plotcm.py

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


def plot_confusion_matrix(
        cm, classes, normalize=False,
        title='Confusion matrix', cmap=plt.cm.Blues):

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print('Normalized confusion matrix')
    else:
        print('Confusion matrix, without normalization')

    plt.subplots(figsize=(10, 10))

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2
    for i, j in product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(
            j, i, format(cm[i, j], fmt),
            horizontalalignment='center',
            color='white' if cm[i, j] > thresh else 'black'
        )

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