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'''|___| CONFUSIONMATRIX |___|'''

def plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=None, cmap=plt.cm.Blues):
    """ This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`."""
    if not title:
        if normalize:
            title = 'Normalized confusion matrix'
        else:
            title = 'Confusion matrix, without normalization'

    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)
    # Only use the labels that appear in the data
    classes = classes[unique_labels(y_true, y_pred)]
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    fig, ax = plt.subplots()
    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
    ax.figure.colorbar(im, ax=ax)
    # We want to show all ticks...
    ax.set(xticks=np.arange(cm.shape[1]), 
           yticks=np.arange(cm.shape[0]), xticklabels=classes, yticklabels=classes,  # ... and label them with the respective list entries
           title=title, ylabel='True label', xlabel='Predicted label')

    plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") # Rotate the tick labels and set their alignment.

    
    fmt = '.2f' if normalize else 'd' # Loop over data dimensions and create text annotations.
    thresh = cm.max() / 2.
    for i in range(cm.shape[0]):
        for j in range(cm.shape[1]):
            ax.text(j, i, format(cm[i, j], fmt), ha="center", va="center", color="white" if cm[i, j] > thresh else "black")
    fig.tight_layout()
    return ax


class_names=str(list("0,0","1,0","2,0","3,0","4,0","1,-1","2,-1","3,-1","4,-1","2,-2","3,-2","4,-2","-3,2","3,-3","4,-3","4,-4",
                    "-1,0","-2,0","-3,0","-4,0","-2,1","-3,1","-4,1","-4,2","-4,3"))#set(test_y)))


print(class_names)

np.set_printoptions(precision=2)

pred_y = model.predict_classes(test_x)     #predict on Test Set

#matrix = confusion_matrix(test_y.argmax(axis=1), pred_y.argmax(axis=1))

# Plot non-normalized confusion matrix
plot_confusion_matrix(test_y, pred_y, classes=class_names, title='Confusion matrix, without normalization')
# Plot normalized confusion matrix
plot_confusion_matrix(test_y, pred_y, classes=class_names, normalize=True, title='Normalized confusion matrix')
plt.show()