def test_gp_search(): # Test that the best estimator contains the right value for foo_param clf = MockDiscreteClassifier() gp_search = GPSearchCV(clf, {'foo_param': ['int', [1, 3]]}) # make sure it selects the smallest parameter in case of ties old_stdout = sys.stdout sys.stdout = StringIO() gp_search.fit(X, y) sys.stdout = old_stdout assert_equal(gp_search.best_estimator_.foo_param, 2) clf = MockContinuousClassifier() gp_search = GPSearchCV(clf, {'foo_param': ['float', [-3, 3]]}, verbose=3) # make sure it selects the smallest parameter in case of ties old_stdout = sys.stdout sys.stdout = StringIO() gp_search.fit(X, y) sys.stdout = old_stdout assert_almost_equal(gp_search.best_estimator_.foo_param, 0, decimal=1) # Smoke test the score etc: gp_search.score(X, y) gp_search.predict_proba(X) gp_search.decision_function(X) gp_search.transform(X) # Test exception handling on scoring gp_search.scoring = 'sklearn' assert_raises(ValueError, gp_search.fit, X, y)
def test_gp_search(): clf = MockClassifier() gp_search = GPSearchCV(clf, {'foo_param': ['int', [1, 3]]}, verbose=3) # make sure it selects the smallest parameter in case of ties old_stdout = sys.stdout sys.stdout = StringIO() gp_search.fit(X, y) sys.stdout = old_stdout assert_equal(gp_search.best_estimator_.foo_param, 2) for i, foo_i in enumerate([1, 2, 3]): assert_true(gp_search.scores_[i][0] == {'foo_param': foo_i}) # Smoke test the score etc: gp_search.score(X, y) gp_search.predict_proba(X) gp_search.decision_function(X) gp_search.transform(X) # Test exception handling on scoring gp_search.scoring = 'sklearn' assert_raises(ValueError, gp_search.fit, X, y)