def test_calibration_multiclass():
    """Test calibration for multiclass """
    # test multi-class setting with classifier that implements
    # only decision function
    clf = LinearSVC()
    X, y_idx = make_blobs(n_samples=100, n_features=2, random_state=42,
                          centers=3, cluster_std=3.0)

    # Use categorical labels to check that CalibratedClassifierCV supports
    # them correctly
    target_names = np.array(['a', 'b', 'c'])
    y = target_names[y_idx]

    X_train, y_train = X[::2], y[::2]
    X_test, y_test = X[1::2], y[1::2]

    clf.fit(X_train, y_train)
    for method in ['isotonic', 'sigmoid']:
        cal_clf = CalibratedClassifierCV(clf, method=method, cv=2)
        cal_clf.fit(X_train, y_train)
        probas = cal_clf.predict_proba(X_test)
        assert_array_almost_equal(np.sum(probas, axis=1), np.ones(len(X_test)))

        # Check that log-loss of calibrated classifier is smaller than
        # log-loss of naively turned OvR decision function to probabilities
        # via softmax
        def softmax(y_pred):
            e = np.exp(-y_pred)
            return e / e.sum(axis=1).reshape(-1, 1)
        uncalibrated_log_loss = \
            log_loss(y_test, softmax(clf.decision_function(X_test)))
        calibrated_log_loss = log_loss(y_test, probas)
        assert_greater_equal(uncalibrated_log_loss, calibrated_log_loss)

    # Test that calibration of a multiclass classifier decreases log-loss
    # for RandomForestClassifier
    X, y = make_blobs(n_samples=100, n_features=2, random_state=42,
                      cluster_std=3.0)
    X_train, y_train = X[::2], y[::2]
    X_test, y_test = X[1::2], y[1::2]

    clf = RandomForestClassifier(n_estimators=10, random_state=42)
    clf.fit(X_train, y_train)
    clf_probs = clf.predict_proba(X_test)
    loss = log_loss(y_test, clf_probs)

    for method in ['isotonic', 'sigmoid']:
        cal_clf = CalibratedClassifierCV(clf, method=method, cv=3)
        cal_clf.fit(X_train, y_train)
        cal_clf_probs = cal_clf.predict_proba(X_test)
        cal_loss = log_loss(y_test, cal_clf_probs)
        assert_greater(loss, cal_loss)
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/tests/test_calibration.py/test_calibration_multiclass/original