From b615257f98fa298b18c32aacae25e6e3ead034d5 Mon Sep 17 00:00:00 2001 From: jeremiedbb Date: Thu, 15 Jun 2023 10:39:18 +0200 Subject: [PATCH 001/264] set version [cd build] --- sklearn/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/__init__.py b/sklearn/__init__.py index 47bb893bd00a0..11185d6b9d918 100644 --- a/sklearn/__init__.py +++ b/sklearn/__init__.py @@ -39,7 +39,7 @@ # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' # -__version__ = "1.3.dev0" +__version__ = "1.3.0rc1" # On OSX, we can get a runtime error due to multiple OpenMP libraries loaded From 936aefd7708c711da97d19829e8d4881eac8575f Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Thu, 15 Jun 2023 10:17:40 +0100 Subject: [PATCH 002/264] CI Fixes tests when matplotlib is not installed (#26588) --- sklearn/model_selection/tests/test_plot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/model_selection/tests/test_plot.py b/sklearn/model_selection/tests/test_plot.py index e1e5003bc8a6b..6baa211d2dc6e 100644 --- a/sklearn/model_selection/tests/test_plot.py +++ b/sklearn/model_selection/tests/test_plot.py @@ -525,7 +525,7 @@ def test_curve_display_plot_kwargs(pyplot, data, CurveDisplay, specific_params): # TODO(1.5): to be removed -def test_learning_curve_display_deprecate_log_scale(data): +def test_learning_curve_display_deprecate_log_scale(data, pyplot): """Check that we warn for the deprecated parameter `log_scale`.""" X, y = data estimator = DecisionTreeClassifier(random_state=0) From 9ea0ec40f8b2753ab1bb4d97407c9b05a883f0f1 Mon Sep 17 00:00:00 2001 From: jeremiedbb Date: Thu, 15 Jun 2023 16:04:19 +0200 Subject: [PATCH 003/264] [cd build] From d7ac21621a13ef121634d01153a812e758a1101a Mon Sep 17 00:00:00 2001 From: Rahil Parikh <75483881+rprkh@users.noreply.github.com> Date: Thu, 15 Jun 2023 14:57:38 +0530 Subject: [PATCH 004/264] DOC update haversine distance formula (#26509) --- sklearn/metrics/pairwise.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py index 67b04e9382acb..b8bd40b8a544f 100644 --- a/sklearn/metrics/pairwise.py +++ b/sklearn/metrics/pairwise.py @@ -919,8 +919,9 @@ def haversine_distances(X, Y=None): in radians. The dimension of the data must be 2. .. math:: - D(x, y) = 2\\arcsin[\\sqrt{\\sin^2((x1 - y1) / 2) - + \\cos(x1)\\cos(y1)\\sin^2((x2 - y2) / 2)}] + D(x, y) = 2\\arcsin[\\sqrt{\\sin^2((x_{lat} - y_{lat}) / 2) + + \\cos(x_{lat})\\cos(y_{lat})\\ + sin^2((x_{lon} - y_{lon}) / 2)}] Parameters ---------- From f26fbe60c2c1d7044bd51b25507967c65e56fd7e Mon Sep 17 00:00:00 2001 From: Gael Varoquaux Date: Fri, 16 Jun 2023 08:05:50 +0200 Subject: [PATCH 005/264] DOC Minor fixes on whats_new 1.3 (#26594) --- doc/whats_new/v1.3.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index 41c03293cf067..7bdbe7841f0d4 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -242,7 +242,7 @@ Changelog :mod:`sklearn.compose` ...................... -- |Fix| `compose.ColumnTransformer` raises an informative error when the individual +- |Fix| :class:`compose.ColumnTransformer` raises an informative error when the individual transformers of `ColumnTransformer` output pandas dataframes with indexes that are not consistent with each other and the output is configured to be pandas. :pr:`26286` by `Thomas Fan`_. @@ -382,10 +382,10 @@ Changelog and :class:`ensemble.AdaBoostRegressor` that was introduced in :pr:`23819`. :pr:`26242` by :user:`Marko Toplak `. -:mod:`sklearn.exception` -........................ +:mod:`sklearn.exceptions` +......................... -- |Feature| Added :class:`exception.InconsistentVersionWarning` which is raised +- |Feature| Added :class:`exceptions.InconsistentVersionWarning` which is raised when a scikit-learn estimator is unpickled with a scikit-learn version that is inconsistent with the sckit-learn version the estimator was pickled with. :pr:`25297` by `Thomas Fan`_. From 72f4def9bda624781b8572cdf7d2dae06418b5ed Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Fri, 16 Jun 2023 18:19:42 +1000 Subject: [PATCH 006/264] DOC Fix typos in math in Target Encoder user guide (#26584) --- doc/modules/preprocessing.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/modules/preprocessing.rst b/doc/modules/preprocessing.rst index 69045147d8af9..c2b3aa1accf63 100644 --- a/doc/modules/preprocessing.rst +++ b/doc/modules/preprocessing.rst @@ -883,11 +883,11 @@ cardinality categories are location based such as zip code or region. For the binary classification target, the target encoding is given by: .. math:: - S_i = \lambda_i\frac{n_{iY}}{n_i} + (1 - \lambda_i)\frac{n_y}{n} + S_i = \lambda_i\frac{n_{iY}}{n_i} + (1 - \lambda_i)\frac{n_Y}{n} where :math:`S_i` is the encoding for category :math:`i`, :math:`n_{iY}` is the number of observations with :math:`Y=1` with category :math:`i`, :math:`n_i` is -the number of observations with category :math:`i`, :math:`n_y` is the number of +the number of observations with category :math:`i`, :math:`n_Y` is the number of observations with :math:`Y=1`, :math:`n` is the number of observations, and :math:`\lambda_i` is a shrinkage factor. The shrinkage factor is given by: @@ -897,14 +897,14 @@ observations with :math:`Y=1`, :math:`n` is the number of observations, and where :math:`m` is a smoothing factor, which is controlled with the `smooth` parameter in :class:`TargetEncoder`. Large smoothing factors will put more weight on the global mean. When `smooth="auto"`, the smoothing factor is -computed as an empirical Bayes estimate: :math:`m=\sigma_c^2/\tau^2`, where +computed as an empirical Bayes estimate: :math:`m=\sigma_i^2/\tau^2`, where :math:`\sigma_i^2` is the variance of `y` with category :math:`i` and :math:`\tau^2` is the global variance of `y`. For continuous targets, the formulation is similar to binary classification: .. math:: - S_i = \lambda_i\frac{\sum_{k\in L_i}y_k}{n_i} + (1 - \lambda_i)\frac{\sum_{k=1}^{n}y_k}{n} + S_i = \lambda_i\frac{\sum_{k\in L_i}Y_k}{n_i} + (1 - \lambda_i)\frac{\sum_{k=1}^{n}Y_k}{n} where :math:`L_i` is the set of observations for which :math:`X=X_i` and :math:`n_i` is the cardinality of :math:`L_i`. From e9436c6790e38bb89ad94f36675dea5afb2b1d5a Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Fri, 16 Jun 2023 11:37:46 +0200 Subject: [PATCH 007/264] FIX seed in test_ridge_sample_weight_consistency [all random seeds] (#26589) --- sklearn/linear_model/tests/test_ridge.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/sklearn/linear_model/tests/test_ridge.py b/sklearn/linear_model/tests/test_ridge.py index 4b9a0765b4caf..64c128ed82046 100644 --- a/sklearn/linear_model/tests/test_ridge.py +++ b/sklearn/linear_model/tests/test_ridge.py @@ -1978,7 +1978,9 @@ def test_lbfgs_solver_error(): @pytest.mark.parametrize("sparseX", [False, True]) @pytest.mark.parametrize("data", ["tall", "wide"]) @pytest.mark.parametrize("solver", SOLVERS + ["lbfgs"]) -def test_ridge_sample_weight_consistency(fit_intercept, sparseX, data, solver): +def test_ridge_sample_weight_consistency( + fit_intercept, sparseX, data, solver, global_random_seed +): """Test that the impact of sample_weight is consistent. Note that this test is stricter than the common test @@ -1989,6 +1991,9 @@ def test_ridge_sample_weight_consistency(fit_intercept, sparseX, data, solver): if solver == "svd" or (solver in ("cholesky", "saga") and fit_intercept): pytest.skip("unsupported configuration") + # XXX: this test is quite sensitive to the seed used to generate the data: + # ideally we would like the test to pass for any global_random_seed but this is not + # the case at the moment. rng = np.random.RandomState(42) n_samples = 12 if data == "tall": @@ -2005,6 +2010,7 @@ def test_ridge_sample_weight_consistency(fit_intercept, sparseX, data, solver): alpha=1.0, solver=solver, positive=(solver == "lbfgs"), + random_state=global_random_seed, # for sag/saga tol=1e-12, ) From 56b16fa31baa221e603f0fcf1124f180ca99e6c6 Mon Sep 17 00:00:00 2001 From: Vijeth Moudgalya <33093576+vijethmoudgalya@users.noreply.github.com> Date: Fri, 16 Jun 2023 18:46:12 +0530 Subject: [PATCH 008/264] DOC Explaining integer+float behavior in HistGradientingBoosting (#26529) --- .../ensemble/_hist_gradient_boosting/gradient_boosting.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py b/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py index 79b640057abe5..e44b6428f8f4e 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py +++ b/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py @@ -1271,6 +1271,9 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting): For each categorical feature, there must be at most `max_bins` unique categories, and each categorical value must be less then `max_bins - 1`. Negative values for categorical features are treated as missing values. + All categorical values are converted to floating point numbers. + This means that categorical values of 1.0 and 1 are treated as + the same category. Read more in the :ref:`User Guide `. @@ -1628,6 +1631,9 @@ class HistGradientBoostingClassifier(ClassifierMixin, BaseHistGradientBoosting): For each categorical feature, there must be at most `max_bins` unique categories, and each categorical value must be less then `max_bins - 1`. Negative values for categorical features are treated as missing values. + All categorical values are converted to floating point numbers. + This means that categorical values of 1.0 and 1 are treated as + the same category. Read more in the :ref:`User Guide `. From c2eb9cec3ef23d34352780b42e563fbccf87013d Mon Sep 17 00:00:00 2001 From: Jarrod Millman Date: Mon, 19 Jun 2023 07:38:40 -0700 Subject: [PATCH 009/264] Upload nightly built wheels to scientific-python-nightly-wheels (#26417) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- .binder/requirements.txt | 2 +- build_tools/azure/install.sh | 2 +- build_tools/github/upload_anaconda.sh | 2 +- doc/developers/advanced_installation.rst | 4 ++-- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.binder/requirements.txt b/.binder/requirements.txt index 51ca95be6785e..0a2352aa9b22b 100644 --- a/.binder/requirements.txt +++ b/.binder/requirements.txt @@ -1,4 +1,4 @@ ---find-links https://pypi.anaconda.org/scipy-wheels-nightly/simple/scikit-learn +--find-links https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/scikit-learn --pre matplotlib scikit-image diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh index 5238cd1121d2e..ced484ae99a1c 100755 --- a/build_tools/azure/install.sh +++ b/build_tools/azure/install.sh @@ -67,7 +67,7 @@ python_environment_install_and_activate() { if [[ "$DISTRIB" == "conda-pip-scipy-dev" ]]; then echo "Installing development dependency wheels" - dev_anaconda_url=https://pypi.anaconda.org/scipy-wheels-nightly/simple + dev_anaconda_url=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple pip install --pre --upgrade --timeout=60 --extra-index $dev_anaconda_url numpy pandas scipy echo "Installing Cython from latest sources" pip install https://github.com/cython/cython/archive/master.zip diff --git a/build_tools/github/upload_anaconda.sh b/build_tools/github/upload_anaconda.sh index 60cab7f8dcf4a..eb9576e222085 100755 --- a/build_tools/github/upload_anaconda.sh +++ b/build_tools/github/upload_anaconda.sh @@ -4,7 +4,7 @@ set -e set -x if [ "$GITHUB_EVENT_NAME" == "schedule" ]; then - ANACONDA_ORG="scipy-wheels-nightly" + ANACONDA_ORG="scientific-python-nightly-wheels" ANACONDA_TOKEN="$SCIKIT_LEARN_NIGHTLY_UPLOAD_TOKEN" else ANACONDA_ORG="scikit-learn-wheels-staging" diff --git a/doc/developers/advanced_installation.rst b/doc/developers/advanced_installation.rst index fe573deb28b83..cef20163dfa1c 100644 --- a/doc/developers/advanced_installation.rst +++ b/doc/developers/advanced_installation.rst @@ -26,12 +26,12 @@ Installing a nightly build is the quickest way to: - check whether a bug you encountered has been fixed since the last release. -You can install the nightly build of scikit-learn using the `scipy-wheels-nightly` +You can install the nightly build of scikit-learn using the `scientific-python-nightly-wheels` index from the PyPI registry of `anaconda.org`: .. prompt:: bash $ - pip install --pre --extra-index https://pypi.anaconda.org/scipy-wheels-nightly/simple scikit-learn + pip install --pre --extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple scikit-learn Note that first uninstalling scikit-learn might be required to be able to install nightly builds of scikit-learn. From 79e433d8679e9006274af28755fbd9f948d06a9c Mon Sep 17 00:00:00 2001 From: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Date: Mon, 19 Jun 2023 23:09:31 +0800 Subject: [PATCH 010/264] DOC search link to sphinx version (#26610) --- doc/developers/contributing.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index 86575dd75d0f1..043c47bb801f8 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -763,7 +763,7 @@ To build the PDF manual, run: versions of Sphinx as possible, the different versions tend to behave slightly differently. To get the best results, you should use the same version as the one we used on CircleCI. Look at this - `github search `_ + `GitHub search `_ to know the exact version. Guidelines for writing documentation From d4074cab4ddf3a718f55710046236691f9478f94 Mon Sep 17 00:00:00 2001 From: Christian Veenhuis <124370897+ChVeen@users.noreply.github.com> Date: Mon, 19 Jun 2023 17:10:29 +0200 Subject: [PATCH 011/264] MAINT Fix broken link in sklearn.datasets._base.py (#26603) --- sklearn/datasets/_base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/datasets/_base.py b/sklearn/datasets/_base.py index aba3a843400e7..014a37ae30b09 100644 --- a/sklearn/datasets/_base.py +++ b/sklearn/datasets/_base.py @@ -717,7 +717,7 @@ def load_breast_cancer(*, return_X_y=False, as_frame=False): The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is downloaded from: - https://goo.gl/U2Uwz2 + https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic Read more in the :ref:`User Guide `. From c4524598c21b3a5b0d5bb761f8ececd0d9cbdaf6 Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Mon, 19 Jun 2023 17:26:52 +0200 Subject: [PATCH 012/264] CI Update nightly token for CirrusCI (#26622) --- build_tools/cirrus/arm_wheel.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/cirrus/arm_wheel.yml b/build_tools/cirrus/arm_wheel.yml index ece984c320249..9fdaa1b4bb48e 100644 --- a/build_tools/cirrus/arm_wheel.yml +++ b/build_tools/cirrus/arm_wheel.yml @@ -94,7 +94,7 @@ wheels_upload_task: env: # Upload tokens have been encrypted via the CirrusCI interface: # https://cirrus-ci.org/guide/writing-tasks/#encrypted-variables - SCIKIT_LEARN_NIGHTLY_UPLOAD_TOKEN: ENCRYPTED[8f20120b18a07d8a11192b98bff1f562883558e1f4c53f8ead1577113785a4105ee6f14ad9b5dacf1803c19c4913fe1c] + SCIKIT_LEARN_NIGHTLY_UPLOAD_TOKEN: ENCRYPTED[9cf0529227577d503f2e19ef31cb690a2272cb243a217fb9a1ceda5cc608e8ccc292050fde9dca94cab766e1dd418519] SCIKIT_LEARN_STAGING_UPLOAD_TOKEN: ENCRYPTED[8fade46af37fa645e57bd1ee21683337aa369ba56f6307ce13889f1e74df94e5bdd21d323baac21e332fd87b8949659a] ARTIFACTS_PATH: wheelhouse upload_script: | From 43536b804b322a203b754a6452f014e30a0fa973 Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Mon, 19 Jun 2023 19:24:16 +0200 Subject: [PATCH 013/264] CI Add a bot to comment on PRs when linter fails (#26553) --- .github/workflows/lint.yml | 83 +++++++++++ build_tools/get_comment.py | 283 +++++++++++++++++++++++++++++++++++++ build_tools/linting.sh | 86 +++++++++-- 3 files changed, 442 insertions(+), 10 deletions(-) create mode 100644 .github/workflows/lint.yml create mode 100644 build_tools/get_comment.py diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml new file mode 100644 index 0000000000000..3ae009e45f26e --- /dev/null +++ b/.github/workflows/lint.yml @@ -0,0 +1,83 @@ +# This linter job on GH actions is used to trigger the commenter bot +# in bot-lint-comment.yml file. It stores the output of the linter to be used +# by the commenter bot. +name: linter + +on: + - pull_request_target + +jobs: + lint: + runs-on: ubuntu-latest + + # setting any permission will set everything else to none for GITHUB_TOKEN + permissions: + pull-requests: none + + steps: + - name: Checkout code + uses: actions/checkout@v3 + with: + ref: ${{ github.event.pull_request.head.sha }} + + - name: Set up Python + uses: actions/setup-python@v3 + with: + python-version: 3.11 + + - name: Install dependencies + run: | + source build_tools/shared.sh + # Include pytest compatibility with mypy + pip install pytest flake8 $(get_dep mypy min) $(get_dep black min) cython-lint + + - name: Run linting + run: ./build_tools/linting.sh &> /tmp/linting_output.txt + + - name: Upload Artifact + if: always() + uses: actions/upload-artifact@v3 + with: + name: lint-log + path: /tmp/linting_output.txt + retention-days: 1 + + comment: + needs: lint + if: always() + runs-on: ubuntu-latest + + # We need these permissions to be able to post / update comments + permissions: + pull-requests: write + issues: write + + steps: + - name: Checkout code + uses: actions/checkout@v3 + + - name: Set up Python + uses: actions/setup-python@v3 + with: + python-version: 3.11 + + - name: Install dependencies + run: python -m pip install requests + + - name: Download artifact + id: download-artifact + uses: actions/download-artifact@v3 + with: + name: lint-log + + - name: Print log + run: cat linting_output.txt + + - name: Process Comments + id: process-comments + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + PR_NUMBER: ${{ github.event.pull_request.number }} + RUN_ID: ${{ github.run_id }} + LOG_FILE: linting_output.txt + run: python ./build_tools/get_comment.py diff --git a/build_tools/get_comment.py b/build_tools/get_comment.py new file mode 100644 index 0000000000000..5115a085ff8b5 --- /dev/null +++ b/build_tools/get_comment.py @@ -0,0 +1,283 @@ +# This script is used to generate a comment for a PR when linting issues are +# detected. It is used by the `Comment on failed linting` GitHub Action. +# This script fails if there are not comments to be posted. + +import os + +import requests + + +def get_step_message(log, start, end, title, message, details): + """Get the message for a specific test. + + Parameters + ---------- + log : str + The log of the linting job. + + start : str + The string that marks the start of the test. + + end : str + The string that marks the end of the test. + + title : str + The title for this section. + + message : str + The message to be added at the beginning of the section. + + details : bool + Whether to add the details of each step. + + Returns + ------- + message : str + The message to be added to the comment. + """ + if end not in log: + return "" + res = ( + "-----------------------------------------------\n" + + f"### {title}\n\n" + + message + + "\n\n" + ) + if details: + res += ( + "
\n\n```\n" + + log[log.find(start) + len(start) + 1 : log.find(end) - 1] + + "\n```\n\n
\n\n" + ) + return res + + +def get_message(log_file, repo, run_id, details): + with open(log_file, "r") as f: + log = f.read() + + message = "" + + # black + message += get_step_message( + log, + start="### Running black ###", + end="Problems detected by black", + title="`black`", + message=( + "`black` detected issues. Please run `black .` locally and push " + "the changes. Here you can see the detected issues. Note that " + "running black might also fix some of the issues which might be " + "detected by `flake8`." + ), + details=details, + ) + + # flake8 + message += get_step_message( + log, + start="### Running flake8 ###", + end="Problems detected by flake8", + title="`flake8`", + message=( + "`flake8` detected issues. Please fix them locally and push the changes. " + "Here you can see the detected issues." + ), + details=details, + ) + + # mypy + message += get_step_message( + log, + start="### Running mypy ###", + end="Problems detected by mypy", + title="`mypy`", + message=( + "`mypy` detected issues. Please fix them locally and push the changes. " + "Here you can see the detected issues." + ), + details=details, + ) + + # cython-lint + message += get_step_message( + log, + start="### Running cython-lint ###", + end="Problems detected by cython-lint", + title="`cython-lint`", + message=( + "`cython-lint` detected issues. Please fix them locally and push " + "the changes. Here you can see the detected issues." + ), + details=details, + ) + + # deprecation order + message += get_step_message( + log, + start="### Checking for bad deprecation order ###", + end="Problems detected by deprecation order check", + title="Deprecation Order", + message=( + "Deprecation order check detected issues. Please fix them locally and " + "push the changes. Here you can see the detected issues." + ), + details=details, + ) + + # doctest directives + message += get_step_message( + log, + start="### Checking for default doctest directives ###", + end="Problems detected by doctest directive check", + title="Doctest Directives", + message=( + "doctest directive check detected issues. Please fix them locally and " + "push the changes. Here you can see the detected issues." + ), + details=details, + ) + + # joblib imports + message += get_step_message( + log, + start="### Checking for joblib imports ###", + end="Problems detected by joblib import check", + title="Joblib Imports", + message=( + "`joblib` import check detected issues. Please fix them locally and " + "push the changes. Here you can see the detected issues." + ), + details=details, + ) + + if not message: + # no issues detected, so this script "fails" + return ( + "## Linting Passed\n" + "All linting checks passed. Your pull request is in excellent shape! ☀️" + ) + + message = ( + "## Linting issues\n\n" + "This PR is introducing linting issues. Here's a summary of the issues. " + "Note that you can avoid having linting issues by enabling `pre-commit` " + "hooks. Instructions to enable them can be found [here](" + "https://scikit-learn.org/dev/developers/contributing.html#how-to-contribute)." + "\n\n" + "You can see the details of the linting issues under the `lint` job [here]" + f"(https://github.com/{repo}/actions/runs/{run_id})\n\n" + + message + ) + + return message + + +def get_headers(token): + """Get the headers for the GitHub API.""" + return { + "Accept": "application/vnd.github+json", + "Authorization": f"Bearer {token}", + "X-GitHub-Api-Version": "2022-11-28", + } + + +def find_lint_bot_comments(repo, token, pr_number): + """Get the comment from the linting bot.""" + # repo is in the form of "org/repo" + # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#list-issue-comments # noqa + response = requests.get( + f"https://api.github.com/repos/{repo}/issues/{pr_number}/comments", + headers=get_headers(token), + ) + response.raise_for_status() + all_comments = response.json() + + failed_comment = "This PR is introducing linting issues. Here's a summary of the" + success_comment = ( + "All linting checks passed. Your pull request is in excellent shape" + ) + + # Find all comments that match the linting bot, and return the first one. + # There should always be only one such comment, or none, if the PR is + # just created. + comments = [ + comment + for comment in all_comments + if comment["user"]["login"] == "github-actions[bot]" + and (failed_comment in comment["body"] or success_comment in comment["body"]) + ] + + if len(all_comments) > 25 and not comments: + # By default the API returns the first 30 comments. If we can't find the + # comment created by the bot in those, then we raise and we skip creating + # a comment in the first place. + raise RuntimeError("Comment not found in the first 30 comments.") + + return comments[0] if comments else None + + +def create_or_update_comment(comment, message, repo, pr_number, token): + """Create a new comment or update existing one.""" + # repo is in the form of "org/repo" + if comment is not None: + print("updating existing comment") + # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#update-an-issue-comment # noqa + response = requests.patch( + f"https://api.github.com/repos/{repo}/issues/comments/{comment['id']}", + headers=get_headers(token), + json={"body": message}, + ) + else: + print("creating new comment") + # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#create-an-issue-comment # noqa + response = requests.post( + f"https://api.github.com/repos/{repo}/issues/{pr_number}/comments", + headers=get_headers(token), + json={"body": message}, + ) + + response.raise_for_status() + + +if __name__ == "__main__": + repo = os.environ["GITHUB_REPOSITORY"] + token = os.environ["GITHUB_TOKEN"] + pr_number = os.environ["PR_NUMBER"] + log_file = os.environ["LOG_FILE"] + run_id = os.environ["RUN_ID"] + + if not repo or not token or not pr_number or not log_file or not run_id: + raise ValueError( + "One of the following environment variables is not set: " + "GITHUB_REPOSITORY, GITHUB_TOKEN, PR_NUMBER, LOG_FILE, RUN_ID" + ) + + try: + comment = find_lint_bot_comments(repo, token, pr_number) + except RuntimeError: + print("Comment not found in the first 30 comments. Skipping!") + exit(0) + + try: + message = get_message(log_file, repo=repo, run_id=run_id, details=True) + create_or_update_comment( + comment=comment, + message=message, + repo=repo, + pr_number=pr_number, + token=token, + ) + print(message) + except requests.HTTPError: + # The above fails if the message is too long. In that case, we + # try again without the details. + message = get_message(log_file, repo=repo, run_id=run_id, details=False) + create_or_update_comment( + comment=comment, + message=message, + repo=repo, + pr_number=pr_number, + token=token, + ) + print(message) diff --git a/build_tools/linting.sh b/build_tools/linting.sh index dd200b9d9cd95..76230abeb434c 100755 --- a/build_tools/linting.sh +++ b/build_tools/linting.sh @@ -1,27 +1,65 @@ #!/bin/bash -set -e +# Note that any change in this file, adding or removing steps or changing the +# printed messages, should be also reflected in the `get_comment.py` file. + +# This script shouldn't exit if a command / pipeline fails +set +e # pipefail is necessary to propagate exit codes set -o pipefail +global_status=0 + +echo -e "### Running black ###\n" black --check --diff . -echo -e "No problem detected by black\n" +status=$? + +if [[ $status -eq 0 ]] +then + echo -e "No problem detected by black\n" +else + echo -e "Problems detected by black, please run black and commit the result\n" + global_status=1 +fi +echo -e "### Running flake8 ###\n" flake8 --show-source . -echo -e "No problem detected by flake8\n" +status=$? +if [[ $status -eq 0 ]] +then + echo -e "No problem detected by flake8\n" +else + echo -e "Problems detected by flake8, please fix them\n" + global_status=1 +fi +echo -e "### Running mypy ###\n" mypy sklearn/ -echo -e "No problem detected by mypy\n" +status=$? +if [[ $status -eq 0 ]] +then + echo -e "No problem detected by mypy\n" +else + echo -e "Problems detected by mypy, please fix them\n" + global_status=1 +fi +echo -e "### Running cython-lint ###\n" cython-lint sklearn/ -echo -e "No problem detected by cython-lint\n" +status=$? +if [[ $status -eq 0 ]] +then + echo -e "No problem detected by cython-lint\n" +else + echo -e "Problems detected by cython-lint, please fix them\n" + global_status=1 +fi # For docstrings and warnings of deprecated attributes to be rendered # properly, the property decorator must come before the deprecated decorator # (else they are treated as functions) -# do not error when grep -B1 "@property" finds nothing -set +e +echo -e "### Checking for bad deprecation order ###\n" bad_deprecation_property_order=`git grep -A 10 "@property" -- "*.py" | awk '/@property/,/def /' | grep -B1 "@deprecated"` if [ ! -z "$bad_deprecation_property_order" ] @@ -29,29 +67,57 @@ then echo "property decorator should come before deprecated decorator" echo "found the following occurrences:" echo $bad_deprecation_property_order - exit 1 + echo -e "\nProblems detected by deprecation order check\n" + global_status=1 +else + echo -e "No problems detected related to deprecation order\n" fi # Check for default doctest directives ELLIPSIS and NORMALIZE_WHITESPACE +echo -e "### Checking for default doctest directives ###\n" doctest_directive="$(git grep -nw -E "# doctest\: \+(ELLIPSIS|NORMALIZE_WHITESPACE)")" if [ ! -z "$doctest_directive" ] then echo "ELLIPSIS and NORMALIZE_WHITESPACE doctest directives are enabled by default, but were found in:" echo "$doctest_directive" - exit 1 + echo -e "\nProblems detected by doctest directive check\n" + global_status=1 +else + echo -e "No problems detected related to doctest directives\n" fi +# Check for joblib.delayed and joblib.Parallel imports + +echo -e "### Checking for joblib imports ###\n" +joblib_status=0 joblib_delayed_import="$(git grep -l -A 10 -E "joblib import.+delayed" -- "*.py" ":!sklearn/utils/_joblib.py" ":!sklearn/utils/parallel.py")" if [ ! -z "$joblib_delayed_import" ]; then echo "Use from sklearn.utils.parallel import delayed instead of joblib delayed. The following files contains imports to joblib.delayed:" echo "$joblib_delayed_import" - exit 1 + joblib_status=1 fi joblib_Parallel_import="$(git grep -l -A 10 -E "joblib import.+Parallel" -- "*.py" ":!sklearn/utils/_joblib.py" ":!sklearn/utils/parallel.py")" if [ ! -z "$joblib_Parallel_import" ]; then echo "Use from sklearn.utils.parallel import Parallel instead of joblib Parallel. The following files contains imports to joblib.Parallel:" echo "$joblib_Parallel_import" + joblib_status=1 +fi + +if [[ $joblib_status -eq 0 ]] +then + echo -e "No problems detected related to joblib imports\n" +else + echo -e "\nProblems detected by joblib import check\n" + global_status=1 +fi + +if [[ $global_status -eq 1 ]] +then + echo -e "Linting failed\n" exit 1 +else + echo -e "Linting passed\n" + exit 0 fi From d9b57fc9dd2a9652174459516c7fa2488d645fbe Mon Sep 17 00:00:00 2001 From: Abhishek Singh Kushwah Date: Tue, 20 Jun 2023 10:33:15 +0200 Subject: [PATCH 014/264] MAINT Added change in doc for push_doc.sh (#26615) Co-authored-by: Thomas J. Fan --- build_tools/circle/push_doc.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/build_tools/circle/push_doc.sh b/build_tools/circle/push_doc.sh index c32a2d31fa811..f959b8b65c85c 100755 --- a/build_tools/circle/push_doc.sh +++ b/build_tools/circle/push_doc.sh @@ -1,8 +1,8 @@ #!/bin/bash # This script is meant to be called in the "deploy" step defined in -# circle.yml. See https://circleci.com/docs/ for more details. +# .circleci/config.yml. See https://circleci.com/docs/ for more details. # The behavior of the script is controlled by environment variable defined -# in the circle.yml in the top level folder of the project. +# in the .circleci/config.yml file. set -ex From 61adc629c93643beace6d41d8a24643801d15a74 Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Tue, 20 Jun 2023 16:02:31 +0200 Subject: [PATCH 015/264] CI Adjust atol for conda-forge to pass (#26628) --- sklearn/linear_model/tests/test_base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/linear_model/tests/test_base.py b/sklearn/linear_model/tests/test_base.py index 92932042ca428..7788e39c804fe 100644 --- a/sklearn/linear_model/tests/test_base.py +++ b/sklearn/linear_model/tests/test_base.py @@ -874,7 +874,7 @@ def test_linear_regression_sample_weight_consistency( # ::test_linear_regression_sample_weight_consistency pass else: - assert_allclose(reg.coef_, coef_0, rtol=1e-6) + assert_allclose(reg.coef_, coef_0, rtol=1e-5) if fit_intercept: assert_allclose(reg.intercept_, intercept_0) From 073322e27e40d6e847502b8fea39b7cbc9cbea87 Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Tue, 20 Jun 2023 16:34:23 +0200 Subject: [PATCH 016/264] MNT replace flake8 with ruff (#26630) --- .circleci/config.yml | 2 +- .github/workflows/lint.yml | 2 +- .pre-commit-config.yaml | 8 ++-- Makefile | 3 +- azure-pipelines.yml | 2 +- build_tools/get_comment.py | 13 ++++--- build_tools/linting.sh | 8 ++-- doc/developers/contributing.rst | 35 ++++++----------- .../plot_select_from_model_diabetes.py | 2 +- ...ot_document_classification_20newsgroups.py | 2 +- pyproject.toml | 37 +++++++++++++++++- setup.cfg | 38 ------------------- sklearn/_min_dependencies.py | 2 +- sklearn/tests/test_pipeline.py | 2 +- 14 files changed, 70 insertions(+), 86 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 4408d2bc36de7..eedc286a5a5f2 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -11,7 +11,7 @@ jobs: command: | source build_tools/shared.sh # Include pytest compatibility with mypy - pip install pytest flake8 $(get_dep mypy min) $(get_dep black min) cython-lint + pip install pytest ruff $(get_dep mypy min) $(get_dep black min) cython-lint - run: name: linting command: ./build_tools/linting.sh diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 3ae009e45f26e..8b527922c4d39 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -29,7 +29,7 @@ jobs: run: | source build_tools/shared.sh # Include pytest compatibility with mypy - pip install pytest flake8 $(get_dep mypy min) $(get_dep black min) cython-lint + pip install pytest ruff $(get_dep mypy min) $(get_dep black min) cython-lint - name: Run linting run: ./build_tools/linting.sh &> /tmp/linting_output.txt diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 26db27bc827b2..98c5cce039652 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,11 +9,11 @@ repos: rev: 23.3.0 hooks: - id: black -- repo: https://github.com/pycqa/flake8 - rev: 4.0.1 +- repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.0.272 hooks: - - id: flake8 - types: [file, python] + - id: ruff - repo: https://github.com/pre-commit/mirrors-mypy rev: v0.961 hooks: diff --git a/Makefile b/Makefile index 5ea64dc0d6cac..e2ae6aa75ca94 100644 --- a/Makefile +++ b/Makefile @@ -61,5 +61,4 @@ doc-noplot: inplace $(MAKE) -C doc html-noplot code-analysis: - flake8 sklearn | grep -v __init__ | grep -v external - pylint -E -i y sklearn/ -d E1103,E0611,E1101 + build_tools/linting.sh diff --git a/azure-pipelines.yml b/azure-pipelines.yml index dfefda5ccddb9..d66acc3d00248 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -35,7 +35,7 @@ jobs: - bash: | source build_tools/shared.sh # Include pytest compatibility with mypy - pip install pytest flake8 $(get_dep mypy min) $(get_dep black min) cython-lint + pip install pytest ruff $(get_dep mypy min) $(get_dep black min) cython-lint displayName: Install linters - bash: | ./build_tools/linting.sh diff --git a/build_tools/get_comment.py b/build_tools/get_comment.py index 5115a085ff8b5..d414fe92975a0 100644 --- a/build_tools/get_comment.py +++ b/build_tools/get_comment.py @@ -68,19 +68,20 @@ def get_message(log_file, repo, run_id, details): "`black` detected issues. Please run `black .` locally and push " "the changes. Here you can see the detected issues. Note that " "running black might also fix some of the issues which might be " - "detected by `flake8`." + "detected by `ruff`." ), details=details, ) - # flake8 + # ruff message += get_step_message( log, - start="### Running flake8 ###", - end="Problems detected by flake8", - title="`flake8`", + start="### Running ruff ###", + end="Problems detected by ruff", + title="`ruff`", message=( - "`flake8` detected issues. Please fix them locally and push the changes. " + "`ruff` detected issues. Please run `ruff --fix --show-source .` " + "locally, fix the remaining issues, and push the changes. " "Here you can see the detected issues." ), details=details, diff --git a/build_tools/linting.sh b/build_tools/linting.sh index 76230abeb434c..0344f8e4f53b2 100755 --- a/build_tools/linting.sh +++ b/build_tools/linting.sh @@ -22,14 +22,14 @@ else global_status=1 fi -echo -e "### Running flake8 ###\n" -flake8 --show-source . +echo -e "### Running ruff ###\n" +ruff check --show-source . status=$? if [[ $status -eq 0 ]] then - echo -e "No problem detected by flake8\n" + echo -e "No problem detected by ruff\n" else - echo -e "Problems detected by flake8, please fix them\n" + echo -e "Problems detected by ruff, please fix them\n" global_status=1 fi diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index 043c47bb801f8..96e0672eb10ea 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -254,7 +254,7 @@ how to set up your git repository: .. prompt:: bash $ - pip install pytest pytest-cov flake8 mypy numpydoc black==23.3.0 + pip install pytest pytest-cov ruff mypy numpydoc black==23.3.0 .. _upstream: @@ -425,30 +425,15 @@ complies with the following rules before marking a PR as ``[MRG]``. The non-regression tests should fail for the code base in the ``main`` branch and pass for the PR code. -5. Run `black` to auto-format your code. - .. prompt:: bash $ - - black . - - See black's - `editor integration documentation `_ - to configure your editor to run `black`. - -6. Run `flake8` to make sure you followed the project coding conventions. - - .. prompt:: bash $ - - flake8 . - -7. Follow the :ref:`coding-guidelines`. +5. Follow the :ref:`coding-guidelines`. -8. When applicable, use the validation tools and scripts in the +6. When applicable, use the validation tools and scripts in the ``sklearn.utils`` submodule. A list of utility routines available for developers can be found in the :ref:`developers-utils` page. -9. Often pull requests resolve one or more other issues (or pull requests). +7. Often pull requests resolve one or more other issues (or pull requests). If merging your pull request means that some other issues/PRs should be closed, you should `use keywords to create link to them `_ @@ -458,7 +443,7 @@ complies with the following rules before marking a PR as ``[MRG]``. The related to some other issues/PRs, create a link to them without using the keywords (e.g., ``See also #1234``). -10. PRs should often substantiate the change, through benchmarks of +8. PRs should often substantiate the change, through benchmarks of performance and efficiency (see :ref:`monitoring_performances`) or through examples of usage. Examples also illustrate the features and intricacies of the library to users. Have a look at other examples in the `examples/ @@ -467,14 +452,14 @@ complies with the following rules before marking a PR as ``[MRG]``. The functionality is useful in practice and, if possible, compare it to other methods available in scikit-learn. -11. New features have some maintenance overhead. We expect PR authors +9. New features have some maintenance overhead. We expect PR authors to take part in the maintenance for the code they submit, at least initially. New features need to be illustrated with narrative documentation in the user guide, with small code snippets. If relevant, please also add references in the literature, with PDF links when possible. -12. The user guide should also include expected time and space complexity +10. The user guide should also include expected time and space complexity of the algorithm and scalability, e.g. "this algorithm can scale to a large number of samples > 100000, but does not scale in dimensionality: n_features is expected to be lower than 100". @@ -534,8 +519,10 @@ Continuous Integration (CI) * Azure pipelines are used for testing scikit-learn on Linux, Mac and Windows, with different dependencies and settings. -* CircleCI is used to build the docs for viewing, for linting with flake8, and - for testing with ARM64 / aarch64 on Linux +* CircleCI is used to build the docs for viewing. +* Github Actions are used for various tasks, including building wheels and + source distributions. +* Cirrus CI is used to build on ARM. Please note that if one of the following markers appear in the latest commit message, the following actions are taken. diff --git a/examples/feature_selection/plot_select_from_model_diabetes.py b/examples/feature_selection/plot_select_from_model_diabetes.py index 16f63868feae0..6e8a6434e1079 100644 --- a/examples/feature_selection/plot_select_from_model_diabetes.py +++ b/examples/feature_selection/plot_select_from_model_diabetes.py @@ -43,7 +43,7 @@ # were already standardized. # For a more complete example on the interpretations of the coefficients of # linear models, you may refer to -# :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`. +# :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`. # noqa: E501 import matplotlib.pyplot as plt import numpy as np from sklearn.linear_model import RidgeCV diff --git a/examples/text/plot_document_classification_20newsgroups.py b/examples/text/plot_document_classification_20newsgroups.py index ffee60087d0c3..633a0602d421b 100644 --- a/examples/text/plot_document_classification_20newsgroups.py +++ b/examples/text/plot_document_classification_20newsgroups.py @@ -358,7 +358,7 @@ def benchmark(clf, custom_name=False): # Notice that the most important hyperparameters values were tuned using a grid # search procedure not shown in this notebook for the sake of simplicity. See # the example script -# :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` +# :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` # noqa: E501 # for a demo on how such tuning can be done. from sklearn.linear_model import LogisticRegression diff --git a/pyproject.toml b/pyproject.toml index 7e39589216956..bed85b074dbfb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,8 +39,43 @@ exclude = ''' )/ ''' +[tool.ruff] +# max line length for black +line-length = 88 +target-version = "py38" + +ignore=[ + # space before : (needed for how black formats slicing) + "E203", + # do not assign a lambda expression, use a def + "E731", + # do not use variables named 'l', 'O', or 'I' + "E741", +] + +exclude=[ + ".git", + "__pycache__", + "dist", + "sklearn/externals", + "doc/_build", + "doc/auto_examples", + "doc/tutorial", + "build", + "asv_benchmarks/env", + "asv_benchmarks/html", + "asv_benchmarks/results", + "asv_benchmarks/benchmarks/cache", +] + +[tool.ruff.per-file-ignores] +# It's fine not to put the import at the top of the file in the examples +# folder. +"examples/*"=["E402"] +"doc/conf.py"=["E402"] + [tool.cython-lint] -# Ignore the same error codes as flake8 +# Ignore the same error codes as ruff # + E501 (line too long) because keeping it < 88 in cython # often makes code less readable. ignore = [ diff --git a/setup.cfg b/setup.cfg index 19f2bebeb7280..65aa4658d579b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -25,44 +25,6 @@ addopts = filterwarnings = ignore:the matrix subclass:PendingDeprecationWarning -[flake8] -# max line length for black -max-line-length = 88 -target-version = ['py37'] -# Default flake8 3.5 ignored flags -ignore= - # check ignored by default in flake8. Meaning unclear. - E24, - # space before : (needed for how black formats slicing) - E203, - # do not assign a lambda expression, use a def - E731, - # do not use variables named 'l', 'O', or 'I' - E741, - # line break before binary operator - W503, - # line break after binary operator - W504 -exclude= - .git, - __pycache__, - dist, - sklearn/externals, - doc/_build, - doc/auto_examples, - doc/tutorial, - build, - asv_benchmarks/env, - asv_benchmarks/html, - asv_benchmarks/results, - asv_benchmarks/benchmarks/cache - -# It's fine not to put the import at the top of the file in the examples -# folder. -per-file-ignores = - examples/*: E402 - doc/conf.py: E402 - [mypy] ignore_missing_imports = True allow_redefinition = True diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py index b6dd0656987b5..9abab236d29d8 100644 --- a/sklearn/_min_dependencies.py +++ b/sklearn/_min_dependencies.py @@ -37,7 +37,7 @@ "memory_profiler": ("0.57.0", "benchmark, docs"), "pytest": (PYTEST_MIN_VERSION, "tests"), "pytest-cov": ("2.9.0", "tests"), - "flake8": ("3.8.2", "tests"), + "ruff": ("0.0.272", "tests"), "black": ("23.3.0", "tests"), "mypy": ("0.961", "tests"), "pyamg": ("4.0.0", "tests"), diff --git a/sklearn/tests/test_pipeline.py b/sklearn/tests/test_pipeline.py index 28067ea316074..ae65e00f0f99c 100644 --- a/sklearn/tests/test_pipeline.py +++ b/sklearn/tests/test_pipeline.py @@ -229,7 +229,7 @@ def test_pipeline_invalid_parameters(): # Test clone pipe2 = clone(pipe) - assert not pipe.named_steps["svc"] is pipe2.named_steps["svc"] + assert pipe.named_steps["svc"] is not pipe2.named_steps["svc"] # Check that apart from estimators, the parameters are the same params = pipe.get_params(deep=True) From 38c3caa335e678d2f998d4f51d631e4ecc0aa817 Mon Sep 17 00:00:00 2001 From: Gael Varoquaux Date: Tue, 20 Jun 2023 16:35:59 +0200 Subject: [PATCH 017/264] DOC: Add HGBT on landing page (#26631) --- doc/developers/maintainer.rst | 4 +++- doc/templates/index.html | 45 +++++++---------------------------- 2 files changed, 11 insertions(+), 38 deletions(-) diff --git a/doc/developers/maintainer.rst b/doc/developers/maintainer.rst index 6b49103774d9c..d2a1d21523f78 100644 --- a/doc/developers/maintainer.rst +++ b/doc/developers/maintainer.rst @@ -208,7 +208,9 @@ Making a release - Update the release date in ``whats_new.rst`` - Edit the ``doc/templates/index.html`` to change the 'News' entry of the - front page (with the release month as well). + front page (with the release month as well). Do not forget to remove + the old entries (two years or three releases are typically good + enough) 2. On the branch for releasing, update the version number in ``sklearn/__init__.py``, the ``__version__``. diff --git a/doc/templates/index.html b/doc/templates/index.html index db5d02ab9d9ab..190f7291a7e3f 100644 --- a/doc/templates/index.html +++ b/doc/templates/index.html @@ -42,9 +42,10 @@

Machine Learning in

Identifying which category an object belongs to.

Applications: Spam detection, image recognition.
Algorithms: - SVM, + Gradient boosting, nearest neighbors, random forest, + logistic regression, and more...

@@ -62,9 +63,10 @@

Machine Learning in

Predicting a continuous-valued attribute associated with an object.

Applications: Drug response, Stock prices.
Algorithms: - SVR, + Gradient boosting, nearest neighbors, random forest, + ridge, and more...

@@ -83,8 +85,9 @@

Machine Learning in

Applications: Customer segmentation, Grouping experiment outcomes
Algorithms: k-Means, - spectral clustering, - mean-shift, + HDBSCAN, + hierarchical + clustering, and more...

