alibi_detect.od.vae

Constants

logger

logger: logging.Logger = <Logger alibi_detect.od.vae (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.

OutlierVAE

Inherits from: BaseDetector, FitMixin, ThresholdMixin, ABC

Constructor

OutlierVAE(self, threshold: float = None, score_type: str = 'mse', vae: keras.src.models.model.Model = None, encoder_net: keras.src.models.model.Model = None, decoder_net: keras.src.models.model.Model = None, latent_dim: int = None, samples: int = 10, beta: float = 1.0, data_type: str = None) -> None

Name	Type	Default	Description
threshold	Optional[float]	None	Threshold used for outlier score to determine outliers.
score_type	str	'mse'	Metric used for outlier scores. Either 'mse' (mean squared error) or 'proba' (reconstruction probabilities) supported.
vae	Optional[keras.src.models.model.Model]	None	A trained tf.keras model if available.
encoder_net	Optional[keras.src.models.model.Model]	None	Layers for the encoder wrapped in a tf.keras.Sequential class if no 'vae' is specified.
decoder_net	Optional[keras.src.models.model.Model]	None	Layers for the decoder wrapped in a tf.keras.Sequential class if no 'vae' is specified.
latent_dim	Optional[int]	None	Dimensionality of the latent space.
samples	int	10	Number of samples sampled to evaluate each instance.
beta	float	1.0	Beta parameter for KL-divergence loss term.
data_type	Optional[str]	None	Optionally specify the data type (tabular, image or time-series). Added to metadata.

Methods

feature_score

feature_score(X_orig: numpy.ndarray, X_recon: numpy.ndarray) -> numpy.ndarray

Compute feature level outlier scores.

Name	Type	Default	Description
X_orig	numpy.ndarray		Batch of original instances.
X_recon	numpy.ndarray		Batch of reconstructed instances.

Returns

• Type: numpy.ndarray

fit

fit(X: numpy.ndarray, loss_fn: .tensorflow.keras.losses = <function elbo at 0x28ee8c040>, 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'>, cov_elbo: dict = {'sim': 0.05}, epochs: int = 20, batch_size: int = 64, verbose: bool = True, log_metric: Tuple[str, ForwardRef('tf.keras.metrics')] = None, callbacks: .tensorflow.keras.callbacks = None) -> None

Train VAE model.

Name	Type	Default	Description
X	numpy.ndarray		Training batch.
loss_fn	.tensorflow.keras.losses	<function elbo at 0x28ee8c040>	Loss function used for training.
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.
cov_elbo	dict	{'sim': 0.05}	Dictionary with covariance matrix options in case the elbo loss function is used. Either use the full covariance matrix inferred from X (dict(cov_full=None)), only the variance (dict(cov_diag=None)) or a float representing the same standard deviation for each feature (e.g. dict(sim=.05)).
epochs	int	20	Number of training epochs.
batch_size	int	64	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.

Returns

• Type: None

infer_threshold

infer_threshold(X: numpy.ndarray, outlier_type: str = 'instance', outlier_perc: float = 100.0, threshold_perc: float = 95.0, batch_size: int = 10000000000) -> None

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

outliers in a sample of the dataset.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances.
outlier_type	str	'instance'	Predict outliers at the 'feature' or 'instance' level.
outlier_perc	float	100.0	Percentage of sorted feature level outlier scores used to predict instance level outlier.
threshold_perc	float	95.0	Percentage of X considered to be normal based on the outlier score.
batch_size	int	10000000000	Batch size used when making predictions with the VAE.

Returns

• Type: None

instance_score

instance_score(fscore: numpy.ndarray, outlier_perc: float = 100.0) -> numpy.ndarray

Compute instance level outlier scores.

Name	Type	Default	Description
fscore	numpy.ndarray		Feature level outlier scores.
outlier_perc	float	100.0	Percentage of sorted feature level outlier scores used to predict instance level outlier.

Returns

• Type: numpy.ndarray

predict

predict(X: numpy.ndarray, outlier_type: str = 'instance', outlier_perc: float = 100.0, batch_size: int = 10000000000, return_feature_score: bool = True, return_instance_score: bool = True) -> Dict[Dict[str, str], Dict[numpy.ndarray, numpy.ndarray]]

Predict whether instances are outliers or not.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances.
outlier_type	str	'instance'	Predict outliers at the 'feature' or 'instance' level.
outlier_perc	float	100.0	Percentage of sorted feature level outlier scores used to predict instance level outlier.
batch_size	int	10000000000	Batch size used when making predictions with the VAE.
return_feature_score	bool	True	Whether to return feature level outlier scores.
return_instance_score	bool	True	Whether to return instance level outlier scores.

Returns

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

score

score(X: numpy.ndarray, outlier_perc: float = 100.0, batch_size: int = 10000000000) -> Tuple[numpy.ndarray, numpy.ndarray]

Compute feature and instance level outlier scores.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances.
outlier_perc	float	100.0	Percentage of sorted feature level outlier scores used to predict instance level outlier.
batch_size	int	10000000000	Batch size used when making predictions with the VAE.

Returns

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