deprecate make_blobs, use sklearn make_blobs in plot_metrics

Author: amueller

mglearn/make_blobs.py

@@ -3,8 +3,10 @@
 
 from sklearn.utils import check_array, check_random_state
 from sklearn.utils import shuffle as shuffle_
+from sklearn.utils.deprecation import deprecated
 
 
+@deprecated("Please import make_blobs directly from scikit-learn")
 def make_blobs(n_samples=100, n_features=2, centers=2, cluster_std=1.0,
                center_box=(-10.0, 10.0), shuffle=True, random_state=None):
     """Generate isotropic Gaussian blobs for clustering.

mglearn/plot_grid_search.py

@@ -14,7 +14,8 @@ def plot_cross_val_selection():
 
     param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],
                   'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}
-    grid_search = GridSearchCV(SVC(), param_grid, cv=5)
+    grid_search = GridSearchCV(SVC(), param_grid, cv=5,
+                               return_train_score=True)
     grid_search.fit(X_trainval, y_trainval)
     results = pd.DataFrame(grid_search.cv_results_)[15:]
 

mglearn/plot_metrics.py

@@ -36,11 +36,11 @@ def plot_binary_confusion_matrix():
 
 
 def plot_decision_threshold():
-    from mglearn.datasets import make_blobs
+    from sklearn.datasets import make_blobs
     from sklearn.svm import SVC
     from sklearn.model_selection import train_test_split
 
-    X, y = make_blobs(n_samples=(400, 50), centers=2, cluster_std=[7.0, 2],
+    X, y = make_blobs(n_samples=(400, 50), cluster_std=[7.0, 2],
                       random_state=22)
     X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)