@@ -171,39 +174,7 @@

News

  • January 2023. scikit-learn 1.2.1 is available for download (Changelog).
  • December 2022. scikit-learn 1.2.0 is available for download (Changelog). -
    • -
  • October 2022. scikit-learn 1.1.3 is available for download (Changelog). -
    • -
  • August 2022. scikit-learn 1.1.2 is available for download (Changelog). -
    • -
  • May 2022. scikit-learn 1.1.1 is available for download (Changelog). -
    • -
  • May 2022. scikit-learn 1.1.0 is available for download (Changelog). -
    • -
  • December 2021. scikit-learn 1.0.2 is available for download (Changelog). -
    • -
  • October 2021. scikit-learn 1.0.1 is available for download (Changelog). -
    • -
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    • +
    From 4c069ff9dca39f1296dee809d4d2ee0f1b28091a Mon Sep 17 00:00:00 2001 From: JB Mountford Date: Tue, 20 Jun 2023 16:13:09 +0100 Subject: [PATCH 018/264] DOC Update plot_digits_agglomeration.py (#26635) --- examples/cluster/plot_digits_agglomeration.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/cluster/plot_digits_agglomeration.py b/examples/cluster/plot_digits_agglomeration.py index 77e11328415d2..627a9a28d7665 100644 --- a/examples/cluster/plot_digits_agglomeration.py +++ b/examples/cluster/plot_digits_agglomeration.py @@ -3,7 +3,7 @@ Feature agglomeration ========================================================= -These images how similar features are merged together using +These images show how similar features are merged together using feature agglomeration. """ From ad0b74b5105585c302f0e3d4233d6352f5fefb23 Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Tue, 20 Jun 2023 18:01:38 +0200 Subject: [PATCH 019/264] MNT update to mypy=1.3 (#26632) --- .pre-commit-config.yaml | 2 +- setup.cfg | 2 ++ sklearn/_min_dependencies.py | 2 +- sklearn/cluster/_hdbscan/hdbscan.py | 2 +- sklearn/tests/test_metaestimators_metadata_routing.py | 2 +- 5 files changed, 6 insertions(+), 4 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 98c5cce039652..643c2141819d5 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,7 +15,7 @@ repos: hooks: - id: ruff - repo: https://github.com/pre-commit/mirrors-mypy - rev: v0.961 + rev: v1.3.0 hooks: - id: mypy files: sklearn/ diff --git a/setup.cfg b/setup.cfg index 65aa4658d579b..d91a27344c575 100644 --- a/setup.cfg +++ b/setup.cfg @@ -28,6 +28,8 @@ filterwarnings = [mypy] ignore_missing_imports = True allow_redefinition = True +exclude= + sklearn/externals [check-manifest] # ignore files missing in VCS diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py index 9abab236d29d8..72ee14d64e958 100644 --- a/sklearn/_min_dependencies.py +++ b/sklearn/_min_dependencies.py @@ -39,7 +39,7 @@ "pytest-cov": ("2.9.0", "tests"), "ruff": ("0.0.272", "tests"), "black": ("23.3.0", "tests"), - "mypy": ("0.961", "tests"), + "mypy": ("1.3", "tests"), "pyamg": ("4.0.0", "tests"), "sphinx": ("4.0.1", "docs"), "sphinx-gallery": ("0.7.0", "docs"), diff --git a/sklearn/cluster/_hdbscan/hdbscan.py b/sklearn/cluster/_hdbscan/hdbscan.py index 3d8ec08ac3997..f1584f46d6f82 100644 --- a/sklearn/cluster/_hdbscan/hdbscan.py +++ b/sklearn/cluster/_hdbscan/hdbscan.py @@ -62,7 +62,7 @@ # Encodings are arbitrary but must be strictly negative. # The current encodings are chosen as extensions to the -1 noise label. # Avoided enums so that the end user only deals with simple labels. -_OUTLIER_ENCODING = { +_OUTLIER_ENCODING: dict = { "infinite": { "label": -2, # The probability could also be 1, since infinite points are certainly diff --git a/sklearn/tests/test_metaestimators_metadata_routing.py b/sklearn/tests/test_metaestimators_metadata_routing.py index 892b0f21dbe8a..5160a4b46b538 100644 --- a/sklearn/tests/test_metaestimators_metadata_routing.py +++ b/sklearn/tests/test_metaestimators_metadata_routing.py @@ -171,7 +171,7 @@ def predict_log_proba(self, X, sample_weight="default", metadata="default"): # return np.zeros(shape=(len(X), 2)) -METAESTIMATORS = [ +METAESTIMATORS: list = [ { "metaestimator": MultiOutputRegressor, "estimator_name": "estimator", From d98a1488e182c4076469b7b747354cf333d946a8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 20 Jun 2023 19:00:23 +0200 Subject: [PATCH 020/264] MNT error when conda version is likely to affect conda-lock (#26620) Co-authored-by: Thomas J. Fan --- .../update_environments_and_lock_files.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 28910a07d899a..a4a5c92b077d2 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -38,6 +38,8 @@ import logging from importlib.metadata import version +from packaging.version import Version + import click from jinja2 import Environment @@ -539,6 +541,22 @@ def check_conda_lock_version(): ) +def check_conda_version(): + # Avoid issues with glibc (https://github.com/conda/conda-lock/issues/292) + # or osx (https://github.com/conda/conda-lock/issues/408) virtual package. + # The glibc one has been fixed in conda 23.1.0 and the osx has been fixed + # in main and will be fixed when conda > 23.5.0 is released. + conda_info_output = execute_command(["conda", "info", "--json"]) + + conda_info = json.loads(conda_info_output) + conda_version = Version(conda_info["conda_version"]) + + if Version("22.9.0") < conda_version <= Version("23.5.0"): + raise RuntimeError( + f"conda version should be <= 22.9.0 or > 23.5.0, got: {conda_version}" + ) + + @click.command() @click.option( "--select-build", @@ -547,6 +565,7 @@ def check_conda_lock_version(): ) def main(select_build): check_conda_lock_version() + check_conda_version() filtered_conda_build_metadata_list = [ each for each in conda_build_metadata_list From 352a25b6a4fc83cef8c629dbef607d427100ed58 Mon Sep 17 00:00:00 2001 From: Gael Varoquaux Date: Wed, 21 Jun 2023 14:57:59 +0200 Subject: [PATCH 021/264] DOC a simple implementation of folding (#26625) --- doc/conf.py | 20 +++++++ doc/modules/model_evaluation.rst | 16 +++++ .../scikit-learn-modern/static/css/theme.css | 58 +++++++++++++++++++ 3 files changed, 94 insertions(+) diff --git a/doc/conf.py b/doc/conf.py index 176a0d8b3a7d1..6ac1b4e231822 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -299,6 +299,26 @@ # Not showing the search summary makes the search page load faster. html_show_search_summary = False + +rst_prolog = """ +.. |details-start| raw:: html + +
    + + +.. |details-split| raw:: html + + Click for more details + +
    + +.. |details-end| raw:: html + +
    +
    + +""" + # -- Options for LaTeX output ------------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst index 670e661d92ef7..9cc1025047802 100644 --- a/doc/modules/model_evaluation.rst +++ b/doc/modules/model_evaluation.rst @@ -162,6 +162,12 @@ the :func:`fbeta_score` function:: >>> grid = GridSearchCV(LinearSVC(dual="auto"), param_grid={'C': [1, 10]}, ... scoring=ftwo_scorer, cv=5) + +|details-start| +**Custom scorer objects** +|details-split| + + The second use case is to build a completely custom scorer object from a simple python function using :func:`make_scorer`, which can take several parameters: @@ -202,13 +208,21 @@ Here is an example of building custom scorers, and of using the >>> score(clf, X, y) -0.69... +|details-end| .. _diy_scoring: Implementing your own scoring object ------------------------------------ + You can generate even more flexible model scorers by constructing your own scoring object from scratch, without using the :func:`make_scorer` factory. + + +|details-start| +**How to build a scorer from scratch** +|details-split| + For a callable to be a scorer, it needs to meet the protocol specified by the following two rules: @@ -249,6 +263,8 @@ the following two rules: ... cv=5, ... n_jobs=-1) # doctest: +SKIP +|details-end| + .. _multimetric_scoring: Using multiple metric evaluation diff --git a/doc/themes/scikit-learn-modern/static/css/theme.css b/doc/themes/scikit-learn-modern/static/css/theme.css index 90cfeb9300490..fb729223b57ab 100644 --- a/doc/themes/scikit-learn-modern/static/css/theme.css +++ b/doc/themes/scikit-learn-modern/static/css/theme.css @@ -147,6 +147,61 @@ div.clearer { clear: both; } +/* details / summary */ + +div.sk-page-content details { + margin: 4ex 0pt; +} + +div.sk-page-content summary.btn { + display: list-item; + padding: 6px 20px; + border: 1pt solid #999; +} + +div.sk-page-content details div.card { + padding: 0pt .5ex; + margin: 1ex 0pt; + border: 1px solid #e9ecef; + border-left-width: .25rem; + border-radius: .25rem; + background: rgb(250, 252, 253) +} + +div.sk-page-content summary { + position: relative; /* Needed for the tooltips */ +} + +div.sk-page-content summary .tooltiptext { + visibility: hidden; + width: 120px; + background-color: black; + color: #fff; + text-align: center; + border-radius: 6px; + padding: 5px 0; + position: absolute; + z-index: 1; + bottom: 150%; + left: 50%; + margin-left: -60px; +} + +div.sk-page-content summary .tooltiptext::after { + content: ""; + position: absolute; + top: 100%; + left: 50%; + margin-left: -5px; + border-width: 5px; + border-style: solid; + border-color: black transparent transparent transparent; +} + +div.sk-page-content summary:hover .tooltiptext { + visibility: visible; +} + /* Button */ .sk-btn-primary { @@ -606,6 +661,7 @@ div.sk-sidebar-global-toc ul ul { div.sk-page-content h1 { background-color: #cde8ef; padding: 0.5rem; + margin-top: calc(max(2.5rem, 1vh)); border-radius: 0 1rem; text-align: center; font-size: 2rem; @@ -617,6 +673,7 @@ div.sk-page-content h2 { background-color: #BED4EB; border-radius: 0.3rem; font-size: 1.5rem; + margin-top: calc(max(2rem, .7vh)); margin-bottom: 1rem; word-wrap: break-word; } @@ -627,6 +684,7 @@ div.sk-page-content h3 { border-radius: 0.3rem; font-size: 1.2rem; word-wrap: break-word; + margin-top: 1.5rem; } div.sk-page-content h4 { From 8a2bf9086b077173b2f8b6749a25a91557698803 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Wed, 21 Jun 2023 23:04:36 +1000 Subject: [PATCH 022/264] DOC Fix typos/nitpicks in `TargetEncoder` docstring (#26645) --- sklearn/preprocessing/_target_encoder.py | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/sklearn/preprocessing/_target_encoder.py b/sklearn/preprocessing/_target_encoder.py index 9dd33ddfa3cce..460058835e3a8 100644 --- a/sklearn/preprocessing/_target_encoder.py +++ b/sklearn/preprocessing/_target_encoder.py @@ -30,13 +30,13 @@ class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder): .. note:: `fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a cross-validation scheme is used in `fit_transform` for encoding. See the - :ref:`User Guide `. for details. + :ref:`User Guide ` for details. .. versionadded:: 1.3 Parameters ---------- - categories : "auto" or a list of array-like, default="auto" + categories : "auto" or list of shape (n_features,) of array-like, default="auto" Categories (unique values) per feature: - `"auto"` : Determine categories automatically from the training data. @@ -44,7 +44,7 @@ class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder): passed categories should not mix strings and numeric values within a single feature, and should be sorted in case of numeric values. - The used categories is stored in the `categories_` fitted attribute. + The used categories are stored in the `categories_` fitted attribute. target_type : {"auto", "continuous", "binary"}, default="auto" Type of target. @@ -56,16 +56,17 @@ class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder): .. note:: The type of target inferred with `"auto"` may not be the desired target - type used for modeling. For example, if the target consistent of integers + type used for modeling. For example, if the target consisted of integers between 0 and 100, then :func:`~sklearn.utils.multiclass.type_of_target` will infer the target as `"multiclass"`. In this case, setting - `target_type="continuous"` will understand the target as a regression + `target_type="continuous"` will specify the target as a regression problem. The `target_type_` attribute gives the target type used by the encoder. smooth : "auto" or float, default="auto" - The amount of mixing of the categorical encoding with the global target mean. A - larger `smooth` value will put more weight on the global target mean. + The amount of mixing of the target mean conditioned on the value of the + category with the global target mean. A larger `smooth` value will put + more weight on the global target mean. If `"auto"`, then `smooth` is set to an empirical Bayes estimate. cv : int, default=5 @@ -75,7 +76,7 @@ class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder): shuffle : bool, default=True Whether to shuffle the data in :meth:`fit_transform` before splitting into - batches. Note that the samples within each split will not be shuffled. + folds. Note that the samples within each split will not be shuffled. random_state : int, RandomState instance or None, default=None When `shuffle` is True, `random_state` affects the ordering of the @@ -87,11 +88,13 @@ class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder): Attributes ---------- encodings_ : list of shape (n_features,) of ndarray - For feature `i`, `encodings_[i]` is the encoding matching the + Encodings learnt on all of `X`. + For feature `i`, `encodings_[i]` are the encodings matching the categories listed in `categories_[i]`. categories_ : list of shape (n_features,) of ndarray - The categories of each feature determined during fitting + The categories of each feature determined during fitting or specified + in `categories` (in order of the features in `X` and corresponding with the output of :meth:`transform`). From 9477fc2c24d174cb4a88de1e544261864084da97 Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Wed, 21 Jun 2023 16:34:58 +0200 Subject: [PATCH 023/264] CI Linter Bot: add concurrency group and a link to the commit (#26638) --- .github/workflows/lint.yml | 11 ++++++++++- build_tools/get_comment.py | 32 +++++++++++++++++++++++++++----- 2 files changed, 37 insertions(+), 6 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 8b527922c4d39..fb919989738fb 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -6,6 +6,10 @@ name: linter on: - pull_request_target +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: lint: runs-on: ubuntu-latest @@ -32,7 +36,11 @@ jobs: pip install pytest ruff $(get_dep mypy min) $(get_dep black min) cython-lint - name: Run linting - run: ./build_tools/linting.sh &> /tmp/linting_output.txt + # We download the linting script from main, since this workflow is run + # from main itself. + run: | + curl https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/build_tools/linting.sh -o ./build_tools/linting.sh + ./build_tools/linting.sh &> /tmp/linting_output.txt - name: Upload Artifact if: always() @@ -78,6 +86,7 @@ jobs: env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} PR_NUMBER: ${{ github.event.pull_request.number }} + BRANCH_SHA: ${{ github.event.pull_request.head.sha }} RUN_ID: ${{ github.run_id }} LOG_FILE: linting_output.txt run: python ./build_tools/get_comment.py diff --git a/build_tools/get_comment.py b/build_tools/get_comment.py index d414fe92975a0..d67492957510e 100644 --- a/build_tools/get_comment.py +++ b/build_tools/get_comment.py @@ -52,7 +52,7 @@ def get_step_message(log, start, end, title, message, details): return res -def get_message(log_file, repo, run_id, details): +def get_message(log_file, repo, pr_number, sha, run_id, details): with open(log_file, "r") as f: log = f.read() @@ -152,15 +152,21 @@ def get_message(log_file, repo, run_id, details): details=details, ) + commit_link = ( + "\n\n_Generated for commit:" + f" [{sha[:7]}](https://github.com/{repo}/pull/{pr_number}/commits/{sha})_" + ) + if not message: # no issues detected, so this script "fails" return ( - "## Linting Passed\n" + "## ✔️ Linting Passed\n" "All linting checks passed. Your pull request is in excellent shape! ☀️" + + commit_link ) message = ( - "## Linting issues\n\n" + "## ❌ Linting issues\n\n" "This PR is introducing linting issues. Here's a summary of the issues. " "Note that you can avoid having linting issues by enabling `pre-commit` " "hooks. Instructions to enable them can be found [here](" @@ -169,6 +175,7 @@ def get_message(log_file, repo, run_id, details): "You can see the details of the linting issues under the `lint` job [here]" f"(https://github.com/{repo}/actions/runs/{run_id})\n\n" + message + + commit_link ) return message @@ -245,6 +252,7 @@ def create_or_update_comment(comment, message, repo, pr_number, token): repo = os.environ["GITHUB_REPOSITORY"] token = os.environ["GITHUB_TOKEN"] pr_number = os.environ["PR_NUMBER"] + sha = os.environ["BRANCH_SHA"] log_file = os.environ["LOG_FILE"] run_id = os.environ["RUN_ID"] @@ -261,7 +269,14 @@ def create_or_update_comment(comment, message, repo, pr_number, token): exit(0) try: - message = get_message(log_file, repo=repo, run_id=run_id, details=True) + message = get_message( + log_file, + repo=repo, + pr_number=pr_number, + sha=sha, + run_id=run_id, + details=True, + ) create_or_update_comment( comment=comment, message=message, @@ -273,7 +288,14 @@ def create_or_update_comment(comment, message, repo, pr_number, token): except requests.HTTPError: # The above fails if the message is too long. In that case, we # try again without the details. - message = get_message(log_file, repo=repo, run_id=run_id, details=False) + message = get_message( + log_file, + repo=repo, + pr_number=pr_number, + sha=sha, + run_id=run_id, + details=False, + ) create_or_update_comment( comment=comment, message=message, From c21aa5967d48c18ade0c0452579d3310f1ee797c Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Thu, 22 Jun 2023 01:22:14 +1000 Subject: [PATCH 024/264] DOC Update link to best sphinx version for doc build (#26626) --- doc/developers/contributing.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index 96e0672eb10ea..d60e559ea2e50 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -750,7 +750,7 @@ To build the PDF manual, run: versions of Sphinx as possible, the different versions tend to behave slightly differently. To get the best results, you should use the same version as the one we used on CircleCI. Look at this - `GitHub search `_ + `GitHub search `_ to know the exact version. Guidelines for writing documentation From 894b33554f5aa7e68cd2993516ab7a252e7e4527 Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Wed, 21 Jun 2023 17:50:07 +0200 Subject: [PATCH 025/264] MNT add isort to ruff's rules (#26649) --- .github/scripts/label_title_regex.py | 5 +- .pre-commit-config.yaml | 1 + asv_benchmarks/benchmarks/cluster.py | 2 +- asv_benchmarks/benchmarks/common.py | 8 +- asv_benchmarks/benchmarks/datasets.py | 13 +- asv_benchmarks/benchmarks/decomposition.py | 4 +- asv_benchmarks/benchmarks/ensemble.py | 2 +- asv_benchmarks/benchmarks/linear_model.py | 4 +- benchmarks/bench_20newsgroups.py | 19 +- benchmarks/bench_covertype.py | 16 +- benchmarks/bench_feature_expansions.py | 4 +- benchmarks/bench_glm.py | 3 +- benchmarks/bench_glmnet.py | 10 +- benchmarks/bench_hist_gradient_boosting.py | 15 +- .../bench_hist_gradient_boosting_adult.py | 7 +- ...hist_gradient_boosting_categorical_only.py | 3 +- ...bench_hist_gradient_boosting_higgsboson.py | 10 +- .../bench_hist_gradient_boosting_threading.py | 17 +- benchmarks/bench_isolation_forest.py | 7 +- benchmarks/bench_isotonic.py | 10 +- ...kernel_pca_solvers_time_vs_n_components.py | 7 +- ...ch_kernel_pca_solvers_time_vs_n_samples.py | 7 +- benchmarks/bench_lasso.py | 4 +- benchmarks/bench_lof.py | 8 +- benchmarks/bench_mnist.py | 16 +- benchmarks/bench_multilabel_metrics.py | 11 +- benchmarks/bench_online_ocsvm.py | 19 +- benchmarks/bench_plot_incremental_pca.py | 8 +- benchmarks/bench_plot_lasso_path.py | 5 +- benchmarks/bench_plot_neighbors.py | 4 +- benchmarks/bench_plot_nmf.py | 19 +- benchmarks/bench_plot_omp_lars.py | 2 +- benchmarks/bench_plot_parallel_pairwise.py | 3 +- ...ch_plot_polynomial_kernel_approximation.py | 14 +- benchmarks/bench_plot_randomized_svd.py | 27 +-- benchmarks/bench_plot_svd.py | 7 +- benchmarks/bench_plot_ward.py | 2 +- benchmarks/bench_random_projections.py | 6 +- benchmarks/bench_rcv1_logreg_convergence.py | 9 +- benchmarks/bench_saga.py | 8 +- .../bench_sample_without_replacement.py | 6 +- benchmarks/bench_sgd_regression.py | 11 +- benchmarks/bench_sparsify.py | 3 +- benchmarks/bench_text_vectorizers.py | 4 +- benchmarks/bench_tree.py | 5 +- benchmarks/bench_tsne_mnist.py | 7 +- benchmarks/plot_tsne_mnist.py | 7 +- build_tools/azure/get_commit_message.py | 2 +- build_tools/circle/list_versions.py | 2 +- build_tools/generate_authors_table.py | 7 +- build_tools/github/check_wheels.py | 5 +- build_tools/github/vendor.py | 1 - .../update_environments_and_lock_files.py | 12 +- doc/conf.py | 13 +- doc/conftest.py | 12 +- doc/sphinxext/allow_nan_estimators.py | 9 +- doc/sphinxext/doi_role.py | 1 - doc/sphinxext/github_link.py | 4 +- .../plot_cyclical_feature_engineering.py | 15 +- .../applications/plot_digits_denoising.py | 3 +- .../applications/plot_face_recognition.py | 12 +- .../plot_model_complexity_influence.py | 10 +- .../plot_out_of_core_classification.py | 16 +- .../plot_outlier_detection_wine.py | 7 +- .../applications/plot_prediction_latency.py | 13 +- .../plot_species_distribution_modeling.py | 6 +- examples/applications/plot_stock_market.py | 1 + .../plot_tomography_l1_reconstruction.py | 9 +- .../plot_topics_extraction_with_nmf_lda.py | 5 +- examples/applications/svm_gui.py | 8 +- .../wikipedia_principal_eigenvector.py | 6 +- .../bicluster/plot_bicluster_newsgroups.py | 5 +- .../bicluster/plot_spectral_biclustering.py | 3 +- .../bicluster/plot_spectral_coclustering.py | 2 +- examples/calibration/plot_calibration.py | 2 +- .../calibration/plot_calibration_curve.py | 6 +- .../plot_calibration_multiclass.py | 1 + .../plot_classification_probability.py | 8 +- .../plot_classifier_comparison.py | 23 +-- .../plot_digits_classification.py | 2 +- examples/classification/plot_lda.py | 5 +- examples/classification/plot_lda_qda.py | 8 +- examples/cluster/plot_affinity_propagation.py | 2 +- .../cluster/plot_agglomerative_clustering.py | 1 + .../plot_agglomerative_clustering_metrics.py | 2 +- .../cluster/plot_agglomerative_dendrogram.py | 4 +- .../cluster/plot_birch_vs_minibatchkmeans.py | 8 +- examples/cluster/plot_bisect_kmeans.py | 3 +- examples/cluster/plot_cluster_comparison.py | 4 +- examples/cluster/plot_cluster_iris.py | 4 +- examples/cluster/plot_coin_segmentation.py | 5 +- examples/cluster/plot_color_quantization.py | 8 +- examples/cluster/plot_dbscan.py | 3 +- examples/cluster/plot_digits_agglomeration.py | 4 +- examples/cluster/plot_digits_linkage.py | 2 +- ...e_agglomeration_vs_univariate_selection.py | 9 +- examples/cluster/plot_hdbscan.py | 4 +- examples/cluster/plot_inductive_clustering.py | 2 +- examples/cluster/plot_kmeans_assumptions.py | 1 + examples/cluster/plot_kmeans_digits.py | 2 + examples/cluster/plot_kmeans_plusplus.py | 3 +- .../plot_kmeans_silhouette_analysis.py | 10 +- .../plot_kmeans_stability_low_dim_dense.py | 10 +- examples/cluster/plot_linkage_comparison.py | 4 +- examples/cluster/plot_mean_shift.py | 1 + examples/cluster/plot_mini_batch_kmeans.py | 2 + examples/cluster/plot_optics.py | 3 +- examples/cluster/plot_segmentation_toy.py | 3 +- .../plot_ward_structured_vs_unstructured.py | 4 - examples/compose/plot_column_transformer.py | 4 +- .../plot_column_transformer_mixed_types.py | 8 +- examples/compose/plot_compare_reduction.py | 12 +- examples/compose/plot_digits_pipe.py | 4 +- examples/compose/plot_feature_union.py | 6 +- examples/compose/plot_transformed_target.py | 2 + .../covariance/plot_covariance_estimation.py | 5 +- examples/covariance/plot_lw_vs_oas.py | 6 +- .../covariance/plot_mahalanobis_distances.py | 1 + .../plot_robust_vs_empirical_covariance.py | 4 +- examples/covariance/plot_sparse_cov.py | 1 + .../cross_decomposition/plot_pcr_vs_pls.py | 9 +- examples/datasets/plot_digits_last_image.py | 4 +- examples/datasets/plot_random_dataset.py | 4 +- .../plot_random_multilabel_dataset.py | 2 +- .../decomposition/plot_faces_decomposition.py | 5 +- .../plot_ica_blind_source_separation.py | 2 +- .../decomposition/plot_image_denoising.py | 1 - .../decomposition/plot_incremental_pca.py | 2 +- examples/decomposition/plot_pca_3d.py | 5 +- examples/decomposition/plot_pca_iris.py | 8 +- .../plot_pca_vs_fa_model_selection.py | 6 +- examples/decomposition/plot_sparse_coding.py | 2 +- examples/decomposition/plot_varimax_fa.py | 4 +- .../ensemble/plot_adaboost_hastie_10_2.py | 1 + examples/ensemble/plot_adaboost_multiclass.py | 1 - examples/ensemble/plot_adaboost_twoclass.py | 7 +- examples/ensemble/plot_bias_variance.py | 2 +- examples/ensemble/plot_ensemble_oob.py | 3 +- .../ensemble/plot_feature_transformation.py | 6 +- ...ot_forest_hist_grad_boosting_comparison.py | 6 +- examples/ensemble/plot_forest_importances.py | 1 + .../ensemble/plot_forest_importances_faces.py | 1 + examples/ensemble/plot_forest_iris.py | 6 +- .../plot_gradient_boosting_categorical.py | 9 +- .../plot_gradient_boosting_early_stopping.py | 5 +- .../ensemble/plot_gradient_boosting_oob.py | 8 +- .../plot_gradient_boosting_quantile.py | 3 +- .../plot_gradient_boosting_regression.py | 1 + .../plot_gradient_boosting_regularization.py | 5 +- examples/ensemble/plot_isolation_forest.py | 2 + .../ensemble/plot_monotonic_constraints.py | 6 +- .../ensemble/plot_random_forest_embedding.py | 4 +- ...ot_random_forest_regression_multioutput.py | 4 +- examples/ensemble/plot_stack_predictors.py | 7 +- .../ensemble/plot_voting_decision_regions.py | 6 +- examples/ensemble/plot_voting_probas.py | 5 +- examples/ensemble/plot_voting_regressor.py | 8 +- examples/exercises/plot_cv_digits.py | 3 +- .../plot_digits_classification_exercise.py | 2 +- examples/exercises/plot_iris_exercise.py | 3 +- .../feature_selection/plot_f_test_vs_mi.py | 3 +- .../plot_feature_selection.py | 1 + examples/feature_selection/plot_rfe_digits.py | 5 +- .../plot_rfe_with_cross_validation.py | 2 +- .../plot_select_from_model_diabetes.py | 4 +- .../gaussian_process/plot_compare_gpr_krr.py | 4 +- examples/gaussian_process/plot_gpc.py | 4 +- examples/gaussian_process/plot_gpc_iris.py | 3 +- .../plot_gpc_isoprobability.py | 6 +- examples/gaussian_process/plot_gpc_xor.py | 3 +- examples/gaussian_process/plot_gpr_co2.py | 1 + .../plot_gpr_on_structured_data.py | 7 +- ...t_iterative_imputer_variants_comparison.py | 14 +- examples/impute/plot_missing_values.py | 8 +- ...linear_model_coefficient_interpretation.py | 15 +- .../inspection/plot_partial_dependence.py | 11 +- .../inspection/plot_permutation_importance.py | 2 +- ...t_permutation_importance_multicollinear.py | 2 +- .../plot_scalable_poly_kernels.py | 3 +- examples/linear_model/plot_ard.py | 3 +- ...puted_gram_matrix_with_weighted_samples.py | 1 + examples/linear_model/plot_huber_vs_ridge.py | 2 +- examples/linear_model/plot_iris_logistic.py | 3 +- .../linear_model/plot_lasso_and_elasticnet.py | 5 +- .../plot_lasso_coordinate_descent_path.py | 6 +- .../plot_lasso_dense_vs_sparse_data.py | 5 +- examples/linear_model/plot_lasso_lars.py | 5 +- examples/linear_model/plot_lasso_lars_ic.py | 2 +- .../plot_lasso_model_selection.py | 3 +- examples/linear_model/plot_logistic.py | 1 + .../plot_logistic_l1_l2_sparsity.py | 4 +- .../linear_model/plot_logistic_multinomial.py | 5 +- .../plot_multi_task_lasso_support.py | 2 +- examples/linear_model/plot_nnls.py | 3 +- examples/linear_model/plot_ols.py | 1 + examples/linear_model/plot_ols_3d.py | 3 +- .../linear_model/plot_ols_ridge_variance.py | 2 +- examples/linear_model/plot_omp.py | 4 +- ...plot_poisson_regression_non_normal_loss.py | 28 +-- .../plot_polynomial_interpolation.py | 5 +- .../linear_model/plot_quantile_regression.py | 5 +- examples/linear_model/plot_ransac.py | 3 +- examples/linear_model/plot_ridge_path.py | 3 +- examples/linear_model/plot_robust_fit.py | 8 +- examples/linear_model/plot_sgd_comparison.py | 13 +- .../linear_model/plot_sgd_early_stopping.py | 10 +- examples/linear_model/plot_sgd_iris.py | 5 +- .../linear_model/plot_sgd_loss_functions.py | 2 +- examples/linear_model/plot_sgd_penalties.py | 2 +- .../plot_sgd_separating_hyperplane.py | 5 +- .../linear_model/plot_sgd_weighted_samples.py | 3 +- .../linear_model/plot_sgdocsvm_vs_ocsvm.py | 9 +- ...sparse_logistic_regression_20newsgroups.py | 2 +- .../plot_sparse_logistic_regression_mnist.py | 1 + examples/linear_model/plot_theilsen.py | 7 +- ...lot_tweedie_regression_insurance_claims.py | 26 +-- examples/manifold/plot_compare_methods.py | 4 +- examples/manifold/plot_lle_digits.py | 5 +- examples/manifold/plot_manifold_sphere.py | 10 +- examples/manifold/plot_mds.py | 3 +- examples/manifold/plot_swissroll.py | 2 +- examples/manifold/plot_t_sne_perplexity.py | 9 +- .../miscellaneous/plot_anomaly_comparison.py | 8 +- .../plot_display_object_visualization.py | 13 +- .../plot_estimator_representation.py | 7 +- .../miscellaneous/plot_isotonic_regression.py | 4 +- .../plot_johnson_lindenstrauss_bound.py | 13 +- .../plot_kernel_approximation.py | 7 +- .../plot_kernel_ridge_regression.py | 2 +- .../miscellaneous/plot_metadata_routing.py | 30 ++-- examples/miscellaneous/plot_multilabel.py | 6 +- .../plot_multioutput_face_completion.py | 8 +- .../plot_outlier_detection_bench.py | 9 +- ...ot_partial_dependence_visualization_api.py | 8 +- .../miscellaneous/plot_pipeline_display.py | 26 +-- .../plot_roc_curve_visualization_api.py | 5 +- examples/miscellaneous/plot_set_output.py | 8 +- examples/mixture/plot_concentration_prior.py | 4 +- examples/mixture/plot_gmm.py | 4 +- examples/mixture/plot_gmm_covariances.py | 1 - examples/mixture/plot_gmm_init.py | 6 +- examples/mixture/plot_gmm_pdf.py | 3 +- examples/mixture/plot_gmm_sin.py | 4 +- .../model_selection/plot_confusion_matrix.py | 6 +- examples/model_selection/plot_cv_indices.py | 15 +- examples/model_selection/plot_cv_predict.py | 1 + examples/model_selection/plot_det.py | 1 + .../plot_grid_search_refit_callable.py | 2 +- .../model_selection/plot_grid_search_stats.py | 1 + ...lot_grid_search_text_feature_extraction.py | 1 + .../model_selection/plot_learning_curve.py | 1 + .../model_selection/plot_likelihood_ratios.py | 8 +- .../plot_multi_metric_evaluation.py | 3 +- .../plot_nested_cross_validation_iris.py | 7 +- ...ot_permutation_tests_for_classification.py | 3 +- .../model_selection/plot_precision_recall.py | 7 +- .../model_selection/plot_randomized_search.py | 6 +- examples/model_selection/plot_roc.py | 4 +- examples/model_selection/plot_roc_crossval.py | 4 +- .../plot_successive_halving_heatmap.py | 6 +- .../plot_successive_halving_iterations.py | 9 +- .../plot_train_error_vs_test_error.py | 1 + .../plot_underfitting_overfitting.py | 7 +- .../model_selection/plot_validation_curve.py | 2 +- .../plot_classifier_chain_yeast.py | 9 +- .../approximate_nearest_neighbors.py | 1 + .../plot_caching_nearest_neighbors.py | 5 +- examples/neighbors/plot_classification.py | 3 +- .../neighbors/plot_digits_kde_sampling.py | 4 +- examples/neighbors/plot_kde_1d.py | 3 +- .../neighbors/plot_lof_novelty_detection.py | 3 +- examples/neighbors/plot_nca_classification.py | 6 +- examples/neighbors/plot_nca_dim_reduction.py | 5 +- examples/neighbors/plot_nca_illustration.py | 7 +- examples/neighbors/plot_nearest_centroid.py | 6 +- examples/neighbors/plot_regression.py | 3 +- examples/neighbors/plot_species_kde.py | 3 +- examples/neural_networks/plot_mlp_alpha.py | 5 +- .../plot_mlp_training_curves.py | 4 +- .../neural_networks/plot_mnist_filters.py | 4 +- .../plot_rbm_logistic_classification.py | 4 +- examples/preprocessing/plot_all_scaling.py | 24 +-- examples/preprocessing/plot_discretization.py | 2 +- .../plot_discretization_classification.py | 15 +- .../plot_discretization_strategies.py | 4 +- .../preprocessing/plot_map_data_to_normal.py | 6 +- .../preprocessing/plot_scaling_importance.py | 9 +- examples/preprocessing/plot_target_encoder.py | 8 +- .../plot_target_encoder_cross_val.py | 10 +- .../plot_release_highlights_0_22_0.py | 28 +-- .../plot_label_propagation_digits.py | 5 +- ...abel_propagation_digits_active_learning.py | 4 +- .../plot_label_propagation_structure.py | 1 + .../plot_self_training_varying_threshold.py | 7 +- .../plot_semi_supervised_newsgroups.py | 10 +- .../plot_semi_supervised_versus_svm_iris.py | 7 +- examples/svm/plot_custom_kernel.py | 5 +- examples/svm/plot_iris_svc.py | 4 +- .../svm/plot_linearsvc_support_vectors.py | 5 +- examples/svm/plot_oneclass.py | 5 +- examples/svm/plot_rbf_parameters.py | 3 +- examples/svm/plot_separating_hyperplane.py | 2 +- .../plot_separating_hyperplane_unbalanced.py | 1 + examples/svm/plot_svm_anova.py | 4 +- examples/svm/plot_svm_kernels.py | 4 +- examples/svm/plot_svm_margin.py | 3 +- examples/svm/plot_svm_nonlinear.py | 3 +- examples/svm/plot_svm_regression.py | 3 +- examples/svm/plot_svm_scale_c.py | 3 +- examples/svm/plot_svm_tie_breaking.py | 5 +- examples/svm/plot_weighted_samples.py | 3 +- ...ot_document_classification_20newsgroups.py | 19 +- examples/text/plot_document_clustering.py | 10 +- .../text/plot_hashing_vs_dict_vectorizer.py | 1 + examples/tree/plot_cost_complexity_pruning.py | 3 +- examples/tree/plot_iris_dtc.py | 5 +- examples/tree/plot_tree_regression.py | 3 +- .../tree/plot_tree_regression_multioutput.py | 3 +- examples/tree/plot_unveil_tree_structure.py | 4 +- maint_tools/check_pxd_in_installation.py | 5 +- maint_tools/sort_whats_new.py | 2 +- maint_tools/update_tracking_issue.py | 4 +- pyproject.toml | 4 + setup.py | 13 +- sklearn/__init__.py | 11 +- sklearn/_build_utils/__init__.py | 8 +- sklearn/_build_utils/pre_build_helpers.py | 4 +- sklearn/_config.py | 2 +- sklearn/_loss/__init__.py | 13 +- sklearn/_loss/link.py | 1 + sklearn/_loss/loss.py | 26 +-- sklearn/_loss/tests/test_link.py | 7 +- sklearn/_loss/tests/test_loss.py | 9 +- sklearn/_min_dependencies.py | 5 +- sklearn/base.py | 35 ++-- sklearn/calibration.py | 48 +++-- sklearn/cluster/__init__.py | 22 +-- sklearn/cluster/_affinity_propagation.py | 10 +- sklearn/cluster/_agglomerative.py | 15 +- sklearn/cluster/_bicluster.py | 16 +- sklearn/cluster/_birch.py | 19 +- sklearn/cluster/_bisect_k_means.py | 19 +- sklearn/cluster/_dbscan.py | 7 +- sklearn/cluster/_feature_agglomeration.py | 5 +- sklearn/cluster/_hdbscan/hdbscan.py | 11 +- .../_hdbscan/tests/test_reachibility.py | 3 +- sklearn/cluster/_kmeans.py | 58 +++--- sklearn/cluster/_mean_shift.py | 18 +- sklearn/cluster/_optics.py | 23 +-- sklearn/cluster/_spectral.py | 14 +- sklearn/cluster/tests/common.py | 1 - .../tests/test_affinity_propagation.py | 12 +- sklearn/cluster/tests/test_bicluster.py | 28 ++- sklearn/cluster/tests/test_birch.py | 9 +- sklearn/cluster/tests/test_bisect_k_means.py | 2 +- sklearn/cluster/tests/test_dbscan.py | 17 +- .../tests/test_feature_agglomeration.py | 7 +- sklearn/cluster/tests/test_hdbscan.py | 12 +- sklearn/cluster/tests/test_hierarchical.py | 40 ++--- sklearn/cluster/tests/test_k_means.py | 47 +++-- sklearn/cluster/tests/test_mean_shift.py | 13 +- sklearn/cluster/tests/test_optics.py | 17 +- sklearn/cluster/tests/test_spectral.py | 15 +- sklearn/compose/__init__.py | 3 +- sklearn/compose/_column_transformer.py | 29 ++- sklearn/compose/_target.py | 13 +- .../compose/tests/test_column_transformer.py | 23 ++- sklearn/compose/tests/test_target.py | 23 +-- sklearn/conftest.py | 30 ++-- sklearn/covariance/__init__.py | 15 +- sklearn/covariance/_elliptic_envelope.py | 11 +- sklearn/covariance/_empirical_covariance.py | 6 +- sklearn/covariance/_graph_lasso.py | 22 ++- sklearn/covariance/_robust_covariance.py | 7 +- sklearn/covariance/_shrunk_covariance.py | 5 +- sklearn/covariance/tests/test_covariance.py | 19 +- .../tests/test_elliptic_envelope.py | 8 +- .../covariance/tests/test_graphical_lasso.py | 19 +- .../tests/test_robust_covariance.py | 6 +- sklearn/cross_decomposition/__init__.py | 2 +- sklearn/cross_decomposition/_pls.py | 24 +-- sklearn/cross_decomposition/tests/test_pls.py | 13 +- sklearn/datasets/__init__.py | 87 ++++----- sklearn/datasets/_arff_parser.py | 1 - sklearn/datasets/_base.py | 19 +- sklearn/datasets/_california_housing.py | 24 +-- sklearn/datasets/_covtype.py | 23 +-- sklearn/datasets/_kddcup99.py | 22 +-- sklearn/datasets/_lfw.py | 14 +- sklearn/datasets/_olivetti_faces.py | 13 +- sklearn/datasets/_openml.py | 10 +- sklearn/datasets/_rcv1.py | 19 +- sklearn/datasets/_samples_generator.py | 8 +- sklearn/datasets/_species_distributions.py | 11 +- sklearn/datasets/_svmlight_format_io.py | 11 +- sklearn/datasets/_twenty_newsgroups.py | 29 +-- sklearn/datasets/tests/conftest.py | 1 + sklearn/datasets/tests/test_20news.py | 13 +- sklearn/datasets/tests/test_arff_parser.py | 2 +- sklearn/datasets/tests/test_base.py | 32 ++-- .../datasets/tests/test_california_housing.py | 3 +- sklearn/datasets/tests/test_common.py | 2 +- sklearn/datasets/tests/test_covtype.py | 2 + sklearn/datasets/tests/test_kddcup99.py | 9 +- sklearn/datasets/tests/test_lfw.py | 11 +- sklearn/datasets/tests/test_olivetti_faces.py | 3 +- sklearn/datasets/tests/test_openml.py | 20 +-- sklearn/datasets/tests/test_rcv1.py | 9 +- .../datasets/tests/test_samples_generator.py | 52 +++--- .../datasets/tests/test_svmlight_format.py | 23 +-- sklearn/decomposition/__init__.py | 31 ++-- sklearn/decomposition/_base.py | 5 +- sklearn/decomposition/_dict_learning.py | 26 +-- sklearn/decomposition/_factor_analysis.py | 14 +- sklearn/decomposition/_fastica.py | 12 +- sklearn/decomposition/_incremental_pca.py | 5 +- sklearn/decomposition/_kernel_pca.py | 25 +-- sklearn/decomposition/_lda.py | 22 ++- sklearn/decomposition/_nmf.py | 33 ++-- sklearn/decomposition/_pca.py | 10 +- sklearn/decomposition/_sparse_pca.py | 14 +- sklearn/decomposition/_truncated_svd.py | 11 +- .../decomposition/tests/test_dict_learning.py | 51 +++--- .../tests/test_factor_analysis.py | 10 +- sklearn/decomposition/tests/test_fastica.py | 9 +- .../tests/test_incremental_pca.py | 15 +- .../decomposition/tests/test_kernel_pca.py | 23 ++- sklearn/decomposition/tests/test_nmf.py | 25 ++- .../decomposition/tests/test_online_lda.py | 19 +- sklearn/decomposition/tests/test_pca.py | 14 +- .../decomposition/tests/test_sparse_pca.py | 13 +- .../decomposition/tests/test_truncated_svd.py | 7 +- sklearn/discriminant_analysis.py | 27 +-- sklearn/dummy.py | 25 ++- sklearn/ensemble/__init__.py | 31 ++-- sklearn/ensemble/_bagging.py | 25 ++- sklearn/ensemble/_base.py | 11 +- sklearn/ensemble/_forest.py | 36 ++-- sklearn/ensemble/_gb.py | 33 ++-- sklearn/ensemble/_gb_losses.py | 6 +- .../_hist_gradient_boosting/binning.py | 8 +- .../gradient_boosting.py | 39 ++-- .../_hist_gradient_boosting/grower.py | 25 +-- .../_hist_gradient_boosting/predictor.py | 8 +- .../tests/test_binning.py | 10 +- .../tests/test_bitset.py | 4 +- .../tests/test_compare_lightgbm.py | 12 +- .../tests/test_gradient_boosting.py | 31 ++-- .../tests/test_grower.py | 19 +- .../tests/test_histogram.py | 16 +- .../tests/test_monotonic_contraints.py | 17 +- .../tests/test_predictor.py | 22 +-- .../tests/test_splitting.py | 14 +- .../tests/test_warm_start.py | 12 +- sklearn/ensemble/_iforest.py | 16 +- sklearn/ensemble/_stacking.py | 35 ++-- sklearn/ensemble/_voting.py | 27 ++- sklearn/ensemble/_weight_boosting.py | 34 ++-- sklearn/ensemble/tests/test_bagging.py | 38 ++-- sklearn/ensemble/tests/test_base.py | 12 +- sklearn/ensemble/tests/test_common.py | 30 ++-- sklearn/ensemble/tests/test_forest.py | 72 ++++---- .../ensemble/tests/test_gradient_boosting.py | 39 ++-- .../test_gradient_boosting_loss_functions.py | 25 +-- sklearn/ensemble/tests/test_iforest.py | 26 ++- sklearn/ensemble/tests/test_stacking.py | 76 ++++---- sklearn/ensemble/tests/test_voting.py | 38 ++-- .../ensemble/tests/test_weight_boosting.py | 32 ++-- .../experimental/enable_halving_search_cv.py | 5 +- .../enable_hist_gradient_boosting.py | 1 - .../experimental/enable_iterative_imputer.py | 2 +- sklearn/feature_extraction/__init__.py | 4 +- .../feature_extraction/_dict_vectorizer.py | 7 +- sklearn/feature_extraction/_hash.py | 7 +- sklearn/feature_extraction/image.py | 9 +- .../tests/test_dict_vectorizer.py | 7 +- .../tests/test_feature_hasher.py | 2 +- .../feature_extraction/tests/test_image.py | 10 +- sklearn/feature_extraction/tests/test_text.py | 48 +++-- sklearn/feature_extraction/text.py | 19 +- sklearn/feature_selection/__init__.py | 40 ++--- sklearn/feature_selection/_base.py | 6 +- sklearn/feature_selection/_from_model.py | 15 +- sklearn/feature_selection/_mutual_info.py | 9 +- sklearn/feature_selection/_rfe.py | 27 ++- sklearn/feature_selection/_sequential.py | 12 +- .../_univariate_selection.py | 13 +- .../feature_selection/_variance_threshold.py | 8 +- sklearn/feature_selection/tests/test_base.py | 3 +- sklearn/feature_selection/tests/test_chi2.py | 5 +- .../tests/test_feature_select.py | 35 ++-- .../tests/test_from_model.py | 36 ++-- .../tests/test_mutual_info.py | 6 +- sklearn/feature_selection/tests/test_rfe.py | 25 ++- .../tests/test_sequential.py | 14 +- .../tests/test_variance_threshold.py | 4 +- sklearn/gaussian_process/__init__.py | 5 +- sklearn/gaussian_process/_gpc.py | 17 +- sklearn/gaussian_process/_gpr.py | 11 +- sklearn/gaussian_process/kernels.py | 13 +- .../tests/_mini_sequence_kernel.py | 10 +- sklearn/gaussian_process/tests/test_gpc.py | 12 +- sklearn/gaussian_process/tests/test_gpr.py | 22 +-- .../gaussian_process/tests/test_kernels.py | 38 ++-- sklearn/impute/_base.py | 14 +- sklearn/impute/_iterative.py | 22 +-- sklearn/impute/_knn.py | 7 +- sklearn/impute/tests/test_base.py | 8 +- sklearn/impute/tests/test_common.py | 17 +- sklearn/impute/tests/test_impute.py | 36 ++-- sklearn/impute/tests/test_knn.py | 3 +- sklearn/inspection/__init__.py | 4 +- sklearn/inspection/_partial_dependence.py | 38 ++-- sklearn/inspection/_permutation_importance.py | 7 +- sklearn/inspection/_plot/decision_boundary.py | 7 +- .../inspection/_plot/partial_dependence.py | 18 +- .../tests/test_boundary_decision_display.py | 20 +-- .../tests/test_plot_partial_dependence.py | 25 ++- .../tests/test_partial_dependence.py | 48 +++-- sklearn/inspection/tests/test_pd_utils.py | 3 +- .../tests/test_permutation_importance.py | 26 ++- sklearn/isotonic.py | 15 +- sklearn/kernel_approximation.py | 29 +-- sklearn/kernel_ridge.py | 9 +- sklearn/linear_model/__init__.py | 46 +++-- sklearn/linear_model/_base.py | 38 ++-- sklearn/linear_model/_bayes.py | 10 +- sklearn/linear_model/_coordinate_descent.py | 16 +- sklearn/linear_model/_glm/__init__.py | 4 +- sklearn/linear_model/_glm/glm.py | 5 +- sklearn/linear_model/_glm/tests/test_glm.py | 10 +- sklearn/linear_model/_huber.py | 9 +- sklearn/linear_model/_least_angle.py | 21 +-- sklearn/linear_model/_linear_loss.py | 1 + sklearn/linear_model/_logistic.py | 40 +++-- sklearn/linear_model/_omp.py | 14 +- sklearn/linear_model/_passive_aggressive.py | 4 +- sklearn/linear_model/_perceptron.py | 2 +- sklearn/linear_model/_quantile.py | 9 +- sklearn/linear_model/_ransac.py | 29 +-- sklearn/linear_model/_ridge.py | 44 ++--- sklearn/linear_model/_sag.py | 6 +- sklearn/linear_model/_stochastic_gradient.py | 57 +++--- sklearn/linear_model/_theil_sen.py | 15 +- sklearn/linear_model/tests/test_base.py | 31 ++-- sklearn/linear_model/tests/test_bayes.py | 14 +- .../tests/test_coordinate_descent.py | 52 +++--- sklearn/linear_model/tests/test_huber.py | 11 +- .../linear_model/tests/test_least_angle.py | 29 +-- .../linear_model/tests/test_linear_loss.py | 3 +- sklearn/linear_model/tests/test_logistic.py | 47 ++--- sklearn/linear_model/tests/test_omp.py | 23 +-- .../tests/test_passive_aggressive.py | 16 +- sklearn/linear_model/tests/test_perceptron.py | 7 +- sklearn/linear_model/tests/test_quantile.py | 5 +- sklearn/linear_model/tests/test_ransac.py | 19 +- sklearn/linear_model/tests/test_ridge.py | 87 ++++----- sklearn/linear_model/tests/test_sag.py | 25 +-- sklearn/linear_model/tests/test_sgd.py | 35 ++-- .../tests/test_sparse_coordinate_descent.py | 16 +- sklearn/linear_model/tests/test_theil_sen.py | 16 +- sklearn/manifold/__init__.py | 2 +- sklearn/manifold/_isomap.py | 25 +-- sklearn/manifold/_locally_linear.py | 16 +- sklearn/manifold/_mds.py | 14 +- sklearn/manifold/_spectral_embedding.py | 14 +- sklearn/manifold/_t_sne.py | 25 +-- sklearn/manifold/tests/test_isomap.py | 11 +- sklearn/manifold/tests/test_locally_linear.py | 16 +- sklearn/manifold/tests/test_mds.py | 3 +- .../manifold/tests/test_spectral_embedding.py | 26 ++- sklearn/manifold/tests/test_t_sne.py | 58 +++--- sklearn/metrics/__init__.py | 169 +++++++++--------- sklearn/metrics/_classification.py | 29 ++- .../_pairwise_distances_reduction/__init__.py | 4 +- .../_dispatcher.py | 20 +-- sklearn/metrics/_plot/confusion_matrix.py | 4 +- sklearn/metrics/_plot/det_curve.py | 2 +- .../metrics/_plot/precision_recall_curve.py | 3 +- sklearn/metrics/_plot/regression.py | 4 +- sklearn/metrics/_plot/roc_curve.py | 3 +- .../_plot/tests/test_common_curve_display.py | 9 +- .../tests/test_confusion_matrix_display.py | 11 +- .../_plot/tests/test_det_curve_display.py | 6 +- .../tests/test_precision_recall_display.py | 8 +- .../_plot/tests/test_predict_error_display.py | 2 - .../_plot/tests/test_roc_curve_display.py | 14 +- sklearn/metrics/_ranking.py | 22 +-- sklearn/metrics/_regression.py | 11 +- sklearn/metrics/_scorer.py | 84 ++++----- sklearn/metrics/cluster/__init__.py | 40 +++-- sklearn/metrics/cluster/_bicluster.py | 2 +- sklearn/metrics/cluster/_supervised.py | 5 +- sklearn/metrics/cluster/_unsupervised.py | 12 +- .../metrics/cluster/tests/test_bicluster.py | 5 +- sklearn/metrics/cluster/tests/test_common.py | 30 ++-- .../metrics/cluster/tests/test_supervised.py | 37 ++-- .../cluster/tests/test_unsupervised.py | 18 +- sklearn/metrics/pairwise.py | 40 ++--- sklearn/metrics/tests/test_classification.py | 85 +++++---- sklearn/metrics/tests/test_common.py | 114 ++++++------ sklearn/metrics/tests/test_dist_metrics.py | 6 +- sklearn/metrics/tests/test_pairwise.py | 91 +++++----- .../test_pairwise_distances_reduction.py | 8 +- sklearn/metrics/tests/test_ranking.py | 67 +++---- sklearn/metrics/tests/test_regression.py | 52 +++--- sklearn/metrics/tests/test_score_objects.py | 66 +++---- sklearn/mixture/__init__.py | 3 +- sklearn/mixture/_base.py | 8 +- sklearn/mixture/_bayesian_mixture.py | 19 +- sklearn/mixture/_gaussian_mixture.py | 6 +- .../mixture/tests/test_bayesian_mixture.py | 20 +-- .../mixture/tests/test_gaussian_mixture.py | 31 ++-- sklearn/mixture/tests/test_mixture.py | 5 +- sklearn/model_selection/__init__.py | 65 ++++--- sklearn/model_selection/_plot.py | 4 +- sklearn/model_selection/_search.py | 38 ++-- .../_search_successive_halving.py | 13 +- sklearn/model_selection/_split.py | 28 +-- sklearn/model_selection/_validation.py | 29 ++- sklearn/model_selection/tests/test_plot.py | 9 +- sklearn/model_selection/tests/test_search.py | 112 ++++++------ sklearn/model_selection/tests/test_split.py | 82 +++++---- .../tests/test_successive_halving.py | 25 +-- .../model_selection/tests/test_validation.py | 137 +++++++------- sklearn/multiclass.py | 30 ++-- sklearn/multioutput.py | 10 +- sklearn/naive_bayes.py | 15 +- sklearn/neighbors/__init__.py | 19 +- sklearn/neighbors/_base.py | 27 ++- sklearn/neighbors/_classification.py | 17 +- sklearn/neighbors/_graph.py | 8 +- sklearn/neighbors/_kde.py | 6 +- sklearn/neighbors/_lof.py | 12 +- sklearn/neighbors/_nca.py | 26 +-- sklearn/neighbors/_nearest_centroid.py | 15 +- sklearn/neighbors/_regression.py | 6 +- sklearn/neighbors/_unsupervised.py | 4 +- sklearn/neighbors/tests/test_ball_tree.py | 3 +- sklearn/neighbors/tests/test_kd_tree.py | 2 +- sklearn/neighbors/tests/test_kde.py | 13 +- sklearn/neighbors/tests/test_lof.py | 22 +-- sklearn/neighbors/tests/test_nca.py | 13 +- .../neighbors/tests/test_nearest_centroid.py | 4 +- sklearn/neighbors/tests/test_neighbors.py | 28 ++- .../tests/test_neighbors_pipeline.py | 25 ++- .../neighbors/tests/test_neighbors_tree.py | 21 ++- sklearn/neighbors/tests/test_quad_tree.py | 2 +- sklearn/neural_network/__init__.py | 4 +- sklearn/neural_network/_base.py | 1 - .../neural_network/_multilayer_perceptron.py | 42 +++-- sklearn/neural_network/_rbm.py | 18 +- sklearn/neural_network/tests/test_base.py | 5 +- sklearn/neural_network/tests/test_mlp.py | 28 +-- sklearn/neural_network/tests/test_rbm.py | 14 +- .../tests/test_stochastic_optimizers.py | 3 +- sklearn/pipeline.py | 22 +-- sklearn/preprocessing/__init__.py | 57 +++--- sklearn/preprocessing/_data.py | 24 ++- sklearn/preprocessing/_discretization.py | 25 +-- sklearn/preprocessing/_encoders.py | 17 +- .../preprocessing/_function_transformer.py | 5 +- sklearn/preprocessing/_label.py | 19 +- sklearn/preprocessing/_polynomial.py | 19 +- sklearn/preprocessing/_target_encoder.py | 16 +- sklearn/preprocessing/tests/test_common.py | 38 ++-- sklearn/preprocessing/tests/test_data.py | 83 ++++----- .../tests/test_discretization.py | 12 +- sklearn/preprocessing/tests/test_encoders.py | 14 +- .../tests/test_function_transformer.py | 10 +- sklearn/preprocessing/tests/test_label.py | 41 ++--- .../preprocessing/tests/test_polynomial.py | 13 +- .../tests/test_target_encoder.py | 25 +-- sklearn/random_projection.py | 13 +- sklearn/semi_supervised/_label_propagation.py | 9 +- sklearn/semi_supervised/_self_training.py | 7 +- .../tests/test_label_propagation.py | 13 +- .../tests/test_self_training.py | 11 +- sklearn/svm/__init__.py | 2 +- sklearn/svm/_base.py | 35 ++-- sklearn/svm/_bounds.py | 4 +- sklearn/svm/_classes.py | 13 +- sklearn/svm/tests/test_bounds.py | 10 +- sklearn/svm/tests/test_sparse.py | 12 +- sklearn/svm/tests/test_svm.py | 45 +++-- sklearn/tests/random_seed.py | 3 +- sklearn/tests/test_base.py | 37 ++-- sklearn/tests/test_build.py | 3 +- sklearn/tests/test_calibration.py | 59 +++--- sklearn/tests/test_common.py | 85 ++++----- sklearn/tests/test_config.py | 4 +- sklearn/tests/test_discriminant_analysis.py | 33 ++-- sklearn/tests/test_docstring_parameters.py | 42 ++--- sklearn/tests/test_docstrings.py | 14 +- sklearn/tests/test_dummy.py | 16 +- sklearn/tests/test_isotonic.py | 20 +-- sklearn/tests/test_kernel_approximation.py | 31 ++-- sklearn/tests/test_kernel_ridge.py | 7 +- sklearn/tests/test_metadata_routing.py | 41 +++-- sklearn/tests/test_metaestimators.py | 27 +-- sklearn/tests/test_min_dependencies_readme.py | 3 +- sklearn/tests/test_multiclass.py | 59 +++--- sklearn/tests/test_multioutput.py | 68 ++++--- sklearn/tests/test_naive_bayes.py | 31 ++-- sklearn/tests/test_pipeline.py | 51 +++--- sklearn/tests/test_public_functions.py | 12 +- sklearn/tests/test_random_projection.py | 32 ++-- sklearn/tree/__init__.py | 14 +- sklearn/tree/_classes.py | 53 +++--- sklearn/tree/_export.py | 12 +- sklearn/tree/tests/test_export.py | 13 +- sklearn/tree/tests/test_reingold_tilford.py | 3 +- sklearn/tree/tests/test_tree.py | 90 +++++----- sklearn/utils/__init__.py | 37 ++-- sklearn/utils/_array_api.py | 2 +- sklearn/utils/_available_if.py | 3 +- sklearn/utils/_encode.py | 3 +- sklearn/utils/_estimator_html_repr.py | 4 +- sklearn/utils/_joblib.py | 21 ++- sklearn/utils/_mask.py | 3 +- sklearn/utils/_mocking.py | 5 +- sklearn/utils/_param_validation.py | 15 +- sklearn/utils/_plotting.py | 2 +- sklearn/utils/_pprint.py | 2 +- sklearn/utils/_set_output.py | 2 +- sklearn/utils/_show_versions.py | 7 +- sklearn/utils/_testing.py | 47 +++-- sklearn/utils/class_weight.py | 1 - sklearn/utils/deprecation.py | 3 +- sklearn/utils/discovery.py | 8 +- sklearn/utils/estimator_checks.py | 107 ++++++----- sklearn/utils/extmath.py | 2 +- sklearn/utils/fixes.py | 10 +- sklearn/utils/metaestimators.py | 8 +- sklearn/utils/multiclass.py | 9 +- sklearn/utils/optimize.py | 5 +- sklearn/utils/random.py | 3 +- sklearn/utils/sparsefuncs.py | 10 +- sklearn/utils/tests/test_array_api.py | 19 +- sklearn/utils/tests/test_arrayfuncs.py | 2 +- sklearn/utils/tests/test_class_weight.py | 7 +- sklearn/utils/tests/test_cython_blas.py | 33 ++-- sklearn/utils/tests/test_cython_templating.py | 2 + sklearn/utils/tests/test_deprecation.py | 4 +- sklearn/utils/tests/test_encode.py | 5 +- sklearn/utils/tests/test_estimator_checks.py | 57 +++--- .../utils/tests/test_estimator_html_repr.py | 40 ++--- sklearn/utils/tests/test_extmath.py | 52 +++--- sklearn/utils/tests/test_fast_dict.py | 2 +- sklearn/utils/tests/test_fixes.py | 1 - sklearn/utils/tests/test_graph.py | 4 +- sklearn/utils/tests/test_metaestimators.py | 4 +- sklearn/utils/tests/test_mocking.py | 12 +- sklearn/utils/tests/test_multiclass.py | 50 +++--- sklearn/utils/tests/test_murmurhash.py | 4 +- sklearn/utils/tests/test_optimize.py | 3 +- sklearn/utils/tests/test_parallel.py | 3 +- sklearn/utils/tests/test_param_validation.py | 53 +++--- sklearn/utils/tests/test_plotting.py | 2 +- sklearn/utils/tests/test_random.py | 4 +- sklearn/utils/tests/test_response.py | 4 +- sklearn/utils/tests/test_seq_dataset.py | 4 +- sklearn/utils/tests/test_set_output.py | 14 +- sklearn/utils/tests/test_shortest_path.py | 1 + sklearn/utils/tests/test_show_versions.py | 6 +- sklearn/utils/tests/test_sparsefuncs.py | 19 +- sklearn/utils/tests/test_testing.py | 37 ++-- sklearn/utils/tests/test_utils.py | 51 +++--- sklearn/utils/tests/test_validation.py | 93 +++++----- sklearn/utils/tests/test_weight_vector.py | 1 + sklearn/utils/validation.py | 25 ++- 771 files changed, 5512 insertions(+), 5562 deletions(-) diff --git a/.github/scripts/label_title_regex.py b/.github/scripts/label_title_regex.py index ddf9bda3492de..a022c3c4dd2a7 100644 --- a/.github/scripts/label_title_regex.py +++ b/.github/scripts/label_title_regex.py @@ -1,10 +1,11 @@ """Labels PRs based on title. Must be run in a github action with the pull_request_target event.""" -from github import Github -import os import json +import os import re +from github import Github + context_dict = json.loads(os.getenv("CONTEXT_GITHUB")) repo = context_dict["repository"] diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 643c2141819d5..abffbbe149f2c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -14,6 +14,7 @@ repos: rev: v0.0.272 hooks: - id: ruff + args: ["--fix", "--show-source"] - repo: https://github.com/pre-commit/mirrors-mypy rev: v1.3.0 hooks: diff --git a/asv_benchmarks/benchmarks/cluster.py b/asv_benchmarks/benchmarks/cluster.py index ba460e6b503a6..457a15dd938e9 100644 --- a/asv_benchmarks/benchmarks/cluster.py +++ b/asv_benchmarks/benchmarks/cluster.py @@ -1,7 +1,7 @@ from sklearn.cluster import KMeans, MiniBatchKMeans from .common import Benchmark, Estimator, Predictor, Transformer -from .datasets import _blobs_dataset, _20newsgroups_highdim_dataset +from .datasets import _20newsgroups_highdim_dataset, _blobs_dataset from .utils import neg_mean_inertia diff --git a/asv_benchmarks/benchmarks/common.py b/asv_benchmarks/benchmarks/common.py index c3e114a212047..aeea558844587 100644 --- a/asv_benchmarks/benchmarks/common.py +++ b/asv_benchmarks/benchmarks/common.py @@ -1,11 +1,11 @@ -import os +import itertools import json -import timeit +import os import pickle -import itertools +import timeit from abc import ABC, abstractmethod -from pathlib import Path from multiprocessing import cpu_count +from pathlib import Path import numpy as np diff --git a/asv_benchmarks/benchmarks/datasets.py b/asv_benchmarks/benchmarks/datasets.py index dbe0eac0b822c..8f0c915c95e63 100644 --- a/asv_benchmarks/benchmarks/datasets.py +++ b/asv_benchmarks/benchmarks/datasets.py @@ -1,21 +1,22 @@ +from pathlib import Path + import numpy as np import scipy.sparse as sp from joblib import Memory -from pathlib import Path -from sklearn.decomposition import TruncatedSVD from sklearn.datasets import ( - make_blobs, fetch_20newsgroups, + fetch_olivetti_faces, fetch_openml, load_digits, - make_regression, + make_blobs, make_classification, - fetch_olivetti_faces, + make_regression, ) -from sklearn.preprocessing import MaxAbsScaler, StandardScaler +from sklearn.decomposition import TruncatedSVD from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split +from sklearn.preprocessing import MaxAbsScaler, StandardScaler # memory location for caching datasets M = Memory(location=str(Path(__file__).resolve().parent / "cache")) diff --git a/asv_benchmarks/benchmarks/decomposition.py b/asv_benchmarks/benchmarks/decomposition.py index 02a7862caeb69..0a7bb7ad07f3e 100644 --- a/asv_benchmarks/benchmarks/decomposition.py +++ b/asv_benchmarks/benchmarks/decomposition.py @@ -1,8 +1,8 @@ from sklearn.decomposition import PCA, DictionaryLearning, MiniBatchDictionaryLearning from .common import Benchmark, Estimator, Transformer -from .datasets import _olivetti_faces_dataset, _mnist_dataset -from .utils import make_pca_scorers, make_dict_learning_scorers +from .datasets import _mnist_dataset, _olivetti_faces_dataset +from .utils import make_dict_learning_scorers, make_pca_scorers class PCABenchmark(Transformer, Estimator, Benchmark): diff --git a/asv_benchmarks/benchmarks/ensemble.py b/asv_benchmarks/benchmarks/ensemble.py index 8c5a28e3da90f..c336d1e5f8805 100644 --- a/asv_benchmarks/benchmarks/ensemble.py +++ b/asv_benchmarks/benchmarks/ensemble.py @@ -1,7 +1,7 @@ from sklearn.ensemble import ( - RandomForestClassifier, GradientBoostingClassifier, HistGradientBoostingClassifier, + RandomForestClassifier, ) from .common import Benchmark, Estimator, Predictor diff --git a/asv_benchmarks/benchmarks/linear_model.py b/asv_benchmarks/benchmarks/linear_model.py index b694a109329f0..7e7b9d33540c6 100644 --- a/asv_benchmarks/benchmarks/linear_model.py +++ b/asv_benchmarks/benchmarks/linear_model.py @@ -1,9 +1,9 @@ from sklearn.linear_model import ( - LogisticRegression, - Ridge, ElasticNet, Lasso, LinearRegression, + LogisticRegression, + Ridge, SGDRegressor, ) diff --git a/benchmarks/bench_20newsgroups.py b/benchmarks/bench_20newsgroups.py index c542349839178..a559bc59b5f8a 100644 --- a/benchmarks/bench_20newsgroups.py +++ b/benchmarks/bench_20newsgroups.py @@ -1,18 +1,19 @@ -from time import time import argparse -import numpy as np +from time import time -from sklearn.dummy import DummyClassifier +import numpy as np from sklearn.datasets import fetch_20newsgroups_vectorized -from sklearn.metrics import accuracy_score -from sklearn.utils.validation import check_array - -from sklearn.ensemble import RandomForestClassifier -from sklearn.ensemble import ExtraTreesClassifier -from sklearn.ensemble import AdaBoostClassifier +from sklearn.dummy import DummyClassifier +from sklearn.ensemble import ( + AdaBoostClassifier, + ExtraTreesClassifier, + RandomForestClassifier, +) from sklearn.linear_model import LogisticRegression +from sklearn.metrics import accuracy_score from sklearn.naive_bayes import MultinomialNB +from sklearn.utils.validation import check_array ESTIMATORS = { "dummy": DummyClassifier(), diff --git a/benchmarks/bench_covertype.py b/benchmarks/bench_covertype.py index 8a13a2d9806c6..5b8cdd588c8ee 100644 --- a/benchmarks/bench_covertype.py +++ b/benchmarks/bench_covertype.py @@ -45,20 +45,24 @@ # Arnaud Joly # License: BSD 3 clause +import argparse import os from time import time -import argparse + import numpy as np from joblib import Memory from sklearn.datasets import fetch_covtype, get_data_home -from sklearn.svm import LinearSVC -from sklearn.linear_model import SGDClassifier, LogisticRegression +from sklearn.ensemble import ( + ExtraTreesClassifier, + GradientBoostingClassifier, + RandomForestClassifier, +) +from sklearn.linear_model import LogisticRegression, SGDClassifier +from sklearn.metrics import zero_one_loss from sklearn.naive_bayes import GaussianNB +from sklearn.svm import LinearSVC from sklearn.tree import DecisionTreeClassifier -from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier -from sklearn.ensemble import GradientBoostingClassifier -from sklearn.metrics import zero_one_loss from sklearn.utils import check_array # Memoize the data extraction and memory map the resulting diff --git a/benchmarks/bench_feature_expansions.py b/benchmarks/bench_feature_expansions.py index fd5a4f0ebccff..b9d9efbdea4f1 100644 --- a/benchmarks/bench_feature_expansions.py +++ b/benchmarks/bench_feature_expansions.py @@ -1,8 +1,10 @@ +from time import time + import matplotlib.pyplot as plt import numpy as np import scipy.sparse as sparse + from sklearn.preprocessing import PolynomialFeatures -from time import time degree = 2 trials = 3 diff --git a/benchmarks/bench_glm.py b/benchmarks/bench_glm.py index c6c2a6f5fa117..803043398d1ac 100644 --- a/benchmarks/bench_glm.py +++ b/benchmarks/bench_glm.py @@ -5,9 +5,10 @@ """ from datetime import datetime + import numpy as np -from sklearn import linear_model +from sklearn import linear_model if __name__ == "__main__": import matplotlib.pyplot as plt diff --git a/benchmarks/bench_glmnet.py b/benchmarks/bench_glmnet.py index 8a0a0545bb627..7b111f95044e2 100644 --- a/benchmarks/bench_glmnet.py +++ b/benchmarks/bench_glmnet.py @@ -16,9 +16,11 @@ In both cases, only 10% of the features are informative. """ -import numpy as np import gc from time import time + +import numpy as np + from sklearn.datasets import make_regression alpha = 0.1 @@ -45,11 +47,11 @@ def bench(factory, X, Y, X_test, Y_test, ref_coef): if __name__ == "__main__": - from glmnet.elastic_net import Lasso as GlmnetLasso - from sklearn.linear_model import Lasso as ScikitLasso - # Delayed import of matplotlib.pyplot import matplotlib.pyplot as plt + from glmnet.elastic_net import Lasso as GlmnetLasso + + from sklearn.linear_model import Lasso as ScikitLasso scikit_results = [] glmnet_results = [] diff --git a/benchmarks/bench_hist_gradient_boosting.py b/benchmarks/bench_hist_gradient_boosting.py index 163e21f98ed0d..c1dfffabe71c2 100644 --- a/benchmarks/bench_hist_gradient_boosting.py +++ b/benchmarks/bench_hist_gradient_boosting.py @@ -1,15 +1,16 @@ -from time import time import argparse +from time import time import matplotlib.pyplot as plt import numpy as np -from sklearn.model_selection import train_test_split -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.ensemble import HistGradientBoostingClassifier -from sklearn.datasets import make_classification -from sklearn.datasets import make_regression -from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator +from sklearn.datasets import make_classification, make_regression +from sklearn.ensemble import ( + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, +) +from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator +from sklearn.model_selection import train_test_split parser = argparse.ArgumentParser() parser.add_argument("--n-leaf-nodes", type=int, default=31) diff --git a/benchmarks/bench_hist_gradient_boosting_adult.py b/benchmarks/bench_hist_gradient_boosting_adult.py index 1b5905b1cf4e8..5fa5bbae0c35c 100644 --- a/benchmarks/bench_hist_gradient_boosting_adult.py +++ b/benchmarks/bench_hist_gradient_boosting_adult.py @@ -4,15 +4,14 @@ import numpy as np import pandas as pd -from sklearn.model_selection import train_test_split -from sklearn.compose import make_column_transformer, make_column_selector +from sklearn.compose import make_column_selector, make_column_transformer from sklearn.datasets import fetch_openml -from sklearn.metrics import accuracy_score, roc_auc_score from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator +from sklearn.metrics import accuracy_score, roc_auc_score +from sklearn.model_selection import train_test_split from sklearn.preprocessing import OrdinalEncoder - parser = argparse.ArgumentParser() parser.add_argument("--n-leaf-nodes", type=int, default=31) parser.add_argument("--n-trees", type=int, default=100) diff --git a/benchmarks/bench_hist_gradient_boosting_categorical_only.py b/benchmarks/bench_hist_gradient_boosting_categorical_only.py index e8d215170f9c8..1085bbc49f4f8 100644 --- a/benchmarks/bench_hist_gradient_boosting_categorical_only.py +++ b/benchmarks/bench_hist_gradient_boosting_categorical_only.py @@ -1,11 +1,10 @@ import argparse from time import time -from sklearn.preprocessing import KBinsDiscretizer from sklearn.datasets import make_classification from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator - +from sklearn.preprocessing import KBinsDiscretizer parser = argparse.ArgumentParser() parser.add_argument("--n-leaf-nodes", type=int, default=31) diff --git a/benchmarks/bench_hist_gradient_boosting_higgsboson.py b/benchmarks/bench_hist_gradient_boosting_higgsboson.py index d6ed3b8e9700f..65be02ec0c4b9 100644 --- a/benchmarks/bench_hist_gradient_boosting_higgsboson.py +++ b/benchmarks/bench_hist_gradient_boosting_higgsboson.py @@ -1,17 +1,17 @@ -from urllib.request import urlretrieve +import argparse import os from gzip import GzipFile from time import time -import argparse +from urllib.request import urlretrieve import numpy as np import pandas as pd from joblib import Memory -from sklearn.model_selection import train_test_split -from sklearn.metrics import accuracy_score, roc_auc_score + from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator - +from sklearn.metrics import accuracy_score, roc_auc_score +from sklearn.model_selection import train_test_split parser = argparse.ArgumentParser() parser.add_argument("--n-leaf-nodes", type=int, default=31) diff --git a/benchmarks/bench_hist_gradient_boosting_threading.py b/benchmarks/bench_hist_gradient_boosting_threading.py index 70787fd2eb479..9acf65bdbaf6a 100644 --- a/benchmarks/bench_hist_gradient_boosting_threading.py +++ b/benchmarks/bench_hist_gradient_boosting_threading.py @@ -1,18 +1,19 @@ -from time import time import argparse import os from pprint import pprint +from time import time import numpy as np from threadpoolctl import threadpool_limits + import sklearn -from sklearn.model_selection import train_test_split -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.ensemble import HistGradientBoostingClassifier -from sklearn.datasets import make_classification -from sklearn.datasets import make_regression +from sklearn.datasets import make_classification, make_regression +from sklearn.ensemble import ( + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, +) from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator - +from sklearn.model_selection import train_test_split parser = argparse.ArgumentParser() parser.add_argument("--n-leaf-nodes", type=int, default=31) @@ -290,8 +291,8 @@ def one_run(n_threads, n_samples): if args.plot or args.plot_filename: - import matplotlib.pyplot as plt import matplotlib + import matplotlib.pyplot as plt fig, axs = plt.subplots(2, figsize=(12, 12)) diff --git a/benchmarks/bench_isolation_forest.py b/benchmarks/bench_isolation_forest.py index 1c85cfb79d321..021114af56ea6 100644 --- a/benchmarks/bench_isolation_forest.py +++ b/benchmarks/bench_isolation_forest.py @@ -17,12 +17,13 @@ """ from time import time -import numpy as np + import matplotlib.pyplot as plt +import numpy as np +from sklearn.datasets import fetch_covtype, fetch_kddcup99, fetch_openml from sklearn.ensemble import IsolationForest -from sklearn.metrics import roc_curve, auc -from sklearn.datasets import fetch_kddcup99, fetch_covtype, fetch_openml +from sklearn.metrics import auc, roc_curve from sklearn.preprocessing import LabelBinarizer from sklearn.utils import shuffle as sh diff --git a/benchmarks/bench_isotonic.py b/benchmarks/bench_isotonic.py index 458a04a463303..221e6fb12da75 100644 --- a/benchmarks/bench_isotonic.py +++ b/benchmarks/bench_isotonic.py @@ -10,13 +10,15 @@ This allows the scaling of the algorithm with the problem size to be visualized and understood. """ -import numpy as np +import argparse import gc from datetime import datetime -from sklearn.isotonic import isotonic_regression -from scipy.special import expit + import matplotlib.pyplot as plt -import argparse +import numpy as np +from scipy.special import expit + +from sklearn.isotonic import isotonic_regression def generate_perturbed_logarithm_dataset(size): diff --git a/benchmarks/bench_kernel_pca_solvers_time_vs_n_components.py b/benchmarks/bench_kernel_pca_solvers_time_vs_n_components.py index 00721aa7f18a9..6551cb74ff86e 100644 --- a/benchmarks/bench_kernel_pca_solvers_time_vs_n_components.py +++ b/benchmarks/bench_kernel_pca_solvers_time_vs_n_components.py @@ -39,13 +39,12 @@ import time -import numpy as np import matplotlib.pyplot as plt - +import numpy as np from numpy.testing import assert_array_almost_equal -from sklearn.decomposition import KernelPCA -from sklearn.datasets import make_circles +from sklearn.datasets import make_circles +from sklearn.decomposition import KernelPCA print(__doc__) diff --git a/benchmarks/bench_kernel_pca_solvers_time_vs_n_samples.py b/benchmarks/bench_kernel_pca_solvers_time_vs_n_samples.py index a40ddea4506dd..26a45ca9f09ca 100644 --- a/benchmarks/bench_kernel_pca_solvers_time_vs_n_samples.py +++ b/benchmarks/bench_kernel_pca_solvers_time_vs_n_samples.py @@ -41,13 +41,12 @@ import time -import numpy as np import matplotlib.pyplot as plt - +import numpy as np from numpy.testing import assert_array_almost_equal -from sklearn.decomposition import KernelPCA -from sklearn.datasets import make_circles +from sklearn.datasets import make_circles +from sklearn.decomposition import KernelPCA print(__doc__) diff --git a/benchmarks/bench_lasso.py b/benchmarks/bench_lasso.py index 9a893545fbb28..1c49c6f5cabdf 100644 --- a/benchmarks/bench_lasso.py +++ b/benchmarks/bench_lasso.py @@ -13,6 +13,7 @@ """ import gc from time import time + import numpy as np from sklearn.datasets import make_regression @@ -59,9 +60,10 @@ def compute_bench(alpha, n_samples, n_features, precompute): if __name__ == "__main__": - from sklearn.linear_model import Lasso, LassoLars import matplotlib.pyplot as plt + from sklearn.linear_model import Lasso, LassoLars + alpha = 0.01 # regularization parameter n_features = 10 diff --git a/benchmarks/bench_lof.py b/benchmarks/bench_lof.py index 31057e2e4067b..8652073a7203d 100644 --- a/benchmarks/bench_lof.py +++ b/benchmarks/bench_lof.py @@ -18,11 +18,13 @@ """ from time import time -import numpy as np + import matplotlib.pyplot as plt +import numpy as np + +from sklearn.datasets import fetch_covtype, fetch_kddcup99, fetch_openml +from sklearn.metrics import auc, roc_curve from sklearn.neighbors import LocalOutlierFactor -from sklearn.metrics import roc_curve, auc -from sklearn.datasets import fetch_kddcup99, fetch_covtype, fetch_openml from sklearn.preprocessing import LabelBinarizer print(__doc__) diff --git a/benchmarks/bench_mnist.py b/benchmarks/bench_mnist.py index 4bc28ea1a165d..4ba17cb1003c3 100644 --- a/benchmarks/bench_mnist.py +++ b/benchmarks/bench_mnist.py @@ -30,26 +30,24 @@ # Arnaud Joly # License: BSD 3 clause +import argparse import os from time import time -import argparse + import numpy as np from joblib import Memory -from sklearn.datasets import fetch_openml -from sklearn.datasets import get_data_home -from sklearn.ensemble import ExtraTreesClassifier -from sklearn.ensemble import RandomForestClassifier +from sklearn.datasets import fetch_openml, get_data_home from sklearn.dummy import DummyClassifier -from sklearn.kernel_approximation import Nystroem -from sklearn.kernel_approximation import RBFSampler +from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier +from sklearn.kernel_approximation import Nystroem, RBFSampler +from sklearn.linear_model import LogisticRegression from sklearn.metrics import zero_one_loss +from sklearn.neural_network import MLPClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC from sklearn.tree import DecisionTreeClassifier from sklearn.utils import check_array -from sklearn.linear_model import LogisticRegression -from sklearn.neural_network import MLPClassifier # Memoize the data extraction and memory map the resulting # train / test splits in readonly mode diff --git a/benchmarks/bench_multilabel_metrics.py b/benchmarks/bench_multilabel_metrics.py index 2a87b388e91a2..1b8449a24da51 100755 --- a/benchmarks/bench_multilabel_metrics.py +++ b/benchmarks/bench_multilabel_metrics.py @@ -3,26 +3,25 @@ A comparison of multilabel target formats and metrics over them """ -from timeit import timeit -from functools import partial -import itertools import argparse +import itertools import sys +from functools import partial +from timeit import timeit import matplotlib.pyplot as plt -import scipy.sparse as sp import numpy as np +import scipy.sparse as sp from sklearn.datasets import make_multilabel_classification from sklearn.metrics import ( - f1_score, accuracy_score, + f1_score, hamming_loss, jaccard_similarity_score, ) from sklearn.utils._testing import ignore_warnings - METRICS = { "f1": partial(f1_score, average="micro"), "f1-by-sample": partial(f1_score, average="samples"), diff --git a/benchmarks/bench_online_ocsvm.py b/benchmarks/bench_online_ocsvm.py index 37af2fdd76562..9f92150e079dd 100644 --- a/benchmarks/bench_online_ocsvm.py +++ b/benchmarks/bench_online_ocsvm.py @@ -15,21 +15,20 @@ """ from time import time -import numpy as np +import matplotlib +import matplotlib.pyplot as plt +import numpy as np from scipy.interpolate import interp1d -from sklearn.metrics import roc_curve, auc -from sklearn.datasets import fetch_kddcup99, fetch_covtype -from sklearn.preprocessing import LabelBinarizer, StandardScaler -from sklearn.pipeline import make_pipeline -from sklearn.utils import shuffle +from sklearn.datasets import fetch_covtype, fetch_kddcup99 from sklearn.kernel_approximation import Nystroem -from sklearn.svm import OneClassSVM from sklearn.linear_model import SGDOneClassSVM - -import matplotlib.pyplot as plt -import matplotlib +from sklearn.metrics import auc, roc_curve +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import LabelBinarizer, StandardScaler +from sklearn.svm import OneClassSVM +from sklearn.utils import shuffle font = {"weight": "normal", "size": 15} diff --git a/benchmarks/bench_plot_incremental_pca.py b/benchmarks/bench_plot_incremental_pca.py index 0f42e4b630f1d..49b87c8c7060a 100644 --- a/benchmarks/bench_plot_incremental_pca.py +++ b/benchmarks/bench_plot_incremental_pca.py @@ -7,13 +7,15 @@ """ -import numpy as np import gc -from time import time from collections import defaultdict +from time import time + import matplotlib.pyplot as plt +import numpy as np + from sklearn.datasets import fetch_lfw_people -from sklearn.decomposition import IncrementalPCA, PCA +from sklearn.decomposition import PCA, IncrementalPCA def plot_results(X, y, label): diff --git a/benchmarks/bench_plot_lasso_path.py b/benchmarks/bench_plot_lasso_path.py index c372ee07117fc..c996c9c09520f 100644 --- a/benchmarks/bench_plot_lasso_path.py +++ b/benchmarks/bench_plot_lasso_path.py @@ -2,16 +2,15 @@ The input data is mostly low rank but is a fat infinite tail. """ -from collections import defaultdict import gc import sys +from collections import defaultdict from time import time import numpy as np -from sklearn.linear_model import lars_path, lars_path_gram -from sklearn.linear_model import lasso_path from sklearn.datasets import make_regression +from sklearn.linear_model import lars_path, lars_path_gram, lasso_path def compute_bench(samples_range, features_range): diff --git a/benchmarks/bench_plot_neighbors.py b/benchmarks/bench_plot_neighbors.py index c6e5541eda6f3..2d9cf2b08b71d 100644 --- a/benchmarks/bench_plot_neighbors.py +++ b/benchmarks/bench_plot_neighbors.py @@ -3,11 +3,11 @@ """ from time import time -import numpy as np import matplotlib.pyplot as plt +import numpy as np from matplotlib import ticker -from sklearn import neighbors, datasets +from sklearn import datasets, neighbors def get_data(N, D, dataset="dense"): diff --git a/benchmarks/bench_plot_nmf.py b/benchmarks/bench_plot_nmf.py index 78d6ad875cc34..d23191df0fbc9 100644 --- a/benchmarks/bench_plot_nmf.py +++ b/benchmarks/bench_plot_nmf.py @@ -6,28 +6,25 @@ # Anthony Di Franco (projected gradient, Python and NumPy port) # License: BSD 3 clause -from time import time +import numbers import sys import warnings -import numbers +from time import time -import numpy as np import matplotlib.pyplot as plt -from joblib import Memory +import numpy as np import pandas +from joblib import Memory -from sklearn.utils._testing import ignore_warnings -from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.decomposition import NMF -from sklearn.decomposition._nmf import _initialize_nmf -from sklearn.decomposition._nmf import _beta_divergence -from sklearn.decomposition._nmf import _check_init +from sklearn.decomposition._nmf import _beta_divergence, _check_init, _initialize_nmf from sklearn.exceptions import ConvergenceWarning -from sklearn.utils.extmath import safe_sparse_dot, squared_norm +from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.utils import check_array +from sklearn.utils._testing import ignore_warnings +from sklearn.utils.extmath import safe_sparse_dot, squared_norm from sklearn.utils.validation import check_is_fitted, check_non_negative - mem = Memory(cachedir=".", verbose=0) ################### diff --git a/benchmarks/bench_plot_omp_lars.py b/benchmarks/bench_plot_omp_lars.py index a800b3ebe2ba9..ec1bf3281f3a4 100644 --- a/benchmarks/bench_plot_omp_lars.py +++ b/benchmarks/bench_plot_omp_lars.py @@ -9,8 +9,8 @@ import numpy as np -from sklearn.linear_model import lars_path, lars_path_gram, orthogonal_mp from sklearn.datasets import make_sparse_coded_signal +from sklearn.linear_model import lars_path, lars_path_gram, orthogonal_mp def compute_bench(samples_range, features_range): diff --git a/benchmarks/bench_plot_parallel_pairwise.py b/benchmarks/bench_plot_parallel_pairwise.py index a41e3fab20589..ca12972f9be6c 100644 --- a/benchmarks/bench_plot_parallel_pairwise.py +++ b/benchmarks/bench_plot_parallel_pairwise.py @@ -4,9 +4,8 @@ import matplotlib.pyplot as plt +from sklearn.metrics.pairwise import pairwise_distances, pairwise_kernels from sklearn.utils import check_random_state -from sklearn.metrics.pairwise import pairwise_distances -from sklearn.metrics.pairwise import pairwise_kernels def plot(func): diff --git a/benchmarks/bench_plot_polynomial_kernel_approximation.py b/benchmarks/bench_plot_polynomial_kernel_approximation.py index b21589263a49f..ad89d974f3d93 100644 --- a/benchmarks/bench_plot_polynomial_kernel_approximation.py +++ b/benchmarks/bench_plot_polynomial_kernel_approximation.py @@ -42,21 +42,21 @@ # License: BSD 3 clause # Load data manipulation functions -from sklearn.datasets import load_digits -from sklearn.model_selection import train_test_split +# Will use this for timing results +from time import time # Some common libraries import matplotlib.pyplot as plt import numpy as np -# Will use this for timing results -from time import time - -# Import SVM classifiers and feature map approximation algorithms -from sklearn.svm import LinearSVC, SVC +from sklearn.datasets import load_digits from sklearn.kernel_approximation import Nystroem, PolynomialCountSketch +from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline +# Import SVM classifiers and feature map approximation algorithms +from sklearn.svm import SVC, LinearSVC + # Split data in train and test sets X, y = load_digits()["data"], load_digits()["target"] X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7) diff --git a/benchmarks/bench_plot_randomized_svd.py b/benchmarks/bench_plot_randomized_svd.py index 2020096a21b88..9ac4e714cb7dc 100644 --- a/benchmarks/bench_plot_randomized_svd.py +++ b/benchmarks/bench_plot_randomized_svd.py @@ -65,28 +65,29 @@ # Author: Giorgio Patrini -import numpy as np -import scipy as sp -import matplotlib.pyplot as plt - import gc +import os.path import pickle -from time import time from collections import defaultdict -import os.path +from time import time + +import matplotlib.pyplot as plt +import numpy as np +import scipy as sp -from sklearn.utils._arpack import _init_arpack_v0 -from sklearn.utils import gen_batches -from sklearn.utils.validation import check_random_state -from sklearn.utils.extmath import randomized_svd -from sklearn.datasets import make_low_rank_matrix, make_sparse_uncorrelated from sklearn.datasets import ( - fetch_lfw_people, - fetch_openml, fetch_20newsgroups_vectorized, + fetch_lfw_people, fetch_olivetti_faces, + fetch_openml, fetch_rcv1, + make_low_rank_matrix, + make_sparse_uncorrelated, ) +from sklearn.utils import gen_batches +from sklearn.utils._arpack import _init_arpack_v0 +from sklearn.utils.extmath import randomized_svd +from sklearn.utils.validation import check_random_state try: import fbpca diff --git a/benchmarks/bench_plot_svd.py b/benchmarks/bench_plot_svd.py index fc370d1073be1..abd2c6fe9d4d4 100644 --- a/benchmarks/bench_plot_svd.py +++ b/benchmarks/bench_plot_svd.py @@ -3,13 +3,14 @@ The data is mostly low rank but is a fat infinite tail. """ import gc -from time import time -import numpy as np from collections import defaultdict +from time import time +import numpy as np from scipy.linalg import svd -from sklearn.utils.extmath import randomized_svd + from sklearn.datasets import make_low_rank_matrix +from sklearn.utils.extmath import randomized_svd def compute_bench(samples_range, features_range, n_iter=3, rank=50): diff --git a/benchmarks/bench_plot_ward.py b/benchmarks/bench_plot_ward.py index 696e833eede20..fe5cee201dff4 100644 --- a/benchmarks/bench_plot_ward.py +++ b/benchmarks/bench_plot_ward.py @@ -4,9 +4,9 @@ import time +import matplotlib.pyplot as plt import numpy as np from scipy.cluster import hierarchy -import matplotlib.pyplot as plt from sklearn.cluster import AgglomerativeClustering diff --git a/benchmarks/bench_random_projections.py b/benchmarks/bench_random_projections.py index 89a4550944f3f..bd8c62ecba484 100644 --- a/benchmarks/bench_random_projections.py +++ b/benchmarks/bench_random_projections.py @@ -6,19 +6,19 @@ Benchmarks for random projections. """ +import collections import gc -import sys import optparse +import sys from datetime import datetime -import collections import numpy as np import scipy.sparse as sp from sklearn import clone from sklearn.random_projection import ( - SparseRandomProjection, GaussianRandomProjection, + SparseRandomProjection, johnson_lindenstrauss_min_dim, ) diff --git a/benchmarks/bench_rcv1_logreg_convergence.py b/benchmarks/bench_rcv1_logreg_convergence.py index 2254ab81f30a4..166c6c2f5f9d1 100644 --- a/benchmarks/bench_rcv1_logreg_convergence.py +++ b/benchmarks/bench_rcv1_logreg_convergence.py @@ -3,14 +3,15 @@ # # License: BSD 3 clause -import matplotlib.pyplot as plt -from joblib import Memory -import numpy as np import gc import time -from sklearn.linear_model import LogisticRegression, SGDClassifier +import matplotlib.pyplot as plt +import numpy as np +from joblib import Memory + from sklearn.datasets import fetch_rcv1 +from sklearn.linear_model import LogisticRegression, SGDClassifier from sklearn.linear_model._sag import get_auto_step_size try: diff --git a/benchmarks/bench_saga.py b/benchmarks/bench_saga.py index 340549ef240e1..dc2ed093f11d0 100644 --- a/benchmarks/bench_saga.py +++ b/benchmarks/bench_saga.py @@ -4,24 +4,24 @@ in using multinomial logistic regression in term of learning time. """ import json -import time import os +import time -from sklearn.utils.parallel import delayed, Parallel import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import ( + fetch_20newsgroups_vectorized, fetch_rcv1, - load_iris, load_digits, - fetch_20newsgroups_vectorized, + load_iris, ) from sklearn.linear_model import LogisticRegression from sklearn.metrics import log_loss from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelBinarizer, LabelEncoder from sklearn.utils.extmath import safe_sparse_dot, softmax +from sklearn.utils.parallel import Parallel, delayed def fit_single( diff --git a/benchmarks/bench_sample_without_replacement.py b/benchmarks/bench_sample_without_replacement.py index 10baad5a8495f..743292ca5fa61 100644 --- a/benchmarks/bench_sample_without_replacement.py +++ b/benchmarks/bench_sample_without_replacement.py @@ -3,14 +3,14 @@ """ import gc -import sys +import operator import optparse +import random +import sys from datetime import datetime -import operator import matplotlib.pyplot as plt import numpy as np -import random from sklearn.utils.random import sample_without_replacement diff --git a/benchmarks/bench_sgd_regression.py b/benchmarks/bench_sgd_regression.py index 47dd9e9fc758b..4b1b902795feb 100644 --- a/benchmarks/bench_sgd_regression.py +++ b/benchmarks/bench_sgd_regression.py @@ -1,16 +1,15 @@ # Author: Peter Prettenhofer # License: BSD 3 clause -import numpy as np -import matplotlib.pyplot as plt - import gc - from time import time -from sklearn.linear_model import Ridge, SGDRegressor, ElasticNet -from sklearn.metrics import mean_squared_error +import matplotlib.pyplot as plt +import numpy as np + from sklearn.datasets import make_regression +from sklearn.linear_model import ElasticNet, Ridge, SGDRegressor +from sklearn.metrics import mean_squared_error """ Benchmark for SGD regression diff --git a/benchmarks/bench_sparsify.py b/benchmarks/bench_sparsify.py index f1aa482b8b732..1832ca40c6ddb 100644 --- a/benchmarks/bench_sparsify.py +++ b/benchmarks/bench_sparsify.py @@ -43,8 +43,9 @@ 60 300 381409 1271.4 97.1 clf.predict(X_test_sparse) """ -from scipy.sparse import csr_matrix import numpy as np +from scipy.sparse import csr_matrix + from sklearn.linear_model import SGDRegressor from sklearn.metrics import r2_score diff --git a/benchmarks/bench_text_vectorizers.py b/benchmarks/bench_text_vectorizers.py index 6d75d57658500..31d4141d1af97 100644 --- a/benchmarks/bench_text_vectorizers.py +++ b/benchmarks/bench_text_vectorizers.py @@ -8,8 +8,8 @@ * psutil (optional, but recommended) """ -import timeit import itertools +import timeit import numpy as np import pandas as pd @@ -18,8 +18,8 @@ from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import ( CountVectorizer, - TfidfVectorizer, HashingVectorizer, + TfidfVectorizer, ) n_repeat = 3 diff --git a/benchmarks/bench_tree.py b/benchmarks/bench_tree.py index c23ef627e237e..29cd7584432b7 100644 --- a/benchmarks/bench_tree.py +++ b/benchmarks/bench_tree.py @@ -13,11 +13,12 @@ training set, classify a sample and plot the time taken as a function of the number of dimensions. """ -import numpy as np -import matplotlib.pyplot as plt import gc from datetime import datetime +import matplotlib.pyplot as plt +import numpy as np + # to store the results scikit_classifier_results = [] scikit_regressor_results = [] diff --git a/benchmarks/bench_tsne_mnist.py b/benchmarks/bench_tsne_mnist.py index e399e891cb94e..39462b33d9655 100644 --- a/benchmarks/bench_tsne_mnist.py +++ b/benchmarks/bench_tsne_mnist.py @@ -7,18 +7,19 @@ # License: BSD 3 clause +import argparse +import json import os import os.path as op from time import time + import numpy as np -import json -import argparse from joblib import Memory from sklearn.datasets import fetch_openml +from sklearn.decomposition import PCA from sklearn.manifold import TSNE from sklearn.neighbors import NearestNeighbors -from sklearn.decomposition import PCA from sklearn.utils import check_array from sklearn.utils import shuffle as _shuffle from sklearn.utils._openmp_helpers import _openmp_effective_n_threads diff --git a/benchmarks/plot_tsne_mnist.py b/benchmarks/plot_tsne_mnist.py index d32e3dd769d6a..fff71eed0a26c 100644 --- a/benchmarks/plot_tsne_mnist.py +++ b/benchmarks/plot_tsne_mnist.py @@ -1,9 +1,8 @@ -import matplotlib.pyplot as plt -import numpy as np -import os.path as op - import argparse +import os.path as op +import matplotlib.pyplot as plt +import numpy as np LOG_DIR = "mnist_tsne_output" diff --git a/build_tools/azure/get_commit_message.py b/build_tools/azure/get_commit_message.py index 239da5b8c4498..0b1246b8d2724 100644 --- a/build_tools/azure/get_commit_message.py +++ b/build_tools/azure/get_commit_message.py @@ -1,6 +1,6 @@ +import argparse import os import subprocess -import argparse def get_commit_message(): diff --git a/build_tools/circle/list_versions.py b/build_tools/circle/list_versions.py index dfcc600957469..345e08b4bece4 100755 --- a/build_tools/circle/list_versions.py +++ b/build_tools/circle/list_versions.py @@ -4,9 +4,9 @@ import json import re import sys +from urllib.request import urlopen from sklearn.utils.fixes import parse_version -from urllib.request import urlopen def json_urlread(url): diff --git a/build_tools/generate_authors_table.py b/build_tools/generate_authors_table.py index d4da0db5be3c1..c930a9b2956d1 100644 --- a/build_tools/generate_authors_table.py +++ b/build_tools/generate_authors_table.py @@ -6,12 +6,13 @@ The table should be updated for each new inclusion in the teams. Generating the table requires admin rights. """ -import sys -import requests import getpass +import sys import time -from pathlib import Path from os import path +from pathlib import Path + +import requests print("user:", file=sys.stderr) user = input() diff --git a/build_tools/github/check_wheels.py b/build_tools/github/check_wheels.py index 99d319cba4dc5..3860d3e81adb7 100644 --- a/build_tools/github/check_wheels.py +++ b/build_tools/github/check_wheels.py @@ -1,8 +1,9 @@ """Checks that dist/* contains the number of wheels built from the .github/workflows/wheels.yml config.""" -import yaml -from pathlib import Path import sys +from pathlib import Path + +import yaml gh_wheel_path = Path.cwd() / ".github" / "workflows" / "wheels.yml" with gh_wheel_path.open("r") as f: diff --git a/build_tools/github/vendor.py b/build_tools/github/vendor.py index 2997688423b84..3bc1aceb3437c 100644 --- a/build_tools/github/vendor.py +++ b/build_tools/github/vendor.py @@ -7,7 +7,6 @@ import sys import textwrap - TARGET_FOLDER = op.join("sklearn", ".libs") DISTRIBUTOR_INIT = op.join("sklearn", "_distributor_init.py") VCOMP140_SRC_PATH = "C:\\Windows\\System32\\vcomp140.dll" diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index a4a5c92b077d2..960c01d4383b8 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -29,20 +29,18 @@ """ +import json +import logging import re +import shlex import subprocess import sys -from pathlib import Path -import shlex -import json -import logging from importlib.metadata import version - -from packaging.version import Version +from pathlib import Path import click - from jinja2 import Environment +from packaging.version import Version logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) diff --git a/doc/conf.py b/doc/conf.py index 6ac1b4e231822..73281f02899d4 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -10,14 +10,15 @@ # All configuration values have a default; values that are commented out # serve to show the default. -import sys import os -import warnings import re +import sys +import warnings from datetime import datetime -from sklearn.externals._packaging.version import parse -from pathlib import Path from io import StringIO +from pathlib import Path + +from sklearn.externals._packaging.version import parse # If extensions (or modules to document with autodoc) are in another # directory, add these directories to sys.path here. If the directory @@ -25,10 +26,10 @@ # absolute, like shown here. sys.path.insert(0, os.path.abspath("sphinxext")) -from github_link import make_linkcode_resolve import sphinx_gallery +from github_link import make_linkcode_resolve +from sphinx_gallery.notebook import add_code_cell, add_markdown_cell from sphinx_gallery.sorting import ExampleTitleSortKey -from sphinx_gallery.notebook import add_markdown_cell, add_code_cell try: # Configure plotly to integrate its output into the HTML pages generated by diff --git a/doc/conftest.py b/doc/conftest.py index 73848ccf392fb..ca94ad1ed2b60 100644 --- a/doc/conftest.py +++ b/doc/conftest.py @@ -1,16 +1,14 @@ import os -from os.path import exists -from os.path import join -from os import environ import warnings +from os import environ +from os.path import exists, join -from sklearn.utils import IS_PYPY -from sklearn.utils._testing import SkipTest -from sklearn.utils._testing import check_skip_network -from sklearn.utils.fixes import parse_version from sklearn.datasets import get_data_home from sklearn.datasets._base import _pkl_filepath from sklearn.datasets._twenty_newsgroups import CACHE_NAME +from sklearn.utils import IS_PYPY +from sklearn.utils._testing import SkipTest, check_skip_network +from sklearn.utils.fixes import parse_version def setup_labeled_faces(): diff --git a/doc/sphinxext/allow_nan_estimators.py b/doc/sphinxext/allow_nan_estimators.py index e8f94506daaa5..89d7077bce2b5 100755 --- a/doc/sphinxext/allow_nan_estimators.py +++ b/doc/sphinxext/allow_nan_estimators.py @@ -1,11 +1,12 @@ -from sklearn.utils import all_estimators -from sklearn.utils.estimator_checks import _construct_instance -from sklearn.utils._testing import SkipTest -from docutils import nodes from contextlib import suppress +from docutils import nodes from docutils.parsers.rst import Directive +from sklearn.utils import all_estimators +from sklearn.utils._testing import SkipTest +from sklearn.utils.estimator_checks import _construct_instance + class AllowNanEstimators(Directive): @staticmethod diff --git a/doc/sphinxext/doi_role.py b/doc/sphinxext/doi_role.py index 7d188969bb778..32e905fe650ea 100644 --- a/doc/sphinxext/doi_role.py +++ b/doc/sphinxext/doi_role.py @@ -15,7 +15,6 @@ """ from docutils import nodes, utils - from sphinx.util.nodes import split_explicit_title diff --git a/doc/sphinxext/github_link.py b/doc/sphinxext/github_link.py index 3992d814b825e..d3e43c8ed0f5e 100644 --- a/doc/sphinxext/github_link.py +++ b/doc/sphinxext/github_link.py @@ -1,9 +1,9 @@ -from operator import attrgetter import inspect -import subprocess import os +import subprocess import sys from functools import partial +from operator import attrgetter REVISION_CMD = "git rev-parse --short HEAD" diff --git a/examples/applications/plot_cyclical_feature_engineering.py b/examples/applications/plot_cyclical_feature_engineering.py index ecd270354ab76..12e285096726d 100644 --- a/examples/applications/plot_cyclical_feature_engineering.py +++ b/examples/applications/plot_cyclical_feature_engineering.py @@ -35,7 +35,6 @@ # demand around the middle of the days: import matplotlib.pyplot as plt - fig, ax = plt.subplots(figsize=(12, 4)) average_week_demand = df.groupby(["weekday", "hour"])["count"].mean() average_week_demand.plot(ax=ax) @@ -181,12 +180,11 @@ # # The numerical variables need no preprocessing and, for the sake of simplicity, # we only try the default hyper-parameters for this model: -from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import OrdinalEncoder from sklearn.compose import ColumnTransformer from sklearn.ensemble import HistGradientBoostingRegressor from sklearn.model_selection import cross_validate - +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import OrdinalEncoder categorical_columns = [ "weather", @@ -262,11 +260,10 @@ def evaluate(model, X, y, cv): # For consistency, we scale the numerical features to the same 0-1 range using # class:`sklearn.preprocessing.MinMaxScaler`, although in this case it does not # impact the results much because they are already on comparable scales: -from sklearn.preprocessing import OneHotEncoder -from sklearn.preprocessing import MinMaxScaler -from sklearn.linear_model import RidgeCV import numpy as np +from sklearn.linear_model import RidgeCV +from sklearn.preprocessing import MinMaxScaler, OneHotEncoder one_hot_encoder = OneHotEncoder(handle_unknown="ignore", sparse_output=False) alphas = np.logspace(-6, 6, 25) @@ -619,9 +616,8 @@ def periodic_spline_transformer(period, n_splines=None, degree=3): # However, it is possible to use the `PolynomialFeatures` class on coarse # grained spline encoded hours to model the "workingday"/"hours" interaction # explicitly without introducing too many new variables: -from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import FeatureUnion - +from sklearn.preprocessing import PolynomialFeatures hour_workday_interaction = make_pipeline( ColumnTransformer( @@ -668,7 +664,6 @@ def periodic_spline_transformer(period, n_splines=None, degree=3): # polynomial kernel expansion. Let us try the latter: from sklearn.kernel_approximation import Nystroem - cyclic_spline_poly_pipeline = make_pipeline( cyclic_spline_transformer, Nystroem(kernel="poly", degree=2, n_components=300, random_state=0), diff --git a/examples/applications/plot_digits_denoising.py b/examples/applications/plot_digits_denoising.py index 72637b6ab036f..bd8d5b1b7b037 100644 --- a/examples/applications/plot_digits_denoising.py +++ b/examples/applications/plot_digits_denoising.py @@ -32,9 +32,10 @@ # :func:`~sklearn.datasets.fetch_openml` to get this dataset. In addition, we # normalize the dataset such that all pixel values are in the range (0, 1). import numpy as np + from sklearn.datasets import fetch_openml -from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import train_test_split +from sklearn.preprocessing import MinMaxScaler X, y = fetch_openml(data_id=41082, as_frame=False, return_X_y=True, parser="pandas") X = MinMaxScaler().fit_transform(X) diff --git a/examples/applications/plot_face_recognition.py b/examples/applications/plot_face_recognition.py index 878d889f52ce3..1ff4399d60739 100644 --- a/examples/applications/plot_face_recognition.py +++ b/examples/applications/plot_face_recognition.py @@ -13,18 +13,16 @@ """ # %% from time import time + import matplotlib.pyplot as plt +from scipy.stats import loguniform -from sklearn.model_selection import train_test_split -from sklearn.model_selection import RandomizedSearchCV from sklearn.datasets import fetch_lfw_people -from sklearn.metrics import classification_report -from sklearn.metrics import ConfusionMatrixDisplay -from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA +from sklearn.metrics import ConfusionMatrixDisplay, classification_report +from sklearn.model_selection import RandomizedSearchCV, train_test_split +from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC -from scipy.stats import loguniform - # %% # Download the data, if not already on disk and load it as numpy arrays diff --git a/examples/applications/plot_model_complexity_influence.py b/examples/applications/plot_model_complexity_influence.py index 812539aa1ff46..f83be241230c3 100644 --- a/examples/applications/plot_model_complexity_influence.py +++ b/examples/applications/plot_model_complexity_influence.py @@ -42,16 +42,16 @@ # License: BSD 3 clause import time -import numpy as np + import matplotlib.pyplot as plt +import numpy as np from sklearn import datasets -from sklearn.model_selection import train_test_split -from sklearn.metrics import mean_squared_error -from sklearn.svm import NuSVR from sklearn.ensemble import GradientBoostingRegressor from sklearn.linear_model import SGDClassifier -from sklearn.metrics import hamming_loss +from sklearn.metrics import hamming_loss, mean_squared_error +from sklearn.model_selection import train_test_split +from sklearn.svm import NuSVR # Initialize random generator np.random.seed(0) diff --git a/examples/applications/plot_out_of_core_classification.py b/examples/applications/plot_out_of_core_classification.py index 212cbda9cc71e..08ae3000c391c 100644 --- a/examples/applications/plot_out_of_core_classification.py +++ b/examples/applications/plot_out_of_core_classification.py @@ -19,24 +19,22 @@ # License: BSD 3 clause import itertools -from pathlib import Path -from hashlib import sha256 import re +import sys import tarfile import time -import sys +from hashlib import sha256 +from html.parser import HTMLParser +from pathlib import Path +from urllib.request import urlretrieve -import numpy as np import matplotlib.pyplot as plt +import numpy as np from matplotlib import rcParams -from html.parser import HTMLParser -from urllib.request import urlretrieve from sklearn.datasets import get_data_home from sklearn.feature_extraction.text import HashingVectorizer -from sklearn.linear_model import SGDClassifier -from sklearn.linear_model import PassiveAggressiveClassifier -from sklearn.linear_model import Perceptron +from sklearn.linear_model import PassiveAggressiveClassifier, Perceptron, SGDClassifier from sklearn.naive_bayes import MultinomialNB diff --git a/examples/applications/plot_outlier_detection_wine.py b/examples/applications/plot_outlier_detection_wine.py index 45e4c64d9fcc4..c4adfa222a5dd 100644 --- a/examples/applications/plot_outlier_detection_wine.py +++ b/examples/applications/plot_outlier_detection_wine.py @@ -37,12 +37,13 @@ # Author: Virgile Fritsch # License: BSD 3 clause +import matplotlib.font_manager +import matplotlib.pyplot as plt import numpy as np + from sklearn.covariance import EllipticEnvelope -from sklearn.svm import OneClassSVM -import matplotlib.pyplot as plt -import matplotlib.font_manager from sklearn.datasets import load_wine +from sklearn.svm import OneClassSVM # Define "classifiers" to be used classifiers = { diff --git a/examples/applications/plot_prediction_latency.py b/examples/applications/plot_prediction_latency.py index 9b99bcbfdfaf1..8fce81fb9fb4e 100644 --- a/examples/applications/plot_prediction_latency.py +++ b/examples/applications/plot_prediction_latency.py @@ -16,19 +16,18 @@ # Authors: Eustache Diemert # License: BSD 3 clause +import gc +import time from collections import defaultdict -import time -import gc -import numpy as np import matplotlib.pyplot as plt +import numpy as np -from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import train_test_split from sklearn.datasets import make_regression from sklearn.ensemble import RandomForestRegressor -from sklearn.linear_model import Ridge -from sklearn.linear_model import SGDRegressor +from sklearn.linear_model import Ridge, SGDRegressor +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler from sklearn.svm import SVR from sklearn.utils import shuffle diff --git a/examples/applications/plot_species_distribution_modeling.py b/examples/applications/plot_species_distribution_modeling.py index e3d5778f3307d..bdf50918840c2 100644 --- a/examples/applications/plot_species_distribution_modeling.py +++ b/examples/applications/plot_species_distribution_modeling.py @@ -43,12 +43,12 @@ from time import time -import numpy as np import matplotlib.pyplot as plt +import numpy as np -from sklearn.utils import Bunch +from sklearn import metrics, svm from sklearn.datasets import fetch_species_distributions -from sklearn import svm, metrics +from sklearn.utils import Bunch # if basemap is available, we'll use it. # otherwise, we'll improvise later... diff --git a/examples/applications/plot_stock_market.py b/examples/applications/plot_stock_market.py index 91168f434338e..cdf5a36074923 100644 --- a/examples/applications/plot_stock_market.py +++ b/examples/applications/plot_stock_market.py @@ -24,6 +24,7 @@ # `alphavantage.co `_. import sys + import numpy as np import pandas as pd diff --git a/examples/applications/plot_tomography_l1_reconstruction.py b/examples/applications/plot_tomography_l1_reconstruction.py index 9ac351c12206c..d851613402571 100644 --- a/examples/applications/plot_tomography_l1_reconstruction.py +++ b/examples/applications/plot_tomography_l1_reconstruction.py @@ -39,12 +39,11 @@ class :class:`~sklearn.linear_model.Lasso`, that uses the coordinate descent # Author: Emmanuelle Gouillart # License: BSD 3 clause -import numpy as np -from scipy import sparse -from scipy import ndimage -from sklearn.linear_model import Lasso -from sklearn.linear_model import Ridge import matplotlib.pyplot as plt +import numpy as np +from scipy import ndimage, sparse + +from sklearn.linear_model import Lasso, Ridge def _weights(x, dx=1, orig=0): diff --git a/examples/applications/plot_topics_extraction_with_nmf_lda.py b/examples/applications/plot_topics_extraction_with_nmf_lda.py index 38945241ab68b..0385fd7c89333 100644 --- a/examples/applications/plot_topics_extraction_with_nmf_lda.py +++ b/examples/applications/plot_topics_extraction_with_nmf_lda.py @@ -27,11 +27,12 @@ # License: BSD 3 clause from time import time + import matplotlib.pyplot as plt -from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer -from sklearn.decomposition import NMF, MiniBatchNMF, LatentDirichletAllocation from sklearn.datasets import fetch_20newsgroups +from sklearn.decomposition import NMF, LatentDirichletAllocation, MiniBatchNMF +from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer n_samples = 2000 n_features = 1000 diff --git a/examples/applications/svm_gui.py b/examples/applications/svm_gui.py index c8019fa72ae91..cd0e3b6101bb6 100644 --- a/examples/applications/svm_gui.py +++ b/examples/applications/svm_gui.py @@ -30,13 +30,13 @@ from matplotlib.backends.backend_tkagg import ( NavigationToolbar2TkAgg as NavigationToolbar2Tk, ) -from matplotlib.figure import Figure -from matplotlib.contour import ContourSet - import sys -import numpy as np import tkinter as Tk +import numpy as np +from matplotlib.contour import ContourSet +from matplotlib.figure import Figure + from sklearn import svm from sklearn.datasets import dump_svmlight_file diff --git a/examples/applications/wikipedia_principal_eigenvector.py b/examples/applications/wikipedia_principal_eigenvector.py index fcc337b0a4e00..0be1661d7ed5c 100644 --- a/examples/applications/wikipedia_principal_eigenvector.py +++ b/examples/applications/wikipedia_principal_eigenvector.py @@ -33,19 +33,17 @@ # Author: Olivier Grisel # License: BSD 3 clause -from bz2 import BZ2File import os +from bz2 import BZ2File from datetime import datetime from pprint import pprint from time import time +from urllib.request import urlopen import numpy as np - from scipy import sparse from sklearn.decomposition import randomized_svd -from urllib.request import urlopen - # %% # Download data, if not already on disk diff --git a/examples/bicluster/plot_bicluster_newsgroups.py b/examples/bicluster/plot_bicluster_newsgroups.py index a54f7099c9a74..0fef820bb9f2a 100644 --- a/examples/bicluster/plot_bicluster_newsgroups.py +++ b/examples/bicluster/plot_bicluster_newsgroups.py @@ -23,14 +23,13 @@ """ -from collections import defaultdict import operator +from collections import defaultdict from time import time import numpy as np -from sklearn.cluster import SpectralCoclustering -from sklearn.cluster import MiniBatchKMeans +from sklearn.cluster import MiniBatchKMeans, SpectralCoclustering from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.cluster import v_measure_score diff --git a/examples/bicluster/plot_spectral_biclustering.py b/examples/bicluster/plot_spectral_biclustering.py index baf0f0ccbc58f..18ff7ae67b83a 100644 --- a/examples/bicluster/plot_spectral_biclustering.py +++ b/examples/bicluster/plot_spectral_biclustering.py @@ -32,9 +32,10 @@ # # As you can see, the data is distributed over 12 cluster cells and is # relatively well distinguishable. -from sklearn.datasets import make_checkerboard from matplotlib import pyplot as plt +from sklearn.datasets import make_checkerboard + n_clusters = (4, 3) data, rows, columns = make_checkerboard( shape=(300, 300), n_clusters=n_clusters, noise=10, shuffle=False, random_state=42 diff --git a/examples/bicluster/plot_spectral_coclustering.py b/examples/bicluster/plot_spectral_coclustering.py index 0df275e83e3bd..92b10d93956e7 100644 --- a/examples/bicluster/plot_spectral_coclustering.py +++ b/examples/bicluster/plot_spectral_coclustering.py @@ -21,8 +21,8 @@ import numpy as np from matplotlib import pyplot as plt -from sklearn.datasets import make_biclusters from sklearn.cluster import SpectralCoclustering +from sklearn.datasets import make_biclusters from sklearn.metrics import consensus_score data, rows, columns = make_biclusters( diff --git a/examples/calibration/plot_calibration.py b/examples/calibration/plot_calibration.py index 75d1ea15b8fbd..f928ae631b78b 100644 --- a/examples/calibration/plot_calibration.py +++ b/examples/calibration/plot_calibration.py @@ -91,8 +91,8 @@ # %% # Plot data and the predicted probabilities # ----------------------------------------- -from matplotlib import cm import matplotlib.pyplot as plt +from matplotlib import cm plt.figure() y_unique = np.unique(y) diff --git a/examples/calibration/plot_calibration_curve.py b/examples/calibration/plot_calibration_curve.py index dc4e85a5f1678..915d3b7c20cc9 100644 --- a/examples/calibration/plot_calibration_curve.py +++ b/examples/calibration/plot_calibration_curve.py @@ -140,11 +140,11 @@ import pandas as pd from sklearn.metrics import ( - precision_score, - recall_score, - f1_score, brier_score_loss, + f1_score, log_loss, + precision_score, + recall_score, roc_auc_score, ) diff --git a/examples/calibration/plot_calibration_multiclass.py b/examples/calibration/plot_calibration_multiclass.py index 24962a786ea03..fc6349f3dea5f 100644 --- a/examples/calibration/plot_calibration_multiclass.py +++ b/examples/calibration/plot_calibration_multiclass.py @@ -31,6 +31,7 @@ class of an instance (red: class 1, green: class 2, blue: class 3). # License: BSD Style. import numpy as np + from sklearn.datasets import make_blobs np.random.seed(0) diff --git a/examples/classification/plot_classification_probability.py b/examples/classification/plot_classification_probability.py index 87c3f51db5eb2..ec5887b63914d 100644 --- a/examples/classification/plot_classification_probability.py +++ b/examples/classification/plot_classification_probability.py @@ -23,12 +23,12 @@ import matplotlib.pyplot as plt import numpy as np -from sklearn.metrics import accuracy_score -from sklearn.linear_model import LogisticRegression -from sklearn.svm import SVC +from sklearn import datasets from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF -from sklearn import datasets +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import accuracy_score +from sklearn.svm import SVC iris = datasets.load_iris() X = iris.data[:, 0:2] # we only take the first two features for visualization diff --git a/examples/classification/plot_classifier_comparison.py b/examples/classification/plot_classifier_comparison.py index 71e8318aa0acb..75164cff8b492 100644 --- a/examples/classification/plot_classifier_comparison.py +++ b/examples/classification/plot_classifier_comparison.py @@ -24,23 +24,24 @@ # Modified for documentation by Jaques Grobler # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np from matplotlib.colors import ListedColormap + +from sklearn.datasets import make_circles, make_classification, make_moons +from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis +from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier +from sklearn.gaussian_process import GaussianProcessClassifier +from sklearn.gaussian_process.kernels import RBF +from sklearn.inspection import DecisionBoundaryDisplay from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from sklearn.pipeline import make_pipeline -from sklearn.datasets import make_moons, make_circles, make_classification -from sklearn.neural_network import MLPClassifier +from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier +from sklearn.neural_network import MLPClassifier +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC -from sklearn.gaussian_process import GaussianProcessClassifier -from sklearn.gaussian_process.kernels import RBF from sklearn.tree import DecisionTreeClassifier -from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier -from sklearn.naive_bayes import GaussianNB -from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis -from sklearn.inspection import DecisionBoundaryDisplay names = [ "Nearest Neighbors", diff --git a/examples/classification/plot_digits_classification.py b/examples/classification/plot_digits_classification.py index f760916d1f66e..d6208400d5416 100644 --- a/examples/classification/plot_digits_classification.py +++ b/examples/classification/plot_digits_classification.py @@ -15,7 +15,7 @@ import matplotlib.pyplot as plt # Import datasets, classifiers and performance metrics -from sklearn import datasets, svm, metrics +from sklearn import datasets, metrics, svm from sklearn.model_selection import train_test_split ############################################################################### diff --git a/examples/classification/plot_lda.py b/examples/classification/plot_lda.py index 322cc8bb4007c..b24479b91f5ea 100644 --- a/examples/classification/plot_lda.py +++ b/examples/classification/plot_lda.py @@ -8,13 +8,12 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np +from sklearn.covariance import OAS from sklearn.datasets import make_blobs from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -from sklearn.covariance import OAS - n_train = 20 # samples for training n_test = 200 # samples for testing diff --git a/examples/classification/plot_lda_qda.py b/examples/classification/plot_lda_qda.py index 712354f7f7f44..71230d0a9bcd9 100644 --- a/examples/classification/plot_lda_qda.py +++ b/examples/classification/plot_lda_qda.py @@ -15,8 +15,8 @@ class has its own standard deviation with QDA. # Colormap # -------- -import matplotlib.pyplot as plt import matplotlib as mpl +import matplotlib.pyplot as plt from matplotlib import colors cmap = colors.LinearSegmentedColormap( @@ -172,8 +172,10 @@ def plot_qda_cov(qda, splot): fontsize=15, ) -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis +from sklearn.discriminant_analysis import ( + LinearDiscriminantAnalysis, + QuadraticDiscriminantAnalysis, +) for i, (X, y) in enumerate([dataset_fixed_cov(), dataset_cov()]): # Linear Discriminant Analysis diff --git a/examples/cluster/plot_affinity_propagation.py b/examples/cluster/plot_affinity_propagation.py index d2bc345c00b3e..5816ae298f419 100644 --- a/examples/cluster/plot_affinity_propagation.py +++ b/examples/cluster/plot_affinity_propagation.py @@ -10,8 +10,8 @@ """ import numpy as np -from sklearn.cluster import AffinityPropagation from sklearn import metrics +from sklearn.cluster import AffinityPropagation from sklearn.datasets import make_blobs # %% diff --git a/examples/cluster/plot_agglomerative_clustering.py b/examples/cluster/plot_agglomerative_clustering.py index 5bb87a9386bf8..d5e7a8168a648 100644 --- a/examples/cluster/plot_agglomerative_clustering.py +++ b/examples/cluster/plot_agglomerative_clustering.py @@ -28,6 +28,7 @@ # License: BSD 3 clause import time + import matplotlib.pyplot as plt import numpy as np diff --git a/examples/cluster/plot_agglomerative_clustering_metrics.py b/examples/cluster/plot_agglomerative_clustering_metrics.py index f1a77d442dbe8..8eb2ea3f7285f 100644 --- a/examples/cluster/plot_agglomerative_clustering_metrics.py +++ b/examples/cluster/plot_agglomerative_clustering_metrics.py @@ -37,8 +37,8 @@ # Author: Gael Varoquaux # License: BSD 3-Clause or CC-0 -import matplotlib.pyplot as plt import matplotlib.patheffects as PathEffects +import matplotlib.pyplot as plt import numpy as np from sklearn.cluster import AgglomerativeClustering diff --git a/examples/cluster/plot_agglomerative_dendrogram.py b/examples/cluster/plot_agglomerative_dendrogram.py index 2de5030d68f6d..20c22f4f0bb39 100644 --- a/examples/cluster/plot_agglomerative_dendrogram.py +++ b/examples/cluster/plot_agglomerative_dendrogram.py @@ -10,11 +10,11 @@ """ import numpy as np - from matplotlib import pyplot as plt from scipy.cluster.hierarchy import dendrogram -from sklearn.datasets import load_iris + from sklearn.cluster import AgglomerativeClustering +from sklearn.datasets import load_iris def plot_dendrogram(model, **kwargs): diff --git a/examples/cluster/plot_birch_vs_minibatchkmeans.py b/examples/cluster/plot_birch_vs_minibatchkmeans.py index 3d4185dc9368a..c9c213c948913 100644 --- a/examples/cluster/plot_birch_vs_minibatchkmeans.py +++ b/examples/cluster/plot_birch_vs_minibatchkmeans.py @@ -25,17 +25,17 @@ # Alexandre Gramfort # License: BSD 3 clause -from joblib import cpu_count from itertools import cycle from time import time -import numpy as np -import matplotlib.pyplot as plt + import matplotlib.colors as colors +import matplotlib.pyplot as plt +import numpy as np +from joblib import cpu_count from sklearn.cluster import Birch, MiniBatchKMeans from sklearn.datasets import make_blobs - # Generate centers for the blobs so that it forms a 10 X 10 grid. xx = np.linspace(-22, 22, 10) yy = np.linspace(-22, 22, 10) diff --git a/examples/cluster/plot_bisect_kmeans.py b/examples/cluster/plot_bisect_kmeans.py index a6be3545e0b27..3aebdffddaf63 100644 --- a/examples/cluster/plot_bisect_kmeans.py +++ b/examples/cluster/plot_bisect_kmeans.py @@ -15,9 +15,8 @@ """ import matplotlib.pyplot as plt -from sklearn.datasets import make_blobs from sklearn.cluster import BisectingKMeans, KMeans - +from sklearn.datasets import make_blobs print(__doc__) diff --git a/examples/cluster/plot_cluster_comparison.py b/examples/cluster/plot_cluster_comparison.py index 843c629374828..27b4a1c46c415 100644 --- a/examples/cluster/plot_cluster_comparison.py +++ b/examples/cluster/plot_cluster_comparison.py @@ -26,14 +26,14 @@ import time import warnings +from itertools import cycle, islice -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn import cluster, datasets, mixture from sklearn.neighbors import kneighbors_graph from sklearn.preprocessing import StandardScaler -from itertools import cycle, islice np.random.seed(0) diff --git a/examples/cluster/plot_cluster_iris.py b/examples/cluster/plot_cluster_iris.py index 4078d139f8064..b20bc8e38dd78 100644 --- a/examples/cluster/plot_cluster_iris.py +++ b/examples/cluster/plot_cluster_iris.py @@ -22,15 +22,15 @@ # Modified for documentation by Jaques Grobler # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt # Though the following import is not directly being used, it is required # for 3D projection to work with matplotlib < 3.2 import mpl_toolkits.mplot3d # noqa: F401 +import numpy as np -from sklearn.cluster import KMeans from sklearn import datasets +from sklearn.cluster import KMeans np.random.seed(5) diff --git a/examples/cluster/plot_coin_segmentation.py b/examples/cluster/plot_coin_segmentation.py index bec68d1221646..c965dc2bd7ace 100644 --- a/examples/cluster/plot_coin_segmentation.py +++ b/examples/cluster/plot_coin_segmentation.py @@ -27,15 +27,14 @@ import time +import matplotlib.pyplot as plt import numpy as np from scipy.ndimage import gaussian_filter -import matplotlib.pyplot as plt from skimage.data import coins from skimage.transform import rescale -from sklearn.feature_extraction import image from sklearn.cluster import spectral_clustering - +from sklearn.feature_extraction import image # load the coins as a numpy array orig_coins = coins() diff --git a/examples/cluster/plot_color_quantization.py b/examples/cluster/plot_color_quantization.py index ae37673808e56..cc8849b64ab6f 100644 --- a/examples/cluster/plot_color_quantization.py +++ b/examples/cluster/plot_color_quantization.py @@ -25,13 +25,15 @@ # # License: BSD 3 clause -import numpy as np +from time import time + import matplotlib.pyplot as plt +import numpy as np + from sklearn.cluster import KMeans -from sklearn.metrics import pairwise_distances_argmin from sklearn.datasets import load_sample_image +from sklearn.metrics import pairwise_distances_argmin from sklearn.utils import shuffle -from time import time n_colors = 64 diff --git a/examples/cluster/plot_dbscan.py b/examples/cluster/plot_dbscan.py index c762e0bceae08..0b0bd64ecf62b 100644 --- a/examples/cluster/plot_dbscan.py +++ b/examples/cluster/plot_dbscan.py @@ -44,8 +44,9 @@ # the `labels_` attribute. Noisy samples are given the label math:`-1`. import numpy as np -from sklearn.cluster import DBSCAN + from sklearn import metrics +from sklearn.cluster import DBSCAN db = DBSCAN(eps=0.3, min_samples=10).fit(X) labels = db.labels_ diff --git a/examples/cluster/plot_digits_agglomeration.py b/examples/cluster/plot_digits_agglomeration.py index 627a9a28d7665..faedefb8aeed8 100644 --- a/examples/cluster/plot_digits_agglomeration.py +++ b/examples/cluster/plot_digits_agglomeration.py @@ -12,10 +12,10 @@ # Modified for documentation by Jaques Grobler # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np -from sklearn import datasets, cluster +from sklearn import cluster, datasets from sklearn.feature_extraction.image import grid_to_graph digits = datasets.load_digits() diff --git a/examples/cluster/plot_digits_linkage.py b/examples/cluster/plot_digits_linkage.py index 730f85c543356..ae67bd5d8e0f4 100644 --- a/examples/cluster/plot_digits_linkage.py +++ b/examples/cluster/plot_digits_linkage.py @@ -35,7 +35,7 @@ import numpy as np from matplotlib import pyplot as plt -from sklearn import manifold, datasets +from sklearn import datasets, manifold digits = datasets.load_digits() X, y = digits.data, digits.target diff --git a/examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py b/examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py index e2273326b9a12..577d65f314337 100644 --- a/examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py +++ b/examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py @@ -21,18 +21,17 @@ import shutil import tempfile -import numpy as np import matplotlib.pyplot as plt -from scipy import linalg, ndimage +import numpy as np from joblib import Memory +from scipy import linalg, ndimage -from sklearn.feature_extraction.image import grid_to_graph from sklearn import feature_selection from sklearn.cluster import FeatureAgglomeration +from sklearn.feature_extraction.image import grid_to_graph from sklearn.linear_model import BayesianRidge +from sklearn.model_selection import GridSearchCV, KFold from sklearn.pipeline import Pipeline -from sklearn.model_selection import GridSearchCV -from sklearn.model_selection import KFold # %% # Set parameters diff --git a/examples/cluster/plot_hdbscan.py b/examples/cluster/plot_hdbscan.py index b97858ff156e8..a4dfcb6c42bbc 100644 --- a/examples/cluster/plot_hdbscan.py +++ b/examples/cluster/plot_hdbscan.py @@ -13,11 +13,11 @@ We first define a couple utility functions for convenience. """ # %% +import matplotlib.pyplot as plt import numpy as np -from sklearn.cluster import HDBSCAN, DBSCAN +from sklearn.cluster import DBSCAN, HDBSCAN from sklearn.datasets import make_blobs -import matplotlib.pyplot as plt def plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=None): diff --git a/examples/cluster/plot_inductive_clustering.py b/examples/cluster/plot_inductive_clustering.py index e395571a1caad..b6464459160e3 100644 --- a/examples/cluster/plot_inductive_clustering.py +++ b/examples/cluster/plot_inductive_clustering.py @@ -24,6 +24,7 @@ # Christos Aridas import matplotlib.pyplot as plt + from sklearn.base import BaseEstimator, clone from sklearn.cluster import AgglomerativeClustering from sklearn.datasets import make_blobs @@ -32,7 +33,6 @@ from sklearn.utils.metaestimators import available_if from sklearn.utils.validation import check_is_fitted - N_SAMPLES = 5000 RANDOM_STATE = 42 diff --git a/examples/cluster/plot_kmeans_assumptions.py b/examples/cluster/plot_kmeans_assumptions.py index bc1f01cb1cdd7..46a7ec6fa58b5 100644 --- a/examples/cluster/plot_kmeans_assumptions.py +++ b/examples/cluster/plot_kmeans_assumptions.py @@ -21,6 +21,7 @@ # one has to define a linear `transformation`. import numpy as np + from sklearn.datasets import make_blobs n_samples = 1500 diff --git a/examples/cluster/plot_kmeans_digits.py b/examples/cluster/plot_kmeans_digits.py index 94bba2a5c52d9..d61ec91d13d52 100644 --- a/examples/cluster/plot_kmeans_digits.py +++ b/examples/cluster/plot_kmeans_digits.py @@ -34,6 +34,7 @@ # to group images such that the handwritten digits on the image are the same. import numpy as np + from sklearn.datasets import load_digits data, labels = load_digits(return_X_y=True) @@ -53,6 +54,7 @@ # * train and time the pipeline fitting; # * measure the performance of the clustering obtained via different metrics. from time import time + from sklearn import metrics from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler diff --git a/examples/cluster/plot_kmeans_plusplus.py b/examples/cluster/plot_kmeans_plusplus.py index 1f3507c0062ac..69ea738635ddf 100644 --- a/examples/cluster/plot_kmeans_plusplus.py +++ b/examples/cluster/plot_kmeans_plusplus.py @@ -10,9 +10,10 @@ """ +import matplotlib.pyplot as plt + from sklearn.cluster import kmeans_plusplus from sklearn.datasets import make_blobs -import matplotlib.pyplot as plt # Generate sample data n_samples = 4000 diff --git a/examples/cluster/plot_kmeans_silhouette_analysis.py b/examples/cluster/plot_kmeans_silhouette_analysis.py index c7d0dc31d4873..c5817a750c2bb 100644 --- a/examples/cluster/plot_kmeans_silhouette_analysis.py +++ b/examples/cluster/plot_kmeans_silhouette_analysis.py @@ -31,14 +31,14 @@ """ -from sklearn.datasets import make_blobs -from sklearn.cluster import KMeans -from sklearn.metrics import silhouette_samples, silhouette_score - -import matplotlib.pyplot as plt import matplotlib.cm as cm +import matplotlib.pyplot as plt import numpy as np +from sklearn.cluster import KMeans +from sklearn.datasets import make_blobs +from sklearn.metrics import silhouette_samples, silhouette_score + # Generating the sample data from make_blobs # This particular setting has one distinct cluster and 3 clusters placed close # together. diff --git a/examples/cluster/plot_kmeans_stability_low_dim_dense.py b/examples/cluster/plot_kmeans_stability_low_dim_dense.py index c88cf864506f7..9340239a3d00e 100644 --- a/examples/cluster/plot_kmeans_stability_low_dim_dense.py +++ b/examples/cluster/plot_kmeans_stability_low_dim_dense.py @@ -26,14 +26,12 @@ # Author: Olivier Grisel # License: BSD 3 clause -import numpy as np -import matplotlib.pyplot as plt import matplotlib.cm as cm +import matplotlib.pyplot as plt +import numpy as np -from sklearn.utils import shuffle -from sklearn.utils import check_random_state -from sklearn.cluster import MiniBatchKMeans -from sklearn.cluster import KMeans +from sklearn.cluster import KMeans, MiniBatchKMeans +from sklearn.utils import check_random_state, shuffle random_state = np.random.RandomState(0) diff --git a/examples/cluster/plot_linkage_comparison.py b/examples/cluster/plot_linkage_comparison.py index af4c3cd2894af..dc009d0110f7c 100644 --- a/examples/cluster/plot_linkage_comparison.py +++ b/examples/cluster/plot_linkage_comparison.py @@ -25,13 +25,13 @@ import time import warnings +from itertools import cycle, islice -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn import cluster, datasets from sklearn.preprocessing import StandardScaler -from itertools import cycle, islice np.random.seed(0) diff --git a/examples/cluster/plot_mean_shift.py b/examples/cluster/plot_mean_shift.py index 46ded7bc43421..aacbc7f216405 100644 --- a/examples/cluster/plot_mean_shift.py +++ b/examples/cluster/plot_mean_shift.py @@ -12,6 +12,7 @@ """ import numpy as np + from sklearn.cluster import MeanShift, estimate_bandwidth from sklearn.datasets import make_blobs diff --git a/examples/cluster/plot_mini_batch_kmeans.py b/examples/cluster/plot_mini_batch_kmeans.py index 7a9d599704059..3a6e8aa63786b 100644 --- a/examples/cluster/plot_mini_batch_kmeans.py +++ b/examples/cluster/plot_mini_batch_kmeans.py @@ -21,6 +21,7 @@ # We start by generating the blobs of data to be clustered. import numpy as np + from sklearn.datasets import make_blobs np.random.seed(0) @@ -35,6 +36,7 @@ # ------------------------------ import time + from sklearn.cluster import KMeans k_means = KMeans(init="k-means++", n_clusters=3, n_init=10) diff --git a/examples/cluster/plot_optics.py b/examples/cluster/plot_optics.py index 7915abd20ce53..5765f8089f3b2 100644 --- a/examples/cluster/plot_optics.py +++ b/examples/cluster/plot_optics.py @@ -20,11 +20,12 @@ # Adrin Jalali # License: BSD 3 clause -from sklearn.cluster import OPTICS, cluster_optics_dbscan import matplotlib.gridspec as gridspec import matplotlib.pyplot as plt import numpy as np +from sklearn.cluster import OPTICS, cluster_optics_dbscan + # Generate sample data np.random.seed(0) diff --git a/examples/cluster/plot_segmentation_toy.py b/examples/cluster/plot_segmentation_toy.py index 0880cdb893839..6fc41f7a5daf2 100644 --- a/examples/cluster/plot_segmentation_toy.py +++ b/examples/cluster/plot_segmentation_toy.py @@ -78,9 +78,10 @@ # %% # Here we perform spectral clustering using the arpack solver since amg is # numerically unstable on this example. We then plot the results. -from sklearn.cluster import spectral_clustering import matplotlib.pyplot as plt +from sklearn.cluster import spectral_clustering + labels = spectral_clustering(graph, n_clusters=4, eigen_solver="arpack") label_im = np.full(mask.shape, -1.0) label_im[mask] = labels diff --git a/examples/cluster/plot_ward_structured_vs_unstructured.py b/examples/cluster/plot_ward_structured_vs_unstructured.py index 430d00a8b3730..446d744b31e78 100644 --- a/examples/cluster/plot_ward_structured_vs_unstructured.py +++ b/examples/cluster/plot_ward_structured_vs_unstructured.py @@ -29,18 +29,14 @@ # The following import is required # for 3D projection to work with matplotlib < 3.2 - import mpl_toolkits.mplot3d # noqa: F401 - import numpy as np - # %% # Generate data # ------------- # # We start by generating the Swiss Roll dataset. - from sklearn.datasets import make_swiss_roll n_samples = 1500 diff --git a/examples/compose/plot_column_transformer.py b/examples/compose/plot_column_transformer.py index d4798d828b321..669e817cbf81d 100644 --- a/examples/compose/plot_column_transformer.py +++ b/examples/compose/plot_column_transformer.py @@ -24,14 +24,14 @@ import numpy as np -from sklearn.preprocessing import FunctionTransformer +from sklearn.compose import ColumnTransformer from sklearn.datasets import fetch_20newsgroups from sklearn.decomposition import TruncatedSVD from sklearn.feature_extraction import DictVectorizer from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics import classification_report from sklearn.pipeline import Pipeline -from sklearn.compose import ColumnTransformer +from sklearn.preprocessing import FunctionTransformer from sklearn.svm import LinearSVC ############################################################################## diff --git a/examples/compose/plot_column_transformer_mixed_types.py b/examples/compose/plot_column_transformer_mixed_types.py index 5ed3239db8478..d7efd033440ce 100644 --- a/examples/compose/plot_column_transformer_mixed_types.py +++ b/examples/compose/plot_column_transformer_mixed_types.py @@ -34,12 +34,12 @@ from sklearn.compose import ColumnTransformer from sklearn.datasets import fetch_openml -from sklearn.pipeline import Pipeline +from sklearn.feature_selection import SelectPercentile, chi2 from sklearn.impute import SimpleImputer -from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.linear_model import LogisticRegression -from sklearn.model_selection import train_test_split, RandomizedSearchCV -from sklearn.feature_selection import SelectPercentile, chi2 +from sklearn.model_selection import RandomizedSearchCV, train_test_split +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import OneHotEncoder, StandardScaler np.random.seed(0) diff --git a/examples/compose/plot_compare_reduction.py b/examples/compose/plot_compare_reduction.py index 47975f84325b8..529366c6244f2 100644 --- a/examples/compose/plot_compare_reduction.py +++ b/examples/compose/plot_compare_reduction.py @@ -28,15 +28,16 @@ # Illustration of ``Pipeline`` and ``GridSearchCV`` ############################################################################### -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn.datasets import load_digits +from sklearn.decomposition import NMF, PCA +from sklearn.feature_selection import SelectKBest, mutual_info_classif from sklearn.model_selection import GridSearchCV from sklearn.pipeline import Pipeline -from sklearn.svm import LinearSVC -from sklearn.decomposition import PCA, NMF -from sklearn.feature_selection import SelectKBest, mutual_info_classif from sklearn.preprocessing import MinMaxScaler +from sklearn.svm import LinearSVC X, y = load_digits(return_X_y=True) @@ -103,9 +104,10 @@ # cache. Hence, use the ``memory`` constructor parameter when the fitting # of a transformer is costly. -from joblib import Memory from shutil import rmtree +from joblib import Memory + # Create a temporary folder to store the transformers of the pipeline location = "cachedir" memory = Memory(location=location, verbose=10) diff --git a/examples/compose/plot_digits_pipe.py b/examples/compose/plot_digits_pipe.py index 640cd6e529a8d..2769422c404a4 100644 --- a/examples/compose/plot_digits_pipe.py +++ b/examples/compose/plot_digits_pipe.py @@ -14,15 +14,15 @@ # Modified for documentation by Jaques Grobler # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np import pandas as pd from sklearn import datasets from sklearn.decomposition import PCA from sklearn.linear_model import LogisticRegression -from sklearn.pipeline import Pipeline from sklearn.model_selection import GridSearchCV +from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler # Define a pipeline to search for the best combination of PCA truncation diff --git a/examples/compose/plot_feature_union.py b/examples/compose/plot_feature_union.py index e014b8b8808b9..01f7e02bfe44f 100644 --- a/examples/compose/plot_feature_union.py +++ b/examples/compose/plot_feature_union.py @@ -20,12 +20,12 @@ # # License: BSD 3 clause -from sklearn.pipeline import Pipeline, FeatureUnion -from sklearn.model_selection import GridSearchCV -from sklearn.svm import SVC from sklearn.datasets import load_iris from sklearn.decomposition import PCA from sklearn.feature_selection import SelectKBest +from sklearn.model_selection import GridSearchCV +from sklearn.pipeline import FeatureUnion, Pipeline +from sklearn.svm import SVC iris = load_iris() diff --git a/examples/compose/plot_transformed_target.py b/examples/compose/plot_transformed_target.py index 1e550ca0ea837..b01c9fbe37934 100644 --- a/examples/compose/plot_transformed_target.py +++ b/examples/compose/plot_transformed_target.py @@ -32,6 +32,7 @@ # (`np.expm1`) will be used to transform the targets before training a linear # regression model and using it for prediction. import numpy as np + from sklearn.datasets import make_regression X, y = make_regression(n_samples=10_000, noise=100, random_state=0) @@ -42,6 +43,7 @@ # Below we plot the probability density functions of the target # before and after applying the logarithmic functions. import matplotlib.pyplot as plt + from sklearn.model_selection import train_test_split f, (ax0, ax1) = plt.subplots(1, 2) diff --git a/examples/covariance/plot_covariance_estimation.py b/examples/covariance/plot_covariance_estimation.py index be3bf4837eb9f..df9af8ea330ba 100644 --- a/examples/covariance/plot_covariance_estimation.py +++ b/examples/covariance/plot_covariance_estimation.py @@ -37,9 +37,10 @@ # Compute the likelihood on test data # ----------------------------------- -from sklearn.covariance import ShrunkCovariance, empirical_covariance, log_likelihood from scipy import linalg +from sklearn.covariance import ShrunkCovariance, empirical_covariance, log_likelihood + # spanning a range of possible shrinkage coefficient values shrinkages = np.logspace(-2, 0, 30) negative_logliks = [ @@ -73,8 +74,8 @@ # are Gaussian, in particular for small samples. +from sklearn.covariance import OAS, LedoitWolf from sklearn.model_selection import GridSearchCV -from sklearn.covariance import LedoitWolf, OAS # GridSearch for an optimal shrinkage coefficient tuned_parameters = [{"shrinkage": shrinkages}] diff --git a/examples/covariance/plot_lw_vs_oas.py b/examples/covariance/plot_lw_vs_oas.py index 1fd84b180f50a..107f6bd1c29cc 100644 --- a/examples/covariance/plot_lw_vs_oas.py +++ b/examples/covariance/plot_lw_vs_oas.py @@ -21,11 +21,11 @@ """ -import numpy as np import matplotlib.pyplot as plt -from scipy.linalg import toeplitz, cholesky +import numpy as np +from scipy.linalg import cholesky, toeplitz -from sklearn.covariance import LedoitWolf, OAS +from sklearn.covariance import OAS, LedoitWolf np.random.seed(0) # %% diff --git a/examples/covariance/plot_mahalanobis_distances.py b/examples/covariance/plot_mahalanobis_distances.py index b93d68a269706..bd61e5af22147 100644 --- a/examples/covariance/plot_mahalanobis_distances.py +++ b/examples/covariance/plot_mahalanobis_distances.py @@ -103,6 +103,7 @@ # designed to have a much larger variance in feature 2. import matplotlib.pyplot as plt + from sklearn.covariance import EmpiricalCovariance, MinCovDet # fit a MCD robust estimator to data diff --git a/examples/covariance/plot_robust_vs_empirical_covariance.py b/examples/covariance/plot_robust_vs_empirical_covariance.py index 9111ec82bcbf3..c61a97ddd979b 100644 --- a/examples/covariance/plot_robust_vs_empirical_covariance.py +++ b/examples/covariance/plot_robust_vs_empirical_covariance.py @@ -53,9 +53,9 @@ """ -import numpy as np -import matplotlib.pyplot as plt import matplotlib.font_manager +import matplotlib.pyplot as plt +import numpy as np from sklearn.covariance import EmpiricalCovariance, MinCovDet diff --git a/examples/covariance/plot_sparse_cov.py b/examples/covariance/plot_sparse_cov.py index 96a5486dc964e..a088aeb7e69c0 100644 --- a/examples/covariance/plot_sparse_cov.py +++ b/examples/covariance/plot_sparse_cov.py @@ -59,6 +59,7 @@ # ----------------- import numpy as np from scipy import linalg + from sklearn.datasets import make_sparse_spd_matrix n_samples = 60 diff --git a/examples/cross_decomposition/plot_pcr_vs_pls.py b/examples/cross_decomposition/plot_pcr_vs_pls.py index 529225d11eead..895c75dc1a728 100644 --- a/examples/cross_decomposition/plot_pcr_vs_pls.py +++ b/examples/cross_decomposition/plot_pcr_vs_pls.py @@ -41,8 +41,9 @@ # into PCR and PLS, we fit a PCA estimator to display the two principal # components of this dataset, i.e. the two directions that explain the most # variance in the data. -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn.decomposition import PCA rng = np.random.RandomState(0) @@ -99,12 +100,12 @@ # For both models, we plot the projected data onto the first component against # the target. In both cases, this projected data is what the regressors will # use as training data. +from sklearn.cross_decomposition import PLSRegression +from sklearn.decomposition import PCA +from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.pipeline import make_pipeline -from sklearn.linear_model import LinearRegression from sklearn.preprocessing import StandardScaler -from sklearn.decomposition import PCA -from sklearn.cross_decomposition import PLSRegression X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng) diff --git a/examples/datasets/plot_digits_last_image.py b/examples/datasets/plot_digits_last_image.py index 95ce867011a9a..0fde32cc674a8 100644 --- a/examples/datasets/plot_digits_last_image.py +++ b/examples/datasets/plot_digits_last_image.py @@ -18,10 +18,10 @@ # Modified for documentation by Jaques Grobler # License: BSD 3 clause -from sklearn import datasets - import matplotlib.pyplot as plt +from sklearn import datasets + # Load the digits dataset digits = datasets.load_digits() diff --git a/examples/datasets/plot_random_dataset.py b/examples/datasets/plot_random_dataset.py index 4f3fdbbb11ef5..e5cbdb080b59f 100644 --- a/examples/datasets/plot_random_dataset.py +++ b/examples/datasets/plot_random_dataset.py @@ -16,9 +16,7 @@ import matplotlib.pyplot as plt -from sklearn.datasets import make_classification -from sklearn.datasets import make_blobs -from sklearn.datasets import make_gaussian_quantiles +from sklearn.datasets import make_blobs, make_classification, make_gaussian_quantiles plt.figure(figsize=(8, 8)) plt.subplots_adjust(bottom=0.05, top=0.9, left=0.05, right=0.95) diff --git a/examples/datasets/plot_random_multilabel_dataset.py b/examples/datasets/plot_random_multilabel_dataset.py index f22c7b9695c42..e6e2d6ad9edcf 100644 --- a/examples/datasets/plot_random_multilabel_dataset.py +++ b/examples/datasets/plot_random_multilabel_dataset.py @@ -35,8 +35,8 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.datasets import make_multilabel_classification as make_ml_clf diff --git a/examples/decomposition/plot_faces_decomposition.py b/examples/decomposition/plot_faces_decomposition.py index 12c091c8e14cb..c29c99b7f3c67 100644 --- a/examples/decomposition/plot_faces_decomposition.py +++ b/examples/decomposition/plot_faces_decomposition.py @@ -21,12 +21,11 @@ import logging -from numpy.random import RandomState import matplotlib.pyplot as plt +from numpy.random import RandomState +from sklearn import cluster, decomposition from sklearn.datasets import fetch_olivetti_faces -from sklearn import cluster -from sklearn import decomposition rng = RandomState(0) diff --git a/examples/decomposition/plot_ica_blind_source_separation.py b/examples/decomposition/plot_ica_blind_source_separation.py index 8c1529a3256fb..584d6b9509589 100644 --- a/examples/decomposition/plot_ica_blind_source_separation.py +++ b/examples/decomposition/plot_ica_blind_source_separation.py @@ -41,7 +41,7 @@ # Fit ICA and PCA models # ---------------------- -from sklearn.decomposition import FastICA, PCA +from sklearn.decomposition import PCA, FastICA # Compute ICA ica = FastICA(n_components=3, whiten="arbitrary-variance") diff --git a/examples/decomposition/plot_image_denoising.py b/examples/decomposition/plot_image_denoising.py index 2840905f0f604..646669d1469ff 100644 --- a/examples/decomposition/plot_image_denoising.py +++ b/examples/decomposition/plot_image_denoising.py @@ -37,7 +37,6 @@ # ------------------------ import numpy as np - try: # Scipy >= 1.10 from scipy.datasets import face except ImportError: diff --git a/examples/decomposition/plot_incremental_pca.py b/examples/decomposition/plot_incremental_pca.py index adc7f83f3cda0..8e5aeccfddc8a 100644 --- a/examples/decomposition/plot_incremental_pca.py +++ b/examples/decomposition/plot_incremental_pca.py @@ -22,8 +22,8 @@ # Authors: Kyle Kastner # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.datasets import load_iris from sklearn.decomposition import PCA, IncrementalPCA diff --git a/examples/decomposition/plot_pca_3d.py b/examples/decomposition/plot_pca_3d.py index 692b9983ed55e..61ce5dde75c89 100644 --- a/examples/decomposition/plot_pca_3d.py +++ b/examples/decomposition/plot_pca_3d.py @@ -19,7 +19,6 @@ # --------------- import numpy as np - from scipy import stats e = np.exp(1) @@ -52,13 +51,13 @@ def pdf(x): # Plot the figures # ---------------- -from sklearn.decomposition import PCA - import matplotlib.pyplot as plt # unused but required import for doing 3d projections with matplotlib < 3.2 import mpl_toolkits.mplot3d # noqa: F401 +from sklearn.decomposition import PCA + def plot_figs(fig_num, elev, azim): fig = plt.figure(fig_num, figsize=(4, 3)) diff --git a/examples/decomposition/plot_pca_iris.py b/examples/decomposition/plot_pca_iris.py index 7c3e69580d298..d025ba34adc27 100644 --- a/examples/decomposition/plot_pca_iris.py +++ b/examples/decomposition/plot_pca_iris.py @@ -13,15 +13,13 @@ # Code source: Gaël Varoquaux # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt - -from sklearn import decomposition -from sklearn import datasets - # unused but required import for doing 3d projections with matplotlib < 3.2 import mpl_toolkits.mplot3d # noqa: F401 +import numpy as np + +from sklearn import datasets, decomposition np.random.seed(5) diff --git a/examples/decomposition/plot_pca_vs_fa_model_selection.py b/examples/decomposition/plot_pca_vs_fa_model_selection.py index 4c934ab756c3e..e269fc6b5c278 100644 --- a/examples/decomposition/plot_pca_vs_fa_model_selection.py +++ b/examples/decomposition/plot_pca_vs_fa_model_selection.py @@ -34,7 +34,6 @@ # --------------- import numpy as np - from scipy import linalg n_samples, n_features, rank = 500, 25, 5 @@ -56,10 +55,9 @@ import matplotlib.pyplot as plt +from sklearn.covariance import LedoitWolf, ShrunkCovariance from sklearn.decomposition import PCA, FactorAnalysis -from sklearn.covariance import ShrunkCovariance, LedoitWolf -from sklearn.model_selection import cross_val_score -from sklearn.model_selection import GridSearchCV +from sklearn.model_selection import GridSearchCV, cross_val_score n_components = np.arange(0, n_features, 5) # options for n_components diff --git a/examples/decomposition/plot_sparse_coding.py b/examples/decomposition/plot_sparse_coding.py index 4f4602f1ff1ac..c45cd3c83b04f 100644 --- a/examples/decomposition/plot_sparse_coding.py +++ b/examples/decomposition/plot_sparse_coding.py @@ -16,8 +16,8 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.decomposition import SparseCoder diff --git a/examples/decomposition/plot_varimax_fa.py b/examples/decomposition/plot_varimax_fa.py index 6e50709620325..9d4c3b9ed1ee7 100644 --- a/examples/decomposition/plot_varimax_fa.py +++ b/examples/decomposition/plot_varimax_fa.py @@ -22,9 +22,9 @@ import matplotlib.pyplot as plt import numpy as np -from sklearn.decomposition import FactorAnalysis, PCA -from sklearn.preprocessing import StandardScaler from sklearn.datasets import load_iris +from sklearn.decomposition import PCA, FactorAnalysis +from sklearn.preprocessing import StandardScaler # %% # Load Iris data diff --git a/examples/ensemble/plot_adaboost_hastie_10_2.py b/examples/ensemble/plot_adaboost_hastie_10_2.py index 13d3a90d3b05c..313056286f6ba 100644 --- a/examples/ensemble/plot_adaboost_hastie_10_2.py +++ b/examples/ensemble/plot_adaboost_hastie_10_2.py @@ -94,6 +94,7 @@ # added to the ensemble. import numpy as np + from sklearn.metrics import zero_one_loss ada_discrete_err = np.zeros((n_estimators,)) diff --git a/examples/ensemble/plot_adaboost_multiclass.py b/examples/ensemble/plot_adaboost_multiclass.py index fae87b4a42d3d..f12aa8c75e213 100644 --- a/examples/ensemble/plot_adaboost_multiclass.py +++ b/examples/ensemble/plot_adaboost_multiclass.py @@ -35,7 +35,6 @@ from sklearn.metrics import accuracy_score from sklearn.tree import DecisionTreeClassifier - X, y = make_gaussian_quantiles( n_samples=13000, n_features=10, n_classes=3, random_state=1 ) diff --git a/examples/ensemble/plot_adaboost_twoclass.py b/examples/ensemble/plot_adaboost_twoclass.py index 19679c6285d3b..d1e89c47b7fcf 100644 --- a/examples/ensemble/plot_adaboost_twoclass.py +++ b/examples/ensemble/plot_adaboost_twoclass.py @@ -21,14 +21,13 @@ # # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np -from sklearn.ensemble import AdaBoostClassifier -from sklearn.tree import DecisionTreeClassifier from sklearn.datasets import make_gaussian_quantiles +from sklearn.ensemble import AdaBoostClassifier from sklearn.inspection import DecisionBoundaryDisplay - +from sklearn.tree import DecisionTreeClassifier # Construct dataset X1, y1 = make_gaussian_quantiles( diff --git a/examples/ensemble/plot_bias_variance.py b/examples/ensemble/plot_bias_variance.py index 4f57b90019e94..9239603115db1 100644 --- a/examples/ensemble/plot_bias_variance.py +++ b/examples/ensemble/plot_bias_variance.py @@ -66,8 +66,8 @@ # Author: Gilles Louppe # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.ensemble import BaggingRegressor from sklearn.tree import DecisionTreeRegressor diff --git a/examples/ensemble/plot_ensemble_oob.py b/examples/ensemble/plot_ensemble_oob.py index bd678af42a7d1..972ca1f6259aa 100644 --- a/examples/ensemble/plot_ensemble_oob.py +++ b/examples/ensemble/plot_ensemble_oob.py @@ -26,9 +26,10 @@ # # License: BSD 3 Clause +from collections import OrderedDict + import matplotlib.pyplot as plt -from collections import OrderedDict from sklearn.datasets import make_classification from sklearn.ensemble import RandomForestClassifier diff --git a/examples/ensemble/plot_feature_transformation.py b/examples/ensemble/plot_feature_transformation.py index 36eb87bb757cd..8a17dd9d74194 100644 --- a/examples/ensemble/plot_feature_transformation.py +++ b/examples/ensemble/plot_feature_transformation.py @@ -59,7 +59,7 @@ # First, we will start by training the random forest and gradient boosting on # the separated training set -from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier +from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier random_forest = RandomForestClassifier( n_estimators=n_estimators, max_depth=max_depth, random_state=10 @@ -105,8 +105,7 @@ # method `apply`. The pipeline in scikit-learn expects a call to `transform`. # Therefore, we wrapped the call to `apply` within a `FunctionTransformer`. -from sklearn.preprocessing import FunctionTransformer -from sklearn.preprocessing import OneHotEncoder +from sklearn.preprocessing import FunctionTransformer, OneHotEncoder def rf_apply(X, model): @@ -143,6 +142,7 @@ def gbdt_apply(X, model): # We can finally show the different ROC curves for all the models. import matplotlib.pyplot as plt + from sklearn.metrics import RocCurveDisplay fig, ax = plt.subplots() diff --git a/examples/ensemble/plot_forest_hist_grad_boosting_comparison.py b/examples/ensemble/plot_forest_hist_grad_boosting_comparison.py index b4a1993471474..cc48e47e9ad34 100644 --- a/examples/ensemble/plot_forest_hist_grad_boosting_comparison.py +++ b/examples/ensemble/plot_forest_hist_grad_boosting_comparison.py @@ -78,8 +78,8 @@ # here to keep the example simple. import pandas as pd -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.ensemble import RandomForestRegressor + +from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor from sklearn.model_selection import GridSearchCV, KFold models = { @@ -123,8 +123,8 @@ # Error bars correspond to one standard deviation as computed in the different # folds of the cross-validation. -import plotly.express as px import plotly.colors as colors +import plotly.express as px from plotly.subplots import make_subplots fig = make_subplots( diff --git a/examples/ensemble/plot_forest_importances.py b/examples/ensemble/plot_forest_importances.py index fbda63b26faee..269451168dd7a 100644 --- a/examples/ensemble/plot_forest_importances.py +++ b/examples/ensemble/plot_forest_importances.py @@ -57,6 +57,7 @@ # cardinality** features (many unique values). See # :ref:`permutation_importance` as an alternative below. import time + import numpy as np start_time = time.time() diff --git a/examples/ensemble/plot_forest_importances_faces.py b/examples/ensemble/plot_forest_importances_faces.py index 3848873c297de..8b8e8751ec5a2 100644 --- a/examples/ensemble/plot_forest_importances_faces.py +++ b/examples/ensemble/plot_forest_importances_faces.py @@ -59,6 +59,7 @@ # cardinality** features (many unique values). See # :ref:`permutation_importance` as an alternative. import time + import matplotlib.pyplot as plt start_time = time.time() diff --git a/examples/ensemble/plot_forest_iris.py b/examples/ensemble/plot_forest_iris.py index ee414db7125dc..6aaceea88efd2 100644 --- a/examples/ensemble/plot_forest_iris.py +++ b/examples/ensemble/plot_forest_iris.py @@ -42,15 +42,15 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np from matplotlib.colors import ListedColormap from sklearn.datasets import load_iris from sklearn.ensemble import ( - RandomForestClassifier, - ExtraTreesClassifier, AdaBoostClassifier, + ExtraTreesClassifier, + RandomForestClassifier, ) from sklearn.tree import DecisionTreeClassifier diff --git a/examples/ensemble/plot_gradient_boosting_categorical.py b/examples/ensemble/plot_gradient_boosting_categorical.py index fa4b68be9cbb7..0dd0a84243b4d 100644 --- a/examples/ensemble/plot_gradient_boosting_categorical.py +++ b/examples/ensemble/plot_gradient_boosting_categorical.py @@ -77,10 +77,9 @@ # As a baseline, we create an estimator where the categorical features are # dropped: +from sklearn.compose import make_column_selector, make_column_transformer from sklearn.ensemble import HistGradientBoostingRegressor from sklearn.pipeline import make_pipeline -from sklearn.compose import make_column_transformer -from sklearn.compose import make_column_selector dropper = make_column_transformer( ("drop", make_column_selector(dtype_include="category")), remainder="passthrough" @@ -114,9 +113,10 @@ # were ordered quantities, i.e. the categories will be encoded as 0, 1, 2, # etc., and treated as continuous features. -from sklearn.preprocessing import OrdinalEncoder import numpy as np +from sklearn.preprocessing import OrdinalEncoder + ordinal_encoder = make_column_transformer( ( OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=np.nan), @@ -166,9 +166,10 @@ # models performance in terms of # :func:`~metrics.mean_absolute_percentage_error` and fit times. -from sklearn.model_selection import cross_validate import matplotlib.pyplot as plt +from sklearn.model_selection import cross_validate + scoring = "neg_mean_absolute_percentage_error" n_cv_folds = 3 diff --git a/examples/ensemble/plot_gradient_boosting_early_stopping.py b/examples/ensemble/plot_gradient_boosting_early_stopping.py index 6f1013eed9564..f271f80a07c55 100644 --- a/examples/ensemble/plot_gradient_boosting_early_stopping.py +++ b/examples/ensemble/plot_gradient_boosting_early_stopping.py @@ -38,11 +38,10 @@ import time -import numpy as np import matplotlib.pyplot as plt +import numpy as np -from sklearn import ensemble -from sklearn import datasets +from sklearn import datasets, ensemble from sklearn.model_selection import train_test_split data_list = [ diff --git a/examples/ensemble/plot_gradient_boosting_oob.py b/examples/ensemble/plot_gradient_boosting_oob.py index dd7f19a1fe245..0cb40ad2c11ea 100644 --- a/examples/ensemble/plot_gradient_boosting_oob.py +++ b/examples/ensemble/plot_gradient_boosting_oob.py @@ -26,15 +26,13 @@ # # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np +from scipy.special import expit from sklearn import ensemble -from sklearn.model_selection import KFold -from sklearn.model_selection import train_test_split from sklearn.metrics import log_loss - -from scipy.special import expit +from sklearn.model_selection import KFold, train_test_split # Generate data (adapted from G. Ridgeway's gbm example) n_samples = 1000 diff --git a/examples/ensemble/plot_gradient_boosting_quantile.py b/examples/ensemble/plot_gradient_boosting_quantile.py index 2aa04c3988d9e..36a78dfeb94ca 100644 --- a/examples/ensemble/plot_gradient_boosting_quantile.py +++ b/examples/ensemble/plot_gradient_boosting_quantile.py @@ -12,6 +12,7 @@ # Generate some data for a synthetic regression problem by applying the # function f to uniformly sampled random inputs. import numpy as np + from sklearn.model_selection import train_test_split @@ -58,7 +59,6 @@ def f(x): from sklearn.ensemble import GradientBoostingRegressor from sklearn.metrics import mean_pinball_loss, mean_squared_error - all_models = {} common_params = dict( learning_rate=0.05, @@ -93,7 +93,6 @@ def f(x): # 90% interval (from 5th to 95th conditional percentiles). import matplotlib.pyplot as plt - y_pred = all_models["mse"].predict(xx) y_lower = all_models["q 0.05"].predict(xx) y_upper = all_models["q 0.95"].predict(xx) diff --git a/examples/ensemble/plot_gradient_boosting_regression.py b/examples/ensemble/plot_gradient_boosting_regression.py index 3e378e8af7203..94705ccfeca24 100644 --- a/examples/ensemble/plot_gradient_boosting_regression.py +++ b/examples/ensemble/plot_gradient_boosting_regression.py @@ -23,6 +23,7 @@ import matplotlib.pyplot as plt import numpy as np + from sklearn import datasets, ensemble from sklearn.inspection import permutation_importance from sklearn.metrics import mean_squared_error diff --git a/examples/ensemble/plot_gradient_boosting_regularization.py b/examples/ensemble/plot_gradient_boosting_regularization.py index a4ac69a822b92..218d69d5ac7d7 100644 --- a/examples/ensemble/plot_gradient_boosting_regularization.py +++ b/examples/ensemble/plot_gradient_boosting_regularization.py @@ -25,11 +25,10 @@ # # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np -from sklearn import ensemble -from sklearn import datasets +from sklearn import datasets, ensemble from sklearn.metrics import log_loss from sklearn.model_selection import train_test_split diff --git a/examples/ensemble/plot_isolation_forest.py b/examples/ensemble/plot_isolation_forest.py index aeabb60203ac6..f5fad1d7b9ea9 100644 --- a/examples/ensemble/plot_isolation_forest.py +++ b/examples/ensemble/plot_isolation_forest.py @@ -31,6 +31,7 @@ # the label `-1`. import numpy as np + from sklearn.model_selection import train_test_split n_samples, n_outliers = 120, 40 @@ -78,6 +79,7 @@ # or not. The scatter plot displays the true labels. import matplotlib.pyplot as plt + from sklearn.inspection import DecisionBoundaryDisplay disp = DecisionBoundaryDisplay.from_estimator( diff --git a/examples/ensemble/plot_monotonic_constraints.py b/examples/ensemble/plot_monotonic_constraints.py index b1f7ca8ed24ed..15ad8e9524243 100644 --- a/examples/ensemble/plot_monotonic_constraints.py +++ b/examples/ensemble/plot_monotonic_constraints.py @@ -20,11 +20,11 @@ """ # %% -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.inspection import PartialDependenceDisplay -import numpy as np import matplotlib.pyplot as plt +import numpy as np +from sklearn.ensemble import HistGradientBoostingRegressor +from sklearn.inspection import PartialDependenceDisplay rng = np.random.RandomState(0) diff --git a/examples/ensemble/plot_random_forest_embedding.py b/examples/ensemble/plot_random_forest_embedding.py index 000b83e67b92a..fe26e04ca7789 100644 --- a/examples/ensemble/plot_random_forest_embedding.py +++ b/examples/ensemble/plot_random_forest_embedding.py @@ -26,12 +26,12 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.datasets import make_circles -from sklearn.ensemble import RandomTreesEmbedding, ExtraTreesClassifier from sklearn.decomposition import TruncatedSVD +from sklearn.ensemble import ExtraTreesClassifier, RandomTreesEmbedding from sklearn.naive_bayes import BernoulliNB # make a synthetic dataset diff --git a/examples/ensemble/plot_random_forest_regression_multioutput.py b/examples/ensemble/plot_random_forest_regression_multioutput.py index 4b3d4f4a9a728..ce8346c329127 100644 --- a/examples/ensemble/plot_random_forest_regression_multioutput.py +++ b/examples/ensemble/plot_random_forest_regression_multioutput.py @@ -25,13 +25,13 @@ # # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split from sklearn.multioutput import MultiOutputRegressor - # Create a random dataset rng = np.random.RandomState(1) X = np.sort(200 * rng.rand(600, 1) - 100, axis=0) diff --git a/examples/ensemble/plot_stack_predictors.py b/examples/ensemble/plot_stack_predictors.py index 56a82ded5b725..aac7ccc8a3ef8 100644 --- a/examples/ensemble/plot_stack_predictors.py +++ b/examples/ensemble/plot_stack_predictors.py @@ -131,8 +131,7 @@ def load_ames_housing(): # Then, we will now define the preprocessor used when the ending regressor # is a linear model. -from sklearn.preprocessing import OneHotEncoder -from sklearn.preprocessing import StandardScaler +from sklearn.preprocessing import OneHotEncoder, StandardScaler cat_linear_processor = OneHotEncoder(handle_unknown="ignore") num_linear_processor = make_pipeline( @@ -206,9 +205,11 @@ def load_ames_housing(): import time + import matplotlib.pyplot as plt + from sklearn.metrics import PredictionErrorDisplay -from sklearn.model_selection import cross_validate, cross_val_predict +from sklearn.model_selection import cross_val_predict, cross_validate fig, axs = plt.subplots(2, 2, figsize=(9, 7)) axs = np.ravel(axs) diff --git a/examples/ensemble/plot_voting_decision_regions.py b/examples/ensemble/plot_voting_decision_regions.py index e6dc68eeadf98..90441c6d28339 100644 --- a/examples/ensemble/plot_voting_decision_regions.py +++ b/examples/ensemble/plot_voting_decision_regions.py @@ -28,11 +28,11 @@ import matplotlib.pyplot as plt from sklearn import datasets -from sklearn.tree import DecisionTreeClassifier -from sklearn.neighbors import KNeighborsClassifier -from sklearn.svm import SVC from sklearn.ensemble import VotingClassifier from sklearn.inspection import DecisionBoundaryDisplay +from sklearn.neighbors import KNeighborsClassifier +from sklearn.svm import SVC +from sklearn.tree import DecisionTreeClassifier # Loading some example data iris = datasets.load_iris() diff --git a/examples/ensemble/plot_voting_probas.py b/examples/ensemble/plot_voting_probas.py index 54c290c3073e0..14f4f4330c045 100644 --- a/examples/ensemble/plot_voting_probas.py +++ b/examples/ensemble/plot_voting_probas.py @@ -23,13 +23,12 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np +from sklearn.ensemble import RandomForestClassifier, VotingClassifier from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB -from sklearn.ensemble import RandomForestClassifier -from sklearn.ensemble import VotingClassifier clf1 = LogisticRegression(max_iter=1000, random_state=123) clf2 = RandomForestClassifier(n_estimators=100, random_state=123) diff --git a/examples/ensemble/plot_voting_regressor.py b/examples/ensemble/plot_voting_regressor.py index 23e709cc9e62a..d33becca505e3 100644 --- a/examples/ensemble/plot_voting_regressor.py +++ b/examples/ensemble/plot_voting_regressor.py @@ -26,10 +26,12 @@ import matplotlib.pyplot as plt from sklearn.datasets import load_diabetes -from sklearn.ensemble import GradientBoostingRegressor -from sklearn.ensemble import RandomForestRegressor +from sklearn.ensemble import ( + GradientBoostingRegressor, + RandomForestRegressor, + VotingRegressor, +) from sklearn.linear_model import LinearRegression -from sklearn.ensemble import VotingRegressor # %% # Training classifiers diff --git a/examples/exercises/plot_cv_digits.py b/examples/exercises/plot_cv_digits.py index e43bbd86bb027..ebad3a55098b5 100644 --- a/examples/exercises/plot_cv_digits.py +++ b/examples/exercises/plot_cv_digits.py @@ -11,8 +11,9 @@ """ import numpy as np -from sklearn.model_selection import cross_val_score + from sklearn import datasets, svm +from sklearn.model_selection import cross_val_score X, y = datasets.load_digits(return_X_y=True) diff --git a/examples/exercises/plot_digits_classification_exercise.py b/examples/exercises/plot_digits_classification_exercise.py index 877e615659743..25b0171c66421 100644 --- a/examples/exercises/plot_digits_classification_exercise.py +++ b/examples/exercises/plot_digits_classification_exercise.py @@ -12,7 +12,7 @@ """ -from sklearn import datasets, neighbors, linear_model +from sklearn import datasets, linear_model, neighbors X_digits, y_digits = datasets.load_digits(return_X_y=True) X_digits = X_digits / X_digits.max() diff --git a/examples/exercises/plot_iris_exercise.py b/examples/exercises/plot_iris_exercise.py index 74da8c27889c9..07687b920e1b8 100644 --- a/examples/exercises/plot_iris_exercise.py +++ b/examples/exercises/plot_iris_exercise.py @@ -10,8 +10,9 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn import datasets, svm iris = datasets.load_iris() diff --git a/examples/feature_selection/plot_f_test_vs_mi.py b/examples/feature_selection/plot_f_test_vs_mi.py index ba82625a7cfaf..5c015e7e4fd58 100644 --- a/examples/feature_selection/plot_f_test_vs_mi.py +++ b/examples/feature_selection/plot_f_test_vs_mi.py @@ -23,8 +23,9 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn.feature_selection import f_regression, mutual_info_regression np.random.seed(0) diff --git a/examples/feature_selection/plot_feature_selection.py b/examples/feature_selection/plot_feature_selection.py index ce2bad8626a79..c57a2d5d6b6f9 100644 --- a/examples/feature_selection/plot_feature_selection.py +++ b/examples/feature_selection/plot_feature_selection.py @@ -21,6 +21,7 @@ # -------------------- # import numpy as np + from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split diff --git a/examples/feature_selection/plot_rfe_digits.py b/examples/feature_selection/plot_rfe_digits.py index 9684f5fabd383..553f38f9c674f 100644 --- a/examples/feature_selection/plot_rfe_digits.py +++ b/examples/feature_selection/plot_rfe_digits.py @@ -12,10 +12,11 @@ """ # noqa: E501 -from sklearn.svm import SVC +import matplotlib.pyplot as plt + from sklearn.datasets import load_digits from sklearn.feature_selection import RFE -import matplotlib.pyplot as plt +from sklearn.svm import SVC # Load the digits dataset digits = load_digits() diff --git a/examples/feature_selection/plot_rfe_with_cross_validation.py b/examples/feature_selection/plot_rfe_with_cross_validation.py index 2d52ea5a3fdf3..693e21fe21787 100644 --- a/examples/feature_selection/plot_rfe_with_cross_validation.py +++ b/examples/feature_selection/plot_rfe_with_cross_validation.py @@ -39,8 +39,8 @@ # strategy "accuracy" optimizes the proportion of correctly classified samples. from sklearn.feature_selection import RFECV -from sklearn.model_selection import StratifiedKFold from sklearn.linear_model import LogisticRegression +from sklearn.model_selection import StratifiedKFold min_features_to_select = 1 # Minimum number of features to consider clf = LogisticRegression() diff --git a/examples/feature_selection/plot_select_from_model_diabetes.py b/examples/feature_selection/plot_select_from_model_diabetes.py index 6e8a6434e1079..38276efcbd770 100644 --- a/examples/feature_selection/plot_select_from_model_diabetes.py +++ b/examples/feature_selection/plot_select_from_model_diabetes.py @@ -46,6 +46,7 @@ # :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`. # noqa: E501 import matplotlib.pyplot as plt import numpy as np + from sklearn.linear_model import RidgeCV ridge = RidgeCV(alphas=np.logspace(-6, 6, num=5)).fit(X, y) @@ -67,9 +68,10 @@ # # Since we want to select only 2 features, we will set this threshold slightly # above the coefficient of third most important feature. -from sklearn.feature_selection import SelectFromModel from time import time +from sklearn.feature_selection import SelectFromModel + threshold = np.sort(importance)[-3] + 0.01 tic = time() diff --git a/examples/gaussian_process/plot_compare_gpr_krr.py b/examples/gaussian_process/plot_compare_gpr_krr.py index 7a58ba437278f..8379baf148256 100644 --- a/examples/gaussian_process/plot_compare_gpr_krr.py +++ b/examples/gaussian_process/plot_compare_gpr_krr.py @@ -125,6 +125,7 @@ # # Thus, let's use such a :class:`~sklearn.kernel_ridge.KernelRidge`. import time + from sklearn.gaussian_process.kernels import ExpSineSquared from sklearn.kernel_ridge import KernelRidge @@ -176,9 +177,10 @@ # parameter and the kernel parameters. # %% -from sklearn.model_selection import RandomizedSearchCV from scipy.stats import loguniform +from sklearn.model_selection import RandomizedSearchCV + param_distributions = { "alpha": loguniform(1e0, 1e3), "kernel__length_scale": loguniform(1e-2, 1e2), diff --git a/examples/gaussian_process/plot_gpc.py b/examples/gaussian_process/plot_gpc.py index e2d78fa23f09e..21a99065e06ce 100644 --- a/examples/gaussian_process/plot_gpc.py +++ b/examples/gaussian_process/plot_gpc.py @@ -27,13 +27,11 @@ # License: BSD 3 clause import numpy as np - from matplotlib import pyplot as plt -from sklearn.metrics import accuracy_score, log_loss from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF - +from sklearn.metrics import accuracy_score, log_loss # Generate data train_size = 50 diff --git a/examples/gaussian_process/plot_gpc_iris.py b/examples/gaussian_process/plot_gpc_iris.py index ce0ed066a1377..88c536d8824c8 100644 --- a/examples/gaussian_process/plot_gpc_iris.py +++ b/examples/gaussian_process/plot_gpc_iris.py @@ -10,8 +10,9 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn import datasets from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF diff --git a/examples/gaussian_process/plot_gpc_isoprobability.py b/examples/gaussian_process/plot_gpc_isoprobability.py index cc036244bc17a..a986d285632b7 100644 --- a/examples/gaussian_process/plot_gpc_isoprobability.py +++ b/examples/gaussian_process/plot_gpc_isoprobability.py @@ -14,12 +14,12 @@ # License: BSD 3 clause import numpy as np - -from matplotlib import pyplot as plt from matplotlib import cm +from matplotlib import pyplot as plt from sklearn.gaussian_process import GaussianProcessClassifier -from sklearn.gaussian_process.kernels import DotProduct, ConstantKernel as C +from sklearn.gaussian_process.kernels import ConstantKernel as C +from sklearn.gaussian_process.kernels import DotProduct # A few constants lim = 8 diff --git a/examples/gaussian_process/plot_gpc_xor.py b/examples/gaussian_process/plot_gpc_xor.py index 6e6217dba8b9e..4439a5ee722b6 100644 --- a/examples/gaussian_process/plot_gpc_xor.py +++ b/examples/gaussian_process/plot_gpc_xor.py @@ -15,13 +15,12 @@ # # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF, DotProduct - xx, yy = np.meshgrid(np.linspace(-3, 3, 50), np.linspace(-3, 3, 50)) rng = np.random.RandomState(0) X = rng.randn(200, 2) diff --git a/examples/gaussian_process/plot_gpr_co2.py b/examples/gaussian_process/plot_gpr_co2.py index bfc1c21631b26..a3acd1dbfcbd3 100644 --- a/examples/gaussian_process/plot_gpr_co2.py +++ b/examples/gaussian_process/plot_gpr_co2.py @@ -172,6 +172,7 @@ # Thus, we create synthetic data from 1958 to the current month. In addition, # we need to add the subtracted mean computed during training. import datetime + import numpy as np today = datetime.datetime.now() diff --git a/examples/gaussian_process/plot_gpr_on_structured_data.py b/examples/gaussian_process/plot_gpr_on_structured_data.py index ada50a0edf06b..e702f1fe0769a 100644 --- a/examples/gaussian_process/plot_gpr_on_structured_data.py +++ b/examples/gaussian_process/plot_gpr_on_structured_data.py @@ -40,11 +40,10 @@ # %% import numpy as np -from sklearn.gaussian_process.kernels import Kernel, Hyperparameter -from sklearn.gaussian_process.kernels import GenericKernelMixin -from sklearn.gaussian_process import GaussianProcessRegressor -from sklearn.gaussian_process import GaussianProcessClassifier + from sklearn.base import clone +from sklearn.gaussian_process import GaussianProcessClassifier, GaussianProcessRegressor +from sklearn.gaussian_process.kernels import GenericKernelMixin, Hyperparameter, Kernel class SequenceKernel(GenericKernelMixin, Kernel): diff --git a/examples/impute/plot_iterative_imputer_variants_comparison.py b/examples/impute/plot_iterative_imputer_variants_comparison.py index d83922817e5de..9dc8b6c831710 100644 --- a/examples/impute/plot_iterative_imputer_variants_comparison.py +++ b/examples/impute/plot_iterative_imputer_variants_comparison.py @@ -44,21 +44,21 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np import pandas as pd +from sklearn.datasets import fetch_california_housing +from sklearn.ensemble import RandomForestRegressor + # To use this experimental feature, we need to explicitly ask for it: from sklearn.experimental import enable_iterative_imputer # noqa -from sklearn.datasets import fetch_california_housing -from sklearn.impute import SimpleImputer -from sklearn.impute import IterativeImputer -from sklearn.linear_model import BayesianRidge, Ridge +from sklearn.impute import IterativeImputer, SimpleImputer from sklearn.kernel_approximation import Nystroem -from sklearn.ensemble import RandomForestRegressor +from sklearn.linear_model import BayesianRidge, Ridge +from sklearn.model_selection import cross_val_score from sklearn.neighbors import KNeighborsRegressor from sklearn.pipeline import make_pipeline -from sklearn.model_selection import cross_val_score N_SPLITS = 5 diff --git a/examples/impute/plot_missing_values.py b/examples/impute/plot_missing_values.py index f6350ad2544dd..4b9f8ae079d8a 100644 --- a/examples/impute/plot_missing_values.py +++ b/examples/impute/plot_missing_values.py @@ -44,9 +44,7 @@ import numpy as np -from sklearn.datasets import fetch_california_housing -from sklearn.datasets import load_diabetes - +from sklearn.datasets import fetch_california_housing, load_diabetes rng = np.random.RandomState(42) @@ -95,11 +93,10 @@ def add_missing_values(X_full, y_full): # To use the experimental IterativeImputer, we need to explicitly ask for it: from sklearn.experimental import enable_iterative_imputer # noqa -from sklearn.impute import SimpleImputer, KNNImputer, IterativeImputer +from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer from sklearn.model_selection import cross_val_score from sklearn.pipeline import make_pipeline - N_SPLITS = 4 regressor = RandomForestRegressor(random_state=0) @@ -260,7 +257,6 @@ def get_impute_iterative(X_missing, y_missing): import matplotlib.pyplot as plt - n_bars = len(mses_diabetes) xval = np.arange(n_bars) diff --git a/examples/inspection/plot_linear_model_coefficient_interpretation.py b/examples/inspection/plot_linear_model_coefficient_interpretation.py index d978ee860636c..eb935ee41ae67 100644 --- a/examples/inspection/plot_linear_model_coefficient_interpretation.py +++ b/examples/inspection/plot_linear_model_coefficient_interpretation.py @@ -40,10 +40,10 @@ """ # %% +import matplotlib.pyplot as plt import numpy as np -import scipy as sp import pandas as pd -import matplotlib.pyplot as plt +import scipy as sp import seaborn as sns # %% @@ -53,7 +53,6 @@ # We fetch the data from `OpenML `_. # Note that setting the parameter `as_frame` to True will retrieve the data # as a pandas dataframe. - from sklearn.datasets import fetch_openml survey = fetch_openml(data_id=534, as_frame=True, parser="pandas") @@ -154,9 +153,9 @@ # To describe the dataset as a linear model we use a ridge regressor # with a very small regularization and to model the logarithm of the WAGE. -from sklearn.pipeline import make_pipeline -from sklearn.linear_model import Ridge from sklearn.compose import TransformedTargetRegressor +from sklearn.linear_model import Ridge +from sklearn.pipeline import make_pipeline model = make_pipeline( preprocessor, @@ -178,8 +177,7 @@ # on the test set and computing, # for example, the median absolute error of the model. -from sklearn.metrics import median_absolute_error -from sklearn.metrics import PredictionErrorDisplay +from sklearn.metrics import PredictionErrorDisplay, median_absolute_error mae_train = median_absolute_error(y_train, model.predict(X_train)) y_pred = model.predict(X_test) @@ -319,8 +317,7 @@ # their robustness is not guaranteed, and they should probably be interpreted # with caution. -from sklearn.model_selection import cross_validate -from sklearn.model_selection import RepeatedKFold +from sklearn.model_selection import RepeatedKFold, cross_validate cv = RepeatedKFold(n_splits=5, n_repeats=5, random_state=0) cv_model = cross_validate( diff --git a/examples/inspection/plot_partial_dependence.py b/examples/inspection/plot_partial_dependence.py index 43404b356d829..ed7a656da9926 100644 --- a/examples/inspection/plot_partial_dependence.py +++ b/examples/inspection/plot_partial_dependence.py @@ -100,8 +100,9 @@ # We plot the average number of bike rentals by grouping the data by season and # by year. from itertools import product -import numpy as np + import matplotlib.pyplot as plt +import numpy as np days = ("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat") hours = tuple(range(24)) @@ -157,8 +158,7 @@ # numerical features and encode the categorical features with a # :class:`~sklearn.preprocessing.OneHotEncoder`. from sklearn.compose import ColumnTransformer -from sklearn.preprocessing import QuantileTransformer -from sklearn.preprocessing import OneHotEncoder +from sklearn.preprocessing import OneHotEncoder, QuantileTransformer mlp_preprocessor = ColumnTransformer( transformers=[ @@ -203,6 +203,7 @@ # Let's fit a :class:`~sklearn.neural_network.MLPRegressor` and compute # single-variable partial dependence plots. from time import time + from sklearn.neural_network import MLPRegressor from sklearn.pipeline import make_pipeline @@ -242,6 +243,7 @@ # # We will plot the averaged partial dependence. import matplotlib.pyplot as plt + from sklearn.inspection import PartialDependenceDisplay common_params = { @@ -529,10 +531,9 @@ # # Let's make the same partial dependence plot for the 2 features interaction, # this time in 3 dimensions. -import numpy as np - # unused but required import for doing 3d projections with matplotlib < 3.2 import mpl_toolkits.mplot3d # noqa: F401 +import numpy as np from sklearn.inspection import partial_dependence diff --git a/examples/inspection/plot_permutation_importance.py b/examples/inspection/plot_permutation_importance.py index cf0907ce3fd37..789506e892e3a 100644 --- a/examples/inspection/plot_permutation_importance.py +++ b/examples/inspection/plot_permutation_importance.py @@ -64,9 +64,9 @@ # categorical features; # - use :class:`~sklearn.impute.SimpleImputer` to fill missing values for # numerical features using a mean strategy. +from sklearn.compose import ColumnTransformer from sklearn.ensemble import RandomForestClassifier from sklearn.impute import SimpleImputer -from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from sklearn.preprocessing import OrdinalEncoder diff --git a/examples/inspection/plot_permutation_importance_multicollinear.py b/examples/inspection/plot_permutation_importance_multicollinear.py index 59871c00946a6..e14916e808af9 100644 --- a/examples/inspection/plot_permutation_importance_multicollinear.py +++ b/examples/inspection/plot_permutation_importance_multicollinear.py @@ -22,9 +22,9 @@ import matplotlib.pyplot as plt import numpy as np -from scipy.stats import spearmanr from scipy.cluster import hierarchy from scipy.spatial.distance import squareform +from scipy.stats import spearmanr from sklearn.datasets import load_breast_cancer from sklearn.ensemble import RandomForestClassifier diff --git a/examples/kernel_approximation/plot_scalable_poly_kernels.py b/examples/kernel_approximation/plot_scalable_poly_kernels.py index 1a46e4bc2aa9c..8e5854bd8500c 100644 --- a/examples/kernel_approximation/plot_scalable_poly_kernels.py +++ b/examples/kernel_approximation/plot_scalable_poly_kernels.py @@ -64,8 +64,8 @@ # the LIBSVM webpage, and then normalize to unit length as done in the # original Tensor Sketch paper [1]. -from sklearn.preprocessing import MinMaxScaler, Normalizer from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import MinMaxScaler, Normalizer mm = make_pipeline(MinMaxScaler(), Normalizer()) X_train = mm.fit_transform(X_train) @@ -80,6 +80,7 @@ # plot them later. import time + from sklearn.svm import LinearSVC results = {} diff --git a/examples/linear_model/plot_ard.py b/examples/linear_model/plot_ard.py index 261fec8aeee3b..79b49fb76ef9a 100644 --- a/examples/linear_model/plot_ard.py +++ b/examples/linear_model/plot_ard.py @@ -54,7 +54,8 @@ # coefficients. import pandas as pd -from sklearn.linear_model import ARDRegression, LinearRegression, BayesianRidge + +from sklearn.linear_model import ARDRegression, BayesianRidge, LinearRegression olr = LinearRegression().fit(X, y) brr = BayesianRidge(compute_score=True, n_iter=30).fit(X, y) diff --git a/examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py b/examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py index 3bca3101758ff..8313b0b56922e 100644 --- a/examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py +++ b/examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py @@ -20,6 +20,7 @@ # %% # Let's start by loading the dataset and creating some sample weights. import numpy as np + from sklearn.datasets import make_regression rng = np.random.RandomState(0) diff --git a/examples/linear_model/plot_huber_vs_ridge.py b/examples/linear_model/plot_huber_vs_ridge.py index 2ea5a190e35d8..7c0222b71a721 100644 --- a/examples/linear_model/plot_huber_vs_ridge.py +++ b/examples/linear_model/plot_huber_vs_ridge.py @@ -16,8 +16,8 @@ # Authors: Manoj Kumar mks542@nyu.edu # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.datasets import make_regression from sklearn.linear_model import HuberRegressor, Ridge diff --git a/examples/linear_model/plot_iris_logistic.py b/examples/linear_model/plot_iris_logistic.py index faf547c783609..b1e4d76c7f221 100644 --- a/examples/linear_model/plot_iris_logistic.py +++ b/examples/linear_model/plot_iris_logistic.py @@ -15,9 +15,10 @@ # License: BSD 3 clause import matplotlib.pyplot as plt -from sklearn.linear_model import LogisticRegression + from sklearn import datasets from sklearn.inspection import DecisionBoundaryDisplay +from sklearn.linear_model import LogisticRegression # import some data to play with iris = datasets.load_iris() diff --git a/examples/linear_model/plot_lasso_and_elasticnet.py b/examples/linear_model/plot_lasso_and_elasticnet.py index b08837304730a..075d8a50d2f62 100644 --- a/examples/linear_model/plot_lasso_and_elasticnet.py +++ b/examples/linear_model/plot_lasso_and_elasticnet.py @@ -112,9 +112,10 @@ # :class:`~sklearn.model_selection.TimeSeriesSplit` cross-validation strategy to a # :class:`~sklearn.linear_model.LassoCV`. To keep the example simple and fast to # execute, we directly set the optimal value for alpha here. +from time import time + from sklearn.linear_model import Lasso from sklearn.metrics import r2_score -from time import time t0 = time() lasso = Lasso(alpha=0.14).fit(X_train, y_train) @@ -181,8 +182,8 @@ # and estimated coefficients of the respective linear models. import matplotlib.pyplot as plt -import seaborn as sns import pandas as pd +import seaborn as sns from matplotlib.colors import SymLogNorm df = pd.DataFrame( diff --git a/examples/linear_model/plot_lasso_coordinate_descent_path.py b/examples/linear_model/plot_lasso_coordinate_descent_path.py index 1796dc5011644..ee2f09f000d23 100644 --- a/examples/linear_model/plot_lasso_coordinate_descent_path.py +++ b/examples/linear_model/plot_lasso_coordinate_descent_path.py @@ -14,12 +14,12 @@ # License: BSD 3 clause from itertools import cycle -import numpy as np + import matplotlib.pyplot as plt +import numpy as np -from sklearn.linear_model import lasso_path, enet_path from sklearn import datasets - +from sklearn.linear_model import enet_path, lasso_path X, y = datasets.load_diabetes(return_X_y=True) diff --git a/examples/linear_model/plot_lasso_dense_vs_sparse_data.py b/examples/linear_model/plot_lasso_dense_vs_sparse_data.py index 8da1820c0b0c4..a797d5d708160 100644 --- a/examples/linear_model/plot_lasso_dense_vs_sparse_data.py +++ b/examples/linear_model/plot_lasso_dense_vs_sparse_data.py @@ -9,13 +9,12 @@ """ from time import time -from scipy import sparse -from scipy import linalg + +from scipy import linalg, sparse from sklearn.datasets import make_regression from sklearn.linear_model import Lasso - # %% # Comparing the two Lasso implementations on Dense data # ----------------------------------------------------- diff --git a/examples/linear_model/plot_lasso_lars.py b/examples/linear_model/plot_lasso_lars.py index 6788b8b1d1598..5444aeec90c65 100644 --- a/examples/linear_model/plot_lasso_lars.py +++ b/examples/linear_model/plot_lasso_lars.py @@ -14,11 +14,10 @@ # Alexandre Gramfort # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np -from sklearn import linear_model -from sklearn import datasets +from sklearn import datasets, linear_model X, y = datasets.load_diabetes(return_X_y=True) diff --git a/examples/linear_model/plot_lasso_lars_ic.py b/examples/linear_model/plot_lasso_lars_ic.py index 95c0d0d66608d..8b265130f2f10 100644 --- a/examples/linear_model/plot_lasso_lars_ic.py +++ b/examples/linear_model/plot_lasso_lars_ic.py @@ -45,9 +45,9 @@ # # In the following, we are going to fit two models to compare the values # reported by AIC and BIC. -from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LassoLarsIC from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler lasso_lars_ic = make_pipeline(StandardScaler(), LassoLarsIC(criterion="aic")).fit(X, y) diff --git a/examples/linear_model/plot_lasso_model_selection.py b/examples/linear_model/plot_lasso_model_selection.py index 7735f01987aa9..169d85ed81644 100644 --- a/examples/linear_model/plot_lasso_model_selection.py +++ b/examples/linear_model/plot_lasso_model_selection.py @@ -59,9 +59,10 @@ # # We will first fit a Lasso model with the AIC criterion. import time -from sklearn.preprocessing import StandardScaler + from sklearn.linear_model import LassoLarsIC from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler start_time = time.time() lasso_lars_ic = make_pipeline(StandardScaler(), LassoLarsIC(criterion="aic")).fit(X, y) diff --git a/examples/linear_model/plot_logistic.py b/examples/linear_model/plot_logistic.py index 801c893e5e28e..6ed3c86e8c27b 100644 --- a/examples/linear_model/plot_logistic.py +++ b/examples/linear_model/plot_logistic.py @@ -15,6 +15,7 @@ import matplotlib.pyplot as plt import numpy as np from scipy.special import expit + from sklearn.linear_model import LinearRegression, LogisticRegression # Generate a toy dataset, it's just a straight line with some Gaussian noise: diff --git a/examples/linear_model/plot_logistic_l1_l2_sparsity.py b/examples/linear_model/plot_logistic_l1_l2_sparsity.py index e8f5a2d51b637..80374d3833151 100644 --- a/examples/linear_model/plot_logistic_l1_l2_sparsity.py +++ b/examples/linear_model/plot_logistic_l1_l2_sparsity.py @@ -20,11 +20,11 @@ # Andreas Mueller # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np -from sklearn.linear_model import LogisticRegression from sklearn import datasets +from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler X, y = datasets.load_digits(return_X_y=True) diff --git a/examples/linear_model/plot_logistic_multinomial.py b/examples/linear_model/plot_logistic_multinomial.py index 814eeadaa68c4..791a788b2238b 100644 --- a/examples/linear_model/plot_logistic_multinomial.py +++ b/examples/linear_model/plot_logistic_multinomial.py @@ -12,11 +12,12 @@ # Authors: Tom Dupre la Tour # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn.datasets import make_blobs -from sklearn.linear_model import LogisticRegression from sklearn.inspection import DecisionBoundaryDisplay +from sklearn.linear_model import LogisticRegression # make 3-class dataset for classification centers = [[-5, 0], [0, 1.5], [5, -1]] diff --git a/examples/linear_model/plot_multi_task_lasso_support.py b/examples/linear_model/plot_multi_task_lasso_support.py index a30b51ed7a7fe..9b6ea64ce4d85 100644 --- a/examples/linear_model/plot_multi_task_lasso_support.py +++ b/examples/linear_model/plot_multi_task_lasso_support.py @@ -39,7 +39,7 @@ # Fit models # ---------- -from sklearn.linear_model import MultiTaskLasso, Lasso +from sklearn.linear_model import Lasso, MultiTaskLasso coef_lasso_ = np.array([Lasso(alpha=0.5).fit(X, y).coef_ for y in Y.T]) coef_multi_task_lasso_ = MultiTaskLasso(alpha=1.0).fit(X, Y).coef_ diff --git a/examples/linear_model/plot_nnls.py b/examples/linear_model/plot_nnls.py index c8ba2914d783a..05a8550ec166b 100644 --- a/examples/linear_model/plot_nnls.py +++ b/examples/linear_model/plot_nnls.py @@ -9,8 +9,9 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn.metrics import r2_score # %% diff --git a/examples/linear_model/plot_ols.py b/examples/linear_model/plot_ols.py index 0618f545306db..244bd86387474 100644 --- a/examples/linear_model/plot_ols.py +++ b/examples/linear_model/plot_ols.py @@ -19,6 +19,7 @@ import matplotlib.pyplot as plt import numpy as np + from sklearn import datasets, linear_model from sklearn.metrics import mean_squared_error, r2_score diff --git a/examples/linear_model/plot_ols_3d.py b/examples/linear_model/plot_ols_3d.py index 7288cc9ae6594..0c95d483f1bf3 100644 --- a/examples/linear_model/plot_ols_3d.py +++ b/examples/linear_model/plot_ols_3d.py @@ -16,9 +16,10 @@ # %% # First we load the diabetes dataset. -from sklearn import datasets import numpy as np +from sklearn import datasets + X, y = datasets.load_diabetes(return_X_y=True) indices = (0, 1) diff --git a/examples/linear_model/plot_ols_ridge_variance.py b/examples/linear_model/plot_ols_ridge_variance.py index b02ab193842d4..a03d9c253c1cf 100644 --- a/examples/linear_model/plot_ols_ridge_variance.py +++ b/examples/linear_model/plot_ols_ridge_variance.py @@ -24,8 +24,8 @@ # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn import linear_model diff --git a/examples/linear_model/plot_omp.py b/examples/linear_model/plot_omp.py index 9329962cce4f6..aa6044173b8ce 100644 --- a/examples/linear_model/plot_omp.py +++ b/examples/linear_model/plot_omp.py @@ -10,9 +10,9 @@ import matplotlib.pyplot as plt import numpy as np -from sklearn.linear_model import OrthogonalMatchingPursuit -from sklearn.linear_model import OrthogonalMatchingPursuitCV + from sklearn.datasets import make_sparse_coded_signal +from sklearn.linear_model import OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV n_components, n_features = 512, 100 n_nonzero_coefs = 17 diff --git a/examples/linear_model/plot_poisson_regression_non_normal_loss.py b/examples/linear_model/plot_poisson_regression_non_normal_loss.py index 46f5c23578b55..cf38ca520f076 100644 --- a/examples/linear_model/plot_poisson_regression_non_normal_loss.py +++ b/examples/linear_model/plot_poisson_regression_non_normal_loss.py @@ -41,21 +41,18 @@ # Olivier Grisel # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np import pandas as pd - ############################################################################## # The French Motor Third-Party Liability Claims dataset # ----------------------------------------------------- # # Let's load the motor claim dataset from OpenML: # https://www.openml.org/d/41214 - from sklearn.datasets import fetch_openml - df = fetch_openml(data_id=41214, as_frame=True, parser="pandas").frame df @@ -97,11 +94,14 @@ # In order to fit linear models with those predictors it is therefore # necessary to perform standard feature transformations as follows: -from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import FunctionTransformer, OneHotEncoder -from sklearn.preprocessing import StandardScaler, KBinsDiscretizer from sklearn.compose import ColumnTransformer - +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import ( + FunctionTransformer, + KBinsDiscretizer, + OneHotEncoder, + StandardScaler, +) log_scale_transformer = make_pipeline( FunctionTransformer(np.log, validate=False), StandardScaler() @@ -139,8 +139,8 @@ # the training sample. from sklearn.dummy import DummyRegressor -from sklearn.pipeline import Pipeline from sklearn.model_selection import train_test_split +from sklearn.pipeline import Pipeline df_train, df_test = train_test_split(df, test_size=0.33, random_state=0) @@ -156,9 +156,11 @@ # Let's compute the performance of this constant prediction baseline with 3 # different regression metrics: -from sklearn.metrics import mean_squared_error -from sklearn.metrics import mean_absolute_error -from sklearn.metrics import mean_poisson_deviance +from sklearn.metrics import ( + mean_absolute_error, + mean_poisson_deviance, + mean_squared_error, +) def score_estimator(estimator, df_test): @@ -213,7 +215,6 @@ def score_estimator(estimator, df_test): from sklearn.linear_model import Ridge - ridge_glm = Pipeline( [ ("preprocessor", linear_model_preprocessor), @@ -285,7 +286,6 @@ def score_estimator(estimator, df_test): from sklearn.ensemble import HistGradientBoostingRegressor from sklearn.preprocessing import OrdinalEncoder - tree_preprocessor = ColumnTransformer( [ ( diff --git a/examples/linear_model/plot_polynomial_interpolation.py b/examples/linear_model/plot_polynomial_interpolation.py index ac2fe28de870d..f648b7aea762d 100644 --- a/examples/linear_model/plot_polynomial_interpolation.py +++ b/examples/linear_model/plot_polynomial_interpolation.py @@ -42,13 +42,12 @@ # Malte Londschien # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.linear_model import Ridge -from sklearn.preprocessing import PolynomialFeatures, SplineTransformer from sklearn.pipeline import make_pipeline - +from sklearn.preprocessing import PolynomialFeatures, SplineTransformer # %% # We start by defining a function that we intend to approximate and prepare diff --git a/examples/linear_model/plot_quantile_regression.py b/examples/linear_model/plot_quantile_regression.py index b66434fa1c0c1..715e6129cdef8 100644 --- a/examples/linear_model/plot_quantile_regression.py +++ b/examples/linear_model/plot_quantile_regression.py @@ -111,7 +111,7 @@ # # We will use the quantiles at 5% and 95% to find the outliers in the training # sample beyond the central 90% interval. -from sklearn.utils.fixes import sp_version, parse_version +from sklearn.utils.fixes import parse_version, sp_version # This is line is to avoid incompatibility if older SciPy version. # You should use `solver="highs"` with recent version of SciPy. @@ -253,8 +253,7 @@ # distributed target to make it more interesting as mean and median are not # equal. from sklearn.linear_model import LinearRegression -from sklearn.metrics import mean_absolute_error -from sklearn.metrics import mean_squared_error +from sklearn.metrics import mean_absolute_error, mean_squared_error linear_regression = LinearRegression() quantile_regression = QuantileRegressor(quantile=0.5, alpha=0, solver=solver) diff --git a/examples/linear_model/plot_ransac.py b/examples/linear_model/plot_ransac.py index 0301dd0ba0088..7b89150c4bd20 100644 --- a/examples/linear_model/plot_ransac.py +++ b/examples/linear_model/plot_ransac.py @@ -18,8 +18,7 @@ import numpy as np from matplotlib import pyplot as plt -from sklearn import linear_model, datasets - +from sklearn import datasets, linear_model n_samples = 1000 n_outliers = 50 diff --git a/examples/linear_model/plot_ridge_path.py b/examples/linear_model/plot_ridge_path.py index 66f8fd9eb6c23..01f9d45a63f8d 100644 --- a/examples/linear_model/plot_ridge_path.py +++ b/examples/linear_model/plot_ridge_path.py @@ -30,8 +30,9 @@ # Author: Fabian Pedregosa -- # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn import linear_model # X is the 10x10 Hilbert matrix diff --git a/examples/linear_model/plot_robust_fit.py b/examples/linear_model/plot_robust_fit.py index c9fe49fc0d416..79213c9a8e83e 100644 --- a/examples/linear_model/plot_robust_fit.py +++ b/examples/linear_model/plot_robust_fit.py @@ -30,18 +30,18 @@ """ -from matplotlib import pyplot as plt import numpy as np +from matplotlib import pyplot as plt from sklearn.linear_model import ( + HuberRegressor, LinearRegression, - TheilSenRegressor, RANSACRegressor, - HuberRegressor, + TheilSenRegressor, ) from sklearn.metrics import mean_squared_error -from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import PolynomialFeatures np.random.seed(42) diff --git a/examples/linear_model/plot_sgd_comparison.py b/examples/linear_model/plot_sgd_comparison.py index 5ab0d6b1b2827..0477e42cf5947 100644 --- a/examples/linear_model/plot_sgd_comparison.py +++ b/examples/linear_model/plot_sgd_comparison.py @@ -9,14 +9,17 @@ # Author: Rob Zinkov # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt -from sklearn import datasets +import numpy as np +from sklearn import datasets +from sklearn.linear_model import ( + LogisticRegression, + PassiveAggressiveClassifier, + Perceptron, + SGDClassifier, +) from sklearn.model_selection import train_test_split -from sklearn.linear_model import SGDClassifier, Perceptron -from sklearn.linear_model import PassiveAggressiveClassifier -from sklearn.linear_model import LogisticRegression heldout = [0.95, 0.90, 0.75, 0.50, 0.01] # Number of rounds to fit and evaluate an estimator. diff --git a/examples/linear_model/plot_sgd_early_stopping.py b/examples/linear_model/plot_sgd_early_stopping.py index 4fb884804492d..6713a74342ba2 100644 --- a/examples/linear_model/plot_sgd_early_stopping.py +++ b/examples/linear_model/plot_sgd_early_stopping.py @@ -41,19 +41,19 @@ # # License: BSD 3 clause -import time import sys +import time -import pandas as pd -import numpy as np import matplotlib.pyplot as plt +import numpy as np +import pandas as pd from sklearn import linear_model from sklearn.datasets import fetch_openml -from sklearn.model_selection import train_test_split -from sklearn.utils._testing import ignore_warnings from sklearn.exceptions import ConvergenceWarning +from sklearn.model_selection import train_test_split from sklearn.utils import shuffle +from sklearn.utils._testing import ignore_warnings def load_mnist(n_samples=None, class_0="0", class_1="8"): diff --git a/examples/linear_model/plot_sgd_iris.py b/examples/linear_model/plot_sgd_iris.py index 64dca07396d54..5d9b923f9b444 100644 --- a/examples/linear_model/plot_sgd_iris.py +++ b/examples/linear_model/plot_sgd_iris.py @@ -9,11 +9,12 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn import datasets -from sklearn.linear_model import SGDClassifier from sklearn.inspection import DecisionBoundaryDisplay +from sklearn.linear_model import SGDClassifier # import some data to play with iris = datasets.load_iris() diff --git a/examples/linear_model/plot_sgd_loss_functions.py b/examples/linear_model/plot_sgd_loss_functions.py index a1f74dca4d6af..140562184b946 100644 --- a/examples/linear_model/plot_sgd_loss_functions.py +++ b/examples/linear_model/plot_sgd_loss_functions.py @@ -8,8 +8,8 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np def modified_huber_loss(y_true, y_pred): diff --git a/examples/linear_model/plot_sgd_penalties.py b/examples/linear_model/plot_sgd_penalties.py index 0413751fb41a9..ff71dba5f20a3 100644 --- a/examples/linear_model/plot_sgd_penalties.py +++ b/examples/linear_model/plot_sgd_penalties.py @@ -11,8 +11,8 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np l1_color = "navy" l2_color = "c" diff --git a/examples/linear_model/plot_sgd_separating_hyperplane.py b/examples/linear_model/plot_sgd_separating_hyperplane.py index af288fcd3dde0..e84ab7c519ae9 100644 --- a/examples/linear_model/plot_sgd_separating_hyperplane.py +++ b/examples/linear_model/plot_sgd_separating_hyperplane.py @@ -9,10 +9,11 @@ """ -import numpy as np import matplotlib.pyplot as plt -from sklearn.linear_model import SGDClassifier +import numpy as np + from sklearn.datasets import make_blobs +from sklearn.linear_model import SGDClassifier # we create 50 separable points X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60) diff --git a/examples/linear_model/plot_sgd_weighted_samples.py b/examples/linear_model/plot_sgd_weighted_samples.py index 2db52042b075f..4d605e99b4e49 100644 --- a/examples/linear_model/plot_sgd_weighted_samples.py +++ b/examples/linear_model/plot_sgd_weighted_samples.py @@ -8,8 +8,9 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn import linear_model # we create 20 points diff --git a/examples/linear_model/plot_sgdocsvm_vs_ocsvm.py b/examples/linear_model/plot_sgdocsvm_vs_ocsvm.py index c25f4a84d91e0..2f03768f50532 100644 --- a/examples/linear_model/plot_sgdocsvm_vs_ocsvm.py +++ b/examples/linear_model/plot_sgdocsvm_vs_ocsvm.py @@ -19,13 +19,14 @@ """ # noqa: E501 -import numpy as np -import matplotlib.pyplot as plt import matplotlib -from sklearn.svm import OneClassSVM -from sklearn.linear_model import SGDOneClassSVM +import matplotlib.pyplot as plt +import numpy as np + from sklearn.kernel_approximation import Nystroem +from sklearn.linear_model import SGDOneClassSVM from sklearn.pipeline import make_pipeline +from sklearn.svm import OneClassSVM font = {"weight": "normal", "size": 15} diff --git a/examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py b/examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py index 507dda5c76901..f62208aab154a 100644 --- a/examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py +++ b/examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py @@ -29,9 +29,9 @@ import numpy as np from sklearn.datasets import fetch_20newsgroups_vectorized +from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split -from sklearn.exceptions import ConvergenceWarning warnings.filterwarnings("ignore", category=ConvergenceWarning, module="sklearn") t0 = timeit.default_timer() diff --git a/examples/linear_model/plot_sparse_logistic_regression_mnist.py b/examples/linear_model/plot_sparse_logistic_regression_mnist.py index 37327aeaa4cb7..e6746b8fb0896 100644 --- a/examples/linear_model/plot_sparse_logistic_regression_mnist.py +++ b/examples/linear_model/plot_sparse_logistic_regression_mnist.py @@ -21,6 +21,7 @@ # License: BSD 3 clause import time + import matplotlib.pyplot as plt import numpy as np diff --git a/examples/linear_model/plot_theilsen.py b/examples/linear_model/plot_theilsen.py index b380baf705a76..eb0ac4966841d 100644 --- a/examples/linear_model/plot_theilsen.py +++ b/examples/linear_model/plot_theilsen.py @@ -39,10 +39,11 @@ # License: BSD 3 clause import time -import numpy as np + import matplotlib.pyplot as plt -from sklearn.linear_model import LinearRegression, TheilSenRegressor -from sklearn.linear_model import RANSACRegressor +import numpy as np + +from sklearn.linear_model import LinearRegression, RANSACRegressor, TheilSenRegressor estimators = [ ("OLS", LinearRegression()), diff --git a/examples/linear_model/plot_tweedie_regression_insurance_claims.py b/examples/linear_model/plot_tweedie_regression_insurance_claims.py index 1d7a5c5ed179f..2ee4b4b18fd7b 100644 --- a/examples/linear_model/plot_tweedie_regression_insurance_claims.py +++ b/examples/linear_model/plot_tweedie_regression_insurance_claims.py @@ -46,14 +46,16 @@ from functools import partial -import numpy as np import matplotlib.pyplot as plt +import numpy as np import pandas as pd from sklearn.datasets import fetch_openml -from sklearn.metrics import mean_tweedie_deviance -from sklearn.metrics import mean_absolute_error -from sklearn.metrics import mean_squared_error +from sklearn.metrics import ( + mean_absolute_error, + mean_squared_error, + mean_tweedie_deviance, +) def load_mtpl2(n_samples=None): @@ -209,11 +211,14 @@ def score_estimator( # containing the number of claims (``ClaimNb``), with the freMTPL2sev table, # containing the claim amount (``ClaimAmount``) for the same policy ids # (``IDpol``). -from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import FunctionTransformer, OneHotEncoder -from sklearn.preprocessing import StandardScaler, KBinsDiscretizer from sklearn.compose import ColumnTransformer - +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import ( + FunctionTransformer, + KBinsDiscretizer, + OneHotEncoder, + StandardScaler, +) df = load_mtpl2() @@ -274,9 +279,8 @@ def score_estimator( # constant rate in a given time interval (``Exposure``, in units of years). # Here we model the frequency ``y = ClaimNb / Exposure``, which is still a # (scaled) Poisson distribution, and use ``Exposure`` as `sample_weight`. -from sklearn.model_selection import train_test_split from sklearn.linear_model import PoissonRegressor - +from sklearn.model_selection import train_test_split df_train, df_test, X_train, X_test = train_test_split(df, X, random_state=0) @@ -396,7 +400,6 @@ def score_estimator( # more than one claim. from sklearn.linear_model import GammaRegressor - mask_train = df_train["ClaimAmount"] > 0 mask_test = df_test["ClaimAmount"] > 0 @@ -540,7 +543,6 @@ def score_estimator( # regardless of `power`. from sklearn.linear_model import TweedieRegressor - glm_pure_premium = TweedieRegressor(power=1.9, alpha=0.1, solver="newton-cholesky") glm_pure_premium.fit( X_train, df_train["PurePremium"], sample_weight=df_train["Exposure"] diff --git a/examples/manifold/plot_compare_methods.py b/examples/manifold/plot_compare_methods.py index 3773f11605241..88ce0f26b8dde 100644 --- a/examples/manifold/plot_compare_methods.py +++ b/examples/manifold/plot_compare_methods.py @@ -29,12 +29,12 @@ # We start by generating the S-curve dataset. import matplotlib.pyplot as plt -from matplotlib import ticker # unused but required import for doing 3d projections with matplotlib < 3.2 import mpl_toolkits.mplot3d # noqa: F401 +from matplotlib import ticker -from sklearn import manifold, datasets +from sklearn import datasets, manifold n_samples = 1500 S_points, S_color = datasets.make_s_curve(n_samples, random_state=0) diff --git a/examples/manifold/plot_lle_digits.py b/examples/manifold/plot_lle_digits.py index 7d4b6610cee49..4424d700789ff 100644 --- a/examples/manifold/plot_lle_digits.py +++ b/examples/manifold/plot_lle_digits.py @@ -45,6 +45,7 @@ # scattered across it. import numpy as np from matplotlib import offsetbox + from sklearn.preprocessing import MinMaxScaler @@ -103,11 +104,11 @@ def plot_embedding(X, title): from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.ensemble import RandomTreesEmbedding from sklearn.manifold import ( + MDS, + TSNE, Isomap, LocallyLinearEmbedding, - MDS, SpectralEmbedding, - TSNE, ) from sklearn.neighbors import NeighborhoodComponentsAnalysis from sklearn.pipeline import make_pipeline diff --git a/examples/manifold/plot_manifold_sphere.py b/examples/manifold/plot_manifold_sphere.py index 46db3f9f60e6d..624206ff4d5e0 100644 --- a/examples/manifold/plot_manifold_sphere.py +++ b/examples/manifold/plot_manifold_sphere.py @@ -29,14 +29,16 @@ # License: BSD 3 clause from time import time -import numpy as np + import matplotlib.pyplot as plt -from matplotlib.ticker import NullFormatter -from sklearn import manifold -from sklearn.utils import check_random_state # Unused but required import for doing 3d projections with matplotlib < 3.2 import mpl_toolkits.mplot3d # noqa: F401 +import numpy as np +from matplotlib.ticker import NullFormatter + +from sklearn import manifold +from sklearn.utils import check_random_state # Variables for manifold learning. n_neighbors = 10 diff --git a/examples/manifold/plot_mds.py b/examples/manifold/plot_mds.py index 51f9745a33f59..2bb56f1f4ed2a 100644 --- a/examples/manifold/plot_mds.py +++ b/examples/manifold/plot_mds.py @@ -14,13 +14,12 @@ # License: BSD import numpy as np - from matplotlib import pyplot as plt from matplotlib.collections import LineCollection from sklearn import manifold -from sklearn.metrics import euclidean_distances from sklearn.decomposition import PCA +from sklearn.metrics import euclidean_distances EPSILON = np.finfo(np.float32).eps n_samples = 20 diff --git a/examples/manifold/plot_swissroll.py b/examples/manifold/plot_swissroll.py index 4a71eb83cc972..fe17d9f80030f 100644 --- a/examples/manifold/plot_swissroll.py +++ b/examples/manifold/plot_swissroll.py @@ -15,8 +15,8 @@ # We start by generating the Swiss Roll dataset. import matplotlib.pyplot as plt -from sklearn import manifold, datasets +from sklearn import datasets, manifold sr_points, sr_color = datasets.make_swiss_roll(n_samples=1500, random_state=0) diff --git a/examples/manifold/plot_t_sne_perplexity.py b/examples/manifold/plot_t_sne_perplexity.py index 014114a8a37d7..314458427f593 100644 --- a/examples/manifold/plot_t_sne_perplexity.py +++ b/examples/manifold/plot_t_sne_perplexity.py @@ -27,12 +27,13 @@ # Author: Narine Kokhlikyan # License: BSD -import numpy as np -import matplotlib.pyplot as plt +from time import time +import matplotlib.pyplot as plt +import numpy as np from matplotlib.ticker import NullFormatter -from sklearn import manifold, datasets -from time import time + +from sklearn import datasets, manifold n_samples = 150 n_components = 2 diff --git a/examples/miscellaneous/plot_anomaly_comparison.py b/examples/miscellaneous/plot_anomaly_comparison.py index ef274bf98fbe5..7fb6b71e2a5c6 100644 --- a/examples/miscellaneous/plot_anomaly_comparison.py +++ b/examples/miscellaneous/plot_anomaly_comparison.py @@ -68,17 +68,17 @@ import time -import numpy as np import matplotlib import matplotlib.pyplot as plt +import numpy as np from sklearn import svm -from sklearn.datasets import make_moons, make_blobs from sklearn.covariance import EllipticEnvelope +from sklearn.datasets import make_blobs, make_moons from sklearn.ensemble import IsolationForest -from sklearn.neighbors import LocalOutlierFactor -from sklearn.linear_model import SGDOneClassSVM from sklearn.kernel_approximation import Nystroem +from sklearn.linear_model import SGDOneClassSVM +from sklearn.neighbors import LocalOutlierFactor from sklearn.pipeline import make_pipeline matplotlib.rcParams["contour.negative_linestyle"] = "solid" diff --git a/examples/miscellaneous/plot_display_object_visualization.py b/examples/miscellaneous/plot_display_object_visualization.py index f108beced7a00..24095de3b5cae 100644 --- a/examples/miscellaneous/plot_display_object_visualization.py +++ b/examples/miscellaneous/plot_display_object_visualization.py @@ -24,10 +24,10 @@ # data is split into a train and test dataset and a logistic regression is # fitted with the train dataset. from sklearn.datasets import fetch_openml -from sklearn.preprocessing import StandardScaler -from sklearn.pipeline import make_pipeline from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler X, y = fetch_openml(data_id=1464, return_X_y=True, parser="pandas") X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y) @@ -41,8 +41,7 @@ # With the fitted model, we compute the predictions of the model on the test # dataset. These predictions are used to compute the confustion matrix which # is plotted with the :class:`ConfusionMatrixDisplay` -from sklearn.metrics import confusion_matrix -from sklearn.metrics import ConfusionMatrixDisplay +from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix y_pred = clf.predict(X_test) cm = confusion_matrix(y_test, y_pred) @@ -56,8 +55,7 @@ # The roc curve requires either the probabilities or the non-thresholded # decision values from the estimator. Since the logistic regression provides # a decision function, we will use it to plot the roc curve: -from sklearn.metrics import roc_curve -from sklearn.metrics import RocCurveDisplay +from sklearn.metrics import RocCurveDisplay, roc_curve y_score = clf.decision_function(X_test) @@ -69,8 +67,7 @@ ############################################################################## # Similarly, the precision recall curve can be plotted using `y_score` from # the prevision sections. -from sklearn.metrics import precision_recall_curve -from sklearn.metrics import PrecisionRecallDisplay +from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve prec, recall, _ = precision_recall_curve(y_test, y_score, pos_label=clf.classes_[1]) pr_display = PrecisionRecallDisplay(precision=prec, recall=recall).plot() diff --git a/examples/miscellaneous/plot_estimator_representation.py b/examples/miscellaneous/plot_estimator_representation.py index 304bb055e6762..1c9e3745db0de 100644 --- a/examples/miscellaneous/plot_estimator_representation.py +++ b/examples/miscellaneous/plot_estimator_representation.py @@ -7,12 +7,11 @@ displayed. """ -from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import OneHotEncoder, StandardScaler -from sklearn.impute import SimpleImputer from sklearn.compose import make_column_transformer +from sklearn.impute import SimpleImputer from sklearn.linear_model import LogisticRegression - +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import OneHotEncoder, StandardScaler # %% # Compact text representation diff --git a/examples/miscellaneous/plot_isotonic_regression.py b/examples/miscellaneous/plot_isotonic_regression.py index 0240a8dec34b5..a1c1174c9e9de 100644 --- a/examples/miscellaneous/plot_isotonic_regression.py +++ b/examples/miscellaneous/plot_isotonic_regression.py @@ -23,12 +23,12 @@ # Alexandre Gramfort # License: BSD -import numpy as np import matplotlib.pyplot as plt +import numpy as np from matplotlib.collections import LineCollection -from sklearn.linear_model import LinearRegression from sklearn.isotonic import IsotonicRegression +from sklearn.linear_model import LinearRegression from sklearn.utils import check_random_state n = 100 diff --git a/examples/miscellaneous/plot_johnson_lindenstrauss_bound.py b/examples/miscellaneous/plot_johnson_lindenstrauss_bound.py index 6fd9d3614804c..85161a6ee51bb 100644 --- a/examples/miscellaneous/plot_johnson_lindenstrauss_bound.py +++ b/examples/miscellaneous/plot_johnson_lindenstrauss_bound.py @@ -15,13 +15,16 @@ import sys from time import time -import numpy as np + import matplotlib.pyplot as plt -from sklearn.random_projection import johnson_lindenstrauss_min_dim -from sklearn.random_projection import SparseRandomProjection -from sklearn.datasets import fetch_20newsgroups_vectorized -from sklearn.datasets import load_digits +import numpy as np + +from sklearn.datasets import fetch_20newsgroups_vectorized, load_digits from sklearn.metrics.pairwise import euclidean_distances +from sklearn.random_projection import ( + SparseRandomProjection, + johnson_lindenstrauss_min_dim, +) # %% # Theoretical bounds diff --git a/examples/miscellaneous/plot_kernel_approximation.py b/examples/miscellaneous/plot_kernel_approximation.py index ffb5d3940a055..7faf7900c7eb5 100644 --- a/examples/miscellaneous/plot_kernel_approximation.py +++ b/examples/miscellaneous/plot_kernel_approximation.py @@ -39,14 +39,15 @@ # License: BSD 3 clause # Standard scientific Python imports +from time import time + import matplotlib.pyplot as plt import numpy as np -from time import time # Import datasets, classifiers and performance metrics -from sklearn import datasets, svm, pipeline -from sklearn.kernel_approximation import RBFSampler, Nystroem +from sklearn import datasets, pipeline, svm from sklearn.decomposition import PCA +from sklearn.kernel_approximation import Nystroem, RBFSampler # The digits dataset digits = datasets.load_digits(n_class=9) diff --git a/examples/miscellaneous/plot_kernel_ridge_regression.py b/examples/miscellaneous/plot_kernel_ridge_regression.py index fa7cb15446473..6d2288936179a 100644 --- a/examples/miscellaneous/plot_kernel_ridge_regression.py +++ b/examples/miscellaneous/plot_kernel_ridge_regression.py @@ -40,9 +40,9 @@ # Construct the kernel-based regression models # -------------------------------------------- +from sklearn.kernel_ridge import KernelRidge from sklearn.model_selection import GridSearchCV from sklearn.svm import SVR -from sklearn.kernel_ridge import KernelRidge train_size = 100 diff --git a/examples/miscellaneous/plot_metadata_routing.py b/examples/miscellaneous/plot_metadata_routing.py index 81e3b6fc9a01d..b189f320cdd24 100644 --- a/examples/miscellaneous/plot_metadata_routing.py +++ b/examples/miscellaneous/plot_metadata_routing.py @@ -22,23 +22,29 @@ """ # %% -import numpy as np import warnings from pprint import pprint + +import numpy as np + from sklearn import set_config -from sklearn.base import BaseEstimator -from sklearn.base import ClassifierMixin -from sklearn.base import RegressorMixin -from sklearn.base import MetaEstimatorMixin -from sklearn.base import TransformerMixin -from sklearn.base import clone +from sklearn.base import ( + BaseEstimator, + ClassifierMixin, + MetaEstimatorMixin, + RegressorMixin, + TransformerMixin, + clone, +) +from sklearn.linear_model import LinearRegression from sklearn.utils import metadata_routing -from sklearn.utils.metadata_routing import get_routing_for_object -from sklearn.utils.metadata_routing import MetadataRouter -from sklearn.utils.metadata_routing import MethodMapping -from sklearn.utils.metadata_routing import process_routing +from sklearn.utils.metadata_routing import ( + MetadataRouter, + MethodMapping, + get_routing_for_object, + process_routing, +) from sklearn.utils.validation import check_is_fitted -from sklearn.linear_model import LinearRegression n_samples, n_features = 100, 4 rng = np.random.RandomState(42) diff --git a/examples/miscellaneous/plot_multilabel.py b/examples/miscellaneous/plot_multilabel.py index aded595258fea..b424c3253104a 100644 --- a/examples/miscellaneous/plot_multilabel.py +++ b/examples/miscellaneous/plot_multilabel.py @@ -32,14 +32,14 @@ # Authors: Vlad Niculae, Mathieu Blondel # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np +from sklearn.cross_decomposition import CCA from sklearn.datasets import make_multilabel_classification +from sklearn.decomposition import PCA from sklearn.multiclass import OneVsRestClassifier from sklearn.svm import SVC -from sklearn.decomposition import PCA -from sklearn.cross_decomposition import CCA def plot_hyperplane(clf, min_x, max_x, linestyle, label): diff --git a/examples/miscellaneous/plot_multioutput_face_completion.py b/examples/miscellaneous/plot_multioutput_face_completion.py index 31e73195747a5..62070bc05e488 100644 --- a/examples/miscellaneous/plot_multioutput_face_completion.py +++ b/examples/miscellaneous/plot_multioutput_face_completion.py @@ -12,16 +12,14 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.datasets import fetch_olivetti_faces -from sklearn.utils.validation import check_random_state - from sklearn.ensemble import ExtraTreesRegressor +from sklearn.linear_model import LinearRegression, RidgeCV from sklearn.neighbors import KNeighborsRegressor -from sklearn.linear_model import LinearRegression -from sklearn.linear_model import RidgeCV +from sklearn.utils.validation import check_random_state # Load the faces datasets data, targets = fetch_olivetti_faces(return_X_y=True) diff --git a/examples/miscellaneous/plot_outlier_detection_bench.py b/examples/miscellaneous/plot_outlier_detection_bench.py index 781fa515f50e8..9b530ccab0807 100644 --- a/examples/miscellaneous/plot_outlier_detection_bench.py +++ b/examples/miscellaneous/plot_outlier_detection_bench.py @@ -32,10 +32,11 @@ # The `preprocess_dataset` function returns data and target. import numpy as np -from sklearn.datasets import fetch_kddcup99, fetch_covtype, fetch_openml -from sklearn.preprocessing import LabelBinarizer import pandas as pd +from sklearn.datasets import fetch_covtype, fetch_kddcup99, fetch_openml +from sklearn.preprocessing import LabelBinarizer + rng = np.random.RandomState(42) @@ -117,8 +118,8 @@ def preprocess_dataset(dataset_name): # `compute_prediction` function returns average outlier score of X. -from sklearn.neighbors import LocalOutlierFactor from sklearn.ensemble import IsolationForest +from sklearn.neighbors import LocalOutlierFactor def compute_prediction(X, model_name): @@ -145,7 +146,9 @@ def compute_prediction(X, model_name): import math + import matplotlib.pyplot as plt + from sklearn.metrics import RocCurveDisplay datasets_name = [ diff --git a/examples/miscellaneous/plot_partial_dependence_visualization_api.py b/examples/miscellaneous/plot_partial_dependence_visualization_api.py index 336d7c36d1661..38a984fa5b0cd 100644 --- a/examples/miscellaneous/plot_partial_dependence_visualization_api.py +++ b/examples/miscellaneous/plot_partial_dependence_visualization_api.py @@ -13,15 +13,15 @@ """ # noqa: E501 -import pandas as pd import matplotlib.pyplot as plt +import pandas as pd + from sklearn.datasets import load_diabetes +from sklearn.inspection import PartialDependenceDisplay from sklearn.neural_network import MLPRegressor -from sklearn.preprocessing import StandardScaler from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler from sklearn.tree import DecisionTreeRegressor -from sklearn.inspection import PartialDependenceDisplay - # %% # Train models on the diabetes dataset diff --git a/examples/miscellaneous/plot_pipeline_display.py b/examples/miscellaneous/plot_pipeline_display.py index f0fea8d2f3a27..9642bb56b903f 100755 --- a/examples/miscellaneous/plot_pipeline_display.py +++ b/examples/miscellaneous/plot_pipeline_display.py @@ -19,10 +19,10 @@ # :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual # representation. +from sklearn import set_config +from sklearn.linear_model import LogisticRegression from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler -from sklearn.linear_model import LogisticRegression -from sklearn import set_config steps = [ ("preprocessing", StandardScaler()), @@ -53,9 +53,9 @@ # :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual # representation. -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler, PolynomialFeatures from sklearn.linear_model import LogisticRegression +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import PolynomialFeatures, StandardScaler steps = [ ("standard_scaler", StandardScaler()), @@ -73,9 +73,9 @@ # a classifier, :class:`~sklearn.svm.SVC`, and displays its visual # representation. +from sklearn.decomposition import PCA from sklearn.pipeline import Pipeline from sklearn.svm import SVC -from sklearn.decomposition import PCA steps = [("reduce_dim", PCA(n_components=4)), ("classifier", SVC(kernel="linear"))] pipe = Pipeline(steps) @@ -90,12 +90,12 @@ # representation. import numpy as np -from sklearn.pipeline import make_pipeline -from sklearn.pipeline import Pipeline -from sklearn.impute import SimpleImputer + from sklearn.compose import ColumnTransformer -from sklearn.preprocessing import OneHotEncoder, StandardScaler +from sklearn.impute import SimpleImputer from sklearn.linear_model import LogisticRegression +from sklearn.pipeline import Pipeline, make_pipeline +from sklearn.preprocessing import OneHotEncoder, StandardScaler numeric_preprocessor = Pipeline( steps=[ @@ -133,13 +133,13 @@ # representation. import numpy as np -from sklearn.pipeline import make_pipeline -from sklearn.pipeline import Pipeline -from sklearn.impute import SimpleImputer + from sklearn.compose import ColumnTransformer -from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn.ensemble import RandomForestClassifier +from sklearn.impute import SimpleImputer from sklearn.model_selection import GridSearchCV +from sklearn.pipeline import Pipeline, make_pipeline +from sklearn.preprocessing import OneHotEncoder, StandardScaler numeric_preprocessor = Pipeline( steps=[ diff --git a/examples/miscellaneous/plot_roc_curve_visualization_api.py b/examples/miscellaneous/plot_roc_curve_visualization_api.py index b4e08493c77d4..7fc8df9724337 100644 --- a/examples/miscellaneous/plot_roc_curve_visualization_api.py +++ b/examples/miscellaneous/plot_roc_curve_visualization_api.py @@ -15,11 +15,12 @@ # First, we load the wine dataset and convert it to a binary classification # problem. Then, we train a support vector classifier on a training dataset. import matplotlib.pyplot as plt -from sklearn.svm import SVC + +from sklearn.datasets import load_wine from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import RocCurveDisplay -from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split +from sklearn.svm import SVC X, y = load_wine(return_X_y=True) y = y == 2 diff --git a/examples/miscellaneous/plot_set_output.py b/examples/miscellaneous/plot_set_output.py index a2088ae48adc3..725e04894614c 100644 --- a/examples/miscellaneous/plot_set_output.py +++ b/examples/miscellaneous/plot_set_output.py @@ -48,9 +48,9 @@ # %% # In a :class:`pipeline.Pipeline`, `set_output` configures all steps to output # DataFrames. -from sklearn.pipeline import make_pipeline -from sklearn.linear_model import LogisticRegression from sklearn.feature_selection import SelectPercentile +from sklearn.linear_model import LogisticRegression +from sklearn.pipeline import make_pipeline clf = make_pipeline( StandardScaler(), SelectPercentile(percentile=75), LogisticRegression() @@ -76,10 +76,10 @@ # %% # The `set_output` API can be configured globally by using :func:`set_config` and # setting `transform_output` to `"pandas"`. +from sklearn import set_config from sklearn.compose import ColumnTransformer -from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn.impute import SimpleImputer -from sklearn import set_config +from sklearn.preprocessing import OneHotEncoder, StandardScaler set_config(transform_output="pandas") diff --git a/examples/mixture/plot_concentration_prior.py b/examples/mixture/plot_concentration_prior.py index a56ec6325068b..6561186adb119 100644 --- a/examples/mixture/plot_concentration_prior.py +++ b/examples/mixture/plot_concentration_prior.py @@ -32,10 +32,10 @@ # Author: Thierry Guillemot # License: BSD 3 clause -import numpy as np import matplotlib as mpl -import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec +import matplotlib.pyplot as plt +import numpy as np from sklearn.mixture import BayesianGaussianMixture diff --git a/examples/mixture/plot_gmm.py b/examples/mixture/plot_gmm.py index efc89baa8159a..82e48a8d13eb0 100644 --- a/examples/mixture/plot_gmm.py +++ b/examples/mixture/plot_gmm.py @@ -26,10 +26,10 @@ import itertools +import matplotlib as mpl +import matplotlib.pyplot as plt import numpy as np from scipy import linalg -import matplotlib.pyplot as plt -import matplotlib as mpl from sklearn import mixture diff --git a/examples/mixture/plot_gmm_covariances.py b/examples/mixture/plot_gmm_covariances.py index aa0b78ab42a0b..9466e11749966 100644 --- a/examples/mixture/plot_gmm_covariances.py +++ b/examples/mixture/plot_gmm_covariances.py @@ -33,7 +33,6 @@ import matplotlib as mpl import matplotlib.pyplot as plt - import numpy as np from sklearn import datasets diff --git a/examples/mixture/plot_gmm_init.py b/examples/mixture/plot_gmm_init.py index 3b4beefe8c99a..aa0266c98ff7a 100644 --- a/examples/mixture/plot_gmm_init.py +++ b/examples/mixture/plot_gmm_init.py @@ -37,12 +37,14 @@ # Author: Gordon Walsh # Data generation code from Jake Vanderplas +from timeit import default_timer as timer + import matplotlib.pyplot as plt import numpy as np + +from sklearn.datasets._samples_generator import make_blobs from sklearn.mixture import GaussianMixture from sklearn.utils.extmath import row_norms -from sklearn.datasets._samples_generator import make_blobs -from timeit import default_timer as timer print(__doc__) diff --git a/examples/mixture/plot_gmm_pdf.py b/examples/mixture/plot_gmm_pdf.py index 70d58f22f8f41..062bdfd4d6d67 100644 --- a/examples/mixture/plot_gmm_pdf.py +++ b/examples/mixture/plot_gmm_pdf.py @@ -9,9 +9,10 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np from matplotlib.colors import LogNorm + from sklearn import mixture n_samples = 300 diff --git a/examples/mixture/plot_gmm_sin.py b/examples/mixture/plot_gmm_sin.py index c8656a69fe9fb..34af17b8920bc 100644 --- a/examples/mixture/plot_gmm_sin.py +++ b/examples/mixture/plot_gmm_sin.py @@ -41,10 +41,10 @@ import itertools +import matplotlib as mpl +import matplotlib.pyplot as plt import numpy as np from scipy import linalg -import matplotlib.pyplot as plt -import matplotlib as mpl from sklearn import mixture diff --git a/examples/model_selection/plot_confusion_matrix.py b/examples/model_selection/plot_confusion_matrix.py index b891564db4025..278083a994e58 100644 --- a/examples/model_selection/plot_confusion_matrix.py +++ b/examples/model_selection/plot_confusion_matrix.py @@ -24,12 +24,12 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np -from sklearn import svm, datasets -from sklearn.model_selection import train_test_split +from sklearn import datasets, svm from sklearn.metrics import ConfusionMatrixDisplay +from sklearn.model_selection import train_test_split # import some data to play with iris = datasets.load_iris() diff --git a/examples/model_selection/plot_cv_indices.py b/examples/model_selection/plot_cv_indices.py index 8b70191e4abd1..e6c3580c787f0 100644 --- a/examples/model_selection/plot_cv_indices.py +++ b/examples/model_selection/plot_cv_indices.py @@ -12,19 +12,20 @@ """ +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.patches import Patch + from sklearn.model_selection import ( - TimeSeriesSplit, + GroupKFold, + GroupShuffleSplit, KFold, ShuffleSplit, + StratifiedGroupKFold, StratifiedKFold, - GroupShuffleSplit, - GroupKFold, StratifiedShuffleSplit, - StratifiedGroupKFold, + TimeSeriesSplit, ) -import numpy as np -import matplotlib.pyplot as plt -from matplotlib.patches import Patch rng = np.random.RandomState(1338) cmap_data = plt.cm.Paired diff --git a/examples/model_selection/plot_cv_predict.py b/examples/model_selection/plot_cv_predict.py index 7fd843c535c85..65517d85f3fd1 100644 --- a/examples/model_selection/plot_cv_predict.py +++ b/examples/model_selection/plot_cv_predict.py @@ -37,6 +37,7 @@ # residuals (i.e. the difference between the observed values and the predicted # values) vs. the predicted values. import matplotlib.pyplot as plt + from sklearn.metrics import PredictionErrorDisplay fig, axs = plt.subplots(ncols=2, figsize=(8, 4)) diff --git a/examples/model_selection/plot_det.py b/examples/model_selection/plot_det.py index 97dbe771e6407..7f7a5be32f976 100644 --- a/examples/model_selection/plot_det.py +++ b/examples/model_selection/plot_det.py @@ -82,6 +82,7 @@ # :func:`scipy.stats.norm`. import matplotlib.pyplot as plt + from sklearn.metrics import DetCurveDisplay, RocCurveDisplay fig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(11, 5)) diff --git a/examples/model_selection/plot_grid_search_refit_callable.py b/examples/model_selection/plot_grid_search_refit_callable.py index 7a7dd8ea3e463..a8dab986a48d2 100644 --- a/examples/model_selection/plot_grid_search_refit_callable.py +++ b/examples/model_selection/plot_grid_search_refit_callable.py @@ -20,8 +20,8 @@ # Author: Wenhao Zhang -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.datasets import load_digits from sklearn.decomposition import PCA diff --git a/examples/model_selection/plot_grid_search_stats.py b/examples/model_selection/plot_grid_search_stats.py index 179d860b42128..fbeb485d8db44 100644 --- a/examples/model_selection/plot_grid_search_stats.py +++ b/examples/model_selection/plot_grid_search_stats.py @@ -16,6 +16,7 @@ import matplotlib.pyplot as plt import seaborn as sns + from sklearn.datasets import make_moons X, y = make_moons(noise=0.352, random_state=1, n_samples=100) diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py index 9ad4296aad9b4..17c2e2bfd5d99 100644 --- a/examples/model_selection/plot_grid_search_text_feature_extraction.py +++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py @@ -105,6 +105,7 @@ # via the parameter `n_jobs`. from pprint import pprint + from sklearn.model_selection import RandomizedSearchCV random_search = RandomizedSearchCV( diff --git a/examples/model_selection/plot_learning_curve.py b/examples/model_selection/plot_learning_curve.py index 956c70aaabd82..450392679095f 100644 --- a/examples/model_selection/plot_learning_curve.py +++ b/examples/model_selection/plot_learning_curve.py @@ -38,6 +38,7 @@ # a cross-validation procedure. import matplotlib.pyplot as plt import numpy as np + from sklearn.model_selection import LearningCurveDisplay, ShuffleSplit fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 6), sharey=True) diff --git a/examples/model_selection/plot_likelihood_ratios.py b/examples/model_selection/plot_likelihood_ratios.py index e6ec94fc50cf9..9a3f29def9e98 100644 --- a/examples/model_selection/plot_likelihood_ratios.py +++ b/examples/model_selection/plot_likelihood_ratios.py @@ -55,8 +55,8 @@ class proportion than the target application. # ratio to evaluate the usefulness of this classifier as a disease diagnosis # tool: -from sklearn.metrics import class_likelihood_ratios from sklearn.linear_model import LogisticRegression +from sklearn.metrics import class_likelihood_ratios estimator = LogisticRegression().fit(X_train, y_train) y_pred = estimator.predict(X_test) @@ -166,10 +166,12 @@ def extract_score(cv_results): # label `1` corresponds to the positive class "disease", whereas the label `0` # stands for "no-disease". -import numpy as np +from collections import defaultdict + import matplotlib.pyplot as plt +import numpy as np + from sklearn.inspection import DecisionBoundaryDisplay -from collections import defaultdict populations = defaultdict(list) common_params = { diff --git a/examples/model_selection/plot_multi_metric_evaluation.py b/examples/model_selection/plot_multi_metric_evaluation.py index e47e67e086ccb..674bf8bc1b07c 100644 --- a/examples/model_selection/plot_multi_metric_evaluation.py +++ b/examples/model_selection/plot_multi_metric_evaluation.py @@ -23,9 +23,8 @@ from matplotlib import pyplot as plt from sklearn.datasets import make_hastie_10_2 +from sklearn.metrics import accuracy_score, make_scorer from sklearn.model_selection import GridSearchCV -from sklearn.metrics import make_scorer -from sklearn.metrics import accuracy_score from sklearn.tree import DecisionTreeClassifier # %% diff --git a/examples/model_selection/plot_nested_cross_validation_iris.py b/examples/model_selection/plot_nested_cross_validation_iris.py index b6f45255e8a09..7513a078b68ce 100644 --- a/examples/model_selection/plot_nested_cross_validation_iris.py +++ b/examples/model_selection/plot_nested_cross_validation_iris.py @@ -44,11 +44,12 @@ """ -from sklearn.datasets import load_iris +import numpy as np from matplotlib import pyplot as plt + +from sklearn.datasets import load_iris +from sklearn.model_selection import GridSearchCV, KFold, cross_val_score from sklearn.svm import SVC -from sklearn.model_selection import GridSearchCV, cross_val_score, KFold -import numpy as np # Number of random trials NUM_TRIALS = 30 diff --git a/examples/model_selection/plot_permutation_tests_for_classification.py b/examples/model_selection/plot_permutation_tests_for_classification.py index c9fcaebb549fe..a02f6d188f006 100644 --- a/examples/model_selection/plot_permutation_tests_for_classification.py +++ b/examples/model_selection/plot_permutation_tests_for_classification.py @@ -58,9 +58,8 @@ # the percentage of permutations for which the score obtained is greater # that the score obtained using the original data. +from sklearn.model_selection import StratifiedKFold, permutation_test_score from sklearn.svm import SVC -from sklearn.model_selection import StratifiedKFold -from sklearn.model_selection import permutation_test_score clf = SVC(kernel="linear", random_state=7) cv = StratifiedKFold(2, shuffle=True, random_state=0) diff --git a/examples/model_selection/plot_precision_recall.py b/examples/model_selection/plot_precision_recall.py index d11d6e10cdff6..52d85691af439 100644 --- a/examples/model_selection/plot_precision_recall.py +++ b/examples/model_selection/plot_precision_recall.py @@ -100,6 +100,7 @@ # # We will use a Linear SVC classifier to differentiate two types of irises. import numpy as np + from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split @@ -198,8 +199,7 @@ # %% # The average precision score in multi-label settings # ................................................... -from sklearn.metrics import precision_recall_curve -from sklearn.metrics import average_precision_score +from sklearn.metrics import average_precision_score, precision_recall_curve # For each class precision = dict() @@ -232,9 +232,10 @@ # %% # Plot Precision-Recall curve for each class and iso-f1 curves # ............................................................ -import matplotlib.pyplot as plt from itertools import cycle +import matplotlib.pyplot as plt + # setup plot details colors = cycle(["navy", "turquoise", "darkorange", "cornflowerblue", "teal"]) diff --git a/examples/model_selection/plot_randomized_search.py b/examples/model_selection/plot_randomized_search.py index 9ffc26a5abc84..140b359ff1934 100644 --- a/examples/model_selection/plot_randomized_search.py +++ b/examples/model_selection/plot_randomized_search.py @@ -20,14 +20,14 @@ """ -import numpy as np - from time import time + +import numpy as np import scipy.stats as stats -from sklearn.model_selection import GridSearchCV, RandomizedSearchCV from sklearn.datasets import load_digits from sklearn.linear_model import SGDClassifier +from sklearn.model_selection import GridSearchCV, RandomizedSearchCV # get some data X, y = load_digits(return_X_y=True, n_class=3) diff --git a/examples/model_selection/plot_roc.py b/examples/model_selection/plot_roc.py index 34346780def26..3fa1374f1b8a0 100644 --- a/examples/model_selection/plot_roc.py +++ b/examples/model_selection/plot_roc.py @@ -44,6 +44,7 @@ # Here we binarize the output and add noisy features to make the problem harder. import numpy as np + from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split @@ -118,6 +119,7 @@ # %% import matplotlib.pyplot as plt + from sklearn.metrics import RocCurveDisplay RocCurveDisplay.from_predictions( @@ -191,7 +193,7 @@ # :class:`~sklearn.metrics.roc_curve` and then the area under the curve with # :class:`~sklearn.metrics.auc` for the raveled true and predicted classes. -from sklearn.metrics import roc_curve, auc +from sklearn.metrics import auc, roc_curve # store the fpr, tpr, and roc_auc for all averaging strategies fpr, tpr, roc_auc = dict(), dict(), dict() diff --git a/examples/model_selection/plot_roc_crossval.py b/examples/model_selection/plot_roc_crossval.py index cf4c0496f54fb..a3663aa040b56 100644 --- a/examples/model_selection/plot_roc_crossval.py +++ b/examples/model_selection/plot_roc_crossval.py @@ -41,6 +41,7 @@ # (`class_id=0`). import numpy as np + from sklearn.datasets import load_iris iris = load_iris() @@ -66,8 +67,7 @@ import matplotlib.pyplot as plt from sklearn import svm -from sklearn.metrics import auc -from sklearn.metrics import RocCurveDisplay +from sklearn.metrics import RocCurveDisplay, auc from sklearn.model_selection import StratifiedKFold n_splits = 6 diff --git a/examples/model_selection/plot_successive_halving_heatmap.py b/examples/model_selection/plot_successive_halving_heatmap.py index ecdae48e64011..9b079e4b1351f 100644 --- a/examples/model_selection/plot_successive_halving_heatmap.py +++ b/examples/model_selection/plot_successive_halving_heatmap.py @@ -14,12 +14,10 @@ import numpy as np import pandas as pd -from sklearn.svm import SVC from sklearn import datasets -from sklearn.model_selection import GridSearchCV from sklearn.experimental import enable_halving_search_cv # noqa -from sklearn.model_selection import HalvingGridSearchCV - +from sklearn.model_selection import GridSearchCV, HalvingGridSearchCV +from sklearn.svm import SVC # %% # We first define the parameter space for an :class:`~sklearn.svm.SVC` diff --git a/examples/model_selection/plot_successive_halving_iterations.py b/examples/model_selection/plot_successive_halving_iterations.py index bd2d5635e376e..31805d308e269 100644 --- a/examples/model_selection/plot_successive_halving_iterations.py +++ b/examples/model_selection/plot_successive_halving_iterations.py @@ -10,16 +10,15 @@ """ -import pandas as pd -from sklearn import datasets import matplotlib.pyplot as plt -from scipy.stats import randint import numpy as np +import pandas as pd +from scipy.stats import randint +from sklearn import datasets +from sklearn.ensemble import RandomForestClassifier from sklearn.experimental import enable_halving_search_cv # noqa from sklearn.model_selection import HalvingRandomSearchCV -from sklearn.ensemble import RandomForestClassifier - # %% # We first define the parameter space and train a diff --git a/examples/model_selection/plot_train_error_vs_test_error.py b/examples/model_selection/plot_train_error_vs_test_error.py index 1aba6f4892cbe..af7e7d14cdac0 100644 --- a/examples/model_selection/plot_train_error_vs_test_error.py +++ b/examples/model_selection/plot_train_error_vs_test_error.py @@ -19,6 +19,7 @@ # Generate sample data # -------------------- import numpy as np + from sklearn import linear_model from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split diff --git a/examples/model_selection/plot_underfitting_overfitting.py b/examples/model_selection/plot_underfitting_overfitting.py index ae8450b50cea9..412946fc9ca8b 100644 --- a/examples/model_selection/plot_underfitting_overfitting.py +++ b/examples/model_selection/plot_underfitting_overfitting.py @@ -21,12 +21,13 @@ """ -import numpy as np import matplotlib.pyplot as plt -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import PolynomialFeatures +import numpy as np + from sklearn.linear_model import LinearRegression from sklearn.model_selection import cross_val_score +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import PolynomialFeatures def true_fun(X): diff --git a/examples/model_selection/plot_validation_curve.py b/examples/model_selection/plot_validation_curve.py index 48aa19dfbc556..947d8ac2b2fdb 100644 --- a/examples/model_selection/plot_validation_curve.py +++ b/examples/model_selection/plot_validation_curve.py @@ -17,8 +17,8 @@ import numpy as np from sklearn.datasets import load_digits -from sklearn.svm import SVC from sklearn.model_selection import ValidationCurveDisplay +from sklearn.svm import SVC X, y = load_digits(return_X_y=True) subset_mask = np.isin(y, [1, 2]) # binary classification: 1 vs 2 diff --git a/examples/multioutput/plot_classifier_chain_yeast.py b/examples/multioutput/plot_classifier_chain_yeast.py index e1f9feed43a97..1df4ee3b8346b 100644 --- a/examples/multioutput/plot_classifier_chain_yeast.py +++ b/examples/multioutput/plot_classifier_chain_yeast.py @@ -36,14 +36,15 @@ # Author: Adam Kleczewski # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn.datasets import fetch_openml -from sklearn.multioutput import ClassifierChain +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import jaccard_score from sklearn.model_selection import train_test_split from sklearn.multiclass import OneVsRestClassifier -from sklearn.metrics import jaccard_score -from sklearn.linear_model import LogisticRegression +from sklearn.multioutput import ClassifierChain # Load a multi-label dataset from https://www.openml.org/d/40597 X, Y = fetch_openml("yeast", version=4, return_X_y=True, parser="pandas") diff --git a/examples/neighbors/approximate_nearest_neighbors.py b/examples/neighbors/approximate_nearest_neighbors.py index ee848cdc66428..faff31d7a85c9 100644 --- a/examples/neighbors/approximate_nearest_neighbors.py +++ b/examples/neighbors/approximate_nearest_neighbors.py @@ -40,6 +40,7 @@ import joblib import numpy as np from scipy.sparse import csr_matrix + from sklearn.base import BaseEstimator, TransformerMixin from sklearn.datasets import fetch_openml from sklearn.utils import shuffle diff --git a/examples/neighbors/plot_caching_nearest_neighbors.py b/examples/neighbors/plot_caching_nearest_neighbors.py index 00be6470c1591..10c0d315da7af 100644 --- a/examples/neighbors/plot_caching_nearest_neighbors.py +++ b/examples/neighbors/plot_caching_nearest_neighbors.py @@ -22,11 +22,12 @@ # # License: BSD 3 clause from tempfile import TemporaryDirectory + import matplotlib.pyplot as plt -from sklearn.neighbors import KNeighborsTransformer, KNeighborsClassifier -from sklearn.model_selection import GridSearchCV from sklearn.datasets import load_digits +from sklearn.model_selection import GridSearchCV +from sklearn.neighbors import KNeighborsClassifier, KNeighborsTransformer from sklearn.pipeline import Pipeline X, y = load_digits(return_X_y=True) diff --git a/examples/neighbors/plot_classification.py b/examples/neighbors/plot_classification.py index cc4f0864ba926..4ed23862ae455 100644 --- a/examples/neighbors/plot_classification.py +++ b/examples/neighbors/plot_classification.py @@ -11,7 +11,8 @@ import matplotlib.pyplot as plt import seaborn as sns from matplotlib.colors import ListedColormap -from sklearn import neighbors, datasets + +from sklearn import datasets, neighbors from sklearn.inspection import DecisionBoundaryDisplay n_neighbors = 15 diff --git a/examples/neighbors/plot_digits_kde_sampling.py b/examples/neighbors/plot_digits_kde_sampling.py index e580f9fa178bc..045058eab09cc 100644 --- a/examples/neighbors/plot_digits_kde_sampling.py +++ b/examples/neighbors/plot_digits_kde_sampling.py @@ -11,13 +11,13 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.datasets import load_digits -from sklearn.neighbors import KernelDensity from sklearn.decomposition import PCA from sklearn.model_selection import GridSearchCV +from sklearn.neighbors import KernelDensity # load the data digits = load_digits() diff --git a/examples/neighbors/plot_kde_1d.py b/examples/neighbors/plot_kde_1d.py index 8b139d4cc2335..fc5b1914f23de 100644 --- a/examples/neighbors/plot_kde_1d.py +++ b/examples/neighbors/plot_kde_1d.py @@ -30,9 +30,10 @@ # Author: Jake Vanderplas # -import numpy as np import matplotlib.pyplot as plt +import numpy as np from scipy.stats import norm + from sklearn.neighbors import KernelDensity # ---------------------------------------------------------------------- diff --git a/examples/neighbors/plot_lof_novelty_detection.py b/examples/neighbors/plot_lof_novelty_detection.py index 277134cc77673..91e40661c6dfe 100644 --- a/examples/neighbors/plot_lof_novelty_detection.py +++ b/examples/neighbors/plot_lof_novelty_detection.py @@ -25,9 +25,10 @@ """ -import numpy as np import matplotlib import matplotlib.pyplot as plt +import numpy as np + from sklearn.neighbors import LocalOutlierFactor np.random.seed(42) diff --git a/examples/neighbors/plot_nca_classification.py b/examples/neighbors/plot_nca_classification.py index a08bbe8be3756..f76770640ed03 100644 --- a/examples/neighbors/plot_nca_classification.py +++ b/examples/neighbors/plot_nca_classification.py @@ -19,13 +19,13 @@ import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap + from sklearn import datasets +from sklearn.inspection import DecisionBoundaryDisplay from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler from sklearn.neighbors import KNeighborsClassifier, NeighborhoodComponentsAnalysis from sklearn.pipeline import Pipeline -from sklearn.inspection import DecisionBoundaryDisplay - +from sklearn.preprocessing import StandardScaler n_neighbors = 1 diff --git a/examples/neighbors/plot_nca_dim_reduction.py b/examples/neighbors/plot_nca_dim_reduction.py index d245e0223ccfa..82fd35616929e 100644 --- a/examples/neighbors/plot_nca_dim_reduction.py +++ b/examples/neighbors/plot_nca_dim_reduction.py @@ -30,12 +30,13 @@ # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn import datasets -from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA from sklearn.discriminant_analysis import LinearDiscriminantAnalysis +from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier, NeighborhoodComponentsAnalysis from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler diff --git a/examples/neighbors/plot_nca_illustration.py b/examples/neighbors/plot_nca_illustration.py index d722ffa5be033..e5fd2f9cb67bd 100644 --- a/examples/neighbors/plot_nca_illustration.py +++ b/examples/neighbors/plot_nca_illustration.py @@ -12,13 +12,14 @@ # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt -from sklearn.datasets import make_classification -from sklearn.neighbors import NeighborhoodComponentsAnalysis +import numpy as np from matplotlib import cm from scipy.special import logsumexp +from sklearn.datasets import make_classification +from sklearn.neighbors import NeighborhoodComponentsAnalysis + # %% # Original points # --------------- diff --git a/examples/neighbors/plot_nearest_centroid.py b/examples/neighbors/plot_nearest_centroid.py index 4eb0e0388a30b..c8f710d0a0377 100644 --- a/examples/neighbors/plot_nearest_centroid.py +++ b/examples/neighbors/plot_nearest_centroid.py @@ -8,13 +8,13 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np from matplotlib.colors import ListedColormap + from sklearn import datasets -from sklearn.neighbors import NearestCentroid from sklearn.inspection import DecisionBoundaryDisplay - +from sklearn.neighbors import NearestCentroid # import some data to play with iris = datasets.load_iris() diff --git a/examples/neighbors/plot_regression.py b/examples/neighbors/plot_regression.py index 78b850d1a4e2c..d5ceba8a34860 100644 --- a/examples/neighbors/plot_regression.py +++ b/examples/neighbors/plot_regression.py @@ -18,8 +18,9 @@ # %% # Generate sample data # -------------------- -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn import neighbors np.random.seed(0) diff --git a/examples/neighbors/plot_species_kde.py b/examples/neighbors/plot_species_kde.py index 35ea40158a45c..3783138dfcb76 100644 --- a/examples/neighbors/plot_species_kde.py +++ b/examples/neighbors/plot_species_kde.py @@ -40,8 +40,9 @@ # # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn.datasets import fetch_species_distributions from sklearn.neighbors import KernelDensity diff --git a/examples/neural_networks/plot_mlp_alpha.py b/examples/neural_networks/plot_mlp_alpha.py index 443d41f4707bf..b53beef54c115 100644 --- a/examples/neural_networks/plot_mlp_alpha.py +++ b/examples/neural_networks/plot_mlp_alpha.py @@ -23,11 +23,12 @@ import numpy as np from matplotlib import pyplot as plt from matplotlib.colors import ListedColormap + +from sklearn.datasets import make_circles, make_classification, make_moons from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from sklearn.datasets import make_moons, make_circles, make_classification from sklearn.neural_network import MLPClassifier from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler h = 0.02 # step size in the mesh diff --git a/examples/neural_networks/plot_mlp_training_curves.py b/examples/neural_networks/plot_mlp_training_curves.py index 3fbddda879162..a9f03c2599a8e 100644 --- a/examples/neural_networks/plot_mlp_training_curves.py +++ b/examples/neural_networks/plot_mlp_training_curves.py @@ -18,10 +18,10 @@ import matplotlib.pyplot as plt -from sklearn.neural_network import MLPClassifier -from sklearn.preprocessing import MinMaxScaler from sklearn import datasets from sklearn.exceptions import ConvergenceWarning +from sklearn.neural_network import MLPClassifier +from sklearn.preprocessing import MinMaxScaler # different learning rate schedules and momentum parameters params = [ diff --git a/examples/neural_networks/plot_mnist_filters.py b/examples/neural_networks/plot_mnist_filters.py index 03f615786e830..43e6a171fb696 100644 --- a/examples/neural_networks/plot_mnist_filters.py +++ b/examples/neural_networks/plot_mnist_filters.py @@ -25,11 +25,13 @@ """ import warnings + import matplotlib.pyplot as plt + from sklearn.datasets import fetch_openml from sklearn.exceptions import ConvergenceWarning -from sklearn.neural_network import MLPClassifier from sklearn.model_selection import train_test_split +from sklearn.neural_network import MLPClassifier # Load data from https://www.openml.org/d/554 X, y = fetch_openml( diff --git a/examples/neural_networks/plot_rbm_logistic_classification.py b/examples/neural_networks/plot_rbm_logistic_classification.py index de939922d9514..3ba878d4ad191 100644 --- a/examples/neural_networks/plot_rbm_logistic_classification.py +++ b/examples/neural_networks/plot_rbm_logistic_classification.py @@ -23,13 +23,11 @@ # linear shifts of 1 pixel in each direction. import numpy as np - from scipy.ndimage import convolve from sklearn import datasets -from sklearn.preprocessing import minmax_scale - from sklearn.model_selection import train_test_split +from sklearn.preprocessing import minmax_scale def nudge_dataset(X, Y): diff --git a/examples/preprocessing/plot_all_scaling.py b/examples/preprocessing/plot_all_scaling.py index 2893f5cf01ccb..c53c81a89727a 100644 --- a/examples/preprocessing/plot_all_scaling.py +++ b/examples/preprocessing/plot_all_scaling.py @@ -45,22 +45,22 @@ # Thomas Unterthiner # License: BSD 3 clause -import numpy as np - import matplotlib as mpl -from matplotlib import pyplot as plt +import numpy as np from matplotlib import cm - -from sklearn.preprocessing import MinMaxScaler -from sklearn.preprocessing import minmax_scale -from sklearn.preprocessing import MaxAbsScaler -from sklearn.preprocessing import StandardScaler -from sklearn.preprocessing import RobustScaler -from sklearn.preprocessing import Normalizer -from sklearn.preprocessing import QuantileTransformer -from sklearn.preprocessing import PowerTransformer +from matplotlib import pyplot as plt from sklearn.datasets import fetch_california_housing +from sklearn.preprocessing import ( + MaxAbsScaler, + MinMaxScaler, + Normalizer, + PowerTransformer, + QuantileTransformer, + RobustScaler, + StandardScaler, + minmax_scale, +) dataset = fetch_california_housing() X_full, y_full = dataset.data, dataset.target diff --git a/examples/preprocessing/plot_discretization.py b/examples/preprocessing/plot_discretization.py index ffb3f9403634d..002d606da0c9d 100644 --- a/examples/preprocessing/plot_discretization.py +++ b/examples/preprocessing/plot_discretization.py @@ -31,8 +31,8 @@ # Hanmin Qin # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.linear_model import LinearRegression from sklearn.preprocessing import KBinsDiscretizer diff --git a/examples/preprocessing/plot_discretization_classification.py b/examples/preprocessing/plot_discretization_classification.py index a35c56ea683d6..71adf44474aa3 100644 --- a/examples/preprocessing/plot_discretization_classification.py +++ b/examples/preprocessing/plot_discretization_classification.py @@ -33,20 +33,19 @@ # # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np from matplotlib.colors import ListedColormap -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from sklearn.datasets import make_moons, make_circles, make_classification + +from sklearn.datasets import make_circles, make_classification, make_moons +from sklearn.ensemble import GradientBoostingClassifier +from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LogisticRegression -from sklearn.model_selection import GridSearchCV +from sklearn.model_selection import GridSearchCV, train_test_split from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import KBinsDiscretizer +from sklearn.preprocessing import KBinsDiscretizer, StandardScaler from sklearn.svm import SVC, LinearSVC -from sklearn.ensemble import GradientBoostingClassifier from sklearn.utils._testing import ignore_warnings -from sklearn.exceptions import ConvergenceWarning h = 0.02 # step size in the mesh diff --git a/examples/preprocessing/plot_discretization_strategies.py b/examples/preprocessing/plot_discretization_strategies.py index 91904246540dd..b4c2f3ca1858d 100644 --- a/examples/preprocessing/plot_discretization_strategies.py +++ b/examples/preprocessing/plot_discretization_strategies.py @@ -19,11 +19,11 @@ # Author: Tom Dupré la Tour # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np -from sklearn.preprocessing import KBinsDiscretizer from sklearn.datasets import make_blobs +from sklearn.preprocessing import KBinsDiscretizer strategies = ["uniform", "quantile", "kmeans"] diff --git a/examples/preprocessing/plot_map_data_to_normal.py b/examples/preprocessing/plot_map_data_to_normal.py index 42a61d84fa384..a521039098871 100644 --- a/examples/preprocessing/plot_map_data_to_normal.py +++ b/examples/preprocessing/plot_map_data_to_normal.py @@ -38,13 +38,11 @@ # Nicolas Hug # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np -from sklearn.preprocessing import PowerTransformer -from sklearn.preprocessing import QuantileTransformer from sklearn.model_selection import train_test_split - +from sklearn.preprocessing import PowerTransformer, QuantileTransformer N_SAMPLES = 1000 FONT_SIZE = 6 diff --git a/examples/preprocessing/plot_scaling_importance.py b/examples/preprocessing/plot_scaling_importance.py index 4e8f87b68b1d4..6e0ae0ae1c109 100644 --- a/examples/preprocessing/plot_scaling_importance.py +++ b/examples/preprocessing/plot_scaling_importance.py @@ -65,10 +65,10 @@ # of features. import matplotlib.pyplot as plt + from sklearn.inspection import DecisionBoundaryDisplay from sklearn.neighbors import KNeighborsClassifier - X_plot = X[["proline", "hue"]] X_plot_scaled = scaler.fit_transform(X_plot) clf = KNeighborsClassifier(n_neighbors=20) @@ -122,6 +122,7 @@ def fit_and_plot_model(X_plot, y, clf, ax): # We can inspect the first principal components using all the original features: import pandas as pd + from sklearn.decomposition import PCA pca = PCA(n_components=2).fit(X_train) @@ -199,8 +200,9 @@ def fit_and_plot_model(X_plot, y, clf, ax): # non-scaling of the data: import numpy as np -from sklearn.pipeline import make_pipeline + from sklearn.linear_model import LogisticRegressionCV +from sklearn.pipeline import make_pipeline Cs = np.logspace(-5, 5, 20) @@ -218,8 +220,7 @@ def fit_and_plot_model(X_plot, y, clf, ax): # was not scaled before applying PCA. We now evaluate the effect of scaling on # the accuracy and the mean log-loss of the optimal models: -from sklearn.metrics import accuracy_score -from sklearn.metrics import log_loss +from sklearn.metrics import accuracy_score, log_loss y_pred = unscaled_clf.predict(X_test) y_pred_scaled = scaled_clf.predict(X_test) diff --git a/examples/preprocessing/plot_target_encoder.py b/examples/preprocessing/plot_target_encoder.py index a50f0199e5ba8..d35990cfb2a9f 100644 --- a/examples/preprocessing/plot_target_encoder.py +++ b/examples/preprocessing/plot_target_encoder.py @@ -55,9 +55,7 @@ # strategies. First, we list out the encoders we will be using to preprocess # the categorical features: from sklearn.compose import ColumnTransformer -from sklearn.preprocessing import OrdinalEncoder -from sklearn.preprocessing import OneHotEncoder -from sklearn.preprocessing import TargetEncoder +from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, TargetEncoder categorical_preprocessors = [ ("drop", "drop"), @@ -71,9 +69,9 @@ # %% # Next, we evaluate the models using cross validation and record the results: -from sklearn.pipeline import make_pipeline -from sklearn.model_selection import cross_validate from sklearn.ensemble import HistGradientBoostingRegressor +from sklearn.model_selection import cross_validate +from sklearn.pipeline import make_pipeline n_cv_folds = 3 max_iter = 20 diff --git a/examples/preprocessing/plot_target_encoder_cross_val.py b/examples/preprocessing/plot_target_encoder_cross_val.py index 455625cc47460..a7066eeab29cb 100644 --- a/examples/preprocessing/plot_target_encoder_cross_val.py +++ b/examples/preprocessing/plot_target_encoder_cross_val.py @@ -21,9 +21,10 @@ # feature with medium cardinality, an uninformative feature with medium cardinality, # and an uninformative feature with high cardinality. First, we generate the informative # feature: -from sklearn.preprocessing import KBinsDiscretizer import numpy as np +from sklearn.preprocessing import KBinsDiscretizer + n_samples = 50_000 rng = np.random.RandomState(42) @@ -60,9 +61,10 @@ # %% # Finally, we assemble the dataset and perform a train test split: -from sklearn.model_selection import train_test_split import pandas as pd +from sklearn.model_selection import train_test_split + X = pd.DataFrame( np.concatenate( [X_informative, X_shuffled, X_near_unique_categories], @@ -80,8 +82,8 @@ # interval cross validation. First, we see the Ridge model trained on the # raw features will have low performance, because the order of the informative # feature is not informative: -from sklearn.linear_model import Ridge import sklearn +from sklearn.linear_model import Ridge # Configure transformers to always output DataFrames sklearn.set_config(transform_output="pandas") @@ -107,8 +109,8 @@ # %% # The coefficients of the linear model shows that most of the weight is on the # feature at column index 0, which is the informative feature -import pandas as pd import matplotlib.pyplot as plt +import pandas as pd plt.rcParams["figure.constrained_layout.use"] = True diff --git a/examples/release_highlights/plot_release_highlights_0_22_0.py b/examples/release_highlights/plot_release_highlights_0_22_0.py index 02b99df3491ee..ca09013703592 100644 --- a/examples/release_highlights/plot_release_highlights_0_22_0.py +++ b/examples/release_highlights/plot_release_highlights_0_22_0.py @@ -34,15 +34,15 @@ # :class:`~metrics.plot_confusion_matrix`. Read more about this new API in the # :ref:`User Guide `. -from sklearn.model_selection import train_test_split -from sklearn.svm import SVC +import matplotlib.pyplot as plt + +from sklearn.datasets import make_classification +from sklearn.ensemble import RandomForestClassifier # from sklearn.metrics import plot_roc_curve from sklearn.metrics import RocCurveDisplay - -from sklearn.ensemble import RandomForestClassifier -from sklearn.datasets import make_classification -import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +from sklearn.svm import SVC X, y = make_classification(random_state=0) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) @@ -79,12 +79,12 @@ # Read more in the :ref:`User Guide `. from sklearn.datasets import load_iris -from sklearn.svm import LinearSVC -from sklearn.linear_model import LogisticRegression -from sklearn.preprocessing import StandardScaler -from sklearn.pipeline import make_pipeline from sklearn.ensemble import StackingClassifier +from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler +from sklearn.svm import LinearSVC X, y = load_iris(return_X_y=True) estimators = [ @@ -102,8 +102,9 @@ # The :func:`inspection.permutation_importance` can be used to get an # estimate of the importance of each feature, for any fitted estimator: -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn.datasets import make_classification from sklearn.ensemble import RandomForestClassifier from sklearn.inspection import permutation_importance @@ -155,8 +156,9 @@ # See more details in the :ref:`User Guide `. from tempfile import TemporaryDirectory -from sklearn.neighbors import KNeighborsTransformer + from sklearn.manifold import Isomap +from sklearn.neighbors import KNeighborsTransformer from sklearn.pipeline import make_pipeline X, y = make_classification(random_state=0) @@ -272,8 +274,8 @@ def test_sklearn_compatible_estimator(estimator, check): from sklearn.datasets import make_classification -from sklearn.svm import SVC from sklearn.metrics import roc_auc_score +from sklearn.svm import SVC X, y = make_classification(n_classes=4, n_informative=16) clf = SVC(decision_function_shape="ovo", probability=True).fit(X, y) diff --git a/examples/semi_supervised/plot_label_propagation_digits.py b/examples/semi_supervised/plot_label_propagation_digits.py index f848e3b76e084..bfdff8e362e47 100644 --- a/examples/semi_supervised/plot_label_propagation_digits.py +++ b/examples/semi_supervised/plot_label_propagation_digits.py @@ -24,9 +24,10 @@ class will be very good. # --------------- # # We use the digits dataset. We only use a subset of randomly selected samples. -from sklearn import datasets import numpy as np +from sklearn import datasets + digits = datasets.load_digits() rng = np.random.RandomState(2) indices = np.arange(len(digits.data)) @@ -59,8 +60,8 @@ class will be very good. # # We fit a :class:`~sklearn.semi_supervised.LabelSpreading` and use it to predict # the unknown labels. -from sklearn.semi_supervised import LabelSpreading from sklearn.metrics import classification_report +from sklearn.semi_supervised import LabelSpreading lp_model = LabelSpreading(gamma=0.25, max_iter=20) lp_model.fit(X, y_train) diff --git a/examples/semi_supervised/plot_label_propagation_digits_active_learning.py b/examples/semi_supervised/plot_label_propagation_digits_active_learning.py index 3a1f533c8a281..215655a287c2d 100644 --- a/examples/semi_supervised/plot_label_propagation_digits_active_learning.py +++ b/examples/semi_supervised/plot_label_propagation_digits_active_learning.py @@ -23,13 +23,13 @@ # Authors: Clay Woolam # License: BSD -import numpy as np import matplotlib.pyplot as plt +import numpy as np from scipy import stats from sklearn import datasets -from sklearn.semi_supervised import LabelSpreading from sklearn.metrics import classification_report, confusion_matrix +from sklearn.semi_supervised import LabelSpreading digits = datasets.load_digits() rng = np.random.RandomState(0) diff --git a/examples/semi_supervised/plot_label_propagation_structure.py b/examples/semi_supervised/plot_label_propagation_structure.py index 5de6e9f20a7e3..cfcd1c1bf5a54 100644 --- a/examples/semi_supervised/plot_label_propagation_structure.py +++ b/examples/semi_supervised/plot_label_propagation_structure.py @@ -22,6 +22,7 @@ # Here, all labels but two are tagged as unknown. import numpy as np + from sklearn.datasets import make_circles n_samples = 200 diff --git a/examples/semi_supervised/plot_self_training_varying_threshold.py b/examples/semi_supervised/plot_self_training_varying_threshold.py index 801e48b8411f5..2c7a485d06eb0 100644 --- a/examples/semi_supervised/plot_self_training_varying_threshold.py +++ b/examples/semi_supervised/plot_self_training_varying_threshold.py @@ -32,13 +32,14 @@ # Authors: Oliver Rausch # License: BSD -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn import datasets -from sklearn.svm import SVC +from sklearn.metrics import accuracy_score from sklearn.model_selection import StratifiedKFold from sklearn.semi_supervised import SelfTrainingClassifier -from sklearn.metrics import accuracy_score +from sklearn.svm import SVC from sklearn.utils import shuffle n_splits = 3 diff --git a/examples/semi_supervised/plot_semi_supervised_newsgroups.py b/examples/semi_supervised/plot_semi_supervised_newsgroups.py index 609f5d10247c2..58c7f6e42f408 100644 --- a/examples/semi_supervised/plot_semi_supervised_newsgroups.py +++ b/examples/semi_supervised/plot_semi_supervised_newsgroups.py @@ -15,15 +15,13 @@ import numpy as np from sklearn.datasets import fetch_20newsgroups -from sklearn.feature_extraction.text import CountVectorizer -from sklearn.feature_extraction.text import TfidfTransformer -from sklearn.preprocessing import FunctionTransformer +from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer from sklearn.linear_model import SGDClassifier +from sklearn.metrics import f1_score from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline -from sklearn.semi_supervised import SelfTrainingClassifier -from sklearn.semi_supervised import LabelSpreading -from sklearn.metrics import f1_score +from sklearn.preprocessing import FunctionTransformer +from sklearn.semi_supervised import LabelSpreading, SelfTrainingClassifier # Loading dataset containing first five categories data = fetch_20newsgroups( diff --git a/examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py b/examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py index 402cd41d6a0f2..766f7ea0a79c6 100644 --- a/examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py +++ b/examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py @@ -18,13 +18,12 @@ # Oliver Rausch # License: BSD -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn import datasets +from sklearn.semi_supervised import LabelSpreading, SelfTrainingClassifier from sklearn.svm import SVC -from sklearn.semi_supervised import LabelSpreading -from sklearn.semi_supervised import SelfTrainingClassifier - iris = datasets.load_iris() diff --git a/examples/svm/plot_custom_kernel.py b/examples/svm/plot_custom_kernel.py index c2c3bc6e6ba28..cacd67ed056ac 100644 --- a/examples/svm/plot_custom_kernel.py +++ b/examples/svm/plot_custom_kernel.py @@ -8,9 +8,10 @@ """ -import numpy as np import matplotlib.pyplot as plt -from sklearn import svm, datasets +import numpy as np + +from sklearn import datasets, svm from sklearn.inspection import DecisionBoundaryDisplay # import some data to play with diff --git a/examples/svm/plot_iris_svc.py b/examples/svm/plot_iris_svc.py index 5bcc81dd91d04..61aba3cc06602 100644 --- a/examples/svm/plot_iris_svc.py +++ b/examples/svm/plot_iris_svc.py @@ -35,9 +35,9 @@ """ import matplotlib.pyplot as plt -from sklearn import svm, datasets -from sklearn.inspection import DecisionBoundaryDisplay +from sklearn import datasets, svm +from sklearn.inspection import DecisionBoundaryDisplay # import some data to play with iris = datasets.load_iris() diff --git a/examples/svm/plot_linearsvc_support_vectors.py b/examples/svm/plot_linearsvc_support_vectors.py index 638579f36f3c3..60e9a3e6f32f9 100644 --- a/examples/svm/plot_linearsvc_support_vectors.py +++ b/examples/svm/plot_linearsvc_support_vectors.py @@ -9,11 +9,12 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn.datasets import make_blobs -from sklearn.svm import LinearSVC from sklearn.inspection import DecisionBoundaryDisplay +from sklearn.svm import LinearSVC X, y = make_blobs(n_samples=40, centers=2, random_state=0) diff --git a/examples/svm/plot_oneclass.py b/examples/svm/plot_oneclass.py index 082cbcd6de2be..d4348fa0ec435 100644 --- a/examples/svm/plot_oneclass.py +++ b/examples/svm/plot_oneclass.py @@ -11,9 +11,10 @@ """ -import numpy as np -import matplotlib.pyplot as plt import matplotlib.font_manager +import matplotlib.pyplot as plt +import numpy as np + from sklearn import svm xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500)) diff --git a/examples/svm/plot_rbf_parameters.py b/examples/svm/plot_rbf_parameters.py index fa4310134487a..ba0154b477b46 100644 --- a/examples/svm/plot_rbf_parameters.py +++ b/examples/svm/plot_rbf_parameters.py @@ -135,9 +135,8 @@ def __call__(self, value, clip=None): # 10 is often helpful. Using a basis of 2, a finer # tuning can be achieved but at a much higher cost. +from sklearn.model_selection import GridSearchCV, StratifiedShuffleSplit from sklearn.svm import SVC -from sklearn.model_selection import StratifiedShuffleSplit -from sklearn.model_selection import GridSearchCV C_range = np.logspace(-2, 10, 13) gamma_range = np.logspace(-9, 3, 13) diff --git a/examples/svm/plot_separating_hyperplane.py b/examples/svm/plot_separating_hyperplane.py index 45bacff6a2b97..23f464169f516 100644 --- a/examples/svm/plot_separating_hyperplane.py +++ b/examples/svm/plot_separating_hyperplane.py @@ -10,11 +10,11 @@ """ import matplotlib.pyplot as plt + from sklearn import svm from sklearn.datasets import make_blobs from sklearn.inspection import DecisionBoundaryDisplay - # we create 40 separable points X, y = make_blobs(n_samples=40, centers=2, random_state=6) diff --git a/examples/svm/plot_separating_hyperplane_unbalanced.py b/examples/svm/plot_separating_hyperplane_unbalanced.py index fe71420ffd0b3..6fd7de98f3fb6 100644 --- a/examples/svm/plot_separating_hyperplane_unbalanced.py +++ b/examples/svm/plot_separating_hyperplane_unbalanced.py @@ -26,6 +26,7 @@ """ import matplotlib.pyplot as plt + from sklearn import svm from sklearn.datasets import make_blobs from sklearn.inspection import DecisionBoundaryDisplay diff --git a/examples/svm/plot_svm_anova.py b/examples/svm/plot_svm_anova.py index 3652fae3e979a..3d5a934bf4884 100644 --- a/examples/svm/plot_svm_anova.py +++ b/examples/svm/plot_svm_anova.py @@ -14,6 +14,7 @@ # Load some data to play with # --------------------------- import numpy as np + from sklearn.datasets import load_iris X, y = load_iris(return_X_y=True) @@ -25,8 +26,8 @@ # %% # Create the pipeline # ------------------- -from sklearn.pipeline import Pipeline from sklearn.feature_selection import SelectPercentile, f_classif +from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC @@ -45,6 +46,7 @@ # Plot the cross-validation score as a function of percentile of features # ----------------------------------------------------------------------- import matplotlib.pyplot as plt + from sklearn.model_selection import cross_val_score score_means = list() diff --git a/examples/svm/plot_svm_kernels.py b/examples/svm/plot_svm_kernels.py index fac86e8a93c7a..7ff2486e1c867 100644 --- a/examples/svm/plot_svm_kernels.py +++ b/examples/svm/plot_svm_kernels.py @@ -13,10 +13,10 @@ # Code source: Gaël Varoquaux # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt -from sklearn import svm +import numpy as np +from sklearn import svm # Our dataset and targets X = np.c_[ diff --git a/examples/svm/plot_svm_margin.py b/examples/svm/plot_svm_margin.py index f3717ecaa24ed..b8253264a4ad0 100644 --- a/examples/svm/plot_svm_margin.py +++ b/examples/svm/plot_svm_margin.py @@ -17,8 +17,9 @@ # Modified for documentation by Jaques Grobler # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn import svm # we create 40 separable points diff --git a/examples/svm/plot_svm_nonlinear.py b/examples/svm/plot_svm_nonlinear.py index f88231b4b6af4..4990e509661a1 100644 --- a/examples/svm/plot_svm_nonlinear.py +++ b/examples/svm/plot_svm_nonlinear.py @@ -11,8 +11,9 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn import svm xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500)) diff --git a/examples/svm/plot_svm_regression.py b/examples/svm/plot_svm_regression.py index 75a16b571c3ea..ab34528a37af6 100644 --- a/examples/svm/plot_svm_regression.py +++ b/examples/svm/plot_svm_regression.py @@ -7,9 +7,10 @@ """ +import matplotlib.pyplot as plt import numpy as np + from sklearn.svm import SVR -import matplotlib.pyplot as plt # %% # Generate sample data diff --git a/examples/svm/plot_svm_scale_c.py b/examples/svm/plot_svm_scale_c.py index 4ba025cffac8e..1e44fb361e6ba 100644 --- a/examples/svm/plot_svm_scale_c.py +++ b/examples/svm/plot_svm_scale_c.py @@ -76,7 +76,8 @@ # We will compute the mean test score for different values of `C`. import numpy as np import pandas as pd -from sklearn.model_selection import validation_curve, ShuffleSplit + +from sklearn.model_selection import ShuffleSplit, validation_curve Cs = np.logspace(-2.3, -1.3, 10) train_sizes = np.linspace(0.3, 0.7, 3) diff --git a/examples/svm/plot_svm_tie_breaking.py b/examples/svm/plot_svm_tie_breaking.py index 93148225b0bb3..848b81dee9c69 100644 --- a/examples/svm/plot_svm_tie_breaking.py +++ b/examples/svm/plot_svm_tie_breaking.py @@ -17,10 +17,11 @@ # Code source: Andreas Mueller, Adrin Jalali # License: BSD 3 clause -import numpy as np import matplotlib.pyplot as plt -from sklearn.svm import SVC +import numpy as np + from sklearn.datasets import make_blobs +from sklearn.svm import SVC X, y = make_blobs(random_state=27) diff --git a/examples/svm/plot_weighted_samples.py b/examples/svm/plot_weighted_samples.py index f346599300aba..c17742e091390 100644 --- a/examples/svm/plot_weighted_samples.py +++ b/examples/svm/plot_weighted_samples.py @@ -14,8 +14,9 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn import svm diff --git a/examples/text/plot_document_classification_20newsgroups.py b/examples/text/plot_document_classification_20newsgroups.py index 633a0602d421b..04aad46c8451a 100644 --- a/examples/text/plot_document_classification_20newsgroups.py +++ b/examples/text/plot_document_classification_20newsgroups.py @@ -36,9 +36,10 @@ # the classification problem "too easy". This is achieved using simple # heuristics that are neither perfect nor standard, hence disabled by default. +from time import time + from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import TfidfVectorizer -from time import time categories = [ "alt.atheism", @@ -158,6 +159,7 @@ def load_dataset(verbose=False, remove=()): # in the classification errors. import matplotlib.pyplot as plt + from sklearn.metrics import ConfusionMatrixDisplay fig, ax = plt.subplots(figsize=(10, 5)) @@ -182,8 +184,8 @@ def load_dataset(verbose=False, remove=()): # We can gain a deeper understanding of how this classifier makes its decisions # by looking at the words with the highest average feature effects: -import pandas as pd import numpy as np +import pandas as pd def plot_feature_effects(): @@ -315,8 +317,8 @@ def plot_feature_effects(): # training time and testing time. For such purpose we define the following # benchmarking utilities: -from sklearn.utils.extmath import density from sklearn import metrics +from sklearn.utils.extmath import density def benchmark(clf, custom_name=False): @@ -361,14 +363,11 @@ def benchmark(clf, custom_name=False): # :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` # noqa: E501 # for a demo on how such tuning can be done. -from sklearn.linear_model import LogisticRegression -from sklearn.svm import LinearSVC -from sklearn.linear_model import SGDClassifier -from sklearn.naive_bayes import ComplementNB -from sklearn.neighbors import KNeighborsClassifier -from sklearn.neighbors import NearestCentroid from sklearn.ensemble import RandomForestClassifier - +from sklearn.linear_model import LogisticRegression, SGDClassifier +from sklearn.naive_bayes import ComplementNB +from sklearn.neighbors import KNeighborsClassifier, NearestCentroid +from sklearn.svm import LinearSVC results = [] for clf, name in ( diff --git a/examples/text/plot_document_clustering.py b/examples/text/plot_document_clustering.py index 368cf7cea60ae..fa68b8bd312ea 100644 --- a/examples/text/plot_document_clustering.py +++ b/examples/text/plot_document_clustering.py @@ -46,6 +46,7 @@ # strip those features and have a more sensible clustering problem. import numpy as np + from sklearn.datasets import fetch_20newsgroups categories = [ @@ -104,9 +105,10 @@ # For more reference, see :ref:`clustering_evaluation`. from collections import defaultdict -from sklearn import metrics from time import time +from sklearn import metrics + evaluations = [] evaluations_std = [] @@ -277,7 +279,6 @@ def fit_and_evaluate(km, X, name=None, n_runs=5): from sklearn.pipeline import make_pipeline from sklearn.preprocessing import Normalizer - lsa = make_pipeline(TruncatedSVD(n_components=100), Normalizer(copy=False)) t0 = time() X_lsa = lsa.fit_transform(X_tfidf) @@ -353,8 +354,7 @@ def fit_and_evaluate(km, X, name=None, n_runs=5): # case we also add LSA to the pipeline to reduce the dimension and sparcity of # the hashed vector space. -from sklearn.feature_extraction.text import HashingVectorizer -from sklearn.feature_extraction.text import TfidfTransformer +from sklearn.feature_extraction.text import HashingVectorizer, TfidfTransformer lsa_vectorizer = make_pipeline( HashingVectorizer(stop_words="english", n_features=50_000), @@ -394,8 +394,8 @@ def fit_and_evaluate(km, X, name=None, n_runs=5): # Clustering evaluation summary # ============================== -import pandas as pd import matplotlib.pyplot as plt +import pandas as pd fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(16, 6), sharey=True) diff --git a/examples/text/plot_hashing_vs_dict_vectorizer.py b/examples/text/plot_hashing_vs_dict_vectorizer.py index 8200c646f69ee..ce2dcc2d13c41 100644 --- a/examples/text/plot_hashing_vs_dict_vectorizer.py +++ b/examples/text/plot_hashing_vs_dict_vectorizer.py @@ -118,6 +118,7 @@ def token_freqs(doc): # both of them receive dictionaries as input. from time import time + from sklearn.feature_extraction import DictVectorizer dict_count_vectorizers = defaultdict(list) diff --git a/examples/tree/plot_cost_complexity_pruning.py b/examples/tree/plot_cost_complexity_pruning.py index d21d163c9a1e3..b232389ea9ded 100644 --- a/examples/tree/plot_cost_complexity_pruning.py +++ b/examples/tree/plot_cost_complexity_pruning.py @@ -18,8 +18,9 @@ """ import matplotlib.pyplot as plt -from sklearn.model_selection import train_test_split + from sklearn.datasets import load_breast_cancer +from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier # %% diff --git a/examples/tree/plot_iris_dtc.py b/examples/tree/plot_iris_dtc.py index 14f6506b5810f..b3d834da5d067 100644 --- a/examples/tree/plot_iris_dtc.py +++ b/examples/tree/plot_iris_dtc.py @@ -23,13 +23,12 @@ # %% # Display the decision functions of trees trained on all pairs of features. -import numpy as np import matplotlib.pyplot as plt +import numpy as np from sklearn.datasets import load_iris -from sklearn.tree import DecisionTreeClassifier from sklearn.inspection import DecisionBoundaryDisplay - +from sklearn.tree import DecisionTreeClassifier # Parameters n_classes = 3 diff --git a/examples/tree/plot_tree_regression.py b/examples/tree/plot_tree_regression.py index 6ed28a5cbfa99..5a3da0b7b6d06 100644 --- a/examples/tree/plot_tree_regression.py +++ b/examples/tree/plot_tree_regression.py @@ -15,9 +15,10 @@ """ # Import the necessary modules and libraries +import matplotlib.pyplot as plt import numpy as np + from sklearn.tree import DecisionTreeRegressor -import matplotlib.pyplot as plt # Create a random dataset rng = np.random.RandomState(1) diff --git a/examples/tree/plot_tree_regression_multioutput.py b/examples/tree/plot_tree_regression_multioutput.py index a75652a6ddd56..b6d2800d2732d 100644 --- a/examples/tree/plot_tree_regression_multioutput.py +++ b/examples/tree/plot_tree_regression_multioutput.py @@ -15,8 +15,9 @@ details of the training data and learn from the noise, i.e. they overfit. """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from sklearn.tree import DecisionTreeRegressor # Create a random dataset diff --git a/examples/tree/plot_unveil_tree_structure.py b/examples/tree/plot_unveil_tree_structure.py index 6313d0ccbb74f..d4009e3111f7f 100644 --- a/examples/tree/plot_unveil_tree_structure.py +++ b/examples/tree/plot_unveil_tree_structure.py @@ -19,10 +19,10 @@ import numpy as np from matplotlib import pyplot as plt -from sklearn.model_selection import train_test_split +from sklearn import tree from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier -from sklearn import tree ############################################################################## # Train tree classifier diff --git a/maint_tools/check_pxd_in_installation.py b/maint_tools/check_pxd_in_installation.py index ac1a8f9627a95..996d45d64d42a 100644 --- a/maint_tools/check_pxd_in_installation.py +++ b/maint_tools/check_pxd_in_installation.py @@ -6,12 +6,11 @@ """ import os -import sys import pathlib +import subprocess +import sys import tempfile import textwrap -import subprocess - sklearn_dir = pathlib.Path(sys.argv[1]) pxd_files = list(sklearn_dir.glob("**/*.pxd")) diff --git a/maint_tools/sort_whats_new.py b/maint_tools/sort_whats_new.py index 178e33bc87e5f..7241059176b66 100755 --- a/maint_tools/sort_whats_new.py +++ b/maint_tools/sort_whats_new.py @@ -2,8 +2,8 @@ # Sorts what's new entries with per-module headings. # Pass what's new entries on stdin. -import sys import re +import sys from collections import defaultdict LABEL_ORDER = ["MajorFeature", "Feature", "Efficiency", "Enhancement", "Fix", "API"] diff --git a/maint_tools/update_tracking_issue.py b/maint_tools/update_tracking_issue.py index 4ddc9d1bfe8e6..725802416fb6c 100644 --- a/maint_tools/update_tracking_issue.py +++ b/maint_tools/update_tracking_issue.py @@ -11,10 +11,10 @@ github account that does **not** have commit access to the public repo. """ -from pathlib import Path -import sys import argparse +import sys from datetime import datetime, timezone +from pathlib import Path import defusedxml.ElementTree as ET from github import Github diff --git a/pyproject.toml b/pyproject.toml index bed85b074dbfb..efd72adf44392 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,6 +40,9 @@ exclude = ''' ''' [tool.ruff] +# all rules can be found here: https://beta.ruff.rs/docs/rules/ +select = ["E", "F", "W", "I"] + # max line length for black line-length = 88 target-version = "py38" @@ -74,6 +77,7 @@ exclude=[ "examples/*"=["E402"] "doc/conf.py"=["E402"] + [tool.cython-lint] # Ignore the same error codes as ruff # + E501 (line too long) because keeping it < 88 in cython diff --git a/setup.py b/setup.py index 33d105a213a7c..5af738f5f841f 100755 --- a/setup.py +++ b/setup.py @@ -4,18 +4,17 @@ # 2010 Fabian Pedregosa # License: 3-clause BSD -import sys +import importlib import os -from os.path import join import platform import shutil +import sys +import traceback +from os.path import join from setuptools import Command, Extension, setup from setuptools.command.build_ext import build_ext -import traceback -import importlib - try: import builtins except ImportError: @@ -454,10 +453,10 @@ def configure_extension_modules(): if "sdist" in sys.argv or "--help" in sys.argv: return [] - from sklearn._build_utils import cythonize_extensions - from sklearn._build_utils import gen_from_templates import numpy + from sklearn._build_utils import cythonize_extensions, gen_from_templates + is_pypy = platform.python_implementation() == "PyPy" np_include = numpy.get_include() default_optimization_level = "O2" diff --git a/sklearn/__init__.py b/sklearn/__init__.py index 11185d6b9d918..1b51e495b7cdf 100644 --- a/sklearn/__init__.py +++ b/sklearn/__init__.py @@ -12,13 +12,12 @@ See http://scikit-learn.org for complete documentation. """ -import sys import logging import os import random +import sys - -from ._config import get_config, set_config, config_context +from ._config import config_context, get_config, set_config logger = logging.getLogger(__name__) @@ -77,8 +76,10 @@ # It is necessary to do this prior to importing show_versions as the # later is linked to the OpenMP runtime to make it possible to introspect # it and importing it first would fail if the OpenMP dll cannot be found. - from . import _distributor_init # noqa: F401 - from . import __check_build # noqa: F401 + from . import ( + __check_build, # noqa: F401 + _distributor_init, # noqa: F401 + ) from .base import clone from .utils._show_versions import show_versions diff --git a/sklearn/_build_utils/__init__.py b/sklearn/_build_utils/__init__.py index f84dfa09a9f94..056215e162647 100644 --- a/sklearn/_build_utils/__init__.py +++ b/sklearn/_build_utils/__init__.py @@ -5,15 +5,15 @@ # license: BSD +import contextlib import os + import sklearn -import contextlib -from .pre_build_helpers import basic_check_build -from .openmp_helpers import check_openmp_support from .._min_dependencies import CYTHON_MIN_VERSION from ..externals._packaging.version import parse - +from .openmp_helpers import check_openmp_support +from .pre_build_helpers import basic_check_build DEFAULT_ROOT = "sklearn" diff --git a/sklearn/_build_utils/pre_build_helpers.py b/sklearn/_build_utils/pre_build_helpers.py index c1d50abd3ae0c..f3eb054bb037e 100644 --- a/sklearn/_build_utils/pre_build_helpers.py +++ b/sklearn/_build_utils/pre_build_helpers.py @@ -1,11 +1,11 @@ """Helpers to check build environment before actual build of scikit-learn""" +import glob import os +import subprocess import sys -import glob import tempfile import textwrap -import subprocess from setuptools.command.build_ext import customize_compiler, new_compiler diff --git a/sklearn/_config.py b/sklearn/_config.py index 43755071e54e9..e84dc9ef5b228 100644 --- a/sklearn/_config.py +++ b/sklearn/_config.py @@ -1,8 +1,8 @@ """Global configuration state and functions for management """ import os -from contextlib import contextmanager as contextmanager import threading +from contextlib import contextmanager as contextmanager _global_config = { "assume_finite": bool(os.environ.get("SKLEARN_ASSUME_FINITE", False)), diff --git a/sklearn/_loss/__init__.py b/sklearn/_loss/__init__.py index 78b1eb8543c8d..ee15e693c16f6 100644 --- a/sklearn/_loss/__init__.py +++ b/sklearn/_loss/__init__.py @@ -4,19 +4,18 @@ """ from .loss import ( - HalfSquaredError, AbsoluteError, - PinballLoss, - HuberLoss, - HalfPoissonLoss, + HalfBinomialLoss, HalfGammaLoss, + HalfMultinomialLoss, + HalfPoissonLoss, + HalfSquaredError, HalfTweedieLoss, HalfTweedieLossIdentity, - HalfBinomialLoss, - HalfMultinomialLoss, + HuberLoss, + PinballLoss, ) - __all__ = [ "HalfSquaredError", "AbsoluteError", diff --git a/sklearn/_loss/link.py b/sklearn/_loss/link.py index 510ef80c641fc..9459844f6b89a 100644 --- a/sklearn/_loss/link.py +++ b/sklearn/_loss/link.py @@ -9,6 +9,7 @@ import numpy as np from scipy.special import expit, logit from scipy.stats import gmean + from ..utils.extmath import softmax diff --git a/sklearn/_loss/loss.py b/sklearn/_loss/loss.py index 037d933aa5491..f3b61da0915d5 100644 --- a/sklearn/_loss/loss.py +++ b/sklearn/_loss/loss.py @@ -16,31 +16,33 @@ # - Replace link module of GLMs. import numbers + import numpy as np from scipy.special import xlogy + +from ..utils import check_scalar +from ..utils.stats import _weighted_percentile from ._loss import ( - CyHalfSquaredError, CyAbsoluteError, - CyPinballLoss, - CyHuberLoss, - CyHalfPoissonLoss, + CyExponentialLoss, + CyHalfBinomialLoss, CyHalfGammaLoss, + CyHalfMultinomialLoss, + CyHalfPoissonLoss, + CyHalfSquaredError, CyHalfTweedieLoss, CyHalfTweedieLossIdentity, - CyHalfBinomialLoss, - CyHalfMultinomialLoss, - CyExponentialLoss, + CyHuberLoss, + CyPinballLoss, ) from .link import ( - Interval, + HalfLogitLink, IdentityLink, - LogLink, + Interval, LogitLink, - HalfLogitLink, + LogLink, MultinomialLogit, ) -from ..utils import check_scalar -from ..utils.stats import _weighted_percentile # Note: The shape of raw_prediction for multiclass classifications are diff --git a/sklearn/_loss/tests/test_link.py b/sklearn/_loss/tests/test_link.py index 8421fd3fd7a77..e5a665f8d48ac 100644 --- a/sklearn/_loss/tests/test_link.py +++ b/sklearn/_loss/tests/test_link.py @@ -1,16 +1,15 @@ import numpy as np -from numpy.testing import assert_allclose, assert_array_equal import pytest +from numpy.testing import assert_allclose, assert_array_equal from sklearn._loss.link import ( _LINKS, - _inclusive_low_high, HalfLogitLink, - MultinomialLogit, Interval, + MultinomialLogit, + _inclusive_low_high, ) - LINK_FUNCTIONS = list(_LINKS.values()) diff --git a/sklearn/_loss/tests/test_loss.py b/sklearn/_loss/tests/test_loss.py index dbfe5b3829dda..d279a2f06a182 100644 --- a/sklearn/_loss/tests/test_loss.py +++ b/sklearn/_loss/tests/test_loss.py @@ -1,22 +1,22 @@ import pickle import numpy as np -from numpy.testing import assert_allclose, assert_array_equal import pytest +from numpy.testing import assert_allclose, assert_array_equal from pytest import approx from scipy.optimize import ( + LinearConstraint, minimize, minimize_scalar, newton, - LinearConstraint, ) from scipy.special import logsumexp -from sklearn._loss.link import _inclusive_low_high, IdentityLink +from sklearn._loss.link import IdentityLink, _inclusive_low_high from sklearn._loss.loss import ( _LOSSES, - BaseLoss, AbsoluteError, + BaseLoss, HalfBinomialLoss, HalfGammaLoss, HalfMultinomialLoss, @@ -30,7 +30,6 @@ from sklearn.utils import assert_all_finite from sklearn.utils._testing import create_memmap_backed_data, skip_if_32bit - ALL_LOSSES = list(_LOSSES.values()) LOSS_INSTANCES = [loss() for loss in ALL_LOSSES] diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py index 72ee14d64e958..e12720dbd5b94 100644 --- a/sklearn/_min_dependencies.py +++ b/sklearn/_min_dependencies.py @@ -1,8 +1,7 @@ """All minimum dependencies for scikit-learn.""" -from collections import defaultdict -import platform import argparse - +import platform +from collections import defaultdict # scipy and cython should by in sync with pyproject.toml diff --git a/sklearn/base.py b/sklearn/base.py index 13bbcab96aa61..fb26012964e7b 100644 --- a/sklearn/base.py +++ b/sklearn/base.py @@ -5,33 +5,36 @@ import copy import functools -import warnings -from collections import defaultdict -import platform import inspect +import platform import re +import warnings +from collections import defaultdict import numpy as np from . import __version__ -from ._config import get_config, config_context +from ._config import config_context, get_config +from .exceptions import InconsistentVersionWarning from .utils import _IS_32BIT +from .utils._estimator_html_repr import estimator_html_repr +from .utils._metadata_requests import _MetadataRequester +from .utils._param_validation import validate_parameter_constraints from .utils._set_output import _SetOutputMixin from .utils._tags import ( _DEFAULT_TAGS, ) -from .exceptions import InconsistentVersionWarning -from .utils.validation import check_X_y -from .utils.validation import check_array -from .utils.validation import _check_y -from .utils.validation import _num_features -from .utils.validation import _check_feature_names_in -from .utils.validation import _generate_get_feature_names_out -from .utils.validation import _is_fitted, check_is_fitted -from .utils._metadata_requests import _MetadataRequester -from .utils.validation import _get_feature_names -from .utils._estimator_html_repr import estimator_html_repr -from .utils._param_validation import validate_parameter_constraints +from .utils.validation import ( + _check_feature_names_in, + _check_y, + _generate_get_feature_names_out, + _get_feature_names, + _is_fitted, + _num_features, + check_array, + check_is_fitted, + check_X_y, +) def clone(estimator, *, safe=True): diff --git a/sklearn/calibration.py b/sklearn/calibration.py index e4869387f4166..42df0b3248733 100644 --- a/sklearn/calibration.py +++ b/sklearn/calibration.py @@ -7,43 +7,51 @@ # # License: BSD 3 clause -from numbers import Integral, Real import warnings -from inspect import signature from functools import partial - +from inspect import signature from math import log -import numpy as np +from numbers import Integral, Real -from scipy.special import expit -from scipy.special import xlogy +import numpy as np from scipy.optimize import fmin_bfgs +from scipy.special import expit, xlogy + +from sklearn.utils import Bunch from .base import ( BaseEstimator, ClassifierMixin, - RegressorMixin, - clone, MetaEstimatorMixin, + RegressorMixin, _fit_context, + clone, ) -from .preprocessing import label_binarize, LabelEncoder +from .isotonic import IsotonicRegression +from .model_selection import check_cv, cross_val_predict +from .preprocessing import LabelEncoder, label_binarize +from .svm import LinearSVC from .utils import ( + _safe_indexing, column_or_1d, indexable, - _safe_indexing, ) - -from .utils.multiclass import check_classification_targets -from .utils.parallel import delayed, Parallel from .utils._param_validation import ( - StrOptions, HasMethods, Hidden, - validate_params, Interval, + StrOptions, + validate_params, ) from .utils._plotting import _BinaryClassifierCurveDisplayMixin +from .utils.metadata_routing import ( + MetadataRouter, + MethodMapping, + _routing_enabled, + process_routing, +) +from .utils.multiclass import check_classification_targets +from .utils.parallel import Parallel, delayed from .utils.validation import ( _check_fit_params, _check_pos_label_consistency, @@ -52,16 +60,6 @@ check_consistent_length, check_is_fitted, ) -from .isotonic import IsotonicRegression -from .svm import LinearSVC -from .model_selection import check_cv, cross_val_predict -from sklearn.utils import Bunch -from .utils.metadata_routing import ( - MetadataRouter, - MethodMapping, - process_routing, - _routing_enabled, -) class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator): diff --git a/sklearn/cluster/__init__.py b/sklearn/cluster/__init__.py index 40b89ea0da8ba..f5d3104d816bf 100644 --- a/sklearn/cluster/__init__.py +++ b/sklearn/cluster/__init__.py @@ -3,27 +3,27 @@ algorithms. """ -from ._spectral import spectral_clustering, SpectralClustering -from ._mean_shift import mean_shift, MeanShift, estimate_bandwidth, get_bin_seeds -from ._affinity_propagation import affinity_propagation, AffinityPropagation +from ._affinity_propagation import AffinityPropagation, affinity_propagation from ._agglomerative import ( - ward_tree, AgglomerativeClustering, - linkage_tree, FeatureAgglomeration, + linkage_tree, + ward_tree, ) -from ._kmeans import k_means, KMeans, MiniBatchKMeans, kmeans_plusplus +from ._bicluster import SpectralBiclustering, SpectralCoclustering +from ._birch import Birch from ._bisect_k_means import BisectingKMeans -from ._dbscan import dbscan, DBSCAN +from ._dbscan import DBSCAN, dbscan +from ._hdbscan.hdbscan import HDBSCAN +from ._kmeans import KMeans, MiniBatchKMeans, k_means, kmeans_plusplus +from ._mean_shift import MeanShift, estimate_bandwidth, get_bin_seeds, mean_shift from ._optics import ( OPTICS, cluster_optics_dbscan, - compute_optics_graph, cluster_optics_xi, + compute_optics_graph, ) -from ._bicluster import SpectralBiclustering, SpectralCoclustering -from ._birch import Birch -from ._hdbscan.hdbscan import HDBSCAN +from ._spectral import SpectralClustering, spectral_clustering __all__ = [ "AffinityPropagation", diff --git a/sklearn/cluster/_affinity_propagation.py b/sklearn/cluster/_affinity_propagation.py index 1ffc5f07e8c50..6e7f67fed5199 100644 --- a/sklearn/cluster/_affinity_propagation.py +++ b/sklearn/cluster/_affinity_propagation.py @@ -5,20 +5,18 @@ # License: BSD 3 clause -from numbers import Integral, Real import warnings +from numbers import Integral, Real import numpy as np +from .._config import config_context +from ..base import BaseEstimator, ClusterMixin, _fit_context from ..exceptions import ConvergenceWarning -from ..base import BaseEstimator, ClusterMixin -from ..base import _fit_context +from ..metrics import euclidean_distances, pairwise_distances_argmin from ..utils import check_random_state from ..utils._param_validation import Interval, StrOptions, validate_params from ..utils.validation import check_is_fitted -from ..metrics import euclidean_distances -from ..metrics import pairwise_distances_argmin -from .._config import config_context def _equal_similarities_and_preferences(S, preference): diff --git a/sklearn/cluster/_agglomerative.py b/sklearn/cluster/_agglomerative.py index b7d08a45dcd80..553908104c92b 100644 --- a/sklearn/cluster/_agglomerative.py +++ b/sklearn/cluster/_agglomerative.py @@ -15,22 +15,25 @@ from scipy import sparse from scipy.sparse.csgraph import connected_components -from ..base import BaseEstimator, ClusterMixin, ClassNamePrefixFeaturesOutMixin -from ..base import _fit_context -from ..metrics.pairwise import paired_distances -from ..metrics.pairwise import _VALID_METRICS +from ..base import ( + BaseEstimator, + ClassNamePrefixFeaturesOutMixin, + ClusterMixin, + _fit_context, +) from ..metrics import DistanceMetric from ..metrics._dist_metrics import METRIC_MAPPING64 +from ..metrics.pairwise import _VALID_METRICS, paired_distances from ..utils import check_array from ..utils._fast_dict import IntFloatDict -from ..utils.graph import _fix_connected_components from ..utils._param_validation import ( + HasMethods, Hidden, Interval, StrOptions, - HasMethods, validate_params, ) +from ..utils.graph import _fix_connected_components from ..utils.validation import check_memory # mypy error: Module 'sklearn.cluster' has no attribute '_hierarchical_fast' diff --git a/sklearn/cluster/_bicluster.py b/sklearn/cluster/_bicluster.py index 4133264626ebb..65280c06319d9 100644 --- a/sklearn/cluster/_bicluster.py +++ b/sklearn/cluster/_bicluster.py @@ -3,25 +3,19 @@ # License: BSD 3 clause from abc import ABCMeta, abstractmethod - -import numpy as np from numbers import Integral +import numpy as np from scipy.linalg import norm from scipy.sparse import dia_matrix, issparse from scipy.sparse.linalg import eigsh, svds -from . import KMeans, MiniBatchKMeans -from ..base import BaseEstimator, BiclusterMixin -from ..base import _fit_context -from ..utils import check_random_state -from ..utils import check_scalar - +from ..base import BaseEstimator, BiclusterMixin, _fit_context +from ..utils import check_random_state, check_scalar +from ..utils._param_validation import Interval, StrOptions from ..utils.extmath import make_nonnegative, randomized_svd, safe_sparse_dot - from ..utils.validation import assert_all_finite -from ..utils._param_validation import Interval, StrOptions - +from ._kmeans import KMeans, MiniBatchKMeans __all__ = ["SpectralCoclustering", "SpectralBiclustering"] diff --git a/sklearn/cluster/_birch.py b/sklearn/cluster/_birch.py index e74630572a014..d62fb880ba8b2 100644 --- a/sklearn/cluster/_birch.py +++ b/sklearn/cluster/_birch.py @@ -4,26 +4,27 @@ # License: BSD 3 clause import warnings -import numpy as np +from math import sqrt from numbers import Integral, Real + +import numpy as np from scipy import sparse -from math import sqrt -from ..metrics import pairwise_distances_argmin -from ..metrics.pairwise import euclidean_distances +from .._config import config_context from ..base import ( - TransformerMixin, - ClusterMixin, BaseEstimator, ClassNamePrefixFeaturesOutMixin, + ClusterMixin, + TransformerMixin, _fit_context, ) -from ..utils.extmath import row_norms +from ..exceptions import ConvergenceWarning +from ..metrics import pairwise_distances_argmin +from ..metrics.pairwise import euclidean_distances from ..utils._param_validation import Interval +from ..utils.extmath import row_norms from ..utils.validation import check_is_fitted -from ..exceptions import ConvergenceWarning from . import AgglomerativeClustering -from .._config import config_context def _iterate_sparse_X(X): diff --git a/sklearn/cluster/_bisect_k_means.py b/sklearn/cluster/_bisect_k_means.py index 959d78ae85009..9091445261f70 100644 --- a/sklearn/cluster/_bisect_k_means.py +++ b/sklearn/cluster/_bisect_k_means.py @@ -7,18 +7,17 @@ import scipy.sparse as sp from ..base import _fit_context -from ._kmeans import _BaseKMeans -from ._kmeans import _kmeans_single_elkan -from ._kmeans import _kmeans_single_lloyd -from ._kmeans import _labels_inertia_threadpool_limit -from ._k_means_common import _inertia_dense -from ._k_means_common import _inertia_sparse -from ..utils.extmath import row_norms from ..utils._openmp_helpers import _openmp_effective_n_threads -from ..utils.validation import check_is_fitted -from ..utils.validation import _check_sample_weight -from ..utils.validation import check_random_state from ..utils._param_validation import StrOptions +from ..utils.extmath import row_norms +from ..utils.validation import _check_sample_weight, check_is_fitted, check_random_state +from ._k_means_common import _inertia_dense, _inertia_sparse +from ._kmeans import ( + _BaseKMeans, + _kmeans_single_elkan, + _kmeans_single_lloyd, + _labels_inertia_threadpool_limit, +) class _BisectingTree: diff --git a/sklearn/cluster/_dbscan.py b/sklearn/cluster/_dbscan.py index 3c753935ac046..e3ba62dbfdf01 100644 --- a/sklearn/cluster/_dbscan.py +++ b/sklearn/cluster/_dbscan.py @@ -14,12 +14,11 @@ import numpy as np from scipy import sparse +from ..base import BaseEstimator, ClusterMixin, _fit_context from ..metrics.pairwise import _VALID_METRICS -from ..base import BaseEstimator, ClusterMixin -from ..base import _fit_context -from ..utils.validation import _check_sample_weight -from ..utils._param_validation import Interval, StrOptions from ..neighbors import NearestNeighbors +from ..utils._param_validation import Interval, StrOptions +from ..utils.validation import _check_sample_weight from ._dbscan_inner import dbscan_inner diff --git a/sklearn/cluster/_feature_agglomeration.py b/sklearn/cluster/_feature_agglomeration.py index 55baf247a2931..f84f18c1c18b3 100644 --- a/sklearn/cluster/_feature_agglomeration.py +++ b/sklearn/cluster/_feature_agglomeration.py @@ -6,12 +6,13 @@ # License: BSD 3 clause import warnings + import numpy as np +from scipy.sparse import issparse from ..base import TransformerMixin -from ..utils.validation import check_is_fitted from ..utils import metadata_routing -from scipy.sparse import issparse +from ..utils.validation import check_is_fitted ############################################################################### # Mixin class for feature agglomeration. diff --git a/sklearn/cluster/_hdbscan/hdbscan.py b/sklearn/cluster/_hdbscan/hdbscan.py index f1584f46d6f82..fa6c1950b1164 100644 --- a/sklearn/cluster/_hdbscan/hdbscan.py +++ b/sklearn/cluster/_hdbscan/hdbscan.py @@ -46,16 +46,15 @@ from ...metrics._dist_metrics import DistanceMetric from ...neighbors import BallTree, KDTree, NearestNeighbors from ...utils._param_validation import Interval, StrOptions -from ...utils.validation import _assert_all_finite, _allclose_dense_sparse -from ._reachability import mutual_reachability_graph +from ...utils.validation import _allclose_dense_sparse, _assert_all_finite from ._linkage import ( + MST_edge_dtype, make_single_linkage, - mst_from_mutual_reachability, mst_from_data_matrix, - MST_edge_dtype, + mst_from_mutual_reachability, ) -from ._tree import tree_to_labels, labelling_at_cut -from ._tree import HIERARCHY_dtype +from ._reachability import mutual_reachability_graph +from ._tree import HIERARCHY_dtype, labelling_at_cut, tree_to_labels FAST_METRICS = set(KDTree.valid_metrics() + BallTree.valid_metrics()) diff --git a/sklearn/cluster/_hdbscan/tests/test_reachibility.py b/sklearn/cluster/_hdbscan/tests/test_reachibility.py index c8ba28d0af25b..c25b6baf4b65c 100644 --- a/sklearn/cluster/_hdbscan/tests/test_reachibility.py +++ b/sklearn/cluster/_hdbscan/tests/test_reachibility.py @@ -1,13 +1,12 @@ import numpy as np import pytest +from sklearn.cluster._hdbscan._reachability import mutual_reachability_graph from sklearn.utils._testing import ( _convert_container, assert_allclose, ) -from sklearn.cluster._hdbscan._reachability import mutual_reachability_graph - def test_mutual_reachability_graph_error_sparse_format(): """Check that we raise an error if the sparse format is not CSR.""" diff --git a/sklearn/cluster/_kmeans.py b/sklearn/cluster/_kmeans.py index b36999885a14e..79aa8b3825170 100644 --- a/sklearn/cluster/_kmeans.py +++ b/sklearn/cluster/_kmeans.py @@ -11,50 +11,48 @@ # Robert Layton # License: BSD 3 clause +import warnings from abc import ABC, abstractmethod from numbers import Integral, Real -import warnings import numpy as np import scipy.sparse as sp from ..base import ( BaseEstimator, + ClassNamePrefixFeaturesOutMixin, ClusterMixin, TransformerMixin, - ClassNamePrefixFeaturesOutMixin, _fit_context, ) -from ..metrics.pairwise import euclidean_distances -from ..metrics.pairwise import _euclidean_distances +from ..exceptions import ConvergenceWarning +from ..metrics.pairwise import _euclidean_distances, euclidean_distances +from ..utils import check_array, check_random_state +from ..utils._openmp_helpers import _openmp_effective_n_threads +from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params from ..utils.extmath import row_norms, stable_cumsum -from ..utils.fixes import threadpool_limits -from ..utils.fixes import threadpool_info -from ..utils.sparsefuncs_fast import assign_rows_csr +from ..utils.fixes import threadpool_info, threadpool_limits from ..utils.sparsefuncs import mean_variance_axis -from ..utils import check_array -from ..utils import check_random_state -from ..utils.validation import check_is_fitted, _check_sample_weight -from ..utils.validation import _is_arraylike_not_scalar -from ..utils._param_validation import Hidden -from ..utils._param_validation import Interval -from ..utils._param_validation import StrOptions -from ..utils._param_validation import validate_params -from ..utils._openmp_helpers import _openmp_effective_n_threads -from ..exceptions import ConvergenceWarning -from ._k_means_common import CHUNK_SIZE -from ._k_means_common import _inertia_dense -from ._k_means_common import _inertia_sparse -from ._k_means_common import _is_same_clustering -from ._k_means_minibatch import _minibatch_update_dense -from ._k_means_minibatch import _minibatch_update_sparse -from ._k_means_lloyd import lloyd_iter_chunked_dense -from ._k_means_lloyd import lloyd_iter_chunked_sparse -from ._k_means_elkan import init_bounds_dense -from ._k_means_elkan import init_bounds_sparse -from ._k_means_elkan import elkan_iter_chunked_dense -from ._k_means_elkan import elkan_iter_chunked_sparse - +from ..utils.sparsefuncs_fast import assign_rows_csr +from ..utils.validation import ( + _check_sample_weight, + _is_arraylike_not_scalar, + check_is_fitted, +) +from ._k_means_common import ( + CHUNK_SIZE, + _inertia_dense, + _inertia_sparse, + _is_same_clustering, +) +from ._k_means_elkan import ( + elkan_iter_chunked_dense, + elkan_iter_chunked_sparse, + init_bounds_dense, + init_bounds_sparse, +) +from ._k_means_lloyd import lloyd_iter_chunked_dense, lloyd_iter_chunked_sparse +from ._k_means_minibatch import _minibatch_update_dense, _minibatch_update_sparse ############################################################################### # Initialization heuristic diff --git a/sklearn/cluster/_mean_shift.py b/sklearn/cluster/_mean_shift.py index 6b0f227d011f9..ab9b8e85beadb 100644 --- a/sklearn/cluster/_mean_shift.py +++ b/sklearn/cluster/_mean_shift.py @@ -14,20 +14,20 @@ # Gael Varoquaux # Martino Sorbaro -import numpy as np import warnings +from collections import defaultdict from numbers import Integral, Real -from collections import defaultdict +import numpy as np + +from .._config import config_context +from ..base import BaseEstimator, ClusterMixin, _fit_context +from ..metrics.pairwise import pairwise_distances_argmin +from ..neighbors import NearestNeighbors +from ..utils import check_array, check_random_state, gen_batches from ..utils._param_validation import Interval, validate_params +from ..utils.parallel import Parallel, delayed from ..utils.validation import check_is_fitted -from ..utils.parallel import delayed, Parallel -from ..utils import check_random_state, gen_batches, check_array -from ..base import BaseEstimator, ClusterMixin -from ..base import _fit_context -from ..neighbors import NearestNeighbors -from ..metrics.pairwise import pairwise_distances_argmin -from .._config import config_context @validate_params( diff --git a/sklearn/cluster/_optics.py b/sklearn/cluster/_optics.py index ca1c74d6f44e7..8a91cd6f5a383 100755 --- a/sklearn/cluster/_optics.py +++ b/sklearn/cluster/_optics.py @@ -10,23 +10,26 @@ License: BSD 3 clause """ +import warnings from numbers import Integral, Real -import warnings import numpy as np +from scipy.sparse import SparseEfficiencyWarning, issparse +from ..base import BaseEstimator, ClusterMixin, _fit_context from ..exceptions import DataConversionWarning -from ..metrics.pairwise import PAIRWISE_BOOLEAN_FUNCTIONS -from ..metrics.pairwise import _VALID_METRICS +from ..metrics import pairwise_distances +from ..metrics.pairwise import _VALID_METRICS, PAIRWISE_BOOLEAN_FUNCTIONS +from ..neighbors import NearestNeighbors from ..utils import gen_batches, get_chunk_n_rows -from ..utils._param_validation import Interval, HasMethods, StrOptions, validate_params -from ..utils._param_validation import RealNotInt +from ..utils._param_validation import ( + HasMethods, + Interval, + RealNotInt, + StrOptions, + validate_params, +) from ..utils.validation import check_memory -from ..neighbors import NearestNeighbors -from ..base import BaseEstimator, ClusterMixin -from ..base import _fit_context -from ..metrics import pairwise_distances -from scipy.sparse import issparse, SparseEfficiencyWarning class OPTICS(ClusterMixin, BaseEstimator): diff --git a/sklearn/cluster/_spectral.py b/sklearn/cluster/_spectral.py index f72db4b7c1da3..d5fc9d4fdc68f 100644 --- a/sklearn/cluster/_spectral.py +++ b/sklearn/cluster/_spectral.py @@ -6,21 +6,19 @@ # Andrew Knyazev # License: BSD 3 clause -from numbers import Integral, Real import warnings +from numbers import Integral, Real import numpy as np - from scipy.linalg import LinAlgError, qr, svd from scipy.sparse import csc_matrix -from ..base import BaseEstimator, ClusterMixin -from ..base import _fit_context -from ..utils._param_validation import Interval, StrOptions, validate_params -from ..utils import check_random_state, as_float_array -from ..metrics.pairwise import pairwise_kernels, KERNEL_PARAMS -from ..neighbors import kneighbors_graph, NearestNeighbors +from ..base import BaseEstimator, ClusterMixin, _fit_context from ..manifold import spectral_embedding +from ..metrics.pairwise import KERNEL_PARAMS, pairwise_kernels +from ..neighbors import NearestNeighbors, kneighbors_graph +from ..utils import as_float_array, check_random_state +from ..utils._param_validation import Interval, StrOptions, validate_params from ._kmeans import k_means diff --git a/sklearn/cluster/tests/common.py b/sklearn/cluster/tests/common.py index 0f4bd9e14926d..b1fe047fe230a 100644 --- a/sklearn/cluster/tests/common.py +++ b/sklearn/cluster/tests/common.py @@ -5,7 +5,6 @@ import numpy as np - ############################################################################### # Generate sample data diff --git a/sklearn/cluster/tests/test_affinity_propagation.py b/sklearn/cluster/tests/test_affinity_propagation.py index 52007c375f667..136d2fe6fd781 100644 --- a/sklearn/cluster/tests/test_affinity_propagation.py +++ b/sklearn/cluster/tests/test_affinity_propagation.py @@ -3,20 +3,18 @@ """ -import numpy as np -import pytest import warnings +import numpy as np +import pytest from scipy.sparse import csr_matrix -from sklearn.exceptions import ConvergenceWarning, NotFittedError -from sklearn.utils._testing import assert_array_equal, assert_allclose - -from sklearn.cluster import AffinityPropagation +from sklearn.cluster import AffinityPropagation, affinity_propagation from sklearn.cluster._affinity_propagation import _equal_similarities_and_preferences -from sklearn.cluster import affinity_propagation from sklearn.datasets import make_blobs +from sklearn.exceptions import ConvergenceWarning, NotFittedError from sklearn.metrics import euclidean_distances +from sklearn.utils._testing import assert_allclose, assert_array_equal n_clusters = 3 centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10 diff --git a/sklearn/cluster/tests/test_bicluster.py b/sklearn/cluster/tests/test_bicluster.py index 0a68e97d6fb22..6d4a1067c4048 100644 --- a/sklearn/cluster/tests/test_bicluster.py +++ b/sklearn/cluster/tests/test_bicluster.py @@ -4,23 +4,21 @@ import pytest from scipy.sparse import csr_matrix, issparse -from sklearn.model_selection import ParameterGrid - -from sklearn.utils._testing import assert_almost_equal -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_array_almost_equal - from sklearn.base import BaseEstimator, BiclusterMixin - -from sklearn.cluster import SpectralCoclustering -from sklearn.cluster import SpectralBiclustering -from sklearn.cluster._bicluster import _scale_normalize -from sklearn.cluster._bicluster import _bistochastic_normalize -from sklearn.cluster._bicluster import _log_normalize - -from sklearn.metrics import consensus_score, v_measure_score - +from sklearn.cluster import SpectralBiclustering, SpectralCoclustering +from sklearn.cluster._bicluster import ( + _bistochastic_normalize, + _log_normalize, + _scale_normalize, +) from sklearn.datasets import make_biclusters, make_checkerboard +from sklearn.metrics import consensus_score, v_measure_score +from sklearn.model_selection import ParameterGrid +from sklearn.utils._testing import ( + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, +) class MockBiclustering(BiclusterMixin, BaseEstimator): diff --git a/sklearn/cluster/tests/test_birch.py b/sklearn/cluster/tests/test_birch.py index c2f3c06d15ba7..7fb83f0803f02 100644 --- a/sklearn/cluster/tests/test_birch.py +++ b/sklearn/cluster/tests/test_birch.py @@ -2,19 +2,16 @@ Tests for the birch clustering algorithm. """ -from scipy import sparse import numpy as np import pytest +from scipy import sparse +from sklearn.cluster import AgglomerativeClustering, Birch from sklearn.cluster.tests.common import generate_clustered_data -from sklearn.cluster import Birch -from sklearn.cluster import AgglomerativeClustering from sklearn.datasets import make_blobs from sklearn.exceptions import ConvergenceWarning from sklearn.metrics import pairwise_distances_argmin, v_measure_score - -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_allclose +from sklearn.utils._testing import assert_allclose, assert_array_equal def test_n_samples_leaves_roots(global_random_seed, global_dtype): diff --git a/sklearn/cluster/tests/test_bisect_k_means.py b/sklearn/cluster/tests/test_bisect_k_means.py index c79cd0bcca3e8..01afd4be9c8b5 100644 --- a/sklearn/cluster/tests/test_bisect_k_means.py +++ b/sklearn/cluster/tests/test_bisect_k_means.py @@ -2,9 +2,9 @@ import pytest import scipy.sparse as sp -from sklearn.utils._testing import assert_array_equal, assert_allclose from sklearn.cluster import BisectingKMeans from sklearn.metrics import v_measure_score +from sklearn.utils._testing import assert_allclose, assert_array_equal @pytest.mark.parametrize("bisecting_strategy", ["biggest_inertia", "largest_cluster"]) diff --git a/sklearn/cluster/tests/test_dbscan.py b/sklearn/cluster/tests/test_dbscan.py index f36eb19caeb0f..972820c6cc137 100644 --- a/sklearn/cluster/tests/test_dbscan.py +++ b/sklearn/cluster/tests/test_dbscan.py @@ -3,23 +3,18 @@ """ import pickle - -import numpy as np - import warnings -from scipy.spatial import distance -from scipy import sparse - +import numpy as np import pytest +from scipy import sparse +from scipy.spatial import distance -from sklearn.utils._testing import assert_array_equal -from sklearn.neighbors import NearestNeighbors -from sklearn.cluster import DBSCAN -from sklearn.cluster import dbscan +from sklearn.cluster import DBSCAN, dbscan from sklearn.cluster.tests.common import generate_clustered_data from sklearn.metrics.pairwise import pairwise_distances - +from sklearn.neighbors import NearestNeighbors +from sklearn.utils._testing import assert_array_equal n_clusters = 3 X = generate_clustered_data(n_clusters=n_clusters) diff --git a/sklearn/cluster/tests/test_feature_agglomeration.py b/sklearn/cluster/tests/test_feature_agglomeration.py index 3db2862384c74..121e8f2cfe400 100644 --- a/sklearn/cluster/tests/test_feature_agglomeration.py +++ b/sklearn/cluster/tests/test_feature_agglomeration.py @@ -3,13 +3,14 @@ """ # Authors: Sergul Aydore 2017 import warnings -import numpy as np -from numpy.testing import assert_array_equal +import numpy as np import pytest +from numpy.testing import assert_array_equal + from sklearn.cluster import FeatureAgglomeration -from sklearn.utils._testing import assert_array_almost_equal from sklearn.datasets import make_blobs +from sklearn.utils._testing import assert_array_almost_equal def test_feature_agglomeration(): diff --git a/sklearn/cluster/tests/test_hdbscan.py b/sklearn/cluster/tests/test_hdbscan.py index b652a99aa221f..d1ff6452a5a08 100644 --- a/sklearn/cluster/tests/test_hdbscan.py +++ b/sklearn/cluster/tests/test_hdbscan.py @@ -8,6 +8,12 @@ from scipy.spatial import distance from sklearn.cluster import HDBSCAN +from sklearn.cluster._hdbscan._tree import ( + CONDENSED_dtype, + _condense_tree, + _do_labelling, +) +from sklearn.cluster._hdbscan.hdbscan import _OUTLIER_ENCODING from sklearn.datasets import make_blobs from sklearn.metrics import fowlkes_mallows_score from sklearn.metrics.pairwise import _VALID_METRICS, euclidean_distances @@ -15,12 +21,6 @@ from sklearn.preprocessing import StandardScaler from sklearn.utils import shuffle from sklearn.utils._testing import assert_allclose, assert_array_equal -from sklearn.cluster._hdbscan.hdbscan import _OUTLIER_ENCODING -from sklearn.cluster._hdbscan._tree import ( - _do_labelling, - _condense_tree, - CONDENSED_dtype, -) n_clusters_true = 3 X, y = make_blobs(n_samples=200, random_state=10) diff --git a/sklearn/cluster/tests/test_hierarchical.py b/sklearn/cluster/tests/test_hierarchical.py index acaf3c27bedb1..95f28413d132d 100644 --- a/sklearn/cluster/tests/test_hierarchical.py +++ b/sklearn/cluster/tests/test_hierarchical.py @@ -6,48 +6,48 @@ # Matteo Visconti di Oleggio Castello 2014 # License: BSD 3 clause import itertools -from tempfile import mkdtemp import shutil -import pytest from functools import partial +from tempfile import mkdtemp import numpy as np +import pytest from scipy import sparse from scipy.cluster import hierarchy from scipy.sparse.csgraph import connected_components -from sklearn.metrics.cluster import adjusted_rand_score -from sklearn.metrics.tests.test_dist_metrics import METRICS_DEFAULT_PARAMS -from sklearn.utils._testing import assert_almost_equal, create_memmap_backed_data -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import ignore_warnings - -from sklearn.cluster import ward_tree -from sklearn.cluster import AgglomerativeClustering, FeatureAgglomeration +from sklearn.cluster import AgglomerativeClustering, FeatureAgglomeration, ward_tree from sklearn.cluster._agglomerative import ( - _hc_cut, _TREE_BUILDERS, - linkage_tree, _fix_connectivity, + _hc_cut, + linkage_tree, +) +from sklearn.cluster._hierarchical_fast import ( + average_merge, + max_merge, + mst_linkage_core, ) +from sklearn.datasets import make_circles, make_moons from sklearn.feature_extraction.image import grid_to_graph from sklearn.metrics import DistanceMetric +from sklearn.metrics.cluster import adjusted_rand_score, normalized_mutual_info_score from sklearn.metrics.pairwise import ( PAIRED_DISTANCES, cosine_distances, manhattan_distances, pairwise_distances, ) -from sklearn.metrics.cluster import normalized_mutual_info_score +from sklearn.metrics.tests.test_dist_metrics import METRICS_DEFAULT_PARAMS from sklearn.neighbors import kneighbors_graph -from sklearn.cluster._hierarchical_fast import ( - average_merge, - max_merge, - mst_linkage_core, -) from sklearn.utils._fast_dict import IntFloatDict -from sklearn.utils._testing import assert_array_equal -from sklearn.datasets import make_moons, make_circles +from sklearn.utils._testing import ( + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, + create_memmap_backed_data, + ignore_warnings, +) def test_linkage_misc(): diff --git a/sklearn/cluster/tests/test_k_means.py b/sklearn/cluster/tests/test_k_means.py index c11d5dd3165c0..a61f548ba11a0 100644 --- a/sklearn/cluster/tests/test_k_means.py +++ b/sklearn/cluster/tests/test_k_means.py @@ -2,37 +2,36 @@ import re import sys import warnings +from io import StringIO import numpy as np -from scipy import sparse as sp - import pytest +from scipy import sparse as sp -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_allclose -from sklearn.utils.fixes import threadpool_limits from sklearn.base import clone +from sklearn.cluster import KMeans, MiniBatchKMeans, k_means, kmeans_plusplus +from sklearn.cluster._k_means_common import ( + _euclidean_dense_dense_wrapper, + _euclidean_sparse_dense_wrapper, + _inertia_dense, + _inertia_sparse, + _is_same_clustering, + _relocate_empty_clusters_dense, + _relocate_empty_clusters_sparse, +) +from sklearn.cluster._kmeans import _labels_inertia, _mini_batch_step +from sklearn.datasets import make_blobs from sklearn.exceptions import ConvergenceWarning - -from sklearn.utils.extmath import row_norms -from sklearn.metrics import pairwise_distances -from sklearn.metrics import pairwise_distances_argmin -from sklearn.metrics.pairwise import euclidean_distances +from sklearn.metrics import pairwise_distances, pairwise_distances_argmin from sklearn.metrics.cluster import v_measure_score -from sklearn.cluster import KMeans, k_means, kmeans_plusplus -from sklearn.cluster import MiniBatchKMeans -from sklearn.cluster._kmeans import _labels_inertia -from sklearn.cluster._kmeans import _mini_batch_step -from sklearn.cluster._k_means_common import _relocate_empty_clusters_dense -from sklearn.cluster._k_means_common import _relocate_empty_clusters_sparse -from sklearn.cluster._k_means_common import _euclidean_dense_dense_wrapper -from sklearn.cluster._k_means_common import _euclidean_sparse_dense_wrapper -from sklearn.cluster._k_means_common import _inertia_dense -from sklearn.cluster._k_means_common import _inertia_sparse -from sklearn.cluster._k_means_common import _is_same_clustering -from sklearn.utils._testing import create_memmap_backed_data -from sklearn.datasets import make_blobs -from io import StringIO +from sklearn.metrics.pairwise import euclidean_distances +from sklearn.utils._testing import ( + assert_allclose, + assert_array_equal, + create_memmap_backed_data, +) +from sklearn.utils.extmath import row_norms +from sklearn.utils.fixes import threadpool_limits # TODO(1.4): Remove msg = ( diff --git a/sklearn/cluster/tests/test_mean_shift.py b/sklearn/cluster/tests/test_mean_shift.py index db13e4d18650f..265c72d0c4ce1 100644 --- a/sklearn/cluster/tests/test_mean_shift.py +++ b/sklearn/cluster/tests/test_mean_shift.py @@ -3,20 +3,15 @@ """ -import numpy as np import warnings -import pytest -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_allclose +import numpy as np +import pytest -from sklearn.cluster import MeanShift -from sklearn.cluster import mean_shift -from sklearn.cluster import estimate_bandwidth -from sklearn.cluster import get_bin_seeds +from sklearn.cluster import MeanShift, estimate_bandwidth, get_bin_seeds, mean_shift from sklearn.datasets import make_blobs from sklearn.metrics import v_measure_score - +from sklearn.utils._testing import assert_allclose, assert_array_equal n_clusters = 3 centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10 diff --git a/sklearn/cluster/tests/test_optics.py b/sklearn/cluster/tests/test_optics.py index 0acf818912c0f..d7bf4034ab98a 100644 --- a/sklearn/cluster/tests/test_optics.py +++ b/sklearn/cluster/tests/test_optics.py @@ -1,24 +1,21 @@ # Authors: Shane Grigsby # Adrin Jalali # License: BSD 3 clause +import warnings + import numpy as np import pytest from scipy import sparse -import warnings -from sklearn.datasets import make_blobs -from sklearn.cluster import OPTICS +from sklearn.cluster import DBSCAN, OPTICS from sklearn.cluster._optics import _extend_region, _extract_xi_labels -from sklearn.exceptions import DataConversionWarning +from sklearn.cluster.tests.common import generate_clustered_data +from sklearn.datasets import make_blobs +from sklearn.exceptions import DataConversionWarning, EfficiencyWarning from sklearn.metrics.cluster import contingency_matrix from sklearn.metrics.pairwise import pairwise_distances -from sklearn.cluster import DBSCAN from sklearn.utils import shuffle -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_allclose -from sklearn.exceptions import EfficiencyWarning -from sklearn.cluster.tests.common import generate_clustered_data - +from sklearn.utils._testing import assert_allclose, assert_array_equal rng = np.random.RandomState(0) n_points_per_cluster = 10 diff --git a/sklearn/cluster/tests/test_spectral.py b/sklearn/cluster/tests/test_spectral.py index d301f06e92075..33968a542691a 100644 --- a/sklearn/cluster/tests/test_spectral.py +++ b/sklearn/cluster/tests/test_spectral.py @@ -1,24 +1,21 @@ """Testing for Spectral Clustering methods""" +import pickle import re import numpy as np +import pytest from scipy import sparse from scipy.linalg import LinAlgError -import pytest - -import pickle - -from sklearn.utils import check_random_state -from sklearn.utils._testing import assert_array_equal - from sklearn.cluster import SpectralClustering, spectral_clustering -from sklearn.cluster._spectral import discretize, cluster_qr +from sklearn.cluster._spectral import cluster_qr, discretize +from sklearn.datasets import make_blobs from sklearn.feature_extraction import img_to_graph from sklearn.metrics import adjusted_rand_score from sklearn.metrics.pairwise import kernel_metrics, rbf_kernel from sklearn.neighbors import NearestNeighbors -from sklearn.datasets import make_blobs +from sklearn.utils import check_random_state +from sklearn.utils._testing import assert_array_equal try: from pyamg import smoothed_aggregation_solver # noqa diff --git a/sklearn/compose/__init__.py b/sklearn/compose/__init__.py index 8be8d17040e82..7b137cdf9e07f 100644 --- a/sklearn/compose/__init__.py +++ b/sklearn/compose/__init__.py @@ -7,12 +7,11 @@ from ._column_transformer import ( ColumnTransformer, - make_column_transformer, make_column_selector, + make_column_transformer, ) from ._target import TransformedTargetRegressor - __all__ = [ "ColumnTransformer", "make_column_transformer", diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py index 14349662cfee9..1f5854eac663e 100644 --- a/sklearn/compose/_column_transformer.py +++ b/sklearn/compose/_column_transformer.py @@ -6,29 +6,28 @@ # Author: Andreas Mueller # Joris Van den Bossche # License: BSD -from numbers import Integral, Real -from itertools import chain from collections import Counter +from itertools import chain +from numbers import Integral, Real import numpy as np from scipy import sparse -from ..base import clone, TransformerMixin -from ..base import _fit_context -from ..utils._estimator_html_repr import _VisualBlock -from ..pipeline import _fit_transform_one, _transform_one, _name_estimators +from ..base import TransformerMixin, _fit_context, clone +from ..pipeline import _fit_transform_one, _name_estimators, _transform_one from ..preprocessing import FunctionTransformer -from ..utils import Bunch -from ..utils import _safe_indexing -from ..utils import _get_column_indices -from ..utils._param_validation import HasMethods, Interval, StrOptions, Hidden +from ..utils import Bunch, _get_column_indices, _safe_indexing, check_pandas_support +from ..utils._estimator_html_repr import _VisualBlock +from ..utils._param_validation import HasMethods, Hidden, Interval, StrOptions from ..utils._set_output import _get_output_config, _safe_set_output -from ..utils import check_pandas_support from ..utils.metaestimators import _BaseComposition -from ..utils.validation import check_array, check_is_fitted, _check_feature_names_in -from ..utils.validation import _num_samples -from ..utils.parallel import delayed, Parallel - +from ..utils.parallel import Parallel, delayed +from ..utils.validation import ( + _check_feature_names_in, + _num_samples, + check_array, + check_is_fitted, +) __all__ = ["ColumnTransformer", "make_column_transformer", "make_column_selector"] diff --git a/sklearn/compose/_target.py b/sklearn/compose/_target.py index e926ed7abe324..348cdda48ea1c 100644 --- a/sklearn/compose/_target.py +++ b/sklearn/compose/_target.py @@ -6,14 +6,13 @@ import numpy as np -from ..base import BaseEstimator, RegressorMixin, clone -from ..base import _fit_context -from ..utils.validation import check_is_fitted -from ..utils._tags import _safe_tags -from ..utils import check_array, _safe_indexing -from ..utils._param_validation import HasMethods -from ..preprocessing import FunctionTransformer +from ..base import BaseEstimator, RegressorMixin, _fit_context, clone from ..exceptions import NotFittedError +from ..preprocessing import FunctionTransformer +from ..utils import _safe_indexing, check_array +from ..utils._param_validation import HasMethods +from ..utils._tags import _safe_tags +from ..utils.validation import check_is_fitted __all__ = ["TransformedTargetRegressor"] diff --git a/sklearn/compose/tests/test_column_transformer.py b/sklearn/compose/tests/test_column_transformer.py index cb9ddc0b4f344..dcf84273b3f14 100644 --- a/sklearn/compose/tests/test_column_transformer.py +++ b/sklearn/compose/tests/test_column_transformer.py @@ -1,28 +1,33 @@ """ Test the ColumnTransformer. """ -import re import pickle +import re import numpy as np -from scipy import sparse import pytest - from numpy.testing import assert_allclose -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_allclose_dense_sparse -from sklearn.utils._testing import assert_almost_equal +from scipy import sparse from sklearn.base import BaseEstimator, TransformerMixin from sklearn.compose import ( ColumnTransformer, - make_column_transformer, make_column_selector, + make_column_transformer, ) from sklearn.exceptions import NotFittedError -from sklearn.preprocessing import FunctionTransformer -from sklearn.preprocessing import StandardScaler, Normalizer, OneHotEncoder from sklearn.feature_selection import VarianceThreshold +from sklearn.preprocessing import ( + FunctionTransformer, + Normalizer, + OneHotEncoder, + StandardScaler, +) +from sklearn.utils._testing import ( + assert_allclose_dense_sparse, + assert_almost_equal, + assert_array_equal, +) class Trans(TransformerMixin, BaseEstimator): diff --git a/sklearn/compose/tests/test_target.py b/sklearn/compose/tests/test_target.py index f0d63c00c2772..53242b7e0277b 100644 --- a/sklearn/compose/tests/test_target.py +++ b/sklearn/compose/tests/test_target.py @@ -1,25 +1,14 @@ import numpy as np import pytest -from sklearn.base import clone -from sklearn.base import BaseEstimator -from sklearn.base import TransformerMixin - -from sklearn.dummy import DummyRegressor - -from sklearn.utils._testing import assert_allclose -from sklearn.utils._testing import assert_no_warnings - -from sklearn.preprocessing import FunctionTransformer -from sklearn.preprocessing import StandardScaler - -from sklearn.pipeline import Pipeline - -from sklearn.linear_model import LinearRegression, OrthogonalMatchingPursuit - from sklearn import datasets - +from sklearn.base import BaseEstimator, TransformerMixin, clone from sklearn.compose import TransformedTargetRegressor +from sklearn.dummy import DummyRegressor +from sklearn.linear_model import LinearRegression, OrthogonalMatchingPursuit +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import FunctionTransformer, StandardScaler +from sklearn.utils._testing import assert_allclose, assert_no_warnings friedman = datasets.make_friedman1(random_state=0) diff --git a/sklearn/conftest.py b/sklearn/conftest.py index 5d5f80d2e22d5..3d2c73b99a801 100644 --- a/sklearn/conftest.py +++ b/sklearn/conftest.py @@ -1,29 +1,29 @@ -from os import environ -from functools import wraps import platform import sys from contextlib import suppress +from functools import wraps +from os import environ from unittest import SkipTest import joblib -import pytest import numpy as np -from threadpoolctl import threadpool_limits +import pytest from _pytest.doctest import DoctestItem +from threadpoolctl import threadpool_limits -from sklearn.utils import _IS_32BIT from sklearn._min_dependencies import PYTEST_MIN_VERSION -from sklearn.utils.fixes import sp_version -from sklearn.utils.fixes import parse_version -from sklearn.datasets import fetch_20newsgroups -from sklearn.datasets import fetch_20newsgroups_vectorized -from sklearn.datasets import fetch_california_housing -from sklearn.datasets import fetch_covtype -from sklearn.datasets import fetch_kddcup99 -from sklearn.datasets import fetch_olivetti_faces -from sklearn.datasets import fetch_rcv1 +from sklearn.datasets import ( + fetch_20newsgroups, + fetch_20newsgroups_vectorized, + fetch_california_housing, + fetch_covtype, + fetch_kddcup99, + fetch_olivetti_faces, + fetch_rcv1, +) from sklearn.tests import random_seed - +from sklearn.utils import _IS_32BIT +from sklearn.utils.fixes import parse_version, sp_version if parse_version(pytest.__version__) < parse_version(PYTEST_MIN_VERSION): raise ImportError( diff --git a/sklearn/covariance/__init__.py b/sklearn/covariance/__init__.py index 011fde3647145..8fcf8c68444e5 100644 --- a/sklearn/covariance/__init__.py +++ b/sklearn/covariance/__init__.py @@ -6,24 +6,23 @@ Models. """ +from ._elliptic_envelope import EllipticEnvelope from ._empirical_covariance import ( - empirical_covariance, EmpiricalCovariance, + empirical_covariance, log_likelihood, ) +from ._graph_lasso import GraphicalLasso, GraphicalLassoCV, graphical_lasso +from ._robust_covariance import MinCovDet, fast_mcd from ._shrunk_covariance import ( - shrunk_covariance, + OAS, + LedoitWolf, ShrunkCovariance, ledoit_wolf, ledoit_wolf_shrinkage, - LedoitWolf, oas, - OAS, + shrunk_covariance, ) -from ._robust_covariance import fast_mcd, MinCovDet -from ._graph_lasso import graphical_lasso, GraphicalLasso, GraphicalLassoCV -from ._elliptic_envelope import EllipticEnvelope - __all__ = [ "EllipticEnvelope", diff --git a/sklearn/covariance/_elliptic_envelope.py b/sklearn/covariance/_elliptic_envelope.py index c99f200592580..fe109dddd5303 100644 --- a/sklearn/covariance/_elliptic_envelope.py +++ b/sklearn/covariance/_elliptic_envelope.py @@ -2,14 +2,15 @@ # # License: BSD 3 clause -import numpy as np from numbers import Real -from . import MinCovDet + +import numpy as np + +from ..base import OutlierMixin, _fit_context +from ..metrics import accuracy_score from ..utils._param_validation import Interval from ..utils.validation import check_is_fitted -from ..metrics import accuracy_score -from ..base import OutlierMixin -from ..base import _fit_context +from ._robust_covariance import MinCovDet class EllipticEnvelope(OutlierMixin, MinCovDet): diff --git a/sklearn/covariance/_empirical_covariance.py b/sklearn/covariance/_empirical_covariance.py index 8083bfd2e1aa1..e39c18017cdf0 100644 --- a/sklearn/covariance/_empirical_covariance.py +++ b/sklearn/covariance/_empirical_covariance.py @@ -11,16 +11,16 @@ # avoid division truncation import warnings + import numpy as np from scipy import linalg from .. import config_context -from ..base import BaseEstimator -from ..base import _fit_context +from ..base import BaseEstimator, _fit_context +from ..metrics.pairwise import pairwise_distances from ..utils import check_array from ..utils._param_validation import validate_params from ..utils.extmath import fast_logdet -from ..metrics.pairwise import pairwise_distances def log_likelihood(emp_cov, precision): diff --git a/sklearn/covariance/_graph_lasso.py b/sklearn/covariance/_graph_lasso.py index 8575cc4f75801..2b3248eb0300e 100644 --- a/sklearn/covariance/_graph_lasso.py +++ b/sklearn/covariance/_graph_lasso.py @@ -5,32 +5,30 @@ # Author: Gael Varoquaux # License: BSD 3 clause # Copyright: INRIA -import warnings import operator import sys import time - +import warnings from numbers import Integral, Real + import numpy as np from scipy import linalg -from . import empirical_covariance, EmpiricalCovariance, log_likelihood - from ..base import _fit_context from ..exceptions import ConvergenceWarning -from ..utils.validation import ( - _is_arraylike_not_scalar, - check_random_state, - check_scalar, -) -from ..utils.parallel import delayed, Parallel -from ..utils._param_validation import Interval, StrOptions -from ..utils._param_validation import validate_params # mypy error: Module 'sklearn.linear_model' has no attribute '_cd_fast' from ..linear_model import _cd_fast as cd_fast # type: ignore from ..linear_model import lars_path_gram from ..model_selection import check_cv, cross_val_score +from ..utils._param_validation import Interval, StrOptions, validate_params +from ..utils.parallel import Parallel, delayed +from ..utils.validation import ( + _is_arraylike_not_scalar, + check_random_state, + check_scalar, +) +from . import EmpiricalCovariance, empirical_covariance, log_likelihood # Helper functions to compute the objective and dual objective functions diff --git a/sklearn/covariance/_robust_covariance.py b/sklearn/covariance/_robust_covariance.py index c723bba7a097b..a6b32e50a6c1f 100644 --- a/sklearn/covariance/_robust_covariance.py +++ b/sklearn/covariance/_robust_covariance.py @@ -10,15 +10,16 @@ import warnings from numbers import Integral, Real + import numpy as np from scipy import linalg from scipy.stats import chi2 -from . import empirical_covariance, EmpiricalCovariance from ..base import _fit_context -from ..utils.extmath import fast_logdet -from ..utils import check_random_state, check_array +from ..utils import check_array, check_random_state from ..utils._param_validation import Interval +from ..utils.extmath import fast_logdet +from ._empirical_covariance import EmpiricalCovariance, empirical_covariance # Minimum Covariance Determinant diff --git a/sklearn/covariance/_shrunk_covariance.py b/sklearn/covariance/_shrunk_covariance.py index 21d2e034b45d7..06d65c46faef7 100644 --- a/sklearn/covariance/_shrunk_covariance.py +++ b/sklearn/covariance/_shrunk_covariance.py @@ -14,13 +14,14 @@ # avoid division truncation import warnings -from numbers import Real, Integral +from numbers import Integral, Real + import numpy as np -from . import empirical_covariance, EmpiricalCovariance from ..base import _fit_context from ..utils import check_array from ..utils._param_validation import Interval, validate_params +from . import EmpiricalCovariance, empirical_covariance def _ledoit_wolf(X, *, assume_centered, block_size): diff --git a/sklearn/covariance/tests/test_covariance.py b/sklearn/covariance/tests/test_covariance.py index bbd3a4757a835..0866c209a10c3 100644 --- a/sklearn/covariance/tests/test_covariance.py +++ b/sklearn/covariance/tests/test_covariance.py @@ -7,24 +7,25 @@ import numpy as np import pytest -from sklearn.utils._testing import assert_allclose -from sklearn.utils._testing import assert_almost_equal -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_array_equal - from sklearn import datasets from sklearn.covariance import ( - empirical_covariance, + OAS, EmpiricalCovariance, - ShrunkCovariance, - shrunk_covariance, LedoitWolf, + ShrunkCovariance, + empirical_covariance, ledoit_wolf, ledoit_wolf_shrinkage, - OAS, oas, + shrunk_covariance, ) from sklearn.covariance._shrunk_covariance import _ledoit_wolf +from sklearn.utils._testing import ( + assert_allclose, + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, +) from .._shrunk_covariance import _oas diff --git a/sklearn/covariance/tests/test_elliptic_envelope.py b/sklearn/covariance/tests/test_elliptic_envelope.py index 122d4c8bfb4cc..ca85717fb3782 100644 --- a/sklearn/covariance/tests/test_elliptic_envelope.py +++ b/sklearn/covariance/tests/test_elliptic_envelope.py @@ -6,10 +6,12 @@ import pytest from sklearn.covariance import EllipticEnvelope -from sklearn.utils._testing import assert_almost_equal -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_array_equal from sklearn.exceptions import NotFittedError +from sklearn.utils._testing import ( + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, +) def test_elliptic_envelope(global_random_seed): diff --git a/sklearn/covariance/tests/test_graphical_lasso.py b/sklearn/covariance/tests/test_graphical_lasso.py index 44a60f3e05103..317bf2aa85124 100644 --- a/sklearn/covariance/tests/test_graphical_lasso.py +++ b/sklearn/covariance/tests/test_graphical_lasso.py @@ -1,26 +1,27 @@ """ Test the graphical_lasso module. """ import sys -import pytest +from io import StringIO import numpy as np -from scipy import linalg - +import pytest from numpy.testing import assert_allclose -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_array_less -from sklearn.utils._testing import _convert_container +from scipy import linalg +from sklearn import datasets from sklearn.covariance import ( - graphical_lasso, GraphicalLasso, GraphicalLassoCV, empirical_covariance, + graphical_lasso, ) from sklearn.datasets import make_sparse_spd_matrix -from io import StringIO from sklearn.utils import check_random_state -from sklearn import datasets +from sklearn.utils._testing import ( + _convert_container, + assert_array_almost_equal, + assert_array_less, +) def test_graphical_lasso(random_state=0): diff --git a/sklearn/covariance/tests/test_robust_covariance.py b/sklearn/covariance/tests/test_robust_covariance.py index 213f3d7e8f04b..44dcdbbbf8249 100644 --- a/sklearn/covariance/tests/test_robust_covariance.py +++ b/sklearn/covariance/tests/test_robust_covariance.py @@ -9,11 +9,9 @@ import numpy as np import pytest -from sklearn.utils._testing import assert_array_almost_equal - from sklearn import datasets -from sklearn.covariance import empirical_covariance, MinCovDet -from sklearn.covariance import fast_mcd +from sklearn.covariance import MinCovDet, empirical_covariance, fast_mcd +from sklearn.utils._testing import assert_array_almost_equal X = datasets.load_iris().data X_1d = X[:, 0] diff --git a/sklearn/cross_decomposition/__init__.py b/sklearn/cross_decomposition/__init__.py index ec2f5fb3049af..47b78783caf9c 100644 --- a/sklearn/cross_decomposition/__init__.py +++ b/sklearn/cross_decomposition/__init__.py @@ -1,3 +1,3 @@ -from ._pls import PLSCanonical, PLSRegression, PLSSVD, CCA +from ._pls import CCA, PLSSVD, PLSCanonical, PLSRegression __all__ = ["PLSCanonical", "PLSRegression", "PLSSVD", "CCA"] diff --git a/sklearn/cross_decomposition/_pls.py b/sklearn/cross_decomposition/_pls.py index da395d8f060fb..f1fc90af11d82 100644 --- a/sklearn/cross_decomposition/_pls.py +++ b/sklearn/cross_decomposition/_pls.py @@ -5,25 +5,27 @@ # Author: Edouard Duchesnay # License: BSD 3 clause -from numbers import Integral, Real - import warnings from abc import ABCMeta, abstractmethod +from numbers import Integral, Real import numpy as np from scipy.linalg import svd -from ..base import BaseEstimator, RegressorMixin, TransformerMixin -from ..base import MultiOutputMixin -from ..base import ClassNamePrefixFeaturesOutMixin -from ..base import _fit_context +from ..base import ( + BaseEstimator, + ClassNamePrefixFeaturesOutMixin, + MultiOutputMixin, + RegressorMixin, + TransformerMixin, + _fit_context, +) +from ..exceptions import ConvergenceWarning from ..utils import check_array, check_consistent_length -from ..utils.fixes import sp_version -from ..utils.fixes import parse_version -from ..utils.extmath import svd_flip -from ..utils.validation import check_is_fitted, FLOAT_DTYPES from ..utils._param_validation import Interval, StrOptions -from ..exceptions import ConvergenceWarning +from ..utils.extmath import svd_flip +from ..utils.fixes import parse_version, sp_version +from ..utils.validation import FLOAT_DTYPES, check_is_fitted __all__ = ["PLSCanonical", "PLSRegression", "PLSSVD"] diff --git a/sklearn/cross_decomposition/tests/test_pls.py b/sklearn/cross_decomposition/tests/test_pls.py index 8f4840c9b9f21..fcdd927efb389 100644 --- a/sklearn/cross_decomposition/tests/test_pls.py +++ b/sklearn/cross_decomposition/tests/test_pls.py @@ -1,21 +1,20 @@ -import pytest import warnings + import numpy as np -from numpy.testing import assert_array_almost_equal, assert_array_equal, assert_allclose +import pytest +from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal -from sklearn.datasets import load_linnerud +from sklearn.cross_decomposition import CCA, PLSSVD, PLSCanonical, PLSRegression from sklearn.cross_decomposition._pls import ( _center_scale_xy, _get_first_singular_vectors_power_method, _get_first_singular_vectors_svd, _svd_flip_1d, ) -from sklearn.cross_decomposition import CCA -from sklearn.cross_decomposition import PLSSVD, PLSRegression, PLSCanonical -from sklearn.datasets import make_regression +from sklearn.datasets import load_linnerud, make_regression +from sklearn.exceptions import ConvergenceWarning from sklearn.utils import check_random_state from sklearn.utils.extmath import svd_flip -from sklearn.exceptions import ConvergenceWarning def assert_matrix_orthogonal(M): diff --git a/sklearn/datasets/__init__.py b/sklearn/datasets/__init__.py index 465d4159a32c4..7ae7902f3365c 100644 --- a/sklearn/datasets/__init__.py +++ b/sklearn/datasets/__init__.py @@ -5,52 +5,55 @@ """ import textwrap -from ._base import load_breast_cancer -from ._base import load_diabetes -from ._base import load_digits -from ._base import load_files -from ._base import load_iris -from ._base import load_linnerud -from ._base import load_sample_images -from ._base import load_sample_image -from ._base import load_wine -from ._base import get_data_home -from ._base import clear_data_home +from ._base import ( + clear_data_home, + get_data_home, + load_breast_cancer, + load_diabetes, + load_digits, + load_files, + load_iris, + load_linnerud, + load_sample_image, + load_sample_images, + load_wine, +) +from ._california_housing import fetch_california_housing from ._covtype import fetch_covtype from ._kddcup99 import fetch_kddcup99 -from ._lfw import fetch_lfw_pairs -from ._lfw import fetch_lfw_people -from ._twenty_newsgroups import fetch_20newsgroups -from ._twenty_newsgroups import fetch_20newsgroups_vectorized -from ._openml import fetch_openml -from ._samples_generator import make_classification -from ._samples_generator import make_multilabel_classification -from ._samples_generator import make_hastie_10_2 -from ._samples_generator import make_regression -from ._samples_generator import make_blobs -from ._samples_generator import make_moons -from ._samples_generator import make_circles -from ._samples_generator import make_friedman1 -from ._samples_generator import make_friedman2 -from ._samples_generator import make_friedman3 -from ._samples_generator import make_low_rank_matrix -from ._samples_generator import make_sparse_coded_signal -from ._samples_generator import make_sparse_uncorrelated -from ._samples_generator import make_spd_matrix -from ._samples_generator import make_swiss_roll -from ._samples_generator import make_s_curve -from ._samples_generator import make_sparse_spd_matrix -from ._samples_generator import make_gaussian_quantiles -from ._samples_generator import make_biclusters -from ._samples_generator import make_checkerboard -from ._svmlight_format_io import load_svmlight_file -from ._svmlight_format_io import load_svmlight_files -from ._svmlight_format_io import dump_svmlight_file +from ._lfw import fetch_lfw_pairs, fetch_lfw_people from ._olivetti_faces import fetch_olivetti_faces -from ._species_distributions import fetch_species_distributions -from ._california_housing import fetch_california_housing +from ._openml import fetch_openml from ._rcv1 import fetch_rcv1 - +from ._samples_generator import ( + make_biclusters, + make_blobs, + make_checkerboard, + make_circles, + make_classification, + make_friedman1, + make_friedman2, + make_friedman3, + make_gaussian_quantiles, + make_hastie_10_2, + make_low_rank_matrix, + make_moons, + make_multilabel_classification, + make_regression, + make_s_curve, + make_sparse_coded_signal, + make_sparse_spd_matrix, + make_sparse_uncorrelated, + make_spd_matrix, + make_swiss_roll, +) +from ._species_distributions import fetch_species_distributions +from ._svmlight_format_io import ( + dump_svmlight_file, + load_svmlight_file, + load_svmlight_files, +) +from ._twenty_newsgroups import fetch_20newsgroups, fetch_20newsgroups_vectorized __all__ = [ "clear_data_home", diff --git a/sklearn/datasets/_arff_parser.py b/sklearn/datasets/_arff_parser.py index bba06fbb74021..d9cc42de71f66 100644 --- a/sklearn/datasets/_arff_parser.py +++ b/sklearn/datasets/_arff_parser.py @@ -8,7 +8,6 @@ import numpy as np import scipy as sp - from ..externals import _arff from ..externals._arff import ArffSparseDataType from ..utils import ( diff --git a/sklearn/datasets/_base.py b/sklearn/datasets/_base.py index 014a37ae30b09..7dad2f1eb7cd1 100644 --- a/sklearn/datasets/_base.py +++ b/sklearn/datasets/_base.py @@ -7,26 +7,23 @@ # 2010 Olivier Grisel # License: BSD 3 clause import csv -import hashlib import gzip +import hashlib +import os import shutil from collections import namedtuple -import os +from numbers import Integral from os import environ, listdir, makedirs from os.path import expanduser, isdir, join, splitext from pathlib import Path -from numbers import Integral - -from ..preprocessing import scale -from ..utils import Bunch -from ..utils import check_random_state -from ..utils import check_pandas_support -from ..utils.fixes import _open_binary, _open_text, _read_text, _contents -from ..utils._param_validation import validate_params, Interval, StrOptions +from urllib.request import urlretrieve import numpy as np -from urllib.request import urlretrieve +from ..preprocessing import scale +from ..utils import Bunch, check_pandas_support, check_random_state +from ..utils._param_validation import Interval, StrOptions, validate_params +from ..utils.fixes import _contents, _open_binary, _open_text, _read_text DATA_MODULE = "sklearn.datasets.data" DESCR_MODULE = "sklearn.datasets.descr" diff --git a/sklearn/datasets/_california_housing.py b/sklearn/datasets/_california_housing.py index 96443c95f9979..0c06544e88317 100644 --- a/sklearn/datasets/_california_housing.py +++ b/sklearn/datasets/_california_housing.py @@ -21,24 +21,24 @@ # Authors: Peter Prettenhofer # License: BSD 3 clause -from os.path import exists -from os import makedirs, remove -import tarfile - -import numpy as np import logging +import tarfile +from os import makedirs, remove +from os.path import exists import joblib +import numpy as np -from . import get_data_home -from ._base import _convert_data_dataframe -from ._base import _fetch_remote -from ._base import _pkl_filepath -from ._base import RemoteFileMetadata -from ._base import load_descr from ..utils import Bunch from ..utils._param_validation import validate_params - +from . import get_data_home +from ._base import ( + RemoteFileMetadata, + _convert_data_dataframe, + _fetch_remote, + _pkl_filepath, + load_descr, +) # The original data can be found at: # https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz diff --git a/sklearn/datasets/_covtype.py b/sklearn/datasets/_covtype.py index 83bd8ad229924..236e69727b7ef 100644 --- a/sklearn/datasets/_covtype.py +++ b/sklearn/datasets/_covtype.py @@ -14,24 +14,25 @@ # Peter Prettenhofer # License: BSD 3 clause -from gzip import GzipFile import logging -from os.path import exists, join import os +from gzip import GzipFile +from os.path import exists, join from tempfile import TemporaryDirectory -import numpy as np import joblib +import numpy as np -from . import get_data_home -from ._base import _convert_data_dataframe -from ._base import _fetch_remote -from ._base import RemoteFileMetadata -from ._base import load_descr -from ..utils import Bunch -from ._base import _pkl_filepath -from ..utils import check_random_state +from ..utils import Bunch, check_random_state from ..utils._param_validation import validate_params +from . import get_data_home +from ._base import ( + RemoteFileMetadata, + _convert_data_dataframe, + _fetch_remote, + _pkl_filepath, + load_descr, +) # The original data can be found in: # https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz diff --git a/sklearn/datasets/_kddcup99.py b/sklearn/datasets/_kddcup99.py index 749e15cd53522..30a535c1f4cd4 100644 --- a/sklearn/datasets/_kddcup99.py +++ b/sklearn/datasets/_kddcup99.py @@ -9,24 +9,24 @@ """ import errno -from gzip import GzipFile import logging import os +from gzip import GzipFile from os.path import exists, join -import numpy as np import joblib +import numpy as np -from ._base import _fetch_remote -from ._base import _convert_data_dataframe -from . import get_data_home -from ._base import RemoteFileMetadata -from ._base import load_descr -from ..utils._param_validation import StrOptions, validate_params -from ..utils import Bunch -from ..utils import check_random_state +from ..utils import Bunch, check_random_state from ..utils import shuffle as shuffle_method - +from ..utils._param_validation import StrOptions, validate_params +from . import get_data_home +from ._base import ( + RemoteFileMetadata, + _convert_data_dataframe, + _fetch_remote, + load_descr, +) # The original data can be found at: # https://archive.ics.uci.edu/ml/machine-learning-databases/kddcup99-mld/kddcup.data.gz diff --git a/sklearn/datasets/_lfw.py b/sklearn/datasets/_lfw.py index 7f6cf8f235d3f..e758eef5dc427 100644 --- a/sklearn/datasets/_lfw.py +++ b/sklearn/datasets/_lfw.py @@ -8,22 +8,22 @@ # Copyright (c) 2011 Olivier Grisel # License: BSD 3 clause -from os import listdir, makedirs, remove -from os.path import join, exists, isdir -from ..utils._param_validation import validate_params, Interval, Hidden, StrOptions -from numbers import Integral, Real import logging +from numbers import Integral, Real +from os import listdir, makedirs, remove +from os.path import exists, isdir, join import numpy as np from joblib import Memory +from ..utils import Bunch +from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params from ._base import ( - get_data_home, - _fetch_remote, RemoteFileMetadata, + _fetch_remote, + get_data_home, load_descr, ) -from ..utils import Bunch logger = logging.getLogger(__name__) diff --git a/sklearn/datasets/_olivetti_faces.py b/sklearn/datasets/_olivetti_faces.py index 55f4b856c6cf0..5ef5cb6286c9f 100644 --- a/sklearn/datasets/_olivetti_faces.py +++ b/sklearn/datasets/_olivetti_faces.py @@ -13,20 +13,17 @@ # Copyright (c) 2011 David Warde-Farley # License: BSD 3 clause -from os.path import exists from os import makedirs, remove +from os.path import exists +import joblib import numpy as np from scipy.io import loadmat -import joblib -from . import get_data_home -from ._base import _fetch_remote -from ._base import RemoteFileMetadata -from ._base import _pkl_filepath -from ._base import load_descr -from ..utils import check_random_state, Bunch +from ..utils import Bunch, check_random_state from ..utils._param_validation import validate_params +from . import get_data_home +from ._base import RemoteFileMetadata, _fetch_remote, _pkl_filepath, load_descr # The original data can be found at: # https://cs.nyu.edu/~roweis/data/olivettifaces.mat diff --git a/sklearn/datasets/_openml.py b/sklearn/datasets/_openml.py index 21d8eb99858bb..3f525c3433a90 100644 --- a/sklearn/datasets/_openml.py +++ b/sklearn/datasets/_openml.py @@ -7,18 +7,20 @@ from contextlib import closing from functools import wraps from os.path import join -from typing import Callable, Optional, Dict, Tuple, List, Any, Union from tempfile import TemporaryDirectory +from typing import Any, Callable, Dict, List, Optional, Tuple, Union from urllib.error import HTTPError, URLError -from urllib.request import urlopen, Request +from urllib.request import Request, urlopen from warnings import warn import numpy as np +from ..utils import ( + Bunch, + check_pandas_support, # noqa +) from . import get_data_home from ._arff_parser import load_arff_from_gzip_file -from ..utils import Bunch -from ..utils import check_pandas_support # noqa __all__ = ["fetch_openml"] diff --git a/sklearn/datasets/_rcv1.py b/sklearn/datasets/_rcv1.py index ae391edbad113..a0780edaba9da 100644 --- a/sklearn/datasets/_rcv1.py +++ b/sklearn/datasets/_rcv1.py @@ -9,25 +9,20 @@ # License: BSD 3 clause import logging - -from os import remove, makedirs -from os.path import exists, join from gzip import GzipFile +from os import makedirs, remove +from os.path import exists, join +import joblib import numpy as np import scipy.sparse as sp -import joblib +from ..utils import Bunch +from ..utils import shuffle as shuffle_ +from ..utils._param_validation import StrOptions, validate_params from . import get_data_home -from ._base import _pkl_filepath -from ._base import _fetch_remote -from ._base import RemoteFileMetadata -from ._base import load_descr +from ._base import RemoteFileMetadata, _fetch_remote, _pkl_filepath, load_descr from ._svmlight_format_io import load_svmlight_files -from ..utils import shuffle as shuffle_ -from ..utils import Bunch -from ..utils._param_validation import validate_params, StrOptions - # The original vectorized data can be found at: # http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a13-vector-files/lyrl2004_vectors_test_pt0.dat.gz diff --git a/sklearn/datasets/_samples_generator.py b/sklearn/datasets/_samples_generator.py index cb3b36d944eb2..9a34c995c0546 100644 --- a/sklearn/datasets/_samples_generator.py +++ b/sklearn/datasets/_samples_generator.py @@ -6,20 +6,20 @@ # G. Louppe, J. Nothman # License: BSD 3 clause -from numbers import Integral, Real -import numbers import array +import numbers import warnings from collections.abc import Iterable +from numbers import Integral, Real import numpy as np -from scipy import linalg import scipy.sparse as sp +from scipy import linalg from ..preprocessing import MultiLabelBinarizer from ..utils import check_array, check_random_state -from ..utils._param_validation import Interval, validate_params, Hidden, StrOptions from ..utils import shuffle as util_shuffle +from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params from ..utils.random import sample_without_replacement diff --git a/sklearn/datasets/_species_distributions.py b/sklearn/datasets/_species_distributions.py index 3387217349e20..8f5a0881bdf6b 100644 --- a/sklearn/datasets/_species_distributions.py +++ b/sklearn/datasets/_species_distributions.py @@ -37,21 +37,18 @@ # # License: BSD 3 clause +import logging from io import BytesIO from os import makedirs, remove from os.path import exists -import logging -import numpy as np - import joblib +import numpy as np -from . import get_data_home -from ._base import _fetch_remote -from ._base import RemoteFileMetadata from ..utils import Bunch -from ._base import _pkl_filepath from ..utils._param_validation import validate_params +from . import get_data_home +from ._base import RemoteFileMetadata, _fetch_remote, _pkl_filepath # The original data can be found at: # https://biodiversityinformatics.amnh.org/open_source/maxent/samples.zip diff --git a/sklearn/datasets/_svmlight_format_io.py b/sklearn/datasets/_svmlight_format_io.py index e04d90e15dceb..a48eab7938336 100644 --- a/sklearn/datasets/_svmlight_format_io.py +++ b/sklearn/datasets/_svmlight_format_io.py @@ -15,22 +15,21 @@ # Olivier Grisel # License: BSD 3 clause -from contextlib import closing import os.path +from contextlib import closing +from numbers import Integral import numpy as np import scipy.sparse as sp -from numbers import Integral from .. import __version__ - -from ..utils import check_array, IS_PYPY -from ..utils._param_validation import validate_params, HasMethods, Interval, StrOptions +from ..utils import IS_PYPY, check_array +from ..utils._param_validation import HasMethods, Interval, StrOptions, validate_params if not IS_PYPY: from ._svmlight_format_fast import ( - _load_svmlight_file, _dump_svmlight_file, + _load_svmlight_file, ) else: diff --git a/sklearn/datasets/_twenty_newsgroups.py b/sklearn/datasets/_twenty_newsgroups.py index 512b7974a497d..2e2dd6aa73234 100644 --- a/sklearn/datasets/_twenty_newsgroups.py +++ b/sklearn/datasets/_twenty_newsgroups.py @@ -24,29 +24,30 @@ # Copyright (c) 2011 Olivier Grisel # License: BSD 3 clause -import os +import codecs import logging -import tarfile +import os import pickle -import shutil import re -import codecs +import shutil +import tarfile +import joblib import numpy as np import scipy.sparse as sp -import joblib -from . import get_data_home -from . import load_files -from ._base import _convert_data_dataframe -from ._base import _pkl_filepath -from ._base import _fetch_remote -from ._base import RemoteFileMetadata -from ._base import load_descr -from ..feature_extraction.text import CountVectorizer from .. import preprocessing -from ..utils import check_random_state, Bunch +from ..feature_extraction.text import CountVectorizer +from ..utils import Bunch, check_random_state from ..utils._param_validation import StrOptions, validate_params +from . import get_data_home, load_files +from ._base import ( + RemoteFileMetadata, + _convert_data_dataframe, + _fetch_remote, + _pkl_filepath, + load_descr, +) logger = logging.getLogger(__name__) diff --git a/sklearn/datasets/tests/conftest.py b/sklearn/datasets/tests/conftest.py index ef1280f6218b1..c8ab1cd04ee6e 100644 --- a/sklearn/datasets/tests/conftest.py +++ b/sklearn/datasets/tests/conftest.py @@ -1,6 +1,7 @@ """ Network tests are only run, if data is already locally available, or if download is specifically requested by environment variable.""" import builtins + import pytest diff --git a/sklearn/datasets/tests/test_20news.py b/sklearn/datasets/tests/test_20news.py index e30348c894559..af308e49c5ebf 100644 --- a/sklearn/datasets/tests/test_20news.py +++ b/sklearn/datasets/tests/test_20news.py @@ -4,16 +4,17 @@ from functools import partial from unittest.mock import patch -import pytest - import numpy as np +import pytest import scipy.sparse as sp -from sklearn.datasets.tests.test_common import check_as_frame -from sklearn.datasets.tests.test_common import check_pandas_dependency_message -from sklearn.datasets.tests.test_common import check_return_X_y -from sklearn.utils._testing import assert_allclose_dense_sparse +from sklearn.datasets.tests.test_common import ( + check_as_frame, + check_pandas_dependency_message, + check_return_X_y, +) from sklearn.preprocessing import normalize +from sklearn.utils._testing import assert_allclose_dense_sparse def test_20news(fetch_20newsgroups_fxt): diff --git a/sklearn/datasets/tests/test_arff_parser.py b/sklearn/datasets/tests/test_arff_parser.py index 8465289d187ee..b675439cd2e9d 100644 --- a/sklearn/datasets/tests/test_arff_parser.py +++ b/sklearn/datasets/tests/test_arff_parser.py @@ -1,5 +1,5 @@ -from io import BytesIO import textwrap +from io import BytesIO import pytest diff --git a/sklearn/datasets/tests/test_base.py b/sklearn/datasets/tests/test_base.py index 23dc78570fc9d..f31f20636c0c1 100644 --- a/sklearn/datasets/tests/test_base.py +++ b/sklearn/datasets/tests/test_base.py @@ -2,31 +2,33 @@ import shutil import tempfile import warnings -from pickle import loads -from pickle import dumps from functools import partial +from pickle import dumps, loads -import pytest import numpy as np -from sklearn.datasets import get_data_home -from sklearn.datasets import clear_data_home -from sklearn.datasets import load_files -from sklearn.datasets import load_sample_images -from sklearn.datasets import load_sample_image -from sklearn.datasets import load_digits -from sklearn.datasets import load_diabetes -from sklearn.datasets import load_linnerud -from sklearn.datasets import load_iris -from sklearn.datasets import load_breast_cancer -from sklearn.datasets import load_wine +import pytest + +from sklearn.datasets import ( + clear_data_home, + get_data_home, + load_breast_cancer, + load_diabetes, + load_digits, + load_files, + load_iris, + load_linnerud, + load_sample_image, + load_sample_images, + load_wine, +) from sklearn.datasets._base import ( load_csv_data, load_gzip_compressed_csv_data, ) +from sklearn.datasets.tests.test_common import check_as_frame from sklearn.preprocessing import scale from sklearn.utils import Bunch from sklearn.utils.fixes import _is_resource -from sklearn.datasets.tests.test_common import check_as_frame def _remove_dir(path): diff --git a/sklearn/datasets/tests/test_california_housing.py b/sklearn/datasets/tests/test_california_housing.py index 495becccd820f..ef6fc95db80bf 100644 --- a/sklearn/datasets/tests/test_california_housing.py +++ b/sklearn/datasets/tests/test_california_housing.py @@ -1,10 +1,11 @@ """Test the california_housing loader, if the data is available, or if specifically requested via environment variable (e.g. for CI jobs).""" +from functools import partial + import pytest from sklearn.datasets.tests.test_common import check_return_X_y -from functools import partial def test_fetch(fetch_california_housing_fxt): diff --git a/sklearn/datasets/tests/test_common.py b/sklearn/datasets/tests/test_common.py index 5f21bdc66b4dc..8048a31041ddc 100644 --- a/sklearn/datasets/tests/test_common.py +++ b/sklearn/datasets/tests/test_common.py @@ -2,8 +2,8 @@ import inspect import os -import pytest import numpy as np +import pytest import sklearn.datasets diff --git a/sklearn/datasets/tests/test_covtype.py b/sklearn/datasets/tests/test_covtype.py index 2cc2fed81bad6..e44fdaae69ec3 100644 --- a/sklearn/datasets/tests/test_covtype.py +++ b/sklearn/datasets/tests/test_covtype.py @@ -2,7 +2,9 @@ or if specifically requested via environment variable (e.g. for CI jobs).""" from functools import partial + import pytest + from sklearn.datasets.tests.test_common import check_return_X_y diff --git a/sklearn/datasets/tests/test_kddcup99.py b/sklearn/datasets/tests/test_kddcup99.py index 8eb1d6ec71eb3..5f6e9c83a30b8 100644 --- a/sklearn/datasets/tests/test_kddcup99.py +++ b/sklearn/datasets/tests/test_kddcup99.py @@ -7,11 +7,14 @@ """ from functools import partial + import pytest -from sklearn.datasets.tests.test_common import check_as_frame -from sklearn.datasets.tests.test_common import check_pandas_dependency_message -from sklearn.datasets.tests.test_common import check_return_X_y +from sklearn.datasets.tests.test_common import ( + check_as_frame, + check_pandas_dependency_message, + check_return_X_y, +) @pytest.mark.parametrize("as_frame", [True, False]) diff --git a/sklearn/datasets/tests/test_lfw.py b/sklearn/datasets/tests/test_lfw.py index 36f33d8a10289..92edb99ce3b0b 100644 --- a/sklearn/datasets/tests/test_lfw.py +++ b/sklearn/datasets/tests/test_lfw.py @@ -8,19 +8,18 @@ joblib, successive runs will be fast (less than 200ms). """ -import random import os +import random import shutil import tempfile +from functools import partial + import numpy as np import pytest -from functools import partial -from sklearn.datasets import fetch_lfw_pairs -from sklearn.datasets import fetch_lfw_people -from sklearn.utils._testing import assert_array_equal +from sklearn.datasets import fetch_lfw_pairs, fetch_lfw_people from sklearn.datasets.tests.test_common import check_return_X_y - +from sklearn.utils._testing import assert_array_equal SCIKIT_LEARN_DATA = None SCIKIT_LEARN_EMPTY_DATA = None diff --git a/sklearn/datasets/tests/test_olivetti_faces.py b/sklearn/datasets/tests/test_olivetti_faces.py index 18fceb0ed8b0e..e5d6c853aa454 100644 --- a/sklearn/datasets/tests/test_olivetti_faces.py +++ b/sklearn/datasets/tests/test_olivetti_faces.py @@ -4,9 +4,8 @@ import numpy as np -from sklearn.utils import Bunch from sklearn.datasets.tests.test_common import check_return_X_y - +from sklearn.utils import Bunch from sklearn.utils._testing import assert_array_equal diff --git a/sklearn/datasets/tests/test_openml.py b/sklearn/datasets/tests/test_openml.py index c13b82dd769d3..8c78b753f336f 100644 --- a/sklearn/datasets/tests/test_openml.py +++ b/sklearn/datasets/tests/test_openml.py @@ -8,28 +8,26 @@ from urllib.error import HTTPError import numpy as np -import scipy.sparse import pytest +import scipy.sparse import sklearn from sklearn import config_context +from sklearn.datasets import fetch_openml as fetch_openml_orig +from sklearn.datasets._openml import ( + _OPENML_PREFIX, + _get_local_path, + _open_openml_url, + _retry_with_clean_cache, +) from sklearn.utils import Bunch, check_pandas_support -from sklearn.utils.fixes import _open_binary from sklearn.utils._testing import ( SkipTest, assert_allclose, assert_array_equal, fails_if_pypy, ) - -from sklearn.datasets import fetch_openml as fetch_openml_orig -from sklearn.datasets._openml import ( - _OPENML_PREFIX, - _open_openml_url, - _get_local_path, - _retry_with_clean_cache, -) - +from sklearn.utils.fixes import _open_binary OPENML_TEST_DATA_MODULE = "sklearn.datasets.tests.data.openml" # if True, urlopen will be monkey patched to only use local files diff --git a/sklearn/datasets/tests/test_rcv1.py b/sklearn/datasets/tests/test_rcv1.py index 11d0335f4fb8c..fbb9d67015a30 100644 --- a/sklearn/datasets/tests/test_rcv1.py +++ b/sklearn/datasets/tests/test_rcv1.py @@ -2,12 +2,13 @@ or if specifically requested via environment variable (e.g. for CI jobs).""" -import scipy.sparse as sp -import numpy as np from functools import partial + +import numpy as np +import scipy.sparse as sp + from sklearn.datasets.tests.test_common import check_return_X_y -from sklearn.utils._testing import assert_almost_equal -from sklearn.utils._testing import assert_array_equal +from sklearn.utils._testing import assert_almost_equal, assert_array_equal def test_fetch_rcv1(fetch_rcv1_fxt, global_random_seed): diff --git a/sklearn/datasets/tests/test_samples_generator.py b/sklearn/datasets/tests/test_samples_generator.py index cd23fc5016672..ad6569f0863bf 100644 --- a/sklearn/datasets/tests/test_samples_generator.py +++ b/sklearn/datasets/tests/test_samples_generator.py @@ -6,31 +6,33 @@ import pytest import scipy.sparse as sp -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_almost_equal -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_allclose -from sklearn.utils._testing import ignore_warnings - -from sklearn.datasets import make_classification -from sklearn.datasets import make_multilabel_classification -from sklearn.datasets import make_hastie_10_2 -from sklearn.datasets import make_regression -from sklearn.datasets import make_blobs -from sklearn.datasets import make_friedman1 -from sklearn.datasets import make_friedman2 -from sklearn.datasets import make_friedman3 -from sklearn.datasets import make_low_rank_matrix -from sklearn.datasets import make_moons -from sklearn.datasets import make_circles -from sklearn.datasets import make_sparse_coded_signal -from sklearn.datasets import make_sparse_uncorrelated -from sklearn.datasets import make_spd_matrix -from sklearn.datasets import make_swiss_roll -from sklearn.datasets import make_s_curve -from sklearn.datasets import make_biclusters -from sklearn.datasets import make_checkerboard - +from sklearn.datasets import ( + make_biclusters, + make_blobs, + make_checkerboard, + make_circles, + make_classification, + make_friedman1, + make_friedman2, + make_friedman3, + make_hastie_10_2, + make_low_rank_matrix, + make_moons, + make_multilabel_classification, + make_regression, + make_s_curve, + make_sparse_coded_signal, + make_sparse_uncorrelated, + make_spd_matrix, + make_swiss_roll, +) +from sklearn.utils._testing import ( + assert_allclose, + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, + ignore_warnings, +) from sklearn.utils.validation import assert_all_finite diff --git a/sklearn/datasets/tests/test_svmlight_format.py b/sklearn/datasets/tests/test_svmlight_format.py index 0b76cce3c5a4d..213e9095a73da 100644 --- a/sklearn/datasets/tests/test_svmlight_format.py +++ b/sklearn/datasets/tests/test_svmlight_format.py @@ -1,22 +1,23 @@ -from bz2 import BZ2File import gzip -from io import BytesIO -import numpy as np -import scipy.sparse as sp import os import shutil +from bz2 import BZ2File +from io import BytesIO from tempfile import NamedTemporaryFile +import numpy as np import pytest - -from sklearn.utils.fixes import _open_binary, _path -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_array_almost_equal, assert_allclose -from sklearn.utils._testing import fails_if_pypy +import scipy.sparse as sp import sklearn -from sklearn.datasets import load_svmlight_file, load_svmlight_files, dump_svmlight_file - +from sklearn.datasets import dump_svmlight_file, load_svmlight_file, load_svmlight_files +from sklearn.utils._testing import ( + assert_allclose, + assert_array_almost_equal, + assert_array_equal, + fails_if_pypy, +) +from sklearn.utils.fixes import _open_binary, _path TEST_DATA_MODULE = "sklearn.datasets.tests.data" datafile = "svmlight_classification.txt" diff --git a/sklearn/decomposition/__init__.py b/sklearn/decomposition/__init__.py index c5f323d3c5d72..1f9cfe07dc0e8 100644 --- a/sklearn/decomposition/__init__.py +++ b/sklearn/decomposition/__init__.py @@ -5,29 +5,28 @@ """ -from ._nmf import ( - NMF, - MiniBatchNMF, - non_negative_factorization, -) -from ._pca import PCA -from ._incremental_pca import IncrementalPCA -from ._kernel_pca import KernelPCA -from ._sparse_pca import SparsePCA, MiniBatchSparsePCA -from ._truncated_svd import TruncatedSVD -from ._fastica import FastICA, fastica +from ..utils.extmath import randomized_svd from ._dict_learning import ( - dict_learning, - dict_learning_online, - sparse_encode, DictionaryLearning, MiniBatchDictionaryLearning, SparseCoder, + dict_learning, + dict_learning_online, + sparse_encode, ) from ._factor_analysis import FactorAnalysis -from ..utils.extmath import randomized_svd +from ._fastica import FastICA, fastica +from ._incremental_pca import IncrementalPCA +from ._kernel_pca import KernelPCA from ._lda import LatentDirichletAllocation - +from ._nmf import ( + NMF, + MiniBatchNMF, + non_negative_factorization, +) +from ._pca import PCA +from ._sparse_pca import MiniBatchSparsePCA, SparsePCA +from ._truncated_svd import TruncatedSVD __all__ = [ "DictionaryLearning", diff --git a/sklearn/decomposition/_base.py b/sklearn/decomposition/_base.py index 20bf7af4f284a..9634395a335ba 100644 --- a/sklearn/decomposition/_base.py +++ b/sklearn/decomposition/_base.py @@ -8,12 +8,13 @@ # # License: BSD 3 clause +from abc import ABCMeta, abstractmethod + import numpy as np from scipy import linalg -from ..base import BaseEstimator, TransformerMixin, ClassNamePrefixFeaturesOutMixin +from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin from ..utils.validation import check_is_fitted -from abc import ABCMeta, abstractmethod class _BasePCA( diff --git a/sklearn/decomposition/_dict_learning.py b/sklearn/decomposition/_dict_learning.py index 54b3590f5b62e..b6972235dca7d 100644 --- a/sklearn/decomposition/_dict_learning.py +++ b/sklearn/decomposition/_dict_learning.py @@ -3,27 +3,29 @@ # Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort # License: BSD 3 clause -import time -import sys import itertools -from numbers import Integral, Real +import sys +import time import warnings - from math import ceil +from numbers import Integral, Real import numpy as np -from scipy import linalg from joblib import effective_n_jobs +from scipy import linalg -from ..base import BaseEstimator, TransformerMixin, ClassNamePrefixFeaturesOutMixin -from ..base import _fit_context -from ..utils import check_array, check_random_state, gen_even_slices, gen_batches -from ..utils._param_validation import Hidden, Interval, StrOptions -from ..utils._param_validation import validate_params +from ..base import ( + BaseEstimator, + ClassNamePrefixFeaturesOutMixin, + TransformerMixin, + _fit_context, +) +from ..linear_model import Lars, Lasso, LassoLars, orthogonal_mp_gram +from ..utils import check_array, check_random_state, gen_batches, gen_even_slices +from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params from ..utils.extmath import randomized_svd, row_norms, svd_flip +from ..utils.parallel import Parallel, delayed from ..utils.validation import check_is_fitted -from ..utils.parallel import delayed, Parallel -from ..linear_model import Lasso, orthogonal_mp_gram, LassoLars, Lars def _check_positive_coding(method, positive): diff --git a/sklearn/decomposition/_factor_analysis.py b/sklearn/decomposition/_factor_analysis.py index 8c3d590b2c814..af3498d534483 100644 --- a/sklearn/decomposition/_factor_analysis.py +++ b/sklearn/decomposition/_factor_analysis.py @@ -20,19 +20,23 @@ # License: BSD3 import warnings -from math import sqrt, log +from math import log, sqrt from numbers import Integral, Real + import numpy as np from scipy import linalg - -from ..base import BaseEstimator, TransformerMixin, ClassNamePrefixFeaturesOutMixin -from ..base import _fit_context +from ..base import ( + BaseEstimator, + ClassNamePrefixFeaturesOutMixin, + TransformerMixin, + _fit_context, +) +from ..exceptions import ConvergenceWarning from ..utils import check_random_state from ..utils._param_validation import Interval, StrOptions from ..utils.extmath import fast_logdet, randomized_svd, squared_norm from ..utils.validation import check_is_fitted -from ..exceptions import ConvergenceWarning class FactorAnalysis(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator): diff --git a/sklearn/decomposition/_fastica.py b/sklearn/decomposition/_fastica.py index 6dcf62c0ace3b..da7f6393c2b7f 100644 --- a/sklearn/decomposition/_fastica.py +++ b/sklearn/decomposition/_fastica.py @@ -15,12 +15,16 @@ import numpy as np from scipy import linalg -from ..base import BaseEstimator, TransformerMixin, ClassNamePrefixFeaturesOutMixin -from ..base import _fit_context +from ..base import ( + BaseEstimator, + ClassNamePrefixFeaturesOutMixin, + TransformerMixin, + _fit_context, +) from ..exceptions import ConvergenceWarning -from ..utils import check_array, as_float_array, check_random_state +from ..utils import as_float_array, check_array, check_random_state +from ..utils._param_validation import Interval, Options, StrOptions, validate_params from ..utils.validation import check_is_fitted -from ..utils._param_validation import Interval, StrOptions, Options, validate_params __all__ = ["fastica", "FastICA"] diff --git a/sklearn/decomposition/_incremental_pca.py b/sklearn/decomposition/_incremental_pca.py index 5ae5d58b06ca4..f05e2dacc66b2 100644 --- a/sklearn/decomposition/_incremental_pca.py +++ b/sklearn/decomposition/_incremental_pca.py @@ -5,14 +5,15 @@ # License: BSD 3 clause from numbers import Integral + import numpy as np from scipy import linalg, sparse -from ._base import _BasePCA from ..base import _fit_context from ..utils import gen_batches from ..utils._param_validation import Interval -from ..utils.extmath import svd_flip, _incremental_mean_and_var +from ..utils.extmath import _incremental_mean_and_var, svd_flip +from ._base import _BasePCA class IncrementalPCA(_BasePCA): diff --git a/sklearn/decomposition/_kernel_pca.py b/sklearn/decomposition/_kernel_pca.py index 61d502a006c5e..ccf79e896f210 100644 --- a/sklearn/decomposition/_kernel_pca.py +++ b/sklearn/decomposition/_kernel_pca.py @@ -4,24 +4,29 @@ # Sylvain Marie # License: BSD 3 clause -import numpy as np from numbers import Integral, Real + +import numpy as np from scipy import linalg -from scipy.sparse.linalg import eigsh from scipy.linalg import eigh +from scipy.sparse.linalg import eigsh +from ..base import ( + BaseEstimator, + ClassNamePrefixFeaturesOutMixin, + TransformerMixin, + _fit_context, +) +from ..exceptions import NotFittedError +from ..metrics.pairwise import pairwise_kernels +from ..preprocessing import KernelCenterer from ..utils._arpack import _init_arpack_v0 -from ..utils.extmath import svd_flip, _randomized_eigsh +from ..utils._param_validation import Interval, StrOptions +from ..utils.extmath import _randomized_eigsh, svd_flip from ..utils.validation import ( - check_is_fitted, _check_psd_eigenvalues, + check_is_fitted, ) -from ..utils._param_validation import Interval, StrOptions -from ..exceptions import NotFittedError -from ..base import BaseEstimator, TransformerMixin, ClassNamePrefixFeaturesOutMixin -from ..base import _fit_context -from ..preprocessing import KernelCenterer -from ..metrics.pairwise import pairwise_kernels class KernelPCA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator): diff --git a/sklearn/decomposition/_lda.py b/sklearn/decomposition/_lda.py index ab1ea5ebb5460..9e161c178b9e3 100644 --- a/sklearn/decomposition/_lda.py +++ b/sklearn/decomposition/_lda.py @@ -14,22 +14,28 @@ import numpy as np import scipy.sparse as sp -from scipy.special import gammaln, logsumexp from joblib import effective_n_jobs +from scipy.special import gammaln, logsumexp -from ..base import BaseEstimator, TransformerMixin, ClassNamePrefixFeaturesOutMixin -from ..base import _fit_context +from ..base import ( + BaseEstimator, + ClassNamePrefixFeaturesOutMixin, + TransformerMixin, + _fit_context, +) from ..utils import check_random_state, gen_batches, gen_even_slices -from ..utils.validation import check_non_negative -from ..utils.validation import check_is_fitted -from ..utils.parallel import delayed, Parallel from ..utils._param_validation import Interval, StrOptions - +from ..utils.parallel import Parallel, delayed +from ..utils.validation import check_is_fitted, check_non_negative from ._online_lda_fast import ( - mean_change as cy_mean_change, _dirichlet_expectation_1d as cy_dirichlet_expectation_1d, +) +from ._online_lda_fast import ( _dirichlet_expectation_2d, ) +from ._online_lda_fast import ( + mean_change as cy_mean_change, +) EPS = np.finfo(float).eps diff --git a/sklearn/decomposition/_nmf.py b/sklearn/decomposition/_nmf.py index d561583dec205..40db8edd0b2fd 100644 --- a/sklearn/decomposition/_nmf.py +++ b/sklearn/decomposition/_nmf.py @@ -6,34 +6,37 @@ # Tom Dupre la Tour # License: BSD 3 clause +import itertools +import time +import warnings from abc import ABC +from math import sqrt from numbers import Integral, Real + import numpy as np import scipy.sparse as sp -import time -import itertools -import warnings -from math import sqrt from scipy import linalg -from ._cdnmf_fast import _update_cdnmf_fast from .._config import config_context -from ..base import BaseEstimator, TransformerMixin, ClassNamePrefixFeaturesOutMixin -from ..base import _fit_context -from ..exceptions import ConvergenceWarning -from ..utils import check_random_state, check_array, gen_batches -from ..utils.extmath import randomized_svd, safe_sparse_dot, squared_norm -from ..utils.validation import ( - check_is_fitted, - check_non_negative, +from ..base import ( + BaseEstimator, + ClassNamePrefixFeaturesOutMixin, + TransformerMixin, + _fit_context, ) +from ..exceptions import ConvergenceWarning +from ..utils import check_array, check_random_state, gen_batches, metadata_routing from ..utils._param_validation import ( Interval, StrOptions, validate_params, ) -from ..utils import metadata_routing - +from ..utils.extmath import randomized_svd, safe_sparse_dot, squared_norm +from ..utils.validation import ( + check_is_fitted, + check_non_negative, +) +from ._cdnmf_fast import _update_cdnmf_fast EPSILON = np.finfo(np.float32).eps diff --git a/sklearn/decomposition/_pca.py b/sklearn/decomposition/_pca.py index 1d3c0678aca89..96931324d7cae 100644 --- a/sklearn/decomposition/_pca.py +++ b/sklearn/decomposition/_pca.py @@ -15,20 +15,18 @@ import numpy as np from scipy import linalg -from scipy.special import gammaln from scipy.sparse import issparse from scipy.sparse.linalg import svds +from scipy.special import gammaln -from ._base import _BasePCA from ..base import _fit_context from ..utils import check_random_state from ..utils._arpack import _init_arpack_v0 +from ..utils._param_validation import Interval, RealNotInt, StrOptions from ..utils.deprecation import deprecated -from ..utils.extmath import fast_logdet, randomized_svd, svd_flip -from ..utils.extmath import stable_cumsum +from ..utils.extmath import fast_logdet, randomized_svd, stable_cumsum, svd_flip from ..utils.validation import check_is_fitted -from ..utils._param_validation import Interval, StrOptions -from ..utils._param_validation import RealNotInt +from ._base import _BasePCA def _assess_dimension(spectrum, rank, n_samples): diff --git a/sklearn/decomposition/_sparse_pca.py b/sklearn/decomposition/_sparse_pca.py index 93e4a2164a87f..aa4dec2fb7ee9 100644 --- a/sklearn/decomposition/_sparse_pca.py +++ b/sklearn/decomposition/_sparse_pca.py @@ -6,14 +6,18 @@ import numpy as np +from ..base import ( + BaseEstimator, + ClassNamePrefixFeaturesOutMixin, + TransformerMixin, + _fit_context, +) +from ..linear_model import ridge_regression from ..utils import check_random_state -from ..utils.extmath import svd_flip from ..utils._param_validation import Hidden, Interval, StrOptions +from ..utils.extmath import svd_flip from ..utils.validation import check_array, check_is_fitted -from ..linear_model import ridge_regression -from ..base import BaseEstimator, TransformerMixin, ClassNamePrefixFeaturesOutMixin -from ..base import _fit_context -from ._dict_learning import dict_learning, MiniBatchDictionaryLearning +from ._dict_learning import MiniBatchDictionaryLearning, dict_learning class _BaseSparsePCA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator): diff --git a/sklearn/decomposition/_truncated_svd.py b/sklearn/decomposition/_truncated_svd.py index 67f5c73028f15..725683e8d46c6 100644 --- a/sklearn/decomposition/_truncated_svd.py +++ b/sklearn/decomposition/_truncated_svd.py @@ -7,18 +7,23 @@ # License: 3-clause BSD. from numbers import Integral, Real + import numpy as np import scipy.sparse as sp from scipy.sparse.linalg import svds -from ..base import BaseEstimator, TransformerMixin, ClassNamePrefixFeaturesOutMixin -from ..base import _fit_context +from ..base import ( + BaseEstimator, + ClassNamePrefixFeaturesOutMixin, + TransformerMixin, + _fit_context, +) from ..utils import check_array, check_random_state from ..utils._arpack import _init_arpack_v0 +from ..utils._param_validation import Interval, StrOptions from ..utils.extmath import randomized_svd, safe_sparse_dot, svd_flip from ..utils.sparsefuncs import mean_variance_axis from ..utils.validation import check_is_fitted -from ..utils._param_validation import Interval, StrOptions __all__ = ["TruncatedSVD"] diff --git a/sklearn/decomposition/tests/test_dict_learning.py b/sklearn/decomposition/tests/test_dict_learning.py index 6e6ddd20acb8c..0986bc6b3feed 100644 --- a/sklearn/decomposition/tests/test_dict_learning.py +++ b/sklearn/decomposition/tests/test_dict_learning.py @@ -1,38 +1,37 @@ -import pytest +import itertools import warnings +from functools import partial import numpy as np -from functools import partial -import itertools +import pytest import sklearn - from sklearn.base import clone - +from sklearn.decomposition import ( + DictionaryLearning, + MiniBatchDictionaryLearning, + SparseCoder, + dict_learning, + dict_learning_online, + sparse_encode, +) +from sklearn.decomposition._dict_learning import _update_dict from sklearn.exceptions import ConvergenceWarning - from sklearn.utils import check_array +from sklearn.utils._testing import ( + TempMemmap, + assert_allclose, + assert_array_almost_equal, + assert_array_equal, + ignore_warnings, +) +from sklearn.utils.estimator_checks import ( + check_transformer_data_not_an_array, + check_transformer_general, + check_transformers_unfitted, +) from sklearn.utils.parallel import Parallel -from sklearn.utils._testing import assert_allclose -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import ignore_warnings -from sklearn.utils._testing import TempMemmap - -from sklearn.decomposition import DictionaryLearning -from sklearn.decomposition import MiniBatchDictionaryLearning -from sklearn.decomposition import SparseCoder -from sklearn.decomposition import dict_learning -from sklearn.decomposition import dict_learning_online -from sklearn.decomposition import sparse_encode -from sklearn.utils.estimator_checks import check_transformer_data_not_an_array -from sklearn.utils.estimator_checks import check_transformer_general -from sklearn.utils.estimator_checks import check_transformers_unfitted - -from sklearn.decomposition._dict_learning import _update_dict - - rng_global = np.random.RandomState(0) n_samples, n_features = 10, 8 X = rng_global.randn(n_samples, n_features) @@ -397,8 +396,8 @@ def test_dict_learning_online_positivity(positive_code, positive_dict): def test_dict_learning_online_verbosity(): # test verbosity for better coverage n_components = 5 - from io import StringIO import sys + from io import StringIO old_stdout = sys.stdout try: diff --git a/sklearn/decomposition/tests/test_factor_analysis.py b/sklearn/decomposition/tests/test_factor_analysis.py index 4284327f3eeb4..2ff14f8d71722 100644 --- a/sklearn/decomposition/tests/test_factor_analysis.py +++ b/sklearn/decomposition/tests/test_factor_analysis.py @@ -7,12 +7,14 @@ import numpy as np import pytest -from sklearn.utils._testing import assert_almost_equal -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.exceptions import ConvergenceWarning from sklearn.decomposition import FactorAnalysis -from sklearn.utils._testing import ignore_warnings from sklearn.decomposition._factor_analysis import _ortho_rotation +from sklearn.exceptions import ConvergenceWarning +from sklearn.utils._testing import ( + assert_almost_equal, + assert_array_almost_equal, + ignore_warnings, +) # Ignore warnings from switching to more power iterations in randomized_svd diff --git a/sklearn/decomposition/tests/test_fastica.py b/sklearn/decomposition/tests/test_fastica.py index 14938b3787a98..6a376b01ecb19 100644 --- a/sklearn/decomposition/tests/test_fastica.py +++ b/sklearn/decomposition/tests/test_fastica.py @@ -2,18 +2,17 @@ Test the fastica algorithm. """ import itertools -import pytest -import warnings import os +import warnings import numpy as np +import pytest from scipy import stats -from sklearn.utils._testing import assert_allclose - -from sklearn.decomposition import FastICA, fastica, PCA +from sklearn.decomposition import PCA, FastICA, fastica from sklearn.decomposition._fastica import _gs_decorrelation from sklearn.exceptions import ConvergenceWarning +from sklearn.utils._testing import assert_allclose def center_and_norm(x, axis=-1): diff --git a/sklearn/decomposition/tests/test_incremental_pca.py b/sklearn/decomposition/tests/test_incremental_pca.py index d8402dad24c04..6ef500b42026b 100644 --- a/sklearn/decomposition/tests/test_incremental_pca.py +++ b/sklearn/decomposition/tests/test_incremental_pca.py @@ -1,17 +1,18 @@ """Tests for Incremental PCA.""" -import numpy as np -import pytest import warnings -from sklearn.utils._testing import assert_almost_equal -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_allclose_dense_sparse +import numpy as np +import pytest from numpy.testing import assert_array_equal +from scipy import sparse from sklearn import datasets from sklearn.decomposition import PCA, IncrementalPCA - -from scipy import sparse +from sklearn.utils._testing import ( + assert_allclose_dense_sparse, + assert_almost_equal, + assert_array_almost_equal, +) iris = datasets.load_iris() diff --git a/sklearn/decomposition/tests/test_kernel_pca.py b/sklearn/decomposition/tests/test_kernel_pca.py index 39aa32a3e9694..3c95454749b4a 100644 --- a/sklearn/decomposition/tests/test_kernel_pca.py +++ b/sklearn/decomposition/tests/test_kernel_pca.py @@ -1,23 +1,22 @@ -import numpy as np -import scipy.sparse as sp -import pytest import warnings -from sklearn.utils._testing import ( - assert_array_almost_equal, - assert_array_equal, - assert_allclose, -) +import numpy as np +import pytest +import scipy.sparse as sp +from sklearn.datasets import make_blobs, make_circles from sklearn.decomposition import PCA, KernelPCA -from sklearn.datasets import make_circles -from sklearn.datasets import make_blobs from sklearn.exceptions import NotFittedError from sklearn.linear_model import Perceptron +from sklearn.metrics.pairwise import rbf_kernel +from sklearn.model_selection import GridSearchCV from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import GridSearchCV -from sklearn.metrics.pairwise import rbf_kernel +from sklearn.utils._testing import ( + assert_allclose, + assert_array_almost_equal, + assert_array_equal, +) from sklearn.utils.validation import _check_psd_eigenvalues diff --git a/sklearn/decomposition/tests/test_nmf.py b/sklearn/decomposition/tests/test_nmf.py index 2b1ed4d91be5e..2cd027f90cdd6 100644 --- a/sklearn/decomposition/tests/test_nmf.py +++ b/sklearn/decomposition/tests/test_nmf.py @@ -1,27 +1,26 @@ import re import sys -from io import StringIO import warnings +from io import StringIO import numpy as np +import pytest import scipy.sparse as sp - from scipy import linalg -from sklearn.decomposition import NMF, MiniBatchNMF -from sklearn.decomposition import non_negative_factorization -from sklearn.decomposition import _nmf as nmf # For testing internals from scipy.sparse import csc_matrix -import pytest - -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_almost_equal -from sklearn.utils._testing import assert_allclose -from sklearn.utils._testing import ignore_warnings -from sklearn.utils.extmath import squared_norm from sklearn.base import clone +from sklearn.decomposition import NMF, MiniBatchNMF, non_negative_factorization +from sklearn.decomposition import _nmf as nmf # For testing internals from sklearn.exceptions import ConvergenceWarning +from sklearn.utils._testing import ( + assert_allclose, + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, + ignore_warnings, +) +from sklearn.utils.extmath import squared_norm @pytest.mark.parametrize( diff --git a/sklearn/decomposition/tests/test_online_lda.py b/sklearn/decomposition/tests/test_online_lda.py index 872bd55916fcb..50c812bcb9f14 100644 --- a/sklearn/decomposition/tests/test_online_lda.py +++ b/sklearn/decomposition/tests/test_online_lda.py @@ -1,26 +1,25 @@ import sys +from io import StringIO import numpy as np +import pytest +from numpy.testing import assert_array_equal from scipy.linalg import block_diag from scipy.sparse import csr_matrix from scipy.special import psi -from numpy.testing import assert_array_equal - -import pytest from sklearn.decomposition import LatentDirichletAllocation from sklearn.decomposition._online_lda_fast import ( _dirichlet_expectation_1d, _dirichlet_expectation_2d, ) - -from sklearn.utils._testing import assert_allclose -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_almost_equal -from sklearn.utils._testing import if_safe_multiprocessing_with_blas - from sklearn.exceptions import NotFittedError -from io import StringIO +from sklearn.utils._testing import ( + assert_allclose, + assert_almost_equal, + assert_array_almost_equal, + if_safe_multiprocessing_with_blas, +) def _build_sparse_mtx(): diff --git a/sklearn/decomposition/tests/test_pca.py b/sklearn/decomposition/tests/test_pca.py index 5bf893f92fd16..0176ebd0be9e7 100644 --- a/sklearn/decomposition/tests/test_pca.py +++ b/sklearn/decomposition/tests/test_pca.py @@ -1,17 +1,15 @@ +import warnings + import numpy as np +import pytest import scipy as sp from numpy.testing import assert_array_equal -import pytest -import warnings - -from sklearn.utils._testing import assert_allclose - from sklearn import datasets -from sklearn.decomposition import PCA from sklearn.datasets import load_iris -from sklearn.decomposition._pca import _assess_dimension -from sklearn.decomposition._pca import _infer_dimension +from sklearn.decomposition import PCA +from sklearn.decomposition._pca import _assess_dimension, _infer_dimension +from sklearn.utils._testing import assert_allclose iris = datasets.load_iris() PCA_SOLVERS = ["full", "arpack", "randomized", "auto"] diff --git a/sklearn/decomposition/tests/test_sparse_pca.py b/sklearn/decomposition/tests/test_sparse_pca.py index cf237014c6049..4abbbd515aeb9 100644 --- a/sklearn/decomposition/tests/test_sparse_pca.py +++ b/sklearn/decomposition/tests/test_sparse_pca.py @@ -2,17 +2,18 @@ # License: BSD 3 clause import sys -import pytest import numpy as np +import pytest from numpy.testing import assert_array_equal -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_allclose -from sklearn.utils._testing import if_safe_multiprocessing_with_blas - -from sklearn.decomposition import SparsePCA, MiniBatchSparsePCA, PCA +from sklearn.decomposition import PCA, MiniBatchSparsePCA, SparsePCA from sklearn.utils import check_random_state +from sklearn.utils._testing import ( + assert_allclose, + assert_array_almost_equal, + if_safe_multiprocessing_with_blas, +) def generate_toy_data(n_components, n_samples, image_size, random_state=None): diff --git a/sklearn/decomposition/tests/test_truncated_svd.py b/sklearn/decomposition/tests/test_truncated_svd.py index bd0bde6e08aa7..4edb7d4a11109 100644 --- a/sklearn/decomposition/tests/test_truncated_svd.py +++ b/sklearn/decomposition/tests/test_truncated_svd.py @@ -1,13 +1,12 @@ """Test truncated SVD transformer.""" import numpy as np -import scipy.sparse as sp - import pytest +import scipy.sparse as sp -from sklearn.decomposition import TruncatedSVD, PCA +from sklearn.decomposition import PCA, TruncatedSVD from sklearn.utils import check_random_state -from sklearn.utils._testing import assert_array_less, assert_allclose +from sklearn.utils._testing import assert_allclose, assert_array_less SVD_SOLVERS = ["arpack", "randomized"] diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py index 275f4ae4d3b30..29146ca857694 100644 --- a/sklearn/discriminant_analysis.py +++ b/sklearn/discriminant_analysis.py @@ -10,24 +10,27 @@ # License: BSD 3-Clause import warnings +from numbers import Integral, Real + import numpy as np import scipy.linalg from scipy import linalg -from numbers import Real, Integral -from .base import BaseEstimator, TransformerMixin, ClassifierMixin -from .base import ClassNamePrefixFeaturesOutMixin -from .base import _fit_context +from .base import ( + BaseEstimator, + ClassifierMixin, + ClassNamePrefixFeaturesOutMixin, + TransformerMixin, + _fit_context, +) +from .covariance import empirical_covariance, ledoit_wolf, shrunk_covariance from .linear_model._base import LinearClassifierMixin -from .covariance import ledoit_wolf, empirical_covariance, shrunk_covariance -from .utils.multiclass import unique_labels -from .utils.validation import check_is_fitted -from .utils._array_api import get_namespace, _expit, device, size -from .utils.multiclass import check_classification_targets -from .utils.extmath import softmax -from .utils._param_validation import StrOptions, Interval, HasMethods from .preprocessing import StandardScaler - +from .utils._array_api import _expit, device, get_namespace, size +from .utils._param_validation import HasMethods, Interval, StrOptions +from .utils.extmath import softmax +from .utils.multiclass import check_classification_targets, unique_labels +from .utils.validation import check_is_fitted __all__ = ["LinearDiscriminantAnalysis", "QuadraticDiscriminantAnalysis"] diff --git a/sklearn/dummy.py b/sklearn/dummy.py index 0d8519484d7a5..1db664826f5c9 100644 --- a/sklearn/dummy.py +++ b/sklearn/dummy.py @@ -9,18 +9,25 @@ import numpy as np import scipy.sparse as sp -from .base import BaseEstimator, ClassifierMixin, RegressorMixin -from .base import MultiOutputMixin -from .base import _fit_context +from .base import ( + BaseEstimator, + ClassifierMixin, + MultiOutputMixin, + RegressorMixin, + _fit_context, +) from .utils import check_random_state -from .utils._param_validation import StrOptions, Interval -from .utils.validation import _num_samples -from .utils.validation import check_array -from .utils.validation import check_consistent_length -from .utils.validation import check_is_fitted, _check_sample_weight +from .utils._param_validation import Interval, StrOptions +from .utils.multiclass import class_distribution from .utils.random import _random_choice_csc from .utils.stats import _weighted_percentile -from .utils.multiclass import class_distribution +from .utils.validation import ( + _check_sample_weight, + _num_samples, + check_array, + check_consistent_length, + check_is_fitted, +) class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator): diff --git a/sklearn/ensemble/__init__.py b/sklearn/ensemble/__init__.py index e892d36a0ce46..f4a3756bdaf1d 100644 --- a/sklearn/ensemble/__init__.py +++ b/sklearn/ensemble/__init__.py @@ -2,27 +2,24 @@ The :mod:`sklearn.ensemble` module includes ensemble-based methods for classification, regression and anomaly detection. """ +from ._bagging import BaggingClassifier, BaggingRegressor from ._base import BaseEnsemble -from ._forest import RandomForestClassifier -from ._forest import RandomForestRegressor -from ._forest import RandomTreesEmbedding -from ._forest import ExtraTreesClassifier -from ._forest import ExtraTreesRegressor -from ._bagging import BaggingClassifier -from ._bagging import BaggingRegressor -from ._iforest import IsolationForest -from ._weight_boosting import AdaBoostClassifier -from ._weight_boosting import AdaBoostRegressor -from ._gb import GradientBoostingClassifier -from ._gb import GradientBoostingRegressor -from ._voting import VotingClassifier -from ._voting import VotingRegressor -from ._stacking import StackingClassifier -from ._stacking import StackingRegressor +from ._forest import ( + ExtraTreesClassifier, + ExtraTreesRegressor, + RandomForestClassifier, + RandomForestRegressor, + RandomTreesEmbedding, +) +from ._gb import GradientBoostingClassifier, GradientBoostingRegressor from ._hist_gradient_boosting.gradient_boosting import ( - HistGradientBoostingRegressor, HistGradientBoostingClassifier, + HistGradientBoostingRegressor, ) +from ._iforest import IsolationForest +from ._stacking import StackingClassifier, StackingRegressor +from ._voting import VotingClassifier, VotingRegressor +from ._weight_boosting import AdaBoostClassifier, AdaBoostRegressor __all__ = [ "BaseEnsemble", diff --git a/sklearn/ensemble/_bagging.py b/sklearn/ensemble/_bagging.py index 0354413fdebfe..117bf470c509f 100644 --- a/sklearn/ensemble/_bagging.py +++ b/sklearn/ensemble/_bagging.py @@ -6,28 +6,25 @@ import itertools import numbers -import numpy as np from abc import ABCMeta, abstractmethod +from functools import partial from numbers import Integral from warnings import warn -from functools import partial -from ._base import BaseEnsemble, _partition_estimators -from ..base import ClassifierMixin, RegressorMixin -from ..base import _fit_context -from ..metrics import r2_score, accuracy_score +import numpy as np + +from ..base import ClassifierMixin, RegressorMixin, _fit_context +from ..metrics import accuracy_score, r2_score from ..tree import DecisionTreeClassifier, DecisionTreeRegressor -from ..utils import check_random_state, column_or_1d -from ..utils import indices_to_mask +from ..utils import check_random_state, column_or_1d, indices_to_mask +from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions +from ..utils._tags import _safe_tags from ..utils.metaestimators import available_if from ..utils.multiclass import check_classification_targets +from ..utils.parallel import Parallel, delayed from ..utils.random import sample_without_replacement -from ..utils._param_validation import Interval, HasMethods, StrOptions -from ..utils._param_validation import RealNotInt -from ..utils.validation import has_fit_parameter, check_is_fitted, _check_sample_weight -from ..utils._tags import _safe_tags -from ..utils.parallel import delayed, Parallel - +from ..utils.validation import _check_sample_weight, check_is_fitted, has_fit_parameter +from ._base import BaseEnsemble, _partition_estimators __all__ = ["BaggingClassifier", "BaggingRegressor"] diff --git a/sklearn/ensemble/_base.py b/sklearn/ensemble/_base.py index 3850fa724f11a..3107b4cf9a6c5 100644 --- a/sklearn/ensemble/_base.py +++ b/sklearn/ensemble/_base.py @@ -3,20 +3,15 @@ # Authors: Gilles Louppe # License: BSD 3 clause +import warnings from abc import ABCMeta, abstractmethod from typing import List -import warnings import numpy as np - from joblib import effective_n_jobs -from ..base import clone -from ..base import is_classifier, is_regressor -from ..base import BaseEstimator -from ..base import MetaEstimatorMixin -from ..utils import Bunch, _print_elapsed_time, deprecated -from ..utils import check_random_state +from ..base import BaseEstimator, MetaEstimatorMixin, clone, is_classifier, is_regressor +from ..utils import Bunch, _print_elapsed_time, check_random_state, deprecated from ..utils.metaestimators import _BaseComposition diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py index ce3a6f78b241d..df8ecc974dd34 100644 --- a/sklearn/ensemble/_forest.py +++ b/sklearn/ensemble/_forest.py @@ -40,19 +40,24 @@ class calls the ``fit`` method of each sub-estimator on random samples # License: BSD 3 clause +import threading +from abc import ABCMeta, abstractmethod from numbers import Integral, Real from warnings import catch_warnings, simplefilter, warn -import threading -from abc import ABCMeta, abstractmethod import numpy as np -from scipy.sparse import issparse from scipy.sparse import hstack as sparse_hstack +from scipy.sparse import issparse -from ..base import is_classifier -from ..base import ClassifierMixin, MultiOutputMixin, RegressorMixin, TransformerMixin -from ..base import _fit_context - +from ..base import ( + ClassifierMixin, + MultiOutputMixin, + RegressorMixin, + TransformerMixin, + _fit_context, + is_classifier, +) +from ..exceptions import DataConversionWarning from ..metrics import accuracy_score, r2_score from ..preprocessing import OneHotEncoder from ..tree import ( @@ -62,21 +67,18 @@ class calls the ``fit`` method of each sub-estimator on random samples ExtraTreeClassifier, ExtraTreeRegressor, ) -from ..tree._tree import DTYPE, DOUBLE +from ..tree._tree import DOUBLE, DTYPE from ..utils import check_random_state, compute_sample_weight -from ..exceptions import DataConversionWarning -from ._base import BaseEnsemble, _partition_estimators -from ..utils.parallel import delayed, Parallel +from ..utils._param_validation import Interval, RealNotInt, StrOptions from ..utils.multiclass import check_classification_targets, type_of_target +from ..utils.parallel import Parallel, delayed from ..utils.validation import ( - check_is_fitted, - _check_sample_weight, _check_feature_names_in, + _check_sample_weight, + _num_samples, + check_is_fitted, ) -from ..utils.validation import _num_samples -from ..utils._param_validation import Interval, StrOptions -from ..utils._param_validation import RealNotInt - +from ._base import BaseEnsemble, _partition_estimators __all__ = [ "RandomForestClassifier", diff --git a/sklearn/ensemble/_gb.py b/sklearn/ensemble/_gb.py index 1b924749f52bd..777e1a18d8396 100644 --- a/sklearn/ensemble/_gb.py +++ b/sklearn/ensemble/_gb.py @@ -20,37 +20,26 @@ # Arnaud Joly, Jacob Schreiber # License: BSD 3 clause -from abc import ABCMeta -from abc import abstractmethod -from numbers import Integral, Real import warnings - -from ._base import BaseEnsemble -from ..base import ClassifierMixin, RegressorMixin -from ..base import is_classifier -from ..base import _fit_context - -from ._gradient_boosting import predict_stages -from ._gradient_boosting import predict_stage -from ._gradient_boosting import _random_sample_mask +from abc import ABCMeta, abstractmethod +from numbers import Integral, Real +from time import time import numpy as np +from scipy.sparse import csc_matrix, csr_matrix, issparse -from scipy.sparse import csc_matrix -from scipy.sparse import csr_matrix -from scipy.sparse import issparse - -from time import time +from ..base import ClassifierMixin, RegressorMixin, _fit_context, is_classifier +from ..exceptions import NotFittedError from ..model_selection import train_test_split from ..tree import DecisionTreeRegressor -from ..tree._tree import DTYPE, DOUBLE -from . import _gb_losses - +from ..tree._tree import DOUBLE, DTYPE from ..utils import check_array, check_random_state, column_or_1d from ..utils._param_validation import HasMethods, Interval, StrOptions -from ..utils.validation import check_is_fitted, _check_sample_weight from ..utils.multiclass import check_classification_targets -from ..exceptions import NotFittedError +from ..utils.validation import _check_sample_weight, check_is_fitted +from . import _gb_losses +from ._base import BaseEnsemble +from ._gradient_boosting import _random_sample_mask, predict_stage, predict_stages class VerboseReporter: diff --git a/sklearn/ensemble/_gb_losses.py b/sklearn/ensemble/_gb_losses.py index db2116d9aa2e1..7fb7e4726c325 100644 --- a/sklearn/ensemble/_gb_losses.py +++ b/sklearn/ensemble/_gb_losses.py @@ -2,16 +2,14 @@ decision trees. """ -from abc import ABCMeta -from abc import abstractmethod +from abc import ABCMeta, abstractmethod import numpy as np from scipy.special import expit, logsumexp +from ..dummy import DummyClassifier, DummyRegressor from ..tree._tree import TREE_LEAF from ..utils.stats import _weighted_percentile -from ..dummy import DummyClassifier -from ..dummy import DummyRegressor class LossFunction(metaclass=ABCMeta): diff --git a/sklearn/ensemble/_hist_gradient_boosting/binning.py b/sklearn/ensemble/_hist_gradient_boosting/binning.py index 805a13b2d361b..8786e866d7be3 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/binning.py +++ b/sklearn/ensemble/_hist_gradient_boosting/binning.py @@ -9,14 +9,14 @@ import numpy as np -from ...utils import check_random_state, check_array from ...base import BaseEstimator, TransformerMixin -from ...utils.validation import check_is_fitted -from ...utils.fixes import percentile +from ...utils import check_array, check_random_state from ...utils._openmp_helpers import _openmp_effective_n_threads +from ...utils.fixes import percentile +from ...utils.validation import check_is_fitted from ._binning import _map_to_bins -from .common import X_DTYPE, X_BINNED_DTYPE, ALMOST_INF, X_BITSET_INNER_DTYPE from ._bitset import set_bitset_memoryview +from .common import ALMOST_INF, X_BINNED_DTYPE, X_BITSET_INNER_DTYPE, X_DTYPE def _find_binning_thresholds(col_data, max_bins): diff --git a/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py b/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py index e44b6428f8f4e..136e8c3b29efe 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py +++ b/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py @@ -1,13 +1,14 @@ """Fast Gradient Boosting decision trees for classification and regression.""" # Author: Nicolas Hug +import itertools from abc import ABC, abstractmethod from functools import partial -import itertools -from numbers import Real, Integral +from numbers import Integral, Real +from timeit import default_timer as time import numpy as np -from timeit import default_timer as time + from ..._loss.loss import ( _LOSSES, BaseLoss, @@ -17,29 +18,31 @@ HalfPoissonLoss, PinballLoss, ) -from ...base import BaseEstimator, RegressorMixin, ClassifierMixin, is_classifier -from ...base import _fit_context -from ...utils import check_random_state, resample, compute_sample_weight -from ...utils.validation import ( - check_is_fitted, - check_consistent_length, - _check_sample_weight, - _check_monotonic_cst, +from ...base import ( + BaseEstimator, + ClassifierMixin, + RegressorMixin, + _fit_context, + is_classifier, ) -from ...utils._param_validation import Interval, StrOptions -from ...utils._param_validation import RealNotInt -from ...utils._openmp_helpers import _openmp_effective_n_threads -from ...utils.multiclass import check_classification_targets from ...metrics import check_scoring from ...model_selection import train_test_split from ...preprocessing import LabelEncoder +from ...utils import check_random_state, compute_sample_weight, resample +from ...utils._openmp_helpers import _openmp_effective_n_threads +from ...utils._param_validation import Interval, RealNotInt, StrOptions +from ...utils.multiclass import check_classification_targets +from ...utils.validation import ( + _check_monotonic_cst, + _check_sample_weight, + check_consistent_length, + check_is_fitted, +) from ._gradient_boosting import _update_raw_predictions -from .common import Y_DTYPE, X_DTYPE, G_H_DTYPE - from .binning import _BinMapper +from .common import G_H_DTYPE, X_DTYPE, Y_DTYPE from .grower import TreeGrower - _LOSSES = _LOSSES.copy() _LOSSES.update( { diff --git a/sklearn/ensemble/_hist_gradient_boosting/grower.py b/sklearn/ensemble/_hist_gradient_boosting/grower.py index b8c0c17969e99..4ed6041ecaa30 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/grower.py +++ b/sklearn/ensemble/_hist_gradient_boosting/grower.py @@ -6,22 +6,25 @@ """ # Author: Nicolas Hug -from heapq import heappush, heappop -import numpy as np -from timeit import default_timer as time import numbers +from heapq import heappop, heappush +from timeit import default_timer as time -from .splitting import Splitter +import numpy as np + +from sklearn.utils._openmp_helpers import _openmp_effective_n_threads + +from ._bitset import set_raw_bitset_from_binned_bitset +from .common import ( + PREDICTOR_RECORD_DTYPE, + X_BITSET_INNER_DTYPE, + Y_DTYPE, + MonotonicConstraint, +) from .histogram import HistogramBuilder from .predictor import TreePredictor +from .splitting import Splitter from .utils import sum_parallel -from .common import PREDICTOR_RECORD_DTYPE -from .common import X_BITSET_INNER_DTYPE -from .common import Y_DTYPE -from .common import MonotonicConstraint -from ._bitset import set_raw_bitset_from_binned_bitset -from sklearn.utils._openmp_helpers import _openmp_effective_n_threads - EPS = np.finfo(Y_DTYPE).eps # to avoid zero division errors diff --git a/sklearn/ensemble/_hist_gradient_boosting/predictor.py b/sklearn/ensemble/_hist_gradient_boosting/predictor.py index 746fa34753121..600e55e43467f 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/predictor.py +++ b/sklearn/ensemble/_hist_gradient_boosting/predictor.py @@ -5,10 +5,12 @@ import numpy as np +from ._predictor import ( + _compute_partial_dependence, + _predict_from_binned_data, + _predict_from_raw_data, +) from .common import Y_DTYPE -from ._predictor import _predict_from_raw_data -from ._predictor import _predict_from_binned_data -from ._predictor import _compute_partial_dependence class TreePredictor: diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_binning.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_binning.py index 08bfebfcbf6c9..6f9fcd0057141 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_binning.py +++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_binning.py @@ -1,15 +1,17 @@ import numpy as np -from numpy.testing import assert_array_equal, assert_allclose import pytest +from numpy.testing import assert_allclose, assert_array_equal from sklearn.ensemble._hist_gradient_boosting.binning import ( _BinMapper, _find_binning_thresholds, _map_to_bins, ) -from sklearn.ensemble._hist_gradient_boosting.common import X_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import X_BINNED_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import ALMOST_INF +from sklearn.ensemble._hist_gradient_boosting.common import ( + ALMOST_INF, + X_BINNED_DTYPE, + X_DTYPE, +) from sklearn.utils._openmp_helpers import _openmp_effective_n_threads n_threads = _openmp_effective_n_threads() diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_bitset.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_bitset.py index e058781cefcef..c02d66b666f80 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_bitset.py +++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_bitset.py @@ -1,10 +1,10 @@ -import pytest import numpy as np +import pytest from numpy.testing import assert_allclose from sklearn.ensemble._hist_gradient_boosting._bitset import ( - set_bitset_memoryview, in_bitset_memoryview, + set_bitset_memoryview, set_raw_bitset_from_binned_bitset, ) from sklearn.ensemble._hist_gradient_boosting.common import X_DTYPE diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py index 6bd5b38d5a4ee..bbdcb38ef013a 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py +++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py @@ -1,13 +1,15 @@ -from sklearn.model_selection import train_test_split -from sklearn.metrics import accuracy_score -from sklearn.datasets import make_classification, make_regression import numpy as np import pytest -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.ensemble import HistGradientBoostingClassifier +from sklearn.datasets import make_classification, make_regression +from sklearn.ensemble import ( + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, +) from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator +from sklearn.metrics import accuracy_score +from sklearn.model_selection import train_test_split @pytest.mark.parametrize("seed", range(5)) diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py index 94d8960b6e813..4851c8e129203 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py +++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py @@ -1,36 +1,35 @@ +import re import warnings -import re import numpy as np import pytest from numpy.testing import assert_allclose, assert_array_equal + from sklearn._loss.loss import ( AbsoluteError, HalfBinomialLoss, HalfSquaredError, PinballLoss, ) -from sklearn.datasets import make_classification, make_regression -from sklearn.datasets import make_low_rank_matrix -from sklearn.preprocessing import KBinsDiscretizer, MinMaxScaler, OneHotEncoder -from sklearn.model_selection import train_test_split, cross_val_score -from sklearn.base import clone, BaseEstimator, TransformerMixin -from sklearn.base import is_regressor -from sklearn.pipeline import make_pipeline -from sklearn.metrics import mean_gamma_deviance, mean_poisson_deviance -from sklearn.dummy import DummyRegressor -from sklearn.exceptions import NotFittedError +from sklearn.base import BaseEstimator, TransformerMixin, clone, is_regressor from sklearn.compose import make_column_transformer - -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.ensemble import HistGradientBoostingClassifier -from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower +from sklearn.datasets import make_classification, make_low_rank_matrix, make_regression +from sklearn.dummy import DummyRegressor +from sklearn.ensemble import ( + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, +) from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper from sklearn.ensemble._hist_gradient_boosting.common import G_H_DTYPE +from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower +from sklearn.exceptions import NotFittedError +from sklearn.metrics import mean_gamma_deviance, mean_poisson_deviance +from sklearn.model_selection import cross_val_score, train_test_split +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import KBinsDiscretizer, MinMaxScaler, OneHotEncoder from sklearn.utils import shuffle from sklearn.utils._openmp_helpers import _openmp_effective_n_threads - n_threads = _openmp_effective_n_threads() X_classification, y_classification = make_classification(random_state=0) diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py index f3380fbf2af6d..a55cb871e3c72 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py +++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py @@ -1,17 +1,18 @@ import numpy as np import pytest +from numpy.testing import assert_allclose, assert_array_equal from pytest import approx -from numpy.testing import assert_array_equal -from numpy.testing import assert_allclose -from sklearn.preprocessing import OneHotEncoder -from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper -from sklearn.ensemble._hist_gradient_boosting.common import X_BINNED_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import X_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import Y_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import G_H_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import X_BITSET_INNER_DTYPE +from sklearn.ensemble._hist_gradient_boosting.common import ( + G_H_DTYPE, + X_BINNED_DTYPE, + X_BITSET_INNER_DTYPE, + X_DTYPE, + Y_DTYPE, +) +from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower +from sklearn.preprocessing import OneHotEncoder from sklearn.utils._openmp_helpers import _openmp_effective_n_threads n_threads = _openmp_effective_n_threads() diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_histogram.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_histogram.py index 1d5963d20739b..99f74b0f542ee 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_histogram.py +++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_histogram.py @@ -1,20 +1,20 @@ import numpy as np import pytest +from numpy.testing import assert_allclose, assert_array_equal -from numpy.testing import assert_allclose -from numpy.testing import assert_array_equal - +from sklearn.ensemble._hist_gradient_boosting.common import ( + G_H_DTYPE, + HISTOGRAM_DTYPE, + X_BINNED_DTYPE, +) from sklearn.ensemble._hist_gradient_boosting.histogram import ( - _build_histogram_naive, _build_histogram, + _build_histogram_naive, _build_histogram_no_hessian, - _build_histogram_root_no_hessian, _build_histogram_root, + _build_histogram_root_no_hessian, _subtract_histograms, ) -from sklearn.ensemble._hist_gradient_boosting.common import HISTOGRAM_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import G_H_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import X_BINNED_DTYPE @pytest.mark.parametrize("build_func", [_build_histogram_naive, _build_histogram]) diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py index f11bec3bd77db..7782b5b32eb68 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py +++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py @@ -1,18 +1,23 @@ import re + import numpy as np import pytest +from sklearn.ensemble import ( + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, +) +from sklearn.ensemble._hist_gradient_boosting.common import ( + G_H_DTYPE, + X_BINNED_DTYPE, + MonotonicConstraint, +) from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower -from sklearn.ensemble._hist_gradient_boosting.common import G_H_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import X_BINNED_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import MonotonicConstraint +from sklearn.ensemble._hist_gradient_boosting.histogram import HistogramBuilder from sklearn.ensemble._hist_gradient_boosting.splitting import ( Splitter, compute_node_value, ) -from sklearn.ensemble._hist_gradient_boosting.histogram import HistogramBuilder -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.utils._openmp_helpers import _openmp_effective_n_threads from sklearn.utils._testing import _convert_container diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_predictor.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_predictor.py index 856ab180459d2..3c3c9ae81bac2 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_predictor.py +++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_predictor.py @@ -1,25 +1,25 @@ import numpy as np -from numpy.testing import assert_allclose -from sklearn.datasets import make_regression -from sklearn.model_selection import train_test_split -from sklearn.metrics import r2_score import pytest +from numpy.testing import assert_allclose +from sklearn.datasets import make_regression +from sklearn.ensemble._hist_gradient_boosting._bitset import ( + set_bitset_memoryview, + set_raw_bitset_from_binned_bitset, +) from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper -from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower -from sklearn.ensemble._hist_gradient_boosting.predictor import TreePredictor from sklearn.ensemble._hist_gradient_boosting.common import ( + ALMOST_INF, G_H_DTYPE, PREDICTOR_RECORD_DTYPE, - ALMOST_INF, X_BINNED_DTYPE, X_BITSET_INNER_DTYPE, X_DTYPE, ) -from sklearn.ensemble._hist_gradient_boosting._bitset import ( - set_bitset_memoryview, - set_raw_bitset_from_binned_bitset, -) +from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower +from sklearn.ensemble._hist_gradient_boosting.predictor import TreePredictor +from sklearn.metrics import r2_score +from sklearn.model_selection import train_test_split from sklearn.utils._openmp_helpers import _openmp_effective_n_threads n_threads = _openmp_effective_n_threads() diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_splitting.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_splitting.py index 255d13bb08456..f862273beadf5 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_splitting.py +++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_splitting.py @@ -2,17 +2,19 @@ import pytest from numpy.testing import assert_array_equal -from sklearn.ensemble._hist_gradient_boosting.common import HISTOGRAM_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import G_H_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import X_BINNED_DTYPE -from sklearn.ensemble._hist_gradient_boosting.common import MonotonicConstraint +from sklearn.ensemble._hist_gradient_boosting.common import ( + G_H_DTYPE, + HISTOGRAM_DTYPE, + X_BINNED_DTYPE, + MonotonicConstraint, +) +from sklearn.ensemble._hist_gradient_boosting.histogram import HistogramBuilder from sklearn.ensemble._hist_gradient_boosting.splitting import ( Splitter, compute_node_value, ) -from sklearn.ensemble._hist_gradient_boosting.histogram import HistogramBuilder -from sklearn.utils._testing import skip_if_32bit from sklearn.utils._openmp_helpers import _openmp_effective_n_threads +from sklearn.utils._testing import skip_if_32bit n_threads = _openmp_effective_n_threads() diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py index f8d7533ec38bc..03a2720b36127 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py +++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py @@ -1,17 +1,15 @@ import numpy as np -from numpy.testing import assert_array_equal -from numpy.testing import assert_allclose - import pytest +from numpy.testing import assert_allclose, assert_array_equal from sklearn.base import clone from sklearn.datasets import make_classification, make_regression - -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.ensemble import HistGradientBoostingClassifier +from sklearn.ensemble import ( + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, +) from sklearn.metrics import check_scoring - X_classification, y_classification = make_classification(random_state=0) X_regression, y_regression = make_regression(random_state=0) diff --git a/sklearn/ensemble/_iforest.py b/sklearn/ensemble/_iforest.py index 048a1d69395e2..9371d2e4e6c5b 100644 --- a/sklearn/ensemble/_iforest.py +++ b/sklearn/ensemble/_iforest.py @@ -3,25 +3,23 @@ # License: BSD 3 clause import numbers +from numbers import Integral, Real +from warnings import warn + import numpy as np from scipy.sparse import issparse -from warnings import warn -from numbers import Integral, Real +from ..base import OutlierMixin, _fit_context from ..tree import ExtraTreeRegressor from ..tree._tree import DTYPE as tree_dtype from ..utils import ( - check_random_state, check_array, + check_random_state, gen_batches, get_chunk_n_rows, ) -from ..utils._param_validation import Interval, StrOptions -from ..utils._param_validation import RealNotInt -from ..utils.validation import check_is_fitted, _num_samples -from ..base import OutlierMixin -from ..base import _fit_context - +from ..utils._param_validation import Interval, RealNotInt, StrOptions +from ..utils.validation import _num_samples, check_is_fitted from ._bagging import BaseBagging __all__ = ["IsolationForest"] diff --git a/sklearn/ensemble/_stacking.py b/sklearn/ensemble/_stacking.py index 5b3486edfeb33..2129e4d9a0134 100644 --- a/sklearn/ensemble/_stacking.py +++ b/sklearn/ensemble/_stacking.py @@ -10,35 +10,32 @@ import numpy as np import scipy.sparse as sparse -from ..base import clone -from ..base import ClassifierMixin, RegressorMixin, TransformerMixin -from ..base import is_classifier, is_regressor -from ..base import _fit_context +from ..base import ( + ClassifierMixin, + RegressorMixin, + TransformerMixin, + _fit_context, + clone, + is_classifier, + is_regressor, +) from ..exceptions import NotFittedError -from ..utils._estimator_html_repr import _VisualBlock - -from ._base import _fit_single_estimator -from ._base import _BaseHeterogeneousEnsemble - -from ..linear_model import LogisticRegression -from ..linear_model import RidgeCV - -from ..model_selection import cross_val_predict -from ..model_selection import check_cv - +from ..linear_model import LogisticRegression, RidgeCV +from ..model_selection import check_cv, cross_val_predict from ..preprocessing import LabelEncoder - from ..utils import Bunch -from ..utils.multiclass import check_classification_targets, type_of_target -from ..utils.metaestimators import available_if -from ..utils.parallel import delayed, Parallel +from ..utils._estimator_html_repr import _VisualBlock from ..utils._param_validation import HasMethods, StrOptions +from ..utils.metaestimators import available_if +from ..utils.multiclass import check_classification_targets, type_of_target +from ..utils.parallel import Parallel, delayed from ..utils.validation import ( _check_feature_names_in, _check_response_method, check_is_fitted, column_or_1d, ) +from ._base import _BaseHeterogeneousEnsemble, _fit_single_estimator def _estimator_has(attr): diff --git a/sklearn/ensemble/_voting.py b/sklearn/ensemble/_voting.py index f8f4d2c4c197f..50670a5a52699 100644 --- a/sklearn/ensemble/_voting.py +++ b/sklearn/ensemble/_voting.py @@ -18,24 +18,23 @@ import numpy as np -from ..base import ClassifierMixin -from ..base import RegressorMixin -from ..base import TransformerMixin -from ..base import clone -from ..base import _fit_context -from ._base import _fit_single_estimator -from ._base import _BaseHeterogeneousEnsemble +from ..base import ( + ClassifierMixin, + RegressorMixin, + TransformerMixin, + _fit_context, + clone, +) +from ..exceptions import NotFittedError from ..preprocessing import LabelEncoder from ..utils import Bunch +from ..utils._estimator_html_repr import _VisualBlock +from ..utils._param_validation import StrOptions from ..utils.metaestimators import available_if -from ..utils.validation import check_is_fitted -from ..utils.validation import _check_feature_names_in from ..utils.multiclass import check_classification_targets -from ..utils.validation import column_or_1d -from ..utils._param_validation import StrOptions -from ..exceptions import NotFittedError -from ..utils._estimator_html_repr import _VisualBlock -from ..utils.parallel import delayed, Parallel +from ..utils.parallel import Parallel, delayed +from ..utils.validation import _check_feature_names_in, check_is_fitted, column_or_1d +from ._base import _BaseHeterogeneousEnsemble, _fit_single_estimator class _BaseVoting(TransformerMixin, _BaseHeterogeneousEnsemble): diff --git a/sklearn/ensemble/_weight_boosting.py b/sklearn/ensemble/_weight_boosting.py index 569609e6326e5..4beee0f09e6f2 100644 --- a/sklearn/ensemble/_weight_boosting.py +++ b/sklearn/ensemble/_weight_boosting.py @@ -23,28 +23,32 @@ # # License: BSD 3 clause +import warnings from abc import ABCMeta, abstractmethod - from numbers import Integral, Real -import numpy as np - -import warnings +import numpy as np from scipy.special import xlogy -from ._base import BaseEnsemble -from ..base import ClassifierMixin, RegressorMixin, is_classifier, is_regressor -from ..base import _fit_context -from ..tree import DecisionTreeClassifier, DecisionTreeRegressor -from ..utils import check_random_state, _safe_indexing -from ..utils.extmath import softmax -from ..utils.extmath import stable_cumsum +from ..base import ( + ClassifierMixin, + RegressorMixin, + _fit_context, + is_classifier, + is_regressor, +) from ..metrics import accuracy_score, r2_score -from ..utils.validation import check_is_fitted -from ..utils.validation import _check_sample_weight -from ..utils.validation import has_fit_parameter -from ..utils.validation import _num_samples +from ..tree import DecisionTreeClassifier, DecisionTreeRegressor +from ..utils import _safe_indexing, check_random_state from ..utils._param_validation import HasMethods, Interval, StrOptions +from ..utils.extmath import softmax, stable_cumsum +from ..utils.validation import ( + _check_sample_weight, + _num_samples, + check_is_fitted, + has_fit_parameter, +) +from ._base import BaseEnsemble __all__ = [ "AdaBoostClassifier", diff --git a/sklearn/ensemble/tests/test_bagging.py b/sklearn/ensemble/tests/test_bagging.py index f6311e8c459d4..2c1067ccfc248 100644 --- a/sklearn/ensemble/tests/test_bagging.py +++ b/sklearn/ensemble/tests/test_bagging.py @@ -4,35 +4,33 @@ # Author: Gilles Louppe # License: BSD 3 clause -from itertools import product +from itertools import cycle, product -import numpy as np import joblib +import numpy as np import pytest +from scipy.sparse import csc_matrix, csr_matrix from sklearn.base import BaseEstimator - -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_array_almost_equal +from sklearn.datasets import load_diabetes, load_iris, make_hastie_10_2 from sklearn.dummy import DummyClassifier, DummyRegressor -from sklearn.model_selection import GridSearchCV, ParameterGrid -from sklearn.ensemble import BaggingClassifier, BaggingRegressor -from sklearn.linear_model import Perceptron, LogisticRegression +from sklearn.ensemble import ( + BaggingClassifier, + BaggingRegressor, + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, +) +from sklearn.feature_selection import SelectKBest +from sklearn.linear_model import LogisticRegression, Perceptron +from sklearn.model_selection import GridSearchCV, ParameterGrid, train_test_split from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor -from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor -from sklearn.svm import SVC, SVR -from sklearn.random_projection import SparseRandomProjection from sklearn.pipeline import make_pipeline -from sklearn.feature_selection import SelectKBest -from sklearn.model_selection import train_test_split -from sklearn.ensemble import HistGradientBoostingClassifier -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.datasets import load_diabetes, load_iris, make_hastie_10_2 -from sklearn.utils import check_random_state from sklearn.preprocessing import FunctionTransformer, scale -from itertools import cycle - -from scipy.sparse import csc_matrix, csr_matrix +from sklearn.random_projection import SparseRandomProjection +from sklearn.svm import SVC, SVR +from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor +from sklearn.utils import check_random_state +from sklearn.utils._testing import assert_array_almost_equal, assert_array_equal rng = check_random_state(0) diff --git a/sklearn/ensemble/tests/test_base.py b/sklearn/ensemble/tests/test_base.py index fe4b1e33ae7b3..8687d91053a22 100644 --- a/sklearn/ensemble/tests/test_base.py +++ b/sklearn/ensemble/tests/test_base.py @@ -5,19 +5,19 @@ # Authors: Gilles Louppe # License: BSD 3 clause +from collections import OrderedDict + import numpy as np import pytest +from sklearn import ensemble from sklearn.datasets import load_iris +from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.ensemble import BaggingClassifier from sklearn.ensemble._base import _set_random_states -from sklearn.linear_model import Perceptron -from sklearn.linear_model import Ridge, LogisticRegression -from collections import OrderedDict -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -from sklearn.pipeline import Pipeline from sklearn.feature_selection import SelectFromModel -from sklearn import ensemble +from sklearn.linear_model import LogisticRegression, Perceptron, Ridge +from sklearn.pipeline import Pipeline def test_base(): diff --git a/sklearn/ensemble/tests/test_common.py b/sklearn/ensemble/tests/test_common.py index 5bafe08881ae9..7e14b34993d6f 100644 --- a/sklearn/ensemble/tests/test_common.py +++ b/sklearn/ensemble/tests/test_common.py @@ -1,21 +1,25 @@ import numpy as np import pytest -from sklearn.base import clone -from sklearn.base import ClassifierMixin -from sklearn.base import is_classifier - -from sklearn.datasets import make_classification -from sklearn.datasets import make_regression -from sklearn.datasets import load_iris, load_diabetes +from sklearn.base import ClassifierMixin, clone, is_classifier +from sklearn.datasets import ( + load_diabetes, + load_iris, + make_classification, + make_regression, +) +from sklearn.ensemble import ( + RandomForestClassifier, + RandomForestRegressor, + StackingClassifier, + StackingRegressor, + VotingClassifier, + VotingRegressor, +) from sklearn.impute import SimpleImputer -from sklearn.linear_model import LogisticRegression, LinearRegression -from sklearn.svm import LinearSVC, LinearSVR, SVC, SVR +from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.pipeline import make_pipeline -from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor - -from sklearn.ensemble import StackingClassifier, StackingRegressor -from sklearn.ensemble import VotingClassifier, VotingRegressor +from sklearn.svm import SVC, SVR, LinearSVC, LinearSVR X, y = load_iris(return_X_y=True) diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py index 9ee29f717af88..15d2999b5ef4d 100644 --- a/sklearn/ensemble/tests/test_forest.py +++ b/sklearn/ensemble/tests/test_forest.py @@ -8,58 +8,54 @@ # Arnaud Joly # License: BSD 3 clause -import pickle +import itertools import math +import pickle from collections import defaultdict from functools import partial -import itertools -from itertools import combinations -from itertools import product -from typing import Dict, Any - -import numpy as np -from scipy.sparse import csr_matrix -from scipy.sparse import csc_matrix -from scipy.sparse import coo_matrix -from scipy.special import comb +from itertools import combinations, product +from typing import Any, Dict +from unittest.mock import patch import joblib - +import numpy as np import pytest +from scipy.sparse import coo_matrix, csc_matrix, csr_matrix +from scipy.special import comb import sklearn -from sklearn.dummy import DummyRegressor -from sklearn.metrics import mean_poisson_deviance -from sklearn.utils._testing import assert_almost_equal -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import _convert_container -from sklearn.utils._testing import ignore_warnings -from sklearn.utils._testing import skip_if_no_parallel - -from sklearn.exceptions import NotFittedError - from sklearn import datasets -from sklearn.decomposition import TruncatedSVD from sklearn.datasets import make_classification -from sklearn.ensemble import ExtraTreesClassifier -from sklearn.ensemble import ExtraTreesRegressor -from sklearn.ensemble import RandomForestClassifier -from sklearn.ensemble import RandomForestRegressor -from sklearn.ensemble import RandomTreesEmbedding -from sklearn.metrics import explained_variance_score, f1_score -from sklearn.model_selection import train_test_split, cross_val_score -from sklearn.model_selection import GridSearchCV +from sklearn.decomposition import TruncatedSVD +from sklearn.dummy import DummyRegressor +from sklearn.ensemble import ( + ExtraTreesClassifier, + ExtraTreesRegressor, + RandomForestClassifier, + RandomForestRegressor, + RandomTreesEmbedding, +) +from sklearn.exceptions import NotFittedError +from sklearn.metrics import ( + explained_variance_score, + f1_score, + mean_poisson_deviance, + mean_squared_error, +) +from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split from sklearn.svm import LinearSVC +from sklearn.tree._classes import SPARSE_SPLITTERS +from sklearn.utils._testing import ( + _convert_container, + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, + ignore_warnings, + skip_if_no_parallel, +) from sklearn.utils.parallel import Parallel from sklearn.utils.validation import check_random_state -from sklearn.metrics import mean_squared_error - -from sklearn.tree._classes import SPARSE_SPLITTERS - -from unittest.mock import patch - # toy sample X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]] y = [-1, -1, -1, 1, 1, 1] diff --git a/sklearn/ensemble/tests/test_gradient_boosting.py b/sklearn/ensemble/tests/test_gradient_boosting.py index ad31b2ed732e9..f46bf9959fa29 100644 --- a/sklearn/ensemble/tests/test_gradient_boosting.py +++ b/sklearn/ensemble/tests/test_gradient_boosting.py @@ -3,38 +3,34 @@ """ import re import warnings + import numpy as np +import pytest from numpy.testing import assert_allclose - -from scipy.sparse import csr_matrix -from scipy.sparse import csc_matrix -from scipy.sparse import coo_matrix +from scipy.sparse import coo_matrix, csc_matrix, csr_matrix from scipy.special import expit -import pytest - from sklearn import datasets from sklearn.base import clone from sklearn.datasets import make_classification, make_regression -from sklearn.ensemble import GradientBoostingClassifier -from sklearn.ensemble import GradientBoostingRegressor +from sklearn.dummy import DummyClassifier, DummyRegressor +from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor from sklearn.ensemble._gradient_boosting import predict_stages -from sklearn.preprocessing import scale +from sklearn.exceptions import DataConversionWarning, NotFittedError +from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import scale +from sklearn.svm import NuSVR from sklearn.utils import check_random_state, tosequence from sklearn.utils._mocking import NoSampleWeightWrapper -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import skip_if_32bit from sklearn.utils._param_validation import InvalidParameterError -from sklearn.exceptions import DataConversionWarning -from sklearn.exceptions import NotFittedError -from sklearn.dummy import DummyClassifier, DummyRegressor -from sklearn.pipeline import make_pipeline -from sklearn.linear_model import LinearRegression -from sklearn.svm import NuSVR - +from sklearn.utils._testing import ( + assert_array_almost_equal, + assert_array_equal, + skip_if_32bit, +) GRADIENT_BOOSTING_ESTIMATORS = [GradientBoostingClassifier, GradientBoostingRegressor] @@ -674,9 +670,8 @@ def test_oob_multilcass_iris(): def test_verbose_output(): # Check verbose=1 does not cause error. - from io import StringIO - import sys + from io import StringIO old_stdout = sys.stdout sys.stdout = StringIO() @@ -706,8 +701,8 @@ def test_verbose_output(): def test_more_verbose_output(): # Check verbose=2 does not cause error. - from io import StringIO import sys + from io import StringIO old_stdout = sys.stdout sys.stdout = StringIO() diff --git a/sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py b/sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py index e710be9504be3..df92c68801da2 100644 --- a/sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py +++ b/sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py @@ -2,22 +2,25 @@ Testing for the gradient boosting loss functions and initial estimators. """ from itertools import product + import numpy as np -from numpy.testing import assert_allclose import pytest +from numpy.testing import assert_allclose from pytest import approx -from sklearn.utils import check_random_state +from sklearn.ensemble._gb_losses import ( + LOSS_FUNCTIONS, + BinomialDeviance, + ExponentialLoss, + HuberLossFunction, + LeastAbsoluteError, + LeastSquaresError, + MultinomialDeviance, + QuantileLossFunction, + RegressionLossFunction, +) from sklearn.metrics import mean_pinball_loss -from sklearn.ensemble._gb_losses import RegressionLossFunction -from sklearn.ensemble._gb_losses import LeastSquaresError -from sklearn.ensemble._gb_losses import LeastAbsoluteError -from sklearn.ensemble._gb_losses import HuberLossFunction -from sklearn.ensemble._gb_losses import QuantileLossFunction -from sklearn.ensemble._gb_losses import BinomialDeviance -from sklearn.ensemble._gb_losses import MultinomialDeviance -from sklearn.ensemble._gb_losses import ExponentialLoss -from sklearn.ensemble._gb_losses import LOSS_FUNCTIONS +from sklearn.utils import check_random_state def test_binomial_deviance(): diff --git a/sklearn/ensemble/tests/test_iforest.py b/sklearn/ensemble/tests/test_iforest.py index 7650dd5c14ce4..854ebdb701014 100644 --- a/sklearn/ensemble/tests/test_iforest.py +++ b/sklearn/ensemble/tests/test_iforest.py @@ -6,27 +6,25 @@ # Alexandre Gramfort # License: BSD 3 clause -import pytest import warnings +from unittest.mock import Mock, patch import numpy as np +import pytest +from scipy.sparse import csc_matrix, csr_matrix -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import ignore_warnings -from sklearn.utils._testing import assert_allclose - -from sklearn.model_selection import ParameterGrid +from sklearn.datasets import load_diabetes, load_iris, make_classification from sklearn.ensemble import IsolationForest from sklearn.ensemble._iforest import _average_path_length -from sklearn.model_selection import train_test_split -from sklearn.datasets import load_diabetes, load_iris, make_classification -from sklearn.utils import check_random_state from sklearn.metrics import roc_auc_score - -from scipy.sparse import csc_matrix, csr_matrix -from unittest.mock import Mock, patch - +from sklearn.model_selection import ParameterGrid, train_test_split +from sklearn.utils import check_random_state +from sklearn.utils._testing import ( + assert_allclose, + assert_array_almost_equal, + assert_array_equal, + ignore_warnings, +) # load iris & diabetes dataset iris = load_iris() diff --git a/sklearn/ensemble/tests/test_stacking.py b/sklearn/ensemble/tests/test_stacking.py index 2c04171fcd0f4..006b9cdb9e966 100644 --- a/sklearn/ensemble/tests/test_stacking.py +++ b/sklearn/ensemble/tests/test_stacking.py @@ -3,55 +3,47 @@ # Authors: Guillaume Lemaitre # License: BSD 3 clause -import pytest +from unittest.mock import Mock + import numpy as np -from numpy.testing import assert_array_equal +import pytest import scipy.sparse as sparse +from numpy.testing import assert_array_equal -from sklearn.base import BaseEstimator -from sklearn.base import ClassifierMixin -from sklearn.base import RegressorMixin -from sklearn.base import clone - -from sklearn.exceptions import ConvergenceWarning - -from sklearn.datasets import load_iris -from sklearn.datasets import load_diabetes -from sklearn.datasets import load_breast_cancer -from sklearn.datasets import make_regression -from sklearn.datasets import make_classification -from sklearn.datasets import make_multilabel_classification - -from sklearn.dummy import DummyClassifier -from sklearn.dummy import DummyRegressor -from sklearn.linear_model import LogisticRegression -from sklearn.linear_model import LinearRegression -from sklearn.linear_model import Ridge -from sklearn.linear_model import RidgeClassifier -from sklearn.svm import LinearSVC -from sklearn.svm import LinearSVR -from sklearn.svm import SVC -from sklearn.ensemble import RandomForestClassifier -from sklearn.ensemble import RandomForestRegressor +from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, clone +from sklearn.datasets import ( + load_breast_cancer, + load_diabetes, + load_iris, + make_classification, + make_multilabel_classification, + make_regression, +) +from sklearn.dummy import DummyClassifier, DummyRegressor +from sklearn.ensemble import ( + RandomForestClassifier, + RandomForestRegressor, + StackingClassifier, + StackingRegressor, +) +from sklearn.exceptions import ConvergenceWarning, NotFittedError +from sklearn.linear_model import ( + LinearRegression, + LogisticRegression, + Ridge, + RidgeClassifier, +) +from sklearn.model_selection import KFold, StratifiedKFold, train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.neural_network import MLPClassifier from sklearn.preprocessing import scale - -from sklearn.ensemble import StackingClassifier -from sklearn.ensemble import StackingRegressor - -from sklearn.model_selection import train_test_split -from sklearn.model_selection import StratifiedKFold -from sklearn.model_selection import KFold - +from sklearn.svm import SVC, LinearSVC, LinearSVR from sklearn.utils._mocking import CheckingClassifier -from sklearn.utils._testing import assert_allclose -from sklearn.utils._testing import assert_allclose_dense_sparse -from sklearn.utils._testing import ignore_warnings - -from sklearn.exceptions import NotFittedError - -from unittest.mock import Mock +from sklearn.utils._testing import ( + assert_allclose, + assert_allclose_dense_sparse, + ignore_warnings, +) diabetes = load_diabetes() X_diabetes, y_diabetes = diabetes.data, diabetes.target diff --git a/sklearn/ensemble/tests/test_voting.py b/sklearn/ensemble/tests/test_voting.py index 56db8b3c7fbf5..52734fc031fde 100644 --- a/sklearn/ensemble/tests/test_voting.py +++ b/sklearn/ensemble/tests/test_voting.py @@ -1,30 +1,34 @@ """Testing for the VotingClassifier and VotingRegressor""" -import pytest import re + import numpy as np +import pytest -from sklearn.utils._testing import assert_almost_equal, assert_array_equal -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.exceptions import NotFittedError -from sklearn.linear_model import LinearRegression -from sklearn.linear_model import LogisticRegression -from sklearn.naive_bayes import GaussianNB -from sklearn.ensemble import RandomForestClassifier -from sklearn.ensemble import RandomForestRegressor -from sklearn.ensemble import VotingClassifier, VotingRegressor -from sklearn.tree import DecisionTreeClassifier -from sklearn.tree import DecisionTreeRegressor -from sklearn.model_selection import GridSearchCV from sklearn import datasets -from sklearn.model_selection import cross_val_score, train_test_split +from sklearn.base import BaseEstimator, ClassifierMixin, clone from sklearn.datasets import make_multilabel_classification -from sklearn.svm import SVC +from sklearn.dummy import DummyRegressor +from sklearn.ensemble import ( + RandomForestClassifier, + RandomForestRegressor, + VotingClassifier, + VotingRegressor, +) +from sklearn.exceptions import NotFittedError +from sklearn.linear_model import LinearRegression, LogisticRegression +from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split from sklearn.multiclass import OneVsRestClassifier +from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier -from sklearn.base import BaseEstimator, ClassifierMixin, clone -from sklearn.dummy import DummyRegressor from sklearn.preprocessing import StandardScaler +from sklearn.svm import SVC +from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor +from sklearn.utils._testing import ( + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, +) # Load datasets iris = datasets.load_iris() diff --git a/sklearn/ensemble/tests/test_weight_boosting.py b/sklearn/ensemble/tests/test_weight_boosting.py index a5b0f7a49ce47..a8e0f06340dc4 100755 --- a/sklearn/ensemble/tests/test_weight_boosting.py +++ b/sklearn/ensemble/tests/test_weight_boosting.py @@ -1,33 +1,27 @@ """Testing for the boost module (sklearn.ensemble.boost).""" -import numpy as np -import pytest import re -from scipy.sparse import csc_matrix -from scipy.sparse import csr_matrix -from scipy.sparse import coo_matrix -from scipy.sparse import dok_matrix -from scipy.sparse import lil_matrix - -from sklearn.utils._testing import assert_array_equal, assert_array_less -from sklearn.utils._testing import assert_array_almost_equal +import numpy as np +import pytest +from scipy.sparse import coo_matrix, csc_matrix, csr_matrix, dok_matrix, lil_matrix -from sklearn.base import BaseEstimator -from sklearn.base import clone +from sklearn import datasets +from sklearn.base import BaseEstimator, clone from sklearn.dummy import DummyClassifier, DummyRegressor -from sklearn.linear_model import LinearRegression -from sklearn.model_selection import train_test_split -from sklearn.model_selection import GridSearchCV -from sklearn.ensemble import AdaBoostClassifier -from sklearn.ensemble import AdaBoostRegressor +from sklearn.ensemble import AdaBoostClassifier, AdaBoostRegressor from sklearn.ensemble._weight_boosting import _samme_proba +from sklearn.linear_model import LinearRegression +from sklearn.model_selection import GridSearchCV, train_test_split from sklearn.svm import SVC, SVR from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor from sklearn.utils import shuffle from sklearn.utils._mocking import NoSampleWeightWrapper -from sklearn import datasets - +from sklearn.utils._testing import ( + assert_array_almost_equal, + assert_array_equal, + assert_array_less, +) # Common random state rng = np.random.RandomState(0) diff --git a/sklearn/experimental/enable_halving_search_cv.py b/sklearn/experimental/enable_halving_search_cv.py index f6937b0d14c01..dd399ef35b6f7 100644 --- a/sklearn/experimental/enable_halving_search_cv.py +++ b/sklearn/experimental/enable_halving_search_cv.py @@ -19,13 +19,12 @@ flake8 to ignore the import, which appears as unused. """ +from .. import model_selection from ..model_selection._search_successive_halving import ( - HalvingRandomSearchCV, HalvingGridSearchCV, + HalvingRandomSearchCV, ) -from .. import model_selection - # use settattr to avoid mypy errors when monkeypatching setattr(model_selection, "HalvingRandomSearchCV", HalvingRandomSearchCV) setattr(model_selection, "HalvingGridSearchCV", HalvingGridSearchCV) diff --git a/sklearn/experimental/enable_hist_gradient_boosting.py b/sklearn/experimental/enable_hist_gradient_boosting.py index f0416ac013e96..d287400c7999f 100644 --- a/sklearn/experimental/enable_hist_gradient_boosting.py +++ b/sklearn/experimental/enable_hist_gradient_boosting.py @@ -12,7 +12,6 @@ import warnings - warnings.warn( "Since version 1.0, " "it is not needed to import enable_hist_gradient_boosting anymore. " diff --git a/sklearn/experimental/enable_iterative_imputer.py b/sklearn/experimental/enable_iterative_imputer.py index 9ef9f6a0dbdf0..0b906961ca184 100644 --- a/sklearn/experimental/enable_iterative_imputer.py +++ b/sklearn/experimental/enable_iterative_imputer.py @@ -12,8 +12,8 @@ >>> from sklearn.impute import IterativeImputer """ -from ..impute._iterative import IterativeImputer from .. import impute +from ..impute._iterative import IterativeImputer # use settattr to avoid mypy errors when monkeypatching setattr(impute, "IterativeImputer", IterativeImputer) diff --git a/sklearn/feature_extraction/__init__.py b/sklearn/feature_extraction/__init__.py index a9c1496181b3b..f4db85303f4b6 100644 --- a/sklearn/feature_extraction/__init__.py +++ b/sklearn/feature_extraction/__init__.py @@ -4,10 +4,10 @@ images. """ +from . import text from ._dict_vectorizer import DictVectorizer from ._hash import FeatureHasher -from .image import img_to_graph, grid_to_graph -from . import text +from .image import grid_to_graph, img_to_graph __all__ = [ "DictVectorizer", diff --git a/sklearn/feature_extraction/_dict_vectorizer.py b/sklearn/feature_extraction/_dict_vectorizer.py index 60e2cb3b7ad84..e32de4be42462 100644 --- a/sklearn/feature_extraction/_dict_vectorizer.py +++ b/sklearn/feature_extraction/_dict_vectorizer.py @@ -3,15 +3,14 @@ # License: BSD 3 clause from array import array -from collections.abc import Mapping, Iterable -from operator import itemgetter +from collections.abc import Iterable, Mapping from numbers import Number +from operator import itemgetter import numpy as np import scipy.sparse as sp -from ..base import BaseEstimator, TransformerMixin -from ..base import _fit_context +from ..base import BaseEstimator, TransformerMixin, _fit_context from ..utils import check_array from ..utils.validation import check_is_fitted diff --git a/sklearn/feature_extraction/_hash.py b/sklearn/feature_extraction/_hash.py index e1b5e5f2561fe..e0941ed1dac97 100644 --- a/sklearn/feature_extraction/_hash.py +++ b/sklearn/feature_extraction/_hash.py @@ -1,16 +1,15 @@ # Author: Lars Buitinck # License: BSD 3 clause -from numbers import Integral from itertools import chain +from numbers import Integral import numpy as np import scipy.sparse as sp -from ..base import BaseEstimator, TransformerMixin -from ..base import _fit_context -from ._hashing_fast import transform as _hashing_transform +from ..base import BaseEstimator, TransformerMixin, _fit_context from ..utils._param_validation import Interval, StrOptions +from ._hashing_fast import transform as _hashing_transform def _iteritems(d): diff --git a/sklearn/feature_extraction/image.py b/sklearn/feature_extraction/image.py index beea3e23e0adc..da5d26b76e8ad 100644 --- a/sklearn/feature_extraction/image.py +++ b/sklearn/feature_extraction/image.py @@ -11,15 +11,14 @@ from itertools import product from numbers import Integral, Number, Real + import numpy as np -from scipy import sparse from numpy.lib.stride_tricks import as_strided +from scipy import sparse -from ..base import BaseEstimator, TransformerMixin -from ..base import _fit_context +from ..base import BaseEstimator, TransformerMixin, _fit_context from ..utils import check_array, check_random_state -from ..utils._param_validation import Hidden, Interval, validate_params -from ..utils._param_validation import RealNotInt +from ..utils._param_validation import Hidden, Interval, RealNotInt, validate_params __all__ = [ "PatchExtractor", diff --git a/sklearn/feature_extraction/tests/test_dict_vectorizer.py b/sklearn/feature_extraction/tests/test_dict_vectorizer.py index c8b9aaa8b5c8a..7e3c7f259ea03 100644 --- a/sklearn/feature_extraction/tests/test_dict_vectorizer.py +++ b/sklearn/feature_extraction/tests/test_dict_vectorizer.py @@ -3,12 +3,11 @@ # License: BSD 3 clause from random import Random -import numpy as np -import scipy.sparse as sp -from numpy.testing import assert_array_equal -from numpy.testing import assert_allclose +import numpy as np import pytest +import scipy.sparse as sp +from numpy.testing import assert_allclose, assert_array_equal from sklearn.feature_extraction import DictVectorizer from sklearn.feature_selection import SelectKBest, chi2 diff --git a/sklearn/feature_extraction/tests/test_feature_hasher.py b/sklearn/feature_extraction/tests/test_feature_hasher.py index b074620f8c029..945a7cb3ca8f9 100644 --- a/sklearn/feature_extraction/tests/test_feature_hasher.py +++ b/sklearn/feature_extraction/tests/test_feature_hasher.py @@ -1,6 +1,6 @@ import numpy as np -from numpy.testing import assert_array_equal import pytest +from numpy.testing import assert_array_equal from sklearn.feature_extraction import FeatureHasher from sklearn.feature_extraction._hashing_fast import transform as _hashing_transform diff --git a/sklearn/feature_extraction/tests/test_image.py b/sklearn/feature_extraction/tests/test_image.py index 5a89062e7de19..375652c848db6 100644 --- a/sklearn/feature_extraction/tests/test_image.py +++ b/sklearn/feature_extraction/tests/test_image.py @@ -3,17 +3,17 @@ # License: BSD 3 clause import numpy as np +import pytest from scipy import ndimage from scipy.sparse.csgraph import connected_components -import pytest from sklearn.feature_extraction.image import ( - img_to_graph, - grid_to_graph, - extract_patches_2d, - reconstruct_from_patches_2d, PatchExtractor, _extract_patches, + extract_patches_2d, + grid_to_graph, + img_to_graph, + reconstruct_from_patches_2d, ) diff --git a/sklearn/feature_extraction/tests/test_text.py b/sklearn/feature_extraction/tests/test_text.py index 80a42aaea5af0..fc35053b40251 100644 --- a/sklearn/feature_extraction/tests/test_text.py +++ b/sklearn/feature_extraction/tests/test_text.py @@ -1,43 +1,37 @@ -from collections.abc import Mapping +import pickle import re +import warnings +from collections import defaultdict +from collections.abc import Mapping +from functools import partial +from io import StringIO +import numpy as np import pytest -import warnings +from numpy.testing import assert_array_almost_equal, assert_array_equal from scipy import sparse -from sklearn.feature_extraction.text import strip_tags -from sklearn.feature_extraction.text import strip_accents_unicode -from sklearn.feature_extraction.text import strip_accents_ascii - -from sklearn.feature_extraction.text import HashingVectorizer -from sklearn.feature_extraction.text import CountVectorizer -from sklearn.feature_extraction.text import TfidfTransformer -from sklearn.feature_extraction.text import TfidfVectorizer - -from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS - -from sklearn.model_selection import train_test_split -from sklearn.model_selection import cross_val_score -from sklearn.model_selection import GridSearchCV +from sklearn.base import clone +from sklearn.feature_extraction.text import ( + ENGLISH_STOP_WORDS, + CountVectorizer, + HashingVectorizer, + TfidfTransformer, + TfidfVectorizer, + strip_accents_ascii, + strip_accents_unicode, + strip_tags, +) +from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split from sklearn.pipeline import Pipeline from sklearn.svm import LinearSVC - -from sklearn.base import clone - -import numpy as np -from numpy.testing import assert_array_almost_equal -from numpy.testing import assert_array_equal from sklearn.utils import IS_PYPY from sklearn.utils._testing import ( + assert_allclose_dense_sparse, assert_almost_equal, fails_if_pypy, - assert_allclose_dense_sparse, skip_if_32bit, ) -from collections import defaultdict -from functools import partial -import pickle -from io import StringIO JUNK_FOOD_DOCS = ( "the pizza pizza beer copyright", diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py index 3201e3a0d51bb..4b4b4396d1863 100644 --- a/sklearn/feature_extraction/text.py +++ b/sklearn/feature_extraction/text.py @@ -12,29 +12,26 @@ """ import array +import re +import unicodedata +import warnings from collections import defaultdict from collections.abc import Mapping from functools import partial from numbers import Integral from operator import itemgetter -import re -import unicodedata -import warnings import numpy as np import scipy.sparse as sp -from ..base import BaseEstimator, TransformerMixin, OneToOneFeatureMixin -from ..base import _fit_context +from ..base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin, _fit_context +from ..exceptions import NotFittedError from ..preprocessing import normalize +from ..utils import _IS_32BIT +from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions +from ..utils.validation import FLOAT_DTYPES, check_array, check_is_fitted from ._hash import FeatureHasher from ._stop_words import ENGLISH_STOP_WORDS -from ..utils.validation import check_is_fitted, check_array, FLOAT_DTYPES -from ..utils import _IS_32BIT -from ..exceptions import NotFittedError -from ..utils._param_validation import StrOptions, Interval, HasMethods -from ..utils._param_validation import RealNotInt - __all__ = [ "HashingVectorizer", diff --git a/sklearn/feature_selection/__init__.py b/sklearn/feature_selection/__init__.py index ce5fbc10ee459..4fbc631155078 100644 --- a/sklearn/feature_selection/__init__.py +++ b/sklearn/feature_selection/__init__.py @@ -4,31 +4,25 @@ recursive feature elimination algorithm. """ -from ._univariate_selection import chi2 -from ._univariate_selection import f_classif -from ._univariate_selection import f_oneway -from ._univariate_selection import f_regression -from ._univariate_selection import r_regression -from ._univariate_selection import SelectPercentile -from ._univariate_selection import SelectKBest -from ._univariate_selection import SelectFpr -from ._univariate_selection import SelectFdr -from ._univariate_selection import SelectFwe -from ._univariate_selection import GenericUnivariateSelect - -from ._variance_threshold import VarianceThreshold - -from ._rfe import RFE -from ._rfe import RFECV - +from ._base import SelectorMixin from ._from_model import SelectFromModel - +from ._mutual_info import mutual_info_classif, mutual_info_regression +from ._rfe import RFE, RFECV from ._sequential import SequentialFeatureSelector - -from ._mutual_info import mutual_info_regression, mutual_info_classif - -from ._base import SelectorMixin - +from ._univariate_selection import ( + GenericUnivariateSelect, + SelectFdr, + SelectFpr, + SelectFwe, + SelectKBest, + SelectPercentile, + chi2, + f_classif, + f_oneway, + f_regression, + r_regression, +) +from ._variance_threshold import VarianceThreshold __all__ = [ "GenericUnivariateSelect", diff --git a/sklearn/feature_selection/_base.py b/sklearn/feature_selection/_base.py index 100af272038ad..9ede37c98c75b 100644 --- a/sklearn/feature_selection/_base.py +++ b/sklearn/feature_selection/_base.py @@ -8,16 +8,16 @@ from operator import attrgetter import numpy as np -from scipy.sparse import issparse, csc_matrix +from scipy.sparse import csc_matrix, issparse from ..base import TransformerMixin from ..utils import ( + _safe_indexing, check_array, safe_sqr, ) -from ..utils._tags import _safe_tags -from ..utils import _safe_indexing from ..utils._set_output import _get_output_config +from ..utils._tags import _safe_tags from ..utils.validation import _check_feature_names_in, check_is_fitted diff --git a/sklearn/feature_selection/_from_model.py b/sklearn/feature_selection/_from_model.py index 47f98d89e8abe..d3a287007bd49 100644 --- a/sklearn/feature_selection/_from_model.py +++ b/sklearn/feature_selection/_from_model.py @@ -2,20 +2,17 @@ # License: BSD 3 clause from copy import deepcopy - -import numpy as np from numbers import Integral, Real -from ._base import SelectorMixin -from ._base import _get_feature_importances -from ..base import BaseEstimator, clone, MetaEstimatorMixin -from ..base import _fit_context -from ..utils._tags import _safe_tags -from ..utils.validation import check_is_fitted, check_scalar, _num_features -from ..utils._param_validation import HasMethods, Interval, Options +import numpy as np +from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone from ..exceptions import NotFittedError +from ..utils._param_validation import HasMethods, Interval, Options +from ..utils._tags import _safe_tags from ..utils.metaestimators import available_if +from ..utils.validation import _num_features, check_is_fitted, check_scalar +from ._base import SelectorMixin, _get_feature_importances def _calculate_threshold(estimator, importances, threshold): diff --git a/sklearn/feature_selection/_mutual_info.py b/sklearn/feature_selection/_mutual_info.py index 9cacfc3890784..b3de388c0811a 100644 --- a/sklearn/feature_selection/_mutual_info.py +++ b/sklearn/feature_selection/_mutual_info.py @@ -1,18 +1,19 @@ # Author: Nikolay Mayorov # License: 3-clause BSD -import numpy as np from numbers import Integral + +import numpy as np from scipy.sparse import issparse from scipy.special import digamma from ..metrics.cluster import mutual_info_score -from ..neighbors import NearestNeighbors, KDTree +from ..neighbors import KDTree, NearestNeighbors from ..preprocessing import scale from ..utils import check_random_state -from ..utils.validation import check_array, check_X_y -from ..utils.multiclass import check_classification_targets from ..utils._param_validation import Interval, StrOptions, validate_params +from ..utils.multiclass import check_classification_targets +from ..utils.validation import check_array, check_X_y def _compute_mi_cc(x, y, n_neighbors): diff --git a/sklearn/feature_selection/_rfe.py b/sklearn/feature_selection/_rfe.py index 932d66449ae22..11cf083992653 100644 --- a/sklearn/feature_selection/_rfe.py +++ b/sklearn/feature_selection/_rfe.py @@ -6,28 +6,21 @@ """Recursive feature elimination for feature ranking""" -import numpy as np from numbers import Integral -from joblib import effective_n_jobs +import numpy as np +from joblib import effective_n_jobs -from ..utils.metaestimators import available_if -from ..utils.metaestimators import _safe_split -from ..utils._param_validation import HasMethods, Interval -from ..utils._param_validation import RealNotInt -from ..utils._tags import _safe_tags -from ..utils.validation import check_is_fitted -from ..utils.parallel import delayed, Parallel -from ..base import BaseEstimator -from ..base import MetaEstimatorMixin -from ..base import clone -from ..base import is_classifier -from ..base import _fit_context +from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier +from ..metrics import check_scoring from ..model_selection import check_cv from ..model_selection._validation import _score -from ..metrics import check_scoring -from ._base import SelectorMixin -from ._base import _get_feature_importances +from ..utils._param_validation import HasMethods, Interval, RealNotInt +from ..utils._tags import _safe_tags +from ..utils.metaestimators import _safe_split, available_if +from ..utils.parallel import Parallel, delayed +from ..utils.validation import check_is_fitted +from ._base import SelectorMixin, _get_feature_importances def _rfe_single_fit(rfe, estimator, X, y, train, test, scorer): diff --git a/sklearn/feature_selection/_sequential.py b/sklearn/feature_selection/_sequential.py index 0fbe91273053b..78a1c86df49de 100644 --- a/sklearn/feature_selection/_sequential.py +++ b/sklearn/feature_selection/_sequential.py @@ -5,15 +5,13 @@ import numpy as np -from ._base import SelectorMixin -from ..base import BaseEstimator, MetaEstimatorMixin, clone, is_classifier -from ..base import _fit_context -from ..utils._param_validation import HasMethods, Interval, StrOptions -from ..utils._param_validation import RealNotInt +from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier +from ..metrics import get_scorer_names +from ..model_selection import check_cv, cross_val_score +from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions from ..utils._tags import _safe_tags from ..utils.validation import check_is_fitted -from ..model_selection import cross_val_score, check_cv -from ..metrics import get_scorer_names +from ._base import SelectorMixin class SequentialFeatureSelector(SelectorMixin, MetaEstimatorMixin, BaseEstimator): diff --git a/sklearn/feature_selection/_univariate_selection.py b/sklearn/feature_selection/_univariate_selection.py index f4355c39f88cd..fc1fcbc01a151 100644 --- a/sklearn/feature_selection/_univariate_selection.py +++ b/sklearn/feature_selection/_univariate_selection.py @@ -5,20 +5,19 @@ # License: BSD 3 clause -import numpy as np import warnings - from numbers import Integral, Real + +import numpy as np from scipy import special, stats from scipy.sparse import issparse -from ..base import BaseEstimator -from ..base import _fit_context +from ..base import BaseEstimator, _fit_context from ..preprocessing import LabelBinarizer -from ..utils import as_float_array, check_array, check_X_y, safe_sqr, safe_mask -from ..utils.extmath import safe_sparse_dot, row_norms -from ..utils.validation import check_is_fitted +from ..utils import as_float_array, check_array, check_X_y, safe_mask, safe_sqr from ..utils._param_validation import Interval, StrOptions, validate_params +from ..utils.extmath import row_norms, safe_sparse_dot +from ..utils.validation import check_is_fitted from ._base import SelectorMixin diff --git a/sklearn/feature_selection/_variance_threshold.py b/sklearn/feature_selection/_variance_threshold.py index 073a22c6ad92b..f97c75db1e34b 100644 --- a/sklearn/feature_selection/_variance_threshold.py +++ b/sklearn/feature_selection/_variance_threshold.py @@ -3,12 +3,12 @@ from numbers import Real import numpy as np -from ..base import BaseEstimator -from ..base import _fit_context -from ._base import SelectorMixin + +from ..base import BaseEstimator, _fit_context +from ..utils._param_validation import Interval from ..utils.sparsefuncs import mean_variance_axis, min_max_axis from ..utils.validation import check_is_fitted -from ..utils._param_validation import Interval +from ._base import SelectorMixin class VarianceThreshold(SelectorMixin, BaseEstimator): diff --git a/sklearn/feature_selection/tests/test_base.py b/sklearn/feature_selection/tests/test_base.py index 9869a1c03e677..bf883797ddabd 100644 --- a/sklearn/feature_selection/tests/test_base.py +++ b/sklearn/feature_selection/tests/test_base.py @@ -1,8 +1,7 @@ import numpy as np import pytest -from scipy import sparse as sp - from numpy.testing import assert_array_equal +from scipy import sparse as sp from sklearn.base import BaseEstimator from sklearn.feature_selection._base import SelectorMixin diff --git a/sklearn/feature_selection/tests/test_chi2.py b/sklearn/feature_selection/tests/test_chi2.py index d7d830459e455..4fdc652a998a9 100644 --- a/sklearn/feature_selection/tests/test_chi2.py +++ b/sklearn/feature_selection/tests/test_chi2.py @@ -7,13 +7,12 @@ import numpy as np import pytest -from scipy.sparse import coo_matrix, csr_matrix import scipy.stats +from scipy.sparse import coo_matrix, csr_matrix from sklearn.feature_selection import SelectKBest, chi2 from sklearn.feature_selection._univariate_selection import _chisquare -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_array_equal +from sklearn.utils._testing import assert_array_almost_equal, assert_array_equal # Feature 0 is highly informative for class 1; # feature 1 is the same everywhere; diff --git a/sklearn/feature_selection/tests/test_feature_select.py b/sklearn/feature_selection/tests/test_feature_select.py index ff51243bb1378..b182aca270e06 100644 --- a/sklearn/feature_selection/tests/test_feature_select.py +++ b/sklearn/feature_selection/tests/test_feature_select.py @@ -3,35 +3,36 @@ """ import itertools import warnings -import numpy as np -from numpy.testing import assert_allclose -from scipy import stats, sparse +import numpy as np import pytest +from numpy.testing import assert_allclose +from scipy import sparse, stats -from sklearn.utils._testing import assert_almost_equal, _convert_container -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import ignore_warnings -from sklearn.utils import safe_mask - -from sklearn.datasets import make_classification, make_regression, load_iris +from sklearn.datasets import load_iris, make_classification, make_regression from sklearn.feature_selection import ( + GenericUnivariateSelect, + SelectFdr, + SelectFpr, + SelectFwe, + SelectKBest, + SelectPercentile, chi2, f_classif, f_oneway, f_regression, - GenericUnivariateSelect, mutual_info_classif, mutual_info_regression, r_regression, - SelectPercentile, - SelectKBest, - SelectFpr, - SelectFdr, - SelectFwe, ) - +from sklearn.utils import safe_mask +from sklearn.utils._testing import ( + _convert_container, + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, + ignore_warnings, +) ############################################################################## # Test the score functions diff --git a/sklearn/feature_selection/tests/test_from_model.py b/sklearn/feature_selection/tests/test_from_model.py index 7b408201bc7f5..aa802136c2f39 100644 --- a/sklearn/feature_selection/tests/test_from_model.py +++ b/sklearn/feature_selection/tests/test_from_model.py @@ -1,34 +1,36 @@ import re -import pytest -import numpy as np import warnings from unittest.mock import Mock -from sklearn.utils._testing import assert_array_almost_equal -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_allclose -from sklearn.utils._testing import skip_if_32bit -from sklearn.utils._testing import MinimalClassifier +import numpy as np +import pytest from sklearn import datasets +from sklearn.base import BaseEstimator from sklearn.cross_decomposition import CCA, PLSCanonical, PLSRegression from sklearn.datasets import make_friedman1 +from sklearn.decomposition import PCA +from sklearn.ensemble import HistGradientBoostingClassifier, RandomForestClassifier from sklearn.exceptions import NotFittedError +from sklearn.feature_selection import SelectFromModel from sklearn.linear_model import ( - LogisticRegression, - SGDClassifier, - Lasso, - LassoCV, ElasticNet, ElasticNetCV, + Lasso, + LassoCV, + LogisticRegression, + PassiveAggressiveClassifier, + SGDClassifier, ) -from sklearn.svm import LinearSVC -from sklearn.feature_selection import SelectFromModel -from sklearn.ensemble import RandomForestClassifier, HistGradientBoostingClassifier -from sklearn.linear_model import PassiveAggressiveClassifier -from sklearn.base import BaseEstimator from sklearn.pipeline import make_pipeline -from sklearn.decomposition import PCA +from sklearn.svm import LinearSVC +from sklearn.utils._testing import ( + MinimalClassifier, + assert_allclose, + assert_array_almost_equal, + assert_array_equal, + skip_if_32bit, +) class NaNTag(BaseEstimator): diff --git a/sklearn/feature_selection/tests/test_mutual_info.py b/sklearn/feature_selection/tests/test_mutual_info.py index f39e4a5738b21..f7b4af0a393f9 100644 --- a/sklearn/feature_selection/tests/test_mutual_info.py +++ b/sklearn/feature_selection/tests/test_mutual_info.py @@ -2,13 +2,13 @@ import pytest from scipy.sparse import csr_matrix +from sklearn.feature_selection import mutual_info_classif, mutual_info_regression +from sklearn.feature_selection._mutual_info import _compute_mi from sklearn.utils import check_random_state from sklearn.utils._testing import ( - assert_array_equal, assert_allclose, + assert_array_equal, ) -from sklearn.feature_selection._mutual_info import _compute_mi -from sklearn.feature_selection import mutual_info_regression, mutual_info_classif def test_compute_mi_dd(): diff --git a/sklearn/feature_selection/tests/test_rfe.py b/sklearn/feature_selection/tests/test_rfe.py index fa7aeea19be6c..0f141f3461d7f 100644 --- a/sklearn/feature_selection/tests/test_rfe.py +++ b/sklearn/feature_selection/tests/test_rfe.py @@ -4,31 +4,26 @@ from operator import attrgetter -import pytest import numpy as np -from numpy.testing import assert_array_almost_equal, assert_array_equal, assert_allclose +import pytest +from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal from scipy import sparse from sklearn.base import BaseEstimator, ClassifierMixin -from sklearn.cross_decomposition import PLSCanonical, PLSRegression, CCA -from sklearn.feature_selection import RFE, RFECV +from sklearn.compose import TransformedTargetRegressor +from sklearn.cross_decomposition import CCA, PLSCanonical, PLSRegression from sklearn.datasets import load_iris, make_friedman1 -from sklearn.metrics import zero_one_loss -from sklearn.svm import SVC, SVR, LinearSVR -from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier -from sklearn.model_selection import cross_val_score -from sklearn.model_selection import GroupKFold -from sklearn.compose import TransformedTargetRegressor +from sklearn.feature_selection import RFE, RFECV +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import get_scorer, make_scorer, zero_one_loss +from sklearn.model_selection import GroupKFold, cross_val_score from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler - +from sklearn.svm import SVC, SVR, LinearSVR from sklearn.utils import check_random_state from sklearn.utils._testing import ignore_warnings -from sklearn.metrics import make_scorer -from sklearn.metrics import get_scorer - class MockClassifier: """ @@ -278,8 +273,8 @@ def test_rfecv_mockclassifier(): def test_rfecv_verbose_output(): # Check verbose=1 is producing an output. - from io import StringIO import sys + from io import StringIO sys.stdout = StringIO() diff --git a/sklearn/feature_selection/tests/test_sequential.py b/sklearn/feature_selection/tests/test_sequential.py index a1ea1d4677dd4..a515bf22cdda3 100644 --- a/sklearn/feature_selection/tests/test_sequential.py +++ b/sklearn/feature_selection/tests/test_sequential.py @@ -1,17 +1,17 @@ +import numpy as np import pytest import scipy -import numpy as np from numpy.testing import assert_array_equal -from sklearn.preprocessing import StandardScaler -from sklearn.pipeline import make_pipeline +from sklearn.cluster import KMeans +from sklearn.datasets import make_blobs, make_classification, make_regression +from sklearn.ensemble import HistGradientBoostingRegressor from sklearn.feature_selection import SequentialFeatureSelector -from sklearn.datasets import make_regression, make_blobs, make_classification from sklearn.linear_model import LinearRegression -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.model_selection import cross_val_score, LeaveOneGroupOut -from sklearn.cluster import KMeans +from sklearn.model_selection import LeaveOneGroupOut, cross_val_score from sklearn.neighbors import KNeighborsClassifier +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler def test_bad_n_features_to_select(): diff --git a/sklearn/feature_selection/tests/test_variance_threshold.py b/sklearn/feature_selection/tests/test_variance_threshold.py index 4bce46556a666..190d016952980 100644 --- a/sklearn/feature_selection/tests/test_variance_threshold.py +++ b/sklearn/feature_selection/tests/test_variance_threshold.py @@ -1,11 +1,9 @@ import numpy as np import pytest - -from sklearn.utils._testing import assert_array_equal - from scipy.sparse import bsr_matrix, csc_matrix, csr_matrix from sklearn.feature_selection import VarianceThreshold +from sklearn.utils._testing import assert_array_equal data = [[0, 1, 2, 3, 4], [0, 2, 2, 3, 5], [1, 1, 2, 4, 0]] diff --git a/sklearn/gaussian_process/__init__.py b/sklearn/gaussian_process/__init__.py index 719208b7951be..bc0d902b45b18 100644 --- a/sklearn/gaussian_process/__init__.py +++ b/sklearn/gaussian_process/__init__.py @@ -8,9 +8,8 @@ based regression and classification. """ -from ._gpr import GaussianProcessRegressor -from ._gpc import GaussianProcessClassifier from . import kernels - +from ._gpc import GaussianProcessClassifier +from ._gpr import GaussianProcessRegressor __all__ = ["GaussianProcessRegressor", "GaussianProcessClassifier", "kernels"] diff --git a/sklearn/gaussian_process/_gpc.py b/sklearn/gaussian_process/_gpc.py index 50a8739372972..013815795a853 100644 --- a/sklearn/gaussian_process/_gpc.py +++ b/sklearn/gaussian_process/_gpc.py @@ -8,20 +8,19 @@ from operator import itemgetter import numpy as np -from scipy.linalg import cholesky, cho_solve, solve import scipy.optimize +from scipy.linalg import cho_solve, cholesky, solve from scipy.special import erf, expit -from ..base import BaseEstimator, ClassifierMixin, clone -from ..base import _fit_context -from .kernels import Kernel, RBF, CompoundKernel, ConstantKernel as C -from ..utils.validation import check_is_fitted +from ..base import BaseEstimator, ClassifierMixin, _fit_context, clone +from ..multiclass import OneVsOneClassifier, OneVsRestClassifier +from ..preprocessing import LabelEncoder from ..utils import check_random_state -from ..utils.optimize import _check_optimize_result from ..utils._param_validation import Interval, StrOptions -from ..preprocessing import LabelEncoder -from ..multiclass import OneVsRestClassifier, OneVsOneClassifier - +from ..utils.optimize import _check_optimize_result +from ..utils.validation import check_is_fitted +from .kernels import RBF, CompoundKernel, Kernel +from .kernels import ConstantKernel as C # Values required for approximating the logistic sigmoid by # error functions. coefs are obtained via: diff --git a/sklearn/gaussian_process/_gpr.py b/sklearn/gaussian_process/_gpr.py index 49fcab40c25f8..90bbe7e446917 100644 --- a/sklearn/gaussian_process/_gpr.py +++ b/sklearn/gaussian_process/_gpr.py @@ -9,17 +9,16 @@ from operator import itemgetter import numpy as np -from scipy.linalg import cholesky, cho_solve, solve_triangular import scipy.optimize +from scipy.linalg import cho_solve, cholesky, solve_triangular -from ..base import BaseEstimator, RegressorMixin, clone -from ..base import MultiOutputMixin -from ..base import _fit_context -from .kernels import Kernel, RBF, ConstantKernel as C +from ..base import BaseEstimator, MultiOutputMixin, RegressorMixin, _fit_context, clone from ..preprocessing._data import _handle_zeros_in_scale from ..utils import check_random_state -from ..utils.optimize import _check_optimize_result from ..utils._param_validation import Interval, StrOptions +from ..utils.optimize import _check_optimize_result +from .kernels import RBF, Kernel +from .kernels import ConstantKernel as C GPR_CHOLESKY_LOWER = True diff --git a/sklearn/gaussian_process/kernels.py b/sklearn/gaussian_process/kernels.py index 1e0866afb6a4d..95db7b13c33ff 100644 --- a/sklearn/gaussian_process/kernels.py +++ b/sklearn/gaussian_process/kernels.py @@ -19,21 +19,20 @@ # Note: this module is strongly inspired by the kernel module of the george # package. +import math +import warnings from abc import ABCMeta, abstractmethod from collections import namedtuple -import math from inspect import signature import numpy as np -from scipy.special import kv, gamma -from scipy.spatial.distance import pdist, cdist, squareform +from scipy.spatial.distance import cdist, pdist, squareform +from scipy.special import gamma, kv -from ..metrics.pairwise import pairwise_kernels from ..base import clone -from ..utils.validation import _num_samples from ..exceptions import ConvergenceWarning - -import warnings +from ..metrics.pairwise import pairwise_kernels +from ..utils.validation import _num_samples def _check_length_scale(X, length_scale): diff --git a/sklearn/gaussian_process/tests/_mini_sequence_kernel.py b/sklearn/gaussian_process/tests/_mini_sequence_kernel.py index ad81890680168..4667329aff9b8 100644 --- a/sklearn/gaussian_process/tests/_mini_sequence_kernel.py +++ b/sklearn/gaussian_process/tests/_mini_sequence_kernel.py @@ -1,8 +1,12 @@ -from sklearn.gaussian_process.kernels import Kernel, Hyperparameter -from sklearn.gaussian_process.kernels import GenericKernelMixin -from sklearn.gaussian_process.kernels import StationaryKernelMixin import numpy as np + from sklearn.base import clone +from sklearn.gaussian_process.kernels import ( + GenericKernelMixin, + Hyperparameter, + Kernel, + StationaryKernelMixin, +) class MiniSeqKernel(GenericKernelMixin, StationaryKernelMixin, Kernel): diff --git a/sklearn/gaussian_process/tests/test_gpc.py b/sklearn/gaussian_process/tests/test_gpc.py index aefdb2e8ff0e2..842159f13ac04 100644 --- a/sklearn/gaussian_process/tests/test_gpc.py +++ b/sklearn/gaussian_process/tests/test_gpc.py @@ -4,22 +4,22 @@ # License: BSD 3 clause import warnings -import numpy as np - -from scipy.optimize import approx_fprime +import numpy as np import pytest +from scipy.optimize import approx_fprime +from sklearn.exceptions import ConvergenceWarning from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import ( RBF, CompoundKernel, - ConstantKernel as C, WhiteKernel, ) +from sklearn.gaussian_process.kernels import ( + ConstantKernel as C, +) from sklearn.gaussian_process.tests._mini_sequence_kernel import MiniSeqKernel -from sklearn.exceptions import ConvergenceWarning - from sklearn.utils._testing import assert_almost_equal, assert_array_equal diff --git a/sklearn/gaussian_process/tests/test_gpr.py b/sklearn/gaussian_process/tests/test_gpr.py index 2de35d4659ce6..d890dc05d9f02 100644 --- a/sklearn/gaussian_process/tests/test_gpr.py +++ b/sklearn/gaussian_process/tests/test_gpr.py @@ -4,29 +4,31 @@ # Modified by: Pete Green # License: BSD 3 clause -import warnings -import sys import re -import numpy as np - -from scipy.optimize import approx_fprime +import sys +import warnings +import numpy as np import pytest +from scipy.optimize import approx_fprime +from sklearn.exceptions import ConvergenceWarning from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import ( RBF, - ConstantKernel as C, + DotProduct, + ExpSineSquared, WhiteKernel, ) -from sklearn.gaussian_process.kernels import DotProduct, ExpSineSquared +from sklearn.gaussian_process.kernels import ( + ConstantKernel as C, +) from sklearn.gaussian_process.tests._mini_sequence_kernel import MiniSeqKernel -from sklearn.exceptions import ConvergenceWarning from sklearn.utils._testing import ( - assert_array_less, + assert_allclose, assert_almost_equal, assert_array_almost_equal, - assert_allclose, + assert_array_less, ) diff --git a/sklearn/gaussian_process/tests/test_kernels.py b/sklearn/gaussian_process/tests/test_kernels.py index 56ab9c8b6c2bf..8733f94c94e06 100644 --- a/sklearn/gaussian_process/tests/test_kernels.py +++ b/sklearn/gaussian_process/tests/test_kernels.py @@ -3,40 +3,38 @@ # Author: Jan Hendrik Metzen # License: BSD 3 clause -import pytest -import numpy as np from inspect import signature -from sklearn.gaussian_process.kernels import _approx_fprime +import numpy as np +import pytest -from sklearn.metrics.pairwise import ( - PAIRWISE_KERNEL_FUNCTIONS, - euclidean_distances, - pairwise_kernels, -) +from sklearn.base import clone from sklearn.gaussian_process.kernels import ( RBF, + CompoundKernel, + ConstantKernel, + DotProduct, + Exponentiation, + ExpSineSquared, + KernelOperator, Matern, + PairwiseKernel, RationalQuadratic, - ExpSineSquared, - DotProduct, - ConstantKernel, WhiteKernel, - PairwiseKernel, - KernelOperator, - Exponentiation, - CompoundKernel, + _approx_fprime, +) +from sklearn.metrics.pairwise import ( + PAIRWISE_KERNEL_FUNCTIONS, + euclidean_distances, + pairwise_kernels, ) -from sklearn.base import clone - from sklearn.utils._testing import ( + assert_allclose, assert_almost_equal, - assert_array_equal, assert_array_almost_equal, - assert_allclose, + assert_array_equal, ) - X = np.random.RandomState(0).normal(0, 1, (5, 2)) Y = np.random.RandomState(0).normal(0, 1, (6, 2)) diff --git a/sklearn/impute/_base.py b/sklearn/impute/_base.py index 37fc43731514a..9245a107adf4f 100644 --- a/sklearn/impute/_base.py +++ b/sklearn/impute/_base.py @@ -10,17 +10,13 @@ import numpy.ma as ma from scipy import sparse as sp -from ..base import BaseEstimator, TransformerMixin -from ..base import _fit_context -from ..utils._param_validation import StrOptions, MissingValues +from ..base import BaseEstimator, TransformerMixin, _fit_context +from ..utils import _is_pandas_na, is_scalar_nan +from ..utils._mask import _get_mask +from ..utils._param_validation import MissingValues, StrOptions from ..utils.fixes import _mode from ..utils.sparsefuncs import _get_median -from ..utils.validation import check_is_fitted -from ..utils.validation import FLOAT_DTYPES -from ..utils.validation import _check_feature_names_in -from ..utils._mask import _get_mask -from ..utils import _is_pandas_na -from ..utils import is_scalar_nan +from ..utils.validation import FLOAT_DTYPES, _check_feature_names_in, check_is_fitted def _check_inputs_dtype(X, missing_values): diff --git a/sklearn/impute/_iterative.py b/sklearn/impute/_iterative.py index f977e5bc23e6c..a0087a5a10d55 100644 --- a/sklearn/impute/_iterative.py +++ b/sklearn/impute/_iterative.py @@ -1,31 +1,25 @@ -from time import time +import warnings from collections import namedtuple from numbers import Integral, Real -import warnings +from time import time -from scipy import stats import numpy as np +from scipy import stats -from ..base import clone -from ..base import _fit_context +from ..base import _fit_context, clone from ..exceptions import ConvergenceWarning from ..preprocessing import normalize from ..utils import ( + _safe_assign, + _safe_indexing, check_array, check_random_state, is_scalar_nan, - _safe_assign, - _safe_indexing, ) -from ..utils.validation import FLOAT_DTYPES, check_is_fitted -from ..utils.validation import _check_feature_names_in from ..utils._mask import _get_mask from ..utils._param_validation import HasMethods, Interval, StrOptions - -from ._base import _BaseImputer -from ._base import SimpleImputer -from ._base import _check_inputs_dtype - +from ..utils.validation import FLOAT_DTYPES, _check_feature_names_in, check_is_fitted +from ._base import SimpleImputer, _BaseImputer, _check_inputs_dtype _ImputerTriplet = namedtuple( "_ImputerTriplet", ["feat_idx", "neighbor_feat_idx", "estimator"] diff --git a/sklearn/impute/_knn.py b/sklearn/impute/_knn.py index 915f8cbdb3fcb..db0da278b39ef 100644 --- a/sklearn/impute/_knn.py +++ b/sklearn/impute/_knn.py @@ -3,19 +3,18 @@ # License: BSD 3 clause from numbers import Integral + import numpy as np -from ._base import _BaseImputer from ..base import _fit_context -from ..utils.validation import FLOAT_DTYPES from ..metrics import pairwise_distances_chunked from ..metrics.pairwise import _NAN_METRICS from ..neighbors._base import _get_weights from ..utils import is_scalar_nan from ..utils._mask import _get_mask -from ..utils.validation import check_is_fitted -from ..utils.validation import _check_feature_names_in from ..utils._param_validation import Hidden, Interval, StrOptions +from ..utils.validation import FLOAT_DTYPES, _check_feature_names_in, check_is_fitted +from ._base import _BaseImputer class KNNImputer(_BaseImputer): diff --git a/sklearn/impute/tests/test_base.py b/sklearn/impute/tests/test_base.py index fedfdebb20a1f..0c1bd83f7ca9e 100644 --- a/sklearn/impute/tests/test_base.py +++ b/sklearn/impute/tests/test_base.py @@ -1,12 +1,10 @@ -import pytest - import numpy as np - -from sklearn.utils._mask import _get_mask -from sklearn.utils._testing import _convert_container, assert_allclose +import pytest from sklearn.impute._base import _BaseImputer from sklearn.impute._iterative import _assign_where +from sklearn.utils._mask import _get_mask +from sklearn.utils._testing import _convert_container, assert_allclose @pytest.fixture diff --git a/sklearn/impute/tests/test_common.py b/sklearn/impute/tests/test_common.py index 00521ca090dc5..aad7eb12a0a92 100644 --- a/sklearn/impute/tests/test_common.py +++ b/sklearn/impute/tests/test_common.py @@ -1,17 +1,14 @@ -import pytest - import numpy as np +import pytest from scipy import sparse -from sklearn.utils._testing import assert_allclose -from sklearn.utils._testing import assert_allclose_dense_sparse -from sklearn.utils._testing import assert_array_equal - from sklearn.experimental import enable_iterative_imputer # noqa - -from sklearn.impute import IterativeImputer -from sklearn.impute import KNNImputer -from sklearn.impute import SimpleImputer +from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer +from sklearn.utils._testing import ( + assert_allclose, + assert_allclose_dense_sparse, + assert_array_equal, +) def imputers(): diff --git a/sklearn/impute/tests/test_impute.py b/sklearn/impute/tests/test_impute.py index 24b070d21ef06..936847e55e324 100644 --- a/sklearn/impute/tests/test_impute.py +++ b/sklearn/impute/tests/test_impute.py @@ -1,33 +1,31 @@ -import pytest +import io import warnings import numpy as np +import pytest from scipy import sparse from scipy.stats import kstest -import io - -from sklearn.utils._testing import _convert_container -from sklearn.utils._testing import assert_allclose -from sklearn.utils._testing import assert_allclose_dense_sparse -from sklearn.utils._testing import assert_array_equal -from sklearn.utils._testing import assert_array_almost_equal +from sklearn import tree +from sklearn.datasets import load_diabetes +from sklearn.dummy import DummyRegressor +from sklearn.exceptions import ConvergenceWarning # make IterativeImputer available from sklearn.experimental import enable_iterative_imputer # noqa - -from sklearn.datasets import load_diabetes -from sklearn.impute import MissingIndicator -from sklearn.impute import SimpleImputer, IterativeImputer, KNNImputer -from sklearn.dummy import DummyRegressor -from sklearn.linear_model import BayesianRidge, ARDRegression, RidgeCV -from sklearn.pipeline import Pipeline -from sklearn.pipeline import make_union +from sklearn.impute import IterativeImputer, KNNImputer, MissingIndicator, SimpleImputer +from sklearn.impute._base import _most_frequent +from sklearn.linear_model import ARDRegression, BayesianRidge, RidgeCV from sklearn.model_selection import GridSearchCV -from sklearn import tree +from sklearn.pipeline import Pipeline, make_union from sklearn.random_projection import _sparse_random_matrix -from sklearn.exceptions import ConvergenceWarning -from sklearn.impute._base import _most_frequent +from sklearn.utils._testing import ( + _convert_container, + assert_allclose, + assert_allclose_dense_sparse, + assert_array_almost_equal, + assert_array_equal, +) def _assert_array_equal_and_same_dtype(x, y): diff --git a/sklearn/impute/tests/test_knn.py b/sklearn/impute/tests/test_knn.py index 80ee1d0c2b574..141c2ea90dbd9 100644 --- a/sklearn/impute/tests/test_knn.py +++ b/sklearn/impute/tests/test_knn.py @@ -3,8 +3,7 @@ from sklearn import config_context from sklearn.impute import KNNImputer -from sklearn.metrics.pairwise import nan_euclidean_distances -from sklearn.metrics.pairwise import pairwise_distances +from sklearn.metrics.pairwise import nan_euclidean_distances, pairwise_distances from sklearn.neighbors import KNeighborsRegressor from sklearn.utils._testing import assert_allclose diff --git a/sklearn/inspection/__init__.py b/sklearn/inspection/__init__.py index f73ffe8cff26f..f8e08785e8358 100644 --- a/sklearn/inspection/__init__.py +++ b/sklearn/inspection/__init__.py @@ -1,13 +1,11 @@ """The :mod:`sklearn.inspection` module includes tools for model inspection.""" +from ._partial_dependence import partial_dependence from ._permutation_importance import permutation_importance from ._plot.decision_boundary import DecisionBoundaryDisplay - -from ._partial_dependence import partial_dependence from ._plot.partial_dependence import PartialDependenceDisplay - __all__ = [ "partial_dependence", "permutation_importance", diff --git a/sklearn/inspection/_partial_dependence.py b/sklearn/inspection/_partial_dependence.py index e3af7dda1e505..59a9212aff440 100644 --- a/sklearn/inspection/_partial_dependence.py +++ b/sklearn/inspection/_partial_dependence.py @@ -11,18 +11,23 @@ from scipy import sparse from scipy.stats.mstats import mquantiles -from ._pd_utils import _check_feature_names, _get_feature_index from ..base import is_classifier, is_regressor -from ..utils.extmath import cartesian -from ..utils import check_array -from ..utils import check_matplotlib_support # noqa -from ..utils import _safe_indexing -from ..utils import _safe_assign -from ..utils import _determine_key_type -from ..utils import _get_column_indices -from ..utils.validation import _check_sample_weight -from ..utils.validation import check_is_fitted -from ..utils import Bunch +from ..ensemble import RandomForestRegressor +from ..ensemble._gb import BaseGradientBoosting +from ..ensemble._hist_gradient_boosting.gradient_boosting import ( + BaseHistGradientBoosting, +) +from ..exceptions import NotFittedError +from ..tree import DecisionTreeRegressor +from ..utils import ( + Bunch, + _determine_key_type, + _get_column_indices, + _safe_assign, + _safe_indexing, + check_array, + check_matplotlib_support, # noqa +) from ..utils._param_validation import ( HasMethods, Integral, @@ -30,14 +35,9 @@ StrOptions, validate_params, ) -from ..tree import DecisionTreeRegressor -from ..ensemble import RandomForestRegressor -from ..exceptions import NotFittedError -from ..ensemble._gb import BaseGradientBoosting -from ..ensemble._hist_gradient_boosting.gradient_boosting import ( - BaseHistGradientBoosting, -) - +from ..utils.extmath import cartesian +from ..utils.validation import _check_sample_weight, check_is_fitted +from ._pd_utils import _check_feature_names, _get_feature_index __all__ = [ "partial_dependence", diff --git a/sklearn/inspection/_permutation_importance.py b/sklearn/inspection/_permutation_importance.py index 9330589a04794..f8e1fba2967c5 100644 --- a/sklearn/inspection/_permutation_importance.py +++ b/sklearn/inspection/_permutation_importance.py @@ -1,15 +1,13 @@ """Permutation importance for estimators.""" import numbers + import numpy as np from ..ensemble._bagging import _generate_indices from ..metrics import check_scoring, get_scorer_names from ..metrics._scorer import _check_multimetric_scoring, _MultimetricScorer from ..model_selection._validation import _aggregate_score_dicts -from ..utils import Bunch, _safe_indexing -from ..utils import check_random_state -from ..utils import check_array -from ..utils.parallel import delayed, Parallel +from ..utils import Bunch, _safe_indexing, check_array, check_random_state from ..utils._param_validation import ( HasMethods, Integral, @@ -18,6 +16,7 @@ StrOptions, validate_params, ) +from ..utils.parallel import Parallel, delayed def _weights_scorer(scorer, estimator, X, y, sample_weight): diff --git a/sklearn/inspection/_plot/decision_boundary.py b/sklearn/inspection/_plot/decision_boundary.py index 22b4590d9bc3c..e588edbef7626 100644 --- a/sklearn/inspection/_plot/decision_boundary.py +++ b/sklearn/inspection/_plot/decision_boundary.py @@ -2,14 +2,13 @@ import numpy as np -from ...preprocessing import LabelEncoder -from ...utils import check_matplotlib_support -from ...utils import _safe_indexing from ...base import is_regressor +from ...preprocessing import LabelEncoder +from ...utils import _safe_indexing, check_matplotlib_support from ...utils.validation import ( - check_is_fitted, _is_arraylike_not_scalar, _num_features, + check_is_fitted, ) diff --git a/sklearn/inspection/_plot/partial_dependence.py b/sklearn/inspection/_plot/partial_dependence.py index 48e151cefedbe..46d2c78d78d2e 100644 --- a/sklearn/inspection/_plot/partial_dependence.py +++ b/sklearn/inspection/_plot/partial_dependence.py @@ -6,16 +6,18 @@ from scipy import sparse from scipy.stats.mstats import mquantiles -from .. import partial_dependence -from .._pd_utils import _check_feature_names, _get_feature_index from ...base import is_regressor -from ...utils import Bunch -from ...utils import check_array -from ...utils import check_matplotlib_support # noqa -from ...utils import check_random_state -from ...utils import _safe_indexing -from ...utils.parallel import delayed, Parallel +from ...utils import ( + Bunch, + _safe_indexing, + check_array, + check_matplotlib_support, # noqa + check_random_state, +) from ...utils._encode import _unique +from ...utils.parallel import Parallel, delayed +from .. import partial_dependence +from .._pd_utils import _check_feature_names, _get_feature_index class PartialDependenceDisplay: diff --git a/sklearn/inspection/_plot/tests/test_boundary_decision_display.py b/sklearn/inspection/_plot/tests/test_boundary_decision_display.py index 73cfe187d7f6e..47c21e4521c35 100644 --- a/sklearn/inspection/_plot/tests/test_boundary_decision_display.py +++ b/sklearn/inspection/_plot/tests/test_boundary_decision_display.py @@ -1,21 +1,19 @@ import warnings -import pytest import numpy as np +import pytest from numpy.testing import assert_allclose -from sklearn.base import BaseEstimator -from sklearn.base import ClassifierMixin -from sklearn.datasets import make_classification -from sklearn.linear_model import LogisticRegression -from sklearn.datasets import load_iris -from sklearn.datasets import make_multilabel_classification -from sklearn.tree import DecisionTreeRegressor -from sklearn.tree import DecisionTreeClassifier - +from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.datasets import ( + load_iris, + make_classification, + make_multilabel_classification, +) from sklearn.inspection import DecisionBoundaryDisplay from sklearn.inspection._plot.decision_boundary import _check_boundary_response_method - +from sklearn.linear_model import LogisticRegression +from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor # TODO: Remove when https://github.com/numpy/numpy/issues/14397 is resolved pytestmark = pytest.mark.filterwarnings( diff --git a/sklearn/inspection/_plot/tests/test_plot_partial_dependence.py b/sklearn/inspection/_plot/tests/test_plot_partial_dependence.py index acda2d001144e..106819b5a25d5 100644 --- a/sklearn/inspection/_plot/tests/test_plot_partial_dependence.py +++ b/sklearn/inspection/_plot/tests/test_plot_partial_dependence.py @@ -1,22 +1,21 @@ import numpy as np -from scipy.stats.mstats import mquantiles - import pytest from numpy.testing import assert_allclose +from scipy.stats.mstats import mquantiles -from sklearn.datasets import load_diabetes -from sklearn.datasets import load_iris -from sklearn.datasets import make_classification, make_regression -from sklearn.ensemble import GradientBoostingRegressor -from sklearn.ensemble import GradientBoostingClassifier -from sklearn.linear_model import LinearRegression -from sklearn.utils._testing import _convert_container from sklearn.compose import make_column_transformer -from sklearn.preprocessing import OneHotEncoder -from sklearn.pipeline import make_pipeline - +from sklearn.datasets import ( + load_diabetes, + load_iris, + make_classification, + make_regression, +) +from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor from sklearn.inspection import PartialDependenceDisplay - +from sklearn.linear_model import LinearRegression +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import OneHotEncoder +from sklearn.utils._testing import _convert_container # TODO: Remove when https://github.com/numpy/numpy/issues/14397 is resolved pytestmark = pytest.mark.filterwarnings( diff --git a/sklearn/inspection/tests/test_partial_dependence.py b/sklearn/inspection/tests/test_partial_dependence.py index 4e93985f4d02a..0336dc4b827fe 100644 --- a/sklearn/inspection/tests/test_partial_dependence.py +++ b/sklearn/inspection/tests/test_partial_dependence.py @@ -7,41 +7,39 @@ import pytest import sklearn +from sklearn.base import BaseEstimator, ClassifierMixin, clone, is_regressor +from sklearn.cluster import KMeans +from sklearn.compose import make_column_transformer +from sklearn.datasets import load_iris, make_classification, make_regression +from sklearn.dummy import DummyClassifier +from sklearn.ensemble import ( + GradientBoostingClassifier, + GradientBoostingRegressor, + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, + RandomForestRegressor, +) +from sklearn.exceptions import NotFittedError from sklearn.inspection import partial_dependence from sklearn.inspection._partial_dependence import ( _grid_from_X, _partial_dependence_brute, _partial_dependence_recursion, ) -from sklearn.ensemble import GradientBoostingClassifier -from sklearn.ensemble import GradientBoostingRegressor -from sklearn.ensemble import RandomForestRegressor -from sklearn.ensemble import HistGradientBoostingClassifier -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.linear_model import LinearRegression -from sklearn.linear_model import LogisticRegression -from sklearn.linear_model import MultiTaskLasso -from sklearn.tree import DecisionTreeRegressor -from sklearn.datasets import load_iris -from sklearn.datasets import make_classification, make_regression -from sklearn.cluster import KMeans -from sklearn.compose import make_column_transformer +from sklearn.linear_model import LinearRegression, LogisticRegression, MultiTaskLasso from sklearn.metrics import r2_score -from sklearn.preprocessing import PolynomialFeatures -from sklearn.preprocessing import StandardScaler -from sklearn.preprocessing import RobustScaler -from sklearn.preprocessing import scale from sklearn.pipeline import make_pipeline -from sklearn.dummy import DummyClassifier -from sklearn.base import BaseEstimator, ClassifierMixin, clone -from sklearn.base import is_regressor -from sklearn.exceptions import NotFittedError -from sklearn.utils._testing import assert_allclose -from sklearn.utils._testing import assert_array_equal +from sklearn.preprocessing import ( + PolynomialFeatures, + RobustScaler, + StandardScaler, + scale, +) +from sklearn.tree import DecisionTreeRegressor +from sklearn.tree.tests.test_tree import assert_is_subtree from sklearn.utils import _IS_32BIT +from sklearn.utils._testing import assert_allclose, assert_array_equal from sklearn.utils.validation import check_random_state -from sklearn.tree.tests.test_tree import assert_is_subtree - # toy sample X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]] diff --git a/sklearn/inspection/tests/test_pd_utils.py b/sklearn/inspection/tests/test_pd_utils.py index 5f461ad498f5b..5dea3834a77a7 100644 --- a/sklearn/inspection/tests/test_pd_utils.py +++ b/sklearn/inspection/tests/test_pd_utils.py @@ -1,9 +1,8 @@ import numpy as np import pytest -from sklearn.utils._testing import _convert_container - from sklearn.inspection._pd_utils import _check_feature_names, _get_feature_index +from sklearn.utils._testing import _convert_container @pytest.mark.parametrize( diff --git a/sklearn/inspection/tests/test_permutation_importance.py b/sklearn/inspection/tests/test_permutation_importance.py index 307d17188e852..b1a680646afe1 100644 --- a/sklearn/inspection/tests/test_permutation_importance.py +++ b/sklearn/inspection/tests/test_permutation_importance.py @@ -1,31 +1,27 @@ -import pytest import numpy as np - +import pytest from numpy.testing import assert_allclose from sklearn.compose import ColumnTransformer -from sklearn.datasets import load_diabetes -from sklearn.datasets import load_iris -from sklearn.datasets import make_classification -from sklearn.datasets import make_regression +from sklearn.datasets import ( + load_diabetes, + load_iris, + make_classification, + make_regression, +) from sklearn.dummy import DummyClassifier -from sklearn.ensemble import RandomForestRegressor -from sklearn.ensemble import RandomForestClassifier -from sklearn.linear_model import LinearRegression -from sklearn.linear_model import LogisticRegression +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor from sklearn.impute import SimpleImputer from sklearn.inspection import permutation_importance -from sklearn.model_selection import train_test_split +from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.metrics import ( get_scorer, mean_squared_error, r2_score, ) +from sklearn.model_selection import train_test_split from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import KBinsDiscretizer -from sklearn.preprocessing import OneHotEncoder -from sklearn.preprocessing import StandardScaler -from sklearn.preprocessing import scale +from sklearn.preprocessing import KBinsDiscretizer, OneHotEncoder, StandardScaler, scale from sklearn.utils import parallel_backend from sklearn.utils._testing import _convert_container diff --git a/sklearn/isotonic.py b/sklearn/isotonic.py index a1cf95b95591b..4e5f7f7b0034f 100644 --- a/sklearn/isotonic.py +++ b/sklearn/isotonic.py @@ -3,20 +3,19 @@ # Nelle Varoquaux # License: BSD 3 clause +import math +import warnings +from numbers import Real + import numpy as np from scipy import interpolate from scipy.stats import spearmanr -from numbers import Real -import warnings -import math -from .base import BaseEstimator, TransformerMixin, RegressorMixin -from .base import _fit_context +from ._isotonic import _inplace_contiguous_isotonic_regression, _make_unique +from .base import BaseEstimator, RegressorMixin, TransformerMixin, _fit_context from .utils import check_array, check_consistent_length -from .utils.validation import _check_sample_weight, check_is_fitted from .utils._param_validation import Interval, StrOptions -from ._isotonic import _inplace_contiguous_isotonic_regression, _make_unique - +from .utils.validation import _check_sample_weight, check_is_fitted __all__ = ["check_increasing", "isotonic_regression", "IsotonicRegression"] diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py index 7f190a2b66823..11e6e91236437 100644 --- a/sklearn/kernel_approximation.py +++ b/sklearn/kernel_approximation.py @@ -8,8 +8,8 @@ # License: BSD 3 clause -from numbers import Integral, Real import warnings +from numbers import Integral, Real import numpy as np import scipy.sparse as sp @@ -20,20 +20,21 @@ except ImportError: # scipy < 1.4 from scipy.fftpack import fft, ifft -from .base import BaseEstimator -from .base import TransformerMixin -from .base import ClassNamePrefixFeaturesOutMixin -from .base import _fit_context -from .utils import check_random_state -from .utils import deprecated +from .base import ( + BaseEstimator, + ClassNamePrefixFeaturesOutMixin, + TransformerMixin, + _fit_context, +) +from .metrics.pairwise import KERNEL_PARAMS, PAIRWISE_KERNEL_FUNCTIONS, pairwise_kernels +from .utils import check_random_state, deprecated +from .utils._param_validation import Interval, StrOptions from .utils.extmath import safe_sparse_dot -from .utils.validation import check_is_fitted -from .utils.validation import _check_feature_names_in -from .metrics.pairwise import pairwise_kernels, KERNEL_PARAMS -from .utils.validation import check_non_negative -from .utils._param_validation import Interval -from .utils._param_validation import StrOptions -from .metrics.pairwise import PAIRWISE_KERNEL_FUNCTIONS +from .utils.validation import ( + _check_feature_names_in, + check_is_fitted, + check_non_negative, +) class PolynomialCountSketch( diff --git a/sklearn/kernel_ridge.py b/sklearn/kernel_ridge.py index a7bfeefaef651..f418c8946510d 100644 --- a/sklearn/kernel_ridge.py +++ b/sklearn/kernel_ridge.py @@ -7,12 +7,11 @@ import numpy as np -from .base import BaseEstimator, RegressorMixin, MultiOutputMixin -from .base import _fit_context -from .utils._param_validation import Interval, StrOptions -from .metrics.pairwise import PAIRWISE_KERNEL_FUNCTIONS, pairwise_kernels +from .base import BaseEstimator, MultiOutputMixin, RegressorMixin, _fit_context from .linear_model._ridge import _solve_cholesky_kernel -from .utils.validation import check_is_fitted, _check_sample_weight +from .metrics.pairwise import PAIRWISE_KERNEL_FUNCTIONS, pairwise_kernels +from .utils._param_validation import Interval, StrOptions +from .utils.validation import _check_sample_weight, check_is_fitted class KernelRidge(MultiOutputMixin, RegressorMixin, BaseEstimator): diff --git a/sklearn/linear_model/__init__.py b/sklearn/linear_model/__init__.py index d5a14756c41a9..45c99d4d36df1 100644 --- a/sklearn/linear_model/__init__.py +++ b/sklearn/linear_model/__init__.py @@ -7,46 +7,44 @@ # complete documentation. from ._base import LinearRegression -from ._bayes import BayesianRidge, ARDRegression -from ._least_angle import ( - Lars, - LassoLars, - lars_path, - lars_path_gram, - LarsCV, - LassoLarsCV, - LassoLarsIC, -) +from ._bayes import ARDRegression, BayesianRidge from ._coordinate_descent import ( - Lasso, ElasticNet, - LassoCV, ElasticNetCV, - lasso_path, - enet_path, - MultiTaskLasso, + Lasso, + LassoCV, MultiTaskElasticNet, MultiTaskElasticNetCV, + MultiTaskLasso, MultiTaskLassoCV, + enet_path, + lasso_path, ) -from ._glm import PoissonRegressor, GammaRegressor, TweedieRegressor +from ._glm import GammaRegressor, PoissonRegressor, TweedieRegressor from ._huber import HuberRegressor -from ._sgd_fast import Hinge, Log, ModifiedHuber, SquaredLoss, Huber -from ._stochastic_gradient import SGDClassifier, SGDRegressor, SGDOneClassSVM -from ._ridge import Ridge, RidgeCV, RidgeClassifier, RidgeClassifierCV, ridge_regression +from ._least_angle import ( + Lars, + LarsCV, + LassoLars, + LassoLarsCV, + LassoLarsIC, + lars_path, + lars_path_gram, +) from ._logistic import LogisticRegression, LogisticRegressionCV from ._omp import ( - orthogonal_mp, - orthogonal_mp_gram, OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV, + orthogonal_mp, + orthogonal_mp_gram, ) -from ._passive_aggressive import PassiveAggressiveClassifier -from ._passive_aggressive import PassiveAggressiveRegressor +from ._passive_aggressive import PassiveAggressiveClassifier, PassiveAggressiveRegressor from ._perceptron import Perceptron - from ._quantile import QuantileRegressor from ._ransac import RANSACRegressor +from ._ridge import Ridge, RidgeClassifier, RidgeClassifierCV, RidgeCV, ridge_regression +from ._sgd_fast import Hinge, Huber, Log, ModifiedHuber, SquaredLoss +from ._stochastic_gradient import SGDClassifier, SGDOneClassSVM, SGDRegressor from ._theil_sen import TheilSenRegressor __all__ = [ diff --git a/sklearn/linear_model/_base.py b/sklearn/linear_model/_base.py index 92c067c850225..249c13da179c0 100644 --- a/sklearn/linear_model/_base.py +++ b/sklearn/linear_model/_base.py @@ -14,33 +14,37 @@ # Maria Telenczuk # License: BSD 3 clause -from abc import ABCMeta, abstractmethod import numbers import warnings +from abc import ABCMeta, abstractmethod +from numbers import Integral import numpy as np import scipy.sparse as sp -from scipy import linalg -from scipy import optimize -from scipy import sparse +from scipy import linalg, optimize, sparse from scipy.sparse.linalg import lsqr from scipy.special import expit -from numbers import Integral -from ..base import BaseEstimator, ClassifierMixin, RegressorMixin, MultiOutputMixin -from ..base import _fit_context +from ..base import ( + BaseEstimator, + ClassifierMixin, + MultiOutputMixin, + RegressorMixin, + _fit_context, +) from ..preprocessing._data import _is_constant_feature -from ..utils import check_array -from ..utils.validation import FLOAT_DTYPES -from ..utils import check_random_state -from ..utils.extmath import safe_sparse_dot -from ..utils.extmath import _incremental_mean_and_var -from ..utils.sparsefuncs import mean_variance_axis, inplace_column_scale +from ..utils import check_array, check_random_state from ..utils._array_api import get_namespace -from ..utils._seq_dataset import ArrayDataset32, CSRDataset32 -from ..utils._seq_dataset import ArrayDataset64, CSRDataset64 -from ..utils.validation import check_is_fitted, _check_sample_weight -from ..utils.parallel import delayed, Parallel +from ..utils._seq_dataset import ( + ArrayDataset32, + ArrayDataset64, + CSRDataset32, + CSRDataset64, +) +from ..utils.extmath import _incremental_mean_and_var, safe_sparse_dot +from ..utils.parallel import Parallel, delayed +from ..utils.sparsefuncs import inplace_column_scale, mean_variance_axis +from ..utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted # TODO: bayesian_ridge_regression and bayesian_regression_ard # should be squashed into its respective objects. diff --git a/sklearn/linear_model/_bayes.py b/sklearn/linear_model/_bayes.py index 37dc3b81511f5..7b64e91f18c17 100644 --- a/sklearn/linear_model/_bayes.py +++ b/sklearn/linear_model/_bayes.py @@ -8,16 +8,16 @@ import warnings from math import log from numbers import Integral, Real + import numpy as np from scipy import linalg +from scipy.linalg import pinvh -from ._base import LinearModel, _preprocess_data, _rescale_data -from ..base import RegressorMixin -from ..base import _fit_context +from ..base import RegressorMixin, _fit_context +from ..utils._param_validation import Hidden, Interval, StrOptions from ..utils.extmath import fast_logdet -from scipy.linalg import pinvh from ..utils.validation import _check_sample_weight -from ..utils._param_validation import Interval, Hidden, StrOptions +from ._base import LinearModel, _preprocess_data, _rescale_data # TODO(1.5) Remove diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py index 829c0ab6149f1..c7caeab2090fe 100644 --- a/sklearn/linear_model/_coordinate_descent.py +++ b/sklearn/linear_model/_coordinate_descent.py @@ -5,36 +5,34 @@ # # License: BSD 3 clause +import numbers import sys import warnings -import numbers from abc import ABC, abstractmethod from functools import partial from numbers import Integral, Real import numpy as np -from scipy import sparse from joblib import effective_n_jobs +from scipy import sparse -from ._base import LinearModel, _pre_fit -from ..base import RegressorMixin, MultiOutputMixin -from ..base import _fit_context -from ._base import _preprocess_data +from ..base import MultiOutputMixin, RegressorMixin, _fit_context +from ..model_selection import check_cv from ..utils import check_array, check_scalar -from ..utils.validation import check_random_state from ..utils._param_validation import Interval, StrOptions -from ..model_selection import check_cv from ..utils.extmath import safe_sparse_dot +from ..utils.parallel import Parallel, delayed from ..utils.validation import ( _check_sample_weight, check_consistent_length, check_is_fitted, + check_random_state, column_or_1d, ) -from ..utils.parallel import delayed, Parallel # mypy error: Module 'sklearn.linear_model' has no attribute '_cd_fast' from . import _cd_fast as cd_fast # type: ignore +from ._base import LinearModel, _pre_fit, _preprocess_data def _set_order(X, y, order="C"): diff --git a/sklearn/linear_model/_glm/__init__.py b/sklearn/linear_model/_glm/__init__.py index fea9c4d4cf6ba..1b82bbd77bcf9 100644 --- a/sklearn/linear_model/_glm/__init__.py +++ b/sklearn/linear_model/_glm/__init__.py @@ -1,10 +1,10 @@ # License: BSD 3 clause from .glm import ( - _GeneralizedLinearRegressor, - PoissonRegressor, GammaRegressor, + PoissonRegressor, TweedieRegressor, + _GeneralizedLinearRegressor, ) __all__ = [ diff --git a/sklearn/linear_model/_glm/glm.py b/sklearn/linear_model/_glm/glm.py index b1bc460f24dff..3dc0bbdc66bff 100644 --- a/sklearn/linear_model/_glm/glm.py +++ b/sklearn/linear_model/_glm/glm.py @@ -11,7 +11,6 @@ import numpy as np import scipy.optimize -from ._newton_solver import NewtonCholeskySolver, NewtonSolver from ..._loss.loss import ( HalfGammaLoss, HalfPoissonLoss, @@ -19,14 +18,14 @@ HalfTweedieLoss, HalfTweedieLossIdentity, ) -from ...base import BaseEstimator, RegressorMixin -from ...base import _fit_context +from ...base import BaseEstimator, RegressorMixin, _fit_context from ...utils import check_array from ...utils._openmp_helpers import _openmp_effective_n_threads from ...utils._param_validation import Hidden, Interval, StrOptions from ...utils.optimize import _check_optimize_result from ...utils.validation import _check_sample_weight, check_is_fitted from .._linear_loss import LinearModelLoss +from ._newton_solver import NewtonCholeskySolver, NewtonSolver class _GeneralizedLinearRegressor(RegressorMixin, BaseEstimator): diff --git a/sklearn/linear_model/_glm/tests/test_glm.py b/sklearn/linear_model/_glm/tests/test_glm.py index c92ef5f99ca8a..5256a5f370272 100644 --- a/sklearn/linear_model/_glm/tests/test_glm.py +++ b/sklearn/linear_model/_glm/tests/test_glm.py @@ -2,22 +2,22 @@ # # License: BSD 3 clause -from functools import partial import itertools import warnings +from functools import partial import numpy as np -from numpy.testing import assert_allclose import pytest import scipy +from numpy.testing import assert_allclose from scipy import linalg from scipy.optimize import minimize, root -from sklearn.base import clone from sklearn._loss import HalfBinomialLoss, HalfPoissonLoss, HalfTweedieLoss from sklearn._loss.link import IdentityLink, LogLink - +from sklearn.base import clone from sklearn.datasets import make_low_rank_matrix, make_regression +from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import ( GammaRegressor, PoissonRegressor, @@ -27,11 +27,9 @@ from sklearn.linear_model._glm import _GeneralizedLinearRegressor from sklearn.linear_model._glm._newton_solver import NewtonCholeskySolver from sklearn.linear_model._linear_loss import LinearModelLoss -from sklearn.exceptions import ConvergenceWarning from sklearn.metrics import d2_tweedie_score, mean_poisson_deviance from sklearn.model_selection import train_test_split - SOLVERS = ["lbfgs", "newton-cholesky"] diff --git a/sklearn/linear_model/_huber.py b/sklearn/linear_model/_huber.py index def2ae273d5c4..554f693061116 100644 --- a/sklearn/linear_model/_huber.py +++ b/sklearn/linear_model/_huber.py @@ -2,18 +2,17 @@ # License: BSD 3 clause from numbers import Integral, Real -import numpy as np +import numpy as np from scipy import optimize -from ..base import BaseEstimator, RegressorMixin -from ..base import _fit_context -from ._base import LinearModel +from ..base import BaseEstimator, RegressorMixin, _fit_context from ..utils import axis0_safe_slice from ..utils._param_validation import Interval -from ..utils.validation import _check_sample_weight from ..utils.extmath import safe_sparse_dot from ..utils.optimize import _check_optimize_result +from ..utils.validation import _check_sample_weight +from ._base import LinearModel def _huber_loss_and_gradient(w, X, y, epsilon, alpha, sample_weight=None): diff --git a/sklearn/linear_model/_least_angle.py b/sklearn/linear_model/_least_angle.py index e6c653eb80bb3..439ba636d159f 100644 --- a/sklearn/linear_model/_least_angle.py +++ b/sklearn/linear_model/_least_angle.py @@ -8,27 +8,24 @@ # # License: BSD 3 clause -from math import log import sys import warnings - +from math import log from numbers import Integral, Real + import numpy as np -from scipy import linalg, interpolate +from scipy import interpolate, linalg from scipy.linalg.lapack import get_lapack_funcs -from ._base import LinearModel, LinearRegression -from ._base import _deprecate_normalize, _preprocess_data -from ..base import RegressorMixin, MultiOutputMixin -from ..base import _fit_context +from ..base import MultiOutputMixin, RegressorMixin, _fit_context +from ..exceptions import ConvergenceWarning +from ..model_selection import check_cv # mypy error: Module 'sklearn.utils' has no attribute 'arrayfuncs' -from ..utils import arrayfuncs, as_float_array # type: ignore -from ..utils import check_random_state +from ..utils import arrayfuncs, as_float_array, check_random_state # type: ignore from ..utils._param_validation import Hidden, Interval, StrOptions -from ..model_selection import check_cv -from ..exceptions import ConvergenceWarning -from ..utils.parallel import delayed, Parallel +from ..utils.parallel import Parallel, delayed +from ._base import LinearModel, LinearRegression, _deprecate_normalize, _preprocess_data SOLVE_TRIANGULAR_ARGS = {"check_finite": False} diff --git a/sklearn/linear_model/_linear_loss.py b/sklearn/linear_model/_linear_loss.py index f70d78fb42871..92a203abc87ab 100644 --- a/sklearn/linear_model/_linear_loss.py +++ b/sklearn/linear_model/_linear_loss.py @@ -3,6 +3,7 @@ """ import numpy as np from scipy import sparse + from ..utils.extmath import squared_norm diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py index 30a0f40a0f2fd..6bdc4b7368ef0 100644 --- a/sklearn/linear_model/_logistic.py +++ b/sklearn/linear_model/_logistic.py @@ -11,35 +11,37 @@ # Arthur Mensch
    +
    +

