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'''|___| CONFUSIONMATRIX |___|'''
def plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=None, cmap=plt.cm.Blues):
""" This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`."""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
# Compute confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Only use the labels that appear in the data
classes = classes[unique_labels(y_true, y_pred)]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]), xticklabels=classes, yticklabels=classes, # ... and label them with the respective list entries
title=title, ylabel='True label', xlabel='Predicted label')
plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") # Rotate the tick labels and set their alignment.
fmt = '.2f' if normalize else 'd' # Loop over data dimensions and create text annotations.
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt), ha="center", va="center", color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
return ax
class_names=str(list("0,0","1,0","2,0","3,0","4,0","1,-1","2,-1","3,-1","4,-1","2,-2","3,-2","4,-2","-3,2","3,-3","4,-3","4,-4",
"-1,0","-2,0","-3,0","-4,0","-2,1","-3,1","-4,1","-4,2","-4,3"))#set(test_y)))
print(class_names)
np.set_printoptions(precision=2)
pred_y = model.predict_classes(test_x) #predict on Test Set
#matrix = confusion_matrix(test_y.argmax(axis=1), pred_y.argmax(axis=1))
# Plot non-normalized confusion matrix
plot_confusion_matrix(test_y, pred_y, classes=class_names, title='Confusion matrix, without normalization')
# Plot normalized confusion matrix
plot_confusion_matrix(test_y, pred_y, classes=class_names, normalize=True, title='Normalized confusion matrix')
plt.show()