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jeremiedbbogrisel
jeremiedbb
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ogrisel
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FEA Add writeable parameter to check_array (#29018)
Co-authored-by: Olivier Grisel <[email protected]>
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19 files changed

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‎doc/whats_new/v1.5.rst‎

Lines changed: 19 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -20,15 +20,19 @@ Version 1.5.1
2020

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**TODO**
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23-
Changelog
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---------
25-
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Changes impacting many modules
2724
------------------------------
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26+
- |Fix| Fixed a regression in the validation of the input data of all estimators where
27+
an unexpected error was raised when passing a DataFrame backed by a read-only buffer.
28+
:pr:`29018` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
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2930
- |Fix| Fixed a regression causing a dead-lock at import time in some settings.
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:pr:`29235` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
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33+
Changelog
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---------
35+
3236
:mod:`sklearn.metrics`
3337
......................
3438

@@ -37,6 +41,10 @@ Changes impacting many modules
3741
instead of implicitly converting those inputs as regular NumPy arrays.
3842
:pr:`29119` by :user:`Olivier Grisel`.
3943

44+
- |Fix| Fix a regression in :func:`metrics.zero_one_loss` causing an error
45+
for Array API dispatch with multilabel inputs.
46+
:pr:`29269` by :user:`Yaroslav Korobko <Tialo>`.
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4048
:mod:`sklearn.model_selection`
4149
..............................
4250

@@ -48,6 +56,14 @@ Changes impacting many modules
4856
grids that have estimators as parameter values.
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:pr:`29179` by :user:`Marco Gorelli<MarcoGorelli>`.
5058

59+
:mod:`sklearn.utils`
60+
....................
61+
62+
- |API| :func:`utils.validation.check_array` has a new parameter, `force_writeable`, to
63+
control the writeability of the output array. If set to `True`, the output array will
64+
be guaranteed to be writeable and a copy will be made if the input array is read-only.
65+
If set to `False`, no guarantee is made about the writeability of the output array.
66+
:pr:`29018` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
5167

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.. _changes_1_5:
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‎sklearn/cluster/_affinity_propagation.py‎

Lines changed: 3 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -504,13 +504,10 @@ def fit(self, X, y=None):
504504
Returns the instance itself.
505505
"""
506506
if self.affinity == "precomputed":
507-
accept_sparse = False
508-
else:
509-
accept_sparse = "csr"
510-
X = self._validate_data(X, accept_sparse=accept_sparse)
511-
if self.affinity == "precomputed":
512-
self.affinity_matrix_ = X.copy() if self.copy else X
507+
X = self._validate_data(X, copy=self.copy, force_writeable=True)
508+
self.affinity_matrix_ = X
513509
else: # self.affinity == "euclidean"
510+
X = self._validate_data(X, accept_sparse="csr")
514511
self.affinity_matrix_ = -euclidean_distances(X, squared=True)
515512

516513
if self.affinity_matrix_.shape[0] != self.affinity_matrix_.shape[1]:

‎sklearn/cluster/_hdbscan/hdbscan.py‎

Lines changed: 4 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -770,14 +770,17 @@ def fit(self, X, y=None):
770770
X,
771771
accept_sparse=["csr", "lil"],
772772
dtype=np.float64,
773+
force_writeable=True,
773774
)
774775
else:
775776
# Only non-sparse, precomputed distance matrices are handled here
776777
# and thereby allowed to contain numpy.inf for missing distances
777778

778779
# Perform data validation after removing infinite values (numpy.inf)
779780
# from the given distance matrix.
780-
X = self._validate_data(X, force_all_finite=False, dtype=np.float64)
781+
X = self._validate_data(
782+
X, force_all_finite=False, dtype=np.float64, force_writeable=True
783+
)
781784
if np.isnan(X).any():
782785
# TODO: Support np.nan in Cython implementation for precomputed
783786
# dense HDBSCAN

‎sklearn/cross_decomposition/_pls.py‎

Lines changed: 22 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -263,10 +263,19 @@ def fit(self, X, y=None, Y=None):
263263

