Created
July 6, 2019 20:02
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Handy confusion matrix plotting
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from sklearn.metrics import confusion_matrix | |
from matplotlib import pyplot as plt | |
# Plot 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`. | |
""" | |
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) | |
if normalize: | |
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] | |
cm = np.round(cm,2) | |
print("Normalized confusion matrix") | |
else: | |
print('Modelling: Confusion matrix, without normalization') | |
thresh = cm.max() / 2. | |
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): | |
plt.text(j, i, 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') |
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