def fit(self,
x: np.ndarray,
y: np.ndarray,
batch_size: int = 128,
nb_epochs: int = 10,
**kwargs):
"""
Fit the classifier on the training set `(x, y)`.
:param x: Training data.
:param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
(nb_samples,).
:param batch_size: Batch size.
:key nb_epochs: Number of epochs to use for training
:param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
and providing it takes no effect.
:type kwargs: `dict`
:return: `None`
"""
RandomizedSmoothingMixin.fit(self,
x,
y,
batch_size=batch_size,
nb_epochs=nb_epochs,
**kwargs)
def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
"""
Perform prediction of the given classifier for a batch of inputs, taking an expectation over transformations.
:param x: Test set.
:param batch_size: Batch size.
:param is_abstain: True if function will abstain from prediction and return 0s. Default: True
:type is_abstain: `boolean`
:return: Array of predictions of shape `(nb_inputs, nb_classes)`.
"""
return RandomizedSmoothingMixin.predict(self, x, batch_size=128, **kwargs)
