Revert "FIX: make LinearRegression perfectly consistent across sparse…

… or dense (scikit-learn#13279)"

This reverts commit 41f106c.

doc/whats_new/v0.21.rst

@@ -200,11 +200,6 @@ 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`
 ............................
 

sklearn/linear_model/base.py

@@ -467,34 +467,21 @@ 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,
-            return_mean=True)
+            copy=self.copy_X, sample_weight=sample_weight)
 
         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)
-
             if y.ndim < 2:
-                out = sparse_lsqr(X_centered, y)
+                out = sparse_lsqr(X, 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_centered, y[:, j].ravel())
+                    delayed(sparse_lsqr)(X, 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])

sklearn/linear_model/tests/test_base.py

@@ -154,26 +154,6 @@ 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)