FIX division by zero warning in LassoLarsIC

Fixes scikit-learn#3576. First time I named a variable after myself.

Author: larsmans

sklearn/linear_model/least_angle.py

@@ -1287,7 +1287,8 @@ def fit(self, X, y, copy_X=True):
             df[k] = np.sum(mask)
 
         self.alphas_ = alphas_
-        self.criterion_ = n_samples * np.log(mean_squared_error) + K * df
+        with np.errstate(divide='ignore'):
+            self.criterion_ = n_samples * np.log(mean_squared_error) + K * df
         n_best = np.argmin(self.criterion_)
 
         self.alpha_ = alphas_[n_best]

sklearn/linear_model/tests/test_least_angle.py

@@ -9,7 +9,7 @@
 from sklearn.utils.testing import assert_greater
 from sklearn.utils.testing import assert_raises
 from sklearn.utils.testing import ignore_warnings, assert_warns_message
-from sklearn.utils.testing import assert_warns
+from sklearn.utils.testing import assert_no_warnings, assert_warns
 from sklearn.utils import ConvergenceWarning
 from sklearn import linear_model, datasets
 
@@ -468,6 +468,15 @@ def test_lasso_lars_ic():
     assert_raises(ValueError, lars_broken.fit, X, y)
 
 
+def test_no_warning_for_zero_mse():
+    """LassoLarsIC should not warn for log of zero MSE."""
+    y = np.arange(10, dtype=float)
+    X = y.reshape(-1, 1)
+    lars = linear_model.LassoLarsIC(normalize=False)
+    assert_no_warnings(lars.fit, X, y)
+    assert_true(np.any(np.isinf(lars.criterion_)))
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()