FIX: ENetCV and LassoCV now accept np.float32 input

Author: MechCoder

sklearn/linear_model/base.py

@@ -48,10 +48,12 @@ def sparse_center_data(X, y, fit_intercept, normalize=False):
     if fit_intercept:
         # we might require not to change the csr matrix sometimes
         # store a copy if normalize is True.
+        # Change dtype to float64 since mean_variance_axis0 accepts
+        # it that way.
         if sp.isspmatrix(X) and X.getformat() == 'csr':
-            X = sp.csr_matrix(X, copy=normalize)
+            X = sp.csr_matrix(X, copy=normalize, dtype=np.float64)
         else:
-            X = sp.csc_matrix(X, copy=normalize)
+            X = sp.csc_matrix(X, copy=normalize, dtype=np.float64)
 
         X_mean, X_var = mean_variance_axis0(X)
         if normalize:

sklearn/linear_model/tests/test_coordinate_descent.py

@@ -5,7 +5,7 @@
 from sys import version_info
 
 import numpy as np
-from scipy import interpolate
+from scipy import interpolate, sparse
 
 from sklearn.utils.testing import assert_array_almost_equal
 from sklearn.utils.testing import assert_almost_equal
@@ -443,6 +443,23 @@ def test_1d_multioutput_lasso_and_multitask_lasso_cv():
     assert_almost_equal(clf.intercept_, clf1.intercept_[0])
 
 
+def test_sparse_input_dtype_enet_and_lassocv():
+    X, y, _, _ = build_dataset(n_features=10)
+    clf = ElasticNetCV(n_alphas=5)
+    clf.fit(sparse.csr_matrix(X), y)
+    clf1 = ElasticNetCV(n_alphas=5)
+    clf1.fit(sparse.csr_matrix(X, dtype=np.float32), y)
+    assert_almost_equal(clf.alpha_, clf1.alpha_, decimal=6)
+    assert_almost_equal(clf.coef_, clf1.coef_, decimal=6)
+
+    clf = LassoCV(n_alphas=5)
+    clf.fit(sparse.csr_matrix(X), y)
+    clf1 = LassoCV(n_alphas=5)
+    clf1.fit(sparse.csr_matrix(X, dtype=np.float32), y)
+    assert_almost_equal(clf.alpha_, clf1.alpha_, decimal=6)
+    assert_almost_equal(clf.coef_, clf1.coef_, decimal=6)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()