def lr_pzi(self, classifier, X_train, X_test, y_train, y_test, steps):
    classifier.fit(X_train, y_train, epochs=steps, verbose=0)
    theta_i = classifier.predict(X_test)
    loss_and_metrics = classifier.evaluate(X_test, y_test)
    print(theta_i[1:10])
    eval_model = accuracy_score(y_test, np.where((theta_i > 0.5), 1, 0))
    print('eval model', eval_model)
    weights = classifier.get_weights()
    return (theta_i, eval_model, loss_and_metrics, weights[0])
def lr_pzi(self, classifier, X_train, X_test, y_train, y_test, steps):
    classifier.fit(X_train, y_train, epochs=steps, verbose=0)
    theta_i = classifier.predict(X_test)
    loss_and_metrics = classifier.evaluate(X_test, y_test)
    print(theta_i[1:10])
    eval_model = accuracy_score(y_test, np.where((theta_i > 0.5), 1, 0))
    print('eval model', eval_model)
    weights = classifier.get_weights()
    return (theta_i, eval_model, loss_and_metrics, weights[0])