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import numpy as np
from sklearn.metrics import (accuracy_score, classification_report,
confusion_matrix, f1_score, precision_score,
recall_score, roc_auc_score)
def get_confusionmatrix_fnd(preds, labels):
# label_predicted = np.argmax(preds, axis=1)
label_predicted = preds
print (accuracy_score(labels, label_predicted))
print(classification_report(labels, label_predicted, labels=[0.0, 1.0], target_names=['real', 'fake'],digits=4))
print (confusion_matrix(labels, label_predicted, labels=[0,1]))
def metrics(y_true, y_pred):
metrics = {}
metrics['auc'] = roc_auc_score(y_true, y_pred, average='macro')
y_pred = np.around(np.array(y_pred)).astype(int)
metrics['f1'] = f1_score(y_true, y_pred, average='macro')
metrics['recall'] = recall_score(y_true, y_pred, average='macro')
metrics['precision'] = precision_score(y_true, y_pred, average='macro')
metrics['acc'] = accuracy_score(y_true, y_pred)
return metrics |