fix TheilSenRegressor input validation, LassoLarsIC input validation

Author: amueller

sklearn/linear_model/least_angle.py

@@ -1295,7 +1295,7 @@ def fit(self, X, y, copy_X=True):
             returns an instance of self.
         """
         self.fit_path = True
-        X, y = check_X_y(X, y, multi_output=True, y_numeric=True)
+        X, y = check_X_y(X, y, y_numeric=True)
 
         X, y, Xmean, ymean, Xstd = LinearModel._center_data(
             X, y, self.fit_intercept, self.normalize, self.copy_X)

sklearn/linear_model/theil_sen.py

@@ -19,8 +19,8 @@
 
 from .base import LinearModel
 from ..base import RegressorMixin
-from ..utils import check_array, check_random_state, ConvergenceWarning
-from ..utils import check_consistent_length, _get_n_jobs
+from ..utils import check_random_state, ConvergenceWarning
+from ..utils import check_X_y, _get_n_jobs
 from ..utils.random import choice
 from ..externals.joblib import Parallel, delayed
 from ..externals.six.moves import xrange as range
@@ -343,9 +343,7 @@ def fit(self, X, y):
         self : returns an instance of self.
         """
         random_state = check_random_state(self.random_state)
-        X = check_array(X)
-        y = check_array(y, ensure_2d=False)
-        check_consistent_length(X, y)
+        X, y = check_X_y(X, y, y_numeric=True)
         n_samples, n_features = X.shape
         n_subsamples, self.n_subpopulation_ = self._check_subparams(n_samples,
                                                                     n_features)

sklearn/utils/estimator_checks.py

@@ -841,7 +841,7 @@ def check_estimators_unfitted(name, Estimator):
 
 def check_supervised_y_2d(name, Estimator):
     if "MultiTask" in name:
-        # These only work on 2d, this test makes not sense
+        # These only work on 2d, so this test makes no sense
         return
     rnd = np.random.RandomState(0)
     X = rnd.uniform(size=(10, 3))
@@ -866,9 +866,9 @@ def check_supervised_y_2d(name, Estimator):
     msg = "expected 1 DataConversionWarning, got: %s" % (
         ", ".join([str(w_x) for w_x in w]))
     if name not in MULTI_OUTPUT:
-        # warned if we don't support multi-output
+        # check that we warned if we don't support multi-output
         assert_equal(len(w), 1, msg)
-    assert_array_equal(y_pred.ravel(), y_pred_2d.ravel())
+    assert_array_almost_equal(y_pred.ravel(), y_pred_2d.ravel())
 
 
 def check_classifiers_classes(name, Classifier):