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MAINT Clean deprecations in MiniBatchDictionaryLearning #25357

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Jan 11, 2023
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MAINT Clean deprecations in MiniBatchDictionaryLearning #25357

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7ab6ff2
cln deprecations
jeremiedbb Jan 11, 2023
87b6f3d
lint
jeremiedbb Jan 11, 2023
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146 changes: 30 additions & 116 deletions 146 sklearn/decomposition/_dict_learning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,6 @@

from ..base import BaseEstimator, TransformerMixin, ClassNamePrefixFeaturesOutMixin
from ..utils import check_array, check_random_state, gen_even_slices, gen_batches
from ..utils import deprecated
from ..utils._param_validation import Hidden, Interval, StrOptions
from ..utils._param_validation import validate_params
from ..utils.extmath import randomized_svd, row_norms, svd_flip
Expand Down Expand Up @@ -627,7 +626,7 @@ def _dict_learning(
def _check_warn_deprecated(param, name, default, additional_message=None):
if param != "deprecated":
msg = (
f"'{name}' is deprecated in version 1.1 and will be removed in version 1.3."
f"'{name}' is deprecated in version 1.1 and will be removed in version 1.4."
)
if additional_message:
msg += f" {additional_message}"
Expand All @@ -647,7 +646,7 @@ def dict_learning_online(
return_code=True,
dict_init=None,
callback=None,
batch_size="warn",
batch_size=256,
verbose=False,
shuffle=True,
n_jobs=None,
Expand Down Expand Up @@ -717,11 +716,11 @@ def dict_learning_online(
callback : callable, default=None
A callable that gets invoked at the end of each iteration.

batch_size : int, default=3
batch_size : int, default=256
The number of samples to take in each batch.

.. versionchanged:: 1.3
The default value of `batch_size` will change from 3 to 256 in version 1.3.
The default value of `batch_size` changed from 3 to 256 in version 1.3.

verbose : bool, default=False
To control the verbosity of the procedure.
Expand All @@ -747,7 +746,7 @@ def dict_learning_online(
initialization.

.. deprecated:: 1.1
`iter_offset` serves internal purpose only and will be removed in 1.3.
`iter_offset` serves internal purpose only and will be removed in 1.4.

random_state : int, RandomState instance or None, default=None
Used for initializing the dictionary when ``dict_init`` is not
Expand All @@ -763,7 +762,7 @@ def dict_learning_online(
ignored.

.. deprecated:: 1.1
`return_inner_stats` serves internal purpose only and will be removed in 1.3.
`return_inner_stats` serves internal purpose only and will be removed in 1.4.

inner_stats : tuple of (A, B) ndarrays, default=None
Inner sufficient statistics that are kept by the algorithm.
Expand All @@ -773,13 +772,13 @@ def dict_learning_online(
`B` `(n_features, n_components)` is the data approximation matrix.

.. deprecated:: 1.1
`inner_stats` serves internal purpose only and will be removed in 1.3.
`inner_stats` serves internal purpose only and will be removed in 1.4.

return_n_iter : bool, default=False
Whether or not to return the number of iterations.

.. deprecated:: 1.1
`return_n_iter` will be removed in 1.3 and n_iter will always be returned.
`return_n_iter` will be removed in 1.4 and n_iter will never be returned.

positive_dict : bool, default=False
Whether to enforce positivity when finding the dictionary.
Expand Down Expand Up @@ -848,15 +847,15 @@ def dict_learning_online(
return_inner_stats,
"return_inner_stats",
default=False,
additional_message="From 1.3 inner_stats will never be returned.",
additional_message="From 1.4 inner_stats will never be returned.",
)
inner_stats = _check_warn_deprecated(inner_stats, "inner_stats", default=None)
return_n_iter = _check_warn_deprecated(
return_n_iter,
"return_n_iter",
default=False,
additional_message=(
"From 1.3 'n_iter' will never be returned. Refer to the 'n_iter_' and "
"From 1.4 'n_iter' will never be returned. Refer to the 'n_iter_' and "
"'n_steps_' attributes of the MiniBatchDictionaryLearning object instead."
),
)
Expand Down Expand Up @@ -891,20 +890,13 @@ def dict_learning_online(
code = est.transform(X)
return code, est.components_

