test_gradient_boosting.py 文件源码

 复制代码def test_probability_exponential():
    # Predict probabilities.
    clf = GradientBoostingClassifier(loss='exponential',
                                     n_estimators=100, random_state=1)

    assert_raises(ValueError, clf.predict_proba, T)

    clf.fit(X, y)
    assert_array_equal(clf.predict(T), true_result)

    # check if probabilities are in [0, 1].
    y_proba = clf.predict_proba(T)
    assert_true(np.all(y_proba >= 0.0))
    assert_true(np.all(y_proba <= 1.0))
    score = clf.decision_function(T).ravel()
    assert_array_almost_equal(y_proba[:, 1],
                              1.0 / (1.0 + np.exp(-2 * score)))

    # derive predictions from probabilities
    y_pred = clf.classes_.take(y_proba.argmax(axis=1), axis=0)
    assert_array_equal(y_pred, true_result)