    Omar Salman

    +
    +

    Bertrand Thirion

    From e4c3c4d103a7493885f5c8da279b979e47d2c352 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Mon, 10 Jul 2023 17:01:32 +0200 Subject: [PATCH 083/264] MNT clean up old scipy sparse.linalg.cg fallback code (#26813) --- sklearn/linear_model/_ridge.py | 18 ++++-------------- 1 file changed, 4 insertions(+), 14 deletions(-) diff --git a/sklearn/linear_model/_ridge.py b/sklearn/linear_model/_ridge.py index cdc48d8bf2d34..aa30db0525715 100644 --- a/sklearn/linear_model/_ridge.py +++ b/sklearn/linear_model/_ridge.py @@ -105,12 +105,7 @@ def _mv(x): C = sp_linalg.LinearOperator( (n_samples, n_samples), matvec=mv, dtype=X.dtype ) - # FIXME atol - try: - coef, info = sp_linalg.cg(C, y_column, tol=tol, atol="legacy") - except TypeError: - # old scipy - coef, info = sp_linalg.cg(C, y_column, tol=tol) + coef, info = sp_linalg.cg(C, y_column, tol=tol, atol="legacy") coefs[i] = X1.rmatvec(coef) else: # linear ridge @@ -119,14 +114,9 @@ def _mv(x): C = sp_linalg.LinearOperator( (n_features, n_features), matvec=mv, dtype=X.dtype ) - # FIXME atol - try: - coefs[i], info = sp_linalg.cg( - C, y_column, maxiter=max_iter, tol=tol, atol="legacy" - ) - except TypeError: - # old scipy - coefs[i], info = sp_linalg.cg(C, y_column, maxiter=max_iter, tol=tol) + coefs[i], info = sp_linalg.cg( + C, y_column, maxiter=max_iter, tol=tol, atol="legacy" + ) if info < 0: raise ValueError("Failed with error code %d" % info) From 85b5a7efba1feab3f15420b5d3cc50641e8ae1ef Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Mon, 10 Jul 2023 18:05:15 +0200 Subject: [PATCH 084/264] DOC fix SLEP020 link in governance (#26816) --- doc/governance.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/governance.rst b/doc/governance.rst index 5b153aed7a0ce..b756c52850e5c 100644 --- a/doc/governance.rst +++ b/doc/governance.rst @@ -158,8 +158,8 @@ are made according to the following rules: versions** happen via a :ref:`slep` and follows the decision-making process outlined above. -* **Changes to the governance model** follow the process outlined in [ - SLEP020](https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep020/proposal.html). +* **Changes to the governance model** follow the process outlined in `SLEP020 + `__. If a veto -1 vote is cast on a lazy consensus, the proposer can appeal to the community and maintainers and the change can be approved or rejected using From 84b430fdf91a2d9d50cf5a4a35c5123837356b0f Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Mon, 10 Jul 2023 20:51:42 +0200 Subject: [PATCH 085/264] DOC add Maren Westermann to the contributor experience team (#26815) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> --- doc/contributor_experience_team.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/doc/contributor_experience_team.rst b/doc/contributor_experience_team.rst index 00b658632302e..a51f30bede265 100644 --- a/doc/contributor_experience_team.rst +++ b/doc/contributor_experience_team.rst @@ -41,4 +41,8 @@

