Autoencoder

class capymoa.anomaly.Autoencoder

Bases: AnomalyDetector

Autoencoder anomaly detector

This is a simple autoencoder anomaly detector that uses a single hidden layer.

Reference:

Contextual One-Class Classification in Data Streams. Richard Hugh Moulton, Herna L. Viktor, Nathalie Japkowicz, and João Gama. arXiv:1907.04233, 2019.

Example:

>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.anomaly import Autoencoder
>>> from capymoa.evaluation import AnomalyDetectionEvaluator
>>> stream = ElectricityTiny()
>>> schema = stream.get_schema()
>>> learner = Autoencoder(schema=schema)
>>> evaluator = AnomalyDetectionEvaluator(schema)
>>> while stream.has_more_instances():
...     instance = stream.next_instance()
...     proba = learner.score_instance(instance)
...     evaluator.update(instance.y_index, proba)
...     learner.train(instance)
>>> auc = evaluator.auc()
>>> print(f"AUC: {auc:.2f}")
AUC: 0.42

__init__(

schema=None,

hidden_layer=2,

learning_rate=0.5,

threshold=0.6,

random_seed=1,

)

Construct an Autoencoder anomaly detector

Parameters :param schema: The schema of the input data :param hidden_layer: Number of neurons in the hidden layer. The number should less than the number of input features. :param learning_rate: Learning rate :param threshold: Anomaly threshold :param random_seed: Random seed

train(instance: Instance)

predict(instance: Instance) → int

score_instance(instance: Instance) → float64