# `Autoencoder`

### *class* capymoa.anomaly.Autoencoder[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/anomaly/_autoencoder.py#L8)

Bases: [`AnomalyDetector`](capymoa.base.AnomalyDetector.md#capymoa.base.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.](https://arxiv.org/pdf/1907.04233)

Example:

```pycon
>>> from capymoa.anomaly.datasets import TinyBlobs
>>> from capymoa.anomaly import Autoencoder
>>> from capymoa.evaluation import AnomalyDetectionEvaluator
>>> stream = TinyBlobs()
>>> 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: 0.85
```

#### \_\_init_\_(schema=None, hidden_layer=2, learning_rate=0.5, threshold=0.6, random_seed=1)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/anomaly/_autoencoder.py#L40)

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

#### *classmethod* from_params(schema: [Any](https://docs.python.org/3/library/typing.html#typing.Any) = None, params: [dict](https://docs.python.org/3/builtins/stdtypes.html#dict)[[str](https://docs.python.org/3/builtins/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)] | [None](https://docs.python.org/3/builtins/constants.html#None) = None, random_seed: [int](https://docs.python.org/3/builtins/functions.html#int) = 1) → [Any](https://docs.python.org/3/library/typing.html#typing.Any)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_learner_params.py#L170)

Construct an instance from parameters produced by `get_params`.

#### get_params() → [dict](https://docs.python.org/3/builtins/stdtypes.html#dict)[[str](https://docs.python.org/3/builtins/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)][[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_learner_params.py#L163)

Return the hyper-parameters captured from the constructor.

#### predict(instance: [Instance](capymoa.core.Instance.md#capymoa.core.Instance)) → [int](https://docs.python.org/3/builtins/functions.html#int)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/anomaly/_autoencoder.py#L111)

#### score_instance(instance: [Instance](capymoa.core.Instance.md#capymoa.core.Instance)) → [float](https://docs.python.org/3/builtins/functions.html#float)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/anomaly/_autoencoder.py#L117)

Returns the anomaly score for the instance.

A high score is indicative of an anomaly.

* **Parameters:**
  **instance** – The instance for which the anomaly score is calculated.
* **Returns:**
  The anomaly score for the instance.

#### train(instance: [Instance](capymoa.core.Instance.md#capymoa.core.Instance))[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/anomaly/_autoencoder.py#L96)