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ENH Adds n_features_in_ to ensemble module #19326

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Merged
merged 20 commits into from
Feb 22, 2021
Merged

ENH Adds n_features_in_ to ensemble module #19326

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14778ba
ENH Adds n_features_in_ checking in bagging
lorentzenchr Feb 1, 2021
186e7a2
ENH Adds n_features_in_ checking in weighted boosting
lorentzenchr Feb 1, 2021
cb12f64
ENH Adds n_features_in_ checking in HGBT
lorentzenchr Feb 3, 2021
5366110
ENH Adds n_features_in_ checking in GradientBoosting
lorentzenchr Feb 3, 2021
f11e457
ENH Adds n_features_in_ checking in forests
lorentzenchr Feb 3, 2021
65edf9f
ENH Adds n_features_in_ checking in IsolationForest
lorentzenchr Feb 3, 2021
4f40b0f
DEP n_features_ in forests
lorentzenchr Feb 4, 2021
679443c
DEP n_features_ in bagging
lorentzenchr Feb 4, 2021
a04fbad
DEP n_features_ in IsolationForest
lorentzenchr Feb 4, 2021
9568948
DEP n_features_ in GradientBoosting
lorentzenchr Feb 4, 2021
d89cb0b
TST add test for deprecated attribute n_features_
lorentzenchr Feb 4, 2021
4e1bac9
TST remove ensemble from N_FEATURES_IN_AFTER_FIT_MODULES_TO_IGNORE
lorentzenchr Feb 4, 2021
905d85c
TST add ExtraTreesClassifier etc to deprecation test of n_features_
lorentzenchr Feb 4, 2021
803461d
CLN remove ensemble from N_FEATURES_IN_AFTER_FIT_MODULES_TO_IGNORE
lorentzenchr Feb 4, 2021
f30d144
CLN mark as code in docstrings
lorentzenchr Feb 5, 2021
43be0d8
Merge branch 'main' into n_features_ensemble
lorentzenchr Feb 5, 2021
44e21c1
Add comment from code review
lorentzenchr Feb 6, 2021
bc43b6a
Merge remote-tracking branch 'origin/main' into pr/lorentzenchr/19326
glemaitre Feb 11, 2021
b1eb01c
CLN version 1.0. to 1.0
lorentzenchr Feb 11, 2021
bed7034
Merge remote-tracking branch 'origin/main' into pr/lorentzenchr/19326
glemaitre Feb 11, 2021
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62 changes: 31 additions & 31 deletions 62 sklearn/ensemble/_bagging.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@
from ..base import ClassifierMixin, RegressorMixin
from ..metrics import r2_score, accuracy_score
from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
from ..utils import check_random_state, check_array, column_or_1d
from ..utils import check_random_state, column_or_1d, deprecated
from ..utils import indices_to_mask
from ..utils.metaestimators import if_delegate_has_method
from ..utils.multiclass import check_classification_targets
Expand Down Expand Up @@ -287,7 +287,7 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
sample_weight = _check_sample_weight(sample_weight, X, dtype=None)

# Remap output
n_samples, self.n_features_ = X.shape
n_samples = X.shape[0]
self._n_samples = n_samples
y = self._validate_y(y)

Expand All @@ -313,11 +313,11 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
if isinstance(self.max_features, numbers.Integral):
max_features = self.max_features
elif isinstance(self.max_features, float):
max_features = self.max_features * self.n_features_
max_features = self.max_features * self.n_features_in_
else:
raise ValueError("max_features must be int or float")

if not (0 < max_features <= self.n_features_):
if not (0 < max_features <= self.n_features_in_):
raise ValueError("max_features must be in (0, n_features]")

max_features = max(1, int(max_features))
Expand Down Expand Up @@ -408,7 +408,7 @@ def _get_estimators_indices(self):
# to those in `_parallel_build_estimators()`
feature_indices, sample_indices = _generate_bagging_indices(
seed, self.bootstrap_features, self.bootstrap,
self.n_features_, self._n_samples, self._max_features,
self.n_features_in_, self._n_samples, self._max_features,
self._max_samples)

yield feature_indices, sample_indices
Expand All @@ -429,6 +429,16 @@ def estimators_samples_(self):
return [sample_indices
for _, sample_indices in self._get_estimators_indices()]

# TODO: Remove in 1.2
# mypy error: Decorated property not supported
@deprecated( # type: ignore
"Attribute n_features_ was deprecated in version 1.0 and will be "
"removed in 1.2. Use 'n_features_in_' instead."
)
@property
def n_features_(self):
return self.n_features_in_


class BaggingClassifier(ClassifierMixin, BaseBagging):
"""A Bagging classifier.
Expand Down Expand Up @@ -523,6 +533,10 @@ class BaggingClassifier(ClassifierMixin, BaseBagging):
n_features_ : int
The number of features when :meth:`fit` is performed.

