speak_lp.py 文件源码

def train_test(X, Y, choice):
    estimators = []
    if choice == 1:
        estimators = build_model_mlp()
    elif choice == 2:
        estimators = build_model_rbm()
    elif choice == 3:
        estimators = build_model_rf()
    elif choice == 4:
        estimators = build_model_svc()
    elif choice == 5:
        estimators = build_model_latent()

    clf = Pipeline(estimators)

    mean_tpr = 0.0
    mean_fpr = np.linspace(0., 1., 30)

    auc_all = []

    num_of_exp = 20
    for i in range(1, num_of_exp+1):
        print "?%d???,%d/%d." % (i, i, num_of_exp)
        x_train, x_test, y_train, y_test = \
            train_test_split(X, Y,
                             test_size=0.2,
                             random_state=np.random.randint(1, 100))
        clf.fit(x_train, y_train)
        y_pred = clf.predict_proba(x_test)[:, 1]
        fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
        mean_tpr += interp(mean_fpr, fpr, tpr)
        mean_tpr[0] = 0.0
        auc_all.append(metrics.roc_auc_score(y_test, y_pred))

    mean_tpr /= num_of_exp

    auc_array = np.array(auc_all)
    auc = auc_array.mean()
    auc_std = auc_array.std()
    mean_tpr[-1] = 1.0
    return mean_tpr, auc, auc_std