    Albert Thomas

    +
    +
    +

    Maren Westermann

    +
    From 6f35782df20d8c3d364ac8b4e43eacd38055e2a1 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 10 Jul 2023 22:25:35 +0200 Subject: [PATCH 086/264] DOC fixes intersphinx warnings (#26779) Co-authored-by: Thomas J. Fan --- doc/common_pitfalls.rst | 8 +- doc/datasets/loading_other_datasets.rst | 4 +- doc/developers/develop.rst | 10 +- doc/developers/plotting.rst | 2 +- doc/developers/utilities.rst | 2 +- doc/glossary.rst | 35 +++--- doc/modules/biclustering.rst | 5 +- doc/modules/classes.rst | 1 + doc/modules/clustering.rst | 2 +- doc/modules/compose.rst | 6 +- doc/modules/decomposition.rst | 34 +++--- doc/modules/ensemble.rst | 15 ++- doc/modules/feature_selection.rst | 11 +- doc/modules/gaussian_process.rst | 18 +-- doc/modules/grid_search.rst | 2 +- doc/modules/impute.rst | 51 +++++---- doc/modules/kernel_approximation.rst | 2 +- doc/modules/linear_model.rst | 19 +-- doc/modules/model_evaluation.rst | 8 +- doc/modules/naive_bayes.rst | 6 +- doc/modules/neighbors.rst | 7 +- doc/modules/neural_networks_supervised.rst | 13 ++- doc/modules/sgd.rst | 8 +- doc/modules/tree.rst | 2 +- doc/modules/unsupervised_reduction.rst | 5 +- doc/whats_new/older_versions.rst | 108 +++++++++--------- doc/whats_new/v0.13.rst | 62 +++++----- doc/whats_new/v0.14.rst | 62 +++++----- doc/whats_new/v0.15.rst | 60 +++++----- doc/whats_new/v0.16.rst | 81 +++++++------ doc/whats_new/v0.17.rst | 62 +++++----- doc/whats_new/v0.18.rst | 69 ++++++----- doc/whats_new/v0.19.rst | 58 +++++----- doc/whats_new/v0.20.rst | 80 ++++++------- doc/whats_new/v0.21.rst | 78 ++++++------- doc/whats_new/v0.22.rst | 77 +++++++------ doc/whats_new/v0.23.rst | 33 +++--- doc/whats_new/v0.24.rst | 67 ++++++----- doc/whats_new/v1.0.rst | 38 +++--- doc/whats_new/v1.1.rst | 17 +-- doc/whats_new/v1.2.rst | 31 ++--- doc/whats_new/v1.3.rst | 41 ++++--- examples/bicluster/README.txt | 2 +- .../bicluster/plot_spectral_biclustering.py | 2 +- .../decomposition/plot_faces_decomposition.py | 48 ++++---- .../plot_gradient_boosting_quantile.py | 8 +- .../plot_select_from_model_diabetes.py | 2 +- ...t_iterative_imputer_variants_comparison.py | 4 +- .../plot_scalable_poly_kernels.py | 8 +- ...puted_gram_matrix_with_weighted_samples.py | 4 +- examples/linear_model/plot_lasso_lars_ic.py | 2 +- ...lot_tweedie_regression_insurance_claims.py | 6 +- ...lot_grid_search_text_feature_extraction.py | 2 +- examples/model_selection/plot_roc.py | 4 +- .../plot_target_encoder_cross_val.py | 2 +- .../plot_release_highlights_0_22_0.py | 12 +- .../plot_release_highlights_1_1_0.py | 49 ++++---- sklearn/_config.py | 2 +- sklearn/base.py | 14 +-- sklearn/calibration.py | 2 +- sklearn/cluster/_dbscan.py | 6 +- sklearn/cluster/_hdbscan/hdbscan.py | 2 +- sklearn/cluster/_optics.py | 26 ++--- sklearn/cluster/_spectral.py | 4 +- sklearn/compose/_column_transformer.py | 20 ++-- sklearn/covariance/_graph_lasso.py | 2 +- sklearn/decomposition/_dict_learning.py | 3 +- sklearn/ensemble/_forest.py | 2 +- sklearn/feature_extraction/text.py | 4 +- sklearn/feature_selection/_sequential.py | 8 +- sklearn/impute/_base.py | 6 +- sklearn/inspection/_plot/decision_boundary.py | 8 +- sklearn/kernel_ridge.py | 2 +- sklearn/linear_model/_coordinate_descent.py | 8 +- sklearn/linear_model/_least_angle.py | 4 +- sklearn/linear_model/_omp.py | 2 +- sklearn/linear_model/_passive_aggressive.py | 2 +- sklearn/linear_model/_ransac.py | 5 +- sklearn/metrics/_classification.py | 2 +- sklearn/metrics/_plot/det_curve.py | 2 +- .../metrics/_plot/precision_recall_curve.py | 10 +- sklearn/metrics/_plot/regression.py | 3 +- sklearn/metrics/_plot/roc_curve.py | 4 +- sklearn/metrics/cluster/_supervised.py | 13 ++- sklearn/metrics/cluster/_unsupervised.py | 5 +- sklearn/metrics/pairwise.py | 7 +- sklearn/model_selection/_plot.py | 4 +- sklearn/multioutput.py | 8 +- sklearn/neighbors/_lof.py | 2 +- sklearn/semi_supervised/_label_propagation.py | 1 - sklearn/utils/_metadata_requests.py | 28 ++--- sklearn/utils/estimator_checks.py | 4 +- 92 files changed, 852 insertions(+), 828 deletions(-) diff --git a/doc/common_pitfalls.rst b/doc/common_pitfalls.rst index 77341047857b5..eef995754d2bb 100644 --- a/doc/common_pitfalls.rst +++ b/doc/common_pitfalls.rst @@ -416,7 +416,7 @@ it will allow the estimator RNG to vary for each fold. **Cloning** Another subtle side effect of passing `RandomState` instances is how -:func:`~sklearn.clone` will work:: +:func:`~sklearn.base.clone` will work:: >>> from sklearn import clone >>> from sklearn.ensemble import RandomForestClassifier @@ -439,10 +439,10 @@ If an integer were passed, `a` and `b` would be exact clones and they would not influence each other. .. warning:: - Even though :func:`~sklearn.clone` is rarely used in user code, it is + Even though :func:`~sklearn.base.clone` is rarely used in user code, it is called pervasively throughout scikit-learn codebase: in particular, most meta-estimators that accept non-fitted estimators call - :func:`~sklearn.clone` internally + :func:`~sklearn.base.clone` internally (:class:`~sklearn.model_selection.GridSearchCV`, :class:`~sklearn.ensemble.StackingClassifier`, :class:`~sklearn.calibration.CalibratedClassifierCV`, etc.). @@ -553,7 +553,7 @@ When we evaluate a randomized estimator performance by cross-validation, we want to make sure that the estimator can yield accurate predictions for new data, but we also want to make sure that the estimator is robust w.r.t. its random initialization. For example, we would like the random weights -initialization of a :class:`~sklearn.linear_model.SGDCLassifier` to be +initialization of a :class:`~sklearn.linear_model.SGDClassifier` to be consistently good across all folds: otherwise, when we train that estimator on new data, we might get unlucky and the random initialization may lead to bad performance. Similarly, we want a random forest to be robust w.r.t the diff --git a/doc/datasets/loading_other_datasets.rst b/doc/datasets/loading_other_datasets.rst index a376a69f26dc3..3a6669799f5a3 100644 --- a/doc/datasets/loading_other_datasets.rst +++ b/doc/datasets/loading_other_datasets.rst @@ -290,9 +290,9 @@ format usable by scikit-learn: context such as .mat and .arff * `numpy/routines.io `_ for standard loading of columnar data into numpy arrays -* scikit-learn's :func:`datasets.load_svmlight_file` for the svmlight or libSVM +* scikit-learn's :func:`load_svmlight_file` for the svmlight or libSVM sparse format -* scikit-learn's :func:`datasets.load_files` for directories of text files where +* scikit-learn's :func:`load_files` for directories of text files where the name of each directory is the name of each category and each file inside of each directory corresponds to one sample from that category diff --git a/doc/developers/develop.rst b/doc/developers/develop.rst index f4fd4898865ea..95f5950ff978d 100644 --- a/doc/developers/develop.rst +++ b/doc/developers/develop.rst @@ -414,7 +414,7 @@ trailing ``_`` is used to check if the estimator has been fitted. Cloning ------- -For use with the :mod:`model_selection` module, +For use with the :mod:`~sklearn.model_selection` module, an estimator must support the ``base.clone`` function to replicate an estimator. This can be done by providing a ``get_params`` method. If ``get_params`` is present, then ``clone(estimator)`` will be an instance of @@ -508,7 +508,7 @@ independent term is stored in ``intercept_``. ``sklearn.linear_model._base`` contains a few base classes and mixins that implement common linear model patterns. -The :mod:`sklearn.utils.multiclass` module contains useful functions +The :mod:`~sklearn.utils.multiclass` module contains useful functions for working with multiclass and multilabel problems. .. _estimator_tags: @@ -568,7 +568,7 @@ pairwise (default=False) or a cross validation procedure that extracts a sub-sample of data intended for a pairwise estimator, where the data needs to be indexed on both axes. Specifically, this tag is used by - :func:`~sklearn.utils.metaestimators._safe_split` to slice rows and + `sklearn.utils.metaestimators._safe_split` to slice rows and columns. preserves_dtype (default=``[np.float64]``) @@ -855,7 +855,7 @@ Numerical assertions in tests ----------------------------- When asserting the quasi-equality of arrays of continuous values, -do use :func:`sklearn.utils._testing.assert_allclose`. +do use `sklearn.utils._testing.assert_allclose`. The relative tolerance is automatically inferred from the provided arrays dtypes (for float32 and float64 dtypes in particular) but you can override @@ -865,4 +865,4 @@ When comparing arrays of zero-elements, please do provide a non-zero value for the absolute tolerance via ``atol``. For more information, please refer to the docstring of -:func:`sklearn.utils._testing.assert_allclose`. +`sklearn.utils._testing.assert_allclose`. diff --git a/doc/developers/plotting.rst b/doc/developers/plotting.rst index b0e8b3b43ee45..90b45ff4ef6b3 100644 --- a/doc/developers/plotting.rst +++ b/doc/developers/plotting.rst @@ -87,7 +87,7 @@ be placed. In this case, we suggest using matplotlib's By default, the `ax` keyword in `plot` is `None`. In this case, the single axes is created and the gridspec api is used to create the regions to plot in. -See for example, :func:`~sklearn.inspection.PartialDependenceDisplay.from_estimator +See for example, :meth:`~sklearn.inspection.PartialDependenceDisplay.from_estimator` which plots multiple lines and contours using this API. The axes defining the bounding box is saved in a `bounding_ax_` attribute. The individual axes created are stored in an `axes_` ndarray, corresponding to the axes position on diff --git a/doc/developers/utilities.rst b/doc/developers/utilities.rst index 8b3612afda82a..2525b2b1365ed 100644 --- a/doc/developers/utilities.rst +++ b/doc/developers/utilities.rst @@ -97,7 +97,7 @@ Efficient Linear Algebra & Array Operations fast on large matrices on which you wish to extract only a small number of components. -- :func:`arrayfuncs.cholesky_delete`: +- `arrayfuncs.cholesky_delete`: (used in :func:`~sklearn.linear_model.lars_path`) Remove an item from a cholesky factorization. diff --git a/doc/glossary.rst b/doc/glossary.rst index d1b92beae8753..36afcd9483684 100644 --- a/doc/glossary.rst +++ b/doc/glossary.rst @@ -344,8 +344,8 @@ General Concepts evaluation metric evaluation metrics Evaluation metrics give a measure of how well a model performs. We may - use this term specifically to refer to the functions in :mod:`metrics` - (disregarding :mod:`metrics.pairwise`), as distinct from the + use this term specifically to refer to the functions in :mod:`~sklearn.metrics` + (disregarding :mod:`~sklearn.metrics.pairwise`), as distinct from the :term:`score` method and the :term:`scoring` API used in cross validation. See :ref:`model_evaluation`. @@ -360,7 +360,7 @@ General Concepts the scoring API. Note that some estimators can calculate metrics that are not included - in :mod:`metrics` and are estimator-specific, notably model + in :mod:`~sklearn.metrics` and are estimator-specific, notably model likelihoods. estimator tags @@ -494,8 +494,8 @@ General Concepts applying a :term:`transformer` to the entirety of a dataset rather than each training portion in a cross validation split. - We aim to provide interfaces (such as :mod:`pipeline` and - :mod:`model_selection`) that shield the user from data leakage. + We aim to provide interfaces (such as :mod:`~sklearn.pipeline` and + :mod:`~sklearn.model_selection`) that shield the user from data leakage. memmapping memory map @@ -575,7 +575,7 @@ General Concepts params We mostly use *parameter* to refer to the aspects of an estimator that can be specified in its construction. For example, ``max_depth`` and - ``random_state`` are parameters of :class:`RandomForestClassifier`. + ``random_state`` are parameters of :class:`~ensemble.RandomForestClassifier`. Parameters to an estimator's constructor are stored unmodified as attributes on the estimator instance, and conventionally start with an alphabetic character and end with an alphanumeric character. Each @@ -620,7 +620,7 @@ General Concepts implementations of distance metrics (as well as improper metrics like Cosine Distance) through :func:`metrics.pairwise_distances`, and of kernel functions (a constrained class of similarity functions) in - :func:`metrics.pairwise_kernels`. These can compute pairwise distance + :func:`metrics.pairwise.pairwise_kernels`. These can compute pairwise distance matrices that are symmetric and hence store data redundantly. See also :term:`precomputed` and :term:`metric`. @@ -1154,7 +1154,7 @@ Target Types XXX: For simplicity, we may not always support string class labels for multiclass multioutput, and integer class labels should be used. - :mod:`multioutput` provides estimators which estimate multi-output + :mod:`~sklearn.multioutput` provides estimators which estimate multi-output problems using multiple single-output estimators. This may not fully account for dependencies among the different outputs, which methods natively handling the multioutput case (e.g. decision trees, nearest @@ -1506,7 +1506,7 @@ functions or non-estimator constructors. 1: 1}, {0: 1, 1: 1}]`` instead of ``[{1:1}, {2:5}, {3:1}, {4:1}]``. The ``class_weight`` parameter is validated and interpreted with - :func:`utils.compute_class_weight`. + :func:`utils.class_weight.compute_class_weight`. ``cv`` Determines a cross validation splitting strategy, as used in @@ -1532,16 +1532,17 @@ functions or non-estimator constructors. With some exceptions (especially where not using cross validation at all is an option), the default is 5-fold. - ``cv`` values are validated and interpreted with :func:`utils.check_cv`. + ``cv`` values are validated and interpreted with + :func:`model_selection.check_cv`. ``kernel`` Specifies the kernel function to be used by Kernel Method algorithms. - For example, the estimators :class:`SVC` and - :class:`GaussianProcessClassifier` both have a ``kernel`` parameter - that takes the name of the kernel to use as string or a callable - kernel function used to compute the kernel matrix. For more reference, - see the :ref:`kernel_approximation` and the :ref:`gaussian_process` - user guides. + For example, the estimators :class:`svm.SVC` and + :class:`gaussian_process.GaussianProcessClassifier` both have a + ``kernel`` parameter that takes the name of the kernel to use as string + or a callable kernel function used to compute the kernel matrix. For + more reference, see the :ref:`kernel_approximation` and the + :ref:`gaussian_process` user guides. ``max_iter`` For estimators involving iterative optimization, this determines the @@ -1702,7 +1703,7 @@ functions or non-estimator constructors. is an interaction between ``warm_start`` and the parameter controlling the number of iterations of the estimator. - For estimators imported from :mod:`ensemble`, + For estimators imported from :mod:`~sklearn.ensemble`, ``warm_start`` will interact with ``n_estimators`` or ``max_iter``. For these models, the number of iterations, reported via ``len(estimators_)`` or ``n_iter_``, corresponds the total number of diff --git a/doc/modules/biclustering.rst b/doc/modules/biclustering.rst index 44a996ed0ffd6..2189e85e0f0ef 100644 --- a/doc/modules/biclustering.rst +++ b/doc/modules/biclustering.rst @@ -4,8 +4,7 @@ Biclustering ============ -Biclustering can be performed with the module -:mod:`sklearn.cluster.bicluster`. Biclustering algorithms simultaneously +Biclustering algorithms simultaneously cluster rows and columns of a data matrix. These clusters of rows and columns are known as biclusters. Each determines a submatrix of the original data matrix with some desired properties. @@ -82,7 +81,7 @@ diagonal and checkerboard bicluster structures. these alternate names. -.. currentmodule:: sklearn.cluster.bicluster +.. currentmodule:: sklearn.cluster .. _spectral_coclustering: diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst index 204c300b1a9b8..7f6f5c910a3fc 100644 --- a/doc/modules/classes.rst +++ b/doc/modules/classes.rst @@ -36,6 +36,7 @@ Base classes base.TransformerMixin base.MetaEstimatorMixin base.OneToOneFeatureMixin + base.OutlierMixin base.ClassNamePrefixFeaturesOutMixin feature_selection.SelectorMixin diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst index f976110ad8712..1f05ce71b549b 100644 --- a/doc/modules/clustering.rst +++ b/doc/modules/clustering.rst @@ -931,7 +931,7 @@ by black points below. which avoids calculating the full distance matrix (as was done in scikit-learn versions before 0.14). The possibility to use custom metrics is retained; - for details, see :class:`NearestNeighbors`. + for details, see :class:`~sklearn.neighbors.NearestNeighbors`. .. topic:: Memory consumption for large sample sizes diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst index 5bcee9550b968..faba9a76ab94c 100644 --- a/doc/modules/compose.rst +++ b/doc/modules/compose.rst @@ -238,9 +238,9 @@ object:: Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. - In following example, accessing the :class:`PCA` instance ``pca2`` - will raise an ``AttributeError`` since ``pca2`` will be an unfitted - transformer. + In following example, accessing the :class:`~sklearn.decomposition.PCA` + instance ``pca2`` will raise an ``AttributeError`` since ``pca2`` will be an + unfitted transformer. Instead, use the attribute ``named_steps`` to inspect estimators within the pipeline:: diff --git a/doc/modules/decomposition.rst b/doc/modules/decomposition.rst index 6a55895b65f07..b852a6133d542 100644 --- a/doc/modules/decomposition.rst +++ b/doc/modules/decomposition.rst @@ -808,7 +808,7 @@ faces dataset, in comparison with the PCA eigenfaces. .. centered:: |pca_img5| |nmf_img5| -The :attr:`init` attribute determines the initialization method applied, which +The `init` attribute determines the initialization method applied, which has a great impact on the performance of the method. :class:`NMF` implements the method Nonnegative Double Singular Value Decomposition. NNDSVD [4]_ is based on two SVD processes, one approximating the data matrix, the other approximating @@ -825,20 +825,20 @@ basic NNDSVD algorithm which introduces a lot of zeros; in this case, NNDSVDa or NNDSVDar should be preferred. :class:`NMF` can also be initialized with correctly scaled random non-negative -matrices by setting :attr:`init="random"`. An integer seed or a -``RandomState`` can also be passed to :attr:`random_state` to control +matrices by setting `init="random"`. An integer seed or a +``RandomState`` can also be passed to `random_state` to control reproducibility. -In :class:`NMF`, L1 and L2 priors can be added to the loss function in order -to regularize the model. The L2 prior uses the Frobenius norm, while the L1 -prior uses an elementwise L1 norm. As in :class:`ElasticNet`, we control the -combination of L1 and L2 with the :attr:`l1_ratio` (:math:`\rho`) parameter, -and the intensity of the regularization with the :attr:`alpha_W` and :attr:`alpha_H` -(:math:`\alpha_W` and :math:`\alpha_H`) parameters. The priors are scaled by the number -of samples (:math:`n\_samples`) for `H` and the number of features (:math:`n\_features`) -for `W` to keep their impact balanced with respect to one another and to the data fit -term as independent as possible of the size of the training set. Then the priors terms -are: +In :class:`NMF`, L1 and L2 priors can be added to the loss function in order to +regularize the model. The L2 prior uses the Frobenius norm, while the L1 prior +uses an elementwise L1 norm. As in :class:`~sklearn.linear_model.ElasticNet`, +we control the combination of L1 and L2 with the `l1_ratio` (:math:`\rho`) +parameter, and the intensity of the regularization with the `alpha_W` and +`alpha_H` (:math:`\alpha_W` and :math:`\alpha_H`) parameters. The priors are +scaled by the number of samples (:math:`n\_samples`) for `H` and the number of +features (:math:`n\_features`) for `W` to keep their impact balanced with +respect to one another and to the data fit term as independent as possible of +the size of the training set. Then the priors terms are: .. math:: (\alpha_W \rho ||W||_1 + \frac{\alpha_W(1-\rho)}{2} ||W||_{\mathrm{Fro}} ^ 2) * n\_features @@ -1002,16 +1002,16 @@ structure. When modeling text corpora, the model assumes the following generative process for a corpus with :math:`D` documents and :math:`K` topics, with :math:`K` -corresponding to :attr:`n_components` in the API: +corresponding to `n_components` in the API: 1. For each topic :math:`k \in K`, draw :math:`\beta_k \sim \mathrm{Dirichlet}(\eta)`. This provides a distribution over the words, i.e. the probability of a word appearing in topic :math:`k`. - :math:`\eta` corresponds to :attr:`topic_word_prior`. + :math:`\eta` corresponds to `topic_word_prior`. 2. For each document :math:`d \in D`, draw the topic proportions :math:`\theta_d \sim \mathrm{Dirichlet}(\alpha)`. :math:`\alpha` - corresponds to :attr:`doc_topic_prior`. + corresponds to `doc_topic_prior`. 3. For each word :math:`i` in document :math:`d`: @@ -1054,7 +1054,7 @@ points. When :class:`LatentDirichletAllocation` is applied on a "document-term" matrix, the matrix will be decomposed into a "topic-term" matrix and a "document-topic" matrix. While -"topic-term" matrix is stored as :attr:`components_` in the model, "document-topic" matrix +"topic-term" matrix is stored as `components_` in the model, "document-topic" matrix can be calculated from ``transform`` method. :class:`LatentDirichletAllocation` also implements ``partial_fit`` method. This is used diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst index fdcc216a96848..c3ea63bc6e944 100644 --- a/doc/modules/ensemble.rst +++ b/doc/modules/ensemble.rst @@ -499,8 +499,8 @@ with least squares loss and 500 base learners to the diabetes dataset (:func:`sklearn.datasets.load_diabetes`). The plot shows the train and test error at each iteration. The train error at each iteration is stored in the -:attr:`~GradientBoostingRegressor.train_score_` attribute -of the gradient boosting model. The test error at each iterations can be obtained +`train_score_` attribute of the gradient boosting model. +The test error at each iterations can be obtained via the :meth:`~GradientBoostingRegressor.staged_predict` method which returns a generator that yields the predictions at each stage. Plots like these can be used to determine the optimal number of trees (i.e. ``n_estimators``) by early stopping. @@ -770,10 +770,9 @@ parameter. Stochastic gradient boosting allows to compute out-of-bag estimates of the test deviance by computing the improvement in deviance on the examples that are not included in the bootstrap sample (i.e. the out-of-bag examples). -The improvements are stored in the attribute -:attr:`~GradientBoostingRegressor.oob_improvement_`. ``oob_improvement_[i]`` holds -the improvement in terms of the loss on the OOB samples if you add the i-th stage -to the current predictions. +The improvements are stored in the attribute `oob_improvement_`. +``oob_improvement_[i]`` holds the improvement in terms of the loss on the OOB samples +if you add the i-th stage to the current predictions. Out-of-bag estimates can be used for model selection, for example to determine the optimal number of iterations. OOB estimates are usually very pessimistic thus we recommend to use cross-validation instead and only use OOB if cross-validation @@ -1180,8 +1179,8 @@ samples and features are drawn with or without replacement. When using a subset of the available samples the generalization accuracy can be estimated with the out-of-bag samples by setting ``oob_score=True``. As an example, the snippet below illustrates how to instantiate a bagging ensemble of -:class:`KNeighborsClassifier` estimators, each built on random subsets of -50% of the samples and 50% of the features. +:class:`~sklearn.neighbors.KNeighborsClassifier` estimators, each built on random +subsets of 50% of the samples and 50% of the features. >>> from sklearn.ensemble import BaggingClassifier >>> from sklearn.neighbors import KNeighborsClassifier diff --git a/doc/modules/feature_selection.rst b/doc/modules/feature_selection.rst index ec902979d5600..01e2d079c45a4 100644 --- a/doc/modules/feature_selection.rst +++ b/doc/modules/feature_selection.rst @@ -215,11 +215,12 @@ alpha parameter, the fewer features selected. There is no general rule to select an alpha parameter for recovery of non-zero coefficients. It can by set by cross-validation - (:class:`LassoCV` or :class:`LassoLarsCV`), though this may lead to - under-penalized models: including a small number of non-relevant - variables is not detrimental to prediction score. BIC - (:class:`LassoLarsIC`) tends, on the opposite, to set high values of - alpha. + (:class:`~sklearn.linear_model.LassoCV` or + :class:`~sklearn.linear_model.LassoLarsCV`), though this may lead to + under-penalized models: including a small number of non-relevant variables + is not detrimental to prediction score. BIC + (:class:`~sklearn.linear_model.LassoLarsIC`) tends, on the opposite, to set + high values of alpha. **Reference** Richard G. Baraniuk "Compressive Sensing", IEEE Signal Processing Magazine [120] July 2007 diff --git a/doc/modules/gaussian_process.rst b/doc/modules/gaussian_process.rst index db490bc1309d3..b8a095eb5b472 100644 --- a/doc/modules/gaussian_process.rst +++ b/doc/modules/gaussian_process.rst @@ -401,15 +401,17 @@ The specification of each hyperparameter is stored in the form of an instance of hyperparameter with name "x" must have the attributes self.x and self.x_bounds. The abstract base class for all kernels is :class:`Kernel`. Kernel implements a -similar interface as :class:`Estimator`, providing the methods ``get_params()``, -``set_params()``, and ``clone()``. This allows setting kernel values also via -meta-estimators such as :class:`Pipeline` or :class:`GridSearch`. Note that due to the nested +similar interface as :class:`~sklearn.base.BaseEstimator`, providing the +methods ``get_params()``, ``set_params()``, and ``clone()``. This allows +setting kernel values also via meta-estimators such as +:class:`~sklearn.pipeline.Pipeline` or +:class:`~sklearn.model_selection.GridSearchCV`. Note that due to the nested structure of kernels (by applying kernel operators, see below), the names of -kernel parameters might become relatively complicated. In general, for a -binary kernel operator, parameters of the left operand are prefixed with ``k1__`` -and parameters of the right operand with ``k2__``. An additional convenience -method is ``clone_with_theta(theta)``, which returns a cloned version of the -kernel but with the hyperparameters set to ``theta``. An illustrative example: +kernel parameters might become relatively complicated. In general, for a binary +kernel operator, parameters of the left operand are prefixed with ``k1__`` and +parameters of the right operand with ``k2__``. An additional convenience method +is ``clone_with_theta(theta)``, which returns a cloned version of the kernel +but with the hyperparameters set to ``theta``. An illustrative example: >>> from sklearn.gaussian_process.kernels import ConstantKernel, RBF >>> kernel = ConstantKernel(constant_value=1.0, constant_value_bounds=(0.0, 10.0)) * RBF(length_scale=0.5, length_scale_bounds=(0.0, 10.0)) + RBF(length_scale=2.0, length_scale_bounds=(0.0, 10.0)) diff --git a/doc/modules/grid_search.rst b/doc/modules/grid_search.rst index 851f9a202fa2f..3f99e7841bb00 100644 --- a/doc/modules/grid_search.rst +++ b/doc/modules/grid_search.rst @@ -81,7 +81,7 @@ evaluated and the best combination is retained. of Grid Search coupling parameters from a text documents feature extractor (n-gram count vectorizer and TF-IDF transformer) with a classifier (here a linear SVM trained with SGD with either elastic - net or L2 penalty) using a :class:`pipeline.Pipeline` instance. + net or L2 penalty) using a :class:`~sklearn.pipeline.Pipeline` instance. - See :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py` for an example of Grid Search within a cross validation loop on the iris diff --git a/doc/modules/impute.rst b/doc/modules/impute.rst index 6314b2ea71737..a1985bc460b77 100644 --- a/doc/modules/impute.rst +++ b/doc/modules/impute.rst @@ -22,9 +22,9 @@ Univariate vs. Multivariate Imputation One type of imputation algorithm is univariate, which imputes values in the i-th feature dimension using only non-missing values in that feature dimension -(e.g. :class:`impute.SimpleImputer`). By contrast, multivariate imputation +(e.g. :class:`SimpleImputer`). By contrast, multivariate imputation algorithms use the entire set of available feature dimensions to estimate the -missing values (e.g. :class:`impute.IterativeImputer`). +missing values (e.g. :class:`IterativeImputer`). .. _single_imputer: @@ -190,19 +190,20 @@ Nearest neighbors imputation The :class:`KNNImputer` class provides imputation for filling in missing values using the k-Nearest Neighbors approach. By default, a euclidean distance metric -that supports missing values, :func:`~sklearn.metrics.nan_euclidean_distances`, -is used to find the nearest neighbors. Each missing feature is imputed using -values from ``n_neighbors`` nearest neighbors that have a value for the -feature. The feature of the neighbors are averaged uniformly or weighted by -distance to each neighbor. If a sample has more than one feature missing, then -the neighbors for that sample can be different depending on the particular -feature being imputed. When the number of available neighbors is less than -`n_neighbors` and there are no defined distances to the training set, the -training set average for that feature is used during imputation. If there is at -least one neighbor with a defined distance, the weighted or unweighted average -of the remaining neighbors will be used during imputation. If a feature is -always missing in training, it is removed during `transform`. For more -information on the methodology, see ref. [OL2001]_. +that supports missing values, +:func:`~sklearn.metrics.pairwise.nan_euclidean_distances`, is used to find the +nearest neighbors. Each missing feature is imputed using values from +``n_neighbors`` nearest neighbors that have a value for the feature. The +feature of the neighbors are averaged uniformly or weighted by distance to each +neighbor. If a sample has more than one feature missing, then the neighbors for +that sample can be different depending on the particular feature being imputed. +When the number of available neighbors is less than `n_neighbors` and there are +no defined distances to the training set, the training set average for that +feature is used during imputation. If there is at least one neighbor with a +defined distance, the weighted or unweighted average of the remaining neighbors +will be used during imputation. If a feature is always missing in training, it +is removed during `transform`. For more information on the methodology, see +ref. [OL2001]_. The following snippet demonstrates how to replace missing values, encoded as ``np.nan``, using the mean feature value of the two nearest @@ -303,10 +304,12 @@ whether or not they contain missing values:: >>> indicator.features_ array([0, 1, 2, 3]) -When using the :class:`MissingIndicator` in a :class:`Pipeline`, be sure to use -the :class:`FeatureUnion` or :class:`ColumnTransformer` to add the indicator -features to the regular features. First we obtain the `iris` dataset, and add -some missing values to it. +When using the :class:`MissingIndicator` in a +:class:`~sklearn.pipeline.Pipeline`, be sure to use the +:class:`~sklearn.pipeline.FeatureUnion` or +:class:`~sklearn.compose.ColumnTransformer` to add the indicator features to +the regular features. First we obtain the `iris` dataset, and add some missing +values to it. >>> from sklearn.datasets import load_iris >>> from sklearn.impute import SimpleImputer, MissingIndicator @@ -319,9 +322,9 @@ some missing values to it. >>> X_train, X_test, y_train, _ = train_test_split(X, y, test_size=100, ... random_state=0) -Now we create a :class:`FeatureUnion`. All features will be imputed using -:class:`SimpleImputer`, in order to enable classifiers to work with this data. -Additionally, it adds the indicator variables from +Now we create a :class:`~sklearn.pipeline.FeatureUnion`. All features will be +imputed using :class:`SimpleImputer`, in order to enable classifiers to work +with this data. Additionally, it adds the indicator variables from :class:`MissingIndicator`. >>> transformer = FeatureUnion( @@ -334,8 +337,8 @@ Additionally, it adds the indicator variables from (100, 8) Of course, we cannot use the transformer to make any predictions. We should -wrap this in a :class:`Pipeline` with a classifier (e.g., a -:class:`DecisionTreeClassifier`) to be able to make predictions. +wrap this in a :class:`~sklearn.pipeline.Pipeline` with a classifier (e.g., a +:class:`~sklearn.tree.DecisionTreeClassifier`) to be able to make predictions. >>> clf = make_pipeline(transformer, DecisionTreeClassifier()) >>> clf = clf.fit(X_train, y_train) diff --git a/doc/modules/kernel_approximation.rst b/doc/modules/kernel_approximation.rst index 40e8e8b526d1e..2166227daf247 100644 --- a/doc/modules/kernel_approximation.rst +++ b/doc/modules/kernel_approximation.rst @@ -108,7 +108,7 @@ The additive chi squared kernel as used here is given by k(x, y) = \sum_i \frac{2x_iy_i}{x_i+y_i} -This is not exactly the same as :func:`sklearn.metrics.additive_chi2_kernel`. +This is not exactly the same as :func:`sklearn.metrics.pairwise.additive_chi2_kernel`. The authors of [VZ2010]_ prefer the version above as it is always positive definite. Since the kernel is additive, it is possible to treat all components diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index 154bbe5ee5cd7..43356763d69c3 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -1062,15 +1062,16 @@ with `loss="log_loss"`, which might be even faster but requires more tuning. .. topic:: Differences from liblinear: There might be a difference in the scores obtained between - :class:`LogisticRegression` with ``solver=liblinear`` - or :class:`LinearSVC` and the external liblinear library directly, - when ``fit_intercept=False`` and the fit ``coef_`` (or) the data to - be predicted are zeroes. This is because for the sample(s) with - ``decision_function`` zero, :class:`LogisticRegression` and :class:`LinearSVC` - predict the negative class, while liblinear predicts the positive class. - Note that a model with ``fit_intercept=False`` and having many samples with - ``decision_function`` zero, is likely to be a underfit, bad model and you are - advised to set ``fit_intercept=True`` and increase the intercept_scaling. + :class:`LogisticRegression` with ``solver=liblinear`` or + :class:`~sklearn.svm.LinearSVC` and the external liblinear library directly, + when ``fit_intercept=False`` and the fit ``coef_`` (or) the data to be + predicted are zeroes. This is because for the sample(s) with + ``decision_function`` zero, :class:`LogisticRegression` and + :class:`~sklearn.svm.LinearSVC` predict the negative class, while liblinear + predicts the positive class. Note that a model with ``fit_intercept=False`` + and having many samples with ``decision_function`` zero, is likely to be a + underfit, bad model and you are advised to set ``fit_intercept=True`` and + increase the intercept_scaling. .. note:: **Feature selection with sparse logistic regression** diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst index 9cc1025047802..32c2fc22ec0f6 100644 --- a/doc/modules/model_evaluation.rst +++ b/doc/modules/model_evaluation.rst @@ -823,8 +823,8 @@ binary case. The :func:`average_precision_score` function supports multiclass and multilabel formats by computing each class score in a One-vs-the-rest (OvR) fashion and averaging them or not depending of its ``average`` argument value. -The :func:`PredictionRecallDisplay.from_estimator` and -:func:`PredictionRecallDisplay.from_predictions` functions will plot the +The :func:`PrecisionRecallDisplay.from_estimator` and +:func:`PrecisionRecallDisplay.from_predictions` functions will plot the precision-recall curve as follows. .. image:: ../auto_examples/model_selection/images/sphx_glr_plot_precision_recall_001.png @@ -2785,8 +2785,8 @@ model would grow with the predicted value of `E[y|X]` (either linearly for Poisson or quadratically for Gamma). When fitting a linear least squares regression model (see -:class:`~sklearn.linear_mnodel.LinearRegression` and -:class:`~sklearn.linear_mnodel.Ridge`), we can use this plot to check +:class:`~sklearn.linear_model.LinearRegression` and +:class:`~sklearn.linear_model.Ridge`), we can use this plot to check if some of the `model assumptions `_ are met, in particular that the residuals should be uncorrelated, their diff --git a/doc/modules/naive_bayes.rst b/doc/modules/naive_bayes.rst index 1cb8aa0d6dedf..b0b32c28e455a 100644 --- a/doc/modules/naive_bayes.rst +++ b/doc/modules/naive_bayes.rst @@ -253,9 +253,9 @@ to class :math:`c`, :math:`N_{c} = |\{ j \in J\mid y_j = c\}|` is the number of samples with class c, :math:`\alpha` is a smoothing parameter and :math:`n_i` is the number of available categories of feature :math:`i`. -:class:`CategoricalNB` assumes that the sample matrix :math:`X` is encoded -(for instance with the help of :class:`OrdinalEncoder`) such that all -categories for each feature :math:`i` are represented with numbers +:class:`CategoricalNB` assumes that the sample matrix :math:`X` is encoded (for +instance with the help of :class:`~sklearn.preprocessing.OrdinalEncoder`) such +that all categories for each feature :math:`i` are represented with numbers :math:`0, ..., n_i - 1` where :math:`n_i` is the number of available categories of feature :math:`i`. diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst index fd5fc55572451..d11287e7c29b1 100644 --- a/doc/modules/neighbors.rst +++ b/doc/modules/neighbors.rst @@ -472,9 +472,10 @@ leaf nodes. The level of this switch can be specified with the parameter Valid Metrics for Nearest Neighbor Algorithms --------------------------------------------- -For a list of available metrics, see the documentation of the :class:`DistanceMetric` -class and the metrics listed in `sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`. -Note that the "cosine" metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`. +For a list of available metrics, see the documentation of the +:class:`~sklearn.metrics.DistanceMetric` class and the metrics listed in +`sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`. Note that the "cosine" +metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`. A list of valid metrics for any of the above algorithms can be obtained by using their ``valid_metric`` attribute. For example, valid metrics for ``KDTree`` can be generated by: diff --git a/doc/modules/neural_networks_supervised.rst b/doc/modules/neural_networks_supervised.rst index 995faa9e6d19c..388f32e7c6925 100644 --- a/doc/modules/neural_networks_supervised.rst +++ b/doc/modules/neural_networks_supervised.rst @@ -316,7 +316,7 @@ Tips on Practical Use attribute on the input vector X to [0, 1] or [-1, +1], or standardize it to have mean 0 and variance 1. Note that you must apply the *same* scaling to the test set for meaningful results. - You can use :class:`StandardScaler` for standardization. + You can use :class:`~sklearn.preprocessing.StandardScaler` for standardization. >>> from sklearn.preprocessing import StandardScaler # doctest: +SKIP >>> scaler = StandardScaler() # doctest: +SKIP @@ -326,12 +326,13 @@ Tips on Practical Use >>> # apply same transformation to test data >>> X_test = scaler.transform(X_test) # doctest: +SKIP - An alternative and recommended approach is to use :class:`StandardScaler` - in a :class:`Pipeline` + An alternative and recommended approach is to use + :class:`~sklearn.preprocessing.StandardScaler` in a + :class:`~sklearn.pipeline.Pipeline` - * Finding a reasonable regularization parameter :math:`\alpha` is - best done using :class:`GridSearchCV`, usually in the - range ``10.0 ** -np.arange(1, 7)``. + * Finding a reasonable regularization parameter :math:`\alpha` is best done + using :class:`~sklearn.model_selection.GridSearchCV`, usually in the range + ``10.0 ** -np.arange(1, 7)``. * Empirically, we observed that `L-BFGS` converges faster and with better solutions on small datasets. For relatively large diff --git a/doc/modules/sgd.rst b/doc/modules/sgd.rst index 4b72e7c5f2bac..3f587d25b3450 100644 --- a/doc/modules/sgd.rst +++ b/doc/modules/sgd.rst @@ -348,9 +348,9 @@ Tips on Practical Use * Stochastic Gradient Descent is sensitive to feature scaling, so it is highly recommended to scale your data. For example, scale each attribute on the input vector X to [0,1] or [-1,+1], or standardize - it to have mean 0 and variance 1. Note that the *same* scaling - must be applied to the test vector to obtain meaningful - results. This can be easily done using :class:`StandardScaler`:: + it to have mean 0 and variance 1. Note that the *same* scaling must be + applied to the test vector to obtain meaningful results. This can be easily + done using :class:`~sklearn.preprocessing.StandardScaler`:: from sklearn.preprocessing import StandardScaler scaler = StandardScaler() @@ -503,7 +503,7 @@ where :math:`t` is the time step (there are a total of `n_samples * n_iter` time steps), :math:`t_0` is determined based on a heuristic proposed by Léon Bottou such that the expected initial updates are comparable with the expected size of the weights (this assuming that the norm of the training samples is -approx. 1). The exact definition can be found in ``_init_t`` in :class:`BaseSGD`. +approx. 1). The exact definition can be found in ``_init_t`` in `BaseSGD`. For regression the default learning rate schedule is inverse scaling diff --git a/doc/modules/tree.rst b/doc/modules/tree.rst index f7d43c5a3d7da..7f4be9f6240a8 100644 --- a/doc/modules/tree.rst +++ b/doc/modules/tree.rst @@ -577,7 +577,7 @@ Note that it fits much slower than the MSE criterion. Missing Values Support ====================== -:class:`~tree.DecisionTreeClassifier` and :class:`~tree.DecisionTreeRegressor` +:class:`DecisionTreeClassifier` and :class:`DecisionTreeRegressor` have built-in support for missing values when `splitter='best'` and criterion is `'gini'`, `'entropy`', or `'log_loss'`, for classification or `'squared_error'`, `'friedman_mse'`, or `'poisson'` for regression. diff --git a/doc/modules/unsupervised_reduction.rst b/doc/modules/unsupervised_reduction.rst index 6e16886064cfc..90c80714c3131 100644 --- a/doc/modules/unsupervised_reduction.rst +++ b/doc/modules/unsupervised_reduction.rst @@ -31,7 +31,7 @@ capture well the variance of the original features. See :ref:`decompositions`. Random projections ------------------- -The module: :mod:`random_projection` provides several tools for data +The module: :mod:`~sklearn.random_projection` provides several tools for data reduction by random projections. See the relevant section of the documentation: :ref:`random_projection`. @@ -55,6 +55,5 @@ similarly. Note that if features have very different scaling or statistical properties, :class:`cluster.FeatureAgglomeration` may not be able to - capture the links between related features. Using a + capture the links between related features. Using a :class:`preprocessing.StandardScaler` can be useful in these settings. - diff --git a/doc/whats_new/older_versions.rst b/doc/whats_new/older_versions.rst index 221de4cdb7e4c..cdc37f25ed997 100644 --- a/doc/whats_new/older_versions.rst +++ b/doc/whats_new/older_versions.rst @@ -101,7 +101,7 @@ Changelog - Add MultiTaskLasso and MultiTaskElasticNet for joint feature selection, by `Alexandre Gramfort`_. -- Added :func:`metrics.auc_score` and +- Added `metrics.auc_score` and :func:`metrics.average_precision_score` convenience functions by `Andreas Müller`_. @@ -121,7 +121,7 @@ Changelog and OrthogonalMatchingPursuit) by `Vlad Niculae`_ and `Alexandre Gramfort`_. -- Fixes in :class:`~decomposition.ProbabilisticPCA` score function by Wei Li. +- Fixes in `decomposition.ProbabilisticPCA` score function by Wei Li. - Fixed feature importance computation in :ref:`gradient_boosting`. @@ -136,8 +136,8 @@ API changes summary with it's order reversed, in order to keep it consistent with the order of the returned ``fpr`` and ``tpr``. -- In :class:`hmm` objects, like :class:`~hmm.GaussianHMM`, - :class:`~hmm.MultinomialHMM`, etc., all parameters must be passed to the +- In `hmm` objects, like `hmm.GaussianHMM`, + `hmm.MultinomialHMM`, etc., all parameters must be passed to the object when initialising it and not through ``fit``. Now ``fit`` will only accept the data as an input parameter. @@ -180,7 +180,7 @@ API changes summary :meth:`~ensemble.GradientBoostingClassifier.staged_predict_proba`, and :meth:`~ensemble.GradientBoostingClassifier.staged_predict`. -- :class:`~svm.sparse.SVC` and other sparse SVM classes are now deprecated. +- `svm.sparse.SVC` and other sparse SVM classes are now deprecated. The all classes in the :ref:`svm` module now automatically select the sparse or dense representation base on the input. @@ -282,8 +282,8 @@ Highlights - Added BIC/AIC model selection to classical :ref:`gmm` and unified the API with the remainder of scikit-learn, by `Bertrand Thirion`_ -- Added :class:`~sklearn.cross_validation.StratifiedShuffleSplit`, which is - a :class:`~sklearn.cross_validation.ShuffleSplit` with balanced splits, +- Added `sklearn.cross_validation.StratifiedShuffleSplit`, which is + a `sklearn.cross_validation.ShuffleSplit` with balanced splits, by Yannick Schwartz. - :class:`~sklearn.neighbors.NearestCentroid` classifier added, along with a @@ -307,15 +307,15 @@ Other changes - Regressors can now be used as base estimator in the :ref:`multiclass` module by `Mathieu Blondel`_. -- Added n_jobs option to :func:`metrics.pairwise.pairwise_distances` +- Added n_jobs option to :func:`metrics.pairwise_distances` and :func:`metrics.pairwise.pairwise_kernels` for parallel computation, by `Mathieu Blondel`_. - :ref:`k_means` can now be run in parallel, using the ``n_jobs`` argument - to either :ref:`k_means` or :class:`KMeans`, by `Robert Layton`_. + to either :ref:`k_means` or :class:`cluster.KMeans`, by `Robert Layton`_. - Improved :ref:`cross_validation` and :ref:`grid_search` documentation - and introduced the new :func:`cross_validation.train_test_split` + and introduced the new `cross_validation.train_test_split` helper function by `Olivier Grisel`_ - :class:`~svm.SVC` members ``coef_`` and ``intercept_`` changed sign for @@ -330,7 +330,7 @@ Other changes API and fixed a bug that caused possible negative IDF, by `Olivier Grisel`_. -- Beam pruning option in :class:`_BaseHMM` module has been removed since it +- Beam pruning option in `_BaseHMM` module has been removed since it is difficult to Cythonize. If you are interested in contributing a Cython version, you can use the python version in the git history as a reference. @@ -340,31 +340,31 @@ Other changes API changes summary ------------------- -- :class:`~covariance.EllipticEnvelop` is now deprecated - Please use :class:`~covariance.EllipticEnvelope` - instead. +- `covariance.EllipticEnvelop` is now deprecated. + Please use :class:`~covariance.EllipticEnvelope` instead. - ``NeighborsClassifier`` and ``NeighborsRegressor`` are gone in the module - :ref:`neighbors`. Use the classes :class:`KNeighborsClassifier`, - :class:`RadiusNeighborsClassifier`, :class:`KNeighborsRegressor` - and/or :class:`RadiusNeighborsRegressor` instead. + :ref:`neighbors`. Use the classes :class:`~neighors.KNeighborsClassifier`, + :class:`~neighors.RadiusNeighborsClassifier`, :class:`~neighors.KNeighborsRegressor` + and/or :class:`~neighors.RadiusNeighborsRegressor` instead. - Sparse classes in the :ref:`sgd` module are now deprecated. -- In :class:`~mixture.GMM`, :class:`~mixture.DPGMM` and :class:`~mixture.VBGMM`, +- In `mixture.GMM`, `mixture.DPGMM` and `mixture.VBGMM`, parameters must be passed to an object when initialising it and not through ``fit``. Now ``fit`` will only accept the data as an input parameter. -- methods ``rvs`` and ``decode`` in :class:`GMM` module are now deprecated. +- methods ``rvs`` and ``decode`` in `GMM` module are now deprecated. ``sample`` and ``score`` or ``predict`` should be used instead. - attribute ``_scores`` and ``_pvalues`` in univariate feature selection objects are now deprecated. ``scores_`` or ``pvalues_`` should be used instead. -- In :class:`LogisticRegression`, :class:`LinearSVC`, :class:`SVC` and - :class:`NuSVC`, the ``class_weight`` parameter is now an initialization - parameter, not a parameter to fit. This makes grid searches - over this parameter possible. +- In :class:`~linear_model.LogisticRegression`, :class:`~svm.LinearSVC`, + :class:`~svm.SVC` and :class:`~svm.NuSVC`, the ``class_weight`` parameter is + now an initialization parameter, not a parameter to fit. This makes grid + searches over this parameter possible. - LFW ``data`` is now always shape ``(n_samples, n_features)`` to be consistent with the Olivetti faces dataset. Use ``images`` and @@ -375,14 +375,14 @@ API changes summary ``'ovr'`` being the default. This does not change the default behavior but hopefully is less confusing. -- Class :class:`~feature_selection.text.Vectorizer` is deprecated and - replaced by :class:`~feature_selection.text.TfidfVectorizer`. +- Class `feature_selection.text.Vectorizer` is deprecated and + replaced by `feature_selection.text.TfidfVectorizer`. - The preprocessor / analyzer nested structure for text feature extraction has been removed. All those features are now directly passed as flat constructor arguments - to :class:`~feature_selection.text.TfidfVectorizer` and - :class:`~feature_selection.text.CountVectorizer`, in particular the + to `feature_selection.text.TfidfVectorizer` and + `feature_selection.text.CountVectorizer`, in particular the following parameters are now used: - ``analyzer`` can be ``'word'`` or ``'char'`` to switch the default @@ -401,27 +401,27 @@ API changes summary ``vocabulary_`` attribute to be consistent with the project conventions. -- Class :class:`~feature_selection.text.TfidfVectorizer` now derives directly - from :class:`~feature_selection.text.CountVectorizer` to make grid +- Class `feature_selection.text.TfidfVectorizer` now derives directly + from `feature_selection.text.CountVectorizer` to make grid search trivial. -- methods ``rvs`` in :class:`_BaseHMM` module are now deprecated. +- methods ``rvs`` in `_BaseHMM` module are now deprecated. ``sample`` should be used instead. -- Beam pruning option in :class:`_BaseHMM` module is removed since it is +- Beam pruning option in `_BaseHMM` module is removed since it is difficult to be Cythonized. If you are interested, you can look in the history codes by git. - The SVMlight format loader now supports files with both zero-based and one-based column indices, since both occur "in the wild". -- Arguments in class :class:`ShuffleSplit` are now consistent with - :class:`StratifiedShuffleSplit`. Arguments ``test_fraction`` and +- Arguments in class :class:`~model_selection.ShuffleSplit` are now consistent with + :class:`~model_selection.StratifiedShuffleSplit`. Arguments ``test_fraction`` and ``train_fraction`` are deprecated and renamed to ``test_size`` and ``train_size`` and can accept both ``float`` and ``int``. -- Arguments in class :class:`Bootstrap` are now consistent with - :class:`StratifiedShuffleSplit`. Arguments ``n_test`` and +- Arguments in class `Bootstrap` are now consistent with + :class:`~model_selection.StratifiedShuffleSplit`. Arguments ``n_test`` and ``n_train`` are deprecated and renamed to ``test_size`` and ``train_size`` and can accept both ``float`` and ``int``. @@ -557,7 +557,7 @@ Changelog by `Mathieu Blondel`_. - Make :func:`~sklearn.preprocessing.scale` and - :class:`~sklearn.preprocessing.Scaler` work on sparse matrices by + `sklearn.preprocessing.Scaler` work on sparse matrices by `Olivier Grisel`_ - Feature importances using decision trees and/or forest of trees, @@ -566,7 +566,7 @@ Changelog - Parallel implementation of forests of randomized trees by `Gilles Louppe`_. -- :class:`~sklearn.cross_validation.ShuffleSplit` can subsample the train +- `sklearn.cross_validation.ShuffleSplit` can subsample the train sets as well as the test sets by `Olivier Grisel`_. - Errors in the build of the documentation fixed by `Andreas Müller`_. @@ -582,7 +582,7 @@ version 0.9: had ``overwrite_`` parameters; these have been replaced with ``copy_`` parameters with exactly the opposite meaning. - This particularly affects some of the estimators in :mod:`linear_model`. + This particularly affects some of the estimators in :mod:`~sklearn.linear_model`. The default behavior is still to copy everything passed in. - The SVMlight dataset loader :func:`~sklearn.datasets.load_svmlight_file` no @@ -596,10 +596,10 @@ version 0.9: - The :ref:`covariance` module now has a robust estimator of covariance, the Minimum Covariance Determinant estimator. -- Cluster evaluation metrics in :mod:`metrics.cluster` have been refactored +- Cluster evaluation metrics in :mod:`~sklearn.metrics.cluster` have been refactored but the changes are backwards compatible. They have been moved to the - :mod:`metrics.cluster.supervised`, along with - :mod:`metrics.cluster.unsupervised` which contains the Silhouette + `metrics.cluster.supervised`, along with + `metrics.cluster.unsupervised` which contains the Silhouette Coefficient. - The ``permutation_test_score`` function now behaves the same way as @@ -622,7 +622,7 @@ version 0.9: - ``BaseDictionaryLearning`` class replaced by ``SparseCodingMixin``. -- :func:`~sklearn.utils.extmath.fast_svd` has been renamed +- `sklearn.utils.extmath.fast_svd` has been renamed :func:`~sklearn.utils.extmath.randomized_svd` and the default oversampling is now fixed to 10 additional random vectors instead of doubling the number of components to extract. The new behavior @@ -744,7 +744,7 @@ Changelog - Text feature extraction optimizations by Lars Buitinck - Chi-Square feature selection - (:func:`feature_selection.univariate_selection.chi2`) by `Lars Buitinck`_. + (:func:`feature_selection.chi2`) by `Lars Buitinck`_. - :ref:`sample_generators` module refactoring by `Gilles Louppe`_ @@ -778,7 +778,7 @@ Changelog - Scalability improvements to :func:`metrics.roc_curve` by Olivier Hervieu -- Distance helper functions :func:`metrics.pairwise.pairwise_distances` +- Distance helper functions :func:`metrics.pairwise_distances` and :func:`metrics.pairwise.pairwise_kernels` by Robert Layton - :class:`Mini-Batch K-Means ` by Nelle Varoquaux and Peter Prettenhofer. @@ -1047,7 +1047,7 @@ Changelog - Sanity checks for SVM-based classes [`Mathieu Blondel`_]. -- Refactoring of :class:`~neighbors.NeighborsClassifier` and +- Refactoring of `neighbors.NeighborsClassifier` and :func:`neighbors.kneighbors_graph`: added different algorithms for the k-Nearest Neighbor Search and implemented a more stable algorithm for finding barycenter weights. Also added some @@ -1055,7 +1055,7 @@ Changelog `notes_neighbors `_ for more information [`Fabian Pedregosa`_]. -- Documentation improvements: Added :class:`~pca.RandomizedPCA` and +- Documentation improvements: Added `pca.RandomizedPCA` and :class:`~linear_model.LogisticRegression` to the class reference. Also added references of matrices used for clustering and other fixes [`Gael Varoquaux`_, `Fabian Pedregosa`_, `Mathieu @@ -1067,12 +1067,12 @@ Changelog :class:`~linear_model.LogisticRegression` [`Fabian Pedregosa`_]. - Performance and API improvements to - :func:`metrics.euclidean_distances` and to - :class:`~pca.RandomizedPCA` [`James Bergstra`_]. + :func:`metrics.pairwise.euclidean_distances` and to + `pca.RandomizedPCA` [`James Bergstra`_]. - Fix compilation issues under NetBSD [Kamel Ibn Hassen Derouiche] -- Allow input sequences of different lengths in :class:`~hmm.GaussianHMM` +- Allow input sequences of different lengths in `hmm.GaussianHMM` [`Ron Weiss`_]. - Fix bug in affinity propagation caused by incorrect indexing [Xinfan Meng] @@ -1141,7 +1141,7 @@ Changelog extraction. - Improved sparse matrix support, both in main classes - (:class:`~grid_search.GridSearchCV`) as in modules + (:class:`~model_selection.GridSearchCV`) as in modules sklearn.svm.sparse and sklearn.linear_model.sparse. - Lots of cool new examples and a new section that uses real-world @@ -1218,9 +1218,9 @@ New classes ----------- - Support for sparse matrices in some classifiers of modules - ``svm`` and ``linear_model`` (see :class:`~svm.sparse.SVC`, - :class:`~svm.sparse.SVR`, :class:`~svm.sparse.LinearSVC`, - :class:`~linear_model.sparse.Lasso`, :class:`~linear_model.sparse.ElasticNet`) + ``svm`` and ``linear_model`` (see `svm.sparse.SVC`, + `svm.sparse.SVR`, `svm.sparse.LinearSVC`, + `linear_model.sparse.Lasso`, `linear_model.sparse.ElasticNet`) - New :class:`~pipeline.Pipeline` object to compose different estimators. @@ -1237,8 +1237,7 @@ New classes :class:`~linear_model.LassoLars`. - New Hidden Markov Models module (see classes - :class:`~hmm.GaussianHMM`, :class:`~hmm.MultinomialHMM`, - :class:`~hmm.GMMHMM`) + `hmm.GaussianHMM`, `hmm.MultinomialHMM`, `hmm.GMMHMM`) - New module feature_extraction (see :ref:`class reference `) @@ -1383,4 +1382,3 @@ Earlier versions Earlier versions included contributions by Fred Mailhot, David Cooke, David Huard, Dave Morrill, Ed Schofield, Travis Oliphant, Pearu Peterson. - diff --git a/doc/whats_new/v0.13.rst b/doc/whats_new/v0.13.rst index 10b4d3b5b783f..00be322bf38fc 100644 --- a/doc/whats_new/v0.13.rst +++ b/doc/whats_new/v0.13.rst @@ -14,7 +14,7 @@ The 0.13.1 release only fixes some bugs and does not add any new functionality. Changelog --------- -- Fixed a testing error caused by the function :func:`cross_validation.train_test_split` being +- Fixed a testing error caused by the function `cross_validation.train_test_split` being interpreted as a test by `Yaroslav Halchenko`_. - Fixed a bug in the reassignment of small clusters in the :class:`cluster.MiniBatchKMeans` @@ -128,7 +128,7 @@ Changelog trees, by `Peter Prettenhofer`_ and `Gilles Louppe`_. - Partial dependence plots for :ref:`gradient_boosting` in - :func:`ensemble.partial_dependence.partial_dependence` by `Peter + `ensemble.partial_dependence.partial_dependence` by `Peter Prettenhofer`_. See :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py` for an example. @@ -161,7 +161,7 @@ Changelog - Faster and more robust :func:`metrics.confusion_matrix` and :ref:`clustering_evaluation` by Wei Li. -- :func:`cross_validation.cross_val_score` now works with precomputed kernels +- `cross_validation.cross_val_score` now works with precomputed kernels and affinity matrices, by `Andreas Müller`_. - LARS algorithm made more numerically stable with heuristics to drop @@ -171,7 +171,7 @@ Changelog - Faster implementation of :func:`metrics.precision_recall_curve` by Conrad Lee. -- New kernel :class:`metrics.chi2_kernel` by `Andreas Müller`_, often used +- New kernel `metrics.chi2_kernel` by `Andreas Müller`_, often used in computer vision applications. - Fix of longstanding bug in :class:`naive_bayes.BernoulliNB` fixed by @@ -184,7 +184,7 @@ Changelog :class:`ensemble.GradientBoostingRegressor` and :class:`ensemble.GradientBoostingClassifier` use the estimator :class:`tree.DecisionTreeRegressor` instead of the - :class:`tree._tree.Tree` data structure by `Arnaud Joly`_. + `tree._tree.Tree` data structure by `Arnaud Joly`_. - Fixed a floating point exception in the :ref:`decision trees ` module, by Seberg. @@ -209,7 +209,7 @@ Changelog - Fixed a bug in :class:`sklearn.svm.SVC` when using csr-matrices with unsorted indices by Xinfan Meng and `Andreas Müller`_. -- :class:`MiniBatchKMeans`: Add random reassignment of cluster centers +- :class:`cluster.MiniBatchKMeans`: Add random reassignment of cluster centers with little observations attached to them, by `Gael Varoquaux`_. @@ -221,18 +221,18 @@ API changes summary :func:`decomposition.dict_learning`, :func:`decomposition.dict_learning_online`. - Renamed all occurrences of ``max_iters`` to ``max_iter`` for consistency. - This applies to :class:`semi_supervised.LabelPropagation` and - :class:`semi_supervised.label_propagation.LabelSpreading`. + This applies to `semi_supervised.LabelPropagation` and + `semi_supervised.label_propagation.LabelSpreading`. - Renamed all occurrences of ``learn_rate`` to ``learning_rate`` for - consistency in :class:`ensemble.BaseGradientBoosting` and + consistency in `ensemble.BaseGradientBoosting` and :class:`ensemble.GradientBoostingRegressor`. - The module ``sklearn.linear_model.sparse`` is gone. Sparse matrix support was already integrated into the "regular" linear models. -- :func:`sklearn.metrics.mean_square_error`, which incorrectly returned the - accumulated error, was removed. Use ``mean_squared_error`` instead. +- `sklearn.metrics.mean_square_error`, which incorrectly returned the + accumulated error, was removed. Use :func:`metrics.mean_squared_error` instead. - Passing ``class_weight`` parameters to ``fit`` methods is no longer supported. Pass them to estimator constructors instead. @@ -244,17 +244,18 @@ API changes summary deprecated and will be removed in v0.14. Use the constructor option instead. -- :class:`feature_extraction.text.DictVectorizer` now returns sparse +- `feature_extraction.text.DictVectorizer` now returns sparse matrices in the CSR format, instead of COO. -- Renamed ``k`` in :class:`cross_validation.KFold` and - :class:`cross_validation.StratifiedKFold` to ``n_folds``, renamed +- Renamed ``k`` in `cross_validation.KFold` and + `cross_validation.StratifiedKFold` to ``n_folds``, renamed ``n_bootstraps`` to ``n_iter`` in ``cross_validation.Bootstrap``. - Renamed all occurrences of ``n_iterations`` to ``n_iter`` for consistency. - This applies to :class:`cross_validation.ShuffleSplit`, - :class:`cross_validation.StratifiedShuffleSplit`, - :func:`utils.randomized_range_finder` and :func:`utils.randomized_svd`. + This applies to `cross_validation.ShuffleSplit`, + `cross_validation.StratifiedShuffleSplit`, + :func:`utils.extmath.randomized_range_finder` and + :func:`utils.extmath.randomized_svd`. - Replaced ``rho`` in :class:`linear_model.ElasticNet` and :class:`linear_model.SGDClassifier` by ``l1_ratio``. The ``rho`` parameter @@ -267,10 +268,10 @@ API changes summary store a list of paths in the case of multiple targets, rather than an array of paths. -- The attribute ``gmm`` of :class:`hmm.GMMHMM` was renamed to ``gmm_`` +- The attribute ``gmm`` of `hmm.GMMHMM` was renamed to ``gmm_`` to adhere more strictly with the API. -- :func:`cluster.spectral_embedding` was moved to +- `cluster.spectral_embedding` was moved to :func:`manifold.spectral_embedding`. - Renamed ``eig_tol`` in :func:`manifold.spectral_embedding`, @@ -286,9 +287,9 @@ API changes summary multi-output problems. - The ``estimators_`` attribute of - :class:`ensemble.gradient_boosting.GradientBoostingRegressor` and - :class:`ensemble.gradient_boosting.GradientBoostingClassifier` is now an - array of :class:'tree.DecisionTreeRegressor'. + :class:`ensemble.GradientBoostingRegressor` and + :class:`ensemble.GradientBoostingClassifier` is now an + array of :class:`tree.DecisionTreeRegressor`. - Renamed ``chunk_size`` to ``batch_size`` in :class:`decomposition.MiniBatchDictionaryLearning` and @@ -299,18 +300,18 @@ API changes summary Also, the dtype returned by ``predict`` now reflects the dtype of ``y`` during ``fit`` (used to be ``np.float``). -- Changed default test_size in :func:`cross_validation.train_test_split` +- Changed default test_size in `cross_validation.train_test_split` to None, added possibility to infer ``test_size`` from ``train_size`` in - :class:`cross_validation.ShuffleSplit` and - :class:`cross_validation.StratifiedShuffleSplit`. + `cross_validation.ShuffleSplit` and + `cross_validation.StratifiedShuffleSplit`. -- Renamed function :func:`sklearn.metrics.zero_one` to - :func:`sklearn.metrics.zero_one_loss`. Be aware that the default behavior - in :func:`sklearn.metrics.zero_one_loss` is different from - :func:`sklearn.metrics.zero_one`: ``normalize=False`` is changed to +- Renamed function `sklearn.metrics.zero_one` to + `sklearn.metrics.zero_one_loss`. Be aware that the default behavior + in `sklearn.metrics.zero_one_loss` is different from + `sklearn.metrics.zero_one`: ``normalize=False`` is changed to ``normalize=True``. -- Renamed function :func:`metrics.zero_one_score` to +- Renamed function `metrics.zero_one_score` to :func:`metrics.accuracy_score`. - :func:`datasets.make_circles` now has the same number of inner and outer points. @@ -388,4 +389,3 @@ List of contributors for release 0.13 by number of commits. * 1 dengemann * 1 emanuele * 1 x006 - diff --git a/doc/whats_new/v0.14.rst b/doc/whats_new/v0.14.rst index 5abe7d12d2051..4bd04ad180c4e 100644 --- a/doc/whats_new/v0.14.rst +++ b/doc/whats_new/v0.14.rst @@ -13,7 +13,7 @@ Changelog --------- - Missing values with sparse and dense matrices can be imputed with the - transformer :class:`preprocessing.Imputer` by `Nicolas Trésegnie`_. + transformer `preprocessing.Imputer` by `Nicolas Trésegnie`_. - The core implementation of decisions trees has been rewritten from scratch, allowing for faster tree induction and lower memory @@ -24,13 +24,13 @@ Changelog `Gilles Louppe`_. See the :ref:`AdaBoost ` section of the user guide for details and examples. -- Added :class:`grid_search.RandomizedSearchCV` and - :class:`grid_search.ParameterSampler` for randomized hyperparameter +- Added `grid_search.RandomizedSearchCV` and + `grid_search.ParameterSampler` for randomized hyperparameter optimization. By `Andreas Müller`_. - Added :ref:`biclustering ` algorithms - (:class:`sklearn.cluster.bicluster.SpectralCoclustering` and - :class:`sklearn.cluster.bicluster.SpectralBiclustering`), data + (`sklearn.cluster.bicluster.SpectralCoclustering` and + `sklearn.cluster.bicluster.SpectralBiclustering`), data generation methods (:func:`sklearn.datasets.make_biclusters` and :func:`sklearn.datasets.make_checkerboard`), and scoring metrics (:func:`sklearn.metrics.consensus_score`). By `Kemal Eren`_. @@ -45,7 +45,7 @@ Changelog - Ability to pass one penalty (alpha value) per target in :class:`linear_model.Ridge`, by @eickenberg and `Mathieu Blondel`_. -- Fixed :mod:`sklearn.linear_model.stochastic_gradient.py` L2 regularization +- Fixed `sklearn.linear_model.stochastic_gradient.py` L2 regularization issue (minor practical significance). By :user:`Norbert Crombach ` and `Mathieu Blondel`_ . @@ -55,8 +55,8 @@ Changelog to the documentation. See :ref:`Choosing the right estimator `. By `Jaques Grobler`_. -- :class:`grid_search.GridSearchCV` and - :func:`cross_validation.cross_val_score` now support the use of advanced +- `grid_search.GridSearchCV` and + `cross_validation.cross_val_score` now support the use of advanced scoring function such as area under the ROC curve and f-beta scores. See :ref:`scoring_parameter` for details. By `Andreas Müller`_ and `Lars Buitinck`_. @@ -71,7 +71,7 @@ Changelog by `Arnaud Joly`_. - Two new metrics :func:`metrics.hamming_loss` and - :func:`metrics.jaccard_similarity_score` + `metrics.jaccard_similarity_score` are added with multi-label support by `Arnaud Joly`_. - Speed and memory usage improvements in @@ -121,8 +121,8 @@ Changelog - Feature selectors now share a mixin providing consistent ``transform``, ``inverse_transform`` and ``get_support`` methods. By `Joel Nothman`_. -- A fitted :class:`grid_search.GridSearchCV` or - :class:`grid_search.RandomizedSearchCV` can now generally be pickled. +- A fitted `grid_search.GridSearchCV` or + `grid_search.RandomizedSearchCV` can now generally be pickled. By `Joel Nothman`_. - Refactored and vectorized implementation of :func:`metrics.roc_curve` @@ -138,7 +138,7 @@ Changelog By :user:`Eustache Diemert `. - The default number of components for - :class:`sklearn.decomposition.RandomizedPCA` is now correctly documented + `sklearn.decomposition.RandomizedPCA` is now correctly documented to be ``n_features``. This was the default behavior, so programs using it will continue to work as they did. @@ -149,12 +149,12 @@ Changelog - Reduce memory footprint of FastICA by `Denis Engemann`_ and `Alexandre Gramfort`_. -- Verbose output in :mod:`sklearn.ensemble.gradient_boosting` now uses +- Verbose output in `sklearn.ensemble.gradient_boosting` now uses a column format and prints progress in decreasing frequency. It also shows the remaining time. By `Peter Prettenhofer`_. -- :mod:`sklearn.ensemble.gradient_boosting` provides out-of-bag improvement - :attr:`~sklearn.ensemble.GradientBoostingRegressor.oob_improvement_` +- `sklearn.ensemble.gradient_boosting` provides out-of-bag improvement + `oob_improvement_` rather than the OOB score for model selection. An example that shows how to use OOB estimates to select the number of trees was added. By `Peter Prettenhofer`_. @@ -165,17 +165,17 @@ Changelog - New OrthogonalMatchingPursuitCV class by `Alexandre Gramfort`_ and `Vlad Niculae`_. -- Fixed a bug in :class:`sklearn.covariance.GraphLassoCV`: the +- Fixed a bug in `sklearn.covariance.GraphLassoCV`: the 'alphas' parameter now works as expected when given a list of values. By Philippe Gervais. -- Fixed an important bug in :class:`sklearn.covariance.GraphLassoCV` +- Fixed an important bug in `sklearn.covariance.GraphLassoCV` that prevented all folds provided by a CV object to be used (only the first 3 were used). When providing a CV object, execution time may thus increase significantly compared to the previous version (bug results are correct now). By Philippe Gervais. -- :class:`cross_validation.cross_val_score` and the :mod:`grid_search` +- `cross_validation.cross_val_score` and the `grid_search` module is now tested with multi-output data by `Arnaud Joly`_. - :func:`datasets.make_multilabel_classification` can now return @@ -187,8 +187,8 @@ Changelog :class:`neighbors.RadiusNeighborsClassifier` support multioutput data by `Arnaud Joly`_. -- Random state in LibSVM-based estimators (:class:`svm.SVC`, :class:`NuSVC`, - :class:`OneClassSVM`, :class:`svm.SVR`, :class:`svm.NuSVR`) can now be +- Random state in LibSVM-based estimators (:class:`svm.SVC`, :class:`svm.NuSVC`, + :class:`svm.OneClassSVM`, :class:`svm.SVR`, :class:`svm.NuSVR`) can now be controlled. This is useful to ensure consistency in the probability estimates for the classifiers trained with ``probability=True``. By `Vlad Niculae`_. @@ -204,10 +204,10 @@ Changelog - Improved documentation on :ref:`multi-class, multi-label and multi-output classification ` by `Yannick Schwartz`_ and `Arnaud Joly`_. -- Better input and error handling in the :mod:`metrics` module by +- Better input and error handling in the :mod:`sklearn.metrics` module by `Arnaud Joly`_ and `Joel Nothman`_. -- Speed optimization of the :mod:`hmm` module by :user:`Mikhail Korobov ` +- Speed optimization of the `hmm` module by :user:`Mikhail Korobov ` - Significant speed improvements for :class:`sklearn.cluster.DBSCAN` by `cleverless `_ @@ -216,7 +216,7 @@ Changelog API changes summary ------------------- -- The :func:`auc_score` was renamed :func:`roc_auc_score`. +- The `auc_score` was renamed :func:`metrics.roc_auc_score`. - Testing scikit-learn with ``sklearn.test()`` is deprecated. Use ``nosetests sklearn`` from the command line. @@ -233,10 +233,9 @@ API changes summary setting the ``return_models`` parameter to ``False``. By `Jaques Grobler`_ and `Alexandre Gramfort`_ -- :class:`grid_search.IterGrid` was renamed to - :class:`grid_search.ParameterGrid`. +- `grid_search.IterGrid` was renamed to `grid_search.ParameterGrid`. -- Fixed bug in :class:`KFold` causing imperfect class balance in some +- Fixed bug in `KFold` causing imperfect class balance in some cases. By `Alexandre Gramfort`_ and Tadej Janež. - :class:`sklearn.neighbors.BallTree` has been refactored, and a @@ -249,8 +248,8 @@ API changes summary By `Jake Vanderplas`_ - Support for scipy.spatial.cKDTree within neighbors queries has been - removed, and the functionality replaced with the new :class:`KDTree` - class. + removed, and the functionality replaced with the new + :class:`sklearn.neighbors.KDTree` class. - :class:`sklearn.neighbors.KernelDensity` has been added, which performs efficient kernel density estimation with a variety of kernels. @@ -264,11 +263,11 @@ API changes summary - ``gcv_mode="auto"`` no longer tries to perform SVD on a densified sparse matrix in :class:`sklearn.linear_model.RidgeCV`. -- Sparse matrix support in :class:`sklearn.decomposition.RandomizedPCA` +- Sparse matrix support in `sklearn.decomposition.RandomizedPCA` is now deprecated in favor of the new ``TruncatedSVD``. -- :class:`cross_validation.KFold` and - :class:`cross_validation.StratifiedKFold` now enforce `n_folds >= 2` +- `cross_validation.KFold` and + `cross_validation.StratifiedKFold` now enforce `n_folds >= 2` otherwise a ``ValueError`` is raised. By `Olivier Grisel`_. - :func:`datasets.load_files`'s ``charset`` and ``charset_errors`` @@ -386,4 +385,3 @@ List of contributors for release 0.14 by number of commits. * 1 Sturla Molden * 1 Thomas Jarosch * 1 Yaroslav Halchenko - diff --git a/doc/whats_new/v0.15.rst b/doc/whats_new/v0.15.rst index a2eafc63b0617..50412425dd7a5 100644 --- a/doc/whats_new/v0.15.rst +++ b/doc/whats_new/v0.15.rst @@ -58,9 +58,9 @@ Version 0.15.1 Bug fixes --------- -- Made :func:`cross_validation.cross_val_score` use - :class:`cross_validation.KFold` instead of - :class:`cross_validation.StratifiedKFold` on multi-output classification +- Made `cross_validation.cross_val_score` use + `cross_validation.KFold` instead of + `cross_validation.StratifiedKFold` on multi-output classification problems. By :user:`Nikolay Mayorov `. - Support unseen labels :class:`preprocessing.LabelBinarizer` to restore @@ -75,7 +75,7 @@ Bug fixes per-class sum of prediction scores. By `Andreas Müller`_. - Made :func:`cross_validation.cross_val_score` and - :class:`grid_search.GridSearchCV` accept Python lists as input data. + `grid_search.GridSearchCV` accept Python lists as input data. This is especially useful for cross-validation and model selection of text processing pipelines. By `Andreas Müller`_. @@ -141,7 +141,7 @@ New features - Shorthand constructors :func:`pipeline.make_pipeline` and :func:`pipeline.make_union` were added by `Lars Buitinck`_. -- Shuffle option for :class:`cross_validation.StratifiedKFold`. +- Shuffle option for `cross_validation.StratifiedKFold`. By :user:`Jeffrey Blackburne `. - Incremental learning (``partial_fit``) for Gaussian Naive Bayes by @@ -151,7 +151,7 @@ New features ` By :user:`Danny Sullivan `. -- Added :func:`learning_curve ` utility to +- Added `learning_curve` utility to chart performance with respect to training size. See :ref:`sphx_glr_auto_examples_model_selection_plot_learning_curve.py`. By Alexander Fabisch. @@ -203,16 +203,16 @@ Enhancements threading backend of joblib 0.8 and releasing the GIL in the tree fitting Cython code. By `Olivier Grisel`_ and `Gilles Louppe`_. -- Speed improvement of the :mod:`sklearn.ensemble.gradient_boosting` module. +- Speed improvement of the `sklearn.ensemble.gradient_boosting` module. By `Gilles Louppe`_ and `Peter Prettenhofer`_. -- Various enhancements to the :mod:`sklearn.ensemble.gradient_boosting` +- Various enhancements to the `sklearn.ensemble.gradient_boosting` module: a ``warm_start`` argument to fit additional trees, a ``max_leaf_nodes`` argument to fit GBM style trees, a ``monitor`` fit argument to inspect the estimator during training, and refactoring of the verbose code. By `Peter Prettenhofer`_. -- Faster :class:`sklearn.ensemble.ExtraTrees` by caching feature values. +- Faster `sklearn.ensemble.ExtraTrees` by caching feature values. By `Arnaud Joly`_. - Faster depth-based tree building algorithm such as decision tree, @@ -246,7 +246,7 @@ Enhancements significantly speedup computation by `Denis Engemann`_, and `Alexandre Gramfort`_. -- Changed :class:`cross_validation.StratifiedKFold` to try and +- Changed `cross_validation.StratifiedKFold` to try and preserve as much of the original ordering of samples as possible so as not to hide overfitting on datasets with a non-negligible level of samples dependency. @@ -282,9 +282,8 @@ Enhancements By `Lars Buitinck`_. - Grid search and cross validation allow NaNs in the input arrays so that - preprocessors such as :class:`preprocessing.Imputer - ` can be trained within the cross validation loop, - avoiding potentially skewed results. + preprocessors such as `preprocessing.Imputer` can be trained within the cross + validation loop, avoiding potentially skewed results. - Ridge regression can now deal with sample weights in feature space (only sample space until then). By :user:`Michael Eickenberg `. @@ -333,7 +332,7 @@ Bug fixes - Fixed bug in :class:`decomposition.MiniBatchDictionaryLearning` : ``partial_fit`` was not working properly. -- Fixed bug in :class:`linear_model.stochastic_gradient` : +- Fixed bug in `linear_model.stochastic_gradient` : ``l1_ratio`` was used as ``(1.0 - l1_ratio)`` . - Fixed bug in :class:`multiclass.OneVsOneClassifier` with string @@ -353,10 +352,10 @@ Bug fixes By `Olivier Grisel`_. - Raise error in :class:`cluster.FeatureAgglomeration` and - :class:`cluster.WardAgglomeration` when no samples are given, + `cluster.WardAgglomeration` when no samples are given, rather than returning meaningless clustering. -- Fixed bug in :class:`gradient_boosting.GradientBoostingRegressor` with +- Fixed bug in `gradient_boosting.GradientBoostingRegressor` with ``loss='huber'``: ``gamma`` might have not been initialized. - Fixed feature importances as computed with a forest of randomized trees @@ -366,36 +365,36 @@ Bug fixes API changes summary ------------------- -- :mod:`sklearn.hmm` is deprecated. Its removal is planned +- `sklearn.hmm` is deprecated. Its removal is planned for the 0.17 release. -- Use of :class:`covariance.EllipticEnvelop` has now been removed after +- Use of `covariance.EllipticEnvelop` has now been removed after deprecation. Please use :class:`covariance.EllipticEnvelope` instead. -- :class:`cluster.Ward` is deprecated. Use +- `cluster.Ward` is deprecated. Use :class:`cluster.AgglomerativeClustering` instead. -- :class:`cluster.WardClustering` is deprecated. Use +- `cluster.WardClustering` is deprecated. Use - :class:`cluster.AgglomerativeClustering` instead. -- :class:`cross_validation.Bootstrap` is deprecated. - :class:`cross_validation.KFold` or - :class:`cross_validation.ShuffleSplit` are recommended instead. +- `cross_validation.Bootstrap` is deprecated. + `cross_validation.KFold` or + `cross_validation.ShuffleSplit` are recommended instead. - Direct support for the sequence of sequences (or list of lists) multilabel format is deprecated. To convert to and from the supported binary indicator matrix format, use - :class:`MultiLabelBinarizer `. + :class:`preprocessing.MultiLabelBinarizer`. By `Joel Nothman`_. -- Add score method to :class:`PCA ` following the model of +- Add score method to :class:`decomposition.PCA` following the model of probabilistic PCA and deprecate - :class:`ProbabilisticPCA ` model whose + `ProbabilisticPCA` model whose score implementation is not correct. The computation now also exploits the matrix inversion lemma for faster computation. By `Alexandre Gramfort`_. -- The score method of :class:`FactorAnalysis ` +- The score method of :class:`decomposition.FactorAnalysis` now returns the average log-likelihood of the samples. Use score_samples to get log-likelihood of each sample. By `Alexandre Gramfort`_. @@ -410,7 +409,7 @@ API changes summary from version 0.13 in some classifiers. By `Joel Nothman`_. - Fix wrong ``explained_variance_ratio_`` attribute in - :class:`RandomizedPCA `. + `RandomizedPCA`. By `Alexandre Gramfort`_. - Fit alphas for each ``l1_ratio`` instead of ``mean_l1_ratio`` in @@ -445,11 +444,11 @@ API changes summary performance, you should modify the value of ``max_features``. By `Arnaud Joly`_. -- Fix :func:`utils.compute_class_weight` when ``class_weight=="auto"``. +- Fix :func:`utils.class_weight.compute_class_weight` when ``class_weight=="auto"``. Previously it was broken for input of non-integer ``dtype`` and the weighted array that was returned was wrong. By `Manoj Kumar`_. -- Fix :class:`cross_validation.Bootstrap` to return ``ValueError`` +- Fix `cross_validation.Bootstrap` to return ``ValueError`` when ``n_train + n_test > n``. By :user:`Ronald Phlypo `. @@ -620,4 +619,3 @@ List of contributors for release 0.15 by number of commits. * 1 Andrew Ash * 1 Pietro Zambelli * 1 staubda - diff --git a/doc/whats_new/v0.16.rst b/doc/whats_new/v0.16.rst index a9c9f0b2614fd..ba96d03683bd0 100644 --- a/doc/whats_new/v0.16.rst +++ b/doc/whats_new/v0.16.rst @@ -60,7 +60,7 @@ Highlights - :class:`cluster.Birch` clustering method for large-scale datasets. - Scalable approximate nearest neighbors search with Locality-sensitive - hashing forests in :class:`neighbors.LSHForest`. + hashing forests in `neighbors.LSHForest`. - Improved error messages and better validation when using malformed input data. @@ -72,7 +72,7 @@ Changelog New features ............ -- The new :class:`neighbors.LSHForest` implements locality-sensitive hashing +- The new `neighbors.LSHForest` implements locality-sensitive hashing for approximate nearest neighbors search. By :user:`Maheshakya Wijewardena`. - Added :class:`svm.LinearSVR`. This class uses the liblinear implementation @@ -109,7 +109,7 @@ New features and :class:`SGDRegressor ` By :user:`Danny Sullivan `. -- Added :func:`cross_val_predict ` +- Added `cross_val_predict` function which computes cross-validated estimates. By `Luis Pedro Coelho`_ - Added :class:`linear_model.TheilSenRegressor`, a robust @@ -131,7 +131,7 @@ New features - All solvers in :class:`linear_model.Ridge` now support `sample_weight`. By `Mathieu Blondel`_. -- Added :class:`cross_validation.PredefinedSplit` cross-validation +- Added `cross_validation.PredefinedSplit` cross-validation for fixed user-provided cross-validation folds. By :user:`Thomas Unterthiner `. @@ -144,10 +144,10 @@ New features Enhancements ............ -- Add option ``return_distance`` in :func:`hierarchical.ward_tree` +- Add option ``return_distance`` in `hierarchical.ward_tree` to return distances between nodes for both structured and unstructured versions of the algorithm. By `Matteo Visconti di Oleggio Castello`_. - The same option was added in :func:`hierarchical.linkage_tree`. + The same option was added in `hierarchical.linkage_tree`. By `Manoj Kumar`_ - Add support for sample weights in scorer objects. Metrics with sample @@ -162,7 +162,7 @@ Enhancements and related. By `Manoj Kumar`_. - Add ``sample_weight`` parameter to - :func:`metrics.jaccard_similarity_score` and :func:`metrics.log_loss`. + `metrics.jaccard_similarity_score` and :func:`metrics.log_loss`. By :user:`Jatin Shah `. - Support sparse multilabel indicator representation in @@ -191,11 +191,11 @@ Enhancements single pass, when giving the option ``sort=False``. By :user:`Dan Blanchard `. -- :class:`GridSearchCV` and :class:`RandomizedSearchCV` can now be - configured to work with estimators that may fail and raise errors on - individual folds. This option is controlled by the `error_score` - parameter. This does not affect errors raised on re-fit. By - :user:`Michal Romaniuk `. +- :class:`model_selection.GridSearchCV` and + :class:`model.selection.RandomizedSearchCV` can now be configured to work + with estimators that may fail and raise errors on individual folds. This + option is controlled by the `error_score` parameter. This does not affect + errors raised on re-fit. By :user:`Michal Romaniuk `. - Add ``digits`` parameter to `metrics.classification_report` to allow report to show different precision of floating point numbers. By @@ -223,14 +223,14 @@ Enhancements - Added decision function for :class:`multiclass.OneVsOneClassifier` By `Raghav RV`_ and :user:`Kyle Beauchamp `. -- :func:`neighbors.kneighbors_graph` and :func:`radius_neighbors_graph` +- `neighbors.kneighbors_graph` and `radius_neighbors_graph` support non-Euclidean metrics. By `Manoj Kumar`_ - Parameter ``connectivity`` in :class:`cluster.AgglomerativeClustering` and family now accept callables that return a connectivity matrix. By `Manoj Kumar`_. -- Sparse support for :func:`paired_distances`. By `Joel Nothman`_. +- Sparse support for :func:`metrics.pairwise.paired_distances`. By `Joel Nothman`_. - :class:`cluster.DBSCAN` now supports sparse input and sample weights and has been optimized: the inner loop has been rewritten in Cython and @@ -242,10 +242,10 @@ Enhancements :class:`tree.DecisionTreeClassifier`, :class:`ensemble.ExtraTreesClassifier` and :class:`tree.ExtraTreeClassifier`. By `Trevor Stephens`_. -- :class:`grid_search.RandomizedSearchCV` now does sampling without +- `grid_search.RandomizedSearchCV` now does sampling without replacement if all parameters are given as lists. By `Andreas Müller`_. -- Parallelized calculation of :func:`pairwise_distances` is now supported +- Parallelized calculation of :func:`metrics.pairwise_distances` is now supported for scipy metrics and custom callables. By `Joel Nothman`_. - Allow the fitting and scoring of all clustering algorithms in @@ -254,8 +254,8 @@ Enhancements - More robust seeding and improved error messages in :class:`cluster.MeanShift` by `Andreas Müller`_. -- Make the stopping criterion for :class:`mixture.GMM`, - :class:`mixture.DPGMM` and :class:`mixture.VBGMM` less dependent on the +- Make the stopping criterion for `mixture.GMM`, + `mixture.DPGMM` and `mixture.VBGMM` less dependent on the number of samples by thresholding the average log-likelihood change instead of its sum over all samples. By `Hervé Bredin`_. @@ -271,14 +271,14 @@ Enhancements - :class:`svm.SVC` fitted on sparse input now implements ``decision_function``. By `Rob Zinkov`_ and `Andreas Müller`_. -- :func:`cross_validation.train_test_split` now preserves the input type, +- `cross_validation.train_test_split` now preserves the input type, instead of converting to numpy arrays. Documentation improvements .......................... -- Added example of using :class:`FeatureUnion` for heterogeneous input. +- Added example of using :class:`pipeline.FeatureUnion` for heterogeneous input. By :user:`Matt Terry ` - Documentation on scorers was improved, to highlight the handling of loss @@ -306,16 +306,16 @@ Bug fixes ......... - Metaestimators now support ducktyping for the presence of ``decision_function``, ``predict_proba`` and other methods. This fixes behavior of - :class:`grid_search.GridSearchCV`, - :class:`grid_search.RandomizedSearchCV`, :class:`pipeline.Pipeline`, + `grid_search.GridSearchCV`, + `grid_search.RandomizedSearchCV`, :class:`pipeline.Pipeline`, :class:`feature_selection.RFE`, :class:`feature_selection.RFECV` when nested. By `Joel Nothman`_ - The ``scoring`` attribute of grid-search and cross-validation methods is no longer - ignored when a :class:`grid_search.GridSearchCV` is given as a base estimator or + ignored when a `grid_search.GridSearchCV` is given as a base estimator or the base estimator doesn't have predict. -- The function :func:`hierarchical.ward_tree` now returns the children in +- The function `hierarchical.ward_tree` now returns the children in the same order for both the structured and unstructured versions. By `Matteo Visconti di Oleggio Castello`_. @@ -327,7 +327,7 @@ Bug fixes length. By :user:`Michael Eickenberg `. - Fix incomplete download of the dataset when - :func:`datasets.download_20newsgroups` is called. By `Manoj Kumar`_. + `datasets.download_20newsgroups` is called. By `Manoj Kumar`_. - Various fixes to the Gaussian processes subpackage by Vincent Dubourg and Jan Hendrik Metzen. @@ -384,7 +384,7 @@ Bug fixes :class:`sklearn.neighbors.NearestNeighbors` and family, when the query data is not the same as fit data. By `Manoj Kumar`_. -- Fix log-density calculation in the :class:`mixture.GMM` with +- Fix log-density calculation in the `mixture.GMM` with tied covariance. By `Will Dawson`_ - Fixed a scaling error in :class:`feature_selection.SelectFdr` @@ -415,15 +415,15 @@ Bug fixes API changes summary ------------------- -- :class:`GridSearchCV ` and - :func:`cross_val_score ` and other +- `GridSearchCV` and + `cross_val_score` and other meta-estimators don't convert pandas DataFrames into arrays any more, allowing DataFrame specific operations in custom estimators. -- :func:`multiclass.fit_ovr`, :func:`multiclass.predict_ovr`, - :func:`predict_proba_ovr`, - :func:`multiclass.fit_ovo`, :func:`multiclass.predict_ovo`, - :func:`multiclass.fit_ecoc` and :func:`multiclass.predict_ecoc` +- `multiclass.fit_ovr`, `multiclass.predict_ovr`, + `predict_proba_ovr`, + `multiclass.fit_ovo`, `multiclass.predict_ovo`, + `multiclass.fit_ecoc` and `multiclass.predict_ecoc` are deprecated. Use the underlying estimators instead. - Nearest neighbors estimators used to take arbitrary keyword arguments @@ -439,11 +439,11 @@ API changes summary but previous versions accidentally returned only the positive probability. Fixed by Will Lamond and `Lars Buitinck`_. -- Change default value of precompute in :class:`ElasticNet` and :class:`Lasso` - to False. Setting precompute to "auto" was found to be slower when - n_samples > n_features since the computation of the Gram matrix is - computationally expensive and outweighs the benefit of fitting the Gram - for just one alpha. +- Change default value of precompute in :class:`linear_model.ElasticNet` and + :class:`linear_model.Lasso` to False. Setting precompute to "auto" was found + to be slower when n_samples > n_features since the computation of the Gram + matrix is computationally expensive and outweighs the benefit of fitting the + Gram for just one alpha. ``precompute="auto"`` is now deprecated and will be removed in 0.18 By `Manoj Kumar`_. @@ -467,8 +467,8 @@ API changes summary been removed. They were deprecated since 0.14 - From now onwards, all estimators will uniformly raise ``NotFittedError`` - (:class:`utils.validation.NotFittedError`), when any of the ``predict`` - like methods are called before the model is fit. By `Raghav RV`_. + when any of the ``predict`` like methods are called before the model is fit. + By `Raghav RV`_. - Input data validation was refactored for more consistent input validation. The ``check_arrays`` function was replaced by ``check_array`` @@ -486,7 +486,7 @@ API changes summary as the first nearest neighbor. - `thresh` parameter is deprecated in favor of new `tol` parameter in - :class:`GMM`, :class:`DPGMM` and :class:`VBGMM`. See `Enhancements` + `GMM`, `DPGMM` and `VBGMM`. See `Enhancements` section for details. By `Hervé Bredin`_. - Estimators will treat input with dtype object as numeric when possible. @@ -538,4 +538,3 @@ terrycojones, Thomas Delteil, Thomas Unterthiner, Tomas Kazmar, trevorstephens, tttthomasssss, Tzu-Ming Kuo, ugurcaliskan, ugurthemaster, Vinayak Mehta, Vincent Dubourg, Vjacheslav Murashkin, Vlad Niculae, wadawson, Wei Xue, Will Lamond, Wu Jiang, x0l, Xinfan Meng, Yan Yi, Yu-Chin - diff --git a/doc/whats_new/v0.17.rst b/doc/whats_new/v0.17.rst index 7657d07712ab5..4e0cf0280ee4d 100644 --- a/doc/whats_new/v0.17.rst +++ b/doc/whats_new/v0.17.rst @@ -75,10 +75,10 @@ New features function into a ``Pipeline``-compatible transformer object. By Joe Jevnik. -- The new classes :class:`cross_validation.LabelKFold` and - :class:`cross_validation.LabelShuffleSplit` generate train-test folds, - respectively similar to :class:`cross_validation.KFold` and - :class:`cross_validation.ShuffleSplit`, except that the folds are +- The new classes `cross_validation.LabelKFold` and + `cross_validation.LabelShuffleSplit` generate train-test folds, + respectively similar to `cross_validation.KFold` and + `cross_validation.ShuffleSplit`, except that the folds are conditioned on a label array. By `Brian McFee`_, :user:`Jean Kossaifi ` and `Gilles Louppe`_. @@ -97,7 +97,7 @@ New features :class:`decomposition.NMF`. Previous solver based on Projected Gradient is still available setting new parameter ``solver`` to ``pg``, but is deprecated and will be removed in 0.19, along with - :class:`decomposition.ProjectedGradientNMF` and parameters ``sparseness``, + `decomposition.ProjectedGradientNMF` and parameters ``sparseness``, ``eta``, ``beta`` and ``nls_max_iter``. New parameters ``alpha`` and ``l1_ratio`` control L1 and L2 regularization, and ``shuffle`` adds a shuffling step in the ``cd`` solver. @@ -109,7 +109,7 @@ Enhancements Barnes-Hut method, leading to much faster fitting. By Christopher Erick Moody. (:issue:`4025`) -- :class:`cluster.mean_shift_.MeanShift` now supports parallel execution, +- :class:`cluster.MeanShift` now supports parallel execution, as implemented in the ``mean_shift`` function. By :user:`Martino Sorbaro `. @@ -119,7 +119,7 @@ Enhancements - :class:`dummy.DummyClassifier` now supports a prior fitting strategy. By `Arnaud Joly`_. -- Added a ``fit_predict`` method for :class:`mixture.GMM` and subclasses. +- Added a ``fit_predict`` method for `mixture.GMM` and subclasses. By :user:`Cory Lorenz `. - Added the :func:`metrics.label_ranking_loss` metric. @@ -133,7 +133,7 @@ Enhancements - Added option to use multi-output regression metrics without averaging. By Konstantin Shmelkov and :user:`Michael Eickenberg`. -- Added ``stratify`` option to :func:`cross_validation.train_test_split` +- Added ``stratify`` option to `cross_validation.train_test_split` for stratified splitting. By Miroslav Batchkarov. - The :func:`tree.export_graphviz` function now supports aesthetic @@ -172,8 +172,8 @@ Enhancements :func:`sklearn.metrics.pairwise.cosine_similarity`. By :user:`Jaidev Deshpande `. -- Add :func:`minmax_scale` to provide a function interface for - :class:`MinMaxScaler`. By :user:`Thomas Unterthiner `. +- Add :func:`preprocessing.minmax_scale` to provide a function interface for + :class:`preprocessing.MinMaxScaler`. By :user:`Thomas Unterthiner `. - ``dump_svmlight_file`` now handles multi-label datasets. By Chih-Wei Chang. @@ -183,12 +183,12 @@ Enhancements - The "Wisconsin Breast Cancer" classical two-class classification dataset is now included in scikit-learn, available with - :func:`sklearn.dataset.load_breast_cancer`. + :func:`datasets.load_breast_cancer`. - Upgraded to joblib 0.9.3 to benefit from the new automatic batching of short tasks. This makes it possible for scikit-learn to benefit from parallelism when many very short tasks are executed in parallel, for - instance by the :class:`grid_search.GridSearchCV` meta-estimator + instance by the `grid_search.GridSearchCV` meta-estimator with ``n_jobs > 1`` used with a large grid of parameters on a small dataset. By `Vlad Niculae`_, `Olivier Grisel`_ and `Loic Esteve`_. @@ -196,7 +196,7 @@ Enhancements https://github.com/joblib/joblib/blob/master/CHANGES.rst#release-093 - Improved speed (3 times per iteration) of - :class:`decomposition.DictLearning` with coordinate descent method + `decomposition.DictLearning` with coordinate descent method from :class:`linear_model.Lasso`. By :user:`Arthur Mensch `. - Parallel processing (threaded) for queries of nearest neighbors @@ -264,7 +264,7 @@ Enhancements - Added :func:`metrics.pairwise.laplacian_kernel`. By `Clyde Fare `_. -- :class:`covariance.GraphLasso` allows separate control of the convergence criterion +- `covariance.GraphLasso` allows separate control of the convergence criterion for the Elastic-Net subproblem via the ``enet_tol`` parameter. - Improved verbosity in :class:`decomposition.DictionaryLearning`. @@ -283,7 +283,7 @@ Enhancements - Added the ``fit_predict`` method to :class:`pipeline.Pipeline`. -- Added the :func:`preprocessing.min_max_scale` function. +- Added the :func:`preprocessing.minmax_scale` function. Bug fixes ......... @@ -294,16 +294,16 @@ Bug fixes - Fixed the output shape of :class:`linear_model.RANSACRegressor` to ``(n_samples, )``. By `Andreas Müller`_. -- Fixed bug in :class:`decomposition.DictLearning` when ``n_jobs < 0``. By +- Fixed bug in `decomposition.DictLearning` when ``n_jobs < 0``. By `Andreas Müller`_. -- Fixed bug where :class:`grid_search.RandomizedSearchCV` could consume a +- Fixed bug where `grid_search.RandomizedSearchCV` could consume a lot of memory for large discrete grids. By `Joel Nothman`_. - Fixed bug in :class:`linear_model.LogisticRegressionCV` where `penalty` was ignored in the final fit. By `Manoj Kumar`_. -- Fixed bug in :class:`ensemble.forest.ForestClassifier` while computing +- Fixed bug in `ensemble.forest.ForestClassifier` while computing oob_score and X is a sparse.csc_matrix. By :user:`Ankur Ankan `. - All regressors now consistently handle and warn when given ``y`` that is of @@ -313,17 +313,17 @@ Bug fixes - Fix in :class:`cluster.KMeans` cluster reassignment for sparse input by `Lars Buitinck`_. -- Fixed a bug in :class:`lda.LDA` that could cause asymmetric covariance +- Fixed a bug in :class:`decomposition.LDA` that could cause asymmetric covariance matrices when using shrinkage. By `Martin Billinger`_. -- Fixed :func:`cross_validation.cross_val_predict` for estimators with +- Fixed `cross_validation.cross_val_predict` for estimators with sparse predictions. By Buddha Prakash. - Fixed the ``predict_proba`` method of :class:`linear_model.LogisticRegression` to use soft-max instead of one-vs-rest normalization. By `Manoj Kumar`_. (:issue:`5182`) -- Fixed the :func:`partial_fit` method of :class:`linear_model.SGDClassifier` +- Fixed the `partial_fit` method of :class:`linear_model.SGDClassifier` when called with ``average=True``. By :user:`Andrew Lamb `. (:issue:`5282`) @@ -339,17 +339,17 @@ Bug fixes automatically changes the solver to 'sag' in this case. :issue:`5360` by `Tom Dupre la Tour`_. -- Fixed a performance bug in :class:`decomposition.RandomizedPCA` on data +- Fixed a performance bug in `decomposition.RandomizedPCA` on data with a large number of features and fewer samples. (:issue:`4478`) By `Andreas Müller`_, `Loic Esteve`_ and :user:`Giorgio Patrini `. -- Fixed bug in :class:`cross_decomposition.PLS` that yielded unstable and +- Fixed bug in `cross_decomposition.PLS` that yielded unstable and platform dependent output, and failed on `fit_transform`. By :user:`Arthur Mensch `. - Fixes to the ``Bunch`` class used to store datasets. -- Fixed :func:`ensemble.plot_partial_dependence` ignoring the +- Fixed `ensemble.plot_partial_dependence` ignoring the ``percentiles`` parameter. - Providing a ``set`` as vocabulary in ``CountVectorizer`` no longer @@ -361,8 +361,8 @@ Bug fixes :class:`linear_model.Lasso` and :class:`linear_model.ElasticNet`. - Fixed inconsistent memory layout in the coordinate descent solver - that affected :class:`linear_model.DictionaryLearning` and - :class:`covariance.GraphLasso`. (:issue:`5337`) + that affected `linear_model.DictionaryLearning` and + `covariance.GraphLasso`. (:issue:`5337`) By `Olivier Grisel`_. - :class:`manifold.LocallyLinearEmbedding` no longer ignores the ``reg`` @@ -396,7 +396,7 @@ API changes summary in :class:`preprocessing.StandardScaler` is deprecated and superseded by `scale_`; it won't be available in 0.19. By :user:`Giorgio Patrini `. -- :class:`svm.SVC`` and :class:`svm.NuSVC` now have an ``decision_function_shape`` +- :class:`svm.SVC` and :class:`svm.NuSVC` now have an ``decision_function_shape`` parameter to make their decision function of shape ``(n_samples, n_classes)`` by setting ``decision_function_shape='ovr'``. This will be the default behavior starting in 0.19. By `Andreas Müller`_. @@ -407,7 +407,7 @@ API changes summary to be explicitly shaped ``(n_samples, n_features)``. By :user:`Vighnesh Birodkar `. -- :class:`lda.LDA` and :class:`qda.QDA` have been moved to +- `lda.LDA` and `qda.QDA` have been moved to :class:`discriminant_analysis.LinearDiscriminantAnalysis` and :class:`discriminant_analysis.QuadraticDiscriminantAnalysis`. @@ -438,7 +438,7 @@ API changes summary - The ``decision_function`` on all regressors was deprecated and will be removed in 0.19. Use ``predict`` instead. -- :func:`datasets.load_lfw_pairs` is deprecated and will be removed in 0.19. +- `datasets.load_lfw_pairs` is deprecated and will be removed in 0.19. Use :func:`datasets.fetch_lfw_pairs` instead. - The deprecated ``hmm`` module was removed. @@ -446,9 +446,9 @@ API changes summary - The deprecated ``Bootstrap`` cross-validation iterator was removed. - The deprecated ``Ward`` and ``WardAgglomerative`` classes have been removed. - Use :class:`clustering.AgglomerativeClustering` instead. + Use :class:`cluster.AgglomerativeClustering` instead. -- :func:`cross_validation.check_cv` is now a public function. +- `cross_validation.check_cv` is now a public function. - The property ``residues_`` of :class:`linear_model.LinearRegression` is deprecated and will be removed in 0.19. diff --git a/doc/whats_new/v0.18.rst b/doc/whats_new/v0.18.rst index ea3548c0b9a0c..d4cd1025d69ef 100644 --- a/doc/whats_new/v0.18.rst +++ b/doc/whats_new/v0.18.rst @@ -189,8 +189,8 @@ Model Selection Enhancements and API Changes - **The model_selection module** The new module :mod:`sklearn.model_selection`, which groups together the - functionalities of formerly :mod:`sklearn.cross_validation`, - :mod:`sklearn.grid_search` and :mod:`sklearn.learning_curve`, introduces new + functionalities of formerly `sklearn.cross_validation`, + `sklearn.grid_search` and `sklearn.learning_curve`, introduces new possibilities such as nested cross-validation and better manipulation of parameter searches with Pandas. @@ -202,7 +202,7 @@ Model Selection Enhancements and API Changes The new cross-validation splitters, defined in the :mod:`sklearn.model_selection`, are no longer initialized with any data-dependent parameters such as ``y``. Instead they expose a - :func:`split` method that takes in the data and yields a generator for the + `split` method that takes in the data and yields a generator for the different splits. This change makes it possible to use the cross-validation splitters to @@ -258,7 +258,7 @@ Model Selection Enhancements and API Changes - **Fit parameter labels renamed to groups** - The ``labels`` parameter in the :func:`split` method of the newly renamed + The ``labels`` parameter in the `split` method of the newly renamed splitters :class:`model_selection.GroupKFold`, :class:`model_selection.LeaveOneGroupOut`, :class:`model_selection.LeavePGroupsOut`, @@ -314,7 +314,7 @@ Other estimators for sounder results. :issue:`7295` by :user:`Wei Xue ` and :user:`Thierry Guillemot `. -- Class :class:`decomposition.RandomizedPCA` is now factored into :class:`decomposition.PCA` +- Class `decomposition.RandomizedPCA` is now factored into :class:`decomposition.PCA` and it is available calling with parameter ``svd_solver='randomized'``. The default number of ``n_iter`` for ``'randomized'`` has changed to 4. The old behavior of PCA is recovered by ``svd_solver='full'``. An additional solver @@ -337,11 +337,11 @@ Other estimators Model selection and evaluation -- Added :func:`metrics.cluster.fowlkes_mallows_score`, the Fowlkes Mallows +- Added :func:`metrics.fowlkes_mallows_score`, the Fowlkes Mallows Index which measures the similarity of two clusterings of a set of points By :user:`Arnaud Fouchet ` and :user:`Thierry Guillemot `. -- Added :func:`metrics.calinski_harabaz_score`, which computes the Calinski +- Added `metrics.calinski_harabaz_score`, which computes the Calinski and Harabaz score to evaluate the resulting clustering of a set of points. By :user:`Arnaud Fouchet ` and :user:`Thierry Guillemot `. @@ -384,7 +384,7 @@ Trees and ensembles :issue:`6667` by :user:`Nelson Liu `. - The memory footprint is reduced (sometimes greatly) for - :class:`ensemble.bagging.BaseBagging` and classes that inherit from it, + `ensemble.bagging.BaseBagging` and classes that inherit from it, i.e, :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor`, and :class:`ensemble.IsolationForest`, by dynamically generating attribute ``estimators_samples_`` only when it is @@ -462,7 +462,7 @@ Model evaluation and meta-estimators - Added support for substituting or disabling :class:`pipeline.Pipeline` and :class:`pipeline.FeatureUnion` components using the ``set_params`` - interface that powers :mod:`sklearn.grid_search`. + interface that powers `sklearn.grid_search`. See :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py` By `Joel Nothman`_ and :user:`Robert McGibbon `. @@ -489,7 +489,7 @@ Metrics :user:`Mads Jensen ` and :user:`Nelson Liu `. - Support sparse contingency matrices in cluster evaluation - (:mod:`metrics.cluster.supervised`) to scale to a large number of + (`metrics.cluster.supervised`) to scale to a large number of clusters. :issue:`7419` by :user:`Gregory Stupp ` and `Joel Nothman`_. @@ -512,22 +512,22 @@ Miscellaneous C/C++ files. By :user:`Arthur Mensch `. - Reduce the memory usage for 32-bit float input arrays of - :func:`utils.sparse_func.mean_variance_axis` and - :func:`utils.sparse_func.incr_mean_variance_axis` by supporting cython + `utils.sparse_func.mean_variance_axis` and + `utils.sparse_func.incr_mean_variance_axis` by supporting cython fused types. By :user:`YenChen Lin `. -- The :func:`ignore_warnings` now accept a category argument to ignore only +- The `ignore_warnings` now accept a category argument to ignore only the warnings of a specified type. By :user:`Thierry Guillemot `. - Added parameter ``return_X_y`` and return type ``(data, target) : tuple`` option to - :func:`load_iris` dataset + :func:`datasets.load_iris` dataset :issue:`7049`, - :func:`load_breast_cancer` dataset + :func:`datasets.load_breast_cancer` dataset :issue:`7152`, - :func:`load_digits` dataset, - :func:`load_diabetes` dataset, - :func:`load_linnerud` dataset, - :func:`load_boston` dataset + :func:`datasets.load_digits` dataset, + :func:`datasets.load_diabetes` dataset, + :func:`datasets.load_linnerud` dataset, + `datasets.load_boston` dataset :issue:`7154` by :user:`Manvendra Singh`. @@ -584,7 +584,7 @@ Linear, kernelized and related models Decomposition, manifold learning and clustering -- :class:`decomposition.RandomizedPCA` default number of `iterated_power` is 4 instead of 3. +- `decomposition.RandomizedPCA` default number of `iterated_power` is 4 instead of 3. :issue:`5141` by :user:`Giorgio Patrini `. - :func:`utils.extmath.randomized_svd` performs 4 power iterations by default, instead or 0. @@ -595,15 +595,15 @@ Decomposition, manifold learning and clustering :issue:`5299` by :user:`Giorgio Patrini`. - Whiten/non-whiten inconsistency between components of :class:`decomposition.PCA` - and :class:`decomposition.RandomizedPCA` (now factored into PCA, see the + and `decomposition.RandomizedPCA` (now factored into PCA, see the New features) is fixed. `components_` are stored with no whitening. :issue:`5299` by :user:`Giorgio Patrini `. - Fixed bug in :func:`manifold.spectral_embedding` where diagonal of unnormalized Laplacian matrix was incorrectly set to 1. :issue:`4995` by :user:`Peter Fischer `. -- Fixed incorrect initialization of :func:`utils.arpack.eigsh` on all - occurrences. Affects :class:`cluster.bicluster.SpectralBiclustering`, +- Fixed incorrect initialization of `utils.arpack.eigsh` on all + occurrences. Affects `cluster.bicluster.SpectralBiclustering`, :class:`decomposition.KernelPCA`, :class:`manifold.LocallyLinearEmbedding`, and :class:`manifold.SpectralEmbedding` (:issue:`5012`). By :user:`Peter Fischer `. @@ -614,7 +614,7 @@ Decomposition, manifold learning and clustering Preprocessing and feature selection -- :func:`preprocessing.data._transform_selected` now always passes a copy +- `preprocessing.data._transform_selected` now always passes a copy of ``X`` to transform function when ``copy=True`` (:issue:`7194`). By `Caio Oliveira `_. @@ -633,8 +633,8 @@ Model evaluation and meta-estimators return splits of size ``train_size`` and ``test_size`` in all cases (:issue:`6472`). By `Andreas Müller`_. -- Cross-validation of :class:`OneVsOneClassifier` and - :class:`OneVsRestClassifier` now works with precomputed kernels. +- Cross-validation of :class:`multiclass.OneVsOneClassifier` and + :class:`multiclass.OneVsRestClassifier` now works with precomputed kernels. :issue:`7350` by :user:`Russell Smith `. - Fix incomplete ``predict_proba`` method delegation from @@ -654,7 +654,7 @@ Metrics - Fix bug where expected and adjusted mutual information were incorrect if cluster contingency cells exceeded ``2**16``. By `Joel Nothman`_. -- :func:`metrics.pairwise.pairwise_distances` now converts arrays to +- :func:`metrics.pairwise_distances` now converts arrays to boolean arrays when required in ``scipy.spatial.distance``. :issue:`5460` by `Tom Dupre la Tour`_. @@ -667,7 +667,7 @@ Metrics Miscellaneous -- :func:`model_selection.tests._search._check_param_grid` now works correctly with all types +- `model_selection.tests._search._check_param_grid` now works correctly with all types that extends/implements `Sequence` (except string), including range (Python 3.x) and xrange (Python 2.x). :issue:`7323` by Viacheslav Kovalevskyi. @@ -698,7 +698,7 @@ Linear, kernelized and related models Decomposition, manifold learning and clustering -- The old :class:`mixture.DPGMM` is deprecated in favor of the new +- The old `mixture.DPGMM` is deprecated in favor of the new :class:`mixture.BayesianGaussianMixture` (with the parameter ``weight_concentration_prior_type='dirichlet_process'``). The new class solves the computational @@ -706,7 +706,7 @@ Decomposition, manifold learning and clustering Dirichlet process prior faster than before. :issue:`7295` by :user:`Wei Xue ` and :user:`Thierry Guillemot `. -- The old :class:`mixture.VBGMM` is deprecated in favor of the new +- The old `mixture.VBGMM` is deprecated in favor of the new :class:`mixture.BayesianGaussianMixture` (with the parameter ``weight_concentration_prior_type='dirichlet_distribution'``). The new class solves the computational @@ -714,15 +714,15 @@ Decomposition, manifold learning and clustering mixture faster than before. :issue:`6651` by :user:`Wei Xue ` and :user:`Thierry Guillemot `. -- The old :class:`mixture.GMM` is deprecated in favor of the new +- The old `mixture.GMM` is deprecated in favor of the new :class:`mixture.GaussianMixture`. The new class computes the Gaussian mixture faster than before and some of computational problems have been solved. :issue:`6666` by :user:`Wei Xue ` and :user:`Thierry Guillemot `. Model evaluation and meta-estimators -- The :mod:`sklearn.cross_validation`, :mod:`sklearn.grid_search` and - :mod:`sklearn.learning_curve` have been deprecated and the classes and +- The `sklearn.cross_validation`, `sklearn.grid_search` and + `sklearn.learning_curve` have been deprecated and the classes and functions have been reorganized into the :mod:`sklearn.model_selection` module. Ref :ref:`model_selection_changes` for more information. :issue:`4294` by `Raghav RV`_. @@ -747,7 +747,7 @@ Model evaluation and meta-estimators :class:`model_selection.GroupShuffleSplit`, :class:`model_selection.LeaveOneGroupOut` and :class:`model_selection.LeavePGroupsOut` respectively. - Also the parameter ``labels`` in the :func:`split` method of the newly + Also the parameter ``labels`` in the `split` method of the newly renamed splitters :class:`model_selection.LeaveOneGroupOut` and :class:`model_selection.LeavePGroupsOut` is renamed to ``groups``. Additionally in :class:`model_selection.LeavePGroupsOut`, @@ -813,4 +813,3 @@ Hauck, trevorstephens, Tue Vo, Varun, Varun Jewalikar, Viacheslav, Vighnesh Birodkar, Vikram, Villu Ruusmann, Vinayak Mehta, walter, waterponey, Wenhua Yang, Wenjian Huang, Will Welch, wyseguy7, xyguo, yanlend, Yaroslav Halchenko, yelite, Yen, YenChenLin, Yichuan Liu, Yoav Ram, Yoshiki, Zheng RuiFeng, zivori, Óscar Nájera - diff --git a/doc/whats_new/v0.19.rst b/doc/whats_new/v0.19.rst index c1a91af9f1ed4..b06e0b36b96a0 100644 --- a/doc/whats_new/v0.19.rst +++ b/doc/whats_new/v0.19.rst @@ -94,9 +94,9 @@ Regressions in 0.19.0 fixed in 0.19.1: longer accepted ``X`` as a list. :issue:`9600` by :user:`Rasul Kerimov `. -- Fixed handling of :func:`cross_val_predict` for binary classification with - ``method='decision_function'``. :issue:`9593` by :user:`Reiichiro Nakano - ` and core devs. +- Fixed handling of :func:`model_selection.cross_val_predict` for binary + classification with ``method='decision_function'``. :issue:`9593` by + :user:`Reiichiro Nakano ` and core devs. - Fix regression in :class:`pipeline.Pipeline` where it no longer accepted ``steps`` as a tuple. :issue:`9604` by :user:`Joris Van den Bossche @@ -119,7 +119,7 @@ Regressions in 0.19.0 fixed in 0.19.1: Enhancements ............ -- Our test suite and :func:`utils.estimator_checks.check_estimators` can now be +- Our test suite and :func:`utils.estimator_checks.check_estimator` can now be run without Nose installed. :issue:`9697` by :user:`Joan Massich `. - To improve usability of version 0.19's :class:`pipeline.Pipeline` @@ -362,11 +362,11 @@ Linear, kernelized and related models Other predictors -- Custom metrics for the :mod:`neighbors` binary trees now have +- Custom metrics for the :mod:`sklearn.neighbors` binary trees now have fewer constraints: they must take two 1d-arrays and return a float. :issue:`6288` by `Jake Vanderplas`_. -- ``algorithm='auto`` in :mod:`neighbors` estimators now chooses the most +- ``algorithm='auto`` in :mod:`sklearn.neighbors` estimators now chooses the most appropriate algorithm for all input types and metrics. :issue:`9145` by :user:`Herilalaina Rakotoarison ` and :user:`Reddy Chinthala `. @@ -396,7 +396,7 @@ Decomposition, manifold learning and clustering - Memory usage enhancements: Prevent cast from float32 to float64 in :class:`decomposition.PCA` and - :func:`decomposition.randomized_svd_low_rank`. + `decomposition.randomized_svd_low_rank`. :issue:`9067` by `Raghav RV`_. Preprocessing and feature selection @@ -409,7 +409,7 @@ Preprocessing and feature selection with ``center=True``. :issue:`8065` by :user:`Daniel LeJeune `. - Small performance improvement to n-gram creation in - :mod:`feature_extraction.text` by binding methods for loops and + :mod:`sklearn.feature_extraction.text` by binding methods for loops and special-casing unigrams. :issue:`7567` by :user:`Jaye Doepke ` - Relax assumption on the data for the @@ -486,12 +486,12 @@ Metrics Miscellaneous -- :func:`utils.check_estimator` now attempts to ensure that methods +- :func:`utils.estimator_checks.check_estimator` now attempts to ensure that methods transform, predict, etc. do not set attributes on the estimator. :issue:`7533` by :user:`Ekaterina Krivich `. - Added type checking to the ``accept_sparse`` parameter in - :mod:`utils.validation` methods. This parameter now accepts only boolean, + :mod:`sklearn.utils.validation` methods. This parameter now accepts only boolean, string, or list/tuple of strings. ``accept_sparse=None`` is deprecated and should be replaced by ``accept_sparse=False``. :issue:`7880` by :user:`Josh Karnofsky `. @@ -570,7 +570,7 @@ Linear, kernelized and related models the same result as the LassoLars implementation available in R (lars library). :issue:`7849` by :user:`Jair Montoya Martinez `. -- Fixed a bug in :class:`linear_model.RandomizedLasso`, +- Fixed a bug in `linear_model.RandomizedLasso`, :class:`linear_model.Lars`, :class:`linear_model.LassoLars`, :class:`linear_model.LarsCV` and :class:`linear_model.LassoLarsCV`, where the parameter ``precompute`` was not used consistently across @@ -611,7 +611,7 @@ Linear, kernelized and related models Other predictors -- Fix :class:`semi_supervised.BaseLabelPropagation` to correctly implement +- Fix `semi_supervised.BaseLabelPropagation` to correctly implement ``LabelPropagation`` and ``LabelSpreading`` as done in the referenced papers. :issue:`9239` by :user:`Andre Ambrosio Boechat `, :user:`Utkarsh Upadhyay @@ -642,7 +642,7 @@ Decomposition, manifold learning and clustering - Fixed the implementation of ``explained_variance_`` in :class:`decomposition.PCA`, - :class:`decomposition.RandomizedPCA` and + `decomposition.RandomizedPCA` and :class:`decomposition.IncrementalPCA`. :issue:`9105` by `Hanmin Qin `_. @@ -674,13 +674,13 @@ Decomposition, manifold learning and clustering - Fixed improper scaling in :class:`cross_decomposition.PLSRegression` with ``scale=True``. :issue:`7819` by :user:`jayzed82 `. -- :class:`cluster.bicluster.SpectralCoclustering` and - :class:`cluster.bicluster.SpectralBiclustering` ``fit`` method conforms +- :class:`cluster.SpectralCoclustering` and + :class:`cluster.SpectralBiclustering` ``fit`` method conforms with API by accepting ``y`` and returning the object. :issue:`6126`, :issue:`7814` by :user:`Laurent Direr ` and :user:`Maniteja Nandana `. -- Fix bug where :mod:`mixture` ``sample`` methods did not return as many +- Fix bug where :mod:`sklearn.mixture` ``sample`` methods did not return as many samples as requested. :issue:`7702` by :user:`Levi John Wolf `. - Fixed the shrinkage implementation in :class:`neighbors.NearestCentroid`. @@ -698,8 +698,8 @@ Preprocessing and feature selection selected fewer features than it should. :issue:`7490` by :user:`Peng Meng `. -- Fixed a bug where :class:`linear_model.RandomizedLasso` and - :class:`linear_model.RandomizedLogisticRegression` breaks for +- Fixed a bug where `linear_model.RandomizedLasso` and + `linear_model.RandomizedLogisticRegression` breaks for sparse input. :issue:`8259` by :user:`Aman Dalmia `. - Fix a bug where :class:`feature_extraction.FeatureHasher` @@ -715,14 +715,14 @@ Preprocessing and feature selection Model evaluation and meta-estimators -- Fixed a bug where :func:`model_selection.BaseSearchCV.inverse_transform` +- Fixed a bug where `model_selection.BaseSearchCV.inverse_transform` returns ``self.best_estimator_.transform()`` instead of ``self.best_estimator_.inverse_transform()``. :issue:`8344` by :user:`Akshay Gupta ` and :user:`Rasmus Eriksson `. - Added ``classes_`` attribute to :class:`model_selection.GridSearchCV`, - :class:`model_selection.RandomizedSearchCV`, :class:`grid_search.GridSearchCV`, - and :class:`grid_search.RandomizedSearchCV` that matches the ``classes_`` + :class:`model_selection.RandomizedSearchCV`, `grid_search.GridSearchCV`, + and `grid_search.RandomizedSearchCV` that matches the ``classes_`` attribute of ``best_estimator_``. :issue:`7661` and :issue:`8295` by :user:`Alyssa Batula `, :user:`Dylan Werner-Meier `, and :user:`Stephen Hoover `. @@ -760,7 +760,7 @@ Metrics (`#7356 `_). By :user:`Nick Dingwall ` and `Gael Varoquaux`_. -- Fix a bug in :func:`metrics.classification._check_targets` +- Fix a bug in `metrics.classification._check_targets` which would return ``'binary'`` if ``y_true`` and ``y_pred`` were both ``'binary'`` but the union of ``y_true`` and ``y_pred`` was ``'multiclass'``. :issue:`8377` by `Loic Esteve`_. @@ -784,7 +784,7 @@ Miscellaneous incorrect result when ``n_samples`` is odd. :issue:`8198` by :user:`Josh Levy `. -- Some ``fetch_`` functions in :mod:`datasets` were ignoring the +- Some ``fetch_`` functions in :mod:`sklearn.datasets` were ignoring the ``download_if_missing`` keyword. :issue:`7944` by :user:`Ralf Gommers `. - Fix estimators to accept a ``sample_weight`` parameter of type @@ -795,7 +795,7 @@ Miscellaneous raising an exception if instability is identified. :issue:`7376` and :issue:`7331` by `Joel Nothman`_ and :user:`yangarbiter`. -- Fix a bug where :meth:`base.BaseEstimator.__getstate__` +- Fix a bug where `base.BaseEstimator.__getstate__` obstructed pickling customizations of child-classes, when used in a multiple inheritance context. :issue:`8316` by :user:`Holger Peters `. @@ -837,7 +837,7 @@ Linear, kernelized and related models Other predictors -- :class:`neighbors.LSHForest` has been deprecated and will be +- `neighbors.LSHForest` has been deprecated and will be removed in 0.21 due to poor performance. :issue:`9078` by :user:`Laurent Direr `. @@ -884,8 +884,8 @@ Preprocessing and feature selection ``alternate_sign``. :issue:`7565` by :user:`Roman Yurchak `. -- :class:`linear_model.RandomizedLogisticRegression`, - and :class:`linear_model.RandomizedLasso` have been deprecated and will +- `linear_model.RandomizedLogisticRegression`, + and `linear_model.RandomizedLasso` have been deprecated and will be removed in version 0.21. :issue:`8995` by :user:`Ramana.S `. @@ -944,7 +944,7 @@ Miscellaneous - SciPy >= 0.13.3 and NumPy >= 1.8.2 are now the minimum supported versions for scikit-learn. The following backported functions in - :mod:`utils` have been removed or deprecated accordingly. + :mod:`sklearn.utils` have been removed or deprecated accordingly. :issue:`8854` and :issue:`8874` by :user:`Naoya Kanai ` - The ``store_covariances`` and ``covariances_`` parameters of @@ -994,7 +994,7 @@ Miscellaneous - Ensure that estimators' attributes ending with ``_`` are not set in the constructor but only in the ``fit`` method. Most notably, - ensemble estimators (deriving from :class:`ensemble.BaseEnsemble`) + ensemble estimators (deriving from `ensemble.BaseEnsemble`) now only have ``self.estimators_`` available after ``fit``. :issue:`7464` by `Lars Buitinck`_ and `Loic Esteve`_. diff --git a/doc/whats_new/v0.20.rst b/doc/whats_new/v0.20.rst index 1f899bfccc838..1c62006907231 100644 --- a/doc/whats_new/v0.20.rst +++ b/doc/whats_new/v0.20.rst @@ -34,7 +34,7 @@ The bundled version of joblib was upgraded from 0.13.0 to 0.13.2. :mod:`sklearn.decomposition` ............................ -- |Fix| Fixed a bug in :class:`cross_decomposition.CCA` improving numerical +- |Fix| Fixed a bug in :class:`cross_decomposition.CCA` improving numerical stability when `Y` is close to zero. :pr:`13903` by `Thomas Fan`_. @@ -104,7 +104,7 @@ Changelog :mod:`sklearn.feature_extraction` ................................. -- |Fix| Fixed a bug in :class:`feature_extraction.text.CountVectorizer` which +- |Fix| Fixed a bug in :class:`feature_extraction.text.CountVectorizer` which would result in the sparse feature matrix having conflicting `indptr` and `indices` precisions under very large vocabularies. :issue:`11295` by :user:`Gabriel Vacaliuc `. @@ -209,7 +209,7 @@ Changelog :mod:`sklearn.neighbors` ........................ -- |Fix| Fixed :class:`sklearn.neighbors.DistanceMetric` jaccard distance +- |Fix| Fixed `sklearn.neighbors.DistanceMetric` jaccard distance function to return 0 when two all-zero vectors are compared. :issue:`12685` by :user:`Thomas Fan `. @@ -342,7 +342,7 @@ Changelog those estimators as part of parallel parameter search or cross-validation. :issue:`12122` by :user:`Olivier Grisel `. -- |Fix| Fixed a bug affecting :class:`SGDClassifier` in the multiclass +- |Fix| Fixed a bug affecting :class:`linear_model.SGDClassifier` in the multiclass case. Each one-versus-all step is run in a :class:`joblib.Parallel` call and mutating a common parameter, causing a segmentation fault if called within a backend using processes and not threads. We now use ``require=sharedmem`` @@ -352,16 +352,16 @@ Changelog :mod:`sklearn.metrics` ...................... -- |Fix| Fixed a bug in :func:`metrics.pairwise.pairwise_distances_argmin_min` +- |Fix| Fixed a bug in `metrics.pairwise.pairwise_distances_argmin_min` which returned the square root of the distance when the metric parameter was set to "euclidean". :issue:`12481` by :user:`Jérémie du Boisberranger `. -- |Fix| Fixed a bug in :func:`metrics.pairwise.pairwise_distances_chunked` +- |Fix| Fixed a bug in `metrics.pairwise.pairwise_distances_chunked` which didn't ensure the diagonal is zero for euclidean distances. :issue:`12612` by :user:`Andreas Müller `. -- |API| The :func:`metrics.calinski_harabaz_score` has been renamed to +- |API| The `metrics.calinski_harabaz_score` has been renamed to :func:`metrics.calinski_harabasz_score` and will be removed in version 0.23. :issue:`12211` by :user:`Lisa Thomas `, :user:`Mark Hannel ` and :user:`Melissa Ferrari `. @@ -399,7 +399,7 @@ Changelog :issue:`12522` by :user:`Nicolas Hug`. - |Fix| Fixed a bug in :class:`preprocessing.OneHotEncoder` where transform - failed when set to ignore unknown numpy strings of different lengths + failed when set to ignore unknown numpy strings of different lengths :issue:`12471` by :user:`Gabriel Marzinotto`. - |API| The default value of the :code:`method` argument in @@ -419,7 +419,7 @@ Changelog - |Fix| Calling :func:`utils.check_array` on `pandas.Series`, which raised an error in 0.20.0, now returns the expected output again. :issue:`12625` by `Andreas Müller`_ - + Miscellaneous ............. @@ -493,7 +493,7 @@ including missing values, categorical variables, heterogeneous data, and features/targets with unusual distributions. Missing values in features, represented by NaNs, are now accepted in column-wise preprocessing such as scalers. Each feature is fitted disregarding -NaNs, and data containing NaNs can be transformed. The new :mod:`impute` +NaNs, and data containing NaNs can be transformed. The new :mod:`sklearn.impute` module provides estimators for learning despite missing data. :class:`~compose.ColumnTransformer` handles the case where different features @@ -545,7 +545,7 @@ random sampling procedures. - :class:`linear_model.SGDRegressor` (bug fix) - :class:`metrics.roc_auc_score` (bug fix) - :class:`metrics.roc_curve` (bug fix) -- :class:`neural_network.BaseMultilayerPerceptron` (bug fix) +- `neural_network.BaseMultilayerPerceptron` (bug fix) - :class:`neural_network.MLPClassifier` (bug fix) - :class:`neural_network.MLPRegressor` (bug fix) - The v0.19.0 release notes failed to mention a backwards incompatibility with @@ -616,7 +616,7 @@ Support for Python 3.3 has been officially dropped. by :user:`Jan Margeta `, :user:`Guillaume Lemaitre `, and :user:`Devansh D. `. -- |Fix| Fixed a bug in :func:`cluster.k_means_elkan` where the returned +- |Fix| Fixed a bug in `cluster.k_means_elkan` where the returned ``iteration`` was 1 less than the correct value. Also added the missing ``n_iter_`` attribute in the docstring of :class:`cluster.KMeans`. :issue:`11353` by :user:`Jeremie du Boisberranger `. @@ -654,8 +654,8 @@ Support for Python 3.3 has been officially dropped. - |Efficiency| Runtime improvements to :class:`covariance.GraphicalLasso`. :issue:`9858` by :user:`Steven Brown `. -- |API| The :func:`covariance.graph_lasso`, - :class:`covariance.GraphLasso` and :class:`covariance.GraphLassoCV` have been +- |API| The `covariance.graph_lasso`, + `covariance.GraphLasso` and `covariance.GraphLassoCV` have been renamed to :func:`covariance.graphical_lasso`, :class:`covariance.GraphicalLasso` and :class:`covariance.GraphicalLassoCV` respectively and will be removed in version 0.22. @@ -675,14 +675,14 @@ Support for Python 3.3 has been officially dropped. cluster. :issue:`8617` by :user:`Maskani Filali Mohamed ` and :user:`Konstantinos Katrioplas `. -- |Feature| Add ``filename`` attribute to :mod:`datasets` that have a CSV file. +- |Feature| Add ``filename`` attribute to :mod:`sklearn.datasets` that have a CSV file. :issue:`9101` by :user:`alex-33 ` and :user:`Maskani Filali Mohamed `. - |Feature| ``return_X_y`` parameter has been added to several dataset loaders. :issue:`10774` by :user:`Chris Catalfo `. -- |Fix| Fixed a bug in :func:`datasets.load_boston` which had a wrong data +- |Fix| Fixed a bug in `datasets.load_boston` which had a wrong data point. :issue:`10795` by :user:`Takeshi Yoshizawa `. - |Fix| Fixed a bug in :func:`datasets.load_iris` which had two wrong data points. @@ -696,7 +696,7 @@ Support for Python 3.3 has been officially dropped. data points could be generated. :issue:`10045` by :user:`Christian Braune `. -- |API| Deprecated :func:`sklearn.datasets.fetch_mldata` to be removed in +- |API| Deprecated `sklearn.datasets.fetch_mldata` to be removed in version 0.22. mldata.org is no longer operational. Until removal it will remain possible to load cached datasets. :issue:`11466` by `Joel Nothman`_. @@ -751,8 +751,8 @@ Support for Python 3.3 has been officially dropped. :mod:`sklearn.discriminant_analysis` .................................... -- |Efficiency| Memory usage improvement for :func:`_class_means` and - :func:`_class_cov` in :mod:`discriminant_analysis`. :issue:`10898` by +- |Efficiency| Memory usage improvement for `_class_means` and + `_class_cov` in :mod:`sklearn.discriminant_analysis`. :issue:`10898` by :user:`Nanxin Chen `. @@ -809,14 +809,14 @@ Support for Python 3.3 has been officially dropped. to 100 in 0.22. A FutureWarning is raised when the default value is used. :issue:`11542` by :user:`Anna Ayzenshtat `. -- |API| Classes derived from :class:`ensemble.BaseBagging`. The attribute +- |API| Classes derived from `ensemble.BaseBagging`. The attribute ``estimators_samples_`` will return a list of arrays containing the indices selected for each bootstrap instead of a list of arrays containing the mask of the samples selected for each bootstrap. Indices allows to repeat samples while mask does not allow this functionality. :issue:`9524` by :user:`Guillaume Lemaitre `. -- |Fix| :class:`ensemble.BaseBagging` where one could not deterministically +- |Fix| `ensemble.BaseBagging` where one could not deterministically reproduce ``fit`` result using the object attributes when ``random_state`` is set. :issue:`9723` by :user:`Guillaume Lemaitre `. @@ -925,7 +925,7 @@ Support for Python 3.3 has been officially dropped. :class:`linear_model.BayesianRidge` for weighted linear regression. :issue:`10112` by :user:`Peter St. John `. -- |Fix| Fixed a bug in :func:`logistic.logistic_regression_path` to ensure +- |Fix| Fixed a bug in `logistic.logistic_regression_path` to ensure that the returned coefficients are correct when ``multiclass='multinomial'``. Previously, some of the coefficients would override each other, leading to incorrect results in :class:`linear_model.LogisticRegressionCV`. @@ -1027,7 +1027,7 @@ Support for Python 3.3 has been officially dropped. - |Feature| Support sparse input in :meth:`manifold.Isomap.fit`. :issue:`8554` by :user:`Leland McInnes `. -- |Feature| :func:`manifold.t_sne.trustworthiness` accepts metrics other than +- |Feature| `manifold.t_sne.trustworthiness` accepts metrics other than Euclidean. :issue:`9775` by :user:`William de Vazelhes `. - |Fix| Fixed a bug in :func:`manifold.spectral_embedding` where the @@ -1037,14 +1037,14 @@ Support for Python 3.3 has been officially dropped. `. - |API| |Feature| Deprecate ``precomputed`` parameter in function - :func:`manifold.t_sne.trustworthiness`. Instead, the new parameter ``metric`` + `manifold.t_sne.trustworthiness`. Instead, the new parameter ``metric`` should be used with any compatible metric including 'precomputed', in which case the input matrix ``X`` should be a matrix of pairwise distances or squared distances. :issue:`9775` by :user:`William de Vazelhes `. - |API| Deprecate ``precomputed`` parameter in function - :func:`manifold.t_sne.trustworthiness`. Instead, the new parameter + `manifold.t_sne.trustworthiness`. Instead, the new parameter ``metric`` should be used with any compatible metric including 'precomputed', in which case the input matrix ``X`` should be a matrix of pairwise distances or squared distances. :issue:`9775` by @@ -1161,12 +1161,12 @@ Support for Python 3.3 has been officially dropped. calling :term:`fit` and :term:`predict`. :issue:`10336` by :user:`Shu Haoran ` and :user:`Andrew Peng `. -- |Fix| Fixed a bug in :class:`mixture.BaseMixture` where the reported `n_iter_` was +- |Fix| Fixed a bug in `mixture.BaseMixture` where the reported `n_iter_` was missing an iteration. It affected :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture`. :issue:`10740` by :user:`Erich Schubert ` and :user:`Guillaume Lemaitre `. -- |Fix| Fixed a bug in :class:`mixture.BaseMixture` and its subclasses +- |Fix| Fixed a bug in `mixture.BaseMixture` and its subclasses :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture` where the ``lower_bound_`` was not the max lower bound across all initializations (when ``n_init > 1``), but just the lower bound of the last @@ -1192,7 +1192,7 @@ Support for Python 3.3 has been officially dropped. :func:`model_selection.cross_val_score`, :func:`model_selection.learning_curve` and :func:`model_selection.validation_curve` to control the behavior triggered - when an error occurs in :func:`model_selection._fit_and_score`. + when an error occurs in `model_selection._fit_and_score`. :issue:`11576` by :user:`Samuel O. Ronsin `. - |Feature| `BaseSearchCV` now has an experimental, private interface to @@ -1271,7 +1271,7 @@ Support for Python 3.3 has been officially dropped. parallelized according to ``n_jobs`` regardless of ``algorithm``. :issue:`10887` by :user:`Joël Billaud `. -- |Efficiency| :mod:`Nearest neighbors ` query methods are now more +- |Efficiency| :mod:`sklearn.neighbors` query methods are now more memory efficient when ``algorithm='brute'``. :issue:`11136` by `Joel Nothman`_ and :user:`Aman Dalmia `. @@ -1305,7 +1305,7 @@ Support for Python 3.3 has been officially dropped. :issue:`11556` by :user:`Jake VanderPlas ` - |Fix| Fixed a bug in :class:`neighbors.KDTree` and :class:`neighbors.BallTree` where - pickled tree objects would change their type to the super class :class:`BinaryTree`. + pickled tree objects would change their type to the super class `BinaryTree`. :issue:`11774` by :user:`Nicolas Hug `. @@ -1313,13 +1313,13 @@ Support for Python 3.3 has been officially dropped. ............................. - |Feature| Add `n_iter_no_change` parameter in - :class:`neural_network.BaseMultilayerPerceptron`, + `neural_network.BaseMultilayerPerceptron`, :class:`neural_network.MLPRegressor`, and :class:`neural_network.MLPClassifier` to give control over maximum number of epochs to not meet ``tol`` improvement. :issue:`9456` by :user:`Nicholas Nadeau `. -- |Fix| Fixed a bug in :class:`neural_network.BaseMultilayerPerceptron`, +- |Fix| Fixed a bug in `neural_network.BaseMultilayerPerceptron`, :class:`neural_network.MLPRegressor`, and :class:`neural_network.MLPClassifier` with new ``n_iter_no_change`` parameter now at 10 from previously hardcoded 2. @@ -1441,13 +1441,13 @@ Support for Python 3.3 has been officially dropped. :class:`compose.ColumnTransformer`. :issue:`10521` by `Joris Van den Bossche`_. -- |API| Deprecate :class:`preprocessing.Imputer` and move +- |API| Deprecate `preprocessing.Imputer` and move the corresponding module to :class:`impute.SimpleImputer`. :issue:`9726` by :user:`Kumar Ashutosh `. - |API| The ``axis`` parameter that was in - :class:`preprocessing.Imputer` is no longer present in + `preprocessing.Imputer` is no longer present in :class:`impute.SimpleImputer`. The behavior is equivalent to ``axis=0`` (impute along columns). Row-wise imputation can be performed with FunctionTransformer @@ -1458,7 +1458,7 @@ Support for Python 3.3 has been officially dropped. - |API| The NaN marker for the missing values has been changed between the :class:`preprocessing.Imputer` and the - :class:`impute.SimpleImputer`. + `impute.SimpleImputer`. ``missing_values='NaN'`` should now be ``missing_values=np.nan``. :issue:`11211` by :user:`Jeremie du Boisberranger `. @@ -1491,15 +1491,15 @@ Support for Python 3.3 has been officially dropped. ................... - |Enhancement| Although private (and hence not assured API stability), - :class:`tree._criterion.ClassificationCriterion` and - :class:`tree._criterion.RegressionCriterion` may now be cimported and + `tree._criterion.ClassificationCriterion` and + `tree._criterion.RegressionCriterion` may now be cimported and extended. :issue:`10325` by :user:`Camil Staps `. -- |Fix| Fixed a bug in :class:`tree.BaseDecisionTree` with `splitter="best"` +- |Fix| Fixed a bug in `tree.BaseDecisionTree` with `splitter="best"` where split threshold could become infinite when values in X were near infinite. :issue:`10536` by :user:`Jonathan Ohayon `. -- |Fix| Fixed a bug in :class:`tree.MAE` to ensure sample weights are being +- |Fix| Fixed a bug in `tree.MAE` to ensure sample weights are being used during the calculation of tree MAE impurity. Previous behaviour could cause suboptimal splits to be chosen since the impurity calculation considered all samples to be of equal weight importance. @@ -1559,7 +1559,7 @@ Multiple modules - |API| Changed warning type from :class:`UserWarning` to :class:`exceptions.ConvergenceWarning` for failing convergence in - :func:`linear_model.logistic_regression_path`, + `linear_model.logistic_regression_path`, :class:`linear_model.RANSACRegressor`, :func:`linear_model.ridge_regression`, :class:`gaussian_process.GaussianProcessRegressor`, :class:`gaussian_process.GaussianProcessClassifier`, diff --git a/doc/whats_new/v0.21.rst b/doc/whats_new/v0.21.rst index 6f6e7eed19bc2..40bdf7540bfc4 100644 --- a/doc/whats_new/v0.21.rst +++ b/doc/whats_new/v0.21.rst @@ -67,8 +67,8 @@ Changelog :mod:`sklearn.ensemble` ....................... -- |Fix| Fix zero division error in :func:`HistGradientBoostingClassifier` and - :func:`HistGradientBoostingRegressor`. +- |Fix| Fix zero division error in :class:`ensemble.HistGradientBoostingClassifier` and + :class:`ensemble.HistGradientBoostingRegressor`. :pr:`14024` by `Nicolas Hug `. :mod:`sklearn.impute` @@ -81,7 +81,7 @@ Changelog :mod:`sklearn.inspection` ......................... -- |Fix| Fixed a bug in :func:`inspection.plot_partial_dependence` where +- |Fix| Fixed a bug in :func:`inspection.plot_partial_dependence` where ``target`` parameter was not being taken into account for multiclass problems. :pr:`14393` by :user:`Guillem G. Subies `. @@ -109,7 +109,7 @@ Changelog :mod:`sklearn.tree` ................... -- |Fix| Fixed bug in :func:`tree.export_text` when the tree has one feature and +- |Fix| Fixed bug in :func:`tree.export_text` when the tree has one feature and a single feature name is passed in. :pr:`14053` by `Thomas Fan`. - |Fix| Fixed an issue with :func:`plot_tree` where it displayed @@ -129,7 +129,7 @@ Changelog :mod:`sklearn.decomposition` ............................ -- |Fix| Fixed a bug in :class:`cross_decomposition.CCA` improving numerical +- |Fix| Fixed a bug in :class:`cross_decomposition.CCA` improving numerical stability when `Y` is close to zero. :pr:`13903` by `Thomas Fan`_. :mod:`sklearn.metrics` @@ -148,11 +148,11 @@ Changelog by :user:`James Myatt `. -:mod:`sklearn.utils.sparsefuncs` -................................ +`sklearn.utils.sparsefuncs` +........................... -- |Fix| Fixed a bug where :func:`min_max_axis` would fail on 32-bit systems - for certain large inputs. This affects :class:`preprocessing.MaxAbsScaler`, +- |Fix| Fixed a bug where `min_max_axis` would fail on 32-bit systems + for certain large inputs. This affects :class:`preprocessing.MaxAbsScaler`, :func:`preprocessing.normalize` and :class:`preprocessing.LabelBinarizer`. :pr:`13741` by :user:`Roddy MacSween `. @@ -230,7 +230,7 @@ random sampling procedures. - :func:`svm.SVC.decision_function` and :func:`multiclass.OneVsOneClassifier.decision_function`. |Fix| - :class:`linear_model.SGDClassifier` and any derived classifiers. |Fix| -- Any model using the :func:`linear_model._sag.sag_solver` function with a `0` +- Any model using the `linear_model._sag.sag_solver` function with a `0` seed, including :class:`linear_model.LogisticRegression`, :class:`linear_model.LogisticRegressionCV`, :class:`linear_model.Ridge`, and :class:`linear_model.RidgeCV` with 'sag' solver. |Fix| @@ -420,7 +420,7 @@ Support for Python 3.4 and below has been officially dropped. >>> from sklearn.experimental import enable_hist_gradient_boosting # noqa >>> # now you can import normally from sklearn.ensemble >>> from sklearn.ensemble import HistGradientBoostingClassifier - + .. note:: Update: since version 1.0, these estimators are not experimental anymore and you don't need to use `from sklearn.experimental import @@ -508,24 +508,24 @@ Support for Python 3.4 and below has been officially dropped. if any targets were strings. :pr:`12834` by :user:`Elizabeth Sander `. -- |Fix| Fixed a bug in :class:`ensemble.gradient_boosting.LossFunction` and - :class:`ensemble.gradient_boosting.LeastSquaresError` where the default +- |Fix| Fixed a bug in `ensemble.gradient_boosting.LossFunction` and + `ensemble.gradient_boosting.LeastSquaresError` where the default value of ``learning_rate`` in ``update_terminal_regions`` is not consistent with the document and the caller functions. Note however that directly using these loss functions is deprecated. :pr:`6463` by :user:`movelikeriver `. -- |Fix| :func:`ensemble.partial_dependence` (and consequently the new +- |Fix| `ensemble.partial_dependence` (and consequently the new version :func:`sklearn.inspection.partial_dependence`) now takes sample weights into account for the partial dependence computation when the gradient boosting model has been trained with sample weights. :pr:`13193` by :user:`Samuel O. Ronsin `. -- |API| :func:`ensemble.partial_dependence` and - :func:`ensemble.plot_partial_dependence` are now deprecated in favor of +- |API| `ensemble.partial_dependence` and + `ensemble.plot_partial_dependence` are now deprecated in favor of :func:`inspection.partial_dependence` and - :func:`inspection.plot_partial_dependence`. + `inspection.plot_partial_dependence`. :pr:`12599` by :user:`Trevor Stephens` and :user:`Nicolas Hug`. @@ -540,10 +540,10 @@ Support for Python 3.4 and below has been officially dropped. :class:`pipeline.FeatureUnion` and :class:`compose.ColumnTransformer`). :pr:`13780` by :user:`Guillaume Lemaitre `. -:mod:`sklearn.externals` -........................ +`sklearn.externals` +................... -- |API| Deprecated :mod:`externals.six` since we have dropped support for +- |API| Deprecated `externals.six` since we have dropped support for Python 2.7. :pr:`12916` by :user:`Hanmin Qin `. :mod:`sklearn.feature_extraction` @@ -599,7 +599,7 @@ Support for Python 3.4 and below has been officially dropped. (new subpackage) - |Feature| Partial dependence plots - (:func:`inspection.plot_partial_dependence`) are now supported for + (`inspection.plot_partial_dependence`) are now supported for any regressor or classifier (provided that they have a `predict_proba` method). :pr:`12599` by :user:`Trevor Stephens ` and :user:`Nicolas Hug `. @@ -627,7 +627,7 @@ Support for Python 3.4 and below has been officially dropped. users to compute :class:`linear_model.lars_path` without providing ``X`` and ``y``. :pr:`11699` by :user:`Kuai Yu `. -- |Efficiency| :func:`linear_model.make_dataset` now preserves +- |Efficiency| `linear_model.make_dataset` now preserves ``float32`` and ``float64`` dtypes, reducing memory consumption in stochastic gradient, SAG and SAGA solvers. :pr:`8769` and :pr:`11000` by @@ -683,7 +683,7 @@ Support for Python 3.4 and below has been officially dropped. case. :pr:`13389` by :user:`Pierre Glaser `. - |Fix| Fixed a bug in - :class:`linear_model.stochastic_gradient.BaseSGDClassifier` that was not + `linear_model.stochastic_gradient.BaseSGDClassifier` that was not deterministic when trained in a multi-class setting on several threads. :pr:`13422` by :user:`Clément Doumouro `. @@ -708,7 +708,7 @@ Support for Python 3.4 and below has been officially dropped. in version 0.23. Use :class:`linear_model.lars_path_gram` instead. :pr:`11699` by :user:`Kuai Yu `. -- |API| :func:`linear_model.logistic_regression_path` is deprecated +- |API| `linear_model.logistic_regression_path` is deprecated in version 0.21 and will be removed in version 0.23. :pr:`12821` by :user:`Nicolas Hug `. @@ -719,7 +719,7 @@ Support for Python 3.4 and below has been officially dropped. :mod:`sklearn.manifold` ....................... -- |Efficiency| Make :func:`manifold.tsne.trustworthiness` use an inverted index +- |Efficiency| Make :func:`manifold.trustworthiness` use an inverted index instead of an `np.where` lookup to find the rank of neighbors in the input space. This improves efficiency in particular when computed with lots of neighbors and/or small datasets. @@ -789,13 +789,13 @@ Support for Python 3.4 and below has been officially dropped. in version 0.21 and will be removed in version 0.23. :pr:`10580` by :user:`Reshama Shaikh ` and :user:`Sandra Mitrovic `. -- |Fix| The function :func:`metrics.pairwise.euclidean_distances`, and - therefore several estimators with ``metric='euclidean'``, suffered from - numerical precision issues with ``float32`` features. Precision has been - increased at the cost of a small drop of performance. :pr:`13554` by +- |Fix| The function :func:`metrics.pairwise.euclidean_distances`, and + therefore several estimators with ``metric='euclidean'``, suffered from + numerical precision issues with ``float32`` features. Precision has been + increased at the cost of a small drop of performance. :pr:`13554` by :user:`Celelibi` and :user:`Jérémie du Boisberranger `. -- |API| :func:`metrics.jaccard_similarity_score` is deprecated in favour of +- |API| `metrics.jaccard_similarity_score` is deprecated in favour of the more consistent :func:`metrics.jaccard_score`. The former behavior for binary and multiclass targets is broken. :pr:`13151` by `Joel Nothman`_. @@ -803,7 +803,7 @@ Support for Python 3.4 and below has been officially dropped. :mod:`sklearn.mixture` ...................... -- |Fix| Fixed a bug in :class:`mixture.BaseMixture` and therefore on estimators +- |Fix| Fixed a bug in `mixture.BaseMixture` and therefore on estimators based on it, i.e. :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture`, where ``fit_predict`` and ``fit.predict`` were not equivalent. :pr:`13142` by @@ -865,7 +865,7 @@ Support for Python 3.4 and below has been officially dropped. `predict_proba` method incorrectly checked for `predict_proba` attribute in the estimator object. :pr:`12222` by :user:`Rebekah Kim ` - + :mod:`sklearn.neighbors` ........................ @@ -958,7 +958,7 @@ Support for Python 3.4 and below has been officially dropped. - |API| The default value of `copy` in :func:`preprocessing.quantile_transform` will change from False to True in 0.23 in order to make it more consistent with the default `copy` values of other functions in - :mod:`preprocessing` and prevent unexpected side effects by modifying + :mod:`sklearn.preprocessing` and prevent unexpected side effects by modifying the value of `X` inplace. :pr:`13459` by :user:`Hunter McGushion `. @@ -976,7 +976,7 @@ Support for Python 3.4 and below has been officially dropped. ................... - |Feature| Decision Trees can now be plotted with matplotlib using - :func:`tree.plot_tree` without relying on the ``dot`` library, + `tree.plot_tree` without relying on the ``dot`` library, removing a hard-to-install dependency. :pr:`8508` by `Andreas Müller`_. - |Feature| Decision Trees can now be exported in a human readable @@ -984,7 +984,7 @@ Support for Python 3.4 and below has been officially dropped. :pr:`6261` by `Giuseppe Vettigli `. - |Feature| ``get_n_leaves()`` and ``get_depth()`` have been added to - :class:`tree.BaseDecisionTree` and consequently all estimators based + `tree.BaseDecisionTree` and consequently all estimators based on it, including :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier`, and :class:`tree.ExtraTreeRegressor`. @@ -994,7 +994,7 @@ Support for Python 3.4 and below has been officially dropped. classification targets with string labels, despite accepting them in `fit`. :pr:`11458` by :user:`Mitar Milutinovic `. -- |Fix| Fixed an issue with :class:`tree.BaseDecisionTree` +- |Fix| Fixed an issue with `tree.BaseDecisionTree` and consequently all estimators based on it, including :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier`, @@ -1013,7 +1013,7 @@ Support for Python 3.4 and below has been officially dropped. - |API| Deprecated ``warn_on_dtype`` parameter from :func:`utils.check_array` and :func:`utils.check_X_y`. Added explicit warning for dtype conversion - in :func:`check_pairwise_arrays` if the ``metric`` being passed is a + in `check_pairwise_arrays` if the ``metric`` being passed is a pairwise boolean metric. :pr:`13382` by :user:`Prathmesh Savale `. @@ -1038,7 +1038,7 @@ Multiple modules dtype in multiple estimators. :pr:`11973` by :user:`Roman Yurchak `. -- |Fix| Fixed a bug in the implementation of the :func:`our_rand_r` +- |Fix| Fixed a bug in the implementation of the `our_rand_r` helper function that was not behaving consistently across platforms. :pr:`13422` by :user:`Madhura Parikh ` and :user:`Clément Doumouro `. @@ -1083,7 +1083,7 @@ Baibak, daten-kieker, Denis Kataev, Didi Bar-Zev, Dillon Gardner, Dmitry Mottl, Dmitry Vukolov, Dougal J. Sutherland, Dowon, drewmjohnston, Dror Atariah, Edward J Brown, Ekaterina Krivich, Elizabeth Sander, Emmanuel Arias, Eric Chang, Eric Larson, Erich Schubert, esvhd, Falak, Feda Curic, Federico Caselli, -Frank Hoang, Fibinse Xavier`, Finn O'Shea, Gabriel Marzinotto, Gabriel Vacaliuc, +Frank Hoang, Fibinse Xavier`, Finn O'Shea, Gabriel Marzinotto, Gabriel Vacaliuc, Gabriele Calvo, Gael Varoquaux, GauravAhlawat, Giuseppe Vettigli, Greg Gandenberger, Guillaume Fournier, Guillaume Lemaitre, Gustavo De Mari Pereira, Hanmin Qin, haroldfox, hhu-luqi, Hunter McGushion, Ian Sanders, JackLangerman, Jacopo diff --git a/doc/whats_new/v0.22.rst b/doc/whats_new/v0.22.rst index 0aae7626e61e6..fea27b0c1c1a4 100644 --- a/doc/whats_new/v0.22.rst +++ b/doc/whats_new/v0.22.rst @@ -27,13 +27,13 @@ Changelog :mod:`sklearn.metrics` ...................... -- |Fix| Fixed a bug in :func:`metrics.plot_roc_curve` where +- |Fix| Fixed a bug in `metrics.plot_roc_curve` where the name of the estimator was passed in the :class:`metrics.RocCurveDisplay` instead of the parameter `name`. It results in a different plot when calling :meth:`metrics.RocCurveDisplay.plot` for the subsequent times. :pr:`16500` by :user:`Guillaume Lemaitre `. -- |Fix| Fixed a bug in :func:`metrics.plot_precision_recall_curve` where the +- |Fix| Fixed a bug in `metrics.plot_precision_recall_curve` where the name of the estimator was passed in the :class:`metrics.PrecisionRecallDisplay` instead of the parameter `name`. It results in a different plot when calling @@ -41,12 +41,12 @@ Changelog :pr:`16505` by :user:`Guillaume Lemaitre `. :mod:`sklearn.neighbors` -.............................. +........................ -- |Fix| Fix a bug which converted a list of arrays into a 2-D object +- |Fix| Fix a bug which converted a list of arrays into a 2-D object array instead of a 1-D array containing NumPy arrays. This bug was affecting :meth:`neighbors.NearestNeighbors.radius_neighbors`. - :pr:`16076` by :user:`Guillaume Lemaitre ` and + :pr:`16076` by :user:`Guillaume Lemaitre ` and :user:`Alex Shacked `. .. _changes_0_22_1: @@ -82,18 +82,18 @@ Changelog Follow-up of :pr:`15898` by :user:`Shivam Gargsya `. :pr:`15933` by :user:`Guillaume Lemaitre ` and `Olivier Grisel`_. -- |Fix| :func:`inspection.plot_partial_dependence` and +- |Fix| `inspection.plot_partial_dependence` and :meth:`inspection.PartialDependenceDisplay.plot` now consistently checks the number of axes passed in. :pr:`15760` by `Thomas Fan`_. :mod:`sklearn.metrics` ...................... -- |Fix| :func:`metrics.plot_confusion_matrix` now raises error when `normalize` +- |Fix| `metrics.plot_confusion_matrix` now raises error when `normalize` is invalid. Previously, it runs fine with no normalization. :pr:`15888` by `Hanmin Qin`_. -- |Fix| :func:`metrics.plot_confusion_matrix` now colors the label color +- |Fix| `metrics.plot_confusion_matrix` now colors the label color correctly to maximize contrast with its background. :pr:`15936` by `Thomas Fan`_ and :user:`DizietAsahi`. @@ -101,8 +101,8 @@ Changelog value of the ``zero_division`` keyword argument. :pr:`15879` by :user:`Bibhash Chandra Mitra `. -- |Fix| Fixed a bug in :func:`metrics.plot_confusion_matrix` to correctly - pass the `values_format` parameter to the :class:`ConfusionMatrixDisplay` +- |Fix| Fixed a bug in `metrics.plot_confusion_matrix` to correctly + pass the `values_format` parameter to the :class:`metrics.ConfusionMatrixDisplay` plot() call. :pr:`15937` by :user:`Stephen Blystone `. :mod:`sklearn.model_selection` @@ -118,7 +118,7 @@ Changelog .......................... - |Fix| Removed `abstractmethod` decorator for the method `_check_X` in - :class:`naive_bayes.BaseNB` that could break downstream projects inheriting + `naive_bayes.BaseNB` that could break downstream projects inheriting from this deprecated public base class. :pr:`15996` by :user:`Brigitta Sipőcz `. @@ -143,7 +143,7 @@ Changelog - |Fix| :func:`utils.check_array` now correctly converts pandas DataFrame with boolean columns to floats. :pr:`15797` by `Thomas Fan`_. -- |Fix| :func:`utils.check_is_fitted` accepts back an explicit ``attributes`` +- |Fix| :func:`utils.validation.check_is_fitted` accepts back an explicit ``attributes`` argument to check for specific attributes as explicit markers of a fitted estimator. When no explicit ``attributes`` are provided, only the attributes that end with a underscore and do not start with double underscore are used @@ -390,12 +390,12 @@ Changelog :mod:`sklearn.decomposition` ............................ -- |Efficiency| :class:`decomposition.NMF(solver='mu')` fitted on sparse input +- |Efficiency| :class:`decomposition.NMF` with `solver="mu"` fitted on sparse input matrices now uses batching to avoid briefly allocating an array with size (#non-zero elements, n_components). :pr:`15257` by `Mart Willocx `_. -- |Enhancement| :func:`decomposition.dict_learning()` and - :func:`decomposition.dict_learning_online()` now accept `method_max_iter` and +- |Enhancement| :func:`decomposition.dict_learning` and + :func:`decomposition.dict_learning_online` now accept `method_max_iter` and pass it to :meth:`decomposition.sparse_encode`. :issue:`12650` by `Adrin Jalali`_. @@ -451,7 +451,7 @@ Changelog - |Feature| Estimators now have an additional `warm_start` parameter that enables warm starting. :pr:`14012` by :user:`Johann Faouzi `. - |Feature| :func:`inspection.partial_dependence` and - :func:`inspection.plot_partial_dependence` now support the fast 'recursion' + `inspection.plot_partial_dependence` now support the fast 'recursion' method for both estimators. :pr:`13769` by `Nicolas Hug`_. - |Enhancement| for :class:`ensemble.HistGradientBoostingClassifier` the training loss or score is now monitored on a class-wise stratified @@ -503,7 +503,7 @@ Changelog - |Fix| Stacking and Voting estimators now ensure that their underlying estimators are either all classifiers or all regressors. :class:`ensemble.StackingClassifier`, :class:`ensemble.StackingRegressor`, - and :class:`ensemble.VotingClassifier` and :class:`VotingRegressor` + and :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor` now raise consistent error messages. :pr:`15084` by `Guillaume Lemaitre`_. @@ -529,10 +529,10 @@ Changelog :pr:`14602` by :user:`Gaurav Chawla `. - |Fix| Functions created by ``build_preprocessor`` and ``build_analyzer`` of - :class:`feature_extraction.text.VectorizerMixin` can now be pickled. + `feature_extraction.text.VectorizerMixin` can now be pickled. :pr:`14430` by :user:`Dillon Niederhut `. -- |Fix| :func:`feature_extraction.text.strip_accents_unicode` now correctly +- |Fix| `feature_extraction.text.strip_accents_unicode` now correctly removes accents from strings that are in NFKD normalized form. :pr:`15100` by :user:`Daniel Grady `. @@ -548,8 +548,8 @@ Changelog :mod:`sklearn.feature_selection` ................................ -- |Enhancement| Updated the following :mod:`feature_selection` estimators to allow - NaN/Inf values in ``transform`` and ``fit``: +- |Enhancement| Updated the following :mod:`sklearn.feature_selection` + estimators to allow NaN/Inf values in ``transform`` and ``fit``: :class:`feature_selection.RFE`, :class:`feature_selection.RFECV`, :class:`feature_selection.SelectFromModel`, and :class:`feature_selection.VarianceThreshold`. Note that if the underlying @@ -570,7 +570,7 @@ Changelog of generic objects (e.g. strings, trees, graphs, etc.) as the ``X`` argument to their training/prediction methods. A user-defined kernel should be provided for computing the kernel matrix among - the generic objects, and should inherit from :class:`gaussian_process.kernels.GenericKernelMixin` + the generic objects, and should inherit from `gaussian_process.kernels.GenericKernelMixin` to notify the GPR/GPC model that it handles non-vectorial samples. :pr:`15557` by :user:`Yu-Hang Tang `. @@ -616,18 +616,18 @@ Changelog respect to a given scoring function. :issue:`13146` by `Thomas Fan`_. - |Feature| :func:`inspection.partial_dependence` and - :func:`inspection.plot_partial_dependence` now support the fast 'recursion' + `inspection.plot_partial_dependence` now support the fast 'recursion' method for :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor`. :pr:`13769` by `Nicolas Hug`_. -- |Enhancement| :func:`inspection.plot_partial_dependence` has been extended to +- |Enhancement| `inspection.plot_partial_dependence` has been extended to now support the new visualization API described in the :ref:`User Guide `. :pr:`14646` by `Thomas Fan`_. - |Enhancement| :func:`inspection.partial_dependence` accepts pandas DataFrame and :class:`pipeline.Pipeline` containing :class:`compose.ColumnTransformer`. - In addition :func:`inspection.plot_partial_dependence` will use the column + In addition `inspection.plot_partial_dependence` will use the column names by default when a dataframe is passed. :pr:`14028` and :pr:`15429` by `Guillaume Lemaitre`_. @@ -712,14 +712,15 @@ Changelog :mod:`sklearn.metrics` ...................... -- |MajorFeature| :func:`metrics.plot_roc_curve` has been added to plot roc +- |MajorFeature| `metrics.plot_roc_curve` has been added to plot roc curves. This function introduces the visualization API described in the :ref:`User Guide `. :pr:`14357` by `Thomas Fan`_. - |Feature| Added a new parameter ``zero_division`` to multiple classification - metrics: :func:`precision_score`, :func:`recall_score`, :func:`f1_score`, - :func:`fbeta_score`, :func:`precision_recall_fscore_support`, - :func:`classification_report`. This allows to set returned value for + metrics: :func:`metrics.precision_score`, :func:`metrics.recall_score`, + :func:`metrics.f1_score`, :func:`metrics.fbeta_score`, + :func:`metrics.precision_recall_fscore_support`, + :func:`metrics.classification_report`. This allows to set returned value for ill-defined metrics. :pr:`14900` by :user:`Marc Torrellas Socastro `. @@ -732,16 +733,16 @@ Changelog Gain and Normalized Discounted Cumulative Gain. :pr:`9951` by :user:`Jérôme Dockès `. -- |Feature| :func:`metrics.plot_precision_recall_curve` has been added to plot +- |Feature| `metrics.plot_precision_recall_curve` has been added to plot precision recall curves. :pr:`14936` by `Thomas Fan`_. -- |Feature| :func:`metrics.plot_confusion_matrix` has been added to plot +- |Feature| `metrics.plot_confusion_matrix` has been added to plot confusion matrices. :pr:`15083` by `Thomas Fan`_. - |Feature| Added multiclass support to :func:`metrics.roc_auc_score` with corresponding scorers `'roc_auc_ovr'`, `'roc_auc_ovo'`, `'roc_auc_ovr_weighted'`, and `'roc_auc_ovo_weighted'`. - :pr:`12789` and :pr:`15274` by + :pr:`12789` and :pr:`15274` by :user:`Kathy Chen `, :user:`Mohamed Maskani `, and `Thomas Fan`_. @@ -877,7 +878,7 @@ Changelog ............................. - |Feature| Add `max_fun` parameter in - :class:`neural_network.BaseMultilayerPerceptron`, + `neural_network.BaseMultilayerPerceptron`, :class:`neural_network.MLPRegressor`, and :class:`neural_network.MLPClassifier` to give control over maximum number of function evaluation to not meet ``tol`` improvement. @@ -949,7 +950,7 @@ Changelog :class:`svm.OneClassSVM` was previously non-initialized, and had size 2. It has now size 1 with the correct value. :pr:`15099` by `Nicolas Hug`_. -- |Fix| fixed a bug in :class:`BaseLibSVM._sparse_fit` where n_SV=0 raised a +- |Fix| fixed a bug in `BaseLibSVM._sparse_fit` where n_SV=0 raised a ZeroDivisionError. :pr:`14894` by :user:`Danna Naser `. - |Fix| The liblinear solver now supports ``sample_weight``. @@ -993,14 +994,14 @@ Changelog :func:`~utils.estimator_checks.parametrize_with_checks`, to parametrize estimator checks for a list of estimators. :pr:`14381` by `Thomas Fan`_. -- |Feature| A new random variable, :class:`utils.fixes.loguniform` implements a +- |Feature| A new random variable, `utils.fixes.loguniform` implements a log-uniform random variable (e.g., for use in RandomizedSearchCV). For example, the outcomes ``1``, ``10`` and ``100`` are all equally likely for ``loguniform(1, 100)``. See :issue:`11232` by :user:`Scott Sievert ` and :user:`Nathaniel Saul `, and `SciPy PR 10815 `. -- |Enhancement| :func:`utils.safe_indexing` (now deprecated) accepts an +- |Enhancement| `utils.safe_indexing` (now deprecated) accepts an ``axis`` parameter to index array-like across rows and columns. The column indexing can be done on NumPy array, SciPy sparse matrix, and Pandas DataFrame. An additional refactoring was done. :pr:`14035` and :pr:`14475` @@ -1092,8 +1093,8 @@ These changes mostly affect library developers. :pr:`14336` by :user:`Gregory Dexter `. - Added two common multioutput estimator tests - :func:`~utils.estimator_checks.check_classifier_multioutput` and - :func:`~utils.estimator_checks.check_regressor_multioutput`. + `utils.estimator_checks.check_classifier_multioutput` and + `utils.estimator_checks.check_regressor_multioutput`. :pr:`13392` by :user:`Rok Mihevc `. - |Fix| Added ``check_transformer_data_not_an_array`` to checks where missing diff --git a/doc/whats_new/v0.23.rst b/doc/whats_new/v0.23.rst index 9603836496ca2..4c3fd66cbd599 100644 --- a/doc/whats_new/v0.23.rst +++ b/doc/whats_new/v0.23.rst @@ -65,7 +65,7 @@ Changelog :mod:`sklearn.ensemble` ....................... -- |Fix| Fixed bug in :class:`ensemble.MultinomialDeviance` where the +- |Fix| Fixed bug in `ensemble.MultinomialDeviance` where the average of logloss was incorrectly calculated as sum of logloss. :pr:`17694` by :user:`Markus Rempfler ` and :user:`Tsutomu Kusanagi `. @@ -210,7 +210,7 @@ random sampling procedures. - |Fix| :class:`preprocessing.StandardScaler` with `partial_fit` and sparse input. - |Fix| :class:`preprocessing.Normalizer` with norm='max' -- |Fix| Any model using the :func:`svm.libsvm` or the :func:`svm.liblinear` solver, +- |Fix| Any model using the `svm.libsvm` or the `svm.liblinear` solver, including :class:`svm.LinearSVC`, :class:`svm.LinearSVR`, :class:`svm.NuSVC`, :class:`svm.NuSVR`, :class:`svm.OneClassSVM`, :class:`svm.SVC`, :class:`svm.SVR`, :class:`linear_model.LogisticRegression`. @@ -269,7 +269,7 @@ Changelog could not have a `np.int64` type. :pr:`16484` by :user:`Jeremie du Boisberranger `. -- |Fix| :class:`cluster.AgglomerativeCluClustering` add specific error when +- |Fix| :class:`cluster.AgglomerativeClustering` add specific error when distance matrix is not square and `affinity=precomputed`. :pr:`16257` by :user:`Simona Maggio `. @@ -320,10 +320,11 @@ Changelog by :user:`Stephanie Andrews ` and :user:`Reshama Shaikh `. -- |Feature| embedded dataset loaders :func:`load_breast_cancer`, - :func:`load_diabetes`, :func:`load_digits`, :func:`load_iris`, - :func:`load_linnerud` and :func:`load_wine` now support loading as a pandas - ``DataFrame`` by setting `as_frame=True`. :pr:`15980` by :user:`wconnell` and +- |Feature| embedded dataset loaders :func:`datasets.load_breast_cancer`, + :func:`datasets.load_diabetes`, :func:`datasets.load_digits`, + :func:`datasets.load_iris`, :func:`datasets.load_linnerud` and + :func:`datasets.load_wine` now support loading as a pandas ``DataFrame`` by + setting `as_frame=True`. :pr:`15980` by :user:`wconnell` and :user:`Reshama Shaikh `. - |Enhancement| Added ``return_centers`` parameter in @@ -353,8 +354,8 @@ Changelog :func:`decomposition.non_negative_factorization` now preserves float32 dtype. :pr:`16280` by :user:`Jeremie du Boisberranger `. -- |Enhancement| :func:`TruncatedSVD.transform` is now faster on given sparse - ``csc`` matrices. :pr:`16837` by :user:`wornbb`. +- |Enhancement| :func:`decomposition.TruncatedSVD.transform` is now faster on + given sparse ``csc`` matrices. :pr:`16837` by :user:`wornbb`. - |Fix| :class:`decomposition.PCA` with a float `n_components` parameter, will exclusively choose the components that explain the variance greater than @@ -484,7 +485,7 @@ Changelog ......................... - |Feature| :func:`inspection.partial_dependence` and - :func:`inspection.plot_partial_dependence` now support the fast 'recursion' + `inspection.plot_partial_dependence` now support the fast 'recursion' method for :class:`ensemble.RandomForestRegressor` and :class:`tree.DecisionTreeRegressor`. :pr:`15864` by `Nicolas Hug`_. @@ -565,7 +566,7 @@ Changelog :mod:`sklearn.metrics` ...................... -- |Enhancement| :func:`metrics.pairwise.pairwise_distances_chunked` now allows +- |Enhancement| :func:`metrics.pairwise_distances_chunked` now allows its ``reduce_func`` to not have a return value, enabling in-place operations. :pr:`16397` by `Joel Nothman`_. @@ -584,7 +585,7 @@ Changelog - |API| Changed the formatting of values in :meth:`metrics.ConfusionMatrixDisplay.plot` and - :func:`metrics.plot_confusion_matrix` to pick the shorter format (either '2g' + `metrics.plot_confusion_matrix` to pick the shorter format (either '2g' or 'd'). :pr:`16159` by :user:`Rick Mackenbach ` and `Thomas Fan`_. @@ -607,7 +608,7 @@ Changelog `method="predict_proba"` when `y=None`. :pr:`15918` by :user:`Luca Kubin `. -- |Fix| :func:`model_selection.fit_grid_point` is deprecated in 0.23 and will +- |Fix| `model_selection.fit_grid_point` is deprecated in 0.23 and will be removed in 0.25. :pr:`16401` by :user:`Arie Pratama Sutiono ` @@ -703,7 +704,7 @@ Changelog crude "modulo" postprocessor used to get a random number in a bounded interval was replaced by the tweaked Lemire method as suggested by `this blog post `_. - Any model using the :func:`svm.libsvm` or the :func:`svm.liblinear` solver, + Any model using the `svm.libsvm` or the `svm.liblinear` solver, including :class:`svm.LinearSVC`, :class:`svm.LinearSVR`, :class:`svm.NuSVC`, :class:`svm.NuSVR`, :class:`svm.OneClassSVM`, :class:`svm.SVC`, :class:`svm.SVR`, :class:`linear_model.LogisticRegression`, @@ -756,7 +757,7 @@ Changelog matrix from a pandas DataFrame that contains only `SparseArray` columns. :pr:`16728` by `Thomas Fan`_. -- |Enhancement| :func:`utils.validation.check_array` supports pandas' +- |Enhancement| :func:`utils.check_array` supports pandas' nullable integer dtype with missing values when `force_all_finite` is set to `False` or `'allow-nan'` in which case the data is converted to floating point values where `pd.NA` values are replaced by `np.nan`. As a consequence, @@ -776,7 +777,7 @@ Changelog in the MRO for `_get_tags()` to work properly. :pr:`16950` by `Nicolas Hug`_. -- |FIX| :func:`utils.all_estimators` now only returns public estimators. +- |FIX| `utils.all_estimators` now only returns public estimators. :pr:`15380` by `Thomas Fan`_. Miscellaneous diff --git a/doc/whats_new/v0.24.rst b/doc/whats_new/v0.24.rst index 35a3c34d7861c..757b9ed476275 100644 --- a/doc/whats_new/v0.24.rst +++ b/doc/whats_new/v0.24.rst @@ -42,8 +42,8 @@ Changelog with `sample_weight` parameter and `least_absolute_deviation` loss function. :pr:`19407` by :user:`Vadim Ushtanit `. -:mod:`feature_extraction` -......................... +:mod:`sklearn.feature_extraction` +................................. - |Fix| Fixed a bug to support multiple strings for a category when `sparse=False` in :class:`feature_extraction.DictVectorizer`. @@ -119,7 +119,7 @@ Changelog :class:`preprocessing.OrdinalEncoder`. :pr:`19727` by :user:`Andrew Delong `. -- |Fix| :meth:`preprocessing.OrdinalEncoder.transfrom` correctly handles +- |Fix| :meth:`preprocessing.OrdinalEncoder.transform` correctly handles unknown values for string dtypes. :pr:`19888` by `Thomas Fan`_. - |Fix| :meth:`preprocessing.OneHotEncoder.fit` no longer alters the `drop` @@ -135,7 +135,7 @@ Changelog :mod:`sklearn.tree` ................... -- |Fix| Fix a bug in `fit` of :class:`tree.BaseDecisionTree` that caused +- |Fix| Fix a bug in `fit` of `tree.BaseDecisionTree` that caused segmentation faults under certain conditions. `fit` now deep copies the `Criterion` object to prevent shared concurrent accesses. :pr:`19580` by :user:`Samuel Brice ` and @@ -320,12 +320,6 @@ Changelog - |Fix| Increases the stability of :class:`cross_decomposition.CCA` :pr:`18746` by `Thomas Fan`_. -- |API| For :class:`cross_decomposition.NMF`, - the `init` value, when 'init=None' and - n_components <= min(n_samples, n_features) will be changed from - `'nndsvd'` to `'nndsvda'` in 1.1 (renaming of 0.26). - :pr:`18525` by :user:`Chiara Marmo `. - - |API| The bounds of the `n_components` parameter is now restricted: - into `[1, min(n_samples, n_features, n_targets)]`, for @@ -395,6 +389,12 @@ Changelog :mod:`sklearn.decomposition` ............................ +- |API| For :class:`decomposition.NMF`, + the `init` value, when 'init=None' and + n_components <= min(n_samples, n_features) will be changed from + `'nndsvd'` to `'nndsvda'` in 1.1 (renaming of 0.26). + :pr:`18525` by :user:`Chiara Marmo `. + - |Enhancement| :func:`decomposition.FactorAnalysis` now supports the optional argument `rotation`, which can take the value `None`, `'varimax'` or `'quartimax'`. :pr:`11064` by :user:`Jona Sassenhagen `. @@ -418,8 +418,9 @@ Changelog parameter. :pr:`17679` by :user:`Xavier Dupré `. -- |Fix| :meth:`TruncatedSVD.fit_transform` consistently returns the same - as :meth:`TruncatedSVD.fit` followed by :meth:`TruncatedSVD.transform`. +- |Fix| :meth:`decomposition.TruncatedSVD.fit_transform` consistently returns + the same as :meth:`decomposition.TruncatedSVD.fit` followed by + :meth:`decomposition.TruncatedSVD.transform`. :pr:`18528` by :user:`Albert Villanova del Moral ` and :user:`Ruifeng Zheng `. @@ -474,8 +475,8 @@ Changelog :mod:`sklearn.exceptions` ......................... -- |API| :class:`exceptions.ChangedBehaviorWarning` and - :class:`exceptions.NonBLASDotWarning` are deprecated and will be removed in +- |API| `exceptions.ChangedBehaviorWarning` and + `exceptions.NonBLASDotWarning` are deprecated and will be removed in 1.1 (renaming of 0.26). :pr:`17804` by `Adrin Jalali`_. @@ -486,7 +487,7 @@ Changelog values for one categorical feature. :pr:`17367` by :user:`Peng Yu ` and :user:`Chiara Marmo `. -- |Fix| :class:`feature_extraction.CountVectorizer` raises an issue if a +- |Fix| :class:`feature_extraction.text.CountVectorizer` raises an issue if a custom token pattern which capture more than one group is provided. :pr:`15427` by :user:`Gangesh Gudmalwar ` and :user:`Erin R Hoffman `. @@ -520,7 +521,7 @@ Changelog ............................... - |Enhancement| A new method - :meth:`gaussian_process.Kernel._check_bounds_params` is called after + `gaussian_process.kernel._check_bounds_params` is called after fitting a Gaussian Process and raises a ``ConvergenceWarning`` if the bounds of the hyperparameters are too tight. :issue:`12638` by :user:`Sylvain Lannuzel `. @@ -555,7 +556,7 @@ Changelog ......................... - |Feature| :func:`inspection.partial_dependence` and - :func:`inspection.plot_partial_dependence` now support calculating and + `inspection.plot_partial_dependence` now support calculating and plotting Individual Conditional Expectation (ICE) curves controlled by the ``kind`` parameter. :pr:`16619` by :user:`Madhura Jayratne `. @@ -652,7 +653,7 @@ Changelog generalization of :func:`metrics.top_k_accuracy_score`, the difference is that a prediction is considered correct as long as the true label is associated with one of the `k` highest predicted scores. - :func:`accuracy_score` is the special case of `k = 1`. + :func:`metrics.accuracy_score` is the special case of `k = 1`. :pr:`16625` by :user:`Geoffrey Bolmier `. - |Feature| Added :func:`metrics.det_curve` to compute Detection Error Tradeoff @@ -660,7 +661,7 @@ Changelog :pr:`10591` by :user:`Jeremy Karnowski ` and :user:`Daniel Mohns `. -- |Feature| Added :func:`metrics.plot_det_curve` and +- |Feature| Added `metrics.plot_det_curve` and :class:`metrics.DetCurveDisplay` to ease the plot of DET curves. :pr:`18176` by :user:`Guillaume Lemaitre `. @@ -674,25 +675,21 @@ Changelog Rand index. :pr:`17412` by :user:`Uwe F Mayer `. -- |Feature| :func:`metrics.plot_confusion_matrix` now supports making colorbar +- |Feature| `metrics.plot_confusion_matrix` now supports making colorbar optional in the matplotlib plot by setting `colorbar=False`. :pr:`17192` by :user:`Avi Gupta ` -- |Feature| :func:`metrics.plot_confusion_matrix` now supports making colorbar - optional in the matplotlib plot by setting colorbar=False. :pr:`17192` by - :user:`Avi Gupta `. - - |Enhancement| Add `sample_weight` parameter to :func:`metrics.median_absolute_error`. :pr:`17225` by :user:`Lucy Liu `. - |Enhancement| Add `pos_label` parameter in - :func:`metrics.plot_precision_recall_curve` in order to specify the positive + `metrics.plot_precision_recall_curve` in order to specify the positive class to be used when computing the precision and recall statistics. :pr:`17569` by :user:`Guillaume Lemaitre `. - |Enhancement| Add `pos_label` parameter in - :func:`metrics.plot_roc_curve` in order to specify the positive + `metrics.plot_roc_curve` in order to specify the positive class to be used when computing the roc auc statistics. :pr:`17651` by :user:`Clara Matos `. @@ -724,7 +721,7 @@ Changelog classifiers directly with string labeled target classes. :pr:`18114` by :user:`Guillaume Lemaitre `. -- |Fix| Fixed bug in :func:`metrics.plot_confusion_matrix` where error occurs +- |Fix| Fixed bug in `metrics.plot_confusion_matrix` where error occurs when `y_true` contains labels that were not previously seen by the classifier while the `labels` and `display_labels` parameters are set to `None`. :pr:`18405` by :user:`Thomas J. Fan ` and @@ -834,7 +831,7 @@ Changelog ........................ - |Efficiency| Speed up ``seuclidean``, ``wminkowski``, ``mahalanobis`` and - ``haversine`` metrics in :class:`neighbors.DistanceMetric` by avoiding + ``haversine`` metrics in `neighbors.DistanceMetric` by avoiding unexpected GIL acquiring in Cython when setting ``n_jobs>1`` in :class:`neighbors.KNeighborsClassifier`, :class:`neighbors.KNeighborsRegressor`, @@ -844,13 +841,13 @@ Changelog and by validating data out of loops. :pr:`17038` by :user:`Wenbo Zhao `. -- |Efficiency| :class:`neighbors.NeighborsBase` benefits of an improved +- |Efficiency| `neighbors.NeighborsBase` benefits of an improved `algorithm = 'auto'` heuristic. In addition to the previous set of rules, now, when the number of features exceeds 15, `brute` is selected, assuming the data intrinsic dimensionality is too high for tree-based methods. :pr:`17148` by :user:`Geoffrey Bolmier `. -- |Fix| :class:`neighbors.BinaryTree` +- |Fix| `neighbors.BinaryTree` will raise a `ValueError` when fitting on data array having points with different dimensions. :pr:`18691` by :user:`Chiara Marmo `. @@ -883,7 +880,7 @@ Changelog :class:`neural_network.MLPRegressor`. :pr:`17759` by :user:`Srimukh Sripada `. -- |Fix| Fix method :func:`fit` of :class:`neural_network.MLPClassifier` +- |Fix| Fix method :meth:`neural_network.MLPClassifier.fit` not iterating to ``max_iter`` if warm started. :pr:`18269` by :user:`Norbert Preining ` and :user:`Guillaume Lemaitre `. @@ -961,7 +958,7 @@ Changelog - |Enhancement| invoke SciPy BLAS API for SVM kernel function in ``fit``, ``predict`` and related methods of :class:`svm.SVC`, :class:`svm.NuSVC`, - :class:`svm.SVR`, :class:`svm.NuSVR`, :class:`OneClassSVM`. + :class:`svm.SVR`, :class:`svm.NuSVR`, :class:`svm.OneClassSVM`. :pr:`16530` by :user:`Shuhua Fan `. :mod:`sklearn.tree` @@ -988,10 +985,10 @@ Changelog with different sample order :pr:`17598` by :user:`Jason Ngo `. - |Enhancement| Add support for weights in - :func:`utils.sparse_func.incr_mean_variance_axis`. + `utils.sparse_func.incr_mean_variance_axis`. By :user:`Maria Telenczuk ` and :user:`Alex Gramfort `. -- |Fix| Raise ValueError with clear error message in :func:`check_array` +- |Fix| Raise ValueError with clear error message in :func:`utils.check_array` for sparse DataFrames with mixed types. :pr:`17992` by :user:`Thomas J. Fan ` and :user:`Alex Shacked `. @@ -1001,7 +998,7 @@ Changelog :pr:`17644` by :user:`Qi Zhang `. - |Fix| Check that we raise proper error when axis=1 and the - dimensions do not match in :func:`utils.sparse_func.incr_mean_variance_axis`. + dimensions do not match in `utils.sparse_func.incr_mean_variance_axis`. By :user:`Alex Gramfort `. Miscellaneous diff --git a/doc/whats_new/v1.0.rst b/doc/whats_new/v1.0.rst index 9d23e98838e98..0119b5bf26011 100644 --- a/doc/whats_new/v1.0.rst +++ b/doc/whats_new/v1.0.rst @@ -96,7 +96,7 @@ Changelog This fixes a regression introduced in 1.0.0 with respect to 0.24.2. :pr:`21694` by :user:`Julien Jerphanion `. -- |Fix| All :class:`sklearn.metrics.MinkowskiDistance` now accepts a weight +- |Fix| All `sklearn.metrics.MinkowskiDistance` now accepts a weight parameter that makes it possible to write code that behaves consistently both with scipy 1.8 and earlier versions. In turns this means that all neighbors-based estimators (except those that use `algorithm="kd_tree"`) now @@ -205,8 +205,8 @@ Fixed models longer checks for uppercase characters in the provided vocabulary. :pr:`21251` by :user:`Jérémie du Boisberranger `. -- |Fix| Fixed a bug in :class:`feature_extraction.CountVectorizer` and - :class:`feature_extraction.TfidfVectorizer` by raising an +- |Fix| Fixed a bug in :class:`feature_extraction.text.CountVectorizer` and + :class:`feature_extraction.text.TfidfVectorizer` by raising an error when 'min_idf' or 'max_idf' are floating-point numbers greater than 1. :pr:`20752` by :user:`Alek Lefebvre `. @@ -250,7 +250,7 @@ Fixed models :mod:`sklearn.utils` .................... -- |Enhancement| :func:`utils.validation._check_sample_weight` can perform a +- |Enhancement| `utils.validation._check_sample_weight` can perform a non-negativity check on the sample weights. It can be turned on using the only_non_negative bool parameter. Estimators that check for non-negative weights are updated: @@ -569,7 +569,7 @@ Changelog - |Fix| :func:`datasets.fetch_kddcup99` returns dataframes when `return_X_y=True` and `as_frame=True`. :pr:`19011` by `Thomas Fan`_. -- |API| Deprecates :func:`datasets.load_boston` in 1.0 and it will be removed +- |API| Deprecates `datasets.load_boston` in 1.0 and it will be removed in 1.2. Alternative code snippets to load similar datasets are provided. Please report to the docstring of the function for details. :pr:`20729` by `Guillaume Lemaitre`_. @@ -587,7 +587,7 @@ Changelog - |Fix| Fixes incorrect multiple data-conversion warnings when clustering boolean data. :pr:`19046` by :user:`Surya Prakash `. -- |Fix| Fixed :func:`dict_learning`, used by +- |Fix| Fixed :func:`decomposition.dict_learning`, used by :class:`decomposition.DictionaryLearning`, to ensure determinism of the output. Achieved by flipping signs of the SVD output which is used to initialize the code. :pr:`18433` by :user:`Bruno Charron `. @@ -613,7 +613,7 @@ Changelog to `alpha` instead of 1.0 by default starting from version 1.2 :pr:`19159` by :user:`Benoît Malézieux `. -- |API| Rename variable names in :class:`KernelPCA` to improve +- |API| Rename variable names in :class:`decomposition.KernelPCA` to improve readability. `lambdas_` and `alphas_` are renamed to `eigenvalues_` and `eigenvectors_`, respectively. `lambdas_` and `alphas_` are deprecated and will be removed in 1.2. @@ -744,7 +744,7 @@ Changelog :pr:`20431` by :user:`Oliver Pfaffel `. - |Enhancement| Add kwargs to format ICE and PD lines separately in partial - dependence plots :func:`inspection.plot_partial_dependence` and + dependence plots `inspection.plot_partial_dependence` and :meth:`inspection.PartialDependenceDisplay.plot`. :pr:`19428` by :user:`Mehdi Hamoumi `. @@ -754,7 +754,7 @@ Changelog - |API| :class:`inspection.PartialDependenceDisplay` exposes a class method: :func:`~inspection.PartialDependenceDisplay.from_estimator`. - :func:`inspection.plot_partial_dependence` is deprecated in favor of the + `inspection.plot_partial_dependence` is deprecated in favor of the class method and will be removed in 1.2. :pr:`20959` by `Thomas Fan`_. :mod:`sklearn.kernel_approximation` @@ -939,7 +939,7 @@ Changelog :pr:`18328` by :user:`Albert Villanova del Moral ` and :user:`Alonso Silva Allende `. -- |Fix| avoid overflow in :func:`metrics.cluster.adjusted_rand_score` with +- |Fix| avoid overflow in :func:`metrics.adjusted_rand_score` with large amount of data. :pr:`20312` by :user:`Divyanshu Deoli `. @@ -947,7 +947,7 @@ Changelog :func:`~metrics.ConfusionMatrixDisplay.from_estimator` and :func:`~metrics.ConfusionMatrixDisplay.from_predictions` allowing to create a confusion matrix plot using an estimator or the predictions. - :func:`metrics.plot_confusion_matrix` is deprecated in favor of these two + `metrics.plot_confusion_matrix` is deprecated in favor of these two class methods and will be removed in 1.2. :pr:`18543` by `Guillaume Lemaitre`_. @@ -955,7 +955,7 @@ Changelog :func:`~metrics.PrecisionRecallDisplay.from_estimator` and :func:`~metrics.PrecisionRecallDisplay.from_predictions` allowing to create a precision-recall curve using an estimator or the predictions. - :func:`metrics.plot_precision_recall_curve` is deprecated in favor of these + `metrics.plot_precision_recall_curve` is deprecated in favor of these two class methods and will be removed in 1.2. :pr:`20552` by `Guillaume Lemaitre`_. @@ -963,7 +963,7 @@ Changelog :func:`~metrics.DetCurveDisplay.from_estimator` and :func:`~metrics.DetCurveDisplay.from_predictions` allowing to create a confusion matrix plot using an estimator or the predictions. - :func:`metrics.plot_det_curve` is deprecated in favor of these two + `metrics.plot_det_curve` is deprecated in favor of these two class methods and will be removed in 1.2. :pr:`19278` by `Guillaume Lemaitre`_. @@ -990,7 +990,7 @@ Changelog - |Enhancement| warn only once in the main process for per-split fit failures in cross-validation. :pr:`20619` by :user:`Loïc Estève ` -- |Enhancement| The :class:`model_selection.BaseShuffleSplit` base class is +- |Enhancement| The `model_selection.BaseShuffleSplit` base class is now public. :pr:`20056` by :user:`pabloduque0`. - |Fix| Avoid premature overflow in :func:`model_selection.train_test_split`. @@ -1020,7 +1020,7 @@ Changelog :pr:`19473` by :user:`jiefangxuanyan ` and :user:`Julien Jerphanion `. -- |FIX| :class:`neighbors.DistanceMetric` subclasses now support readonly +- |FIX| `neighbors.DistanceMetric` subclasses now support readonly memory-mapped datasets. :pr:`19883` by :user:`Julien Jerphanion `. - |FIX| :class:`neighbors.NearestNeighbors`, :class:`neighbors.KNeighborsClassifier`, @@ -1178,11 +1178,11 @@ Changelog :func:`utils.deprecated` are now properly wrapped. :pr:`20385` by `Thomas Fan`_. -- |Fix| :func:`utils.stats._weighted_percentile` now correctly ignores +- |Fix| `utils.stats._weighted_percentile` now correctly ignores zero-weighted observations smaller than the smallest observation with positive weight for ``percentile=0``. Affected classes are :class:`dummy.DummyRegressor` for ``quantile=0`` and - :class:`ensemble.HuberLossFunction` and :class:`ensemble.HuberLossFunction` + `ensemble.HuberLossFunction` and `ensemble.HuberLossFunction` for ``alpha=0``. :pr:`20528` by :user:`Malte Londschien `. - |Fix| :func:`utils._safe_indexing` explicitly takes a dataframe copy when @@ -1194,7 +1194,7 @@ Changelog :func:`model_selection.cross_val_predict`). :pr:`20673` by :user:`Joris Van den Bossche `. -- |Fix| Fix a regression in :func:`utils.is_scalar_nan` where large Python +- |Fix| Fix a regression in `utils.is_scalar_nan` where large Python numbers would raise an error due to overflow in C types (`np.float64` or `np.int64`). :pr:`20727` by `Guillaume Lemaitre`_. @@ -1208,7 +1208,7 @@ Changelog manager instead. Note that these functions were not documented and part from the public API. :pr:`20521` by :user:`Olivier Grisel `. -- |API| Fixed several bugs in :func:`utils.graph.graph_shortest_path`, which is +- |API| Fixed several bugs in `utils.graph.graph_shortest_path`, which is now deprecated. Use `scipy.sparse.csgraph.shortest_path` instead. :pr:`20531` by `Tom Dupre la Tour`_. diff --git a/doc/whats_new/v1.1.rst b/doc/whats_new/v1.1.rst index e2ac0be0a08cc..c5e64bbd5882b 100644 --- a/doc/whats_new/v1.1.rst +++ b/doc/whats_new/v1.1.rst @@ -62,7 +62,7 @@ Changelog :mod:`sklearn.base` ................... -- |Fix| The `get_params` method of the :class:`BaseEstimator` class now supports +- |Fix| The `get_params` method of the :class:`base.BaseEstimator` class now supports estimators with `type`-type params that have the `get_params` method. :pr:`24017` by :user:`Henry Sorsky `. @@ -544,7 +544,7 @@ Changelog :pr:`22002` by :user:`Takeshi Oura `. - |Enhancement| :class:`decomposition.PCA` exposes a parameter `n_oversamples` to tune - :func:`utils.randomized_svd` and get accurate results when the number of + :func:`utils.extmath.randomized_svd` and get accurate results when the number of features is large. :pr:`21109` by :user:`Smile `. @@ -591,13 +591,14 @@ Changelog `Thomas Fan`_. - |Enhancement| :class:`decomposition.TruncatedSVD` exposes the parameter - `n_oversamples` and `power_iteration_normalizer` to tune :func:`utils.randomized_svd` - and get accurate results when the number of features is large, the rank of the matrix - is high, or other features of the matrix make low rank approximation difficult. + `n_oversamples` and `power_iteration_normalizer` to tune + :func:`utils.extmath.randomized_svd` and get accurate results when the number + of features is large, the rank of the matrix is high, or other features of + the matrix make low rank approximation difficult. :pr:`21705` by :user:`Jay S. Stanley III `. - |Enhancement| :class:`decomposition.PCA` exposes the parameter - `power_iteration_normalizer` to tune :func:`utils.randomized_svd` and + `power_iteration_normalizer` to tune :func:`utils.extmath.randomized_svd` and get more accurate results when low rank approximation is difficult. :pr:`21705` by :user:`Jay S. Stanley III `. @@ -661,7 +662,7 @@ Changelog The quantile level can be specified with the new parameter `quantile`. :pr:`21800` and :pr:`20567` by :user:`Christian Lorentzen `. -- |Efficiency| :meth:`fit` of :class:`ensemble.GradientBoostingClassifier` +- |Efficiency| `fit` of :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` now calls :func:`utils.check_array` with parameter `force_all_finite=False` for non initial warm-start runs as it has already been checked before. @@ -838,7 +839,7 @@ Changelog - |Enhancement| :meth:`inspection.PartialDependenceDisplay.from_estimator`, :meth:`inspection.PartialDependenceDisplay.plot`, and - :func:`inspection.plot_partial_dependence` now support plotting centered + `inspection.plot_partial_dependence` now support plotting centered Individual Conditional Expectation (cICE) and centered PDP curves controlled by setting the parameter `centered`. :pr:`18310` by :user:`Johannes Elfner ` and diff --git a/doc/whats_new/v1.2.rst b/doc/whats_new/v1.2.rst index f2b352a16a10a..f77af841608e3 100644 --- a/doc/whats_new/v1.2.rst +++ b/doc/whats_new/v1.2.rst @@ -76,7 +76,7 @@ Changelog :mod:`sklearn.preprocessing` ............................ -- |Fix| :attr:`preprocessing.OneHotEncoder.drop_idx_` now properly +- |Fix| `preprocessing.OneHotEncoder.drop_idx_` now properly references the dropped category in the `categories_` attribute when there are infrequent categories. :pr:`25589` by `Thomas Fan`_. @@ -118,9 +118,10 @@ parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures. -- |Fix| The fitted components in :class:`MiniBatchDictionaryLearning` might differ. The - online updates of the sufficient statistics now properly take the sizes of the batches - into account. +- |Fix| The fitted components in + :class:`decomposition.MiniBatchDictionaryLearning` might differ. The online + updates of the sufficient statistics now properly take the sizes of the + batches into account. :pr:`25354` by :user:`Jérémie du Boisberranger `. - |Fix| The `categories_` attribute of :class:`preprocessing.OneHotEncoder` now @@ -227,7 +228,7 @@ Changelog - |Fix| Improves error message in :class:`neural_network.MLPClassifier` and :class:`neural_network.MLPRegressor`, when `early_stopping=True` and - :meth:`partial_fit` is called. :pr:`25694` by `Thomas Fan`_. + `partial_fit` is called. :pr:`25694` by `Thomas Fan`_. :mod:`sklearn.preprocessing` ............................ @@ -255,7 +256,7 @@ Changelog boolean. The type is maintained, instead of converting to `float64.` :pr:`25147` by :user:`Tim Head `. -- |API| :func:`utils.fixes.delayed` is deprecated in 1.2.1 and will be removed +- |API| `utils.fixes.delayed` is deprecated in 1.2.1 and will be removed in 1.5. Instead, import :func:`utils.parallel.delayed` and use it in conjunction with the newly introduced :func:`utils.parallel.Parallel` to ensure proper propagation of the scikit-learn configuration to @@ -294,7 +295,7 @@ random sampling procedures. to a tiny value. Moreover, `verbose` is now properly propagated to L-BFGS-B. :pr:`23619` by :user:`Christian Lorentzen `. -- |Enhancement| The default value for `eps` :func:`metrics.logloss` has changed +- |Enhancement| The default value for `eps` :func:`metrics.log_loss` has changed from `1e-15` to `"auto"`. `"auto"` sets `eps` to `np.finfo(y_pred.dtype).eps`. :pr:`24354` by :user:`Safiuddin Khaja ` and :user:`gsiisg `. @@ -306,7 +307,7 @@ random sampling procedures. :pr:`22527` by :user:`Meekail Zain ` and `Thomas Fan`_. - |Fix| The condition for early stopping has now been changed in - :func:`linear_model._sgd_fast._plain_sgd` which is used by + `linear_model._sgd_fast._plain_sgd` which is used by :class:`linear_model.SGDRegressor` and :class:`linear_model.SGDClassifier`. The old condition did not disambiguate between training and validation set and had an effect of overscaling the error tolerance. @@ -319,7 +320,7 @@ random sampling procedures. - |API| The default value of `tol` was changed from `1e-3` to `1e-4` for :func:`linear_model.ridge_regression`, :class:`linear_model.Ridge` and - :class:`linear_model.`RidgeClassifier`. + :class:`linear_model.RidgeClassifier`. :pr:`24465` by :user:`Christian Lorentzen `. Changes impacting all modules @@ -358,8 +359,8 @@ Changes impacting all modules - :class:`sklearn.semi_supervised.LabelPropagation` - :class:`sklearn.semi_supervised.LabelSpreading` - For instance :class:`sklearn.neighbors.NearestNeighbors.kneighbors` and - :class:`sklearn.neighbors.NearestNeighbors.radius_neighbors` + For instance :meth:`sklearn.neighbors.NearestNeighbors.kneighbors` and + :meth:`sklearn.neighbors.NearestNeighbors.radius_neighbors` can respectively be up to ×20 and ×5 faster than previously on a laptop. Moreover, implementations of those two algorithms are now suitable @@ -796,7 +797,7 @@ Changelog (`average="micro"`) for the One-vs-Rest multiclass case (`multi_class="ovr"`). :pr:`24338` by :user:`Arturo Amor `. -- |Enhancement| Adds an `"auto"` option to `eps` in :func:`metrics.logloss`. +- |Enhancement| Adds an `"auto"` option to `eps` in :func:`metrics.log_loss`. This option will automatically set the `eps` value depending on the data type of `y_pred`. In addition, the default value of `eps` is changed from `1e-15` to the new `"auto"` option. @@ -887,7 +888,7 @@ Changelog :pr:`10468` by :user:`Ruben ` and :pr:`22993` by :user:`Jovan Stojanovic `. -- |Enhancement| :class:`neighbors.NeighborsBase` now accepts +- |Enhancement| `neighbors.NeighborsBase` now accepts Minkowski semi-metric (i.e. when :math:`0 < p < 1` for `metric="minkowski"`) for `algorithm="auto"` or `algorithm="brute"`. :pr:`24750` by :user:`Rudresh Veerkhare ` @@ -970,7 +971,7 @@ Changelog - |Enhancement| :func:`utils.validation.column_or_1d` now accepts a `dtype` parameter to specific `y`'s dtype. :pr:`22629` by `Thomas Fan`_. -- |Enhancement| :func:`utils.extmath.cartesian` now accepts arrays with different +- |Enhancement| `utils.extmath.cartesian` now accepts arrays with different `dtype` and will cast the output to the most permissive `dtype`. :pr:`25067` by :user:`Guillaume Lemaitre `. @@ -1062,4 +1063,4 @@ Pitters, Tom Dupré la Tour, tomiock, Tom Mathews, Tom McTiernan, tspeng, Tyler Egashira, Valentin Laurent, Varun Jain, Vera Komeyer, Vicente Reyes-Puerta, Vinayak Mehta, Vincent M, Vishal, Vyom Pathak, wattai, wchathura, WEN Hao, William M, x110, Xiao Yuan, Xunius, yanhong-zhao-ef, Yusuf Raji, Z Adil Khwaja, -zeeshan lone \ No newline at end of file +zeeshan lone diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index 237072fd938da..8d39ca2fed143 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -204,8 +204,8 @@ Changelog :pr:`123456` by :user:`Joe Bloggs `. where 123456 is the *pull request* number, not the issue number. -:mod:`sklearn` -.............. +`sklearn` +......... - |Feature| Added a new option `skip_parameter_validation`, to the function :func:`sklearn.set_config` and context manager :func:`sklearn.config_context`, that @@ -302,7 +302,7 @@ Changelog :pr:`26033` by :user:`Genesis Valencia `. - |API| Adds `eps` parameter in :class:`covariance.GraphicalLasso`, - :func:`covariance.graphical_lasso_path`, and :class:`covariance.GraphicalLassoCV`. + :func:`covariance.graphical_lasso`, and :class:`covariance.GraphicalLassoCV`. :pr:`26033` by :user:`Genesis Valencia `. :mod:`sklearn.datasets` @@ -321,7 +321,7 @@ Changelog the pandas parser. The parameter `read_csv_kwargs` allows to overwrite this behaviour. :pr:`26551` by :user:`Guillaume Lemaitre `. -- |Fix| :func:`dataasets.fetch_openml` will consistenly use `np.nan` as missing marker +- |Fix| :func:`datasets.fetch_openml` will consistenly use `np.nan` as missing marker with both parsers `"pandas"` and `"liac-arff"`. :pr:`26579` by :user:`Guillaume Lemaitre `. @@ -461,6 +461,13 @@ Changelog and the `values` key will be removed in 1.5. :pr:`21809` and :pr:`25732` by `Thomas Fan`_. +:mod:`sklearn.kernel_approximation` +................................... + +- |Fix| :class:`kernel_approximation.AdditiveChi2Sampler` is now stateless. + The `sample_interval_` attribute is deprecated and will be removed in 1.5. + :pr:`25190` by :user:`Vincent Maladière `. + :mod:`sklearn.linear_model` ........................... @@ -480,13 +487,14 @@ Changelog :pr:`25697` by :user:`John Pangas `. - |Fix| Use a more robust criterion to detect convergence of - :class:`linear_model.LogisticRegression(penalty="l1", solver="liblinear")` + :class:`linear_model.LogisticRegression` with `penalty="l1"` and `solver="liblinear"` on linearly separable problems. :pr:`25214` by `Tom Dupre la Tour`_. - |Fix| Fix a crash when calling `fit` on - :class:`linear_model.LogisticRegression(solver="newton-cholesky", max_iter=0)` - which failed to inspect the state of the model prior to the first parameter update. + :class:`linear_model.LogisticRegression` with `solver="newton-cholesky"` and + `max_iter=0` which failed to inspect the state of the model prior to the + first parameter update. :pr:`26653` by :user:`Olivier Grisel `. - |API| Deprecates `n_iter` in favor of `max_iter` in @@ -550,7 +558,7 @@ Changelog chance level. This line is exposed in the `chance_level_` attribute. :pr:`26019` by :user:`Yao Xiao `. -- |Fix| :func:`metrics.manhattan_distances` now supports readonly sparse datasets. +- |Fix| :func:`metrics.pairwise.manhattan_distances` now supports readonly sparse datasets. :pr:`25432` by :user:`Julien Jerphanion `. - |Fix| Fixed :func:`metrics.classification_report` so that empty input will return @@ -691,9 +699,10 @@ Changelog CSR matrix. :pr:`24145` by :user:`Christian Lorentzen `. - |Enhancement| Adds a `feature_name_combiner` parameter to - :class:`preprocessing.OneHotEncoder`. This specifies a custom callable to create - feature names to be returned by :meth:`get_feature_names_out`. - The callable combines input arguments `(input_feature, category)` to a string. + :class:`preprocessing.OneHotEncoder`. This specifies a custom callable to + create feature names to be returned by + :meth:`preprocessing.OneHotEncoder.get_feature_names_out`. The callable + combines input arguments `(input_feature, category)` to a string. :pr:`22506` by :user:`Mario Kostelac `. - |Enhancement| Added support for `sample_weight` in @@ -707,10 +716,6 @@ Changelog :class:`preprocessing.KBinsDiscretizer` regardless of the strategy used. :pr:`26424` by :user:`Jérémie du Boisberranger `. -- |Fix| :class:`preprocessing.AdditiveChi2Sampler` is now stateless. - The `sample_interval_` attribute is deprecated and will be removed in 1.5. - :pr:`25190` by :user:`Vincent Maladière `. - - |Fix| :class:`preprocessing.PowerTransformer` now correctly preserves the Pandas Index when the `set_config(transform_output="pandas")`. :pr:`26454` by `Thomas Fan`_. @@ -756,14 +761,14 @@ Changelog :mod:`sklearn.utils` .................... -- |FIX| Fixes :func:`utils.validation.check_array` to properly convert pandas +- |FIX| Fixes :func:`utils.check_array` to properly convert pandas extension arrays. :pr:`25813` and :pr:`26106` by `Thomas Fan`_. -- |Fix| :func:`utils.validation.check_array` now supports pandas DataFrames with +- |Fix| :func:`utils.check_array` now supports pandas DataFrames with extension arrays and object dtypes by return an ndarray with object dtype. :pr:`25814` by `Thomas Fan`_. -- |API| :func:`utils.estimator_checks.check_transformers_unfitted_stateless` has been +- |API| `utils.estimator_checks.check_transformers_unfitted_stateless` has been introduced to ensure stateless transformers don't raise `NotFittedError` during `transform` with no prior call to `fit` or `fit_transform`. :pr:`25190` by :user:`Vincent Maladière `. diff --git a/examples/bicluster/README.txt b/examples/bicluster/README.txt index 468e2524eb310..0b2bda2522b63 100644 --- a/examples/bicluster/README.txt +++ b/examples/bicluster/README.txt @@ -3,4 +3,4 @@ Biclustering ------------ -Examples concerning the :mod:`sklearn.cluster.bicluster` module. +Examples concerning biclustering techniques. diff --git a/examples/bicluster/plot_spectral_biclustering.py b/examples/bicluster/plot_spectral_biclustering.py index 18ff7ae67b83a..853b529a4aeba 100644 --- a/examples/bicluster/plot_spectral_biclustering.py +++ b/examples/bicluster/plot_spectral_biclustering.py @@ -47,7 +47,7 @@ # %% # We shuffle the data and the goal is to reconstruct it afterwards using -# :class:`~sklearn.bicluster.SpectralBiclustering`. +# :class:`~sklearn.cluster.SpectralBiclustering`. import numpy as np # Creating lists of shuffled row and column indices diff --git a/examples/decomposition/plot_faces_decomposition.py b/examples/decomposition/plot_faces_decomposition.py index c29c99b7f3c67..59c030dbab2ae 100644 --- a/examples/decomposition/plot_faces_decomposition.py +++ b/examples/decomposition/plot_faces_decomposition.py @@ -5,7 +5,7 @@ This example applies to :ref:`olivetti_faces_dataset` different unsupervised matrix decomposition (dimension reduction) methods from the module -:py:mod:`sklearn.decomposition` (see the documentation chapter +:mod:`sklearn.decomposition` (see the documentation chapter :ref:`decompositions`). @@ -146,9 +146,10 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # Sparse components - MiniBatchSparsePCA # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # -# Mini-batch sparse PCA (`MiniBatchSparsePCA`) extracts the set of sparse -# components that best reconstruct the data. This variant is faster but -# less accurate than the similar :py:mod:`sklearn.decomposition.SparsePCA`. +# Mini-batch sparse PCA (:class:`~sklearn.decomposition.MiniBatchSparsePCA`) +# extracts the set of sparse components that best reconstruct the data. This +# variant is faster but less accurate than the similar +# :class:`~sklearn.decomposition.SparsePCA`. # %% batch_pca_estimator = decomposition.MiniBatchSparsePCA( @@ -164,9 +165,9 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # Dictionary learning # ^^^^^^^^^^^^^^^^^^^ # -# By default, :class:`MiniBatchDictionaryLearning` divides the data into -# mini-batches and optimizes in an online manner by cycling over the -# mini-batches for the specified number of iterations. +# By default, :class:`~sklearn.decomposition.MiniBatchDictionaryLearning` +# divides the data into mini-batches and optimizes in an online manner by +# cycling over the mini-batches for the specified number of iterations. # %% batch_dict_estimator = decomposition.MiniBatchDictionaryLearning( @@ -179,9 +180,11 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # Cluster centers - MiniBatchKMeans # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # -# `MiniBatchKMeans` is computationally efficient and implements on-line -# learning with a `partial_fit` method. That is why it could be beneficial -# to enhance some time-consuming algorithms with `MiniBatchKMeans`. +# :class:`sklearn.cluster.MiniBatchKMeans` is computationally efficient and +# implements on-line learning with a +# :meth:`~sklearn.decomposition.MiniBatchKMeans.partial_fit` method. That is +# why it could be beneficial to enhance some time-consuming algorithms with +# :class:`~sklearn.cluster.MiniBatchKMeans`. # %% kmeans_estimator = cluster.MiniBatchKMeans( @@ -203,10 +206,10 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # Factor Analysis components - FA # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # -# `Factor Analysis` is similar to `PCA` but has the advantage of modelling the -# variance in every direction of the input space independently -# (heteroscedastic noise). -# Read more in the :ref:`User Guide `. +# :class:`~sklearn.decomposition.FactorAnalysis` is similar to +# :class:`~sklearn.decomposition.PCA` but has the advantage of modelling the +# variance in every direction of the input space independently (heteroscedastic +# noise). Read more in the :ref:`User Guide `. # %% fa_estimator = decomposition.FactorAnalysis(n_components=n_components, max_iter=20) @@ -239,9 +242,10 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # a dictionary. It is possible to constrain the dictionary and/or coding coefficients # to be positive to match constraints that may be present in the data. # -# :class:`MiniBatchDictionaryLearning` implements a faster, but less accurate -# version of the dictionary learning algorithm that is better suited for large -# datasets. Read more in the :ref:`User Guide `. +# :class:`~sklearn.decomposition.MiniBatchDictionaryLearning` implements a +# faster, but less accurate version of the dictionary learning algorithm that +# is better suited for large datasets. Read more in the :ref:`User Guide +# `. # %% # Plot the same samples from our dataset but with another colormap. @@ -252,11 +256,11 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # %% # Similar to the previous examples, we change parameters and train -# `MiniBatchDictionaryLearning` estimator on all images. Generally, -# the dictionary learning and sparse encoding decompose input data -# into the dictionary and the coding coefficients matrices. -# :math:`X \approx UV`, where :math:`X = [x_1, . . . , x_n]`, -# :math:`X \in \mathbb{R}^{m×n}`, dictionary :math:`U \in \mathbb{R}^{m×k}`, coding +# :class:`~sklearn.decomposition.MiniBatchDictionaryLearning` estimator on all +# images. Generally, the dictionary learning and sparse encoding decompose +# input data into the dictionary and the coding coefficients matrices. :math:`X +# \approx UV`, where :math:`X = [x_1, . . . , x_n]`, :math:`X \in +# \mathbb{R}^{m×n}`, dictionary :math:`U \in \mathbb{R}^{m×k}`, coding # coefficients :math:`V \in \mathbb{R}^{k×n}`. # # Also below are the results when the dictionary and coding diff --git a/examples/ensemble/plot_gradient_boosting_quantile.py b/examples/ensemble/plot_gradient_boosting_quantile.py index 36a78dfeb94ca..d1464ba92c572 100644 --- a/examples/ensemble/plot_gradient_boosting_quantile.py +++ b/examples/ensemble/plot_gradient_boosting_quantile.py @@ -128,8 +128,8 @@ def f(x): # Analysis of the error metrics # ----------------------------- # -# Measure the models with :func:`mean_squared_error` and -# :func:`mean_pinball_loss` metrics on the training dataset. +# Measure the models with :func:`~sklearn.metrics.mean_squared_error` and +# :func:`~sklearn.metrics.mean_pinball_loss` metrics on the training dataset. import pandas as pd @@ -156,7 +156,7 @@ def highlight_min(x): # training converged. # # Note that because the target distribution is asymmetric, the expected -# conditional mean and conditional median are signficiantly different and +# conditional mean and conditional median are significantly different and # therefore one could not use the squared error model get a good estimation of # the conditional median nor the converse. # @@ -194,7 +194,7 @@ def highlight_min(x): # -------------------------------------- # # We can also evaluate the ability of the two extreme quantile estimators at -# producing a well-calibrated conditational 90%-confidence interval. +# producing a well-calibrated conditional 90%-confidence interval. # # To do this we can compute the fraction of observations that fall between the # predictions: diff --git a/examples/feature_selection/plot_select_from_model_diabetes.py b/examples/feature_selection/plot_select_from_model_diabetes.py index 38276efcbd770..688c2b4ba8079 100644 --- a/examples/feature_selection/plot_select_from_model_diabetes.py +++ b/examples/feature_selection/plot_select_from_model_diabetes.py @@ -6,7 +6,7 @@ This example illustrates and compares two approaches for feature selection: :class:`~sklearn.feature_selection.SelectFromModel` which is based on feature importance, and -:class:`~sklearn.feature_selection.SequentialFeatureSelection` which relies +:class:`~sklearn.feature_selection.SequentialFeatureSelector` which relies on a greedy approach. We use the Diabetes dataset, which consists of 10 features collected from 442 diff --git a/examples/impute/plot_iterative_imputer_variants_comparison.py b/examples/impute/plot_iterative_imputer_variants_comparison.py index 9dc8b6c831710..445a08c05f02f 100644 --- a/examples/impute/plot_iterative_imputer_variants_comparison.py +++ b/examples/impute/plot_iterative_imputer_variants_comparison.py @@ -13,8 +13,8 @@ imputation with :class:`~impute.IterativeImputer`: * :class:`~linear_model.BayesianRidge`: regularized linear regression -* :class:`~tree.RandomForestRegressor`: Forests of randomized trees regression -* :func:`~pipeline.make_pipeline`(:class:`~kernel_approximation.Nystroem`, +* :class:`~ensemble.RandomForestRegressor`: Forests of randomized trees regression +* :func:`~pipeline.make_pipeline` (:class:`~kernel_approximation.Nystroem`, :class:`~linear_model.Ridge`): a pipeline with the expansion of a degree 2 polynomial kernel and regularized linear regression * :class:`~neighbors.KNeighborsRegressor`: comparable to other KNN diff --git a/examples/kernel_approximation/plot_scalable_poly_kernels.py b/examples/kernel_approximation/plot_scalable_poly_kernels.py index 8e5854bd8500c..c3fe5b405d0d0 100644 --- a/examples/kernel_approximation/plot_scalable_poly_kernels.py +++ b/examples/kernel_approximation/plot_scalable_poly_kernels.py @@ -1,15 +1,15 @@ """ -======================================================= +====================================================== Scalable learning with polynomial kernel approximation -======================================================= +====================================================== + +.. currentmodule:: sklearn.kernel_approximation This example illustrates the use of :class:`PolynomialCountSketch` to efficiently generate polynomial kernel feature-space approximations. This is used to train linear classifiers that approximate the accuracy of kernelized ones. -.. currentmodule:: sklearn.kernel_approximation - We use the Covtype dataset [2], trying to reproduce the experiments on the original paper of Tensor Sketch [1], i.e. the algorithm implemented by :class:`PolynomialCountSketch`. diff --git a/examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py b/examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py index 8313b0b56922e..b31d95348c083 100644 --- a/examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py +++ b/examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py @@ -4,7 +4,7 @@ ========================================================================== The following example shows how to precompute the gram matrix -while using weighted samples with an ElasticNet. +while using weighted samples with an :class:`~sklearn.linear_model.ElasticNet`. If weighted samples are used, the design matrix must be centered and then rescaled by the square root of the weight vector before the gram matrix @@ -13,7 +13,7 @@ .. note:: `sample_weight` vector is also rescaled to sum to `n_samples`, see the documentation for the `sample_weight` parameter to - :func:`linear_model.ElasticNet.fit`. + :meth:`~sklearn.linear_model.ElasticNet.fit`. """ diff --git a/examples/linear_model/plot_lasso_lars_ic.py b/examples/linear_model/plot_lasso_lars_ic.py index 8b265130f2f10..8f1e7034a108a 100644 --- a/examples/linear_model/plot_lasso_lars_ic.py +++ b/examples/linear_model/plot_lasso_lars_ic.py @@ -38,7 +38,7 @@ # %% # Scikit-learn provides an estimator called -# :class:`~sklearn.linear_model.LinearLarsIC` that uses either Akaike's +# :class:`~sklearn.linear_model.LassoLarsIC` that uses either Akaike's # information criterion (AIC) or the Bayesian information criterion (BIC) to # select the best model. Before fitting # this model, we will scale the dataset. diff --git a/examples/linear_model/plot_tweedie_regression_insurance_claims.py b/examples/linear_model/plot_tweedie_regression_insurance_claims.py index 2ee4b4b18fd7b..a1894eaa88ed2 100644 --- a/examples/linear_model/plot_tweedie_regression_insurance_claims.py +++ b/examples/linear_model/plot_tweedie_regression_insurance_claims.py @@ -454,9 +454,9 @@ def score_estimator( # %% # # We conclude that the claim amount is very challenging to predict. Still, the -# :class:`~sklearn.linear.GammaRegressor` is able to leverage some information -# from the input features to slightly improve upon the mean baseline in terms -# of D². +# :class:`~sklearn.linear_model.GammaRegressor` is able to leverage some +# information from the input features to slightly improve upon the mean +# baseline in terms of D². # # Note that the resulting model is the average claim amount per claim. As such, # it is conditional on having at least one claim, and cannot be used to predict diff --git a/examples/model_selection/plot_grid_search_text_feature_extraction.py b/examples/model_selection/plot_grid_search_text_feature_extraction.py index 17c2e2bfd5d99..f82cd82b13112 100644 --- a/examples/model_selection/plot_grid_search_text_feature_extraction.py +++ b/examples/model_selection/plot_grid_search_text_feature_extraction.py @@ -25,7 +25,7 @@ # ------------ # We load two categories from the training set. You can adjust the number of # categories by adding their names to the list or setting `categories=None` when -# calling the dataset loader :func:`~sklearn.datasets.fetch20newsgroups` to get +# calling the dataset loader :func:`~sklearn.datasets.fetch_20newsgroups` to get # the 20 of them. from sklearn.datasets import fetch_20newsgroups diff --git a/examples/model_selection/plot_roc.py b/examples/model_selection/plot_roc.py index 3fa1374f1b8a0..79cfde38bad0f 100644 --- a/examples/model_selection/plot_roc.py +++ b/examples/model_selection/plot_roc.py @@ -141,13 +141,13 @@ # ---------------------------------- # # Micro-averaging aggregates the contributions from all the classes (using -# :func:`np.ravel`) to compute the average metrics as follows: +# :func:`numpy.ravel`) to compute the average metrics as follows: # # :math:`TPR=\frac{\sum_{c}TP_c}{\sum_{c}(TP_c + FN_c)}` ; # # :math:`FPR=\frac{\sum_{c}FP_c}{\sum_{c}(FP_c + TN_c)}` . # -# We can briefly demo the effect of :func:`np.ravel`: +# We can briefly demo the effect of :func:`numpy.ravel`: print(f"y_score:\n{y_score[0:2,:]}") print() diff --git a/examples/preprocessing/plot_target_encoder_cross_val.py b/examples/preprocessing/plot_target_encoder_cross_val.py index 46d8324a3b304..f4ff643d8b48e 100644 --- a/examples/preprocessing/plot_target_encoder_cross_val.py +++ b/examples/preprocessing/plot_target_encoder_cross_val.py @@ -5,7 +5,7 @@ .. currentmodule:: sklearn.preprocessing -The :class:`TargetEnocoder` replaces each category of a categorical feature with +The :class:`TargetEncoder` replaces each category of a categorical feature with the mean of the target variable for that category. This method is useful in cases where there is a strong relationship between the categorical feature and the target. To prevent overfitting, :meth:`TargetEncoder.fit_transform` uses diff --git a/examples/release_highlights/plot_release_highlights_0_22_0.py b/examples/release_highlights/plot_release_highlights_0_22_0.py index ca09013703592..2fc3c5782cf53 100644 --- a/examples/release_highlights/plot_release_highlights_0_22_0.py +++ b/examples/release_highlights/plot_release_highlights_0_22_0.py @@ -27,11 +27,11 @@ # A new plotting API is available for creating visualizations. This new API # allows for quickly adjusting the visuals of a plot without involving any # recomputation. It is also possible to add different plots to the same -# figure. The following example illustrates :class:`~metrics.plot_roc_curve`, +# figure. The following example illustrates `plot_roc_curve`, # but other plots utilities are supported like -# :class:`~inspection.plot_partial_dependence`, -# :class:`~metrics.plot_precision_recall_curve`, and -# :class:`~metrics.plot_confusion_matrix`. Read more about this new API in the +# `plot_partial_dependence`, +# `plot_precision_recall_curve`, and +# `plot_confusion_matrix`. Read more about this new API in the # :ref:`User Guide `. import matplotlib.pyplot as plt @@ -187,7 +187,7 @@ # close if the features that neither is missing are close. # By default, a euclidean distance metric # that supports missing values, -# :func:`~metrics.nan_euclidean_distances`, is used to find the nearest +# :func:`~sklearn.metrics.pairwise.nan_euclidean_distances`, is used to find the nearest # neighbors. # # Read more in the :ref:`User Guide `. @@ -260,7 +260,7 @@ def test_sklearn_compatible_estimator(estimator, check): # %% # ROC AUC now supports multiclass classification # ---------------------------------------------- -# The :func:`roc_auc_score` function can also be used in multi-class +# The :func:`~sklearn.metrics.roc_auc_score` function can also be used in multi-class # classification. Two averaging strategies are currently supported: the # one-vs-one algorithm computes the average of the pairwise ROC AUC scores, and # the one-vs-rest algorithm computes the average of the ROC AUC scores for each diff --git a/examples/release_highlights/plot_release_highlights_1_1_0.py b/examples/release_highlights/plot_release_highlights_1_1_0.py index 16887b5b1b493..f6432cf15037c 100644 --- a/examples/release_highlights/plot_release_highlights_1_1_0.py +++ b/examples/release_highlights/plot_release_highlights_1_1_0.py @@ -24,7 +24,7 @@ # %% # Quantile loss in :class:`ensemble.HistGradientBoostingRegressor` # ---------------------------------------------------------------- -# :class:`ensemble.HistGradientBoostingRegressor` can model quantiles with +# :class:`~ensemble.HistGradientBoostingRegressor` can model quantiles with # `loss="quantile"` and the new parameter `quantile`. from sklearn.ensemble import HistGradientBoostingRegressor import numpy as np @@ -56,7 +56,7 @@ # `get_feature_names_out` Available in all Transformers # ----------------------------------------------------- # :term:`get_feature_names_out` is now available in all Transformers. This enables -# :class:`pipeline.Pipeline` to construct the output feature names for more complex +# :class:`~pipeline.Pipeline` to construct the output feature names for more complex # pipelines: from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder, StandardScaler @@ -101,12 +101,13 @@ # %% -# Grouping infrequent categories in :class:`OneHotEncoder` -# -------------------------------------------------------- -# :class:`OneHotEncoder` supports aggregating infrequent categories into a single -# output for each feature. The parameters to enable the gathering of infrequent -# categories are `min_frequency` and `max_categories`. See the -# :ref:`User Guide ` for more details. +# Grouping infrequent categories in :class:`~preprocessing.OneHotEncoder` +# ----------------------------------------------------------------------- +# :class:`~preprocessing.OneHotEncoder` supports aggregating infrequent +# categories into a single output for each feature. The parameters to enable +# the gathering of infrequent categories are `min_frequency` and +# `max_categories`. See the :ref:`User Guide ` +# for more details. from sklearn.preprocessing import OneHotEncoder import numpy as np @@ -165,14 +166,15 @@ # - :class:`linear_model.TweedieRegressor` # %% -# MiniBatchNMF: an online version of NMF -# -------------------------------------- -# The new class :class:`decomposition.MiniBatchNMF` implements a faster but less -# accurate version of non-negative matrix factorization (:class:`decomposition.NMF`). -# :class:`MiniBatchNMF` divides the data into mini-batches and optimizes the NMF model -# in an online manner by cycling over the mini-batches, making it better suited for -# large datasets. In particular, it implements `partial_fit`, which can be used for -# online learning when the data is not readily available from the start, or when the +# :class:`~decomposition.MiniBatchNMF`: an online version of NMF +# -------------------------------------------------------------- +# The new class :class:`~decomposition.MiniBatchNMF` implements a faster but +# less accurate version of non-negative matrix factorization +# (:class:`~decomposition.NMF`). :class:`~decomposition.MiniBatchNMF` divides the +# data into mini-batches and optimizes the NMF model in an online manner by +# cycling over the mini-batches, making it better suited for large datasets. In +# particular, it implements `partial_fit`, which can be used for online +# learning when the data is not readily available from the start, or when the # data does not fit into memory. import numpy as np from sklearn.decomposition import MiniBatchNMF @@ -198,13 +200,14 @@ ) # %% -# BisectingKMeans: divide and cluster -# ----------------------------------- -# The new class :class:`cluster.BisectingKMeans` is a variant of :class:`KMeans`, using -# divisive hierarchical clustering. Instead of creating all centroids at once, centroids -# are picked progressively based on a previous clustering: a cluster is split into two -# new clusters repeatedly until the target number of clusters is reached, giving a -# hierarchical structure to the clustering. +# :class:`~cluster.BisectingKMeans`: divide and cluster +# ----------------------------------------------------- +# The new class :class:`~cluster.BisectingKMeans` is a variant of +# :class:`~cluster.KMeans`, using divisive hierarchical clustering. Instead of +# creating all centroids at once, centroids are picked progressively based on a +# previous clustering: a cluster is split into two new clusters repeatedly +# until the target number of clusters is reached, giving a hierarchical +# structure to the clustering. from sklearn.datasets import make_blobs from sklearn.cluster import KMeans, BisectingKMeans import matplotlib.pyplot as plt diff --git a/sklearn/_config.py b/sklearn/_config.py index e84dc9ef5b228..91d149c81dc59 100644 --- a/sklearn/_config.py +++ b/sklearn/_config.py @@ -59,7 +59,7 @@ def set_config( enable_metadata_routing=None, skip_parameter_validation=None, ): - """Set global scikit-learn configuration + """Set global scikit-learn configuration. .. versionadded:: 0.19 diff --git a/sklearn/base.py b/sklearn/base.py index fb26012964e7b..5986d7288c773 100644 --- a/sklearn/base.py +++ b/sklearn/base.py @@ -875,12 +875,12 @@ def get_submatrix(self, i, data): class TransformerMixin(_SetOutputMixin): """Mixin class for all transformers in scikit-learn. - If :term:`get_feature_names_out` is defined, then `BaseEstimator` will + If :term:`get_feature_names_out` is defined, then :class:`BaseEstimator` will automatically wrap `transform` and `fit_transform` to follow the `set_output` API. See the :ref:`developer_api_set_output` for details. - :class:`base.OneToOneFeatureMixin` and - :class:`base.ClassNamePrefixFeaturesOutMixin` are helpful mixins for + :class:`OneToOneFeatureMixin` and + :class:`ClassNamePrefixFeaturesOutMixin` are helpful mixins for defining :term:`get_feature_names_out`. """ @@ -922,7 +922,7 @@ class OneToOneFeatureMixin: """Provides `get_feature_names_out` for simple transformers. This mixin assumes there's a 1-to-1 correspondence between input features - and output features, such as :class:`~preprocessing.StandardScaler`. + and output features, such as :class:`~sklearn.preprocessing.StandardScaler`. """ def get_feature_names_out(self, input_features=None): @@ -953,8 +953,8 @@ class ClassNamePrefixFeaturesOutMixin: """Mixin class for transformers that generate their own names by prefixing. This mixin is useful when the transformer needs to generate its own feature - names out, such as :class:`~decomposition.PCA`. For example, if - :class:`~decomposition.PCA` outputs 3 features, then the generated feature + names out, such as :class:`~sklearn.decomposition.PCA`. For example, if + :class:`~sklearn.decomposition.PCA` outputs 3 features, then the generated feature names out are: `["pca0", "pca1", "pca2"]`. This mixin assumes that a `_n_features_out` attribute is defined when the @@ -972,7 +972,7 @@ def get_feature_names_out(self, input_features=None): Parameters ---------- input_features : array-like of str or None, default=None - Only used to validate feature names with the names seen in :meth:`fit`. + Only used to validate feature names with the names seen in `fit`. Returns ------- diff --git a/sklearn/calibration.py b/sklearn/calibration.py index ada8b8d2ece3c..2b694234955e7 100644 --- a/sklearn/calibration.py +++ b/sklearn/calibration.py @@ -532,7 +532,7 @@ def get_metadata_routing(self): Returns ------- routing : MetadataRouter - A :class:`~utils.metadata_routing.MetadataRouter` encapsulating + A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. """ router = ( diff --git a/sklearn/cluster/_dbscan.py b/sklearn/cluster/_dbscan.py index e3ba62dbfdf01..7280bc31423ae 100644 --- a/sklearn/cluster/_dbscan.py +++ b/sklearn/cluster/_dbscan.py @@ -134,8 +134,8 @@ def dbscan( Another way to reduce memory and computation time is to remove (near-)duplicate points and use ``sample_weight`` instead. - :func:`cluster.optics ` provides a similar - clustering with lower memory usage. + :class:`~sklearn.cluster.OPTICS` provides a similar clustering with lower + memory usage. References ---------- @@ -274,7 +274,7 @@ class DBSCAN(ClusterMixin, BaseEstimator): Another way to reduce memory and computation time is to remove (near-)duplicate points and use ``sample_weight`` instead. - :class:`cluster.OPTICS` provides a similar clustering with lower memory + :class:`~sklearn.cluster.OPTICS` provides a similar clustering with lower memory usage. References diff --git a/sklearn/cluster/_hdbscan/hdbscan.py b/sklearn/cluster/_hdbscan/hdbscan.py index ec0d04bb0d403..ddfe0c8e08b65 100644 --- a/sklearn/cluster/_hdbscan/hdbscan.py +++ b/sklearn/cluster/_hdbscan/hdbscan.py @@ -187,7 +187,7 @@ def _hdbscan_brute( feature array. - If metric is a string or callable, it must be one of - the options allowed by :func:`~sklearn.metrics.pairwise.pairwise_distances` + the options allowed by :func:`~sklearn.metrics.pairwise_distances` for its metric parameter. - If metric is "precomputed", X is assumed to be a distance matrix and diff --git a/sklearn/cluster/_optics.py b/sklearn/cluster/_optics.py index 05f63d6324095..ddc8ca51e907a 100755 --- a/sklearn/cluster/_optics.py +++ b/sklearn/cluster/_optics.py @@ -138,8 +138,8 @@ class OPTICS(ClusterMixin, BaseEstimator): algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' Algorithm used to compute the nearest neighbors: - - 'ball_tree' will use :class:`BallTree`. - - 'kd_tree' will use :class:`KDTree`. + - 'ball_tree' will use :class:`~sklearn.neighbors.BallTree`. + - 'kd_tree' will use :class:`~sklearn.neighbors.KDTree`. - 'brute' will use a brute-force search. - 'auto' (default) will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method. @@ -148,10 +148,10 @@ class OPTICS(ClusterMixin, BaseEstimator): this parameter, using brute force. leaf_size : int, default=30 - Leaf size passed to :class:`BallTree` or :class:`KDTree`. This can - affect the speed of the construction and query, as well as the memory - required to store the tree. The optimal value depends on the - nature of the problem. + Leaf size passed to :class:`~sklearn.neighbors.BallTree` or + :class:`~sklearn.neighbors.KDTree`. This can affect the speed of the + construction and query, as well as the memory required to store the + tree. The optimal value depends on the nature of the problem. memory : str or object with the joblib.Memory interface, default=None Used to cache the output of the computation of the tree. @@ -515,20 +515,20 @@ def compute_optics_graph( algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' Algorithm used to compute the nearest neighbors: - - 'ball_tree' will use :class:`BallTree`. - - 'kd_tree' will use :class:`KDTree`. + - 'ball_tree' will use :class:`~sklearn.neighbors.BallTree`. + - 'kd_tree' will use :class:`~sklearn.neighbors.KDTree`. - 'brute' will use a brute-force search. - 'auto' will attempt to decide the most appropriate algorithm - based on the values passed to :meth:`fit` method. (default) + based on the values passed to `fit` method. (default) Note: fitting on sparse input will override the setting of this parameter, using brute force. leaf_size : int, default=30 - Leaf size passed to :class:`BallTree` or :class:`KDTree`. This can - affect the speed of the construction and query, as well as the memory - required to store the tree. The optimal value depends on the - nature of the problem. + Leaf size passed to :class:`~sklearn.neighbors.BallTree` or + :class:`~sklearn.neighbors.KDTree`. This can affect the speed of the + construction and query, as well as the memory required to store the + tree. The optimal value depends on the nature of the problem. n_jobs : int, default=None The number of parallel jobs to run for neighbors search. diff --git a/sklearn/cluster/_spectral.py b/sklearn/cluster/_spectral.py index 5be6b219a0a6b..975800fae6d89 100644 --- a/sklearn/cluster/_spectral.py +++ b/sklearn/cluster/_spectral.py @@ -439,7 +439,7 @@ class SpectralClustering(ClusterMixin, BaseEstimator): of precomputed distances, and construct a binary affinity matrix from the ``n_neighbors`` nearest neighbors of each instance. - one of the kernels supported by - :func:`~sklearn.metrics.pairwise_kernels`. + :func:`~sklearn.metrics.pairwise.pairwise_kernels`. Only kernels that produce similarity scores (non-negative values that increase with similarity) should be used. This property is not checked @@ -450,7 +450,7 @@ class SpectralClustering(ClusterMixin, BaseEstimator): the nearest neighbors method. Ignored for ``affinity='rbf'``. eigen_tol : float, default="auto" - Stopping criterion for eigendecomposition of the Laplacian matrix. + Stopping criterion for eigen decomposition of the Laplacian matrix. If `eigen_tol="auto"` then the passed tolerance will depend on the `eigen_solver`: diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py index 1f5854eac663e..20877775f843c 100644 --- a/sklearn/compose/_column_transformer.py +++ b/sklearn/compose/_column_transformer.py @@ -117,10 +117,12 @@ class ColumnTransformer(TransformerMixin, _BaseComposition): printed as it is completed. verbose_feature_names_out : bool, default=True - If True, :meth:`get_feature_names_out` will prefix all feature names - with the name of the transformer that generated that feature. - If False, :meth:`get_feature_names_out` will not prefix any feature - names and will error if feature names are not unique. + If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix + all feature names with the name of the transformer that generated that + feature. + If False, :meth:`ColumnTransformer.get_feature_names_out` will not + prefix any feature names and will error if feature names are not + unique. .. versionadded:: 1.0 @@ -1023,10 +1025,12 @@ def make_column_transformer( printed as it is completed. verbose_feature_names_out : bool, default=True - If True, :meth:`get_feature_names_out` will prefix all feature names - with the name of the transformer that generated that feature. - If False, :meth:`get_feature_names_out` will not prefix any feature - names and will error if feature names are not unique. + If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix + all feature names with the name of the transformer that generated that + feature. + If False, :meth:`ColumnTransformer.get_feature_names_out` will not + prefix any feature names and will error if feature names are not + unique. .. versionadded:: 1.0 diff --git a/sklearn/covariance/_graph_lasso.py b/sklearn/covariance/_graph_lasso.py index 3d26041afb83f..3b715a5d53bcc 100644 --- a/sklearn/covariance/_graph_lasso.py +++ b/sklearn/covariance/_graph_lasso.py @@ -737,7 +737,7 @@ class GraphicalLassoCV(BaseGraphicalLasso): - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. - For integer/None inputs :class:`KFold` is used. + For integer/None inputs :class:`~sklearn.model_selection.KFold` is used. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. diff --git a/sklearn/decomposition/_dict_learning.py b/sklearn/decomposition/_dict_learning.py index 48beac4508348..5ba8b628316ab 100644 --- a/sklearn/decomposition/_dict_learning.py +++ b/sklearn/decomposition/_dict_learning.py @@ -736,7 +736,8 @@ def dict_learning_online( dict_init : ndarray of shape (n_components, n_features), default=None Initial values for the dictionary for warm restart scenarios. If `None`, the initial values for the dictionary are created - with an SVD decomposition of the data via :func:`~sklearn.utils.randomized_svd`. + with an SVD decomposition of the data via + :func:`~sklearn.utils.extmath.randomized_svd`. callback : callable, default=None A callable that gets invoked at the end of each iteration. diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py index df8ecc974dd34..4dbe4314f8933 100644 --- a/sklearn/ensemble/_forest.py +++ b/sklearn/ensemble/_forest.py @@ -1977,7 +1977,7 @@ class ExtraTreesClassifier(ForestClassifier): Attributes ---------- - estimator_ : :class:`~sklearn.tree.ExtraTreesClassifier` + estimator_ : :class:`~sklearn.tree.ExtraTreeClassifier` The child estimator template used to create the collection of fitted sub-estimators. diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py index 6e6dd92af51b3..4fd785038696e 100644 --- a/sklearn/feature_extraction/text.py +++ b/sklearn/feature_extraction/text.py @@ -1545,7 +1545,7 @@ class TfidfTransformer( similarity between two vectors is their dot product when l2 norm has been applied. - 'l1': Sum of absolute values of vector elements is 1. - See :func:`preprocessing.normalize`. + See :func:`~sklearn.preprocessing.normalize`. - None: No normalization. use_idf : bool, default=True @@ -1894,7 +1894,7 @@ class TfidfVectorizer(CountVectorizer): similarity between two vectors is their dot product when l2 norm has been applied. - 'l1': Sum of absolute values of vector elements is 1. - See :func:`preprocessing.normalize`. + See :func:`~sklearn.preprocessing.normalize`. - None: No normalization. use_idf : bool, default=True diff --git a/sklearn/feature_selection/_sequential.py b/sklearn/feature_selection/_sequential.py index 78a1c86df49de..f124fff8fed3e 100644 --- a/sklearn/feature_selection/_sequential.py +++ b/sklearn/feature_selection/_sequential.py @@ -83,9 +83,11 @@ class SequentialFeatureSelector(SelectorMixin, MetaEstimatorMixin, BaseEstimator - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if the estimator is a classifier and ``y`` is - either binary or multiclass, :class:`StratifiedKFold` is used. In all - other cases, :class:`KFold` is used. These splitters are instantiated - with `shuffle=False` so the splits will be the same across calls. + either binary or multiclass, + :class:`~sklearn.model_selection.StratifiedKFold` is used. In all other + cases, :class:`~sklearn.model_selection.KFold` is used. These splitters + are instantiated with `shuffle=False` so the splits will be the same + across calls. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. diff --git a/sklearn/impute/_base.py b/sklearn/impute/_base.py index 9245a107adf4f..a65210bce6960 100644 --- a/sklearn/impute/_base.py +++ b/sklearn/impute/_base.py @@ -695,8 +695,10 @@ class MissingIndicator(TransformerMixin, BaseEstimator): """Binary indicators for missing values. Note that this component typically should not be used in a vanilla - :class:`Pipeline` consisting of transformers and a classifier, but rather - could be added using a :class:`FeatureUnion` or :class:`ColumnTransformer`. + :class:`~sklearn.pipeline.Pipeline` consisting of transformers and a + classifier, but rather could be added using a + :class:`~sklearn.pipeline.FeatureUnion` or + :class:`~sklearn.compose.ColumnTransformer`. Read more in the :ref:`User Guide `. diff --git a/sklearn/inspection/_plot/decision_boundary.py b/sklearn/inspection/_plot/decision_boundary.py index e588edbef7626..c9d2a52b6e9ab 100644 --- a/sklearn/inspection/_plot/decision_boundary.py +++ b/sklearn/inspection/_plot/decision_boundary.py @@ -274,10 +274,10 @@ def from_estimator( See Also -------- DecisionBoundaryDisplay : Decision boundary visualization. - ConfusionMatrixDisplay.from_estimator : Plot the confusion matrix - given an estimator, the data, and the label. - ConfusionMatrixDisplay.from_predictions : Plot the confusion matrix - given the true and predicted labels. + sklearn.metrics.ConfusionMatrixDisplay.from_estimator : Plot the + confusion matrix given an estimator, the data, and the label. + sklearn.metrics.ConfusionMatrixDisplay.from_predictions : Plot the + confusion matrix given the true and predicted labels. Examples -------- diff --git a/sklearn/kernel_ridge.py b/sklearn/kernel_ridge.py index f418c8946510d..8842b31693394 100644 --- a/sklearn/kernel_ridge.py +++ b/sklearn/kernel_ridge.py @@ -51,7 +51,7 @@ class KernelRidge(MultiOutputMixin, RegressorMixin, BaseEstimator): kernel : str or callable, default="linear" Kernel mapping used internally. This parameter is directly passed to - :class:`~sklearn.metrics.pairwise.pairwise_kernel`. + :class:`~sklearn.metrics.pairwise.pairwise_kernels`. If `kernel` is a string, it must be one of the metrics in `pairwise.PAIRWISE_KERNEL_FUNCTIONS` or "precomputed". If `kernel` is "precomputed", X is assumed to be a kernel matrix. diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py index c7caeab2090fe..1d01b0a534675 100644 --- a/sklearn/linear_model/_coordinate_descent.py +++ b/sklearn/linear_model/_coordinate_descent.py @@ -1785,7 +1785,7 @@ class LassoCV(RegressorMixin, LinearModelCV): - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. - For int/None inputs, :class:`KFold` is used. + For int/None inputs, :class:`~sklearn.model_selection.KFold` is used. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. @@ -2004,7 +2004,7 @@ class ElasticNetCV(RegressorMixin, LinearModelCV): - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. - For int/None inputs, :class:`KFold` is used. + For int/None inputs, :class:`~sklearn.model_selection.KFold` is used. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. @@ -2644,7 +2644,7 @@ class MultiTaskElasticNetCV(RegressorMixin, LinearModelCV): - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. - For int/None inputs, :class:`KFold` is used. + For int/None inputs, :class:`~sklearn.model_selection.KFold` is used. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. @@ -2878,7 +2878,7 @@ class MultiTaskLassoCV(RegressorMixin, LinearModelCV): - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. - For int/None inputs, :class:`KFold` is used. + For int/None inputs, :class:`~sklearn.model_selection.KFold` is used. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. diff --git a/sklearn/linear_model/_least_angle.py b/sklearn/linear_model/_least_angle.py index 439ba636d159f..dc6b385f70c73 100644 --- a/sklearn/linear_model/_least_angle.py +++ b/sklearn/linear_model/_least_angle.py @@ -1540,7 +1540,7 @@ class LarsCV(Lars): - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. - For integer/None inputs, :class:`KFold` is used. + For integer/None inputs, :class:`~sklearn.model_selection.KFold` is used. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. @@ -1844,7 +1844,7 @@ class LassoLarsCV(LarsCV): - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. - For integer/None inputs, :class:`KFold` is used. + For integer/None inputs, :class:`~sklearn.model_selection.KFold` is used. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. diff --git a/sklearn/linear_model/_omp.py b/sklearn/linear_model/_omp.py index df89e71a195f8..cb7eee8643457 100644 --- a/sklearn/linear_model/_omp.py +++ b/sklearn/linear_model/_omp.py @@ -934,7 +934,7 @@ class OrthogonalMatchingPursuitCV(RegressorMixin, LinearModel): - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. - For integer/None inputs, :class:`KFold` is used. + For integer/None inputs, :class:`~sklearn.model_selection.KFold` is used. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. diff --git a/sklearn/linear_model/_passive_aggressive.py b/sklearn/linear_model/_passive_aggressive.py index 3fc4b72103bca..d27cc928ca056 100644 --- a/sklearn/linear_model/_passive_aggressive.py +++ b/sklearn/linear_model/_passive_aggressive.py @@ -24,7 +24,7 @@ class PassiveAggressiveClassifier(BaseSGDClassifier): max_iter : int, default=1000 The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the ``fit`` method, and not the - :meth:`partial_fit` method. + :meth:`PassiveAggressive.partial_fit` method. .. versionadded:: 0.19 diff --git a/sklearn/linear_model/_ransac.py b/sklearn/linear_model/_ransac.py index c9f05f3c2601c..01d3207ab5736 100644 --- a/sklearn/linear_model/_ransac.py +++ b/sklearn/linear_model/_ransac.py @@ -101,10 +101,11 @@ class RANSACRegressor( relative number `ceil(min_samples * X.shape[0])` for `min_samples < 1`. This is typically chosen as the minimal number of samples necessary to estimate the given `estimator`. By default a - ``sklearn.linear_model.LinearRegression()`` estimator is assumed and + :class:`~sklearn.linear_model.LinearRegression` estimator is assumed and `min_samples` is chosen as ``X.shape[1] + 1``. This parameter is highly dependent upon the model, so if a `estimator` other than - :class:`linear_model.LinearRegression` is used, the user must provide a value. + :class:`~sklearn.linear_model.LinearRegression` is used, the user must + provide a value. residual_threshold : float, default=None Maximum residual for a data sample to be classified as an inlier. diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py index e0ef359aa1a85..f916b7f86da38 100644 --- a/sklearn/metrics/_classification.py +++ b/sklearn/metrics/_classification.py @@ -2784,7 +2784,7 @@ def log_loss( the probabilities provided are assumed to be that of the positive class. The labels in ``y_pred`` are assumed to be ordered alphabetically, as done by - :class:`preprocessing.LabelBinarizer`. + :class:`~sklearn.preprocessing.LabelBinarizer`. eps : float or "auto", default="auto" Log loss is undefined for p=0 or p=1, so probabilities are diff --git a/sklearn/metrics/_plot/det_curve.py b/sklearn/metrics/_plot/det_curve.py index d794cc9409d60..98997e01750bc 100644 --- a/sklearn/metrics/_plot/det_curve.py +++ b/sklearn/metrics/_plot/det_curve.py @@ -292,7 +292,7 @@ def plot(self, ax=None, *, name=None, **kwargs): Returns ------- - display : :class:`~sklearn.metrics.plot.DetCurveDisplay` + display : :class:`~sklearn.metrics.DetCurveDisplay` Object that stores computed values. """ self.ax_, self.figure_, name = self._validate_plot_params(ax=ax, name=name) diff --git a/sklearn/metrics/_plot/precision_recall_curve.py b/sklearn/metrics/_plot/precision_recall_curve.py index a4e92dcf829d6..1c13377cfe137 100644 --- a/sklearn/metrics/_plot/precision_recall_curve.py +++ b/sklearn/metrics/_plot/precision_recall_curve.py @@ -10,7 +10,7 @@ class PrecisionRecallDisplay(_BinaryClassifierCurveDisplayMixin): It is recommend to use :func:`~sklearn.metrics.PrecisionRecallDisplay.from_estimator` or :func:`~sklearn.metrics.PrecisionRecallDisplay.from_predictions` to create - a :class:`~sklearn.metrics.PredictionRecallDisplay`. All parameters are + a :class:`~sklearn.metrics.PrecisionRecallDisplay`. All parameters are stored as attributes. Read more in the :ref:`User Guide `. @@ -69,7 +69,7 @@ class PrecisionRecallDisplay(_BinaryClassifierCurveDisplayMixin): Notes ----- - The average precision (cf. :func:`~sklearn.metrics.average_precision`) in + The average precision (cf. :func:`~sklearn.metrics.average_precision_score`) in scikit-learn is computed without any interpolation. To be consistent with this metric, the precision-recall curve is plotted without any interpolation as well (step-wise style). @@ -164,7 +164,7 @@ def plot( Notes ----- - The average precision (cf. :func:`~sklearn.metrics.average_precision`) + The average precision (cf. :func:`~sklearn.metrics.average_precision_score`) in scikit-learn is computed without any interpolation. To be consistent with this metric, the precision-recall curve is plotted without any interpolation as well (step-wise style). @@ -312,7 +312,7 @@ def from_estimator( Notes ----- - The average precision (cf. :func:`~sklearn.metrics.average_precision`) + The average precision (cf. :func:`~sklearn.metrics.average_precision_score`) in scikit-learn is computed without any interpolation. To be consistent with this metric, the precision-recall curve is plotted without any interpolation as well (step-wise style). @@ -434,7 +434,7 @@ def from_predictions( Notes ----- - The average precision (cf. :func:`~sklearn.metrics.average_precision`) + The average precision (cf. :func:`~sklearn.metrics.average_precision_score`) in scikit-learn is computed without any interpolation. To be consistent with this metric, the precision-recall curve is plotted without any interpolation as well (step-wise style). diff --git a/sklearn/metrics/_plot/regression.py b/sklearn/metrics/_plot/regression.py index b4be7a01ce5dc..0b855d679866f 100644 --- a/sklearn/metrics/_plot/regression.py +++ b/sklearn/metrics/_plot/regression.py @@ -115,7 +115,8 @@ def plot( Returns ------- - display : :class:`~sklearn.metrics.plot.PredictionErrorDisplay` + display : :class:`~sklearn.metrics.PredictionErrorDisplay` + Object that stores computed values. """ check_matplotlib_support(f"{self.__class__.__name__}.plot") diff --git a/sklearn/metrics/_plot/roc_curve.py b/sklearn/metrics/_plot/roc_curve.py index 9e4ad18f48cc8..9fc401383c45f 100644 --- a/sklearn/metrics/_plot/roc_curve.py +++ b/sklearn/metrics/_plot/roc_curve.py @@ -121,7 +121,7 @@ def plot( Returns ------- - display : :class:`~sklearn.metrics.plot.RocCurveDisplay` + display : :class:`~sklearn.metrics.RocCurveDisplay` Object that stores computed values. """ self.ax_, self.figure_, name = self._validate_plot_params(ax=ax, name=name) @@ -240,7 +240,7 @@ def from_estimator( Returns ------- - display : :class:`~sklearn.metrics.plot.RocCurveDisplay` + display : :class:`~sklearn.metrics.RocCurveDisplay` The ROC Curve display. See Also diff --git a/sklearn/metrics/cluster/_supervised.py b/sklearn/metrics/cluster/_supervised.py index 5bd94f8ee1341..2b7a00d90eb18 100644 --- a/sklearn/metrics/cluster/_supervised.py +++ b/sklearn/metrics/cluster/_supervised.py @@ -207,9 +207,9 @@ def pair_confusion_matrix(labels_true, labels_pred): See Also -------- - rand_score: Rand Score. - adjusted_rand_score: Adjusted Rand Score. - adjusted_mutual_info_score: Adjusted Mutual Information. + rand_score : Rand Score. + adjusted_rand_score : Adjusted Rand Score. + adjusted_mutual_info_score : Adjusted Mutual Information. References ---------- @@ -838,9 +838,10 @@ def mutual_info_score(labels_true, labels_pred, *, contingency=None): contingency : {array-like, sparse matrix} of shape \ (n_classes_true, n_classes_pred), default=None - A contingency matrix given by the :func:`contingency_matrix` function. - If value is ``None``, it will be computed, otherwise the given value is - used, with ``labels_true`` and ``labels_pred`` ignored. + A contingency matrix given by the + :func:`~sklearn.metrics.cluster.contingency_matrix` function. If value + is ``None``, it will be computed, otherwise the given value is used, + with ``labels_true`` and ``labels_pred`` ignored. Returns ------- diff --git a/sklearn/metrics/cluster/_unsupervised.py b/sklearn/metrics/cluster/_unsupervised.py index a0ff6b9f3a92d..10749c23dacbe 100644 --- a/sklearn/metrics/cluster/_unsupervised.py +++ b/sklearn/metrics/cluster/_unsupervised.py @@ -84,8 +84,7 @@ def silhouette_score( metric : str or callable, default='euclidean' The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options - allowed by :func:`metrics.pairwise.pairwise_distances - `. If ``X`` is + allowed by :func:`~sklearn.metrics.pairwise_distances`. If ``X`` is the distance array itself, use ``metric="precomputed"``. sample_size : int, default=None @@ -230,7 +229,7 @@ def silhouette_samples(X, labels, *, metric="euclidean", **kwds): metric : str or callable, default='euclidean' The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options - allowed by :func:`sklearn.metrics.pairwise.pairwise_distances`. + allowed by :func:`~sklearn.metrics.pairwise_distances`. If ``X`` is the distance array itself, use "precomputed" as the metric. Precomputed distance matrices must have 0 along the diagonal. diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py index 2f3bd2473794d..ad0a7b01eb30d 100644 --- a/sklearn/metrics/pairwise.py +++ b/sklearn/metrics/pairwise.py @@ -1266,7 +1266,8 @@ def paired_distances(X, Y, *, metric="euclidean", **kwds): See Also -------- - pairwise_distances : Computes the distance between every pair of samples. + sklearn.metrics.pairwise_distances : Computes the distance between every pair of + samples. Examples -------- @@ -2140,8 +2141,8 @@ def pairwise_distances( pairwise_distances_chunked : Performs the same calculation as this function, but returns a generator of chunks of the distance matrix, in order to limit memory usage. - paired_distances : Computes the distances between corresponding elements - of two arrays. + sklearn.metrics.pairwise.paired_distances : Computes the distances between + corresponding elements of two arrays. """ if ( metric not in _VALID_METRICS diff --git a/sklearn/model_selection/_plot.py b/sklearn/model_selection/_plot.py index 26cbdea8acea6..336fc8d22221a 100644 --- a/sklearn/model_selection/_plot.py +++ b/sklearn/model_selection/_plot.py @@ -380,7 +380,7 @@ def from_estimator( For int/None inputs, if the estimator is a classifier and `y` is either binary or multiclass, :class:`~sklearn.model_selection.StratifiedKFold` is used. In all - other cases, :class:`~sklearn.model_selectionKFold` is used. These + other cases, :class:`~sklearn.model_selection.KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. @@ -772,7 +772,7 @@ def from_estimator( For int/None inputs, if the estimator is a classifier and `y` is either binary or multiclass, :class:`~sklearn.model_selection.StratifiedKFold` is used. In all - other cases, :class:`~sklearn.model_selectionKFold` is used. These + other cases, :class:`~sklearn.model_selection.KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. diff --git a/sklearn/multioutput.py b/sklearn/multioutput.py index f61451c34a644..433dc0f7b0fa6 100644 --- a/sklearn/multioutput.py +++ b/sklearn/multioutput.py @@ -322,7 +322,7 @@ def get_metadata_routing(self): Returns ------- routing : MetadataRouter - A :class:`~utils.metadata_routing.MetadataRouter` encapsulating + A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. """ router = MetadataRouter(owner=self.__class__.__name__).add( @@ -859,7 +859,7 @@ class labels for each estimator in the chain. See Also -------- RegressorChain : Equivalent for regression. - MultioutputClassifier : Classifies each output independently rather than + MultiOutputClassifier : Classifies each output independently rather than chaining. References @@ -1006,7 +1006,7 @@ def get_metadata_routing(self): Returns ------- routing : MetadataRouter - A :class:`~utils.metadata_routing.MetadataRouter` encapsulating + A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. """ router = MetadataRouter(owner=self.__class__.__name__).add( @@ -1155,7 +1155,7 @@ def get_metadata_routing(self): Returns ------- routing : MetadataRouter - A :class:`~utils.metadata_routing.MetadataRouter` encapsulating + A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. """ router = MetadataRouter(owner=self.__class__.__name__).add( diff --git a/sklearn/neighbors/_lof.py b/sklearn/neighbors/_lof.py index 12a4ced709e28..147e3a35bd3bc 100644 --- a/sklearn/neighbors/_lof.py +++ b/sklearn/neighbors/_lof.py @@ -78,7 +78,7 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase): p : int, default=2 Parameter for the Minkowski metric from - :func:`sklearn.metrics.pairwise.pairwise_distances`. When p = 1, this + :func:`sklearn.metrics.pairwise_distances`. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. diff --git a/sklearn/semi_supervised/_label_propagation.py b/sklearn/semi_supervised/_label_propagation.py index b6b6b9c4bfe4a..d482cce7585d2 100644 --- a/sklearn/semi_supervised/_label_propagation.py +++ b/sklearn/semi_supervised/_label_propagation.py @@ -397,7 +397,6 @@ class LabelPropagation(BaseLabelPropagation): See Also -------- - BaseLabelPropagation : Base class for label propagation module. LabelSpreading : Alternate label propagation strategy more robust to noise. References diff --git a/sklearn/utils/_metadata_requests.py b/sklearn/utils/_metadata_requests.py index b04bc5c41b6bb..443e7a736e177 100644 --- a/sklearn/utils/_metadata_requests.py +++ b/sklearn/utils/_metadata_requests.py @@ -367,7 +367,7 @@ def __str__(self): class MetadataRequest: """Contains the metadata request info of a consumer. - Instances of :class:`MethodMetadataRequest` are used in this class for each + Instances of `MethodMetadataRequest` are used in this class for each available method under `metadatarequest.