def _calculate_auc(classifier, Xt, yt):
w = classifier.coef_
b = classifier.intercept_[0]
lin = np.dot(Xt, w.T) + b
prob = sigmoid(lin)
fpr, tpr, thresholds = roc_curve(yt, prob,
thresholds=np.linspace(prob.min(),prob.max(),1e3))
auc_score = auc(fpr, tpr)
return auc_score, fpr, tpr
def calculate_auc_score(nn, X, y):
probabilities = np.array([nn.activate(row).tolist() for row in X])
fpr, tpr, thresholds = roc_curve(y, probabilities,
thresholds=np.linspace(0,1,1e3))
auc_score = auc(fpr, tpr)
return auc_score, fpr, tpr
