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Example-2 (How to plot via matplotlib)

⚠️ This example is deprecated. You can use plot method from version 3.0

Environment check

Checking that the notebook is running on Google Colab or not.

import sys
try:
  import google.colab
  !{sys.executable} -m pip -q -q install pycm
except:
  pass

Install matplotlib

!{sys.executable} -m pip -q -q install matplotlib;

Keyring is skipped due to an exception: invalid syntax (core.py, line 48)

Plotting

import numpy as np
import matplotlib.pyplot as plt
import itertools
from pycm import ConfusionMatrix

def plot_confusion_matrix(cm,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function modified to plots the ConfusionMatrix object.
    Normalization can be applied by setting `normalize=True`.
    
    Code Reference : 
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
    
    """

    plt_cm = []
    for i in cm.classes :
        row=[]
        for j in cm.classes:
            row.append(cm.table[i][j])
        plt_cm.append(row)
    plt_cm = np.array(plt_cm)
    if normalize:
        plt_cm = plt_cm.astype('float') / plt_cm.sum(axis=1)[:, np.newaxis]     
    plt.imshow(plt_cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(cm.classes))
    plt.xticks(tick_marks, cm.classes, rotation=45)
    plt.yticks(tick_marks, cm.classes)

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

    plt.tight_layout()
    plt.ylabel('Actual')
    plt.xlabel('Predict')

cm = ConfusionMatrix(matrix={0: {0:13, 1:0, 2:0}, 1:{0:0, 1:10, 2:6}, 2: {0:0, 1:0, 2:9}})

plt.figure()
plot_confusion_matrix(cm,title='cm')
plt.figure()
plot_confusion_matrix(cm,title='cm(Normalized)', normalize=True)
plt.show()