{method}`. Consumer-only classes such as simple estimators return a serialized @@ -573,7 +573,7 @@ def from_str(cls, route): Returns ------- obj : MethodMapping - A :class:`~utils.metadata_requests.MethodMapping` instance + A :class:`~sklearn.utils.metadata_routing.MethodMapping` instance constructed from the given string. """ routing = cls() @@ -599,10 +599,10 @@ class MetadataRouter: This class is used by router objects to store and handle metadata routing. Routing information is stored as a dictionary of the form ``{"object_name": RouteMappingPair(method_mapping, routing_info)}``, where ``method_mapping`` - is an instance of :class:`~utils.metadata_requests.MethodMapping` and + is an instance of :class:`~sklearn.utils.metadata_routing.MethodMapping` and ``routing_info`` is either a - :class:`~utils.metadata_requests.MetadataRequest` or a - :class:`~utils.metadata_requests.MetadataRouter` instance. + :class:`~utils.metadata_routing.MetadataRequest` or a + :class:`~utils.metadata_routing.MetadataRouter` instance. .. versionadded:: 1.3 @@ -632,7 +632,7 @@ def add_self_request(self, obj): This method is used if the router is also a consumer, and hence the router itself needs to be included in the routing. The passed object can be an estimator or a - :class:``~utils.metadata_requests.MetadataRequest``. + :class:`~utils.metadata_routing.MetadataRequest`. A router should add itself using this method instead of `add` since it should be treated differently than the other objects to which metadata @@ -669,12 +669,12 @@ def add(self, *, method_mapping, **objs): ---------- method_mapping : MethodMapping or str The mapping between the child and the parent's methods. If str, the - output of :func:`~utils.metadata_requests.MethodMapping.from_str` + output of :func:`~sklearn.utils.metadata_routing.MethodMapping.from_str` is used. **objs : dict A dictionary of objects from which metadata is extracted by calling - :func:`~utils.metadata_requests.get_routing_for_object` on them. + :func:`~sklearn.utils.metadata_routing.get_routing_for_object` on them. Returns ------- @@ -758,7 +758,7 @@ def _route_params(self, *, params, method): Returns ------- params : Bunch - A :class:`~utils.Bunch` of {prop: value} which can be given to the + A :class:`~sklearn.utils.Bunch` of {prop: value} which can be given to the corresponding method. """ res = Bunch() @@ -897,8 +897,8 @@ def get_routing_for_object(obj=None): """Get a ``Metadata{Router, Request}`` instance from the given object. This function returns a - :class:`~utils.metadata_request.MetadataRouter` or a - :class:`~utils.metadata_request.MetadataRequest` from the given input. + :class:`~sklearn.utils.metadata_routing.MetadataRouter` or a + :class:`~sklearn.utils.metadata_routing.MetadataRequest` from the given input. This function always returns a copy or an instance constructed from the input, such that changing the output of this function will not change the @@ -910,12 +910,12 @@ def get_routing_for_object(obj=None): ---------- obj : object - If the object is already a - :class:`~utils.metadata_requests.MetadataRequest` or a - :class:`~utils.metadata_requests.MetadataRouter`, return a copy + :class:`~sklearn.utils.metadata_routing.MetadataRequest` or a + :class:`~sklearn.utils.metadata_routing.MetadataRouter`, return a copy of that. - If the object provides a `get_metadata_routing` method, return a copy of the output of that method. - - Returns an empty :class:`~utils.metadata_requests.MetadataRequest` + - Returns an empty :class:`~sklearn.utils.metadata_routing.MetadataRequest` otherwise. Returns diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py index 48086afc6510c..39654acd35d51 100644 --- a/sklearn/utils/estimator_checks.py +++ b/sklearn/utils/estimator_checks.py @@ -576,8 +576,8 @@ def check_estimator(estimator=None, generate_only=False): independently and report the checks that are failing. scikit-learn provides a pytest specific decorator, - :func:`~sklearn.utils.parametrize_with_checks`, making it easier to test - multiple estimators. + :func:`~sklearn.utils.estimator_checks.parametrize_with_checks`, making it + easier to test multiple estimators. Parameters ---------- From c3fe8648a84bbbefe776921e23635215ff792c12 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Tue, 11 Jul 2023 11:11:26 +0200 Subject: [PATCH 087/264] DOC notebook style and improved description for plot ridge coeffs example (#26554) Co-authored-by: Guillaume Lemaitre --- examples/linear_model/plot_ridge_coeffs.py | 235 ++++++++++++++------- 1 file changed, 163 insertions(+), 72 deletions(-) diff --git a/examples/linear_model/plot_ridge_coeffs.py b/examples/linear_model/plot_ridge_coeffs.py index bfe6d818b2f37..4f5a90d5848ef 100644 --- a/examples/linear_model/plot_ridge_coeffs.py +++ b/examples/linear_model/plot_ridge_coeffs.py @@ -1,89 +1,180 @@ """ -============================================================== -Plot Ridge coefficients as a function of the L2 regularization -============================================================== - -.. currentmodule:: sklearn.linear_model - -:class:`Ridge` Regression is the estimator used in this example. -Each color in the left plot represents one different dimension of the -coefficient vector, and this is displayed as a function of the -regularization parameter. The right plot shows how exact the solution -is. This example illustrates how a well defined solution is -found by Ridge regression and how regularization affects the -coefficients and their values. The plot on the right shows how -the difference of the coefficients from the estimator changes -as a function of regularization. - -In this example the dependent variable Y is set as a function -of the input features: y = X*w + c. The coefficient vector w is -randomly sampled from a normal distribution, whereas the bias term c is -set to a constant. - -As alpha tends toward zero the coefficients found by Ridge -regression stabilize towards the randomly sampled vector w. -For big alpha (strong regularisation) the coefficients -are smaller (eventually converging at 0) leading to a -simpler and biased solution. -These dependencies can be observed on the left plot. - -The right plot shows the mean squared error between the -coefficients found by the model and the chosen vector w. -Less regularised models retrieve the exact -coefficients (error is equal to 0), stronger regularised -models increase the error. - -Please note that in this example the data is non-noisy, hence -it is possible to extract the exact coefficients. - +========================================================= +Ridge coefficients as a function of the L2 Regularization +========================================================= + +A model that overfits learns the training data too well, capturing both the +underlying patterns and the noise in the data. However, when applied to unseen +data, the learned associations may not hold. We normally detect this when we +apply our trained predictions to the test data and see the statistical +performance drop significantly compared to the training data. + +One way to overcome overfitting is through regularization, which can be done by +penalizing large weights (coefficients) in linear models, forcing the model to +shrink all coefficients. Regularization reduces a model's reliance on specific +information obtained from the training samples. + +This example illustrates how L2 regularization in a +:class:`~sklearn.linear_model.Ridge` regression affects a model's performance by +adding a penalty term to the loss that increases with the coefficients +:math:`\\beta`. + +The regularized loss function is given by: :math:`\\mathcal{L}(X, y, \\beta) = +\\| y - X \\beta \\|^{2}_{2} + \\alpha \\| \\beta \\|^{2}_{2}` + +where :math:`X` is the input data, :math:`y` is the target variable, +:math:`\\beta` is the vector of coefficients associated with the features, and +:math:`\\alpha` is the regularization strength. + +The regularized loss function aims to balance the trade-off between accurately +predicting the training set and to prevent overfitting. + +In this regularized loss, the left-hand side (e.g. :math:`\\|y - +X\\beta\\|^{2}_{2}`) measures the squared difference between the actual target +variable, :math:`y`, and the predicted values. Minimizing this term alone could +lead to overfitting, as the model may become too complex and sensitive to noise +in the training data. + +To address overfitting, Ridge regularization adds a constraint, called a penalty +term, (:math:`\\alpha \\| \\beta\\|^{2}_{2}`) to the loss function. This penalty +term is the sum of the squares of the model's coefficients, multiplied by the +regularization strength :math:`\\alpha`. By introducing this constraint, Ridge +regularization discourages any single coefficient :math:`\\beta_{i}` from taking +an excessively large value and encourages smaller and more evenly distributed +coefficients. Higher values of :math:`\\alpha` force the coefficients towards +zero. However, an excessively high :math:`\\alpha` can result in an underfit +model that fails to capture important patterns in the data. + +Therefore, the regularized loss function combines the prediction accuracy term +and the penalty term. By adjusting the regularization strength, practitioners +can fine-tune the degree of constraint imposed on the weights, training a model +capable of generalizing well to unseen data while avoiding overfitting. """ # Author: Kornel Kielczewski -- -import matplotlib.pyplot as plt +# %% +# Purpose of this example +# ----------------------- +# For the purpose of showing how Ridge regularization works, we will create a +# non-noisy data set. Then we will train a regularized model on a range of +# regularization strengths (:math:`\alpha`) and plot how the trained +# coefficients and the mean squared error between those and the original values +# behave as functions of the regularization strength. +# +# Creating a non-noisy data set +# ***************************** +# We make a toy data set with 100 samples and 10 features, that's suitable to +# detect regression. Out of the 10 features, 8 are informative and contribute to +# the regression, while the remaining 2 features do not have any effect on the +# target variable (their true coefficients are 0). Please note that in this +# example the data is non-noisy, hence we can expect our regression model to +# recover exactly the true coefficients w. +from sklearn.datasets import make_regression + +X, y, w = make_regression( + n_samples=100, n_features=10, n_informative=8, coef=True, random_state=1 +) + +# Obtain the true coefficients +print(f"The true coefficient of this regression problem are:\n{w}") + +# %% +# Training the Ridge Regressor +# **************************** +# We use :class:`~sklearn.linear_model.Ridge`, a linear model with L2 +# regularization. We train several models, each with a different value for the +# model parameter `alpha`, which is a positive constant that multiplies the +# penalty term, controlling the regularization strength. For each trained model +# we then compute the error between the true coefficients `w` and the +# coefficients found by the model `clf`. We store the identified coefficients +# and the calculated errors for the corresponding coefficients in lists, which +# makes it convenient for us to plot them. import numpy as np -from sklearn.datasets import make_regression from sklearn.linear_model import Ridge from sklearn.metrics import mean_squared_error clf = Ridge() -X, y, w = make_regression( - n_samples=10, n_features=10, coef=True, random_state=1, bias=3.5 -) - +# Generate values for `alpha` that are evenly distributed on a logarithmic scale +alphas = np.logspace(-3, 4, 200) coefs = [] -errors = [] - -alphas = np.logspace(-6, 6, 200) +errors_coefs = [] # Train the model with different regularisation strengths for a in alphas: - clf.set_params(alpha=a) - clf.fit(X, y) + clf.set_params(alpha=a).fit(X, y) coefs.append(clf.coef_) - errors.append(mean_squared_error(clf.coef_, w)) - -# Display results -plt.figure(figsize=(20, 6)) - -plt.subplot(121) -ax = plt.gca() -ax.plot(alphas, coefs) -ax.set_xscale("log") -plt.xlabel("alpha") -plt.ylabel("weights") -plt.title("Ridge coefficients as a function of the regularization") -plt.axis("tight") - -plt.subplot(122) -ax = plt.gca() -ax.plot(alphas, errors) -ax.set_xscale("log") -plt.xlabel("alpha") -plt.ylabel("error") -plt.title("Coefficient error as a function of the regularization") -plt.axis("tight") - -plt.show() + errors_coefs.append(mean_squared_error(clf.coef_, w)) + +# %% +# Plotting trained Coefficients and Mean Squared Errors +# ***************************************************** +# We now plot the 10 different regularized coefficients as a function of the +# regularization parameter `alpha` where each color represents a different +# coefficient. +# +# On the right-hand-side, we plot how the errors of the coefficients from the +# estimator change as a function of regularization. +import matplotlib.pyplot as plt +import pandas as pd + +alphas = pd.Index(alphas, name="alpha") +coefs = pd.DataFrame(coefs, index=alphas, columns=[f"Feature {i}" for i in range(10)]) +errors = pd.Series(errors_coefs, index=alphas, name="Mean squared error") + +fig, axs = plt.subplots(1, 2, figsize=(20, 6)) + +coefs.plot( + ax=axs[0], + logx=True, + title="Ridge coefficients as a function of the regularization strength", +) +axs[0].set_ylabel("Ridge coefficient values") +errors.plot( + ax=axs[1], + logx=True, + title="Coefficient error as a function of the regularization strength", +) +_ = axs[1].set_ylabel("Mean squared error") +# %% +# Interpreting the plots +# ********************** +# The plot on the left-hand side shows how the regularization strength (`alpha`) +# affects the Ridge regression coefficients. Smaller values of `alpha` (weak +# regularization), allow the coefficients to closely resemble the true +# coefficients (`w`) used to generate the data set. This is because no +# additional noise was added to our artificial data set. As `alpha` increases, +# the coefficients shrink towards zero, gradually reducing the impact of the +# features that were formerly more significant. +# +# The right-hand side plot shows the mean squared error (MSE) between the +# coefficients found by the model and the true coefficients (`w`). It provides a +# measure that relates to how exact our ridge model is in comparison to the true +# generative model. A low error means that it found coefficients closer to the +# ones of the true generative model. In this case, since our toy data set was +# non-noisy, we can see that the least regularized model retrieves coefficients +# closest to the true coefficients (`w`) (error is close to 0). +# +# When `alpha` is small, the model captures the intricate details of the +# training data, whether those were caused by noise or by actual information. As +# `alpha` increases, the highest coefficients shrink more rapidly, rendering +# their corresponding features less influential in the training process. This +# can enhance a model's ability to generalize to unseen data (if there was a lot +# of noise to capture), but it also poses the risk of loosing performance if the +# regularization becomes too strong compared to the amount of noise the data +# contained (as in this example). +# +# In real-world scenarios where data typically includes noise, selecting an +# appropriate `alpha` value becomes crucial in striking a balance between an +# overfitting and an underfitting model. +# +# Here, we saw that :class:`~sklearn.linear_model.Ridge` adds a penalty to the +# coefficients to fight overfitting. Another problem that occurs is linked to +# the presence of outliers in the training dataset. An outlier is a data point +# that differs significantly from other observations. Concretely, these outliers +# impact the left-hand side term of the loss function that we showed earlier. +# Some other linear models are formulated to be robust to outliers such as the +# :class:`~sklearn.linear_model.HuberRegressor`. You can learn more about it in +# the :ref:`sphx_glr_auto_examples_linear_model_plot_huber_vs_ridge.py` example. From 242c7bd18bef0e85212cf4a466d08641b14e1cc8 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 11 Jul 2023 22:03:05 +0200 Subject: [PATCH 088/264] DOC add versionadded for auto in LinearSVM (#26792) --- sklearn/svm/_classes.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py index 5ee3a08bd71c7..1772a6773be90 100644 --- a/sklearn/svm/_classes.py +++ b/sklearn/svm/_classes.py @@ -77,7 +77,8 @@ class LinearSVC(LinearClassifierMixin, SparseCoefMixin, BaseEstimator): otherwise it will be set to False. .. versionchanged:: 1.3 - The default value will change from `True` to `"auto"` in 1.5. + The `"auto"` option is added in version 1.3 and will be the default + in version 1.5. tol : float, default=1e-4 Tolerance for stopping criteria. @@ -412,7 +413,8 @@ class LinearSVR(RegressorMixin, LinearModel): then dual will be set to True, otherwise it will be set to False. .. versionchanged:: 1.3 - The default value will change from `True` to `"auto"` in 1.5. + The `"auto"` option is added in version 1.3 and will be the default + in version 1.5. verbose : int, default=0 Enable verbose output. Note that this setting takes advantage of a From 2141b40366581acdfb0a7d078fb51e2f09f1d7a5 Mon Sep 17 00:00:00 2001 From: epimorphic <5407214+epimorphic@users.noreply.github.com> Date: Wed, 12 Jul 2023 03:11:55 -0500 Subject: [PATCH 089/264] DOC fix type of the Minkowski p parameter (#26822) --- sklearn/cluster/_optics.py | 2 +- sklearn/manifold/_isomap.py | 2 +- sklearn/neighbors/_classification.py | 4 ++-- sklearn/neighbors/_graph.py | 8 ++++---- sklearn/neighbors/_lof.py | 2 +- sklearn/neighbors/_regression.py | 4 ++-- 6 files changed, 11 insertions(+), 11 deletions(-) diff --git a/sklearn/cluster/_optics.py b/sklearn/cluster/_optics.py index ddc8ca51e907a..39d86be0c105a 100755 --- a/sklearn/cluster/_optics.py +++ b/sklearn/cluster/_optics.py @@ -503,7 +503,7 @@ def compute_optics_graph( .. note:: `'kulsinski'` is deprecated from SciPy 1.9 and will be removed in SciPy 1.11. - p : int, default=2 + p : float, default=2 Parameter for the Minkowski metric from :class:`~sklearn.metrics.pairwise_distances`. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance diff --git a/sklearn/manifold/_isomap.py b/sklearn/manifold/_isomap.py index fd6ac22144cdc..c6e8bfdc42685 100644 --- a/sklearn/manifold/_isomap.py +++ b/sklearn/manifold/_isomap.py @@ -94,7 +94,7 @@ class Isomap(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator): .. versionadded:: 0.22 - p : int, default=2 + p : float, default=2 Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance diff --git a/sklearn/neighbors/_classification.py b/sklearn/neighbors/_classification.py index 7f6242ab29001..a9b78d6e499c9 100644 --- a/sklearn/neighbors/_classification.py +++ b/sklearn/neighbors/_classification.py @@ -72,7 +72,7 @@ class KNeighborsClassifier(KNeighborsMixin, ClassifierMixin, NeighborsBase): required to store the tree. The optimal value depends on the nature of the problem. - p : int, default=2 + p : float, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. @@ -433,7 +433,7 @@ class RadiusNeighborsClassifier(RadiusNeighborsMixin, ClassifierMixin, Neighbors required to store the tree. The optimal value depends on the nature of the problem. - p : int, default=2 + p : float, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. diff --git a/sklearn/neighbors/_graph.py b/sklearn/neighbors/_graph.py index 347331c480ce6..98cc91141b124 100644 --- a/sklearn/neighbors/_graph.py +++ b/sklearn/neighbors/_graph.py @@ -73,7 +73,7 @@ def kneighbors_graph( :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric values. - p : int, default=2 + p : float, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. @@ -168,7 +168,7 @@ def radius_neighbors_graph( :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric values. - p : int, default=2 + p : float, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. @@ -281,7 +281,7 @@ class KNeighborsTransformer( Distance matrices are not supported. - p : int, default=2 + p : float, default=2 Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance @@ -508,7 +508,7 @@ class RadiusNeighborsTransformer( Distance matrices are not supported. - p : int, default=2 + p : float, default=2 Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance diff --git a/sklearn/neighbors/_lof.py b/sklearn/neighbors/_lof.py index 147e3a35bd3bc..05dfdb13a1cbe 100644 --- a/sklearn/neighbors/_lof.py +++ b/sklearn/neighbors/_lof.py @@ -76,7 +76,7 @@ class LocalOutlierFactor(KNeighborsMixin, OutlierMixin, NeighborsBase): between those vectors. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. - p : int, default=2 + p : float, default=2 Parameter for the Minkowski metric from :func:`sklearn.metrics.pairwise_distances`. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance diff --git a/sklearn/neighbors/_regression.py b/sklearn/neighbors/_regression.py index 1190dae01e97b..b9b7f4030d02c 100644 --- a/sklearn/neighbors/_regression.py +++ b/sklearn/neighbors/_regression.py @@ -66,7 +66,7 @@ class KNeighborsRegressor(KNeighborsMixin, RegressorMixin, NeighborsBase): required to store the tree. The optimal value depends on the nature of the problem. - p : int, default=2 + p : float, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. @@ -309,7 +309,7 @@ class RadiusNeighborsRegressor(RadiusNeighborsMixin, RegressorMixin, NeighborsBa required to store the tree. The optimal value depends on the nature of the problem. - p : int, default=2 + p : float, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. From 0044eb4e918893c7c495844418bcceeb2790cc10 Mon Sep 17 00:00:00 2001 From: Roman Lutz Date: Wed, 12 Jul 2023 05:24:12 -0400 Subject: [PATCH 090/264] DOC fix outdated function name _safe_indexing_column (#26818) --- sklearn/utils/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/utils/__init__.py b/sklearn/utils/__init__.py index 1d935fd7c7e3c..2322765bd3904 100644 --- a/sklearn/utils/__init__.py +++ b/sklearn/utils/__init__.py @@ -398,7 +398,7 @@ def _get_column_indices(X, key): """Get feature column indices for input data X and key. For accepted values of `key`, see the docstring of - :func:`_safe_indexing_column`. + :func:`_safe_indexing`. """ n_columns = X.shape[1] From 200c036b73dde056e52d0e506932681ff966d16f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Wed, 12 Jul 2023 15:09:13 +0200 Subject: [PATCH 091/264] MNT Bump Pyodide version to 0.23.4 (#26824) --- azure-pipelines.yml | 6 +++--- doc/jupyter-lite.json | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index d66acc3d00248..6c3511319e4eb 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -127,9 +127,9 @@ jobs: vmImage: ubuntu-22.04 variables: # Need to match Python version and Emscripten version for the correct - # Pyodide version. For Pyodide version 0.23.2, see - # https://github.com/pyodide/pyodide/blob/0.23.2/Makefile.envs - PYODIDE_VERSION: '0.23.2' + # Pyodide version. For example, for Pyodide version 0.23.4, see + # https://github.com/pyodide/pyodide/blob/0.23.4/Makefile.envs + PYODIDE_VERSION: '0.23.4' EMSCRIPTEN_VERSION: '3.1.32' PYTHON_VERSION: '3.11.2' diff --git a/doc/jupyter-lite.json b/doc/jupyter-lite.json index 32a5e43af987b..ebc627e18b820 100644 --- a/doc/jupyter-lite.json +++ b/doc/jupyter-lite.json @@ -3,8 +3,8 @@ "jupyter-config-data": { "litePluginSettings": { "@jupyterlite/pyodide-kernel-extension:kernel": { - "pyodideUrl": "https://cdn.jsdelivr.net/pyodide/v0.23.1/full/pyodide.js" + "pyodideUrl": "https://cdn.jsdelivr.net/pyodide/v0.23.4/full/pyodide.js" } } } -} +} From ef965aa153c4dc5540e22dea0cb5def546a3e5f9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?S=C3=B8ren=20Fuglede=20J=C3=B8rgensen?= Date: Wed, 12 Jul 2023 18:13:36 +0200 Subject: [PATCH 092/264] DOC fix description of tolerance in OMP (#26823) --- sklearn/linear_model/_omp.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/sklearn/linear_model/_omp.py b/sklearn/linear_model/_omp.py index cb7eee8643457..689d3df53b104 100644 --- a/sklearn/linear_model/_omp.py +++ b/sklearn/linear_model/_omp.py @@ -332,7 +332,7 @@ def orthogonal_mp( default) this value is set to 10% of n_features. tol : float, default=None - Maximum norm of the residual. If not None, overrides n_nonzero_coefs. + Maximum squared norm of the residual. If not None, overrides n_nonzero_coefs. precompute : 'auto' or bool, default=False Whether to perform precomputations. Improves performance when n_targets @@ -479,7 +479,8 @@ def orthogonal_mp_gram( default) this value is set to 10% of n_features. tol : float, default=None - Maximum norm of the residual. If not None, overrides n_nonzero_coefs. + Maximum squared norm of the residual. If not `None`, + overrides `n_nonzero_coefs`. norms_squared : array-like of shape (n_targets,), default=None Squared L2 norms of the lines of y. Required if tol is not None. @@ -611,7 +612,7 @@ class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel): default) this value is set to 10% of n_features. tol : float, default=None - Maximum norm of the residual. If not None, overrides n_nonzero_coefs. + Maximum squared norm of the residual. If not None, overrides n_nonzero_coefs. fit_intercept : bool, default=True Whether to calculate the intercept for this model. If set From bfe74dec791342b7569c60005f18c53a9df57dec Mon Sep 17 00:00:00 2001 From: Julien Jerphanion Date: Wed, 12 Jul 2023 18:34:01 +0200 Subject: [PATCH 093/264] MAINT Remove soon-to-be deprecated scipy.sparse functions (#26751) --- sklearn/cluster/_agglomerative.py | 11 ++++---- sklearn/datasets/tests/test_20news.py | 6 ++--- sklearn/feature_extraction/text.py | 2 +- sklearn/kernel_approximation.py | 2 +- sklearn/linear_model/_base.py | 2 +- sklearn/linear_model/_coordinate_descent.py | 14 +++++----- .../tests/test_coordinate_descent.py | 10 +++---- .../tests/test_sparse_coordinate_descent.py | 2 +- sklearn/manifold/_spectral_embedding.py | 8 +++--- .../_dispatcher.py | 5 ++-- sklearn/model_selection/tests/test_split.py | 6 ++--- sklearn/preprocessing/_data.py | 4 +-- sklearn/preprocessing/_polynomial.py | 9 +++---- sklearn/preprocessing/tests/test_data.py | 12 ++++----- sklearn/preprocessing/tests/test_encoders.py | 2 +- .../preprocessing/tests/test_polynomial.py | 18 +++++++------ sklearn/semi_supervised/_label_propagation.py | 6 ++--- sklearn/svm/_base.py | 6 ++--- sklearn/svm/tests/test_sparse.py | 4 +-- sklearn/tree/_splitter.pyx | 4 +-- sklearn/tree/_tree.pyx | 5 ++-- sklearn/utils/extmath.py | 5 ++-- sklearn/utils/graph.py | 2 +- sklearn/utils/multiclass.py | 4 +-- sklearn/utils/sparsefuncs.py | 26 +++++++++---------- 25 files changed, 87 insertions(+), 88 deletions(-) diff --git a/sklearn/cluster/_agglomerative.py b/sklearn/cluster/_agglomerative.py index 360f55c57b377..9a2aff524a91e 100644 --- a/sklearn/cluster/_agglomerative.py +++ b/sklearn/cluster/_agglomerative.py @@ -90,11 +90,12 @@ def _fix_connectivity(X, connectivity, affinity): connectivity = connectivity + connectivity.T # Convert connectivity matrix to LIL - if not sparse.isspmatrix_lil(connectivity): - if not sparse.isspmatrix(connectivity): - connectivity = sparse.lil_matrix(connectivity) - else: - connectivity = connectivity.tolil() + if not sparse.issparse(connectivity): + connectivity = sparse.lil_matrix(connectivity) + + # `connectivity` is a sparse matrix at this point + if connectivity.format != "lil": + connectivity = connectivity.tolil() # Compute the number of nodes n_connected_components, labels = connected_components(connectivity) diff --git a/sklearn/datasets/tests/test_20news.py b/sklearn/datasets/tests/test_20news.py index af308e49c5ebf..4072d9c8ec67f 100644 --- a/sklearn/datasets/tests/test_20news.py +++ b/sklearn/datasets/tests/test_20news.py @@ -64,7 +64,7 @@ def test_20news_length_consistency(fetch_20newsgroups_fxt): def test_20news_vectorized(fetch_20newsgroups_vectorized_fxt): # test subset = train bunch = fetch_20newsgroups_vectorized_fxt(subset="train") - assert sp.isspmatrix_csr(bunch.data) + assert sp.issparse(bunch.data) and bunch.data.format == "csr" assert bunch.data.shape == (11314, 130107) assert bunch.target.shape[0] == 11314 assert bunch.data.dtype == np.float64 @@ -72,7 +72,7 @@ def test_20news_vectorized(fetch_20newsgroups_vectorized_fxt): # test subset = test bunch = fetch_20newsgroups_vectorized_fxt(subset="test") - assert sp.isspmatrix_csr(bunch.data) + assert sp.issparse(bunch.data) and bunch.data.format == "csr" assert bunch.data.shape == (7532, 130107) assert bunch.target.shape[0] == 7532 assert bunch.data.dtype == np.float64 @@ -84,7 +84,7 @@ def test_20news_vectorized(fetch_20newsgroups_vectorized_fxt): # test subset = all bunch = fetch_20newsgroups_vectorized_fxt(subset="all") - assert sp.isspmatrix_csr(bunch.data) + assert sp.issparse(bunch.data) and bunch.data.format == "csr" assert bunch.data.shape == (11314 + 7532, 130107) assert bunch.target.shape[0] == 11314 + 7532 assert bunch.data.dtype == np.float64 diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py index 4fd785038696e..80a6062fffb16 100644 --- a/sklearn/feature_extraction/text.py +++ b/sklearn/feature_extraction/text.py @@ -917,7 +917,7 @@ def _more_tags(self): def _document_frequency(X): """Count the number of non-zero values for each feature in sparse X.""" - if sp.isspmatrix_csr(X): + if sp.issparse(X) and X.format == "csr": return np.bincount(X.indices, minlength=X.shape[1]) else: return np.diff(X.indptr) diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py index 11e6e91236437..dfe8e33fdf4c2 100644 --- a/sklearn/kernel_approximation.py +++ b/sklearn/kernel_approximation.py @@ -363,7 +363,7 @@ def fit(self, X, y=None): X = self._validate_data(X, accept_sparse="csr") random_state = check_random_state(self.random_state) n_features = X.shape[1] - sparse = sp.isspmatrix(X) + sparse = sp.issparse(X) if self.gamma == "scale": # var = E[X^2] - E[X]^2 if sparse X_var = (X.multiply(X)).mean() - (X.mean()) ** 2 if sparse else X.var() diff --git a/sklearn/linear_model/_base.py b/sklearn/linear_model/_base.py index 249c13da179c0..f3ef92f6c4ecc 100644 --- a/sklearn/linear_model/_base.py +++ b/sklearn/linear_model/_base.py @@ -839,7 +839,7 @@ def _pre_fit( """ n_samples, n_features = X.shape - if sparse.isspmatrix(X): + if sparse.issparse(X): # copy is not needed here as X is not modified inplace when X is sparse precompute = False X, y, X_offset, y_offset, X_scale = _preprocess_data( diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py index 1d01b0a534675..7c26bf553a334 100644 --- a/sklearn/linear_model/_coordinate_descent.py +++ b/sklearn/linear_model/_coordinate_descent.py @@ -137,7 +137,7 @@ def _alpha_grid( sparse_center = False if Xy is None: - X_sparse = sparse.isspmatrix(X) + X_sparse = sparse.issparse(X) sparse_center = X_sparse and fit_intercept X = check_array( X, accept_sparse="csc", copy=(copy_X and fit_intercept and not X_sparse) @@ -524,7 +524,7 @@ def enet_path( raise ValueError("positive=True is not allowed for multi-output (y.ndim != 1)") # MultiTaskElasticNet does not support sparse matrices - if not multi_output and sparse.isspmatrix(X): + if not multi_output and sparse.issparse(X): if X_offset_param is not None: # As sparse matrices are not actually centered we need this to be passed to # the CD solver. @@ -585,7 +585,7 @@ def enet_path( # account for n_samples scaling in objectives between here and cd_fast l1_reg = alpha * l1_ratio * n_samples l2_reg = alpha * (1.0 - l1_ratio) * n_samples - if not multi_output and sparse.isspmatrix(X): + if not multi_output and sparse.issparse(X): model = cd_fast.sparse_enet_coordinate_descent( w=coef_, alpha=l1_reg, @@ -1065,7 +1065,7 @@ def _decision_function(self, X): The predicted decision function. """ check_is_fitted(self) - if sparse.isspmatrix(X): + if sparse.issparse(X): return safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_ else: return super()._decision_function(X) @@ -1510,7 +1510,7 @@ def fit(self, X, y, sample_weight=None): check_y_params = dict( copy=False, dtype=[np.float64, np.float32], ensure_2d=False ) - if isinstance(X, np.ndarray) or sparse.isspmatrix(X): + if isinstance(X, np.ndarray) or sparse.issparse(X): # Keep a reference to X reference_to_old_X = X # Let us not impose fortran ordering so far: it is @@ -1527,7 +1527,7 @@ def fit(self, X, y, sample_weight=None): X, y = self._validate_data( X, y, validate_separately=(check_X_params, check_y_params) ) - if sparse.isspmatrix(X): + if sparse.issparse(X): if hasattr(reference_to_old_X, "data") and not np.may_share_memory( reference_to_old_X.data, X.data ): @@ -1562,7 +1562,7 @@ def fit(self, X, y, sample_weight=None): ) y = column_or_1d(y, warn=True) else: - if sparse.isspmatrix(X): + if sparse.issparse(X): raise TypeError("X should be dense but a sparse matrix waspassed") elif y.ndim == 1: raise ValueError( diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py index ffaa2f828b770..f5791ddb4e081 100644 --- a/sklearn/linear_model/tests/test_coordinate_descent.py +++ b/sklearn/linear_model/tests/test_coordinate_descent.py @@ -84,12 +84,10 @@ def test_set_order_sparse(order, input_order): X = X.asformat(sparse_format) y = X.asformat(sparse_format) X2, y2 = _set_order(X, y, order=order) - if order == "C": - assert sparse.isspmatrix_csr(X2) - assert sparse.isspmatrix_csr(y2) - elif order == "F": - assert sparse.isspmatrix_csc(X2) - assert sparse.isspmatrix_csc(y2) + + format = "csc" if order == "F" else "csr" + assert sparse.issparse(X2) and X2.format == format + assert sparse.issparse(y2) and y2.format == format def test_lasso_zero(): diff --git a/sklearn/linear_model/tests/test_sparse_coordinate_descent.py b/sklearn/linear_model/tests/test_sparse_coordinate_descent.py index 14dfabbbc5fdb..1842f5e77dda3 100644 --- a/sklearn/linear_model/tests/test_sparse_coordinate_descent.py +++ b/sklearn/linear_model/tests/test_sparse_coordinate_descent.py @@ -19,7 +19,7 @@ def test_sparse_coef(): clf = ElasticNet() clf.coef_ = [1, 2, 3] - assert sp.isspmatrix(clf.sparse_coef_) + assert sp.issparse(clf.sparse_coef_) assert clf.sparse_coef_.toarray().tolist()[0] == clf.coef_ diff --git a/sklearn/manifold/_spectral_embedding.py b/sklearn/manifold/_spectral_embedding.py index 27b553b9791e4..e6b21de7951a3 100644 --- a/sklearn/manifold/_spectral_embedding.py +++ b/sklearn/manifold/_spectral_embedding.py @@ -85,7 +85,7 @@ def _graph_is_connected(graph): is_connected : bool True means the graph is fully connected and False means not. """ - if sparse.isspmatrix(graph): + if sparse.issparse(graph): # sparse graph, find all the connected components n_connected_components, _ = connected_components(graph) return n_connected_components == 1 @@ -118,7 +118,7 @@ def _set_diag(laplacian, value, norm_laplacian): """ n_nodes = laplacian.shape[0] # We need all entries in the diagonal to values - if not sparse.isspmatrix(laplacian): + if not sparse.issparse(laplacian): if norm_laplacian: laplacian.flat[:: n_nodes + 1] = value else: @@ -280,7 +280,7 @@ def spectral_embedding( if ( eigen_solver == "arpack" or eigen_solver != "lobpcg" - and (not sparse.isspmatrix(laplacian) or n_nodes < 5 * n_components) + and (not sparse.issparse(laplacian) or n_nodes < 5 * n_components) ): # lobpcg used with eigen_solver='amg' has bugs for low number of nodes # for details see the source code in scipy: @@ -371,7 +371,7 @@ def spectral_embedding( # see note above under arpack why lobpcg has problems with small # number of nodes # lobpcg will fallback to eigh, so we short circuit it - if sparse.isspmatrix(laplacian): + if sparse.issparse(laplacian): laplacian = laplacian.toarray() _, diffusion_map = eigh(laplacian, check_finite=False) embedding = diffusion_map.T[:n_components] diff --git a/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py b/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py index 7413720966d5c..796f15ab6fca0 100644 --- a/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py +++ b/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py @@ -2,7 +2,7 @@ from typing import List import numpy as np -from scipy.sparse import issparse, isspmatrix_csr +from scipy.sparse import issparse from ... import get_config from .._dist_metrics import BOOL_METRICS, METRIC_MAPPING64 @@ -100,7 +100,8 @@ def is_numpy_c_ordered(X): def is_valid_sparse_matrix(X): return ( - isspmatrix_csr(X) + issparse(X) + and X.format == "csr" and # TODO: support CSR matrices without non-zeros elements X.nnz > 0 diff --git a/sklearn/model_selection/tests/test_split.py b/sklearn/model_selection/tests/test_split.py index 8edf99adfb679..d92f624441541 100644 --- a/sklearn/model_selection/tests/test_split.py +++ b/sklearn/model_selection/tests/test_split.py @@ -10,7 +10,7 @@ coo_matrix, csc_matrix, csr_matrix, - isspmatrix_csr, + issparse, ) from scipy.special import comb @@ -1355,8 +1355,8 @@ def test_train_test_split_sparse(): for InputFeatureType in sparse_types: X_s = InputFeatureType(X) X_train, X_test = train_test_split(X_s) - assert isspmatrix_csr(X_train) - assert isspmatrix_csr(X_test) + assert issparse(X_train) and X_train.format == "csr" + assert issparse(X_test) and X_test.format == "csr" def test_train_test_split_mock_pandas(): diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py index 197d709689daa..b51f2af2c3d17 100644 --- a/sklearn/preprocessing/_data.py +++ b/sklearn/preprocessing/_data.py @@ -2394,7 +2394,7 @@ def add_dummy_feature(X, value=1.0): n_samples, n_features = X.shape shape = (n_samples, n_features + 1) if sparse.issparse(X): - if sparse.isspmatrix_coo(X): + if X.format == "coo": # Shift columns to the right. col = X.col + 1 # Column indices of dummy feature are 0 everywhere. @@ -2404,7 +2404,7 @@ def add_dummy_feature(X, value=1.0): # Prepend the dummy feature n_samples times. data = np.concatenate((np.full(n_samples, value), X.data)) return sparse.coo_matrix((data, (row, col)), shape) - elif sparse.isspmatrix_csc(X): + elif X.format == "csc": # Shift index pointers since we need to add n_samples elements. indptr = X.indptr + n_samples # indptr[0] must be 0. diff --git a/sklearn/preprocessing/_polynomial.py b/sklearn/preprocessing/_polynomial.py index 0864aad3403f4..634c1497f36bf 100644 --- a/sklearn/preprocessing/_polynomial.py +++ b/sklearn/preprocessing/_polynomial.py @@ -435,7 +435,7 @@ def transform(self, X): n_samples, n_features = X.shape max_int32 = np.iinfo(np.int32).max - if sparse.isspmatrix_csr(X): + if sparse.issparse(X) and X.format == "csr": if self._max_degree > 3: return self.transform(X.tocsc()).tocsr() to_stack = [] @@ -480,9 +480,9 @@ def transform(self, X): " transformer to produce fewer than 2^31 output features" ) XP = sparse.hstack(to_stack, dtype=X.dtype, format="csr") - elif sparse.isspmatrix_csc(X) and self._max_degree < 4: + elif sparse.issparse(X) and X.format == "csc" and self._max_degree < 4: return self.transform(X.tocsr()).tocsc() - elif sparse.isspmatrix(X): + elif sparse.issparse(X): combinations = self._combinations( n_features=n_features, min_degree=self._min_degree, @@ -1119,8 +1119,7 @@ def transform(self, X): XBS[mask, i * n_splines + k] = linear_extr if use_sparse: - if not sparse.isspmatrix_csr(XBS_sparse): - XBS_sparse = XBS_sparse.tocsr() + XBS_sparse = XBS_sparse.tocsr() output_list.append(XBS_sparse) if use_sparse: diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py index e9cfff4d8febb..189d8875bc8f2 100644 --- a/sklearn/preprocessing/tests/test_data.py +++ b/sklearn/preprocessing/tests/test_data.py @@ -1846,7 +1846,7 @@ def test_normalizer_l1(): X_norm = normalizer = Normalizer(norm="l2", copy=False).transform(X) assert X_norm is not X - assert sparse.isspmatrix_csr(X_norm) + assert sparse.issparse(X_norm) and X_norm.format == "csr" X_norm = toarray(X_norm) for i in range(3): @@ -1893,7 +1893,7 @@ def test_normalizer_l2(): X_norm = normalizer = Normalizer(norm="l2", copy=False).transform(X) assert X_norm is not X - assert sparse.isspmatrix_csr(X_norm) + assert sparse.issparse(X_norm) and X_norm.format == "csr" X_norm = toarray(X_norm) for i in range(3): @@ -1941,7 +1941,7 @@ def test_normalizer_max(): X_norm = normalizer = Normalizer(norm="l2", copy=False).transform(X) assert X_norm is not X - assert sparse.isspmatrix_csr(X_norm) + assert sparse.issparse(X_norm) and X_norm.format == "csr" X_norm = toarray(X_norm) for i in range(3): @@ -2205,21 +2205,21 @@ def test_add_dummy_feature(): def test_add_dummy_feature_coo(): X = sparse.coo_matrix([[1, 0], [0, 1], [0, 1]]) X = add_dummy_feature(X) - assert sparse.isspmatrix_coo(X), X + assert sparse.issparse(X) and X.format == "coo", X assert_array_equal(X.toarray(), [[1, 1, 0], [1, 0, 1], [1, 0, 1]]) def test_add_dummy_feature_csc(): X = sparse.csc_matrix([[1, 0], [0, 1], [0, 1]]) X = add_dummy_feature(X) - assert sparse.isspmatrix_csc(X), X + assert sparse.issparse(X) and X.format == "csc", X assert_array_equal(X.toarray(), [[1, 1, 0], [1, 0, 1], [1, 0, 1]]) def test_add_dummy_feature_csr(): X = sparse.csr_matrix([[1, 0], [0, 1], [0, 1]]) X = add_dummy_feature(X) - assert sparse.isspmatrix_csr(X), X + assert sparse.issparse(X) and X.format == "csr", X assert_array_equal(X.toarray(), [[1, 1, 0], [1, 0, 1], [1, 0, 1]]) diff --git a/sklearn/preprocessing/tests/test_encoders.py b/sklearn/preprocessing/tests/test_encoders.py index 6cf0a887f5744..ca809dd513cf3 100644 --- a/sklearn/preprocessing/tests/test_encoders.py +++ b/sklearn/preprocessing/tests/test_encoders.py @@ -240,7 +240,7 @@ def check_categorical_onehot(X): assert_allclose(Xtr1.toarray(), Xtr2) - assert sparse.isspmatrix_csr(Xtr1) + assert sparse.issparse(Xtr1) and Xtr1.format == "csr" return Xtr1.toarray() diff --git a/sklearn/preprocessing/tests/test_polynomial.py b/sklearn/preprocessing/tests/test_polynomial.py index db194396cf848..70a927e1a814d 100644 --- a/sklearn/preprocessing/tests/test_polynomial.py +++ b/sklearn/preprocessing/tests/test_polynomial.py @@ -423,8 +423,10 @@ def test_spline_transformer_sparse_output( splt_dense.fit(X) splt_sparse.fit(X) - assert sparse.isspmatrix_csr(splt_sparse.transform(X)) - assert_allclose(splt_dense.transform(X), splt_sparse.transform(X).toarray()) + X_trans_sparse = splt_sparse.transform(X) + X_trans_dense = splt_dense.transform(X) + assert sparse.issparse(X_trans_sparse) and X_trans_sparse.format == "csr" + assert_allclose(X_trans_dense, X_trans_sparse.toarray()) # extrapolation regime X_min = np.amin(X, axis=0) @@ -721,7 +723,7 @@ def test_polynomial_features_csc_X(deg, include_bias, interaction_only, dtype): Xt_csc = est.fit_transform(X_csc.astype(dtype)) Xt_dense = est.fit_transform(X.astype(dtype)) - assert sparse.isspmatrix_csc(Xt_csc) + assert sparse.issparse(Xt_csc) and Xt_csc.format == "csc" assert Xt_csc.dtype == Xt_dense.dtype assert_array_almost_equal(Xt_csc.A, Xt_dense) @@ -748,7 +750,7 @@ def test_polynomial_features_csr_X(deg, include_bias, interaction_only, dtype): Xt_csr = est.fit_transform(X_csr.astype(dtype)) Xt_dense = est.fit_transform(X.astype(dtype, copy=False)) - assert sparse.isspmatrix_csr(Xt_csr) + assert sparse.issparse(Xt_csr) and Xt_csr.format == "csr" assert Xt_csr.dtype == Xt_dense.dtype assert_array_almost_equal(Xt_csr.A, Xt_dense) @@ -807,7 +809,7 @@ def test_polynomial_features_csr_X_floats(deg, include_bias, interaction_only, d Xt_csr = est.fit_transform(X_csr.astype(dtype)) Xt_dense = est.fit_transform(X.astype(dtype)) - assert sparse.isspmatrix_csr(Xt_csr) + assert sparse.issparse(Xt_csr) and Xt_csr.format == "csr" assert Xt_csr.dtype == Xt_dense.dtype assert_array_almost_equal(Xt_csr.A, Xt_dense) @@ -838,7 +840,7 @@ def test_polynomial_features_csr_X_zero_row(zero_row_index, deg, interaction_onl Xt_csr = est.fit_transform(X_csr) Xt_dense = est.fit_transform(X) - assert sparse.isspmatrix_csr(Xt_csr) + assert sparse.issparse(Xt_csr) and Xt_csr.format == "csr" assert Xt_csr.dtype == Xt_dense.dtype assert_array_almost_equal(Xt_csr.A, Xt_dense) @@ -859,7 +861,7 @@ def test_polynomial_features_csr_X_degree_4(include_bias, interaction_only): Xt_csr = est.fit_transform(X_csr) Xt_dense = est.fit_transform(X) - assert sparse.isspmatrix_csr(X_csr) + assert sparse.issparse(Xt_csr) and Xt_csr.format == "csr" assert Xt_csr.dtype == Xt_dense.dtype assert_array_almost_equal(Xt_csr.A, Xt_dense) @@ -887,7 +889,7 @@ def test_polynomial_features_csr_X_dim_edges(deg, dim, interaction_only): Xt_csr = est.fit_transform(X_csr) Xt_dense = est.fit_transform(X) - assert sparse.isspmatrix_csr(Xt_csr) + assert sparse.issparse(Xt_csr) and Xt_csr.format == "csr" assert Xt_csr.dtype == Xt_dense.dtype assert_array_almost_equal(Xt_csr.A, Xt_dense) diff --git a/sklearn/semi_supervised/_label_propagation.py b/sklearn/semi_supervised/_label_propagation.py index d482cce7585d2..2b3d0e2018430 100644 --- a/sklearn/semi_supervised/_label_propagation.py +++ b/sklearn/semi_supervised/_label_propagation.py @@ -294,7 +294,7 @@ def fit(self, X, y): l_previous = np.zeros((self.X_.shape[0], n_classes)) unlabeled = unlabeled[:, np.newaxis] - if sparse.isspmatrix(graph_matrix): + if sparse.issparse(graph_matrix): graph_matrix = graph_matrix.tocsr() for self.n_iter_ in range(self.max_iter): @@ -455,7 +455,7 @@ class distributions will exceed 1 (normalization may be desired). self.nn_fit = None affinity_matrix = self._get_kernel(self.X_) normalizer = affinity_matrix.sum(axis=0) - if sparse.isspmatrix(affinity_matrix): + if sparse.issparse(affinity_matrix): affinity_matrix.data /= np.diag(np.array(normalizer)) else: affinity_matrix /= normalizer[:, np.newaxis] @@ -615,7 +615,7 @@ def _build_graph(self): affinity_matrix = self._get_kernel(self.X_) laplacian = csgraph.laplacian(affinity_matrix, normed=True) laplacian = -laplacian - if sparse.isspmatrix(laplacian): + if sparse.issparse(laplacian): diag_mask = laplacian.row == laplacian.col laplacian.data[diag_mask] = 0.0 else: diff --git a/sklearn/svm/_base.py b/sklearn/svm/_base.py index 43ec06f7859ba..2fc5e04b5df83 100644 --- a/sklearn/svm/_base.py +++ b/sklearn/svm/_base.py @@ -179,7 +179,7 @@ def fit(self, X, y, sample_weight=None): """ rnd = check_random_state(self.random_state) - sparse = sp.isspmatrix(X) + sparse = sp.issparse(X) if sparse and self.kernel == "precomputed": raise TypeError("Sparse precomputed kernels are not supported.") self._sparse = sparse and not callable(self.kernel) @@ -617,7 +617,7 @@ def _validate_for_predict(self, X): reset=False, ) - if self._sparse and not sp.isspmatrix(X): + if self._sparse and not sp.issparse(X): X = sp.csr_matrix(X) if self._sparse: X.sort_indices() @@ -1222,7 +1222,7 @@ def _fit_liblinear( raw_coef_, n_iter_ = liblinear.train_wrap( X, y_ind, - sp.isspmatrix(X), + sp.issparse(X), solver_type, tol, bias, diff --git a/sklearn/svm/tests/test_sparse.py b/sklearn/svm/tests/test_sparse.py index 6ea67c80020ff..b8a9ef651221e 100644 --- a/sklearn/svm/tests/test_sparse.py +++ b/sklearn/svm/tests/test_sparse.py @@ -45,7 +45,7 @@ def check_svm_model_equal(dense_svm, sparse_svm, X_train, y_train, X_test): dense_svm.fit(X_train.toarray(), y_train) - if sparse.isspmatrix(X_test): + if sparse.issparse(X_test): X_test_dense = X_test.toarray() else: X_test_dense = X_test @@ -77,7 +77,7 @@ def check_svm_model_equal(dense_svm, sparse_svm, X_train, y_train, X_test): dense_svm.predict_proba(X_test_dense), sparse_svm.predict_proba(X_test), 4 ) msg = "cannot use sparse input in 'SVC' trained on dense data" - if sparse.isspmatrix(X_test): + if sparse.issparse(X_test): with pytest.raises(ValueError, match=msg): dense_svm.predict(X_test) diff --git a/sklearn/tree/_splitter.pyx b/sklearn/tree/_splitter.pyx index c52f9477d6f98..e578d115dec8e 100644 --- a/sklearn/tree/_splitter.pyx +++ b/sklearn/tree/_splitter.pyx @@ -20,7 +20,7 @@ from cython cimport final import numpy as np -from scipy.sparse import isspmatrix_csc +from scipy.sparse import issparse from ._utils cimport log from ._utils cimport rand_int @@ -1046,7 +1046,7 @@ cdef class SparsePartitioner: DTYPE_t[::1] feature_values, const unsigned char[::1] missing_values_in_feature_mask, ): - if not isspmatrix_csc(X): + if not (issparse(X) and X.format == "csc"): raise ValueError("X should be in csc format") self.samples = samples diff --git a/sklearn/tree/_tree.pyx b/sklearn/tree/_tree.pyx index f4fa759252dd0..057ef9aa865a9 100644 --- a/sklearn/tree/_tree.pyx +++ b/sklearn/tree/_tree.pyx @@ -32,7 +32,6 @@ cnp.import_array() from scipy.sparse import issparse from scipy.sparse import csr_matrix -from scipy.sparse import isspmatrix_csr from ._utils cimport safe_realloc from ._utils cimport sizet_ptr_to_ndarray @@ -885,7 +884,7 @@ cdef class Tree: """Finds the terminal region (=leaf node) for each sample in sparse X. """ # Check input - if not isspmatrix_csr(X): + if not (issparse(X) and X.format == 'csr'): raise ValueError("X should be in csr_matrix format, got %s" % type(X)) @@ -1013,7 +1012,7 @@ cdef class Tree: """Finds the decision path (=node) for each sample in X.""" # Check input - if not isspmatrix_csr(X): + if not (issparse(X) and X.format == "csr"): raise ValueError("X should be in csr_matrix format, got %s" % type(X)) diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py index 17798e8c9b54e..e548204463fba 100644 --- a/sklearn/utils/extmath.py +++ b/sklearn/utils/extmath.py @@ -72,8 +72,7 @@ def row_norms(X, squared=False): The row-wise (squared) Euclidean norm of X. """ if sparse.issparse(X): - if not sparse.isspmatrix_csr(X): - X = sparse.csr_matrix(X) + X = X.tocsr() norms = csr_row_norms(X) else: norms = np.einsum("ij,ij->i", X, X) @@ -425,7 +424,7 @@ def randomized_svd( >>> U.shape, s.shape, Vh.shape ((3, 2), (2,), (2, 4)) """ - if sparse.isspmatrix_lil(M) or sparse.isspmatrix_dok(M): + if sparse.issparse(M) and M.format in ("lil", "dok"): warnings.warn( "Calculating SVD of a {} is expensive. " "csr_matrix is more efficient.".format(type(M).__name__), diff --git a/sklearn/utils/graph.py b/sklearn/utils/graph.py index ffd4f63a466de..65b088197f46e 100644 --- a/sklearn/utils/graph.py +++ b/sklearn/utils/graph.py @@ -54,7 +54,7 @@ def single_source_shortest_path_length(graph, source, *, cutoff=None): >>> sorted(single_source_shortest_path_length(graph, 2).items()) [(0, 1), (1, 1), (2, 0), (3, 1), (4, 1), (5, 1)] """ - if sparse.isspmatrix(graph): + if sparse.issparse(graph): graph = graph.tolil() else: graph = sparse.lil_matrix(graph) diff --git a/sklearn/utils/multiclass.py b/sklearn/utils/multiclass.py index 83492c852f745..892d77c7e01e5 100644 --- a/sklearn/utils/multiclass.py +++ b/sklearn/utils/multiclass.py @@ -11,7 +11,7 @@ from itertools import chain import numpy as np -from scipy.sparse import issparse, isspmatrix_dok, isspmatrix_lil +from scipy.sparse import issparse from ..utils._array_api import get_namespace from .validation import _assert_all_finite, check_array @@ -176,7 +176,7 @@ def is_multilabel(y): return False if issparse(y): - if isspmatrix_dok(y) or isspmatrix_lil(y): + if y.format in ("dok", "lil"): y = y.tocsr() labels = xp.unique_values(y.data) return ( diff --git a/sklearn/utils/sparsefuncs.py b/sklearn/utils/sparsefuncs.py index 111f67c44f391..b2d0e725b8453 100644 --- a/sklearn/utils/sparsefuncs.py +++ b/sklearn/utils/sparsefuncs.py @@ -107,7 +107,7 @@ def mean_variance_axis(X, axis, weights=None, return_sum_weights=False): """ _raise_error_wrong_axis(axis) - if sp.isspmatrix_csr(X): + if sp.issparse(X) and X.format == "csr": if axis == 0: return _csr_mean_var_axis0( X, weights=weights, return_sum_weights=return_sum_weights @@ -116,7 +116,7 @@ def mean_variance_axis(X, axis, weights=None, return_sum_weights=False): return _csc_mean_var_axis0( X.T, weights=weights, return_sum_weights=return_sum_weights ) - elif sp.isspmatrix_csc(X): + elif sp.issparse(X) and X.format == "csc": if axis == 0: return _csc_mean_var_axis0( X, weights=weights, return_sum_weights=return_sum_weights @@ -191,7 +191,7 @@ def incr_mean_variance_axis(X, *, axis, last_mean, last_var, last_n, weights=Non """ _raise_error_wrong_axis(axis) - if not (sp.isspmatrix_csr(X) or sp.isspmatrix_csc(X)): + if not (sp.issparse(X) and X.format in ("csc", "csr")): _raise_typeerror(X) if np.size(last_n) == 1: @@ -238,9 +238,9 @@ def inplace_column_scale(X, scale): scale : ndarray of shape (n_features,), dtype={np.float32, np.float64} Array of precomputed feature-wise values to use for scaling. """ - if sp.isspmatrix_csc(X): + if sp.issparse(X) and X.format == "csc": inplace_csr_row_scale(X.T, scale) - elif sp.isspmatrix_csr(X): + elif sp.issparse(X) and X.format == "csr": inplace_csr_column_scale(X, scale) else: _raise_typeerror(X) @@ -260,9 +260,9 @@ def inplace_row_scale(X, scale): scale : ndarray of shape (n_features,), dtype={np.float32, np.float64} Array of precomputed sample-wise values to use for scaling. """ - if sp.isspmatrix_csc(X): + if sp.issparse(X) and X.format == "csc": inplace_csr_column_scale(X.T, scale) - elif sp.isspmatrix_csr(X): + elif sp.issparse(X) and X.format == "csr": inplace_csr_row_scale(X, scale) else: _raise_typeerror(X) @@ -376,9 +376,9 @@ def inplace_swap_row(X, m, n): n : int Index of the row of X to be swapped. """ - if sp.isspmatrix_csc(X): + if sp.issparse(X) and X.format == "csc": inplace_swap_row_csc(X, m, n) - elif sp.isspmatrix_csr(X): + elif sp.issparse(X) and X.format == "csr": inplace_swap_row_csr(X, m, n) else: _raise_typeerror(X) @@ -404,9 +404,9 @@ def inplace_swap_column(X, m, n): m += X.shape[1] if n < 0: n += X.shape[1] - if sp.isspmatrix_csc(X): + if sp.issparse(X) and X.format == "csc": inplace_swap_row_csr(X, m, n) - elif sp.isspmatrix_csr(X): + elif sp.issparse(X) and X.format == "csr": inplace_swap_row_csc(X, m, n) else: _raise_typeerror(X) @@ -505,7 +505,7 @@ def min_max_axis(X, axis, ignore_nan=False): maxs : ndarray of shape (n_features,), dtype={np.float32, np.float64} Feature-wise maxima. """ - if sp.isspmatrix_csr(X) or sp.isspmatrix_csc(X): + if sp.issparse(X) and X.format in ("csr", "csc"): if ignore_nan: return _sparse_nan_min_max(X, axis=axis) else: @@ -614,7 +614,7 @@ def csc_median_axis_0(X): median : ndarray of shape (n_features,) Median. """ - if not sp.isspmatrix_csc(X): + if not (sp.issparse(X) and X.format == "csc"): raise TypeError("Expected matrix of CSC format, got %s" % X.format) indptr = X.indptr From d451609cc15438fb859afbd21383389c834d4462 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Thu, 13 Jul 2023 19:43:39 +0200 Subject: [PATCH 094/264] TST Adaboost: Fixed ValueError in test_multidimensional_X (#26829) --- sklearn/ensemble/tests/test_weight_boosting.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/sklearn/ensemble/tests/test_weight_boosting.py b/sklearn/ensemble/tests/test_weight_boosting.py index a8e0f06340dc4..5982affcf8c60 100755 --- a/sklearn/ensemble/tests/test_weight_boosting.py +++ b/sklearn/ensemble/tests/test_weight_boosting.py @@ -475,9 +475,9 @@ def test_multidimensional_X(): """ rng = np.random.RandomState(0) - X = rng.randn(50, 3, 3) - yc = rng.choice([0, 1], 50) - yr = rng.randn(50) + X = rng.randn(51, 3, 3) + yc = rng.choice([0, 1], 51) + yr = rng.randn(51) boost = AdaBoostClassifier(DummyClassifier(strategy="most_frequent")) boost.fit(X, yc) From ebe10e679bfe485d37273a49ada6f5edbb3c0b65 Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Thu, 13 Jul 2023 23:16:29 +0200 Subject: [PATCH 095/264] FIX clone to handle dict and GridSearchCV changing original params (#26786) Co-authored-by: Thomas J. Fan --- sklearn/base.py | 5 ++-- sklearn/model_selection/_search.py | 11 ++++--- sklearn/model_selection/_validation.py | 13 ++++----- sklearn/model_selection/tests/test_search.py | 30 ++++++++++++++++++++ sklearn/tests/test_base.py | 7 +++++ 5 files changed, 52 insertions(+), 14 deletions(-) diff --git a/sklearn/base.py b/sklearn/base.py index 5986d7288c773..893925c8104a0 100644 --- a/sklearn/base.py +++ b/sklearn/base.py @@ -80,8 +80,9 @@ def _clone_parametrized(estimator, *, safe=True): """Default implementation of clone. See :func:`sklearn.base.clone` for details.""" estimator_type = type(estimator) - # XXX: not handling dictionaries - if estimator_type in (list, tuple, set, frozenset): + if estimator_type is dict: + return {k: clone(v, safe=safe) for k, v in estimator.items()} + elif estimator_type in (list, tuple, set, frozenset): return estimator_type([clone(e, safe=safe) for e in estimator]) elif not hasattr(estimator, "get_params") or isinstance(estimator, type): if not safe: diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py index b3925865eb775..c2e94712ea97a 100644 --- a/sklearn/model_selection/_search.py +++ b/sklearn/model_selection/_search.py @@ -923,11 +923,14 @@ def evaluate_candidates(candidate_params, cv=None, more_results=None): self.best_params_ = results["params"][self.best_index_] if self.refit: - # we clone again after setting params in case some - # of the params are estimators as well. - self.best_estimator_ = clone( - clone(base_estimator).set_params(**self.best_params_) + # here we clone the estimator as well as the parameters, since + # sometimes the parameters themselves might be estimators, e.g. + # when we search over different estimators in a pipeline. + # ref: https://github.com/scikit-learn/scikit-learn/pull/26786 + self.best_estimator_ = clone(base_estimator).set_params( + **clone(self.best_params_, safe=False) ) + refit_start_time = time.time() if y is not None: self.best_estimator_.fit(X, y, **fit_params) diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py index f91cf05224695..033eb6ec26bd9 100644 --- a/sklearn/model_selection/_validation.py +++ b/sklearn/model_selection/_validation.py @@ -710,14 +710,11 @@ def _fit_and_score( fit_params = _check_fit_params(X, fit_params, train) if parameters is not None: - # clone after setting parameters in case any parameters - # are estimators (like pipeline steps) - # because pipeline doesn't clone steps in fit - cloned_parameters = {} - for k, v in parameters.items(): - cloned_parameters[k] = clone(v, safe=False) - - estimator = estimator.set_params(**cloned_parameters) + # here we clone the parameters, since sometimes the parameters + # themselves might be estimators, e.g. when we search over different + # estimators in a pipeline. + # ref: https://github.com/scikit-learn/scikit-learn/pull/26786 + estimator = estimator.set_params(**clone(parameters, safe=False)) start_time = time.time() diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py index 1bafcf3e76a4a..6ea52049f3ced 100644 --- a/sklearn/model_selection/tests/test_search.py +++ b/sklearn/model_selection/tests/test_search.py @@ -22,6 +22,7 @@ make_multilabel_classification, ) from sklearn.ensemble import HistGradientBoostingClassifier +from sklearn.experimental import enable_halving_search_cv # noqa from sklearn.impute import SimpleImputer from sklearn.linear_model import LinearRegression, Ridge, SGDClassifier from sklearn.metrics import ( @@ -38,6 +39,7 @@ GridSearchCV, GroupKFold, GroupShuffleSplit, + HalvingGridSearchCV, KFold, LeaveOneGroupOut, LeavePGroupsOut, @@ -2420,3 +2422,31 @@ def test_search_cv_verbose_3(capsys, return_train_score): else: match = re.findall(r"score=[\d\.]+", captured) assert len(match) == 3 + + +@pytest.mark.parametrize( + "SearchCV, param_search", + [ + (GridSearchCV, "param_grid"), + (RandomizedSearchCV, "param_distributions"), + (HalvingGridSearchCV, "param_grid"), + ], +) +def test_search_estimator_param(SearchCV, param_search): + # test that SearchCV object doesn't change the object given in the parameter grid + X, y = make_classification(random_state=42) + + params = {"clf": [LinearSVC(dual="auto")], "clf__C": [0.01]} + orig_C = params["clf"][0].C + + pipe = Pipeline([("trs", MinimalTransformer()), ("clf", None)]) + + param_grid_search = {param_search: params} + gs = SearchCV(pipe, refit=True, cv=2, scoring="accuracy", **param_grid_search).fit( + X, y + ) + + # testing that the original object in params is not changed + assert params["clf"][0].C == orig_C + # testing that the GS is setting the parameter of the step correctly + assert gs.best_estimator_.named_steps["clf"].C == 0.01 diff --git a/sklearn/tests/test_base.py b/sklearn/tests/test_base.py index 21e166428f21f..2f1c746052beb 100644 --- a/sklearn/tests/test_base.py +++ b/sklearn/tests/test_base.py @@ -183,6 +183,13 @@ def test_clone_nan(): assert clf.empty is clf2.empty +def test_clone_dict(): + # test that clone creates a clone of a dict + orig = {"a": MyEstimator()} + cloned = clone(orig) + assert orig["a"] is not cloned["a"] + + def test_clone_sparse_matrices(): sparse_matrix_classes = [ cls From 05d3b7cbb1e1b6055f2d9c9795c58afaf60fe030 Mon Sep 17 00:00:00 2001 From: Kushan Sharma <50699013+greyisbetter@users.noreply.github.com> Date: Mon, 17 Jul 2023 14:47:45 +0530 Subject: [PATCH 096/264] [MRG] DOC add __sklearn_is_fitted__ example (#26618) Co-authored-by: Adrin Jalali --- doc/developers/develop.rst | 14 ++++ examples/developing_estimators/README.txt | 6 ++ .../sklearn_is_fitted.py | 76 +++++++++++++++++++ 3 files changed, 96 insertions(+) create mode 100644 examples/developing_estimators/README.txt create mode 100644 examples/developing_estimators/sklearn_is_fitted.py diff --git a/doc/developers/develop.rst b/doc/developers/develop.rst index 95f5950ff978d..8db8799daf6fe 100644 --- a/doc/developers/develop.rst +++ b/doc/developers/develop.rst @@ -709,6 +709,20 @@ only wrap the first array and not alter the other arrays. See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` for an example on how to use the API. +.. _developer_api_check_is_fitted: + +Developer API for `check_is_fitted` +=================================== + +By default :func:`~sklearn.utils.validation.check_is_fitted` checks if there +are any attributes in the instance with a trailing underscore, e.g. `coef_`. +An estimator can change the behavior by implementing a `__sklearn_is_fitted__` +method taking no input and returning a boolean. If this method exists, +:func:`~sklearn.utils.validation.check_is_fitted` simply returns its output. + +See :ref:`sphx_glr_auto_examples_developing_estimators_sklearn_is_fitted.py` +for an example on how to use the API. + .. _coding-guidelines: Coding guidelines diff --git a/examples/developing_estimators/README.txt b/examples/developing_estimators/README.txt new file mode 100644 index 0000000000000..dc2c2ffde352a --- /dev/null +++ b/examples/developing_estimators/README.txt @@ -0,0 +1,6 @@ +.. _developing_estimator_examples: + +Developing Estimators +--------------------- + +Examples concerning the development of Custom Estimator. \ No newline at end of file diff --git a/examples/developing_estimators/sklearn_is_fitted.py b/examples/developing_estimators/sklearn_is_fitted.py new file mode 100644 index 0000000000000..f426ed2e0b441 --- /dev/null +++ b/examples/developing_estimators/sklearn_is_fitted.py @@ -0,0 +1,76 @@ +""" +======================================== +`__sklearn_is_fitted__` as Developer API +======================================== + +The `__sklearn_is_fitted__` method is a convention used in scikit-learn for +checking whether an estimator object has been fitted or not. This method is +typically implemented in custom estimator classes that are built on top of +scikit-learn's base classes like `BaseEstimator` or its subclasses. + +Developers should use :func:`~sklearn.sklearn.utils.validation.check_is_fitted` +at the beginning of all methods except `fit`. If they need to customize or +speed-up the check, they can implement the `__sklearn_is_fitted__` method as +shown below. + +In this example the custom estimator showcases the usage of the +`__sklearn_is_fitted__` method and the `check_is_fitted` utility function +as developer APIs. The `__sklearn_is_fitted__` method checks fitted status +by verifying the presence of the `_is_fitted` attribute. +""" + +# %% +# An example custom estimator implementing a simple classifier +# ------------------------------------------------------------ +# This code snippet defines a custom estimator class called `CustomEstimator` +# that extends both the `BaseEstimator` and `ClassifierMixin` classes from +# scikit-learn and showcases the usage of the `__sklearn_is_fitted__` method +# and the `check_is_fitted` utility function. + +# Author: Kushan +# +# License: BSD 3 clause + +from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.utils.validation import check_is_fitted + + +class CustomEstimator(BaseEstimator, ClassifierMixin): + def __init__(self, parameter=1): + self.parameter = parameter + + def fit(self, X, y): + """ + Fit the estimator to the training data. + """ + self.classes_ = sorted(set(y)) + # Custom attribute to track if the estimator is fitted + self._is_fitted = True + return self + + def predict(self, X): + """ + Perform Predictions + + If the estimator is not fitted, then raise NotFittedError + """ + check_is_fitted(self) + # Perform prediction logic + predictions = [self.classes_[0]] * len(X) + return predictions + + def score(self, X, y): + """ + Calculate Score + + If the estimator is not fitted, then raise NotFittedError + """ + check_is_fitted(self) + # Perform scoring logic + return 0.5 + + def __sklearn_is_fitted__(self): + """ + Check fitted status and return a Boolean value. + """ + return hasattr(self, "_is_fitted") and self._is_fitted From 4c7c680cf82c66d6f0d46dbe3ecd6208986d820d Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Mon, 17 Jul 2023 23:46:54 -0500 Subject: [PATCH 097/264] CI Only update tracker to succes when all jobs pass on CirrusCI (#26849) --- build_tools/cirrus/arm_wheel.yml | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/build_tools/cirrus/arm_wheel.yml b/build_tools/cirrus/arm_wheel.yml index 9fdaa1b4bb48e..a7023867e1109 100644 --- a/build_tools/cirrus/arm_wheel.yml +++ b/build_tools/cirrus/arm_wheel.yml @@ -31,7 +31,6 @@ macos_arm64_wheel_task: cibuildwheel_script: - bash build_tools/wheels/build_wheels.sh - - bash build_tools/cirrus/update_tracking_issue.sh true on_failure: update_tracker_script: @@ -73,7 +72,6 @@ linux_arm64_wheel_task: cibuildwheel_script: - apt install -y python3 python-is-python3 - bash build_tools/wheels/build_wheels.sh - - bash build_tools/cirrus/update_tracking_issue.sh true on_failure: update_tracker_script: @@ -82,6 +80,17 @@ linux_arm64_wheel_task: wheels_artifacts: path: "wheelhouse/*" +# Update tracker when all jobs are successful +update_tracker_success: + depends_on: + - macos_arm64_wheel + - linux_arm64_wheel + container: + image: python:3.11 + # Only update tracker for nightly builds + only_if: $CIRRUS_CRON == "nightly" + update_script: + - bash build_tools/cirrus/update_tracking_issue.sh true wheels_upload_task: depends_on: From 8c5ebc0f2650c6de97bbfda8397bce27c1f104c9 Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos Orfanos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Tue, 18 Jul 2023 10:20:40 +0200 Subject: [PATCH 098/264] MAINT Fix typos found by codespell (#26852) --- doc/whats_new/older_versions.rst | 6 +++--- examples/bicluster/plot_spectral_biclustering.py | 2 +- examples/cluster/plot_hdbscan.py | 2 +- .../ensemble/plot_forest_hist_grad_boosting_comparison.py | 4 ++-- examples/linear_model/plot_ridge_coeffs.py | 2 +- examples/miscellaneous/plot_display_object_visualization.py | 2 +- examples/miscellaneous/plot_metadata_routing.py | 2 +- sklearn/cluster/_hdbscan/_tree.pyx | 2 +- sklearn/cluster/_hdbscan/hdbscan.py | 2 +- sklearn/datasets/tests/test_openml.py | 2 +- .../ensemble/_hist_gradient_boosting/gradient_boosting.py | 4 ++-- sklearn/metrics/_ranking.py | 2 +- sklearn/preprocessing/tests/test_discretization.py | 2 +- sklearn/preprocessing/tests/test_target_encoder.py | 2 +- sklearn/svm/_classes.py | 4 ++-- sklearn/tests/test_discriminant_analysis.py | 2 +- sklearn/tests/test_metaestimators_metadata_routing.py | 2 +- sklearn/utils/_metadata_requests.py | 2 +- 18 files changed, 23 insertions(+), 23 deletions(-) diff --git a/doc/whats_new/older_versions.rst b/doc/whats_new/older_versions.rst index cdc37f25ed997..5a1d6a1c7c13f 100644 --- a/doc/whats_new/older_versions.rst +++ b/doc/whats_new/older_versions.rst @@ -344,9 +344,9 @@ API changes summary Please use :class:`~covariance.EllipticEnvelope` instead. - ``NeighborsClassifier`` and ``NeighborsRegressor`` are gone in the module - :ref:`neighbors`. Use the classes :class:`~neighors.KNeighborsClassifier`, - :class:`~neighors.RadiusNeighborsClassifier`, :class:`~neighors.KNeighborsRegressor` - and/or :class:`~neighors.RadiusNeighborsRegressor` instead. + :ref:`neighbors`. Use the classes :class:`~neighbors.KNeighborsClassifier`, + :class:`~neighbors.RadiusNeighborsClassifier`, :class:`~neighbors.KNeighborsRegressor` + and/or :class:`~neighbors.RadiusNeighborsRegressor` instead. - Sparse classes in the :ref:`sgd` module are now deprecated. diff --git a/examples/bicluster/plot_spectral_biclustering.py b/examples/bicluster/plot_spectral_biclustering.py index 853b529a4aeba..041ef4c4944f6 100644 --- a/examples/bicluster/plot_spectral_biclustering.py +++ b/examples/bicluster/plot_spectral_biclustering.py @@ -57,7 +57,7 @@ # %% # We redefine the shuffled data and plot it. We observe that we lost the -# strucuture of original data matrix. +# structure of original data matrix. data = data[row_idx_shuffled][:, col_idx_shuffled] plt.matshow(data, cmap=plt.cm.Blues) diff --git a/examples/cluster/plot_hdbscan.py b/examples/cluster/plot_hdbscan.py index a4dfcb6c42bbc..149deb97020cd 100644 --- a/examples/cluster/plot_hdbscan.py +++ b/examples/cluster/plot_hdbscan.py @@ -113,7 +113,7 @@ def plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax= # ---------------------- # HDBSCAN is much more than scale invariant though -- it is capable of # multi-scale clustering, which accounts for clusters with varying density. -# Traditional DBSCAN assumes that any potential clusters are homogenous in +# Traditional DBSCAN assumes that any potential clusters are homogeneous in # density. HDBSCAN is free from such constraints. To demonstrate this we # consider the following dataset centers = [[-0.85, -0.85], [-0.85, 0.85], [3, 3], [3, -3]] diff --git a/examples/ensemble/plot_forest_hist_grad_boosting_comparison.py b/examples/ensemble/plot_forest_hist_grad_boosting_comparison.py index cc48e47e9ad34..7eab9a3437d65 100644 --- a/examples/ensemble/plot_forest_hist_grad_boosting_comparison.py +++ b/examples/ensemble/plot_forest_hist_grad_boosting_comparison.py @@ -11,7 +11,7 @@ The comparison is made by varying the parameters that control the number of trees according to each estimator: -- `n_estimators` controls the number of trees in the forest. It's a fixed numer. +- `n_estimators` controls the number of trees in the forest. It's a fixed number. - `max_iter` is the the maximum number of iterations in a gradient boosting based model. The number of iterations corresponds to the number of trees for regression and binary classification problems. Furthermore, the actual number @@ -202,7 +202,7 @@ # makes fitting and scoring slower. The RF model reaches such plateau earlier # and can never reach the test score of the largest HGBDT model. # -# Note that the results shown on the above plot can change sightly across runs +# Note that the results shown on the above plot can change slightly across runs # and even more significantly when running on other machines: try to run this # example on your own local machine. # diff --git a/examples/linear_model/plot_ridge_coeffs.py b/examples/linear_model/plot_ridge_coeffs.py index 4f5a90d5848ef..4bfb1f4c29325 100644 --- a/examples/linear_model/plot_ridge_coeffs.py +++ b/examples/linear_model/plot_ridge_coeffs.py @@ -162,7 +162,7 @@ # `alpha` increases, the highest coefficients shrink more rapidly, rendering # their corresponding features less influential in the training process. This # can enhance a model's ability to generalize to unseen data (if there was a lot -# of noise to capture), but it also poses the risk of loosing performance if the +# of noise to capture), but it also poses the risk of losing performance if the # regularization becomes too strong compared to the amount of noise the data # contained (as in this example). # diff --git a/examples/miscellaneous/plot_display_object_visualization.py b/examples/miscellaneous/plot_display_object_visualization.py index 24095de3b5cae..059fb5963fe57 100644 --- a/examples/miscellaneous/plot_display_object_visualization.py +++ b/examples/miscellaneous/plot_display_object_visualization.py @@ -39,7 +39,7 @@ # Create :class:`ConfusionMatrixDisplay` ############################################################################## # With the fitted model, we compute the predictions of the model on the test -# dataset. These predictions are used to compute the confustion matrix which +# dataset. These predictions are used to compute the confusion matrix which # is plotted with the :class:`ConfusionMatrixDisplay` from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix diff --git a/examples/miscellaneous/plot_metadata_routing.py b/examples/miscellaneous/plot_metadata_routing.py index 55e40c1ab5a5e..ae1ec857b59b0 100644 --- a/examples/miscellaneous/plot_metadata_routing.py +++ b/examples/miscellaneous/plot_metadata_routing.py @@ -368,7 +368,7 @@ def predict(self, X, **predict_params): # In ``get_metadata_routing``, we add ``self`` to the routing using # ``add_self_request`` to indicate this estimator is consuming # ``sample_weight`` as well as being a router; which also adds a -# ``$self_request`` key to the routing info as illustrated bellow. Now let's +# ``$self_request`` key to the routing info as illustrated below. Now let's # look at some examples: # %% diff --git a/sklearn/cluster/_hdbscan/_tree.pyx b/sklearn/cluster/_hdbscan/_tree.pyx index c5bca48d5ebf9..df63ce4d6b0f4 100644 --- a/sklearn/cluster/_hdbscan/_tree.pyx +++ b/sklearn/cluster/_hdbscan/_tree.pyx @@ -133,7 +133,7 @@ cpdef cnp.ndarray[CONDENSED_t, ndim=1, mode='c'] _condense_tree( A single linkage hierarchy in scipy.cluster.hierarchy format. min_cluster_size : int, optional (default 10) - The minimum size of clusters to consider. Clusters smaler than this + The minimum size of clusters to consider. Clusters smaller than this are pruned from the tree. Returns diff --git a/sklearn/cluster/_hdbscan/hdbscan.py b/sklearn/cluster/_hdbscan/hdbscan.py index ddfe0c8e08b65..57de8962250b1 100644 --- a/sklearn/cluster/_hdbscan/hdbscan.py +++ b/sklearn/cluster/_hdbscan/hdbscan.py @@ -900,7 +900,7 @@ def _weighted_cluster_center(self, X): self.medoids_ = np.empty((n_clusters, X.shape[1]), dtype=np.float64) # Need to handle iteratively seen each cluster may have a different - # number of samples, hence we can't create a homogenous 3D array. + # number of samples, hence we can't create a homogeneous 3D array. for idx in range(n_clusters): mask = self.labels_ == idx data = X[mask] diff --git a/sklearn/datasets/tests/test_openml.py b/sklearn/datasets/tests/test_openml.py index d31237b19bfa7..93dcffef0cd03 100644 --- a/sklearn/datasets/tests/test_openml.py +++ b/sklearn/datasets/tests/test_openml.py @@ -1534,7 +1534,7 @@ def test_fetch_openml_verify_checksum(monkeypatch, as_frame, cache, tmpdir, pars modified_gzip.write(data) # Requests are already mocked by monkey_patch_webbased_functions. - # We want to re-use that mock for all requests except file download, + # We want to reuse that mock for all requests except file download, # hence creating a thin mock over the original mock mocked_openml_url = sklearn.datasets._openml.urlopen diff --git a/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py b/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py index 136e8c3b29efe..5d030d3add5bb 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py +++ b/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py @@ -382,7 +382,7 @@ def fit(self, X, y, sample_weight=None): rng = check_random_state(self.random_state) - # When warm starting, we want to re-use the same seed that was used + # When warm starting, we want to reuse the same seed that was used # the first time fit was called (e.g. for subsampling or for the # train/val split). if not (self.warm_start and self._is_fitted()): @@ -538,7 +538,7 @@ def fit(self, X, y, sample_weight=None): # we're going to compute scoring w.r.t the loss. As losses # take raw predictions as input (unlike the scorers), we # can optimize a bit and avoid repeating computing the - # predictions of the previous trees. We'll re-use + # predictions of the previous trees. We'll reuse # raw_predictions (as it's needed for training anyway) for # evaluating the training loss, and create # raw_predictions_val for storing the raw predictions of diff --git a/sklearn/metrics/_ranking.py b/sklearn/metrics/_ranking.py index d920c092e30f5..166c2ce20eb87 100644 --- a/sklearn/metrics/_ranking.py +++ b/sklearn/metrics/_ranking.py @@ -313,7 +313,7 @@ def det_curve(y_true, y_score, pos_label=None, sample_weight=None): fpr : ndarray of shape (n_thresholds,) False positive rate (FPR) such that element i is the false positive rate of predictions with score >= thresholds[i]. This is occasionally - referred to as false acceptance propability or fall-out. + referred to as false acceptance probability or fall-out. fnr : ndarray of shape (n_thresholds,) False negative rate (FNR) such that element i is the false negative diff --git a/sklearn/preprocessing/tests/test_discretization.py b/sklearn/preprocessing/tests/test_discretization.py index 0994f27151d98..bc0b9470f4aa6 100644 --- a/sklearn/preprocessing/tests/test_discretization.py +++ b/sklearn/preprocessing/tests/test_discretization.py @@ -485,7 +485,7 @@ def test_kbinsdiscretizer_subsample(strategy, global_random_seed): kbd_no_subsampling.set_params(subsample=None) kbd_no_subsampling.fit(X) - # We use a large tolerance because we can't expect the bin edges to be exactely the + # We use a large tolerance because we can't expect the bin edges to be exactly the # same when subsampling is used. assert_allclose( kbd_subsampling.bin_edges_[0], kbd_no_subsampling.bin_edges_[0], rtol=1e-2 diff --git a/sklearn/preprocessing/tests/test_target_encoder.py b/sklearn/preprocessing/tests/test_target_encoder.py index 68fef99ba5049..d39eef0fd3e1e 100644 --- a/sklearn/preprocessing/tests/test_target_encoder.py +++ b/sklearn/preprocessing/tests/test_target_encoder.py @@ -484,7 +484,7 @@ def test_target_encoding_for_linear_regression(smooth, global_random_seed): # itself independent of the target variable: target encoding such a feature # without internal cross-validation should cause catastrophic overfitting # for the downstream regressor, even with shrinkage. This kind of features - # typically represents near unique idenfiers of samples. In general they + # typically represents near unique identifiers of samples. In general they # should be removed from a machine learning datasets but here we want to # study the ability of the default behavior of TargetEncoder to mitigate # them automatically. diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py index 1772a6773be90..7a54c02201ccb 100644 --- a/sklearn/svm/_classes.py +++ b/sklearn/svm/_classes.py @@ -72,7 +72,7 @@ class LinearSVC(LinearClassifierMixin, SparseCoefMixin, BaseEstimator): optimization problem. Prefer dual=False when n_samples > n_features. `dual="auto"` will choose the value of the parameter automatically, based on the values of `n_samples`, `n_features`, `loss`, `multi_class` - and `penalty`. If `n_samples` < `n_features` and optmizer supports + and `penalty`. If `n_samples` < `n_features` and optimizer supports chosen `loss`, `multi_class` and `penalty`, then dual will be set to True, otherwise it will be set to False. @@ -409,7 +409,7 @@ class LinearSVR(RegressorMixin, LinearModel): optimization problem. Prefer dual=False when n_samples > n_features. `dual="auto"` will choose the value of the parameter automatically, based on the values of `n_samples`, `n_features` and `loss`. If - `n_samples` < `n_features` and optmizer supports chosen `loss`, + `n_samples` < `n_features` and optimizer supports chosen `loss`, then dual will be set to True, otherwise it will be set to False. .. versionchanged:: 1.3 diff --git a/sklearn/tests/test_discriminant_analysis.py b/sklearn/tests/test_discriminant_analysis.py index ac379de06d378..27e183fde43e0 100644 --- a/sklearn/tests/test_discriminant_analysis.py +++ b/sklearn/tests/test_discriminant_analysis.py @@ -219,7 +219,7 @@ def discriminant_func(sample, coef, intercept, clazz): assert prob_ref == pytest.approx(prob_ref_2) # check that the probability of LDA are close to the theoretical - # probabilties + # probabilities assert_allclose( lda.predict_proba(sample), np.hstack([prob, prob_ref])[np.newaxis], atol=1e-2 ) diff --git a/sklearn/tests/test_metaestimators_metadata_routing.py b/sklearn/tests/test_metaestimators_metadata_routing.py index 5160a4b46b538..830c0c1a2c95b 100644 --- a/sklearn/tests/test_metaestimators_metadata_routing.py +++ b/sklearn/tests/test_metaestimators_metadata_routing.py @@ -226,7 +226,7 @@ def predict_log_proba(self, X, sample_weight="default", metadata="default"): - routing_methods: list of all methods to check for routing - preserves_metadata: Whether the metaestimator passes the metadata to the sub-estimator without modification or not. If it does, we check that the - values are identical. If it doesn', no check is performed. TODO Maybe + values are identical. If it doesn't, no check is performed. TODO Maybe something smarter could be done if the data is modified. """ diff --git a/sklearn/utils/_metadata_requests.py b/sklearn/utils/_metadata_requests.py index 443e7a736e177..b3957d0dda240 100644 --- a/sklearn/utils/_metadata_requests.py +++ b/sklearn/utils/_metadata_requests.py @@ -614,7 +614,7 @@ class MetadataRouter: # this is here for us to use this attribute's value instead of doing # `isinstance`` in our checks, so that we avoid issues when people vendor - # this file instad of using it directly from scikit-learn. + # this file instead of using it directly from scikit-learn. _type = "metadata_router" def __init__(self, owner): From 73e43cbe6d4ea49e4ee7454bf85b37e5895d077c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 18 Jul 2023 15:46:10 +0200 Subject: [PATCH 099/264] MNT Improve conda version check and update lock files (#26845) --- build_tools/azure/debian_atlas_32bit_lock.txt | 6 +- ...onda_defaults_openblas_linux-64_conda.lock | 33 ++-- .../py38_conda_forge_mkl_win-64_conda.lock | 96 +++++----- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 106 +++++------ ...latest_conda_forge_mkl_linux-64_conda.lock | 14 +- ..._forge_mkl_no_coverage_linux-64_conda.lock | 108 ++++++----- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 123 ++++++------- ...latest_conda_mkl_no_openmp_environment.yml | 2 +- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 14 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 59 +++--- ...pylatest_pip_scipy_dev_linux-64_conda.lock | 22 +-- build_tools/azure/pypy3_linux-64_conda.lock | 76 ++++---- build_tools/azure/python_nogil_lock.txt | 22 +-- .../azure/python_nogil_requirements.txt | 3 + build_tools/azure/ubuntu_atlas_lock.txt | 12 +- build_tools/circle/doc_linux-64_conda.lock | 169 +++++++++--------- .../doc_min_dependencies_linux-64_conda.lock | 44 +++-- .../py39_conda_forge_linux-aarch64_conda.lock | 74 ++++---- .../update_environments_and_lock_files.py | 10 +- sklearn/_min_dependencies.py | 2 +- sklearn/cluster/tests/test_spectral.py | 4 + .../manifold/tests/test_spectral_embedding.py | 11 ++ 22 files changed, 511 insertions(+), 499 deletions(-) diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt index 1a8c4eca7c291..1b3dd213e85ef 100644 --- a/build_tools/azure/debian_atlas_32bit_lock.txt +++ b/build_tools/azure/debian_atlas_32bit_lock.txt @@ -2,13 +2,13 @@ # This file is autogenerated by pip-compile with Python 3.9 # by the following command: # -# pip-compile --output-file=build_tools/azure/debian_atlas_32bit_lock.txt build_tools/azure/debian_atlas_32bit_requirements.txt +# pip-compile --config=pyproject.toml --output-file=build_tools/azure/debian_atlas_32bit_lock.txt build_tools/azure/debian_atlas_32bit_requirements.txt # attrs==23.1.0 # via pytest coverage==7.2.7 # via pytest-cov -cython==0.29.35 +cython==0.29.36 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt iniconfig==2.0.0 # via pytest @@ -16,7 +16,7 @@ joblib==1.1.1 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt packaging==23.1 # via pytest -pluggy==1.0.0 +pluggy==1.2.0 # via pytest py==1.11.0 # via pytest diff --git a/build_tools/azure/py38_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/py38_conda_defaults_openblas_linux-64_conda.lock index 3a15776662079..adcd7c946c808 100644 --- a/build_tools/azure/py38_conda_defaults_openblas_linux-64_conda.lock +++ b/build_tools/azure/py38_conda_defaults_openblas_linux-64_conda.lock @@ -4,7 +4,7 @@ @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-openblas.conda#9ddfcaef10d79366c90128f5dc444be8 -https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.01.10-h06a4308_0.conda#7704989a2ccf6c1f5a50c985509841c4 +https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.05.30-h06a4308_0.conda#979be8dd2368decd342b13e01540d297 https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.38-h1181459_1.conda#68eedfd9c06f2b0e6888d8db345b7f5b https://repo.anaconda.com/pkgs/main/linux-64/libgfortran4-7.5.0-ha8ba4b0_17.conda#e3883581cbf0a98672250c3e80d292bf https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-7.5.0-ha8ba4b0_17.conda#ecb35c8952579d5c8dc56c6e076ba948 @@ -26,40 +26,39 @@ https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.15-h7f8727e_0.conda#ada518 https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_0.conda#53915e9402180a7f22ea619c41089520 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c https://repo.anaconda.com/pkgs/main/linux-64/nspr-4.35-h6a678d5_0.conda#208fff5d60133bcff6998a70c9f5203b -https://repo.anaconda.com/pkgs/main/linux-64/openssl-1.1.1t-h7f8727e_0.conda#0410db682c02665511bd4203ade48a32 +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.9-h7f8727e_0.conda#dc136c954b2d644c79fee77e57e183dc 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a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml index e32b4adc6ea3e..64a33fe7d7522 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml @@ -5,7 +5,7 @@ channels: - defaults dependencies: - python - - numpy + - numpy<1.25 - blas[build=mkl] - scipy - cython diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index 1e1ae5e4ff3e6..95d69f1d03dca 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -1,10 +1,10 @@ # Generated by conda-lock. # platform: osx-64 -# input_hash: 808a9ca502dcdd93d1b689ad8ff08d74228790f74a1f707c0054ee97dad6a742 +# input_hash: fbf2d65e263f99b2d6f22988cb39d83f9afe591cddad646c82ddb61740f5b841 @EXPLICIT https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae776d26d4214c8a https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h1de35cc_0.conda#19fcb113b170fe2a0be96b47801fed7d -https://repo.anaconda.com/pkgs/main/osx-64/ca-certificates-2023.01.10-hecd8cb5_0.conda#4544150389480f19dd67c20b3bb12d61 +https://repo.anaconda.com/pkgs/main/osx-64/ca-certificates-2023.05.30-hecd8cb5_0.conda#1ab4b0336a311064d05fdd2484df5336 https://repo.anaconda.com/pkgs/main/osx-64/giflib-5.2.1-h6c40b1e_3.conda#a5ab49bdb6fdc875fb965221241e3bcf https://repo.anaconda.com/pkgs/main/osx-64/jpeg-9e-h6c40b1e_1.conda#fc3e61fa41309946c9283fe8737d7f41 https://repo.anaconda.com/pkgs/main/osx-64/libbrotlicommon-1.0.9-hca72f7f_7.conda#6c865b9e76fa2fad0c8ac32aa0f01f75 @@ -25,7 +25,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/libbrotlienc-1.0.9-hca72f7f_7.conda#e https://repo.anaconda.com/pkgs/main/osx-64/libgfortran5-11.3.0-h9dfd629_28.conda#1fa1a27ee100b1918c3021dbfa3895a3 https://repo.anaconda.com/pkgs/main/osx-64/libpng-1.6.39-h6c40b1e_0.conda#a3c824835f53ad27aeb86d2b55e47804 https://repo.anaconda.com/pkgs/main/osx-64/lz4-c-1.9.4-hcec6c5f_0.conda#44291e9e6920cfff30caf1299f48db38 -https://repo.anaconda.com/pkgs/main/osx-64/openssl-1.1.1t-hca72f7f_0.conda#5027baac278975d148ee3887b3f4e911 +https://repo.anaconda.com/pkgs/main/osx-64/openssl-3.0.9-hca72f7f_0.conda#da0d587d3348bb1f9cbe3aa7e7237753 https://repo.anaconda.com/pkgs/main/osx-64/readline-8.2-hca72f7f_0.conda#971667436260e523f6f7355fdfa238bf https://repo.anaconda.com/pkgs/main/osx-64/tbb-2021.8.0-ha357a0b_0.conda#fb48530a3eea681c11dafb95b3387c0f https://repo.anaconda.com/pkgs/main/osx-64/tk-8.6.12-h5d9f67b_0.conda#047f0af5486d19163e37fd7f8ae3d29f @@ -37,14 +37,14 @@ https://repo.anaconda.com/pkgs/main/osx-64/sqlite-3.41.2-h6c40b1e_0.conda#6947a5 https://repo.anaconda.com/pkgs/main/osx-64/zstd-1.5.5-hc035e20_0.conda#5e0b7ddb1b7dc6b630e1f9a03499c19c https://repo.anaconda.com/pkgs/main/osx-64/brotli-1.0.9-hca72f7f_7.conda#68e54d12ec67591deb2ffd70348fb00f https://repo.anaconda.com/pkgs/main/osx-64/libtiff-4.5.0-hcec6c5f_2.conda#f0b033a82af1bd028f112cdecef1fe0a -https://repo.anaconda.com/pkgs/main/osx-64/python-3.11.3-h1fd4e5f_0.conda#df6f985ea9100007789662afeca11311 +https://repo.anaconda.com/pkgs/main/osx-64/python-3.11.4-hf27a42d_0.conda#7ad1265574193e18e9beaa16879734dc https://repo.anaconda.com/pkgs/main/noarch/appdirs-1.4.4-pyhd3eb1b0_0.conda#5673d98d06171cb6eed03a6736845c4d https://repo.anaconda.com/pkgs/main/osx-64/attrs-22.1.0-py311hecd8cb5_0.conda#d87b931f00c25263ede3d7ec691389af https://repo.anaconda.com/pkgs/main/osx-64/certifi-2023.5.7-py311hecd8cb5_0.conda#c7cb5a9de1041b8b59f92089bd9aa55e https://repo.anaconda.com/pkgs/main/noarch/charset-normalizer-2.0.4-pyhd3eb1b0_0.conda#e7a441d94234b2b5fafee06e25dbf076 https://repo.anaconda.com/pkgs/main/osx-64/coverage-7.2.2-py311h6c40b1e_0.conda#e15605553450156cf75c3ae38a920475 https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda#f5e365d2cdb66d547eb8c3ab93843aab -https://repo.anaconda.com/pkgs/main/osx-64/cython-0.29.33-py311hcec6c5f_0.conda#9865281df3b2e61f46dc189ae46c5abc +https://repo.anaconda.com/pkgs/main/osx-64/cython-0.29.35-py311hcec6c5f_0.conda#77741cf35f554c28746a4a8151809123 https://repo.anaconda.com/pkgs/main/noarch/execnet-1.9.0-pyhd3eb1b0_0.conda#f895937671af67cebb8af617494b3513 https://repo.anaconda.com/pkgs/main/osx-64/idna-3.4-py311hecd8cb5_0.conda#48ab3e9b53e5607abe86a920cd37e13a https://repo.anaconda.com/pkgs/main/noarch/iniconfig-1.1.1-pyhd3eb1b0_0.tar.bz2#e40edff2c5708f342cef43c7f280c507 @@ -74,12 +74,12 @@ https://repo.anaconda.com/pkgs/main/osx-64/pillow-9.4.0-py311hcec6c5f_0.conda#fc https://repo.anaconda.com/pkgs/main/osx-64/pytest-7.3.1-py311hecd8cb5_0.conda#0247a6236ee44b38f6f0dc54ca3cbe7a https://repo.anaconda.com/pkgs/main/noarch/python-dateutil-2.8.2-pyhd3eb1b0_0.conda#211ee00320b08a1ac9fea6677649f6c9 https://repo.anaconda.com/pkgs/main/osx-64/brotlipy-0.7.0-py311h6c40b1e_1002.conda#214a3acdf6f828a764263d430826688b -https://repo.anaconda.com/pkgs/main/osx-64/cryptography-39.0.1-py311hf6deb26_0.conda#baf00061474e2c639029b0208d3eaf2e +https://repo.anaconda.com/pkgs/main/osx-64/cryptography-39.0.1-py311hf6deb26_2.conda#7b461f984b7da7b2f9ca32a3487ca288 https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-4.0.0-py311hecd8cb5_0.conda#c63893569d344f4297f2ae08e0387ccf https://repo.anaconda.com/pkgs/main/noarch/pytest-forked-1.3.0-pyhd3eb1b0_0.tar.bz2#07970bffdc78f417d7f8f1c7e620f5c4 https://repo.anaconda.com/pkgs/main/osx-64/pyopenssl-23.0.0-py311hecd8cb5_0.conda#d034f753f088967f765030dc5742c1d7 https://repo.anaconda.com/pkgs/main/noarch/pytest-xdist-2.5.0-pyhd3eb1b0_0.conda#d15cdc4207bcf8ca920822597f1d138d -https://repo.anaconda.com/pkgs/main/osx-64/urllib3-1.26.15-py311hecd8cb5_0.conda#2ce7c8e3fe61096e275f3d078485f7b6 +https://repo.anaconda.com/pkgs/main/osx-64/urllib3-1.26.16-py311hecd8cb5_0.conda#8bb53507a1d3ab7602adfc54f29adfe7 https://repo.anaconda.com/pkgs/main/osx-64/requests-2.29.0-py311hecd8cb5_0.conda#5ea75ca544f2a7b0a2660368bf886006 https://repo.anaconda.com/pkgs/main/noarch/pooch-1.4.0-pyhd3eb1b0_0.conda#69ec83cb3d152f9e854115555004f368 https://repo.anaconda.com/pkgs/main/osx-64/bottleneck-1.3.5-py311hb9e55a9_0.conda#5aa1b58b421d4608b16184f8468253ef diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index eab6dc087f26d..6943e080317d7 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -3,7 +3,7 @@ # input_hash: 61862ec58344ddfaad255f4687ca311eb7e2e61001e209d63f0cc92f97178848 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 -https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.01.10-h06a4308_0.conda#7704989a2ccf6c1f5a50c985509841c4 +https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.05.30-h06a4308_0.conda#979be8dd2368decd342b13e01540d297 https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.38-h1181459_1.conda#68eedfd9c06f2b0e6888d8db345b7f5b https://repo.anaconda.com/pkgs/main/noarch/tzdata-2023c-h04d1e81_0.conda#29db02adf8808f7c64642cead3e28acd https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b372c0eea9b60732fdae4b817a63c8cd @@ -12,39 +12,39 @@ https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d28 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda#06e288f9250abef59b9a367d151fc339 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https://files.pythonhosted.org/packages/a7/4b/8b78d126e275efa2379b1c2e09dc52cf70df16fc3b90613ef82531499d73/pytest_cov-4.1.0-py3-none-any.whl#sha256=6ba70b9e97e69fcc3fb45bfeab2d0a138fb65c4d0d6a41ef33983ad114be8c3a # pip pytest-forked @ https://files.pythonhosted.org/packages/f4/af/9c0bda43e486a3c9bf1e0f876d0f241bc3f229d7d65d09331a0868db9629/pytest_forked-1.6.0-py3-none-any.whl#sha256=810958f66a91afb1a1e2ae83089d8dc1cd2437ac96b12963042fbb9fb4d16af0 # pip scikit-image @ https://files.pythonhosted.org/packages/19/bd/a53569a0a698d925eb46dbea0bd3b6b62e7287a9ec88b5a03efa8ebd5b14/scikit_image-0.21.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=78b1e96c59cab640ca5c5b22c501524cfaf34cbe0cb51ba73bd9a9ede3fb6e1d -# pip scikit-learn @ https://files.pythonhosted.org/packages/81/84/756be2b975959a5f94124d5584ead75d7ca99184f2d16664a0157b274b9a/scikit_learn-1.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ea061bf0283bf9a9f36ea3c5d3231ba2176221bbd430abd2603b1c3b2ed85c89 # pip sphinx @ https://files.pythonhosted.org/packages/4b/a9/9760e8373a11a62f5ef66684771b0a5b2c4a699bf0dbbc650ca2b75cec36/sphinx-7.0.1-py3-none-any.whl#sha256=60c5e04756c1709a98845ed27a2eed7a556af3993afb66e77fec48189f742616 -# pip lightgbm @ https://files.pythonhosted.org/packages/38/5c/d9773cf0ea7938f3b777eaacc6f9d58f69ca76a667771364ffefed9095b4/lightgbm-3.3.5-py3-none-manylinux1_x86_64.whl#sha256=044f65664c1a32c98cb619bafa97d8cd9d93c2c2d5053376aadfe509a3a3e7fa # pip numpydoc @ https://files.pythonhosted.org/packages/c4/81/ad9b8837442ff451eca82515b41ac425f87acff7e2fc016fd1bda13fc01a/numpydoc-1.5.0-py3-none-any.whl#sha256=c997759fb6fc32662801cece76491eedbc0ec619b514932ffd2b270ae89c07f9 # pip pytest-xdist @ https://files.pythonhosted.org/packages/21/08/b1945d4b4986eb1aa10cf84efc5293bba39da80a2f95db3573dd90678408/pytest_xdist-2.5.0-py3-none-any.whl#sha256=6fe5c74fec98906deb8f2d2b616b5c782022744978e7bd4695d39c8f42d0ce65 diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 7861c2e43cf99..f6e10e7cab325 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -3,7 +3,7 @@ # input_hash: d7687370ba8c822d5b621703d51324b6767f15f0fc49177381f2a0a81a756684 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 -https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.01.10-h06a4308_0.conda#7704989a2ccf6c1f5a50c985509841c4 +https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.05.30-h06a4308_0.conda#979be8dd2368decd342b13e01540d297 https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.38-h1181459_1.conda#68eedfd9c06f2b0e6888d8db345b7f5b https://repo.anaconda.com/pkgs/main/noarch/tzdata-2023c-h04d1e81_0.conda#29db02adf8808f7c64642cead3e28acd https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b372c0eea9b60732fdae4b817a63c8cd @@ -14,31 +14,31 @@ https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h7b6447c_0.conda#9303f4 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda#06e288f9250abef59b9a367d151fc339 https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-1.1.1t-h7f8727e_0.conda#0410db682c02665511bd4203ade48a32 +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.9-h7f8727e_0.conda#dc136c954b2d644c79fee77e57e183dc https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.2-h5eee18b_0.conda#bcd31de48a0dcb44bc5b99675800c5cc https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31fbfbb5057c92fa845ad6adef93 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda#c7086c9ceb6cfe1c4c729a774a2d88a5 -https://repo.anaconda.com/pkgs/main/linux-64/python-3.11.3-h7a1cb2a_0.conda#d4474259a2525cc6fb272f02ca02873e +https://repo.anaconda.com/pkgs/main/linux-64/python-3.11.4-h955ad1f_0.conda#3c8fb4ea9fdf9a4d27d16cd5fb6fdd16 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-67.8.0-py311h06a4308_0.conda#b65f6b9c4547f1fd81af11d4e8b649c4 https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.38.4-py311h06a4308_0.conda#b3d14884810655c572ea9a91df7de205 -https://repo.anaconda.com/pkgs/main/linux-64/pip-23.0.1-py311h06a4308_0.conda#06ec6690fc9814ab769a62dfeeb26419 +https://repo.anaconda.com/pkgs/main/linux-64/pip-23.1.2-py311h06a4308_0.conda#143f7a0b817b07755e1d2c42f5365e7d # pip alabaster @ https://files.pythonhosted.org/packages/64/88/c7083fc61120ab661c5d0b82cb77079fc1429d3f913a456c1c82cf4658f7/alabaster-0.7.13-py3-none-any.whl#sha256=1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3 # pip babel @ https://files.pythonhosted.org/packages/df/c4/1088865e0246d7ecf56d819a233ab2b72f7d6ab043965ef327d0731b5434/Babel-2.12.1-py3-none-any.whl#sha256=b4246fb7677d3b98f501a39d43396d3cafdc8eadb045f4a31be01863f655c610 # pip certifi @ https://files.pythonhosted.org/packages/9d/19/59961b522e6757f0c9097e4493fa906031b95b3ebe9360b2c3083561a6b4/certifi-2023.5.7-py3-none-any.whl#sha256=c6c2e98f5c7869efca1f8916fed228dd91539f9f1b444c314c06eef02980c716 -# pip charset-normalizer @ https://files.pythonhosted.org/packages/18/36/7ae10a3dd7f9117b61180671f8d1e4802080cca88ad40aaabd3dad8bab0e/charset_normalizer-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=0ca564606d2caafb0abe6d1b5311c2649e8071eb241b2d64e75a0d0065107e62 +# pip charset-normalizer @ https://files.pythonhosted.org/packages/bc/85/ef25d4ba14c7653c3020a1c6e1a7413e6791ef36a0ac177efa605fc2c737/charset_normalizer-3.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=246de67b99b6851627d945db38147d1b209a899311b1305dd84916f2b88526c6 # pip coverage @ https://files.pythonhosted.org/packages/a7/cd/3ce94ad9d407a052dc2a74fbeb1c7947f442155b28264eb467ee78dea812/coverage-7.2.7-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=63426706118b7f5cf6bb6c895dc215d8a418d5952544042c8a2d9fe87fcf09cb # pip docutils @ https://files.pythonhosted.org/packages/26/87/f238c0670b94533ac0353a4e2a1a771a0cc73277b88bff23d3ae35a256c1/docutils-0.20.1-py3-none-any.whl#sha256=96f387a2c5562db4476f09f13bbab2192e764cac08ebbf3a34a95d9b1e4a59d6 -# pip execnet @ https://files.pythonhosted.org/packages/81/c0/3072ecc23f4c5e0a1af35e3a222855cfd9c80a1a105ca67be3b6172637dd/execnet-1.9.0-py2.py3-none-any.whl#sha256=a295f7cc774947aac58dde7fdc85f4aa00c42adf5d8f5468fc630c1acf30a142 +# pip execnet @ https://files.pythonhosted.org/packages/e8/9c/a079946da30fac4924d92dbc617e5367d454954494cf1e71567bcc4e00ee/execnet-2.0.2-py3-none-any.whl#sha256=88256416ae766bc9e8895c76a87928c0012183da3cc4fc18016e6f050e025f41 # pip idna @ https://files.pythonhosted.org/packages/fc/34/3030de6f1370931b9dbb4dad48f6ab1015ab1d32447850b9fc94e60097be/idna-3.4-py3-none-any.whl#sha256=90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl#sha256=b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374 # pip markupsafe @ https://files.pythonhosted.org/packages/fe/21/2eff1de472ca6c99ec3993eab11308787b9879af9ca8bbceb4868cf4f2ca/MarkupSafe-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bfce63a9e7834b12b87c64d6b155fdd9b3b96191b6bd334bf37db7ff1fe457f2 # pip packaging @ https://files.pythonhosted.org/packages/ab/c3/57f0601a2d4fe15de7a553c00adbc901425661bf048f2a22dfc500caf121/packaging-23.1-py3-none-any.whl#sha256=994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61 -# pip platformdirs @ https://files.pythonhosted.org/packages/89/7e/c6ff9ddcf93b9b36c90d88111c4db354afab7f9a58c7ac3257fa717f1268/platformdirs-3.5.1-py3-none-any.whl#sha256=e2378146f1964972c03c085bb5662ae80b2b8c06226c54b2ff4aa9483e8a13a5 -# pip pluggy @ https://files.pythonhosted.org/packages/9e/01/f38e2ff29715251cf25532b9082a1589ab7e4f571ced434f98d0139336dc/pluggy-1.0.0-py2.py3-none-any.whl#sha256=74134bbf457f031a36d68416e1509f34bd5ccc019f0bcc952c7b909d06b37bd3 +# pip platformdirs @ https://files.pythonhosted.org/packages/6d/a7/47b7088a28c8fe5775eb15281bf44d39facdbe4bc011a95ccb89390c2db9/platformdirs-3.9.1-py3-none-any.whl#sha256=ad8291ae0ae5072f66c16945166cb11c63394c7a3ad1b1bc9828ca3162da8c2f +# pip pluggy @ https://files.pythonhosted.org/packages/51/32/4a79112b8b87b21450b066e102d6608907f4c885ed7b04c3fdb085d4d6ae/pluggy-1.2.0-py3-none-any.whl#sha256=c2fd55a7d7a3863cba1a013e4e2414658b1d07b6bc57b3919e0c63c9abb99849 # pip py @ https://files.pythonhosted.org/packages/f6/f0/10642828a8dfb741e5f3fbaac830550a518a775c7fff6f04a007259b0548/py-1.11.0-py2.py3-none-any.whl#sha256=607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378 # pip pygments @ https://files.pythonhosted.org/packages/34/a7/37c8d68532ba71549db4212cb036dbd6161b40e463aba336770e80c72f84/Pygments-2.15.1-py3-none-any.whl#sha256=db2db3deb4b4179f399a09054b023b6a586b76499d36965813c71aa8ed7b5fd1 # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 @@ -49,10 +49,10 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.0.1-py311h06a4308_0.conda#06 # pip sphinxcontrib-jsmath @ https://files.pythonhosted.org/packages/c2/42/4c8646762ee83602e3fb3fbe774c2fac12f317deb0b5dbeeedd2d3ba4b77/sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl#sha256=2ec2eaebfb78f3f2078e73666b1415417a116cc848b72e5172e596c871103178 # pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/2b/14/05f9206cf4e9cfca1afb5fd224c7cd434dcc3a433d6d9e4e0264d29c6cdb/sphinxcontrib_qthelp-1.0.3-py2.py3-none-any.whl#sha256=bd9fc24bcb748a8d51fd4ecaade681350aa63009a347a8c14e637895444dfab6 # pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/c6/77/5464ec50dd0f1c1037e3c93249b040c8fc8078fdda97530eeb02424b6eea/sphinxcontrib_serializinghtml-1.1.5-py2.py3-none-any.whl#sha256=352a9a00ae864471d3a7ead8d7d79f5fc0b57e8b3f95e9867eb9eb28999b92fd -# pip threadpoolctl @ https://files.pythonhosted.org/packages/61/cf/6e354304bcb9c6413c4e02a747b600061c21d38ba51e7e544ac7bc66aecc/threadpoolctl-3.1.0-py3-none-any.whl#sha256=8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b -# pip urllib3 @ https://files.pythonhosted.org/packages/4b/1d/f8383ef593114755429c307449e7717b87044b3bcd5f7860b89b1f759e34/urllib3-2.0.2-py3-none-any.whl#sha256=d055c2f9d38dc53c808f6fdc8eab7360b6fdbbde02340ed25cfbcd817c62469e +# pip threadpoolctl @ https://files.pythonhosted.org/packages/81/12/fd4dea011af9d69e1cad05c75f3f7202cdcbeac9b712eea58ca779a72865/threadpoolctl-3.2.0-py3-none-any.whl#sha256=2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032 +# pip urllib3 @ https://files.pythonhosted.org/packages/8a/03/ad9306a50d05c166e3456fe810f33cee2b8b2a7a6818ec5d4908c4ec6b36/urllib3-2.0.3-py3-none-any.whl#sha256=48e7fafa40319d358848e1bc6809b208340fafe2096f1725d05d67443d0483d1 # pip jinja2 @ https://files.pythonhosted.org/packages/bc/c3/f068337a370801f372f2f8f6bad74a5c140f6fda3d9de154052708dd3c65/Jinja2-3.1.2-py3-none-any.whl#sha256=6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61 -# pip pytest @ https://files.pythonhosted.org/packages/1b/d1/72df649a705af1e3a09ffe14b0c7d3be1fd730da6b98beb4a2ed26b8a023/pytest-7.3.1-py3-none-any.whl#sha256=3799fa815351fea3a5e96ac7e503a96fa51cc9942c3753cda7651b93c1cfa362 +# pip pytest @ https://files.pythonhosted.org/packages/33/b2/741130cbcf2bbfa852ed95a60dc311c9e232c7ed25bac3d9b8880a8df4ae/pytest-7.4.0-py3-none-any.whl#sha256=78bf16451a2eb8c7a2ea98e32dc119fd2aa758f1d5d66dbf0a59d69a3969df32 # pip python-dateutil @ https://files.pythonhosted.org/packages/36/7a/87837f39d0296e723bb9b62bbb257d0355c7f6128853c78955f57342a56d/python_dateutil-2.8.2-py2.py3-none-any.whl#sha256=961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9 # pip requests @ 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+https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.7.2-py39hfcdda22_0.conda#27f24b3402229299a3d5fdeee0b417b9 https://conda.anaconda.org/conda-forge/noarch/pooch-1.7.0-pyha770c72_3.conda#5936894aade8240c867d292aa0d980c6 -https://conda.anaconda.org/conda-forge/noarch/pytest-forked-1.6.0-pyhd8ed1ab_0.conda#a46947638b6e005b63d2d6271da529b0 -https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.7.1-py39h4162558_0.conda#b6ca076a90a7f2a8d7ff976d243dd4c5 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.tar.bz2#1fdd1f3baccf0deb647385c677a1a48e -https://conda.anaconda.org/conda-forge/linux-64/scipy-1.10.1-py39h129f8d9_3.conda#ccc3e84894f1a2b3fea200b4e8946903 -https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.0.0-py39h6728ab1_0.conda#ee14077fae1c48e0ca5154f5a5427521 +https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.7.2-py39h4162558_0.conda#839abb578897996f795648dc0b621b30 +https://conda.anaconda.org/conda-forge/linux-64/scipy-1.11.1-py39h129f8d9_0.conda#cf3f3a39e9bd095064c903cd2fc6a4a0 +https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.0.1-py39h00faaa6_0.conda#d5b46d053aa61bb420230a041afcb136 diff --git a/build_tools/azure/python_nogil_lock.txt b/build_tools/azure/python_nogil_lock.txt index cd44de206adb4..044ac475c1ccf 100644 --- a/build_tools/azure/python_nogil_lock.txt +++ b/build_tools/azure/python_nogil_lock.txt @@ -8,20 +8,22 @@ --extra-index-url https://pypi.org/simple contourpy==1.0.7 - # via matplotlib + # via + # -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt + # matplotlib cycler==0.11.0 # via matplotlib cython==0.29.33 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt -exceptiongroup==1.1.1 +exceptiongroup==1.1.2 # via pytest -execnet==1.9.0 +execnet==2.0.2 # via pytest-xdist -fonttools==4.39.4 +fonttools==4.41.0 # via matplotlib iniconfig==2.0.0 # via pytest -joblib==1.2.0 +joblib==1.3.1 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt kiwisolver==1.4.4 # via matplotlib @@ -39,15 +41,15 @@ packaging==23.1 # pytest pillow==9.5.0 # via matplotlib -pluggy==1.0.0 +pluggy==1.2.0 # via pytest -pyparsing==3.0.9 +pyparsing==3.1.0 # via matplotlib -pytest==7.3.1 +pytest==7.4.0 # via # -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt # pytest-xdist -pytest-xdist==3.3.0 +pytest-xdist==3.3.1 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt python-dateutil==2.8.2 # via matplotlib @@ -55,7 +57,7 @@ scipy==1.9.3 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt six==1.16.0 # via python-dateutil -threadpoolctl==3.1.0 +threadpoolctl==3.2.0 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt tomli==2.0.1 # via pytest diff --git a/build_tools/azure/python_nogil_requirements.txt b/build_tools/azure/python_nogil_requirements.txt index 970059ede81aa..de7e729f77263 100644 --- a/build_tools/azure/python_nogil_requirements.txt +++ b/build_tools/azure/python_nogil_requirements.txt @@ -6,6 +6,9 @@ # the latest cython will be picked up from PyPI, rather than the one from the # python-nogil index matplotlib +# 2023-07-15 contourpy 1.1 needs meson which needs a recent pybind version +# which is not available for python/nogil ... +contourpy<1.1 numpy scipy cython diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index 255d037ccbaee..4242a0b1c2384 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -2,13 +2,13 @@ # This file is autogenerated by pip-compile with Python 3.10 # by the following command: # -# pip-compile --output-file=build_tools/azure/ubuntu_atlas_lock.txt build_tools/azure/ubuntu_atlas_requirements.txt +# pip-compile --config=pyproject.toml --output-file=build_tools/azure/ubuntu_atlas_lock.txt build_tools/azure/ubuntu_atlas_requirements.txt # -cython==0.29.35 +cython==0.29.36 # via -r build_tools/azure/ubuntu_atlas_requirements.txt -exceptiongroup==1.1.1 +exceptiongroup==1.1.2 # via pytest -execnet==1.9.0 +execnet==2.0.2 # via pytest-xdist iniconfig==2.0.0 # via pytest @@ -16,11 +16,11 @@ joblib==1.1.1 # via -r build_tools/azure/ubuntu_atlas_requirements.txt packaging==23.1 # via pytest -pluggy==1.0.0 +pluggy==1.2.0 # via pytest py==1.11.0 # via pytest-forked -pytest==7.3.1 +pytest==7.4.0 # via # -r build_tools/azure/ubuntu_atlas_requirements.txt # pytest-forked diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 24af483b17aab..1caa857463952 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -8,25 +8,25 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-hab24e00_0.tar.bz2#19410c3df09dfb12d1206132a1d357c5 -https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_15.tar.bz2#5dd5127afd710f91f6a75821bac0a4f0 +https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_16.conda#7ca122655873935e02c91279c5b03c8c https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda#7aca3059a1729aa76c597603f10b0dd3 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-devel_linux-64-11.4.0-h922705a_0.conda#4b2b4bbd3b68a5fd72e648ea70208a97 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-devel_linux-64-12.3.0-h8bca6fd_0.conda#b9ae31bc2e565684ebaf82d4bd954d55 https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.1.0-h15d22d2_0.conda#afb656a334c409dd9805508af1c89c7a -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-devel_linux-64-11.4.0-h922705a_0.conda#f6ab69b69e09f83024d1d79a72700c5a +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-devel_linux-64-12.3.0-h8bca6fd_0.conda#7c80158949230e6d837186b20b2fcf13 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.1.0-hfd8a6a1_0.conda#067bcc23164642f4c226da631f2a2e1d https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.9-3_cp39.conda#0dd193187d54e585cac7eab942a8847e https://conda.anaconda.org/conda-forge/noarch/tzdata-2023c-h71feb2d_0.conda#939e3e74d8be4dac89ce83b20de2492a https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.1.0-h69a702a_0.conda#506dc07710dd5b0ba63cbf134897fc10 https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.1.0-he5830b7_0.conda#56ca14d57ac29a75d23a39eb3ee0ddeb -https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.12-he073ed8_15.tar.bz2#66c192522eacf5bb763568b4e415d133 +https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.12-he073ed8_16.conda#071ea8dceff4d30ac511f4a2f8437cd1 https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-hf600244_0.conda#33084421a8c0af6aef1b439707f7662a https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/binutils-2.40-hdd6e379_0.conda#ccc940fddbc3fcd3d79cd4c654c4b5c4 https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hbdbef99_0.conda#8dd723ef0716b6d102d57bbacbe92b22 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.1.0-he5830b7_0.conda#cd93f779ff018dd85c7544c015c9db3c -https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.8-h166bdaf_0.tar.bz2#be733e69048951df1e4b4b7bb8c7666f +https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.9-hd590300_0.conda#a0c6f0e7e1a467f5678f94dea18c8aa7 https://conda.anaconda.org/conda-forge/linux-64/aom-3.5.0-h27087fc_0.tar.bz2#a08150fd2298460cd1fcccf626305642 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h7f98852_4.tar.bz2#a1fd65c7ccbf10880423d82bca54eb54 @@ -42,7 +42,7 @@ https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2#a8832b479f93521a9e7b5b743803be51 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2#76bbff344f0134279f225174e9064c8f https://conda.anaconda.org/conda-forge/linux-64/libaec-1.0.6-hcb278e6_1.conda#0f683578378cddb223e7fd24f785ab2a -https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.0.9-h166bdaf_8.tar.bz2#9194c9bf9428035a05352d031462eae4 +https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.0.9-h166bdaf_9.conda#61641e239f96eae2b8492dc7e755828c https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.18-h0b41bf4_0.conda#6aa9c9de5542ecb07fdda9ca626252d8 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-h516909a_1.tar.bz2#6f8720dff19e17ce5d48cfe7f3d2f0a3 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.5.0-hcb278e6_1.conda#6305a3dd2752c76335295da4e581f2fd @@ -53,9 +53,9 @@ https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.0-h7f98852_0.tar.bz2# https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.4-h7f98852_1.tar.bz2#6e8cc2173440d77708196c5b93771680 https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.23-pthreads_h80387f5_0.conda#9c5ea51ccb8ffae7d06c645869d24ce6 https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2#15345e56d527b330e1cacbdf58676e8f -https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-11.4.0-h4dcbe23_0.conda#62790b01a5710ee704f48b01f64f4743 +https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-12.3.0-h0f45ef3_0.conda#bbc8fef17925480272a671b1d83431fa https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b -https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.3.0-h0b41bf4_0.conda#0d4a7508d8c6c65314f2b9c1f56ad408 +https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.3.1-hd590300_0.conda#82bf6f63eb15ef719b556b63feec3a77 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@@ -126,6 +126,10 @@ def remove_from(alist, to_remove): "conda_dependencies": common_dependencies + ["ccache"], "package_constraints": { "blas": "[build=mkl]", + # TODO: temporary pin for numpy to avoid what seems a loky issue, + # for more details see + # https://github.com/scikit-learn/scikit-learn/pull/26845#issuecomment-1639917135 + "numpy": "<1.25", }, }, { @@ -550,15 +554,15 @@ def check_conda_version(): # Avoid issues with glibc (https://github.com/conda/conda-lock/issues/292) # or osx (https://github.com/conda/conda-lock/issues/408) virtual package. # The glibc one has been fixed in conda 23.1.0 and the osx has been fixed - # in main and will be fixed when conda > 23.5.0 is released. + # in main and will be fixed when conda >= 23.6 is released. conda_info_output = execute_command(["conda", "info", "--json"]) conda_info = json.loads(conda_info_output) conda_version = Version(conda_info["conda_version"]) - if Version("22.9.0") < conda_version <= Version("23.5.0"): + if Version("22.9.0") < conda_version < Version("23.6"): raise RuntimeError( - f"conda version should be <= 22.9.0 or > 23.5.0, got: {conda_version}" + f"conda version should be <= 22.9.0 or >= 23.6 got: {conda_version}" ) diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py index 849bde8db722d..a4829c42b505b 100644 --- a/sklearn/_min_dependencies.py +++ b/sklearn/_min_dependencies.py @@ -51,7 +51,7 @@ "plotly": ("5.14.0", "docs, examples"), # XXX: Pin conda-lock to the latest released version (needs manual update # from time to time) - "conda-lock": ("2.0.0", "maintenance"), + "conda-lock": ("2.1.1", "maintenance"), } diff --git a/sklearn/cluster/tests/test_spectral.py b/sklearn/cluster/tests/test_spectral.py index 33968a542691a..3813a45c13719 100644 --- a/sklearn/cluster/tests/test_spectral.py +++ b/sklearn/cluster/tests/test_spectral.py @@ -224,6 +224,10 @@ def test_discretize(n_samples): @pytest.mark.filterwarnings( "ignore:scipy.linalg.pinv2 is deprecated:DeprecationWarning:pyamg.*" ) +# TODO: Remove when pyamg removes the use of np.find_common_type +@pytest.mark.filterwarnings( + "ignore:np.find_common_type is deprecated:DeprecationWarning:pyamg.*" +) def test_spectral_clustering_with_arpack_amg_solvers(): # Test that spectral_clustering is the same for arpack and amg solver # Based on toy example from plot_segmentation_toy.py diff --git a/sklearn/manifold/tests/test_spectral_embedding.py b/sklearn/manifold/tests/test_spectral_embedding.py index 7e6172d9ad1fa..e194d30529205 100644 --- a/sklearn/manifold/tests/test_spectral_embedding.py +++ b/sklearn/manifold/tests/test_spectral_embedding.py @@ -243,6 +243,9 @@ def test_spectral_embedding_callable_affinity(X, seed=36): @pytest.mark.filterwarnings( "ignore:scipy.linalg.pinv2 is deprecated:DeprecationWarning:pyamg.*" ) +@pytest.mark.filterwarnings( + "ignore:np.find_common_type is deprecated:DeprecationWarning:pyamg.*" +) @pytest.mark.skipif( not pyamg_available, reason="PyAMG is required for the tests in this function." ) @@ -300,6 +303,10 @@ def test_spectral_embedding_amg_solver(dtype, seed=36): @pytest.mark.skipif( not pyamg_available, reason="PyAMG is required for the tests in this function." ) +# TODO: Remove when pyamg removes the use of np.find_common_type +@pytest.mark.filterwarnings( + "ignore:np.find_common_type is deprecated:DeprecationWarning:pyamg.*" +) @pytest.mark.parametrize("dtype", (np.float32, np.float64)) def test_spectral_embedding_amg_solver_failure(dtype, seed=36): # Non-regression test for amg solver failure (issue #13393 on github) @@ -464,6 +471,10 @@ def test_error_pyamg_not_available(): se_precomp.fit_transform(S) +# TODO: Remove when pyamg removes the use of np.find_common_type +@pytest.mark.filterwarnings( + "ignore:np.find_common_type is deprecated:DeprecationWarning:pyamg.*" +) @pytest.mark.parametrize("solver", ["arpack", "amg", "lobpcg"]) def test_spectral_eigen_tol_auto(monkeypatch, solver): """Test that `eigen_tol="auto"` is resolved correctly""" From 934f8bade19a77f2c351cc4c61f14f0bb9e22411 Mon Sep 17 00:00:00 2001 From: Florian Idelberger Date: Tue, 18 Jul 2023 20:59:30 +0200 Subject: [PATCH 100/264] DOC Remove reference to CharNGramAnalyzer (#26846) Co-authored-by: Thomas J. Fan --- doc/tutorial/text_analytics/working_with_text_data.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/tutorial/text_analytics/working_with_text_data.rst b/doc/tutorial/text_analytics/working_with_text_data.rst index a878b766bd4fa..61fd02da05548 100644 --- a/doc/tutorial/text_analytics/working_with_text_data.rst +++ b/doc/tutorial/text_analytics/working_with_text_data.rst @@ -520,7 +520,7 @@ Exercise 1: Language identification ----------------------------------- - Write a text classification pipeline using a custom preprocessor and - ``CharNGramAnalyzer`` using data from Wikipedia articles as training set. + ``TfidfVectorizer`` set up to use character based n-grams, using data from Wikipedia articles as the training set. - Evaluate the performance on some held out test set. From 5082cbfd0e71c84d04af28dbd72f265d47aea5c9 Mon Sep 17 00:00:00 2001 From: Yang Tao <56955779+mchikyt3@users.noreply.github.com> Date: Thu, 20 Jul 2023 11:43:47 +0100 Subject: [PATCH 101/264] FIX Correct the initiatialization of `precisions_cholesky_` from `precisions_init` (#26416) --- sklearn/mixture/_gaussian_mixture.py | 70 +++++++++++++++---- .../mixture/tests/test_gaussian_mixture.py | 52 ++++++++++++++ 2 files changed, 110 insertions(+), 12 deletions(-) diff --git a/sklearn/mixture/_gaussian_mixture.py b/sklearn/mixture/_gaussian_mixture.py index e528d780bc6b5..9fcc791032c48 100644 --- a/sklearn/mixture/_gaussian_mixture.py +++ b/sklearn/mixture/_gaussian_mixture.py @@ -348,6 +348,61 @@ def _compute_precision_cholesky(covariances, covariance_type): return precisions_chol +def _flipudlr(array): + """Reverse the rows and columns of an array.""" + return np.flipud(np.fliplr(array)) + + +def _compute_precision_cholesky_from_precisions(precisions, covariance_type): + r"""Compute the Cholesky decomposition of precisions using precisions themselves. + + As implemented in :func:`_compute_precision_cholesky`, the `precisions_cholesky_` is + an upper-triangular matrix for each Gaussian component, which can be expressed as + the $UU^T$ factorization of the precision matrix for each Gaussian component, where + $U$ is an upper-triangular matrix. + + In order to use the Cholesky decomposition to get $UU^T$, the precision matrix + $\Lambda$ needs to be permutated such that its rows and columns are reversed, which + can be done by applying a similarity transformation with an exchange matrix $J$, + where the 1 elements reside on the anti-diagonal and all other elements are 0. In + particular, the Cholesky decomposition of the transformed precision matrix is + $J\Lambda J=LL^T$, where $L$ is a lower-triangular matrix. Because $\Lambda=UU^T$ + and $J=J^{-1}=J^T$, the `precisions_cholesky_` for each Gaussian component can be + expressed as $JLJ$. + + Refer to #26415 for details. + + Parameters + ---------- + precisions : array-like + The precision matrix of the current components. + The shape depends on the covariance_type. + + covariance_type : {'full', 'tied', 'diag', 'spherical'} + The type of precision matrices. + + Returns + ------- + precisions_cholesky : array-like + The cholesky decomposition of sample precisions of the current + components. The shape depends on the covariance_type. + """ + if covariance_type == "full": + precisions_cholesky = np.array( + [ + _flipudlr(linalg.cholesky(_flipudlr(precision), lower=True)) + for precision in precisions + ] + ) + elif covariance_type == "tied": + precisions_cholesky = _flipudlr( + linalg.cholesky(_flipudlr(precisions), lower=True) + ) + else: + precisions_cholesky = np.sqrt(precisions) + return precisions_cholesky + + ############################################################################### # Gaussian mixture probability estimators def _compute_log_det_cholesky(matrix_chol, covariance_type, n_features): @@ -723,19 +778,10 @@ def _initialize(self, X, resp): self.precisions_cholesky_ = _compute_precision_cholesky( covariances, self.covariance_type ) - elif self.covariance_type == "full": - self.precisions_cholesky_ = np.array( - [ - linalg.cholesky(prec_init, lower=True) - for prec_init in self.precisions_init - ] - ) - elif self.covariance_type == "tied": - self.precisions_cholesky_ = linalg.cholesky( - self.precisions_init, lower=True - ) else: - self.precisions_cholesky_ = np.sqrt(self.precisions_init) + self.precisions_cholesky_ = _compute_precision_cholesky_from_precisions( + self.precisions_init, self.covariance_type + ) def _m_step(self, X, log_resp): """M step. diff --git a/sklearn/mixture/tests/test_gaussian_mixture.py b/sklearn/mixture/tests/test_gaussian_mixture.py index e3289a2d4d73d..2f39033faed6b 100644 --- a/sklearn/mixture/tests/test_gaussian_mixture.py +++ b/sklearn/mixture/tests/test_gaussian_mixture.py @@ -1326,6 +1326,58 @@ def test_gaussian_mixture_precisions_init_diag(): ) +def _generate_data(seed, n_samples, n_features, n_components): + """Randomly generate samples and responsibilities.""" + rs = np.random.RandomState(seed) + X = rs.random_sample((n_samples, n_features)) + resp = rs.random_sample((n_samples, n_components)) + resp /= resp.sum(axis=1)[:, np.newaxis] + return X, resp + + +def _calculate_precisions(X, resp, covariance_type): + """Calculate precision matrix of X and its Cholesky decomposition + for the given covariance type. + """ + reg_covar = 1e-6 + weights, means, covariances = _estimate_gaussian_parameters( + X, resp, reg_covar, covariance_type + ) + precisions_cholesky = _compute_precision_cholesky(covariances, covariance_type) + + _, n_components = resp.shape + # Instantiate a `GaussianMixture` model in order to use its + # `_set_parameters` method to return the `precisions_` and + # `precisions_cholesky_` from matching the `covariance_type` + # provided. + gmm = GaussianMixture(n_components=n_components, covariance_type=covariance_type) + params = (weights, means, covariances, precisions_cholesky) + gmm._set_parameters(params) + return gmm.precisions_, gmm.precisions_cholesky_ + + +@pytest.mark.parametrize("covariance_type", COVARIANCE_TYPE) +def test_gaussian_mixture_precisions_init(covariance_type, global_random_seed): + """Non-regression test for #26415.""" + + X, resp = _generate_data( + seed=global_random_seed, + n_samples=100, + n_features=3, + n_components=4, + ) + + precisions_init, desired_precisions_cholesky = _calculate_precisions( + X, resp, covariance_type + ) + gmm = GaussianMixture( + covariance_type=covariance_type, precisions_init=precisions_init + ) + gmm._initialize(X, resp) + actual_precisions_cholesky = gmm.precisions_cholesky_ + assert_allclose(actual_precisions_cholesky, desired_precisions_cholesky) + + def test_gaussian_mixture_single_component_stable(): """ Non-regression test for #23032 ensuring 1-component GM works on only a From 739290cee5b5a32e215a3beb244c318d64b2fc82 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Fri, 21 Jul 2023 01:06:03 +0200 Subject: [PATCH 102/264] DOC Added information about space complexity to docs DBSCAN (#26783) Co-authored-by: Guillaume Lemaitre --- sklearn/cluster/_dbscan.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/sklearn/cluster/_dbscan.py b/sklearn/cluster/_dbscan.py index 7280bc31423ae..30205f70ae157 100644 --- a/sklearn/cluster/_dbscan.py +++ b/sklearn/cluster/_dbscan.py @@ -172,6 +172,9 @@ class DBSCAN(ClusterMixin, BaseEstimator): Finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density. + The worst case memory complexity of DBSCAN is :math:`O({n}^2)`, which can + occur when the `eps` param is large and `min_samples` is low. + Read more in the :ref:`User Guide `. Parameters @@ -184,8 +187,11 @@ class DBSCAN(ClusterMixin, BaseEstimator): and distance function. min_samples : int, default=5 - The number of samples (or total weight) in a neighborhood for a point - to be considered as a core point. This includes the point itself. + The number of samples (or total weight) in a neighborhood for a point to + be considered as a core point. This includes the point itself. If + `min_samples` is set to a higher value, DBSCAN will find denser clusters, + whereas if it is set to a lower value, the found clusters will be more + sparse. metric : str, or callable, default='euclidean' The metric to use when calculating distance between instances in a From 3ce2353ce1370e9a30e8ec4bcf27349ee5914ddb Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Fri, 21 Jul 2023 17:32:16 +1000 Subject: [PATCH 103/264] DOC Directly import label class in example (#26876) --- sklearn/preprocessing/_label.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py index d621c6c410153..f5044f9c9d417 100644 --- a/sklearn/preprocessing/_label.py +++ b/sklearn/preprocessing/_label.py @@ -56,8 +56,8 @@ class LabelEncoder(TransformerMixin, BaseEstimator): -------- `LabelEncoder` can be used to normalize labels. - >>> from sklearn import preprocessing - >>> le = preprocessing.LabelEncoder() + >>> from sklearn.preprocessing import LabelEncoder + >>> le = LabelEncoder() >>> le.fit([1, 2, 2, 6]) LabelEncoder() >>> le.classes_ @@ -70,7 +70,7 @@ class LabelEncoder(TransformerMixin, BaseEstimator): It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels. - >>> le = preprocessing.LabelEncoder() + >>> le = LabelEncoder() >>> le.fit(["paris", "paris", "tokyo", "amsterdam"]) LabelEncoder() >>> list(le.classes_) @@ -221,8 +221,8 @@ class LabelBinarizer(TransformerMixin, BaseEstimator): Examples -------- - >>> from sklearn import preprocessing - >>> lb = preprocessing.LabelBinarizer() + >>> from sklearn.preprocessing import LabelBinarizer + >>> lb = LabelBinarizer() >>> lb.fit([1, 2, 6, 4, 2]) LabelBinarizer() >>> lb.classes_ @@ -233,7 +233,7 @@ class LabelBinarizer(TransformerMixin, BaseEstimator): Binary targets transform to a column vector - >>> lb = preprocessing.LabelBinarizer() + >>> lb = LabelBinarizer() >>> lb.fit_transform(['yes', 'no', 'no', 'yes']) array([[1], [0], From d854262abcb4e44fd5f83eba2b9c4879f12673df Mon Sep 17 00:00:00 2001 From: Rahil Parikh <75483881+rprkh@users.noreply.github.com> Date: Fri, 21 Jul 2023 14:53:28 +0530 Subject: [PATCH 104/264] DOC update description of support_vectors_ (#26866) --- sklearn/svm/_classes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py index 7a54c02201ccb..3f9f7f956146e 100644 --- a/sklearn/svm/_classes.py +++ b/sklearn/svm/_classes.py @@ -774,7 +774,7 @@ class SVC(BaseSVC): Indices of support vectors. support_vectors_ : ndarray of shape (n_SV, n_features) - Support vectors. + Support vectors. An empty array if kernel is precomputed. n_support_ : ndarray of shape (n_classes,), dtype=int32 Number of support vectors for each class. From d9d2f7995636348979b31ccf2e4bee40992848da Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos Orfanos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Fri, 21 Jul 2023 20:08:43 +0200 Subject: [PATCH 105/264] DOC fix broken links (#26853) Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> --- doc/modules/ensemble.rst | 6 +++--- doc/whats_new/v0.22.rst | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst index c3ea63bc6e944..36eed98da0f6b 100644 --- a/doc/modules/ensemble.rst +++ b/doc/modules/ensemble.rst @@ -10,12 +10,12 @@ Ensembles: Gradient boosting, random forests, bagging, voting, stacking base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator. -Two very famous examples of ensemble methods are `gradient-boosted trees -`_ and `random forests `_. +Two very famous examples of ensemble methods are :ref:`gradient-boosted trees +` and :ref:`random forests `. More generally, ensemble models can be applied to any base learner beyond trees, in averaging methods such as :ref:`Bagging methods `, -`model stacking `_, or `Voting `_, or in +:ref:`model stacking `, or :ref:`Voting `, or in boosting, as :ref:`AdaBoost `. .. contents:: diff --git a/doc/whats_new/v0.22.rst b/doc/whats_new/v0.22.rst index fea27b0c1c1a4..da2f5e8796db8 100644 --- a/doc/whats_new/v0.22.rst +++ b/doc/whats_new/v0.22.rst @@ -392,7 +392,7 @@ Changelog - |Efficiency| :class:`decomposition.NMF` with `solver="mu"` fitted on sparse input matrices now uses batching to avoid briefly allocating an array with size - (#non-zero elements, n_components). :pr:`15257` by `Mart Willocx `_. + (#non-zero elements, n_components). :pr:`15257` by :user:`Mart Willocx `. - |Enhancement| :func:`decomposition.dict_learning` and :func:`decomposition.dict_learning_online` now accept `method_max_iter` and From 04c5593ac10a3892ad5ae2f264f81700349f964f Mon Sep 17 00:00:00 2001 From: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Date: Mon, 24 Jul 2023 14:22:25 +0800 Subject: [PATCH 106/264] FIX ravel prediction of `PLSRegression` when fitted on 1d `y` (#26602) --- sklearn/cross_decomposition/_pls.py | 7 ++++-- sklearn/cross_decomposition/tests/test_pls.py | 23 +++++++++++++++++++ 2 files changed, 28 insertions(+), 2 deletions(-) diff --git a/sklearn/cross_decomposition/_pls.py b/sklearn/cross_decomposition/_pls.py index f1fc90af11d82..822a13064bb08 100644 --- a/sklearn/cross_decomposition/_pls.py +++ b/sklearn/cross_decomposition/_pls.py @@ -238,7 +238,10 @@ def fit(self, X, Y): Y, input_name="Y", dtype=np.float64, copy=self.copy, ensure_2d=False ) if Y.ndim == 1: + self._predict_1d = True Y = Y.reshape(-1, 1) + else: + self._predict_1d = False n = X.shape[0] p = X.shape[1] @@ -469,8 +472,8 @@ def predict(self, X, copy=True): # Normalize X -= self._x_mean X /= self._x_std - Ypred = X @ self.coef_.T - return Ypred + self.intercept_ + Ypred = X @ self.coef_.T + self.intercept_ + return Ypred.ravel() if self._predict_1d else Ypred def fit_transform(self, X, y=None): """Learn and apply the dimension reduction on the train data. diff --git a/sklearn/cross_decomposition/tests/test_pls.py b/sklearn/cross_decomposition/tests/test_pls.py index fcdd927efb389..b8b5cbaa0f275 100644 --- a/sklearn/cross_decomposition/tests/test_pls.py +++ b/sklearn/cross_decomposition/tests/test_pls.py @@ -12,7 +12,9 @@ _svd_flip_1d, ) from sklearn.datasets import load_linnerud, make_regression +from sklearn.ensemble import VotingRegressor from sklearn.exceptions import ConvergenceWarning +from sklearn.linear_model import LinearRegression from sklearn.utils import check_random_state from sklearn.utils.extmath import svd_flip @@ -621,3 +623,24 @@ def test_pls_set_output(Klass): assert isinstance(y_trans, np.ndarray) assert isinstance(X_trans, pd.DataFrame) assert_array_equal(X_trans.columns, est.get_feature_names_out()) + + +def test_pls_regression_fit_1d_y(): + """Check that when fitting with 1d `y`, prediction should also be 1d. + + Non-regression test for Issue #26549. + """ + X = np.array([[1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36]]) + y = np.array([2, 6, 12, 20, 30, 42]) + expected = y.copy() + + plsr = PLSRegression().fit(X, y) + y_pred = plsr.predict(X) + assert y_pred.shape == expected.shape + + # Check that it works in VotingRegressor + lr = LinearRegression().fit(X, y) + vr = VotingRegressor([("lr", lr), ("plsr", plsr)]) + y_pred = vr.fit(X, y).predict(X) + assert y_pred.shape == expected.shape + assert_allclose(y_pred, expected) From 60c5e76d1901bd33108e06f5f6f5e0871cdd3bbe Mon Sep 17 00:00:00 2001 From: Kushan Sharma <50699013+greyisbetter@users.noreply.github.com> Date: Mon, 24 Jul 2023 15:02:15 +0530 Subject: [PATCH 107/264] DOC Add docstring DistanceMetric class (#26795) --- sklearn/metrics/_dist_metrics.pyx.tp | 131 ++++++++++++++++++++++++++- 1 file changed, 128 insertions(+), 3 deletions(-) diff --git a/sklearn/metrics/_dist_metrics.pyx.tp b/sklearn/metrics/_dist_metrics.pyx.tp index bc54e51a7511a..539eef70ec4e6 100644 --- a/sklearn/metrics/_dist_metrics.pyx.tp +++ b/sklearn/metrics/_dist_metrics.pyx.tp @@ -65,6 +65,118 @@ def get_valid_metric_ids(L): if (val.__name__ in L) or (val in L)] cdef class DistanceMetric: + """Uniform interface for fast distance metric functions. + + The `DistanceMetric` class provides a convenient way to compute pairwise distances + between samples. It supports various distance metrics, such as Euclidean distance, + Manhattan distance, and more. + + The `pairwise` method can be used to compute pairwise distances between samples in + the input arrays. It returns a distance matrix representing the distances between + all pairs of samples. + + The :meth:`get_metric` method allows you to retrieve a specific metric using its + string identifier. + + Examples + -------- + >>> from sklearn.metrics import DistanceMetric + >>> dist = DistanceMetric.get_metric('euclidean') + >>> X = [[1, 2], [3, 4], [5, 6]] + >>> Y = [[7, 8], [9, 10]] + >>> dist.pairwise(X,Y) + array([[7.81..., 10.63...] + [5.65..., 8.48...] + [1.41..., 4.24...]]) + + Available Metrics + + The following lists the string metric identifiers and the associated + distance metric classes: + + **Metrics intended for real-valued vector spaces:** + + ============== ==================== ======== =============================== + identifier class name args distance function + -------------- -------------------- -------- ------------------------------- + "euclidean" EuclideanDistance - ``sqrt(sum((x - y)^2))`` + "manhattan" ManhattanDistance - ``sum(|x - y|)`` + "chebyshev" ChebyshevDistance - ``max(|x - y|)`` + "minkowski" MinkowskiDistance p, w ``sum(w * |x - y|^p)^(1/p)`` + "seuclidean" SEuclideanDistance V ``sqrt(sum((x - y)^2 / V))`` + "mahalanobis" MahalanobisDistance V or VI ``sqrt((x - y)' V^-1 (x - y))`` + ============== ==================== ======== =============================== + + **Metrics intended for two-dimensional vector spaces:** Note that the haversine + distance metric requires data in the form of [latitude, longitude] and both + inputs and outputs are in units of radians. + + ============ ================== =============================================================== + identifier class name distance function + ------------ ------------------ --------------------------------------------------------------- + "haversine" HaversineDistance ``2 arcsin(sqrt(sin^2(0.5*dx) + cos(x1)cos(x2)sin^2(0.5*dy)))`` + ============ ================== =============================================================== + + + **Metrics intended for integer-valued vector spaces:** Though intended + for integer-valued vectors, these are also valid metrics in the case of + real-valued vectors. + + ============= ==================== ======================================== + identifier class name distance function + ------------- -------------------- ---------------------------------------- + "hamming" HammingDistance ``N_unequal(x, y) / N_tot`` + "canberra" CanberraDistance ``sum(|x - y| / (|x| + |y|))`` + "braycurtis" BrayCurtisDistance ``sum(|x - y|) / (sum(|x|) + sum(|y|))`` + ============= ==================== ======================================== + + **Metrics intended for boolean-valued vector spaces:** Any nonzero entry + is evaluated to "True". In the listings below, the following + abbreviations are used: + + - N : number of dimensions + - NTT : number of dims in which both values are True + - NTF : number of dims in which the first value is True, second is False + - NFT : number of dims in which the first value is False, second is True + - NFF : number of dims in which both values are False + - NNEQ : number of non-equal dimensions, NNEQ = NTF + NFT + - NNZ : number of nonzero dimensions, NNZ = NTF + NFT + NTT + + ================= ======================= =============================== + identifier class name distance function + ----------------- ----------------------- ------------------------------- + "jaccard" JaccardDistance NNEQ / NNZ + "matching" MatchingDistance NNEQ / N + "dice" DiceDistance NNEQ / (NTT + NNZ) + "kulsinski" KulsinskiDistance (NNEQ + N - NTT) / (NNEQ + N) + "rogerstanimoto" RogersTanimotoDistance 2 * NNEQ / (N + NNEQ) + "russellrao" RussellRaoDistance (N - NTT) / N + "sokalmichener" SokalMichenerDistance 2 * NNEQ / (N + NNEQ) + "sokalsneath" SokalSneathDistance NNEQ / (NNEQ + 0.5 * NTT) + ================= ======================= =============================== + + **User-defined distance:** + + =========== =============== ======= + identifier class name args + ----------- --------------- ------- + "pyfunc" PyFuncDistance func + =========== =============== ======= + + Here ``func`` is a function which takes two one-dimensional numpy + arrays, and returns a distance. Note that in order to be used within + the BallTree, the distance must be a true metric: + i.e. it must satisfy the following properties + + 1) Non-negativity: d(x, y) >= 0 + 2) Identity: d(x, y) = 0 if and only if x == y + 3) Symmetry: d(x, y) = d(y, x) + 4) Triangle Inequality: d(x, y) + d(y, z) >= d(x, z) + + Because of the Python object overhead involved in calling the python + function, this will be fairly slow, but it will have the same + scaling as other distances. + """ @classmethod def get_metric(cls, metric, dtype=np.float64, **kwargs): """Get the given distance metric from the string identifier. @@ -74,11 +186,24 @@ cdef class DistanceMetric: Parameters ---------- metric : str or class name - The distance metric to use + The string identifier or class name of the desired distance metric. + See the documentation of the `DistanceMetric` class for a list of + available metrics. + dtype : {np.float32, np.float64}, default=np.float64 - The dtype of the data on which the metric will be applied + The data type of the input on which the metric will be applied. + This affects the precision of the computed distances. + By default, it is set to `np.float64`. + **kwargs - additional arguments will be passed to the requested metric + Additional keyword arguments that will be passed to the requested metric. + These arguments can be used to customize the behavior of the specific + metric. + + Returns + ------- + metric_obj : instance of the requested metric + An instance of the requested distance metric class. """ if dtype == np.float32: specialized_class = DistanceMetric32 From 61747ffe073eb15421accd9245de310f2fd1fa1f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Mon, 24 Jul 2023 15:32:54 +0200 Subject: [PATCH 108/264] CI Add summary about failures and errors in most builds (#26847) --- azure-pipelines.yml | 1 - build_tools/azure/posix-docker.yml | 1 - build_tools/azure/posix.yml | 1 - build_tools/azure/test_script.sh | 4 ---- setup.cfg | 1 - 5 files changed, 8 deletions(-) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 6c3511319e4eb..464096fb69c29 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -171,7 +171,6 @@ jobs: DISTRIB: 'conda' LOCK_FILE: './build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock' COVERAGE: 'true' - SHOW_SHORT_SUMMARY: 'true' SKLEARN_TESTS_GLOBAL_RANDOM_SEED: '42' # default global random seed # Check compilation with Ubuntu 22.04 LTS (Jammy Jellyfish) and scipy from conda-forge diff --git a/build_tools/azure/posix-docker.yml b/build_tools/azure/posix-docker.yml index af776c4c62f14..b00ca66c378ca 100644 --- a/build_tools/azure/posix-docker.yml +++ b/build_tools/azure/posix-docker.yml @@ -22,7 +22,6 @@ jobs: # Set in azure-pipelines.yml DISTRIB: '' DOCKER_CONTAINER: '' - SHOW_SHORT_SUMMARY: 'false' CREATE_ISSUE_ON_TRACKER: 'true' CCACHE_DIR: $(Pipeline.Workspace)/ccache CCACHE_COMPRESS: '1' diff --git a/build_tools/azure/posix.yml b/build_tools/azure/posix.yml index 2ee03daafd288..35e5165d22c83 100644 --- a/build_tools/azure/posix.yml +++ b/build_tools/azure/posix.yml @@ -22,7 +22,6 @@ jobs: PYTEST_XDIST_VERSION: 'latest' COVERAGE: 'true' CREATE_ISSUE_ON_TRACKER: 'true' - SHOW_SHORT_SUMMARY: 'false' strategy: matrix: ${{ insert }}: ${{ parameters.matrix }} diff --git a/build_tools/azure/test_script.sh b/build_tools/azure/test_script.sh index 98ac2e797b73c..0ba477d3fdbca 100755 --- a/build_tools/azure/test_script.sh +++ b/build_tools/azure/test_script.sh @@ -75,10 +75,6 @@ if [[ "$PYTEST_XDIST_VERSION" != "none" ]]; then TEST_CMD="$TEST_CMD -n$XDIST_WORKERS" fi -if [[ "$SHOW_SHORT_SUMMARY" == "true" ]]; then - TEST_CMD="$TEST_CMD -ra" -fi - if [[ -n "$SELECTED_TESTS" ]]; then TEST_CMD="$TEST_CMD -k $SELECTED_TESTS" diff --git a/setup.cfg b/setup.cfg index d91a27344c575..94ed59f539cb7 100644 --- a/setup.cfg +++ b/setup.cfg @@ -20,7 +20,6 @@ addopts = # correctly on the CI when running `pytest --pyargs sklearn` from the # source folder. -p sklearn.tests.random_seed - -rN filterwarnings = ignore:the matrix subclass:PendingDeprecationWarning From ae941f4368a1d9ae875696c73d69328823998156 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 24 Jul 2023 17:38:45 +0200 Subject: [PATCH 109/264] MAINT make sure to test encoders in common tests (#26859) --- sklearn/preprocessing/_encoders.py | 6 +++--- sklearn/preprocessing/_target_encoder.py | 16 +++++++++++++--- sklearn/utils/_encode.py | 2 +- sklearn/utils/estimator_checks.py | 9 ++++++--- 4 files changed, 23 insertions(+), 10 deletions(-) diff --git a/sklearn/preprocessing/_encoders.py b/sklearn/preprocessing/_encoders.py index 0a0447de95cd8..febc2523845ad 100644 --- a/sklearn/preprocessing/_encoders.py +++ b/sklearn/preprocessing/_encoders.py @@ -176,11 +176,11 @@ def _transform( warn_on_unknown=False, ignore_category_indices=None, ): - self._check_feature_names(X, reset=False) - self._check_n_features(X, reset=False) X_list, n_samples, n_features = self._check_X( X, force_all_finite=force_all_finite ) + self._check_feature_names(X, reset=False) + self._check_n_features(X, reset=False) X_int = np.zeros((n_samples, n_features), dtype=int) X_mask = np.ones((n_samples, n_features), dtype=bool) @@ -437,7 +437,7 @@ def _map_infrequent_categories(self, X_int, X_mask, ignore_category_indices): X_int[rows_to_update, i] = np.take(mapping, X_int[rows_to_update, i]) def _more_tags(self): - return {"X_types": ["categorical"]} + return {"X_types": ["2darray", "categorical"], "allow_nan": True} class OneHotEncoder(_BaseEncoder): diff --git a/sklearn/preprocessing/_target_encoder.py b/sklearn/preprocessing/_target_encoder.py index ea2f3b202bac4..f5ca3b45a07f8 100644 --- a/sklearn/preprocessing/_target_encoder.py +++ b/sklearn/preprocessing/_target_encoder.py @@ -299,8 +299,9 @@ def _fit_encodings_all(self, X, y): inferred_type_of_target = type_of_target(y, input_name="y") if inferred_type_of_target not in accepted_target_types: raise ValueError( - f"Target type was inferred to be {inferred_type_of_target!r}. Only" - f" {accepted_target_types} are supported." + "Unknown label type: Target type was inferred to be " + f"{inferred_type_of_target!r}. Only {accepted_target_types} are " + "supported." ) self.target_type_ = inferred_type_of_target else: @@ -343,4 +344,13 @@ def _transform_X_ordinal( X_out[X_unknown_mask[:, f_idx], f_idx] = y_mean def _more_tags(self): - return {"requires_y": True} + return { + "requires_y": True, + # TargetEncoder is a special case where a transformer uses `y` but + # only accept binary classification and regression targets. For the + # purpose of common tests we use `binary_only` tag to eliminate the + # multiclass tests. TODO: remove this special case when multiclass + # support is added to TargetEncoder. xref: + # https://github.com/scikit-learn/scikit-learn/pull/26674 + "binary_only": True, + } diff --git a/sklearn/utils/_encode.py b/sklearn/utils/_encode.py index 5affa4616be01..fb3912b27dbfe 100644 --- a/sklearn/utils/_encode.py +++ b/sklearn/utils/_encode.py @@ -177,7 +177,7 @@ def _unique_python(values, *, return_inverse, return_counts): except TypeError: types = sorted(t.__qualname__ for t in set(type(v) for v in values)) raise TypeError( - "Encoders require their input to be uniformly " + "Encoders require their input argument must be uniformly " f"strings or numbers. Got {types}" ) ret = (uniques,) diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py index 39654acd35d51..c13e666ec69e3 100644 --- a/sklearn/utils/estimator_checks.py +++ b/sklearn/utils/estimator_checks.py @@ -1261,7 +1261,10 @@ def check_dtype_object(name, estimator_orig): if "string" not in tags["X_types"]: X[0, 0] = {"foo": "bar"} - msg = "argument must be a string.* number" + # This error is raised by: + # - `np.asarray` in `check_array` + # - `_unique_python` for encoders + msg = "argument must be .* string.* number" with raises(TypeError, match=msg): estimator.fit(X, y) else: @@ -3453,7 +3456,6 @@ def _enforce_estimator_tags_y(estimator, y): # Create strictly positive y. The minimal increment above 0 is 1, as # y could be of integer dtype. y += 1 + abs(y.min()) - # Estimators with a `binary_only` tag only accept up to two unique y values if _safe_tags(estimator, key="binary_only") and y.size > 0: y = np.where(y == y.flat[0], y, y.flat[0] + 1) # Estimators in mono_output_task_error raise ValueError if y is of 1-D @@ -3473,7 +3475,8 @@ def _enforce_estimator_tags_X(estimator, X, kernel=linear_kernel): if _safe_tags(estimator, key="requires_positive_X"): X = X - X.min() if "categorical" in _safe_tags(estimator, key="X_types"): - X = (X - X.min()).astype(np.int32) + dtype = np.float64 if _safe_tags(estimator, key="allow_nan") else np.int32 + X = np.round((X - X.min())).astype(dtype) if estimator.__class__.__name__ == "SkewedChi2Sampler": # SkewedChi2Sampler requires X > -skewdness in transform From 6aa66c02fe5fc93fd43f700ce09d78e7c82006b0 Mon Sep 17 00:00:00 2001 From: Malte Londschien <61679398+mlondschien@users.noreply.github.com> Date: Tue, 25 Jul 2023 15:13:04 +0200 Subject: [PATCH 110/264] DOC Specify primal/dual formulation in LogisticRegression (#26294) --- doc/modules/linear_model.rst | 8 ++++++-- sklearn/linear_model/_logistic.py | 10 ++++++---- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index 43356763d69c3..fceb1e09a15bf 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -889,12 +889,16 @@ the probability of the positive class :math:`P(y_i=1|X_i)` as .. math:: \hat{p}(X_i) = \operatorname{expit}(X_i w + w_0) = \frac{1}{1 + \exp(-X_i w - w_0)}. + As an optimization problem, binary class logistic regression with regularization term :math:`r(w)` minimizes the following cost function: -.. math:: \min_{w} C \sum_{i=1}^n \left(-y_i \log(\hat{p}(X_i)) - (1 - y_i) \log(1 - \hat{p}(X_i))\right) + r(w). - +.. math:: + :name: regularized-logistic-loss + + \min_{w} C \sum_{i=1}^n \left(-y_i \log(\hat{p}(X_i)) - (1 - y_i) \log(1 - \hat{p}(X_i))\right) + r(w). + We currently provide four choices for the regularization term :math:`r(w)` via the `penalty` argument: diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py index 6bdc4b7368ef0..6453cee2ac22e 100644 --- a/sklearn/linear_model/_logistic.py +++ b/sklearn/linear_model/_logistic.py @@ -837,8 +837,9 @@ class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator): in 1.4. Use `None` instead. dual : bool, default=False - Dual or primal formulation. Dual formulation is only implemented for - l2 penalty with liblinear solver. Prefer dual=False when + Dual (constrained) or primal (regularized, see also + :ref:`this equation `) formulation. Dual formulation + is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. tol : float, default=1e-4 @@ -1455,8 +1456,9 @@ class LogisticRegressionCV(LogisticRegression, LinearClassifierMixin, BaseEstima ``cv`` default value if None changed from 3-fold to 5-fold. dual : bool, default=False - Dual or primal formulation. Dual formulation is only implemented for - l2 penalty with liblinear solver. Prefer dual=False when + Dual (constrained) or primal (regularized, see also + :ref:`this equation `) formulation. Dual formulation + is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. penalty : {'l1', 'l2', 'elasticnet'}, default='l2' From 97e0c2351b46695f6a6a63f225a6784b37f55dd1 Mon Sep 17 00:00:00 2001 From: Shreesha Kumar Bhat Date: Wed, 26 Jul 2023 21:58:38 +0530 Subject: [PATCH 111/264] FIX Allow 0 Co-authored-by: Meekail Zain <34613774+Micky774@users.noreply.github.com> --- sklearn/metrics/_dist_metrics.pyx.tp | 18 +++++++++++----- sklearn/metrics/tests/test_dist_metrics.py | 25 +++++++++++++++++++--- sklearn/neighbors/tests/test_neighbors.py | 19 ++++++++++++++++ 3 files changed, 54 insertions(+), 8 deletions(-) diff --git a/sklearn/metrics/_dist_metrics.pyx.tp b/sklearn/metrics/_dist_metrics.pyx.tp index 539eef70ec4e6..8eda9b69ec525 100644 --- a/sklearn/metrics/_dist_metrics.pyx.tp +++ b/sklearn/metrics/_dist_metrics.pyx.tp @@ -1396,19 +1396,27 @@ cdef class MinkowskiDistance{{name_suffix}}(DistanceMetric{{name_suffix}}): Parameters ---------- - p : int + p : float The order of the p-norm of the difference (see above). + + .. versionchanged:: 1.4.0 + Minkowski distance allows `p` to be `0= 1 and finite. For p = infinity, - use ChebyshevDistance. + Minkowski Distance requires p > 0 and finite. + When :math:`p \in (0,1)`, it isn't a true metric but is permissible when + the triangular inequality isn't necessary. + For p = infinity, use ChebyshevDistance. Note that for p=1, ManhattanDistance is more efficient, and for p=2, EuclideanDistance is more efficient. + """ def __init__(self, p, w=None): - if p < 1: - raise ValueError("p must be greater than 1") + if p <= 0: + raise ValueError("p must be greater than 0") elif np.isinf(p): raise ValueError("MinkowskiDistance requires finite p. " "For p=inf, use ChebyshevDistance.") diff --git a/sklearn/metrics/tests/test_dist_metrics.py b/sklearn/metrics/tests/test_dist_metrics.py index 16aa5c569b161..be5d12f0414b8 100644 --- a/sklearn/metrics/tests/test_dist_metrics.py +++ b/sklearn/metrics/tests/test_dist_metrics.py @@ -15,6 +15,7 @@ ) from sklearn.utils import check_random_state from sklearn.utils._testing import assert_allclose, create_memmap_backed_data +from sklearn.utils.fixes import parse_version, sp_version def dist_func(x1, x2, p): @@ -42,18 +43,17 @@ def dist_func(x1, x2, p): V = rng.random_sample((d, d)) VI = np.dot(V, V.T) - METRICS_DEFAULT_PARAMS = [ ("euclidean", {}), ("cityblock", {}), - ("minkowski", dict(p=(1, 1.5, 2, 3))), + ("minkowski", dict(p=(0.5, 1, 1.5, 2, 3))), ("chebyshev", {}), ("seuclidean", dict(V=(rng.random_sample(d),))), ("mahalanobis", dict(VI=(VI,))), ("hamming", {}), ("canberra", {}), ("braycurtis", {}), - ("minkowski", dict(p=(1, 1.5, 3), w=(rng.random_sample(d),))), + ("minkowski", dict(p=(0.5, 1, 1.5, 3), w=(rng.random_sample(d),))), ] @@ -76,6 +76,13 @@ def test_cdist(metric_param_grid, X, Y): # with scipy rtol_dict = {"rtol": 1e-6} + # TODO: Remove when scipy minimum version >= 1.7.0 + # scipy supports 0= 1.7.0 + if metric == "minkowski": + p = kwargs["p"] + if sp_version < parse_version("1.7.0") and p < 1: + pytest.skip("scipy does not support 0= 1.7.0 + # scipy supports 0= 1.7.0 + if metric == "minkowski": + p = kwargs["p"] + if sp_version < parse_version("1.7.0") and p < 1: + pytest.skip("scipy does not support 0 Date: Wed, 26 Jul 2023 20:27:10 +0200 Subject: [PATCH 112/264] DOC use the same estimators to demonstrate pipeline construction (#26907) --- doc/modules/compose.rst | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst index faba9a76ab94c..f277c32675c3f 100644 --- a/doc/modules/compose.rst +++ b/doc/modules/compose.rst @@ -66,10 +66,8 @@ it takes a variable number of estimators and returns a pipeline, filling in the names automatically:: >>> from sklearn.pipeline import make_pipeline - >>> from sklearn.naive_bayes import MultinomialNB - >>> from sklearn.preprocessing import Binarizer - >>> make_pipeline(Binarizer(), MultinomialNB()) - Pipeline(steps=[('binarizer', Binarizer()), ('multinomialnb', MultinomialNB())]) + >>> make_pipeline(PCA(), SVC()) + Pipeline(steps=[('pca', PCA()), ('svc', SVC())]) Accessing steps ............... From 6ef2b264cc41e898c395a4aa286e679fef305765 Mon Sep 17 00:00:00 2001 From: Rahil Parikh <75483881+rprkh@users.noreply.github.com> Date: Thu, 27 Jul 2023 01:44:52 +0530 Subject: [PATCH 113/264] DOC example on feature selection using negative `tol` values (#26205) Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> --- .../plot_select_from_model_diabetes.py | 53 +++++++++++++++++-- 1 file changed, 50 insertions(+), 3 deletions(-) diff --git a/examples/feature_selection/plot_select_from_model_diabetes.py b/examples/feature_selection/plot_select_from_model_diabetes.py index 688c2b4ba8079..874d359e9beef 100644 --- a/examples/feature_selection/plot_select_from_model_diabetes.py +++ b/examples/feature_selection/plot_select_from_model_diabetes.py @@ -122,9 +122,6 @@ print(f"Done in {toc_bwd - tic_bwd:.3f}s") # %% -# Discussion -# ---------- -# # Interestingly, forward and backward selection have selected the same set of # features. In general, this isn't the case and the two methods would lead to # different results. @@ -145,3 +142,53 @@ # attribute. The forward SFS is faster than the backward SFS because it only # needs to perform `n_features_to_select = 2` iterations, while the backward # SFS needs to perform `n_features - n_features_to_select = 8` iterations. +# +# Using negative tolerance values +# ------------------------------- +# +# :class:`~sklearn.feature_selection.SequentialFeatureSelector` can be used +# to remove features present in the dataset and return a +# smaller subset of the original features with `direction="backward"` +# and a negative value of `tol`. +# +# We begin by loading the Breast Cancer dataset, consisting of 30 different +# features and 569 samples. +from sklearn.datasets import load_breast_cancer +import numpy as np + +breast_cancer_data = load_breast_cancer() +X, y = breast_cancer_data.data, breast_cancer_data.target +feature_names = np.array(breast_cancer_data.feature_names) +print(breast_cancer_data.DESCR) + +# %% +# We will make use of the :class:`~sklearn.linear_model.LogisticRegression` +# estimator with :class:`~sklearn.feature_selection.SequentialFeatureSelector` +# to perform the feature selection. +from sklearn.linear_model import LogisticRegression +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import roc_auc_score + +for tol in [-1e-2, -1e-3, -1e-4]: + start = time() + feature_selector = SequentialFeatureSelector( + LogisticRegression(), + n_features_to_select="auto", + direction="backward", + scoring="roc_auc", + tol=tol, + n_jobs=2, + ) + model = make_pipeline(StandardScaler(), feature_selector, LogisticRegression()) + model.fit(X, y) + end = time() + print(f"\ntol: {tol}") + print(f"Features selected: {feature_names[model[1].get_support()]}") + print(f"ROC AUC score: {roc_auc_score(y, model.predict_proba(X)[:, 1]):.3f}") + print(f"Done in {end - start:.3f}s") + +# %% +# We can see that the number of features selected tend to increase as negative +# values of `tol` approach to zero. The time taken for feature selection also +# decreases as the values of `tol` come closer to zero. From c7485e7931f52a0173cb255f550ab779d73983a4 Mon Sep 17 00:00:00 2001 From: Meekail Zain <34613774+Micky774@users.noreply.github.com> Date: Thu, 27 Jul 2023 02:58:49 -0400 Subject: [PATCH 114/264] MNT Improve robustness of sparse test in `HDBSCAN` (#26889) --- sklearn/cluster/tests/test_hdbscan.py | 33 +++++++++++++++++++-------- 1 file changed, 24 insertions(+), 9 deletions(-) diff --git a/sklearn/cluster/tests/test_hdbscan.py b/sklearn/cluster/tests/test_hdbscan.py index c0c281ce31475..8f4629f573842 100644 --- a/sklearn/cluster/tests/test_hdbscan.py +++ b/sklearn/cluster/tests/test_hdbscan.py @@ -287,22 +287,37 @@ def test_hdbscan_precomputed_non_brute(tree): def test_hdbscan_sparse(): """ Tests that HDBSCAN works correctly when passing sparse feature data. + Evaluates correctness by comparing against the same data passed as a dense + array. """ - sparse_X = sparse.csr_matrix(X) - labels = HDBSCAN().fit(sparse_X).labels_ - n_clusters = len(set(labels) - OUTLIER_SET) + dense_labels = HDBSCAN().fit(X).labels_ + n_clusters = len(set(dense_labels) - OUTLIER_SET) assert n_clusters == 3 - sparse_X_nan = sparse_X.copy() - sparse_X_nan[0, 0] = np.nan - labels = HDBSCAN().fit(sparse_X_nan).labels_ - n_clusters = len(set(labels) - OUTLIER_SET) - assert n_clusters == 3 + _X_sparse = sparse.csr_matrix(X) + X_sparse = _X_sparse.copy() + sparse_labels = HDBSCAN().fit(X_sparse).labels_ + assert_array_equal(dense_labels, sparse_labels) + + # Compare that the sparse and dense non-precomputed routines return the same labels + # where the 0th observation contains the outlier. + for outlier_val, outlier_type in ((np.inf, "infinite"), (np.nan, "missing")): + X_dense = X.copy() + X_dense[0, 0] = outlier_val + dense_labels = HDBSCAN().fit(X_dense).labels_ + n_clusters = len(set(dense_labels) - OUTLIER_SET) + assert n_clusters == 3 + assert dense_labels[0] == _OUTLIER_ENCODING[outlier_type]["label"] + + X_sparse = _X_sparse.copy() + X_sparse[0, 0] = outlier_val + sparse_labels = HDBSCAN().fit(X_sparse).labels_ + assert_array_equal(dense_labels, sparse_labels) msg = "Sparse data matrices only support algorithm `brute`." with pytest.raises(ValueError, match=msg): - HDBSCAN(metric="euclidean", algorithm="balltree").fit(sparse_X) + HDBSCAN(metric="euclidean", algorithm="balltree").fit(X_sparse) @pytest.mark.parametrize("algorithm", ALGORITHMS) From 0c90414c4c33e747e4b2b8dcfa9a91e9c0ce7ad1 Mon Sep 17 00:00:00 2001 From: Meekail Zain <34613774+Micky774@users.noreply.github.com> Date: Thu, 27 Jul 2023 09:42:46 -0400 Subject: [PATCH 115/264] MNT Fixed linting error in `plot_select_from_model_diabetes.py` (#26915) --- .../feature_selection/plot_select_from_model_diabetes.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/feature_selection/plot_select_from_model_diabetes.py b/examples/feature_selection/plot_select_from_model_diabetes.py index 874d359e9beef..f008d8d6e8b68 100644 --- a/examples/feature_selection/plot_select_from_model_diabetes.py +++ b/examples/feature_selection/plot_select_from_model_diabetes.py @@ -153,9 +153,10 @@ # # We begin by loading the Breast Cancer dataset, consisting of 30 different # features and 569 samples. -from sklearn.datasets import load_breast_cancer import numpy as np +from sklearn.datasets import load_breast_cancer + breast_cancer_data = load_breast_cancer() X, y = breast_cancer_data.data, breast_cancer_data.target feature_names = np.array(breast_cancer_data.feature_names) @@ -166,9 +167,9 @@ # estimator with :class:`~sklearn.feature_selection.SequentialFeatureSelector` # to perform the feature selection. from sklearn.linear_model import LogisticRegression +from sklearn.metrics import roc_auc_score from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler -from sklearn.metrics import roc_auc_score for tol in [-1e-2, -1e-3, -1e-4]: start = time() From da589e8dd823515a27b570e7ec7cca89f4108ecc Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Thu, 27 Jul 2023 23:52:03 +1000 Subject: [PATCH 116/264] DOC Improve `plot_target_encoder_cross_val.py` example (#26677) --- doc/glossary.rst | 23 +++ doc/modules/preprocessing.rst | 16 +- .../plot_target_encoder_cross_val.py | 137 +++++++++++------- sklearn/preprocessing/_target_encoder.py | 14 +- 4 files changed, 121 insertions(+), 69 deletions(-) diff --git a/doc/glossary.rst b/doc/glossary.rst index 36afcd9483684..1dbb7e630c449 100644 --- a/doc/glossary.rst +++ b/doc/glossary.rst @@ -205,6 +205,29 @@ General Concepts exceptional behaviours on the estimator using semantic :term:`estimator tags`. + cross-fitting + cross fitting + A resampling method that iteratively partitions data into mutually + exclusive subsets to fit two stages. During the first stage, the + mutually exclusive subsets enable predictions or transformations to be + computed on data not seen during training. The computed data is then + used in the second stage. The objective is to avoid having any + overfitting in the first stage introduce bias into the input data + distribution of the second stage. + For examples of its use, see: :class:`~preprocessing.TargetEncoder`, + :class:`~ensemble.StackingClassifier`, + :class:`~ensemble.StackingRegressor` and + :class:`~calibration.CalibratedClassifierCV`. + + cross-validation + cross validation + A resampling method that iteratively partitions data into mutually + exclusive 'train' and 'test' subsets so model performance can be + evaluated on unseen data. This conserves data as avoids the need to hold + out a 'validation' dataset and accounts for variability as multiple + rounds of cross validation are generally performed. + See :ref:`User Guide ` for more details. + deprecation We use deprecation to slowly violate our :term:`backwards compatibility` assurances, usually to: diff --git a/doc/modules/preprocessing.rst b/doc/modules/preprocessing.rst index 1d7ad07f7023c..82fecf0c4e9f1 100644 --- a/doc/modules/preprocessing.rst +++ b/doc/modules/preprocessing.rst @@ -910,16 +910,16 @@ For continuous targets, the formulation is similar to binary classification: where :math:`L_i` is the set of observations with category :math:`i` and :math:`n_i` is the number of observations with category :math:`i`. -:meth:`~TargetEncoder.fit_transform` internally relies on a cross fitting +:meth:`~TargetEncoder.fit_transform` internally relies on a :term:`cross fitting` scheme to prevent target information from leaking into the train-time representation, especially for non-informative high-cardinality categorical variables, and help prevent the downstream model from overfitting spurious correlations. Note that as a result, `fit(X, y).transform(X)` does not equal `fit_transform(X, y)`. In :meth:`~TargetEncoder.fit_transform`, the training -data is split into *k* folds (determined by the `cv` parameter) and encodes each -fold using the encodings trained on the other *k-1* folds. The following diagram -shows the cross fitting scheme in :meth:`~TargetEncoder.fit_transform` with -the default `cv=5`: +data is split into *k* folds (determined by the `cv` parameter) and each fold is +encoded using the encodings learnt using the other *k-1* folds. The following +diagram shows the :term:`cross fitting` scheme in +:meth:`~TargetEncoder.fit_transform` with the default `cv=5`: .. image:: ../images/target_encoder_cross_validation.svg :width: 600 @@ -929,10 +929,10 @@ the default `cv=5`: the whole training set. This is never used in :meth:`~TargetEncoder.fit_transform` but is saved to the attribute `encodings_`, for use when :meth:`~TargetEncoder.transform` is called. Note that the encodings -learned for each fold during the cross fitting scheme are not saved to an -attribute. +learned for each fold during the :term:`cross fitting` scheme are not saved to +an attribute. -The :meth:`~TargetEncoder.fit` method does **not** use any cross fitting +The :meth:`~TargetEncoder.fit` method does **not** use any :term:`cross fitting` schemes and learns one encoding on the entire training set, which is used to encode categories in :meth:`~TargetEncoder.transform`. This encoding is the same as the 'full data' diff --git a/examples/preprocessing/plot_target_encoder_cross_val.py b/examples/preprocessing/plot_target_encoder_cross_val.py index f4ff643d8b48e..7244a1bf61cd6 100644 --- a/examples/preprocessing/plot_target_encoder_cross_val.py +++ b/examples/preprocessing/plot_target_encoder_cross_val.py @@ -6,21 +6,26 @@ .. currentmodule:: sklearn.preprocessing The :class:`TargetEncoder` replaces each category of a categorical feature with -the mean of the target variable for that category. This method is useful +the shrunk mean of the target variable for that category. This method is useful in cases where there is a strong relationship between the categorical feature and the target. To prevent overfitting, :meth:`TargetEncoder.fit_transform` uses -an internal cross fitting scheme to encode the training data to be used by a -downstream model. In this example, we demonstrate the importance of the cross fitting -procedure to prevent overfitting. +an internal :term:`cross fitting` scheme to encode the training data to be used +by a downstream model. This scheme involves splitting the data into *k* folds +and encoding each fold using the encodings learnt using the other *k-1* folds. +In this example, we demonstrate the importance of the cross +fitting procedure to prevent overfitting. """ # %% # Create Synthetic Dataset # ======================== -# For this example, we build a dataset with three categorical features: an informative -# feature with medium cardinality, an uninformative feature with medium cardinality, -# and an uninformative feature with high cardinality. First, we generate the informative -# feature: +# For this example, we build a dataset with three categorical features: +# +# * an informative feature with medium cardinality ("informative") +# * an uninformative feature with medium cardinality ("shuffled") +# * an uninformative feature with high cardinality ("near_unique") +# +# First, we generate the informative feature: import numpy as np from sklearn.preprocessing import KBinsDiscretizer @@ -33,12 +38,16 @@ n_categories = 100 kbins = KBinsDiscretizer( - n_bins=n_categories, encode="ordinal", strategy="uniform", random_state=rng + n_bins=n_categories, + encode="ordinal", + strategy="uniform", + random_state=rng, + subsample=None, ) X_informative = kbins.fit_transform((y + noise).reshape(-1, 1)) -# Remove the linear relationship between y and the bin index by permuting the values of -# X_informative +# Remove the linear relationship between y and the bin index by permuting the +# values of X_informative: permuted_categories = rng.permutation(n_categories) X_informative = permuted_categories[X_informative.astype(np.int32)] @@ -48,13 +57,13 @@ X_shuffled = rng.permutation(X_informative) # %% -# The uninformative feature with high cardinality is generated so that is independent of -# the target variable. We will show that target encoding without cross fitting will -# cause catastrophic overfitting for the downstream regressor. These high cardinality -# features are basically unique identifiers for samples which should generally be -# removed from machine learning dataset. In this example, we generate them to show how -# :class:`TargetEncoder`'s default cross fitting behavior mitigates the overfitting -# issue automatically. +# The uninformative feature with high cardinality is generated so that it is +# independent of the target variable. We will show that target encoding without +# :term:`cross fitting` will cause catastrophic overfitting for the downstream +# regressor. These high cardinality features are basically unique identifiers +# for samples which should generally be removed from machine learning datasets. +# In this example, we generate them to show how :class:`TargetEncoder`'s default +# :term:`cross fitting` behavior mitigates the overfitting issue automatically. X_near_unique_categories = rng.choice( int(0.9 * n_samples), size=n_samples, replace=True ).reshape(-1, 1) @@ -79,9 +88,10 @@ # ========================== # In this section, we train a ridge regressor on the dataset with and without # encoding and explore the influence of target encoder with and without the -# internal cross fitting. First, we see the Ridge model trained on the -# raw features will have low performance, because the order of the informative -# feature is not informative: +# internal :term:`cross fitting`. First, we see the Ridge model trained on the +# raw features will have low performance. This is because we permuted the order +# of the informative feature meaning `X_informative` is not informative when +# raw: import sklearn from sklearn.linear_model import Ridge @@ -96,15 +106,15 @@ # %% # Next, we create a pipeline with the target encoder and ridge model. The pipeline -# uses :meth:`TargetEncoder.fit_transform` which uses cross fitting. We see that -# the model fits the data well and generalizes to the test set: +# uses :meth:`TargetEncoder.fit_transform` which uses :term:`cross fitting`. We +# see that the model fits the data well and generalizes to the test set: from sklearn.pipeline import make_pipeline from sklearn.preprocessing import TargetEncoder -model_with_cv = make_pipeline(TargetEncoder(random_state=0), ridge) -model_with_cv.fit(X_train, y_train) -print("Model with CV on training set: ", model_with_cv.score(X_train, y_train)) -print("Model with CV on test set: ", model_with_cv.score(X_test, y_test)) +model_with_cf = make_pipeline(TargetEncoder(random_state=0), ridge) +model_with_cf.fit(X_train, y_train) +print("Model with CF on train set: ", model_with_cf.score(X_train, y_train)) +print("Model with CF on test set: ", model_with_cf.score(X_test, y_test)) # %% # The coefficients of the linear model shows that most of the weight is on the @@ -114,49 +124,68 @@ plt.rcParams["figure.constrained_layout.use"] = True -coefs_cv = pd.Series( - model_with_cv[-1].coef_, index=model_with_cv[-1].feature_names_in_ +coefs_cf = pd.Series( + model_with_cf[-1].coef_, index=model_with_cf[-1].feature_names_in_ ).sort_values() -_ = coefs_cv.plot(kind="barh") +ax = coefs_cf.plot(kind="barh") +_ = ax.set( + title="Target encoded with cross fitting", + xlabel="Ridge coefficient", + ylabel="Feature", +) # %% -# While :meth:`TargetEncoder.fit_transform` uses an internal cross fitting scheme, -# :meth:`TargetEncoder.transform` itself does not perform any cross fitting. -# It uses the aggregation of the complete training set to transform the categorical -# features. Thus, we can use :meth:`TargetEncoder.fit` followed by -# :meth:`TargetEncoder.transform` to disable the cross fitting. This encoding -# is then passed to the ridge model. +# While :meth:`TargetEncoder.fit_transform` uses an internal +# :term:`cross fitting` scheme to learn encodings for the training set, +# :meth:`TargetEncoder.transform` itself does not. +# It uses the complete training set to learn encodings and to transform the +# categorical features. Thus, we can use :meth:`TargetEncoder.fit` followed by +# :meth:`TargetEncoder.transform` to disable the :term:`cross fitting`. This +# encoding is then passed to the ridge model. target_encoder = TargetEncoder(random_state=0) target_encoder.fit(X_train, y_train) -X_train_no_cv_encoding = target_encoder.transform(X_train) -X_test_no_cv_encoding = target_encoder.transform(X_test) +X_train_no_cf_encoding = target_encoder.transform(X_train) +X_test_no_cf_encoding = target_encoder.transform(X_test) -model_no_cv = ridge.fit(X_train_no_cv_encoding, y_train) +model_no_cf = ridge.fit(X_train_no_cf_encoding, y_train) # %% -# We evaluate the model on the non-cross validated encoding and see that it overfits: +# We evaluate the model that did not use :term:`cross fitting` when encoding and +# see that it overfits: print( - "Model without CV on training set: ", - model_no_cv.score(X_train_no_cv_encoding, y_train), + "Model without CF on training set: ", + model_no_cf.score(X_train_no_cf_encoding, y_train), ) print( - "Model without CV on test set: ", model_no_cv.score(X_test_no_cv_encoding, y_test) + "Model without CF on test set: ", + model_no_cf.score( + X_test_no_cf_encoding, + y_test, + ), ) # %% -# The ridge model overfits, because it assigns more weight to the extremely high -# cardinality feature relative to the informative feature. -coefs_no_cv = pd.Series( - model_no_cv.coef_, index=model_no_cv.feature_names_in_ +# The ridge model overfits because it assigns much more weight to the +# uninformative extremely high cardinality ("near_unique") and medium +# cardinality ("shuffled") features than when the model used +# :term:`cross fitting` to encode the features. +coefs_no_cf = pd.Series( + model_no_cf.coef_, index=model_no_cf.feature_names_in_ ).sort_values() -_ = coefs_no_cv.plot(kind="barh") +ax = coefs_no_cf.plot(kind="barh") +_ = ax.set( + title="Target encoded without cross fitting", + xlabel="Ridge coefficient", + ylabel="Feature", +) # %% # Conclusion # ========== -# This example demonstrates the importance of :class:`TargetEncoder`'s internal cross -# fitting. It is important to use :meth:`TargetEncoder.fit_transform` to encode -# training data before passing it to a machine learning model. When a -# :class:`TargetEncoder` is a part of a :class:`~sklearn.pipeline.Pipeline` and the -# pipeline is fitted, the pipeline will correctly call -# :meth:`TargetEncoder.fit_transform` and pass the encoding along. +# This example demonstrates the importance of :class:`TargetEncoder`'s internal +# :term:`cross fitting`. It is important to use +# :meth:`TargetEncoder.fit_transform` to encode training data before passing it +# to a machine learning model. When a :class:`TargetEncoder` is a part of a +# :class:`~sklearn.pipeline.Pipeline` and the pipeline is fitted, the pipeline +# will correctly call :meth:`TargetEncoder.fit_transform` and use +# :term:`cross fitting` when encoding the training data. diff --git a/sklearn/preprocessing/_target_encoder.py b/sklearn/preprocessing/_target_encoder.py index f5ca3b45a07f8..0e26f0e05d22e 100644 --- a/sklearn/preprocessing/_target_encoder.py +++ b/sklearn/preprocessing/_target_encoder.py @@ -27,8 +27,8 @@ class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder): .. note:: `fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a - cross fitting scheme is used in `fit_transform` for encoding. See the - :ref:`User Guide ` for details. + :term:`cross fitting` scheme is used in `fit_transform` for encoding. + See the :ref:`User Guide ` for details. .. versionadded:: 1.3 @@ -68,7 +68,7 @@ class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder): If `"auto"`, then `smooth` is set to an empirical Bayes estimate. cv : int, default=5 - Determines the number of folds in the cross fitting strategy used in + Determines the number of folds in the :term:`cross fitting` strategy used in :meth:`fit_transform`. For classification targets, `StratifiedKFold` is used and for continuous targets, `KFold` is used. @@ -204,8 +204,8 @@ def fit_transform(self, X, y): .. note:: `fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a - cross fitting scheme is used in `fit_transform` for encoding. See the - :ref:`User Guide `. for details. + :term:`cross fitting` scheme is used in `fit_transform` for encoding. + See the :ref:`User Guide `. for details. Parameters ---------- @@ -260,8 +260,8 @@ def transform(self, X): .. note:: `fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a - cross fitting scheme is used in `fit_transform` for encoding. See the - :ref:`User Guide `. for details. + :term:`cross fitting` scheme is used in `fit_transform` for encoding. + See the :ref:`User Guide `. for details. Parameters ---------- From 349cdd90ce6c198caf7181f38982f6446c1d3d84 Mon Sep 17 00:00:00 2001 From: Thomas Roehr Date: Thu, 27 Jul 2023 17:06:17 +0200 Subject: [PATCH 117/264] FIX fix validation of class_names argument for plot_tree (#26903) Co-authored-by: Thomas J. Fan --- doc/whats_new/v1.3.rst | 6 ++++++ sklearn/tree/_export.py | 4 ++-- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index 8d39ca2fed143..e0df3c1beba4b 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -23,6 +23,12 @@ Changelog :attr:`sklearn.neighbors.KDTree.valid_metrics` as public class attributes. :pr:`26754` by :user:`Julien Jerphanion `. +:mod:`sklearn.tree` +................... + +- |Fix| :func:`tree.plot_tree` now accepts `class_names=True` as documented. + :pr:`26903` by :user:`Thomas Roehr <2maz>` + .. _changes_1_3: Version 1.3.0 diff --git a/sklearn/tree/_export.py b/sklearn/tree/_export.py index 02f8ea81404c7..c89333aa49831 100644 --- a/sklearn/tree/_export.py +++ b/sklearn/tree/_export.py @@ -79,7 +79,7 @@ def __repr__(self): "decision_tree": [DecisionTreeClassifier, DecisionTreeRegressor], "max_depth": [Interval(Integral, 0, None, closed="left"), None], "feature_names": [list, None], - "class_names": [list, None], + "class_names": ["array-like", "boolean", None], "label": [StrOptions({"all", "root", "none"})], "filled": ["boolean"], "impurity": ["boolean"], @@ -134,7 +134,7 @@ def plot_tree( Names of each of the features. If None, generic names will be used ("x[0]", "x[1]", ...). - class_names : list of str or bool, default=None + class_names : array-like of str or True, default=None Names of each of the target classes in ascending numerical order. Only relevant for classification and not supported for multi-output. If ``True``, shows a symbolic representation of the class name. From c3e49a96f46f93adcaffd63ffffb143650bb8069 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marek=20Hanu=C5=A1?= Date: Fri, 28 Jul 2023 14:26:57 +0200 Subject: [PATCH 118/264] DOC Add missing cross validation image alt (#26261) Co-authored-by: Adrin Jalali --- doc/modules/cross_validation.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/modules/cross_validation.rst b/doc/modules/cross_validation.rst index 6158e000cb727..8afa467982736 100644 --- a/doc/modules/cross_validation.rst +++ b/doc/modules/cross_validation.rst @@ -102,6 +102,7 @@ where the number of samples is very small. .. image:: ../images/grid_search_cross_validation.png :width: 500px :height: 300px + :alt: A depiction of a 5 fold cross validation on a training set, while holding out a test set. :align: center Computing cross-validated metrics From a27bf2015adc0601bbeed84bdb262127f01d8eb0 Mon Sep 17 00:00:00 2001 From: Fabian Egli Date: Fri, 28 Jul 2023 18:47:13 +0200 Subject: [PATCH 119/264] DOC Note missing value support as advantage of decision trees (#26928) Co-authored-by: Thomas J. Fan --- doc/modules/tree.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/modules/tree.rst b/doc/modules/tree.rst index 7f4be9f6240a8..cb5a752f549a7 100644 --- a/doc/modules/tree.rst +++ b/doc/modules/tree.rst @@ -27,8 +27,8 @@ Some advantages of decision trees are: - Requires little data preparation. Other techniques often require data normalization, dummy variables need to be created and blank values to - be removed. Note however that this module does not support missing - values. + be removed. Some tree and algorithm combinations support + :ref:`missing values `. - The cost of using the tree (i.e., predicting data) is logarithmic in the number of data points used to train the tree. From 3747676bf8e9c9f64659b5e8b98a9b17cb1357eb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Luk=C3=A1=C5=A1=20Folwarczn=C3=BD?= Date: Sun, 30 Jul 2023 12:37:46 +0200 Subject: [PATCH 120/264] DOC backticks around X and y in linear_model.rst (#26929) --- doc/modules/linear_model.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index fceb1e09a15bf..aa9184a2bedc5 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -37,7 +37,7 @@ solves a problem of the form: :align: center :scale: 50% -:class:`LinearRegression` will take in its ``fit`` method arrays X, y +:class:`LinearRegression` will take in its ``fit`` method arrays ``X``, ``y`` and will store the coefficients :math:`w` of the linear model in its ``coef_`` member:: @@ -114,7 +114,7 @@ of shrinkage and thus the coefficients become more robust to collinearity. As with other linear models, :class:`Ridge` will take in its ``fit`` method -arrays X, y and will store the coefficients :math:`w` of the linear model in +arrays ``X``, ``y`` and will store the coefficients :math:`w` of the linear model in its ``coef_`` member:: >>> from sklearn import linear_model From c9e2ea2acf27d5dcab1a7acaa048745a5a27d2eb Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Sun, 30 Jul 2023 13:16:00 +0200 Subject: [PATCH 121/264] DOC update related packages (#26922) --- doc/related_projects.rst | 87 ++++++++++++++++++++-------------------- 1 file changed, 43 insertions(+), 44 deletions(-) diff --git a/doc/related_projects.rst b/doc/related_projects.rst index 9cc70ad89ffff..ef6ed9e5ec266 100644 --- a/doc/related_projects.rst +++ b/doc/related_projects.rst @@ -21,9 +21,6 @@ enhance the functionality of scikit-learn's estimators. **Data formats** -- `Fast svmlight / libsvm file loader `_ - Fast and memory-efficient svmlight / libsvm file loader for Python. - - `sklearn_pandas `_ bridge for scikit-learn pipelines and pandas data frame with dedicated transformers. @@ -64,19 +61,20 @@ enhance the functionality of scikit-learn's estimators. It incorporates multiple modeling libraries under one API, and the objects that EvalML creates use an sklearn-compatible API. -**Experimentation frameworks** +**Experimentation and model registry frameworks** + +- `MLFlow `_ MLflow is an open source platform to manage the ML + lifecycle, including experimentation, reproducibility, deployment, and a central + model registry. - `Neptune `_ Metadata store for MLOps, - built for teams that run a lot of experiments.‌ It gives you a single + built for teams that run a lot of experiments. It gives you a single place to log, store, display, organize, compare, and query all your model building metadata. - `Sacred `_ Tool to help you configure, organize, log and reproduce experiments -- `REP `_ Environment for conducting data-driven - research in a consistent and reproducible way - - `Scikit-Learn Laboratory `_ A command-line wrapper around scikit-learn that makes it easy to run machine learning @@ -91,10 +89,7 @@ enhance the functionality of scikit-learn's estimators. debugging/inspecting machine learning models and explaining their predictions. -- `mlxtend `_ Includes model visualization - utilities. - -- `sklearn-evaluation `_ +- `sklearn-evaluation `_ Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis. Visual analysis, model selection, evaluation and diagnostics. @@ -140,7 +135,7 @@ enhance the functionality of scikit-learn's estimators. - `treelite `_ Compiles tree-based ensemble models into C code for minimizing prediction latency. - + **Model throughput** - `Intel(R) Extension for scikit-learn `_ @@ -161,12 +156,40 @@ project. The following are projects providing interfaces similar to scikit-learn for additional learning algorithms, infrastructures and tasks. -**Structured learning** +**Time series and forecasting** + +- `Darts `_ Darts is a Python library for + user-friendly forecasting and anomaly detection on time series. It contains a variety + of models, from classics such as ARIMA to deep neural networks. The forecasting + models can all be used in the same way, using fit() and predict() functions, similar + to scikit-learn. + +- `sktime `_ A scikit-learn compatible + toolbox for machine learning with time series including time series + classification/regression and (supervised/panel) forecasting. + +- `skforecast `_ A python library + that eases using scikit-learn regressors as multi-step forecasters. It also works + with any regressor compatible with the scikit-learn API. + +- `tslearn `_ A machine learning library for + time series that offers tools for pre-processing and feature extraction as well as + dedicated models for clustering, classification and regression. + +**Gradient (tree) boosting** -- `tslearn `_ A machine learning library for time series - that offers tools for pre-processing and feature extraction as well as dedicated models for clustering, classification and regression. +Note scikit-learn own modern gradient boosting estimators +:class:`~sklearn.ensemble.HistGradientBoostingClassifier` and +:class:`~sklearn.ensemble.HistGradientBoostingRegressor`. -- `sktime `_ A scikit-learn compatible toolbox for machine learning with time series including time series classification/regression and (supervised/panel) forecasting. +- `XGBoost `_ XGBoost is an optimized distributed + gradient boosting library designed to be highly efficient, flexible and portable. + +- `LightGBM `_ LightGBM is a gradient boosting + framework that uses tree based learning algorithms. It is designed to be distributed + and efficient. + +**Structured learning** - `HMMLearn `_ Implementation of hidden markov models that was previously part of scikit-learn. @@ -182,21 +205,9 @@ and tasks. (`CRFsuite `_ wrapper with sklearn-like API). -- `skforecast `_ A python library - that eases using scikit-learn regressors as multi-step forecasters. It also works - with any regressor compatible with the scikit-learn API. **Deep neural networks etc.** -- `nolearn `_ A number of wrappers and - abstractions around existing neural network libraries - -- `Keras `_ High-level API for - TensorFlow with a scikit-learn inspired API. - -- `lasagne `_ A lightweight library to - build and train neural networks in Theano. - - `skorch `_ A scikit-learn compatible neural network library that wraps PyTorch. @@ -219,9 +230,6 @@ and tasks. **Other regression and classification** -- `xgboost `_ Optimised gradient boosted decision - tree library. - - `ML-Ensemble `_ Generalized ensemble learning (stacking, blending, subsemble, deep ensembles, etc.). @@ -232,10 +240,6 @@ and tasks. - `py-earth `_ Multivariate adaptive regression splines -- `Kernel Regression `_ - Implementation of Nadaraya-Watson kernel regression with automatic bandwidth - selection - - `gplearn `_ Genetic Programming for symbolic regression tasks. @@ -245,8 +249,6 @@ and tasks. - `seglearn `_ Time series and sequence learning using sliding window segmentation. -- `libOPF `_ Optimal path forest classifier - - `fastFM `_ Fast factorization machine implementation compatible with scikit-learn @@ -266,6 +268,7 @@ and tasks. - `hdbscan `_ HDBSCAN and Robust Single Linkage clustering algorithms for robust variable density clustering. + As of scikit-learn version 1.3.0, there is :class:`~sklearn.cluster.HDBSCAN`. - `spherecluster `_ Spherical K-means and mixture of von Mises Fisher clustering routines for data on the @@ -276,6 +279,8 @@ and tasks. - `categorical-encoding `_ A library of sklearn compatible categorical variable encoders. + As of scikit-learn version 1.3.0, there is + :class:`~sklearn.preprocessing.TargetEncoder`. - `imbalanced-learn `_ Various @@ -331,9 +336,6 @@ Recommendation Engine packages - `OpenRec `_ TensorFlow-based neural-network inspired recommendation algorithms. -- `Spotlight `_ Pytorch-based - implementation of deep recommender models. - - `Surprise Lib `_ Library for explicit feedback datasets. @@ -355,9 +357,6 @@ Domain specific packages - `AstroML `_ Machine learning for astronomy. -- `MSMBuilder `_ Machine learning for protein - conformational dynamics time series. - Translations of scikit-learn documentation ------------------------------------------ From bc238b7a3ca2a9433c4736052e4158958da83814 Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Mon, 31 Jul 2023 05:02:34 -0400 Subject: [PATCH 122/264] FIX Disable set_output for label encoders (#26940) Co-authored-by: Adrin Jalali Co-authored-by: Omar Salman --- doc/whats_new/v1.3.rst | 6 ++++++ sklearn/preprocessing/_label.py | 6 +++--- sklearn/preprocessing/tests/test_label.py | 14 ++++++++++++++ 3 files changed, 23 insertions(+), 3 deletions(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index e0df3c1beba4b..d486b177b0f2c 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -23,6 +23,12 @@ Changelog :attr:`sklearn.neighbors.KDTree.valid_metrics` as public class attributes. :pr:`26754` by :user:`Julien Jerphanion `. +:mod:`sklearn.preprocessing` +............................ + +- |Fix| :class:`preprocessing.LabelEncoder` correctly accepts `y` as a keyword + argument. :pr:`26940` by `Thomas Fan`_. + :mod:`sklearn.tree` ................... diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py index f5044f9c9d417..3008710d3c3dc 100644 --- a/sklearn/preprocessing/_label.py +++ b/sklearn/preprocessing/_label.py @@ -31,7 +31,7 @@ ] -class LabelEncoder(TransformerMixin, BaseEstimator): +class LabelEncoder(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None): """Encode target labels with value between 0 and n_classes-1. This transformer should be used to encode target values, *i.e.* `y`, and @@ -165,7 +165,7 @@ def _more_tags(self): return {"X_types": ["1dlabels"]} -class LabelBinarizer(TransformerMixin, BaseEstimator): +class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None): """Binarize labels in a one-vs-all fashion. Several regression and binary classification algorithms are @@ -685,7 +685,7 @@ def _inverse_binarize_thresholding(y, output_type, classes, threshold): raise ValueError("{0} format is not supported".format(output_type)) -class MultiLabelBinarizer(TransformerMixin, BaseEstimator): +class MultiLabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None): """Transform between iterable of iterables and a multilabel format. Although a list of sets or tuples is a very intuitive format for multilabel diff --git a/sklearn/preprocessing/tests/test_label.py b/sklearn/preprocessing/tests/test_label.py index 7d413063968e4..633a386c75951 100644 --- a/sklearn/preprocessing/tests/test_label.py +++ b/sklearn/preprocessing/tests/test_label.py @@ -672,3 +672,17 @@ def test_nan_label_encoder(): y_trans = le.transform([np.nan]) assert_array_equal(y_trans, [2]) + + +@pytest.mark.parametrize( + "encoder", [LabelEncoder(), LabelBinarizer(), MultiLabelBinarizer()] +) +def test_label_encoders_do_not_have_set_output(encoder): + """Check that label encoders do not define set_output and work with y as a kwarg. + + Non-regression test for #26854. + """ + assert not hasattr(encoder, "set_output") + y_encoded_with_kwarg = encoder.fit_transform(y=["a", "b", "c"]) + y_encoded_positional = encoder.fit_transform(["a", "b", "c"]) + assert_array_equal(y_encoded_with_kwarg, y_encoded_positional) From 651b7965b20d72c3a97db53998e2366043cef307 Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Mon, 31 Jul 2023 07:40:31 -0400 Subject: [PATCH 123/264] FIX Adds more informative error message for OHE (#26931) --- sklearn/preprocessing/_encoders.py | 9 +++++++++ sklearn/preprocessing/tests/test_encoders.py | 20 ++++++++++++++++++++ sklearn/utils/estimator_checks.py | 4 ++-- 3 files changed, 31 insertions(+), 2 deletions(-) diff --git a/sklearn/preprocessing/_encoders.py b/sklearn/preprocessing/_encoders.py index febc2523845ad..5dc9929ded704 100644 --- a/sklearn/preprocessing/_encoders.py +++ b/sklearn/preprocessing/_encoders.py @@ -14,6 +14,7 @@ from ..utils._encode import _check_unknown, _encode, _get_counts, _unique from ..utils._mask import _get_mask from ..utils._param_validation import Hidden, Interval, RealNotInt, StrOptions +from ..utils._set_output import _get_output_config from ..utils.validation import _check_feature_names_in, check_is_fitted __all__ = ["OneHotEncoder", "OrdinalEncoder"] @@ -1008,6 +1009,14 @@ def transform(self, X): returned. """ check_is_fitted(self) + transform_output = _get_output_config("transform", estimator=self)["dense"] + if transform_output == "pandas" and self.sparse_output: + raise ValueError( + "Pandas output does not support sparse data. Set sparse_output=False to" + " output pandas DataFrames or disable pandas output via" + ' `ohe.set_output(transform="default").' + ) + # validation of X happens in _check_X called by _transform warn_on_unknown = self.drop is not None and self.handle_unknown in { "ignore", diff --git a/sklearn/preprocessing/tests/test_encoders.py b/sklearn/preprocessing/tests/test_encoders.py index ca809dd513cf3..9ba041c90f5de 100644 --- a/sklearn/preprocessing/tests/test_encoders.py +++ b/sklearn/preprocessing/tests/test_encoders.py @@ -1588,6 +1588,26 @@ def test_ohe_drop_first_explicit_categories(handle_unknown): assert_allclose(X_trans, X_expected) +def test_ohe_more_informative_error_message(): + """Raise informative error message when pandas output and sparse_output=True.""" + pd = pytest.importorskip("pandas") + df = pd.DataFrame({"a": [1, 2, 3], "b": ["z", "b", "b"]}, columns=["a", "b"]) + + ohe = OneHotEncoder(sparse_output=True) + ohe.set_output(transform="pandas") + + msg = ( + "Pandas output does not support sparse data. Set " + "sparse_output=False to output pandas DataFrames or disable pandas output" + ) + with pytest.raises(ValueError, match=msg): + ohe.fit_transform(df) + + ohe.fit(df) + with pytest.raises(ValueError, match=msg): + ohe.transform(df) + + def test_ordinal_encoder_passthrough_missing_values_float_errors_dtype(): """Test ordinal encoder with nan passthrough fails when dtype=np.int32.""" diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py index c13e666ec69e3..4c9434b92eda7 100644 --- a/sklearn/utils/estimator_checks.py +++ b/sklearn/utils/estimator_checks.py @@ -4482,7 +4482,7 @@ def check_set_output_transform_pandas(name, transformer_orig): outputs_pandas = _output_from_fit_transform(transformer_pandas, name, X, df, y) except ValueError as e: # transformer does not support sparse data - assert str(e) == "Pandas output does not support sparse data.", e + assert "Pandas output does not support sparse data." in str(e), e return for case in outputs_default: @@ -4528,7 +4528,7 @@ def check_global_output_transform_pandas(name, transformer_orig): ) except ValueError as e: # transformer does not support sparse data - assert str(e) == "Pandas output does not support sparse data.", e + assert "Pandas output does not support sparse data." in str(e), e return for case in outputs_default: From fd49d347b579f10331e1585cb80f8c94bd0d6448 Mon Sep 17 00:00:00 2001 From: Tialo <65392801+Tialo@users.noreply.github.com> Date: Mon, 31 Jul 2023 17:10:01 +0300 Subject: [PATCH 124/264] DOC Fixed typo, added missing comma in plot_forest_hist_grad_boosting_comparison example (#26954) --- .../ensemble/plot_forest_hist_grad_boosting_comparison.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/ensemble/plot_forest_hist_grad_boosting_comparison.py b/examples/ensemble/plot_forest_hist_grad_boosting_comparison.py index 7eab9a3437d65..0dde24116065d 100644 --- a/examples/ensemble/plot_forest_hist_grad_boosting_comparison.py +++ b/examples/ensemble/plot_forest_hist_grad_boosting_comparison.py @@ -12,7 +12,7 @@ trees according to each estimator: - `n_estimators` controls the number of trees in the forest. It's a fixed number. -- `max_iter` is the the maximum number of iterations in a gradient boosting +- `max_iter` is the maximum number of iterations in a gradient boosting based model. The number of iterations corresponds to the number of trees for regression and binary classification problems. Furthermore, the actual number of trees required by the model depends on the stopping criteria. @@ -210,7 +210,7 @@ # models uniformly dominate the Random Forest models in the "test score vs # training speed trade-off" (the HGBDT curve should be on the top left of the RF # curve, without ever crossing). The "test score vs prediction speed" trade-off -# can also be more disputed but it's most often favorable to HGBDT. It's always +# can also be more disputed, but it's most often favorable to HGBDT. It's always # a good idea to check both kinds of model (with hyper-parameter tuning) and # compare their performance on your specific problem to determine which model is # the best fit but **HGBT almost always offers a more favorable speed-accuracy From cf9f4fef02bd0804f1f0002f25c912b1e822d051 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Tue, 1 Aug 2023 17:32:23 +0200 Subject: [PATCH 125/264] MNT name it X_train in GradientBoosting (#26959) --- sklearn/ensemble/_gb.py | 43 ++++++++++++++++++++++++----------------- 1 file changed, 25 insertions(+), 18 deletions(-) diff --git a/sklearn/ensemble/_gb.py b/sklearn/ensemble/_gb.py index 777e1a18d8396..3d02eeab35a75 100644 --- a/sklearn/ensemble/_gb.py +++ b/sklearn/ensemble/_gb.py @@ -432,16 +432,18 @@ def fit(self, X, y, sample_weight=None, monitor=None): if self.n_iter_no_change is not None: stratify = y if is_classifier(self) else None - X, X_val, y, y_val, sample_weight, sample_weight_val = train_test_split( - X, - y, - sample_weight, - random_state=self.random_state, - test_size=self.validation_fraction, - stratify=stratify, + X_train, X_val, y_train, y_val, sample_weight_train, sample_weight_val = ( + train_test_split( + X, + y, + sample_weight, + random_state=self.random_state, + test_size=self.validation_fraction, + stratify=stratify, + ) ) if is_classifier(self): - if self._n_classes != np.unique(y).shape[0]: + if self._n_classes != np.unique(y_train).shape[0]: # We choose to error here. The problem is that the init # estimator would be trained on y, which has some missing # classes now, so its predictions would not have the @@ -452,6 +454,7 @@ def fit(self, X, y, sample_weight=None, monitor=None): "seed." ) else: + X_train, y_train, sample_weight_train = X, y, sample_weight X_val = y_val = sample_weight_val = None if not self._is_initialized(): @@ -461,19 +464,21 @@ def fit(self, X, y, sample_weight=None, monitor=None): # fit initial model and initialize raw predictions if self.init_ == "zero": raw_predictions = np.zeros( - shape=(X.shape[0], self._loss.K), dtype=np.float64 + shape=(X_train.shape[0], self._loss.K), dtype=np.float64 ) else: # XXX clean this once we have a support_sample_weight tag if sample_weight_is_none: - self.init_.fit(X, y) + self.init_.fit(X_train, y_train) else: msg = ( "The initial estimator {} does not support sample " "weights.".format(self.init_.__class__.__name__) ) try: - self.init_.fit(X, y, sample_weight=sample_weight) + self.init_.fit( + X_train, y_train, sample_weight=sample_weight_train + ) except TypeError as e: if "unexpected keyword argument 'sample_weight'" in str(e): # regular estimator without SW support @@ -491,7 +496,9 @@ def fit(self, X, y, sample_weight=None, monitor=None): else: # regular estimator whose input checking failed raise - raw_predictions = self._loss.get_init_raw_predictions(X, self.init_) + raw_predictions = self._loss.get_init_raw_predictions( + X_train, self.init_ + ) begin_at_stage = 0 @@ -511,22 +518,22 @@ def fit(self, X, y, sample_weight=None, monitor=None): # The requirements of _raw_predict # are more constrained than fit. It accepts only CSR # matrices. Finite values have already been checked in _validate_data. - X = check_array( - X, + X_train = check_array( + X_train, dtype=DTYPE, order="C", accept_sparse="csr", force_all_finite=False, ) - raw_predictions = self._raw_predict(X) + raw_predictions = self._raw_predict(X_train) self._resize_state() # fit the boosting stages n_stages = self._fit_stages( - X, - y, + X_train, + y_train, raw_predictions, - sample_weight, + sample_weight_train, self._rng, X_val, y_val, From 778a14cb87109270fe6021b408702df8e6a2a0e1 Mon Sep 17 00:00:00 2001 From: Meekail Zain <34613774+Micky774@users.noreply.github.com> Date: Tue, 1 Aug 2023 14:40:29 -0400 Subject: [PATCH 126/264] MNT Use `curl` instead of `wget` to avoid occasional `SSL` error on Cirrus CI (#26918) --- build_tools/cirrus/build_test_arm.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/cirrus/build_test_arm.sh b/build_tools/cirrus/build_test_arm.sh index 4eeef6ec2dc0c..dfe048da47a7f 100755 --- a/build_tools/cirrus/build_test_arm.sh +++ b/build_tools/cirrus/build_test_arm.sh @@ -25,7 +25,7 @@ setup_ccache() { MAMBAFORGE_URL="https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-aarch64.sh" # Install Mambaforge -wget $MAMBAFORGE_URL -O mambaforge.sh +curl -L $MAMBAFORGE_URL -o mambaforge.sh MAMBAFORGE_PATH=$HOME/mambaforge bash ./mambaforge.sh -b -p $MAMBAFORGE_PATH export PATH=$MAMBAFORGE_PATH/bin:$PATH From 80e3021a970c0cbbb36a0cd358cb1f9658816885 Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Wed, 2 Aug 2023 08:18:21 -0400 Subject: [PATCH 127/264] DOC Reduce whitespace above h1 tag (#26787) Co-authored-by: Gael Varoquaux --- doc/themes/scikit-learn-modern/static/css/theme.css | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/doc/themes/scikit-learn-modern/static/css/theme.css b/doc/themes/scikit-learn-modern/static/css/theme.css index 0a8822cdcd848..40ac5e25ea698 100644 --- a/doc/themes/scikit-learn-modern/static/css/theme.css +++ b/doc/themes/scikit-learn-modern/static/css/theme.css @@ -661,13 +661,19 @@ div.sk-sidebar-global-toc ul ul { div.sk-page-content h1 { background-color: #cde8ef; padding: 0.5rem; - margin-top: calc(max(2.5rem, 1vh)); + margin-top: calc(max(1rem, 1vh)); border-radius: 0 1rem; text-align: center; font-size: 2rem; word-wrap: break-word; } +/* General sibling selector: does not apply to first h1, to avoid gap in + * top of page */ +div.sk-page-content ~ h1 { + margin-top: calc(max(2.5rem, 1vh)); +} + div.sk-page-content h2 { padding: 0.5rem; background-color: #BED4EB; From c6fea5438b91ae0cb3e8d70f27bc75a4cd8dbde5 Mon Sep 17 00:00:00 2001 From: Tamara Date: Wed, 2 Aug 2023 14:22:46 +0200 Subject: [PATCH 128/264] DOC Add random_state to all classifiers in plot_classifier_comparison.py (#26968) Co-authored-by: AnnaWey --- .../classification/plot_classifier_comparison.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/examples/classification/plot_classifier_comparison.py b/examples/classification/plot_classifier_comparison.py index 75164cff8b492..8d7eb7c63c81a 100644 --- a/examples/classification/plot_classifier_comparison.py +++ b/examples/classification/plot_classifier_comparison.py @@ -58,13 +58,15 @@ classifiers = [ KNeighborsClassifier(3), - SVC(kernel="linear", C=0.025), - SVC(gamma=2, C=1), - GaussianProcessClassifier(1.0 * RBF(1.0)), - DecisionTreeClassifier(max_depth=5), - RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1), - MLPClassifier(alpha=1, max_iter=1000), - AdaBoostClassifier(), + SVC(kernel="linear", C=0.025, random_state=42), + SVC(gamma=2, C=1, random_state=42), + GaussianProcessClassifier(1.0 * RBF(1.0), random_state=42), + DecisionTreeClassifier(max_depth=5, random_state=42), + RandomForestClassifier( + max_depth=5, n_estimators=10, max_features=1, random_state=42 + ), + MLPClassifier(alpha=1, max_iter=1000, random_state=42), + AdaBoostClassifier(random_state=42), GaussianNB(), QuadraticDiscriminantAnalysis(), ] From cd68222d9909f483df314edc1089caf7dd4dc2ba Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Wed, 2 Aug 2023 08:56:10 -0400 Subject: [PATCH 129/264] CI Cross compile wheel macos wheels on github actions (#26985) --- .github/workflows/wheels.yml | 12 ++++++++++++ build_tools/cirrus/arm_wheel.yml | 8 ++------ 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml index b43f29ffa4f7f..8026996106d8b 100644 --- a/.github/workflows/wheels.yml +++ b/.github/workflows/wheels.yml @@ -103,6 +103,18 @@ jobs: python: 311 platform_id: macosx_x86_64 + # MacOS arm64 + # The latest Python version is built and tested on CirrusCI + - os: macos-latest + python: 38 + platform_id: macosx_arm64 + - os: macos-latest + python: 39 + platform_id: macosx_arm64 + - os: macos-latest + python: 310 + platform_id: macosx_arm64 + steps: - name: Checkout scikit-learn uses: actions/checkout@v3 diff --git a/build_tools/cirrus/arm_wheel.yml b/build_tools/cirrus/arm_wheel.yml index a7023867e1109..1d19a67e3fb78 100644 --- a/build_tools/cirrus/arm_wheel.yml +++ b/build_tools/cirrus/arm_wheel.yml @@ -16,12 +16,8 @@ macos_arm64_wheel_task: # See `maint_tools/update_tracking_issue.py` for details on the permissions the token requires. BOT_GITHUB_TOKEN: ENCRYPTED[9b50205e2693f9e4ce9a3f0fcb897a259289062fda2f5a3b8aaa6c56d839e0854a15872f894a70fca337dd4787274e0f] matrix: - - env: - CIBW_BUILD: cp38-macosx_arm64 - - env: - CIBW_BUILD: cp39-macosx_arm64 - - env: - CIBW_BUILD: cp310-macosx_arm64 + # Only the latest Python version is built and tested on CirrusCI, the other + # macos arm64 builds are on GitHub Actions - env: CIBW_BUILD: cp311-macosx_arm64 From f66fa3f18a5733f03904beb78a9fffb89983398b Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Wed, 2 Aug 2023 08:59:05 -0400 Subject: [PATCH 130/264] CI Only test latest python version on CirrusCI (#26986) --- build_tools/cirrus/arm_wheel.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/build_tools/cirrus/arm_wheel.yml b/build_tools/cirrus/arm_wheel.yml index 1d19a67e3fb78..5616108315fba 100644 --- a/build_tools/cirrus/arm_wheel.yml +++ b/build_tools/cirrus/arm_wheel.yml @@ -56,12 +56,16 @@ linux_arm64_wheel_task: # See `maint_tools/update_tracking_issue.py` for details on the permissions the token requires. BOT_GITHUB_TOKEN: ENCRYPTED[9b50205e2693f9e4ce9a3f0fcb897a259289062fda2f5a3b8aaa6c56d839e0854a15872f894a70fca337dd4787274e0f] matrix: + # Only the latest Python version is tested - env: CIBW_BUILD: cp38-manylinux_aarch64 + CIBW_TEST_SKIP: "*_aarch64" - env: CIBW_BUILD: cp39-manylinux_aarch64 + CIBW_TEST_SKIP: "*_aarch64" - env: CIBW_BUILD: cp310-manylinux_aarch64 + CIBW_TEST_SKIP: "*_aarch64" - env: CIBW_BUILD: cp311-manylinux_aarch64 From ccd3a3a725d83c32f979a06741f156f7e20bf0c0 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Wed, 2 Aug 2023 23:02:42 +1000 Subject: [PATCH 131/264] MAINT Remove flake8 mentions/ignore comments (#26988) --- doc/tutorial/machine_learning_map/pyparsing.py | 2 +- examples/release_highlights/plot_release_highlights_0_23_0.py | 2 +- examples/release_highlights/plot_release_highlights_0_24_0.py | 2 +- examples/release_highlights/plot_release_highlights_1_0_0.py | 2 +- examples/release_highlights/plot_release_highlights_1_1_0.py | 2 +- examples/release_highlights/plot_release_highlights_1_2_0.py | 2 +- examples/release_highlights/plot_release_highlights_1_3_0.py | 2 +- pyproject.toml | 2 +- sklearn/utils/tests/test_pprint.py | 2 +- 9 files changed, 9 insertions(+), 9 deletions(-) diff --git a/doc/tutorial/machine_learning_map/pyparsing.py b/doc/tutorial/machine_learning_map/pyparsing.py index 0418cf2b51528..88d00e138d02c 100644 --- a/doc/tutorial/machine_learning_map/pyparsing.py +++ b/doc/tutorial/machine_learning_map/pyparsing.py @@ -21,7 +21,7 @@ # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # -# flake8: noqa +# ruff: noqa __doc__ = \ """ diff --git a/examples/release_highlights/plot_release_highlights_0_23_0.py b/examples/release_highlights/plot_release_highlights_0_23_0.py index 7c6836632e3f0..d7ae7465a590b 100644 --- a/examples/release_highlights/plot_release_highlights_0_23_0.py +++ b/examples/release_highlights/plot_release_highlights_0_23_0.py @@ -1,4 +1,4 @@ -# flake8: noqa +# ruff: noqa """ ======================================== Release Highlights for scikit-learn 0.23 diff --git a/examples/release_highlights/plot_release_highlights_0_24_0.py b/examples/release_highlights/plot_release_highlights_0_24_0.py index a55b4aabc7994..29082c1a078f4 100644 --- a/examples/release_highlights/plot_release_highlights_0_24_0.py +++ b/examples/release_highlights/plot_release_highlights_0_24_0.py @@ -1,4 +1,4 @@ -# flake8: noqa +# ruff: noqa """ ======================================== Release Highlights for scikit-learn 0.24 diff --git a/examples/release_highlights/plot_release_highlights_1_0_0.py b/examples/release_highlights/plot_release_highlights_1_0_0.py index 383612e611688..7ac09dd193c0f 100644 --- a/examples/release_highlights/plot_release_highlights_1_0_0.py +++ b/examples/release_highlights/plot_release_highlights_1_0_0.py @@ -1,4 +1,4 @@ -# flake8: noqa +# ruff: noqa """ ======================================= Release Highlights for scikit-learn 1.0 diff --git a/examples/release_highlights/plot_release_highlights_1_1_0.py b/examples/release_highlights/plot_release_highlights_1_1_0.py index f6432cf15037c..b3058a7e0aa27 100644 --- a/examples/release_highlights/plot_release_highlights_1_1_0.py +++ b/examples/release_highlights/plot_release_highlights_1_1_0.py @@ -1,4 +1,4 @@ -# flake8: noqa +# ruff: noqa """ ======================================= Release Highlights for scikit-learn 1.1 diff --git a/examples/release_highlights/plot_release_highlights_1_2_0.py b/examples/release_highlights/plot_release_highlights_1_2_0.py index 8165c3bc4eed0..695e74cfcdd64 100644 --- a/examples/release_highlights/plot_release_highlights_1_2_0.py +++ b/examples/release_highlights/plot_release_highlights_1_2_0.py @@ -1,4 +1,4 @@ -# flake8: noqa +# ruff: noqa """ ======================================= Release Highlights for scikit-learn 1.2 diff --git a/examples/release_highlights/plot_release_highlights_1_3_0.py b/examples/release_highlights/plot_release_highlights_1_3_0.py index 8fa1ea057ac91..5ce2617cd08aa 100644 --- a/examples/release_highlights/plot_release_highlights_1_3_0.py +++ b/examples/release_highlights/plot_release_highlights_1_3_0.py @@ -1,4 +1,4 @@ -# flake8: noqa +# ruff: noqa """ ======================================= Release Highlights for scikit-learn 1.3 diff --git a/pyproject.toml b/pyproject.toml index efd72adf44392..c98ed2130189f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -83,7 +83,7 @@ exclude=[ # + E501 (line too long) because keeping it < 88 in cython # often makes code less readable. ignore = [ - # check ignored by default in flake8. Meaning unclear. + # multiple spaces/tab after comma 'E24', # space before : (needed for how black formats slicing) 'E203', diff --git a/sklearn/utils/tests/test_pprint.py b/sklearn/utils/tests/test_pprint.py index a4aaa8f21b6b7..ec48c4a012574 100644 --- a/sklearn/utils/tests/test_pprint.py +++ b/sklearn/utils/tests/test_pprint.py @@ -12,7 +12,7 @@ # Ignore flake8 (lots of line too long issues) -# flake8: noqa +# ruff: noqa # Constructors excerpted to test pprinting From 1c9986d0e231000b8851554c7b84641a68f0f0fa Mon Sep 17 00:00:00 2001 From: Jon Nordby Date: Wed, 2 Aug 2023 17:43:13 +0200 Subject: [PATCH 132/264] DOC Add 2 related projects for microcontroller export (#26984) Co-authored-by: Meekail Zain <34613774+Micky774@users.noreply.github.com> --- doc/related_projects.rst | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/doc/related_projects.rst b/doc/related_projects.rst index ef6ed9e5ec266..10304a7070be0 100644 --- a/doc/related_projects.rst +++ b/doc/related_projects.rst @@ -136,6 +136,14 @@ enhance the functionality of scikit-learn's estimators. Compiles tree-based ensemble models into C code for minimizing prediction latency. +- `micromlgen `_ + MicroML brings Machine Learning algorithms to microcontrollers. + Supports several scikit-learn classifiers by transpiling them to C code. + +- `emlearn `_ + Implements scikit-learn estimators in C99 for embedded devices and microcontrollers. + Supports several classifier, regression and outlier detection models. + **Model throughput** - `Intel(R) Extension for scikit-learn `_ From ae941d2701c91671fe848817ba055908c9e0e269 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Thu, 3 Aug 2023 15:02:27 +0200 Subject: [PATCH 133/264] DOC improve the KNN classifier example (#26993) Co-authored-by: Adrin Jalali Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> --- doc/modules/neighbors.rst | 8 +- examples/neighbors/plot_classification.py | 107 ++++++++++++++-------- 2 files changed, 71 insertions(+), 44 deletions(-) diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst index d11287e7c29b1..d3a7df74e6348 100644 --- a/doc/modules/neighbors.rst +++ b/doc/modules/neighbors.rst @@ -188,13 +188,9 @@ distance can be supplied to compute the weights. .. |classification_1| image:: ../auto_examples/neighbors/images/sphx_glr_plot_classification_001.png :target: ../auto_examples/neighbors/plot_classification.html - :scale: 50 - -.. |classification_2| image:: ../auto_examples/neighbors/images/sphx_glr_plot_classification_002.png - :target: ../auto_examples/neighbors/plot_classification.html - :scale: 50 + :scale: 75 -.. centered:: |classification_1| |classification_2| +.. centered:: |classification_1| .. topic:: Examples: diff --git a/examples/neighbors/plot_classification.py b/examples/neighbors/plot_classification.py index 4ed23862ae455..43c45558054cf 100644 --- a/examples/neighbors/plot_classification.py +++ b/examples/neighbors/plot_classification.py @@ -3,61 +3,92 @@ Nearest Neighbors Classification ================================ -Sample usage of Nearest Neighbors classification. -It will plot the decision boundaries for each class. - +This example shows how to use :class:`~sklearn.neighbors.KNeighborsClassifier`. +We train such a classifier on the iris dataset and observe the difference of the +decision boundary obtained with regards to the parameter `weights`. """ -import matplotlib.pyplot as plt -import seaborn as sns -from matplotlib.colors import ListedColormap +# %% +# Load the data +# ------------- +# +# In this example, we use the iris dataset. We split the data into a train and test +# dataset. +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split -from sklearn import datasets, neighbors -from sklearn.inspection import DecisionBoundaryDisplay +iris = load_iris(as_frame=True) +X = iris.data[["sepal length (cm)", "sepal width (cm)"]] +y = iris.target +X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) -n_neighbors = 15 +# %% +# K-nearest neighbors classifier +# ------------------------------ +# +# We want to use a k-nearest neighbors classifier considering a neighborhood of 11 data +# points. Since our k-nearest neighbors model uses euclidean distance to find the +# nearest neighbors, it is therefore important to scale the data beforehand. Refer to +# the example entitled +# :ref:`sphx_glr_auto_examples_preprocessing_plot_scaling_importance.py` for more +# detailed information. +# +# Thus, we use a :class:`~sklearn.pipeline.Pipeline` to chain a scaler before to use +# our classifier. +from sklearn.neighbors import KNeighborsClassifier +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import StandardScaler -# import some data to play with -iris = datasets.load_iris() +clf = Pipeline( + steps=[("scaler", StandardScaler()), ("knn", KNeighborsClassifier(n_neighbors=11))] +) -# we only take the first two features. We could avoid this ugly -# slicing by using a two-dim dataset -X = iris.data[:, :2] -y = iris.target +# %% +# Decision boundary +# ----------------- +# +# Now, we fit two classifiers with different values of the parameter +# `weights`. We plot the decision boundary of each classifier as well as the original +# dataset to observe the difference. +import matplotlib.pyplot as plt -# Create color maps -cmap_light = ListedColormap(["orange", "cyan", "cornflowerblue"]) -cmap_bold = ["darkorange", "c", "darkblue"] +from sklearn.inspection import DecisionBoundaryDisplay -for weights in ["uniform", "distance"]: - # we create an instance of Neighbours Classifier and fit the data. - clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights) - clf.fit(X, y) +_, axs = plt.subplots(ncols=2, figsize=(12, 5)) - _, ax = plt.subplots() - DecisionBoundaryDisplay.from_estimator( +for ax, weights in zip(axs, ("uniform", "distance")): + clf.set_params(knn__weights=weights).fit(X_train, y_train) + disp = DecisionBoundaryDisplay.from_estimator( clf, - X, - cmap=cmap_light, - ax=ax, + X_test, response_method="predict", plot_method="pcolormesh", xlabel=iris.feature_names[0], ylabel=iris.feature_names[1], shading="auto", + alpha=0.5, + ax=ax, ) - - # Plot also the training points - sns.scatterplot( - x=X[:, 0], - y=X[:, 1], - hue=iris.target_names[y], - palette=cmap_bold, - alpha=1.0, - edgecolor="black", + scatter = disp.ax_.scatter(X.iloc[:, 0], X.iloc[:, 1], c=y, edgecolors="k") + disp.ax_.legend( + scatter.legend_elements()[0], + iris.target_names, + loc="lower left", + title="Classes", ) - plt.title( - "3-Class classification (k = %i, weights = '%s')" % (n_neighbors, weights) + _ = disp.ax_.set_title( + f"3-Class classification\n(k={clf[-1].n_neighbors}, weights={weights!r})" ) plt.show() + +# %% +# Conclusion +# ---------- +# +# We observe that the parameter `weights` has an impact on the decision boundary. When +# `weights="unifom"` all nearest neighbors will have the same impact on the decision. +# Whereas when `weights="distance"` the weight given to each neighbor is proportional +# to the inverse of the distance from that neighbor to the query point. +# +# In some cases, taking the distance into account might improve the model. From 5b77360791666c36c1fa62cfc76b32b796e42939 Mon Sep 17 00:00:00 2001 From: Yuusuke Hiramatsu <140389350+hiramatsuyuusuke@users.noreply.github.com> Date: Mon, 7 Aug 2023 16:00:10 +0900 Subject: [PATCH 134/264] DOC Fix miniforge link with typo in install.rst (#27019) --- doc/install.rst | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/install.rst b/doc/install.rst index bf2832bf72f24..263e83cdc31a5 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -61,7 +61,7 @@ Installing the latest release >Install python3 and python3-pip using the package manager of the Linux Distribution.Install conda using the Anaconda or miniconda - installers or the miniforge installers + installers or the miniforge installers (no administrator permission required for any of those). @@ -279,14 +279,14 @@ and in the `main`, `conda-forge` and `intel` conda channels: conda install scikit-learn-intelex -This package has an Intel optimized version of many estimators. Whenever -an alternative implementation doesn't exist, scikit-learn implementation -is used as a fallback. Those optimized solvers come from the oneDAL -C++ library and are optimized for the x86_64 architecture, and are +This package has an Intel optimized version of many estimators. Whenever +an alternative implementation doesn't exist, scikit-learn implementation +is used as a fallback. Those optimized solvers come from the oneDAL +C++ library and are optimized for the x86_64 architecture, and are optimized for multi-core Intel CPUs. Note that those solvers are not enabled by default, please refer to the -`scikit-learn-intelex `_ +`scikit-learn-intelex `_ documentation for more details on usage scenarios. Direct export example: .. prompt:: bash $ From 3841a65d8f95ce026a4562f449ed50f0a11d3b4d Mon Sep 17 00:00:00 2001 From: Ben Mares Date: Mon, 7 Aug 2023 10:38:31 +0200 Subject: [PATCH 135/264] MNT Fix good Conda versions for updating lockfile (#26908) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- build_tools/update_environments_and_lock_files.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 97e2ff8d6613f..5c740a805f96e 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -554,15 +554,15 @@ def check_conda_version(): # Avoid issues with glibc (https://github.com/conda/conda-lock/issues/292) # or osx (https://github.com/conda/conda-lock/issues/408) virtual package. # The glibc one has been fixed in conda 23.1.0 and the osx has been fixed - # in main and will be fixed when conda >= 23.6 is released. + # in conda 23.7.0. conda_info_output = execute_command(["conda", "info", "--json"]) conda_info = json.loads(conda_info_output) conda_version = Version(conda_info["conda_version"]) - if Version("22.9.0") < conda_version < Version("23.6"): + if Version("22.9.0") < conda_version < Version("23.7"): raise RuntimeError( - f"conda version should be <= 22.9.0 or >= 23.6 got: {conda_version}" + f"conda version should be <= 22.9.0 or >= 23.7 got: {conda_version}" ) From d82fd2abc71a5e9dc8cfdddc7911d376729786c8 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Mon, 7 Aug 2023 14:09:50 +0200 Subject: [PATCH 136/264] FIX `param_distribution` param of `HalvingRandomSearchCV` accepts list of dicts (#26893) Co-authored-by: Guillaume Lemaitre --- doc/whats_new/v1.3.rst | 4 + .../_search_successive_halving.py | 8 +- sklearn/model_selection/tests/test_search.py | 39 +++++----- .../tests/test_successive_halving.py | 73 ++++++++++++++++++- 4 files changed, 99 insertions(+), 25 deletions(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index d486b177b0f2c..e9961ab7567a3 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -23,6 +23,10 @@ Changelog :attr:`sklearn.neighbors.KDTree.valid_metrics` as public class attributes. :pr:`26754` by :user:`Julien Jerphanion `. +- |Fix| :class:`sklearn.model_selection.HalvingRandomSearchCV` no longer raises + when the input to the `param_distributions` parameter is a list of dicts. + :pr:`26893` by :user:`Stefanie Senger `. + :mod:`sklearn.preprocessing` ............................ diff --git a/sklearn/model_selection/_search_successive_halving.py b/sklearn/model_selection/_search_successive_halving.py index 38fe1e0e7a15c..708092d09a2a5 100644 --- a/sklearn/model_selection/_search_successive_halving.py +++ b/sklearn/model_selection/_search_successive_halving.py @@ -750,11 +750,13 @@ class HalvingRandomSearchCV(BaseSuccessiveHalving): Either estimator needs to provide a ``score`` function, or ``scoring`` must be passed. - param_distributions : dict - Dictionary with parameters names (string) as keys and distributions + param_distributions : dict or list of dicts + Dictionary with parameters names (`str`) as keys and distributions or lists of parameters to try. Distributions must provide a ``rvs`` method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. + If a list of dicts is given, first a dict is sampled uniformly, and + then a parameter is sampled using that dict as above. n_candidates : "exhaust" or int, default="exhaust" The number of candidate parameters to sample, at the first @@ -1024,7 +1026,7 @@ class HalvingRandomSearchCV(BaseSuccessiveHalving): _parameter_constraints: dict = { **BaseSuccessiveHalving._parameter_constraints, - "param_distributions": [dict], + "param_distributions": [dict, list], "n_candidates": [ Interval(Integral, 0, None, closed="neither"), StrOptions({"exhaust"}), diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py index 6ea52049f3ced..04c3f1f156fab 100644 --- a/sklearn/model_selection/tests/test_search.py +++ b/sklearn/model_selection/tests/test_search.py @@ -900,18 +900,16 @@ def check_cv_results_array_types(search, param_keys, score_keys): assert cv_results["rank_test_%s" % key].dtype == np.int32 -def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand): +def check_cv_results_keys(cv_results, param_keys, score_keys, n_cand, extra_keys=()): # Test the search.cv_results_ contains all the required results - assert_array_equal( - sorted(cv_results.keys()), sorted(param_keys + score_keys + ("params",)) - ) + all_keys = param_keys + score_keys + extra_keys + assert_array_equal(sorted(cv_results.keys()), sorted(all_keys + ("params",))) assert all(cv_results[key].shape == (n_cand,) for key in param_keys + score_keys) def test_grid_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) - n_splits = 3 n_grid_points = 6 params = [ dict( @@ -949,9 +947,7 @@ def test_grid_search_cv_results(): ) n_candidates = n_grid_points - search = GridSearchCV( - SVC(), cv=n_splits, param_grid=params, return_train_score=True - ) + search = GridSearchCV(SVC(), cv=3, param_grid=params, return_train_score=True) search.fit(X, y) cv_results = search.cv_results_ # Check if score and timing are reasonable @@ -967,17 +963,20 @@ def test_grid_search_cv_results(): check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) # Check masking cv_results = search.cv_results_ - n_candidates = len(search.cv_results_["params"]) - assert all( + + poly_results = [ ( cv_results["param_C"].mask[i] and cv_results["param_gamma"].mask[i] and not cv_results["param_degree"].mask[i] ) for i in range(n_candidates) - if cv_results["param_kernel"][i] == "linear" - ) - assert all( + if cv_results["param_kernel"][i] == "poly" + ] + assert all(poly_results) + assert len(poly_results) == 2 + + rbf_results = [ ( not cv_results["param_C"].mask[i] and not cv_results["param_gamma"].mask[i] @@ -985,13 +984,14 @@ def test_grid_search_cv_results(): ) for i in range(n_candidates) if cv_results["param_kernel"][i] == "rbf" - ) + ] + assert all(rbf_results) + assert len(rbf_results) == 4 def test_random_search_cv_results(): X, y = make_classification(n_samples=50, n_features=4, random_state=42) - n_splits = 3 n_search_iter = 30 params = [ @@ -1016,12 +1016,12 @@ def test_random_search_cv_results(): "mean_score_time", "std_score_time", ) - n_cand = n_search_iter + n_candidates = n_search_iter search = RandomizedSearchCV( SVC(), n_iter=n_search_iter, - cv=n_splits, + cv=3, param_distributions=params, return_train_score=True, ) @@ -1029,8 +1029,7 @@ def test_random_search_cv_results(): cv_results = search.cv_results_ # Check results structure check_cv_results_array_types(search, param_keys, score_keys) - check_cv_results_keys(cv_results, param_keys, score_keys, n_cand) - n_candidates = len(search.cv_results_["params"]) + check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates) assert all( ( cv_results["param_C"].mask[i] @@ -1038,7 +1037,7 @@ def test_random_search_cv_results(): and not cv_results["param_degree"].mask[i] ) for i in range(n_candidates) - if cv_results["param_kernel"][i] == "linear" + if cv_results["param_kernel"][i] == "poly" ) assert all( ( diff --git a/sklearn/model_selection/tests/test_successive_halving.py b/sklearn/model_selection/tests/test_successive_halving.py index d4cc09ee01044..6c89f89afa684 100644 --- a/sklearn/model_selection/tests/test_successive_halving.py +++ b/sklearn/model_selection/tests/test_successive_halving.py @@ -2,7 +2,7 @@ import numpy as np import pytest -from scipy.stats import norm, randint +from scipy.stats import expon, norm, randint from sklearn.datasets import make_classification from sklearn.dummy import DummyClassifier @@ -23,7 +23,11 @@ _SubsampleMetaSplitter, _top_k, ) -from sklearn.svm import LinearSVC +from sklearn.model_selection.tests.test_search import ( + check_cv_results_array_types, + check_cv_results_keys, +) +from sklearn.svm import SVC, LinearSVC class FastClassifier(DummyClassifier): @@ -777,3 +781,68 @@ def test_select_best_index(SearchCV): # we expect the index of 'i' best_index = SearchCV._select_best_index(None, None, results) assert best_index == 8 + + +def test_halving_random_search_list_of_dicts(): + """Check the behaviour of the `HalvingRandomSearchCV` with `param_distribution` + being a list of dictionary. + """ + X, y = make_classification(n_samples=150, n_features=4, random_state=42) + + params = [ + {"kernel": ["rbf"], "C": expon(scale=10), "gamma": expon(scale=0.1)}, + {"kernel": ["poly"], "degree": [2, 3]}, + ] + param_keys = ( + "param_C", + "param_degree", + "param_gamma", + "param_kernel", + ) + score_keys = ( + "mean_test_score", + "mean_train_score", + "rank_test_score", + "split0_test_score", + "split1_test_score", + "split2_test_score", + "split0_train_score", + "split1_train_score", + "split2_train_score", + "std_test_score", + "std_train_score", + "mean_fit_time", + "std_fit_time", + "mean_score_time", + "std_score_time", + ) + extra_keys = ("n_resources", "iter") + + search = HalvingRandomSearchCV( + SVC(), cv=3, param_distributions=params, return_train_score=True, random_state=0 + ) + search.fit(X, y) + n_candidates = sum(search.n_candidates_) + cv_results = search.cv_results_ + # Check results structure + check_cv_results_keys(cv_results, param_keys, score_keys, n_candidates, extra_keys) + check_cv_results_array_types(search, param_keys, score_keys) + + assert all( + ( + cv_results["param_C"].mask[i] + and cv_results["param_gamma"].mask[i] + and not cv_results["param_degree"].mask[i] + ) + for i in range(n_candidates) + if cv_results["param_kernel"][i] == "poly" + ) + assert all( + ( + not cv_results["param_C"].mask[i] + and not cv_results["param_gamma"].mask[i] + and cv_results["param_degree"].mask[i] + ) + for i in range(n_candidates) + if cv_results["param_kernel"][i] == "rbf" + ) From bd21f1184c6bf56e8a0f1a8587961d15ca9630fe Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Tue, 8 Aug 2023 01:59:50 -0400 Subject: [PATCH 137/264] CI Only run arm tests nightly (#26996) Co-authored-by: Julien Jerphanion --- .cirrus.star | 12 +++++++----- build_tools/cirrus/arm_tests.yml | 8 +++++++- doc/developers/contributing.rst | 1 + 3 files changed, 15 insertions(+), 6 deletions(-) diff --git a/.cirrus.star b/.cirrus.star index 8b3de0d10c532..f0b458d74289a 100644 --- a/.cirrus.star +++ b/.cirrus.star @@ -14,7 +14,7 @@ def main(ctx): # Nightly jobs always run if env.get("CIRRUS_CRON", "") == "nightly": - return fs.read(arm_wheel_yaml) + return fs.read(arm_wheel_yaml) + fs.read(arm_tests_yaml) # Get commit message for event. We can not use `git` here because there is # no command line access in starlark. Thus we need to query the GitHub API @@ -26,10 +26,12 @@ def main(ctx): response = http.get(url).json() commit_msg = response["message"] - if "[skip ci]" in commit_msg: - return [] + jobs_to_run = "" if "[cd build]" in commit_msg or "[cd build cirrus]" in commit_msg: - return fs.read(arm_wheel_yaml) + fs.read(arm_tests_yaml) + jobs_to_run += fs.read(arm_wheel_yaml) + + if "[cirrus arm]" in commit_msg: + jobs_to_run += fs.read(arm_tests_yaml) - return fs.read(arm_tests_yaml) + return jobs_to_run diff --git a/build_tools/cirrus/arm_tests.yml b/build_tools/cirrus/arm_tests.yml index a6e5919ecc32f..d1ac551a749e3 100644 --- a/build_tools/cirrus/arm_tests.yml +++ b/build_tools/cirrus/arm_tests.yml @@ -17,4 +17,10 @@ linux_aarch64_test_task: folder: /root/.conda/pkgs fingerprint_script: cat build_tools/cirrus/py39_conda_forge_linux-aarch64_conda.lock - test_script: bash build_tools/cirrus/build_test_arm.sh + test_script: | + bash build_tools/cirrus/build_test_arm.sh + # On success, this script is run updating the issue. + bash build_tools/cirrus/update_tracking_issue.sh true + + on_failure: + update_tracker_script: bash build_tools/cirrus/update_tracking_issue.sh false diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index fc1ef95dbced0..6aecc524a9a30 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -542,6 +542,7 @@ message, the following actions are taken. [pypy] Build & test with PyPy [pyodide] Build & test with Pyodide [azure parallel] Run Azure CI jobs in parallel + [cirrus arm] Run Cirrus CI ARM test [float32] Run float32 tests by setting `SKLEARN_RUN_FLOAT32_TESTS=1`. See :ref:`environment_variable` for more details [doc skip] Docs are not built [doc quick] Docs built, but excludes example gallery plots From 0d4cda5a4263a9311e314f12efe1a50160554e0e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mateusz=20Sok=C3=B3=C5=82?= <8431159+mtsokol@users.noreply.github.com> Date: Tue, 8 Aug 2023 08:42:48 +0200 Subject: [PATCH 138/264] ENH: Update numpy exceptions imports (#27013) --- build_tools/azure/test_script.sh | 2 +- sklearn/utils/_testing.py | 4 ++-- sklearn/utils/fixes.py | 8 ++++++++ sklearn/utils/multiclass.py | 9 +++++---- 4 files changed, 16 insertions(+), 7 deletions(-) diff --git a/build_tools/azure/test_script.sh b/build_tools/azure/test_script.sh index 0ba477d3fdbca..5117473ea6366 100755 --- a/build_tools/azure/test_script.sh +++ b/build_tools/azure/test_script.sh @@ -49,7 +49,7 @@ if [[ "$COVERAGE" == "true" ]]; then fi if [[ -n "$CHECK_WARNINGS" ]]; then - TEST_CMD="$TEST_CMD -Werror::DeprecationWarning -Werror::FutureWarning -Werror::numpy.VisibleDeprecationWarning" + TEST_CMD="$TEST_CMD -Werror::DeprecationWarning -Werror::FutureWarning -Werror::sklearn.utils.fixes.VisibleDeprecationWarning" # numpy's 1.19.0's tostring() deprecation is ignored until scipy and joblib # removes its usage diff --git a/sklearn/utils/_testing.py b/sklearn/utils/_testing.py index c37e7dac858e3..2c0374eeabba6 100644 --- a/sklearn/utils/_testing.py +++ b/sklearn/utils/_testing.py @@ -47,7 +47,7 @@ _in_unstable_openblas_configuration, ) from sklearn.utils._array_api import _check_array_api_dispatch -from sklearn.utils.fixes import threadpool_info +from sklearn.utils.fixes import VisibleDeprecationWarning, threadpool_info from sklearn.utils.multiclass import check_classification_targets from sklearn.utils.validation import ( check_array, @@ -96,7 +96,7 @@ def assert_no_warnings(func, *args, **kw): result = func(*args, **kw) if hasattr(np, "FutureWarning"): # Filter out numpy-specific warnings in numpy >= 1.9 - w = [e for e in w if e.category is not np.VisibleDeprecationWarning] + w = [e for e in w if e.category is not VisibleDeprecationWarning] if len(w) > 0: raise AssertionError( diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py index 2202a1daaf90a..d33b638358157 100644 --- a/sklearn/utils/fixes.py +++ b/sklearn/utils/fixes.py @@ -158,3 +158,11 @@ def _contents(data_module): ) else: return resources.contents(data_module) + + +# For +1.25 NumPy versions exceptions and warnings are being moved +# to a dedicated submodule. +if np_version >= parse_version("1.25.0"): + from numpy.exceptions import VisibleDeprecationWarning +else: + from numpy import VisibleDeprecationWarning # type: ignore # noqa diff --git a/sklearn/utils/multiclass.py b/sklearn/utils/multiclass.py index 892d77c7e01e5..1f46f6400df98 100644 --- a/sklearn/utils/multiclass.py +++ b/sklearn/utils/multiclass.py @@ -14,6 +14,7 @@ from scipy.sparse import issparse from ..utils._array_api import get_namespace +from ..utils.fixes import VisibleDeprecationWarning from .validation import _assert_all_finite, check_array @@ -161,10 +162,10 @@ def is_multilabel(y): ensure_min_features=0, ) with warnings.catch_warnings(): - warnings.simplefilter("error", np.VisibleDeprecationWarning) + warnings.simplefilter("error", VisibleDeprecationWarning) try: y = check_array(y, dtype=None, **check_y_kwargs) - except (np.VisibleDeprecationWarning, ValueError) as e: + except (VisibleDeprecationWarning, ValueError) as e: if str(e).startswith("Complex data not supported"): raise @@ -324,11 +325,11 @@ def type_of_target(y, input_name=""): ) with warnings.catch_warnings(): - warnings.simplefilter("error", np.VisibleDeprecationWarning) + warnings.simplefilter("error", VisibleDeprecationWarning) if not issparse(y): try: y = check_array(y, dtype=None, **check_y_kwargs) - except (np.VisibleDeprecationWarning, ValueError) as e: + except (VisibleDeprecationWarning, ValueError) as e: if str(e).startswith("Complex data not supported"): raise From c3b66098ee60201e722069dc526759aaad15ae07 Mon Sep 17 00:00:00 2001 From: Shreesha Kumar Bhat Date: Wed, 9 Aug 2023 03:35:07 +0530 Subject: [PATCH 139/264] FIX KNNImputer missing indicator column addition when add_indicator=True (#26600) Co-authored-by: shreesha3112 --- doc/whats_new/v1.3.rst | 7 ++++++ sklearn/impute/_knn.py | 7 +++++- sklearn/impute/tests/test_common.py | 36 +++++++++++++++++++++++++++++ 3 files changed, 49 insertions(+), 1 deletion(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index e9961ab7567a3..0f46f28cd340b 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -12,6 +12,13 @@ Version 1.3.1 Changelog --------- +:mod:`sklearn.impute` +..................... + +- |Fix| :class:`impute.KNNImputer` now correctly adds a missing indicator column in + ``transform`` when ``add_indicator`` is set to ``True`` and missing values are observed + during ``fit``. :pr:`26600` by :user:`Shreesha Kumar Bhat `. + :mod:`sklearn.neighbors` ........................ diff --git a/sklearn/impute/_knn.py b/sklearn/impute/_knn.py index db0da278b39ef..e36b49f262b2d 100644 --- a/sklearn/impute/_knn.py +++ b/sklearn/impute/_knn.py @@ -282,7 +282,12 @@ def transform(self, X): Xc[:, ~valid_mask] = 0 else: Xc = X[:, valid_mask] - return Xc + + # Even if there are no missing values in X, we still concatenate Xc + # with the missing value indicator matrix, X_indicator. + # This is to ensure that the output maintains consistency in terms + # of columns, regardless of whether missing values exist in X or not. + return super()._concatenate_indicator(Xc, X_indicator) row_missing_idx = np.flatnonzero(mask.any(axis=1)) diff --git a/sklearn/impute/tests/test_common.py b/sklearn/impute/tests/test_common.py index aad7eb12a0a92..be2fa6e4d1736 100644 --- a/sklearn/impute/tests/test_common.py +++ b/sklearn/impute/tests/test_common.py @@ -181,3 +181,39 @@ def test_keep_empty_features(imputer, keep_empty_features): assert X_imputed.shape == X.shape else: assert X_imputed.shape == (X.shape[0], X.shape[1] - 1) + + +@pytest.mark.parametrize("imputer", imputers(), ids=lambda x: x.__class__.__name__) +@pytest.mark.parametrize("missing_value_test", [np.nan, 1]) +def test_imputation_adds_missing_indicator_if_add_indicator_is_true( + imputer, missing_value_test +): + """Check that missing indicator always exists when add_indicator=True. + + Non-regression test for gh-26590. + """ + X_train = np.array([[0, np.NaN], [1, 2]]) + + # Test data where missing_value_test variable can be set to np.NaN or 1. + X_test = np.array([[0, missing_value_test], [1, 2]]) + + imputer.set_params(add_indicator=True) + imputer.fit(X_train) + + X_test_imputed_with_indicator = imputer.transform(X_test) + assert X_test_imputed_with_indicator.shape == (2, 3) + + imputer.set_params(add_indicator=False) + imputer.fit(X_train) + X_test_imputed_without_indicator = imputer.transform(X_test) + assert X_test_imputed_without_indicator.shape == (2, 2) + + assert_allclose( + X_test_imputed_with_indicator[:, :-1], X_test_imputed_without_indicator + ) + if np.isnan(missing_value_test): + expected_missing_indicator = [1, 0] + else: + expected_missing_indicator = [0, 0] + + assert_allclose(X_test_imputed_with_indicator[:, -1], expected_missing_indicator) From 4182eaacf70f8ef1a0a28f88ce2608b075318405 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Wed, 9 Aug 2023 08:08:06 +1000 Subject: [PATCH 140/264] CLN Update var name in `TargetEncoder` to make consistent (#27033) --- sklearn/preprocessing/_target_encoder.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sklearn/preprocessing/_target_encoder.py b/sklearn/preprocessing/_target_encoder.py index 0e26f0e05d22e..81afeb6a8bd43 100644 --- a/sklearn/preprocessing/_target_encoder.py +++ b/sklearn/preprocessing/_target_encoder.py @@ -273,14 +273,14 @@ def transform(self, X): X_trans : ndarray of shape (n_samples, n_features) Transformed input. """ - X_ordinal, X_valid = self._transform( + X_ordinal, X_known_mask = self._transform( X, handle_unknown="ignore", force_all_finite="allow-nan" ) X_out = np.empty_like(X_ordinal, dtype=np.float64) self._transform_X_ordinal( X_out, X_ordinal, - ~X_valid, + ~X_known_mask, slice(None), self.encodings_, self.target_mean_, From 75cdfec874d2e9bb6c1fe4342607927a466f071c Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Wed, 9 Aug 2023 11:44:31 +0200 Subject: [PATCH 141/264] MNT fix ruff type vs isinstance errors (#27039) --- sklearn/metrics/tests/test_classification.py | 8 ++++---- sklearn/model_selection/_split.py | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/sklearn/metrics/tests/test_classification.py b/sklearn/metrics/tests/test_classification.py index a05a532ecb3f2..cfcb08a312443 100644 --- a/sklearn/metrics/tests/test_classification.py +++ b/sklearn/metrics/tests/test_classification.py @@ -159,10 +159,10 @@ def test_classification_report_dictionary_output(): for metric in expected_report[key]: assert_almost_equal(expected_report[key][metric], report[key][metric]) - assert type(expected_report["setosa"]["precision"]) == float - assert type(expected_report["macro avg"]["precision"]) == float - assert type(expected_report["setosa"]["support"]) == int - assert type(expected_report["macro avg"]["support"]) == int + assert isinstance(expected_report["setosa"]["precision"], float) + assert isinstance(expected_report["macro avg"]["precision"], float) + assert isinstance(expected_report["setosa"]["support"], int) + assert isinstance(expected_report["macro avg"]["support"], int) def test_classification_report_output_dict_empty_input(): diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py index 178d0aa808790..2dceff9b22126 100644 --- a/sklearn/model_selection/_split.py +++ b/sklearn/model_selection/_split.py @@ -2673,7 +2673,7 @@ def _pprint(params, offset=0, printer=repr): this_line_length = offset line_sep = ",\n" + (1 + offset // 2) * " " for i, (k, v) in enumerate(sorted(params.items())): - if type(v) is float: + if isinstance(v, float): # use str for representing floating point numbers # this way we get consistent representation across # architectures and versions. From 5b1eb928418ca75191391df78e847f3802280f17 Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Wed, 9 Aug 2023 12:30:46 -0400 Subject: [PATCH 142/264] MNT Use assert_no_warnings from numpy.testing (#27031) --- sklearn/utils/_testing.py | 30 +++--------------------------- 1 file changed, 3 insertions(+), 27 deletions(-) diff --git a/sklearn/utils/_testing.py b/sklearn/utils/_testing.py index 2c0374eeabba6..278bf38a65690 100644 --- a/sklearn/utils/_testing.py +++ b/sklearn/utils/_testing.py @@ -38,6 +38,7 @@ assert_array_almost_equal, assert_array_equal, assert_array_less, + assert_no_warnings, ) import sklearn @@ -47,7 +48,7 @@ _in_unstable_openblas_configuration, ) from sklearn.utils._array_api import _check_array_api_dispatch -from sklearn.utils.fixes import VisibleDeprecationWarning, threadpool_info +from sklearn.utils.fixes import threadpool_info from sklearn.utils.multiclass import check_classification_targets from sklearn.utils.validation import ( check_array, @@ -65,6 +66,7 @@ "assert_approx_equal", "assert_allclose", "assert_run_python_script", + "assert_no_warnings", "SkipTest", ] @@ -80,32 +82,6 @@ assert_raises_regexp = assert_raises_regex -# To remove when we support numpy 1.7 -def assert_no_warnings(func, *args, **kw): - """ - Parameters - ---------- - func - *args - **kw - """ - # very important to avoid uncontrolled state propagation - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter("always") - - result = func(*args, **kw) - if hasattr(np, "FutureWarning"): - # Filter out numpy-specific warnings in numpy >= 1.9 - w = [e for e in w if e.category is not VisibleDeprecationWarning] - - if len(w) > 0: - raise AssertionError( - "Got warnings when calling %s: [%s]" - % (func.__name__, ", ".join(str(warning) for warning in w)) - ) - return result - - def ignore_warnings(obj=None, category=Warning): """Context manager and decorator to ignore warnings. From 5c1a8a356ceb99a1125dd456e1ec5cff87e7a9e7 Mon Sep 17 00:00:00 2001 From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Date: Wed, 9 Aug 2023 20:15:05 +0200 Subject: [PATCH 143/264] FIX potentially redundant marker argument (#27043) Co-authored-by: ArturoAmorQ Co-authored-by: Thomas J. Fan --- sklearn/calibration.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/sklearn/calibration.py b/sklearn/calibration.py index 2b694234955e7..39b0e5f83a821 100644 --- a/sklearn/calibration.py +++ b/sklearn/calibration.py @@ -1186,7 +1186,7 @@ def plot(self, *, ax=None, name=None, ref_line=True, **kwargs): f"(Positive class: {self.pos_label})" if self.pos_label is not None else "" ) - line_kwargs = {} + line_kwargs = {"marker": "s", "linestyle": "-"} if name is not None: line_kwargs["label"] = name line_kwargs.update(**kwargs) @@ -1195,9 +1195,7 @@ def plot(self, *, ax=None, name=None, ref_line=True, **kwargs): existing_ref_line = ref_line_label in self.ax_.get_legend_handles_labels()[1] if ref_line and not existing_ref_line: self.ax_.plot([0, 1], [0, 1], "k:", label=ref_line_label) - self.line_ = self.ax_.plot(self.prob_pred, self.prob_true, "s-", **line_kwargs)[ - 0 - ] + self.line_ = self.ax_.plot(self.prob_pred, self.prob_true, **line_kwargs)[0] # We always have to show the legend for at least the reference line self.ax_.legend(loc="lower right") From 7f8dd759b68992dd57cec4e78816d13dd0d0b68d Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Thu, 10 Aug 2023 03:05:40 -0400 Subject: [PATCH 144/264] FIX Fixes set_output with list input (#27044) --- doc/whats_new/v1.3.rst | 6 ++++++ sklearn/utils/_set_output.py | 4 +++- sklearn/utils/tests/test_set_output.py | 29 ++++++++++++++++++++++++++ 3 files changed, 38 insertions(+), 1 deletion(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index 0f46f28cd340b..17d304b379aea 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -9,6 +9,12 @@ Version 1.3.1 **In development** +Changes impacting all modules +----------------------------- + +- |Fix| The `set_output` API correctly works with list input. :pr:`27044` by + `Thomas Fan`_. + Changelog --------- diff --git a/sklearn/utils/_set_output.py b/sklearn/utils/_set_output.py index 9eee7c370e341..bb289535c45ec 100644 --- a/sklearn/utils/_set_output.py +++ b/sklearn/utils/_set_output.py @@ -5,6 +5,7 @@ from .._config import get_config from . import check_pandas_support from ._available_if import available_if +from .validation import _is_pandas_df def _wrap_in_pandas_container( @@ -125,9 +126,10 @@ def _wrap_data_with_container(method, data_to_wrap, original_input, estimator): return data_to_wrap # dense_config == "pandas" + index = original_input.index if _is_pandas_df(original_input) else None return _wrap_in_pandas_container( data_to_wrap=data_to_wrap, - index=getattr(original_input, "index", None), + index=index, columns=estimator.get_feature_names_out, ) diff --git a/sklearn/utils/tests/test_set_output.py b/sklearn/utils/tests/test_set_output.py index 403a5db63ec54..d1722a1553f9c 100644 --- a/sklearn/utils/tests/test_set_output.py +++ b/sklearn/utils/tests/test_set_output.py @@ -315,3 +315,32 @@ def test_set_output_named_tuple_out(): assert isinstance(X_trans, Output) assert_array_equal(X_trans.X, X) assert_array_equal(X_trans.Y, 2 * X) + + +class EstimatorWithListInput(_SetOutputMixin): + def fit(self, X, y=None): + assert isinstance(X, list) + self.n_features_in_ = len(X[0]) + return self + + def transform(self, X, y=None): + return X + + def get_feature_names_out(self, input_features=None): + return np.asarray([f"X{i}" for i in range(self.n_features_in_)], dtype=object) + + +def test_set_output_list_input(): + """Check set_output for list input. + + Non-regression test for #27037. + """ + pd = pytest.importorskip("pandas") + + X = [[0, 1, 2, 3], [4, 5, 6, 7]] + est = EstimatorWithListInput() + est.set_output(transform="pandas") + + X_out = est.fit(X).transform(X) + assert isinstance(X_out, pd.DataFrame) + assert_array_equal(X_out.columns, ["X0", "X1", "X2", "X3"]) From 636200da42acfacf2d91c3f795d27fa4ac251038 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Thu, 10 Aug 2023 15:52:57 +0200 Subject: [PATCH 145/264] DOC Highlight differerence between SVC/R and LinearSVC/R (#26825) --- doc/modules/svm.rst | 34 ++++++++++++------- sklearn/svm/_base.py | 26 +++++++++------ sklearn/svm/_classes.py | 72 ++++++++++++++++++++++++++--------------- 3 files changed, 85 insertions(+), 47 deletions(-) diff --git a/doc/modules/svm.rst b/doc/modules/svm.rst index 7e886366aebae..0ac34cdcb6a10 100644 --- a/doc/modules/svm.rst +++ b/doc/modules/svm.rst @@ -60,14 +60,19 @@ capable of performing binary and multi-class classification on a dataset. :align: center -:class:`SVC` and :class:`NuSVC` are similar methods, but accept -slightly different sets of parameters and have different mathematical -formulations (see section :ref:`svm_mathematical_formulation`). On the -other hand, :class:`LinearSVC` is another (faster) implementation of Support -Vector Classification for the case of a linear kernel. Note that -:class:`LinearSVC` does not accept parameter ``kernel``, as this is -assumed to be linear. It also lacks some of the attributes of -:class:`SVC` and :class:`NuSVC`, like ``support_``. +:class:`SVC` and :class:`NuSVC` are similar methods, but accept slightly +different sets of parameters and have different mathematical formulations (see +section :ref:`svm_mathematical_formulation`). On the other hand, +:class:`LinearSVC` is another (faster) implementation of Support Vector +Classification for the case of a linear kernel. It also +lacks some of the attributes of :class:`SVC` and :class:`NuSVC`, like +`support_`. :class:`LinearSVC` uses `squared_hinge` loss and due to its +implementation in `liblinear` it also regularizes the intercept, if considered. +This effect can however be reduced by carefully fine tuning its +`intercept_scaling` parameter, which allows the intercept term to have a +different regularization behavior compared to the other features. The +classification results and score can therefore differ from the other two +classifiers. As other classifiers, :class:`SVC`, :class:`NuSVC` and :class:`LinearSVC` take as input two arrays: an array `X` of shape @@ -314,10 +319,15 @@ target. There are three different implementations of Support Vector Regression: :class:`SVR`, :class:`NuSVR` and :class:`LinearSVR`. :class:`LinearSVR` -provides a faster implementation than :class:`SVR` but only considers -the linear kernel, while :class:`NuSVR` implements a slightly different -formulation than :class:`SVR` and :class:`LinearSVR`. See -:ref:`svm_implementation_details` for further details. +provides a faster implementation than :class:`SVR` but only considers the +linear kernel, while :class:`NuSVR` implements a slightly different formulation +than :class:`SVR` and :class:`LinearSVR`. Due to its implementation in +`liblinear` :class:`LinearSVR` also regularizes the intercept, if considered. +This effect can however be reduced by carefully fine tuning its +`intercept_scaling` parameter, which allows the intercept term to have a +different regularization behavior compared to the other features. The +classification results and score can therefore differ from the other two +classifiers. See :ref:`svm_implementation_details` for further details. As with classification classes, the fit method will take as argument vectors X, y, only that in this case y is expected to have diff --git a/sklearn/svm/_base.py b/sklearn/svm/_base.py index 2fc5e04b5df83..eb126ec77e526 100644 --- a/sklearn/svm/_base.py +++ b/sklearn/svm/_base.py @@ -825,7 +825,7 @@ def predict(self, X): def _check_proba(self): if not self.probability: raise AttributeError( - "predict_proba is not available when probability=False" + "predict_proba is not available when probability=False" ) if self._impl not in ("c_svc", "nu_svc"): raise AttributeError("predict_proba only implemented for SVC and NuSVC") @@ -835,7 +835,7 @@ def _check_proba(self): def predict_proba(self, X): """Compute probabilities of possible outcomes for samples in X. - The model need to have probability information computed at training + The model needs to have probability information computed at training time: fit with attribute `probability` set to True. Parameters @@ -1095,18 +1095,26 @@ def _fit_liblinear( Target vector relative to X C : float - Inverse of cross-validation parameter. Lower the C, the more + Inverse of cross-validation parameter. The lower the C, the higher the penalization. fit_intercept : bool - Whether or not to fit the intercept, that is to add a intercept - term to the decision function. + Whether or not to fit an intercept. If set to True, the feature vector + is extended to include an intercept term: ``[x_1, ..., x_n, 1]``, where + 1 corresponds to the intercept. If set to False, no intercept will be + used in calculations (i.e. data is expected to be already centered). intercept_scaling : float - LibLinear internally penalizes the intercept and this term is subject - to regularization just like the other terms of the feature vector. - In order to avoid this, one should increase the intercept_scaling. - such that the feature vector becomes [x, intercept_scaling]. + Liblinear internally penalizes the intercept, treating it like any + other term in the feature vector. To reduce the impact of the + regularization on the intercept, the `intercept_scaling` parameter can + be set to a value greater than 1; the higher the value of + `intercept_scaling`, the lower the impact of regularization on it. + Then, the weights become `[w_x_1, ..., w_x_n, + w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent + the feature weights and the intercept weight is scaled by + `intercept_scaling`. This scaling allows the intercept term to have a + different regularization behavior compared to the other features. class_weight : dict or 'balanced', default=None Weights associated with classes in the form ``{class_label: weight}``. diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py index 3f9f7f956146e..dfa48b4937147 100644 --- a/sklearn/svm/_classes.py +++ b/sklearn/svm/_classes.py @@ -49,6 +49,10 @@ class LinearSVC(LinearClassifierMixin, SparseCoefMixin, BaseEstimator): penalties and loss functions and should scale better to large numbers of samples. + The main differences between :class:`~sklearn.svm.LinearSVC` and + :class:`~sklearn.svm.SVC` lie in the loss function used by default, and in + the handling of intercept regularization between those two implementations. + This class supports both dense and sparse input and the multiclass support is handled according to a one-vs-the-rest scheme. @@ -99,20 +103,26 @@ class LinearSVC(LinearClassifierMixin, SparseCoefMixin, BaseEstimator): will be ignored. fit_intercept : bool, default=True - Whether to calculate the intercept for this model. If set - to false, no intercept will be used in calculations - (i.e. data is expected to be already centered). + Whether or not to fit an intercept. If set to True, the feature vector + is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where + 1 corresponds to the intercept. If set to False, no intercept will be + used in calculations (i.e. data is expected to be already centered). intercept_scaling : float, default=1.0 - When self.fit_intercept is True, instance vector x becomes - ``[x, self.intercept_scaling]``, - i.e. a "synthetic" feature with constant value equals to - intercept_scaling is appended to the instance vector. - The intercept becomes intercept_scaling * synthetic feature weight - Note! the synthetic feature weight is subject to l1/l2 regularization - as all other features. - To lessen the effect of regularization on synthetic feature weight - (and therefore on the intercept) intercept_scaling has to be increased. + When `fit_intercept` is True, the instance vector x becomes ``[x_1, + ..., x_n, intercept_scaling]``, i.e. a "synthetic" feature with a + constant value equal to `intercept_scaling` is appended to the instance + vector. The intercept becomes intercept_scaling * synthetic feature + weight. Note that liblinear internally penalizes the intercept, + treating it like any other term in the feature vector. To reduce the + impact of the regularization on the intercept, the `intercept_scaling` + parameter can be set to a value greater than 1; the higher the value of + `intercept_scaling`, the lower the impact of regularization on it. + Then, the weights become `[w_x_1, ..., w_x_n, + w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent + the feature weights and the intercept weight is scaled by + `intercept_scaling`. This scaling allows the intercept term to have a + different regularization behavior compared to the other features. class_weight : dict or 'balanced', default=None Set the parameter C of class i to ``class_weight[i]*C`` for @@ -362,6 +372,10 @@ class LinearSVR(RegressorMixin, LinearModel): penalties and loss functions and should scale better to large numbers of samples. + The main differences between :class:`~sklearn.svm.LinearSVR` and + :class:`~sklearn.svm.SVR` lie in the loss function used by default, and in + the handling of intercept regularization between those two implementations. + This class supports both dense and sparse input. Read more in the :ref:`User Guide `. @@ -389,20 +403,26 @@ class LinearSVR(RegressorMixin, LinearModel): loss ('squared_epsilon_insensitive') is the L2 loss. fit_intercept : bool, default=True - Whether to calculate the intercept for this model. If set - to false, no intercept will be used in calculations - (i.e. data is expected to be already centered). + Whether or not to fit an intercept. If set to True, the feature vector + is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where + 1 corresponds to the intercept. If set to False, no intercept will be + used in calculations (i.e. data is expected to be already centered). intercept_scaling : float, default=1.0 - When self.fit_intercept is True, instance vector x becomes - [x, self.intercept_scaling], - i.e. a "synthetic" feature with constant value equals to - intercept_scaling is appended to the instance vector. - The intercept becomes intercept_scaling * synthetic feature weight - Note! the synthetic feature weight is subject to l1/l2 regularization - as all other features. - To lessen the effect of regularization on synthetic feature weight - (and therefore on the intercept) intercept_scaling has to be increased. + When `fit_intercept` is True, the instance vector x becomes `[x_1, ..., + x_n, intercept_scaling]`, i.e. a "synthetic" feature with a constant + value equal to `intercept_scaling` is appended to the instance vector. + The intercept becomes intercept_scaling * synthetic feature weight. + Note that liblinear internally penalizes the intercept, treating it + like any other term in the feature vector. To reduce the impact of the + regularization on the intercept, the `intercept_scaling` parameter can + be set to a value greater than 1; the higher the value of + `intercept_scaling`, the lower the impact of regularization on it. + Then, the weights become `[w_x_1, ..., w_x_n, + w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent + the feature weights and the intercept weight is scaled by + `intercept_scaling`. This scaling allows the intercept term to have a + different regularization behavior compared to the other features. dual : "auto" or bool, default=True Select the algorithm to either solve the dual or primal @@ -462,8 +482,8 @@ class LinearSVR(RegressorMixin, LinearModel): same library as this class (liblinear). SVR : Implementation of Support Vector Machine regression using libsvm: - the kernel can be non-linear but its SMO algorithm does not - scale to large number of samples as LinearSVC does. + the kernel can be non-linear but its SMO algorithm does not scale to + large number of samples as :class:`~sklearn.svm.LinearSVR` does. sklearn.linear_model.SGDRegressor : SGDRegressor can optimize the same cost function as LinearSVR From ad423f2783bd253e3487447b5c96e13348e16058 Mon Sep 17 00:00:00 2001 From: Ricardo Peres Date: Sat, 12 Aug 2023 00:16:11 +0200 Subject: [PATCH 146/264] DOC Add description for the meaning of `None` for check_X_y (#25997) Co-authored-by: Thomas J. Fan --- sklearn/utils/_mocking.py | 10 +++++++--- sklearn/utils/validation.py | 3 ++- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/sklearn/utils/_mocking.py b/sklearn/utils/_mocking.py index 70fe835870c36..16acabf03755b 100644 --- a/sklearn/utils/_mocking.py +++ b/sklearn/utils/_mocking.py @@ -70,10 +70,12 @@ class CheckingClassifier(ClassifierMixin, BaseEstimator): ---------- check_y, check_X : callable, default=None The callable used to validate `X` and `y`. These callable should return - a bool where `False` will trigger an `AssertionError`. + a bool where `False` will trigger an `AssertionError`. If `None`, the + data is not validated. Default is `None`. check_y_params, check_X_params : dict, default=None - The optional parameters to pass to `check_X` and `check_y`. + The optional parameters to pass to `check_X` and `check_y`. If `None`, + then no parameters are passed in. methods_to_check : "all" or list of str, default="all" The methods in which the checks should be applied. By default, @@ -148,8 +150,10 @@ def _check_X_y(self, X, y=None, should_be_fitted=True): ---------- X : array-like of shape (n_samples, n_features) The data set. + `X` is checked only if `check_X` is not `None` (default is None). y : array-like of shape (n_samples), default=None - The corresponding target, by default None. + The corresponding target, by default `None`. + `y` is checked only if `check_y` is not `None` (default is None). should_be_fitted : bool, default=True Whether or not the classifier should be already fitted. By default True. diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py index 77c342dc31130..95c785d0b2c8d 100644 --- a/sklearn/utils/validation.py +++ b/sklearn/utils/validation.py @@ -1075,7 +1075,8 @@ def check_X_y( performed if the dtype of the input is not in the list. order : {'F', 'C'}, default=None - Whether an array will be forced to be fortran or c-style. + Whether an array will be forced to be fortran or c-style. If + `None`, then the input data's order is preserved when possible. copy : bool, default=False Whether a forced copy will be triggered. If copy=False, a copy might From 09aa60ca8e5cf7de76c1121aad57e4d9c00f136d Mon Sep 17 00:00:00 2001 From: Emma Carballal <80595553+emma-carballal@users.noreply.github.com> Date: Sat, 12 Aug 2023 00:30:56 +0200 Subject: [PATCH 147/264] DOC Add example link to KNeighborsClassifier class (#26979) --- sklearn/neighbors/_classification.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/sklearn/neighbors/_classification.py b/sklearn/neighbors/_classification.py index a9b78d6e499c9..5d8fdab348ab8 100644 --- a/sklearn/neighbors/_classification.py +++ b/sklearn/neighbors/_classification.py @@ -54,6 +54,11 @@ class KNeighborsClassifier(KNeighborsMixin, ClassifierMixin, NeighborsBase): array of distances, and returns an array of the same shape containing the weights. + Refer to the example entitled + :ref:`sphx_glr_auto_examples_neighbors_plot_classification.py` + showing the impact of the `weights` parameter on the decision + boundary. + algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' Algorithm used to compute the nearest neighbors: From 7286da0807492448c4c623a0f5087dd856ff4ff9 Mon Sep 17 00:00:00 2001 From: mecopur <24374426+mecopur@users.noreply.github.com> Date: Sat, 12 Aug 2023 00:39:42 +0200 Subject: [PATCH 148/264] DOC Added example link to Gaussian process regressor (#26980) --- sklearn/gaussian_process/_gpr.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/sklearn/gaussian_process/_gpr.py b/sklearn/gaussian_process/_gpr.py index 90bbe7e446917..d3723016be127 100644 --- a/sklearn/gaussian_process/_gpr.py +++ b/sklearn/gaussian_process/_gpr.py @@ -38,6 +38,10 @@ class GaussianProcessRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator): externally for other ways of selecting hyperparameters, e.g., via Markov chain Monte Carlo. + To learn the difference between a point-estimate approach vs. a more + Bayesian modelling approach, refer to the example entitled + :ref:`sphx_glr_auto_examples_gaussian_process_plot_compare_gpr_krr.py`. + Read more in the :ref:`User Guide `. .. versionadded:: 0.18 From e4fd1f47e7d45a9c7c93853b4df8a0e6abbff5ab Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Sat, 12 Aug 2023 11:39:45 +0200 Subject: [PATCH 149/264] GOV add a documentation team (#26714) --- build_tools/generate_authors_table.py | 21 +++++++++++++++++++-- doc/about.rst | 7 +++++++ doc/documentation_team.rst | 16 ++++++++++++++++ doc/governance.rst | 11 +++++++++++ 4 files changed, 53 insertions(+), 2 deletions(-) create mode 100644 doc/documentation_team.rst diff --git a/build_tools/generate_authors_table.py b/build_tools/generate_authors_table.py index c930a9b2956d1..28d1818c2b779 100644 --- a/build_tools/generate_authors_table.py +++ b/build_tools/generate_authors_table.py @@ -43,17 +43,24 @@ def get_contributors(): """Get the list of contributor profiles. Require admin rights.""" # get core devs and contributor experience team core_devs = [] + documentation_team = [] contributor_experience_team = [] comm_team = [] core_devs_slug = "core-devs" contributor_experience_team_slug = "contributor-experience-team" comm_team_slug = "communication-team" + documentation_team_slug = "documentation-team" entry_point = "https://api.github.com/orgs/scikit-learn/" for team_slug, lst in zip( - (core_devs_slug, contributor_experience_team_slug, comm_team_slug), - (core_devs, contributor_experience_team, comm_team), + ( + core_devs_slug, + contributor_experience_team_slug, + comm_team_slug, + documentation_team_slug, + ), + (core_devs, contributor_experience_team, comm_team, documentation_team), ): for page in [1, 2]: # 30 per page reply = get(f"{entry_point}teams/{team_slug}/members?page={page}") @@ -67,6 +74,7 @@ def get_contributors(): # keep only the logins core_devs = set(c["login"] for c in core_devs) + documentation_team = set(c["login"] for c in documentation_team) contributor_experience_team = set(c["login"] for c in contributor_experience_team) comm_team = set(c["login"] for c in comm_team) members = set(c["login"] for c in members) @@ -96,11 +104,13 @@ def get_contributors(): ] comm_team = [get_profile(login) for login in comm_team] emeritus_comm_team = [get_profile(login) for login in emeritus_comm_team] + documentation_team = [get_profile(login) for login in documentation_team] # sort by last name core_devs = sorted(core_devs, key=key) emeritus = sorted(emeritus, key=key) contributor_experience_team = sorted(contributor_experience_team, key=key) + documentation_team = sorted(documentation_team, key=key) comm_team = sorted(comm_team, key=key) emeritus_comm_team = sorted(emeritus_comm_team, key=key) @@ -110,6 +120,7 @@ def get_contributors(): contributor_experience_team, comm_team, emeritus_comm_team, + documentation_team, ) @@ -179,6 +190,7 @@ def generate_list(contributors): contributor_experience_team, comm_team, emeritus_comm_team, + documentation_team, ) = get_contributors() with open(REPO_FOLDER / "doc" / "authors.rst", "w+", encoding="utf-8") as rst_file: @@ -203,3 +215,8 @@ def generate_list(contributors): REPO_FOLDER / "doc" / "communication_team_emeritus.rst", "w+", encoding="utf-8" ) as rst_file: rst_file.write(generate_list(emeritus_comm_team)) + + with open( + REPO_FOLDER / "doc" / "documentation_team.rst", "w+", encoding="utf-8" + ) as rst_file: + rst_file.write(generate_table(documentation_team)) diff --git a/doc/about.rst b/doc/about.rst index eabd8d5e251d9..4bd001fe391e6 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -39,6 +39,13 @@ in the FAQ. :ref:`How you can contribute to the project ` +Documentation Team +------------------ + +The following people help with documenting the project: + +.. include:: documentation_team.rst + Contributor Experience Team --------------------------- diff --git a/doc/documentation_team.rst b/doc/documentation_team.rst new file mode 100644 index 0000000000000..935a995a7c00e --- /dev/null +++ b/doc/documentation_team.rst @@ -0,0 +1,16 @@ +.. raw :: html + + +
    + +
    +
    +

