ENH extensible parameter search results

examples/grid_search_digits.py

@@ -59,9 +59,11 @@
     print()
     print("Grid scores on development set:")
     print()
-    for params, mean_score, scores in clf.cv_scores_:
+    means = clf.grid_results_['test_score']
+    stds = clf.fold_results_['test_score'].std(axis=1)
+    for params, mean, std in zip(clf.grid_results_['parameters'], means, stds):
         print("%0.3f (+/-%0.03f) for %r"
-              % (mean_score, scores.std() / 2, params))
+              % (mean, std / 2, params))
     print()
 
     print("Detailed classification report:")

examples/svm/plot_rbf_parameters.py

@@ -105,12 +105,8 @@
     pl.axis('tight')
 
 # plot the scores of the grid
-# cv_scores_ contains parameter settings and scores
-score_dict = grid.cv_scores_
-
-# We extract just the scores
-scores = [x[1] for x in score_dict]
-scores = np.array(scores).reshape(len(C_range), len(gamma_range))
+scores = grid.grid_results_['test_score']
+scores = scores.reshape(len(C_range), len(gamma_range))
 
 # draw heatmap of accuracy as a function of gamma and C
 pl.figure(figsize=(8, 6))

examples/svm/plot_svm_scale_c.py

@@ -131,7 +131,7 @@
                             cv=ShuffleSplit(n=n_samples, train_size=train_size,
                                             n_iter=250, random_state=1))
         grid.fit(X, y)
-        scores = [x[1] for x in grid.cv_scores_]
+        scores = grid.grid_results_['test_score']
 
         scales = [(1, 'No scaling'),
                   ((n_samples * train_size), '1/n_samples'),