Pass check_input to _pre_fit in ElasticNet

jakirkham · jakirkham · commit 9a59e7381203 · 2018-07-12T13:32:52.000-04:00
If we want to skip `check_array` calls in `ElasticNet` or `Lasso`, we
should disable them in `_pre_fit` as well. Otherwise we will still have
to do the same checks that we skipped previously.
diff --git a/sklearn/linear_model/coordinate_descent.py b/sklearn/linear_model/coordinate_descent.py
@@ -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
@@ -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:
