alibi_detect.od.vaegmm

Constants

logger

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

OutlierVAEGMM

Inherits from: BaseDetector, FitMixin, ThresholdMixin, ABC

Constructor

OutlierVAEGMM(self, threshold: float = None, vaegmm: keras.src.models.model.Model = None, encoder_net: keras.src.models.model.Model = None, decoder_net: keras.src.models.model.Model = None, gmm_density_net: keras.src.models.model.Model = None, n_gmm: int = None, latent_dim: int = None, samples: int = 10, beta: float = 1.0, recon_features: Callable = <function eucl_cosim_features at 0x280c66430>, data_type: str = None) -> None

Name	Type	Default	Description
threshold	Optional[float]	None	Threshold used for outlier score to determine outliers.
vaegmm	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 'vaegmm' is specified.
decoder_net	Optional[keras.src.models.model.Model]	None	Layers for the decoder wrapped in a tf.keras.Sequential class if no 'vaegmm' is specified.
gmm_density_net	Optional[keras.src.models.model.Model]	None	Layers for the GMM network wrapped in a tf.keras.Sequential class.
n_gmm	Optional[int]	None	Number of components in GMM.
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.
recon_features	Callable	<function eucl_cosim_features at 0x280c66430>	Function to extract features from the reconstructed instance by the decoder.
data_type	Optional[str]	None	Optionally specifiy the data type (tabular, image or time-series). Added to metadata.

Methods

fit

fit(X: numpy.ndarray, loss_fn: .tensorflow.keras.losses = <function loss_vaegmm at 0x28ee8c3a0>, w_recon: float = 1e-07, w_energy: float = 0.1, w_cov_diag: float = 0.005, 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 VAEGMM model.

Name	Type	Default	Description
X	numpy.ndarray		Training batch.
loss_fn	.tensorflow.keras.losses	<function loss_vaegmm at 0x28ee8c3a0>	Loss function used for training.
w_recon	float	1e-07	Weight on elbo loss term if default loss_vaegmm.
w_energy	float	0.1	Weight on sample energy loss term if default loss_vaegmm loss fn is used.
w_cov_diag	float	0.005	Weight on covariance regularizing loss term if default loss_vaegmm loss fn is used.
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, 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.
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 VAEGMM.

Returns

• Type: None

predict

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

Compute outlier scores and transform into outlier predictions.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances.
batch_size	int	10000000000	Batch size used when making predictions with the VAEGMM.
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, batch_size: int = 10000000000) -> numpy.ndarray

Compute outlier scores.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances to analyze.
batch_size	int	10000000000	Batch size used when making predictions with the VAEGMM.

Returns

• Type: numpy.ndarray