264264
check_consistent_length(X, y)
265265
X = self._validate_data(
266-
X, dtype=np.float64, copy=self.copy, ensure_min_samples=2
266+
X,
267+
dtype=np.float64,
268+
force_writeable=True,
269+
copy=self.copy,
270+
ensure_min_samples=2,
267271
)
268272
y = check_array(
269-
y, input_name="y", dtype=np.float64, copy=self.copy, ensure_2d=False
273+
y,
274+
input_name="y",
275+
dtype=np.float64,
276+
force_writeable=True,
277+
copy=self.copy,
278+
ensure_2d=False,
270279
)
271280
if y.ndim == 1:
272281
self._predict_1d = True
@@ -1056,10 +1065,19 @@ def fit(self, X, y=None, Y=None):
10561065
y = _deprecate_Y_when_required(y, Y)
10571066
check_consistent_length(X, y)
10581067
X = self._validate_data(
1059-
X, dtype=np.float64, copy=self.copy, ensure_min_samples=2
1068+
X,
1069+
dtype=np.float64,
1070+
force_writeable=True,
1071+
copy=self.copy,
1072+
ensure_min_samples=2,
10601073
)
10611074
y = check_array(
1062-
y, input_name="y", dtype=np.float64, copy=self.copy, ensure_2d=False
1075+
y,
1076+
input_name="y",
1077+
dtype=np.float64,
1078+
force_writeable=True,
1079+
copy=self.copy,
1080+
ensure_2d=False,
10631081
)
10641082
if y.ndim == 1:
10651083
y = y.reshape(-1, 1)

‎sklearn/decomposition/_factor_analysis.py‎

Lines changed: 3 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -219,7 +219,9 @@ def fit(self, X, y=None):
219219
self : object
220220
FactorAnalysis class instance.
221221
"""
222-
X = self._validate_data(X, copy=self.copy, dtype=np.float64)
222+
X = self._validate_data(
223+
X, copy=self.copy, dtype=np.float64, force_writeable=True
224+
)
223225

224226
n_samples, n_features = X.shape
225227
n_components = self.n_components

‎sklearn/decomposition/_incremental_pca.py‎

Lines changed: 6 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -229,6 +229,7 @@ def fit(self, X, y=None):
229229
accept_sparse=["csr", "csc", "lil"],
230230
copy=self.copy,
231231
dtype=[np.float64, np.float32],
232+
force_writeable=True,
232233
)
233234
n_samples, n_features = X.shape
234235

@@ -278,7 +279,11 @@ def partial_fit(self, X, y=None, check_input=True):
278279
"or use IncrementalPCA.fit to do so in batches."
279280
)
280281
X = self._validate_data(
281-
X, copy=self.copy, dtype=[np.float64, np.float32], reset=first_pass
282+
X,
283+
copy=self.copy,
284+
dtype=[np.float64, np.float32],
285+
force_writeable=True,
286+
reset=first_pass,
282287
)
283288
n_samples, n_features = X.shape
284289
if first_pass:

‎sklearn/decomposition/_pca.py‎

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -511,6 +511,7 @@ def _fit(self, X):
511511
X = self._validate_data(
512512
X,
513513
dtype=[xp.float64, xp.float32],
514+
force_writeable=True,
514515
accept_sparse=("csr", "csc"),
515516
ensure_2d=True,
516517
copy=False,

‎sklearn/impute/_base.py‎

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -334,6 +334,7 @@ def _validate_input(self, X, in_fit):
334334
reset=in_fit,
335335
accept_sparse="csc",
336336
dtype=dtype,
337+
force_writeable=True if not in_fit else None,
337338
force_all_finite=force_all_finite,
338339
copy=self.copy,
339340
)

‎sklearn/impute/_knn.py‎

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -267,6 +267,7 @@ def transform(self, X):
267267
X,
268268
accept_sparse=False,
269269
dtype=FLOAT_DTYPES,
270+
force_writeable=True,
270271
force_all_finite=force_all_finite,
271272
copy=self.copy,
272273
reset=False,

‎sklearn/linear_model/_base.py‎

Lines changed: 6 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -607,7 +607,12 @@ def fit(self, X, y, sample_weight=None):
607607
accept_sparse = False if self.positive else ["csr", "csc", "coo"]
608608

609609
X, y = self._validate_data(
610-
X, y, accept_sparse=accept_sparse, y_numeric=True, multi_output=True
610+
X,
611+
y,
612+
accept_sparse=accept_sparse,
613+
y_numeric=True,
614+
multi_output=True,
615+
force_writeable=True,
611616
)
612617

613618
has_sw = sample_weight is not None

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