test_search.py 文件源码

def test_grid_search():
    # Test that the best estimator contains the right value for foo_param
    clf = MockClassifier()
    grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, verbose=3)
    # make sure it selects the smallest parameter in case of ties
    old_stdout = sys.stdout
    sys.stdout = StringIO()
    grid_search.fit(X, y)
    sys.stdout = old_stdout
    assert_equal(grid_search.best_estimator_.foo_param, 2)

    for i, foo_i in enumerate([1, 2, 3]):
        assert_true(grid_search.grid_scores_[i][0]
                    == {'foo_param': foo_i})
    # Smoke test the score etc:
    grid_search.score(X, y)
    grid_search.predict_proba(X)
    grid_search.decision_function(X)
    grid_search.transform(X)

    # Test exception handling on scoring
    grid_search.scoring = 'sklearn'
    assert_raises(ValueError, grid_search.fit, X, y)