.. deprecated:: 1.0
Attribute `n_features_` was deprecated in version 1.0 and will be
removed in 1.2. Use `n_features_in_` instead.

estimators_ : list of estimators
The collection of fitted base estimators.

Expand Down Expand Up @@ -702,17 +716,11 @@ def predict_proba(self, X):
"""
check_is_fitted(self)
# Check data
X = check_array(
X = self._validate_data(
X, accept_sparse=['csr', 'csc'], dtype=None,
force_all_finite=False
force_all_finite=False, reset=False
)

if self.n_features_ != X.shape[1]:
raise ValueError("Number of features of the model must "
"match the input. Model n_features is {0} and "
"input n_features is {1}."
"".format(self.n_features_, X.shape[1]))

# Parallel loop
n_jobs, n_estimators, starts = _partition_estimators(self.n_estimators,
self.n_jobs)
Expand Down Expand Up @@ -753,17 +761,11 @@ def predict_log_proba(self, X):
check_is_fitted(self)
if hasattr(self.base_estimator_, "predict_log_proba"):
# Check data
X = check_array(
X = self._validate_data(
X, accept_sparse=['csr', 'csc'], dtype=None,
force_all_finite=False
force_all_finite=False, reset=False
)

if self.n_features_ != X.shape[1]:
raise ValueError("Number of features of the model must "
"match the input. Model n_features is {0} "
"and input n_features is {1} "
"".format(self.n_features_, X.shape[1]))

# Parallel loop
n_jobs, n_estimators, starts = _partition_estimators(
self.n_estimators, self.n_jobs)
Expand Down Expand Up @@ -811,17 +813,11 @@ def decision_function(self, X):
check_is_fitted(self)

# Check data
X = check_array(
X = self._validate_data(
X, accept_sparse=['csr', 'csc'], dtype=None,
force_all_finite=False
force_all_finite=False, reset=False
)

if self.n_features_ != X.shape[1]:
raise ValueError("Number of features of the model must "
"match the input. Model n_features is {0} and "
"input n_features is {1} "
"".format(self.n_features_, X.shape[1]))

# Parallel loop
n_jobs, n_estimators, starts = _partition_estimators(self.n_estimators,
self.n_jobs)
Expand Down Expand Up @@ -929,6 +925,10 @@ class BaggingRegressor(RegressorMixin, BaseBagging):
n_features_ : int
The number of features when :meth:`fit` is performed.

.. deprecated:: 1.0
Attribute `n_features_` was deprecated in version 1.0 and will be
removed in 1.2. Use `n_features_in_` instead.

estimators_ : list of estimators
The collection of fitted sub-estimators.

Expand Down Expand Up @@ -1024,9 +1024,9 @@ def predict(self, X):
"""
check_is_fitted(self)
# Check data
X = check_array(
X = self._validate_data(
X, accept_sparse=['csr', 'csc'], dtype=None,
force_all_finite=False
force_all_finite=False, reset=False
)

# Parallel loop
Expand Down
42 changes: 35 additions & 7 deletions 42 sklearn/ensemble/_forest.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,7 @@ class calls the ``fit`` method of each sub-estimator on random samples
from ..tree import (DecisionTreeClassifier, DecisionTreeRegressor,
ExtraTreeClassifier, ExtraTreeRegressor)
from ..tree._tree import DTYPE, DOUBLE
from ..utils import check_random_state, check_array, compute_sample_weight
from ..utils import check_random_state, compute_sample_weight, deprecated
from ..exceptions import DataConversionWarning
from ._base import BaseEnsemble, _partition_estimators
from ..utils.fixes import delayed
Expand Down Expand Up @@ -312,9 +312,6 @@ def fit(self, X, y, sample_weight=None):
# ensemble sorts the indices.
X.sort_indices()

