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FIX: make LinearRegression perfectly consistent across sparse or dense #13279

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Feb 27, 2019
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FIX: make LinearRegression perfectly consistent across sparse or dense #13279

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FIX : make LinearRegression perfectly consistent across sparse or dense
agramfort Feb 26, 2019
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agramfort Feb 26, 2019
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agramfort Feb 26, 2019
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5 changes: 5 additions & 0 deletions doc/whats_new/v0.21.rst
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Expand Up @@ -174,6 +174,11 @@ Support for Python 3.4 and below has been officially dropped.
parameter value ``copy_X=True`` in ``fit``.
:issue:`12972` by :user:`Lucio Fernandez-Arjona <luk-f-a>`

- |Fix| Fixed a bug in :class:`linear_model.LinearRegression` that
was not returning the same coeffecients and intercepts with
``fit_intercept=True`` in sparse and dense case.
:issue:`13279` by `Alexandre Gramfort`_

:mod:`sklearn.manifold`
............................

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19 changes: 16 additions & 3 deletions sklearn/linear_model/base.py
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Expand Up @@ -459,21 +459,34 @@ def fit(self, X, y, sample_weight=None):

X, y, X_offset, y_offset, X_scale = self._preprocess_data(
X, y, fit_intercept=self.fit_intercept, normalize=self.normalize,
copy=self.copy_X, sample_weight=sample_weight)
copy=self.copy_X, sample_weight=sample_weight,
return_mean=True)

if sample_weight is not None:
# Sample weight can be implemented via a simple rescaling.
X, y = _rescale_data(X, y, sample_weight)

if sp.issparse(X):
X_offset_scale = X_offset / X_scale

def matvec(b):
return X.dot(b) - b.dot(X_offset_scale)

def rmatvec(b):
return X.T.dot(b) - X_offset_scale * np.sum(b)

X_centered = sparse.linalg.LinearOperator(shape=X.shape,
matvec=matvec,
rmatvec=rmatvec)
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Very elegant!


if y.ndim < 2:
out = sparse_lsqr(X, y)
out = sparse_lsqr(X_centered, y)
self.coef_ = out[0]
self._residues = out[3]
else:
# sparse_lstsq cannot handle y with shape (M, K)
outs = Parallel(n_jobs=n_jobs_)(
delayed(sparse_lsqr)(X, y[:, j].ravel())
delayed(sparse_lsqr)(X_centered, y[:, j].ravel())
for j in range(y.shape[1]))
self.coef_ = np.vstack([out[0] for out in outs])
self._residues = np.vstack([out[3] for out in outs])
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21 changes: 21 additions & 0 deletions sklearn/linear_model/tests/test_base.py
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Expand Up @@ -13,6 +13,7 @@
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_allclose

from sklearn.linear_model.base import LinearRegression
from sklearn.linear_model.base import _preprocess_data
Expand Down Expand Up @@ -150,6 +151,26 @@ def test_linear_regression_sparse(random_state=0):
assert_array_almost_equal(ols.predict(X) - y.ravel(), 0)


@pytest.mark.parametrize('normalize', [True, False])
@pytest.mark.parametrize('fit_intercept', [True, False])
def test_linear_regression_sparse_equal_dense(normalize, fit_intercept):
# Test that linear regression agrees between sparse and dense
rng = check_random_state(0)
n_samples = 200
n_features = 2
X = rng.randn(n_samples, n_features)
X[X < 0.1] = 0.
Xcsr = sparse.csr_matrix(X)
y = rng.rand(n_samples)
params = dict(normalize=normalize, fit_intercept=fit_intercept)
clf_dense = LinearRegression(**params)
clf_sparse = LinearRegression(**params)
clf_dense.fit(X, y)
clf_sparse.fit(Xcsr, y)
assert clf_dense.intercept_ == pytest.approx(clf_sparse.intercept_)
assert_allclose(clf_dense.coef_, clf_sparse.coef_)


def test_linear_regression_multiple_outcome(random_state=0):
# Test multiple-outcome linear regressions
X, y = make_regression(random_state=random_state)
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