alibi_detect.od.seq2seq

Constants

logger

logger: logging.Logger = <Logger alibi_detect.od.seq2seq (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.

OutlierSeq2Seq

Inherits from: BaseDetector, FitMixin, ThresholdMixin, ABC

Constructor

OutlierSeq2Seq(self, n_features: int, seq_len: int, threshold: Union[float, numpy.ndarray] = None, seq2seq: keras.src.models.model.Model = None, threshold_net: keras.src.models.model.Model = None, latent_dim: int = None, output_activation: str = None, beta: float = 1.0) -> None

Name	Type	Default	Description
n_features	int		Number of features in the time series.
seq_len	int		Sequence length fed into the Seq2Seq model.
threshold	Union[float, numpy.ndarray, None]	None	Threshold used for outlier detection. Can be a float or feature-wise array.
seq2seq	Optional[keras.src.models.model.Model]	None	A trained seq2seq model if available.
threshold_net	Optional[keras.src.models.model.Model]	None	Layers for the threshold estimation network wrapped in a tf.keras.Sequential class if no 'seq2seq' is specified.
latent_dim	Optional[int]	None	Latent dimension of the encoder and decoder.
output_activation	Optional[str]	None	Activation used in the Dense output layer of the decoder.
beta	float	1.0	Weight on the threshold estimation loss term.

Methods

feature_score

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

Compute feature level outlier scores.

Name	Type	Default	Description
X_orig	numpy.ndarray		Original time series.
X_recon	numpy.ndarray		Reconstructed time series.
threshold_est	numpy.ndarray		Estimated threshold from the decoder's latent space.

Returns

• Type: numpy.ndarray

fit

fit(X: numpy.ndarray, loss_fn: .tensorflow.keras.losses = <function mean_squared_error at 0x169a64ca0>, 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 = 64, verbose: bool = True, log_metric: Tuple[str, ForwardRef('tf.keras.metrics')] = None, callbacks: .tensorflow.keras.callbacks = None) -> None

Train Seq2Seq model.

Name	Type	Default	Description
X	numpy.ndarray		Univariate or multivariate time series. Shape equals (batch, features) or (batch, sequence length, features).
loss_fn	.tensorflow.keras.losses	<function mean_squared_error at 0x169a64ca0>	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.
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_perc: Union[int, float] = 100.0, threshold_perc: Union[int, float, numpy.ndarray, list] = 95.0, batch_size: int = 10000000000) -> None

Update the outlier threshold by using a sequence of instances from the dataset

of which the fraction of features which are outliers are known. This fraction can be across all features or per feature.

Name	Type	Default	Description
X	numpy.ndarray		Univariate or multivariate time series.
outlier_perc	Union[int, float]	100.0	Percentage of sorted feature level outlier scores used to predict instance level outlier.
threshold_perc	Union[int, float, numpy.ndarray, list]	95.0	Percentage of X considered to be normal based on the outlier score. Overall (float) or feature-wise (array or list).
batch_size	int	10000000000	Batch size used when making predictions with the seq2seq model.

Returns

• Type: None

instance_score

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

Compute instance level outlier scores. instance in this case means the data along the

first axis of the original time series passed to the predictor.

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]]

Compute outlier scores and transform into outlier predictions.

Name	Type	Default	Description
X	numpy.ndarray		Univariate or multivariate time series.
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 seq2seq model.
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		Univariate or multivariate time series.
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 seq2seq model.

Returns

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