test_logistic.py 文件源码

def test_logreg_predict_proba_multinomial():
    X, y = make_classification(n_samples=10, n_features=20, random_state=0,
                               n_classes=3, n_informative=10)

    # Predicted probabilites using the true-entropy loss should give a
    # smaller loss than those using the ovr method.
    clf_multi = LogisticRegression(multi_class="multinomial", solver="lbfgs")
    clf_multi.fit(X, y)
    clf_multi_loss = log_loss(y, clf_multi.predict_proba(X))
    clf_ovr = LogisticRegression(multi_class="ovr", solver="lbfgs")
    clf_ovr.fit(X, y)
    clf_ovr_loss = log_loss(y, clf_ovr.predict_proba(X))
    assert_greater(clf_ovr_loss, clf_multi_loss)

    # Predicted probabilites using the soft-max function should give a
    # smaller loss than those using the logistic function.
    clf_multi_loss = log_loss(y, clf_multi.predict_proba(X))
    clf_wrong_loss = log_loss(y, clf_multi._predict_proba_lr(X))
    assert_greater(clf_wrong_loss, clf_multi_loss)