Proper confusion matrix

confusion_matrix.py

import numpy as np
from sklearn.metrics import confusion_matrix
from matplotlib import pyplot as plt  
import itertools

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
  """
  This function prints and plots the confusion matrix.
  Normalization can be applied by setting `normalize=True`.
  """
  if normalize:
      cm = cm.astype('float') / np.maximum(cm.sum(axis=1)[:, np.newaxis],1.0)
      print("Normalized confusion matrix")
  else:
      print('Confusion matrix, without normalization')

  print(cm)
  
  plt.imshow(cm, interpolation='nearest', cmap=cmap)
  plt.title(title)
  # https://stackoverflow.com/a/26720422/850760
  plt.colorbar(fraction=0.046, pad=0.04)
  tick_marks = np.arange(len(classes))
  plt.xticks(tick_marks, classes, rotation=45)
  plt.yticks(tick_marks, classes)



  thresh = cm.max() / 2.
  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
      plt.text(j, i, "{:.2f}".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')

from sklearn import svm, datasets
from sklearn.model_selection import train_test_split

iris = datasets.load_iris()
X = iris.data
y = iris.target
class_names = iris.target_names

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)


cnf_matrix = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)

plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
                      title='Normalized confusion matrix')

text modifications:
https://matplotlib.org/users/text_props.html