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@morganmcg1
Created November 29, 2017 17:57
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Plot a confusion, with the option to normalise the values
def plot_confusion_matrix(cm,
classes,
normalize,
title):
'''
PARAMETERS:
- cm: SKL.METRICS confustion matrix
- Classes: the labels used in the classification
- normalize=False : (True will normalise the values, can be useful for large sample with few outliers)
- title : should be set to: 'Confusion matrix'
RETURN:
- Plot of confusion matrix for multi-class classification
'''
import matplotlib.pyplot as plt
import numpy as np
import itertools
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
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]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
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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