# TODO remove the whole old behavior in 1.3
# TODO(1.4) remove the whole old behavior
# Fallback to old behavior

n_iter = _check_warn_deprecated(
n_iter, "n_iter", default=100, additional_message="Use 'max_iter' instead."
)

if batch_size == "warn":
warnings.warn(
"The default value of batch_size will change from 3 to 256 in 1.3.",
FutureWarning,
)
batch_size = 3

if n_components is None:
n_components = X.shape[1]

Expand Down Expand Up @@ -1903,11 +1895,11 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

batch_size : int, default=3
batch_size : int, default=256
Number of samples in each mini-batch.

.. versionchanged:: 1.3
The default value of `batch_size` will change from 3 to 256 in version 1.3.
The default value of `batch_size` changed from 3 to 256 in version 1.3.

shuffle : bool, default=True
Whether to shuffle the samples before forming batches.
Expand Down Expand Up @@ -2006,17 +1998,6 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):
components_ : ndarray of shape (n_components, n_features)
Components extracted from the data.

inner_stats_ : tuple of (A, B) ndarrays
Internal sufficient statistics that are kept by the algorithm.
Keeping them is useful in online settings, to avoid losing the
history of the evolution, but they shouldn't have any use for the
end user.
`A` `(n_components, n_components)` is the dictionary covariance matrix.
`B` `(n_features, n_components)` is the data approximation matrix.

.. deprecated:: 1.1
`inner_stats_` serves internal purpose only and will be removed in 1.3.

n_features_in_ : int
Number of features seen during :term:`fit`.

Expand All @@ -2031,19 +2012,6 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):
n_iter_ : int
Number of iterations over the full dataset.

iter_offset_ : int
The number of iteration on data batches that has been performed before.

.. deprecated:: 1.1
`iter_offset_` has been renamed `n_steps_` and will be removed in 1.3.

random_state_ : RandomState instance
RandomState instance that is generated either from a seed, the random
number generattor or by `np.random`.

.. deprecated:: 1.1
`random_state_` serves internal purpose only and will be removed in 1.3.

n_steps_ : int
Number of mini-batches processed.

Expand Down Expand Up @@ -2100,10 +2068,7 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):
"max_iter": [Interval(Integral, 0, None, closed="left"), None],
"fit_algorithm": [StrOptions({"cd", "lars"})],
"n_jobs": [None, Integral],
"batch_size": [
Interval(Integral, 1, None, closed="left"),
Hidden(StrOptions({"warn"})),
],
"batch_size": [Interval(Integral, 1, None, closed="left")],
"shuffle": ["boolean"],
"dict_init": [None, np.ndarray],
"transform_algorithm": [
Expand Down Expand Up @@ -2131,7 +2096,7 @@ def __init__(
max_iter=None,
fit_algorithm="lars",
n_jobs=None,
batch_size="warn",
batch_size=256,
shuffle=True,
dict_init=None,
transform_algorithm="omp",
Expand Down Expand Up @@ -2173,27 +2138,6 @@ def __init__(
self.max_no_improvement = max_no_improvement
self.tol = tol

@deprecated( # type: ignore
"The attribute `iter_offset_` is deprecated in 1.1 and will be removed in 1.3."
)
@property
def iter_offset_(self):
return self.n_iter_

@deprecated( # type: ignore
"The attribute `random_state_` is deprecated in 1.1 and will be removed in 1.3."
)
@property
def random_state_(self):
return self._random_state

@deprecated( # type: ignore
"The attribute `inner_stats_` is deprecated in 1.1 and will be removed in 1.3."
)
@property
def inner_stats_(self):
return self._inner_stats

def _check_params(self, X):
# n_components
self._n_components = self.n_components
Expand All @@ -2205,8 +2149,7 @@ def _check_params(self, X):
self._fit_algorithm = "lasso_" + self.fit_algorithm

