From 2fc4c96a9014ab37031f9aac45e2025aa32eb65d Mon Sep 17 00:00:00 2001
From: Fabian Pedregosa <fabian.pedregosa@inria.fr>
Date: Sat, 30 Jul 2011 07:49:56 +0200
Subject: [PATCH] Revert "Remove hardcoded n_jobs from examples."
This reverts commit 68871955639b19b1e66427c814b0a41bb9f3e18e.
---
.../plot_feature_agglomeration_vs_univariate_selection.py | 2 +-
examples/gaussian_process/gp_diabetes_dataset.py | 2 +-
examples/grid_search_text_feature_extraction.py | 2 +-
examples/plot_permutation_test_for_classification.py | 2 +-
examples/svm/plot_svm_anova.py | 2 +-
5 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py b/examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py
index fd39f8a81d..951a4f2c6c 100644
--- a/examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py
+++ b/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_)
diff --git a/examples/gaussian_process/gp_diabetes_dataset.py b/examples/gaussian_process/gp_diabetes_dataset.py
index 61e4b79d97..d814fd1a1d 100644
--- a/examples/gaussian_process/gp_diabetes_dataset.py
+++ b/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))
diff --git a/examples/grid_search_text_feature_extraction.py b/examples/grid_search_text_feature_extraction.py
index 054f3022b3..164cd934bd 100644
--- a/examples/grid_search_text_feature_extraction.py
+++ b/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
diff --git a/examples/plot_permutation_test_for_classification.py b/examples/plot_permutation_test_for_classification.py
index 854ec23b7f..d41eb0c3c7 100644
--- a/examples/plot_permutation_test_for_classification.py
+++ b/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)
diff --git a/examples/svm/plot_svm_anova.py b/examples/svm/plot_svm_anova.py
index f018613fc3..6187afe801 100644
--- a/examples/svm/plot_svm_anova.py
+++ b/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())
--
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