def predict(self, X):
n = X.shape[0]
epsilon = np.random.uniform(low=0.0, high=1.0, size=n)
p_hat = self.black_box.predict_proba(X)
grey_box = ProbAccum(p_hat)
S_hat = grey_box.predict_sets(self.alpha_calibrated, epsilon=epsilon)
return S_hat
def predict(self, dataset, X):
n = X.shape[0]
epsilon = np.random.uniform(low=0.0, high=1.0, size=n)
if dataset=='imagenet':
p_hat = X
else:
p_hat = self.black_box.predict_proba(X)
grey_box = ProbAccum(p_hat)
idxs = np.argsort(np.array(self.alpha_calibrated)[:, 0])
alpha_calib = self.alpha_calibrated[idxs[self.gamma]][0]
S_hat = grey_box.predict_sets(alpha_calib, epsilon=epsilon)
return S_hat
def predict(self, X, randomize=True):
prob_y = self.data_model.compute_prob(X)
grey_box = ProbAccum(prob_y)
S = grey_box.predict_sets(self.alpha, randomize=randomize)
return S