[WIP] benchmarks for nested parallelism in sklearn

scikit-learn/bench_cross-valid.py

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+from __future__ import print_function
+
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import cross_val_score
+from sklearn.metrics import classification_report
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.externals.joblib import Memory
+from timeit import default_timer as timer
+
+mem = Memory(cachedir='cache')
+
+@mem.cache
+def make_data():
+    return datasets.make_classification(n_samples=70000, n_features=200, random_state=0)
+
+X, y = make_data()
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)
+
+clf = RandomForestClassifier(n_estimators=100, n_jobs=-1, random_state=0)
+
+t0 = timer()
+cross_val_score(clf, X_train, y_train, n_jobs=5, cv=5)
+t1 = timer()
+print(t1-t0)
+

scikit-learn/bench_grid_search.py

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+from __future__ import print_function
+
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import GridSearchCV
+from sklearn.metrics import classification_report
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.externals.joblib import Memory
+from timeit import default_timer as timer
+
+mem = Memory(cachedir='cache')
+
+@mem.cache
+def make_data():
+    return datasets.make_classification(n_samples=700, n_features=200, random_state=0)
+
+X, y = make_data()
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)
+
+# Set the parameters by cross-validation
+parameters = {
+    'n_estimators': [10, 50, 100, 500],
+    'max_depth':    [5, 10, None],
+    'max_features': [.1, .5, .8, 1.]
+    }
+
+clf = GridSearchCV(RandomForestClassifier(n_jobs=-1), parameters, cv=5, n_jobs=-1,
+                       scoring='precision_macro' % score)
+t0 = timer()
+clf.fit(X_train, y_train)
+t1 = timer()
+print(t1-t0)