def nn_pzi(self, classifier, social_features, y, steps, true_labels):
    classifier.fit(social_features, y, epochs=steps, verbose=0)
    theta_i = classifier.predict(social_features)
    eval_model = accuracy_score(true_labels, np.where((theta_i > theta_i.mean()), 1, 0))
    weights = classifier.get_weights()
    return (theta_i, eval_model, weights[0])
def nn_pzi(self, classifier, social_features, y, steps, true_labels):
    classifier.fit(social_features, y, epochs=steps, verbose=0)
    theta_i = classifier.predict(social_features)
    eval_model = accuracy_score(true_labels, np.where((theta_i > theta_i.mean()), 1, 0))
    weights = classifier.get_weights()
    return (theta_i, eval_model, weights[0])