def predict(self, X: np.ndarray, batch_size: int = 64, return_instance_score: bool = True) \
-> Dict[Dict[str, str], Dict[str, np.ndarray]]:
"""
Predict whether instances are adversarial instances or not.
Parameters
----------
X
Batch of instances.
batch_size
Batch size used when computing scores.
return_instance_score
Whether to return instance level adversarial scores.
Returns
-------
Dictionary containing 'meta' and 'data' dictionaries.
'meta' has the model's metadata.
'data' contains the adversarial predictions and instance level adversarial scores.
"""
adv_score = self.score(X, batch_size=batch_size)
# values above threshold are adversarial
adv_pred = (adv_score > self.threshold).astype(int) # type: ignore
# populate output dict
ad = adversarial_prediction_dict()
ad['meta'] = self.meta
ad['data']['is_adversarial'] = adv_pred
if return_instance_score:
ad['data']['instance_score'] = adv_score
return ad
def predict(
self,
X: np.ndarray,
batch_size: int = int(1e10),
return_instance_score: bool = True,
) -> Dict[Dict[str, str], Dict[str, np.ndarray]]:
assert return_instance_score == self.expected_return_instance_score
ad = adversarial_prediction_dict()
ad["data"]["is_adversarial"] = self.expected_is_adversarial
return ad
