alibi_detect.ad.adversarialae

Constants

logger

logger: logging.Logger = <Logger alibi_detect.ad.adversarialae (WARNING)>

Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting.

AdversarialAE

Inherits from: BaseDetector, FitMixin, ThresholdMixin, ABC

Constructor

AdversarialAE(self, threshold: float = None, ae: keras.src.models.model.Model = None, model: keras.src.models.model.Model = None, encoder_net: keras.src.models.model.Model = None, decoder_net: keras.src.models.model.Model = None, model_hl: List[keras.src.models.model.Model] = None, hidden_layer_kld: dict = None, w_model_hl: list = None, temperature: float = 1.0, data_type: str = None) -> None

Name	Type	Default	Description
threshold	Optional[float]	None	Threshold used for adversarial score to determine adversarial instances.
ae	Optional[keras.src.models.model.Model]	None	A trained tf.keras autoencoder model if available.
model	Optional[keras.src.models.model.Model]	None	A trained tf.keras classification model.
encoder_net	Optional[keras.src.models.model.Model]	None	Layers for the encoder wrapped in a tf.keras.Sequential class if no 'ae' is specified.
decoder_net	Optional[keras.src.models.model.Model]	None	Layers for the decoder wrapped in a tf.keras.Sequential class if no 'ae' is specified.
model_hl	Optional[List[keras.src.models.model.Model]]	None	List with tf.keras models for the hidden layer K-L divergence computation.
hidden_layer_kld	Optional[dict]	None	Dictionary with as keys the hidden layer(s) of the model which are extracted and used during training of the AE, and as values the output dimension for the hidden layer.
w_model_hl	Optional[list]	None	Weights assigned to the loss of each model in model_hl.
temperature	float	1.0	Temperature used for model prediction scaling. Temperature <1 sharpens the prediction probability distribution.
data_type	Optional[str]	None	Optionally specifiy the data type (tabular, image or time-series). Added to metadata.

Methods

correct

correct(X: numpy.ndarray, batch_size: int = 10000000000, return_instance_score: bool = True, return_all_predictions: bool = True) -> Dict[Dict[str, str], Dict[str, numpy.ndarray]]

Correct adversarial instances if the adversarial score is above the threshold.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances.
batch_size	int	10000000000	Batch size used when computing scores.
return_instance_score	bool	True	Whether to return instance level adversarial scores.
return_all_predictions	bool	True	Whether to return the predictions on the original and the reconstructed data.

Returns

• Type: Dict[Dict[str, str], Dict[str, numpy.ndarray]]

fit

fit(X: numpy.ndarray, loss_fn: .tensorflow.keras.losses = <function loss_adv_ae at 0x28ee8c430>, w_model: float = 1.0, w_recon: float = 0.0, optimizer: Union[ForwardRef('tf.keras.optimizers.Optimizer'), ForwardRef('tf.keras.optimizers.legacy.Optimizer'), type[ForwardRef('tf.keras.optimizers.Optimizer')], type[ForwardRef('tf.keras.optimizers.legacy.Optimizer')]] = <class 'keras.src.optimizers.adam.Adam'>, epochs: int = 20, batch_size: int = 128, verbose: bool = True, log_metric: Tuple[str, ForwardRef('tf.keras.metrics')] = None, callbacks: .tensorflow.keras.callbacks = None, preprocess_fn: Callable = None) -> None

Train Adversarial AE model.

Name	Type	Default	Description
X	numpy.ndarray		Training batch.
loss_fn	.tensorflow.keras.losses	<function loss_adv_ae at 0x28ee8c430>	Loss function used for training.
w_model	float	1.0	Weight on model prediction loss term.
w_recon	float	0.0	Weight on MSE reconstruction error loss term.
optimizer	Union[ForwardRef('tf.keras.optimizers.Optimizer'), ForwardRef('tf.keras.optimizers.legacy.Optimizer'), type[ForwardRef('tf.keras.optimizers.Optimizer')], type[ForwardRef('tf.keras.optimizers.legacy.Optimizer')]]	<class 'keras.src.optimizers.adam.Adam'>	Optimizer used for training.
epochs	int	20	Number of training epochs.
batch_size	int	128	Batch size used for training.
verbose	bool	True	Whether to print training progress.
log_metric	Tuple[str, ForwardRef('tf.keras.metrics')]	None	Additional metrics whose progress will be displayed if verbose equals True.
callbacks	.tensorflow.keras.callbacks	None	Callbacks used during training.
preprocess_fn	Callable	None	Preprocessing function applied to each training batch.

Returns

• Type: None

infer_threshold

infer_threshold(X: numpy.ndarray, threshold_perc: float = 99.0, margin: float = 0.0, batch_size: int = 10000000000) -> None

Update threshold by a value inferred from the percentage of instances considered to be

adversarial in a sample of the dataset.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances.
threshold_perc	float	99.0	Percentage of X considered to be normal based on the adversarial score.
margin	float	0.0	Add margin to threshold. Useful if adversarial instances have significantly higher scores and there is no adversarial instance in X.
batch_size	int	10000000000	Batch size used when computing scores.

Returns

• Type: None

predict

predict(X: numpy.ndarray, batch_size: int = 10000000000, return_instance_score: bool = True) -> Dict[Dict[str, str], Dict[str, numpy.ndarray]]

Predict whether instances are adversarial instances or not.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances.
batch_size	int	10000000000	Batch size used when computing scores.
return_instance_score	bool	True	Whether to return instance level adversarial scores.

Returns

• Type: Dict[Dict[str, str], Dict[str, numpy.ndarray]]

score

score(X: numpy.ndarray, batch_size: int = 10000000000, return_predictions: bool = False) -> Union[numpy.ndarray, Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]]

Compute adversarial scores.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances to analyze.
batch_size	int	10000000000	Batch size used when computing scores.
return_predictions	bool	False	Whether to return the predictions of the classifier on the original and reconstructed instances.

Returns

• Type: Union[numpy.ndarray, Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]]

DenseHidden

Inherits from: Model, TensorFlowTrainer, Trainer, Layer, TFLayer, KerasAutoTrackable, AutoTrackable, Trackable, Operation, KerasSaveable

Constructor

DenseHidden(self, model: keras.src.models.model.Model, hidden_layer: int, output_dim: int, hidden_dim: int = None) -> None

Name	Type	Default	Description
model	keras.src.models.model.Model		tf.keras classification model.
hidden_layer	int		Hidden layer from model where feature map is extracted from.
output_dim	int		Output dimension for softmax layer.
hidden_dim	Optional[int]	None	Dimension of optional additional dense layer.

Methods

call

call(x: tensorflow.python.framework.tensor.Tensor) -> tensorflow.python.framework.tensor.Tensor

Name	Type	Default	Description
x	tensorflow.python.framework.tensor.Tensor

Returns

• Type: tensorflow.python.framework.tensor.Tensor