    Arturo Amor

    +
    +
    +
    +

    Lucy Liu

    +
    +
    diff --git a/doc/governance.rst b/doc/governance.rst index b756c52850e5c..33afd7dde8ddb 100644 --- a/doc/governance.rst +++ b/doc/governance.rst @@ -86,6 +86,17 @@ For this, they can operate the scikit-learn accounts on various social networks and produce materials. They also have the required rights to our blog repository and other relevant accounts and platforms. +Documentation team +~~~~~~~~~~~~~~~~~~ + +Members of the documentation team engage with the documentation of the project +among other things. They might also be involved in other aspects of the +project, but their reviews on documentation contributions are considered +authoritative, and can merge such contributions. + +To this end, they have permissions to merge pull requests in scikit-learn's +repository. + Maintainers ~~~~~~~~~~~ From 5de9aff2b012b50b7dabf6cc0dfc788b650eefb8 Mon Sep 17 00:00:00 2001 From: cynthias13w <89746065+cynthias13w@users.noreply.github.com> Date: Sat, 12 Aug 2023 12:22:20 +0200 Subject: [PATCH 150/264] DOC link to example explaining `init` usage in KMeans (#26981) Co-authored-by: Guillaume Lemaitre --- sklearn/cluster/_kmeans.py | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/sklearn/cluster/_kmeans.py b/sklearn/cluster/_kmeans.py index c63b1756941b4..d1da355290073 100644 --- a/sklearn/cluster/_kmeans.py +++ b/sklearn/cluster/_kmeans.py @@ -1224,22 +1224,25 @@ class KMeans(_BaseKMeans): (n_clusters, n_features), default='k-means++' Method for initialization: - 'k-means++' : selects initial cluster centroids using sampling based on - an empirical probability distribution of the points' contribution to the - overall inertia. This technique speeds up convergence. The algorithm - implemented is "greedy k-means++". It differs from the vanilla k-means++ - by making several trials at each sampling step and choosing the best centroid - among them. + * 'k-means++' : selects initial cluster centroids using sampling \ + based on an empirical probability distribution of the points' \ + contribution to the overall inertia. This technique speeds up \ + convergence. The algorithm implemented is "greedy k-means++". It \ + differs from the vanilla k-means++ by making several trials at \ + each sampling step and choosing the best centroid among them. - 'random': choose `n_clusters` observations (rows) at random from data - for the initial centroids. + * 'random': choose `n_clusters` observations (rows) at random from \ + data for the initial centroids. - If an array is passed, it should be of shape (n_clusters, n_features) + * If an array is passed, it should be of shape (n_clusters, n_features)\ and gives the initial centers. - If a callable is passed, it should take arguments X, n_clusters and a + * If a callable is passed, it should take arguments X, n_clusters and a\ random state and return an initialization. + For an example of how to use the different `init` strategy, see the example + entitled :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`. + n_init : 'auto' or int, default=10 Number of times the k-means algorithm is run with different centroid seeds. The final results is the best output of `n_init` consecutive runs From b58962bc8631c0e5a6200a6cb58952075a10850d Mon Sep 17 00:00:00 2001 From: Tialo <65392801+Tialo@users.noreply.github.com> Date: Sat, 12 Aug 2023 13:24:14 +0300 Subject: [PATCH 151/264] DOC Add link to plot_classifier_chain_yeast example in ClassifierChain (#26937) Co-authored-by: Adrin Jalali --- sklearn/multioutput.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/sklearn/multioutput.py b/sklearn/multioutput.py index 433dc0f7b0fa6..02aa5da827605 100644 --- a/sklearn/multioutput.py +++ b/sklearn/multioutput.py @@ -782,6 +782,11 @@ class ClassifierChain(MetaEstimatorMixin, ClassifierMixin, _BaseChain): all of the available features provided to the model plus the predictions of models that are earlier in the chain. + For an example of how to use ``ClassifierChain`` and benefit from its + ensemble, see + :ref:`ClassifierChain on a yeast dataset + ` example. + Read more in the :ref:`User Guide `. .. versionadded:: 0.19 From 80d6946e3e2567fb54be1aac5080a8ec149b56b5 Mon Sep 17 00:00:00 2001 From: Tialo <65392801+Tialo@users.noreply.github.com> Date: Sat, 12 Aug 2023 13:36:44 +0300 Subject: [PATCH 152/264] DOC Add link to text/plot_document_classification_20newsgroups example (#26938) --- sklearn/feature_extraction/text.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py index 80a6062fffb16..c6d826539ef49 100644 --- a/sklearn/feature_extraction/text.py +++ b/sklearn/feature_extraction/text.py @@ -1755,6 +1755,9 @@ class TfidfVectorizer(CountVectorizer): Equivalent to :class:`CountVectorizer` followed by :class:`TfidfTransformer`. + For an example of usage, see + :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`. + Read more in the :ref:`User Guide `. Parameters From cdf31160bb7119f968fa9bae5048961b8a0d765f Mon Sep 17 00:00:00 2001 From: Xuefeng Xu Date: Sat, 12 Aug 2023 19:04:02 +0800 Subject: [PATCH 153/264] DOC fix default dtype in OneHotEncoder (#27054) --- sklearn/preprocessing/_encoders.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/preprocessing/_encoders.py b/sklearn/preprocessing/_encoders.py index 5dc9929ded704..7910976d6007c 100644 --- a/sklearn/preprocessing/_encoders.py +++ b/sklearn/preprocessing/_encoders.py @@ -525,7 +525,7 @@ class OneHotEncoder(_BaseEncoder): .. versionadded:: 1.2 `sparse` was renamed to `sparse_output` - dtype : number type, default=float + dtype : number type, default=np.float64 Desired dtype of output. handle_unknown : {'error', 'ignore', 'infrequent_if_exist'}, \ From d0671b2d8d3e305baca585f0970245f57cf3e03f Mon Sep 17 00:00:00 2001 From: Christian Veenhuis <124370897+ChVeen@users.noreply.github.com> Date: Mon, 14 Aug 2023 18:16:28 +0200 Subject: [PATCH 154/264] MAINT Remove `-Wsign-compare` compilation warning for `_radius_neighbors.pyx.tp` (#27061) --- .../_pairwise_distances_reduction/_radius_neighbors.pyx.tp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp index 1defa30b6325e..8695baad172e0 100644 --- a/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp +++ b/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp @@ -300,7 +300,8 @@ cdef class RadiusNeighbors{{name_suffix}}(BaseDistancesReduction{{name_suffix}}) cdef void compute_exact_distances(self) noexcept nogil: """Convert rank-preserving distances to pairwise distances in parallel.""" cdef: - intp_t i, j + intp_t i + vector[intp_t].size_type j for i in prange(self.n_samples_X, nogil=True, schedule='static', num_threads=self.effective_n_threads): From 079fcf5f009675925abb352d39da98dc05e3daac Mon Sep 17 00:00:00 2001 From: Tim Head Date: Mon, 14 Aug 2023 22:38:39 +0200 Subject: [PATCH 155/264] DOC Remove outdated instructions for Apple Silicon (#27067) --- doc/install.rst | 22 ---------------------- 1 file changed, 22 deletions(-) diff --git a/doc/install.rst b/doc/install.rst index 263e83cdc31a5..a69d68a96ca42 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -146,28 +146,6 @@ purpose. Scikit-learn 1.1 and later requires Python 3.8 or newer. -.. _install_on_apple_silicon_m1: - -Installing on Apple Silicon M1 hardware -======================================= - -The recently introduced `macos/arm64` platform (sometimes also known as -`macos/aarch64`) requires the open source community to upgrade the build -configuration and automation to properly support it. - -At the time of writing (January 2021), the only way to get a working -installation of scikit-learn on this hardware is to install scikit-learn and its -dependencies from the conda-forge distribution, for instance using the miniforge -installers: - -https://github.com/conda-forge/miniforge - -The following issue tracks progress on making it possible to install -scikit-learn from PyPI with pip: - -https://github.com/scikit-learn/scikit-learn/issues/19137 - - .. _install_by_distribution: Third party distributions of scikit-learn From b32cbff8d99775998b0e12dce0c2ed2194b517d3 Mon Sep 17 00:00:00 2001 From: Narendra Singh dangi <82656649+narendrasinghdangi@users.noreply.github.com> Date: Tue, 15 Aug 2023 20:03:35 +0530 Subject: [PATCH 156/264] DOC Documents fixed to increase speed by removing for loop (#27066) --- examples/applications/plot_prediction_latency.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/applications/plot_prediction_latency.py b/examples/applications/plot_prediction_latency.py index 8fce81fb9fb4e..0c966b3b1e28e 100644 --- a/examples/applications/plot_prediction_latency.py +++ b/examples/applications/plot_prediction_latency.py @@ -232,7 +232,7 @@ def plot_n_features_influence(percentiles, percentile): fig, ax1 = plt.subplots(figsize=(10, 6)) colors = ["r", "g", "b"] for i, cls_name in enumerate(percentiles.keys()): - x = np.array(sorted([n for n in percentiles[cls_name].keys()])) + x = np.array(sorted(percentiles[cls_name].keys())) y = np.array([percentiles[cls_name][n] for n in x]) plt.plot( x, From a2353a7271472b615f46bb2966db07c241581b55 Mon Sep 17 00:00:00 2001 From: Tanjina Afroj <67520120+tanjina-hub@users.noreply.github.com> Date: Tue, 15 Aug 2023 17:36:46 +0200 Subject: [PATCH 157/264] DOC Add link to plot_optics.py (#26970) Co-authored-by: Adrin Jalali --- examples/cluster/plot_optics.py | 1 + sklearn/cluster/_optics.py | 3 +++ 2 files changed, 4 insertions(+) diff --git a/examples/cluster/plot_optics.py b/examples/cluster/plot_optics.py index 5765f8089f3b2..523742d43f43d 100644 --- a/examples/cluster/plot_optics.py +++ b/examples/cluster/plot_optics.py @@ -8,6 +8,7 @@ Finds core samples of high density and expands clusters from them. This example uses data that is generated so that the clusters have different densities. + The :class:`~cluster.OPTICS` is first used with its Xi cluster detection method, and then setting specific thresholds on the reachability, which corresponds to :class:`~cluster.DBSCAN`. We can see that the different diff --git a/sklearn/cluster/_optics.py b/sklearn/cluster/_optics.py index 39d86be0c105a..c1665b28d0060 100755 --- a/sklearn/cluster/_optics.py +++ b/sklearn/cluster/_optics.py @@ -233,6 +233,9 @@ class OPTICS(ClusterMixin, BaseEstimator): >>> clustering = OPTICS(min_samples=2).fit(X) >>> clustering.labels_ array([0, 0, 0, 1, 1, 1]) + + For a more detailed example see + :ref:`sphx_glr_auto_examples_cluster_plot_optics.py`. """ _parameter_constraints: dict = { From 5921064eb0e2fdb35cb5e1cf695818e0d4efd251 Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Wed, 16 Aug 2023 11:25:55 -0400 Subject: [PATCH 158/264] CI Enable retry for curl on ARM builds (#27071) --- build_tools/cirrus/arm_wheel.yml | 2 +- build_tools/cirrus/build_test_arm.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/build_tools/cirrus/arm_wheel.yml b/build_tools/cirrus/arm_wheel.yml index 5616108315fba..81acc7ed6a9fa 100644 --- a/build_tools/cirrus/arm_wheel.yml +++ b/build_tools/cirrus/arm_wheel.yml @@ -22,7 +22,7 @@ macos_arm64_wheel_task: CIBW_BUILD: cp311-macosx_arm64 conda_script: - - curl -L -o ~/mambaforge.sh https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh + - curl -L --retry 10 -o ~/mambaforge.sh https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh - bash ~/mambaforge.sh -b -p ~/mambaforge cibuildwheel_script: diff --git a/build_tools/cirrus/build_test_arm.sh b/build_tools/cirrus/build_test_arm.sh index dfe048da47a7f..551dc3689e010 100755 --- a/build_tools/cirrus/build_test_arm.sh +++ b/build_tools/cirrus/build_test_arm.sh @@ -25,7 +25,7 @@ setup_ccache() { MAMBAFORGE_URL="https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-aarch64.sh" # Install Mambaforge -curl -L $MAMBAFORGE_URL -o mambaforge.sh +curl -L --retry 10 $MAMBAFORGE_URL -o mambaforge.sh MAMBAFORGE_PATH=$HOME/mambaforge bash ./mambaforge.sh -b -p $MAMBAFORGE_PATH export PATH=$MAMBAFORGE_PATH/bin:$PATH From a82fa317beda03b747a0d89052dedf299283ad98 Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Wed, 16 Aug 2023 11:26:07 -0400 Subject: [PATCH 159/264] CI Fixes update tracker on ARM CI (#27072) --- build_tools/cirrus/arm_tests.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/build_tools/cirrus/arm_tests.yml b/build_tools/cirrus/arm_tests.yml index d1ac551a749e3..f64adbcdd4748 100644 --- a/build_tools/cirrus/arm_tests.yml +++ b/build_tools/cirrus/arm_tests.yml @@ -17,6 +17,10 @@ linux_aarch64_test_task: folder: /root/.conda/pkgs fingerprint_script: cat build_tools/cirrus/py39_conda_forge_linux-aarch64_conda.lock + install_python_script: | + # Install python so that update_tracking_issue has access to a Python + apt install -y python3 python-is-python3 + test_script: | bash build_tools/cirrus/build_test_arm.sh # On success, this script is run updating the issue. From 8356f6ca5349d150492a77057544a7c221e5f42c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Fri, 18 Aug 2023 10:01:23 +0200 Subject: [PATCH 160/264] MNT Update lock files (#27024) --- build_tools/azure/debian_atlas_32bit_lock.txt | 2 +- ...onda_defaults_openblas_linux-64_conda.lock | 11 +- .../py38_conda_forge_mkl_win-64_conda.lock | 42 ++++---- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 42 ++++---- ...latest_conda_forge_mkl_linux-64_conda.lock | 10 +- ..._forge_mkl_no_coverage_linux-64_conda.lock | 49 ++++----- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 46 ++++---- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 20 ++-- ...st_pip_openblas_pandas_linux-64_conda.lock | 24 ++--- ...pylatest_pip_scipy_dev_linux-64_conda.lock | 16 +-- build_tools/azure/pypy3_linux-64_conda.lock | 16 +-- build_tools/azure/python_nogil_lock.txt | 4 +- build_tools/azure/ubuntu_atlas_lock.txt | 2 +- build_tools/circle/doc_linux-64_conda.lock | 100 ++++++++---------- .../doc_min_dependencies_linux-64_conda.lock | 20 ++-- .../py39_conda_forge_linux-aarch64_conda.lock | 20 ++-- 16 files changed, 208 insertions(+), 216 deletions(-) diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt index 1b3dd213e85ef..e3d33cc1cbd19 100644 --- a/build_tools/azure/debian_atlas_32bit_lock.txt +++ b/build_tools/azure/debian_atlas_32bit_lock.txt @@ -8,7 +8,7 @@ attrs==23.1.0 # via pytest coverage==7.2.7 # via pytest-cov -cython==0.29.36 +cython==3.0.0 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt iniconfig==2.0.0 # via pytest diff --git a/build_tools/azure/py38_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/py38_conda_defaults_openblas_linux-64_conda.lock index adcd7c946c808..5543d7ac963bd 100644 --- a/build_tools/azure/py38_conda_defaults_openblas_linux-64_conda.lock +++ b/build_tools/azure/py38_conda_defaults_openblas_linux-64_conda.lock @@ -26,7 +26,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.15-h7f8727e_0.conda#ada518 https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_0.conda#53915e9402180a7f22ea619c41089520 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c https://repo.anaconda.com/pkgs/main/linux-64/nspr-4.35-h6a678d5_0.conda#208fff5d60133bcff6998a70c9f5203b -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.9-h7f8727e_0.conda#dc136c954b2d644c79fee77e57e183dc +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.10-h7f8727e_0.conda#83d254d7304c7c1220f82668ab1e7b42 https://repo.anaconda.com/pkgs/main/linux-64/pcre-8.45-h295c915_0.conda#b32ccc24d1d9808618c1e898da60f68d https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.2-h5eee18b_0.conda#bcd31de48a0dcb44bc5b99675800c5cc https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31fbfbb5057c92fa845ad6adef93 @@ -56,9 +56,8 @@ https://repo.anaconda.com/pkgs/main/linux-64/libpq-12.15-hdbd6064_1.conda#218227 https://repo.anaconda.com/pkgs/main/linux-64/libwebp-1.2.4-h11a3e52_1.conda#9f9153b30e58e9ce896f74634622cbf1 https://repo.anaconda.com/pkgs/main/linux-64/nss-3.89.1-h6a678d5_0.conda#4d9d28fc3a0ca4916f281d2f5429ac50 https://repo.anaconda.com/pkgs/main/linux-64/python-3.8.17-h955ad1f_0.conda#f901f4fd76d24a2d598788a24e4d7246 -https://repo.anaconda.com/pkgs/main/linux-64/attrs-22.1.0-py38h06a4308_0.conda#51beb64c6f06b5a69529df7ecaccc3f9 https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda#f5e365d2cdb66d547eb8c3ab93843aab -https://repo.anaconda.com/pkgs/main/linux-64/cython-0.29.35-py38h6a678d5_0.conda#8a447d112d648cbd429b9a0e278fa505 +https://repo.anaconda.com/pkgs/main/linux-64/cython-3.0.0-py38h5eee18b_0.conda#c71ed7a68aaeccc56088e4a7474bcdb9 https://repo.anaconda.com/pkgs/main/linux-64/exceptiongroup-1.0.4-py38h06a4308_0.conda#db954e73dca6076c64a1004d71b45784 https://repo.anaconda.com/pkgs/main/noarch/execnet-1.9.0-pyhd3eb1b0_0.conda#f895937671af67cebb8af617494b3513 https://repo.anaconda.com/pkgs/main/noarch/iniconfig-1.1.1-pyhd3eb1b0_0.tar.bz2#e40edff2c5708f342cef43c7f280c507 @@ -73,15 +72,15 @@ https://repo.anaconda.com/pkgs/main/noarch/py-1.11.0-pyhd3eb1b0_0.conda#7205a898 https://repo.anaconda.com/pkgs/main/linux-64/pyparsing-3.0.9-py38h06a4308_0.conda#becbbf51d2b05de228eed968e20f963d https://repo.anaconda.com/pkgs/main/linux-64/pytz-2022.7-py38h06a4308_0.conda#19c9f6a24d5c6f779c645d00f646666b https://repo.anaconda.com/pkgs/main/linux-64/qt-main-5.15.2-h327a75a_7.conda#1868b206ade356f1812a723804e1cc31 -https://repo.anaconda.com/pkgs/main/linux-64/setuptools-67.8.0-py38h06a4308_0.conda#629ffd3b3738163d536a6c06e0b14164 +https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.0.0-py38h06a4308_0.conda#24f9c895455f3992d6b04957fd0e7546 https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#34586824d411d36af2fa40e799c172d0 https://repo.anaconda.com/pkgs/main/noarch/threadpoolctl-2.2.0-pyh0d69192_0.conda#bbfdbae4934150b902f97daaf287efe2 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a https://repo.anaconda.com/pkgs/main/linux-64/tomli-2.0.1-py38h06a4308_0.conda#791cce9de9913e9587b0a85cd8419123 -https://repo.anaconda.com/pkgs/main/linux-64/tornado-6.2-py38h5eee18b_0.conda#db2f7ebc500d97a4af6889dfd0d03dbc +https://repo.anaconda.com/pkgs/main/linux-64/tornado-6.3.2-py38h5eee18b_0.conda#276225b0c9533f993c5d71ac01e72ddf https://repo.anaconda.com/pkgs/main/linux-64/coverage-7.2.2-py38h5eee18b_0.conda#a05c1732d4e67102d2aa8d7e56de778b https://repo.anaconda.com/pkgs/main/linux-64/numpy-1.17.3-py38h7e8d029_0.conda#5f2b196b515f8fe6b37e3d224650577d -https://repo.anaconda.com/pkgs/main/linux-64/pytest-7.3.1-py38h06a4308_0.conda#456f5c7532523cc7bd098e0a87a199dc +https://repo.anaconda.com/pkgs/main/linux-64/pytest-7.4.0-py38h06a4308_0.conda#ba6c58ef1c6ba5247ccc17d41fdd71e5 https://repo.anaconda.com/pkgs/main/noarch/python-dateutil-2.8.2-pyhd3eb1b0_0.conda#211ee00320b08a1ac9fea6677649f6c9 https://repo.anaconda.com/pkgs/main/linux-64/qt-webengine-5.15.9-hd2b0992_4.conda#ed674e212597b93fffa1afc90a3e100c https://repo.anaconda.com/pkgs/main/linux-64/sip-6.6.2-py38h6a678d5_0.conda#cb3f0d10f7f79870945f4dbbe0000f92 diff --git a/build_tools/azure/py38_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/py38_conda_forge_mkl_win-64_conda.lock index db087d3663881..032adf4b697c1 100644 --- a/build_tools/azure/py38_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/py38_conda_forge_mkl_win-64_conda.lock @@ -2,8 +2,8 @@ # platform: win-64 # input_hash: e3af9571d95aff7d02e118db6e2ccbce90cd3cf3c663b4ed8a5e8c3fef5b1318 @EXPLICIT -https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2023.5.7-h56e8100_0.conda#604212634bd8c4d6f20d44b946e8eedb -https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2023.1.0-h57928b3_46319.conda#dbc4636f419722fbf3ab6501377228ba +https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2023.7.22-h56e8100_0.conda#b1c2327b36f1a25d96f2039b0d3e3739 +https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2023.2.0-h57928b3_49496.conda#f2e71622520883ffdbc379b13049534c https://conda.anaconda.org/conda-forge/win-64/mkl-include-2022.1.0-h6a75c08_874.tar.bz2#414f6ab96ad71e7a95bd00d990fa3473 https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2#b0309b72560df66f71a9d5e34a5efdfa https://conda.anaconda.org/conda-forge/win-64/python_abi-3.8-3_cp38.conda#c6df946723dadd4a5830a8ff8c6b9a20 @@ -27,15 +27,15 @@ https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.42.0-hcfcfb64_0.conda# https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.3.1-hcfcfb64_0.conda#f89e765213cac556a8ed72ba8c1b5071 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https://conda.anaconda.org/conda-forge/win-64/libxml2-2.11.4-hc3477c8_0.conda#586627982a63815637f871a6360fe3f9 @@ -45,20 +45,20 @@ https://conda.anaconda.org/conda-forge/win-64/python-3.8.17-h4de0772_0_cpython.c https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.2-h12be248_7.conda#f3c3879d8cda1c5a6885435dd48a470e https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.0.9-hcfcfb64_9.conda#ba8ae6c24cf47da3fb73270e4f119f08 https://conda.anaconda.org/conda-forge/win-64/brotli-python-1.0.9-py38hd3f51b4_9.conda#b451fb48b1337ceb34b7b39de0bc09c8 -https://conda.anaconda.org/conda-forge/noarch/certifi-2023.5.7-pyhd8ed1ab_0.conda#5d1b71c942b8421285934dad1d891ebc +https://conda.anaconda.org/conda-forge/noarch/certifi-2023.7.22-pyhd8ed1ab_0.conda#7f3dbc9179b4dde7da98dfb151d0ad22 https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.2.0-pyhd8ed1ab_0.conda#313516e9a4b08b12dfb1e1cd390a96e3 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https://conda.anaconda.org/conda-forge/noarch/win_inet_pton-1.1.0-pyhd8ed1ab_6.tar.bz2#30878ecc4bd36e8deeea1e3c151b2e0b https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.11-hcd874cb_0.conda#c46ba8712093cb0114404ae8a7582e1a https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.3-hcd874cb_0.tar.bz2#46878ebb6b9cbd8afcf8088d7ef00ece @@ -88,38 +88,38 @@ https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.0-pyhd8ed1ab_1.conda#fb https://conda.anaconda.org/conda-forge/win-64/lcms2-2.15-h3e3b177_1.conda#a76c36ad1b4b87f038d67890122d08ec https://conda.anaconda.org/conda-forge/win-64/libxcb-1.15-hcd874cb_0.conda#090d91b69396f14afef450c285f9758c https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.0-ha2aaf27_2.conda#db0490689232e8e38c312281df6f31a2 -https://conda.anaconda.org/conda-forge/noarch/pip-23.2-pyhd8ed1ab_0.conda#fb90dfe13ce16c0a3374e3d34e3291c5 +https://conda.anaconda.org/conda-forge/noarch/pip-23.2.1-pyhd8ed1ab_0.conda#e2783aa3f9235225eec92f9081c5b801 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https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.7.2-py38h578d9bd_0.conda#ab4f8f744029755778a4876559bec6ec diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index ef503c4e2e010..446ba530c52ef 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -3,7 +3,7 @@ # input_hash: 56e8dae95dcae13cac7ca1898bda12f1408bcea8a1aeb587ced409672f398a4b @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 -https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.5.7-hbcca054_0.conda#f5c65075fc34438d5b456c7f3f5ab695 +https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.7.22-hbcca054_0.conda#a73ecd2988327ad4c8f2c331482917f2 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-https://conda.anaconda.org/conda-forge/linux-64/krb5-1.20.1-h81ceb04_0.conda#89a41adce7106749573d883b2f657d78 +https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.1-h659d440_0.conda#1b5126ec25763eb17ef74c8763d26e84 https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.10.1-h166bdaf_0.tar.bz2#f967fc95089cd247ceed56eda31de3a9 https://conda.anaconda.org/conda-forge/linux-64/libglib-2.76.3-hebfc3b9_0.conda#a64f11b244b2c112cd3fa1cbe9493999 https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.9.1-cuda112_haf10fcf_5.conda#b8996ffa972161676ba6972af4c41384 @@ -106,7 +106,7 @@ https://conda.anaconda.org/conda-forge/noarch/certifi-2023.5.7-pyhd8ed1ab_0.cond https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.2.0-pyhd8ed1ab_0.conda#313516e9a4b08b12dfb1e1cd390a96e3 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 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--- a/build_tools/azure/pylatest_conda_forge_mkl_no_coverage_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_no_coverage_linux-64_conda.lock @@ -3,7 +3,7 @@ # input_hash: 28f25ea7bcf22e93278ac96747ca9700ada47330f6e3ed927edb73ab4a4c153e @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 -https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.5.7-hbcca054_0.conda#f5c65075fc34438d5b456c7f3f5ab695 +https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.7.22-hbcca054_0.conda#a73ecd2988327ad4c8f2c331482917f2 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 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https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2#ecfff944ba3960ecb334b9a2663d708d https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.1.2-pyhd8ed1ab_0.conda#de4cb3384374e1411f0454edcf546cdb https://conda.anaconda.org/conda-forge/noarch/execnet-2.0.2-pyhd8ed1ab_0.conda#67de0d8241e1060a479e3c37793e26f9 @@ -112,10 +113,10 @@ https://conda.anaconda.org/conda-forge/noarch/idna-3.4-pyhd8ed1ab_0.tar.bz2#3427 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.4-py311h4dd048b_1.tar.bz2#46d451f575392c01dc193069bd89766d https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.15-haa2dc70_1.conda#980d8aca0bc23ca73fa8caa3e7c84c28 -https://conda.anaconda.org/conda-forge/linux-64/libclang13-15.0.7-default_h9986a30_2.conda#907344cee64101d44d806bbe0fccb01d 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https://repo.anaconda.com/pkgs/main/osx-64/bottleneck-1.3.5-py311hb9e55a9_0.conda#5aa1b58b421d4608b16184f8468253ef https://repo.anaconda.com/pkgs/main/osx-64/contourpy-1.0.5-py311ha357a0b_0.conda#a130f83ba4b5d008e0c134c73e10b8fb diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 6943e080317d7..a85af464ddcb4 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -12,7 +12,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d28 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda#06e288f9250abef59b9a367d151fc339 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.9-h7f8727e_0.conda#dc136c954b2d644c79fee77e57e183dc +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.10-h7f8727e_0.conda#83d254d7304c7c1220f82668ab1e7b42 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.2-h5eee18b_0.conda#bcd31de48a0dcb44bc5b99675800c5cc https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31fbfbb5057c92fa845ad6adef93 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e @@ -20,19 +20,19 @@ https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be421 https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda#c7086c9ceb6cfe1c4c729a774a2d88a5 https://repo.anaconda.com/pkgs/main/linux-64/python-3.9.17-h955ad1f_0.conda#73d1906010828e497fe284a2ed9fd2cb -https://repo.anaconda.com/pkgs/main/linux-64/setuptools-67.8.0-py39h06a4308_0.conda#3d40bf5ad5f24b0c96624efd2cff1c80 +https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.0.0-py39h06a4308_0.conda#0af0f107fd4cfe0b3b46ce9fe0471873 https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.38.4-py39h06a4308_0.conda#83e731cfecb3797a0f2865615177f433 -https://repo.anaconda.com/pkgs/main/linux-64/pip-23.1.2-py39h06a4308_0.conda#2f158d8e808d38955aa602aad8db0157 +https://repo.anaconda.com/pkgs/main/linux-64/pip-23.2.1-py39h06a4308_0.conda#e337f9c2951f8ad2a1276b91c04d47e4 # pip alabaster @ https://files.pythonhosted.org/packages/64/88/c7083fc61120ab661c5d0b82cb77079fc1429d3f913a456c1c82cf4658f7/alabaster-0.7.13-py3-none-any.whl#sha256=1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3 # pip babel @ 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https://files.pythonhosted.org/packages/eb/3a/023761d323f51b932ba8aa70bfe9c987f5fa094ffbaba9cd9295b8eee429/Pillow-10.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=9f7c16705f44e0504a3a2a14197c1f0b32a95731d251777dcb060aa83022cb2d # pip pluggy @ https://files.pythonhosted.org/packages/51/32/4a79112b8b87b21450b066e102d6608907f4c885ed7b04c3fdb085d4d6ae/pluggy-1.2.0-py3-none-any.whl#sha256=c2fd55a7d7a3863cba1a013e4e2414658b1d07b6bc57b3919e0c63c9abb99849 # pip py @ https://files.pythonhosted.org/packages/f6/f0/10642828a8dfb741e5f3fbaac830550a518a775c7fff6f04a007259b0548/py-1.11.0-py2.py3-none-any.whl#sha256=607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378 -# pip pygments @ https://files.pythonhosted.org/packages/34/a7/37c8d68532ba71549db4212cb036dbd6161b40e463aba336770e80c72f84/Pygments-2.15.1-py3-none-any.whl#sha256=db2db3deb4b4179f399a09054b023b6a586b76499d36965813c71aa8ed7b5fd1 +# pip pygments @ 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https://files.pythonhosted.org/packages/36/7a/87837f39d0296e723bb9b62bbb257d0355c7f6128853c78955f57342a56d/python_dateutil-2.8.2-py2.py3-none-any.whl#sha256=961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9 @@ -75,7 +75,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.1.2-py39h06a4308_0.conda#2f1 # pip requests @ https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl#sha256=58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f # pip scipy @ https://files.pythonhosted.org/packages/08/25/035fe07fc32c5a8b314f882faa9d4817223fa5faf524d3fedcf17a4b9d22/scipy-1.11.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b41a0f322b4eb51b078cb3441e950ad661ede490c3aca66edef66f4b37ab1877 # pip setuptools-scm @ https://files.pythonhosted.org/packages/1d/66/8f42c941be949ef2b22fe905d850c794e7c170a526023612aad5f3a121ad/setuptools_scm-7.1.0-py3-none-any.whl#sha256=73988b6d848709e2af142aa48c986ea29592bbcfca5375678064708205253d8e -# pip tifffile @ https://files.pythonhosted.org/packages/06/a3/68d17088a4f09565bc7341fd20490da8191ec4cddde479daaabbe07bb603/tifffile-2023.7.10-py3-none-any.whl#sha256=94dfdec321ace96abbfe872a66cfd824800c099a2db558443453eebc2c11b304 +# pip tifffile @ https://files.pythonhosted.org/packages/2d/e5/cc8a8ca43685006bb3ca56fab60707f3f74700844b18634db0b1e8b4b93f/tifffile-2023.7.18-py3-none-any.whl#sha256=a9449ab688b82b69f3ddf80e4e0b4de7b5b02549974a56e112061b816b3c5585 # pip lightgbm @ https://files.pythonhosted.org/packages/d8/61/4165b1caf07d661c4f0241534bbc18748e49e1ddb849fd9908c36c1d622c/lightgbm-4.0.0.tar.gz#sha256=03d1b3903aa51cd9a5e3965941236f2a7bf5a69d7a76059dbf68fd9b4fc92d8f # pip matplotlib @ https://files.pythonhosted.org/packages/47/b9/6c0daa9b953a80b4e6933bf6a11a2d0633f257e84ee5995c5fd35de564c9/matplotlib-3.7.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=318c89edde72ff95d8df67d82aca03861240512994a597a435a1011ba18dbc7f # pip pandas @ https://files.pythonhosted.org/packages/9e/0d/91a9fd2c202f2b1d97a38ab591890f86480ecbb596cbc56d035f6f23fdcc/pandas-2.0.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=5ec591c48e29226bcbb316e0c1e9423622bc7a4eaf1ef7c3c9fa1a3981f89641 @@ -83,6 +83,6 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.1.2-py39h06a4308_0.conda#2f1 # pip pytest-cov @ https://files.pythonhosted.org/packages/a7/4b/8b78d126e275efa2379b1c2e09dc52cf70df16fc3b90613ef82531499d73/pytest_cov-4.1.0-py3-none-any.whl#sha256=6ba70b9e97e69fcc3fb45bfeab2d0a138fb65c4d0d6a41ef33983ad114be8c3a # pip pytest-forked @ https://files.pythonhosted.org/packages/f4/af/9c0bda43e486a3c9bf1e0f876d0f241bc3f229d7d65d09331a0868db9629/pytest_forked-1.6.0-py3-none-any.whl#sha256=810958f66a91afb1a1e2ae83089d8dc1cd2437ac96b12963042fbb9fb4d16af0 # pip scikit-image @ https://files.pythonhosted.org/packages/19/bd/a53569a0a698d925eb46dbea0bd3b6b62e7287a9ec88b5a03efa8ebd5b14/scikit_image-0.21.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=78b1e96c59cab640ca5c5b22c501524cfaf34cbe0cb51ba73bd9a9ede3fb6e1d -# pip sphinx @ https://files.pythonhosted.org/packages/4b/a9/9760e8373a11a62f5ef66684771b0a5b2c4a699bf0dbbc650ca2b75cec36/sphinx-7.0.1-py3-none-any.whl#sha256=60c5e04756c1709a98845ed27a2eed7a556af3993afb66e77fec48189f742616 +# pip sphinx @ https://files.pythonhosted.org/packages/48/17/325cf6a257d84751a48ae90752b3d8fe0be8f9535b6253add61c49d0d9bc/sphinx-7.1.2-py3-none-any.whl#sha256=d170a81825b2fcacb6dfd5a0d7f578a053e45d3f2b153fecc948c37344eb4cbe # pip numpydoc @ https://files.pythonhosted.org/packages/c4/81/ad9b8837442ff451eca82515b41ac425f87acff7e2fc016fd1bda13fc01a/numpydoc-1.5.0-py3-none-any.whl#sha256=c997759fb6fc32662801cece76491eedbc0ec619b514932ffd2b270ae89c07f9 # pip pytest-xdist @ https://files.pythonhosted.org/packages/21/08/b1945d4b4986eb1aa10cf84efc5293bba39da80a2f95db3573dd90678408/pytest_xdist-2.5.0-py3-none-any.whl#sha256=6fe5c74fec98906deb8f2d2b616b5c782022744978e7bd4695d39c8f42d0ce65 diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index f6e10e7cab325..2c80d823c0b85 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -14,7 +14,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h7b6447c_0.conda#9303f4 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda#06e288f9250abef59b9a367d151fc339 https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.9-h7f8727e_0.conda#dc136c954b2d644c79fee77e57e183dc +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.10-h7f8727e_0.conda#83d254d7304c7c1220f82668ab1e7b42 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.2-h5eee18b_0.conda#bcd31de48a0dcb44bc5b99675800c5cc https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31fbfbb5057c92fa845ad6adef93 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e @@ -22,12 +22,12 @@ https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be421 https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda#c7086c9ceb6cfe1c4c729a774a2d88a5 https://repo.anaconda.com/pkgs/main/linux-64/python-3.11.4-h955ad1f_0.conda#3c8fb4ea9fdf9a4d27d16cd5fb6fdd16 -https://repo.anaconda.com/pkgs/main/linux-64/setuptools-67.8.0-py311h06a4308_0.conda#b65f6b9c4547f1fd81af11d4e8b649c4 +https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.0.0-py311h06a4308_0.conda#eae51c7be37e9fc2b6f708114b9f2e8d https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.38.4-py311h06a4308_0.conda#b3d14884810655c572ea9a91df7de205 -https://repo.anaconda.com/pkgs/main/linux-64/pip-23.1.2-py311h06a4308_0.conda#143f7a0b817b07755e1d2c42f5365e7d +https://repo.anaconda.com/pkgs/main/linux-64/pip-23.2.1-py311h06a4308_0.conda#112b1357a2afeacaa247b21c01dc9a06 # pip alabaster @ https://files.pythonhosted.org/packages/64/88/c7083fc61120ab661c5d0b82cb77079fc1429d3f913a456c1c82cf4658f7/alabaster-0.7.13-py3-none-any.whl#sha256=1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3 # pip babel @ https://files.pythonhosted.org/packages/df/c4/1088865e0246d7ecf56d819a233ab2b72f7d6ab043965ef327d0731b5434/Babel-2.12.1-py3-none-any.whl#sha256=b4246fb7677d3b98f501a39d43396d3cafdc8eadb045f4a31be01863f655c610 -# pip certifi @ https://files.pythonhosted.org/packages/9d/19/59961b522e6757f0c9097e4493fa906031b95b3ebe9360b2c3083561a6b4/certifi-2023.5.7-py3-none-any.whl#sha256=c6c2e98f5c7869efca1f8916fed228dd91539f9f1b444c314c06eef02980c716 +# pip certifi @ https://files.pythonhosted.org/packages/4c/dd/2234eab22353ffc7d94e8d13177aaa050113286e93e7b40eae01fbf7c3d9/certifi-2023.7.22-py3-none-any.whl#sha256=92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9 # pip charset-normalizer @ https://files.pythonhosted.org/packages/bc/85/ef25d4ba14c7653c3020a1c6e1a7413e6791ef36a0ac177efa605fc2c737/charset_normalizer-3.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=246de67b99b6851627d945db38147d1b209a899311b1305dd84916f2b88526c6 # pip coverage @ https://files.pythonhosted.org/packages/a7/cd/3ce94ad9d407a052dc2a74fbeb1c7947f442155b28264eb467ee78dea812/coverage-7.2.7-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=63426706118b7f5cf6bb6c895dc215d8a418d5952544042c8a2d9fe87fcf09cb # pip docutils @ https://files.pythonhosted.org/packages/26/87/f238c0670b94533ac0353a4e2a1a771a0cc73277b88bff23d3ae35a256c1/docutils-0.20.1-py3-none-any.whl#sha256=96f387a2c5562db4476f09f13bbab2192e764cac08ebbf3a34a95d9b1e4a59d6 @@ -37,10 +37,10 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.1.2-py311h06a4308_0.conda#14 # pip iniconfig @ https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl#sha256=b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374 # pip markupsafe @ https://files.pythonhosted.org/packages/fe/21/2eff1de472ca6c99ec3993eab11308787b9879af9ca8bbceb4868cf4f2ca/MarkupSafe-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bfce63a9e7834b12b87c64d6b155fdd9b3b96191b6bd334bf37db7ff1fe457f2 # pip packaging @ https://files.pythonhosted.org/packages/ab/c3/57f0601a2d4fe15de7a553c00adbc901425661bf048f2a22dfc500caf121/packaging-23.1-py3-none-any.whl#sha256=994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61 -# pip platformdirs @ https://files.pythonhosted.org/packages/6d/a7/47b7088a28c8fe5775eb15281bf44d39facdbe4bc011a95ccb89390c2db9/platformdirs-3.9.1-py3-none-any.whl#sha256=ad8291ae0ae5072f66c16945166cb11c63394c7a3ad1b1bc9828ca3162da8c2f +# pip platformdirs @ https://files.pythonhosted.org/packages/14/51/fe5a0d6ea589f0d4a1b97824fb518962ad48b27cd346dcdfa2405187997a/platformdirs-3.10.0-py3-none-any.whl#sha256=d7c24979f292f916dc9cbf8648319032f551ea8c49a4c9bf2fb556a02070ec1d # pip pluggy @ https://files.pythonhosted.org/packages/51/32/4a79112b8b87b21450b066e102d6608907f4c885ed7b04c3fdb085d4d6ae/pluggy-1.2.0-py3-none-any.whl#sha256=c2fd55a7d7a3863cba1a013e4e2414658b1d07b6bc57b3919e0c63c9abb99849 # pip py @ https://files.pythonhosted.org/packages/f6/f0/10642828a8dfb741e5f3fbaac830550a518a775c7fff6f04a007259b0548/py-1.11.0-py2.py3-none-any.whl#sha256=607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378 -# pip pygments @ https://files.pythonhosted.org/packages/34/a7/37c8d68532ba71549db4212cb036dbd6161b40e463aba336770e80c72f84/Pygments-2.15.1-py3-none-any.whl#sha256=db2db3deb4b4179f399a09054b023b6a586b76499d36965813c71aa8ed7b5fd1 +# pip pygments @ https://files.pythonhosted.org/packages/43/88/29adf0b44ba6ac85045e63734ae0997d3c58d8b1a91c914d240828d0d73d/Pygments-2.16.1-py3-none-any.whl#sha256=13fc09fa63bc8d8671a6d247e1eb303c4b343eaee81d861f3404db2935653692 # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 # pip snowballstemmer @ https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl#sha256=c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a # pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/06/c1/5e2cafbd03105ce50d8500f9b4e8a6e8d02e22d0475b574c3b3e9451a15f/sphinxcontrib_applehelp-1.0.4-py3-none-any.whl#sha256=29d341f67fb0f6f586b23ad80e072c8e6ad0b48417db2bde114a4c9746feb228 @@ -50,7 +50,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.1.2-py311h06a4308_0.conda#14 # pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/2b/14/05f9206cf4e9cfca1afb5fd224c7cd434dcc3a433d6d9e4e0264d29c6cdb/sphinxcontrib_qthelp-1.0.3-py2.py3-none-any.whl#sha256=bd9fc24bcb748a8d51fd4ecaade681350aa63009a347a8c14e637895444dfab6 # pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/c6/77/5464ec50dd0f1c1037e3c93249b040c8fc8078fdda97530eeb02424b6eea/sphinxcontrib_serializinghtml-1.1.5-py2.py3-none-any.whl#sha256=352a9a00ae864471d3a7ead8d7d79f5fc0b57e8b3f95e9867eb9eb28999b92fd # pip threadpoolctl @ https://files.pythonhosted.org/packages/81/12/fd4dea011af9d69e1cad05c75f3f7202cdcbeac9b712eea58ca779a72865/threadpoolctl-3.2.0-py3-none-any.whl#sha256=2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032 -# pip urllib3 @ https://files.pythonhosted.org/packages/8a/03/ad9306a50d05c166e3456fe810f33cee2b8b2a7a6818ec5d4908c4ec6b36/urllib3-2.0.3-py3-none-any.whl#sha256=48e7fafa40319d358848e1bc6809b208340fafe2096f1725d05d67443d0483d1 +# pip urllib3 @ https://files.pythonhosted.org/packages/9b/81/62fd61001fa4b9d0df6e31d47ff49cfa9de4af03adecf339c7bc30656b37/urllib3-2.0.4-py3-none-any.whl#sha256=de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4 # pip jinja2 @ https://files.pythonhosted.org/packages/bc/c3/f068337a370801f372f2f8f6bad74a5c140f6fda3d9de154052708dd3c65/Jinja2-3.1.2-py3-none-any.whl#sha256=6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61 # pip pytest @ https://files.pythonhosted.org/packages/33/b2/741130cbcf2bbfa852ed95a60dc311c9e232c7ed25bac3d9b8880a8df4ae/pytest-7.4.0-py3-none-any.whl#sha256=78bf16451a2eb8c7a2ea98e32dc119fd2aa758f1d5d66dbf0a59d69a3969df32 # pip python-dateutil @ https://files.pythonhosted.org/packages/36/7a/87837f39d0296e723bb9b62bbb257d0355c7f6128853c78955f57342a56d/python_dateutil-2.8.2-py2.py3-none-any.whl#sha256=961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9 @@ -58,6 +58,6 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.1.2-py311h06a4308_0.conda#14 # pip pooch @ https://files.pythonhosted.org/packages/84/8c/4da580db7fb4cfce8f5ed78e7d2aa542e6f201edd69d3d8a96917a8ff63c/pooch-1.7.0-py3-none-any.whl#sha256=74258224fc33d58f53113cf955e8d51bf01386b91492927d0d1b6b341a765ad7 # pip pytest-cov @ https://files.pythonhosted.org/packages/a7/4b/8b78d126e275efa2379b1c2e09dc52cf70df16fc3b90613ef82531499d73/pytest_cov-4.1.0-py3-none-any.whl#sha256=6ba70b9e97e69fcc3fb45bfeab2d0a138fb65c4d0d6a41ef33983ad114be8c3a # pip pytest-forked @ https://files.pythonhosted.org/packages/f4/af/9c0bda43e486a3c9bf1e0f876d0f241bc3f229d7d65d09331a0868db9629/pytest_forked-1.6.0-py3-none-any.whl#sha256=810958f66a91afb1a1e2ae83089d8dc1cd2437ac96b12963042fbb9fb4d16af0 -# pip sphinx @ https://files.pythonhosted.org/packages/4b/a9/9760e8373a11a62f5ef66684771b0a5b2c4a699bf0dbbc650ca2b75cec36/sphinx-7.0.1-py3-none-any.whl#sha256=60c5e04756c1709a98845ed27a2eed7a556af3993afb66e77fec48189f742616 +# pip sphinx @ https://files.pythonhosted.org/packages/48/17/325cf6a257d84751a48ae90752b3d8fe0be8f9535b6253add61c49d0d9bc/sphinx-7.1.2-py3-none-any.whl#sha256=d170a81825b2fcacb6dfd5a0d7f578a053e45d3f2b153fecc948c37344eb4cbe # pip numpydoc @ https://files.pythonhosted.org/packages/c4/81/ad9b8837442ff451eca82515b41ac425f87acff7e2fc016fd1bda13fc01a/numpydoc-1.5.0-py3-none-any.whl#sha256=c997759fb6fc32662801cece76491eedbc0ec619b514932ffd2b270ae89c07f9 # pip pytest-xdist @ https://files.pythonhosted.org/packages/21/08/b1945d4b4986eb1aa10cf84efc5293bba39da80a2f95db3573dd90678408/pytest_xdist-2.5.0-py3-none-any.whl#sha256=6fe5c74fec98906deb8f2d2b616b5c782022744978e7bd4695d39c8f42d0ce65 diff --git a/build_tools/azure/pypy3_linux-64_conda.lock b/build_tools/azure/pypy3_linux-64_conda.lock index c1c7bf6fa609b..5b3d23f9710b3 100644 --- a/build_tools/azure/pypy3_linux-64_conda.lock +++ b/build_tools/azure/pypy3_linux-64_conda.lock @@ -3,7 +3,7 @@ # input_hash: 35e4a4f1db15219fa4cb71af7b54acc24ec7c3b3610c479f979c6c44cbd93db7 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 -https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.5.7-hbcca054_0.conda#f5c65075fc34438d5b456c7f3f5ab695 +https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.7.22-hbcca054_0.conda#a73ecd2988327ad4c8f2c331482917f2 https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.1.0-h15d22d2_0.conda#afb656a334c409dd9805508af1c89c7a https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.1.0-hfd8a6a1_0.conda#067bcc23164642f4c226da631f2a2e1d https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.9-3_pypy39_pp73.conda#6f23be0f8f1e4871998437b188425ea3 @@ -23,7 +23,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.23-pthreads_h803 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.3.1-hd590300_0.conda#82bf6f63eb15ef719b556b63feec3a77 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda#f36c115f1ee199da648e0597ec2047ad https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4-hcb278e6_0.conda#681105bccc2a3f7f1a837d47d39c9179 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.1.1-hd590300_1.conda#2e1d7b458ac8f1e3ca4e18b77add6277 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.1.2-hd590300_0.conda#e5ac5227582d6c83ccf247288c0eb095 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hd590300_0.conda#2c80dc38fface310c9bd81b17037fee5 @@ -62,18 +62,18 @@ https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-17_linux64_open https://conda.anaconda.org/conda-forge/linux-64/python-3.9.16-0_73_pypy.conda#16eebd2564f86026ea0abe5b8e446438 https://conda.anaconda.org/conda-forge/linux-64/blas-2.117-openblas.conda#54b4b02b897156056f3056f992261d0c https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.0.9-py39h0e26352_9.conda#26b12739f1ef41d48e9f8fbadd7afe10 -https://conda.anaconda.org/conda-forge/noarch/certifi-2023.5.7-pyhd8ed1ab_0.conda#5d1b71c942b8421285934dad1d891ebc +https://conda.anaconda.org/conda-forge/noarch/certifi-2023.7.22-pyhd8ed1ab_0.conda#7f3dbc9179b4dde7da98dfb151d0ad22 https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.2.0-pyhd8ed1ab_0.conda#313516e9a4b08b12dfb1e1cd390a96e3 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 https://conda.anaconda.org/conda-forge/noarch/cycler-0.11.0-pyhd8ed1ab_0.tar.bz2#a50559fad0affdbb33729a68669ca1cb -https://conda.anaconda.org/conda-forge/linux-64/cython-0.29.36-py39hc10206b_0.conda#f4286c793e0f1a4bb6a04231bf01a080 +https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.0-py39hc10206b_0.conda#4189d8a66dc8d9111f7d4ba5443991f6 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.1.2-pyhd8ed1ab_0.conda#de4cb3384374e1411f0454edcf546cdb https://conda.anaconda.org/conda-forge/noarch/execnet-2.0.2-pyhd8ed1ab_0.conda#67de0d8241e1060a479e3c37793e26f9 https://conda.anaconda.org/conda-forge/noarch/idna-3.4-pyhd8ed1ab_0.tar.bz2#34272b248891bddccc64479f9a7fffed https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.4-py39h2865249_1.tar.bz2#6b7e75ba141872a00154f312d43d9a8c https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19 -https://conda.anaconda.org/conda-forge/linux-64/numpy-1.25.1-py39h129f8d9_0.conda#5fca5bacc257672ca1dc6e614fe5c3aa +https://conda.anaconda.org/conda-forge/linux-64/numpy-1.25.2-py39h129f8d9_0.conda#134b81b5cb145952044ebbb5c70bb306 https://conda.anaconda.org/conda-forge/noarch/packaging-23.1-pyhd8ed1ab_0.conda#91cda59e66e1e4afe9476f8ef98f5c30 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.0.0-py39hb514683_0.conda#6e21ef6b7b5618ad333edc3602ae4e56 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.2.0-pyhd8ed1ab_0.conda#7263924c642d22e311d9e59b839f1b33 @@ -90,15 +90,15 @@ https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.7.1-pyha770c72 https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-15.0.0-py39h4d8b378_0.tar.bz2#44eea5be274d005065d87df9cf2a9234 https://conda.anaconda.org/conda-forge/noarch/zipp-3.16.2-pyhd8ed1ab_0.conda#2da0451b54c4563c32490cb1b7cf68a1 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.1.0-py39ha90811c_0.conda#9ff9b1b02301cf75a8585895856fff0f -https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.41.0-py39hf860d4a_0.conda#bc97c11560e18fe8572cdc25b2cf04a5 +https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.42.0-py39hf860d4a_0.conda#da1e833718c26334f6807687c03eddff https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.0.0-pyhd8ed1ab_1.conda#a08b6be5bf18b9d2a927d3457750f82e https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.0-pyhd8ed1ab_1.conda#fb4caf6da228ccc487350eade569abae https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.0-pyhd8ed1ab_0.conda#3cfe9b9e958e7238a386933c75d190db https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984 https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.7.1-hd8ed1ab_0.conda#f96688577f1faa58096d06a45136afa2 -https://conda.anaconda.org/conda-forge/noarch/urllib3-2.0.3-pyhd8ed1ab_1.conda#89efeecbb24c62e2f1ed0b8ca959ec69 +https://conda.anaconda.org/conda-forge/noarch/urllib3-2.0.4-pyhd8ed1ab_0.conda#18badd8fa3648d1beb1fcc7f2e0f756e https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.0.0-pyhd8ed1ab_1.conda#d69f29916f934f30adb1dd5fff4d9a8b -https://conda.anaconda.org/conda-forge/noarch/platformdirs-3.9.1-pyhd8ed1ab_0.conda#044e7a1e0ad42c4e67110bd078150a63 +https://conda.anaconda.org/conda-forge/noarch/platformdirs-3.10.0-pyhd8ed1ab_0.conda#0809187ef9b89a3d94a5c24d13936236 https://conda.anaconda.org/conda-forge/noarch/pytest-forked-1.6.0-pyhd8ed1ab_0.conda#a46947638b6e005b63d2d6271da529b0 https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.7.2-py39hfcdda22_0.conda#27f24b3402229299a3d5fdeee0b417b9 diff --git a/build_tools/azure/python_nogil_lock.txt b/build_tools/azure/python_nogil_lock.txt index 044ac475c1ccf..70a9e4fb69e82 100644 --- a/build_tools/azure/python_nogil_lock.txt +++ b/build_tools/azure/python_nogil_lock.txt @@ -19,7 +19,7 @@ exceptiongroup==1.1.2 # via pytest execnet==2.0.2 # via pytest-xdist -fonttools==4.41.0 +fonttools==4.42.0 # via matplotlib iniconfig==2.0.0 # via pytest @@ -43,7 +43,7 @@ pillow==9.5.0 # via matplotlib pluggy==1.2.0 # via pytest -pyparsing==3.1.0 +pyparsing==3.1.1 # via matplotlib pytest==7.4.0 # via diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index 4242a0b1c2384..3c0d757bb94ad 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -4,7 +4,7 @@ # # pip-compile --config=pyproject.toml --output-file=build_tools/azure/ubuntu_atlas_lock.txt build_tools/azure/ubuntu_atlas_requirements.txt # -cython==0.29.36 +cython==3.0.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt exceptiongroup==1.1.2 # via pytest diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 1caa857463952..ed665dbd5f400 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -3,7 +3,7 @@ # input_hash: c5d98c25be4983392d8d1d6b28c2ceea24a4d25b50d0f45ad6192f94abed4d46 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 -https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.5.7-hbcca054_0.conda#f5c65075fc34438d5b456c7f3f5ab695 +https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.7.22-hbcca054_0.conda#a73ecd2988327ad4c8f2c331482917f2 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb @@ -23,14 +23,13 @@ https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.12-he073ed8_16. https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-hf600244_0.conda#33084421a8c0af6aef1b439707f7662a https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/binutils-2.40-hdd6e379_0.conda#ccc940fddbc3fcd3d79cd4c654c4b5c4 -https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hbdbef99_0.conda#8dd723ef0716b6d102d57bbacbe92b22 +https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hbdbef99_1.conda#855757cf4cf920cc9b6130a32fdcaa81 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.1.0-he5830b7_0.conda#cd93f779ff018dd85c7544c015c9db3c https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.9-hd590300_0.conda#a0c6f0e7e1a467f5678f94dea18c8aa7 https://conda.anaconda.org/conda-forge/linux-64/aom-3.5.0-h27087fc_0.tar.bz2#a08150fd2298460cd1fcccf626305642 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h7f98852_4.tar.bz2#a1fd65c7ccbf10880423d82bca54eb54 -https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.19.1-hd590300_0.conda#e8c18d865be43e2fb3f7a145b6adf1f5 https://conda.anaconda.org/conda-forge/linux-64/charls-2.4.2-h59595ed_0.conda#4336bd67920dd504cd8c6761d6a99645 https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda#418c6ca5929a611cbd69204907a83995 https://conda.anaconda.org/conda-forge/linux-64/gettext-0.21.1-h27087fc_0.tar.bz2#14947d8770185e5153fdd04d4673ed37 @@ -44,7 +43,6 @@ https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2#76 https://conda.anaconda.org/conda-forge/linux-64/libaec-1.0.6-hcb278e6_1.conda#0f683578378cddb223e7fd24f785ab2a 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https://files.pythonhosted.org/packages/80/eb/b1a80fcb207bf73b98efe342c8c827e121f465a407d8819a82c6a88f41ee/jupyterlite_sphinx-0.9.1-py3-none-any.whl#sha256=6965a0736f49b4e7c604b41edf8df0ab266711f4f8b6e25b32f078cfb7e67d02 diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index c1045033c5a7a..5d357f651dbd2 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -3,7 +3,7 @@ # input_hash: d465abb23248872aed411e2f2f7293a661f0b783f1a84420ffa5431229814cab @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 -https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.5.7-hbcca054_0.conda#f5c65075fc34438d5b456c7f3f5ab695 +https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.7.22-hbcca054_0.conda#a73ecd2988327ad4c8f2c331482917f2 https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_16.conda#7ca122655873935e02c91279c5b03c8c https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.36.1-hea4e1c9_2.tar.bz2#bd4f2e711b39af170e7ff15163fe87ee https://conda.anaconda.org/conda-forge/linux-64/libgcc-devel_linux-64-7.5.0-hda03d7c_20.tar.bz2#2146b25eb2a762a44fab709338a7b6d9 @@ -34,7 +34,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-0.10.0-he1b5a44_0.t https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda#f36c115f1ee199da648e0597ec2047ad https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4-hcb278e6_0.conda#681105bccc2a3f7f1a837d47d39c9179 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 -https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1u-hd590300_0.conda#cc1c2db83ae28a28871d52b035739488 +https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1v-hd590300_0.conda#b1701dc29287ef4638ccc7f12cf73405 https://conda.anaconda.org/conda-forge/linux-64/pcre-8.45-h9c3ff4c_0.tar.bz2#c05d1820a6d34ff07aaaab7a9b7eddaa https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hd590300_0.conda#2c80dc38fface310c9bd81b17037fee5 @@ -73,9 +73,9 @@ https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.0-hfec8fc6_2.conda# https://conda.anaconda.org/conda-forge/linux-64/python-3.8.6-h852b56e_0_cpython.tar.bz2#dd65401dfb61ac030edc0dc4d15c2c51 https://conda.anaconda.org/conda-forge/noarch/alabaster-0.7.13-pyhd8ed1ab_0.conda#06006184e203b61d3525f90de394471e https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.0.9-py38hfa26641_9.conda#d056745008e5ed73210a31dcfd4d183a -https://conda.anaconda.org/conda-forge/noarch/certifi-2023.5.7-pyhd8ed1ab_0.conda#5d1b71c942b8421285934dad1d891ebc +https://conda.anaconda.org/conda-forge/noarch/certifi-2023.7.22-pyhd8ed1ab_0.conda#7f3dbc9179b4dde7da98dfb151d0ad22 https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.2.0-pyhd8ed1ab_0.conda#313516e9a4b08b12dfb1e1cd390a96e3 -https://conda.anaconda.org/conda-forge/noarch/click-8.1.5-unix_pyh707e725_0.conda#4f63f3e01a26e7058eabfa5f984cd6f3 +https://conda.anaconda.org/conda-forge/noarch/click-8.1.6-unix_pyh707e725_0.conda#64dbb3b205546691a61204d1cfb208e3 https://conda.anaconda.org/conda-forge/noarch/cloudpickle-2.2.1-pyhd8ed1ab_0.conda#b325bfc4cff7d7f8a868f1f7ecc4ed16 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.1.1-0.tar.bz2#1ba267e19dbaf3db9dd0404e6fb9cdb9 @@ -100,7 +100,7 @@ https://conda.anaconda.org/conda-forge/noarch/pluggy-1.2.0-pyhd8ed1ab_0.conda#72 https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.5-py38h1de0b5d_0.conda#92e899e7b0ed27c793014d1fa54f9b7b https://conda.anaconda.org/conda-forge/noarch/py-1.11.0-pyh6c4a22f_0.tar.bz2#b4613d7e7a493916d867842a6a148054 https://conda.anaconda.org/conda-forge/noarch/pygments-2.15.1-pyhd8ed1ab_0.conda#d316679235612869eba305aa7d41d9bf -https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.0-pyhd8ed1ab_0.conda#d3ed087d1f7f8f5590e8e87b57a8ce64 +https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.1-pyhd8ed1ab_0.conda#176f7d56f0cfe9008bdf1bccd7de02fb https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2#2a7de29fb590ca14b5243c4c812c8025 https://conda.anaconda.org/conda-forge/noarch/pytz-2023.3-pyhd8ed1ab_0.conda#d3076b483092a435832603243567bc31 https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0-py38h0a891b7_5.tar.bz2#0856c59f9ddb710c640dc0428d66b1b7 @@ -119,10 +119,10 @@ https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5 https://conda.anaconda.org/conda-forge/noarch/toolz-0.12.0-pyhd8ed1ab_0.tar.bz2#92facfec94bc02d6ccf42e7173831a36 https://conda.anaconda.org/conda-forge/linux-64/tornado-6.3.2-py38h01eb140_0.conda#3db869202b0e523d606d13e81ca79ab6 https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.7.1-pyha770c72_0.conda#c39d6a09fe819de4951c2642629d9115 -https://conda.anaconda.org/conda-forge/noarch/wheel-0.40.0-pyhd8ed1ab_1.conda#c95fac682453aeffa12db7ae5a721bec +https://conda.anaconda.org/conda-forge/noarch/wheel-0.41.1-pyhd8ed1ab_0.conda#8f467ba2db2b5470d297953d9c1f9c7d https://conda.anaconda.org/conda-forge/noarch/zipp-3.16.2-pyhd8ed1ab_0.conda#2da0451b54c4563c32490cb1b7cf68a1 https://conda.anaconda.org/conda-forge/noarch/babel-2.12.1-pyhd8ed1ab_1.conda#ac432e732804a81ddcf29c92ead57cde -https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.12.0-py38h0a891b7_1.tar.bz2#183f6160ab3498b882e903b06be7d430 +https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.12.2-py38h01eb140_0.conda#e9d465b78d0b41beeb6bcceb6714520d https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-hfdff14a_1.tar.bz2#4caaca6356992ee545080c7d7193b5a3 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.14.5-h36ae1b5_2.tar.bz2#00084ab2657be5bf0ba0757ccde797ef https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-6.8.0-pyha770c72_0.conda#4e9f59a060c3be52bc4ddc46ee9b6946 @@ -132,17 +132,17 @@ https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.8.0-20_mkl.tar.bz2#14 https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.8.0-20_mkl.tar.bz2#52c0ae3606eeae7e1d493f37f336f4f5 https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_0.tar.bz2#8b45f9f2b2f7a98b0ec179c8991a4a9b https://conda.anaconda.org/conda-forge/noarch/partd-1.4.0-pyhd8ed1ab_0.conda#721dab5803ea92ce02ddc4ee50aa0c48 -https://conda.anaconda.org/conda-forge/noarch/pip-23.2-pyhd8ed1ab_0.conda#fb90dfe13ce16c0a3374e3d34e3291c5 +https://conda.anaconda.org/conda-forge/noarch/pip-23.2.1-pyhd8ed1ab_0.conda#e2783aa3f9235225eec92f9081c5b801 https://conda.anaconda.org/conda-forge/noarch/plotly-5.14.0-pyhd8ed1ab_0.conda#6a7bcc42ef58dd6cf3da9333ea102433 https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.0-pyhd8ed1ab_0.conda#3cfe9b9e958e7238a386933c75d190db https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984 https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.7.1-hd8ed1ab_0.conda#f96688577f1faa58096d06a45136afa2 -https://conda.anaconda.org/conda-forge/noarch/urllib3-2.0.3-pyhd8ed1ab_1.conda#89efeecbb24c62e2f1ed0b8ca959ec69 +https://conda.anaconda.org/conda-forge/noarch/urllib3-2.0.4-pyhd8ed1ab_0.conda#18badd8fa3648d1beb1fcc7f2e0f756e https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.14.5-h0935bb2_2.tar.bz2#eb125ee86480e00a4a1ed45a577c3311 https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-6.8.0-hd8ed1ab_0.conda#b279b07ce18058034e5b3606ba103a8b https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.8.0-20_mkl.tar.bz2#8274dc30518af9df1de47f5d9e73165c https://conda.anaconda.org/conda-forge/linux-64/numpy-1.17.3-py38h95a1406_0.tar.bz2#bc0cbf611fe2f86eab29b98e51404f5e -https://conda.anaconda.org/conda-forge/noarch/platformdirs-3.9.1-pyhd8ed1ab_0.conda#044e7a1e0ad42c4e67110bd078150a63 +https://conda.anaconda.org/conda-forge/noarch/platformdirs-3.10.0-pyhd8ed1ab_0.conda#0809187ef9b89a3d94a5c24d13936236 https://conda.anaconda.org/conda-forge/noarch/pytest-forked-1.6.0-pyhd8ed1ab_0.conda#a46947638b6e005b63d2d6271da529b0 https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b https://conda.anaconda.org/conda-forge/linux-64/blas-2.20-mkl.tar.bz2#e7d09a07f5413e53dca5282b8fa50bed diff --git a/build_tools/cirrus/py39_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/py39_conda_forge_linux-aarch64_conda.lock index d90c286366c7d..2324d3d372676 100644 --- a/build_tools/cirrus/py39_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/py39_conda_forge_linux-aarch64_conda.lock @@ -2,7 +2,7 @@ # platform: linux-aarch64 # input_hash: de5bfe2a68b349f08233af7b94fc3b2045503b21289e8d3bdb30a1613fd0ddb8 @EXPLICIT -https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2023.5.7-hcefe29a_0.conda#331e624442b88d96bc05a7f2d38c61a4 +https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2023.7.22-hcefe29a_0.conda#95d7f998087114466fa91e7c2887fa2f https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h2d8c526_0.conda#16246d69e945d0b1969a6099e7c5d457 https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-13.1.0-h24e4805_0.conda#069e75bfdbed7744ee64a2b840fccc4e https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-13.1.0-h452befe_0.conda#572f5798bb3d4cc79650f0ca3149aeaa @@ -24,7 +24,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.38.1-hb4cce97_0.c https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.3.1-h31becfc_0.conda#cef7349b72e170880662f4e39864fe44 https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.2.13-h31becfc_5.conda#b213aa87eea9491ef7b129179322e955 https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.4-h2e1726e_0.conda#40beaf447150c2760affc591c7509595 -https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.1.1-h31becfc_1.conda#a8e811c3390d93e5db0cef68e52f349f +https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.1.2-h31becfc_0.conda#fde5a105b2bc9d6eced8a23005492c7e https://conda.anaconda.org/conda-forge/linux-aarch64/pthread-stubs-0.4-hb9de7d4_1001.tar.bz2#d0183ec6ce0b5aaa3486df25fa5f0ded https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxau-1.0.11-h31becfc_0.conda#13de34f69cb73165dbe08c1e9148bedb https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxdmcp-1.1.3-h3557bc0_0.tar.bz2#a6c9016ae1ca5c47a3603ed4cd65fedd @@ -49,11 +49,11 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-16.0.6-h8b0cb96 https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.9.16-hb363c5e_0_cpython.conda#0a7ef29549eaef817898062eeeefebd3 https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.0.9-h4e544f5_9.conda#f172e55e26e979e64e57d5ad28aa0f1d https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-python-1.0.9-py39h3d8bfb9_9.conda#ec5ce3a831ddd7ef4dd731dbe0cdc8e3 -https://conda.anaconda.org/conda-forge/noarch/certifi-2023.5.7-pyhd8ed1ab_0.conda#5d1b71c942b8421285934dad1d891ebc +https://conda.anaconda.org/conda-forge/noarch/certifi-2023.7.22-pyhd8ed1ab_0.conda#7f3dbc9179b4dde7da98dfb151d0ad22 https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.2.0-pyhd8ed1ab_0.conda#313516e9a4b08b12dfb1e1cd390a96e3 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 https://conda.anaconda.org/conda-forge/noarch/cycler-0.11.0-pyhd8ed1ab_0.tar.bz2#a50559fad0affdbb33729a68669ca1cb -https://conda.anaconda.org/conda-forge/linux-aarch64/cython-0.29.36-py39h387a81e_0.conda#18428f05f95972433b4f29725b7e5f65 +https://conda.anaconda.org/conda-forge/linux-aarch64/cython-3.0.0-py39h387a81e_0.conda#9a7721d8c7ddb32842cebda28e980980 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.1.2-pyhd8ed1ab_0.conda#de4cb3384374e1411f0454edcf546cdb https://conda.anaconda.org/conda-forge/noarch/execnet-2.0.2-pyhd8ed1ab_0.conda#67de0d8241e1060a479e3c37793e26f9 https://conda.anaconda.org/conda-forge/noarch/idna-3.4-pyhd8ed1ab_0.tar.bz2#34272b248891bddccc64479f9a7fffed @@ -62,7 +62,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/kiwisolver-1.4.4-py39h11058 https://conda.anaconda.org/conda-forge/linux-aarch64/lcms2-2.15-h3e0bdec_1.conda#5d6c6a9042e2316cec7410dd085814d1 https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-17_linuxaarch64_openblas.conda#1522e3323e898ae9fadd11424a3c0b75 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19 -https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-1.25.1-py39hf88902c_0.conda#1c963c77d842f084b8a84fd5d2338840 +https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-1.25.2-py39hf88902c_0.conda#0d8d2e1a1d2dd0bc581b4682ee9fd95a https://conda.anaconda.org/conda-forge/linux-aarch64/openjpeg-2.5.0-h9508984_2.conda#3d56d402a845c243f8c2dd3c8e836029 https://conda.anaconda.org/conda-forge/noarch/packaging-23.1-pyhd8ed1ab_0.conda#91cda59e66e1e4afe9476f8ef98f5c30 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.2.0-pyhd8ed1ab_0.conda#7263924c642d22e311d9e59b839f1b33 @@ -76,22 +76,22 @@ https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5 https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.3.2-py39h7cc1d5f_0.conda#2c853c8bb419699667c452a01f69749f https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.7.1-pyha770c72_0.conda#c39d6a09fe819de4951c2642629d9115 https://conda.anaconda.org/conda-forge/linux-aarch64/unicodedata2-15.0.0-py39h0fd3b05_0.tar.bz2#835f1a9631e600e0176593e95e85f73f -https://conda.anaconda.org/conda-forge/noarch/wheel-0.40.0-pyhd8ed1ab_1.conda#c95fac682453aeffa12db7ae5a721bec +https://conda.anaconda.org/conda-forge/noarch/wheel-0.41.1-pyhd8ed1ab_0.conda#8f467ba2db2b5470d297953d9c1f9c7d https://conda.anaconda.org/conda-forge/noarch/zipp-3.16.2-pyhd8ed1ab_0.conda#2da0451b54c4563c32490cb1b7cf68a1 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-17_linuxaarch64_openblas.conda#d8a3c0b2b389b2a64b3a1b5e59ae2e09 https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.1.0-py39hd16970a_0.conda#814adec2c3feda6643d00a55d6b9debf -https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.41.0-py39h898b7ef_0.conda#cf7d8c3fedcc6f0e698b86849d4f91e5 +https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.42.0-py39h898b7ef_0.conda#c96b09aadda261154e85c1d78a70631d https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.0.0-pyhd8ed1ab_1.conda#a08b6be5bf18b9d2a927d3457750f82e https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.0-pyhd8ed1ab_1.conda#fb4caf6da228ccc487350eade569abae https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-10.0.0-py39hc5b5638_0.conda#3a11d8338a85e3f93c5bbda05ec8d3c4 -https://conda.anaconda.org/conda-forge/noarch/pip-23.2-pyhd8ed1ab_0.conda#fb90dfe13ce16c0a3374e3d34e3291c5 +https://conda.anaconda.org/conda-forge/noarch/pip-23.2.1-pyhd8ed1ab_0.conda#e2783aa3f9235225eec92f9081c5b801 https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.0-pyhd8ed1ab_0.conda#3cfe9b9e958e7238a386933c75d190db https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984 https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.7.1-hd8ed1ab_0.conda#f96688577f1faa58096d06a45136afa2 -https://conda.anaconda.org/conda-forge/noarch/urllib3-2.0.3-pyhd8ed1ab_1.conda#89efeecbb24c62e2f1ed0b8ca959ec69 +https://conda.anaconda.org/conda-forge/noarch/urllib3-2.0.4-pyhd8ed1ab_0.conda#18badd8fa3648d1beb1fcc7f2e0f756e https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.117-openblas.conda#5f88c5a193286ed9a87afd4b815e8c70 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.0.0-pyhd8ed1ab_1.conda#d69f29916f934f30adb1dd5fff4d9a8b -https://conda.anaconda.org/conda-forge/noarch/platformdirs-3.9.1-pyhd8ed1ab_0.conda#044e7a1e0ad42c4e67110bd078150a63 +https://conda.anaconda.org/conda-forge/noarch/platformdirs-3.10.0-pyhd8ed1ab_0.conda#0809187ef9b89a3d94a5c24d13936236 https://conda.anaconda.org/conda-forge/noarch/pytest-forked-1.6.0-pyhd8ed1ab_0.conda#a46947638b6e005b63d2d6271da529b0 https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.7.2-py39h4ad26d3_0.conda#5f86c21c0c21d294f1c3aa9399e389eb From 213dfe930df501663e6ec2f08fbac6b857f86aa6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Fri, 18 Aug 2023 11:07:20 +0200 Subject: [PATCH 161/264] MNT Ignore ruff errors (#27094) --- sklearn/utils/tests/test_utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/sklearn/utils/tests/test_utils.py b/sklearn/utils/tests/test_utils.py index 4ad48acd345cc..efb9c8666901c 100644 --- a/sklearn/utils/tests/test_utils.py +++ b/sklearn/utils/tests/test_utils.py @@ -525,16 +525,16 @@ def test_shuffle_dont_convert_to_array(): a_s, b_s, c_s, d_s, e_s = shuffle(a, b, c, d, e, random_state=0) assert a_s == ["c", "b", "a"] - assert type(a_s) == list + assert type(a_s) == list # noqa: E721 assert_array_equal(b_s, ["c", "b", "a"]) assert b_s.dtype == object assert c_s == [3, 2, 1] - assert type(c_s) == list + assert type(c_s) == list # noqa: E721 assert_array_equal(d_s, np.array([["c", 2], ["b", 1], ["a", 0]], dtype=object)) - assert type(d_s) == MockDataFrame + assert type(d_s) == MockDataFrame # noqa: E721 assert_array_equal(e_s.toarray(), np.array([[4, 5], [2, 3], [0, 1]])) From e7eb359bf5e573c464de16e0ce5e8ec912dc4679 Mon Sep 17 00:00:00 2001 From: Oleksii Kachaiev Date: Fri, 18 Aug 2023 11:56:49 +0200 Subject: [PATCH 162/264] DOC fix docstring for `sklearn.datasets.get_data_home` (#27073) --- sklearn/datasets/_base.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sklearn/datasets/_base.py b/sklearn/datasets/_base.py index c95c43a8ab942..7a1e584556f89 100644 --- a/sklearn/datasets/_base.py +++ b/sklearn/datasets/_base.py @@ -55,13 +55,13 @@ def get_data_home(data_home=None) -> str: Parameters ---------- - data_home : str, default=None + data_home : str or path-like, default=None The path to scikit-learn data directory. If `None`, the default path is `~/sklearn_learn_data`. Returns ------- - data_home: str or path-like, default=None + data_home: str The path to scikit-learn data directory. """ if data_home is None: From e6d0950f8fc939aabc03e6940d139373bea609cc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Fri, 18 Aug 2023 13:53:11 +0200 Subject: [PATCH 163/264] MNT Remove DeprecationWarning for scipy.sparse.linalg.cg tol vs rtol argument (#26814) --- sklearn/linear_model/_ridge.py | 7 +++---- sklearn/utils/fixes.py | 14 ++++++++++++++ 2 files changed, 17 insertions(+), 4 deletions(-) diff --git a/sklearn/linear_model/_ridge.py b/sklearn/linear_model/_ridge.py index aa30db0525715..03af59376cc8d 100644 --- a/sklearn/linear_model/_ridge.py +++ b/sklearn/linear_model/_ridge.py @@ -33,6 +33,7 @@ ) from ..utils._param_validation import Interval, StrOptions from ..utils.extmath import row_norms, safe_sparse_dot +from ..utils.fixes import _sparse_linalg_cg from ..utils.sparsefuncs import mean_variance_axis from ..utils.validation import _check_sample_weight, check_is_fitted from ._base import LinearClassifierMixin, LinearModel, _preprocess_data, _rescale_data @@ -105,7 +106,7 @@ def _mv(x): C = sp_linalg.LinearOperator( (n_samples, n_samples), matvec=mv, dtype=X.dtype ) - coef, info = sp_linalg.cg(C, y_column, tol=tol, atol="legacy") + coef, info = _sparse_linalg_cg(C, y_column, rtol=tol) coefs[i] = X1.rmatvec(coef) else: # linear ridge @@ -114,9 +115,7 @@ def _mv(x): C = sp_linalg.LinearOperator( (n_features, n_features), matvec=mv, dtype=X.dtype ) - coefs[i], info = sp_linalg.cg( - C, y_column, maxiter=max_iter, tol=tol, atol="legacy" - ) + coefs[i], info = _sparse_linalg_cg(C, y_column, maxiter=max_iter, rtol=tol) if info < 0: raise ValueError("Failed with error code %d" % info) diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py index d33b638358157..ba5ce3c35d07f 100644 --- a/sklearn/utils/fixes.py +++ b/sklearn/utils/fixes.py @@ -15,6 +15,7 @@ import numpy as np import scipy +import scipy.sparse.linalg import scipy.stats import threadpoolctl @@ -109,6 +110,19 @@ def _mode(a, axis=0): return scipy.stats.mode(a, axis=axis) +# TODO: Remove when Scipy 1.12 is the minimum supported version +if sp_base_version >= parse_version("1.12.0"): + _sparse_linalg_cg = scipy.sparse.linalg.cg +else: + + def _sparse_linalg_cg(A, b, **kwargs): + if "rtol" in kwargs: + kwargs["tol"] = kwargs.pop("rtol") + if "atol" not in kwargs: + kwargs["atol"] = "legacy" + return scipy.sparse.linalg.cg(A, b, **kwargs) + + ############################################################################### # Backport of Python 3.9's importlib.resources # TODO: Remove when Python 3.9 is the minimum supported version From 500bd72d4bfb3f753d46c810afe032e1b20201d7 Mon Sep 17 00:00:00 2001 From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Date: Fri, 18 Aug 2023 19:40:57 +0200 Subject: [PATCH 164/264] DOC Fix warning on sklearn.svm._classes.NuSVC docstring (#27106) Co-authored-by: ArturoAmorQ --- sklearn/svm/_classes.py | 1 + 1 file changed, 1 insertion(+) diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py index dfa48b4937147..de342ccbc56b0 100644 --- a/sklearn/svm/_classes.py +++ b/sklearn/svm/_classes.py @@ -1064,6 +1064,7 @@ class NuSVC(BaseSVC): 0 if correctly fitted, 1 if the algorithm did not converge. probA_ : ndarray of shape (n_classes * (n_classes - 1) / 2,) + probB_ : ndarray of shape (n_classes * (n_classes - 1) / 2,) If `probability=True`, it corresponds to the parameters learned in Platt scaling to produce probability estimates from decision values. From c8d4aec23a0297eb5eb51d06ccc9b02201d314a9 Mon Sep 17 00:00:00 2001 From: avm19 Date: Sun, 20 Aug 2023 06:16:00 -0400 Subject: [PATCH 165/264] FIX NumPy DeprecationWarning in a rare branch of _lars_path_solver (#27079) Co-authored-by: avm19 <52547519avm19@users.noreply.github.com> --- sklearn/linear_model/_least_angle.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sklearn/linear_model/_least_angle.py b/sklearn/linear_model/_least_angle.py index dc6b385f70c73..55f2249dec06e 100644 --- a/sklearn/linear_model/_least_angle.py +++ b/sklearn/linear_model/_least_angle.py @@ -649,7 +649,7 @@ def _lars_path_solver( "with an active set of %i regressors, and " "the smallest cholesky pivot element being %.3e." " Reduce max_iter or increase eps parameters." - % (n_iter, alpha, n_active, diag), + % (n_iter, alpha.item(), n_active, diag), ConvergenceWarning, ) @@ -677,7 +677,7 @@ def _lars_path_solver( "are small and the current value of alpha is no " "longer well controlled. %i iterations, alpha=%.3e, " "previous alpha=%.3e, with an active set of %i " - "regressors." % (n_iter, alpha, prev_alpha, n_active), + "regressors." % (n_iter, alpha.item(), prev_alpha.item(), n_active), ConvergenceWarning, ) break From e8aff03318b36aa971e00f6e9b1504928a718845 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Mon, 21 Aug 2023 13:46:58 +0200 Subject: [PATCH 166/264] DOC Add links to `pipelines` examples in docstrings (#26904) Co-authored-by: Adrin Jalali --- sklearn/compose/_column_transformer.py | 8 ++++++++ sklearn/compose/_target.py | 6 +++--- sklearn/pipeline.py | 9 +++++++++ 3 files changed, 20 insertions(+), 3 deletions(-) diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py index 20877775f843c..beaaf2849091f 100644 --- a/sklearn/compose/_column_transformer.py +++ b/sklearn/compose/_column_transformer.py @@ -214,6 +214,9 @@ class ColumnTransformer(TransformerMixin, _BaseComposition): ... [("text_preprocess", FeatureHasher(input_type="string"), "documents"), ... ("num_preprocess", MinMaxScaler(), ["width"])]) >>> X_trans = ct.fit_transform(X) # doctest: +SKIP + + For a more detailed example of usage, see + :ref:`sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py`. """ _required_parameters = ["transformers"] @@ -1078,6 +1081,11 @@ class make_column_selector: columns name with a regex. When using multiple selection criteria, **all** criteria must match for a column to be selected. + For an example of how to use :func:`make_column_selector` within a + :class:`ColumnTransformer` to select columns based on data type (i.e. + `dtype`), refer to + :ref:`sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py`. + Parameters ---------- pattern : str, default=None diff --git a/sklearn/compose/_target.py b/sklearn/compose/_target.py index 348cdda48ea1c..79f8d503a24f9 100644 --- a/sklearn/compose/_target.py +++ b/sklearn/compose/_target.py @@ -108,9 +108,6 @@ class TransformedTargetRegressor(RegressorMixin, BaseEstimator): to be used by scikit-learn transformers. At the time of prediction, the output will be reshaped to a have the same number of dimensions as `y`. - See :ref:`examples/compose/plot_transformed_target.py - `. - Examples -------- >>> import numpy as np @@ -126,6 +123,9 @@ class TransformedTargetRegressor(RegressorMixin, BaseEstimator): 1.0 >>> tt.regressor_.coef_ array([2.]) + + For a more detailed example use case refer to + :ref:`sphx_glr_auto_examples_compose_plot_transformed_target.py`. """ _parameter_constraints: dict = { diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py index 26008c82fef11..ed23a3c33bd70 100644 --- a/sklearn/pipeline.py +++ b/sklearn/pipeline.py @@ -65,6 +65,12 @@ class Pipeline(_BaseComposition): to another estimator, or a transformer removed by setting it to `'passthrough'` or `None`. + For an example use case of `Pipeline` combined with + :class:`~sklearn.model_selection.GridSearchCV`, refer to + :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The + example :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py` shows how + to grid search on a pipeline using `'__'` as a separator in the parameter names. + Read more in the :ref:`User Guide `. .. versionadded:: 0.5 @@ -1056,6 +1062,9 @@ class FeatureUnion(TransformerMixin, _BaseComposition): >>> union.set_params(pca__n_components=1).fit_transform(X) array([[ 1.5 , 3.0...], [-1.5 , 5.7...]]) + + For a more detailed example of usage, see + :ref:`sphx_glr_auto_examples_compose_plot_feature_union.py`. """ _required_parameters = ["transformer_list"] From 53144c03a066f46e71846c40f26940ba02c6bc15 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Mon, 21 Aug 2023 14:05:45 +0200 Subject: [PATCH 167/264] MNT Remove misleading comment (#27115) --- sklearn/linear_model/_least_angle.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/sklearn/linear_model/_least_angle.py b/sklearn/linear_model/_least_angle.py index 55f2249dec06e..df85c9bbb6b52 100644 --- a/sklearn/linear_model/_least_angle.py +++ b/sklearn/linear_model/_least_angle.py @@ -636,12 +636,6 @@ def _lars_path_solver( # The system is becoming too ill-conditioned. # We have degenerate vectors in our active set. # We'll 'drop for good' the last regressor added. - - # Note: this case is very rare. It is no longer triggered by - # the test suite. The `equality_tolerance` margin added in 0.16 - # to get early stopping to work consistently on all versions of - # Python including 32 bit Python under Windows seems to make it - # very difficult to trigger the 'drop for good' strategy. warnings.warn( "Regressors in active set degenerate. " "Dropping a regressor, after %i iterations, " From 0ba978f60b6388b2ee1304654cc614d11435511e Mon Sep 17 00:00:00 2001 From: Camille Troillard <556316+tuscland@users.noreply.github.com> Date: Tue, 22 Aug 2023 10:50:52 +0200 Subject: [PATCH 168/264] DOC fix behavior of copy button in installation instructions (#27052) --- doc/install.rst | 85 ++++++++++++------- .../scikit-learn-modern/static/css/theme.css | 31 ++++++- 2 files changed, 82 insertions(+), 34 deletions(-) diff --git a/doc/install.rst b/doc/install.rst index a69d68a96ca42..d3c0533d4a001 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -69,42 +69,65 @@ Then run: .. raw:: html - 
    python3 -m venv sklearn-venvpython -m venv sklearn-venvpython -m venv sklearn-venvsource sklearn-venv/bin/activatesource sklearn-venv/bin/activatesklearn-venv\Scripts\activatepip install -U scikit-learnpip install -U scikit-learnpip install -U scikit-learnpip3 install -U scikit-learnconda create -n sklearn-env -c conda-forge scikit-learnconda activate sklearn-env
    +
    + 
    pip3 install -U scikit-learn
    + + 
    pip install -U scikit-learn
    + + 
    pip install -U scikit-learn
    + + 
    python3 -m venv sklearn-venv
+  source sklearn-venv/bin/activate
+  pip3 install -U scikit-learn
    + + 
    python -m venv sklearn-venv
+  sklearn-venv\Scripts\activate
+  pip install -U scikit-learn
    + + 
    python -m venv sklearn-venv
+  source sklearn-venv/bin/activate
+  pip install -U scikit-learn
    + + 
    conda create -n sklearn-env -c conda-forge scikit-learn
+  conda activate sklearn-env
    +
    In order to check your installation you can use .. raw:: html - 
    python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installedpython3 -m pip freeze  # to see all packages installed in the active virtualenvpython3 -c "import sklearn; sklearn.show_versions()"python -m pip show scikit-learn  # to see which version and where scikit-learn is installedpython -m pip freeze  # to see all packages installed in the active virtualenvpython -c "import sklearn; sklearn.show_versions()"python -m pip show scikit-learn  # to see which version and where scikit-learn is installedpython -m pip freeze  # to see all packages installed in the active virtualenvpython -c "import sklearn; sklearn.show_versions()"python -m pip show scikit-learn  # to see which version and where scikit-learn is installedpython -m pip freeze  # to see all packages installed in the active virtualenvpython -c "import sklearn; sklearn.show_versions()"conda list scikit-learn  # to see which scikit-learn version is installedconda list  # to see all packages installed in the active conda environmentpython -c "import sklearn; sklearn.show_versions()"
    +
    + 
    python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installed
+  python3 -m pip freeze  # to see all packages installed in the active virtualenv
+  python3 -c "import sklearn; sklearn.show_versions()"
    + + 
    python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
+  python -m pip freeze  # to see all packages installed in the active virtualenv
+  python -c "import sklearn; sklearn.show_versions()"
    + + 
    python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
+  python -m pip freeze  # to see all packages installed in the active virtualenv
+  python -c "import sklearn; sklearn.show_versions()"
    + + 
    python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
+  python -m pip freeze  # to see all packages installed in the active virtualenv
+  python -c "import sklearn; sklearn.show_versions()"
    + + 
    conda list scikit-learn  # to see which scikit-learn version is installed
+  conda list  # to see all packages installed in the active conda environment
+  python -c "import sklearn; sklearn.show_versions()"
    Note that in order to avoid potential conflicts with other packages it is diff --git a/doc/themes/scikit-learn-modern/static/css/theme.css b/doc/themes/scikit-learn-modern/static/css/theme.css index 40ac5e25ea698..aa5bb3c853e51 100644 --- a/doc/themes/scikit-learn-modern/static/css/theme.css +++ b/doc/themes/scikit-learn-modern/static/css/theme.css @@ -1204,9 +1204,8 @@ div.install > input:checked + label { .sk-expandable { display: none; } - -div.highlight span.sk-expandable:before { - content: "$ "; +.sk-expandable + .copybtn { + display: none; } /* Show hidden content when the checkbox is checked */ @@ -1214,15 +1213,24 @@ div.highlight span.sk-expandable:before { #quickstart-conda:checked ~* [data-packager="conda"] { display: block; } +#quickstart-conda:checked ~* [data-packager="conda"] + .copybtn { + display: block; +} #quickstart-conda:checked ~ #quickstart-venv ~ label[for="quickstart-venv"] { display: none; } +#quickstart-conda:checked ~ #quickstart-venv ~ label[for="quickstart-venv"] + .copybtn { + display: none; +} /* for pip */ #quickstart-pip:checked ~* [data-packager="pip"] { display: block; } +#quickstart-pip:checked ~* [data-packager="pip"] + .copybtn { + display: block; +} #quickstart-pip:checked ~ label[for="quickstart-venv"]:before { content: "Use pip virtualenv"; @@ -1231,20 +1239,37 @@ div.highlight span.sk-expandable:before { #quickstart-win:not(:checked) ~* [data-os="windows"] { display: none; } +#quickstart-win:not(:checked) ~* [data-os="windows"] + .copybtn { + display: none; +} + #quickstart-lin:not(:checked) ~* [data-os="linux"] { display: none; } +#quickstart-lin:not(:checked) ~* [data-os="linux"] + .copybtn { + display: none; +} + #quickstart-mac:not(:checked) ~* [data-os="mac"] { display: none; } +#quickstart-mac:not(:checked) ~* [data-os="mac"] + .copybtn { + display: none; +} #quickstart-venv:not(:checked) ~* [data-venv=""] { display: none; } +#quickstart-venv:not(:checked) ~* [data-venv=""] + .copybtn { + display: none; +} #quickstart-venv:checked ~* [data-venv="no"] { display: none; } +#quickstart-venv:checked ~* [data-venv="no"] + .copybtn { + display: none; +} /* Algorithm cheet-sheet */ From 37e821bcef1beb644e4e4fe9c57eb26ee94fb5fa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 22 Aug 2023 11:26:55 +0200 Subject: [PATCH 169/264] MNT Adjust code after NEP 51 numpy scalar formatting changes (#27042) --- sklearn/dummy.py | 2 +- sklearn/impute/tests/test_impute.py | 4 ++-- sklearn/preprocessing/_encoders.py | 4 ++-- sklearn/utils/class_weight.py | 4 +++- sklearn/utils/validation.py | 2 +- 5 files changed, 9 insertions(+), 7 deletions(-) diff --git a/sklearn/dummy.py b/sklearn/dummy.py index 1db664826f5c9..63318b07ce580 100644 --- a/sklearn/dummy.py +++ b/sklearn/dummy.py @@ -227,7 +227,7 @@ def fit(self, X, y, sample_weight=None): "The constant target value must be present in " "the training data. You provided constant={}. " "Possible values are: {}.".format( - self.constant, list(self.classes_[k]) + self.constant, self.classes_[k].tolist() ) ) raise ValueError(err_msg) diff --git a/sklearn/impute/tests/test_impute.py b/sklearn/impute/tests/test_impute.py index 936847e55e324..9d02eb7722cfc 100644 --- a/sklearn/impute/tests/test_impute.py +++ b/sklearn/impute/tests/test_impute.py @@ -259,7 +259,7 @@ def test_imputation_median_special_cases(): @pytest.mark.parametrize("dtype", [None, object, str]) def test_imputation_mean_median_error_invalid_type(strategy, dtype): X = np.array([["a", "b", 3], [4, "e", 6], ["g", "h", 9]], dtype=dtype) - msg = "non-numeric data:\ncould not convert string to float: '" + msg = "non-numeric data:\ncould not convert string to float:" with pytest.raises(ValueError, match=msg): imputer = SimpleImputer(strategy=strategy) imputer.fit_transform(X) @@ -272,7 +272,7 @@ def test_imputation_mean_median_error_invalid_type_list_pandas(strategy, type): if type == "dataframe": pd = pytest.importorskip("pandas") X = pd.DataFrame(X) - msg = "non-numeric data:\ncould not convert string to float: '" + msg = "non-numeric data:\ncould not convert string to float:" with pytest.raises(ValueError, match=msg): imputer = SimpleImputer(strategy=strategy) imputer.fit_transform(X) diff --git a/sklearn/preprocessing/_encoders.py b/sklearn/preprocessing/_encoders.py index 7910976d6007c..c9be60d823e90 100644 --- a/sklearn/preprocessing/_encoders.py +++ b/sklearn/preprocessing/_encoders.py @@ -774,8 +774,8 @@ def _map_drop_idx_to_infrequent(self, feature_idx, drop_idx): if infrequent_indices is not None and drop_idx in infrequent_indices: categories = self.categories_[feature_idx] raise ValueError( - f"Unable to drop category {categories[drop_idx]!r} from feature" - f" {feature_idx} because it is infrequent" + f"Unable to drop category {categories[drop_idx].item()!r} from" + f" feature {feature_idx} because it is infrequent" ) return default_to_infrequent[drop_idx] diff --git a/sklearn/utils/class_weight.py b/sklearn/utils/class_weight.py index 5d1126104eb9d..1ff349347b015 100644 --- a/sklearn/utils/class_weight.py +++ b/sklearn/utils/class_weight.py @@ -68,8 +68,10 @@ def compute_class_weight(class_weight, *, classes, y): n_weighted_classes = len(classes) - len(unweighted_classes) if unweighted_classes and n_weighted_classes != len(class_weight): + unweighted_classes_user_friendly_str = np.array(unweighted_classes).tolist() raise ValueError( - f"The classes, {unweighted_classes}, are not in class_weight" + f"The classes, {unweighted_classes_user_friendly_str}, are not in" + " class_weight" ) return weight diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py index 95c785d0b2c8d..ae1886e0f92e4 100644 --- a/sklearn/utils/validation.py +++ b/sklearn/utils/validation.py @@ -2243,7 +2243,7 @@ def _check_pos_label_consistency(pos_label, y_true): or np.array_equal(classes, [1]) ) ): - classes_repr = ", ".join(repr(c) for c in classes) + classes_repr = ", ".join([repr(c) for c in classes.tolist()]) raise ValueError( f"y_true takes value in {{{classes_repr}}} and pos_label is not " "specified: either make y_true take value in {0, 1} or " From 4e3585471547eeba42ff3dfb7aeab9d0ac320341 Mon Sep 17 00:00:00 2001 From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Date: Tue, 22 Aug 2023 21:43:58 +0200 Subject: [PATCH 170/264] DOC Rework permutation importance with multicollinearity example (#26221) Co-authored-by: ArturoAmorQ Co-authored-by: Guillaume Lemaitre --- ...t_permutation_importance_multicollinear.py | 167 ++++++++++++------ 1 file changed, 110 insertions(+), 57 deletions(-) diff --git a/examples/inspection/plot_permutation_importance_multicollinear.py b/examples/inspection/plot_permutation_importance_multicollinear.py index e14916e808af9..a8fe52b1565d9 100644 --- a/examples/inspection/plot_permutation_importance_multicollinear.py +++ b/examples/inspection/plot_permutation_importance_multicollinear.py @@ -3,12 +3,15 @@ Permutation Importance with Multicollinear or Correlated Features ================================================================= -In this example, we compute the permutation importance on the Wisconsin -breast cancer dataset using :func:`~sklearn.inspection.permutation_importance`. -The :class:`~sklearn.ensemble.RandomForestClassifier` can easily get about 97% -accuracy on a test dataset. Because this dataset contains multicollinear -features, the permutation importance will show that none of the features are -important. One approach to handling multicollinearity is by performing +In this example, we compute the +:func:`~sklearn.inspection.permutation_importance` of the features to a trained +:class:`~sklearn.ensemble.RandomForestClassifier` using the +:ref:`breast_cancer_dataset`. The model can easily get about 97% accuracy on a +test dataset. Because this dataset contains multicollinear features, the +permutation importance shows that none of the features are important, in +contradiction with the high test accuracy. + +We demo a possible approach to handling multicollinearity, which consists of hierarchical clustering on the features' Spearman rank-order correlations, picking a threshold, and keeping a single feature from each cluster. @@ -18,68 +21,106 @@ """ -from collections import defaultdict +# %% +# Random Forest Feature Importance on Breast Cancer Data +# ------------------------------------------------------ +# +# First, we define a function to ease the plotting: +from sklearn.inspection import permutation_importance -import matplotlib.pyplot as plt -import numpy as np -from scipy.cluster import hierarchy -from scipy.spatial.distance import squareform -from scipy.stats import spearmanr +def plot_permutation_importance(clf, X, y, ax): + result = permutation_importance(clf, X, y, n_repeats=10, random_state=42, n_jobs=2) + perm_sorted_idx = result.importances_mean.argsort() + + ax.boxplot( + result.importances[perm_sorted_idx].T, + vert=False, + labels=X.columns[perm_sorted_idx], + ) + ax.axvline(x=0, color="k", linestyle="--") + return ax + + +# %% +# We then train a :class:`~sklearn.ensemble.RandomForestClassifier` on the +# :ref:`breast_cancer_dataset` and evaluate its accuracy on a test set: from sklearn.datasets import load_breast_cancer from sklearn.ensemble import RandomForestClassifier -from sklearn.inspection import permutation_importance from sklearn.model_selection import train_test_split -# %% -# Random Forest Feature Importance on Breast Cancer Data -# ------------------------------------------------------ -# First, we train a random forest on the breast cancer dataset and evaluate -# its accuracy on a test set: -data = load_breast_cancer() -X, y = data.data, data.target +X, y = load_breast_cancer(return_X_y=True, as_frame=True) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) clf = RandomForestClassifier(n_estimators=100, random_state=42) clf.fit(X_train, y_train) -print("Accuracy on test data: {:.2f}".format(clf.score(X_test, y_test))) +print(f"Baseline accuracy on test data: {clf.score(X_test, y_test):.2}") # %% # Next, we plot the tree based feature importance and the permutation -# importance. The permutation importance plot shows that permuting a feature -# drops the accuracy by at most `0.012`, which would suggest that none of the -# features are important. This is in contradiction with the high test accuracy -# computed above: some feature must be important. The permutation importance -# is calculated on the training set to show how much the model relies on each -# feature during training. -result = permutation_importance(clf, X_train, y_train, n_repeats=10, random_state=42) -perm_sorted_idx = result.importances_mean.argsort() +# importance. The permutation importance is calculated on the training set to +# show how much the model relies on each feature during training. +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +mdi_importances = pd.Series(clf.feature_importances_, index=X_train.columns) tree_importance_sorted_idx = np.argsort(clf.feature_importances_) tree_indices = np.arange(0, len(clf.feature_importances_)) + 0.5 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8)) -ax1.barh(tree_indices, clf.feature_importances_[tree_importance_sorted_idx], height=0.7) -ax1.set_yticks(tree_indices) -ax1.set_yticklabels(data.feature_names[tree_importance_sorted_idx]) -ax1.set_ylim((0, len(clf.feature_importances_))) -ax2.boxplot( - result.importances[perm_sorted_idx].T, - vert=False, - labels=data.feature_names[perm_sorted_idx], +mdi_importances.sort_values().plot.barh(ax=ax1) +ax1.set_xlabel("Gini importance") +plot_permutation_importance(clf, X_train, y_train, ax2) +ax2.set_xlabel("Decrease in accuracy score") +fig.suptitle( + "Impurity-based vs. permutation importances on multicollinear features (train set)" ) -fig.tight_layout() -plt.show() +_ = fig.tight_layout() + +# %% +# The plot on the left shows the Gini importance of the model. As the +# scikit-learn implementation of +# :class:`~sklearn.ensemble.RandomForestClassifier` uses a random subsets of +# :math:`\sqrt{n_\text{features}}` features at each split, it is able to dilute +# the dominance of any single correlated feature. As a result, the individual +# feature importance may be distributed more evenly among the correlated +# features. Since the features have large cardinality and the classifier is +# non-overfitted, we can relatively trust those values. +# +# The permutation importance on the right plot shows that permuting a feature +# drops the accuracy by at most `0.012`, which would suggest that none of the +# features are important. This is in contradiction with the high test accuracy +# computed as baseline: some feature must be important. +# +# Similarly, the change in accuracy score computed on the test set appears to be +# driven by chance: + +fig, ax = plt.subplots(figsize=(7, 6)) +plot_permutation_importance(clf, X_test, y_test, ax) +ax.set_title("Permutation Importances on multicollinear features\n(test set)") +ax.set_xlabel("Decrease in accuracy score") +_ = ax.figure.tight_layout() # %% +# Nevertheless, one can still compute a meaningful permutation importance in the +# presence of correlated features, as demonstrated in the following section. +# # Handling Multicollinear Features # -------------------------------- -# When features are collinear, permutating one feature will have little -# effect on the models performance because it can get the same information -# from a correlated feature. One way to handle multicollinear features is by -# performing hierarchical clustering on the Spearman rank-order correlations, -# picking a threshold, and keeping a single feature from each cluster. First, -# we plot a heatmap of the correlated features: +# When features are collinear, permuting one feature has little effect on the +# models performance because it can get the same information from a correlated +# feature. Note that this is not the case for all predictive models and depends +# on their underlying implementation. +# +# One way to handle multicollinear features is by performing hierarchical +# clustering on the Spearman rank-order correlations, picking a threshold, and +# keeping a single feature from each cluster. First, we plot a heatmap of the +# correlated features: +from scipy.cluster import hierarchy +from scipy.spatial.distance import squareform +from scipy.stats import spearmanr + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8)) corr = spearmanr(X).correlation @@ -92,7 +133,7 @@ distance_matrix = 1 - np.abs(corr) dist_linkage = hierarchy.ward(squareform(distance_matrix)) dendro = hierarchy.dendrogram( - dist_linkage, labels=data.feature_names.tolist(), ax=ax1, leaf_rotation=90 + dist_linkage, labels=X.columns.to_list(), ax=ax1, leaf_rotation=90 ) dendro_idx = np.arange(0, len(dendro["ivl"])) @@ -101,28 +142,40 @@ ax2.set_yticks(dendro_idx) ax2.set_xticklabels(dendro["ivl"], rotation="vertical") ax2.set_yticklabels(dendro["ivl"]) -fig.tight_layout() -plt.show() +_ = fig.tight_layout() # %% -# Next, we manually pick a threshold by visual inspection of the dendrogram -# to group our features into clusters and choose a feature from each cluster to +# Next, we manually pick a threshold by visual inspection of the dendrogram to +# group our features into clusters and choose a feature from each cluster to # keep, select those features from our dataset, and train a new random forest. -# The test accuracy of the new random forest did not change much compared to -# the random forest trained on the complete dataset. +# The test accuracy of the new random forest did not change much compared to the +# random forest trained on the complete dataset. +from collections import defaultdict + cluster_ids = hierarchy.fcluster(dist_linkage, 1, criterion="distance") cluster_id_to_feature_ids = defaultdict(list) for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_ids[cluster_id].append(idx) selected_features = [v[0] for v in cluster_id_to_feature_ids.values()] +selected_features_names = X.columns[selected_features] -X_train_sel = X_train[:, selected_features] -X_test_sel = X_test[:, selected_features] +X_train_sel = X_train[selected_features_names] +X_test_sel = X_test[selected_features_names] clf_sel = RandomForestClassifier(n_estimators=100, random_state=42) clf_sel.fit(X_train_sel, y_train) print( - "Accuracy on test data with features removed: {:.2f}".format( - clf_sel.score(X_test_sel, y_test) - ) + "Baseline accuracy on test data with features removed:" + f" {clf_sel.score(X_test_sel, y_test):.2}" ) + +# %% +# We can finally explore the permutation importance of the selected subset of +# features: + +fig, ax = plt.subplots(figsize=(7, 6)) +plot_permutation_importance(clf_sel, X_test_sel, y_test, ax) +ax.set_title("Permutation Importances on selected subset of features\n(test set)") +ax.set_xlabel("Decrease in accuracy score") +ax.figure.tight_layout() +plt.show() From 25d9689c12e6fe0c4fe1f0eb76e93eafaff32f1a Mon Sep 17 00:00:00 2001 From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Date: Tue, 22 Aug 2023 21:44:25 +0200 Subject: [PATCH 171/264] DOC Revisit SVM C scaling example (#25115) Co-authored-by: ArturoAmorQ Co-authored-by: Guillaume Lemaitre --- examples/svm/plot_svm_scale_c.py | 165 +++++++++++++++++++------------ 1 file changed, 101 insertions(+), 64 deletions(-) diff --git a/examples/svm/plot_svm_scale_c.py b/examples/svm/plot_svm_scale_c.py index 1e44fb361e6ba..ea09f03ec7f95 100644 --- a/examples/svm/plot_svm_scale_c.py +++ b/examples/svm/plot_svm_scale_c.py @@ -3,9 +3,8 @@ Scaling the regularization parameter for SVCs ============================================== -The following example illustrates the effect of scaling the -regularization parameter when using :ref:`svm` for -:ref:`classification `. +The following example illustrates the effect of scaling the regularization +parameter when using :ref:`svm` for :ref:`classification `. For SVC classification, we are interested in a risk minimization for the equation: @@ -21,25 +20,18 @@ and our model parameters. - :math:`\Omega` is a `penalty` function of our model parameters -If we consider the loss function to be the individual error per -sample, then the data-fit term, or the sum of the error for each sample, will -increase as we add more samples. The penalization term, however, will not -increase. - -When using, for example, :ref:`cross validation `, to -set the amount of regularization with `C`, there will be a -different amount of samples between the main problem and the smaller problems -within the folds of the cross validation. - -Since our loss function is dependent on the amount of samples, the latter -will influence the selected value of `C`. -The question that arises is "How do we optimally adjust C to -account for the different amount of training samples?" - -In the remainder of this example, we will investigate the effect of scaling -the value of the regularization parameter `C` in regards to the number of -samples for both L1 and L2 penalty. We will generate some synthetic datasets -that are appropriate for each type of regularization. +If we consider the loss function to be the individual error per sample, then the +data-fit term, or the sum of the error for each sample, increases as we add more +samples. The penalization term, however, does not increase. + +When using, for example, :ref:`cross validation `, to set the +amount of regularization with `C`, there would be a different amount of samples +between the main problem and the smaller problems within the folds of the cross +validation. + +Since the loss function dependens on the amount of samples, the latter +influences the selected value of `C`. The question that arises is "How do we +optimally adjust C to account for the different amount of training samples?" """ # Author: Andreas Mueller @@ -47,18 +39,16 @@ # License: BSD 3 clause # %% -# L1-penalty case +# Data generation # --------------- -# In the L1 case, theory says that prediction consistency (i.e. that under -# given hypothesis, the estimator learned predicts as well as a model knowing -# the true distribution) is not possible because of the bias of the L1. It -# does say, however, that model consistency, in terms of finding the right set -# of non-zero parameters as well as their signs, can be achieved by scaling -# `C`. # -# We will demonstrate this effect by using a synthetic dataset. This -# dataset will be sparse, meaning that only a few features will be informative -# and useful for the model. +# In this example we investigate the effect of reparametrizing the regularization +# parameter `C` to account for the number of samples when using either L1 or L2 +# penalty. For such purpose we create a synthetic dataset with a large number of +# features, out of which only a few are informative. We therefore expect the +# regularization to shrink the coefficients towards zero (L2 penalty) or exactly +# zero (L1 penalty). + from sklearn.datasets import make_classification n_samples, n_features = 100, 300 @@ -67,13 +57,25 @@ ) # %% -# Now, we can define a linear SVC with the `l1` penalty. +# L1-penalty case +# --------------- +# In the L1 case, theory says that provided a strong regularization, the +# estimator cannot predict as well as a model knowing the true distribution +# (even in the limit where the sample size grows to infinity) as it may set some +# weights of otherwise predictive features to zero, which induces a bias. It does +# say, however, that it is possible to find the right set of non-zero parameters +# as well as their signs by tuning `C`. +# +# We define a linear SVC with the L1 penalty. + from sklearn.svm import LinearSVC model_l1 = LinearSVC(penalty="l1", loss="squared_hinge", dual=False, tol=1e-3) # %% -# We will compute the mean test score for different values of `C`. +# We compute the mean test score for different values of `C` via +# cross-validation. + import numpy as np import pandas as pd @@ -82,12 +84,23 @@ Cs = np.logspace(-2.3, -1.3, 10) train_sizes = np.linspace(0.3, 0.7, 3) labels = [f"fraction: {train_size}" for train_size in train_sizes] +shuffle_params = { + "test_size": 0.3, + "n_splits": 150, + "random_state": 1, +} results = {"C": Cs} for label, train_size in zip(labels, train_sizes): - cv = ShuffleSplit(train_size=train_size, test_size=0.3, n_splits=50, random_state=1) + cv = ShuffleSplit(train_size=train_size, **shuffle_params) train_scores, test_scores = validation_curve( - model_l1, X, y, param_name="C", param_range=Cs, cv=cv + model_l1, + X, + y, + param_name="C", + param_range=Cs, + cv=cv, + n_jobs=2, ) results[label] = test_scores.mean(axis=1) results = pd.DataFrame(results) @@ -102,47 +115,59 @@ axes[0].set_ylabel("CV score") axes[0].set_title("No scaling") +for label in labels: + best_C = results.loc[results[label].idxmax(), "C"] + axes[0].axvline(x=best_C, linestyle="--", color="grey", alpha=0.7) + # plot results by scaling C for train_size_idx, label in enumerate(labels): + train_size = train_sizes[train_size_idx] results_scaled = results[[label]].assign( - C_scaled=Cs * float(n_samples * train_sizes[train_size_idx]) + C_scaled=Cs * float(n_samples * np.sqrt(train_size)) ) results_scaled.plot(x="C_scaled", ax=axes[1], logx=True, label=label) -axes[1].set_title("Scaling C by 1 / n_samples") + best_C_scaled = results_scaled["C_scaled"].loc[results[label].idxmax()] + axes[1].axvline(x=best_C_scaled, linestyle="--", color="grey", alpha=0.7) + +axes[1].set_title("Scaling C by sqrt(1 / n_samples)") _ = fig.suptitle("Effect of scaling C with L1 penalty") # %% -# Here, we observe that the cross-validation-error correlates best with the -# test-error, when scaling our `C` with the number of samples, `n`. +# In the region of small `C` (strong regularization) all the coefficients +# learned by the models are zero, leading to severe underfitting. Indeed, the +# accuracy in this region is at the chance level. # -# L2-penalty case -# --------------- -# We can repeat a similar experiment with the `l2` penalty. In this case, we -# don't need to use a sparse dataset. +# Using the default scale results in a somewhat stable optimal value of `C`, +# whereas the transition out of the underfitting region depends on the number of +# training samples. The reparametrization leads to even more stable results. # -# In this case, the theory says that in order to achieve prediction -# consistency, the penalty parameter should be kept constant as the number of -# samples grow. +# See e.g. theorem 3 of :arxiv:`On the prediction performance of the Lasso +# <1402.1700>` or :arxiv:`Simultaneous analysis of Lasso and Dantzig selector +# <0801.1095>` where the regularization parameter is always assumed to be +# proportional to 1 / sqrt(n_samples). # -# So we will repeat the same experiment by creating a linear SVC classifier -# with the `l2` penalty and check the test score via cross-validation and -# plot the results with and without scaling the parameter `C`. -rng = np.random.RandomState(1) -y = np.sign(0.5 - rng.rand(n_samples)) -X = rng.randn(n_samples, n_features // 5) + y[:, np.newaxis] -X += 5 * rng.randn(n_samples, n_features // 5) +# L2-penalty case +# --------------- +# We can do a similar experiment with the L2 penalty. In this case, the +# theory says that in order to achieve prediction consistency, the penalty +# parameter should be kept constant as the number of samples grow. -# %% model_l2 = LinearSVC(penalty="l2", loss="squared_hinge", dual=True) -Cs = np.logspace(-4.5, -2, 10) +Cs = np.logspace(-8, 4, 11) labels = [f"fraction: {train_size}" for train_size in train_sizes] results = {"C": Cs} for label, train_size in zip(labels, train_sizes): - cv = ShuffleSplit(train_size=train_size, test_size=0.3, n_splits=50, random_state=1) + cv = ShuffleSplit(train_size=train_size, **shuffle_params) train_scores, test_scores = validation_curve( - model_l2, X, y, param_name="C", param_range=Cs, cv=cv + model_l2, + X, + y, + param_name="C", + param_range=Cs, + cv=cv, + n_jobs=2, ) results[label] = test_scores.mean(axis=1) results = pd.DataFrame(results) @@ -157,17 +182,29 @@ axes[0].set_ylabel("CV score") axes[0].set_title("No scaling") +for label in labels: + best_C = results.loc[results[label].idxmax(), "C"] + axes[0].axvline(x=best_C, linestyle="--", color="grey", alpha=0.8) + # plot results by scaling C for train_size_idx, label in enumerate(labels): results_scaled = results[[label]].assign( - C_scaled=Cs * float(n_samples * train_sizes[train_size_idx]) + C_scaled=Cs * float(n_samples * np.sqrt(train_sizes[train_size_idx])) ) results_scaled.plot(x="C_scaled", ax=axes[1], logx=True, label=label) -axes[1].set_title("Scaling C by 1 / n_samples") + best_C_scaled = results_scaled["C_scaled"].loc[results[label].idxmax()] + axes[1].axvline(x=best_C_scaled, linestyle="--", color="grey", alpha=0.8) +axes[1].set_title("Scaling C by sqrt(1 / n_samples)") -_ = fig.suptitle("Effect of scaling C with L2 penalty") +fig.suptitle("Effect of scaling C with L2 penalty") +plt.show() # %% -# So or the L2 penalty case, the best result comes from the case where `C` is -# not scaled. -plt.show() +# For the L2 penalty case, the reparametrization seems to have a smaller impact +# on the stability of the optimal value for the regularization. The transition +# out of the overfitting region occurs in a more spread range and the accuracy +# does not seem to be degraded up to chance level. +# +# Try increasing the value to `n_splits=1_000` for better results in the L2 +# case, which is not shown here due to the limitations on the documentation +# builder. From 4ec98b099dd0fd211fbc5f01e8070913170923b8 Mon Sep 17 00:00:00 2001 From: Tamara Date: Wed, 23 Aug 2023 11:50:18 +0200 Subject: [PATCH 172/264] FIX Create a cosistent image via random_state additions in "plot_cluster_comparison.py" (#26976) Co-authored-by: AnnaWey --- examples/cluster/plot_cluster_comparison.py | 30 ++++++++++++++------- 1 file changed, 21 insertions(+), 9 deletions(-) diff --git a/examples/cluster/plot_cluster_comparison.py b/examples/cluster/plot_cluster_comparison.py index 27b4a1c46c415..6e7ac7c4dfe56 100644 --- a/examples/cluster/plot_cluster_comparison.py +++ b/examples/cluster/plot_cluster_comparison.py @@ -35,17 +35,19 @@ from sklearn.neighbors import kneighbors_graph from sklearn.preprocessing import StandardScaler -np.random.seed(0) - # ============ # Generate datasets. We choose the size big enough to see the scalability # of the algorithms, but not too big to avoid too long running times # ============ n_samples = 500 -noisy_circles = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05) -noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05) -blobs = datasets.make_blobs(n_samples=n_samples, random_state=8) -no_structure = np.random.rand(n_samples, 2), None +seed = 30 +noisy_circles = datasets.make_circles( + n_samples=n_samples, factor=0.5, noise=0.05, random_state=seed +) +noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05, random_state=seed) +blobs = datasets.make_blobs(n_samples=n_samples, random_state=seed) +rng = np.random.RandomState(seed) +no_structure = rng.rand(n_samples, 2), None # Anisotropicly distributed data random_state = 170 @@ -82,6 +84,7 @@ "allow_single_cluster": True, "hdbscan_min_cluster_size": 15, "hdbscan_min_samples": 3, + "random_state": 42, } datasets = [ @@ -154,7 +157,11 @@ # Create cluster objects # ============ ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True) - two_means = cluster.MiniBatchKMeans(n_clusters=params["n_clusters"], n_init="auto") + two_means = cluster.MiniBatchKMeans( + n_clusters=params["n_clusters"], + n_init="auto", + random_state=params["random_state"], + ) ward = cluster.AgglomerativeClustering( n_clusters=params["n_clusters"], linkage="ward", connectivity=connectivity ) @@ -162,6 +169,7 @@ n_clusters=params["n_clusters"], eigen_solver="arpack", affinity="nearest_neighbors", + random_state=params["random_state"], ) dbscan = cluster.DBSCAN(eps=params["eps"]) hdbscan = cluster.HDBSCAN( @@ -175,7 +183,9 @@ min_cluster_size=params["min_cluster_size"], ) affinity_propagation = cluster.AffinityPropagation( - damping=params["damping"], preference=params["preference"], random_state=0 + damping=params["damping"], + preference=params["preference"], + random_state=params["random_state"], ) average_linkage = cluster.AgglomerativeClustering( linkage="average", @@ -185,7 +195,9 @@ ) birch = cluster.Birch(n_clusters=params["n_clusters"]) gmm = mixture.GaussianMixture( - n_components=params["n_clusters"], covariance_type="full" + n_components=params["n_clusters"], + covariance_type="full", + random_state=params["random_state"], ) clustering_algorithms = ( From 2ab5f84fc89647ae6741dfd4573c9e2ec6248561 Mon Sep 17 00:00:00 2001 From: DaminK Date: Wed, 23 Aug 2023 17:25:40 +0100 Subject: [PATCH 173/264] DOC Error in the notation of the regularization penalties for multinomial logistic regression in the docs (#26517) --- doc/modules/linear_model.rst | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index aa9184a2bedc5..04d58c925e464 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -951,16 +951,17 @@ The objective for the optimization becomes Where :math:`[P]` represents the Iverson bracket which evaluates to :math:`0` if :math:`P` is false, otherwise it evaluates to :math:`1`. We currently provide four choices -for the regularization term :math:`r(W)` via the `penalty` argument: +for the regularization term :math:`r(W)` via the `penalty` argument, where :math:`m` +is the number of features: +----------------+----------------------------------------------------------------------------------+ | penalty | :math:`r(W)` | +================+==================================================================================+ | `None` | :math:`0` | +----------------+----------------------------------------------------------------------------------+ -| :math:`\ell_1` | :math:`\|W\|_{1,1} = \sum_{i=1}^n\sum_{j=1}^{K}|W_{i,j}|` | +| :math:`\ell_1` | :math:`\|W\|_{1,1} = \sum_{i=1}^m\sum_{j=1}^{K}|W_{i,j}|` | +----------------+----------------------------------------------------------------------------------+ -| :math:`\ell_2` | :math:`\frac{1}{2}\|W\|_F^2 = \frac{1}{2}\sum_{i=1}^n\sum_{j=1}^{K} W_{i,j}^2` | +| :math:`\ell_2` | :math:`\frac{1}{2}\|W\|_F^2 = \frac{1}{2}\sum_{i=1}^m\sum_{j=1}^{K} W_{i,j}^2` | +----------------+----------------------------------------------------------------------------------+ | `ElasticNet` | :math:`\frac{1 - \rho}{2}\|W\|_F^2 + \rho \|W\|_{1,1}` | +----------------+----------------------------------------------------------------------------------+ From c36eab0603f4f6ed87c0bdf77db8bdac5f51ecac Mon Sep 17 00:00:00 2001 From: David Brochart Date: Thu, 24 Aug 2023 14:28:56 +0200 Subject: [PATCH 174/264] DOC Fix typo in plot_classifier_comparison.py (#27149) Co-authored-by: Adrin Jalali --- examples/classification/plot_classifier_comparison.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/classification/plot_classifier_comparison.py b/examples/classification/plot_classifier_comparison.py index 8d7eb7c63c81a..37dfa49af0bfd 100644 --- a/examples/classification/plot_classifier_comparison.py +++ b/examples/classification/plot_classifier_comparison.py @@ -3,7 +3,7 @@ Classifier comparison ===================== -A comparison of a several classifiers in scikit-learn on synthetic datasets. +A comparison of several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by From 62372a9fd070eed1d74db4578e5b522b97cb8fcb Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Thu, 24 Aug 2023 15:06:25 +0200 Subject: [PATCH 175/264] DOC Notebook style and enhanced descriptions in SVM kernels example (#26805) --- .../supervised_learning.rst | 16 +- examples/svm/plot_svm_kernels.py | 320 ++++++++++++++---- sklearn/svm/_classes.py | 18 +- 3 files changed, 274 insertions(+), 80 deletions(-) diff --git a/doc/tutorial/statistical_inference/supervised_learning.rst b/doc/tutorial/statistical_inference/supervised_learning.rst index 629d163be4370..d7477b279662d 100644 --- a/doc/tutorial/statistical_inference/supervised_learning.rst +++ b/doc/tutorial/statistical_inference/supervised_learning.rst @@ -465,7 +465,7 @@ Linear kernel >>> svc = svm.SVC(kernel='linear') -.. image:: /auto_examples/svm/images/sphx_glr_plot_svm_kernels_001.png +.. image:: /auto_examples/svm/images/sphx_glr_plot_svm_kernels_002.png :target: ../../auto_examples/svm/plot_svm_kernels.html Polynomial kernel @@ -477,7 +477,7 @@ Polynomial kernel ... degree=3) >>> # degree: polynomial degree -.. image:: /auto_examples/svm/images/sphx_glr_plot_svm_kernels_002.png +.. image:: /auto_examples/svm/images/sphx_glr_plot_svm_kernels_003.png :target: ../../auto_examples/svm/plot_svm_kernels.html RBF kernel (Radial Basis Function) @@ -489,7 +489,17 @@ RBF kernel (Radial Basis Function) >>> # gamma: inverse of size of >>> # radial kernel -.. image:: /auto_examples/svm/images/sphx_glr_plot_svm_kernels_003.png +.. image:: /auto_examples/svm/images/sphx_glr_plot_svm_kernels_004.png + :target: ../../auto_examples/svm/plot_svm_kernels.html + +Sigmoid kernel +^^^^^^^^^^^^^^ + +:: + + >>> svc = svm.SVC(kernel='sigmoid') + +.. image:: /auto_examples/svm/images/sphx_glr_plot_svm_kernels_005.png :target: ../../auto_examples/svm/plot_svm_kernels.html diff --git a/examples/svm/plot_svm_kernels.py b/examples/svm/plot_svm_kernels.py index 7ff2486e1c867..b83d645ac056a 100644 --- a/examples/svm/plot_svm_kernels.py +++ b/examples/svm/plot_svm_kernels.py @@ -1,93 +1,273 @@ """ ========================================================= -SVM-Kernels +Plot classification boundaries with different SVM Kernels ========================================================= +This example shows how different kernels in a :class:`~sklearn.svm.SVC` (Support Vector +Classifier) influence the classification boundaries in a binary, two-dimensional +classification problem. -Three different types of SVM-Kernels are displayed below. -The polynomial and RBF are especially useful when the -data-points are not linearly separable. +SVCs aim to find a hyperplane that effectively separates the classes in their training +data by maximizing the margin between the outermost data points of each class. This is +achieved by finding the best weight vector :math:`w` that defines the decision boundary +hyperplane and minimizes the sum of hinge losses for misclassified samples, as measured +by the :func:`~sklearn.metrics.hinge_loss` function. By default, regularization is +applied with the parameter `C=1`, which allows for a certain degree of misclassification +tolerance. +If the data is not linearly separable in the original feature space, a non-linear kernel +parameter can be set. Depending on the kernel, the process involves adding new features +or transforming existing features to enrich and potentially add meaning to the data. +When a kernel other than `"linear"` is set, the SVC applies the `kernel trick +`__, which +computes the similarity between pairs of data points using the kernel function without +explicitly transforming the entire dataset. The kernel trick surpasses the otherwise +necessary matrix transformation of the whole dataset by only considering the relations +between all pairs of data points. The kernel function maps two vectors (each pair of +observations) to their similarity using their dot product. +The hyperplane can then be calculated using the kernel function as if the dataset were +represented in a higher-dimensional space. Using a kernel function instead of an +explicit matrix transformation improves performance, as the kernel function has a time +complexity of :math:`O({n}^2)`, whereas matrix transformation scales according to the +specific transformation being applied. + +In this example, we compare the most common kernel types of Support Vector Machines: the +linear kernel (`"linear"`), the polynomial kernel (`"poly"`), the radial basis function +kernel (`"rbf"`) and the sigmoid kernel (`"sigmoid"`). """ # Code source: Gaël Varoquaux # License: BSD 3 clause +# %% +# Creating a dataset +# ------------------ +# We create a two-dimensional classification dataset with 16 samples and two classes. We +# plot the samples with the colors matching their respective targets. import matplotlib.pyplot as plt import numpy as np +X = np.array( + [ + [0.4, -0.7], + [-1.5, -1.0], + [-1.4, -0.9], + [-1.3, -1.2], + [-1.1, -0.2], + [-1.2, -0.4], + [-0.5, 1.2], + [-1.5, 2.1], + [1.0, 1.0], + [1.3, 0.8], + [1.2, 0.5], + [0.2, -2.0], + [0.5, -2.4], + [0.2, -2.3], + [0.0, -2.7], + [1.3, 2.1], + ] +) + +y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]) + +# Plotting settings +fig, ax = plt.subplots(figsize=(4, 3)) +x_min, x_max, y_min, y_max = -3, 3, -3, 3 +ax.set(xlim=(x_min, x_max), ylim=(y_min, y_max)) + +# Plot samples by color and add legend +scatter = ax.scatter(X[:, 0], X[:, 1], s=150, c=y, label=y, edgecolors="k") +ax.legend(*scatter.legend_elements(), loc="upper right", title="Classes") +ax.set_title("Samples in two-dimensional feature space") +_ = plt.show() + +# %% +# We can see that the samples are not clearly separable by a straight line. +# +# Training SVC model and plotting decision boundaries +# --------------------------------------------------- +# We define a function that fits a :class:`~sklearn.svm.SVC` classifier, +# allowing the `kernel` parameter as an input, and then plots the decision +# boundaries learned by the model using +# :class:`~sklearn.inspection.DecisionBoundaryDisplay`. +# +# Notice that for the sake of simplicity, the `C` parameter is set to its +# default value (`C=1`) in this example and the `gamma` parameter is set to +# `gamma=2` across all kernels, although it is automatically ignored for the +# linear kernel. In a real classification task, where performance matters, +# parameter tuning (by using :class:`~sklearn.model_selection.GridSearchCV` for +# instance) is highly recommended to capture different structures within the +# data. +# +# Setting `response_method="predict"` in +# :class:`~sklearn.inspection.DecisionBoundaryDisplay` colors the areas based +# on their predicted class. Using `response_method="decision_function"` allows +# us to also plot the decision boundary and the margins to both sides of it. +# Finally the support vectors used during training (which always lay on the +# margins) are identified by means of the `support_vectors_` attribute of +# the trained SVCs, and plotted as well. from sklearn import svm +from sklearn.inspection import DecisionBoundaryDisplay + + +def plot_training_data_with_decision_boundary(kernel): + # Train the SVC + clf = svm.SVC(kernel=kernel, gamma=2).fit(X, y) + + # Settings for plotting + _, ax = plt.subplots(figsize=(4, 3)) + x_min, x_max, y_min, y_max = -3, 3, -3, 3 + ax.set(xlim=(x_min, x_max), ylim=(y_min, y_max)) + + # Plot decision boundary and margins + common_params = {"estimator": clf, "X": X, "ax": ax} + DecisionBoundaryDisplay.from_estimator( + **common_params, + response_method="predict", + plot_method="pcolormesh", + alpha=0.3, + ) + DecisionBoundaryDisplay.from_estimator( + **common_params, + response_method="decision_function", + plot_method="contour", + levels=[-1, 0, 1], + colors=["k", "k", "k"], + linestyles=["--", "-", "--"], + ) -# Our dataset and targets -X = np.c_[ - (0.4, -0.7), - (-1.5, -1), - (-1.4, -0.9), - (-1.3, -1.2), - (-1.1, -0.2), - (-1.2, -0.4), - (-0.5, 1.2), - (-1.5, 2.1), - (1, 1), - # -- - (1.3, 0.8), - (1.2, 0.5), - (0.2, -2), - (0.5, -2.4), - (0.2, -2.3), - (0, -2.7), - (1.3, 2.1), -].T -Y = [0] * 8 + [1] * 8 - -# figure number -fignum = 1 - -# fit the model -for kernel in ("linear", "poly", "rbf"): - clf = svm.SVC(kernel=kernel, gamma=2) - clf.fit(X, Y) - - # plot the line, the points, and the nearest vectors to the plane - plt.figure(fignum, figsize=(4, 3)) - plt.clf() - - plt.scatter( + # Plot bigger circles around samples that serve as support vectors + ax.scatter( clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], - s=80, + s=250, facecolors="none", - zorder=10, edgecolors="k", ) - plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired, edgecolors="k") - - plt.axis("tight") - x_min = -3 - x_max = 3 - y_min = -3 - y_max = 3 - - XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j] - Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()]) - - # Put the result into a color plot - Z = Z.reshape(XX.shape) - plt.figure(fignum, figsize=(4, 3)) - plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired) - plt.contour( - XX, - YY, - Z, - colors=["k", "k", "k"], - linestyles=["--", "-", "--"], - levels=[-0.5, 0, 0.5], - ) + # Plot samples by color and add legend + ax.scatter(X[:, 0], X[:, 1], c=y, s=150, edgecolors="k") + ax.legend(*scatter.legend_elements(), loc="upper right", title="Classes") + ax.set_title(f" Decision boundaries of {kernel} kernel in SVC") + + _ = plt.show() + + +# %% +# Linear kernel +# ************* +# Linear kernel is the dot product of the input samples: +# +# .. math:: K(\mathbf{x}_1, \mathbf{x}_2) = \mathbf{x}_1^\top \mathbf{x}_2 +# +# It is then applied to any combination of two data points (samples) in the +# dataset. The dot product of the two points determines the +# :func:`~sklearn.metrics.pairwise.cosine_similarity` between both points. The +# higher the value, the more similar the points are. +plot_training_data_with_decision_boundary("linear") + +# %% +# Training a :class:`~sklearn.svm.SVC` on a linear kernel results in an +# untransformed feature space, where the hyperplane and the margins are +# straight lines. Due to the lack of expressivity of the linear kernel, the +# trained classes do not perfectly capture the training data. +# +# Polynomial kernel +# ***************** +# The polynomial kernel changes the notion of similarity. The kernel function +# is defined as: +# +# .. math:: +# K(\mathbf{x}_1, \mathbf{x}_2) = (\gamma \cdot \ +# \mathbf{x}_1^\top\mathbf{x}_2 + r)^d +# +# where :math:`{d}` is the degree (`degree`) of the polynomial, :math:`{\gamma}` +# (`gamma`) controls the influence of each individual training sample on the +# decision boundary and :math:`{r}` is the bias term (`coef0`) that shifts the +# data up or down. Here, we use the default value for the degree of the +# polynomal in the kernel funcion (`degree=3`). When `coef0=0` (the default), +# the data is only transformed, but no additional dimension is added. Using a +# polynomial kernel is equivalent to creating +# :class:`~sklearn.preprocessing.PolynomialFeatures` and then fitting a +# :class:`~sklearn.svm.SVC` with a linear kernel on the transformed data, +# although this alternative approach would be computationally expensive for most +# datasets. +plot_training_data_with_decision_boundary("poly") + +# %% +# The polynomial kernel with `gamma=2`` adapts well to the training data, +# causing the margins on both sides of the hyperplane to bend accordingly. +# +# RBF kernel +# ********** +# The radial basis function (RBF) kernel, also known as the Gaussian kernel, is +# the default kernel for Support Vector Machines in scikit-learn. It measures +# similarity between two data points in infinite dimensions and then approaches +# classification by majority vote. The kernel function is defined as: +# +# .. math:: +# K(\mathbf{x}_1, \mathbf{x}_2) = \exp\left(-\gamma \cdot +# {\|\mathbf{x}_1 - \mathbf{x}_2\|^2}\right) +# +# where :math:`{\gamma}` (`gamma`) controls the influence of each individual +# training sample on the decision boundary. +# +# The larger the euclidean distance between two points +# :math:`\|\mathbf{x}_1 - \mathbf{x}_2\|^2` +# the closer the kernel function is to zero. This means that two points far away +# are more likely to be dissimilar. +plot_training_data_with_decision_boundary("rbf") + +# %% +# In the plot we can see how the decision boundaries tend to contract around +# data points that are close to each other. +# +# Sigmoid kernel +# ************** +# The sigmoid kernel function is defined as: +# +# .. math:: +# K(\mathbf{x}_1, \mathbf{x}_2) = \tanh(\gamma \cdot +# \mathbf{x}_1^\top\mathbf{x}_2 + r) +# +# where the kernel coefficient :math:`{\gamma}` (`gamma`) controls the influence +# of each individual training sample on the decision boundary and :math:`{r}` is +# the bias term (`coef0`) that shifts the data up or down. +# +# In the sigmoid kernel, the similarity between two data points is computed +# using the hyperbolic tangent function (:math:`\tanh`). The kernel function +# scales and possibly shifts the dot product of the two points +# (:math:`\mathbf{x}_1` and :math:`\mathbf{x}_2`). - plt.xlim(x_min, x_max) - plt.ylim(y_min, y_max) +plot_training_data_with_decision_boundary("sigmoid") - plt.xticks(()) - plt.yticks(()) - fignum = fignum + 1 -plt.show() +# %% +# We can see that the decision boundaries obtained with the sigmoid kernel +# appear curved and irregular. The decision boundary tries to separate the +# classes by fitting a sigmoid-shaped curve, resulting in a complex boundary +# that may not generalize well to unseen data. From this example it becomes +# obvious, that the sigmoid kernel has very specific use cases, when dealing +# with data that exhibits a sigmoidal shape. In this example, careful fine +# tuning might find more generalizable decision boundaries. Because of it's +# specificity, the sigmoid kernel is less commonly used in practice compared to +# other kernels. +# +# Conclusion +# ---------- +# In this example, we have visualized the decision boundaries trained with the +# provided dataset. The plots serve as an intuitive demonstration of how +# different kernels utilize the training data to determine the classification +# boundaries. +# +# The hyperplanes and margins, although computed indirectly, can be imagined as +# planes in the transformed feature space. However, in the plots, they are +# represented relative to the original feature space, resulting in curved +# decision boundaries for the polynomial, RBF, and sigmoid kernels. +# +# Please note that the plots do not evaluate the individual kernel's accuracy or +# quality. They are intended to provide a visual understanding of how the +# different kernels use the training data. +# +# For a comprehensive evaluation, fine-tuning of :class:`~sklearn.svm.SVC` +# parameters using techniques such as +# :class:`~sklearn.model_selection.GridSearchCV` is recommended to capture the +# underlying structures within the data. diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py index de342ccbc56b0..1ca6a8b8c6067 100644 --- a/sklearn/svm/_classes.py +++ b/sklearn/svm/_classes.py @@ -652,10 +652,12 @@ class SVC(BaseSVC): kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, \ default='rbf' - Specifies the kernel type to be used in the algorithm. - If none is given, 'rbf' will be used. If a callable is given it is - used to pre-compute the kernel matrix from data matrices; that matrix - should be an array of shape ``(n_samples, n_samples)``. + Specifies the kernel type to be used in the algorithm. If + none is given, 'rbf' will be used. If a callable is given it is used to + pre-compute the kernel matrix from data matrices; that matrix should be + an array of shape ``(n_samples, n_samples)``. For an intuitive + visualization of different kernel types see + :ref:`sphx_glr_auto_examples_svm_plot_svm_kernels.py`. degree : int, default=3 Degree of the polynomial kernel function ('poly'). @@ -916,9 +918,11 @@ class NuSVC(BaseSVC): kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, \ default='rbf' - Specifies the kernel type to be used in the algorithm. - If none is given, 'rbf' will be used. If a callable is given it is - used to precompute the kernel matrix. + Specifies the kernel type to be used in the algorithm. + If none is given, 'rbf' will be used. If a callable is given it is + used to precompute the kernel matrix. For an intuitive + visualization of different kernel types see + :ref:`sphx_glr_auto_examples_svm_plot_svm_kernels.py`. degree : int, default=3 Degree of the polynomial kernel function ('poly'). From b3f9243a5407d659f1e23753289a766010adbc02 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Thu, 24 Aug 2023 15:15:41 +0200 Subject: [PATCH 176/264] DOC Add links to preprocessing examples in docstrings and userguide (#26877) Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Co-authored-by: Adrin Jalali Co-authored-by: Olivier Grisel --- doc/modules/preprocessing.rst | 7 +- examples/preprocessing/plot_all_scaling.py | 14 ++++ sklearn/preprocessing/_data.py | 87 ++++++++++------------ sklearn/preprocessing/_discretization.py | 9 +++ sklearn/preprocessing/_encoders.py | 4 + sklearn/preprocessing/_target_encoder.py | 7 +- 6 files changed, 78 insertions(+), 50 deletions(-) diff --git a/doc/modules/preprocessing.rst b/doc/modules/preprocessing.rst index 82fecf0c4e9f1..b968070f4b5da 100644 --- a/doc/modules/preprocessing.rst +++ b/doc/modules/preprocessing.rst @@ -10,9 +10,10 @@ The ``sklearn.preprocessing`` package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream estimators. -In general, learning algorithms benefit from standardization of the data set. If -some outliers are present in the set, robust scalers or transformers are more -appropriate. The behaviors of the different scalers, transformers, and +In general, many learning algorithms such as linear models benefit from standardization of the data set +(see :ref:`sphx_glr_auto_examples_preprocessing_plot_scaling_importance.py`). +If some outliers are present in the set, robust scalers or other transformers can +be more appropriate. The behaviors of the different scalers, transformers, and normalizers on a dataset containing marginal outliers is highlighted in :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. diff --git a/examples/preprocessing/plot_all_scaling.py b/examples/preprocessing/plot_all_scaling.py index c53c81a89727a..5a08976bd2653 100644 --- a/examples/preprocessing/plot_all_scaling.py +++ b/examples/preprocessing/plot_all_scaling.py @@ -265,6 +265,8 @@ def make_plot(item_idx): make_plot(0) # %% +# .. _plot_all_scaling_standard_scaler_section: +# # StandardScaler # -------------- # @@ -285,6 +287,8 @@ def make_plot(item_idx): make_plot(1) # %% +# .. _plot_all_scaling_minmax_scaler_section: +# # MinMaxScaler # ------------ # @@ -301,6 +305,8 @@ def make_plot(item_idx): make_plot(2) # %% +# .. _plot_all_scaling_max_abs_scaler_section: +# # MaxAbsScaler # ------------ # @@ -318,6 +324,8 @@ def make_plot(item_idx): make_plot(3) # %% +# .. _plot_all_scaling_robust_scaler_section: +# # RobustScaler # ------------ # @@ -335,6 +343,8 @@ def make_plot(item_idx): make_plot(4) # %% +# .. _plot_all_scaling_power_transformer_section: +# # PowerTransformer # ---------------- # @@ -353,6 +363,8 @@ def make_plot(item_idx): make_plot(6) # %% +# .. _plot_all_scaling_quantile_transformer_section: +# # QuantileTransformer (uniform output) # ------------------------------------ # @@ -384,6 +396,8 @@ def make_plot(item_idx): make_plot(8) # %% +# .. _plot_all_scaling_normalizer_section: +# # Normalizer # ---------- # diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py index b51f2af2c3d17..05eaa08cde947 100644 --- a/sklearn/preprocessing/_data.py +++ b/sklearn/preprocessing/_data.py @@ -188,8 +188,7 @@ def scale(X, *, axis=0, with_mean=True, with_std=True, copy=True): affect model performance. For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. + see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. .. warning:: Risk of data leak @@ -291,6 +290,12 @@ class MinMaxScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): This transformation is often used as an alternative to zero mean, unit variance scaling. + `MinMaxScaler` doesn't reduce the effect of outliers, but it linearily + scales them down into a fixed range, where the largest occuring data point + corresponds to the maximum value and the smallest one corresponds to the + minimum value. For an example visualization, refer to :ref:`Compare + MinMaxScaler with other scalers `. + Read more in the :ref:`User Guide `. Parameters @@ -364,10 +369,6 @@ class MinMaxScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): NaNs are treated as missing values: disregarded in fit, and maintained in transform. - For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. - Examples -------- >>> from sklearn.preprocessing import MinMaxScaler @@ -629,8 +630,7 @@ def minmax_scale(X, feature_range=(0, 1), *, axis=0, copy=True): Notes ----- For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. + see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. """ # Unlike the scaler object, this function allows 1d input. # If copy is required, it will be done inside the scaler object. @@ -683,6 +683,11 @@ class StandardScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected. + `StandardScaler` is sensitive to outliers, and the features may scale + differently from each other in the presence of outliers. For an example + visualization, refer to :ref:`Compare StandardScaler with other scalers + `. + This scaler can also be applied to sparse CSR or CSC matrices by passing `with_mean=False` to avoid breaking the sparsity structure of the data. @@ -764,10 +769,6 @@ class StandardScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): `numpy.std(x, ddof=0)`. Note that the choice of `ddof` is unlikely to affect model performance. - For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. - Examples -------- >>> from sklearn.preprocessing import StandardScaler @@ -1081,6 +1082,10 @@ class MaxAbsScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): This scaler can also be applied to sparse CSR or CSC matrices. + `MaxAbsScaler` doesn't reduce the effect of outliers; it only linearily + scales them down. For an example visualization, refer to :ref:`Compare + MaxAbsScaler with other scalers `. + .. versionadded:: 0.17 Parameters @@ -1124,10 +1129,6 @@ class MaxAbsScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): NaNs are treated as missing values: disregarded in fit, and maintained in transform. - For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. - Examples -------- >>> from sklearn.preprocessing import MaxAbsScaler @@ -1347,8 +1348,7 @@ def maxabs_scale(X, *, axis=0, copy=True): and maintained during the data transformation. For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. + see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. """ # Unlike the scaler object, this function allows 1d input. @@ -1391,11 +1391,13 @@ class RobustScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): set. Median and interquartile range are then stored to be used on later data using the :meth:`transform` method. - Standardization of a dataset is a common requirement for many - machine learning estimators. Typically this is done by removing the mean - and scaling to unit variance. However, outliers can often influence the - sample mean / variance in a negative way. In such cases, the median and - the interquartile range often give better results. + Standardization of a dataset is a common preprocessing for many machine + learning estimators. Typically this is done by removing the mean and + scaling to unit variance. However, outliers can often influence the sample + mean / variance in a negative way. In such cases, using the median and the + interquartile range often give better results. For an example visualization + and comparison to other scalers, refer to :ref:`Compare RobustScaler with + other scalers `. .. versionadded:: 0.17 @@ -1466,9 +1468,6 @@ class RobustScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): Notes ----- - For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. https://en.wikipedia.org/wiki/Median https://en.wikipedia.org/wiki/Interquartile_range @@ -1731,8 +1730,7 @@ def robust_scale( To avoid memory copy the caller should pass a CSR matrix. For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. + see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. .. warning:: Risk of data leak @@ -1833,8 +1831,7 @@ def normalize(X, norm="l2", *, axis=1, copy=True, return_norm=False): Notes ----- For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. + see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. """ if axis == 0: sparse_format = "csc" @@ -1904,6 +1901,9 @@ class Normalizer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): of the vectors and is the base similarity metric for the Vector Space Model commonly used by the Information Retrieval community. + For an example visualization, refer to :ref:`Compare Normalizer with other + scalers `. + Read more in the :ref:`User Guide `. Parameters @@ -1942,10 +1942,6 @@ class Normalizer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): :meth:`transform`, as parameter validation is only performed in :meth:`fit`. - For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. - Examples -------- >>> from sklearn.preprocessing import Normalizer @@ -2439,6 +2435,9 @@ class QuantileTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator) correlations between variables measured at the same scale but renders variables measured at different scales more directly comparable. + For example visualizations, refer to :ref:`Compare QuantileTransformer with + other scalers `. + Read more in the :ref:`User Guide `. .. versionadded:: 0.19 @@ -2516,10 +2515,6 @@ class QuantileTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator) NaNs are treated as missing values: disregarded in fit, and maintained in transform. - For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. - Examples -------- >>> import numpy as np @@ -2968,8 +2963,7 @@ def quantile_transform( LogisticRegression())`. For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. + see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. Examples -------- @@ -3013,6 +3007,12 @@ class PowerTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): By default, zero-mean, unit-variance normalization is applied to the transformed data. + For an example visualization, refer to :ref:`Compare PowerTransformer with + other scalers `. To see the + effect of Box-Cox and Yeo-Johnson transformations on different + distributions, see: + :ref:`sphx_glr_auto_examples_preprocessing_plot_map_data_to_normal.py`. + Read more in the :ref:`User Guide `. .. versionadded:: 0.20 @@ -3060,10 +3060,6 @@ class PowerTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): NaNs are treated as missing values: disregarded in ``fit``, and maintained in ``transform``. - For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. - References ---------- @@ -3480,8 +3476,7 @@ def power_transform(X, method="yeo-johnson", *, standardize=True, copy=True): in ``transform``. For a comparison of the different scalers, transformers, and normalizers, - see :ref:`examples/preprocessing/plot_all_scaling.py - `. + see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. References ---------- diff --git a/sklearn/preprocessing/_discretization.py b/sklearn/preprocessing/_discretization.py index 549c2f3110b92..691ebefcaa29b 100644 --- a/sklearn/preprocessing/_discretization.py +++ b/sklearn/preprocessing/_discretization.py @@ -55,6 +55,9 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator): - 'kmeans': Values in each bin have the same nearest center of a 1D k-means cluster. + For an example of the different strategies see: + :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_strategies.py`. + dtype : {np.float32, np.float64}, default=None The desired data-type for the output. If None, output dtype is consistent with input dtype. Only np.float32 and np.float64 are @@ -117,6 +120,12 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator): Notes ----- + + For a visualization of discretization on different datasets refer to + :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_classification.py`. + On the effect of discretization on linear models see: + :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization.py`. + In bin edges for feature ``i``, the first and last values are used only for ``inverse_transform``. During transform, bin edges are extended to:: diff --git a/sklearn/preprocessing/_encoders.py b/sklearn/preprocessing/_encoders.py index c9be60d823e90..3bd040fad0d54 100644 --- a/sklearn/preprocessing/_encoders.py +++ b/sklearn/preprocessing/_encoders.py @@ -463,6 +463,8 @@ class OneHotEncoder(_BaseEncoder): instead. Read more in the :ref:`User Guide `. + For a comparison of different encoders, refer to: + :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py`. Parameters ---------- @@ -1243,6 +1245,8 @@ class OrdinalEncoder(OneToOneFeatureMixin, _BaseEncoder): a single column of integers (0 to n_categories - 1) per feature. Read more in the :ref:`User Guide `. + For a comparison of different encoders, refer to: + :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py`. .. versionadded:: 0.20 diff --git a/sklearn/preprocessing/_target_encoder.py b/sklearn/preprocessing/_target_encoder.py index 81afeb6a8bd43..fe9bddecba31c 100644 --- a/sklearn/preprocessing/_target_encoder.py +++ b/sklearn/preprocessing/_target_encoder.py @@ -23,7 +23,12 @@ class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder): that are not seen during :meth:`fit` are encoded with the target mean, i.e. `target_mean_`. - Read more in the :ref:`User Guide `. + For a demo on the importance of the `TargetEncoder` internal cross-fitting, + see + ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder_cross_val.py`. + For a comparison of different encoders, refer to + :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py`. Read + more in the :ref:`User Guide `. .. note:: `fit(X, y).transform(X)` does not equal `fit_transform(X, y)` because a From 2860ca944535a547aeb94a11786bc1d381ab935f Mon Sep 17 00:00:00 2001 From: Tialo <65392801+Tialo@users.noreply.github.com> Date: Thu, 24 Aug 2023 16:28:14 +0300 Subject: [PATCH 177/264] DOC Add links to text/plot_hashing_vs_dict_vectorizer.py example (#26967) --- sklearn/feature_extraction/_dict_vectorizer.py | 3 +++ sklearn/feature_extraction/_hash.py | 3 +++ sklearn/feature_extraction/text.py | 9 +++++++++ 3 files changed, 15 insertions(+) diff --git a/sklearn/feature_extraction/_dict_vectorizer.py b/sklearn/feature_extraction/_dict_vectorizer.py index e32de4be42462..0b9ea2f202cb0 100644 --- a/sklearn/feature_extraction/_dict_vectorizer.py +++ b/sklearn/feature_extraction/_dict_vectorizer.py @@ -42,6 +42,9 @@ class DictVectorizer(TransformerMixin, BaseEstimator): Features that do not occur in a sample (mapping) will have a zero value in the resulting array/matrix. + For an efficiency comparision of the different feature extractors, see + :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. + Read more in the :ref:`User Guide `. Parameters diff --git a/sklearn/feature_extraction/_hash.py b/sklearn/feature_extraction/_hash.py index e0941ed1dac97..2019552ae65bb 100644 --- a/sklearn/feature_extraction/_hash.py +++ b/sklearn/feature_extraction/_hash.py @@ -34,6 +34,9 @@ class FeatureHasher(TransformerMixin, BaseEstimator): where memory is tight, e.g. when running prediction code on embedded devices. + For an efficiency comparision of the different feature extractors, see + :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. + Read more in the :ref:`User Guide `. .. versionadded:: 0.13 diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py index c6d826539ef49..b2ef28de75766 100644 --- a/sklearn/feature_extraction/text.py +++ b/sklearn/feature_extraction/text.py @@ -602,6 +602,9 @@ class HashingVectorizer( The hash function employed is the signed 32-bit version of Murmurhash3. + For an efficiency comparision of the different feature extractors, see + :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. + Read more in the :ref:`User Guide `. Parameters @@ -933,6 +936,9 @@ class CountVectorizer(_VectorizerMixin, BaseEstimator): that does some kind of feature selection then the number of features will be equal to the vocabulary size found by analyzing the data. + For an efficiency comparision of the different feature extractors, see + :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. + Read more in the :ref:`User Guide `. Parameters @@ -1758,6 +1764,9 @@ class TfidfVectorizer(CountVectorizer): For an example of usage, see :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`. + For an efficiency comparision of the different feature extractors, see + :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. + Read more in the :ref:`User Guide `. Parameters From 5933374e78cadb7c0cc76cbbc1b050411c8a6da2 Mon Sep 17 00:00:00 2001 From: Omar Salman Date: Fri, 25 Aug 2023 16:48:03 +0500 Subject: [PATCH 178/264] FIX CalibratedClassifierCV with sigmoid and large confidence scores (#26913) Co-authored-by: Olivier Grisel --- sklearn/calibration.py | 26 ++++++++- sklearn/tests/test_calibration.py | 95 ++++++++++++++++++++++++++++++- 2 files changed, 118 insertions(+), 3 deletions(-) diff --git a/sklearn/calibration.py b/sklearn/calibration.py index 39b0e5f83a821..1fc2eeb4f64fa 100644 --- a/sklearn/calibration.py +++ b/sklearn/calibration.py @@ -823,7 +823,11 @@ def predict_proba(self, X): return proba -def _sigmoid_calibration(predictions, y, sample_weight=None): +# The max_abs_prediction_threshold was approximated using +# logit(np.finfo(np.float64).eps) which is about -36 +def _sigmoid_calibration( + predictions, y, sample_weight=None, max_abs_prediction_threshold=30 +): """Probability Calibration with sigmoid method (Platt 2000) Parameters @@ -854,6 +858,20 @@ def _sigmoid_calibration(predictions, y, sample_weight=None): F = predictions # F follows Platt's notations + scale_constant = 1.0 + max_prediction = np.max(np.abs(F)) + + # If the predictions have large values we scale them in order to bring + # them within a suitable range. This has no effect on the final + # (prediction) result because linear models like Logisitic Regression + # without a penalty are invariant to multiplying the features by a + # constant. + if max_prediction >= max_abs_prediction_threshold: + scale_constant = max_prediction + # We rescale the features in a copy: inplace rescaling could confuse + # the caller and make the code harder to reason about. + F = F / scale_constant + # Bayesian priors (see Platt end of section 2.2): # It corresponds to the number of samples, taking into account the # `sample_weight`. @@ -890,7 +908,11 @@ def grad(AB): AB0 = np.array([0.0, log((prior0 + 1.0) / (prior1 + 1.0))]) AB_ = fmin_bfgs(objective, AB0, fprime=grad, disp=False) - return AB_[0], AB_[1] + + # The tuned multiplicative parameter is converted back to the original + # input feature scale. The offset parameter does not need rescaling since + # we did not rescale the outcome variable. + return AB_[0] / scale_constant, AB_[1] class _SigmoidCalibration(RegressorMixin, BaseEstimator): diff --git a/sklearn/tests/test_calibration.py b/sklearn/tests/test_calibration.py index 00220c2f8291b..df82cbc18e6c6 100644 --- a/sklearn/tests/test_calibration.py +++ b/sklearn/tests/test_calibration.py @@ -25,12 +25,14 @@ from sklearn.feature_extraction import DictVectorizer from sklearn.impute import SimpleImputer from sklearn.isotonic import IsotonicRegression -from sklearn.linear_model import LogisticRegression +from sklearn.linear_model import LogisticRegression, SGDClassifier from sklearn.metrics import brier_score_loss from sklearn.model_selection import ( KFold, LeaveOneOut, + check_cv, cross_val_predict, + cross_val_score, train_test_split, ) from sklearn.naive_bayes import MultinomialNB @@ -996,3 +998,94 @@ def fit(self, X, y, sample_weight=None, fit_param=None): CalibratedClassifierCV(estimator=TestClassifier()).fit( *data, fit_param=np.ones(len(data[1]) + 1) ) + + +def test_calibrated_classifier_cv_works_with_large_confidence_scores( + global_random_seed, +): + """Test that :class:`CalibratedClassifierCV` works with large confidence + scores when using the `sigmoid` method, particularly with the + :class:`SGDClassifier`. + + Non-regression test for issue #26766. + """ + prob = 0.67 + n = 1000 + random_noise = np.random.default_rng(global_random_seed).normal(size=n) + + y = np.array([1] * int(n * prob) + [0] * (n - int(n * prob))) + X = 1e5 * y.reshape((-1, 1)) + random_noise + + # Check that the decision function of SGDClassifier produces predicted + # values that are quite large, for the data under consideration. + cv = check_cv(cv=None, y=y, classifier=True) + indices = cv.split(X, y) + for train, test in indices: + X_train, y_train = X[train], y[train] + X_test = X[test] + sgd_clf = SGDClassifier(loss="squared_hinge", random_state=global_random_seed) + sgd_clf.fit(X_train, y_train) + predictions = sgd_clf.decision_function(X_test) + assert (predictions > 1e4).any() + + # Compare the CalibratedClassifierCV using the sigmoid method with the + # CalibratedClassifierCV using the isotonic method. The isotonic method + # is used for comparison because it is numerically stable. + clf_sigmoid = CalibratedClassifierCV( + SGDClassifier(loss="squared_hinge", random_state=global_random_seed), + method="sigmoid", + ) + score_sigmoid = cross_val_score(clf_sigmoid, X, y, scoring="roc_auc") + + # The isotonic method is used for comparison because it is numerically + # stable. + clf_isotonic = CalibratedClassifierCV( + SGDClassifier(loss="squared_hinge", random_state=global_random_seed), + method="isotonic", + ) + score_isotonic = cross_val_score(clf_isotonic, X, y, scoring="roc_auc") + + # The AUC score should be the same because it is invariant under + # strictly monotonic conditions + assert_allclose(score_sigmoid, score_isotonic) + + +def test_sigmoid_calibration_max_abs_prediction_threshold(global_random_seed): + random_state = np.random.RandomState(seed=global_random_seed) + n = 100 + y = random_state.randint(0, 2, size=n) + + # Check that for small enough predictions ranging from -2 to 2, the + # threshold value has no impact on the outcome + predictions_small = random_state.uniform(low=-2, high=2, size=100) + + # Using a threshold lower than the maximum absolute value of the + # predictions enables internal re-scaling by max(abs(predictions_small)). + threshold_1 = 0.1 + a1, b1 = _sigmoid_calibration( + predictions=predictions_small, + y=y, + max_abs_prediction_threshold=threshold_1, + ) + + # Using a larger threshold disables rescaling. + threshold_2 = 10 + a2, b2 = _sigmoid_calibration( + predictions=predictions_small, + y=y, + max_abs_prediction_threshold=threshold_2, + ) + + # Using default threshold of 30 also disables the scaling. + a3, b3 = _sigmoid_calibration( + predictions=predictions_small, + y=y, + ) + + # Depends on the tolerance of the underlying quasy-newton solver which is + # not too strict by default. + atol = 1e-6 + assert_allclose(a1, a2, atol=atol) + assert_allclose(a2, a3, atol=atol) + assert_allclose(b1, b2, atol=atol) + assert_allclose(b2, b3, atol=atol) From c8ea8725712cca4e742730c85d4566ae23a3d9b0 Mon Sep 17 00:00:00 2001 From: Rahil Parikh <75483881+rprkh@users.noreply.github.com> Date: Fri, 25 Aug 2023 20:04:30 +0530 Subject: [PATCH 179/264] DOC clustering speed with connectivity matrices (#27020) --- examples/cluster/plot_agglomerative_clustering.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/cluster/plot_agglomerative_clustering.py b/examples/cluster/plot_agglomerative_clustering.py index d5e7a8168a648..0cbce55cd3f29 100644 --- a/examples/cluster/plot_agglomerative_clustering.py +++ b/examples/cluster/plot_agglomerative_clustering.py @@ -7,7 +7,7 @@ neighbors. There are two advantages of imposing a connectivity. First, clustering -without a connectivity matrix is much faster. +with sparse connectivity matrices is faster in general. Second, when using a connectivity matrix, single, average and complete linkage are unstable and tend to create a few clusters that grow very From d2cb3b5fd23951ae8a4db2fa61e7db54b3a34d76 Mon Sep 17 00:00:00 2001 From: Julien Jerphanion Date: Sat, 26 Aug 2023 19:16:50 +0200 Subject: [PATCH 180/264] DOC Remove outdated comments regarding memoryviews (#27169) Signed-off-by: Julien Jerphanion --- sklearn/cluster/_k_means_common.pyx | 4 ---- sklearn/cluster/_k_means_elkan.pyx | 4 ---- sklearn/cluster/_k_means_lloyd.pyx | 4 ---- sklearn/cluster/_k_means_minibatch.pyx | 4 ---- 4 files changed, 16 deletions(-) diff --git a/sklearn/cluster/_k_means_common.pyx b/sklearn/cluster/_k_means_common.pyx index 192a4bdec1088..151af55076b7b 100644 --- a/sklearn/cluster/_k_means_common.pyx +++ b/sklearn/cluster/_k_means_common.pyx @@ -4,10 +4,6 @@ # # License: BSD 3 clause -# TODO: We still need to use ndarrays instead of typed memoryviews when using -# fused types and when the array may be read-only (for instance when it's -# provided by the user). This is fixed in cython > 0.3. - import numpy as np from cython cimport floating from cython.parallel cimport prange diff --git a/sklearn/cluster/_k_means_elkan.pyx b/sklearn/cluster/_k_means_elkan.pyx index 60b2d080793db..1f61d99ba5404 100644 --- a/sklearn/cluster/_k_means_elkan.pyx +++ b/sklearn/cluster/_k_means_elkan.pyx @@ -2,10 +2,6 @@ # # Licence: BSD 3 clause -# TODO: We still need to use ndarrays instead of typed memoryviews when using -# fused types and when the array may be read-only (for instance when it's -# provided by the user). This is fixed in cython > 0.3. - from cython cimport floating from cython.parallel import prange, parallel from libc.stdlib cimport calloc, free diff --git a/sklearn/cluster/_k_means_lloyd.pyx b/sklearn/cluster/_k_means_lloyd.pyx index 664ec0da2cea2..a7e68c1fed5a9 100644 --- a/sklearn/cluster/_k_means_lloyd.pyx +++ b/sklearn/cluster/_k_means_lloyd.pyx @@ -1,9 +1,5 @@ # Licence: BSD 3 clause -# TODO: We still need to use ndarrays instead of typed memoryviews when using -# fused types and when the array may be read-only (for instance when it's -# provided by the user). This is fixed in cython > 0.3. - from cython cimport floating from cython.parallel import prange, parallel from libc.stdlib cimport malloc, calloc, free diff --git a/sklearn/cluster/_k_means_minibatch.pyx b/sklearn/cluster/_k_means_minibatch.pyx index 503413a469e3e..22ca5255e3889 100644 --- a/sklearn/cluster/_k_means_minibatch.pyx +++ b/sklearn/cluster/_k_means_minibatch.pyx @@ -1,7 +1,3 @@ -# TODO: We still need to use ndarrays instead of typed memoryviews when using -# fused types and when the array may be read-only (for instance when it's -# provided by the user). This will be fixed in cython >= 0.3. - from cython cimport floating from cython.parallel cimport parallel, prange from libc.stdlib cimport malloc, free From ab6d453cb31c4f1873ba5988fdbeebb42456581a Mon Sep 17 00:00:00 2001 From: Kushan Sharma <50699013+greyisbetter@users.noreply.github.com> Date: Mon, 28 Aug 2023 14:33:10 +0530 Subject: [PATCH 181/264] [MRG] DOC Add dropdowns to Module 1.13 Feature Selection (#26662) --- doc/modules/feature_selection.rst | 65 ++++++++++++++++++------------- 1 file changed, 38 insertions(+), 27 deletions(-) diff --git a/doc/modules/feature_selection.rst b/doc/modules/feature_selection.rst index 01e2d079c45a4..496b27daf447e 100644 --- a/doc/modules/feature_selection.rst +++ b/doc/modules/feature_selection.rst @@ -201,31 +201,36 @@ alpha parameter, the fewer features selected. .. _compressive_sensing: -.. topic:: **L1-recovery and compressive sensing** - - For a good choice of alpha, the :ref:`lasso` can fully recover the - exact set of non-zero variables using only few observations, provided - certain specific conditions are met. In particular, the number of - samples should be "sufficiently large", or L1 models will perform at - random, where "sufficiently large" depends on the number of non-zero - coefficients, the logarithm of the number of features, the amount of - noise, the smallest absolute value of non-zero coefficients, and the - structure of the design matrix X. In addition, the design matrix must - display certain specific properties, such as not being too correlated. - - There is no general rule to select an alpha parameter for recovery of - non-zero coefficients. It can by set by cross-validation - (:class:`~sklearn.linear_model.LassoCV` or - :class:`~sklearn.linear_model.LassoLarsCV`), though this may lead to - under-penalized models: including a small number of non-relevant variables - is not detrimental to prediction score. BIC - (:class:`~sklearn.linear_model.LassoLarsIC`) tends, on the opposite, to set - high values of alpha. - - **Reference** Richard G. Baraniuk "Compressive Sensing", IEEE Signal +|details-start| +**L1-recovery and compressive sensing** +|details-split| + +For a good choice of alpha, the :ref:`lasso` can fully recover the +exact set of non-zero variables using only few observations, provided +certain specific conditions are met. In particular, the number of +samples should be "sufficiently large", or L1 models will perform at +random, where "sufficiently large" depends on the number of non-zero +coefficients, the logarithm of the number of features, the amount of +noise, the smallest absolute value of non-zero coefficients, and the +structure of the design matrix X. In addition, the design matrix must +display certain specific properties, such as not being too correlated. + +There is no general rule to select an alpha parameter for recovery of +non-zero coefficients. It can by set by cross-validation +(:class:`~sklearn.linear_model.LassoCV` or +:class:`~sklearn.linear_model.LassoLarsCV`), though this may lead to +under-penalized models: including a small number of non-relevant variables +is not detrimental to prediction score. BIC +(:class:`~sklearn.linear_model.LassoLarsIC`) tends, on the opposite, to set +high values of alpha. + +.. topic:: Reference + + Richard G. Baraniuk "Compressive Sensing", IEEE Signal Processing Magazine [120] July 2007 http://users.isr.ist.utl.pt/~aguiar/CS_notes.pdf +|details-end| Tree-based feature selection ---------------------------- @@ -282,6 +287,10 @@ instead of starting with no features and greedily adding features, we start with *all* the features and greedily *remove* features from the set. The `direction` parameter controls whether forward or backward SFS is used. +|details-start| +**Detail on Sequential Feature Selection** +|details-split| + In general, forward and backward selection do not yield equivalent results. Also, one may be much faster than the other depending on the requested number of selected features: if we have 10 features and ask for 7 selected features, @@ -299,16 +308,18 @@ cross-validation requires fitting `m * k` models, while :class:`~sklearn.feature_selection.SelectFromModel` always just does a single fit and requires no iterations. -.. topic:: Examples - - * :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py` - -.. topic:: References: +.. topic:: Reference .. [sfs] Ferri et al, `Comparative study of techniques for large-scale feature selection `_. +|details-end| + +.. topic:: Examples + + * :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py` + Feature selection as part of a pipeline ======================================= From 06f02854de0a497f8143115bde33010d4209f62c Mon Sep 17 00:00:00 2001 From: Rahil Parikh <75483881+rprkh@users.noreply.github.com> Date: Mon, 28 Aug 2023 14:52:14 +0530 Subject: [PATCH 182/264] DOC Add dropdowns to module 7.1 Toy datasets (#26710) --- sklearn/datasets/descr/breast_cancer.rst | 28 +++++++++------- sklearn/datasets/descr/digits.rst | 28 +++++++++------- sklearn/datasets/descr/iris.rst | 36 +++++++++++--------- sklearn/datasets/descr/linnerud.rst | 10 ++++-- sklearn/datasets/descr/wine_data.rst | 42 +++++++++++++----------- 5 files changed, 82 insertions(+), 62 deletions(-) diff --git a/sklearn/datasets/descr/breast_cancer.rst b/sklearn/datasets/descr/breast_cancer.rst index bc4d60b9a363d..a532ef960737f 100644 --- a/sklearn/datasets/descr/breast_cancer.rst +++ b/sklearn/datasets/descr/breast_cancer.rst @@ -104,15 +104,19 @@ This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/ -.. topic:: References - - - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction - for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on - Electronic Imaging: Science and Technology, volume 1905, pages 861-870, - San Jose, CA, 1993. - - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and - prognosis via linear programming. Operations Research, 43(4), pages 570-577, - July-August 1995. - - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques - to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) - 163-171. \ No newline at end of file +|details-start| +**References** +|details-split| + +- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction + for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on + Electronic Imaging: Science and Technology, volume 1905, pages 861-870, + San Jose, CA, 1993. +- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and + prognosis via linear programming. Operations Research, 43(4), pages 570-577, + July-August 1995. +- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques + to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) + 163-171. + +|details-end| \ No newline at end of file diff --git a/sklearn/datasets/descr/digits.rst b/sklearn/datasets/descr/digits.rst index 244f34f316865..40d819e92b7ab 100644 --- a/sklearn/datasets/descr/digits.rst +++ b/sklearn/datasets/descr/digits.rst @@ -32,15 +32,19 @@ T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994. -.. topic:: References - - - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their - Applications to Handwritten Digit Recognition, MSc Thesis, Institute of - Graduate Studies in Science and Engineering, Bogazici University. - - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika. - - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. - Linear dimensionalityreduction using relevance weighted LDA. School of - Electrical and Electronic Engineering Nanyang Technological University. - 2005. - - Claudio Gentile. A New Approximate Maximal Margin Classification - Algorithm. NIPS. 2000. +|details-start| +**References** +|details-split| + +- C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their + Applications to Handwritten Digit Recognition, MSc Thesis, Institute of + Graduate Studies in Science and Engineering, Bogazici University. +- E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika. +- Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. + Linear dimensionalityreduction using relevance weighted LDA. School of + Electrical and Electronic Engineering Nanyang Technological University. + 2005. +- Claudio Gentile. A New Approximate Maximal Margin Classification + Algorithm. NIPS. 2000. + +|details-end| \ No newline at end of file diff --git a/sklearn/datasets/descr/iris.rst b/sklearn/datasets/descr/iris.rst index e05206454d218..02236dcb1c19f 100644 --- a/sklearn/datasets/descr/iris.rst +++ b/sklearn/datasets/descr/iris.rst @@ -45,19 +45,23 @@ data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. -.. topic:: References - - - Fisher, R.A. "The use of multiple measurements in taxonomic problems" - Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to - Mathematical Statistics" (John Wiley, NY, 1950). - - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis. - (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218. - - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System - Structure and Classification Rule for Recognition in Partially Exposed - Environments". IEEE Transactions on Pattern Analysis and Machine - Intelligence, Vol. PAMI-2, No. 1, 67-71. - - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions - on Information Theory, May 1972, 431-433. - - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II - conceptual clustering system finds 3 classes in the data. - - Many, many more ... \ No newline at end of file +|details-start| +**References** +|details-split| + +- Fisher, R.A. "The use of multiple measurements in taxonomic problems" + Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to + Mathematical Statistics" (John Wiley, NY, 1950). +- Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis. + (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218. +- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System + Structure and Classification Rule for Recognition in Partially Exposed + Environments". IEEE Transactions on Pattern Analysis and Machine + Intelligence, Vol. PAMI-2, No. 1, 67-71. +- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions + on Information Theory, May 1972, 431-433. +- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II + conceptual clustering system finds 3 classes in the data. +- Many, many more ... + +|details-end| \ No newline at end of file diff --git a/sklearn/datasets/descr/linnerud.rst b/sklearn/datasets/descr/linnerud.rst index f7c10a95423d0..81c970bb6e3e6 100644 --- a/sklearn/datasets/descr/linnerud.rst +++ b/sklearn/datasets/descr/linnerud.rst @@ -18,7 +18,11 @@ twenty middle-aged men in a fitness club: - *exercise* - CSV containing 20 observations on 3 exercise variables: Chins, Situps and Jumps. -.. topic:: References +|details-start| +**References** +|details-split| - * Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: - Editions Technic. +* Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: + Editions Technic. + +|details-end| \ No newline at end of file diff --git a/sklearn/datasets/descr/wine_data.rst b/sklearn/datasets/descr/wine_data.rst index dbe7f38e44aa6..e20efea9ba719 100644 --- a/sklearn/datasets/descr/wine_data.rst +++ b/sklearn/datasets/descr/wine_data.rst @@ -74,22 +74,26 @@ Lichman, M. (2013). UCI Machine Learning Repository [https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. -.. topic:: References - - (1) S. Aeberhard, D. Coomans and O. de Vel, - Comparison of Classifiers in High Dimensional Settings, - Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of - Mathematics and Statistics, James Cook University of North Queensland. - (Also submitted to Technometrics). - - The data was used with many others for comparing various - classifiers. The classes are separable, though only RDA - has achieved 100% correct classification. - (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) - (All results using the leave-one-out technique) - - (2) S. Aeberhard, D. Coomans and O. de Vel, - "THE CLASSIFICATION PERFORMANCE OF RDA" - Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of - Mathematics and Statistics, James Cook University of North Queensland. - (Also submitted to Journal of Chemometrics). +|details-start| +**References** +|details-split| + +(1) S. Aeberhard, D. Coomans and O. de Vel, +Comparison of Classifiers in High Dimensional Settings, +Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of +Mathematics and Statistics, James Cook University of North Queensland. +(Also submitted to Technometrics). + +The data was used with many others for comparing various +classifiers. The classes are separable, though only RDA +has achieved 100% correct classification. +(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) +(All results using the leave-one-out technique) + +(2) S. Aeberhard, D. Coomans and O. de Vel, +"THE CLASSIFICATION PERFORMANCE OF RDA" +Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of +Mathematics and Statistics, James Cook University of North Queensland. +(Also submitted to Journal of Chemometrics). + +|details-end| \ No newline at end of file From ead18158a33d19a3291b14610fab27e12553818e Mon Sep 17 00:00:00 2001 From: Puneeth K <32433964+punndcoder28@users.noreply.github.com> Date: Mon, 28 Aug 2023 14:57:42 +0530 Subject: [PATCH 183/264] DOC Adding dropdown for module 2.2 Manifold Learning (#26720) --- doc/modules/manifold.rst | 70 ++++++++++++++++++++++++++++------------ 1 file changed, 50 insertions(+), 20 deletions(-) diff --git a/doc/modules/manifold.rst b/doc/modules/manifold.rst index 40bbea17a8309..1656c09f1371d 100644 --- a/doc/modules/manifold.rst +++ b/doc/modules/manifold.rst @@ -130,8 +130,10 @@ distances between all points. Isomap can be performed with the object :align: center :scale: 50 -Complexity ----------- +|details-start| +**Complexity** +|details-split| + The Isomap algorithm comprises three stages: 1. **Nearest neighbor search.** Isomap uses @@ -162,6 +164,8 @@ The overall complexity of Isomap is * :math:`k` : number of nearest neighbors * :math:`d` : output dimension +|details-end| + .. topic:: References: * `"A global geometric framework for nonlinear dimensionality reduction" @@ -187,8 +191,9 @@ Locally linear embedding can be performed with function :align: center :scale: 50 -Complexity ----------- +|details-start| +**Complexity** +|details-split| The standard LLE algorithm comprises three stages: @@ -209,6 +214,8 @@ The overall complexity of standard LLE is * :math:`k` : number of nearest neighbors * :math:`d` : output dimension +|details-end| + .. topic:: References: * `"Nonlinear dimensionality reduction by locally linear embedding" @@ -241,8 +248,9 @@ It requires ``n_neighbors > n_components``. :align: center :scale: 50 -Complexity ----------- +|details-start| +**Complexity** +|details-split| The MLLE algorithm comprises three stages: @@ -265,6 +273,8 @@ The overall complexity of MLLE is * :math:`k` : number of nearest neighbors * :math:`d` : output dimension +|details-end| + .. topic:: References: * `"MLLE: Modified Locally Linear Embedding Using Multiple Weights" @@ -291,8 +301,9 @@ It requires ``n_neighbors > n_components * (n_components + 3) / 2``. :align: center :scale: 50 -Complexity ----------- +|details-start| +**Complexity** +|details-split| The HLLE algorithm comprises three stages: @@ -313,6 +324,8 @@ The overall complexity of standard HLLE is * :math:`k` : number of nearest neighbors * :math:`d` : output dimension +|details-end| + .. topic:: References: * `"Hessian Eigenmaps: Locally linear embedding techniques for @@ -335,8 +348,9 @@ preserving local distances. Spectral embedding can be performed with the function :func:`spectral_embedding` or its object-oriented counterpart :class:`SpectralEmbedding`. -Complexity ----------- +|details-start| +**Complexity** +|details-split| The Spectral Embedding (Laplacian Eigenmaps) algorithm comprises three stages: @@ -358,6 +372,8 @@ The overall complexity of spectral embedding is * :math:`k` : number of nearest neighbors * :math:`d` : output dimension +|details-end| + .. topic:: References: * `"Laplacian Eigenmaps for Dimensionality Reduction @@ -383,8 +399,9 @@ tangent spaces to learn the embedding. LTSA can be performed with function :align: center :scale: 50 -Complexity ----------- +|details-start| +**Complexity** +|details-split| The LTSA algorithm comprises three stages: @@ -404,6 +421,8 @@ The overall complexity of standard LTSA is * :math:`k` : number of nearest neighbors * :math:`d` : output dimension +|details-end| + .. topic:: References: * :arxiv:`"Principal manifolds and nonlinear dimensionality reduction via @@ -448,8 +467,9 @@ the similarities chosen in some optimal ways. The objective, called the stress, is then defined by :math:`\sum_{i < j} d_{ij}(X) - \hat{d}_{ij}(X)` -Metric MDS ----------- +|details-start| +**Metric MDS** +|details-split| The simplest metric :class:`MDS` model, called *absolute MDS*, disparities are defined by :math:`\hat{d}_{ij} = S_{ij}`. With absolute MDS, the value :math:`S_{ij}` @@ -458,8 +478,11 @@ should then correspond exactly to the distance between point :math:`i` and Most commonly, disparities are set to :math:`\hat{d}_{ij} = b S_{ij}`. -Nonmetric MDS -------------- +|details-end| + +|details-start| +**Nonmetric MDS** +|details-split| Non metric :class:`MDS` focuses on the ordination of the data. If :math:`S_{ij} > S_{jk}`, then the embedding should enforce :math:`d_{ij} < @@ -490,6 +513,7 @@ in the metric case. :align: center :scale: 60 +|details-end| .. topic:: References: @@ -551,8 +575,10 @@ The disadvantages to using t-SNE are roughly: :align: center :scale: 50 -Optimizing t-SNE ----------------- +|details-start| +**Optimizing t-SNE** +|details-split| + The main purpose of t-SNE is visualization of high-dimensional data. Hence, it works best when the data will be embedded on two or three dimensions. @@ -601,8 +627,11 @@ but less accurate results. provides a good discussion of the effects of the various parameters, as well as interactive plots to explore the effects of different parameters. -Barnes-Hut t-SNE ----------------- +|details-end| + +|details-start| +**Barnes-Hut t-SNE** +|details-split| The Barnes-Hut t-SNE that has been implemented here is usually much slower than other manifold learning algorithms. The optimization is quite difficult @@ -638,6 +667,7 @@ imply that the data cannot be correctly classified by a supervised model. It might be the case that 2 dimensions are not high enough to accurately represent the internal structure of the data. +|details-end| .. topic:: References: From f5b065e205a9ed82e13c6ea19a43f1799de737c9 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Mon, 28 Aug 2023 11:54:18 +0200 Subject: [PATCH 184/264] Fix low level crash in BisectingKMeans.predict on rescaled data (#27167) --- doc/whats_new/v1.3.rst | 7 +++++++ sklearn/cluster/_k_means_elkan.pyx | 16 ++++++++++++++++ sklearn/cluster/_k_means_lloyd.pyx | 17 ++++++++++++++++- sklearn/cluster/tests/test_bisect_k_means.py | 15 +++++++++++++++ 4 files changed, 54 insertions(+), 1 deletion(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index 17d304b379aea..61d3aa3fcee92 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -18,6 +18,13 @@ Changes impacting all modules Changelog --------- +:mod:`sklearn.cluster` +...................... + +- |Fix| :class:`cluster.BisectingKMeans` could crash when predicting on data + with a different scale than the data used to fit the model. + :pr:`27167` by `Olivier Grisel`_. + :mod:`sklearn.impute` ..................... diff --git a/sklearn/cluster/_k_means_elkan.pyx b/sklearn/cluster/_k_means_elkan.pyx index 1f61d99ba5404..0853d5f11d5e6 100644 --- a/sklearn/cluster/_k_means_elkan.pyx +++ b/sklearn/cluster/_k_means_elkan.pyx @@ -259,6 +259,14 @@ def elkan_iter_chunked_dense( int n_features = X.shape[1] int n_clusters = centers_new.shape[0] + if n_samples == 0: + # An empty array was passed, do nothing and return early (before + # attempting to compute n_chunks). This can typically happen when + # calling the prediction function of a bisecting k-means model with a + # large fraction of outiers. + return + + cdef: # hard-coded number of samples per chunk. Splitting in chunks is # necessary to get parallelism. Chunk size chosen to be same as lloyd's int n_samples_chunk = CHUNK_SIZE if n_samples > CHUNK_SIZE else n_samples @@ -494,6 +502,14 @@ def elkan_iter_chunked_sparse( int n_features = X.shape[1] int n_clusters = centers_new.shape[0] + if n_samples == 0: + # An empty array was passed, do nothing and return early (before + # attempting to compute n_chunks). This can typically happen when + # calling the prediction function of a bisecting k-means model with a + # large fraction of outiers. + return + + cdef: floating[::1] X_data = X.data int[::1] X_indices = X.indices int[::1] X_indptr = X.indptr diff --git a/sklearn/cluster/_k_means_lloyd.pyx b/sklearn/cluster/_k_means_lloyd.pyx index a7e68c1fed5a9..db7b4e259f434 100644 --- a/sklearn/cluster/_k_means_lloyd.pyx +++ b/sklearn/cluster/_k_means_lloyd.pyx @@ -78,6 +78,14 @@ def lloyd_iter_chunked_dense( int n_features = X.shape[1] int n_clusters = centers_old.shape[0] + if n_samples == 0: + # An empty array was passed, do nothing and return early (before + # attempting to compute n_chunks). This can typically happen when + # calling the prediction function of a bisecting k-means model with a + # large fraction of outiers. + return + + cdef: # hard-coded number of samples per chunk. Appeared to be close to # optimal in all situations. int n_samples_chunk = CHUNK_SIZE if n_samples > CHUNK_SIZE else n_samples @@ -263,12 +271,19 @@ def lloyd_iter_chunked_sparse( the algorithm. This is useful especially when calling predict on a fitted model. """ - # print(X.indices.dtype) cdef: int n_samples = X.shape[0] int n_features = X.shape[1] int n_clusters = centers_old.shape[0] + if n_samples == 0: + # An empty array was passed, do nothing and return early (before + # attempting to compute n_chunks). This can typically happen when + # calling the prediction function of a bisecting k-means model with a + # large fraction of outiers. + return + + cdef: # Choose same as for dense. Does not have the same impact since with # sparse data the pairwise distances matrix is not precomputed. # However, splitting in chunks is necessary to get parallelism. diff --git a/sklearn/cluster/tests/test_bisect_k_means.py b/sklearn/cluster/tests/test_bisect_k_means.py index 01afd4be9c8b5..fe60cea277558 100644 --- a/sklearn/cluster/tests/test_bisect_k_means.py +++ b/sklearn/cluster/tests/test_bisect_k_means.py @@ -132,3 +132,18 @@ def test_float32_float64_equivalence(is_sparse): assert_allclose(km32.cluster_centers_, km64.cluster_centers_) assert_array_equal(km32.labels_, km64.labels_) + + +@pytest.mark.parametrize("algorithm", ("lloyd", "elkan")) +def test_no_crash_on_empty_bisections(algorithm): + # Non-regression test for: + # https://github.com/scikit-learn/scikit-learn/issues/27081 + rng = np.random.RandomState(0) + X_train = rng.rand(3000, 10) + bkm = BisectingKMeans(n_clusters=10, algorithm=algorithm).fit(X_train) + + # predict on scaled data to trigger pathologic case + # where the inner mask leads to empty bisections. + X_test = 50 * rng.rand(100, 10) + labels = bkm.predict(X_test) # should not crash with idiv by 0 + assert np.isin(np.unique(labels), np.arange(10)).all() From 41f4266b359d61eefbffa84eccdb82e598d48b77 Mon Sep 17 00:00:00 2001 From: Shreesha Kumar Bhat Date: Mon, 28 Aug 2023 17:44:15 +0530 Subject: [PATCH 185/264] Doc Add dropdowns to 1.10.decision trees (#26699) --- doc/modules/tree.rst | 36 +++++++++++++++++++++++++++++------- 1 file changed, 29 insertions(+), 7 deletions(-) diff --git a/doc/modules/tree.rst b/doc/modules/tree.rst index cb5a752f549a7..cd15e7aadb696 100644 --- a/doc/modules/tree.rst +++ b/doc/modules/tree.rst @@ -146,6 +146,10 @@ Once trained, you can plot the tree with the :func:`plot_tree` function:: :scale: 75 :align: center +|details-start| +**Alternative ways to export trees** +|details-split| + We can also export the tree in `Graphviz `_ format using the :func:`export_graphviz` exporter. If you use the `conda `_ package manager, the graphviz binaries @@ -212,6 +216,8 @@ of external libraries and is more compact: | | |--- class: 2 +|details-end| + .. topic:: Examples: * :ref:`sphx_glr_auto_examples_tree_plot_iris_dtc.py` @@ -281,7 +287,6 @@ of shape ``(n_samples, n_outputs)`` then the resulting estimator will: * Output a list of n_output arrays of class probabilities upon ``predict_proba``. - The use of multi-output trees for regression is demonstrated in :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py`. In this example, the input X is a single real value and the outputs Y are the sine and cosine of X. @@ -303,16 +308,20 @@ the lower half of those faces. .. topic:: Examples: - * :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py` - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py` + * :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py` + * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py` -.. topic:: References: +|details-start| +**References** +|details-split| * M. Dumont et al, `Fast multi-class image annotation with random subwindows and multiple output randomized trees `_, International Conference on Computer Vision Theory and Applications 2009 +|details-end| + .. _tree_complexity: Complexity @@ -403,6 +412,10 @@ Tree algorithms: ID3, C4.5, C5.0 and CART What are all the various decision tree algorithms and how do they differ from each other? Which one is implemented in scikit-learn? +|details-start| +**Various decision tree algorithms** +|details-split| + ID3_ (Iterative Dichotomiser 3) was developed in 1986 by Ross Quinlan. The algorithm creates a multiway tree, finding for each node (i.e. in a greedy manner) the categorical feature that will yield the largest @@ -428,6 +441,8 @@ it differs in that it supports numerical target variables (regression) and does not compute rule sets. CART constructs binary trees using the feature and threshold that yield the largest information gain at each node. +|details-end| + scikit-learn uses an optimized version of the CART algorithm; however, the scikit-learn implementation does not support categorical variables for now. @@ -500,8 +515,9 @@ Log Loss or Entropy: H(Q_m) = - \sum_k p_{mk} \log(p_{mk}) - -.. note:: +|details-start| +Shannon entropy: +|details-split| The entropy criterion computes the Shannon entropy of the possible classes. It takes the class frequencies of the training data points that reached a given @@ -531,6 +547,8 @@ Log Loss or Entropy: \mathrm{LL}(D, T) = \sum_{m \in T} \frac{n_m}{n} H(Q_m) +|details-end| + Regression criteria ------------------- @@ -671,7 +689,9 @@ be pruned. This process stops when the pruned tree's minimal * :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py` -.. topic:: References: +|details-start| +**References** +|details-split| .. [BRE] L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, Belmont, CA, 1984. @@ -685,3 +705,5 @@ be pruned. This process stops when the pruned tree's minimal * T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning, Springer, 2009. + +|details-end| From 9f318b1888ac9fab9b5bdf25b1984b68834445af Mon Sep 17 00:00:00 2001 From: Camille Troillard <556316+tuscland@users.noreply.github.com> Date: Mon, 28 Aug 2023 15:01:49 +0200 Subject: [PATCH 186/264] DOC improve code block CSS (#27136) --- doc/conf.py | 1 + doc/developers/advanced_installation.rst | 9 ++- doc/install.rst | 72 +++++++++---------- doc/modules/array_api.rst | 2 +- .../scikit-learn-modern/static/css/theme.css | 4 ++ 5 files changed, 48 insertions(+), 40 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index db69cfedd48a3..bfeb7a1b05940 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -68,6 +68,7 @@ # Specify how to identify the prompt when copying code snippets copybutton_prompt_text = r">>> |\.\.\. " copybutton_prompt_is_regexp = True +copybutton_exclude = "style" try: import jupyterlite_sphinx # noqa: F401 diff --git a/doc/developers/advanced_installation.rst b/doc/developers/advanced_installation.rst index b808911e1b961..a9461e965af2a 100644 --- a/doc/developers/advanced_installation.rst +++ b/doc/developers/advanced_installation.rst @@ -229,10 +229,13 @@ console: For 64-bit Python, configure the build environment by running the following commands in ``cmd`` or an Anaconda Prompt (if you use Anaconda): - :: +.. sphinx-prompt 1.3.0 (used in doc-min-dependencies CI task) does not support `batch` prompt type, +.. so we work around by using a known prompt type and an explicit prompt text. +.. +.. prompt:: bash C:\> - $ SET DISTUTILS_USE_SDK=1 - $ "C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Auxiliary\Build\vcvarsall.bat" x64 + SET DISTUTILS_USE_SDK=1 + "C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Auxiliary\Build\vcvarsall.bat" x64 Replace ``x64`` by ``x86`` to build for 32-bit Python. diff --git a/doc/install.rst b/doc/install.rst index d3c0533d4a001..8b36da24adf08 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -71,32 +71,32 @@ Then run: 
    pip3 install -U scikit-learn
    - + >pip3 install -U scikit-learn + 
    pip install -U scikit-learn
    - + >pip install -U scikit-learn + 
    pip install -U scikit-learn
    - + >pip install -U scikit-learn + 
    python3 -m venv sklearn-venv
-  source sklearn-venv/bin/activate
-  pip3 install -U scikit-learn
    - + >python3 -m venv sklearn-venv + source sklearn-venv/bin/activate + pip3 install -U scikit-learn + 
    python -m venv sklearn-venv
-  sklearn-venv\Scripts\activate
-  pip install -U scikit-learn
    - + >python -m venv sklearn-venv + sklearn-venv\Scripts\activate + pip install -U scikit-learn + 
    python -m venv sklearn-venv
-  source sklearn-venv/bin/activate
-  pip install -U scikit-learn
    + >python -m venv sklearn-venv + source sklearn-venv/bin/activate + pip install -U scikit-learn 
    conda create -n sklearn-env -c conda-forge scikit-learn
-  conda activate sklearn-env
    + >conda create -n sklearn-env -c conda-forge scikit-learn + conda activate sklearn-env
    In order to check your installation you can use @@ -105,29 +105,29 @@ In order to check your installation you can use 
    python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installed
-  python3 -m pip freeze  # to see all packages installed in the active virtualenv
-  python3 -c "import sklearn; sklearn.show_versions()"
    + >python3 -m pip show scikit-learn # to see which version and where scikit-learn is installed + python3 -m pip freeze # to see all packages installed in the active virtualenv + python3 -c "import sklearn; sklearn.show_versions()" 
    python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
-  python -m pip freeze  # to see all packages installed in the active virtualenv
-  python -c "import sklearn; sklearn.show_versions()"
    + >python -m pip show scikit-learn # to see which version and where scikit-learn is installed + python -m pip freeze # to see all packages installed in the active virtualenv + python -c "import sklearn; sklearn.show_versions()" 
    python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
-  python -m pip freeze  # to see all packages installed in the active virtualenv
-  python -c "import sklearn; sklearn.show_versions()"
    + >python -m pip show scikit-learn # to see which version and where scikit-learn is installed + python -m pip freeze # to see all packages installed in the active virtualenv + python -c "import sklearn; sklearn.show_versions()" 
    python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
-  python -m pip freeze  # to see all packages installed in the active virtualenv
-  python -c "import sklearn; sklearn.show_versions()"
    + >python -m pip show scikit-learn # to see which version and where scikit-learn is installed + python -m pip freeze # to see all packages installed in the active virtualenv + python -c "import sklearn; sklearn.show_versions()" 
    conda list scikit-learn  # to see which scikit-learn version is installed
-  conda list  # to see all packages installed in the active conda environment
-  python -c "import sklearn; sklearn.show_versions()"
    + >conda list scikit-learn # to see which scikit-learn version is installed + conda list # to see all packages installed in the active conda environment + python -c "import sklearn; sklearn.show_versions()"
    Note that in order to avoid potential conflicts with other packages it is @@ -290,7 +290,7 @@ Note that those solvers are not enabled by default, please refer to the `scikit-learn-intelex `_ documentation for more details on usage scenarios. Direct export example: -.. prompt:: bash $ +.. prompt:: python >>> from sklearnex.neighbors import NearestNeighbors @@ -340,6 +340,6 @@ using the ``regedit`` tool: #. Reinstall scikit-learn (ignoring the previous broken installation): -.. prompt:: python $ +.. prompt:: bash $ pip install --exists-action=i scikit-learn diff --git a/doc/modules/array_api.rst b/doc/modules/array_api.rst index 71a2e1ce0a6ce..15e78d25f93df 100644 --- a/doc/modules/array_api.rst +++ b/doc/modules/array_api.rst @@ -107,4 +107,4 @@ To run these checks you need to install test environment. To run the full set of checks you need to install both `PyTorch `_ and `CuPy `_ and have a GPU. Checks that can not be executed or have missing dependencies will be -automatically skipped. \ No newline at end of file +automatically skipped. diff --git a/doc/themes/scikit-learn-modern/static/css/theme.css b/doc/themes/scikit-learn-modern/static/css/theme.css index aa5bb3c853e51..21e1a2336a553 100644 --- a/doc/themes/scikit-learn-modern/static/css/theme.css +++ b/doc/themes/scikit-learn-modern/static/css/theme.css @@ -1208,6 +1208,10 @@ div.install > input:checked + label { display: none; } +pre.sk-expandable > span:before { + content: "$ "; +} + /* Show hidden content when the checkbox is checked */ /* for conda */ #quickstart-conda:checked ~* [data-packager="conda"] { From 8d40fc166d56c5fa8cdfab0be83575516a524e50 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 29 Aug 2023 13:04:33 +0200 Subject: [PATCH 187/264] CI Fix scipy-dev issues related to numpy 2.0 changes (#27190) --- sklearn/cluster/_hdbscan/_linkage.pyx | 4 ++-- sklearn/impute/tests/test_common.py | 4 ++-- sklearn/preprocessing/tests/test_encoders.py | 2 +- sklearn/utils/fixes.py | 4 ++-- sklearn/utils/tests/test_validation.py | 2 +- sklearn/utils/validation.py | 4 +--- 6 files changed, 9 insertions(+), 11 deletions(-) diff --git a/sklearn/cluster/_hdbscan/_linkage.pyx b/sklearn/cluster/_hdbscan/_linkage.pyx index 03e91ac8d6833..ee8025c8027aa 100644 --- a/sklearn/cluster/_hdbscan/_linkage.pyx +++ b/sklearn/cluster/_hdbscan/_linkage.pyx @@ -90,7 +90,7 @@ cpdef cnp.ndarray[MST_edge_t, ndim=1, mode='c'] mst_from_mutual_reachability( mst = np.empty(n_samples - 1, dtype=MST_edge_dtype) current_labels = np.arange(n_samples, dtype=np.int64) current_node = 0 - min_reachability = np.full(n_samples, fill_value=np.infty, dtype=np.float64) + min_reachability = np.full(n_samples, fill_value=np.inf, dtype=np.float64) for i in range(0, n_samples - 1): label_filter = current_labels != current_node current_labels = current_labels[label_filter] @@ -156,7 +156,7 @@ cpdef cnp.ndarray[MST_edge_t, ndim=1, mode='c'] mst_from_data_matrix( mst = np.empty(n_samples - 1, dtype=MST_edge_dtype) in_tree = np.zeros(n_samples, dtype=np.uint8) - min_reachability = np.full(n_samples, fill_value=np.infty, dtype=np.float64) + min_reachability = np.full(n_samples, fill_value=np.inf, dtype=np.float64) current_sources = np.ones(n_samples, dtype=np.int64) current_node = 0 diff --git a/sklearn/impute/tests/test_common.py b/sklearn/impute/tests/test_common.py index be2fa6e4d1736..32ac789375b9b 100644 --- a/sklearn/impute/tests/test_common.py +++ b/sklearn/impute/tests/test_common.py @@ -192,9 +192,9 @@ def test_imputation_adds_missing_indicator_if_add_indicator_is_true( Non-regression test for gh-26590. """ - X_train = np.array([[0, np.NaN], [1, 2]]) + X_train = np.array([[0, np.nan], [1, 2]]) - # Test data where missing_value_test variable can be set to np.NaN or 1. + # Test data where missing_value_test variable can be set to np.nan or 1. X_test = np.array([[0, missing_value_test], [1, 2]]) imputer.set_params(add_indicator=True) diff --git a/sklearn/preprocessing/tests/test_encoders.py b/sklearn/preprocessing/tests/test_encoders.py index 9ba041c90f5de..fb57a1993c3b7 100644 --- a/sklearn/preprocessing/tests/test_encoders.py +++ b/sklearn/preprocessing/tests/test_encoders.py @@ -414,7 +414,7 @@ def test_X_is_not_1D_pandas(method): np.object_, ), (np.array([["A", "cat"], ["B", "cat"]]), [["A", "B"], ["cat"]], np.str_), - (np.array([[1, 2], [np.nan, 2]]), [[1, np.nan], [2]], np.float_), + (np.array([[1, 2], [np.nan, 2]]), [[1, np.nan], [2]], np.float64), ( np.array([["A", np.nan], [None, np.nan]], dtype=object), [["A", None], [np.nan]], diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py index ba5ce3c35d07f..1ab7ac9af4db3 100644 --- a/sklearn/utils/fixes.py +++ b/sklearn/utils/fixes.py @@ -177,6 +177,6 @@ def _contents(data_module): # For +1.25 NumPy versions exceptions and warnings are being moved # to a dedicated submodule. if np_version >= parse_version("1.25.0"): - from numpy.exceptions import VisibleDeprecationWarning + from numpy.exceptions import ComplexWarning, VisibleDeprecationWarning else: - from numpy import VisibleDeprecationWarning # type: ignore # noqa + from numpy import ComplexWarning, VisibleDeprecationWarning # type: ignore # noqa diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py index 6e5848612fb97..0a030e90839ed 100644 --- a/sklearn/utils/tests/test_validation.py +++ b/sklearn/utils/tests/test_validation.py @@ -1585,7 +1585,7 @@ def test_check_pandas_sparse_invalid(ntype1, ntype2): @pytest.mark.parametrize( "ntype1, ntype2, expected_subtype", [ - ("longfloat", "longdouble", np.floating), + ("double", "longdouble", np.floating), ("float16", "half", np.floating), ("single", "float32", np.floating), ("double", "float64", np.floating), diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py index ae1886e0f92e4..57c56ebd9307d 100644 --- a/sklearn/utils/validation.py +++ b/sklearn/utils/validation.py @@ -20,12 +20,10 @@ import numpy as np import scipy.sparse as sp -# mypy error: Module 'numpy.core.numeric' has no attribute 'ComplexWarning' -from numpy.core.numeric import ComplexWarning # type: ignore - from .. import get_config as _get_config from ..exceptions import DataConversionWarning, NotFittedError, PositiveSpectrumWarning from ..utils._array_api import _asarray_with_order, _is_numpy_namespace, get_namespace +from ..utils.fixes import ComplexWarning from ._isfinite import FiniteStatus, cy_isfinite from .fixes import _object_dtype_isnan From ddc95a0a685d6af7fa2506c9ac328a14a94d3c19 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 29 Aug 2023 07:10:43 -0400 Subject: [PATCH 188/264] [DOC] Improve tree documentation (#26592) --- examples/tree/plot_unveil_tree_structure.py | 35 ++++++++++++++++++--- sklearn/tree/_tree.pyx | 7 +++++ 2 files changed, 38 insertions(+), 4 deletions(-) diff --git a/examples/tree/plot_unveil_tree_structure.py b/examples/tree/plot_unveil_tree_structure.py index d4009e3111f7f..fc2a9df20ae70 100644 --- a/examples/tree/plot_unveil_tree_structure.py +++ b/examples/tree/plot_unveil_tree_structure.py @@ -44,7 +44,8 @@ # # The decision classifier has an attribute called ``tree_`` which allows access # to low level attributes such as ``node_count``, the total number of nodes, -# and ``max_depth``, the maximal depth of the tree. It also stores the +# and ``max_depth``, the maximal depth of the tree. The tree_.compute_node_depths() +# method computes the depth of each node in the tree. `tree_` also stores the # entire binary tree structure, represented as a number of parallel arrays. The # i-th element of each array holds information about the node ``i``. Node 0 is # the tree's root. Some of the arrays only apply to either leaves or split @@ -63,6 +64,10 @@ # - ``n_node_samples[i]``: the number of training samples reaching node # ``i`` # - ``impurity[i]``: the impurity at node ``i`` +# - ``weighted_n_node_samples[i]``: the weighted number of training samples +# reaching node ``i`` +# - ``value[i, j, k]``: the summary of the training samples that reached node i for +# class j and output k. # # Using the arrays, we can traverse the tree structure to compute various # properties. Below, we will compute the depth of each node and whether or not @@ -73,6 +78,7 @@ children_right = clf.tree_.children_right feature = clf.tree_.feature threshold = clf.tree_.threshold +values = clf.tree_.value node_depth = np.zeros(shape=n_nodes, dtype=np.int64) is_leaves = np.zeros(shape=n_nodes, dtype=bool) @@ -100,13 +106,13 @@ for i in range(n_nodes): if is_leaves[i]: print( - "{space}node={node} is a leaf node.".format( - space=node_depth[i] * "\t", node=i + "{space}node={node} is a leaf node with value={value}.".format( + space=node_depth[i] * "\t", node=i, value=values[i] ) ) else: print( - "{space}node={node} is a split node: " + "{space}node={node} is a split node with value={value}: " "go to node {left} if X[:, {feature}] <= {threshold} " "else to node {right}.".format( space=node_depth[i] * "\t", @@ -115,9 +121,30 @@ feature=feature[i], threshold=threshold[i], right=children_right[i], + value=values[i], ) ) +# %% +# What is the values array used here? +# ----------------------------------- +# The `tree_.value` array is a 3D array of shape +# [``n_nodes``, ``n_classes``, ``n_outputs``] which provides the count of samples +# reaching a node for each class and for each output. Each node has a ``value`` +# array which is the number of weighted samples reaching this +# node for each output and class. +# +# For example, in the above tree built on the iris dataset, the root node has +# ``value = [37, 34, 41]``, indicating there are 37 samples +# of class 0, 34 samples of class 1, and 41 samples of class 2 at the root node. +# Traversing the tree, the samples are split and as a result, the ``value`` array +# reaching each node changes. The left child of the root node has ``value = [37, 0, 0]`` +# because all 37 samples in the left child node are from class 0. +# +# Note: In this example, `n_outputs=1`, but the tree classifier can also handle +# multi-output problems. The `value` array at each node would just be a 2D +# array instead. + ############################################################################## # We can compare the above output to the plot of the decision tree. diff --git a/sklearn/tree/_tree.pyx b/sklearn/tree/_tree.pyx index 057ef9aa865a9..22d3d38ce981a 100644 --- a/sklearn/tree/_tree.pyx +++ b/sklearn/tree/_tree.pyx @@ -581,6 +581,9 @@ cdef class Tree: children_right[i] > i. This child handles the case where X[:, feature[i]] > threshold[i]. + n_leaves : int + Number of leaves in the tree. + feature : array of int, shape [node_count] feature[i] holds the feature to split on, for the internal node i. @@ -600,6 +603,10 @@ cdef class Tree: weighted_n_node_samples : array of double, shape [node_count] weighted_n_node_samples[i] holds the weighted number of training samples reaching node i. + + missing_go_to_left : array of bool, shape [node_count] + missing_go_to_left[i] holds a bool indicating whether or not there were + missing values at node i. """ # Wrap for outside world. # WARNING: these reference the current `nodes` and `value` buffers, which From 4a1b88a1440b45884fa580f75028ecac6fe8839b Mon Sep 17 00:00:00 2001 From: Vladimir Fokow <57260995+VladimirFokow@users.noreply.github.com> Date: Wed, 30 Aug 2023 15:30:16 +0200 Subject: [PATCH 189/264] EXA fix typo in HDBSCAN example (#27158) Co-authored-by: jeremie du boisberranger --- examples/cluster/plot_hdbscan.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/examples/cluster/plot_hdbscan.py b/examples/cluster/plot_hdbscan.py index 149deb97020cd..630ab1f150fcb 100644 --- a/examples/cluster/plot_hdbscan.py +++ b/examples/cluster/plot_hdbscan.py @@ -84,7 +84,7 @@ def plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax= # rescaled versions of the dataset. fig, axes = plt.subplots(3, 1, figsize=(10, 12)) dbs = DBSCAN(eps=0.3) -for idx, scale in enumerate((1, 0.5, 3)): +for idx, scale in enumerate([1, 0.5, 3]): dbs.fit(X * scale) plot(X * scale, dbs.labels_, parameters={"scale": scale, "eps": 0.3}, ax=axes[idx]) @@ -105,9 +105,15 @@ def plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax= # One immediate advantage is that HDBSCAN is scale-invariant. fig, axes = plt.subplots(3, 1, figsize=(10, 12)) hdb = HDBSCAN() -for idx, scale in enumerate((1, 0.5, 3)): - hdb.fit(X) - plot(X, hdb.labels_, hdb.probabilities_, ax=axes[idx], parameters={"scale": scale}) +for idx, scale in enumerate([1, 0.5, 3]): + hdb.fit(X * scale) + plot( + X * scale, + hdb.labels_, + hdb.probabilities_, + ax=axes[idx], + parameters={"scale": scale}, + ) # %% # Multi-Scale Clustering # ---------------------- From 0dfdc01f452c53b834f9e0e80002b5dadd2e920c Mon Sep 17 00:00:00 2001 From: Xuefeng Xu Date: Thu, 31 Aug 2023 23:56:51 +0800 Subject: [PATCH 190/264] DOC add reference DOI in PowerTransformer (#27247) --- sklearn/preprocessing/_data.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py index 05eaa08cde947..87ac8cd2c9819 100644 --- a/sklearn/preprocessing/_data.py +++ b/sklearn/preprocessing/_data.py @@ -3063,12 +3063,13 @@ class PowerTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): References ---------- - .. [1] I.K. Yeo and R.A. Johnson, "A new family of power transformations to - improve normality or symmetry." Biometrika, 87(4), pp.954-959, - (2000). + .. [1] :doi:`I.K. Yeo and R.A. Johnson, "A new family of power + transformations to improve normality or symmetry." Biometrika, + 87(4), pp.954-959, (2000). <10.1093/biomet/87.4.954>` - .. [2] G.E.P. Box and D.R. Cox, "An Analysis of Transformations", Journal - of the Royal Statistical Society B, 26, 211-252 (1964). + .. [2] :doi:`G.E.P. Box and D.R. Cox, "An Analysis of Transformations", + Journal of the Royal Statistical Society B, 26, 211-252 (1964). + <10.1111/j.2517-6161.1964.tb00553.x>` Examples -------- From 9fcfa5d8957dc608ad365c71b97b58039e37fb2f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= <34657725+jeremiedbb@users.noreply.github.com> Date: Fri, 1 Sep 2023 10:57:38 +0200 Subject: [PATCH 191/264] FIX Bisecting kmeans when data has 1 feature (#27243) Co-authored-by: Tim Head --- doc/whats_new/v1.3.rst | 3 +++ sklearn/cluster/_bisect_k_means.py | 9 +++++---- sklearn/cluster/tests/test_bisect_k_means.py | 8 ++++++++ 3 files changed, 16 insertions(+), 4 deletions(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index 61d3aa3fcee92..4de3772453cf5 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -25,6 +25,9 @@ Changelog with a different scale than the data used to fit the model. :pr:`27167` by `Olivier Grisel`_. +- |Fix| :class:`cluster.BisectingKMeans` now works with data that has a single feature. + :pr:`27243` by `Jérémie du Boisberranger `. + :mod:`sklearn.impute` ..................... diff --git a/sklearn/cluster/_bisect_k_means.py b/sklearn/cluster/_bisect_k_means.py index 9091445261f70..b98d58f6cf12b 100644 --- a/sklearn/cluster/_bisect_k_means.py +++ b/sklearn/cluster/_bisect_k_means.py @@ -257,7 +257,7 @@ def _inertia_per_cluster(self, X, centers, labels, sample_weight): X : {ndarray, csr_matrix} of shape (n_samples, n_features) The input samples. - centers : ndarray of shape (n_clusters, n_features) + centers : ndarray of shape (n_clusters=2, n_features) The cluster centers. labels : ndarray of shape (n_samples,) @@ -268,13 +268,14 @@ def _inertia_per_cluster(self, X, centers, labels, sample_weight): Returns ------- - inertia_per_cluster : ndarray of shape (n_clusters,) + inertia_per_cluster : ndarray of shape (n_clusters=2,) Sum of squared errors (inertia) for each cluster. """ + n_clusters = centers.shape[0] # = 2 since centers comes from a bisection _inertia = _inertia_sparse if sp.issparse(X) else _inertia_dense - inertia_per_cluster = np.empty(centers.shape[1]) - for label in range(centers.shape[0]): + inertia_per_cluster = np.empty(n_clusters) + for label in range(n_clusters): inertia_per_cluster[label] = _inertia( X, sample_weight, centers, labels, self._n_threads, single_label=label ) diff --git a/sklearn/cluster/tests/test_bisect_k_means.py b/sklearn/cluster/tests/test_bisect_k_means.py index fe60cea277558..24e8ee60abf9d 100644 --- a/sklearn/cluster/tests/test_bisect_k_means.py +++ b/sklearn/cluster/tests/test_bisect_k_means.py @@ -147,3 +147,11 @@ def test_no_crash_on_empty_bisections(algorithm): X_test = 50 * rng.rand(100, 10) labels = bkm.predict(X_test) # should not crash with idiv by 0 assert np.isin(np.unique(labels), np.arange(10)).all() + + +def test_one_feature(): + # Check that no error is raised when there is only one feature + # Non-regression test for: + # https://github.com/scikit-learn/scikit-learn/issues/27236 + X = np.random.normal(size=(128, 1)) + BisectingKMeans(bisecting_strategy="biggest_inertia", random_state=0).fit(X) From 48d0b63f9855e8a5a3d792bfc671f28918893b8b Mon Sep 17 00:00:00 2001 From: Raphael Date: Fri, 1 Sep 2023 10:25:02 +0100 Subject: [PATCH 192/264] DOC Add dropdown to Module 6.1 Pipelines and composite estimators (#27022) --- doc/modules/compose.rst | 117 ++++++++++++++++++++++------------------ 1 file changed, 65 insertions(+), 52 deletions(-) diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst index f277c32675c3f..d1a20456707ca 100644 --- a/doc/modules/compose.rst +++ b/doc/modules/compose.rst @@ -41,12 +41,22 @@ All estimators in a pipeline, except the last one, must be transformers (i.e. must have a :term:`transform` method). The last estimator may be any type (transformer, classifier, etc.). +.. note:: + + Calling ``fit`` on the pipeline is the same as calling ``fit`` on + each estimator in turn, ``transform`` the input and pass it on to the next step. + The pipeline has all the methods that the last estimator in the pipeline has, + i.e. if the last estimator is a classifier, the :class:`Pipeline` can be used + as a classifier. If the last estimator is a transformer, again, so is the + pipeline. + Usage ----- -Construction -............ +|details-start| +**Construction** +|details-split| The :class:`Pipeline` is built using a list of ``(key, value)`` pairs, where the ``key`` is a string containing the name you want to give this step and ``value`` @@ -69,8 +79,12 @@ filling in the names automatically:: >>> make_pipeline(PCA(), SVC()) Pipeline(steps=[('pca', PCA()), ('svc', SVC())]) -Accessing steps -............... +|details-end| + +|details-start| +**Accessing steps** +|details-split| + The estimators of a pipeline are stored as a list in the ``steps`` attribute, but can be accessed by index or name by indexing (with ``[idx]``) the @@ -99,11 +113,13 @@ permitted). This is convenient for performing only some of the transformations >>> pipe[-1:] Pipeline(steps=[('clf', SVC())]) +|details-end| .. _pipeline_nested_parameters: -Nested parameters -................. +|details-start| +**Nested parameters** +|details-split| Parameters of the estimators in the pipeline can be accessed using the ``__`` syntax:: @@ -172,16 +188,7 @@ You can also provide custom feature names for the input data using * :ref:`composite_grid_search` - -Notes ------ - -Calling ``fit`` on the pipeline is the same as calling ``fit`` on -each estimator in turn, ``transform`` the input and pass it on to the next step. -The pipeline has all the methods that the last estimator in the pipeline has, -i.e. if the last estimator is a classifier, the :class:`Pipeline` can be used -as a classifier. If the last estimator is a transformer, again, so is the -pipeline. +|details-end| .. _pipeline_cache: @@ -217,43 +224,49 @@ object:: >>> # Clear the cache directory when you don't need it anymore >>> rmtree(cachedir) -.. warning:: **Side effect of caching transformers** - - Using a :class:`Pipeline` without cache enabled, it is possible to - inspect the original instance such as:: - - >>> from sklearn.datasets import load_digits - >>> X_digits, y_digits = load_digits(return_X_y=True) - >>> pca1 = PCA() - >>> svm1 = SVC() - >>> pipe = Pipeline([('reduce_dim', pca1), ('clf', svm1)]) - >>> pipe.fit(X_digits, y_digits) - Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC())]) - >>> # The pca instance can be inspected directly - >>> print(pca1.components_) - [[-1.77484909e-19 ... 4.07058917e-18]] - - Enabling caching triggers a clone of the transformers before fitting. - Therefore, the transformer instance given to the pipeline cannot be - inspected directly. - In following example, accessing the :class:`~sklearn.decomposition.PCA` - instance ``pca2`` will raise an ``AttributeError`` since ``pca2`` will be an - unfitted transformer. - Instead, use the attribute ``named_steps`` to inspect estimators within - the pipeline:: - - >>> cachedir = mkdtemp() - >>> pca2 = PCA() - >>> svm2 = SVC() - >>> cached_pipe = Pipeline([('reduce_dim', pca2), ('clf', svm2)], - ... memory=cachedir) - >>> cached_pipe.fit(X_digits, y_digits) - Pipeline(memory=..., +|details-start| +**Warning: Side effect of caching transformers** +|details-split| + +Using a :class:`Pipeline` without cache enabled, it is possible to +inspect the original instance such as:: + + >>> from sklearn.datasets import load_digits + >>> X_digits, y_digits = load_digits(return_X_y=True) + >>> pca1 = PCA() + >>> svm1 = SVC() + >>> pipe = Pipeline([('reduce_dim', pca1), ('clf', svm1)]) + >>> pipe.fit(X_digits, y_digits) + Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC())]) + >>> # The pca instance can be inspected directly + >>> print(pca1.components_) + [[-1.77484909e-19 ... 4.07058917e-18]] + + +Enabling caching triggers a clone of the transformers before fitting. +Therefore, the transformer instance given to the pipeline cannot be +inspected directly. +In following example, accessing the :class:`~sklearn.decomposition.PCA` +instance ``pca2`` will raise an ``AttributeError`` since ``pca2`` will be an +unfitted transformer. +Instead, use the attribute ``named_steps`` to inspect estimators within +the pipeline:: + + >>> cachedir = mkdtemp() + >>> pca2 = PCA() + >>> svm2 = SVC() + >>> cached_pipe = Pipeline([('reduce_dim', pca2), ('clf', svm2)], + ... memory=cachedir) + >>> cached_pipe.fit(X_digits, y_digits) + Pipeline(memory=..., steps=[('reduce_dim', PCA()), ('clf', SVC())]) - >>> print(cached_pipe.named_steps['reduce_dim'].components_) - [[-1.77484909e-19 ... 4.07058917e-18]] - >>> # Remove the cache directory - >>> rmtree(cachedir) + >>> print(cached_pipe.named_steps['reduce_dim'].components_) + [[-1.77484909e-19 ... 4.07058917e-18]] + >>> # Remove the cache directory + >>> rmtree(cachedir) + + +|details-end| .. topic:: Examples: From b81f812334603d41452b8bbf73d33470298e0711 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Fri, 1 Sep 2023 15:16:26 +0200 Subject: [PATCH 193/264] FIX make sure the decision function of weak learner is symmetric (#26521) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> Co-authored-by: Olivier Grisel --- doc/whats_new/v1.3.rst | 8 ++++ sklearn/ensemble/_weight_boosting.py | 13 ++++-- .../ensemble/tests/test_weight_boosting.py | 43 +++++++++++++++++++ 3 files changed, 61 insertions(+), 3 deletions(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index 4de3772453cf5..8bfb99f16f0fa 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -28,6 +28,14 @@ Changelog - |Fix| :class:`cluster.BisectingKMeans` now works with data that has a single feature. :pr:`27243` by `Jérémie du Boisberranger `. +:mod:`sklearn.ensemble` +....................... + +- |Fix| Fix a bug in :class:`ensemble.AdaBoostClassifier` with `algorithm="SAMME"` + where the decision function of each weak learner should be symmetric (i.e. + the sum of the scores should sum to zero for a sample). + :pr:`26521` by :user:`Guillaume Lemaitre `. + :mod:`sklearn.impute` ..................... diff --git a/sklearn/ensemble/_weight_boosting.py b/sklearn/ensemble/_weight_boosting.py index 776078d7fa7aa..45a87ad6521fd 100644 --- a/sklearn/ensemble/_weight_boosting.py +++ b/sklearn/ensemble/_weight_boosting.py @@ -780,7 +780,11 @@ class in ``classes_``, respectively. ) else: # self.algorithm == "SAMME" pred = sum( - (estimator.predict(X) == classes).T * w + np.where( + (estimator.predict(X) == classes).T, + w, + -1 / (n_classes - 1) * w, + ) for estimator, w in zip(self.estimators_, self.estimator_weights_) ) @@ -827,8 +831,11 @@ class in ``classes_``, respectively. # The weights are all 1. for SAMME.R current_pred = _samme_proba(estimator, n_classes, X) else: # elif self.algorithm == "SAMME": - current_pred = estimator.predict(X) - current_pred = (current_pred == classes).T * weight + current_pred = np.where( + (estimator.predict(X) == classes).T, + weight, + -1 / (n_classes - 1) * weight, + ) if pred is None: pred = current_pred diff --git a/sklearn/ensemble/tests/test_weight_boosting.py b/sklearn/ensemble/tests/test_weight_boosting.py index 5982affcf8c60..07d03be9721c0 100755 --- a/sklearn/ensemble/tests/test_weight_boosting.py +++ b/sklearn/ensemble/tests/test_weight_boosting.py @@ -18,6 +18,7 @@ from sklearn.utils import shuffle from sklearn.utils._mocking import NoSampleWeightWrapper from sklearn.utils._testing import ( + assert_allclose, assert_array_almost_equal, assert_array_equal, assert_array_less, @@ -663,3 +664,45 @@ def test_deprecated_base_estimator_parameters_can_be_set(): with pytest.warns(FutureWarning, match="Parameter 'base_estimator' of"): clf.set_params(base_estimator__max_depth=2) + + +@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"]) +def test_adaboost_decision_function(algorithm, global_random_seed): + """Check that the decision function respects the symmetric constraint for weak + learners. + + Non-regression test for: + https://github.com/scikit-learn/scikit-learn/issues/26520 + """ + n_classes = 3 + X, y = datasets.make_classification( + n_classes=n_classes, n_clusters_per_class=1, random_state=global_random_seed + ) + clf = AdaBoostClassifier( + n_estimators=1, random_state=global_random_seed, algorithm=algorithm + ).fit(X, y) + + y_score = clf.decision_function(X) + assert_allclose(y_score.sum(axis=1), 0, atol=1e-8) + + if algorithm == "SAMME": + # With a single learner, we expect to have a decision function in + # {1, - 1 / (n_classes - 1)}. + assert set(np.unique(y_score)) == {1, -1 / (n_classes - 1)} + + # We can assert the same for staged_decision_function since we have a single learner + for y_score in clf.staged_decision_function(X): + assert_allclose(y_score.sum(axis=1), 0, atol=1e-8) + + if algorithm == "SAMME": + # With a single learner, we expect to have a decision function in + # {1, - 1 / (n_classes - 1)}. + assert set(np.unique(y_score)) == {1, -1 / (n_classes - 1)} + + clf.set_params(n_estimators=5).fit(X, y) + + y_score = clf.decision_function(X) + assert_allclose(y_score.sum(axis=1), 0, atol=1e-8) + + for y_score in clf.staged_decision_function(X): + assert_allclose(y_score.sum(axis=1), 0, atol=1e-8) From 28816c89f3b7b2a3e19c1f8b6c389ab4b64e345b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 5 Sep 2023 12:26:08 +0200 Subject: [PATCH 194/264] MNT Avoid np.lib.arraysetops which is private in numpy 2 (#27297) --- sklearn/model_selection/tests/test_split.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/model_selection/tests/test_split.py b/sklearn/model_selection/tests/test_split.py index d92f624441541..ab44aa40174f4 100644 --- a/sklearn/model_selection/tests/test_split.py +++ b/sklearn/model_selection/tests/test_split.py @@ -818,7 +818,7 @@ def test_stratified_shuffle_split_iter(): assert len(train) + len(test) == y.size assert len(train) == train_size assert len(test) == test_size - assert_array_equal(np.lib.arraysetops.intersect1d(train, test), []) + assert_array_equal(np.intersect1d(train, test), []) def test_stratified_shuffle_split_even(): From 66977aed1dc06628286d37a64beacc6e4d4faf9a Mon Sep 17 00:00:00 2001 From: Xiao Yuan Date: Tue, 5 Sep 2023 20:02:48 +0800 Subject: [PATCH 195/264] DOC Fix a broken link in reference (#27299) --- doc/modules/cross_validation.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/modules/cross_validation.rst b/doc/modules/cross_validation.rst index 8afa467982736..fd3d5f170056f 100644 --- a/doc/modules/cross_validation.rst +++ b/doc/modules/cross_validation.rst @@ -452,7 +452,7 @@ fold cross validation should be preferred to LOO. * R. Bharat Rao, G. Fung, R. Rosales, `On the Dangers of Cross-Validation. An Experimental Evaluation `_, SIAM 2008; * G. James, D. Witten, T. Hastie, R Tibshirani, `An Introduction to - Statistical Learning `_, Springer 2013. + Statistical Learning `_, Springer 2013. .. _leave_p_out: From ef76bcf9d4c7ce72ce3f1f6291c335729b19fa4b Mon Sep 17 00:00:00 2001 From: Rahil Parikh <75483881+rprkh@users.noreply.github.com> Date: Wed, 6 Sep 2023 14:32:24 +0530 Subject: [PATCH 196/264] DOC update feature_names and target_names types in load_breast_cancer (#27279) --- sklearn/datasets/_base.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sklearn/datasets/_base.py b/sklearn/datasets/_base.py index 7a1e584556f89..9c38d2a980353 100644 --- a/sklearn/datasets/_base.py +++ b/sklearn/datasets/_base.py @@ -756,9 +756,9 @@ def load_breast_cancer(*, return_X_y=False, as_frame=False): target : {ndarray, Series} of shape (569,) The classification target. If `as_frame=True`, `target` will be a pandas Series. - feature_names : list + feature_names : ndarray of shape (30,) The names of the dataset columns. - target_names : list + target_names : ndarray of shape (2,) The names of target classes. frame : DataFrame of shape (569, 31) Only present when `as_frame=True`. DataFrame with `data` and From 613cadea2e8003387a20976a55f6d1316a269577 Mon Sep 17 00:00:00 2001 From: Rahil Parikh <75483881+rprkh@users.noreply.github.com> Date: Wed, 6 Sep 2023 16:50:30 +0530 Subject: [PATCH 197/264] FIX accept `array-like` in `feature_names` for `plot_tree` (#27292) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> --- doc/whats_new/v1.3.rst | 3 +++ sklearn/tree/_export.py | 4 ++-- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index 8bfb99f16f0fa..e8baa92d1d876 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -70,6 +70,9 @@ Changelog - |Fix| :func:`tree.plot_tree` now accepts `class_names=True` as documented. :pr:`26903` by :user:`Thomas Roehr <2maz>` +- |Fix| The `feature_names` parameter of :func:`tree.plot_tree` now accepts any kind of + array-like instead of just a list. :pr:`27292` by :user:`Rahil Parikh `. + .. _changes_1_3: Version 1.3.0 diff --git a/sklearn/tree/_export.py b/sklearn/tree/_export.py index c89333aa49831..ff0d6db5c25a5 100644 --- a/sklearn/tree/_export.py +++ b/sklearn/tree/_export.py @@ -78,7 +78,7 @@ def __repr__(self): { "decision_tree": [DecisionTreeClassifier, DecisionTreeRegressor], "max_depth": [Interval(Integral, 0, None, closed="left"), None], - "feature_names": [list, None], + "feature_names": ["array-like", None], "class_names": ["array-like", "boolean", None], "label": [StrOptions({"all", "root", "none"})], "filled": ["boolean"], @@ -130,7 +130,7 @@ def plot_tree( The maximum depth of the representation. If None, the tree is fully generated. - feature_names : list of str, default=None + feature_names : array-like of str, default=None Names of each of the features. If None, generic names will be used ("x[0]", "x[1]", ...). From 350daea8d95a4003ebd0c107ffeafcbcb07fee10 Mon Sep 17 00:00:00 2001 From: Bharat Raghunathan Date: Wed, 6 Sep 2023 19:58:45 +0530 Subject: [PATCH 198/264] DOC Fix user name in whats new entry (#27300) --- doc/whats_new/v0.24.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/whats_new/v0.24.rst b/doc/whats_new/v0.24.rst index 757b9ed476275..5f6eb4a62bfa4 100644 --- a/doc/whats_new/v0.24.rst +++ b/doc/whats_new/v0.24.rst @@ -403,7 +403,7 @@ Changelog `regularization`, which can take the values `None`, 'components', 'transformation' or 'both', in accordance with :func:`decomposition.NMF.non_negative_factorization`. - :pr:`17414` by :user:`Bharat Raghunathan `. + :pr:`17414` by :user:`Bharat Raghunathan `. - |Fix| :class:`decomposition.KernelPCA` behaviour is now more consistent between 32-bits and 64-bits data input when the kernel has small positive From 2ae9fab7c36d9508bba69acd09a7f4a7a6650984 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= <34657725+jeremiedbb@users.noreply.github.com> Date: Wed, 6 Sep 2023 16:34:09 +0200 Subject: [PATCH 199/264] BENCH pin dependencies in asv benchmarks (#27264) --- asv_benchmarks/asv.conf.json | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/asv_benchmarks/asv.conf.json b/asv_benchmarks/asv.conf.json index 9f65d194b6d84..f261ce1553c8f 100644 --- a/asv_benchmarks/asv.conf.json +++ b/asv_benchmarks/asv.conf.json @@ -71,13 +71,17 @@ // pip (with all the conda available packages installed first, // followed by the pip installed packages). // + // The versions of the dependencies should be bumped in a dedicated commit + // to easily identify regressions/imrovements due to code changes from + // those due to dependency changes. + // "matrix": { - "numpy": [], - "scipy": [], - "cython": [], - "joblib": [], - "threadpoolctl": [], - "pandas": [] + "numpy": ["1.25.2"], + "scipy": ["1.11.2"], + "cython": ["0.29.36"], + "joblib": ["1.3.2"], + "threadpoolctl": ["3.2.0"], + "pandas": ["2.1.0"] }, // Combinations of libraries/python versions can be excluded/included From 54eeee2f794c5fb0ecd3e2f79c6fc709a404745f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mateusz=20Sok=C3=B3=C5=82?= <8431159+mtsokol@users.noreply.github.com> Date: Wed, 6 Sep 2023 17:06:31 +0200 Subject: [PATCH 200/264] MAINT: Remove np.in1d and np.trapz usages (#27140) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- .../plot_label_propagation_digits_active_learning.py | 2 +- sklearn/datasets/_twenty_newsgroups.py | 2 +- sklearn/feature_extraction/image.py | 2 +- .../metrics/_plot/tests/test_precision_recall_display.py | 5 +++-- sklearn/metrics/_plot/tests/test_roc_curve_display.py | 5 +++-- sklearn/metrics/_ranking.py | 5 +++-- sklearn/model_selection/_split.py | 4 ++-- sklearn/model_selection/tests/test_search.py | 2 +- sklearn/model_selection/tests/test_split.py | 4 ++-- sklearn/naive_bayes.py | 2 +- sklearn/preprocessing/_label.py | 2 +- sklearn/tests/test_isotonic.py | 2 +- sklearn/utils/_encode.py | 2 +- sklearn/utils/class_weight.py | 4 ++-- sklearn/utils/fixes.py | 7 +++++++ 15 files changed, 30 insertions(+), 20 deletions(-) diff --git a/examples/semi_supervised/plot_label_propagation_digits_active_learning.py b/examples/semi_supervised/plot_label_propagation_digits_active_learning.py index 215655a287c2d..45af1d7891b2e 100644 --- a/examples/semi_supervised/plot_label_propagation_digits_active_learning.py +++ b/examples/semi_supervised/plot_label_propagation_digits_active_learning.py @@ -79,7 +79,7 @@ # select up to 5 digit examples that the classifier is most uncertain about uncertainty_index = np.argsort(pred_entropies)[::-1] uncertainty_index = uncertainty_index[ - np.in1d(uncertainty_index, unlabeled_indices) + np.isin(uncertainty_index, unlabeled_indices) ][:5] # keep track of indices that we get labels for diff --git a/sklearn/datasets/_twenty_newsgroups.py b/sklearn/datasets/_twenty_newsgroups.py index 95a7274c20f75..637cf8e4fc8d4 100644 --- a/sklearn/datasets/_twenty_newsgroups.py +++ b/sklearn/datasets/_twenty_newsgroups.py @@ -319,7 +319,7 @@ def fetch_20newsgroups( # Sort the categories to have the ordering of the labels labels.sort() labels, categories = zip(*labels) - mask = np.in1d(data.target, labels) + mask = np.isin(data.target, labels) data.filenames = data.filenames[mask] data.target = data.target[mask] # searchsorted to have continuous labels diff --git a/sklearn/feature_extraction/image.py b/sklearn/feature_extraction/image.py index 1ef3895fe2818..a2a23b9ec4f3d 100644 --- a/sklearn/feature_extraction/image.py +++ b/sklearn/feature_extraction/image.py @@ -76,7 +76,7 @@ def _mask_edges_weights(mask, edges, weights=None): """Apply a mask to edges (weighted or not)""" inds = np.arange(mask.size) inds = inds[mask.ravel()] - ind_mask = np.logical_and(np.in1d(edges[0], inds), np.in1d(edges[1], inds)) + ind_mask = np.logical_and(np.isin(edges[0], inds), np.isin(edges[1], inds)) edges = edges[:, ind_mask] if weights is not None: weights = weights[ind_mask] diff --git a/sklearn/metrics/_plot/tests/test_precision_recall_display.py b/sklearn/metrics/_plot/tests/test_precision_recall_display.py index 3ca94bd96dbe3..5487fe3156bdb 100644 --- a/sklearn/metrics/_plot/tests/test_precision_recall_display.py +++ b/sklearn/metrics/_plot/tests/test_precision_recall_display.py @@ -16,6 +16,7 @@ from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.utils import shuffle +from sklearn.utils.fixes import trapezoid # TODO: Remove when https://github.com/numpy/numpy/issues/14397 is resolved pytestmark = pytest.mark.filterwarnings( @@ -286,7 +287,7 @@ def test_plot_precision_recall_pos_label(pyplot, constructor_name, response_meth # we should obtain the statistics of the "cancer" class avg_prec_limit = 0.65 assert display.average_precision < avg_prec_limit - assert -np.trapz(display.precision, display.recall) < avg_prec_limit + assert -trapezoid(display.precision, display.recall) < avg_prec_limit # otherwise we should obtain the statistics of the "not cancer" class if constructor_name == "from_estimator": @@ -305,7 +306,7 @@ def test_plot_precision_recall_pos_label(pyplot, constructor_name, response_meth ) avg_prec_limit = 0.95 assert display.average_precision > avg_prec_limit - assert -np.trapz(display.precision, display.recall) > avg_prec_limit + assert -trapezoid(display.precision, display.recall) > avg_prec_limit @pytest.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"]) diff --git a/sklearn/metrics/_plot/tests/test_roc_curve_display.py b/sklearn/metrics/_plot/tests/test_roc_curve_display.py index b87005e877b77..56f20204ddc2d 100644 --- a/sklearn/metrics/_plot/tests/test_roc_curve_display.py +++ b/sklearn/metrics/_plot/tests/test_roc_curve_display.py @@ -11,6 +11,7 @@ from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.utils import shuffle +from sklearn.utils.fixes import trapezoid @pytest.fixture(scope="module") @@ -290,7 +291,7 @@ def test_plot_roc_curve_pos_label(pyplot, response_method, constructor_name): roc_auc_limit = 0.95679 assert display.roc_auc == pytest.approx(roc_auc_limit) - assert np.trapz(display.tpr, display.fpr) == pytest.approx(roc_auc_limit) + assert trapezoid(display.tpr, display.fpr) == pytest.approx(roc_auc_limit) if constructor_name == "from_estimator": display = RocCurveDisplay.from_estimator( @@ -308,4 +309,4 @@ def test_plot_roc_curve_pos_label(pyplot, response_method, constructor_name): ) assert display.roc_auc == pytest.approx(roc_auc_limit) - assert np.trapz(display.tpr, display.fpr) == pytest.approx(roc_auc_limit) + assert trapezoid(display.tpr, display.fpr) == pytest.approx(roc_auc_limit) diff --git a/sklearn/metrics/_ranking.py b/sklearn/metrics/_ranking.py index 166c2ce20eb87..a7d4b5ef18d66 100644 --- a/sklearn/metrics/_ranking.py +++ b/sklearn/metrics/_ranking.py @@ -38,6 +38,7 @@ from ..utils._encode import _encode, _unique from ..utils._param_validation import Interval, StrOptions, validate_params from ..utils.extmath import stable_cumsum +from ..utils.fixes import trapezoid from ..utils.multiclass import type_of_target from ..utils.sparsefuncs import count_nonzero from ..utils.validation import _check_pos_label_consistency, _check_sample_weight @@ -104,9 +105,9 @@ def auc(x, y): else: raise ValueError("x is neither increasing nor decreasing : {}.".format(x)) - area = direction * np.trapz(y, x) + area = direction * trapezoid(y, x) if isinstance(area, np.memmap): - # Reductions such as .sum used internally in np.trapz do not return a + # Reductions such as .sum used internally in trapezoid do not return a # scalar by default for numpy.memmap instances contrary to # regular numpy.ndarray instances. area = area.dtype.type(area) diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py index 2dceff9b22126..4d30538023abd 100644 --- a/sklearn/model_selection/_split.py +++ b/sklearn/model_selection/_split.py @@ -1973,8 +1973,8 @@ def _iter_indices(self, X, y, groups): # these are the indices of classes in the partition # invert them into data indices - train = np.flatnonzero(np.in1d(group_indices, group_train)) - test = np.flatnonzero(np.in1d(group_indices, group_test)) + train = np.flatnonzero(np.isin(group_indices, group_train)) + test = np.flatnonzero(np.isin(group_indices, group_test)) yield train, test diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py index 04c3f1f156fab..50b519118a2b3 100644 --- a/sklearn/model_selection/tests/test_search.py +++ b/sklearn/model_selection/tests/test_search.py @@ -1418,7 +1418,7 @@ def test_grid_search_correct_score_results(): expected_keys = ("mean_test_score", "rank_test_score") + tuple( "split%d_test_score" % cv_i for cv_i in range(n_splits) ) - assert all(np.in1d(expected_keys, result_keys)) + assert all(np.isin(expected_keys, result_keys)) cv = StratifiedKFold(n_splits=n_splits) n_splits = grid_search.n_splits_ diff --git a/sklearn/model_selection/tests/test_split.py b/sklearn/model_selection/tests/test_split.py index ab44aa40174f4..600ae920073b3 100644 --- a/sklearn/model_selection/tests/test_split.py +++ b/sklearn/model_selection/tests/test_split.py @@ -975,8 +975,8 @@ def test_group_shuffle_split(): # First test: no train group is in the test set and vice versa l_train_unique = np.unique(l[train]) l_test_unique = np.unique(l[test]) - assert not np.any(np.in1d(l[train], l_test_unique)) - assert not np.any(np.in1d(l[test], l_train_unique)) + assert not np.any(np.isin(l[train], l_test_unique)) + assert not np.any(np.isin(l[test], l_train_unique)) # Second test: train and test add up to all the data assert l[train].size + l[test].size == l.size diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py index 22e65f5062586..9ee664bf8b3a4 100644 --- a/sklearn/naive_bayes.py +++ b/sklearn/naive_bayes.py @@ -467,7 +467,7 @@ def _partial_fit(self, X, y, classes=None, _refit=False, sample_weight=None): classes = self.classes_ unique_y = np.unique(y) - unique_y_in_classes = np.in1d(unique_y, classes) + unique_y_in_classes = np.isin(unique_y, classes) if not np.all(unique_y_in_classes): raise ValueError( diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py index 3008710d3c3dc..41494f2649a01 100644 --- a/sklearn/preprocessing/_label.py +++ b/sklearn/preprocessing/_label.py @@ -553,7 +553,7 @@ def label_binarize(y, *, classes, neg_label=0, pos_label=1, sparse_output=False) y = column_or_1d(y) # pick out the known labels from y - y_in_classes = np.in1d(y, classes) + y_in_classes = np.isin(y, classes) y_seen = y[y_in_classes] indices = np.searchsorted(sorted_class, y_seen) indptr = np.hstack((0, np.cumsum(y_in_classes))) diff --git a/sklearn/tests/test_isotonic.py b/sklearn/tests/test_isotonic.py index 15e0d0d99cfb0..93df0221236b8 100644 --- a/sklearn/tests/test_isotonic.py +++ b/sklearn/tests/test_isotonic.py @@ -595,7 +595,7 @@ def test_isotonic_thresholds(increasing): # the data is already strictly monotonic which is not the case with # this random data) assert X_thresholds.shape[0] < X.shape[0] - assert np.in1d(X_thresholds, X).all() + assert np.isin(X_thresholds, X).all() # Output thresholds lie in the range of the training set: assert y_thresholds.max() <= y.max() diff --git a/sklearn/utils/_encode.py b/sklearn/utils/_encode.py index fb3912b27dbfe..b3bf1c2a317ec 100644 --- a/sklearn/utils/_encode.py +++ b/sklearn/utils/_encode.py @@ -296,7 +296,7 @@ def is_valid(value): diff = np.setdiff1d(unique_values, known_values, assume_unique=True) if return_mask: if diff.size: - valid_mask = np.in1d(values, known_values) + valid_mask = np.isin(values, known_values) else: valid_mask = np.ones(len(values), dtype=bool) diff --git a/sklearn/utils/class_weight.py b/sklearn/utils/class_weight.py index 1ff349347b015..0ee479c77ac76 100644 --- a/sklearn/utils/class_weight.py +++ b/sklearn/utils/class_weight.py @@ -47,7 +47,7 @@ def compute_class_weight(class_weight, *, classes, y): # Find the weight of each class as present in y. le = LabelEncoder() y_ind = le.fit_transform(y) - if not all(np.in1d(classes, le.classes_)): + if not all(np.isin(classes, le.classes_)): raise ValueError("classes should have valid labels that are in y") recip_freq = len(y) / (len(le.classes_) * np.bincount(y_ind).astype(np.float64)) @@ -185,7 +185,7 @@ def compute_sample_weight(class_weight, y, *, indices=None): if classes_missing: # Make missing classes' weight zero - weight_k[np.in1d(y_full, list(classes_missing))] = 0.0 + weight_k[np.isin(y_full, list(classes_missing))] = 0.0 expanded_class_weight.append(weight_k) diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py index 1ab7ac9af4db3..166548e7f88ef 100644 --- a/sklearn/utils/fixes.py +++ b/sklearn/utils/fixes.py @@ -180,3 +180,10 @@ def _contents(data_module): from numpy.exceptions import ComplexWarning, VisibleDeprecationWarning else: from numpy import ComplexWarning, VisibleDeprecationWarning # type: ignore # noqa + + +# TODO: Remove when Scipy 1.6 is the minimum supported version +try: + from scipy.integrate import trapezoid # type: ignore # noqa +except ImportError: + from scipy.integrate import trapz as trapezoid # type: ignore # noqa From 941220263795185ae50eb62a15d6ebc81e9011b1 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Thu, 7 Sep 2023 16:59:28 +1000 Subject: [PATCH 201/264] TST Fix typo, lint `test_target_encoder.py` (#26958) Co-authored-by: Olivier Grisel --- .../tests/test_target_encoder.py | 74 +++++++++++-------- 1 file changed, 45 insertions(+), 29 deletions(-) diff --git a/sklearn/preprocessing/tests/test_target_encoder.py b/sklearn/preprocessing/tests/test_target_encoder.py index d39eef0fd3e1e..38aa0569b9d40 100644 --- a/sklearn/preprocessing/tests/test_target_encoder.py +++ b/sklearn/preprocessing/tests/test_target_encoder.py @@ -61,54 +61,70 @@ def _encode_target(X_ordinal, y_int, n_categories, smooth): @pytest.mark.parametrize("smooth", [5.0, "auto"]) @pytest.mark.parametrize("target_type", ["binary", "continuous"]) def test_encoding(categories, unknown_value, global_random_seed, smooth, target_type): - """Check encoding for binary and continuous targets.""" + """Check encoding for binary and continuous targets. + + Compare the values returned by `TargetEncoder.fit_transform` against the + expected encodings for cv splits from a naive reference Python + implementation in _encode_target. + """ - X_train_array = np.array([[0] * 20 + [1] * 30 + [2] * 40], dtype=np.int64).T - X_test_array = np.array([[0, 1, 2]], dtype=np.int64).T n_categories = 3 - n_samples = X_train_array.shape[0] + X_train_int_array = np.array([[0] * 20 + [1] * 30 + [2] * 40], dtype=np.int64).T + X_test_int_array = np.array([[0, 1, 2]], dtype=np.int64).T + n_samples = X_train_int_array.shape[0] if categories == "auto": - X_train = X_train_array + X_train = X_train_int_array + X_test = X_test_int_array else: - X_train = categories[0][X_train_array] + X_train = categories[0][X_train_int_array] + X_test = categories[0][X_test_int_array] - if categories == "auto": - X_test = X_test_array - else: - X_test = categories[0][X_test_array] X_test = np.concatenate((X_test, [[unknown_value]])) - rng = np.random.RandomState(global_random_seed) - + data_rng = np.random.RandomState(global_random_seed) + n_splits = 3 if target_type == "binary": - y_int = rng.randint(low=0, high=2, size=n_samples) + y_int = data_rng.randint(low=0, high=2, size=n_samples) target_names = np.array(["cat", "dog"], dtype=object) y_train = target_names[y_int] - cv = StratifiedKFold(n_splits=3, random_state=0, shuffle=True) + else: # target_type == continuous - y_int = rng.uniform(low=-10, high=20, size=n_samples) + y_int = data_rng.uniform(low=-10, high=20, size=n_samples) y_train = y_int - cv = KFold(n_splits=3, random_state=0, shuffle=True) - shuffled_idx = rng.permutation(n_samples) - X_train_array = X_train_array[shuffled_idx] + shuffled_idx = data_rng.permutation(n_samples) + X_train_int_array = X_train_int_array[shuffled_idx] X_train = X_train[shuffled_idx] y_train = y_train[shuffled_idx] y_int = y_int[shuffled_idx] - # Get encodings for cv splits to validate `fit_transform` - expected_X_fit_transform = np.empty_like(X_train_array, dtype=np.float64) + # Define our CV splitting strategy + if target_type == "binary": + cv = StratifiedKFold( + n_splits=n_splits, random_state=global_random_seed, shuffle=True + ) + else: + cv = KFold(n_splits=n_splits, random_state=global_random_seed, shuffle=True) + + # Compute the expected values using our reference Python implementation of + # target encoding: + expected_X_fit_transform = np.empty_like(X_train_int_array, dtype=np.float64) - for train_idx, test_idx in cv.split(X_train_array, y_train): - X_, y_ = X_train_array[train_idx, 0], y_int[train_idx] + for train_idx, test_idx in cv.split(X_train_int_array, y_train): + X_, y_ = X_train_int_array[train_idx, 0], y_int[train_idx] cur_encodings = _encode_target(X_, y_, n_categories, smooth) expected_X_fit_transform[test_idx, 0] = cur_encodings[ - X_train_array[test_idx, 0] + X_train_int_array[test_idx, 0] ] + # Check that we can obtain the same encodings by calling `fit_transform` on + # the estimator with the same CV parameters: target_encoder = TargetEncoder( - smooth=smooth, categories=categories, cv=3, random_state=0 + smooth=smooth, + categories=categories, + cv=n_splits, + random_state=global_random_seed, ) X_fit_transform = target_encoder.fit_transform(X_train, y_train) @@ -120,12 +136,12 @@ def test_encoding(categories, unknown_value, global_random_seed, smooth, target_ # compute encodings for all data to validate `transform` y_mean = np.mean(y_int) expected_encodings = _encode_target( - X_train_array[:, 0], y_int, n_categories, smooth + X_train_int_array[:, 0], y_int, n_categories, smooth ) assert_allclose(target_encoder.encodings_[0], expected_encodings) assert target_encoder.target_mean_ == pytest.approx(y_mean) - # Transform on test data, the last value is unknown is it is encoded as the target + # Transform on test data, the last value is unknown so it is encoded as the target # mean expected_X_test_transform = np.concatenate( (expected_encodings, np.array([y_mean])) @@ -394,7 +410,7 @@ def test_smooth_zero(): # it will be encoded as the mean of the second half assert_allclose(X_trans[0], np.mean(y[5:])) - # category 1 does nto exist in the first half, thus it will be encoded as + # category 1 does not exist in the first half, thus it will be encoded as # the mean of the first half assert_allclose(X_trans[-1], np.mean(y[:5])) @@ -402,7 +418,7 @@ def test_smooth_zero(): @pytest.mark.parametrize("smooth", [0.0, 1e3, "auto"]) def test_invariance_of_encoding_under_label_permutation(smooth, global_random_seed): # Check that the encoding does not depend on the integer of the value of - # the integer labels. This is quite of a trivial property but it is helpful + # the integer labels. This is quite a trivial property but it is helpful # to understand the following test. rng = np.random.RandomState(global_random_seed) @@ -440,7 +456,7 @@ def test_invariance_of_encoding_under_label_permutation(smooth, global_random_se @pytest.mark.parametrize("smooth", [0.0, "auto"]) def test_target_encoding_for_linear_regression(smooth, global_random_seed): # Check some expected statistical properties when fitting a linear - # regression model on target encoded features depending on there relation + # regression model on target encoded features depending on their relation # with that target. # In this test, we use the Ridge class with the "lsqr" solver and a little From 6e53d6a2d79db3d7ac8f71706d4b7fdb6efd8e4a Mon Sep 17 00:00:00 2001 From: Julien Jerphanion Date: Thu, 7 Sep 2023 10:19:55 +0200 Subject: [PATCH 202/264] CI Pin `Cython<3` in some builds due to observed regressions (#27266) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- build_tools/azure/debian_atlas_32bit_lock.txt | 8 +- .../azure/debian_atlas_32bit_requirements.txt | 2 +- ...38_conda_defaults_openblas_environment.yml | 2 +- ...onda_defaults_openblas_linux-64_conda.lock | 36 +++--- .../py38_conda_forge_mkl_environment.yml | 2 +- .../py38_conda_forge_mkl_win-64_conda.lock | 50 ++++----- ...forge_openblas_ubuntu_2204_environment.yml | 2 +- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 50 ++++----- ...latest_conda_forge_mkl_linux-64_conda.lock | 8 +- ..._forge_mkl_no_coverage_linux-64_conda.lock | 55 +++++---- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 56 +++++----- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 28 ++--- ...st_pip_openblas_pandas_linux-64_conda.lock | 40 +++---- ...pylatest_pip_scipy_dev_linux-64_conda.lock | 22 ++-- build_tools/azure/pypy3_environment.yml | 2 +- build_tools/azure/pypy3_linux-64_conda.lock | 50 ++++----- build_tools/azure/ubuntu_atlas_lock.txt | 8 +- .../azure/ubuntu_atlas_requirements.txt | 2 +- build_tools/circle/doc_environment.yml | 2 +- build_tools/circle/doc_linux-64_conda.lock | 104 +++++++++--------- .../doc_min_dependencies_linux-64_conda.lock | 34 +++--- .../cirrus/py39_conda_forge_environment.yml | 2 +- .../py39_conda_forge_linux-aarch64_conda.lock | 46 ++++---- .../update_environments_and_lock_files.py | 34 +++++- 24 files changed, 333 insertions(+), 312 deletions(-) diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt index e3d33cc1cbd19..5168bd7b96dc6 100644 --- a/build_tools/azure/debian_atlas_32bit_lock.txt +++ b/build_tools/azure/debian_atlas_32bit_lock.txt @@ -2,13 +2,13 @@ # This file is autogenerated by pip-compile with Python 3.9 # by the following command: # -# pip-compile --config=pyproject.toml --output-file=build_tools/azure/debian_atlas_32bit_lock.txt build_tools/azure/debian_atlas_32bit_requirements.txt +# pip-compile --output-file=build_tools/azure/debian_atlas_32bit_lock.txt build_tools/azure/debian_atlas_32bit_requirements.txt # attrs==23.1.0 # via pytest -coverage==7.2.7 +coverage==7.3.0 # via pytest-cov -cython==3.0.0 +cython==0.29.36 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt iniconfig==2.0.0 # via pytest @@ -16,7 +16,7 @@ joblib==1.1.1 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt packaging==23.1 # via pytest -pluggy==1.2.0 +pluggy==1.3.0 # via pytest py==1.11.0 # via pytest diff --git a/build_tools/azure/debian_atlas_32bit_requirements.txt b/build_tools/azure/debian_atlas_32bit_requirements.txt index 83baf09b14093..a77387862c025 100644 --- a/build_tools/azure/debian_atlas_32bit_requirements.txt +++ b/build_tools/azure/debian_atlas_32bit_requirements.txt @@ -1,7 +1,7 @@ # DO NOT EDIT: this file is generated from the specification found in the # following script to centralize the configuration for CI builds: # build_tools/update_environments_and_lock_files.py -cython +cython<3.0.0 joblib==1.1.1 # min threadpoolctl==2.2.0 pytest==7.1.2 # min diff --git a/build_tools/azure/py38_conda_defaults_openblas_environment.yml b/build_tools/azure/py38_conda_defaults_openblas_environment.yml index 7abb54f99d300..2493606135454 100644 --- a/build_tools/azure/py38_conda_defaults_openblas_environment.yml +++ b/build_tools/azure/py38_conda_defaults_openblas_environment.yml @@ -8,7 +8,7 @@ dependencies: - numpy=1.17.3 # min - blas[build=openblas] - scipy=1.5.0 # min - - cython + - cython<3.0.0 - joblib - threadpoolctl=2.2.0 - matplotlib=3.1.3 # min diff --git a/build_tools/azure/py38_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/py38_conda_defaults_openblas_linux-64_conda.lock index 5543d7ac963bd..bc261623f5757 100644 --- a/build_tools/azure/py38_conda_defaults_openblas_linux-64_conda.lock +++ b/build_tools/azure/py38_conda_defaults_openblas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 79255228ac886c1c3fdbcda6a5d6e899b5ab035d633fa540a755b9ba633c2a2c +# input_hash: cc5492b4677e6d5132ab4ab70eda13c942bdf5f6dd53af977e801c42d5f48132 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-openblas.conda#9ddfcaef10d79366c90128f5dc444be8 @@ -12,7 +12,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b37 https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda#57623d10a70e09e1d048c2b2b6f4e2dd https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d281e9c2192cb3fa425655a8defb85 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 -https://repo.anaconda.com/pkgs/main/linux-64/expat-2.4.9-h6a678d5_0.conda#3a6139fbcd96384855f0e6037502bf28 +https://repo.anaconda.com/pkgs/main/linux-64/expat-2.5.0-h6a678d5_0.conda#9a21d99d49a0a556cf9590430dec8ec0 https://repo.anaconda.com/pkgs/main/linux-64/giflib-5.2.1-h5eee18b_3.conda#aa7d64adb3cd8a75d398167f8c29afc3 https://repo.anaconda.com/pkgs/main/linux-64/icu-58.2-he6710b0_3.conda#48cc14d5ad1a9bcd8dac17211a8deb8b https://repo.anaconda.com/pkgs/main/linux-64/jpeg-9e-h5eee18b_1.conda#ac373800fda872108412d1ccfe3fa572 @@ -26,17 +26,18 @@ https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.15-h7f8727e_0.conda#ada518 https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_0.conda#53915e9402180a7f22ea619c41089520 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c https://repo.anaconda.com/pkgs/main/linux-64/nspr-4.35-h6a678d5_0.conda#208fff5d60133bcff6998a70c9f5203b -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.10-h7f8727e_0.conda#83d254d7304c7c1220f82668ab1e7b42 +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.10-h7f8727e_2.conda#066a828cc9dcd120af8c503381d6a1b8 https://repo.anaconda.com/pkgs/main/linux-64/pcre-8.45-h295c915_0.conda#b32ccc24d1d9808618c1e898da60f68d https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.2-h5eee18b_0.conda#bcd31de48a0dcb44bc5b99675800c5cc https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31fbfbb5057c92fa845ad6adef93 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e https://repo.anaconda.com/pkgs/main/linux-64/glib-2.69.1-he621ea3_2.conda#51cf1899782b3f3744aedd143fbc07f3 +https://repo.anaconda.com/pkgs/main/linux-64/libcups-2.4.2-h2d74bed_1.conda#3f265c2172a9e8c90a74037b6fa13685 https://repo.anaconda.com/pkgs/main/linux-64/libedit-3.1.20221030-h5eee18b_0.conda#7c724a17739aceaf9d1633ff06962137 https://repo.anaconda.com/pkgs/main/linux-64/libevent-2.1.12-hdbd6064_1.conda#99312bf9d90f1ea14534b40afb61ce63 -https://repo.anaconda.com/pkgs/main/linux-64/libllvm10-10.0.1-hbcb73fb_5.conda#198e840fc17a5bff7f1ee543ee1981b2 +https://repo.anaconda.com/pkgs/main/linux-64/libllvm14-14.0.6-hdb19cb5_3.conda#aefea2b45cf32f12b4f1ffaa70aa3201 https://repo.anaconda.com/pkgs/main/linux-64/libpng-1.6.39-h5eee18b_0.conda#f6aee38184512eb05b06c2e94d39ab22 -https://repo.anaconda.com/pkgs/main/linux-64/libxml2-2.9.14-h74e7548_0.conda#2eafeb1cb5f00b034d150f3d70436e52 +https://repo.anaconda.com/pkgs/main/linux-64/libxml2-2.10.4-hcbfbd50_0.conda#c42cffdb0bc28d37a4eb33aed114f554 https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9 https://repo.anaconda.com/pkgs/main/linux-64/zstd-1.5.5-hc292b87_0.conda#0f59d57dc21f585f4c282d60dfb46505 @@ -44,34 +45,36 @@ https://repo.anaconda.com/pkgs/main/linux-64/dbus-1.13.18-hb2f20db_0.conda#6a6a6 https://repo.anaconda.com/pkgs/main/linux-64/freetype-2.12.1-h4a9f257_0.conda#bdc7b5952e9c5dca01bc2f4ccef2f974 https://repo.anaconda.com/pkgs/main/linux-64/gstreamer-1.14.1-h5eee18b_1.conda#f2f26e6f869b5d87f41bd059fae47c3e https://repo.anaconda.com/pkgs/main/linux-64/krb5-1.20.1-h143b758_1.conda#cf1accc86321fa25d6b978cc748039ae -https://repo.anaconda.com/pkgs/main/linux-64/libclang-10.0.1-default_hb85057a_2.conda#9e39ee5217327ba25e341c629b642247 -https://repo.anaconda.com/pkgs/main/linux-64/libtiff-4.5.0-h6a678d5_2.conda#b3391ee6956636eb8ef159c1c454e3da -https://repo.anaconda.com/pkgs/main/linux-64/libxkbcommon-1.0.1-hfa300c1_0.conda#913e6c7c04026ff341960a9700889498 -https://repo.anaconda.com/pkgs/main/linux-64/libxslt-1.1.35-h4e12654_0.conda#328c111d87dccd5a3e471a691833f670 +https://repo.anaconda.com/pkgs/main/linux-64/libclang13-14.0.6-default_he11475f_1.conda#44890feda1cf51639d9c94afbacce011 +https://repo.anaconda.com/pkgs/main/linux-64/libtiff-4.5.1-h6a678d5_0.conda#235a671f74f0c4ecad9f9b3b107e3566 +https://repo.anaconda.com/pkgs/main/linux-64/libxkbcommon-1.0.1-h5eee18b_1.conda#888b2e8f1bbf21017c503826e2d24b50 +https://repo.anaconda.com/pkgs/main/linux-64/libxslt-1.1.37-h2085143_0.conda#680f9676bf55bdafd276eaa12fbb0f28 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda#c7086c9ceb6cfe1c4c729a774a2d88a5 -https://repo.anaconda.com/pkgs/main/linux-64/fontconfig-2.14.1-h52c9d5c_1.conda#cd3f711abef17203045b7bcfc83fac2f +https://repo.anaconda.com/pkgs/main/linux-64/cyrus-sasl-2.1.28-h52b45da_1.conda#d634af1577e4008f9228ae96ce671c44 +https://repo.anaconda.com/pkgs/main/linux-64/fontconfig-2.14.1-h4c34cd2_2.conda#f0b472f5b544f8d57beb09ed4a2932e1 https://repo.anaconda.com/pkgs/main/linux-64/gst-plugins-base-1.14.1-h6a678d5_1.conda#afd9cbe949d670d24cc0a007aaec1fe1 https://repo.anaconda.com/pkgs/main/linux-64/lcms2-2.12-h3be6417_0.conda#719db47afba9f6586eecb5eacac70bff +https://repo.anaconda.com/pkgs/main/linux-64/libclang-14.0.6-default_hc6dbbc7_1.conda#8f12583c4027b2861cff470f6b8837c4 https://repo.anaconda.com/pkgs/main/linux-64/libpq-12.15-hdbd6064_1.conda#218227d255f6056b6f49f52dd0d1731f https://repo.anaconda.com/pkgs/main/linux-64/libwebp-1.2.4-h11a3e52_1.conda#9f9153b30e58e9ce896f74634622cbf1 https://repo.anaconda.com/pkgs/main/linux-64/nss-3.89.1-h6a678d5_0.conda#4d9d28fc3a0ca4916f281d2f5429ac50 https://repo.anaconda.com/pkgs/main/linux-64/python-3.8.17-h955ad1f_0.conda#f901f4fd76d24a2d598788a24e4d7246 https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda#f5e365d2cdb66d547eb8c3ab93843aab -https://repo.anaconda.com/pkgs/main/linux-64/cython-3.0.0-py38h5eee18b_0.conda#c71ed7a68aaeccc56088e4a7474bcdb9 +https://repo.anaconda.com/pkgs/main/linux-64/cython-0.29.36-py38h5eee18b_0.conda#0465e461450c86b395da9ccc3853d7dc https://repo.anaconda.com/pkgs/main/linux-64/exceptiongroup-1.0.4-py38h06a4308_0.conda#db954e73dca6076c64a1004d71b45784 https://repo.anaconda.com/pkgs/main/noarch/execnet-1.9.0-pyhd3eb1b0_0.conda#f895937671af67cebb8af617494b3513 https://repo.anaconda.com/pkgs/main/noarch/iniconfig-1.1.1-pyhd3eb1b0_0.tar.bz2#e40edff2c5708f342cef43c7f280c507 https://repo.anaconda.com/pkgs/main/linux-64/joblib-1.2.0-py38h06a4308_0.conda#ee7f1f50ae15650057e5d5301900ae34 https://repo.anaconda.com/pkgs/main/linux-64/kiwisolver-1.4.4-py38h6a678d5_0.conda#7424aa335d22974192800ec19a68486e +https://repo.anaconda.com/pkgs/main/linux-64/mysql-5.7.24-h721c034_2.conda#dfc19ca2466d275c4c1f73b62c57f37b https://repo.anaconda.com/pkgs/main/linux-64/numpy-base-1.17.3-py38h2f8d375_0.conda#40edbb76ecacefb1e6ab639b514822b1 -https://repo.anaconda.com/pkgs/main/linux-64/packaging-23.0-py38h06a4308_0.conda#87dd3a3af0b6c6f5bbb99b7f205c2612 +https://repo.anaconda.com/pkgs/main/linux-64/packaging-23.1-py38h06a4308_0.conda#9ec9b6ee22dad7f49806c51218befd5b https://repo.anaconda.com/pkgs/main/linux-64/pillow-9.4.0-py38h6a678d5_0.conda#8afd1f4f8b23a1c44fca4975253b17f7 https://repo.anaconda.com/pkgs/main/linux-64/pluggy-1.0.0-py38h06a4308_1.conda#87bb1d3f6cf3e409a1dac38cee99918e https://repo.anaconda.com/pkgs/main/linux-64/ply-3.11-py38_0.conda#d6a69c576c6e4d19e3074eaae3d149f2 https://repo.anaconda.com/pkgs/main/noarch/py-1.11.0-pyhd3eb1b0_0.conda#7205a898ed2abbf6e9b903dff6abe08e https://repo.anaconda.com/pkgs/main/linux-64/pyparsing-3.0.9-py38h06a4308_0.conda#becbbf51d2b05de228eed968e20f963d https://repo.anaconda.com/pkgs/main/linux-64/pytz-2022.7-py38h06a4308_0.conda#19c9f6a24d5c6f779c645d00f646666b -https://repo.anaconda.com/pkgs/main/linux-64/qt-main-5.15.2-h327a75a_7.conda#1868b206ade356f1812a723804e1cc31 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.0.0-py38h06a4308_0.conda#24f9c895455f3992d6b04957fd0e7546 https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#34586824d411d36af2fa40e799c172d0 https://repo.anaconda.com/pkgs/main/noarch/threadpoolctl-2.2.0-pyh0d69192_0.conda#bbfdbae4934150b902f97daaf287efe2 @@ -82,16 +85,17 @@ https://repo.anaconda.com/pkgs/main/linux-64/coverage-7.2.2-py38h5eee18b_0.conda https://repo.anaconda.com/pkgs/main/linux-64/numpy-1.17.3-py38h7e8d029_0.conda#5f2b196b515f8fe6b37e3d224650577d https://repo.anaconda.com/pkgs/main/linux-64/pytest-7.4.0-py38h06a4308_0.conda#ba6c58ef1c6ba5247ccc17d41fdd71e5 https://repo.anaconda.com/pkgs/main/noarch/python-dateutil-2.8.2-pyhd3eb1b0_0.conda#211ee00320b08a1ac9fea6677649f6c9 -https://repo.anaconda.com/pkgs/main/linux-64/qt-webengine-5.15.9-hd2b0992_4.conda#ed674e212597b93fffa1afc90a3e100c +https://repo.anaconda.com/pkgs/main/linux-64/qt-main-5.15.2-h7358343_9.conda#d3eac069d7e4e93b866a07c2274c9ee7 https://repo.anaconda.com/pkgs/main/linux-64/sip-6.6.2-py38h6a678d5_0.conda#cb3f0d10f7f79870945f4dbbe0000f92 https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-base-3.1.3-py38hef1b27d_0.conda#a7ad7d097c25b7beeb76f370d51687a1 https://repo.anaconda.com/pkgs/main/linux-64/pandas-1.2.4-py38ha9443f7_0.conda#5bd3fd807a294f387feabc65821b75d0 https://repo.anaconda.com/pkgs/main/linux-64/pyqt5-sip-12.11.0-py38h6a678d5_1.conda#7bc403c7d55f1465e922964d293d2186 https://repo.anaconda.com/pkgs/main/linux-64/pytest-cov-4.0.0-py38h06a4308_0.conda#54035e39255f285f98ca1141b7f098e7 https://repo.anaconda.com/pkgs/main/noarch/pytest-forked-1.3.0-pyhd3eb1b0_0.tar.bz2#07970bffdc78f417d7f8f1c7e620f5c4 -https://repo.anaconda.com/pkgs/main/linux-64/qtwebkit-5.212-h4eab89a_4.conda#7317bbf3f3e66a0a02b07b860783ecff +https://repo.anaconda.com/pkgs/main/linux-64/qt-webengine-5.15.9-h9ab4d14_7.conda#907aa480f11eabd16bd6c72c81720ef2 https://repo.anaconda.com/pkgs/main/linux-64/scipy-1.5.0-py38habc2bb6_0.conda#a27a97fc2377ab74cbd33ce22d3c3353 https://repo.anaconda.com/pkgs/main/linux-64/pyamg-4.2.3-py38h79cecc1_0.conda#6e7f4f94000b244396de8bf4e6ae8dc4 -https://repo.anaconda.com/pkgs/main/linux-64/pyqt-5.15.7-py38h6a678d5_1.conda#62232dc285be8e7e85ae9596d89b3b95 https://repo.anaconda.com/pkgs/main/noarch/pytest-xdist-2.5.0-pyhd3eb1b0_0.conda#d15cdc4207bcf8ca920822597f1d138d +https://repo.anaconda.com/pkgs/main/linux-64/qtwebkit-5.212-h3fafdc1_5.conda#e811bbc0456e3d3a02cab199492153ee +https://repo.anaconda.com/pkgs/main/linux-64/pyqt-5.15.7-py38h6a678d5_1.conda#62232dc285be8e7e85ae9596d89b3b95 https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-3.1.3-py38_0.conda#70d5f6df438d469dc78f082389ada23d diff --git a/build_tools/azure/py38_conda_forge_mkl_environment.yml b/build_tools/azure/py38_conda_forge_mkl_environment.yml index 2a2955d523a97..e2034fd25ddc8 100644 --- a/build_tools/azure/py38_conda_forge_mkl_environment.yml +++ b/build_tools/azure/py38_conda_forge_mkl_environment.yml @@ -8,7 +8,7 @@ dependencies: - numpy - blas[build=mkl] - scipy - - cython + - cython<3.0.0 - joblib - threadpoolctl - matplotlib diff --git a/build_tools/azure/py38_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/py38_conda_forge_mkl_win-64_conda.lock index 032adf4b697c1..322a6d02f47c9 100644 --- a/build_tools/azure/py38_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/py38_conda_forge_mkl_win-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: win-64 -# input_hash: e3af9571d95aff7d02e118db6e2ccbce90cd3cf3c663b4ed8a5e8c3fef5b1318 +# input_hash: 6608a61d0b91d8f5b96cf9aaa6a208586babc4fe18583a535509c82488bd1ebb @EXPLICIT https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2023.7.22-h56e8100_0.conda#b1c2327b36f1a25d96f2039b0d3e3739 https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2023.2.0-h57928b3_49496.conda#f2e71622520883ffdbc379b13049534c @@ -17,13 +17,13 @@ https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.36.32532-h05e663 https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-h8ffe710_4.tar.bz2#7c03c66026944073040cb19a4f3ec3c9 https://conda.anaconda.org/conda-forge/win-64/icu-72.1-h63175ca_0.conda#a108731562663d787066bd17c9595114 https://conda.anaconda.org/conda-forge/win-64/lerc-4.0.0-h63175ca_0.tar.bz2#1900cb3cab5055833cfddb0ba233b074 -https://conda.anaconda.org/conda-forge/win-64/libbrotlicommon-1.0.9-hcfcfb64_9.conda#5e0f7bd6f1d0747abd5da59cffb6f533 +https://conda.anaconda.org/conda-forge/win-64/libbrotlicommon-1.1.0-hcfcfb64_0.conda#a10abcccb9339c0bfbf7655e2643f6ac https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.18-hcfcfb64_0.conda#493acc14c556ef6f1d13ba00b099c679 https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.2-h8ffe710_5.tar.bz2#2c96d1b6915b408893f9472569dee135 https://conda.anaconda.org/conda-forge/win-64/libiconv-1.17-h8ffe710_0.tar.bz2#050119977a86e4856f0416e2edcf81bb https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-2.1.5.1-hcfcfb64_0.conda#f2fad2ae9f1365e343e4329fdb1e9d63 https://conda.anaconda.org/conda-forge/win-64/libogg-1.3.4-h8ffe710_1.tar.bz2#04286d905a0dcb7f7d4a12bdfe02516d -https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.42.0-hcfcfb64_0.conda#9a71d93deb99cc09d8939d5235b5909a +https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.43.0-hcfcfb64_0.conda#16c6f482e70cb3da41d0bee5d49c6bf3 https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.3.1-hcfcfb64_0.conda#f89e765213cac556a8ed72ba8c1b5071 https://conda.anaconda.org/conda-forge/win-64/libzlib-1.2.13-hcfcfb64_5.conda#5fdb9c6a113b6b6cb5e517fd972d5f41 https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2#066552ac6b907ec6d72c0ddab29050dc @@ -32,59 +32,59 @@ https://conda.anaconda.org/conda-forge/win-64/pthreads-win32-2.9.1-hfa6e2cd_3.ta https://conda.anaconda.org/conda-forge/win-64/tk-8.6.12-h8ffe710_0.tar.bz2#c69a5047cc9291ae40afd4a1ad6f0c0f https://conda.anaconda.org/conda-forge/win-64/xz-5.2.6-h8d14728_0.tar.bz2#515d77642eaa3639413c6b1bc3f94219 https://conda.anaconda.org/conda-forge/win-64/gettext-0.21.1-h5728263_0.tar.bz2#299d4fd6798a45337042ff5a48219e5f -https://conda.anaconda.org/conda-forge/win-64/krb5-1.21.1-heb0366b_0.conda#66fdc07e563f5ae3f6d82c58f492f776 -https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.0.9-hcfcfb64_9.conda#4c502e4846bdc22b944ab9aa5a55a58f -https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.0.9-hcfcfb64_9.conda#19029748649ae0d48871d7012918efef +https://conda.anaconda.org/conda-forge/win-64/krb5-1.21.2-heb0366b_0.conda#6e8b0f22b4eef3b3cb3849bb4c3d47f9 +https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.1.0-hcfcfb64_0.conda#b694da94a046204c19b25673de87f796 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https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.11-hcd874cb_0.conda#c46ba8712093cb0114404ae8a7582e1a https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.3-hcd874cb_0.tar.bz2#46878ebb6b9cbd8afcf8088d7ef00ece https://conda.anaconda.org/conda-forge/noarch/zipp-3.16.2-pyhd8ed1ab_0.conda#2da0451b54c4563c32490cb1b7cf68a1 -https://conda.anaconda.org/conda-forge/win-64/brotli-1.0.9-hcfcfb64_9.conda#5275e2634840f6d156934a16b7c0c813 -https://conda.anaconda.org/conda-forge/win-64/coverage-7.2.7-py38h91455d4_0.conda#2fa3faef0a7b6a5da2bff0faddbfbc68 +https://conda.anaconda.org/conda-forge/win-64/brotli-1.1.0-hcfcfb64_0.conda#4a65489faacc6b6dbd3e0d0572a3a9b9 +https://conda.anaconda.org/conda-forge/win-64/coverage-7.3.0-py38h91455d4_0.conda#738b7e58d7a86a79d73841b2f88941ed https://conda.anaconda.org/conda-forge/win-64/glib-tools-2.76.4-h12be248_0.conda#88237d3ddd338164196043cb7e927246 -https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.0.0-pyhd8ed1ab_1.conda#a08b6be5bf18b9d2a927d3457750f82e -https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.0-pyhd8ed1ab_1.conda#fb4caf6da228ccc487350eade569abae +https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.0.1-pyhd8ed1ab_0.conda#d978c61aa5fc2c69380d53ad56b5ae86 +https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda#4da50d410f553db77e62ab62ffaa1abc https://conda.anaconda.org/conda-forge/win-64/lcms2-2.15-h3e3b177_1.conda#a76c36ad1b4b87f038d67890122d08ec https://conda.anaconda.org/conda-forge/win-64/libxcb-1.15-hcd874cb_0.conda#090d91b69396f14afef450c285f9758c https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.0-ha2aaf27_2.conda#db0490689232e8e38c312281df6f31a2 @@ -95,9 +95,9 @@ https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0 https://conda.anaconda.org/conda-forge/win-64/sip-6.7.11-py38hd3f51b4_0.conda#c5ddea7d96c83d73f33c80e94791c904 https://conda.anaconda.org/conda-forge/win-64/tbb-2021.10.0-h91493d7_0.conda#348275b42ff7638e7798ac61e073f864 https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.7.1-hd8ed1ab_0.conda#f96688577f1faa58096d06a45136afa2 -https://conda.anaconda.org/conda-forge/win-64/fonttools-4.42.0-py38h91455d4_0.conda#834ae21babff592fb62b642856627cbc +https://conda.anaconda.org/conda-forge/win-64/fonttools-4.42.1-py38h91455d4_0.conda#296f16446a00d56f5c6ee0dbdd1c397c https://conda.anaconda.org/conda-forge/win-64/glib-2.76.4-h12be248_0.conda#4d7ae53ee4b7e08f3fbd1d3a7efd4812 -https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.0.0-pyhd8ed1ab_1.conda#d69f29916f934f30adb1dd5fff4d9a8b +https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.0.1-pyhd8ed1ab_0.conda#54661981fd331e20847d8a49543dd9af https://conda.anaconda.org/conda-forge/win-64/mkl-2022.1.0-h6a75c08_874.tar.bz2#2ff89a7337a9636029b4db9466e9f8e3 https://conda.anaconda.org/conda-forge/win-64/pillow-10.0.0-py38ha7eb54a_0.conda#7b0af6e39802b71b97b1688efb9d2d41 https://conda.anaconda.org/conda-forge/noarch/platformdirs-3.10.0-pyhd8ed1ab_0.conda#0809187ef9b89a3d94a5c24d13936236 diff --git a/build_tools/azure/py38_conda_forge_openblas_ubuntu_2204_environment.yml b/build_tools/azure/py38_conda_forge_openblas_ubuntu_2204_environment.yml index bbbb3bb4cef6c..cfafd4c2bddec 100644 --- a/build_tools/azure/py38_conda_forge_openblas_ubuntu_2204_environment.yml +++ b/build_tools/azure/py38_conda_forge_openblas_ubuntu_2204_environment.yml @@ -8,7 +8,7 @@ dependencies: - numpy - blas[build=openblas] - scipy - - cython + - cython<3.0.0 - joblib - threadpoolctl - matplotlib diff --git a/build_tools/azure/py38_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/py38_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index 7328a3073175b..99dea64ba839f 100644 --- a/build_tools/azure/py38_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/py38_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: d249329b78962bdba40d2f7d66c3a94b4caaced25b05b3bc95f39dda6c72aebe +# input_hash: 45129d4891600cbebed054af93f99df16f4911de4fc4048069748c4c59c09eef @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.7.22-hbcca054_0.conda#a73ecd2988327ad4c8f2c331482917f2 @@ -26,7 +26,7 @@ https://conda.anaconda.org/conda-forge/linux-64/icu-72.1-hcb278e6_0.conda#7c8d20 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 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https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-7.3.0-hdb3a94d_0.conda#765bc76c0dfaf24ff9d8a2935b2510df -https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.0.0-pyhd8ed1ab_1.conda#d69f29916f934f30adb1dd5fff4d9a8b +https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.0.1-pyhd8ed1ab_0.conda#54661981fd331e20847d8a49543dd9af https://conda.anaconda.org/conda-forge/linux-64/pandas-2.0.3-py38h01efb38_1.conda#01a2b6144e65631e2fe24e569d0738ee https://conda.anaconda.org/conda-forge/noarch/platformdirs-3.10.0-pyhd8ed1ab_0.conda#0809187ef9b89a3d94a5c24d13936236 https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py38h17151c0_4.conda#95447fd7bd5b420df7e7eb405f19f463 diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 446ba530c52ef..3a3040b5e68e7 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -83,7 +83,7 @@ https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.2-h3eb15da_6.conda#6b63 https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.0.9-h166bdaf_8.tar.bz2#e5613f2bc717e9945840ff474419b8e4 https://conda.anaconda.org/conda-forge/linux-64/ccache-4.8.1-h1fcd64f_0.conda#fd37a0c47d8b3667b73af0549037ce83 https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-hca18f0e_1.conda#e1232042de76d24539a436d37597eb06 -https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.1-h659d440_0.conda#1b5126ec25763eb17ef74c8763d26e84 +https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.2-h659d440_0.conda#cd95826dbd331ed1be26bdf401432844 https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.10.1-h166bdaf_0.tar.bz2#f967fc95089cd247ceed56eda31de3a9 https://conda.anaconda.org/conda-forge/linux-64/libglib-2.76.3-hebfc3b9_0.conda#a64f11b244b2c112cd3fa1cbe9493999 https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.9.1-cuda112_haf10fcf_5.conda#b8996ffa972161676ba6972af4c41384 @@ -106,15 +106,15 @@ https://conda.anaconda.org/conda-forge/noarch/certifi-2023.5.7-pyhd8ed1ab_0.cond https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.2.0-pyhd8ed1ab_0.conda#313516e9a4b08b12dfb1e1cd390a96e3 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 https://conda.anaconda.org/conda-forge/noarch/cycler-0.11.0-pyhd8ed1ab_0.tar.bz2#a50559fad0affdbb33729a68669ca1cb -https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.0-py311hb755f60_0.conda#257dfede48699e2e6372528d08399e5a +https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.2-py311hb755f60_0.conda#81d4eacf7eb2d40beee33aa71e8f94ad https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2#ecfff944ba3960ecb334b9a2663d708d -https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.1.2-pyhd8ed1ab_0.conda#de4cb3384374e1411f0454edcf546cdb +https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.1.3-pyhd8ed1ab_0.conda#e6518222753f519e911e83136d2158d9 https://conda.anaconda.org/conda-forge/noarch/execnet-2.0.2-pyhd8ed1ab_0.conda#67de0d8241e1060a479e3c37793e26f9 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.14.2-h14ed4e7_0.conda#0f69b688f52ff6da70bccb7ff7001d1d https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.76.4-hfc55251_0.conda#76ac435b8668f636a39fcb155c3543fd https://conda.anaconda.org/conda-forge/noarch/idna-3.4-pyhd8ed1ab_0.tar.bz2#34272b248891bddccc64479f9a7fffed https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 -https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.4-py311h4dd048b_1.tar.bz2#46d451f575392c01dc193069bd89766d +https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.5-py311h9547e67_0.conda#f53903649188b99e6b44c560c69f5b23 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.15-haa2dc70_1.conda#980d8aca0bc23ca73fa8caa3e7c84c28 https://conda.anaconda.org/conda-forge/linux-64/libclang13-15.0.7-default_h9986a30_2.conda#907344cee64101d44d806bbe0fccb01d https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h36d4200_3.conda#c9f4416a34bc91e0eb029f912c68f81f diff --git a/build_tools/azure/pylatest_conda_forge_mkl_no_coverage_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_no_coverage_linux-64_conda.lock index 249d842aff090..ca55037ef7936 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_no_coverage_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_no_coverage_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 28f25ea7bcf22e93278ac96747ca9700ada47330f6e3ed927edb73ab4a4c153e +# input_hash: 223cf367742008b437f38ff4642c0e70494f665cf9434d4da5c6483c757397fd @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.7.22-hbcca054_0.conda#a73ecd2988327ad4c8f2c331482917f2 @@ -28,7 +28,7 @@ https://conda.anaconda.org/conda-forge/linux-64/icu-72.1-hcb278e6_0.conda#7c8d20 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2#a8832b479f93521a9e7b5b743803be51 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2#76bbff344f0134279f225174e9064c8f -https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.0.9-h166bdaf_9.conda#61641e239f96eae2b8492dc7e755828c +https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_0.conda#e805cbec4c29feb22e019245f7e47b6c https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.18-h0b41bf4_0.conda#6aa9c9de5542ecb07fdda9ca626252d8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.5.0-hcb278e6_1.conda#6305a3dd2752c76335295da4e581f2fd https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3 @@ -48,7 +48,6 @@ https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec https://conda.anaconda.org/conda-forge/linux-64/openssl-3.1.2-hd590300_0.conda#e5ac5227582d6c83ccf247288c0eb095 https://conda.anaconda.org/conda-forge/linux-64/pixman-0.40.0-h36c2ea0_0.tar.bz2#660e72c82f2e75a6b3fe6a6e75c79f19 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 -https://conda.anaconda.org/conda-forge/linux-64/rocm-smi-5.6.0-h59595ed_1.conda#2980fb3b7777f495dacd46d5cc5d74bb https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hd590300_0.conda#b462a33c0be1421532f28bfe8f4a7514 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hd590300_0.conda#2c80dc38fface310c9bd81b17037fee5 @@ -59,90 +58,90 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-xf86vidmodeproto-2.3.1-h7f9 https://conda.anaconda.org/conda-forge/linux-64/xorg-xproto-7.0.31-h7f98852_1007.tar.bz2#b4a4381d54784606820704f7b5f05a15 https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2#2161070d867d1b1204ea749c8eec4ef0 https://conda.anaconda.org/conda-forge/linux-64/expat-2.5.0-hcb278e6_1.conda#8b9b5aca60558d02ddaa09d599e55920 -https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.0.9-h166bdaf_9.conda#081aa22f4581c08e4372b0b6c2f8478e -https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.0.9-h166bdaf_9.conda#1f0a03af852a9659ed2bf08f2f1704fd +https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hd590300_0.conda#43017394a280a42b48d11d2a6e169901 +https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hd590300_0.conda#8e3e1cb77c4b355a3776bdfb74095bed https://conda.anaconda.org/conda-forge/linux-64/libcap-2.69-h0f662aa_0.conda#25cb5999faa414e5ccb2c1388f62d3d5 https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2#4d331e44109e3f0e19b4cb8f9b82f3e1 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.47-h71f35ed_0.conda#c2097d0b46367996f09b4e8e4920384a 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https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-17_osx64_mkl.conda#5adcad22978f80fa101047022e79d9eb https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-17_osx64_mkl.conda#5557060dea295fcbb224be17b3947d16 @@ -112,21 +112,21 @@ https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda https://conda.anaconda.org/conda-forge/osx-64/blas-devel-3.9.0-17_osx64_mkl.conda#b40b415e2be4d0d2a8d05d0f805240b7 https://conda.anaconda.org/conda-forge/osx-64/clangxx-15.0.7-default_hdb78580_3.conda#58df9ff86fefc7684670be729b41412f https://conda.anaconda.org/conda-forge/osx-64/contourpy-1.1.0-py311h5fe6e05_0.conda#1969042c846644a15c25ea78f487459c -https://conda.anaconda.org/conda-forge/osx-64/pandas-2.0.3-py311hab14417_1.conda#e5c7b1b1f55b11db3adb209089ab6eae +https://conda.anaconda.org/conda-forge/osx-64/pandas-2.1.0-py311hab14417_0.conda#e081bf78c37d2671a7d442a56c3a8728 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https://conda.anaconda.org/conda-forge/osx-64/matplotlib-3.7.2-py311h6eed73b_0.conda#e32f9e5a192246ee550157ac8ffca102 https://conda.anaconda.org/conda-forge/osx-64/pyamg-5.0.1-py311hd5c4f45_0.conda#e6d0a4faf158e01603bb1cef76fa10ab https://conda.anaconda.org/conda-forge/osx-64/clang_osx-64-15.0.7-h03d6864_3.conda#9dfd4e8cbc51c07a7b1ad59ad8415fad https://conda.anaconda.org/conda-forge/osx-64/c-compiler-1.6.0-h63c33a9_0.conda#d7f3b8d3a85b4e7eded31adb611bb665 https://conda.anaconda.org/conda-forge/osx-64/clangxx_osx-64-15.0.7-h2133e9c_3.conda#2ff16b86a981da4b1a2658423db664bb -https://conda.anaconda.org/conda-forge/osx-64/gfortran_osx-64-12.2.0-h18f7dce_1.conda#39a70f12c676b252d980c6b52c608500 +https://conda.anaconda.org/conda-forge/osx-64/gfortran_osx-64-12.3.0-h18f7dce_1.conda#436af2384c47aedb94af78a128e174f1 https://conda.anaconda.org/conda-forge/osx-64/cxx-compiler-1.6.0-h1c7c39f_0.conda#9adaf7c9d4e1e15e70a8dd46befbbab2 -https://conda.anaconda.org/conda-forge/osx-64/gfortran-12.2.0-h2c809b3_1.conda#4a5cb3bf02a98991321a1f8ec4d8c817 +https://conda.anaconda.org/conda-forge/osx-64/gfortran-12.3.0-h2c809b3_1.conda#c48adbaa8944234b80ef287c37e329b0 https://conda.anaconda.org/conda-forge/osx-64/fortran-compiler-1.6.0-h932d759_0.conda#d2bc049eae716dd6879079ddd209ffc3 https://conda.anaconda.org/conda-forge/osx-64/compilers-1.6.0-h694c41f_0.conda#d4c66ca84aa87a6c63f4c8a6498052d9 diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index d0acacc8cd97e..0e987a3ed7de9 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: osx-64 -# input_hash: fbf2d65e263f99b2d6f22988cb39d83f9afe591cddad646c82ddb61740f5b841 +# input_hash: 03f7604aefb9752d2367c457bdf4e4923158be96db35ac0dd1d5dc60a9981cd1 @EXPLICIT https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae776d26d4214c8a https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h1de35cc_0.conda#19fcb113b170fe2a0be96b47801fed7d @@ -25,27 +25,23 @@ https://repo.anaconda.com/pkgs/main/osx-64/libbrotlienc-1.0.9-hca72f7f_7.conda#e https://repo.anaconda.com/pkgs/main/osx-64/libgfortran5-11.3.0-h9dfd629_28.conda#1fa1a27ee100b1918c3021dbfa3895a3 https://repo.anaconda.com/pkgs/main/osx-64/libpng-1.6.39-h6c40b1e_0.conda#a3c824835f53ad27aeb86d2b55e47804 https://repo.anaconda.com/pkgs/main/osx-64/lz4-c-1.9.4-hcec6c5f_0.conda#44291e9e6920cfff30caf1299f48db38 -https://repo.anaconda.com/pkgs/main/osx-64/openssl-3.0.10-hca72f7f_0.conda#6b69fd0cdbfcbf92a5de04163d5e9244 +https://repo.anaconda.com/pkgs/main/osx-64/openssl-3.0.10-hca72f7f_2.conda#2915c036fba59aa6bf4faaf36473619a https://repo.anaconda.com/pkgs/main/osx-64/readline-8.2-hca72f7f_0.conda#971667436260e523f6f7355fdfa238bf https://repo.anaconda.com/pkgs/main/osx-64/tbb-2021.8.0-ha357a0b_0.conda#fb48530a3eea681c11dafb95b3387c0f https://repo.anaconda.com/pkgs/main/osx-64/tk-8.6.12-h5d9f67b_0.conda#047f0af5486d19163e37fd7f8ae3d29f https://repo.anaconda.com/pkgs/main/osx-64/brotli-bin-1.0.9-hca72f7f_7.conda#110bdca1a20710820e61f7fa3047f737 https://repo.anaconda.com/pkgs/main/osx-64/freetype-2.12.1-hd8bbffd_0.conda#1f276af321375ee7fe8056843044fa76 https://repo.anaconda.com/pkgs/main/osx-64/libgfortran-5.0.0-11_3_0_hecd8cb5_28.conda#2eb13b680803f1064e53873ae0aaafb3 -https://repo.anaconda.com/pkgs/main/osx-64/mkl-2023.1.0-h59209a4_43558.conda#898a058caf42cf8b706034be6e5b2d50 +https://repo.anaconda.com/pkgs/main/osx-64/mkl-2023.1.0-h8e150cf_43559.conda#f5a09d45a003f817d5c43935e20ca0c8 https://repo.anaconda.com/pkgs/main/osx-64/sqlite-3.41.2-h6c40b1e_0.conda#6947a501943529c7536b7e4ba53802c1 https://repo.anaconda.com/pkgs/main/osx-64/zstd-1.5.5-hc035e20_0.conda#5e0b7ddb1b7dc6b630e1f9a03499c19c https://repo.anaconda.com/pkgs/main/osx-64/brotli-1.0.9-hca72f7f_7.conda#68e54d12ec67591deb2ffd70348fb00f -https://repo.anaconda.com/pkgs/main/osx-64/libtiff-4.5.0-hcec6c5f_2.conda#f0b033a82af1bd028f112cdecef1fe0a +https://repo.anaconda.com/pkgs/main/osx-64/libtiff-4.5.1-hcec6c5f_0.conda#e127a800ffd9d300ed7d5e1b026944ec https://repo.anaconda.com/pkgs/main/osx-64/python-3.11.4-hf27a42d_0.conda#7ad1265574193e18e9beaa16879734dc -https://repo.anaconda.com/pkgs/main/noarch/appdirs-1.4.4-pyhd3eb1b0_0.conda#5673d98d06171cb6eed03a6736845c4d -https://repo.anaconda.com/pkgs/main/osx-64/certifi-2023.7.22-py311hecd8cb5_0.conda#09c171f0fb0dbe3171e472222944ba5c -https://repo.anaconda.com/pkgs/main/noarch/charset-normalizer-2.0.4-pyhd3eb1b0_0.conda#e7a441d94234b2b5fafee06e25dbf076 https://repo.anaconda.com/pkgs/main/osx-64/coverage-7.2.2-py311h6c40b1e_0.conda#e15605553450156cf75c3ae38a920475 https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda#f5e365d2cdb66d547eb8c3ab93843aab https://repo.anaconda.com/pkgs/main/osx-64/cython-3.0.0-py311h6c40b1e_0.conda#f1831f4c643b4653ecb777477763f9cc https://repo.anaconda.com/pkgs/main/noarch/execnet-1.9.0-pyhd3eb1b0_0.conda#f895937671af67cebb8af617494b3513 -https://repo.anaconda.com/pkgs/main/osx-64/idna-3.4-py311hecd8cb5_0.conda#48ab3e9b53e5607abe86a920cd37e13a https://repo.anaconda.com/pkgs/main/noarch/iniconfig-1.1.1-pyhd3eb1b0_0.tar.bz2#e40edff2c5708f342cef43c7f280c507 https://repo.anaconda.com/pkgs/main/osx-64/joblib-1.2.0-py311hecd8cb5_0.conda#af8c1fcd4e8e0c6fa2a4f4ecda261dc9 https://repo.anaconda.com/pkgs/main/osx-64/kiwisolver-1.4.4-py311hcec6c5f_0.conda#f2cf31e2a762f071fd6bc4d74ea2bfc8 @@ -53,33 +49,25 @@ https://repo.anaconda.com/pkgs/main/osx-64/lcms2-2.12-hf1fd2bf_0.conda#697aba7a3 https://repo.anaconda.com/pkgs/main/osx-64/libwebp-1.2.4-hf6ce154_1.conda#07d0981c3847293d4aea5778298a12d3 https://repo.anaconda.com/pkgs/main/osx-64/mkl-service-2.4.0-py311h6c40b1e_1.conda#f709b80c57a0fcc577319920d1b7228b https://repo.anaconda.com/pkgs/main/noarch/munkres-1.1.4-py_0.conda#148362ba07f92abab76999a680c80084 -https://repo.anaconda.com/pkgs/main/osx-64/packaging-23.0-py311hecd8cb5_0.conda#456989f87701680b35cab3edc49e223d +https://repo.anaconda.com/pkgs/main/osx-64/packaging-23.1-py311hecd8cb5_0.conda#4f5c491cd2de9d61f61c0ea3340ab46a https://repo.anaconda.com/pkgs/main/osx-64/pluggy-1.0.0-py311hecd8cb5_1.conda#98e4da64cd934965a0caf4136280ff35 https://repo.anaconda.com/pkgs/main/noarch/py-1.11.0-pyhd3eb1b0_0.conda#7205a898ed2abbf6e9b903dff6abe08e -https://repo.anaconda.com/pkgs/main/noarch/pycparser-2.21-pyhd3eb1b0_0.conda#135a72ff2a31150a3a3ff0b1edd41ca9 https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-3.0.9-py311hecd8cb5_0.conda#a4262f849ecc82af69f58da0cbcaaf04 -https://repo.anaconda.com/pkgs/main/osx-64/pysocks-1.7.1-py311hecd8cb5_0.conda#6a9c1a311e30a9776b3297fe1480fa38 +https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2023.3-pyhd3eb1b0_0.conda#479c037de0186d114b9911158427624e https://repo.anaconda.com/pkgs/main/osx-64/pytz-2022.7-py311hecd8cb5_0.conda#87c5590ad0bdf9c5c76feb22b7fbd5ba https://repo.anaconda.com/pkgs/main/osx-64/setuptools-68.0.0-py311hecd8cb5_0.conda#ad594daf4f91ef9b89b10b0f4b2c9e10 https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#34586824d411d36af2fa40e799c172d0 https://repo.anaconda.com/pkgs/main/noarch/threadpoolctl-2.2.0-pyh0d69192_0.conda#bbfdbae4934150b902f97daaf287efe2 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a https://repo.anaconda.com/pkgs/main/osx-64/tornado-6.3.2-py311h6c40b1e_0.conda#0dcfb37496c5564e896428ae56ab3e95 -https://repo.anaconda.com/pkgs/main/osx-64/cffi-1.15.1-py311h6c40b1e_3.conda#5eb14a7a7187a7593f09dafc7a26ff23 https://repo.anaconda.com/pkgs/main/noarch/fonttools-4.25.0-pyhd3eb1b0_0.conda#bb9c5b5a6d892fca5efe4bf0203b6a48 https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.24.3-py311h53bf9ac_1.conda#1b1957e3823208a006d0699999335c7d https://repo.anaconda.com/pkgs/main/osx-64/pillow-9.4.0-py311hcec6c5f_0.conda#fccbb731e918b59d44372354ff2e24f9 https://repo.anaconda.com/pkgs/main/osx-64/pytest-7.4.0-py311hecd8cb5_0.conda#8c5496a4a1f36160ac5556495faa4a24 https://repo.anaconda.com/pkgs/main/noarch/python-dateutil-2.8.2-pyhd3eb1b0_0.conda#211ee00320b08a1ac9fea6677649f6c9 -https://repo.anaconda.com/pkgs/main/osx-64/brotlipy-0.7.0-py311h6c40b1e_1002.conda#214a3acdf6f828a764263d430826688b -https://repo.anaconda.com/pkgs/main/osx-64/cryptography-41.0.2-py311h3b477ad_0.conda#ce895538478ebb5d009ee588f2bbd782 https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-4.0.0-py311hecd8cb5_0.conda#c63893569d344f4297f2ae08e0387ccf https://repo.anaconda.com/pkgs/main/noarch/pytest-forked-1.3.0-pyhd3eb1b0_0.tar.bz2#07970bffdc78f417d7f8f1c7e620f5c4 -https://repo.anaconda.com/pkgs/main/osx-64/pyopenssl-23.2.0-py311hecd8cb5_0.conda#cc87a54275ec00c9d5f893d0d872e0b1 https://repo.anaconda.com/pkgs/main/noarch/pytest-xdist-2.5.0-pyhd3eb1b0_0.conda#d15cdc4207bcf8ca920822597f1d138d -https://repo.anaconda.com/pkgs/main/osx-64/urllib3-1.26.16-py311hecd8cb5_0.conda#8bb53507a1d3ab7602adfc54f29adfe7 -https://repo.anaconda.com/pkgs/main/osx-64/requests-2.31.0-py311hecd8cb5_0.conda#454548d88c1db83b3e5e890a9f788e40 -https://repo.anaconda.com/pkgs/main/noarch/pooch-1.4.0-pyhd3eb1b0_0.conda#69ec83cb3d152f9e854115555004f368 https://repo.anaconda.com/pkgs/main/osx-64/bottleneck-1.3.5-py311hb9e55a9_0.conda#5aa1b58b421d4608b16184f8468253ef https://repo.anaconda.com/pkgs/main/osx-64/contourpy-1.0.5-py311ha357a0b_0.conda#a130f83ba4b5d008e0c134c73e10b8fb https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-3.7.1-py311hecd8cb5_1.conda#6ec92c9f01ff593b177da73ab17e9f54 @@ -88,6 +76,6 @@ https://repo.anaconda.com/pkgs/main/osx-64/mkl_fft-1.3.6-py311hdb55bb0_1.conda#d https://repo.anaconda.com/pkgs/main/osx-64/mkl_random-1.2.2-py311hdb55bb0_1.conda#9b1de8f6e280fb8e74f186007a0b4ca4 https://repo.anaconda.com/pkgs/main/osx-64/numpy-1.24.3-py311h728a8a3_1.conda#68069c79ebb0cdd2561026a909a57183 https://repo.anaconda.com/pkgs/main/osx-64/numexpr-2.8.4-py311h728a8a3_1.conda#be9facbd68b7476262684afb69fd2841 -https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.10.1-py311h224febf_1.conda#a3ae336a401d47b73b17c3b5d780de78 -https://repo.anaconda.com/pkgs/main/osx-64/pandas-1.5.3-py311hc5848a5_0.conda#4111406bad69018aa5e1cb04561a4374 +https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.1-py311h224febf_0.conda#ddae5ebff1fd56d3900250861cd2d4b9 +https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.0.3-py311hdb55bb0_0.conda#d23c89f98d12773c3cc8d16bd6206711 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-4.2.3-py311h37a6a59_0.conda#5fca7d043dc68c1d7acc22aa03a24918 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index a85af464ddcb4..865546a447b7b 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 61862ec58344ddfaad255f4687ca311eb7e2e61001e209d63f0cc92f97178848 +# input_hash: d01d23bd27bcd50d2b3643492f966c8e390822d72b69f31bf66c2fe98a265a4c @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.05.30-h06a4308_0.conda#979be8dd2368decd342b13e01540d297 @@ -12,7 +12,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d28 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda#06e288f9250abef59b9a367d151fc339 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.10-h7f8727e_0.conda#83d254d7304c7c1220f82668ab1e7b42 +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.10-h7f8727e_2.conda#066a828cc9dcd120af8c503381d6a1b8 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.2-h5eee18b_0.conda#bcd31de48a0dcb44bc5b99675800c5cc https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31fbfbb5057c92fa845ad6adef93 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e @@ -28,35 +28,30 @@ 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https://files.pythonhosted.org/packages/28/08/9dcdaa5aac4634e4c23af26d92121f7ce445c630efa0d3037881ae2407fb/joblib-1.3.1-py3-none-any.whl#sha256=89cf0529520e01b3de7ac7b74a8102c90d16d54c64b5dd98cafcd14307fdf915 -# pip kiwisolver @ https://files.pythonhosted.org/packages/a4/36/c414d75be311ce97ef7248edcc4fc05afae2998641bf6b592d43a9dee581/kiwisolver-1.4.4-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=7c43e1e1206cd421cd92e6b3280d4385d41d7166b3ed577ac20444b6995a445f +# pip joblib @ https://files.pythonhosted.org/packages/10/40/d551139c85db202f1f384ba8bcf96aca2f329440a844f924c8a0040b6d02/joblib-1.3.2-py3-none-any.whl#sha256=ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9 +# pip kiwisolver @ https://files.pythonhosted.org/packages/c0/a8/841594f11d0b88d8aeb26991bc4dac38baa909dc58d0c4262a4f7893bcbf/kiwisolver-1.4.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=6c3bd3cde54cafb87d74d8db50b909705c62b17c2099b8f2e25b461882e544ff # pip lazy-loader @ https://files.pythonhosted.org/packages/a1/c3/65b3814e155836acacf720e5be3b5757130346670ac454fee29d3eda1381/lazy_loader-0.3-py3-none-any.whl#sha256=1e9e76ee8631e264c62ce10006718e80b2cfc74340d17d1031e0f84af7478554 # pip markupsafe @ https://files.pythonhosted.org/packages/de/63/cb7e71984e9159ec5f45b5e81e896c8bdd0e45fe3fc6ce02ab497f0d790e/MarkupSafe-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=05fb21170423db021895e1ea1e1f3ab3adb85d1c2333cbc2310f2a26bc77272e # pip networkx @ https://files.pythonhosted.org/packages/a8/05/9d4f9b78ead6b2661d6e8ea772e111fc4a9fbd866ad0c81906c11206b55e/networkx-3.1-py3-none-any.whl#sha256=4f33f68cb2afcf86f28a45f43efc27a9386b535d567d2127f8f61d51dec58d36 # pip numpy @ https://files.pythonhosted.org/packages/69/1f/c95b1108a9972a52d7b1b63ed8ca70466b59b8c1811bd121f1e667cc45d8/numpy-1.25.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=d7806500e4f5bdd04095e849265e55de20d8cc4b661b038957354327f6d9b295 # pip packaging @ https://files.pythonhosted.org/packages/ab/c3/57f0601a2d4fe15de7a553c00adbc901425661bf048f2a22dfc500caf121/packaging-23.1-py3-none-any.whl#sha256=994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61 # pip pillow @ https://files.pythonhosted.org/packages/eb/3a/023761d323f51b932ba8aa70bfe9c987f5fa094ffbaba9cd9295b8eee429/Pillow-10.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=9f7c16705f44e0504a3a2a14197c1f0b32a95731d251777dcb060aa83022cb2d -# pip pluggy @ https://files.pythonhosted.org/packages/51/32/4a79112b8b87b21450b066e102d6608907f4c885ed7b04c3fdb085d4d6ae/pluggy-1.2.0-py3-none-any.whl#sha256=c2fd55a7d7a3863cba1a013e4e2414658b1d07b6bc57b3919e0c63c9abb99849 +# pip pluggy @ https://files.pythonhosted.org/packages/05/b8/42ed91898d4784546c5f06c60506400548db3f7a4b3fb441cba4e5c17952/pluggy-1.3.0-py3-none-any.whl#sha256=d89c696a773f8bd377d18e5ecda92b7a3793cbe66c87060a6fb58c7b6e1061f7 # pip py @ https://files.pythonhosted.org/packages/f6/f0/10642828a8dfb741e5f3fbaac830550a518a775c7fff6f04a007259b0548/py-1.11.0-py2.py3-none-any.whl#sha256=607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378 # pip pygments @ https://files.pythonhosted.org/packages/43/88/29adf0b44ba6ac85045e63734ae0997d3c58d8b1a91c914d240828d0d73d/Pygments-2.16.1-py3-none-any.whl#sha256=13fc09fa63bc8d8671a6d247e1eb303c4b343eaee81d861f3404db2935653692 # pip pyparsing @ https://files.pythonhosted.org/packages/6c/10/a7d0fa5baea8fe7b50f448ab742f26f52b80bfca85ac2be9d35cdd9a3246/pyparsing-3.0.9-py3-none-any.whl#sha256=5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc # pip pytz @ https://files.pythonhosted.org/packages/7f/99/ad6bd37e748257dd70d6f85d916cafe79c0b0f5e2e95b11f7fbc82bf3110/pytz-2023.3-py2.py3-none-any.whl#sha256=a151b3abb88eda1d4e34a9814df37de2a80e301e68ba0fd856fb9b46bfbbbffb # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 # pip snowballstemmer @ https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl#sha256=c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a -# pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/06/c1/5e2cafbd03105ce50d8500f9b4e8a6e8d02e22d0475b574c3b3e9451a15f/sphinxcontrib_applehelp-1.0.4-py3-none-any.whl#sha256=29d341f67fb0f6f586b23ad80e072c8e6ad0b48417db2bde114a4c9746feb228 -# pip sphinxcontrib-devhelp @ https://files.pythonhosted.org/packages/c5/09/5de5ed43a521387f18bdf5f5af31d099605c992fd25372b2b9b825ce48ee/sphinxcontrib_devhelp-1.0.2-py2.py3-none-any.whl#sha256=8165223f9a335cc1af7ffe1ed31d2871f325254c0423bc0c4c7cd1c1e4734a2e -# pip 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https://repo.anaconda.com/pkgs/main/linux-64/pip-23.2.1-py39h06a4308_0.conda#e33 # pip urllib3 @ https://files.pythonhosted.org/packages/9b/81/62fd61001fa4b9d0df6e31d47ff49cfa9de4af03adecf339c7bc30656b37/urllib3-2.0.4-py3-none-any.whl#sha256=de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4 # pip zipp @ https://files.pythonhosted.org/packages/8c/08/d3006317aefe25ea79d3b76c9650afabaf6d63d1c8443b236e7405447503/zipp-3.16.2-py3-none-any.whl#sha256=679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0 # pip contourpy @ https://files.pythonhosted.org/packages/38/6f/5382bdff9dda60cb17cef6dfa2bad3e6edacffd5c2243e282e851c63f721/contourpy-1.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=e94bef2580e25b5fdb183bf98a2faa2adc5b638736b2c0a4da98691da641316a -# pip coverage @ 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@ https://files.pythonhosted.org/packages/19/bd/a53569a0a698d925eb46dbea0bd3b6b62e7287a9ec88b5a03efa8ebd5b14/scikit_image-0.21.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=78b1e96c59cab640ca5c5b22c501524cfaf34cbe0cb51ba73bd9a9ede3fb6e1d -# pip sphinx @ https://files.pythonhosted.org/packages/48/17/325cf6a257d84751a48ae90752b3d8fe0be8f9535b6253add61c49d0d9bc/sphinx-7.1.2-py3-none-any.whl#sha256=d170a81825b2fcacb6dfd5a0d7f578a053e45d3f2b153fecc948c37344eb4cbe -# pip numpydoc @ https://files.pythonhosted.org/packages/c4/81/ad9b8837442ff451eca82515b41ac425f87acff7e2fc016fd1bda13fc01a/numpydoc-1.5.0-py3-none-any.whl#sha256=c997759fb6fc32662801cece76491eedbc0ec619b514932ffd2b270ae89c07f9 # pip pytest-xdist @ https://files.pythonhosted.org/packages/21/08/b1945d4b4986eb1aa10cf84efc5293bba39da80a2f95db3573dd90678408/pytest_xdist-2.5.0-py3-none-any.whl#sha256=6fe5c74fec98906deb8f2d2b616b5c782022744978e7bd4695d39c8f42d0ce65 +# pip numpydoc @ https://files.pythonhosted.org/packages/c4/81/ad9b8837442ff451eca82515b41ac425f87acff7e2fc016fd1bda13fc01a/numpydoc-1.5.0-py3-none-any.whl#sha256=c997759fb6fc32662801cece76491eedbc0ec619b514932ffd2b270ae89c07f9 +# pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/c0/0c/261c0949083c0ac635853528bb0070c89e927841d4e533ba0b5563365c06/sphinxcontrib_applehelp-1.0.7-py3-none-any.whl#sha256=094c4d56209d1734e7d252f6e0b3ccc090bd52ee56807a5d9315b19c122ab15d +# pip sphinxcontrib-devhelp @ https://files.pythonhosted.org/packages/c0/03/010ac733ec7b7f71c1dc88e7115743ee466560d6d85373b56fb9916e4586/sphinxcontrib_devhelp-1.0.5-py3-none-any.whl#sha256=fe8009aed765188f08fcaadbb3ea0d90ce8ae2d76710b7e29ea7d047177dae2f +# pip sphinxcontrib-htmlhelp @ https://files.pythonhosted.org/packages/28/7a/958f8e3e6abe8219d0d1f1224886de847ab227b218f4a07b61bc337f64be/sphinxcontrib_htmlhelp-2.0.4-py3-none-any.whl#sha256=8001661c077a73c29beaf4a79968d0726103c5605e27db92b9ebed8bab1359e9 +# pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/1f/e5/1850f3f118e95581c1e30b57028ac979badee1eb29e70ee72b0241f5a185/sphinxcontrib_qthelp-1.0.6-py3-none-any.whl#sha256=bf76886ee7470b934e363da7a954ea2825650013d367728588732c7350f49ea4 +# pip sphinx @ https://files.pythonhosted.org/packages/a6/54/f4fcf7113eb051a46476ecce9485c463f58dbc3887c06dbfe1e67a8ce7c0/sphinx-7.2.5-py3-none-any.whl#sha256=9269f9ed2821c9ebd30e4204f5c2339f5d4980e377bc89cb2cb6f9b17409c20a +# pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/95/d6/2e0bda62b2a808070ac922d21a950aa2cb5e4fcfb87e5ff5f86bc43a2201/sphinxcontrib_serializinghtml-1.1.9-py3-none-any.whl#sha256=9b36e503703ff04f20e9675771df105e58aa029cfcbc23b8ed716019b7416ae1 diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 2c80d823c0b85..c6435269a4d70 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: d7687370ba8c822d5b621703d51324b6767f15f0fc49177381f2a0a81a756684 +# input_hash: 5ead5ba5f8c8179fdc95f486e5db1fa2283387358360c89b1d62ffb6b1018cf0 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.05.30-h06a4308_0.conda#979be8dd2368decd342b13e01540d297 @@ -14,7 +14,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h7b6447c_0.conda#9303f4 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda#06e288f9250abef59b9a367d151fc339 https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.10-h7f8727e_0.conda#83d254d7304c7c1220f82668ab1e7b42 +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.10-h7f8727e_2.conda#066a828cc9dcd120af8c503381d6a1b8 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.2-h5eee18b_0.conda#bcd31de48a0dcb44bc5b99675800c5cc https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31fbfbb5057c92fa845ad6adef93 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e @@ -29,7 +29,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.2.1-py311h06a4308_0.conda#11 # pip babel @ https://files.pythonhosted.org/packages/df/c4/1088865e0246d7ecf56d819a233ab2b72f7d6ab043965ef327d0731b5434/Babel-2.12.1-py3-none-any.whl#sha256=b4246fb7677d3b98f501a39d43396d3cafdc8eadb045f4a31be01863f655c610 # pip certifi @ https://files.pythonhosted.org/packages/4c/dd/2234eab22353ffc7d94e8d13177aaa050113286e93e7b40eae01fbf7c3d9/certifi-2023.7.22-py3-none-any.whl#sha256=92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9 # pip charset-normalizer @ https://files.pythonhosted.org/packages/bc/85/ef25d4ba14c7653c3020a1c6e1a7413e6791ef36a0ac177efa605fc2c737/charset_normalizer-3.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=246de67b99b6851627d945db38147d1b209a899311b1305dd84916f2b88526c6 -# pip coverage @ https://files.pythonhosted.org/packages/a7/cd/3ce94ad9d407a052dc2a74fbeb1c7947f442155b28264eb467ee78dea812/coverage-7.2.7-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=63426706118b7f5cf6bb6c895dc215d8a418d5952544042c8a2d9fe87fcf09cb +# pip coverage @ 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https://repo.anaconda.com/pkgs/main/linux-64/pip-23.2.1-py311h06a4308_0.conda#11 # pip markupsafe @ https://files.pythonhosted.org/packages/fe/21/2eff1de472ca6c99ec3993eab11308787b9879af9ca8bbceb4868cf4f2ca/MarkupSafe-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bfce63a9e7834b12b87c64d6b155fdd9b3b96191b6bd334bf37db7ff1fe457f2 # pip packaging @ https://files.pythonhosted.org/packages/ab/c3/57f0601a2d4fe15de7a553c00adbc901425661bf048f2a22dfc500caf121/packaging-23.1-py3-none-any.whl#sha256=994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61 # pip platformdirs @ https://files.pythonhosted.org/packages/14/51/fe5a0d6ea589f0d4a1b97824fb518962ad48b27cd346dcdfa2405187997a/platformdirs-3.10.0-py3-none-any.whl#sha256=d7c24979f292f916dc9cbf8648319032f551ea8c49a4c9bf2fb556a02070ec1d -# pip pluggy @ https://files.pythonhosted.org/packages/51/32/4a79112b8b87b21450b066e102d6608907f4c885ed7b04c3fdb085d4d6ae/pluggy-1.2.0-py3-none-any.whl#sha256=c2fd55a7d7a3863cba1a013e4e2414658b1d07b6bc57b3919e0c63c9abb99849 +# pip pluggy @ https://files.pythonhosted.org/packages/05/b8/42ed91898d4784546c5f06c60506400548db3f7a4b3fb441cba4e5c17952/pluggy-1.3.0-py3-none-any.whl#sha256=d89c696a773f8bd377d18e5ecda92b7a3793cbe66c87060a6fb58c7b6e1061f7 # pip py @ https://files.pythonhosted.org/packages/f6/f0/10642828a8dfb741e5f3fbaac830550a518a775c7fff6f04a007259b0548/py-1.11.0-py2.py3-none-any.whl#sha256=607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378 # pip pygments @ https://files.pythonhosted.org/packages/43/88/29adf0b44ba6ac85045e63734ae0997d3c58d8b1a91c914d240828d0d73d/Pygments-2.16.1-py3-none-any.whl#sha256=13fc09fa63bc8d8671a6d247e1eb303c4b343eaee81d861f3404db2935653692 # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 # pip snowballstemmer @ https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl#sha256=c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a -# pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/06/c1/5e2cafbd03105ce50d8500f9b4e8a6e8d02e22d0475b574c3b3e9451a15f/sphinxcontrib_applehelp-1.0.4-py3-none-any.whl#sha256=29d341f67fb0f6f586b23ad80e072c8e6ad0b48417db2bde114a4c9746feb228 -# pip sphinxcontrib-devhelp @ https://files.pythonhosted.org/packages/c5/09/5de5ed43a521387f18bdf5f5af31d099605c992fd25372b2b9b825ce48ee/sphinxcontrib_devhelp-1.0.2-py2.py3-none-any.whl#sha256=8165223f9a335cc1af7ffe1ed31d2871f325254c0423bc0c4c7cd1c1e4734a2e -# pip sphinxcontrib-htmlhelp @ https://files.pythonhosted.org/packages/6e/ee/a1f5e39046cbb5f8bc8fba87d1ddf1c6643fbc9194e58d26e606de4b9074/sphinxcontrib_htmlhelp-2.0.1-py3-none-any.whl#sha256=c38cb46dccf316c79de6e5515e1770414b797162b23cd3d06e67020e1d2a6903 # pip sphinxcontrib-jsmath @ https://files.pythonhosted.org/packages/c2/42/4c8646762ee83602e3fb3fbe774c2fac12f317deb0b5dbeeedd2d3ba4b77/sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl#sha256=2ec2eaebfb78f3f2078e73666b1415417a116cc848b72e5172e596c871103178 -# pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/2b/14/05f9206cf4e9cfca1afb5fd224c7cd434dcc3a433d6d9e4e0264d29c6cdb/sphinxcontrib_qthelp-1.0.3-py2.py3-none-any.whl#sha256=bd9fc24bcb748a8d51fd4ecaade681350aa63009a347a8c14e637895444dfab6 -# pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/c6/77/5464ec50dd0f1c1037e3c93249b040c8fc8078fdda97530eeb02424b6eea/sphinxcontrib_serializinghtml-1.1.5-py2.py3-none-any.whl#sha256=352a9a00ae864471d3a7ead8d7d79f5fc0b57e8b3f95e9867eb9eb28999b92fd # pip threadpoolctl @ https://files.pythonhosted.org/packages/81/12/fd4dea011af9d69e1cad05c75f3f7202cdcbeac9b712eea58ca779a72865/threadpoolctl-3.2.0-py3-none-any.whl#sha256=2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032 # pip urllib3 @ https://files.pythonhosted.org/packages/9b/81/62fd61001fa4b9d0df6e31d47ff49cfa9de4af03adecf339c7bc30656b37/urllib3-2.0.4-py3-none-any.whl#sha256=de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4 # pip jinja2 @ https://files.pythonhosted.org/packages/bc/c3/f068337a370801f372f2f8f6bad74a5c140f6fda3d9de154052708dd3c65/Jinja2-3.1.2-py3-none-any.whl#sha256=6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61 @@ -58,6 +53,11 @@ 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pip numpydoc @ https://files.pythonhosted.org/packages/c4/81/ad9b8837442ff451eca82515b41ac425f87acff7e2fc016fd1bda13fc01a/numpydoc-1.5.0-py3-none-any.whl#sha256=c997759fb6fc32662801cece76491eedbc0ec619b514932ffd2b270ae89c07f9 # pip pytest-xdist @ https://files.pythonhosted.org/packages/21/08/b1945d4b4986eb1aa10cf84efc5293bba39da80a2f95db3573dd90678408/pytest_xdist-2.5.0-py3-none-any.whl#sha256=6fe5c74fec98906deb8f2d2b616b5c782022744978e7bd4695d39c8f42d0ce65 +# pip numpydoc @ https://files.pythonhosted.org/packages/c4/81/ad9b8837442ff451eca82515b41ac425f87acff7e2fc016fd1bda13fc01a/numpydoc-1.5.0-py3-none-any.whl#sha256=c997759fb6fc32662801cece76491eedbc0ec619b514932ffd2b270ae89c07f9 +# pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/c0/0c/261c0949083c0ac635853528bb0070c89e927841d4e533ba0b5563365c06/sphinxcontrib_applehelp-1.0.7-py3-none-any.whl#sha256=094c4d56209d1734e7d252f6e0b3ccc090bd52ee56807a5d9315b19c122ab15d +# pip sphinxcontrib-devhelp @ 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https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.0-pyhd8ed1ab_0.conda#3cfe9b9e958e7238a386933c75d190db https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984 https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.7.1-hd8ed1ab_0.conda#f96688577f1faa58096d06a45136afa2 https://conda.anaconda.org/conda-forge/noarch/urllib3-2.0.4-pyhd8ed1ab_0.conda#18badd8fa3648d1beb1fcc7f2e0f756e -https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.0.0-pyhd8ed1ab_1.conda#d69f29916f934f30adb1dd5fff4d9a8b +https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.0.1-pyhd8ed1ab_0.conda#54661981fd331e20847d8a49543dd9af https://conda.anaconda.org/conda-forge/noarch/platformdirs-3.10.0-pyhd8ed1ab_0.conda#0809187ef9b89a3d94a5c24d13936236 https://conda.anaconda.org/conda-forge/noarch/pytest-forked-1.6.0-pyhd8ed1ab_0.conda#a46947638b6e005b63d2d6271da529b0 https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b @@ -105,5 +105,5 @@ https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.7.2-py39hfcdda https://conda.anaconda.org/conda-forge/noarch/pooch-1.7.0-pyha770c72_3.conda#5936894aade8240c867d292aa0d980c6 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.tar.bz2#1fdd1f3baccf0deb647385c677a1a48e https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.7.2-py39h4162558_0.conda#839abb578897996f795648dc0b621b30 -https://conda.anaconda.org/conda-forge/linux-64/scipy-1.11.1-py39h129f8d9_0.conda#cf3f3a39e9bd095064c903cd2fc6a4a0 +https://conda.anaconda.org/conda-forge/linux-64/scipy-1.11.2-py39h129f8d9_0.conda#296411437a9b4e75d088c7200bbc6500 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.0.1-py39h00faaa6_0.conda#d5b46d053aa61bb420230a041afcb136 diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index 3c0d757bb94ad..1e90eaf311711 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -2,11 +2,11 @@ # This file is autogenerated by pip-compile with Python 3.10 # by the following command: # -# pip-compile --config=pyproject.toml --output-file=build_tools/azure/ubuntu_atlas_lock.txt build_tools/azure/ubuntu_atlas_requirements.txt +# pip-compile --output-file=build_tools/azure/ubuntu_atlas_lock.txt build_tools/azure/ubuntu_atlas_requirements.txt # -cython==3.0.0 +cython==0.29.36 # via -r build_tools/azure/ubuntu_atlas_requirements.txt -exceptiongroup==1.1.2 +exceptiongroup==1.1.3 # via pytest execnet==2.0.2 # via pytest-xdist @@ -16,7 +16,7 @@ joblib==1.1.1 # via -r build_tools/azure/ubuntu_atlas_requirements.txt packaging==23.1 # via pytest -pluggy==1.2.0 +pluggy==1.3.0 # via pytest py==1.11.0 # via pytest-forked diff --git a/build_tools/azure/ubuntu_atlas_requirements.txt b/build_tools/azure/ubuntu_atlas_requirements.txt index 57413851e5329..97b6bd8a83a26 100644 --- a/build_tools/azure/ubuntu_atlas_requirements.txt +++ b/build_tools/azure/ubuntu_atlas_requirements.txt @@ -1,7 +1,7 @@ # DO NOT EDIT: this file is generated from the specification found in the # following script to centralize the configuration for CI builds: # build_tools/update_environments_and_lock_files.py -cython +cython<3.0.0 joblib==1.1.1 # min threadpoolctl==2.0.0 # min pytest diff --git a/build_tools/circle/doc_environment.yml b/build_tools/circle/doc_environment.yml index 0c47eb505849a..af00f57a3dc51 100644 --- a/build_tools/circle/doc_environment.yml +++ b/build_tools/circle/doc_environment.yml @@ -8,7 +8,7 @@ dependencies: - numpy - blas - scipy - - cython + - cython<3.0.0 - joblib - threadpoolctl - matplotlib diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index ed665dbd5f400..5cbf6e48f98e5 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: c5d98c25be4983392d8d1d6b28c2ceea24a4d25b50d0f45ad6192f94abed4d46 +# input_hash: e5c4e5603cb530b6ebd880df5ac2a031cdeebbbb3a8188ec94d243cf9851ad37 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.7.22-hbcca054_0.conda#a73ecd2988327ad4c8f2c331482917f2 @@ -87,10 +87,10 @@ https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.47-h71f35ed_0.conda#c2097d0b46367996f09b4e8e4920384a https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.39-h753d276_0.conda#e1c890aebdebbfbf87e2c917187b4416 -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.42.0-h2797004_0.conda#fdaae20a1cf7cd62130a0973190a31b7 +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.43.0-h2797004_0.conda#903fa782a9067d5934210df6d79220f6 https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2#309dec04b70a3cc0f1e84a4013683bc0 https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.15-h0b41bf4_0.conda#33277193f5b92bad9fdd230eb700929c -https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.11.4-h0d562d8_0.conda#e46fad17d5fb57316b956f88dca765e4 +https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.11.5-h0d562d8_0.conda#558ab736404275d7df61c473c1af35aa https://conda.anaconda.org/conda-forge/linux-64/mysql-common-8.0.33-hf1915f5_2.conda#a55ff0ed12efd86cf3a3dfb750adb950 https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.23-pthreads_h855a84d_0.conda#ba8810202f8879562f01b4f9957c1ada https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.40-hc3806b6_0.tar.bz2#69e2c796349cd9b273890bee0febfe1b @@ -98,27 +98,27 @@ https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47 https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.12-h27826a3_0.tar.bz2#5b8c42eb62e9fc961af70bdd6a26e168 https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.4-h7391055_0.conda#93ee23f12bc2e684548181256edd2cf6 https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.13-hd590300_5.conda#68c34ec6149623be41a1933ab996a209 -https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.2-hfc55251_7.conda#32ae18eb2a687912fc9e92a501c0a11b +https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.5-hfc55251_0.conda#04b88013080254850d6c01ed54810589 https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.4-h0f2a231_0.conda#876286b5941933a0f558777e57d883cc https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.0.9-h166bdaf_9.conda#d47dee1856d9cb955b8076eeff304a5b -https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.10.0-hb4ffafa_0.conda#63b397207c1e039b3373dd45af045d8b +https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.10.2-hb4ffafa_0.conda#1a88c95afde6f13403492cac91352568 https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-hca18f0e_1.conda#e1232042de76d24539a436d37597eb06 https://conda.anaconda.org/conda-forge/linux-64/gcc-12.3.0-h8d2909c_1.conda#cb7c7892032ecf45fcad76d67b6a3e9b https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-12.3.0-h76fc315_1.conda#70b65b994853c64c631b6addfbaea487 https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-12.3.0-hfcedea8_0.conda#4cc7e96285bfbca093531d116724ec6d https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-12.3.0-he2b93b0_0.conda#3f00aa0a8f8d3924890fecae937cc6bd -https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.1-h659d440_0.conda#1b5126ec25763eb17ef74c8763d26e84 +https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.2-h659d440_0.conda#cd95826dbd331ed1be26bdf401432844 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-17_linux64_openblas.conda#7ef0969b00fe3d6eef56a8151d3afb29 https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.10.1-h166bdaf_0.tar.bz2#f967fc95089cd247ceed56eda31de3a9 https://conda.anaconda.org/conda-forge/linux-64/libglib-2.76.4-hebfc3b9_0.conda#c6f951789c888f7bbd2dd6858eab69de https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-17_linux64_openblas.conda#a2103882c46492e26500fcb56c03de8b https://conda.anaconda.org/conda-forge/linux-64/libllvm15-15.0.7-h5cf9203_3.conda#9efe82d44b76a7529a1d702e5a37752e -https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.0-hb75c966_0.conda#c648d19cd9c8625898d5d370414de7c7 -https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.5.1-h8b53f26_0.conda#8ad377fb60abab446a9f02c62b3c2190 +https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hbc2eb40_0.conda#38f84d395629e48b7c7b48a8ca740341 +https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.5.1-h8b53f26_1.conda#5b09e13d732dda1a2bc9adc711164f4d https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-16.0.6-h4dfa4b3_0.conda#b096c85c415519259e731d8fb719a3ef https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.0.33-hca2cd23_2.conda#b2f09078f50b9e859aca3f0dc1cc8b7e -https://conda.anaconda.org/conda-forge/linux-64/nss-3.89-he45b914_0.conda#2745719a58eeaab6657256a3f142f099 -https://conda.anaconda.org/conda-forge/linux-64/python-3.9.16-h2782a2a_0_cpython.conda#95c9b7c96a7fd7342e0c9d0a917b8f78 +https://conda.anaconda.org/conda-forge/linux-64/nss-3.92-h1d7d5a4_0.conda#22c89a3d87828fe925b310b9cdf0f574 +https://conda.anaconda.org/conda-forge/linux-64/python-3.9.18-h0755675_0_cpython.conda#3ede353bc605068d9677e700b1847382 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.0-hd590300_1.conda#9bfac7ccd94d54fd21a0501296d60424 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-keysyms-0.4.0-h8ee46fc_1.conda#632413adcd8bc16b515cab87a2932913 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.9-hd590300_1.conda#e995b155d938b6779da6ace6c6b13816 @@ -132,10 +132,10 @@ https://conda.anaconda.org/conda-forge/noarch/certifi-2023.7.22-pyhd8ed1ab_0.con https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.2.0-pyhd8ed1ab_0.conda#313516e9a4b08b12dfb1e1cd390a96e3 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 https://conda.anaconda.org/conda-forge/noarch/cycler-0.11.0-pyhd8ed1ab_0.tar.bz2#a50559fad0affdbb33729a68669ca1cb -https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.0-py39h3d6467e_0.conda#3d700ccea39ca04cb8b6210ac653e0b1 +https://conda.anaconda.org/conda-forge/linux-64/cython-0.29.36-py39h3d6467e_0.conda#879336ef992db7dd6a9dc4eb1e34ce58 https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2#ecfff944ba3960ecb334b9a2663d708d https://conda.anaconda.org/conda-forge/linux-64/docutils-0.19-py39hf3d152e_1.tar.bz2#adb733ec2ee669f6d010758d054da60f -https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.1.2-pyhd8ed1ab_0.conda#de4cb3384374e1411f0454edcf546cdb +https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.1.3-pyhd8ed1ab_0.conda#e6518222753f519e911e83136d2158d9 https://conda.anaconda.org/conda-forge/noarch/execnet-2.0.2-pyhd8ed1ab_0.conda#67de0d8241e1060a479e3c37793e26f9 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.14.2-h14ed4e7_0.conda#0f69b688f52ff6da70bccb7ff7001d1d https://conda.anaconda.org/conda-forge/linux-64/gfortran-12.3.0-h499e0f7_1.conda#d93122c45011981d840864197e7d92a4 @@ -146,13 +146,13 @@ https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-12.3.0-h8a814eb_1.c https://conda.anaconda.org/conda-forge/noarch/idna-3.4-pyhd8ed1ab_0.tar.bz2#34272b248891bddccc64479f9a7fffed https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.bz2#7de5386c8fea29e76b303f37dde4c352 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 -https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.4-py39hf939315_1.tar.bz2#41679a052a8ce841c74df1ebc802e411 -https://conda.anaconda.org/conda-forge/noarch/lazy_loader-0.2-pyhd8ed1ab_0.conda#d060d017720c9882c4eca0544a4a0592 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https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl#sha256=a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 -# pip websocket-client @ https://files.pythonhosted.org/packages/d3/a3/63e9329c8cc9be6153e919e17d0ef5b60d537fed78564872951b95bcc17c/websocket_client-1.6.1-py3-none-any.whl#sha256=f1f9f2ad5291f0225a49efad77abf9e700b6fef553900623060dad6e26503b9d -# pip anyio @ https://files.pythonhosted.org/packages/19/24/44299477fe7dcc9cb58d0a57d5a7588d6af2ff403fdd2d47a246c91a3246/anyio-3.7.1-py3-none-any.whl#sha256=91dee416e570e92c64041bd18b900d1d6fa78dff7048769ce5ac5ddad004fbb5 +# pip websocket-client @ https://files.pythonhosted.org/packages/4b/4a/3176388095e5bae6e6a1fbee66c438809230ae0196e7de4af12c5e75c509/websocket_client-1.6.2-py3-none-any.whl#sha256=ce54f419dfae71f4bdba69ebe65bf7f0a93fe71bc009ad3a010aacc3eebad537 +# pip anyio @ 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pip isoduration @ https://files.pythonhosted.org/packages/7b/55/e5326141505c5d5e34c5e0935d2908a74e4561eca44108fbfb9c13d2911a/isoduration-20.11.0-py3-none-any.whl#sha256=b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042 # pip jsonschema-specifications @ https://files.pythonhosted.org/packages/1c/24/83349ac2189cc2435e84da3f69ba3c97314d3c0622628e55171c6798ed80/jsonschema_specifications-2023.7.1-py3-none-any.whl#sha256=05adf340b659828a004220a9613be00fa3f223f2b82002e273dee62fd50524b1 # pip jupyter-server-terminals @ https://files.pythonhosted.org/packages/ea/7f/36db12bdb90f5237766dcbf59892198daab7260acbcf03fc75e2a2a82672/jupyter_server_terminals-0.4.4-py3-none-any.whl#sha256=75779164661cec02a8758a5311e18bb8eb70c4e86c6b699403100f1585a12a36 -# pip jupyterlite-core @ 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https://files.pythonhosted.org/packages/f4/e7/ef30a90b70eba39e675689b9eaaa92530a71d7435ab8f9cae520814e0caf/nbformat-5.9.2-py3-none-any.whl#sha256=1c5172d786a41b82bcfd0c23f9e6b6f072e8fb49c39250219e4acfff1efe89e9 # pip nbclient @ https://files.pythonhosted.org/packages/ac/5a/d670ca51e6c3d98574b9647599821590efcd811d71f58e9c89fc59a17685/nbclient-0.8.0-py3-none-any.whl#sha256=25e861299e5303a0477568557c4045eccc7a34c17fc08e7959558707b9ebe548 -# pip nbconvert @ https://files.pythonhosted.org/packages/51/bd/ede955c9e981abf6d0e12a7583fd0f9256eedfb461073f25045770f3e717/nbconvert-7.7.3-py3-none-any.whl#sha256=3022adadff3f86578a47fab7c2228bb3ca9c56a24345642a22f917f6168b48fc -# pip jupyter-server @ https://files.pythonhosted.org/packages/f2/8f/914785ff2a0c4e2a7e15217710d57568f6ed6a84befa83a5a8c8b22ed3c6/jupyter_server-2.7.0-py3-none-any.whl#sha256=6a77912aff643e53fa14bdb2634884b52b784a4be77ce8e93f7283faed0f0849 +# pip nbconvert @ 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a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 5d357f651dbd2..78b2081a07b0f 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: d465abb23248872aed411e2f2f7293a661f0b783f1a84420ffa5431229814cab +# input_hash: 65e3ed797c4adee3f15a519d65e7dd7ed27340eb62a52cd1d1b597c377bc77f5 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.7.22-hbcca054_0.conda#a73ecd2988327ad4c8f2c331482917f2 @@ -47,12 +47,12 @@ https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-7.5.0-h56 https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-7.5.0-hd0bb8aa_20.tar.bz2#dbe78fc5fb9c339f8e55426559e12f7b 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https://conda.anaconda.org/conda-forge/noarch/babel-2.12.1-pyhd8ed1ab_1.conda#ac432e732804a81ddcf29c92ead57cde https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.12.2-py38h01eb140_0.conda#e9d465b78d0b41beeb6bcceb6714520d @@ -127,7 +127,7 @@ https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-hfdff14a_1.tar.bz2#4 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.14.5-h36ae1b5_2.tar.bz2#00084ab2657be5bf0ba0757ccde797ef https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-6.8.0-pyha770c72_0.conda#4e9f59a060c3be52bc4ddc46ee9b6946 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.2-pyhd8ed1ab_1.tar.bz2#c8490ed5c70966d232fdd389d0dbed37 -https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.0-pyhd8ed1ab_1.conda#fb4caf6da228ccc487350eade569abae +https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda#4da50d410f553db77e62ab62ffaa1abc https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.8.0-20_mkl.tar.bz2#14b25490fdcc44e879ac6c10fe764f68 https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.8.0-20_mkl.tar.bz2#52c0ae3606eeae7e1d493f37f336f4f5 https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_0.tar.bz2#8b45f9f2b2f7a98b0ec179c8991a4a9b diff --git a/build_tools/cirrus/py39_conda_forge_environment.yml b/build_tools/cirrus/py39_conda_forge_environment.yml index 70aedd73bf883..78a9df9cf2451 100644 --- a/build_tools/cirrus/py39_conda_forge_environment.yml +++ b/build_tools/cirrus/py39_conda_forge_environment.yml @@ -8,7 +8,7 @@ dependencies: - numpy - blas - scipy - - cython + - cython<3.0.0 - joblib - threadpoolctl - matplotlib diff --git a/build_tools/cirrus/py39_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/py39_conda_forge_linux-aarch64_conda.lock index 2324d3d372676..5f9fdd498a3e0 100644 --- a/build_tools/cirrus/py39_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/py39_conda_forge_linux-aarch64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-aarch64 -# input_hash: de5bfe2a68b349f08233af7b94fc3b2045503b21289e8d3bdb30a1613fd0ddb8 +# input_hash: cdaa5d55d8877a790d9ff09cc07901d3dc654506873fdf6b254372ee2f516f41 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2023.7.22-hcefe29a_0.conda#95d7f998087114466fa91e7c2887fa2f https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h2d8c526_0.conda#16246d69e945d0b1969a6099e7c5d457 @@ -13,7 +13,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/_openmp_mutex-4.5-2_kmp_llv https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-13.1.0-h2b4548d_0.conda#02619409d02932e28d694144b509597d https://conda.anaconda.org/conda-forge/linux-aarch64/bzip2-1.0.8-hf897c2e_4.tar.bz2#2d787570a729e273a4e75775ddf3348a 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+https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.0.1-pyhd8ed1ab_0.conda#d978c61aa5fc2c69380d53ad56b5ae86 +https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda#4da50d410f553db77e62ab62ffaa1abc https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-10.0.0-py39hc5b5638_0.conda#3a11d8338a85e3f93c5bbda05ec8d3c4 https://conda.anaconda.org/conda-forge/noarch/pip-23.2.1-pyhd8ed1ab_0.conda#e2783aa3f9235225eec92f9081c5b801 https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.0-pyhd8ed1ab_0.conda#3cfe9b9e958e7238a386933c75d190db @@ -90,7 +90,7 @@ https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0 https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.7.1-hd8ed1ab_0.conda#f96688577f1faa58096d06a45136afa2 https://conda.anaconda.org/conda-forge/noarch/urllib3-2.0.4-pyhd8ed1ab_0.conda#18badd8fa3648d1beb1fcc7f2e0f756e https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.117-openblas.conda#5f88c5a193286ed9a87afd4b815e8c70 -https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.0.0-pyhd8ed1ab_1.conda#d69f29916f934f30adb1dd5fff4d9a8b +https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.0.1-pyhd8ed1ab_0.conda#54661981fd331e20847d8a49543dd9af https://conda.anaconda.org/conda-forge/noarch/platformdirs-3.10.0-pyhd8ed1ab_0.conda#0809187ef9b89a3d94a5c24d13936236 https://conda.anaconda.org/conda-forge/noarch/pytest-forked-1.6.0-pyhd8ed1ab_0.conda#a46947638b6e005b63d2d6271da529b0 https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b @@ -98,4 +98,4 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.7.2-py39h https://conda.anaconda.org/conda-forge/noarch/pooch-1.7.0-pyha770c72_3.conda#5936894aade8240c867d292aa0d980c6 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.tar.bz2#1fdd1f3baccf0deb647385c677a1a48e https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.7.2-py39ha65689a_0.conda#ea8ac50ecf9cd562870764d909a35abc -https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.11.1-py39hf88902c_0.conda#8a00ffb609d552e57ae66f681e89bee6 +https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.11.2-py39hf88902c_0.conda#91d995cdb639f080707d23bbf97d55b0 diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 5c740a805f96e..d5ad0185bd685 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -155,6 +155,9 @@ def remove_from(alist, to_remove): "scipy": "min", "matplotlib": "min", "threadpoolctl": "2.2.0", + # Regression have been observed with Cython>=3.0.0. + # See: https://github.com/scikit-learn/scikit-learn/issues/27086 + "cython": "<3.0.0", }, }, { @@ -163,7 +166,13 @@ def remove_from(alist, to_remove): "platform": "linux-64", "channel": "conda-forge", "conda_dependencies": common_dependencies_without_coverage + ["ccache"], - "package_constraints": {"python": "3.8", "blas": "[build=openblas]"}, + "package_constraints": { + "python": "3.8", + "blas": "[build=openblas]", + # Regression have been observed with Cython>=3.0.0. + # See: https://github.com/scikit-learn/scikit-learn/issues/27086 + "cython": "<3.0.0", + }, }, { "build_name": "pylatest_pip_openblas_pandas", @@ -227,6 +236,9 @@ def remove_from(alist, to_remove): "package_constraints": { "blas": "[build=openblas]", "python": "3.9", + # Regression have been observed with Cython>=3.0.0. + # See: https://github.com/scikit-learn/scikit-learn/issues/27086 + "cython": "<3.0.0", }, }, { @@ -241,6 +253,9 @@ def remove_from(alist, to_remove): "package_constraints": { "python": "3.8", "blas": "[build=mkl]", + # Regression have been observed with Cython>=3.0.0. + # See: https://github.com/scikit-learn/scikit-learn/issues/27086 + "cython": "<3.0.0", }, }, { @@ -303,6 +318,9 @@ def remove_from(alist, to_remove): "python": "3.9", # XXX: sphinx > 6.0 does not correctly generate searchindex.js "sphinx": "6.0.0", + # Regression have been observed with Cython>=3.0.0. + # See: https://github.com/scikit-learn/scikit-learn/issues/27086 + "cython": "<3.0.0", }, }, { @@ -315,6 +333,9 @@ def remove_from(alist, to_remove): ) + ["pip", "ccache"], "package_constraints": { "python": "3.9", + # Regression have been observed with Cython>=3.0.0. + # See: https://github.com/scikit-learn/scikit-learn/issues/27086 + "cython": "<3.0.0", }, }, ] @@ -337,6 +358,9 @@ def remove_from(alist, to_remove): "pytest": "min", "pytest-cov": "min", # no pytest-xdist because it causes issue on 32bit + # Regression have been observed with Cython>=3.0.0. + # See: https://github.com/scikit-learn/scikit-learn/issues/27086 + "cython": "<3.0.0", }, # same Python version as in debian-32 build "python_version": "3.9.2", @@ -351,7 +375,13 @@ def remove_from(alist, to_remove): "pytest", "pytest-xdist", ], - "package_constraints": {"joblib": "min", "threadpoolctl": "min"}, + "package_constraints": { + "joblib": "min", + "threadpoolctl": "min", + # Regression have been observed with Cython>=3.0.0. + # See: https://github.com/scikit-learn/scikit-learn/issues/27086 + "cython": "<3.0.0", + }, "python_version": "3.10.4", }, ] From ba19a4ea3b6b07235bd2cd417ee16e120d85f0e6 Mon Sep 17 00:00:00 2001 From: Tamara Date: Thu, 7 Sep 2023 11:40:34 +0200 Subject: [PATCH 203/264] DOC fix random_state in several example for reproducibility (#27153) --- examples/cluster/plot_linkage_comparison.py | 18 +++++++++--------- examples/preprocessing/plot_all_scaling.py | 8 ++++++-- .../plot_discretization_classification.py | 4 ++-- 3 files changed, 17 insertions(+), 13 deletions(-) diff --git a/examples/cluster/plot_linkage_comparison.py b/examples/cluster/plot_linkage_comparison.py index dc009d0110f7c..793fee059d797 100644 --- a/examples/cluster/plot_linkage_comparison.py +++ b/examples/cluster/plot_linkage_comparison.py @@ -33,28 +33,28 @@ from sklearn import cluster, datasets from sklearn.preprocessing import StandardScaler -np.random.seed(0) - # %% # Generate datasets. We choose the size big enough to see the scalability # of the algorithms, but not too big to avoid too long running times n_samples = 1500 -noisy_circles = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05) -noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05) -blobs = datasets.make_blobs(n_samples=n_samples, random_state=8) -no_structure = np.random.rand(n_samples, 2), None +noisy_circles = datasets.make_circles( + n_samples=n_samples, factor=0.5, noise=0.05, random_state=170 +) +noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05, random_state=170) +blobs = datasets.make_blobs(n_samples=n_samples, random_state=170) +rng = np.random.RandomState(170) +no_structure = rng.rand(n_samples, 2), None # Anisotropicly distributed data -random_state = 170 -X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state) +X, y = datasets.make_blobs(n_samples=n_samples, random_state=170) transformation = [[0.6, -0.6], [-0.4, 0.8]] X_aniso = np.dot(X, transformation) aniso = (X_aniso, y) # blobs with varied variances varied = datasets.make_blobs( - n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state + n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=170 ) # %% diff --git a/examples/preprocessing/plot_all_scaling.py b/examples/preprocessing/plot_all_scaling.py index 5a08976bd2653..f53c50e33875a 100644 --- a/examples/preprocessing/plot_all_scaling.py +++ b/examples/preprocessing/plot_all_scaling.py @@ -102,11 +102,15 @@ ), ( "Data after quantile transformation (uniform pdf)", - QuantileTransformer(output_distribution="uniform").fit_transform(X), + QuantileTransformer( + output_distribution="uniform", random_state=42 + ).fit_transform(X), ), ( "Data after quantile transformation (gaussian pdf)", - QuantileTransformer(output_distribution="normal").fit_transform(X), + QuantileTransformer( + output_distribution="normal", random_state=42 + ).fit_transform(X), ), ("Data after sample-wise L2 normalizing", Normalizer().fit_transform(X)), ] diff --git a/examples/preprocessing/plot_discretization_classification.py b/examples/preprocessing/plot_discretization_classification.py index 71adf44474aa3..f3edcac0011d7 100644 --- a/examples/preprocessing/plot_discretization_classification.py +++ b/examples/preprocessing/plot_discretization_classification.py @@ -74,7 +74,7 @@ def get_name(estimator): ( make_pipeline( StandardScaler(), - KBinsDiscretizer(encode="onehot"), + KBinsDiscretizer(encode="onehot", random_state=0), LogisticRegression(random_state=0), ), { @@ -85,7 +85,7 @@ def get_name(estimator): ( make_pipeline( StandardScaler(), - KBinsDiscretizer(encode="onehot"), + KBinsDiscretizer(encode="onehot", random_state=0), LinearSVC(random_state=0, dual="auto"), ), { From b10c8ab9ca7923c004e25fa2c07476a945ef7591 Mon Sep 17 00:00:00 2001 From: Tamara Date: Thu, 7 Sep 2023 12:01:54 +0200 Subject: [PATCH 204/264] DOC fix random_state in example for reproducibility cont'd (#27238) Co-authored-by: Guillaume Lemaitre --- examples/manifold/plot_compare_methods.py | 2 +- examples/manifold/plot_manifold_sphere.py | 8 +++++--- .../miscellaneous/plot_kernel_approximation.py | 14 ++++++++++---- 3 files changed, 16 insertions(+), 8 deletions(-) diff --git a/examples/manifold/plot_compare_methods.py b/examples/manifold/plot_compare_methods.py index 88ce0f26b8dde..139964cfe93d4 100644 --- a/examples/manifold/plot_compare_methods.py +++ b/examples/manifold/plot_compare_methods.py @@ -182,7 +182,7 @@ def add_2d_scatter(ax, points, points_color, title=None): # Read more in the :ref:`User Guide `. spectral = manifold.SpectralEmbedding( - n_components=n_components, n_neighbors=n_neighbors + n_components=n_components, n_neighbors=n_neighbors, random_state=42 ) S_spectral = spectral.fit_transform(S_points) diff --git a/examples/manifold/plot_manifold_sphere.py b/examples/manifold/plot_manifold_sphere.py index 624206ff4d5e0..144d696904c37 100644 --- a/examples/manifold/plot_manifold_sphere.py +++ b/examples/manifold/plot_manifold_sphere.py @@ -78,7 +78,7 @@ t0 = time() trans_data = ( manifold.LocallyLinearEmbedding( - n_neighbors=n_neighbors, n_components=2, method=method + n_neighbors=n_neighbors, n_components=2, method=method, random_state=42 ) .fit_transform(sphere_data) .T @@ -112,7 +112,7 @@ # Perform Multi-dimensional scaling. t0 = time() -mds = manifold.MDS(2, max_iter=100, n_init=1, normalized_stress="auto") +mds = manifold.MDS(2, max_iter=100, n_init=1, normalized_stress="auto", random_state=42) trans_data = mds.fit_transform(sphere_data).T t1 = time() print("MDS: %.2g sec" % (t1 - t0)) @@ -126,7 +126,9 @@ # Perform Spectral Embedding. t0 = time() -se = manifold.SpectralEmbedding(n_components=2, n_neighbors=n_neighbors) +se = manifold.SpectralEmbedding( + n_components=2, n_neighbors=n_neighbors, random_state=42 +) trans_data = se.fit_transform(sphere_data).T t1 = time() print("Spectral Embedding: %.2g sec" % (t1 - t0)) diff --git a/examples/miscellaneous/plot_kernel_approximation.py b/examples/miscellaneous/plot_kernel_approximation.py index 7faf7900c7eb5..199739016efa8 100644 --- a/examples/miscellaneous/plot_kernel_approximation.py +++ b/examples/miscellaneous/plot_kernel_approximation.py @@ -72,18 +72,24 @@ # Create a classifier: a support vector classifier kernel_svm = svm.SVC(gamma=0.2) -linear_svm = svm.LinearSVC(dual="auto") +linear_svm = svm.LinearSVC(dual="auto", random_state=42) # create pipeline from kernel approximation # and linear svm feature_map_fourier = RBFSampler(gamma=0.2, random_state=1) feature_map_nystroem = Nystroem(gamma=0.2, random_state=1) fourier_approx_svm = pipeline.Pipeline( - [("feature_map", feature_map_fourier), ("svm", svm.LinearSVC(dual="auto"))] + [ + ("feature_map", feature_map_fourier), + ("svm", svm.LinearSVC(dual="auto", random_state=42)), + ] ) nystroem_approx_svm = pipeline.Pipeline( - [("feature_map", feature_map_nystroem), ("svm", svm.LinearSVC(dual="auto"))] + [ + ("feature_map", feature_map_nystroem), + ("svm", svm.LinearSVC(dual="auto", random_state=42)), + ] ) # fit and predict using linear and kernel svm: @@ -192,7 +198,7 @@ # visualize the decision surface, projected down to the first # two principal components of the dataset -pca = PCA(n_components=8).fit(data_train) +pca = PCA(n_components=8, random_state=42).fit(data_train) X = pca.transform(data_train) From 82499776e8d6a94fd25ad2ba28cefa95a914af95 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elisabeth=20G=C3=BCnther?= Date: Thu, 7 Sep 2023 16:05:31 +0200 Subject: [PATCH 205/264] DOC improve iris example (#26973) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Elisabeth Günther Co-authored-by: Guillaume Lemaitre --- examples/datasets/plot_iris_dataset.py | 78 +++++++++++++++----------- sklearn/datasets/_base.py | 3 + 2 files changed, 49 insertions(+), 32 deletions(-) diff --git a/examples/datasets/plot_iris_dataset.py b/examples/datasets/plot_iris_dataset.py index 16edcdf37b70d..32aba8918547e 100644 --- a/examples/datasets/plot_iris_dataset.py +++ b/examples/datasets/plot_iris_dataset.py @@ -1,7 +1,7 @@ """ -========================================================= +================ The Iris Dataset -========================================================= +================ This data sets consists of 3 different types of irises' (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy.ndarray @@ -19,37 +19,47 @@ # Modified for documentation by Jaques Grobler # License: BSD 3 clause -import matplotlib.pyplot as plt - -# unused but required import for doing 3d projections with matplotlib < 3.2 -import mpl_toolkits.mplot3d # noqa: F401 - +# %% +# Loading the iris dataset +# ------------------------ from sklearn import datasets -from sklearn.decomposition import PCA -# import some data to play with iris = datasets.load_iris() -X = iris.data[:, :2] # we only take the first two features. -y = iris.target -x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5 -y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5 -plt.figure(2, figsize=(8, 6)) -plt.clf() +# %% +# Scatter Plot of the Iris dataset +# -------------------------------- +import matplotlib.pyplot as plt + +_, ax = plt.subplots() +scatter = ax.scatter(iris.data[:, 0], iris.data[:, 1], c=iris.target) +ax.set(xlabel=iris.feature_names[0], ylabel=iris.feature_names[1]) +_ = ax.legend( + scatter.legend_elements()[0], iris.target_names, loc="lower right", title="Classes" +) -# Plot the training points -plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1, edgecolor="k") -plt.xlabel("Sepal length") -plt.ylabel("Sepal width") +# %% +# Each point in the scatter plot refers to one of the 150 iris flowers +# in the dataset, with the color indicating their respective type +# (Setosa, Versicolour, and Virginica). +# You can already see a pattern regarding the Setosa type, which is +# easily identifiable based on its short and wide sepal. Only +# considering these 2 dimensions, sepal width and length, there's still +# overlap between the Versicolor and Virginica types. + +# %% +# Plot a PCA representation +# ------------------------- +# Let's apply a Principal Component Analysis (PCA) to the iris dataset +# and then plot the irises across the first three PCA dimensions. +# This will allow us to better differentiate between the three types! -plt.xlim(x_min, x_max) -plt.ylim(y_min, y_max) -plt.xticks(()) -plt.yticks(()) +# unused but required import for doing 3d projections with matplotlib < 3.2 +import mpl_toolkits.mplot3d # noqa: F401 + +from sklearn.decomposition import PCA -# To getter a better understanding of interaction of the dimensions -# plot the first three PCA dimensions fig = plt.figure(1, figsize=(8, 6)) ax = fig.add_subplot(111, projection="3d", elev=-150, azim=110) @@ -58,18 +68,22 @@ X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], - c=y, - cmap=plt.cm.Set1, - edgecolor="k", + c=iris.target, s=40, ) -ax.set_title("First three PCA directions") -ax.set_xlabel("1st eigenvector") +ax.set_title("First three PCA dimensions") +ax.set_xlabel("1st Eigenvector") ax.xaxis.set_ticklabels([]) -ax.set_ylabel("2nd eigenvector") +ax.set_ylabel("2nd Eigenvector") ax.yaxis.set_ticklabels([]) -ax.set_zlabel("3rd eigenvector") +ax.set_zlabel("3rd Eigenvector") ax.zaxis.set_ticklabels([]) plt.show() + +# %% +# PCA will create 3 new features that are a linear combination of the +# 4 original features. In addition, this transform maximizes the variance. +# With this transformation, we see that we can identify each species using +# only the first feature (i.e. first eigenvalues). diff --git a/sklearn/datasets/_base.py b/sklearn/datasets/_base.py index 9c38d2a980353..b2d198ecf8c2f 100644 --- a/sklearn/datasets/_base.py +++ b/sklearn/datasets/_base.py @@ -667,6 +667,9 @@ def load_iris(*, return_X_y=False, as_frame=False): array([0, 0, 1]) >>> list(data.target_names) ['setosa', 'versicolor', 'virginica'] + + See :ref:`sphx_glr_auto_examples_datasets_plot_iris_dataset.py` for a more + detailed example of how to work with the iris dataset. """ data_file_name = "iris.csv" data, target, target_names, fdescr = load_csv_data( From 90defd536e5995e10b354dc7feaac3ff6d5a5707 Mon Sep 17 00:00:00 2001 From: Yuusuke Hiramatsu <140389350+hiramatsuyuusuke@users.noreply.github.com> Date: Mon, 11 Sep 2023 16:38:05 +0900 Subject: [PATCH 206/264] DOC take `Examples` out of a dropdown (#27034) --- doc/modules/svm.rst | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/doc/modules/svm.rst b/doc/modules/svm.rst index 0ac34cdcb6a10..8f97b8dee8806 100644 --- a/doc/modules/svm.rst +++ b/doc/modules/svm.rst @@ -521,9 +521,8 @@ is advised to use :class:`~sklearn.model_selection.GridSearchCV` with * :ref:`sphx_glr_auto_examples_svm_plot_rbf_parameters.py` * :ref:`sphx_glr_auto_examples_svm_plot_svm_nonlinear.py` -|details-start| -**Custom Kernels** -|details-split| +Custom Kernels +-------------- You can define your own kernels by either giving the kernel as a python function or by precomputing the Gram matrix. @@ -539,8 +538,9 @@ classifiers, except that: use of ``fit()`` and ``predict()`` you will have unexpected results. -Using Python functions as kernels -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +|details-start| +**Using Python functions as kernels** +|details-split| You can use your own defined kernels by passing a function to the ``kernel`` parameter. @@ -558,13 +558,13 @@ instance that will use that kernel:: ... return np.dot(X, Y.T) ... >>> clf = svm.SVC(kernel=my_kernel) + +|details-end| -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_svm_plot_custom_kernel.py`. -Using the Gram matrix -~~~~~~~~~~~~~~~~~~~~~ +|details-start| +**Using the Gram matrix** +|details-split| You can pass pre-computed kernels by using the ``kernel='precomputed'`` option. You should then pass Gram matrix instead of X to the `fit` and @@ -589,6 +589,10 @@ test vectors must be provided: |details-end| +.. topic:: Examples: + + * :ref:`sphx_glr_auto_examples_svm_plot_custom_kernel.py`. + .. _svm_mathematical_formulation: Mathematical formulation From dee4500077f17150675b08e8d870e7465c846b14 Mon Sep 17 00:00:00 2001 From: Puneeth K <32433964+punndcoder28@users.noreply.github.com> Date: Mon, 11 Sep 2023 13:14:54 +0530 Subject: [PATCH 207/264] DOC Adding dropdown for module 2.1 Gaussian Mixtures (#26694) --- doc/modules/mixture.rst | 125 +++++++++++++++++++++------------------- 1 file changed, 67 insertions(+), 58 deletions(-) diff --git a/doc/modules/mixture.rst b/doc/modules/mixture.rst index fbf0551da93a4..e9cc94b1d493d 100644 --- a/doc/modules/mixture.rst +++ b/doc/modules/mixture.rst @@ -68,33 +68,36 @@ full covariance. * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_pdf.py` for an example on plotting the density estimation. -Pros and cons of class :class:`GaussianMixture` ------------------------------------------------ +|details-start| +**Pros and cons of class GaussianMixture** +|details-split| -Pros -.... +.. topic:: Pros: -:Speed: It is the fastest algorithm for learning mixture models + :Speed: It is the fastest algorithm for learning mixture models -:Agnostic: As this algorithm maximizes only the likelihood, it - will not bias the means towards zero, or bias the cluster sizes to - have specific structures that might or might not apply. + :Agnostic: As this algorithm maximizes only the likelihood, it + will not bias the means towards zero, or bias the cluster sizes to + have specific structures that might or might not apply. -Cons -.... +.. topic:: Cons: -:Singularities: When one has insufficiently many points per - mixture, estimating the covariance matrices becomes difficult, - and the algorithm is known to diverge and find solutions with - infinite likelihood unless one regularizes the covariances artificially. + :Singularities: When one has insufficiently many points per + mixture, estimating the covariance matrices becomes difficult, + and the algorithm is known to diverge and find solutions with + infinite likelihood unless one regularizes the covariances artificially. -:Number of components: This algorithm will always use all the - components it has access to, needing held-out data - or information theoretical criteria to decide how many components to use - in the absence of external cues. + :Number of components: This algorithm will always use all the + components it has access to, needing held-out data + or information theoretical criteria to decide how many components to use + in the absence of external cues. -Selecting the number of components in a classical Gaussian Mixture Model ------------------------------------------------------------------------- +|details-end| + + +|details-start| +**Selecting the number of components in a classical Gaussian Mixture model** +|details-split| The BIC criterion can be used to select the number of components in a Gaussian Mixture in an efficient way. In theory, it recovers the true number of @@ -114,10 +117,13 @@ model. * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_selection.py` for an example of model selection performed with classical Gaussian mixture. +|details-end| + .. _expectation_maximization: -Estimation algorithm Expectation-maximization ------------------------------------------------ +|details-start| +**Estimation algorithm expectation-maximization** +|details-split| The main difficulty in learning Gaussian mixture models from unlabeled data is that one usually doesn't know which points came from @@ -135,8 +141,11 @@ parameters to maximize the likelihood of the data given those assignments. Repeating this process is guaranteed to always converge to a local optimum. -Choice of the Initialization Method ------------------------------------ +|details-end| + +|details-start| +**Choice of the Initialization method** +|details-split| There is a choice of four initialization methods (as well as inputting user defined initial means) to generate the initial centers for the model components: @@ -172,6 +181,8 @@ random * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_init.py` for an example of using different initializations in Gaussian Mixture. +|details-end| + .. _bgmm: Variational Bayesian Gaussian Mixture @@ -183,8 +194,7 @@ similar to the one defined by :class:`GaussianMixture`. .. _variational_inference: -Estimation algorithm: variational inference ---------------------------------------------- +**Estimation algorithm: variational inference** Variational inference is an extension of expectation-maximization that maximizes a lower bound on model evidence (including @@ -282,48 +292,47 @@ from the two resulting mixtures. ``weight_concentration_prior_type`` for different values of the parameter ``weight_concentration_prior``. +|details-start| +**Pros and cons of variational inference with BayesianGaussianMixture** +|details-split| -Pros and cons of variational inference with :class:`BayesianGaussianMixture` ----------------------------------------------------------------------------- - -Pros -..... +.. topic:: Pros: -:Automatic selection: when ``weight_concentration_prior`` is small enough and - ``n_components`` is larger than what is found necessary by the model, the - Variational Bayesian mixture model has a natural tendency to set some mixture - weights values close to zero. This makes it possible to let the model choose - a suitable number of effective components automatically. Only an upper bound - of this number needs to be provided. Note however that the "ideal" number of - active components is very application specific and is typically ill-defined - in a data exploration setting. + :Automatic selection: when ``weight_concentration_prior`` is small enough and + ``n_components`` is larger than what is found necessary by the model, the + Variational Bayesian mixture model has a natural tendency to set some mixture + weights values close to zero. This makes it possible to let the model choose + a suitable number of effective components automatically. Only an upper bound + of this number needs to be provided. Note however that the "ideal" number of + active components is very application specific and is typically ill-defined + in a data exploration setting. -:Less sensitivity to the number of parameters: unlike finite models, which will - almost always use all components as much as they can, and hence will produce - wildly different solutions for different numbers of components, the - variational inference with a Dirichlet process prior - (``weight_concentration_prior_type='dirichlet_process'``) won't change much - with changes to the parameters, leading to more stability and less tuning. + :Less sensitivity to the number of parameters: unlike finite models, which will + almost always use all components as much as they can, and hence will produce + wildly different solutions for different numbers of components, the + variational inference with a Dirichlet process prior + (``weight_concentration_prior_type='dirichlet_process'``) won't change much + with changes to the parameters, leading to more stability and less tuning. -:Regularization: due to the incorporation of prior information, - variational solutions have less pathological special cases than - expectation-maximization solutions. + :Regularization: due to the incorporation of prior information, + variational solutions have less pathological special cases than + expectation-maximization solutions. -Cons -..... +.. topic:: Cons: -:Speed: the extra parametrization necessary for variational inference makes - inference slower, although not by much. + :Speed: the extra parametrization necessary for variational inference makes + inference slower, although not by much. -:Hyperparameters: this algorithm needs an extra hyperparameter - that might need experimental tuning via cross-validation. + :Hyperparameters: this algorithm needs an extra hyperparameter + that might need experimental tuning via cross-validation. -:Bias: there are many implicit biases in the inference algorithms (and also in - the Dirichlet process if used), and whenever there is a mismatch between - these biases and the data it might be possible to fit better models using a - finite mixture. + :Bias: there are many implicit biases in the inference algorithms (and also in + the Dirichlet process if used), and whenever there is a mismatch between + these biases and the data it might be possible to fit better models using a + finite mixture. +|details-end| .. _dirichlet_process: From 5c447809c69a30b1e19aba2c580e0c4fbe02d6c8 Mon Sep 17 00:00:00 2001 From: Puneeth K <32433964+punndcoder28@users.noreply.github.com> Date: Mon, 11 Sep 2023 13:20:14 +0530 Subject: [PATCH 208/264] DOC Adding Dropdown to module 7.2 Realworld Datasets (#26693) --- sklearn/datasets/descr/lfw.rst | 12 +++++++----- sklearn/datasets/descr/twenty_newsgroups.rst | 19 +++++++++++++------ 2 files changed, 20 insertions(+), 11 deletions(-) diff --git a/sklearn/datasets/descr/lfw.rst b/sklearn/datasets/descr/lfw.rst index e7fc35c3caabc..8105d7d6d633a 100644 --- a/sklearn/datasets/descr/lfw.rst +++ b/sklearn/datasets/descr/lfw.rst @@ -32,8 +32,9 @@ face detector from various online websites. Features real, between 0 and 255 ================= ======================= -Usage -~~~~~ +|details-start| +**Usage** +|details-split| ``scikit-learn`` provides two loaders that will automatically download, cache, parse the metadata files, decode the jpeg and convert the @@ -111,6 +112,8 @@ The :func:`sklearn.datasets.fetch_lfw_pairs` datasets is subdivided into an evaluation ``10_folds`` set meant to compute performance metrics using a 10-folds cross validation scheme. +|details-end| + .. topic:: References: * `Labeled Faces in the Wild: A Database for Studying Face Recognition @@ -120,7 +123,6 @@ an evaluation ``10_folds`` set meant to compute performance metrics using a University of Massachusetts, Amherst, Technical Report 07-49, October, 2007. -Examples -~~~~~~~~ +.. topic:: Examples: -:ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py` + * :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py` diff --git a/sklearn/datasets/descr/twenty_newsgroups.rst b/sklearn/datasets/descr/twenty_newsgroups.rst index 8e373c6ec3e74..669e158244134 100644 --- a/sklearn/datasets/descr/twenty_newsgroups.rst +++ b/sklearn/datasets/descr/twenty_newsgroups.rst @@ -27,8 +27,9 @@ extractor. Features text ================= ========== -Usage -~~~~~ +|details-start| +**Usage** +|details-split| The :func:`sklearn.datasets.fetch_20newsgroups` function is a data fetching / caching functions that downloads the data archive from @@ -89,8 +90,11 @@ list of the categories to load to the >>> newsgroups_train.target[:10] array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0]) -Converting text to vectors -~~~~~~~~~~~~~~~~~~~~~~~~~~ +|details-end| + +|details-start| +**Converting text to vectors** +|details-split| In order to feed predictive or clustering models with the text data, one first need to turn the text into vectors of numerical values suitable @@ -122,9 +126,11 @@ returns ready-to-use token counts features instead of file names. .. _`20 newsgroups website`: http://people.csail.mit.edu/jrennie/20Newsgroups/ .. _`TF-IDF`: https://en.wikipedia.org/wiki/Tf-idf +|details-end| -Filtering text for more realistic training -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +|details-start| +**Filtering text for more realistic training** +|details-split| It is easy for a classifier to overfit on particular things that appear in the 20 Newsgroups data, such as newsgroup headers. Many classifiers achieve very @@ -218,6 +224,7 @@ It loses even more if we also strip this metadata from the training data: Some other classifiers cope better with this harder version of the task. Try the :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` example with and without the `remove` option to compare the results. +|details-end| .. topic:: Data Considerations From 7b4d44bb827bc3aed5003d5176d674031b63d0e3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Marcus=20Fraa=C3=9F?= Date: Mon, 11 Sep 2023 12:42:58 +0200 Subject: [PATCH 209/264] DOC Add missing links to examples/impute (#27053) --- doc/modules/impute.rst | 4 ++++ sklearn/impute/_base.py | 3 +++ sklearn/impute/_iterative.py | 4 ++++ sklearn/impute/_knn.py | 3 +++ 4 files changed, 14 insertions(+) diff --git a/doc/modules/impute.rst b/doc/modules/impute.rst index a1985bc460b77..0bb62fa03bc63 100644 --- a/doc/modules/impute.rst +++ b/doc/modules/impute.rst @@ -87,6 +87,8 @@ string values or pandas categoricals when using the ``'most_frequent'`` or ['a' 'y'] ['b' 'y']] +For another example on usage, see :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py`. + .. _iterative_imputer: @@ -220,6 +222,8 @@ neighbors of samples with missing values:: [5.5, 6. , 5. ], [8. , 8. , 7. ]]) +For another example on usage, see :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py`. + .. topic:: References .. [OL2001] Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, diff --git a/sklearn/impute/_base.py b/sklearn/impute/_base.py index a65210bce6960..e182a6ca73e61 100644 --- a/sklearn/impute/_base.py +++ b/sklearn/impute/_base.py @@ -256,6 +256,9 @@ class SimpleImputer(_BaseImputer): [[ 7. 2. 3. ] [ 4. 3.5 6. ] [10. 3.5 9. ]] + + For a more detailed example see + :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py`. """ _parameter_constraints: dict = { diff --git a/sklearn/impute/_iterative.py b/sklearn/impute/_iterative.py index a0087a5a10d55..e08f1bd773a34 100644 --- a/sklearn/impute/_iterative.py +++ b/sklearn/impute/_iterative.py @@ -273,6 +273,10 @@ class IterativeImputer(_BaseImputer): array([[ 6.9584..., 2. , 3. ], [ 4. , 2.6000..., 6. ], [10. , 4.9999..., 9. ]]) + + For a more detailed example see + :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py` or + :ref:`sphx_glr_auto_examples_impute_plot_iterative_imputer_variants_comparison.py`. """ _parameter_constraints: dict = { diff --git a/sklearn/impute/_knn.py b/sklearn/impute/_knn.py index e36b49f262b2d..7da7785369c0d 100644 --- a/sklearn/impute/_knn.py +++ b/sklearn/impute/_knn.py @@ -121,6 +121,9 @@ class KNNImputer(_BaseImputer): [3. , 4. , 3. ], [5.5, 6. , 5. ], [8. , 8. , 7. ]]) + + For a more detailed example see + :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py`. """ _parameter_constraints: dict = { From 37796c012bf85c990a4fed8b00a8e327288aea34 Mon Sep 17 00:00:00 2001 From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Date: Mon, 11 Sep 2023 18:26:17 +0200 Subject: [PATCH 210/264] DOC Rework outlier detection estimators example (#25878) Co-authored-by: ArturoAmorQ Co-authored-by: Tim Head Co-authored-by: Julien Jerphanion Co-authored-by: Guillaume Lemaitre --- .../plot_outlier_detection_bench.py | 565 +++++++++++++----- .../preprocessing/plot_scaling_importance.py | 2 + 2 files changed, 412 insertions(+), 155 deletions(-) diff --git a/examples/miscellaneous/plot_outlier_detection_bench.py b/examples/miscellaneous/plot_outlier_detection_bench.py index 9b530ccab0807..b4fae93131971 100644 --- a/examples/miscellaneous/plot_outlier_detection_bench.py +++ b/examples/miscellaneous/plot_outlier_detection_bench.py @@ -3,196 +3,451 @@ Evaluation of outlier detection estimators ========================================== -This example benchmarks outlier detection algorithms, :ref:`local_outlier_factor` -(LOF) and :ref:`isolation_forest` (IForest), using ROC curves on -classical anomaly detection datasets. The algorithm performance -is assessed in an outlier detection context: +This example compares two outlier detection algorithms, namely +:ref:`local_outlier_factor` (LOF) and :ref:`isolation_forest` (IForest), on +real-world datasets available in :class:`sklearn.datasets`. The goal is to show +that different algorithms perform well on different datasets. -1. The algorithms are trained on the whole dataset which is assumed to -contain outliers. +The algorithms are trained in an outlier detection context: -2. The ROC curve from :class:`~sklearn.metrics.RocCurveDisplay` is computed -on the same dataset using the knowledge of the labels. +1. The ROC curves are computed using knowledge of the ground-truth labels +and displayed using :class:`~sklearn.metrics.RocCurveDisplay`. +2. The performance is assessed in terms of the ROC-AUC. """ # Author: Pharuj Rajborirug +# Arturo Amor # License: BSD 3 clause -print(__doc__) +# %% +# Dataset preprocessing and model training +# ======================================== +# +# Different outlier detection models require different preprocessing. In the +# presence of categorical variables, +# :class:`~sklearn.preprocessing.OrdinalEncoder` is often a good strategy for +# tree-based models such as :class:`~sklearn.ensemble.IsolationForest`, whereas +# neighbors-based models such as :class:`~sklearn.neighbors.LocalOutlierFactor` +# would be impacted by the ordering induced by ordinal encoding. To avoid +# inducing an ordering, on should rather use +# :class:`~sklearn.preprocessing.OneHotEncoder`. +# +# Neighbors-based models may also require scaling of the numerical features (see +# for instance :ref:`neighbors_scaling`). In the presence of outliers, a good +# option is to use a :class:`~sklearn.preprocessing.RobustScaler`. + +from sklearn.compose import ColumnTransformer +from sklearn.ensemble import IsolationForest +from sklearn.neighbors import LocalOutlierFactor +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import ( + OneHotEncoder, + OrdinalEncoder, + RobustScaler, +) + + +def make_estimator(name, categorical_columns=None, iforest_kw=None, lof_kw=None): + """Create an outlier detection estimator based on its name.""" + if name == "LOF": + outlier_detector = LocalOutlierFactor(**(lof_kw or {})) + if categorical_columns is None: + preprocessor = RobustScaler() + else: + preprocessor = ColumnTransformer( + transformers=[("categorical", OneHotEncoder(), categorical_columns)], + remainder=RobustScaler(), + ) + else: # name == "IForest" + outlier_detector = IsolationForest(**(iforest_kw or {})) + if categorical_columns is None: + preprocessor = None + else: + ordinal_encoder = OrdinalEncoder( + handle_unknown="use_encoded_value", unknown_value=-1 + ) + preprocessor = ColumnTransformer( + transformers=[ + ("categorical", ordinal_encoder, categorical_columns), + ], + remainder="passthrough", + ) + + return make_pipeline(preprocessor, outlier_detector) + # %% -# Define a data preprocessing function -# ------------------------------------ +# The following `fit_predict` function returns the average outlier score of X. + +from time import perf_counter + + +def fit_predict(estimator, X): + tic = perf_counter() + if estimator[-1].__class__.__name__ == "LocalOutlierFactor": + estimator.fit(X) + y_pred = estimator[-1].negative_outlier_factor_ + else: # "IsolationForest" + y_pred = estimator.fit(X).decision_function(X) + toc = perf_counter() + print(f"Duration for {model_name}: {toc - tic:.2f} s") + return y_pred + + +# %% +# On the rest of the example we process one dataset per section. After loading +# the data, the targets are modified to consist of two classes: 0 representing +# inliers and 1 representing outliers. Due to computational constraints of the +# scikit-learn documentation, the sample size of some datasets is reduced using +# a stratified :class:`~sklearn.model_selection.train_test_split`. +# +# Furthermore, we set `n_neighbors` to match the expected number of anomalies +# `expected_n_anomalies = n_samples * expected_anomaly_fraction`. This is a good +# heuristic as long as the proportion of outliers is not very low, the reason +# being that `n_neighbors` should be at least greater than the number of samples +# in the less populated cluster (see +# :ref:`sphx_glr_auto_examples_neighbors_plot_lof_outlier_detection.py`). +# +# KDDCup99 - SA dataset +# --------------------- # -# The example uses real-world datasets available in -# :class:`sklearn.datasets` and the sample size of some datasets is reduced -# to speed up computation. After the data preprocessing, the datasets' targets -# will have two classes, 0 representing inliers and 1 representing outliers. -# The `preprocess_dataset` function returns data and target. +# The :ref:`kddcup99_dataset` was generated using a closed network and +# hand-injected attacks. The SA dataset is a subset of it obtained by simply +# selecting all the normal data and an anomaly proportion of around 3%. +# %% import numpy as np -import pandas as pd - -from sklearn.datasets import fetch_covtype, fetch_kddcup99, fetch_openml -from sklearn.preprocessing import LabelBinarizer - -rng = np.random.RandomState(42) - - -def preprocess_dataset(dataset_name): - # loading and vectorization - print(f"Loading {dataset_name} data") - if dataset_name in ["http", "smtp", "SA", "SF"]: - dataset = fetch_kddcup99(subset=dataset_name, percent10=True, random_state=rng) - X = dataset.data - y = dataset.target - lb = LabelBinarizer() - - if dataset_name == "SF": - idx = rng.choice(X.shape[0], int(X.shape[0] * 0.1), replace=False) - X = X[idx] # reduce the sample size - y = y[idx] - x1 = lb.fit_transform(X[:, 1].astype(str)) - X = np.c_[X[:, :1], x1, X[:, 2:]] - elif dataset_name == "SA": - idx = rng.choice(X.shape[0], int(X.shape[0] * 0.1), replace=False) - X = X[idx] # reduce the sample size - y = y[idx] - x1 = lb.fit_transform(X[:, 1].astype(str)) - x2 = lb.fit_transform(X[:, 2].astype(str)) - x3 = lb.fit_transform(X[:, 3].astype(str)) - X = np.c_[X[:, :1], x1, x2, x3, X[:, 4:]] - y = (y != b"normal.").astype(int) - if dataset_name == "forestcover": - dataset = fetch_covtype() - X = dataset.data - y = dataset.target - idx = rng.choice(X.shape[0], int(X.shape[0] * 0.1), replace=False) - X = X[idx] # reduce the sample size - y = y[idx] - - # inliers are those with attribute 2 - # outliers are those with attribute 4 - s = (y == 2) + (y == 4) - X = X[s, :] - y = y[s] - y = (y != 2).astype(int) - if dataset_name in ["glass", "wdbc", "cardiotocography"]: - dataset = fetch_openml( - name=dataset_name, version=1, as_frame=False, parser="pandas" - ) - X = dataset.data - y = dataset.target - - if dataset_name == "glass": - s = y == "tableware" - y = s.astype(int) - if dataset_name == "wdbc": - s = y == "2" - y = s.astype(int) - X_mal, y_mal = X[s], y[s] - X_ben, y_ben = X[~s], y[~s] - - # downsampled to 39 points (9.8% outliers) - idx = rng.choice(y_mal.shape[0], 39, replace=False) - X_mal2 = X_mal[idx] - y_mal2 = y_mal[idx] - X = np.concatenate((X_ben, X_mal2), axis=0) - y = np.concatenate((y_ben, y_mal2), axis=0) - if dataset_name == "cardiotocography": - s = y == "3" - y = s.astype(int) - # 0 represents inliers, and 1 represents outliers - y = pd.Series(y, dtype="category") - return (X, y) - - -# %% -# Define an outlier prediction function -# ------------------------------------- -# There is no particular reason to choose algorithms -# :class:`~sklearn.neighbors.LocalOutlierFactor` and -# :class:`~sklearn.ensemble.IsolationForest`. The goal is to show that -# different algorithm performs well on different datasets. The following -# `compute_prediction` function returns average outlier score of X. +from sklearn.datasets import fetch_kddcup99 +from sklearn.model_selection import train_test_split -from sklearn.ensemble import IsolationForest -from sklearn.neighbors import LocalOutlierFactor +X, y = fetch_kddcup99( + subset="SA", percent10=True, random_state=42, return_X_y=True, as_frame=True +) +y = (y != b"normal.").astype(np.int32) +X, _, y, _ = train_test_split(X, y, train_size=0.1, stratify=y, random_state=42) +n_samples, anomaly_frac = X.shape[0], y.mean() +print(f"{n_samples} datapoints with {y.sum()} anomalies ({anomaly_frac:.02%})") -def compute_prediction(X, model_name): - print(f"Computing {model_name} prediction...") - if model_name == "LOF": - clf = LocalOutlierFactor(n_neighbors=20, contamination="auto") - clf.fit(X) - y_pred = clf.negative_outlier_factor_ - if model_name == "IForest": - clf = IsolationForest(random_state=rng, contamination="auto") - y_pred = clf.fit(X).decision_function(X) - return y_pred +# %% +# The SA dataset contains 41 features out of which 3 are categorical: +# "protocol_type", "service" and "flag". +# %% +y_true = {} +y_pred = {"LOF": {}, "IForest": {}} +model_names = ["LOF", "IForest"] +cat_columns = ["protocol_type", "service", "flag"] + +y_true["KDDCup99 - SA"] = y +for model_name in model_names: + model = make_estimator( + name=model_name, + categorical_columns=cat_columns, + lof_kw={"n_neighbors": int(n_samples * anomaly_frac)}, + iforest_kw={"random_state": 42}, + ) + y_pred[model_name]["KDDCup99 - SA"] = fit_predict(model, X) # %% -# Plot and interpret results -# -------------------------- +# Forest covertypes dataset +# ------------------------- # -# The algorithm performance relates to how good the true positive rate (TPR) -# is at low value of the false positive rate (FPR). The best algorithms -# have the curve on the top-left of the plot and the area under curve (AUC) -# close to 1. The diagonal dashed line represents a random classification -# of outliers and inliers. +# The :ref:`covtype_dataset` is a multiclass dataset where the target is the +# dominant species of tree in a given patch of forest. It contains 54 features, +# some of which ("Wilderness_Area" and "Soil_Type") are already binary encoded. +# Though originally meant as a classification task, one can regard inliers as +# samples encoded with label 2 and outliers as those with label 4. +# %% +from sklearn.datasets import fetch_covtype -import math +X, y = fetch_covtype(return_X_y=True, as_frame=True) +s = (y == 2) + (y == 4) +X = X.loc[s] +y = y.loc[s] +y = (y != 2).astype(np.int32) + +X, _, y, _ = train_test_split(X, y, train_size=0.05, stratify=y, random_state=42) +X_forestcover = X # save X for later use +n_samples, anomaly_frac = X.shape[0], y.mean() +print(f"{n_samples} datapoints with {y.sum()} anomalies ({anomaly_frac:.02%})") + +# %% +y_true["forestcover"] = y +for model_name in model_names: + model = make_estimator( + name=model_name, + lof_kw={"n_neighbors": int(n_samples * anomaly_frac)}, + iforest_kw={"random_state": 42}, + ) + y_pred[model_name]["forestcover"] = fit_predict(model, X) + +# %% +# Ames Housing dataset +# -------------------- +# +# The `Ames housing dataset `_ is originally a +# regression dataset where the target are sales prices of houses in Ames, Iowa. +# Here we convert it into an outlier detection problem by regarding houses with +# price over 70 USD/sqft. To make the problem easier, we drop intermediate +# prices between 40 and 70 USD/sqft. + +# %% import matplotlib.pyplot as plt -from sklearn.metrics import RocCurveDisplay +from sklearn.datasets import fetch_openml -datasets_name = [ - "http", - "smtp", - "SA", - "SF", - "forestcover", - "glass", - "wdbc", - "cardiotocography", -] +X, y = fetch_openml( + name="ames_housing", version=1, return_X_y=True, as_frame=True, parser="pandas" +) +y = y.div(X["Lot_Area"]) -models_name = [ - "LOF", - "IForest", -] +# None values in pandas 1.5.1 were mapped to np.nan in pandas 2.0.1 +X["Misc_Feature"] = X["Misc_Feature"].cat.add_categories("NoInfo").fillna("NoInfo") +X["Mas_Vnr_Type"] = X["Mas_Vnr_Type"].cat.add_categories("NoInfo").fillna("NoInfo") + +X.drop(columns="Lot_Area", inplace=True) +mask = (y < 40) | (y > 70) +X = X.loc[mask] +y = y.loc[mask] +y.hist(bins=20, edgecolor="black") +plt.xlabel("House price in USD/sqft") +_ = plt.title("Distribution of house prices in Ames") + +# %% +y = (y > 70).astype(np.int32) + +n_samples, anomaly_frac = X.shape[0], y.mean() +print(f"{n_samples} datapoints with {y.sum()} anomalies ({anomaly_frac:.02%})") + +# %% +# The dataset contains 46 categorical features. In this case it is easier use a +# :class:`~sklearn.compose.make_column_selector` to find them instead of passing +# a list made by hand. + +# %% +from sklearn.compose import make_column_selector as selector + +categorical_columns_selector = selector(dtype_include="category") +cat_columns = categorical_columns_selector(X) + +y_true["ames_housing"] = y +for model_name in model_names: + model = make_estimator( + name=model_name, + categorical_columns=cat_columns, + lof_kw={"n_neighbors": int(n_samples * anomaly_frac)}, + iforest_kw={"random_state": 42}, + ) + y_pred[model_name]["ames_housing"] = fit_predict(model, X) + +# %% +# Cardiotocography dataset +# ------------------------ +# +# The `Cardiotocography dataset `_ is a multiclass +# dataset of fetal cardiotocograms, the classes being the fetal heart rate (FHR) +# pattern encoded with labels from 1 to 10. Here we set class 3 (the minority +# class) to represent the outliers. It contains 30 numerical features, some of +# which are binary encoded and some are continuous. + +# %% +X, y = fetch_openml( + name="cardiotocography", version=1, return_X_y=True, as_frame=False, parser="pandas" +) +X_cardiotocography = X # save X for later use +s = y == "3" +y = s.astype(np.int32) + +n_samples, anomaly_frac = X.shape[0], y.mean() +print(f"{n_samples} datapoints with {y.sum()} anomalies ({anomaly_frac:.02%})") + +# %% +y_true["cardiotocography"] = y +for model_name in model_names: + model = make_estimator( + name=model_name, + lof_kw={"n_neighbors": int(n_samples * anomaly_frac)}, + iforest_kw={"random_state": 42}, + ) + y_pred[model_name]["cardiotocography"] = fit_predict(model, X) + +# %% +# Plot and interpret results +# ========================== +# +# The algorithm performance relates to how good the true positive rate (TPR) is +# at low value of the false positive rate (FPR). The best algorithms have the +# curve on the top-left of the plot and the area under curve (AUC) close to 1. +# The diagonal dashed line represents a random classification of outliers and +# inliers. + +# %% +import math + +from sklearn.metrics import RocCurveDisplay -# plotting parameters cols = 2 -linewidth = 1 pos_label = 0 # mean 0 belongs to positive class -rows = math.ceil(len(datasets_name) / cols) - -fig, axs = plt.subplots(rows, cols, figsize=(10, rows * 3), sharex=True, sharey=True) +datasets_names = y_true.keys() +rows = math.ceil(len(datasets_names) / cols) -for i, dataset_name in enumerate(datasets_name): - (X, y) = preprocess_dataset(dataset_name=dataset_name) +fig, axs = plt.subplots(nrows=rows, ncols=cols, squeeze=False, figsize=(10, rows * 4)) - for model_idx, model_name in enumerate(models_name): - y_pred = compute_prediction(X, model_name=model_name) +for ax, dataset_name in zip(axs.ravel(), datasets_names): + for model_idx, model_name in enumerate(model_names): display = RocCurveDisplay.from_predictions( - y, - y_pred, + y_true[dataset_name], + y_pred[model_name][dataset_name], pos_label=pos_label, name=model_name, - linewidth=linewidth, - ax=axs[i // cols, i % cols], - plot_chance_level=(model_idx == len(models_name) - 1), - chance_level_kw={ - "linewidth": linewidth, - "linestyle": ":", - }, + ax=ax, + plot_chance_level=(model_idx == len(model_names) - 1), + chance_level_kw={"linestyle": ":"}, ) - axs[i // cols, i % cols].set_title(dataset_name) -plt.tight_layout(pad=2.0) # spacing between subplots + ax.set_title(dataset_name) +_ = plt.tight_layout(pad=2.0) # spacing between subplots + +# %% +# We observe that once the number of neighbors is tuned, LOF and IForest perform +# similarly in terms of ROC AUC for the forestcover and cardiotocography +# datasets. The score for IForest is slightly better for the SA dataset and LOF +# performs considerably better on the Ames housing dataset than IForest. +# +# Ablation study +# ============== +# +# In this section we explore the impact of the hyperparameter `n_neighbors` and +# the choice of scaling the numerical variables on the LOF model. Here we use +# the :ref:`covtype_dataset` dataset as the binary encoded categories introduce +# a natural scale of euclidean distances between 0 and 1. We then want a scaling +# method to avoid granting a privilege to non-binary features and that is robust +# enough to outliers so that the task of finding them does not become too +# difficult. + +# %% +X = X_forestcover +y = y_true["forestcover"] + +n_samples = X.shape[0] +n_neighbors_list = (n_samples * np.array([0.2, 0.02, 0.01, 0.001])).astype(np.int32) +model = make_pipeline(RobustScaler(), LocalOutlierFactor()) + +linestyles = ["solid", "dashed", "dashdot", ":", (5, (10, 3))] + +fig, ax = plt.subplots() +for model_idx, (linestyle, n_neighbors) in enumerate(zip(linestyles, n_neighbors_list)): + model.set_params(localoutlierfactor__n_neighbors=n_neighbors) + model.fit(X) + y_pred = model[-1].negative_outlier_factor_ + display = RocCurveDisplay.from_predictions( + y, + y_pred, + pos_label=pos_label, + name=f"n_neighbors = {n_neighbors}", + ax=ax, + plot_chance_level=(model_idx == len(n_neighbors_list) - 1), + chance_level_kw={"linestyle": (0, (1, 10))}, + linestyle=linestyle, + linewidth=2, + ) +_ = ax.set_title("RobustScaler with varying n_neighbors\non forestcover dataset") + +# %% +# We observe that the number of neighbors has a big impact on the performance of +# the model. If one has access to (at least some) ground truth labels, it is +# then important to tune `n_neighbors` accordingly. A convenient way to do so is +# to explore values for `n_neighbors` of the order of magnitud of the expected +# contamination. + +# %% +from sklearn.preprocessing import MinMaxScaler, SplineTransformer, StandardScaler + +preprocessor_list = [ + None, + RobustScaler(), + StandardScaler(), + MinMaxScaler(), + SplineTransformer(), +] +expected_anomaly_fraction = 0.02 +lof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction)) + +fig, ax = plt.subplots() +for model_idx, (linestyle, preprocessor) in enumerate( + zip(linestyles, preprocessor_list) +): + model = make_pipeline(preprocessor, lof) + model.fit(X) + y_pred = model[-1].negative_outlier_factor_ + display = RocCurveDisplay.from_predictions( + y, + y_pred, + pos_label=pos_label, + name=str(preprocessor).split("(")[0], + ax=ax, + plot_chance_level=(model_idx == len(preprocessor_list) - 1), + chance_level_kw={"linestyle": (0, (1, 10))}, + linestyle=linestyle, + linewidth=2, + ) +_ = ax.set_title("Fixed n_neighbors with varying preprocessing\non forestcover dataset") + +# %% +# On the one hand, :class:`~sklearn.preprocessing.RobustScaler` scales each +# feature independently by using the interquartile range (IQR) by default, which +# is the range between the 25th and 75th percentiles of the data. It centers the +# data by subtracting the median and then scale it by dividing by the IQR. The +# IQR is robust to outliers: the median and interquartile range are less +# affected by extreme values than the range, the mean and the standard +# deviation. Furthermore, :class:`~sklearn.preprocessing.RobustScaler` does not +# squash marginal outlier values, contrary to +# :class:`~sklearn.preprocessing.StandardScaler`. +# +# On the other hand, :class:`~sklearn.preprocessing.MinMaxScaler` scales each +# feature individually such that its range maps into the range between zero and +# one. If there are outliers in the data, they can skew it towards either the +# minimum or maximum values, leading to a completely different distribution of +# data with large marginal outliers: all non-outlier values can be collapsed +# almost together as a result. +# +# We also evaluated no preprocessing at all (by passing `None` to the pipeline), +# :class:`~sklearn.preprocessing.StandardScaler` and +# :class:`~sklearn.preprocessing.SplineTransformer`. Please refer to their +# respective documentation for more details. +# +# Note that the optimal preprocessing depends on the dataset, as shown below: + +# %% +X = X_cardiotocography +y = y_true["cardiotocography"] + +n_samples, expected_anomaly_fraction = X.shape[0], 0.025 +lof = LocalOutlierFactor(n_neighbors=int(n_samples * expected_anomaly_fraction)) + +fig, ax = plt.subplots() +for model_idx, (linestyle, preprocessor) in enumerate( + zip(linestyles, preprocessor_list) +): + model = make_pipeline(preprocessor, lof) + model.fit(X) + y_pred = model[-1].negative_outlier_factor_ + display = RocCurveDisplay.from_predictions( + y, + y_pred, + pos_label=pos_label, + name=str(preprocessor).split("(")[0], + ax=ax, + plot_chance_level=(model_idx == len(preprocessor_list) - 1), + chance_level_kw={"linestyle": (0, (1, 10))}, + linestyle=linestyle, + linewidth=2, + ) +ax.set_title( + "Fixed n_neighbors with varying preprocessing\non cardiotocography dataset" +) plt.show() diff --git a/examples/preprocessing/plot_scaling_importance.py b/examples/preprocessing/plot_scaling_importance.py index 6e0ae0ae1c109..5b78fe0636d8c 100644 --- a/examples/preprocessing/plot_scaling_importance.py +++ b/examples/preprocessing/plot_scaling_importance.py @@ -52,6 +52,8 @@ scaled_X_train = scaler.fit_transform(X_train) # %% +# .. _neighbors_scaling: +# # Effect of rescaling on a k-neighbors models # =========================================== # From 9fcb54def33028a28e378f2fccb091e5811af560 Mon Sep 17 00:00:00 2001 From: tongyu <119610311+tongyu0924@users.noreply.github.com> Date: Tue, 12 Sep 2023 16:16:44 +0800 Subject: [PATCH 211/264] DOC Add dropdowns to module 2.8. Density Estimation (#26757) --- doc/modules/density.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/doc/modules/density.rst b/doc/modules/density.rst index fc0530ed262c0..5a9b456010aa3 100644 --- a/doc/modules/density.rst +++ b/doc/modules/density.rst @@ -113,6 +113,10 @@ forms, which are shown in the following figure: .. centered:: |kde_kernels| +|details-start| +**kernels' mathematical expressions** +|details-split| + The form of these kernels is as follows: * Gaussian kernel (``kernel = 'gaussian'``) @@ -139,6 +143,8 @@ The form of these kernels is as follows: :math:`K(x; h) \propto \cos(\frac{\pi x}{2h})` if :math:`x < h` +|details-end| + The kernel density estimator can be used with any of the valid distance metrics (see :class:`~sklearn.metrics.DistanceMetric` for a list of available metrics), though the results are properly normalized only From 267aa664eb2286347ca0308badbf7b0f46f89774 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 12 Sep 2023 11:03:35 +0200 Subject: [PATCH 212/264] DOC add cross-reference to examples instead of duplicating content for GPR (#20003) Co-authored-by: Julien Jerphanion --- doc/modules/gaussian_process.rst | 212 ++++---------------- examples/gaussian_process/plot_gpr_co2.py | 6 +- examples/gaussian_process/plot_gpr_noisy.py | 6 +- 3 files changed, 49 insertions(+), 175 deletions(-) diff --git a/doc/modules/gaussian_process.rst b/doc/modules/gaussian_process.rst index b8a095eb5b472..75cbf95dae049 100644 --- a/doc/modules/gaussian_process.rst +++ b/doc/modules/gaussian_process.rst @@ -1,5 +1,3 @@ - - .. _gaussian_process: ================== @@ -8,7 +6,7 @@ Gaussian Processes .. currentmodule:: sklearn.gaussian_process -**Gaussian Processes (GP)** are a generic supervised learning method designed +**Gaussian Processes (GP)** are a nonparametric supervised learning method used to solve *regression* and *probabilistic classification* problems. The advantages of Gaussian processes are: @@ -27,8 +25,8 @@ The advantages of Gaussian processes are: The disadvantages of Gaussian processes include: - - They are not sparse, i.e., they use the whole samples/features information to - perform the prediction. + - Our implementation is not sparse, i.e., they use the whole samples/features + information to perform the prediction. - They lose efficiency in high dimensional spaces -- namely when the number of features exceeds a few dozens. @@ -42,31 +40,44 @@ Gaussian Process Regression (GPR) .. currentmodule:: sklearn.gaussian_process The :class:`GaussianProcessRegressor` implements Gaussian processes (GP) for -regression purposes. For this, the prior of the GP needs to be specified. The -prior mean is assumed to be constant and zero (for ``normalize_y=False``) or the -training data's mean (for ``normalize_y=True``). The prior's -covariance is specified by passing a :ref:`kernel ` object. The -hyperparameters of the kernel are optimized during fitting of -GaussianProcessRegressor by maximizing the log-marginal-likelihood (LML) based -on the passed ``optimizer``. As the LML may have multiple local optima, the -optimizer can be started repeatedly by specifying ``n_restarts_optimizer``. The -first run is always conducted starting from the initial hyperparameter values -of the kernel; subsequent runs are conducted from hyperparameter values -that have been chosen randomly from the range of allowed values. -If the initial hyperparameters should be kept fixed, `None` can be passed as -optimizer. +regression purposes. For this, the prior of the GP needs to be specified. GP +will combine this prior and the likelihood function based on training samples. +It allows to give a probabilistic approach to prediction by giving the mean and +standard deviation as output when predicting. -The noise level in the targets can be specified by passing it via the -parameter ``alpha``, either globally as a scalar or per datapoint. -Note that a moderate noise level can also be helpful for dealing with numeric -issues during fitting as it is effectively implemented as Tikhonov -regularization, i.e., by adding it to the diagonal of the kernel matrix. An -alternative to specifying the noise level explicitly is to include a -WhiteKernel component into the kernel, which can estimate the global noise -level from the data (see example below). +.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_targets_002.png + :target: ../auto_examples/gaussian_process/plot_gpr_noisy_targets.html + :align: center + +The prior mean is assumed to be constant and zero (for `normalize_y=False`) or +the training data's mean (for `normalize_y=True`). The prior's covariance is +specified by passing a :ref:`kernel ` object. The hyperparameters +of the kernel are optimized when fitting the :class:`GaussianProcessRegressor` +by maximizing the log-marginal-likelihood (LML) based on the passed +`optimizer`. As the LML may have multiple local optima, the optimizer can be +started repeatedly by specifying `n_restarts_optimizer`. The first run is +always conducted starting from the initial hyperparameter values of the kernel; +subsequent runs are conducted from hyperparameter values that have been chosen +randomly from the range of allowed values. If the initial hyperparameters +should be kept fixed, `None` can be passed as optimizer. + +The noise level in the targets can be specified by passing it via the parameter +`alpha`, either globally as a scalar or per datapoint. Note that a moderate +noise level can also be helpful for dealing with numeric instabilities during +fitting as it is effectively implemented as Tikhonov regularization, i.e., by +adding it to the diagonal of the kernel matrix. An alternative to specifying +the noise level explicitly is to include a +:class:`~sklearn.gaussian_process.kernels.WhiteKernel` component into the +kernel, which can estimate the global noise level from the data (see example +below). The figure below shows the effect of noisy target handled by setting +the parameter `alpha`. + +.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_targets_003.png + :target: ../auto_examples/gaussian_process/plot_gpr_noisy_targets.html + :align: center The implementation is based on Algorithm 2.1 of [RW2006]_. In addition to -the API of standard scikit-learn estimators, GaussianProcessRegressor: +the API of standard scikit-learn estimators, :class:`GaussianProcessRegressor`: * allows prediction without prior fitting (based on the GP prior) @@ -77,149 +88,12 @@ the API of standard scikit-learn estimators, GaussianProcessRegressor: externally for other ways of selecting hyperparameters, e.g., via Markov chain Monte Carlo. +.. topic:: Examples -GPR examples -============ - -GPR with noise-level estimation -------------------------------- -This example illustrates that GPR with a sum-kernel including a WhiteKernel can -estimate the noise level of data. An illustration of the -log-marginal-likelihood (LML) landscape shows that there exist two local -maxima of LML. - -.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_003.png - :target: ../auto_examples/gaussian_process/plot_gpr_noisy.html - :align: center - -The first corresponds to a model with a high noise level and a -large length scale, which explains all variations in the data by noise. - -.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_004.png - :target: ../auto_examples/gaussian_process/plot_gpr_noisy.html - :align: center - -The second one has a smaller noise level and shorter length scale, which explains -most of the variation by the noise-free functional relationship. The second -model has a higher likelihood; however, depending on the initial value for the -hyperparameters, the gradient-based optimization might also converge to the -high-noise solution. It is thus important to repeat the optimization several -times for different initializations. - -.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_005.png - :target: ../auto_examples/gaussian_process/plot_gpr_noisy.html - :align: center - - -Comparison of GPR and Kernel Ridge Regression ---------------------------------------------- - -Both kernel ridge regression (KRR) and GPR learn -a target function by employing internally the "kernel trick". KRR learns a -linear function in the space induced by the respective kernel which corresponds -to a non-linear function in the original space. The linear function in the -kernel space is chosen based on the mean-squared error loss with -ridge regularization. GPR uses the kernel to define the covariance of -a prior distribution over the target functions and uses the observed training -data to define a likelihood function. Based on Bayes theorem, a (Gaussian) -posterior distribution over target functions is defined, whose mean is used -for prediction. - -A major difference is that GPR can choose the kernel's hyperparameters based -on gradient-ascent on the marginal likelihood function while KRR needs to -perform a grid search on a cross-validated loss function (mean-squared error -loss). A further difference is that GPR learns a generative, probabilistic -model of the target function and can thus provide meaningful confidence -intervals and posterior samples along with the predictions while KRR only -provides predictions. - -The following figure illustrates both methods on an artificial dataset, which -consists of a sinusoidal target function and strong noise. The figure compares -the learned model of KRR and GPR based on a ExpSineSquared kernel, which is -suited for learning periodic functions. The kernel's hyperparameters control -the smoothness (length_scale) and periodicity of the kernel (periodicity). -Moreover, the noise level -of the data is learned explicitly by GPR by an additional WhiteKernel component -in the kernel and by the regularization parameter alpha of KRR. - -.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_compare_gpr_krr_005.png - :target: ../auto_examples/gaussian_process/plot_compare_gpr_krr.html - :align: center - -The figure shows that both methods learn reasonable models of the target -function. GPR provides reasonable confidence bounds on the prediction which are not -available for KRR. A major difference between the two methods is the time -required for fitting and predicting: while fitting KRR is fast in principle, -the grid-search for hyperparameter optimization scales exponentially with the -number of hyperparameters ("curse of dimensionality"). The gradient-based -optimization of the parameters in GPR does not suffer from this exponential -scaling and is thus considerably faster on this example with 3-dimensional -hyperparameter space. The time for predicting is similar; however, generating -the variance of the predictive distribution of GPR takes considerably longer -than just predicting the mean. - -GPR on Mauna Loa CO2 data -------------------------- - -This example is based on Section 5.4.3 of [RW2006]_. -It illustrates an example of complex kernel engineering and -hyperparameter optimization using gradient ascent on the -log-marginal-likelihood. The data consists of the monthly average atmospheric -CO2 concentrations (in parts per million by volume (ppmv)) collected at the -Mauna Loa Observatory in Hawaii, between 1958 and 1997. The objective is to -model the CO2 concentration as a function of the time t. - -The kernel is composed of several terms that are responsible for explaining -different properties of the signal: - -- a long term, smooth rising trend is to be explained by an RBF kernel. The - RBF kernel with a large length-scale enforces this component to be smooth; - it is not enforced that the trend is rising which leaves this choice to the - GP. The specific length-scale and the amplitude are free hyperparameters. - -- a seasonal component, which is to be explained by the periodic - ExpSineSquared kernel with a fixed periodicity of 1 year. The length-scale - of this periodic component, controlling its smoothness, is a free parameter. - In order to allow decaying away from exact periodicity, the product with an - RBF kernel is taken. The length-scale of this RBF component controls the - decay time and is a further free parameter. - -- smaller, medium term irregularities are to be explained by a - RationalQuadratic kernel component, whose length-scale and alpha parameter, - which determines the diffuseness of the length-scales, are to be determined. - According to [RW2006]_, these irregularities can better be explained by - a RationalQuadratic than an RBF kernel component, probably because it can - accommodate several length-scales. - -- a "noise" term, consisting of an RBF kernel contribution, which shall - explain the correlated noise components such as local weather phenomena, - and a WhiteKernel contribution for the white noise. The relative amplitudes - and the RBF's length scale are further free parameters. - -Maximizing the log-marginal-likelihood after subtracting the target's mean -yields the following kernel with an LML of -83.214: - -:: - - 34.4**2 * RBF(length_scale=41.8) - + 3.27**2 * RBF(length_scale=180) * ExpSineSquared(length_scale=1.44, - periodicity=1) - + 0.446**2 * RationalQuadratic(alpha=17.7, length_scale=0.957) - + 0.197**2 * RBF(length_scale=0.138) + WhiteKernel(noise_level=0.0336) - -Thus, most of the target signal (34.4ppm) is explained by a long-term rising -trend (length-scale 41.8 years). The periodic component has an amplitude of -3.27ppm, a decay time of 180 years and a length-scale of 1.44. The long decay -time indicates that we have a locally very close to periodic seasonal -component. The correlated noise has an amplitude of 0.197ppm with a length -scale of 0.138 years and a white-noise contribution of 0.197ppm. Thus, the -overall noise level is very small, indicating that the data can be very well -explained by the model. The figure shows also that the model makes very -confident predictions until around 2015 - -.. figure:: ../auto_examples/gaussian_process/images/sphx_glr_plot_gpr_co2_003.png - :target: ../auto_examples/gaussian_process/plot_gpr_co2.html - :align: center + * :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy_targets.py` + * :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy.py` + * :ref:`sphx_glr_auto_examples_gaussian_process_plot_compare_gpr_krr.py` + * :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_co2.py` .. _gpc: diff --git a/examples/gaussian_process/plot_gpr_co2.py b/examples/gaussian_process/plot_gpr_co2.py index a3acd1dbfcbd3..787574a65a9d3 100644 --- a/examples/gaussian_process/plot_gpr_co2.py +++ b/examples/gaussian_process/plot_gpr_co2.py @@ -1,7 +1,7 @@ """ -======================================================= -Gaussian process regression (GPR) on Mauna Loa CO2 data -======================================================= +==================================================================================== +Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR) +==================================================================================== This example is based on Section 5.4.3 of "Gaussian Processes for Machine Learning" [RW2006]_. It illustrates an example of complex kernel engineering diff --git a/examples/gaussian_process/plot_gpr_noisy.py b/examples/gaussian_process/plot_gpr_noisy.py index b76fc745e7df7..31d3b149aa47f 100644 --- a/examples/gaussian_process/plot_gpr_noisy.py +++ b/examples/gaussian_process/plot_gpr_noisy.py @@ -1,7 +1,7 @@ """ -============================================================= -Gaussian process regression (GPR) with noise-level estimation -============================================================= +========================================================================= +Ability of Gaussian process regression (GPR) to estimate data noise-level +========================================================================= This example shows the ability of the :class:`~sklearn.gaussian_process.kernels.WhiteKernel` to estimate the noise From 012ad62621a19b1869d86390ec4f7046dce47f5d Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 12 Sep 2023 15:04:31 +0200 Subject: [PATCH 213/264] DOC add feature_names_in_ in the docsring of ColumnTransformer (#27130) --- sklearn/compose/_column_transformer.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py index beaaf2849091f..1b5373dbbc316 100644 --- a/sklearn/compose/_column_transformer.py +++ b/sklearn/compose/_column_transformer.py @@ -164,6 +164,12 @@ class ColumnTransformer(TransformerMixin, _BaseComposition): .. versionadded:: 0.24 + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + See Also -------- make_column_transformer : Convenience function for From ffd1d562b927d23453a04965f832fc9d433ef1aa Mon Sep 17 00:00:00 2001 From: Tialo <65392801+Tialo@users.noreply.github.com> Date: Wed, 13 Sep 2023 12:45:04 +0300 Subject: [PATCH 214/264] DOC remove unnecessary backtick (#27354) --- doc/glossary.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/glossary.rst b/doc/glossary.rst index 1dbb7e630c449..57e7242825cac 100644 --- a/doc/glossary.rst +++ b/doc/glossary.rst @@ -1731,7 +1731,7 @@ functions or non-estimator constructors. For these models, the number of iterations, reported via ``len(estimators_)`` or ``n_iter_``, corresponds the total number of estimators/iterations learnt since the initialization of the model. - Thus, if a model was already initialized with `N`` estimators, and `fit` + Thus, if a model was already initialized with `N` estimators, and `fit` is called with ``n_estimators`` or ``max_iter`` set to `M`, the model will train `M - N` new estimators. From 5cc9ce285b6e129888c0723c66251b2aa6ba04c8 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Wed, 13 Sep 2023 12:01:56 +0200 Subject: [PATCH 215/264] MAINT update supported Python version in README (#27348) Co-authored-by: Guillaume Lemaitre --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 80de41a8890a1..4042420ba2977 100644 --- a/README.rst +++ b/README.rst @@ -17,7 +17,7 @@ .. |Nightly wheels| image:: https://github.com/scikit-learn/scikit-learn/workflows/Wheel%20builder/badge.svg?event=schedule .. _`Nightly wheels`: https://github.com/scikit-learn/scikit-learn/actions?query=workflow%3A%22Wheel+builder%22+event%3Aschedule -.. |PythonVersion| image:: https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue +.. |PythonVersion| image:: https://img.shields.io/pypi/pyversions/scikit-learn.svg .. _PythonVersion: https://pypi.org/project/scikit-learn/ .. |PyPi| image:: https://img.shields.io/pypi/v/scikit-learn From 294ca38559a5df28f37699969b3e6823c06a4e13 Mon Sep 17 00:00:00 2001 From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Date: Wed, 13 Sep 2023 12:09:51 +0200 Subject: [PATCH 216/264] FIX potential AttributeError when passing list to ValidationCurveDisplay (#27311) Co-authored-by: ArturoAmorQ Co-authored-by: Olivier Grisel Co-authored-by: Guillaume Lemaitre --- doc/whats_new/v1.3.rst | 4 ++++ sklearn/model_selection/_plot.py | 4 ++-- sklearn/model_selection/tests/test_plot.py | 24 +++++++++++++++++++++- 3 files changed, 29 insertions(+), 3 deletions(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index e8baa92d1d876..25e698caa8605 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -133,6 +133,10 @@ Changed displays past behaviour. :pr:`25120` by :user:`Guillaume Lemaitre `. +- |Fix| :class:`model_selection.ValidationCurveDisplay` now accepts passing a + list to the `param_range` parameter. + :pr:`27311` by :user:`Arturo Amor `. + Changes impacting all modules ----------------------------- diff --git a/sklearn/model_selection/_plot.py b/sklearn/model_selection/_plot.py index 336fc8d22221a..b36f16d415c7a 100644 --- a/sklearn/model_selection/_plot.py +++ b/sklearn/model_selection/_plot.py @@ -552,7 +552,7 @@ class ValidationCurveDisplay(_BaseCurveDisplay): param_name : str Name of the parameter that has been varied. - param_range : ndarray of shape (n_ticks,) + param_range : array-like of shape (n_ticks,) The values of the parameter that have been evaluated. train_scores : ndarray of shape (n_ticks, n_cv_folds) @@ -891,7 +891,7 @@ def from_estimator( viz = cls( param_name=param_name, - param_range=param_range, + param_range=np.array(param_range, copy=False), train_scores=train_scores, test_scores=test_scores, score_name=score_name, diff --git a/sklearn/model_selection/tests/test_plot.py b/sklearn/model_selection/tests/test_plot.py index fe9fd45111de5..a3dad60f7bf40 100644 --- a/sklearn/model_selection/tests/test_plot.py +++ b/sklearn/model_selection/tests/test_plot.py @@ -116,7 +116,6 @@ def test_validation_curve_display_default_usage(pyplot, data): estimator, X, y, param_name=param_name, param_range=param_range ) - assert display.param_range == param_range assert_array_equal(display.param_range, param_range) assert_allclose(display.train_scores, train_scores) assert_allclose(display.test_scores, test_scores) @@ -548,3 +547,26 @@ def test_learning_curve_display_deprecate_log_scale(data, pyplot): assert display.ax_.get_xscale() == "linear" assert display.ax_.get_yscale() == "linear" + + +@pytest.mark.parametrize( + "param_range, xscale", + [([5, 10, 15], "linear"), ([-50, 5, 50, 500], "symlog"), ([5, 50, 500], "log")], +) +def test_validation_curve_xscale_from_param_range_provided_as_a_list( + pyplot, data, param_range, xscale +): + """Check the induced xscale from the provided param_range values.""" + X, y = data + estimator = DecisionTreeClassifier(random_state=0) + + param_name = "max_depth" + display = ValidationCurveDisplay.from_estimator( + estimator, + X, + y, + param_name=param_name, + param_range=param_range, + ) + + assert display.ax_.get_xscale() == xscale From d364dfb0804084d4c72518d84da75176a7a0bf57 Mon Sep 17 00:00:00 2001 From: Kishan Ved <124593234+Kishan-Ved@users.noreply.github.com> Date: Wed, 13 Sep 2023 17:24:34 +0530 Subject: [PATCH 217/264] DOC Added dropdowns to 6.2 feature-extraction (#26807) --- doc/modules/feature_extraction.rst | 44 ++++++++++++++++++++++++------ 1 file changed, 35 insertions(+), 9 deletions(-) diff --git a/doc/modules/feature_extraction.rst b/doc/modules/feature_extraction.rst index 1f4e974d6c087..ba8f7373e1304 100644 --- a/doc/modules/feature_extraction.rst +++ b/doc/modules/feature_extraction.rst @@ -206,8 +206,9 @@ Note the use of a generator comprehension, which introduces laziness into the feature extraction: tokens are only processed on demand from the hasher. -Implementation details ----------------------- +|details-start| +**Implementation details** +|details-split| :class:`FeatureHasher` uses the signed 32-bit variant of MurmurHash3. As a result (and because of limitations in ``scipy.sparse``), @@ -223,6 +224,11 @@ Since a simple modulo is used to transform the hash function to a column index, it is advisable to use a power of two as the ``n_features`` parameter; otherwise the features will not be mapped evenly to the columns. +.. topic:: References: + + * `MurmurHash3 `_. + +|details-end| .. topic:: References: @@ -230,9 +236,6 @@ otherwise the features will not be mapped evenly to the columns. Josh Attenberg (2009). `Feature hashing for large scale multitask learning `_. Proc. ICML. - * `MurmurHash3 `_. - - .. _text_feature_extraction: Text feature extraction @@ -395,8 +398,9 @@ last document:: .. _stop_words: -Using stop words -................ +|details-start| +**Using stop words** +|details-split| Stop words are words like "and", "the", "him", which are presumed to be uninformative in representing the content of a text, and which may be @@ -426,6 +430,9 @@ identify and warn about some kinds of inconsistencies. `__. In *Proc. Workshop for NLP Open Source Software*. + +|details-end| + .. _tfidf: Tf–idf term weighting @@ -490,6 +497,10 @@ class:: Again please see the :ref:`reference documentation ` for the details on all the parameters. +|details-start| +**Numeric example of a tf-idf matrix** +|details-split| + Let's take an example with the following counts. The first term is present 100% of the time hence not very interesting. The two other features only in less than 50% of the time hence probably more representative of the @@ -609,6 +620,7 @@ feature extractor with a classifier: * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` +|details-end| Decoding text files ------------------- @@ -637,6 +649,10 @@ or ``"replace"``. See the documentation for the Python function ``bytes.decode`` for more details (type ``help(bytes.decode)`` at the Python prompt). +|details-start| +**Troubleshooting decoding text** +|details-split| + If you are having trouble decoding text, here are some things to try: - Find out what the actual encoding of the text is. The file might come @@ -690,6 +706,7 @@ About Unicode `_. .. _`ftfy`: https://github.com/LuminosoInsight/python-ftfy +|details-end| Applications and examples ------------------------- @@ -870,8 +887,9 @@ The :class:`HashingVectorizer` also comes with the following limitations: model. A :class:`TfidfTransformer` can be appended to it in a pipeline if required. -Performing out-of-core scaling with HashingVectorizer ------------------------------------------------------- +|details-start| +**Performing out-of-core scaling with HashingVectorizer** +|details-split| An interesting development of using a :class:`HashingVectorizer` is the ability to perform `out-of-core`_ scaling. This means that we can learn from data that @@ -890,6 +908,8 @@ time is often limited by the CPU time one wants to spend on the task. For a full-fledged example of out-of-core scaling in a text classification task see :ref:`sphx_glr_auto_examples_applications_plot_out_of_core_classification.py`. +|details-end| + Customizing the vectorizer classes ---------------------------------- @@ -928,6 +948,10 @@ parameters it is possible to derive from the class and override the ``build_preprocessor``, ``build_tokenizer`` and ``build_analyzer`` factory methods instead of passing custom functions. +|details-start| +**Tips and tricks** +|details-split| + Some tips and tricks: * If documents are pre-tokenized by an external package, then store them in @@ -982,6 +1006,8 @@ Some tips and tricks: Customizing the vectorizer can also be useful when handling Asian languages that do not use an explicit word separator such as whitespace. +|details-end| + .. _image_feature_extraction: Image feature extraction From 643955a1fb6086ab2a9407cb434de04345fd16c2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Wed, 13 Sep 2023 18:37:26 +0200 Subject: [PATCH 218/264] CI Do not run doctests for numpy 2 (#27345) Co-authored-by: Olivier Grisel --- doc/conftest.py | 36 +++++++++++++++++++++++++++++++++++- sklearn/conftest.py | 6 +++++- sklearn/utils/fixes.py | 1 + 3 files changed, 41 insertions(+), 2 deletions(-) diff --git a/doc/conftest.py b/doc/conftest.py index ca94ad1ed2b60..563105bc6757e 100644 --- a/doc/conftest.py +++ b/doc/conftest.py @@ -3,12 +3,15 @@ from os import environ from os.path import exists, join +import pytest +from _pytest.doctest import DoctestItem + from sklearn.datasets import get_data_home from sklearn.datasets._base import _pkl_filepath from sklearn.datasets._twenty_newsgroups import CACHE_NAME from sklearn.utils import IS_PYPY from sklearn.utils._testing import SkipTest, check_skip_network -from sklearn.utils.fixes import parse_version +from sklearn.utils.fixes import np_base_version, parse_version def setup_labeled_faces(): @@ -172,3 +175,34 @@ def pytest_configure(config): matplotlib.use("agg") except ImportError: pass + + +def pytest_collection_modifyitems(config, items): + """Called after collect is completed. + + Parameters + ---------- + config : pytest config + items : list of collected items + """ + skip_doctests = False + if np_base_version >= parse_version("2"): + # Skip doctests when using numpy 2 for now. See the following discussion + # to decide what to do in the longer term: + # https://github.com/scikit-learn/scikit-learn/issues/27339 + reason = "Due to NEP 51 numpy scalar repr has changed in numpy 2" + skip_doctests = True + + # Normally doctest has the entire module's scope. Here we set globs to an empty dict + # to remove the module's scope: + # https://docs.python.org/3/library/doctest.html#what-s-the-execution-context + for item in items: + if isinstance(item, DoctestItem): + item.dtest.globs = {} + + if skip_doctests: + skip_marker = pytest.mark.skip(reason=reason) + + for item in items: + if isinstance(item, DoctestItem): + item.add_marker(skip_marker) diff --git a/sklearn/conftest.py b/sklearn/conftest.py index 3d2c73b99a801..9eed56011a505 100644 --- a/sklearn/conftest.py +++ b/sklearn/conftest.py @@ -23,7 +23,7 @@ ) from sklearn.tests import random_seed from sklearn.utils import _IS_32BIT -from sklearn.utils.fixes import parse_version, sp_version +from sklearn.utils.fixes import np_base_version, parse_version, sp_version if parse_version(pytest.__version__) < parse_version(PYTEST_MIN_VERSION): raise ImportError( @@ -178,6 +178,10 @@ def pytest_collection_modifyitems(config, items): ) skip_doctests = True + if np_base_version >= parse_version("2"): + reason = "Due to NEP 51 numpy scalar repr has changed in numpy 2" + skip_doctests = True + # Normally doctest has the entire module's scope. Here we set globs to an empty dict # to remove the module's scope: # https://docs.python.org/3/library/doctest.html#what-s-the-execution-context diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py index 166548e7f88ef..bcdc227d2f93d 100644 --- a/sklearn/utils/fixes.py +++ b/sklearn/utils/fixes.py @@ -25,6 +25,7 @@ from .deprecation import deprecated np_version = parse_version(np.__version__) +np_base_version = parse_version(np_version.base_version) sp_version = parse_version(scipy.__version__) sp_base_version = parse_version(sp_version.base_version) From 63ae48c9f133a291fcd3bbbd42395eb9097f8453 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Thu, 14 Sep 2023 13:08:35 +0200 Subject: [PATCH 219/264] MAINT use .toarray instead of .A that is deprecated (#27367) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- .../_hdbscan/tests/test_reachibility.py | 2 +- .../tests/test_dict_vectorizer.py | 4 ++- .../tests/test_feature_hasher.py | 2 +- sklearn/impute/tests/test_impute.py | 4 +-- sklearn/manifold/tests/test_t_sne.py | 2 +- sklearn/neighbors/tests/test_neighbors.py | 26 +++++++++++-------- sklearn/preprocessing/tests/test_common.py | 4 +-- .../preprocessing/tests/test_polynomial.py | 12 ++++----- sklearn/tests/test_kernel_approximation.py | 4 +-- sklearn/utils/estimator_checks.py | 2 +- sklearn/utils/tests/test_sparsefuncs.py | 6 ++--- 11 files changed, 37 insertions(+), 31 deletions(-) diff --git a/sklearn/cluster/_hdbscan/tests/test_reachibility.py b/sklearn/cluster/_hdbscan/tests/test_reachibility.py index c25b6baf4b65c..53096dd7cbec7 100644 --- a/sklearn/cluster/_hdbscan/tests/test_reachibility.py +++ b/sklearn/cluster/_hdbscan/tests/test_reachibility.py @@ -45,7 +45,7 @@ def test_mutual_reachability_graph_equivalence_dense_sparse(): mr_graph_dense = mutual_reachability_graph(X_dense, min_samples=3) mr_graph_sparse = mutual_reachability_graph(X_sparse, min_samples=3) - assert_allclose(mr_graph_dense, mr_graph_sparse.A) + assert_allclose(mr_graph_dense, mr_graph_sparse.toarray()) @pytest.mark.parametrize("array_type", ["array", "sparse_csr"]) diff --git a/sklearn/feature_extraction/tests/test_dict_vectorizer.py b/sklearn/feature_extraction/tests/test_dict_vectorizer.py index 7e3c7f259ea03..3066d7669546b 100644 --- a/sklearn/feature_extraction/tests/test_dict_vectorizer.py +++ b/sklearn/feature_extraction/tests/test_dict_vectorizer.py @@ -30,7 +30,9 @@ def test_dictvectorizer(sparse, dtype, sort, iterable): if sparse: # CSR matrices can't be compared for equality - assert_array_equal(X.A, v.transform(iter(D) if iterable else D).A) + assert_array_equal( + X.toarray(), v.transform(iter(D) if iterable else D).toarray() + ) else: assert_array_equal(X, v.transform(iter(D) if iterable else D)) diff --git a/sklearn/feature_extraction/tests/test_feature_hasher.py b/sklearn/feature_extraction/tests/test_feature_hasher.py index 945a7cb3ca8f9..276d0d48b0770 100644 --- a/sklearn/feature_extraction/tests/test_feature_hasher.py +++ b/sklearn/feature_extraction/tests/test_feature_hasher.py @@ -125,7 +125,7 @@ def test_hash_empty_input(): feature_hasher = FeatureHasher(n_features=n_features, input_type="string") X = feature_hasher.transform(raw_X) - assert_array_equal(X.A, np.zeros((len(raw_X), n_features))) + assert_array_equal(X.toarray(), np.zeros((len(raw_X), n_features))) def test_hasher_zeros(): diff --git a/sklearn/impute/tests/test_impute.py b/sklearn/impute/tests/test_impute.py index 9d02eb7722cfc..57f9a3d5159e0 100644 --- a/sklearn/impute/tests/test_impute.py +++ b/sklearn/impute/tests/test_impute.py @@ -1669,7 +1669,7 @@ def test_simple_imputer_constant_keep_empty_features(array_type, keep_empty_feat X_imputed = getattr(imputer, method)(X) assert X_imputed.shape == X.shape constant_feature = ( - X_imputed[:, 0].A if array_type == "sparse" else X_imputed[:, 0] + X_imputed[:, 0].toarray() if array_type == "sparse" else X_imputed[:, 0] ) assert_array_equal(constant_feature, fill_value) @@ -1690,7 +1690,7 @@ def test_simple_imputer_keep_empty_features(strategy, array_type, keep_empty_fea if keep_empty_features: assert X_imputed.shape == X.shape constant_feature = ( - X_imputed[:, 0].A if array_type == "sparse" else X_imputed[:, 0] + X_imputed[:, 0].toarray() if array_type == "sparse" else X_imputed[:, 0] ) assert_array_equal(constant_feature, 0) else: diff --git a/sklearn/manifold/tests/test_t_sne.py b/sklearn/manifold/tests/test_t_sne.py index ea9a0e70c8252..813d72a3de8ae 100644 --- a/sklearn/manifold/tests/test_t_sne.py +++ b/sklearn/manifold/tests/test_t_sne.py @@ -447,7 +447,7 @@ def test_sparse_precomputed_distance(): D_sparse = kneighbors_graph(X, n_neighbors=100, mode="distance", include_self=True) D = pairwise_distances(X) assert sp.issparse(D_sparse) - assert_almost_equal(D_sparse.A, D) + assert_almost_equal(D_sparse.toarray(), D) tsne = TSNE( metric="precomputed", random_state=0, init="random", learning_rate="auto" diff --git a/sklearn/neighbors/tests/test_neighbors.py b/sklearn/neighbors/tests/test_neighbors.py index d24fc618d184c..369d61be740b8 100644 --- a/sklearn/neighbors/tests/test_neighbors.py +++ b/sklearn/neighbors/tests/test_neighbors.py @@ -1770,7 +1770,7 @@ def test_non_euclidean_kneighbors(): X, radius, metric=metric, mode="connectivity", include_self=True ).toarray() nbrs1 = neighbors.NearestNeighbors(metric=metric, radius=radius).fit(X) - assert_array_equal(nbrs_graph, nbrs1.radius_neighbors_graph(X).A) + assert_array_equal(nbrs_graph, nbrs1.radius_neighbors_graph(X).toarray()) # Raise error when wrong parameters are supplied, X_nbrs = neighbors.NearestNeighbors(n_neighbors=3, metric="manhattan") @@ -1807,13 +1807,15 @@ def test_k_and_radius_neighbors_train_is_not_query(): check_object_arrays(ind, [[1], [0, 1]]) # Test the graph variants. - assert_array_equal(nn.kneighbors_graph(test_data).A, [[0.0, 1.0], [0.0, 1.0]]) assert_array_equal( - nn.kneighbors_graph([[2], [1]], mode="distance").A, + nn.kneighbors_graph(test_data).toarray(), [[0.0, 1.0], [0.0, 1.0]] + ) + assert_array_equal( + nn.kneighbors_graph([[2], [1]], mode="distance").toarray(), np.array([[0.0, 1.0], [0.0, 0.0]]), ) rng = nn.radius_neighbors_graph([[2], [1]], radius=1.5) - assert_array_equal(rng.A, [[0, 1], [1, 1]]) + assert_array_equal(rng.toarray(), [[0, 1], [1, 1]]) @pytest.mark.parametrize("algorithm", ALGORITHMS) @@ -1835,7 +1837,7 @@ def test_k_and_radius_neighbors_X_None(algorithm): rng = nn.radius_neighbors_graph(None, radius=1.5) kng = nn.kneighbors_graph(None) for graph in [rng, kng]: - assert_array_equal(graph.A, [[0, 1], [1, 0]]) + assert_array_equal(graph.toarray(), [[0, 1], [1, 0]]) assert_array_equal(graph.data, [1, 1]) assert_array_equal(graph.indices, [1, 0]) @@ -1843,7 +1845,7 @@ def test_k_and_radius_neighbors_X_None(algorithm): nn = neighbors.NearestNeighbors(n_neighbors=2, algorithm=algorithm) nn.fit(X) assert_array_equal( - nn.kneighbors_graph().A, + nn.kneighbors_graph().toarray(), np.array([[0.0, 1.0, 1.0], [1.0, 0.0, 1.0], [1.0, 1.0, 0]]), ) @@ -1901,13 +1903,15 @@ def test_k_and_radius_neighbors_duplicates(algorithm): def test_include_self_neighbors_graph(): # Test include_self parameter in neighbors_graph X = [[2, 3], [4, 5]] - kng = neighbors.kneighbors_graph(X, 1, include_self=True).A - kng_not_self = neighbors.kneighbors_graph(X, 1, include_self=False).A + kng = neighbors.kneighbors_graph(X, 1, include_self=True).toarray() + kng_not_self = neighbors.kneighbors_graph(X, 1, include_self=False).toarray() assert_array_equal(kng, [[1.0, 0.0], [0.0, 1.0]]) assert_array_equal(kng_not_self, [[0.0, 1.0], [1.0, 0.0]]) - rng = neighbors.radius_neighbors_graph(X, 5.0, include_self=True).A - rng_not_self = neighbors.radius_neighbors_graph(X, 5.0, include_self=False).A + rng = neighbors.radius_neighbors_graph(X, 5.0, include_self=True).toarray() + rng_not_self = neighbors.radius_neighbors_graph( + X, 5.0, include_self=False + ).toarray() assert_array_equal(rng, [[1.0, 1.0], [1.0, 1.0]]) assert_array_equal(rng_not_self, [[0.0, 1.0], [1.0, 0.0]]) @@ -1996,7 +2000,7 @@ def test_dtype_convert(): def test_sparse_metric_callable(): def sparse_metric(x, y): # Metric accepting sparse matrix input (only) assert issparse(x) and issparse(y) - return x.dot(y.T).A.item() + return x.dot(y.T).toarray().item() X = csr_matrix( [[1, 1, 1, 1, 1], [1, 0, 1, 0, 1], [0, 0, 1, 0, 0]] # Population matrix diff --git a/sklearn/preprocessing/tests/test_common.py b/sklearn/preprocessing/tests/test_common.py index 9ebef6c000050..653ce58e0b68c 100644 --- a/sklearn/preprocessing/tests/test_common.py +++ b/sklearn/preprocessing/tests/test_common.py @@ -131,13 +131,13 @@ def test_missing_value_handling( warnings.simplefilter("error", RuntimeWarning) Xt_sp = est_sparse.fit(X_train_sp).transform(X_test_sp) - assert_allclose(Xt_sp.A, Xt_dense) + assert_allclose(Xt_sp.toarray(), Xt_dense) with warnings.catch_warnings(): warnings.simplefilter("ignore", PendingDeprecationWarning) warnings.simplefilter("error", RuntimeWarning) Xt_inv_sp = est_sparse.inverse_transform(Xt_sp) - assert_allclose(Xt_inv_sp.A, Xt_inv_dense) + assert_allclose(Xt_inv_sp.toarray(), Xt_inv_dense) @pytest.mark.parametrize( diff --git a/sklearn/preprocessing/tests/test_polynomial.py b/sklearn/preprocessing/tests/test_polynomial.py index 70a927e1a814d..de1f29026a62b 100644 --- a/sklearn/preprocessing/tests/test_polynomial.py +++ b/sklearn/preprocessing/tests/test_polynomial.py @@ -725,7 +725,7 @@ def test_polynomial_features_csc_X(deg, include_bias, interaction_only, dtype): assert sparse.issparse(Xt_csc) and Xt_csc.format == "csc" assert Xt_csc.dtype == Xt_dense.dtype - assert_array_almost_equal(Xt_csc.A, Xt_dense) + assert_array_almost_equal(Xt_csc.toarray(), Xt_dense) @pytest.mark.parametrize( @@ -752,7 +752,7 @@ def test_polynomial_features_csr_X(deg, include_bias, interaction_only, dtype): assert sparse.issparse(Xt_csr) and Xt_csr.format == "csr" assert Xt_csr.dtype == Xt_dense.dtype - assert_array_almost_equal(Xt_csr.A, Xt_dense) + assert_array_almost_equal(Xt_csr.toarray(), Xt_dense) @pytest.mark.parametrize("n_features", [1, 4, 5]) @@ -811,7 +811,7 @@ def test_polynomial_features_csr_X_floats(deg, include_bias, interaction_only, d assert sparse.issparse(Xt_csr) and Xt_csr.format == "csr" assert Xt_csr.dtype == Xt_dense.dtype - assert_array_almost_equal(Xt_csr.A, Xt_dense) + assert_array_almost_equal(Xt_csr.toarray(), Xt_dense) @pytest.mark.parametrize( @@ -842,7 +842,7 @@ def test_polynomial_features_csr_X_zero_row(zero_row_index, deg, interaction_onl assert sparse.issparse(Xt_csr) and Xt_csr.format == "csr" assert Xt_csr.dtype == Xt_dense.dtype - assert_array_almost_equal(Xt_csr.A, Xt_dense) + assert_array_almost_equal(Xt_csr.toarray(), Xt_dense) # This degree should always be one more than the highest degree supported by @@ -863,7 +863,7 @@ def test_polynomial_features_csr_X_degree_4(include_bias, interaction_only): assert sparse.issparse(Xt_csr) and Xt_csr.format == "csr" assert Xt_csr.dtype == Xt_dense.dtype - assert_array_almost_equal(Xt_csr.A, Xt_dense) + assert_array_almost_equal(Xt_csr.toarray(), Xt_dense) @pytest.mark.parametrize( @@ -891,7 +891,7 @@ def test_polynomial_features_csr_X_dim_edges(deg, dim, interaction_only): assert sparse.issparse(Xt_csr) and Xt_csr.format == "csr" assert Xt_csr.dtype == Xt_dense.dtype - assert_array_almost_equal(Xt_csr.A, Xt_dense) + assert_array_almost_equal(Xt_csr.toarray(), Xt_dense) @pytest.mark.parametrize("interaction_only", [True, False]) diff --git a/sklearn/tests/test_kernel_approximation.py b/sklearn/tests/test_kernel_approximation.py index 17d268e587394..c541b3c545be2 100644 --- a/sklearn/tests/test_kernel_approximation.py +++ b/sklearn/tests/test_kernel_approximation.py @@ -113,8 +113,8 @@ def test_additive_chi2_sampler(): X_sp_trans = transform.fit_transform(csr_matrix(X)) Y_sp_trans = transform.transform(csr_matrix(Y)) - assert_array_equal(X_trans, X_sp_trans.A) - assert_array_equal(Y_trans, Y_sp_trans.A) + assert_array_equal(X_trans, X_sp_trans.toarray()) + assert_array_equal(Y_trans, Y_sp_trans.toarray()) # test error is raised on negative input Y_neg = Y.copy() diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py index 4c9434b92eda7..ce9dd73ad3747 100644 --- a/sklearn/utils/estimator_checks.py +++ b/sklearn/utils/estimator_checks.py @@ -1430,7 +1430,7 @@ def _apply_on_subsets(func, X): if sparse.issparse(result_full): result_full = result_full.A - result_by_batch = [x.A for x in result_by_batch] + result_by_batch = [x.toarray() for x in result_by_batch] return np.ravel(result_full), np.ravel(result_by_batch) diff --git a/sklearn/utils/tests/test_sparsefuncs.py b/sklearn/utils/tests/test_sparsefuncs.py index f3bcaf56bb561..886367d480fc1 100644 --- a/sklearn/utils/tests/test_sparsefuncs.py +++ b/sklearn/utils/tests/test_sparsefuncs.py @@ -455,9 +455,9 @@ def test_incr_mean_variance_axis_equivalence_mean_variance(X1, X2): X2, axis=axis, last_mean=updated_mean, last_var=updated_var, last_n=updated_n ) X = sp.vstack([X1, X2]) - assert_allclose(updated_mean, np.nanmean(X.A, axis=axis)) - assert_allclose(updated_var, np.nanvar(X.A, axis=axis)) - assert_allclose(updated_n, np.count_nonzero(~np.isnan(X.A), axis=0)) + assert_allclose(updated_mean, np.nanmean(X.toarray(), axis=axis)) + assert_allclose(updated_var, np.nanvar(X.toarray(), axis=axis)) + assert_allclose(updated_n, np.count_nonzero(~np.isnan(X.toarray()), axis=0)) def test_incr_mean_variance_no_new_n(): From 0139118eff21e700508d4473435c5d839d1a8d57 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Fri, 15 Sep 2023 10:39:17 +0200 Subject: [PATCH 220/264] DOC add link to example in user guide on interaction constraints (#27156) --- doc/modules/ensemble.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst index 36eed98da0f6b..552e47a3f049c 100644 --- a/doc/modules/ensemble.rst +++ b/doc/modules/ensemble.rst @@ -347,6 +347,10 @@ Note that features not listed in ``interaction_cst`` are automatically assigned an interaction group for themselves. With again 3 features, this means that ``[{0}]`` is equivalent to ``[{0}, {1, 2}]``. +.. topic:: Examples: + + * :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py` + .. topic:: References .. [Mayer2022] M. Mayer, S.C. Bourassa, M. Hoesli, and D.F. Scognamiglio. From 96b897b4138220aa8538ddf0264ffdd88a54d760 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Fri, 15 Sep 2023 17:43:19 +0200 Subject: [PATCH 221/264] MNT Remove setuptools dependency in our test suite (#27355) --- sklearn/tests/test_common.py | 3 +++ sklearn/tests/test_docstring_parameters.py | 23 ---------------------- 2 files changed, 3 insertions(+), 23 deletions(-) diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py index 9ff5bf21c9a7d..a5f485ee1a882 100644 --- a/sklearn/tests/test_common.py +++ b/sklearn/tests/test_common.py @@ -217,6 +217,9 @@ def test_import_all_consistency(): for modname in submods + ["sklearn"]: if ".tests." in modname: continue + # Avoid test suite depending on setuptools + if modname == "sklearn._build_utils.pre_build_helpers": + continue if IS_PYPY and ( "_svmlight_format_io" in modname or "feature_extraction._hashing_fast" in modname diff --git a/sklearn/tests/test_docstring_parameters.py b/sklearn/tests/test_docstring_parameters.py index d395b6c7ba2cd..c6cba68e340f6 100644 --- a/sklearn/tests/test_docstring_parameters.py +++ b/sklearn/tests/test_docstring_parameters.py @@ -153,29 +153,6 @@ def test_docstring_parameters(): raise AssertionError("Docstring Error:\n" + msg) -@ignore_warnings(category=FutureWarning) -def test_tabs(): - # Test that there are no tabs in our source files - for importer, modname, ispkg in walk_packages(sklearn.__path__, prefix="sklearn."): - if IS_PYPY and ( - "_svmlight_format_io" in modname - or "feature_extraction._hashing_fast" in modname - ): - continue - - # because we don't import - mod = importlib.import_module(modname) - - try: - source = inspect.getsource(mod) - except IOError: # user probably should have run "make clean" - continue - assert "\t" not in source, ( - '"%s" has tabs, please remove them ', - "or add it to the ignore list" % modname, - ) - - def _construct_searchcv_instance(SearchCV): return SearchCV(LogisticRegression(), {"C": [0.1, 1]}) From 3d5d7a30cb56c5a3a8eb8829799d137e57451720 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Fri, 15 Sep 2023 17:49:16 +0200 Subject: [PATCH 222/264] CI Use the libmamba to install conda-lock on Azure Pipelines (#27382) --- build_tools/azure/install.sh | 5 ++++- build_tools/azure/install_win.sh | 4 ++++ 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh index ced484ae99a1c..675fedd5d9771 100755 --- a/build_tools/azure/install.sh +++ b/build_tools/azure/install.sh @@ -49,7 +49,10 @@ pre_python_environment_install() { python_environment_install_and_activate() { if [[ "$DISTRIB" == "conda"* ]]; then - conda update -n base conda -y + # Install/update conda with the libmamba solver because the legacy + # solver can be slow at installing a specific version of conda-lock. + conda install -n base conda conda-libmamba-solver -y + conda config --set solver libmamba conda install -c conda-forge "$(get_dep conda-lock min)" -y conda-lock install --name $VIRTUALENV $LOCK_FILE source activate $VIRTUALENV diff --git a/build_tools/azure/install_win.sh b/build_tools/azure/install_win.sh index ab559a1878971..b8f2cac096471 100755 --- a/build_tools/azure/install_win.sh +++ b/build_tools/azure/install_win.sh @@ -7,6 +7,10 @@ set -x source build_tools/shared.sh if [[ "$DISTRIB" == "conda" ]]; then + # Install/update conda with the libmamba solver because the legacy solver + # can be slow at installing a specific version of conda-lock. + conda install -n base conda conda-libmamba-solver -y + conda config --set solver libmamba conda install -c conda-forge "$(get_dep conda-lock min)" -y conda-lock install --name $VIRTUALENV $LOCK_FILE source activate $VIRTUALENV From f586d9e2d2b9f4ffe3ba96e425a700a8df6c0297 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Fri, 15 Sep 2023 20:32:46 +0200 Subject: [PATCH 223/264] MAINT ignore ConvergenceWarning instead of FutureWarning in test_check_estimator_clones (#27385) --- sklearn/utils/tests/test_estimator_checks.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/sklearn/utils/tests/test_estimator_checks.py b/sklearn/utils/tests/test_estimator_checks.py index 87357078ad9bf..535c821623490 100644 --- a/sklearn/utils/tests/test_estimator_checks.py +++ b/sklearn/utils/tests/test_estimator_checks.py @@ -18,7 +18,7 @@ from sklearn.datasets import make_multilabel_classification from sklearn.decomposition import PCA from sklearn.ensemble import ExtraTreesClassifier -from sklearn.exceptions import SkipTestWarning +from sklearn.exceptions import ConvergenceWarning, SkipTestWarning from sklearn.linear_model import ( LinearRegression, LogisticRegression, @@ -707,22 +707,20 @@ def test_check_estimator_clones(): ExtraTreesClassifier, MiniBatchKMeans, ]: - with ignore_warnings(category=FutureWarning): - # when 'est = SGDClassifier()' + # without fitting + with ignore_warnings(category=ConvergenceWarning): est = Estimator() _set_checking_parameters(est) set_random_state(est) - # without fitting old_hash = joblib.hash(est) check_estimator(est) assert old_hash == joblib.hash(est) - with ignore_warnings(category=FutureWarning): - # when 'est = SGDClassifier()' + # with fitting + with ignore_warnings(category=ConvergenceWarning): est = Estimator() _set_checking_parameters(est) set_random_state(est) - # with fitting est.fit(iris.data + 10, iris.target) old_hash = joblib.hash(est) check_estimator(est) From 622809d16a1e1fa5e339def0ae7b53f077eed7a4 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Sat, 16 Sep 2023 11:09:15 +0200 Subject: [PATCH 224/264] TST Speed up some of the slowest tests (#27383) --- sklearn/decomposition/_dict_learning.py | 14 ++++++------- .../decomposition/tests/test_dict_learning.py | 2 +- .../decomposition/tests/test_sparse_pca.py | 4 ++-- .../ensemble/tests/test_gradient_boosting.py | 21 ++++++++++++------- sklearn/neighbors/tests/test_neighbors.py | 9 ++++---- sklearn/tree/tests/test_tree.py | 4 ++-- 6 files changed, 29 insertions(+), 25 deletions(-) diff --git a/sklearn/decomposition/_dict_learning.py b/sklearn/decomposition/_dict_learning.py index 5ba8b628316ab..7fc0915f2ea8e 100644 --- a/sklearn/decomposition/_dict_learning.py +++ b/sklearn/decomposition/_dict_learning.py @@ -1692,7 +1692,7 @@ class DictionaryLearning(_BaseSparseCoding, BaseEstimator): >>> from sklearn.datasets import make_sparse_coded_signal >>> from sklearn.decomposition import DictionaryLearning >>> X, dictionary, code = make_sparse_coded_signal( - ... n_samples=100, n_components=15, n_features=20, n_nonzero_coefs=10, + ... n_samples=30, n_components=15, n_features=20, n_nonzero_coefs=10, ... random_state=42, ... ) >>> dict_learner = DictionaryLearning( @@ -1704,7 +1704,7 @@ class DictionaryLearning(_BaseSparseCoding, BaseEstimator): We can check the level of sparsity of `X_transformed`: >>> np.mean(X_transformed == 0) - 0.41... + 0.52... We can compare the average squared euclidean norm of the reconstruction error of the sparse coded signal relative to the squared euclidean norm of @@ -1712,7 +1712,7 @@ class DictionaryLearning(_BaseSparseCoding, BaseEstimator): >>> X_hat = X_transformed @ dict_learner.components_ >>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1)) - 0.07... + 0.05... """ _parameter_constraints: dict = { @@ -2062,16 +2062,16 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator): >>> from sklearn.datasets import make_sparse_coded_signal >>> from sklearn.decomposition import MiniBatchDictionaryLearning >>> X, dictionary, code = make_sparse_coded_signal( - ... n_samples=100, n_components=15, n_features=20, n_nonzero_coefs=10, + ... n_samples=30, n_components=15, n_features=20, n_nonzero_coefs=10, ... random_state=42) >>> dict_learner = MiniBatchDictionaryLearning( ... n_components=15, batch_size=3, transform_algorithm='lasso_lars', - ... transform_alpha=0.1, random_state=42) + ... transform_alpha=0.1, max_iter=20, random_state=42) >>> X_transformed = dict_learner.fit_transform(X) We can check the level of sparsity of `X_transformed`: - >>> np.mean(X_transformed == 0) < 0.5 + >>> np.mean(X_transformed == 0) > 0.5 True We can compare the average squared euclidean norm of the reconstruction @@ -2080,7 +2080,7 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator): >>> X_hat = X_transformed @ dict_learner.components_ >>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1)) - 0.057... + 0.052... """ _parameter_constraints: dict = { diff --git a/sklearn/decomposition/tests/test_dict_learning.py b/sklearn/decomposition/tests/test_dict_learning.py index 0986bc6b3feed..b98d75eccbee3 100644 --- a/sklearn/decomposition/tests/test_dict_learning.py +++ b/sklearn/decomposition/tests/test_dict_learning.py @@ -43,7 +43,7 @@ def test_sparse_encode_shapes_omp(): for n_components, n_samples in itertools.product([1, 5], [1, 9]): X_ = rng.randn(n_samples, n_features) dictionary = rng.randn(n_components, n_features) - for algorithm, n_jobs in itertools.product(algorithms, [1, 3]): + for algorithm, n_jobs in itertools.product(algorithms, [1, 2]): code = sparse_encode(X_, dictionary, algorithm=algorithm, n_jobs=n_jobs) assert code.shape == (n_samples, n_components) diff --git a/sklearn/decomposition/tests/test_sparse_pca.py b/sklearn/decomposition/tests/test_sparse_pca.py index 4abbbd515aeb9..848d5d9d7ee34 100644 --- a/sklearn/decomposition/tests/test_sparse_pca.py +++ b/sklearn/decomposition/tests/test_sparse_pca.py @@ -120,12 +120,12 @@ def test_initialization(): def test_mini_batch_correct_shapes(): rng = np.random.RandomState(0) X = rng.randn(12, 10) - pca = MiniBatchSparsePCA(n_components=8, random_state=rng) + pca = MiniBatchSparsePCA(n_components=8, max_iter=1, random_state=rng) U = pca.fit_transform(X) assert pca.components_.shape == (8, 10) assert U.shape == (12, 8) # test overcomplete decomposition - pca = MiniBatchSparsePCA(n_components=13, random_state=rng) + pca = MiniBatchSparsePCA(n_components=13, max_iter=1, random_state=rng) U = pca.fit_transform(X) assert pca.components_.shape == (13, 10) assert U.shape == (12, 13) diff --git a/sklearn/ensemble/tests/test_gradient_boosting.py b/sklearn/ensemble/tests/test_gradient_boosting.py index f46bf9959fa29..6e8335402c78c 100644 --- a/sklearn/ensemble/tests/test_gradient_boosting.py +++ b/sklearn/ensemble/tests/test_gradient_boosting.py @@ -80,10 +80,15 @@ def test_classification_toy(loss, global_random_seed): def test_classification_synthetic(loss, global_random_seed): # Test GradientBoostingClassifier on synthetic dataset used by # Hastie et al. in ESLII - Figure 10.9 - X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=global_random_seed) + # Note that Figure 10.9 reuses the dataset generated for figure 10.2 + # and should have 2_000 train data points and 10_000 test data points. + # Here we intentionally use a smaller variant to make the test run faster, + # but the conclusions are still the same, despite the smaller datasets. + X, y = datasets.make_hastie_10_2(n_samples=2000, random_state=global_random_seed) - X_train, X_test = X[:2000], X[2000:] - y_train, y_test = y[:2000], y[2000:] + split_idx = 500 + X_train, X_test = X[:split_idx], X[split_idx:] + y_train, y_test = y[:split_idx], y[split_idx:] # Increasing the number of trees should decrease the test error common_params = { @@ -92,13 +97,13 @@ def test_classification_synthetic(loss, global_random_seed): "loss": loss, "random_state": global_random_seed, } - gbrt_100_stumps = GradientBoostingClassifier(n_estimators=100, **common_params) - gbrt_100_stumps.fit(X_train, y_train) + gbrt_10_stumps = GradientBoostingClassifier(n_estimators=10, **common_params) + gbrt_10_stumps.fit(X_train, y_train) - gbrt_200_stumps = GradientBoostingClassifier(n_estimators=200, **common_params) - gbrt_200_stumps.fit(X_train, y_train) + gbrt_50_stumps = GradientBoostingClassifier(n_estimators=50, **common_params) + gbrt_50_stumps.fit(X_train, y_train) - assert gbrt_100_stumps.score(X_test, y_test) < gbrt_200_stumps.score(X_test, y_test) + assert gbrt_10_stumps.score(X_test, y_test) < gbrt_50_stumps.score(X_test, y_test) # Decision stumps are better suited for this dataset with a large number of # estimators. diff --git a/sklearn/neighbors/tests/test_neighbors.py b/sklearn/neighbors/tests/test_neighbors.py index 369d61be740b8..339861dac6ab3 100644 --- a/sklearn/neighbors/tests/test_neighbors.py +++ b/sklearn/neighbors/tests/test_neighbors.py @@ -70,7 +70,6 @@ set.intersection(*map(set, neighbors.VALID_METRICS.values())) ) # type: ignore P = (1, 2, 3, 4, np.inf) -JOBLIB_BACKENDS = list(joblib.parallel.BACKENDS.keys()) # Filter deprecation warnings. neighbors.kneighbors_graph = ignore_warnings(neighbors.kneighbors_graph) @@ -1967,10 +1966,10 @@ def test_same_radius_neighbors_parallel(algorithm): assert_allclose(graph, graph_parallel) -@pytest.mark.parametrize("backend", JOBLIB_BACKENDS) +@pytest.mark.parametrize("backend", ["threading", "loky"]) @pytest.mark.parametrize("algorithm", ALGORITHMS) def test_knn_forcing_backend(backend, algorithm): - # Non-regression test which ensure the knn methods are properly working + # Non-regression test which ensures the knn methods are properly working # even when forcing the global joblib backend. with joblib.parallel_backend(backend): X, y = datasets.make_classification( @@ -1979,12 +1978,12 @@ def test_knn_forcing_backend(backend, algorithm): X_train, X_test, y_train, y_test = train_test_split(X, y) clf = neighbors.KNeighborsClassifier( - n_neighbors=3, algorithm=algorithm, n_jobs=3 + n_neighbors=3, algorithm=algorithm, n_jobs=2 ) clf.fit(X_train, y_train) clf.predict(X_test) clf.kneighbors(X_test) - clf.kneighbors_graph(X_test, mode="distance").toarray() + clf.kneighbors_graph(X_test, mode="distance") def test_dtype_convert(): diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index cadbe2c9f702e..82c68b1327296 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -802,10 +802,10 @@ def test_min_weight_fraction_leaf_with_min_samples_leaf_on_sparse_input(name): check_min_weight_fraction_leaf_with_min_samples_leaf(name, "multilabel", True) -def test_min_impurity_decrease(): +def test_min_impurity_decrease(global_random_seed): # test if min_impurity_decrease ensure that a split is made only if # if the impurity decrease is at least that value - X, y = datasets.make_classification(n_samples=10000, random_state=42) + X, y = datasets.make_classification(n_samples=100, random_state=global_random_seed) # test both DepthFirstTreeBuilder and BestFirstTreeBuilder # by setting max_leaf_nodes From 41beb327df31712ccc1e3c8dc0110318f4506d37 Mon Sep 17 00:00:00 2001 From: Daniel Schmitz <40656107+dschmitz89@users.noreply.github.com> Date: Sat, 16 Sep 2023 16:27:19 +0200 Subject: [PATCH 225/264] DOC: add note about restarting conda environment after creation (#27387) --- doc/developers/advanced_installation.rst | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/doc/developers/advanced_installation.rst b/doc/developers/advanced_installation.rst index a9461e965af2a..2eab1bb06d979 100644 --- a/doc/developers/advanced_installation.rst +++ b/doc/developers/advanced_installation.rst @@ -69,6 +69,12 @@ feature, code or documentation improvement). .. prompt:: bash $ conda create -n sklearn-env -c conda-forge python=3.9 numpy scipy cython + + It is not always necessary but it is safer to open a new prompt before + activating the newly created conda environment. + + .. prompt:: bash $ + conda activate sklearn-env #. **Alternative to conda:** If you run Linux or similar, you can instead use @@ -287,6 +293,12 @@ scikit-learn from source: conda create -n sklearn-dev -c conda-forge python numpy scipy cython \ joblib threadpoolctl pytest compilers llvm-openmp + +It is not always necessary but it is safer to open a new prompt before +activating the newly created conda environment. + +.. prompt:: bash $ + conda activate sklearn-dev make clean pip install -v --no-use-pep517 --no-build-isolation -e . @@ -307,12 +319,6 @@ forge using the following command: which should include ``compilers`` and ``llvm-openmp``. -.. note:: - - If you installed these packages after creating and activating a new conda - environment, you will need to first deactivate and then reactivate the - environment for these changes to take effect. - The compilers meta-package will automatically set custom environment variables: @@ -428,6 +434,12 @@ in the user folder using conda: conda create -n sklearn-dev -c conda-forge python numpy scipy cython \ joblib threadpoolctl pytest compilers + +It is not always necessary but it is safer to open a new prompt before +activating the newly created conda environment. + +.. prompt:: bash $ + conda activate sklearn-dev pip install -v --no-use-pep517 --no-build-isolation -e . From bfe045f09e1f249f5e1d7c09de8eb92b52505bbb Mon Sep 17 00:00:00 2001 From: MN193 <114568256+MN193@users.noreply.github.com> Date: Sun, 17 Sep 2023 13:09:29 +0200 Subject: [PATCH 226/264] DOC add link to plot_cv_indices example in _split (#27244) Co-authored-by: Adrin Jalali --- sklearn/model_selection/_split.py | 32 +++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py index 4d30538023abd..959a8c361d879 100644 --- a/sklearn/model_selection/_split.py +++ b/sklearn/model_selection/_split.py @@ -410,6 +410,10 @@ class KFold(_BaseKFold): Read more in the :ref:`User Guide `. + For visualisation of cross-validation behaviour and + comparison between common scikit-learn split methods + refer to :ref:`sphx_glr_auto_examples_model_selection_plot_cv_indices.py` + Parameters ---------- n_splits : int, default=5 @@ -502,6 +506,10 @@ class GroupKFold(GroupsConsumerMixin, _BaseKFold): Read more in the :ref:`User Guide `. + For visualisation of cross-validation behaviour and + comparison between common scikit-learn split methods + refer to :ref:`sphx_glr_auto_examples_model_selection_plot_cv_indices.py` + Parameters ---------- n_splits : int, default=5 @@ -626,6 +634,10 @@ class StratifiedKFold(_BaseKFold): Read more in the :ref:`User Guide `. + For visualisation of cross-validation behaviour and + comparison between common scikit-learn split methods + refer to :ref:`sphx_glr_auto_examples_model_selection_plot_cv_indices.py` + Parameters ---------- n_splits : int, default=5 @@ -817,6 +829,10 @@ class StratifiedGroupKFold(GroupsConsumerMixin, _BaseKFold): Read more in the :ref:`User Guide `. + For visualisation of cross-validation behaviour and + comparison between common scikit-learn split methods + refer to :ref:`sphx_glr_auto_examples_model_selection_plot_cv_indices.py` + Parameters ---------- n_splits : int, default=5 @@ -1016,6 +1032,10 @@ class TimeSeriesSplit(_BaseKFold): Read more in the :ref:`User Guide `. + For visualisation of cross-validation behaviour and + comparison between common scikit-learn split methods + refer to :ref:`sphx_glr_auto_examples_model_selection_plot_cv_indices.py` + .. versionadded:: 0.18 Parameters @@ -1766,6 +1786,10 @@ class ShuffleSplit(BaseShuffleSplit): Read more in the :ref:`User Guide `. + For visualisation of cross-validation behaviour and + comparison between common scikit-learn split methods + refer to :ref:`sphx_glr_auto_examples_model_selection_plot_cv_indices.py` + Parameters ---------- n_splits : int, default=10 @@ -1896,6 +1920,10 @@ class GroupShuffleSplit(GroupsConsumerMixin, ShuffleSplit): Read more in the :ref:`User Guide `. + For visualisation of cross-validation behaviour and + comparison between common scikit-learn split methods + refer to :ref:`sphx_glr_auto_examples_model_selection_plot_cv_indices.py` + Parameters ---------- n_splits : int, default=5 @@ -2026,6 +2054,10 @@ class StratifiedShuffleSplit(BaseShuffleSplit): Read more in the :ref:`User Guide `. + For visualisation of cross-validation behaviour and + comparison between common scikit-learn split methods + refer to :ref:`sphx_glr_auto_examples_model_selection_plot_cv_indices.py` + Parameters ---------- n_splits : int, default=10 From 07f990aeef79cd4bba95601c17b327abe80ba535 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Sun, 17 Sep 2023 15:15:47 +0200 Subject: [PATCH 227/264] FIX accept multilabel-indicator in _get_response_values (#27002) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Thomas J. Fan Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> Co-authored-by: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Co-authored-by: Vladimir Fokow <57260995+VladimirFokow@users.noreply.github.com> Co-authored-by: jeremie du boisberranger Co-authored-by: Xuefeng Xu Co-authored-by: Tim Head Co-authored-by: Raphael Co-authored-by: Olivier Grisel Co-authored-by: Sayed Qaiser Ali <66676360+sqali@users.noreply.github.com> Co-authored-by: Loïc Estève Co-authored-by: Xiao Yuan --- doc/whats_new/v1.3.rst | 7 + sklearn/metrics/tests/test_score_objects.py | 45 +++++- sklearn/utils/_response.py | 159 +++++++++++++++++--- sklearn/utils/tests/test_response.py | 31 +++- 4 files changed, 208 insertions(+), 34 deletions(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index 25e698caa8605..7d155c392423a 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -43,6 +43,13 @@ Changelog ``transform`` when ``add_indicator`` is set to ``True`` and missing values are observed during ``fit``. :pr:`26600` by :user:`Shreesha Kumar Bhat `. +:mod:`sklearn.metrics` +....................... + +- |Fix| Scorers used with :func:`metrics.get_scorer` handle properly + multilabel-indicator matrix. + :pr:`27002` by :user:`Guillaume Lemaitre `. + :mod:`sklearn.neighbors` ........................ diff --git a/sklearn/metrics/tests/test_score_objects.py b/sklearn/metrics/tests/test_score_objects.py index ffcb0ed4fda10..a72392e44d786 100644 --- a/sklearn/metrics/tests/test_score_objects.py +++ b/sklearn/metrics/tests/test_score_objects.py @@ -541,12 +541,15 @@ def test_thresholded_scorers_multilabel_indicator_data(): # Multi-output multi-class decision_function # TODO Is there any yet? - clf = DecisionTreeClassifier() - clf.fit(X_train, y_train) - clf._predict_proba = clf.predict_proba - clf.predict_proba = None - clf.decision_function = lambda X: [p[:, 1] for p in clf._predict_proba(X)] + class TreeWithDecisionFunction(DecisionTreeClassifier): + # disable predict_proba + predict_proba = None + + def decision_function(self, X): + return [p[:, 1] for p in DecisionTreeClassifier.predict_proba(self, X)] + clf = TreeWithDecisionFunction() + clf.fit(X_train, y_train) y_proba = clf.decision_function(X_test) score1 = get_scorer("roc_auc")(clf, X_test, y_test) score2 = roc_auc_score(y_test, np.vstack([p for p in y_proba]).T) @@ -799,7 +802,19 @@ def test_multimetric_scorer_calls_method_once( assert decision_function_func.call_count == expected_decision_func_count -def test_multimetric_scorer_calls_method_once_classifier_no_decision(): +@pytest.mark.parametrize( + "scorers", + [ + (["roc_auc", "neg_log_loss"]), + ( + { + "roc_auc": make_scorer(roc_auc_score, needs_threshold=True), + "neg_log_loss": make_scorer(log_loss, needs_proba=True), + } + ), + ], +) +def test_multimetric_scorer_calls_method_once_classifier_no_decision(scorers): predict_proba_call_cnt = 0 class MockKNeighborsClassifier(KNeighborsClassifier): @@ -814,7 +829,6 @@ def predict_proba(self, X): clf = MockKNeighborsClassifier(n_neighbors=1) clf.fit(X, y) - scorers = ["roc_auc", "neg_log_loss"] scorer_dict = _check_multimetric_scoring(clf, scorers) scorer = _MultimetricScorer(scorers=scorer_dict) scorer(clf, X, y) @@ -837,7 +851,7 @@ def predict(self, X): clf = MockDecisionTreeRegressor() clf.fit(X, y) - scorers = {"neg_mse": "neg_mean_squared_error", "r2": "roc_auc"} + scorers = {"neg_mse": "neg_mean_squared_error", "r2": "r2"} scorer_dict = _check_multimetric_scoring(clf, scorers) scorer = _MultimetricScorer(scorers=scorer_dict) scorer(clf, X, y) @@ -1346,3 +1360,18 @@ def score(y_true, y_pred, param=None): with config_context(enable_metadata_routing=False): with pytest.raises(ValueError, match="kwargs is only supported if"): scorer(clf, X, y, param="blah") + + +def test_get_scorer_multilabel_indicator(): + """Check that our scorer deal with multi-label indicator matrices. + + Non-regression test for: + https://github.com/scikit-learn/scikit-learn/issues/26817 + """ + X, Y = make_multilabel_classification(n_samples=72, n_classes=3, random_state=0) + X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=0) + + estimator = KNeighborsClassifier().fit(X_train, Y_train) + + score = get_scorer("average_precision")(estimator, X_test, Y_test) + assert score > 0.8 diff --git a/sklearn/utils/_response.py b/sklearn/utils/_response.py index e753ced045e1e..a8504099eb194 100644 --- a/sklearn/utils/_response.py +++ b/sklearn/utils/_response.py @@ -5,9 +5,110 @@ import numpy as np from ..base import is_classifier +from .multiclass import type_of_target from .validation import _check_response_method, check_is_fitted +def _process_predict_proba(*, y_pred, target_type, classes, pos_label): + """Get the response values when the response method is `predict_proba`. + + This function process the `y_pred` array in the binary and multi-label cases. + In the binary case, it selects the column corresponding to the positive + class. In the multi-label case, it stacks the predictions if they are not + in the "compressed" format `(n_samples, n_outputs)`. + + Parameters + ---------- + y_pred : ndarray + Output of `estimator.predict_proba`. The shape depends on the target type: + + - for binary classification, it is a 2d array of shape `(n_samples, 2)`; + - for multiclass classification, it is a 2d array of shape + `(n_samples, n_classes)`; + - for multilabel classification, it is either a list of 2d arrays of shape + `(n_samples, 2)` (e.g. `RandomForestClassifier` or `KNeighborsClassifier`) or + an array of shape `(n_samples, n_outputs)` (e.g. `MLPClassifier` or + `RidgeClassifier`). + + target_type : {"binary", "multiclass", "multilabel-indicator"} + Type of the target. + + classes : ndarray of shape (n_classes,) or list of such arrays + Class labels as reported by `estimator.classes_`. + + pos_label : int, float, bool or str + Only used with binary and multiclass targets. + + Returns + ------- + y_pred : ndarray of shape (n_samples,), (n_samples, n_classes) or \ + (n_samples, n_output) + Compressed predictions format as requested by the metrics. + """ + if target_type == "binary" and y_pred.shape[1] < 2: + # We don't handle classifiers trained on a single class. + raise ValueError( + f"Got predict_proba of shape {y_pred.shape}, but need " + "classifier with two classes." + ) + + if target_type == "binary": + col_idx = np.flatnonzero(classes == pos_label)[0] + return y_pred[:, col_idx] + elif target_type == "multilabel-indicator": + # Use a compress format of shape `(n_samples, n_output)`. + # Only `MLPClassifier` and `RidgeClassifier` return an array of shape + # `(n_samples, n_outputs)`. + if isinstance(y_pred, list): + # list of arrays of shape `(n_samples, 2)` + return np.vstack([p[:, -1] for p in y_pred]).T + else: + # array of shape `(n_samples, n_outputs)` + return y_pred + + return y_pred + + +def _process_decision_function(*, y_pred, target_type, classes, pos_label): + """Get the response values when the response method is `decision_function`. + + This function process the `y_pred` array in the binary and multi-label cases. + In the binary case, it inverts the sign of the score if the positive label + is not `classes[1]`. In the multi-label case, it stacks the predictions if + they are not in the "compressed" format `(n_samples, n_outputs)`. + + Parameters + ---------- + y_pred : ndarray + Output of `estimator.predict_proba`. The shape depends on the target type: + + - for binary classification, it is a 1d array of shape `(n_samples,)` where the + sign is assuming that `classes[1]` is the positive class; + - for multiclass classification, it is a 2d array of shape + `(n_samples, n_classes)`; + - for multilabel classification, it is a 2d array of shape `(n_samples, + n_outputs)`. + + target_type : {"binary", "multiclass", "multilabel-indicator"} + Type of the target. + + classes : ndarray of shape (n_classes,) or list of such arrays + Class labels as reported by `estimator.classes_`. + + pos_label : int, float, bool or str + Only used with binary and multiclass targets. + + Returns + ------- + y_pred : ndarray of shape (n_samples,), (n_samples, n_classes) or \ + (n_samples, n_output) + Compressed predictions format as requested by the metrics. + """ + if target_type == "binary" and pos_label == classes[0]: + return -1 * y_pred + return y_pred + + def _get_response_values( estimator, X, @@ -16,12 +117,18 @@ def _get_response_values( ): """Compute the response values of a classifier or a regressor. - The response values are predictions, one scalar value for each sample in X - that depends on the specific choice of `response_method`. + The response values are predictions such that it follows the following shape: + + - for binary classification, it is a 1d array of shape `(n_samples,)`; + - for multiclass classification, it is a 2d array of shape `(n_samples, n_classes)`; + - for multilabel classification, it is a 2d array of shape `(n_samples, n_outputs)`; + - for regression, it is a 1d array of shape `(n_samples,)`. If `estimator` is a binary classifier, also return the label for the effective positive class. + This utility is used primarily in the displays and the scikit-learn scorers. + .. versionadded:: 1.3 Parameters @@ -51,8 +158,9 @@ def _get_response_values( Returns ------- - y_pred : ndarray of shape (n_samples,) - Target scores calculated from the provided response_method + y_pred : ndarray of shape (n_samples,), (n_samples, n_classes) or \ + (n_samples, n_outputs) + Target scores calculated from the provided `response_method` and `pos_label`. pos_label : int, float, bool, str or None @@ -72,32 +180,33 @@ def _get_response_values( if is_classifier(estimator): prediction_method = _check_response_method(estimator, response_method) classes = estimator.classes_ - target_type = "binary" if len(classes) <= 2 else "multiclass" + target_type = type_of_target(classes) - if pos_label is not None and pos_label not in classes.tolist(): - raise ValueError( - f"pos_label={pos_label} is not a valid label: It should be " - f"one of {classes}" - ) - elif pos_label is None and target_type == "binary": - pos_label = pos_label if pos_label is not None else classes[-1] + if target_type in ("binary", "multiclass"): + if pos_label is not None and pos_label not in classes.tolist(): + raise ValueError( + f"pos_label={pos_label} is not a valid label: It should be " + f"one of {classes}" + ) + elif pos_label is None and target_type == "binary": + pos_label = classes[-1] y_pred = prediction_method(X) + if prediction_method.__name__ == "predict_proba": - if target_type == "binary" and y_pred.shape[1] <= 2: - if y_pred.shape[1] == 2: - col_idx = np.flatnonzero(classes == pos_label)[0] - y_pred = y_pred[:, col_idx] - else: - err_msg = ( - f"Got predict_proba of shape {y_pred.shape}, but need " - "classifier with two classes." - ) - raise ValueError(err_msg) + y_pred = _process_predict_proba( + y_pred=y_pred, + target_type=target_type, + classes=classes, + pos_label=pos_label, + ) elif prediction_method.__name__ == "decision_function": - if target_type == "binary": - if pos_label == classes[0]: - y_pred *= -1 + y_pred = _process_decision_function( + y_pred=y_pred, + target_type=target_type, + classes=classes, + pos_label=pos_label, + ) else: # estimator is a regressor if response_method != "predict": raise ValueError( diff --git a/sklearn/utils/tests/test_response.py b/sklearn/utils/tests/test_response.py index a67346e5697ec..e715e94cc60b1 100644 --- a/sklearn/utils/tests/test_response.py +++ b/sklearn/utils/tests/test_response.py @@ -1,11 +1,17 @@ import numpy as np import pytest -from sklearn.datasets import load_iris, make_classification, make_regression +from sklearn.datasets import ( + load_iris, + make_classification, + make_multilabel_classification, + make_regression, +) from sklearn.linear_model import ( LinearRegression, LogisticRegression, ) +from sklearn.multioutput import ClassifierChain from sklearn.preprocessing import scale from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor from sklearn.utils._mocking import _MockEstimatorOnOffPrediction @@ -230,3 +236,26 @@ def test_get_response_values_multiclass(estimator, response_method): assert predictions.shape == (X.shape[0], len(estimator.classes_)) if response_method == "predict_proba": assert np.logical_and(predictions >= 0, predictions <= 1).all() + + +@pytest.mark.parametrize( + "response_method", ["predict_proba", "decision_function", "predict"] +) +def test_get_response_values_multilabel_indicator(response_method): + X, Y = make_multilabel_classification(random_state=0) + estimator = ClassifierChain(LogisticRegression()).fit(X, Y) + + y_pred, pos_label = _get_response_values( + estimator, X, response_method=response_method + ) + assert pos_label is None + assert y_pred.shape == Y.shape + + if response_method == "predict_proba": + assert np.logical_and(y_pred >= 0, y_pred <= 1).all() + elif response_method == "decision_function": + # values returned by `decision_function` are not bounded in [0, 1] + assert (y_pred < 0).sum() > 0 + assert (y_pred > 1).sum() > 0 + else: # response_method == "predict" + assert np.logical_or(y_pred == 0, y_pred == 1).all() From ce26663d4e1c128db856a8d29eae6e7222a2e920 Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Mon, 18 Sep 2023 11:51:42 +0200 Subject: [PATCH 228/264] DOC fix unknown link error in plot_unveil_tree_structure.py (#27399) --- examples/tree/plot_unveil_tree_structure.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/examples/tree/plot_unveil_tree_structure.py b/examples/tree/plot_unveil_tree_structure.py index fc2a9df20ae70..9ce67132ef12a 100644 --- a/examples/tree/plot_unveil_tree_structure.py +++ b/examples/tree/plot_unveil_tree_structure.py @@ -44,14 +44,15 @@ # # The decision classifier has an attribute called ``tree_`` which allows access # to low level attributes such as ``node_count``, the total number of nodes, -# and ``max_depth``, the maximal depth of the tree. The tree_.compute_node_depths() -# method computes the depth of each node in the tree. `tree_` also stores the -# entire binary tree structure, represented as a number of parallel arrays. The -# i-th element of each array holds information about the node ``i``. Node 0 is -# the tree's root. Some of the arrays only apply to either leaves or split -# nodes. In this case the values of the nodes of the other type is arbitrary. -# For example, the arrays ``feature`` and ``threshold`` only apply to split -# nodes. The values for leaf nodes in these arrays are therefore arbitrary. +# and ``max_depth``, the maximal depth of the tree. The +# ``tree_.compute_node_depths()`` method computes the depth of each node in the +# tree. `tree_` also stores the entire binary tree structure, represented as a +# number of parallel arrays. The i-th element of each array holds information +# about the node ``i``. Node 0 is the tree's root. Some of the arrays only +# apply to either leaves or split nodes. In this case the values of the nodes +# of the other type is arbitrary. For example, the arrays ``feature`` and +# ``threshold`` only apply to split nodes. The values for leaf nodes in these +# arrays are therefore arbitrary. # # Among these arrays, we have: # From 03dab4997ab6e9b35cf13d32c67ecdf973c9d55e Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 18 Sep 2023 17:37:34 +0200 Subject: [PATCH 229/264] MAINT backport _is_pandas_df from main --- sklearn/utils/_set_output.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/sklearn/utils/_set_output.py b/sklearn/utils/_set_output.py index bb289535c45ec..814cab9e568e4 100644 --- a/sklearn/utils/_set_output.py +++ b/sklearn/utils/_set_output.py @@ -5,7 +5,6 @@ from .._config import get_config from . import check_pandas_support from ._available_if import available_if -from .validation import _is_pandas_df def _wrap_in_pandas_container( @@ -125,6 +124,22 @@ def _wrap_data_with_container(method, data_to_wrap, original_input, estimator): if output_config["dense"] == "default" or not _auto_wrap_is_configured(estimator): return data_to_wrap + def _is_pandas_df(X): + """Return True if the X is a pandas dataframe. + + This is is backport from 1.4 in 1.3.1 for compatibility. + """ + import sys + + if hasattr(X, "columns") and hasattr(X, "iloc"): + # Likely a pandas DataFrame, we explicitly check the type to confirm. + try: + pd = sys.modules["pandas"] + except KeyError: + return False + return isinstance(X, pd.DataFrame) + return False + # dense_config == "pandas" index = original_input.index if _is_pandas_df(original_input) else None return _wrap_in_pandas_container( From 398fc0fd430bca9131d099c48e3d99d9b6b46f97 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 18 Sep 2023 17:34:27 +0200 Subject: [PATCH 230/264] DOC move some fixes from 1.4 to 1.3.1 (#27402) --- doc/whats_new/v1.3.rst | 54 ++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 52 insertions(+), 2 deletions(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index 7d155c392423a..ce30f27918434 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -9,6 +9,20 @@ Version 1.3.1 **In development** +Changed models +-------------- + +The following estimators and functions, when fit with the same data and +parameters, may produce different models from the previous version. This often +occurs due to changes in the modelling logic (bug fixes or enhancements), or in +random sampling procedures. + +- |Fix| Ridge models with `solver='sparse_cg'` may have slightly different + results with scipy>=1.12, because of an underlying change in the scipy solver + (see `scipy#18488 `_ for more + details) + :pr:`26814` by :user:`Loïc Estève ` + Changes impacting all modules ----------------------------- @@ -18,6 +32,13 @@ Changes impacting all modules Changelog --------- +:mod:`sklearn.calibration` +.......................... + +- |Fix| :class:`calibration.CalibratedClassifierCV` can now handle models that + produce large prediction scores. Before it was numerically unstable. + :pr:`26913` by :user:`Omar Salman `. + :mod:`sklearn.cluster` ...................... @@ -26,7 +47,14 @@ Changelog :pr:`27167` by `Olivier Grisel`_. - |Fix| :class:`cluster.BisectingKMeans` now works with data that has a single feature. - :pr:`27243` by `Jérémie du Boisberranger `. + :pr:`27243` by :user:`Jérémie du Boisberranger `. + +:mod:`sklearn.cross_decomposition` +.................................. + +- |Fix| :class:`cross_decomposition.PLSRegression` now automatically ravels the output + of `predict` if fitted with one dimensional `y`. + :pr:`26602` by :user:`Yao Xiao `. :mod:`sklearn.ensemble` ....................... @@ -36,6 +64,12 @@ Changelog the sum of the scores should sum to zero for a sample). :pr:`26521` by :user:`Guillaume Lemaitre `. +:mod:`sklearn.feature_selection` +................................ + +- |Fix| :func:`feature_selection.mutual_info_regression` now correctly computes the + result when `X` is of integer dtype. :pr:`26748` by :user:`Yao Xiao `. + :mod:`sklearn.impute` ..................... @@ -44,12 +78,20 @@ Changelog during ``fit``. :pr:`26600` by :user:`Shreesha Kumar Bhat `. :mod:`sklearn.metrics` -....................... +...................... - |Fix| Scorers used with :func:`metrics.get_scorer` handle properly multilabel-indicator matrix. :pr:`27002` by :user:`Guillaume Lemaitre `. +:mod:`sklearn.mixture` +...................... + +- |Fix| The initialization of :class:`mixture.GaussianMixture` from user-provided + `precisions_init` for `covariance_type` of `full` or `tied` was not correct, + and has been fixed. + :pr:`26416` by :user:`Yang Tao `. + :mod:`sklearn.neighbors` ........................ @@ -65,12 +107,20 @@ Changelog when the input to the `param_distributions` parameter is a list of dicts. :pr:`26893` by :user:`Stefanie Senger `. +- |Fix| Neighbors based estimators now correctly work when `metric="minkowski"` and the + metric parameter `p` is in the range `0 < p < 1`, regardless of the `dtype` of `X`. + :pr:`26760` by :user:`Shreesha Kumar Bhat `. + :mod:`sklearn.preprocessing` ............................ - |Fix| :class:`preprocessing.LabelEncoder` correctly accepts `y` as a keyword argument. :pr:`26940` by `Thomas Fan`_. +- |Fix| :class:`preprocessing.OneHotEncoder` shows a more informative error message + when `sparse_output=True` and the output is configured to be pandas. + :pr:`26931` by `Thomas Fan`_. + :mod:`sklearn.tree` ................... From 415c032d65abaa87ee065853682a3b9712ba8f8a Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 18 Sep 2023 17:44:32 +0200 Subject: [PATCH 231/264] MAINT remove unintended merged code due to git auto-merge --- sklearn/tree/_criterion.pyx | 30 ------------------------------ 1 file changed, 30 deletions(-) diff --git a/sklearn/tree/_criterion.pyx b/sklearn/tree/_criterion.pyx index 7569c50f4b8fa..91c347735c5e0 100644 --- a/sklearn/tree/_criterion.pyx +++ b/sklearn/tree/_criterion.pyx @@ -211,36 +211,6 @@ cdef class Criterion: - (self.weighted_n_left / self.weighted_n_node_samples * impurity_left))) - cdef bint check_monotonicity( - self, - cnp.int8_t monotonic_cst, - double lower_bound, - double upper_bound, - ) noexcept nogil: - pass - - cdef inline bint _check_monotonicity( - self, - cnp.int8_t monotonic_cst, - double lower_bound, - double upper_bound, - double value_left, - double value_right, - ) noexcept nogil: - cdef: - bint check_lower_bound = ( - (value_left >= lower_bound) & - (value_right >= lower_bound) - ) - bint check_upper_bound = ( - (value_left <= upper_bound) & - (value_right <= upper_bound) - ) - bint check_monotonic_cst = ( - (value_left - value_right) * monotonic_cst <= 0 - ) - return check_lower_bound & check_upper_bound & check_monotonic_cst - cdef void init_sum_missing(self): """Init sum_missing to hold sums for missing values.""" From fbc344dcbb2e7e7ac86dc420d26b82978a9c72ea Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 18 Sep 2023 18:16:02 +0200 Subject: [PATCH 232/264] MAINT update the SECURITY.md file --- SECURITY.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/SECURITY.md b/SECURITY.md index 6b1e4fc87709c..fffe1d46b2ed0 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -4,8 +4,8 @@ | Version | Supported | | --------- | ------------------ | -| 1.3.0 | :white_check_mark: | -| < 1.3.0 | :x: | +| 1.3.1 | :white_check_mark: | +| < 1.3.1 | :x: | ## Reporting a Vulnerability From 167622f80f1d83173cdd7492c5fb92b49d596de3 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 18 Sep 2023 18:16:56 +0200 Subject: [PATCH 233/264] MAINT bumpversion to 1.3.1 --- sklearn/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/__init__.py b/sklearn/__init__.py index 82680d85e99da..8df2c91fb2ca9 100644 --- a/sklearn/__init__.py +++ b/sklearn/__init__.py @@ -38,7 +38,7 @@ # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' # -__version__ = "1.3.0" +__version__ = "1.3.1" # On OSX, we can get a runtime error due to multiple OpenMP libraries loaded From 93a0ac35efb8e81f27588d898c8115b4ff7011ce Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 19 Sep 2023 18:13:46 +0200 Subject: [PATCH 234/264] MAINT pin Cython<3.0 as build dependencies --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index c98ed2130189f..e7bb9aaa9542b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = [ "setuptools", "wheel", - "Cython>=0.29.33", + "Cython>=0.29.33 and Cython<3.0", # use oldest-supported-numpy which provides the oldest numpy version with # wheels on PyPI From 02254cd8f3bb4d17d7cbeb920be8275c4d52d18a Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 19 Sep 2023 18:21:37 +0200 Subject: [PATCH 235/264] MAINT do not allow for NumPy >= 2.0 --- setup.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/setup.py b/setup.py index 5af738f5f841f..17819f401d743 100755 --- a/setup.py +++ b/setup.py @@ -601,6 +601,11 @@ def setup_package(): }, ) + # Overwrite the dependencies to not allow for NumPy >= 2.0 + metadata["install_requires"] = [ + f"{dep},<2.0" for dep in metadata["install_requires"] if dep.startswith("numpy") + ] + commands = [arg for arg in sys.argv[1:] if not arg.startswith("-")] if not all( command in ("egg_info", "dist_info", "clean", "check") for command in commands From 82c273fded6e08654663b4d0fb943c41c6da222a Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 19 Sep 2023 18:36:29 +0200 Subject: [PATCH 236/264] fix upper bound cython --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index e7bb9aaa9542b..e662c6cd42e2f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = [ "setuptools", "wheel", - "Cython>=0.29.33 and Cython<3.0", + "Cython>=0.29.33,<3.0", # use oldest-supported-numpy which provides the oldest numpy version with # wheels on PyPI From 164e4200b1b5fd91862a0a861f408051a8226f8a Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 19 Sep 2023 17:23:47 +0200 Subject: [PATCH 237/264] DOC update release dates for 1.3.1 (#27407) --- doc/templates/index.html | 2 ++ doc/whats_new/v1.3.rst | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/templates/index.html b/doc/templates/index.html index fc0362f4e379f..40f58f7bbefe3 100644 --- a/doc/templates/index.html +++ b/doc/templates/index.html @@ -169,6 +169,8 @@

