xgb_tf_profile.py 文件源码

def run_model(model):
    '''Train model'''
    # Call global variables
    x_train, x_test, y_train, y_test = X_TRAIN, X_TEST, Y_TRAIN, Y_TEST

    model.fit(x_train, y_train)
    # make predictions for test data
    y_pred = model.predict(x_test)

    # Accuracy
    acc = metrics.accuracy_score(y_test, y_pred)
    print('Accuracy: %.2f%%' % (acc * 100.0))

    # F1_score
    # f1_score = metrics.f1_score(y_test, y_pred)
    # print("F1_score: %.2f%%" % (f1_score * 100.0))

    # AUC of ROC
    fpr, tpr, _ = metrics.roc_curve(y_test, y_pred)
    auc = metrics.auc(fpr, tpr)
    print('AUC: %.3f' % (auc))

    # Logs for each fold
    crossvalidation_acc.append(acc)
    crossvalidation_auc.append(auc)

    if ARGS.m:
        cnf_matrix = confusion_matrix(y_test, y_pred)
        print(cnf_matrix)
        np.set_printoptions(precision=2)
        if ARGS.t == '2':
            classes = np.asarray(['Spiced', 'Non-spliced'])
            plot_confusion_matrix(cnf_matrix, classes=classes, normalize=True)
        elif ARGS.t == '3':
            classes = np.asarray(['Low', 'Medium', 'High'])
            plot_confusion_matrix(cnf_matrix, classes=classes, normalize=True)
        plt.show()
    if ARGS.f:
        feature_selection(imp=IMP, model=model)

    print()