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November 12, 2017 17:37
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Proper confusion matrix
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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') |
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