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Merged
merged 9 commits into from
Jan 2, 2021
Merged

ENH Adds n_features_in_ checks to linear and svm modules #18578

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83a538c
ENH Adds n_features_in_ checks to linear module
thomasjpfan Oct 9, 2020
61ba6d5
REV Reduces diff
thomasjpfan Oct 9, 2020
345d432
Merge remote-tracking branch 'upstream/master' into n_feature_in_svm
thomasjpfan Oct 13, 2020
6b44811
CLN Remove unreachable code
thomasjpfan Oct 13, 2020
7201313
REV Reduces diff
thomasjpfan Oct 13, 2020
81b3fc7
Expect base estimator names
ogrisel Oct 14, 2020
5c8aae9
Merge branch 'master' into n_feature_in_svm
ogrisel Oct 14, 2020
7d15672
Merge remote-tracking branch 'upstream/master' into n_feature_in_svm
thomasjpfan Nov 23, 2020
926bf4c
Merge remote-tracking branch 'upstream/master' into n_feature_in_svm
thomasjpfan Dec 17, 2020
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11 changes: 3 additions & 8 deletions sklearn/linear_model/_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -217,7 +217,8 @@ def fit(self, X, y):
def _decision_function(self, X):
check_is_fitted(self)

X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
X = self._validate_data(X, accept_sparse=['csr', 'csc', 'coo'],
reset=False)
return safe_sparse_dot(X, self.coef_.T,
dense_output=True) + self.intercept_

Expand Down Expand Up @@ -281,13 +282,7 @@ class would be predicted.
"""
check_is_fitted(self)

X = check_array(X, accept_sparse='csr')

n_features = self.coef_.shape[1]
if X.shape[1] != n_features:
raise ValueError("X has %d features per sample; expecting %d"
% (X.shape[1], n_features))

X = self._validate_data(X, accept_sparse='csr', reset=False)
scores = safe_sparse_dot(X, self.coef_.T,
dense_output=True) + self.intercept_
return scores.ravel() if scores.shape[1] == 1 else scores
Expand Down
13 changes: 6 additions & 7 deletions sklearn/linear_model/_glm/glm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,6 @@
import scipy.optimize

from ...base import BaseEstimator, RegressorMixin
from ...utils import check_array, check_X_y
from ...utils.optimize import _check_optimize_result
from ...utils.validation import check_is_fitted, _check_sample_weight
from ..._loss.glm_distribution import (
Expand Down Expand Up @@ -221,9 +220,9 @@ def fit(self, X, y, sample_weight=None):
family = self._family_instance
link = self._link_instance

X, y = check_X_y(X, y, accept_sparse=['csc', 'csr'],
dtype=[np.float64, np.float32],
y_numeric=True, multi_output=False)
X, y = self._validate_data(X, y, accept_sparse=['csc', 'csr'],
dtype=[np.float64, np.float32],
y_numeric=True, multi_output=False)

weights = _check_sample_weight(sample_weight, X)

Expand Down Expand Up @@ -311,9 +310,9 @@ def _linear_predictor(self, X):
Returns predicted values of linear predictor.
"""
check_is_fitted(self)
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'],
dtype=[np.float64, np.float32], ensure_2d=True,
allow_nd=False)
X = self._validate_data(X, accept_sparse=['csr', 'csc', 'coo'],
dtype=[np.float64, np.float32], ensure_2d=True,
allow_nd=False, reset=False)
return X @ self.coef_ + self.intercept_

def predict(self, X):
Expand Down
18 changes: 9 additions & 9 deletions sklearn/linear_model/_stochastic_gradient.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@
from ._base import LinearClassifierMixin, SparseCoefMixin
from ._base import make_dataset
from ..base import BaseEstimator, RegressorMixin
from ..utils import check_array, check_random_state, check_X_y
from ..utils import check_random_state
from ..utils.extmath import safe_sparse_dot
from ..utils.multiclass import _check_partial_fit_first_call
from ..utils.validation import check_is_fitted, _check_sample_weight
Expand Down Expand Up @@ -488,8 +488,10 @@ def _partial_fit(self, X, y, alpha, C,
loss, learning_rate, max_iter,
classes, sample_weight,
coef_init, intercept_init):
X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64,
order="C", accept_large_sparse=False)
first_call = not hasattr(self, "classes_")
X, y = self._validate_data(X, y, accept_sparse='csr', dtype=np.float64,
order="C", accept_large_sparse=False,
reset=first_call)

n_samples, n_features = X.shape

Expand Down Expand Up @@ -1135,22 +1137,20 @@ def __init__(self, loss="squared_loss", *, penalty="l2", alpha=0.0001,

def _partial_fit(self, X, y, alpha, C, loss, learning_rate,
max_iter, sample_weight, coef_init, intercept_init):
first_call = getattr(self, "coef_", None) is None
X, y = self._validate_data(X, y, accept_sparse="csr", copy=False,
order='C', dtype=np.float64,
accept_large_sparse=False)
accept_large_sparse=False, reset=first_call)
y = y.astype(np.float64, copy=False)

n_samples, n_features = X.shape

sample_weight = _check_sample_weight(sample_weight, X)

# Allocate datastructures from input arguments
if getattr(self, "coef_", None) is None:
if first_call:
self._allocate_parameter_mem(1, n_features, coef_init,
intercept_init)
elif n_features != self.coef_.shape[-1]:
raise ValueError("Number of features %d does not match previous "
"data %d." % (n_features, self.coef_.shape[-1]))
if self.average > 0 and getattr(self, "_average_coef", None) is None:
self._average_coef = np.zeros(n_features,
dtype=np.float64,
Expand Down Expand Up @@ -1266,7 +1266,7 @@ def _decision_function(self, X):
"""
check_is_fitted(self)

X = check_array(X, accept_sparse='csr')
X = self._validate_data(X, accept_sparse='csr', reset=False)

scores = safe_sparse_dot(X, self.coef_.T,
dense_output=True) + self.intercept_
Expand Down
9 changes: 3 additions & 6 deletions sklearn/svm/_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -471,8 +471,9 @@ def _validate_for_predict(self, X):
check_is_fitted(self)

if not callable(self.kernel):
X = check_array(X, accept_sparse='csr', dtype=np.float64,
order="C", accept_large_sparse=False)
X = self._validate_data(X, accept_sparse='csr', dtype=np.float64,
order="C", accept_large_sparse=False,
reset=False)

if self._sparse and not sp.isspmatrix(X):
X = sp.csr_matrix(X)
Expand All @@ -489,10 +490,6 @@ def _validate_for_predict(self, X):
raise ValueError("X.shape[1] = %d should be equal to %d, "
"the number of samples at training time" %
(X.shape[1], self.shape_fit_[0]))
elif not callable(self.kernel) and X.shape[1] != self.shape_fit_[1]:
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do we still need shape_fit_ then?

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shape_fit_[0] is still being used in some places of the codebase.

raise ValueError("X.shape[1] = %d should be equal to %d, "
"the number of features at training time" %
(X.shape[1], self.shape_fit_[1]))
return X

@property
Expand Down
2 changes: 0 additions & 2 deletions sklearn/tests/test_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -272,7 +272,6 @@ def test_search_cv(estimator, check, request):
'feature_selection',
'gaussian_process',
'isotonic',
'linear_model',
'manifold',
'mixture',
'model_selection',
Expand All @@ -283,7 +282,6 @@ def test_search_cv(estimator, check, request):
'pipeline',
'random_projection',
'semi_supervised',
'svm',
}

N_FEATURES_IN_AFTER_FIT_ESTIMATORS = [
Expand Down