pretty_confusion_matrix.py

# Confusion Matrix (improved version with total numbers)
def plot_confusion_matrix(conf_arr, labels, title='Confusion matrix', cmap=plt.cm.Blues):
    #plt.imshow(cm, interpolation='nearest', cmap=cmap)
    norm_conf = []
    for i in conf_arr:
        a = 0
        tmp_arr = []
        a = sum(i, 0)
        for j in i:
            tmp_arr.append(float(j)/float(a))
        norm_conf.append(tmp_arr)

    fig = plt.figure()
    plt.clf()
    ax = fig.add_subplot(111)
    ax.set_aspect(1)
    res = ax.imshow(np.array(norm_conf), cmap=cmap, 
                    interpolation='nearest')

    width = len(conf_arr)
    height = len(conf_arr[0])

    for x in xrange(width):
        for y in xrange(height):
            if conf_arr[x][y] != 0:
                ax.annotate("%.2f" % conf_arr[x][y], xy=(y, x), 
                            horizontalalignment='center',
                            verticalalignment='center')
    plt.title(title)
    plt.colorbar(res)
    tick_marks = np.arange(len(labels))
    plt.xticks(tick_marks, labels, rotation=45)
    plt.yticks(tick_marks, labels)
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

def ConfusionMatrix(y_test, y_pred, labels):
    from sklearn.metrics import confusion_matrix
    # Compute confusion matrix
    folds = 5 # like classification report
    cm = confusion_matrix(y_test, y_pred)

    nObjects = cm.sum()
    misRate = float(nObjects - np.diag(cm).sum()) * 100 / nObjects

    cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    np.set_printoptions(precision=2)
    print('Confusion matrix (Classification Accuracy: %3.2f%%)' % (100 - misRate))
    plot_confusion_matrix(cm_normalized, labels, title='Confusion matrix (Classification Accuracy: %3.2f%%)' % (100 - misRate))

    plt.show()