import itertools

import numpy as np

import matplotlib.pyplot as plt

from sklearn.metrics import confusion_matrix

def plot_confusion_matrix(y_true, y_pred, sub, title = "Confusion matrix - 2a",

                          cmap=plt.cm.Blues, save_flg=True):

    y_pred = y_pred.cpu().detach().numpy()

    y_true = y_true.cpu().detach().numpy()

    classes = [str(i) for i in range(4)]

    labels = range(4)

    cm = confusion_matrix(y_true, y_pred, labels=labels, normalize='true')

    plt.figure(figsize=(14, 12))

    plt.imshow(cm, interpolation='nearest', cmap=cmap)

    plt.title(title, fontsize=40)

    plt.colorbar()

    tick_marks = np.arange(len(classes))

    plt.xticks(tick_marks, classes, fontsize=20)

    plt.yticks(tick_marks, classes, fontsize=20)

    # print('Confusion matrix, without normalization')

    thresh = cm.max() / 2.

    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):

        plt.text(j, i, format(cm[i, j], '.2f'),

                 horizontalalignment="center",

                 color="white" if cm[i, j] > thresh else "black",

                 fontsize=30)

    plt.ylabel('True label', fontsize=30)

    plt.xlabel('Predicted label', fontsize=30)

    if save_flg:

        plt.savefig("confusion_matrix" + str(sub) + ".png")

    # plt.show()