plot_cm.py

from textwrap import wrap
import re
import os
import itertools
#import tfplot
import matplotlib
import numpy as np
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt


def plot_confusion_matrix_values(correct_labels, predict_labels, labels, title='Confusion matrix', filename='confusion_matrix', normalize=False):
    cm = confusion_matrix(correct_labels, predict_labels)


def plot_confusion_matrix(cm, labels, title='Confusion matrix', filename = 'confusion_matrix', normalize=False):
    '''
    Parameters:
        correct_labels                  : These are your true classification categories.
        predict_labels                  : These are you predicted classification categories
        labels                          : This is a list of labels which will be used to display the axis labels
        title='Confusion matrix'        : Title for your matrix
        filename = 'confusion_matrix'   : Name for the output summay tensor

    Returns:
        summary: TensorFlow summary

    Other itema to note:
        - Depending on the number of category and the data , you may have to modify the figzie, font sizes etc.
        - Currently, some of the ticks dont line up due to rotations.
    '''

    if normalize:
        cm = cm.astype('float')*10 / cm.sum(axis=1)[:, np.newaxis]
        cm = np.nan_to_num(cm, copy=True)
        cm = cm.astype('int')

    np.set_printoptions(precision=2)
    fig = plt.figure(figsize=(3, 3), dpi=320, facecolor='w', edgecolor='k')
    ax = fig.add_subplot(1, 1, 1)
    im = ax.imshow(cm, cmap='Oranges')

    classes = [re.sub(r'([a-z](?=[A-Z])|[A-Z](?=[A-Z][a-z]))', r'\1 ', x) for x in labels]
    classes = ['\n'.join(wrap(l, 40)) for l in classes]

    tick_marks = np.arange(len(classes))

    ax.set_xlabel('Predicted', fontsize=7)
    ax.set_xticks(tick_marks)
    c = ax.set_xticklabels(classes, fontsize=4, rotation=-90,  ha='center')
    ax.xaxis.set_label_position('bottom')
    ax.xaxis.tick_bottom()

    ax.set_ylabel('True Label', fontsize=7)
    ax.set_yticks(tick_marks)
    ax.set_yticklabels(classes, fontsize=4, va ='center')
    ax.yaxis.set_label_position('left')
    ax.yaxis.tick_left()

    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        ax.text(j, i, format(cm[i, j], 'd') if cm[i,j]!=0 else '.', horizontalalignment="center", fontsize=6, verticalalignment='center', color= "black")
    fig.set_tight_layout(True)

    ImageDirAndName = os.path.join('/Users/tomrink', filename)
    fig.savefig(ImageDirAndName)