alibi_detect.od.ae module

class alibi_detect.od.ae.OutlierAE(threshold=None, ae=None, encoder_net=None, decoder_net=None, data_type=None)[source]

Bases: alibi_detect.base.BaseDetector, alibi_detect.base.FitMixin, alibi_detect.base.ThresholdMixin

__init__(threshold=None, ae=None, encoder_net=None, decoder_net=None, data_type=None)[source]

AE-based outlier detector.

Parameters
  • threshold (Optional[float]) – Threshold used for outlier score to determine outliers.

  • ae (Optional[Model]) – A trained tf.keras model if available.

  • encoder_net (Optional[Sequential]) – Layers for the encoder wrapped in a tf.keras.Sequential class if no ‘ae’ is specified.

  • decoder_net (Optional[Sequential]) – Layers for the decoder wrapped in a tf.keras.Sequential class if no ‘ae’ is specified.

  • data_type (Optional[str]) – Optionally specify the data type (tabular, image or time-series). Added to metadata.

Return type

None

feature_score(X_orig, X_recon)[source]

Compute feature level outlier scores.

Parameters
  • X_orig (ndarray) – Batch of original instances.

  • X_recon (ndarray) – Batch of reconstructed instances.

Return type

ndarray

Returns

Feature level outlier scores.

fit(X, loss_fn=tensorflow.keras.losses.MeanSquaredError, optimizer=tensorflow.keras.optimizers.Adam, epochs=20, batch_size=64, verbose=True, log_metric=None, callbacks=None)[source]

Train AE model.

Parameters
  • X – Training batch.

  • 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_type='instance', outlier_perc=100.0, threshold_perc=95.0, batch_size=10000000000)[source]

Update threshold by a value inferred from the percentage of instances considered to be outliers in a sample of the dataset.

Parameters
  • X (ndarray) – Batch of instances.

  • 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.

  • threshold_perc (float) – Percentage of X considered to be normal based on the outlier score.

  • batch_size (int) – Batch size used when making predictions with the autoencoder.

Return type

None

instance_score(fscore, outlier_perc=100.0)[source]

Compute instance level outlier scores.

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]

Predict whether instances are outliers or not.

Parameters
  • X (ndarray) – Batch of instances.

  • 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 autoencoder.

  • return_feature_score (bool) – Whether to return feature level outlier scores.

  • return_instance_score (bool) – Whether to return instance level outlier scores.

Return type

Dict[Dict[str, str], Dict[ndarray, ndarray]]

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.

score(X, outlier_perc=100.0, batch_size=10000000000)[source]

Compute feature and instance level outlier scores.

Parameters
  • X (ndarray) – Batch of instances.

  • 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 autoencoder.

Return type

Tuple[ndarray, ndarray]

Returns

Feature and instance level outlier scores.