# Remap output
self.n_features_ = X.shape[1]

y = np.atleast_1d(y)
if y.ndim == 2 and y.shape[1] == 1:
warn("A column-vector y was passed when a 1d array was"
Expand Down Expand Up @@ -446,7 +443,8 @@ def _compute_oob_predictions(self, X, y):
(n_samples, 1, n_outputs)
The OOB predictions.
"""
X = check_array(X, dtype=DTYPE, accept_sparse='csr')
X = self._validate_data(X, dtype=DTYPE, accept_sparse='csr',
reset=False)

n_samples = y.shape[0]
n_outputs = self.n_outputs_
Expand Down Expand Up @@ -530,12 +528,22 @@ def feature_importances_(self):
for tree in self.estimators_ if tree.tree_.node_count > 1)

if not all_importances:
return np.zeros(self.n_features_, dtype=np.float64)
return np.zeros(self.n_features_in_, dtype=np.float64)

all_importances = np.mean(all_importances,
axis=0, dtype=np.float64)
return all_importances / np.sum(all_importances)

# TODO: Remove in 1.2
# mypy error: Decorated property not supported
@deprecated( # type: ignore
"Attribute n_features_ was deprecated in version 1.0 and will be "
"removed in 1.2. Use 'n_features_in_' instead."
)
@property
def n_features_(self):
return self.n_features_in_


def _accumulate_prediction(predict, X, out, lock):
"""
Expand Down Expand Up @@ -1163,6 +1171,10 @@ class labels (multi-output problem).
n_features_ : int
The number of features when ``fit`` is performed.

.. deprecated:: 1.0
Attribute `n_features_` was deprecated in version 1.0 and will be
removed in 1.2. Use `n_features_in_` instead.

n_outputs_ : int
The number of outputs when ``fit`` is performed.

Expand Down Expand Up @@ -1463,6 +1475,10 @@ class RandomForestRegressor(ForestRegressor):
n_features_ : int
The number of features when ``fit`` is performed.

.. deprecated:: 1.0
Attribute `n_features_` was deprecated in version 1.0 and will be
removed in 1.2. Use `n_features_in_` instead.

n_outputs_ : int
The number of outputs when ``fit`` is performed.

Expand Down Expand Up @@ -1783,6 +1799,10 @@ class labels (multi-output problem).
n_features_ : int
The number of features when ``fit`` is performed.

.. deprecated:: 1.0
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Attribute `n_features_` was deprecated in version 1.0 and will be
removed in 1.2. Use `n_features_in_` instead.

n_outputs_ : int
The number of outputs when ``fit`` is performed.

Expand Down Expand Up @@ -2068,6 +2088,10 @@ class ExtraTreesRegressor(ForestRegressor):
n_features_ : int
The number of features.

.. deprecated:: 1.0
Attribute `n_features_` was deprecated in version 1.0 and will be
removed in 1.2. Use `n_features_in_` instead.

n_outputs_ : int
The number of outputs.

Expand Down Expand Up @@ -2292,6 +2316,10 @@ class RandomTreesEmbedding(BaseForest):
n_features_ : int
The number of features when ``fit`` is performed.

.. deprecated:: 1.0
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1.0. -> 1.0

Attribute `n_features_` was deprecated in version 1.0 and will be
removed in 1.2. Use `n_features_in_` instead.

n_outputs_ : int
The number of outputs when ``fit`` is performed.

Expand Down Expand Up @@ -2421,7 +2449,7 @@ def fit_transform(self, X, y=None, sample_weight=None):
X_transformed : sparse matrix of shape (n_samples, n_out)
Transformed dataset.
"""
X = check_array(X, accept_sparse=['csc'])
X = self._validate_data(X, accept_sparse=['csc'])
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Uhm what is the reason that the common test where not failing for this transformer since we did not introduce _validate_data before

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test_check_n_features_in_after_fitting is applied to all estimators except those from modules listed in N_FEATURES_IN_AFTER_FIT_MODULES_TO_IGNORE. Every module where we add n_features_in_ has to be removed from that list. This is done in this PR form ensemble.

Or do you think of another test?

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I was thinking about check_n_features_in(name, estimator_orig) but I can check on the side.
Sorry we merged a new PR that added some conflicts in test_bagging again.

if issparse(X):
# Pre-sort indices to avoid that each individual tree of the
# ensemble sorts the indices.
Expand Down
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