# batch_size
if hasattr(self, "_batch_size"):
self._batch_size = min(self._batch_size, X.shape[0])
self._batch_size = min(self.batch_size, X.shape[0])

def _initialize_dict(self, X, random_state):
"""Initialization of the dictionary."""
Expand Down Expand Up @@ -2245,11 +2188,10 @@ def _update_inner_stats(self, X, code, batch_size, step):
theta = batch_size**2 + step + 1 - batch_size
beta = (theta + 1 - batch_size) / (theta + 1)

A, B = self._inner_stats
A *= beta
A += code.T @ code
B *= beta
B += X.T @ code
self._A *= beta
self._A += code.T @ code
self._B *= beta
self._B += X.T @ code

def _minibatch_step(self, X, dictionary, random_state, step):
"""Perform the update on the dictionary for one minibatch."""
Expand Down Expand Up @@ -2277,13 +2219,12 @@ def _minibatch_step(self, X, dictionary, random_state, step):
self._update_inner_stats(X, code, batch_size, step)

# Update dictionary
A, B = self._inner_stats
_update_dict(
dictionary,
X,
code,
A,
B,
self._A,
self._B,
verbose=self.verbose,
random_state=random_state,
positive=self.positive_dict,
Expand Down Expand Up @@ -2378,14 +2319,6 @@ def fit(self, X, y=None):
"""
self._validate_params()

self._batch_size = self.batch_size
if self.batch_size == "warn":
warnings.warn(
"The default value of batch_size will change from 3 to 256 in 1.3.",
FutureWarning,
)
self._batch_size = 3

X = self._validate_data(
X, dtype=[np.float64, np.float32], order="C", copy=False
)
Expand Down Expand Up @@ -2419,10 +2352,10 @@ def fit(self, X, y=None):
print("[dict_learning]")

# Inner stats
self._inner_stats = (
np.zeros((self._n_components, self._n_components), dtype=X_train.dtype),
np.zeros((n_features, self._n_components), dtype=X_train.dtype),
self._A = np.zeros(
(self._n_components, self._n_components), dtype=X_train.dtype
)
self._B = np.zeros((n_features, self._n_components), dtype=X_train.dtype)

if self.max_iter is not None:

Expand Down Expand Up @@ -2483,7 +2416,7 @@ def fit(self, X, y=None):

return self

def partial_fit(self, X, y=None, iter_offset="deprecated"):
def partial_fit(self, X, y=None):
"""Update the model using the data in X as a mini-batch.

Parameters
Expand All @@ -2495,15 +2428,6 @@ def partial_fit(self, X, y=None, iter_offset="deprecated"):
y : Ignored
Not used, present for API consistency by convention.

iter_offset : int, default=None
The number of iteration on data batches that has been
performed before this call to `partial_fit`. This is optional:
if no number is passed, the memory of the object is
used.

.. deprecated:: 1.1
``iter_offset`` will be removed in 1.3.

Returns
-------
self : object
Expand All @@ -2518,27 +2442,17 @@ def partial_fit(self, X, y=None, iter_offset="deprecated"):
X, dtype=[np.float64, np.float32], order="C", reset=not has_components
)

if iter_offset != "deprecated":
warnings.warn(
"'iter_offset' is deprecated in version 1.1 and "
"will be removed in version 1.3",
FutureWarning,
)
self.n_steps_ = iter_offset
else:
self.n_steps_ = getattr(self, "n_steps_", 0)

if not has_components:
# This instance has not been fitted yet (fit or partial_fit)
self._check_params(X)
self._random_state = check_random_state(self.random_state)

dictionary = self._initialize_dict(X, self._random_state)

self._inner_stats = (
np.zeros((self._n_components, self._n_components), dtype=X.dtype),
np.zeros((X.shape[1], self._n_components), dtype=X.dtype),
)
self.n_steps_ = 0

self._A = np.zeros((self._n_components, self._n_components), dtype=X.dtype)
self._B = np.zeros((X.shape[1], self._n_components), dtype=X.dtype)
else:
dictionary = self.components_

Expand Down
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