cm_plot.py

import numpy as np


def plot_confusion_matrix(cm,
                          target_names,
                          title='Confusion matrix',
                          cmap=None,
                          normalize=True):
    """
  given a sklearn confusion matrix (cm), make a nice plot

  Arguments
  ---------
  cm:           confusion matrix from sklearn.metrics.confusion_matrix

  target_names: given classification classes such as [0, 1, 2]
                the class names, for example: ['high', 'medium', 'low']

  title:        the text to display at the top of the matrix

  cmap:         the gradient of the values displayed from matplotlib.pyplot.cm
                see http://matplotlib.org/examples/color/colormaps_reference.html
                plt.get_cmap('jet') or plt.cm.Blues

  normalize:    If False, plot the raw numbers
                If True, plot the proportions

  Usage
  -----
  plot_confusion_matrix(cm           = cm,                  # confusion matrix created by
                                                            # sklearn.metrics.confusion_matrix
                        normalize    = True,                # show proportions
                        target_names = y_labels_vals,       # list of names of the classes
                        title        = best_estimator_name) # title of graph

  Citiation
  ---------
  http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

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

    if cmap is None:
        cmap = plt.get_cmap('Blues')

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

    # plt.figure(figsize=(8, 6))
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()

    if target_names is not None:
        tick_marks = np.arange(len(target_names))
        plt.xticks(tick_marks, target_names)
        plt.yticks(tick_marks, target_names)

    thresh = cm.max() / 1.5 if normalize else cm.max() / 2
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        if normalize:
            plt.text(j, i, "{:0.2f}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")
        else:
            plt.text(j, i, "{:,}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()

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