# `RobustRandomCutForest`

### *class* capymoa.anomaly.RobustRandomCutForest[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/anomaly/_robust_random_cut_forest.py#L498)

Bases: [`AnomalyDetector`](capymoa.base.AnomalyDetector.md#capymoa.base.AnomalyDetector)

Robust Random Cut Forest.

Robust Random Cut Forest (RRCF) <sup>[1](#f0)</sup> is an algorithm for anomaly detection in
dynamic data streams. It maintains a random cut-based data structure (the forest)
that acts as a compact sketch or synopsis of the input stream. Anomalies are defined
non-parametrically in terms of the “externality” a new point imposes on the existing
data—that is, how much the new point influences the structure of the forest.

This implementation is adapted from [https://klabum.github.io/rrcf/](https://klabum.github.io/rrcf/)

```pycon
>>> from capymoa.anomaly.datasets import TinyBlobs
>>> from capymoa.anomaly import RobustRandomCutForest
>>> from capymoa.evaluation import AnomalyDetectionEvaluator
```

```pycon
>>> stream = TinyBlobs()
>>> schema = stream.get_schema()
>>> learner = RobustRandomCutForest(schema, tree_size=50, n_trees=10, random_state=42)
>>> 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: 0.97
```

* <a id='f0'>**[1]**</a> Guha, S., Mishra, N., Roy, G., & Schrijvers, O. (2016, June). Robust random cut forest based anomaly detection on streams. In International conference on machine learning (pp. 2712-2721). PMLR.

#### \_\_init_\_(schema: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema), tree_size=1000, n_trees=100, random_state=42)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/anomaly/_robust_random_cut_forest.py#L532)

#### *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) | [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/anomaly/_robust_random_cut_forest.py#L555)

#### 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/_robust_random_cut_forest.py#L558)

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/_robust_random_cut_forest.py#L551)