Created
May 6, 2019 19:51
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import itertools | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn import svm, datasets | |
from sklearn.model_selection import train_test_split | |
from sklearn.metrics import confusion_matrix | |
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') / cm.sum(axis=1)[:, np.newaxis] | |
print("Normalized confusion matrix") | |
else: | |
print('Confusion matrix, without normalization') | |
print(cm) | |
plt.imshow(cm, interpolation='nearest', cmap=cmap) | |
plt.title(title) | |
plt.colorbar() | |
tick_marks = np.arange(len(classes)) | |
plt.xticks(tick_marks, classes, rotation=45) | |
plt.yticks(tick_marks, classes) | |
fmt = '.2f' if normalize else 'd' | |
thresh = cm.max() / 2. | |
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): | |
plt.text(j, i, format(cm[i, j], fmt), | |
horizontalalignment="center", | |
color="white" if cm[i, j] > thresh else "black") | |
plt.ylabel('True label') | |
plt.xlabel('Predicted label') | |
plt.tight_layout() | |
# Compute confusion matrix | |
cnf_matrix = confusion_matrix(y_test, y_pred) | |
np.set_printoptions(precision=2) | |
# Plot non-normalized confusion matrix | |
plt.figure() | |
plot_confusion_matrix(cnf_matrix, classes=class_names, | |
title='Confusion matrix, without normalization') | |
# Plot normalized confusion matrix | |
plt.figure() | |
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True, | |
title='Normalized confusion matrix') | |
plt.show() |
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