fix travis

ngoix · ngoix · commit 3d759a496cd4 · 2015-09-07T18:27:00.000+02:00
diff --git a/sklearn/ensemble/iforest.py b/sklearn/ensemble/iforest.py
@@ -151,9 +151,8 @@ def fit(self, X, y=None, sample_weight=None):
         # ensure that max_sample is in [1, n_samples]:
         n_samples = X.shape[0]
         if not (self.max_samples <= n_samples):
-            warn("max_samples is larger than the total number of samples"
-                 " n_samples. Corrected as max_samples=n_samples")
-            self.max_samples = n_samples
+            raise ValueError("max_samples (default=256) is greater than the total" 
+                             " number of samples")
         if not (0 < self.max_samples):
             raise ValueError("max_samples has to be positive")
 
diff --git a/sklearn/ensemble/tests/test_iforest.py b/sklearn/ensemble/tests/test_iforest.py
@@ -95,7 +95,7 @@ def test_iforest_error():
                   IsolationForest(max_samples=0.0).fit, X)
     assert_raises(ValueError,
                   IsolationForest(max_samples=2.0).fit, X)
-    assert_warns(UserWarning,
+    assert_raises(ValueError,
                  IsolationForest(max_samples=1000).fit, X)
     # cannot check for string values
 
@@ -133,7 +133,8 @@ def test_iforest_gridsearch():
     # Grid search with scoring based on decision_function
     parameters = {'n_estimators': (1, 2, 100)}
 
-    grid_search = GridSearchCV(IsolationForest(max_samples=0.1),
+    grid_search = GridSearchCV(IsolationForest(random_state=0,
+                                               max_samples=0.1),
                                parameters,
                                scoring="roc_auc").fit(X, y)
     best_score = grid_search.best_score_
