8000 [MRG] MNT: Optionally skip another input check in ElasticNet, Lasso by jakirkham · Pull Request #11487 · scikit-learn/scikit-learn · GitHub
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[MRG] MNT: Optionally skip another input check in ElasticNet, Lasso #11487

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Merged
merged 4 commits into from
Jul 27, 2018
Merged

[MRG] MNT: Optionally skip another input check in ElasticNet, Lasso #11487

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87dbe43
Add `check_input` option to `_preprocess_data`
jakirkham Jul 12, 2018
f21ba6f
Add option to disable `check_input` in `_pre_fit`
jakirkham Jul 12, 2018
9a59e73
Pass `check_input` to `_pre_fit` in `ElasticNet`
jakirkham Jul 12, 2018
387fb13
TST: Run _preprocess_data on sparse without checks
jakirkham Jul 18, 2018
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21 changes: 15 additions & 6 deletions sklearn/linear_model/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,7 @@ def make_dataset(X, y, sample_weight, random_state=None):


def _preprocess_data(X, y, fit_intercept, normalize=False, copy=True,
sample_weight=None, return_mean=False):
sample_weight=None, return_mean=False, check_input=True):
"""
Centers data to have mean zero along axis 0. If fit_intercept=False or if
the X is a sparse matrix, no centering is done, but normalization can still
Expand All @@ -90,8 +90,15 @@ def _preprocess_data(X, y, fit_intercept, normalize=False, copy=True,
if isinstance(sample_weight, numbers.Number):
sample_weight = None

X = check_array(X, copy=copy, accept_sparse=['csr', 'csc'],
dtype=FLOAT_DTYPES)
if check_input:
X = check_array(X, copy=copy, accept_sparse=['csr', 'csc'],
dtype=FLOAT_DTYPES)
elif copy:
if sp.issparse(X):
X = X.copy()
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This isn't covered by tests.

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Ok, think I've fixed this.

else:
X = X.copy(order='K')

y = np.asarray(y, dtype=X.dtype)

if fit_intercept:
Expand Down Expand Up @@ -441,7 +448,8 @@ def fit(self, X, y, sample_weight=None):
return self


def _pre_fit(X, y, Xy, precompute, normalize, fit_intercept, copy):
def _pre_fit(X, y, Xy, precompute, normalize, fit_intercept, copy,
check_input=True):
"""Aux function used at beginning of fit in linear models"""
n_samples, n_features = X.shape

Expand All @@ -450,11 +458,12 @@ def _pre_fit(X, y, Xy, precompute, normalize, fit_intercept, copy):
precompute = False
X, y, X_offset, y_offset, X_scale = _preprocess_data(
X, y, fit_intercept=fit_intercept, normalize=normalize,
copy=False, return_mean=True)
copy=False, return_mean=True, check_input=check_input)
else:
# copy was done in fit if necessary
X, y, X_offset, y_offset, X_scale = _preprocess_data(
X, y, fit_intercept=fit_intercept, normalize=normalize, copy=copy)
X, y, fit_intercept=fit_intercept, normalize=normalize, copy=copy,
check_input=check_input)
if hasattr(precompute, '__array__') and (
fit_intercept and not np.allclose(X_offset, np.zeros(n_features)) or
normalize and not np.allclose(X_scale, np.ones(n_features))):
Expand Down
5 changes: 3 additions & 2 deletions sklearn/linear_model/coordinate_descent.py
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Expand Up @@ -418,7 +418,7 @@ def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
if check_input:
X, y, X_offset, y_offset, X_scale, precompute, Xy = \
_pre_fit(X, y, Xy, precompute, normalize=False,
fit_intercept=False, copy=False)
fit_intercept=False, copy=False, check_input=check_input)
if alphas is None:
# No need to normalize of fit_intercept: it has been done
# above
Expand Down Expand Up @@ -717,7 +717,8 @@ def fit(self, X, y, check_input=True):
should_copy = self.copy_X and not X_copied
X, y, X_offset, y_offset, X_scale, precompute, Xy = \
_pre_fit(X, y, None, self.precompute, self.normalize,
self.fit_intercept, copy=should_copy)
self.fit_intercept, copy=should_copy,
check_input=check_input)
if y.ndim == 1:
y = y[:, np.newaxis]
if Xy is not None and Xy.ndim == 1:
Expand Down
24 changes: 24 additions & 0 deletions sklearn/linear_model/tests/test_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,8 @@
#
# License: BSD 3 clause

import pytest

import numpy as np
from scipy import sparse
from scipy import linalg
Expand Down Expand Up @@ -321,6 +323,28 @@ def test_csr_preprocess_data():
assert_equal(csr_.getformat(), 'csr')


@pytest.mark.parametrize('is_sparse', (True, False))
@pytest.mark.parametrize('to_copy', (True, False))
def test_preprocess_copy_data_no_checks(is_sparse, to_copy):
X, y = make_regression()
X[X < 2.5] = 0.0

if is_sparse:
X = sparse.csr_matrix(X)

X_, y_, _, _, _ = _preprocess_data(X, y, True,
copy=to_copy, check_input=False)

if to_copy and is_sparse:
assert not np.may_share_memory(X_.data, X.data)
elif to_copy:
assert not np.may_share_memory(X_, X)
elif is_sparse:
assert np.may_share_memory(X_.data, X.data)
else:
assert np.may_share_memory(X_, X)


def test_dtype_preprocess_data():
n_samples = 200
n_features = 2
Expand Down
0