def display_metrics(classifier_list: list, test_labels_all, pred: list, proba: list, label_list: list):
"""
Function that display some metrics in regard of the training results
:arg
self (Trainer): instance of the class
classifier_list (list): List with selected classifier names
test_labels_all (numpy array): Numpy array containing target values of the testing set
pred (list): list of the prediction. Each index of the list contains a numpy array
proba (list): list of the probability. Each index of the list contains a numpy array
label_list (list): list of the name of pathologies
:return
None
"""
metrics = Metrics()
plt.figure()
plt.title('ROC Curve')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
for i, value in enumerate(proba):
fpr, tpr = metrics.roc_metrics(test_labels_all, value)
plt.plot(fpr, tpr, label=classifier_list[i])
if len(classifier_list) > 1:
mean_proba = np.dstack(proba)
mean_proba = np.mean(mean_proba, axis=2)
fpr, tpr = metrics.roc_metrics(test_labels_all, mean_proba)
plt.plot(fpr, tpr, label='Voting classifiers')
plt.legend(loc='lower right')
plt.show(block=False)
plt.figure()
plt.title('Precision-Recall Curve')
plt.xlabel('Recall')
plt.ylabel('Precision')
for i, value in enumerate(proba):
precision, recall = metrics.precision_recall(test_labels_all, value)
plt.plot(precision, recall, label=classifier_list[i])
if len(classifier_list) > 1:
mean_proba = np.dstack(proba)
mean_proba = np.mean(mean_proba, axis=2)
fpr, tpr = metrics.precision_recall(test_labels_all, mean_proba)
plt.plot(fpr, tpr, label='Voting classifiers')
plt.legend(loc='lower left')
plt.show(block=False)
if len(classifier_list) > 1:
mean_pred = np.dstack(pred)
mean_pred = np.mean(mean_pred, axis=2)
mean_pred[mean_pred >= 0.5] = 1
mean_pred[mean_pred < 0.5] = 0
pred = mean_pred
else:
pred = pred[0]
cohen_kappa_score, kappa_class = metrics.cohen_kappa_score(test_labels_all, pred)
f1_score, f1_class = metrics.f1_score(test_labels_all, pred)
accuracy, accuracy_class = metrics.accuracy(test_labels_all, pred)
precision, precision_class = metrics.precision(test_labels_all, pred)
recall, recall_class = metrics.recall(test_labels_all, pred)
#
print('Cohen: {}'.format(cohen_kappa_score))
print('F1: {}'.format(f1_score))
print('Accuracy: {}'.format(accuracy))
print('Precision: {}'.format(precision))
print('Recall: {}'.format(recall))
titles = ['names', 'Cohen', 'F1_score', 'Accuracy', 'Precision', 'Recall']
kappa_class_disp = ['%.4f' % elem for elem in kappa_class]
f1_class_disp = ['%.4f' % elem for elem in f1_class]
accuracy_class_disp = ['%.4f' % elem for elem in accuracy_class]
precision_class_disp = ['%.4f' % elem for elem in precision_class]
recall_class_disp = ['%.4f' % elem for elem in recall_class]
element = [titles] + list(
zip(label_list, kappa_class_disp, f1_class_disp, accuracy_class_disp, precision_class_disp,
recall_class_disp))
for i, d in enumerate(element):
line = '|'.join(str(x).ljust(19) for x in d)
print(line)
if i == 0:
print('-' * (len(line)-10))