    News

  • On-going development: What's new (Changelog)
    • +
  • September 2023. scikit-learn 1.3.1 is available for download (Changelog). +
  • June 2023. scikit-learn 1.3.0 is available for download (Changelog).
  • March 2023. scikit-learn 1.2.2 is available for download (Changelog). diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index ce30f27918434..4999aba4c2e71 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -7,7 +7,7 @@ Version 1.3.1 ============= -**In development** +**September 2023** Changed models -------------- From d7e2b7a657cdaeabe843f7801a6b0e76fdb3dee1 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 19 Sep 2023 17:21:59 +0200 Subject: [PATCH 238/264] MAINT update author list and code to generate table (#27403) --- build_tools/generate_authors_table.py | 29 ++++++++++++++++++++++++++- doc/authors_emeritus.rst | 1 - doc/contributor_experience_team.rst | 4 ---- 3 files changed, 28 insertions(+), 6 deletions(-) diff --git a/build_tools/generate_authors_table.py b/build_tools/generate_authors_table.py index 28d1818c2b779..f438927772619 100644 --- a/build_tools/generate_authors_table.py +++ b/build_tools/generate_authors_table.py @@ -89,11 +89,23 @@ def get_contributors(): core_devs # remove ogrisel from contributor_experience_team ) - emeritus = members - core_devs - contributor_experience_team - comm_team + emeritus = ( + members + - core_devs + - contributor_experience_team + - comm_team + - documentation_team + ) # hard coded + emeritus_contributor_experience_team = { + "cmarmo", + } emeritus_comm_team = {"reshamas"} + # Up-to-now, we can subtract the team emeritus from the original emeritus + emeritus -= emeritus_contributor_experience_team | emeritus_comm_team + comm_team -= {"reshamas"} # in the comm team but not on the web page # get profiles from GitHub @@ -102,6 +114,9 @@ def get_contributors(): contributor_experience_team = [ get_profile(login) for login in contributor_experience_team ] + emeritus_contributor_experience_team = [ + get_profile(login) for login in emeritus_contributor_experience_team + ] comm_team = [get_profile(login) for login in comm_team] emeritus_comm_team = [get_profile(login) for login in emeritus_comm_team] documentation_team = [get_profile(login) for login in documentation_team] @@ -110,6 +125,9 @@ def get_contributors(): core_devs = sorted(core_devs, key=key) emeritus = sorted(emeritus, key=key) contributor_experience_team = sorted(contributor_experience_team, key=key) + emeritus_contributor_experience_team = sorted( + emeritus_contributor_experience_team, key=key + ) documentation_team = sorted(documentation_team, key=key) comm_team = sorted(comm_team, key=key) emeritus_comm_team = sorted(emeritus_comm_team, key=key) @@ -118,6 +136,7 @@ def get_contributors(): core_devs, emeritus, contributor_experience_team, + emeritus_contributor_experience_team, comm_team, emeritus_comm_team, documentation_team, @@ -188,6 +207,7 @@ def generate_list(contributors): core_devs, emeritus, contributor_experience_team, + emeritus_contributor_experience_team, comm_team, emeritus_comm_team, documentation_team, @@ -206,6 +226,13 @@ def generate_list(contributors): ) as rst_file: rst_file.write(generate_table(contributor_experience_team)) + with open( + REPO_FOLDER / "doc" / "contributor_experience_team_emeritus.rst", + "w+", + encoding="utf-8", + ) as rst_file: + rst_file.write(generate_list(emeritus_contributor_experience_team)) + with open( REPO_FOLDER / "doc" / "communication_team.rst", "w+", encoding="utf-8" ) as rst_file: diff --git a/doc/authors_emeritus.rst b/doc/authors_emeritus.rst index a56e2bc408ff4..b979b77bba974 100644 --- a/doc/authors_emeritus.rst +++ b/doc/authors_emeritus.rst @@ -20,7 +20,6 @@ - Wei Li - Paolo Losi - Gilles Louppe -- Chiara Marmo - Vincent Michel - Jarrod Millman - Alexandre Passos diff --git a/doc/contributor_experience_team.rst b/doc/contributor_experience_team.rst index a51f30bede265..7d942a07e6a7d 100644 --- a/doc/contributor_experience_team.rst +++ b/doc/contributor_experience_team.rst @@ -6,10 +6,6 @@ img.avatar {border-radius: 10px;}
    -
    -

    Arturo Amor

    -
    -

    Juan Carlos Alfaro Jiménez

    From bbe996065b91eb56f95acc9368205f08b882d920 Mon Sep 17 00:00:00 2001 From: Tim Head Date: Tue, 19 Sep 2023 14:49:20 +0200 Subject: [PATCH 239/264] MNT Update to Pyodide 0.24 (#27405) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- build_tools/circle/doc_linux-64_conda.lock | 164 +++++++++++---------- doc/jupyter-lite.json | 2 +- 2 files changed, 84 insertions(+), 82 deletions(-) diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 5cbf6e48f98e5..96176da5e65fb 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: e5c4e5603cb530b6ebd880df5ac2a031cdeebbbb3a8188ec94d243cf9851ad37 +# input_hash: b41f095377c6c64519b9c000301e61a7405086d523d7eec1238cdf4f8b896bb2 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.7.22-hbcca054_0.conda#a73ecd2988327ad4c8f2c331482917f2 @@ -10,23 +10,21 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77 https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-hab24e00_0.tar.bz2#19410c3df09dfb12d1206132a1d357c5 https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_16.conda#7ca122655873935e02c91279c5b03c8c https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda#7aca3059a1729aa76c597603f10b0dd3 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-devel_linux-64-12.3.0-h8bca6fd_0.conda#b9ae31bc2e565684ebaf82d4bd954d55 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.1.0-h15d22d2_0.conda#afb656a334c409dd9805508af1c89c7a 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+https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hbdbef99_2.conda#adfebae9fdc63a598495dfe3b006973a https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.1.0-he5830b7_0.conda#cd93f779ff018dd85c7544c015c9db3c -https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.9-hd590300_0.conda#a0c6f0e7e1a467f5678f94dea18c8aa7 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h807b86a_1.conda#ff8999574b465089ba0aa25a5e865bd0 +https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.10-hd590300_0.conda#75dae9a4201732aa78a530b826ee5fe0 https://conda.anaconda.org/conda-forge/linux-64/aom-3.5.0-h27087fc_0.tar.bz2#a08150fd2298460cd1fcccf626305642 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h7f98852_4.tar.bz2#a1fd65c7ccbf10880423d82bca54eb54 @@ -35,25 +33,25 @@ https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda#418 https://conda.anaconda.org/conda-forge/linux-64/gettext-0.21.1-h27087fc_0.tar.bz2#14947d8770185e5153fdd04d4673ed37 https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.1-h0b41bf4_3.conda#96f3b11872ef6fad973eac856cd2624f https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h58526e2_1001.tar.bz2#8c54672728e8ec6aa6db90cf2806d220 -https://conda.anaconda.org/conda-forge/linux-64/icu-72.1-hcb278e6_0.conda#7c8d20d847bb45f56bd941578fcfa146 +https://conda.anaconda.org/conda-forge/linux-64/icu-73.2-h59595ed_0.conda#cc47e1facc155f91abd89b11e48e72ff https://conda.anaconda.org/conda-forge/linux-64/jxrlib-1.1-h7f98852_2.tar.bz2#8e787b08fe19986d99d034b839df2961 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 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https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.8.2-pyhd8ed1 # pip python-json-logger @ https://files.pythonhosted.org/packages/35/a6/145655273568ee78a581e734cf35beb9e33a370b29c5d3c8fee3744de29f/python_json_logger-2.0.7-py3-none-any.whl#sha256=f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd # pip pyyaml @ https://files.pythonhosted.org/packages/7d/39/472f2554a0f1e825bd7c5afc11c817cd7a2f3657460f7159f691fbb37c51/PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c # pip rfc3986-validator @ https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl#sha256=2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9 -# pip rpds-py @ 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https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.8.2-pyhd8ed1 # pip argon2-cffi @ https://files.pythonhosted.org/packages/a4/6a/e8a041599e78b6b3752da48000b14c8d1e8a04ded09c88c714ba047f34f5/argon2_cffi-23.1.0-py3-none-any.whl#sha256=c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea # pip jsonschema @ https://files.pythonhosted.org/packages/2b/ff/af59fd34bc4d7ac3e6e0cd1f3c10317d329b6c1aee179e8b24ad9a79fbac/jsonschema-4.19.0-py3-none-any.whl#sha256=043dc26a3845ff09d20e4420d6012a9c91c9aa8999fa184e7efcfeccb41e32cb # pip jupyter-client @ https://files.pythonhosted.org/packages/73/d4/3c13d6a300be9e894561aea0b81e7aed46e8f98029b7d9369d90b1fc7ac5/jupyter_client-8.3.1-py3-none-any.whl#sha256=5eb9f55eb0650e81de6b7e34308d8b92d04fe4ec41cd8193a913979e33d8e1a5 -# pip jupyterlite-pyodide-kernel @ 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"pyodideUrl": "https://cdn.jsdelivr.net/pyodide/v0.23.4/full/pyodide.js" + "pyodideUrl": "https://cdn.jsdelivr.net/pyodide/v0.24.0/full/pyodide.js" } } } From 91163a688c7623e6df02044b3e9f8314851bfc81 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 19 Sep 2023 19:24:34 +0200 Subject: [PATCH 240/264] TST skip test checking the Cython version in pyproject toml --- sklearn/tests/test_min_dependencies_readme.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/sklearn/tests/test_min_dependencies_readme.py b/sklearn/tests/test_min_dependencies_readme.py index c6d9cc42b0a03..ce53cc8d28d82 100644 --- a/sklearn/tests/test_min_dependencies_readme.py +++ b/sklearn/tests/test_min_dependencies_readme.py @@ -53,6 +53,13 @@ def test_min_dependencies_readme(): assert version == min_version, f"{package} has a mismatched version" +@pytest.mark.skip( + reason=( + "For the release purpose, the upper limit of Cython is set to 3.0 since it " + "leads to performance regression. Scikit-learn can still be built with " + "Cython 3." + ) +) def test_min_dependencies_pyproject_toml(): """Check versions in pyproject.toml is consistent with _min_dependencies.""" # tomllib is available in Python 3.11 From 9f7cba31552f69f83e6d29d5713d91b164ee509e Mon Sep 17 00:00:00 2001 From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Date: Wed, 20 Sep 2023 11:19:23 +0200 Subject: [PATCH 241/264] DOC Fix dropdown-related warnings (#27418) Co-authored-by: ArturoAmorQ Co-authored-by: Guillaume Lemaitre --- doc/modules/compose.rst | 120 ++++++++++++++++------------- doc/modules/feature_extraction.rst | 9 +-- 2 files changed, 68 insertions(+), 61 deletions(-) diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst index d1a20456707ca..5b3b809dbe995 100644 --- a/doc/modules/compose.rst +++ b/doc/modules/compose.rst @@ -54,9 +54,8 @@ The last estimator may be any type (transformer, classifier, etc.). Usage ----- -|details-start| -**Construction** -|details-split| +Build a pipeline +................ The :class:`Pipeline` is built using a list of ``(key, value)`` pairs, where the ``key`` is a string containing the name you want to give this step and ``value`` @@ -70,6 +69,10 @@ is an estimator object:: >>> pipe Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC())]) +|details-start| +**Shortand version using :func:`make_pipeline`** +|details-split| + The utility function :func:`make_pipeline` is a shorthand for constructing pipelines; it takes a variable number of estimators and returns a pipeline, @@ -81,14 +84,26 @@ filling in the names automatically:: |details-end| +Access pipeline steps +..................... + +The estimators of a pipeline are stored as a list in the ``steps`` attribute. +A sub-pipeline can be extracted using the slicing notation commonly used +for Python Sequences such as lists or strings (although only a step of 1 is +permitted). This is convenient for performing only some of the transformations +(or their inverse): + + >>> pipe[:1] + Pipeline(steps=[('reduce_dim', PCA())]) + >>> pipe[-1:] + Pipeline(steps=[('clf', SVC())]) + |details-start| -**Accessing steps** +**Accessing a step by name or position** |details-split| - -The estimators of a pipeline are stored as a list in the ``steps`` attribute, -but can be accessed by index or name by indexing (with ``[idx]``) the -Pipeline:: +A specific step can also be accessed by index or name by indexing (with ``[idx]``) the +pipeline:: >>> pipe.steps[0] ('reduce_dim', PCA()) @@ -97,36 +112,61 @@ Pipeline:: >>> pipe['reduce_dim'] PCA() -Pipeline's `named_steps` attribute allows accessing steps by name with tab +`Pipeline`'s `named_steps` attribute allows accessing steps by name with tab completion in interactive environments:: >>> pipe.named_steps.reduce_dim is pipe['reduce_dim'] True -A sub-pipeline can also be extracted using the slicing notation commonly used -for Python Sequences such as lists or strings (although only a step of 1 is -permitted). This is convenient for performing only some of the transformations -(or their inverse): +|details-end| - >>> pipe[:1] - Pipeline(steps=[('reduce_dim', PCA())]) - >>> pipe[-1:] - Pipeline(steps=[('clf', SVC())]) +Tracking feature names in a pipeline +.................................... -|details-end| +To enable model inspection, :class:`~sklearn.pipeline.Pipeline` has a +``get_feature_names_out()`` method, just like all transformers. You can use +pipeline slicing to get the feature names going into each step:: -.. _pipeline_nested_parameters: + >>> from sklearn.datasets import load_iris + >>> from sklearn.feature_selection import SelectKBest + >>> iris = load_iris() + >>> pipe = Pipeline(steps=[ + ... ('select', SelectKBest(k=2)), + ... ('clf', LogisticRegression())]) + >>> pipe.fit(iris.data, iris.target) + Pipeline(steps=[('select', SelectKBest(...)), ('clf', LogisticRegression(...))]) + >>> pipe[:-1].get_feature_names_out() + array(['x2', 'x3'], ...) |details-start| -**Nested parameters** +**Customize feature names** |details-split| -Parameters of the estimators in the pipeline can be accessed using the -``__`` syntax:: +You can also provide custom feature names for the input data using +``get_feature_names_out``:: + + >>> pipe[:-1].get_feature_names_out(iris.feature_names) + array(['petal length (cm)', 'petal width (cm)'], ...) + +|details-end| + +.. _pipeline_nested_parameters: + +Access to nested parameters +........................... + +It is common to adjust the parameters of an estimator within a pipeline. This parameter +is therefore nested because it belongs to a particular sub-step. Parameters of the +estimators in the pipeline are accessible using the ``__`` +syntax:: >>> pipe.set_params(clf__C=10) Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC(C=10))]) +|details-start| +**When does it matter?** +|details-split| + This is particularly important for doing grid searches:: >>> from sklearn.model_selection import GridSearchCV @@ -143,36 +183,11 @@ ignored by setting them to ``'passthrough'``:: ... clf__C=[0.1, 10, 100]) >>> grid_search = GridSearchCV(pipe, param_grid=param_grid) -The estimators of the pipeline can be retrieved by index: - - >>> pipe[0] - PCA() - -or by name:: - - >>> pipe['reduce_dim'] - PCA() - -To enable model inspection, :class:`~sklearn.pipeline.Pipeline` has a -``get_feature_names_out()`` method, just like all transformers. You can use -pipeline slicing to get the feature names going into each step:: - - >>> from sklearn.datasets import load_iris - >>> from sklearn.feature_selection import SelectKBest - >>> iris = load_iris() - >>> pipe = Pipeline(steps=[ - ... ('select', SelectKBest(k=2)), - ... ('clf', LogisticRegression())]) - >>> pipe.fit(iris.data, iris.target) - Pipeline(steps=[('select', SelectKBest(...)), ('clf', LogisticRegression(...))]) - >>> pipe[:-1].get_feature_names_out() - array(['x2', 'x3'], ...) +.. topic:: See Also: -You can also provide custom feature names for the input data using -``get_feature_names_out``:: + * :ref:`composite_grid_search` - >>> pipe[:-1].get_feature_names_out(iris.feature_names) - array(['petal length (cm)', 'petal width (cm)'], ...) +|details-end| .. topic:: Examples: @@ -184,11 +199,6 @@ You can also provide custom feature names for the input data using * :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py` * :ref:`sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py` -.. topic:: See Also: - - * :ref:`composite_grid_search` - -|details-end| .. _pipeline_cache: diff --git a/doc/modules/feature_extraction.rst b/doc/modules/feature_extraction.rst index ba8f7373e1304..9653ba9d7b646 100644 --- a/doc/modules/feature_extraction.rst +++ b/doc/modules/feature_extraction.rst @@ -225,7 +225,7 @@ it is advisable to use a power of two as the ``n_features`` parameter; otherwise the features will not be mapped evenly to the columns. .. topic:: References: - + * `MurmurHash3 `_. |details-end| @@ -398,9 +398,8 @@ last document:: .. _stop_words: -|details-start| -**Using stop words** -|details-split| +Using stop words +---------------- Stop words are words like "and", "the", "him", which are presumed to be uninformative in representing the content of a text, and which may be @@ -431,8 +430,6 @@ identify and warn about some kinds of inconsistencies. In *Proc. Workshop for NLP Open Source Software*. -|details-end| - .. _tfidf: Tf–idf term weighting From bfeef9dc7df3ac201d8223119591125ce4dc6078 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 20 Sep 2023 11:38:24 +0200 Subject: [PATCH 242/264] DOC fix typo in title of section (#27424) --- doc/modules/compose.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst index 5b3b809dbe995..8e21043ea6343 100644 --- a/doc/modules/compose.rst +++ b/doc/modules/compose.rst @@ -70,7 +70,7 @@ is an estimator object:: Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC())]) |details-start| -**Shortand version using :func:`make_pipeline`** +**Shorthand version using :func:`make_pipeline`** |details-split| The utility function :func:`make_pipeline` is a shorthand From 69a4e4c0faeeaba907f0b26c558280baa627aac3 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 20 Sep 2023 11:49:25 +0200 Subject: [PATCH 243/264] DOC fix examples in compose.rst due to change in code snippet order (#27425) --- doc/modules/compose.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst index 8e21043ea6343..965765ac1f7b2 100644 --- a/doc/modules/compose.rst +++ b/doc/modules/compose.rst @@ -128,6 +128,7 @@ To enable model inspection, :class:`~sklearn.pipeline.Pipeline` has a pipeline slicing to get the feature names going into each step:: >>> from sklearn.datasets import load_iris + >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.feature_selection import SelectKBest >>> iris = load_iris() >>> pipe = Pipeline(steps=[ @@ -160,6 +161,7 @@ is therefore nested because it belongs to a particular sub-step. Parameters of t estimators in the pipeline are accessible using the ``__`` syntax:: + >>> pipe = Pipeline(steps=[("reduce_dim", PCA()), ("clf", SVC())]) >>> pipe.set_params(clf__C=10) Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC(C=10))]) @@ -177,7 +179,6 @@ This is particularly important for doing grid searches:: Individual steps may also be replaced as parameters, and non-final steps may be ignored by setting them to ``'passthrough'``:: - >>> from sklearn.linear_model import LogisticRegression >>> param_grid = dict(reduce_dim=['passthrough', PCA(5), PCA(10)], ... clf=[SVC(), LogisticRegression()], ... clf__C=[0.1, 10, 100]) From 90c9a4d41834a16b0dc2d323300f171a5316a8ed Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 19 Sep 2023 22:09:19 +0200 Subject: [PATCH 244/264] MNT Remove test suite setuptools dependency for good (#27420) --- sklearn/tests/test_common.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py index a5f485ee1a882..8b407d18f90d8 100644 --- a/sklearn/tests/test_common.py +++ b/sklearn/tests/test_common.py @@ -218,7 +218,7 @@ def test_import_all_consistency(): if ".tests." in modname: continue # Avoid test suite depending on setuptools - if modname == "sklearn._build_utils.pre_build_helpers": + if "sklearn._build_utils" in modname: continue if IS_PYPY and ( "_svmlight_format_io" in modname From 55a65a2fa5653257225d7e184da3d0c00ff852b1 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 20 Sep 2023 12:01:00 +0200 Subject: [PATCH 245/264] [cd build] FIX only change numpy when limiting upper limit --- setup.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 17819f401d743..f78b7ec3545a4 100755 --- a/setup.py +++ b/setup.py @@ -603,7 +603,8 @@ def setup_package(): # Overwrite the dependencies to not allow for NumPy >= 2.0 metadata["install_requires"] = [ - f"{dep},<2.0" for dep in metadata["install_requires"] if dep.startswith("numpy") + f"{dep},<2.0" if dep.startswith("numpy") else dep + for dep in metadata["install_requires"] ] commands = [arg for arg in sys.argv[1:] if not arg.startswith("-")] From 2d8e03f4d5b3c466fb4542360bcef742a3a4e0a1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Thu, 28 Sep 2023 17:08:08 +0200 Subject: [PATCH 246/264] MAINT Update Pyodide to 0.24.1 for JupyterLite button (#27474) (#27489) --- doc/jupyter-lite.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/jupyter-lite.json b/doc/jupyter-lite.json index d5fb63ceb4a84..05a02b1080fa4 100644 --- a/doc/jupyter-lite.json +++ b/doc/jupyter-lite.json @@ -3,7 +3,7 @@ "jupyter-config-data": { "litePluginSettings": { "@jupyterlite/pyodide-kernel-extension:kernel": { - "pyodideUrl": "https://cdn.jsdelivr.net/pyodide/v0.24.0/full/pyodide.js" + "pyodideUrl": "https://cdn.jsdelivr.net/pyodide/v0.24.1/full/pyodide.js" } } } From 5309a51113c54b7f673cc472273ba842c4966fbf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Mon, 2 Oct 2023 14:03:09 +0200 Subject: [PATCH 247/264] CI Build and test Python 3.12 wheels (#27027) Co-authored-by: Olivier Grisel --- .github/workflows/wheels.yml | 20 ++++++++++++++++++- build_tools/cirrus/arm_wheel.yml | 18 ++++++++++++++--- .../github/build_minimal_windows_image.sh | 3 +++ setup.py | 1 + 4 files changed, 38 insertions(+), 4 deletions(-) diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml index 8026996106d8b..42f35362a5739 100644 --- a/.github/workflows/wheels.yml +++ b/.github/workflows/wheels.yml @@ -67,6 +67,11 @@ jobs: - os: windows-latest python: 311 platform_id: win_amd64 + - os: windows-latest + python: 312 + platform_id: win_amd64 + # TODO: remove when Python 3.12 is released + prerelease: "True" # Linux 64 bit manylinux2014 - os: ubuntu-latest @@ -88,6 +93,12 @@ jobs: python: 311 platform_id: manylinux_x86_64 manylinux_image: manylinux2014 + - os: ubuntu-latest + python: 312 + platform_id: manylinux_x86_64 + manylinux_image: manylinux2014 + # TODO: remove when Python 3.12 is released + prerelease: "True" # MacOS x86_64 - os: macos-latest @@ -104,7 +115,10 @@ jobs: platform_id: macosx_x86_64 # MacOS arm64 - # The latest Python version is built and tested on CirrusCI + # The wheel for the latest Python version is built and tested on + # Cirrus CI but due to limited build time for free accounts on Cirrus + # CI, we build the macOS arm64 wheels for the other Python versions on + # Github Actions via cross-compilation (without running the tests). - os: macos-latest python: 38 platform_id: macosx_arm64 @@ -114,6 +128,9 @@ jobs: - os: macos-latest python: 310 platform_id: macosx_arm64 + - os: macos-latest + python: 311 + platform_id: macosx_arm64 steps: - name: Checkout scikit-learn @@ -128,6 +145,7 @@ jobs: env: CONFTEST_PATH: ${{ github.workspace }}/conftest.py CONFTEST_NAME: conftest.py + CIBW_PRERELEASE_PYTHONS: ${{ matrix.prerelease }} CIBW_ENVIRONMENT: SKLEARN_SKIP_NETWORK_TESTS=1 SKLEARN_BUILD_PARALLEL=3 CIBW_BUILD: cp${{ matrix.python }}-${{ matrix.platform_id }} diff --git a/build_tools/cirrus/arm_wheel.yml b/build_tools/cirrus/arm_wheel.yml index 81acc7ed6a9fa..c5f5a34afa490 100644 --- a/build_tools/cirrus/arm_wheel.yml +++ b/build_tools/cirrus/arm_wheel.yml @@ -16,10 +16,17 @@ macos_arm64_wheel_task: # See `maint_tools/update_tracking_issue.py` for details on the permissions the token requires. BOT_GITHUB_TOKEN: ENCRYPTED[9b50205e2693f9e4ce9a3f0fcb897a259289062fda2f5a3b8aaa6c56d839e0854a15872f894a70fca337dd4787274e0f] matrix: - # Only the latest Python version is built and tested on CirrusCI, the other - # macos arm64 builds are on GitHub Actions + # Only the latest Python version is built and tested on Cirrus CI, the other + # macOS arm64 builds are on GitHub Actions. The reason is that macOS time is + # 5x more expensive than Linux times on Cirrus CI and the credits are limited + # (for free accounts). + # Note that the macOS arm64 builds are cross compiled on GitHub Actions (without + # running the tests) and while the macOS arm64 build for the latest Python version + # is actually tested on Cirrus CI. - env: - CIBW_BUILD: cp311-macosx_arm64 + CIBW_BUILD: cp312-macosx_arm64 + # TODO: remove when Python 3.12 is released + CIBW_PRERELEASE_PYTHONS: True conda_script: - curl -L --retry 10 -o ~/mambaforge.sh https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh @@ -68,6 +75,11 @@ linux_arm64_wheel_task: CIBW_TEST_SKIP: "*_aarch64" - env: CIBW_BUILD: cp311-manylinux_aarch64 + CIBW_TEST_SKIP: "*_aarch64" + - env: + CIBW_BUILD: cp312-manylinux_aarch64 + # TODO: remove when Python 3.12 is released + CIBW_PRERELEASE_PYTHONS: True cibuildwheel_script: - apt install -y python3 python-is-python3 diff --git a/build_tools/github/build_minimal_windows_image.sh b/build_tools/github/build_minimal_windows_image.sh index 4399bfa80704e..aa7bfc3e31f9f 100755 --- a/build_tools/github/build_minimal_windows_image.sh +++ b/build_tools/github/build_minimal_windows_image.sh @@ -14,6 +14,9 @@ cp $WHEEL_PATH $WHEEL_NAME # Dot the Python version for identyfing the base Docker image PYTHON_VERSION=$(echo ${PYTHON_VERSION:0:1}.${PYTHON_VERSION:1:2}) +if [[ "$CIBW_PRERELEASE_PYTHONS" == "True" ]]; then + PYTHON_VERSION="$PYTHON_VERSION-rc" +fi # Build a minimal Windows Docker image for testing the wheels docker build --build-arg PYTHON_VERSION=$PYTHON_VERSION \ --build-arg WHEEL_NAME=$WHEEL_NAME \ diff --git a/setup.py b/setup.py index f78b7ec3545a4..d3f70c548316c 100755 --- a/setup.py +++ b/setup.py @@ -587,6 +587,7 @@ def setup_package(): "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", ], From c69cabda001fafd069870f5455903b3d63417430 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 2 Oct 2023 14:54:05 +0200 Subject: [PATCH 248/264] [cd build][azure parallel] Trigger building wheels for Python 3.12 From 19b8119c12c419bd0bfd560f5093ac11d59ba93c Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 3 Oct 2023 10:01:15 +0200 Subject: [PATCH 249/264] MAINT add missing wheel for MacOS x86_64 (#27520) Co-authored-by: Olivier Grisel --- .github/workflows/wheels.yml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml index 42f35362a5739..ae8f182450202 100644 --- a/.github/workflows/wheels.yml +++ b/.github/workflows/wheels.yml @@ -113,6 +113,11 @@ jobs: - os: macos-latest python: 311 platform_id: macosx_x86_64 + - os: macos-latest + python: 312 + platform_id: macosx_x86_64 + # TODO: remove when Python 3.12 is released + prerelease: "True" # MacOS arm64 # The wheel for the latest Python version is built and tested on From d99b728b3a7952b2111cf5e0cb5d14f92c6f3a80 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 3 Oct 2023 10:04:35 +0200 Subject: [PATCH 250/264] [cd build][azure parallel] trigger CD From 1df363c73b70a3d07da97ac735662dc48650662e Mon Sep 17 00:00:00 2001 From: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Date: Thu, 28 Sep 2023 19:19:36 +0800 Subject: [PATCH 251/264] FIX make dataset fetchers accept `os.Pathlike` for `data_home` (#27468) Co-authored-by: Guillaume Lemaitre --- sklearn/datasets/_base.py | 4 ++-- sklearn/datasets/_california_housing.py | 6 +++--- sklearn/datasets/_covtype.py | 4 ++-- sklearn/datasets/_kddcup99.py | 4 ++-- sklearn/datasets/_lfw.py | 10 +++++----- sklearn/datasets/_olivetti_faces.py | 6 +++--- sklearn/datasets/_openml.py | 6 +++--- sklearn/datasets/_rcv1.py | 6 +++--- sklearn/datasets/_species_distributions.py | 6 +++--- sklearn/datasets/_twenty_newsgroups.py | 8 ++++---- sklearn/datasets/tests/test_base.py | 18 +++++++++++++++++- 11 files changed, 47 insertions(+), 31 deletions(-) diff --git a/sklearn/datasets/_base.py b/sklearn/datasets/_base.py index b2d198ecf8c2f..5675798137824 100644 --- a/sklearn/datasets/_base.py +++ b/sklearn/datasets/_base.py @@ -57,7 +57,7 @@ def get_data_home(data_home=None) -> str: ---------- data_home : str or path-like, default=None The path to scikit-learn data directory. If `None`, the default path - is `~/sklearn_learn_data`. + is `~/scikit_learn_data`. Returns ------- @@ -84,7 +84,7 @@ def clear_data_home(data_home=None): ---------- data_home : str or path-like, default=None The path to scikit-learn data directory. If `None`, the default path - is `~/sklearn_learn_data`. + is `~/scikit_learn_data`. """ data_home = get_data_home(data_home) shutil.rmtree(data_home) diff --git a/sklearn/datasets/_california_housing.py b/sklearn/datasets/_california_housing.py index b48e7e10bdc4b..3153f0dd03f72 100644 --- a/sklearn/datasets/_california_housing.py +++ b/sklearn/datasets/_california_housing.py @@ -23,7 +23,7 @@ import logging import tarfile -from os import makedirs, remove +from os import PathLike, makedirs, remove from os.path import exists import joblib @@ -53,7 +53,7 @@ @validate_params( { - "data_home": [str, None], + "data_home": [str, PathLike, None], "download_if_missing": ["boolean"], "return_X_y": ["boolean"], "as_frame": ["boolean"], @@ -76,7 +76,7 @@ def fetch_california_housing( Parameters ---------- - data_home : str, default=None + data_home : str or path-like, default=None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. diff --git a/sklearn/datasets/_covtype.py b/sklearn/datasets/_covtype.py index 557899bc88e97..7620e08c5ec92 100644 --- a/sklearn/datasets/_covtype.py +++ b/sklearn/datasets/_covtype.py @@ -65,7 +65,7 @@ @validate_params( { - "data_home": [str, None], + "data_home": [str, os.PathLike, None], "download_if_missing": ["boolean"], "random_state": ["random_state"], "shuffle": ["boolean"], @@ -98,7 +98,7 @@ def fetch_covtype( Parameters ---------- - data_home : str, default=None + data_home : str or path-like, default=None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. diff --git a/sklearn/datasets/_kddcup99.py b/sklearn/datasets/_kddcup99.py index 17c49161c3bc2..444bd01737901 100644 --- a/sklearn/datasets/_kddcup99.py +++ b/sklearn/datasets/_kddcup99.py @@ -50,7 +50,7 @@ @validate_params( { "subset": [StrOptions({"SA", "SF", "http", "smtp"}), None], - "data_home": [str, None], + "data_home": [str, os.PathLike, None], "shuffle": ["boolean"], "random_state": ["random_state"], "percent10": ["boolean"], @@ -92,7 +92,7 @@ def fetch_kddcup99( To return the corresponding classical subsets of kddcup 99. If None, return the entire kddcup 99 dataset. - data_home : str, default=None + data_home : str or path-like, default=None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. diff --git a/sklearn/datasets/_lfw.py b/sklearn/datasets/_lfw.py index 345f56e89a03b..d06d29f21d0a5 100644 --- a/sklearn/datasets/_lfw.py +++ b/sklearn/datasets/_lfw.py @@ -10,7 +10,7 @@ import logging from numbers import Integral, Real -from os import listdir, makedirs, remove +from os import PathLike, listdir, makedirs, remove from os.path import exists, isdir, join import numpy as np @@ -234,7 +234,7 @@ def _fetch_lfw_people( @validate_params( { - "data_home": [str, None], + "data_home": [str, PathLike, None], "funneled": ["boolean"], "resize": [Interval(Real, 0, None, closed="neither"), None], "min_faces_per_person": [Interval(Integral, 0, None, closed="left"), None], @@ -272,7 +272,7 @@ def fetch_lfw_people( Parameters ---------- - data_home : str, default=None + data_home : str or path-like, default=None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. @@ -431,7 +431,7 @@ def _fetch_lfw_pairs( @validate_params( { "subset": [StrOptions({"train", "test", "10_folds"})], - "data_home": [str, None], + "data_home": [str, PathLike, None], "funneled": ["boolean"], "resize": [Interval(Real, 0, None, closed="neither"), None], "color": ["boolean"], @@ -480,7 +480,7 @@ def fetch_lfw_pairs( official evaluation set that is meant to be used with a 10-folds cross validation. - data_home : str, default=None + data_home : str or path-like, default=None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. diff --git a/sklearn/datasets/_olivetti_faces.py b/sklearn/datasets/_olivetti_faces.py index 51710faccc417..8e1b3c91e254b 100644 --- a/sklearn/datasets/_olivetti_faces.py +++ b/sklearn/datasets/_olivetti_faces.py @@ -13,7 +13,7 @@ # Copyright (c) 2011 David Warde-Farley # License: BSD 3 clause -from os import makedirs, remove +from os import PathLike, makedirs, remove from os.path import exists import joblib @@ -36,7 +36,7 @@ @validate_params( { - "data_home": [str, None], + "data_home": [str, PathLike, None], "shuffle": ["boolean"], "random_state": ["random_state"], "download_if_missing": ["boolean"], @@ -67,7 +67,7 @@ def fetch_olivetti_faces( Parameters ---------- - data_home : str, default=None + data_home : str or path-like, default=None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. diff --git a/sklearn/datasets/_openml.py b/sklearn/datasets/_openml.py index 1c36dc8a25ce1..c9d09dc3ce46a 100644 --- a/sklearn/datasets/_openml.py +++ b/sklearn/datasets/_openml.py @@ -749,7 +749,7 @@ def _valid_data_column_names(features_list, target_columns): "name": [str, None], "version": [Interval(Integral, 1, None, closed="left"), StrOptions({"active"})], "data_id": [Interval(Integral, 1, None, closed="left"), None], - "data_home": [str, None], + "data_home": [str, os.PathLike, None], "target_column": [str, list, None], "cache": [bool], "return_X_y": [bool], @@ -769,7 +769,7 @@ def fetch_openml( *, version: Union[str, int] = "active", data_id: Optional[int] = None, - data_home: Optional[str] = None, + data_home: Optional[Union[str, os.PathLike]] = None, target_column: Optional[Union[str, List]] = "default-target", cache: bool = True, return_X_y: bool = False, @@ -815,7 +815,7 @@ def fetch_openml( dataset. If data_id is not given, name (and potential version) are used to obtain a dataset. - data_home : str, default=None + data_home : str or path-like, default=None Specify another download and cache folder for the data sets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. diff --git a/sklearn/datasets/_rcv1.py b/sklearn/datasets/_rcv1.py index a807d8e311466..d9f392d872216 100644 --- a/sklearn/datasets/_rcv1.py +++ b/sklearn/datasets/_rcv1.py @@ -10,7 +10,7 @@ import logging from gzip import GzipFile -from os import makedirs, remove +from os import PathLike, makedirs, remove from os.path import exists, join import joblib @@ -74,7 +74,7 @@ @validate_params( { - "data_home": [str, None], + "data_home": [str, PathLike, None], "subset": [StrOptions({"train", "test", "all"})], "download_if_missing": ["boolean"], "random_state": ["random_state"], @@ -111,7 +111,7 @@ def fetch_rcv1( Parameters ---------- - data_home : str, default=None + data_home : str or path-like, default=None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. diff --git a/sklearn/datasets/_species_distributions.py b/sklearn/datasets/_species_distributions.py index 0bfc4bb0fdaf5..a1e654d41e071 100644 --- a/sklearn/datasets/_species_distributions.py +++ b/sklearn/datasets/_species_distributions.py @@ -39,7 +39,7 @@ import logging from io import BytesIO -from os import makedirs, remove +from os import PathLike, makedirs, remove from os.path import exists import joblib @@ -136,7 +136,7 @@ def construct_grids(batch): @validate_params( - {"data_home": [str, None], "download_if_missing": ["boolean"]}, + {"data_home": [str, PathLike, None], "download_if_missing": ["boolean"]}, prefer_skip_nested_validation=True, ) def fetch_species_distributions(*, data_home=None, download_if_missing=True): @@ -146,7 +146,7 @@ def fetch_species_distributions(*, data_home=None, download_if_missing=True): Parameters ---------- - data_home : str, default=None + data_home : str or path-like, default=None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. diff --git a/sklearn/datasets/_twenty_newsgroups.py b/sklearn/datasets/_twenty_newsgroups.py index 637cf8e4fc8d4..5973e998c34b9 100644 --- a/sklearn/datasets/_twenty_newsgroups.py +++ b/sklearn/datasets/_twenty_newsgroups.py @@ -153,7 +153,7 @@ def strip_newsgroup_footer(text): @validate_params( { - "data_home": [str, None], + "data_home": [str, os.PathLike, None], "subset": [StrOptions({"train", "test", "all"})], "categories": ["array-like", None], "shuffle": ["boolean"], @@ -191,7 +191,7 @@ def fetch_20newsgroups( Parameters ---------- - data_home : str, default=None + data_home : str or path-like, default=None Specify a download and cache folder for the datasets. If None, all scikit-learn data is stored in '~/scikit_learn_data' subfolders. @@ -351,7 +351,7 @@ def fetch_20newsgroups( { "subset": [StrOptions({"train", "test", "all"})], "remove": [tuple], - "data_home": [str, None], + "data_home": [str, os.PathLike, None], "download_if_missing": ["boolean"], "return_X_y": ["boolean"], "normalize": ["boolean"], @@ -411,7 +411,7 @@ def fetch_20newsgroups_vectorized( ends of posts that look like signatures, and 'quotes' removes lines that appear to be quoting another post. - data_home : str, default=None + data_home : str or path-like, default=None Specify an download and cache folder for the datasets. If None, all scikit-learn data is stored in '~/scikit_learn_data' subfolders. diff --git a/sklearn/datasets/tests/test_base.py b/sklearn/datasets/tests/test_base.py index f31f20636c0c1..f84c275d67cf9 100644 --- a/sklearn/datasets/tests/test_base.py +++ b/sklearn/datasets/tests/test_base.py @@ -3,6 +3,7 @@ import tempfile import warnings from functools import partial +from pathlib import Path from pickle import dumps, loads import numpy as np @@ -31,6 +32,16 @@ from sklearn.utils.fixes import _is_resource +class _DummyPath: + """Minimal class that implements the os.PathLike interface.""" + + def __init__(self, path): + self.path = path + + def __fspath__(self): + return self.path + + def _remove_dir(path): if os.path.isdir(path): shutil.rmtree(path) @@ -67,13 +78,18 @@ def test_category_dir_2(load_files_root): _remove_dir(test_category_dir2) -def test_data_home(data_home): +@pytest.mark.parametrize("path_container", [None, Path, _DummyPath]) +def test_data_home(path_container, data_home): # get_data_home will point to a pre-existing folder + if path_container is not None: + data_home = path_container(data_home) data_home = get_data_home(data_home=data_home) assert data_home == data_home assert os.path.exists(data_home) # clear_data_home will delete both the content and the folder it-self + if path_container is not None: + data_home = path_container(data_home) clear_data_home(data_home=data_home) assert not os.path.exists(data_home) From d53756ec02b24cab193d087f157575f8c7c7309d Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Fri, 13 Oct 2023 10:47:11 +0200 Subject: [PATCH 252/264] FIX validate properly zero_division=np.nan when used in parallel processing (#27573) --- sklearn/metrics/_classification.py | 18 ++++++++----- sklearn/metrics/tests/test_classification.py | 27 ++++++++++++++++++++ sklearn/utils/_param_validation.py | 3 +++ sklearn/utils/tests/test_param_validation.py | 3 +++ 4 files changed, 45 insertions(+), 6 deletions(-) diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py index f916b7f86da38..0819abb463e2b 100644 --- a/sklearn/metrics/_classification.py +++ b/sklearn/metrics/_classification.py @@ -1079,7 +1079,8 @@ def zero_one_loss(y_true, y_pred, *, normalize=True, sample_weight=None): ], "sample_weight": ["array-like", None], "zero_division": [ - Options(Real, {0.0, 1.0, np.nan}), + Options(Real, {0.0, 1.0}), + "nan", StrOptions({"warn"}), ], }, @@ -1260,7 +1261,8 @@ def f1_score( ], "sample_weight": ["array-like", None], "zero_division": [ - Options(Real, {0.0, 1.0, np.nan}), + Options(Real, {0.0, 1.0}), + "nan", StrOptions({"warn"}), ], }, @@ -1542,7 +1544,8 @@ def _check_set_wise_labels(y_true, y_pred, average, labels, pos_label): "warn_for": [list, tuple, set], "sample_weight": ["array-like", None], "zero_division": [ - Options(Real, {0.0, 1.0, np.nan}), + Options(Real, {0.0, 1.0}), + "nan", StrOptions({"warn"}), ], }, @@ -1979,7 +1982,8 @@ class after being classified as negative. This is the case when the ], "sample_weight": ["array-like", None], "zero_division": [ - Options(Real, {0.0, 1.0, np.nan}), + Options(Real, {0.0, 1.0}), + "nan", StrOptions({"warn"}), ], }, @@ -2149,7 +2153,8 @@ def precision_score( ], "sample_weight": ["array-like", None], "zero_division": [ - Options(Real, {0.0, 1.0, np.nan}), + Options(Real, {0.0, 1.0}), + "nan", StrOptions({"warn"}), ], }, @@ -2412,7 +2417,8 @@ def balanced_accuracy_score(y_true, y_pred, *, sample_weight=None, adjusted=Fals "digits": [Interval(Integral, 0, None, closed="left")], "output_dict": ["boolean"], "zero_division": [ - Options(Real, {0.0, 1.0, np.nan}), + Options(Real, {0.0, 1.0}), + "nan", StrOptions({"warn"}), ], }, diff --git a/sklearn/metrics/tests/test_classification.py b/sklearn/metrics/tests/test_classification.py index cfcb08a312443..afa3b90d5e8a9 100644 --- a/sklearn/metrics/tests/test_classification.py +++ b/sklearn/metrics/tests/test_classification.py @@ -27,6 +27,7 @@ hinge_loss, jaccard_score, log_loss, + make_scorer, matthews_corrcoef, multilabel_confusion_matrix, precision_recall_fscore_support, @@ -35,7 +36,9 @@ zero_one_loss, ) from sklearn.metrics._classification import _check_targets +from sklearn.model_selection import cross_val_score from sklearn.preprocessing import LabelBinarizer, label_binarize +from sklearn.tree import DecisionTreeClassifier from sklearn.utils._mocking import MockDataFrame from sklearn.utils._testing import ( assert_allclose, @@ -2802,3 +2805,27 @@ def test_classification_metric_pos_label_types(metric, classes): y_pred = y_true.copy() result = metric(y_true, y_pred, pos_label=pos_label) assert not np.any(np.isnan(result)) + + +@pytest.mark.parametrize( + "scoring", + [ + make_scorer(f1_score, zero_division=np.nan), + make_scorer(fbeta_score, beta=2, zero_division=np.nan), + make_scorer(precision_score, zero_division=np.nan), + make_scorer(recall_score, zero_division=np.nan), + ], +) +def test_classification_metric_division_by_zero_nan_validaton(scoring): + """Check that we validate `np.nan` properly for classification metrics. + + With `n_jobs=2` in cross-validation, the `np.nan` used for the singleton will be + different in the sub-process and we should not use the `is` operator but + `math.isnan`. + + Non-regression test for: + https://github.com/scikit-learn/scikit-learn/issues/27563 + """ + X, y = datasets.make_classification(random_state=0) + classifier = DecisionTreeClassifier(max_depth=3, random_state=0).fit(X, y) + cross_val_score(classifier, X, y, scoring=scoring, n_jobs=2, error_score="raise") diff --git a/sklearn/utils/_param_validation.py b/sklearn/utils/_param_validation.py index 0e30627ab06cc..bf063a1945621 100644 --- a/sklearn/utils/_param_validation.py +++ b/sklearn/utils/_param_validation.py @@ -46,6 +46,7 @@ def validate_parameter_constraints(parameter_constraints, params, caller_name): - the string "boolean" - the string "verbose" - the string "cv_object" + - the string "nan" - a MissingValues object representing markers for missing values - a HasMethods object, representing method(s) an object must have - a Hidden object, representing a constraint not meant to be exposed to the user @@ -137,6 +138,8 @@ def make_constraint(constraint): constraint = make_constraint(constraint.constraint) constraint.hidden = True return constraint + if isinstance(constraint, str) and constraint == "nan": + return _NanConstraint() raise ValueError(f"Unknown constraint type: {constraint}") diff --git a/sklearn/utils/tests/test_param_validation.py b/sklearn/utils/tests/test_param_validation.py index ab6465538b863..2af84707cd2ed 100644 --- a/sklearn/utils/tests/test_param_validation.py +++ b/sklearn/utils/tests/test_param_validation.py @@ -23,6 +23,7 @@ _CVObjects, _InstancesOf, _IterablesNotString, + _NanConstraint, _NoneConstraint, _PandasNAConstraint, _RandomStates, @@ -387,6 +388,7 @@ def test_generate_valid_param(constraint): (Real, 0.5), ("boolean", False), ("verbose", 1), + ("nan", np.nan), (MissingValues(), -1), (MissingValues(), -1.0), (MissingValues(), None), @@ -420,6 +422,7 @@ def test_is_satisfied_by(constraint_declaration, value): (MissingValues(numeric_only=True), MissingValues), (HasMethods("fit"), HasMethods), ("cv_object", _CVObjects), + ("nan", _NanConstraint), ], ) def test_make_constraint(constraint_declaration, expected_constraint_class): From 9b977e9245d3916d7d29fe086ad81b8fbc414d7d Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 16 Oct 2023 15:26:36 +0200 Subject: [PATCH 253/264] FIX make sure that KernelPCA works with pandas output and arpack solver (#27583) --- sklearn/decomposition/_kernel_pca.py | 2 +- sklearn/decomposition/tests/test_kernel_pca.py | 15 ++++++++++++++- 2 files changed, 15 insertions(+), 2 deletions(-) diff --git a/sklearn/decomposition/_kernel_pca.py b/sklearn/decomposition/_kernel_pca.py index ccf79e896f210..800b472a9b3a6 100644 --- a/sklearn/decomposition/_kernel_pca.py +++ b/sklearn/decomposition/_kernel_pca.py @@ -432,7 +432,7 @@ def fit(self, X, y=None): raise ValueError("Cannot fit_inverse_transform with a precomputed kernel.") X = self._validate_data(X, accept_sparse="csr", copy=self.copy_X) self.gamma_ = 1 / X.shape[1] if self.gamma is None else self.gamma - self._centerer = KernelCenterer() + self._centerer = KernelCenterer().set_output(transform="default") K = self._get_kernel(X) self._fit_transform(K) diff --git a/sklearn/decomposition/tests/test_kernel_pca.py b/sklearn/decomposition/tests/test_kernel_pca.py index 3c95454749b4a..fdaa71314f43f 100644 --- a/sklearn/decomposition/tests/test_kernel_pca.py +++ b/sklearn/decomposition/tests/test_kernel_pca.py @@ -4,7 +4,8 @@ import pytest import scipy.sparse as sp -from sklearn.datasets import make_blobs, make_circles +import sklearn +from sklearn.datasets import load_iris, make_blobs, make_circles from sklearn.decomposition import PCA, KernelPCA from sklearn.exceptions import NotFittedError from sklearn.linear_model import Perceptron @@ -550,3 +551,15 @@ def test_kernel_pca_inverse_correct_gamma(): X2_recon = kpca2.inverse_transform(kpca1.transform(X)) assert_allclose(X1_recon, X2_recon) + + +def test_kernel_pca_pandas_output(): + """Check that KernelPCA works with pandas output when the solver is arpack. + + Non-regression test for: + https://github.com/scikit-learn/scikit-learn/issues/27579 + """ + pytest.importorskip("pandas") + X, _ = load_iris(as_frame=True, return_X_y=True) + with sklearn.config_context(transform_output="pandas"): + KernelPCA(n_components=2, eigen_solver="arpack").fit_transform(X) From 8a3c16cf5e70a5677653d148ad7466b57fc20b78 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 17 Oct 2023 14:25:17 +0200 Subject: [PATCH 254/264] FIX Make decision tree pickles deterministic (#27580) Co-authored-by: Olivier Grisel --- doc/whats_new/v1.3.rst | 17 +++++++++++++++++ sklearn/tree/_tree.pyx | 4 +++- sklearn/tree/tests/test_tree.py | 13 +++++++++++++ 3 files changed, 33 insertions(+), 1 deletion(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index 4999aba4c2e71..d6ea7352d4f3f 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -2,6 +2,23 @@ .. currentmodule:: sklearn +.. _changes_1_3_2: + +Version 1.3.2 +============= + +**October 2023** + +Changelog +--------- + +:mod:`sklearn.tree` +................... + +- |Fix| Do not leak data via non-initialized memory in decision tree pickle files and make + the generation of those files deterministic. :pr:`27580` by :user:`Loïc Estève `. + + .. _changes_1_3_1: Version 1.3.1 diff --git a/sklearn/tree/_tree.pyx b/sklearn/tree/_tree.pyx index 22d3d38ce981a..c843ad7500480 100644 --- a/sklearn/tree/_tree.pyx +++ b/sklearn/tree/_tree.pyx @@ -771,11 +771,13 @@ cdef class Tree: safe_realloc(&self.nodes, capacity) safe_realloc(&self.value, capacity * self.value_stride) - # value memory is initialised to 0 to enable classifier argmax if capacity > self.capacity: + # value memory is initialised to 0 to enable classifier argmax memset((self.value + self.capacity * self.value_stride), 0, (capacity - self.capacity) * self.value_stride * sizeof(double)) + # node memory is initialised to 0 to ensure deterministic pickle (padding in Node struct) + memset((self.nodes + self.capacity), 0, (capacity - self.capacity) * sizeof(Node)) # if capacity smaller than node_count, adjust the counter if capacity < self.node_count: diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index 82c68b1327296..3dac1b38693c0 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -2626,3 +2626,16 @@ def test_sample_weight_non_uniform(make_data, Tree): tree_samples_removed.fit(X[1::2, :], y[1::2]) assert_allclose(tree_samples_removed.predict(X), tree_with_sw.predict(X)) + + +def test_deterministic_pickle(): + # Non-regression test for: + # https://github.com/scikit-learn/scikit-learn/issues/27268 + # Uninitialised memory would lead to the two pickle strings being different. + tree1 = DecisionTreeClassifier(random_state=0).fit(iris.data, iris.target) + tree2 = DecisionTreeClassifier(random_state=0).fit(iris.data, iris.target) + + pickle1 = pickle.dumps(tree1) + pickle2 = pickle.dumps(tree2) + + assert pickle1 == pickle2 From 6d086c23e023b6701a6eaf40a9aaed9a0635104e Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 17 Oct 2023 14:54:36 +0200 Subject: [PATCH 255/264] REL bump to 1.3.2 --- sklearn/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/__init__.py b/sklearn/__init__.py index 8df2c91fb2ca9..48d907fa5ad23 100644 --- a/sklearn/__init__.py +++ b/sklearn/__init__.py @@ -38,7 +38,7 @@ # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' # -__version__ = "1.3.1" +__version__ = "1.3.2" # On OSX, we can get a runtime error due to multiple OpenMP libraries loaded From b54a13aeceff868023e54bf4c720c26dffe494ef Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 18 Oct 2023 11:10:51 +0200 Subject: [PATCH 256/264] DOC add version 1.3.2 into landing page (#27604) --- doc/templates/index.html | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/templates/index.html b/doc/templates/index.html index 40f58f7bbefe3..b41ede9382a49 100644 --- a/doc/templates/index.html +++ b/doc/templates/index.html @@ -169,7 +169,9 @@

    News

  • On-going development: What's new (Changelog)
    • -
  • September 2023. scikit-learn 1.3.1 is available for download (Changelog). +
  • October 2023. scikit-learn 1.3.2 is available for download (Changelog). +
    • +
  • September 2023. scikit-learn 1.3.1 is available for download (Changelog).
  • June 2023. scikit-learn 1.3.0 is available for download (Changelog).
    • From e43ad8b4ef0c751d03d7b260f6e23d23028fa1cd Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 18 Oct 2023 16:05:39 +0200 Subject: [PATCH 257/264] MAINT remove prerelease flag for Python 3.12 (#27605) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- .github/workflows/wheels.yml | 6 ------ build_tools/cirrus/arm_wheel.yml | 4 ---- 2 files changed, 10 deletions(-) diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml index ae8f182450202..b82a114bff1af 100644 --- a/.github/workflows/wheels.yml +++ b/.github/workflows/wheels.yml @@ -70,8 +70,6 @@ jobs: - os: windows-latest python: 312 platform_id: win_amd64 - # TODO: remove when Python 3.12 is released - prerelease: "True" # Linux 64 bit manylinux2014 - os: ubuntu-latest @@ -97,8 +95,6 @@ jobs: python: 312 platform_id: manylinux_x86_64 manylinux_image: manylinux2014 - # TODO: remove when Python 3.12 is released - prerelease: "True" # MacOS x86_64 - os: macos-latest @@ -116,8 +112,6 @@ jobs: - os: macos-latest python: 312 platform_id: macosx_x86_64 - # TODO: remove when Python 3.12 is released - prerelease: "True" # MacOS arm64 # The wheel for the latest Python version is built and tested on diff --git a/build_tools/cirrus/arm_wheel.yml b/build_tools/cirrus/arm_wheel.yml index c5f5a34afa490..f210eea817601 100644 --- a/build_tools/cirrus/arm_wheel.yml +++ b/build_tools/cirrus/arm_wheel.yml @@ -25,8 +25,6 @@ macos_arm64_wheel_task: # is actually tested on Cirrus CI. - env: CIBW_BUILD: cp312-macosx_arm64 - # TODO: remove when Python 3.12 is released - CIBW_PRERELEASE_PYTHONS: True conda_script: - curl -L --retry 10 -o ~/mambaforge.sh https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh @@ -78,8 +76,6 @@ linux_arm64_wheel_task: CIBW_TEST_SKIP: "*_aarch64" - env: CIBW_BUILD: cp312-manylinux_aarch64 - # TODO: remove when Python 3.12 is released - CIBW_PRERELEASE_PYTHONS: True cibuildwheel_script: - apt install -y python3 python-is-python3 From 7e3705dad82d7189fae254344176de57f1ae729b Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 18 Oct 2023 16:52:28 +0200 Subject: [PATCH 258/264] DOC move somes fixes from 1.4 to 1.3.2 (#27602) Co-authored-by: Olivier Grisel --- doc/whats_new/v1.3.rst | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index d6ea7352d4f3f..70d14ab285eea 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -12,6 +12,30 @@ Version 1.3.2 Changelog --------- +:mod:`sklearn.datasets` +....................... + +- |Fix| All dataset fetchers now accept `data_home` as any object that implements + the :class:`os.PathLike` interface, for instance, :class:`pathlib.Path`. + :pr:`27468` by :user:`Yao Xiao `. + +:mod:`sklearn.decomposition` +............................ + +- |Fix| Fixes a bug in :class:`decomposition.KernelPCA` by forcing the output of + the internal :class:`preprocessing.KernelCenterer` to be a default array. When the + arpack solver is used, it expects an array with a `dtype` attribute. + :pr:`27583` by :user:`Guillaume Lemaitre `. + +:mod:`sklearn.metrics` +...................... + +- |Fix| Fixes a bug for metrics using `zero_division=np.nan` + (e.g. :func:`~metrics.precision_score`) within a paralell loop + (e.g. :func:`~model_selection.cross_val_score`) where the singleton for `np.nan` + will be different in the sub-processes. + :pr:`27573` by :user:`Guillaume Lemaitre `. + :mod:`sklearn.tree` ................... From fc2454237c98cba44c7e338b85acbfdeb8af38fe Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Thu, 19 Oct 2023 12:35:45 +0200 Subject: [PATCH 259/264] TST change random seed to make graphical lasso test pass (#27616) --- sklearn/covariance/tests/test_graphical_lasso.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/sklearn/covariance/tests/test_graphical_lasso.py b/sklearn/covariance/tests/test_graphical_lasso.py index 317bf2aa85124..aaee1919e8dcc 100644 --- a/sklearn/covariance/tests/test_graphical_lasso.py +++ b/sklearn/covariance/tests/test_graphical_lasso.py @@ -24,7 +24,12 @@ ) -def test_graphical_lasso(random_state=0): +def test_graphical_lassos(random_state=1): + """Test the graphical lasso solvers. + + This checks is unstable for some random seeds where the covariance found with "cd" + and "lars" solvers are different (4 cases / 100 tries). + """ # Sample data from a sparse multivariate normal dim = 20 n_samples = 100 @@ -46,10 +51,11 @@ def test_graphical_lasso(random_state=0): costs, dual_gap = np.array(costs).T # Check that the costs always decrease (doesn't hold if alpha == 0) if not alpha == 0: - assert_array_less(np.diff(costs), 0) + # use 1e-12 since the cost can be exactly 0 + assert_array_less(np.diff(costs), 1e-12) # Check that the 2 approaches give similar results - assert_array_almost_equal(covs["cd"], covs["lars"], decimal=4) - assert_array_almost_equal(icovs["cd"], icovs["lars"], decimal=4) + assert_allclose(covs["cd"], covs["lars"], atol=1e-4) + assert_allclose(icovs["cd"], icovs["lars"], atol=1e-4) # Smoke test the estimator model = GraphicalLasso(alpha=0.25).fit(X) From 093e0cf14aff026cca6097e8c42f83b735d26358 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 23 Oct 2023 12:10:50 +0200 Subject: [PATCH 260/264] [cd build][azure parallel] trigger CI/CD From b08cdfabc09074c8bc525839f092b0671fe259fe Mon Sep 17 00:00:00 2001 From: Gael Varoquaux Date: Fri, 27 Oct 2023 11:08:35 +0200 Subject: [PATCH 261/264] DOC update the sponsors list on landing and about pages (#27660) --- doc/about.rst | 114 ++++++++++++++++++++++----------- doc/images/bcg-small.png | Bin 17039 -> 0 bytes doc/images/fujitsu-small.png | Bin 6618 -> 0 bytes doc/images/microsoft-small.png | Bin 8047 -> 0 bytes doc/templates/index.html | 6 +- 5 files changed, 79 insertions(+), 41 deletions(-) delete mode 100644 doc/images/bcg-small.png delete mode 100644 doc/images/fujitsu-small.png delete mode 100644 doc/images/microsoft-small.png diff --git a/doc/about.rst b/doc/about.rst index 4bd001fe391e6..e2242d28f73e7 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -168,14 +168,6 @@ Grisel, Guillaume Lemaitre, and Jérémie du Boisberranger. -.. |msn| image:: images/microsoft.png - :width: 100pt - :target: https://www.microsoft.com/ - -.. |bcg| image:: images/bcg.png - :width: 100pt - :target: https://www.bcg.com/beyond-consulting/bcg-gamma/default.aspx - .. |axa| image:: images/axa.png :width: 50pt :target: https://www.axa.fr/ @@ -184,17 +176,17 @@ Grisel, Guillaume Lemaitre, and Jérémie du Boisberranger. :width: 150pt :target: https://www.bnpparibascardif.com/ -.. |fujitsu| image:: images/fujitsu.png - :width: 100pt - :target: https://www.fujitsu.com/global/ - .. |dataiku| image:: images/dataiku.png :width: 70pt :target: https://www.dataiku.com/ -.. |aphp| image:: images/logo_APHP_text.png - :width: 150pt - :target: https://aphp.fr/ +.. |hf| image:: images/huggingface_logo-noborder.png + :width: 70pt + :target: https://huggingface.co + +.. |nvidia| image:: images/nvidia.png + :width: 70pt + :target: https://www.nvidia.com .. |inria| image:: images/inria-logo.jpg :width: 100pt @@ -208,25 +200,21 @@ Grisel, Guillaume Lemaitre, and Jérémie du Boisberranger. .. table:: :class: sk-sponsor-table align-default - +---------+----------+ - | |bcg| | - +---------+----------+ - | | - +---------+----------+ - | |axa| | |bnp| | - +---------+----------+ - ||fujitsu|| |msn| | - +---------+----------+ - | | - +---------+----------+ - | |dataiku| | - +---------+----------+ - | |aphp| | - +---------+----------+ - | | - +---------+----------+ - | |inria| | - +---------+----------+ + +----------+-----------+ + | |axa| | |bnp| | + +----------+-----------+ + | | + +----------+-----------+ + | |nvidia| | |hf| | + +----------+-----------+ + | | + +----------+-----------+ + | |dataiku| | + +----------+-----------+ + | | + +----------+-----------+ + | |inria| | + +----------+-----------+ .. raw:: html @@ -240,7 +228,35 @@ Grisel, Guillaume Lemaitre, and Jérémie du Boisberranger.
    -`Hugging Face `_ funds Adrin Jalali since 2022. +`NVidia `_ funds Tim Head since 2022 +and is part of the scikit-learn consortium at Inria. + +.. raw:: html + +
    + +
    + +.. image:: images/nvidia.png + :width: 55pt + :align: center + :target: https://nvidia.com + +.. raw:: html + +
    +
    + + +.......... + +.. raw:: html + +
    +
    + +`Hugging Face `_ funded Adrin Jalali in 2022, +2023 and is part of the scikit-learn consortium at Inria. .. raw:: html @@ -290,7 +306,8 @@ Grisel, Guillaume Lemaitre, and Jérémie du Boisberranger.
    -`Quansight Labs `_ funds Thomas J. Fan since 2021. +`Quansight Labs `_ funds Lucy Liu and +Meekail Zain since 2022 and funded Thomas J. Fan from 2021 to 2023. .. raw:: html @@ -573,6 +590,31 @@ The `NeuroDebian `_ project providing `Debian `Dr. James V. Haxby `_ (`Dartmouth College `_). +................... + +The following organizations funded the scikit-learn consortium at Inria in +the past: + +.. |msn| image:: images/microsoft.png + :width: 100pt + :target: https://www.microsoft.com/ + +.. |bcg| image:: images/bcg.png + :width: 100pt + :target: https://www.bcg.com/beyond-consulting/bcg-gamma/default.aspx + +.. |fujitsu| image:: images/fujitsu.png + :width: 100pt + :target: https://www.fujitsu.com/global/ + +.. |aphp| image:: images/logo_APHP_text.png + :width: 150pt + :target: https://aphp.fr/ + + +|bcg| |msn| |fujitsu| |aphp| + + Sprints ------- diff --git a/doc/images/bcg-small.png b/doc/images/bcg-small.png deleted file mode 100644 index 8ff377969003a23cc3b827efa2a2ae8ddbc46f4e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 17039 zcmV)-K!?AHP) zaB^>EX>4U6ba`-PAZ2)IW&i+q+O3;uvRpZCh5vIEUIGcu%i$Q|9eDZv4l^ZHsny-l z4og+QKl{QABl{#uUDg?>z{{I*Wt=R^MU`n^^9>xhDV-(tUBcq6j# 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    scikit-learn development and maintenance are financially supported by

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The `Members `_ of the `Scikit-Learn Consortium at Inria Foundation -`_ fund Olivier -Grisel, Guillaume Lemaitre, and Jérémie du Boisberranger. +`_ fund Arturo Amor, +François Goupil, Guillaume Lemaitre, Jérémie du Boisberranger, and Olivier Grisel. .. raw:: html +.. |chanel| image:: images/chanel.png + :width: 70pt + :target: https://www.chanel.com + .. |axa| image:: images/axa.png :width: 50pt :target: https://www.axa.fr/ @@ -200,6 +204,10 @@ Grisel, Guillaume Lemaitre, and Jérémie du Boisberranger. .. table:: :class: sk-sponsor-table align-default + +----------+-----------+ + | |chanel| | + +----------+-----------+ + | | +----------+-----------+ | |axa| | |bnp| | +----------+-----------+ diff --git a/doc/images/chanel-small.png b/doc/images/chanel-small.png new file mode 100644 index 0000000000000000000000000000000000000000..b1965b714a42f37f84682c82f635ef7f60d14f4a GIT binary patch literal 4428 zcmZ`-2UJtr(oSg7i$S`SfD|dALon1JAqW>K(yIv|Qi2eqD$+$jr9%isK#^XgOHmM{ 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    + From a3100bcc8fbd8659ab5e7c80339b6ad53b623eb9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Wed, 18 Dec 2024 17:31:33 +0100 Subject: [PATCH 264/264] MNT Fetch script from main branch in lint.yml (#30505) --- .github/workflows/lint.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 9648f66d840d4..cb86f84baa494 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -31,6 +31,7 @@ jobs: - name: Install dependencies run: | + curl https://raw.githubusercontent.com/${{ github.repository }}/main/build_tools/shared.sh --retry 5 -o ./build_tools/shared.sh source build_tools/shared.sh # Include pytest compatibility with mypy pip install pytest ruff $(get_dep mypy min) $(get_dep black min) cython-lint