import numpy as np
import itertools
import matplotlib.pyplot as plt


def history_save(history, save_path, metrics_name='accuracy'):
    acc = history.history[metrics_name]
    val_acc = history.history['val_{0}'.format(metrics_name)]

    loss = history.history['loss']
    val_loss = history.history['val_loss']

    plt.figure(figsize=(8, 8))
    plt.subplot(2, 1, 1)
    plt.plot(acc, label='Training Accuracy')
    plt.plot(val_acc, label='Validation Accuracy')
    plt.legend(loc='lower right')
    plt.ylabel('Accuracy')
    plt.ylim([min(plt.ylim()), 1])
    plt.title('Training and Validation Accuracy')

    plt.subplot(2, 1, 2)
    plt.plot(loss, label='Training Loss')
    plt.plot(val_loss, label='Validation Loss')
    plt.legend(loc='upper right')
    plt.ylabel('Cross Entropy')
    plt.ylim([0, 1.0])
    plt.title('Training and Validation Loss')
    plt.xlabel('epoch')
    # plt.show()
    plt.savefig(save_path)


def plot_confusion_matrix(cm, class_names, save_path):
    """
    Returns a matplotlib figure containing the plotted confusion matrix.

    Args:
        cm (array, shape = [n, n]): a confusion matrix of integer classes
        class_names (array, shape = [n]): String names of the integer classes
        save_path (str): figure save path
    """
    figure = plt.figure(figsize=(8, 8))
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
    plt.title("Confusion matrix")
    plt.colorbar()
    tick_marks = np.arange(len(class_names))
    plt.xticks(tick_marks, class_names, rotation=45)
    plt.yticks(tick_marks, class_names)

    # Compute the labels from the normalized confusion matrix.
    labels = np.around(cm.astype('float') / cm.sum(axis=1)[:, np.newaxis], decimals=2)
    # labels = cm.astype('int')

    # Use white text if squares are dark; otherwise black.
    threshold = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        color = "white" if cm[i, j] > threshold else "black"
        plt.text(j, i, labels[i, j], horizontalalignment="center", color=color)

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.savefig(save_path)