alibi_detect.od.seq2seq module
- class alibi_detect.od.seq2seq.OutlierSeq2Seq(n_features, seq_len, threshold=None, seq2seq=None, threshold_net=None, latent_dim=None, output_activation=None, beta=1.0)[source]
Bases:
BaseDetector,FitMixin,ThresholdMixin- __init__(n_features, seq_len, threshold=None, seq2seq=None, threshold_net=None, latent_dim=None, output_activation=None, beta=1.0)[source]
Seq2Seq-based outlier detector.
- Parameters:
n_features (
int) – Number of features in the time series.seq_len (
int) – Sequence length fed into the Seq2Seq model.threshold (
Union[float,ndarray,None]) – Threshold used for outlier detection. Can be a float or feature-wise array.seq2seq (
Optional[Model]) – A trained seq2seq model if available.threshold_net (
Optional[Model]) – Layers for the threshold estimation network wrapped in a tf.keras.Sequential class if no ‘seq2seq’ is specified.latent_dim (
Optional[int]) – Latent dimension of the encoder and decoder.output_activation (
Optional[str]) – Activation used in the Dense output layer of the decoder.beta (
float) – Weight on the threshold estimation loss term.
- feature_score(X_orig, X_recon, threshold_est)[source]
Compute feature level outlier scores.
- Parameters:
X_orig (
ndarray) – Original time series.X_recon (
ndarray) – Reconstructed time series.threshold_est (
ndarray) – Estimated threshold from the decoder’s latent space.
- Return type:
ndarray- Returns:
Feature level outlier scores. Scores above 0 are outliers.
- fit(X, loss_fn=tensorflow.keras.losses.mse, optimizer=tensorflow.keras.optimizers.Adam, epochs=20, batch_size=64, verbose=True, log_metric=None, callbacks=None)[source]
Train Seq2Seq model.
- Parameters:
X – Univariate or multivariate time series. Shape equals (batch, features) or (batch, sequence length, features).
loss_fn – Loss function used for training.
optimizer – Optimizer used for training.
epochs – Number of training epochs.
batch_size – Batch size used for training.
verbose – Whether to print training progress.
log_metric – Additional metrics whose progress will be displayed if verbose equals True.
callbacks – Callbacks used during training.
- infer_threshold(X, outlier_perc=100.0, threshold_perc=95.0, batch_size=10000000000)[source]
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.
- Parameters:
X (
ndarray) – Univariate or multivariate time series.outlier_perc (
Union[int,float]) – Percentage of sorted feature level outlier scores used to predict instance level outlier.threshold_perc (
Union[int,float,ndarray,list]) – Percentage of X considered to be normal based on the outlier score. Overall (float) or feature-wise (array or list).batch_size (
int) – Batch size used when making predictions with the seq2seq model.
- Return type:
- instance_score(fscore, outlier_perc=100.0)[source]
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.
- Parameters:
fscore (
ndarray) – Feature level outlier scores.outlier_perc (
float) – Percentage of sorted feature level outlier scores used to predict instance level outlier.
- Return type:
ndarray- Returns:
Instance level outlier scores.
- predict(X, outlier_type='instance', outlier_perc=100.0, batch_size=10000000000, return_feature_score=True, return_instance_score=True)[source]
Compute outlier scores and transform into outlier predictions.
- Parameters:
X (
ndarray) – Univariate or multivariate time series.outlier_type (
str) – Predict outliers at the ‘feature’ or ‘instance’ level.outlier_perc (
float) – Percentage of sorted feature level outlier scores used to predict instance level outlier.batch_size (
int) – Batch size used when making predictions with the seq2seq model.return_feature_score (
bool) – Whether to return feature level outlier scores.return_instance_score (
bool) – Whether to return instance level outlier scores.
- Return type:
- Returns:
Dictionary containing
'meta'and'data'dictionaries. –'meta'has the model’s metadata.'data'contains the outlier predictions and both feature and instance level outlier scores.