import numpy as np

import random

from sklearn.metrics import roc_auc_score, average_precision_score

def f_get_minibatch_set(mb_size_, x_set_, y_, m_):

    idx = range(np.shape(y_)[0])

    idx = random.sample(idx, mb_size_)

    x_set_mb = {}

    for m in range(len(x_set_)):

        x_set_mb[m] = x_set_[m][idx].astype(float)    

    y_mb   = y_[idx].astype(float)

    m_mb  = m_[idx].astype(float)

    return x_set_mb, y_mb, m_mb

def evaluate(true_y_, pred_y_, y_type_):

    if y_type_ == 'categorical':

        acc = np.mean(np.argmax(true_y_, axis=1) == np.argmax(pred_y_, axis=1))

        return acc

    elif y_type_ == 'binary':

        auroc = roc_auc_score(true_y_[:, 1], pred_y_[:,1])

        auprc = average_precision_score(true_y_[:, 1], pred_y_[:,1])

        return auroc, auprc

    elif y_type_ == 'continuous':

        mse   = np.mean((true_y_.reshape([-1]) - pred_y_.reshape([-1]))**2)

        return mse

    else:

        ValueError(print("y_type should be {'categorical', 'binary', 'continuous'}"))