ConfusionMatrix.py

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


y_true = np.load('Fer2013_True_y.npy')
y_pred = np.load('Fer2013_Predict_y.npy')
print(len(y_true))
print(len(y_pred))
print(accuracy_score(y_true, y_pred))
# print(confusion_matrix(y_true, y_pred))
cm = confusion_matrix(y_true,y_pred)
# cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
title='Confusion matrix'
print(cm)

plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(labels))
plt.xticks(tick_marks, labels, rotation=45)
plt.yticks(tick_marks, labels)
fmt = '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()
plt.show()