def fit(self, x: np.ndarray, y: np.ndarray) -> None:
"""
Train the attack model.
:param x: Input to training process. Includes all features used to train the original model.
:param y: True labels of the features.
"""
# Checks:
if self.single_index_feature and self.attack_feature >= x.shape[1]:
raise ValueError(
"attack_feature must be a valid index to a feature in x")
# get vector of attacked feature
attacked_feature = x[:, self.attack_feature]
if self.single_index_feature:
y_one_hot = float_to_categorical(attacked_feature)
else:
y_one_hot = floats_to_one_hot(attacked_feature)
y_ready = check_and_transform_label_format(
y_one_hot, len(np.unique(attacked_feature)), return_one_hot=True)
# create training set for attack model
normalized_labels = y * self.prediction_normal_factor
x_train = np.concatenate(
(np.delete(x, self.attack_feature, 1), normalized_labels),
axis=1).astype(np.float32)
# train attack model
self.attack_model.fit(x_train, y_ready)
def fit(self, x: np.ndarray) -> None:
"""
Train the attack model.
:param x: Input to training process. Includes all features used to train the original model.
"""
# Checks:
if isinstance(self.attack_feature,
int) and self.attack_feature >= x.shape[1]:
raise ValueError(
"attack_feature must be a valid index to a feature in x")
# get vector of attacked feature
y = x[:, self.attack_feature]
self._values = get_feature_values(y,
isinstance(self.attack_feature, int))
if isinstance(self.attack_feature, int):
y_one_hot = float_to_categorical(y)
else:
y_one_hot = floats_to_one_hot(y)
y_ready = check_and_transform_label_format(y_one_hot,
len(np.unique(y)),
return_one_hot=True)
if y_ready is None:
raise ValueError("None value detected.")
# create training set for attack model
x_train = np.delete(x, self.attack_feature, 1).astype(np.float32)
# train attack model
self.attack_model.fit(x_train, y_ready)
def fit(self, x: np.ndarray) -> None:
"""
Train the attack model.
:param x: Input to training process. Includes all features used to train the original model.
"""
# Checks:
if self.estimator.input_shape[0] != x.shape[1]:
raise ValueError(
"Shape of x does not match input_shape of classifier")
if self.attack_feature >= x.shape[1]:
raise ValueError(
"attack_feature must be a valid index to a feature in x")
# get model's predictions for x
predictions = np.array([
np.argmax(arr) for arr in self.estimator.predict(x)
]).reshape(-1, 1)
# get vector of attacked feature
y = x[:, self.attack_feature]
y_one_hot = float_to_categorical(y)
y_ready = check_and_transform_label_format(y_one_hot,
len(np.unique(y)),
return_one_hot=True)
# create training set for attack model
x_train = np.concatenate(
(np.delete(x, self.attack_feature, 1), predictions),
axis=1).astype(np.float32)
# train attack model
self.attack_model.fit(x_train, y_ready)
def fit(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> None:
"""
Train the attack model.
:param x: Input to training process. Includes all features used to train the original model.
:param y: True labels for x.
"""
# Checks:
if self.estimator.input_shape is not None:
if self.estimator.input_shape[0] != x.shape[1]:
raise ValueError(
"Shape of x does not match input_shape of model")
if isinstance(self.attack_feature,
int) and self.attack_feature >= x.shape[1]:
raise ValueError(
"`attack_feature` must be a valid index to a feature in x")
# get model's predictions for x
if ClassifierMixin in type(self.estimator).__mro__:
predictions = np.array([
np.argmax(arr) for arr in self.estimator.predict(x)
]).reshape(-1, 1)
else: # Regression model
if self.scale_range is not None:
predictions = minmax_scale(self.estimator.predict(x).reshape(
-1, 1),
feature_range=self.scale_range)
if y is not None:
y = minmax_scale(y, feature_range=self.scale_range)
else:
predictions = self.estimator.predict(x).reshape(
-1, 1) * self.prediction_normal_factor
if y is not None:
y = y * self.prediction_normal_factor
# get vector of attacked feature
y_attack = x[:, self.attack_feature]
self._values = get_feature_values(y_attack,
isinstance(self.attack_feature, int))
if isinstance(self.attack_feature, int):
y_one_hot = float_to_categorical(y_attack)
else:
y_one_hot = floats_to_one_hot(y_attack)
y_attack_ready = check_and_transform_label_format(
y_one_hot, len(np.unique(y_attack)), return_one_hot=True)
# create training set for attack model
x_train = np.concatenate(
(np.delete(x, self.attack_feature, 1), predictions),
axis=1).astype(np.float32)
if y is not None:
y = check_and_transform_label_format(y, return_one_hot=True)
x_train = np.concatenate((x_train, y), axis=1)
# train attack model
self.attack_model.fit(x_train, y_attack_ready)
def fit(self, x: np.ndarray, y: np.ndarray) -> None:
"""
Train the attack model.
:param x: Input to training process. Includes all features used to train the original model.
:param y: True labels of the features.
"""
# Checks:
if isinstance(self.attack_feature,
int) and self.attack_feature >= x.shape[1]:
raise ValueError(
"attack_feature must be a valid index to a feature in x")
# get vector of attacked feature
attacked_feature = x[:, self.attack_feature]
self._values = get_feature_values(attacked_feature,
isinstance(self.attack_feature, int))
if isinstance(self.attack_feature, int):
y_one_hot = float_to_categorical(attacked_feature)
else:
y_one_hot = floats_to_one_hot(attacked_feature)
y_ready = check_and_transform_label_format(
y_one_hot, len(np.unique(attacked_feature)), return_one_hot=True)
if y_ready is None:
raise ValueError("None value detected.")
# create training set for attack model
if self.scale_range is not None:
normalized_labels = minmax_scale(y, feature_range=self.scale_range)
else:
normalized_labels = y * self.prediction_normal_factor
normalized_labels = check_and_transform_label_format(
normalized_labels, return_one_hot=True)
x_train = np.concatenate(
(np.delete(x, self.attack_feature, 1), normalized_labels),
axis=1).astype(np.float32)
# train attack model
self.attack_model.fit(x_train, y_ready)
