Revert "Remove hardcoded n_jobs from examples."

This reverts commit 68871955.

examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py

@@ -67,7 +67,7 @@ ward = WardAgglomeration(n_clusters=10, connectivity=A, memory=mem,
                          n_components=1)
 clf = Pipeline([('ward', ward), ('ridge', ridge)])
 # Select the optimal number of parcels with grid search
-clf = GridSearchCV(clf, {'ward__n_clusters': [10, 20, 30]})
+clf = GridSearchCV(clf, {'ward__n_clusters': [10, 20, 30]}, n_jobs=1)
 clf.fit(X, y, cv=cv) # set the best parameters
 coef_ = clf.best_estimator.steps[-1][1].coef_
 coef_ = clf.best_estimator.steps[0][1].inverse_transform(coef_)

examples/gaussian_process/gp_diabetes_dataset.py

@@ -46,6 +46,6 @@ gp.thetaL, gp.thetaU = None, None # None bounds deactivate MLE
 # Perform a cross-validation estimate of the coefficient of determination using
 # the cross_val module using all CPUs available on the machine
 K = 20 # folds
-R2 = cross_val_score(gp, X, y=y, cv=KFold(y.size, K)).mean()
+R2 = cross_val_score(gp, X, y=y, cv=KFold(y.size, K), n_jobs=-1).mean()
 print("The %d-Folds estimate of the coefficient of determination is R2 = %s"
     % (K, R2))

examples/grid_search_text_feature_extraction.py

@@ -105,7 +105,7 @@ parameters = {
 
 # find the best parameters for both the feature extraction and the
 # classifier
-grid_search = GridSearchCV(pipeline, parameters)
+grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1)
 
 # cross-validation doesn't work if the length of the data is not known,
 # hence use lists instead of iterators

examples/plot_permutation_test_for_classification.py

@@ -44,7 +44,7 @@ cv = StratifiedKFold(y, 2)
 
 score, permutation_scores, pvalue = permutation_test_score(svm, X, y,
                                             zero_one_score, cv=cv,
-                                            n_permutations=100)
+                                            n_permutations=100, n_jobs=1)
 
 print "Classification score %s (pvalue : %s)" % (score, pvalue)
 

examples/svm/plot_svm_anova.py

@@ -42,7 +42,7 @@ percentiles = (1, 3, 6, 10, 15, 20, 30, 40, 60, 80, 100)
 for percentile in percentiles:
     clf._set_params(anova__percentile=percentile)
     # Compute cross-validation score using all CPUs
-    this_scores = cross_val.cross_val_score(clf, X, y)
+    this_scores = cross_val.cross_val_score(clf, X, y, n_jobs=1)
     score_means.append(this_scores.mean())
     score_stds.append(this_scores.std())