# `MOAFeatureImportanceClassifier`

### *class* capymoa.feature.MOAFeatureImportanceClassifier[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/feature/_feature_importance.py#L136)

Bases: [`FeatureImportanceClassifier`](capymoa.feature.FeatureImportanceClassifier.md#capymoa.feature.FeatureImportanceClassifier), [`MOAClassifier`](capymoa.base.MOAClassifier.md#capymoa.base.MOAClassifier)

MOA-backed feature-importance classifier.

Instantiate this class when the underlying learner is a MOA classifier.
Pure Python implementations should subclass
[`FeatureImportanceClassifier`](capymoa.feature.FeatureImportanceClassifier.md#capymoa.feature.FeatureImportanceClassifier) instead.

This wrapper is currently restricted to:

- `HoeffdingTree` learners and subclasses, which are wrapped with
  `FeatureImportanceHoeffdingTree`
- MOA ensembles built from `HoeffdingTree` learners, which are wrapped
  with `FeatureImportanceHoeffdingTreeEnsemble`

If MOA adds other feature-importance learner families in the future, they
will not automatically work through this class. In that case this wrapper
should be refactored to support those learners explicitly.

Accepted `base_learner` inputs:
- CapyMOA `MOAClassifier` instance
- CapyMOA `MOAClassifier` class
- raw MOA learner instance
- raw MOA learner class

#### \_\_init_\_(schema: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema) | [None](https://docs.python.org/3/builtins/constants.html#None) = None, base_learner: [Any](https://docs.python.org/3/library/typing.html#typing.Any) = None, random_seed: [int](https://docs.python.org/3/builtins/functions.html#int) = 1, window_size: [int](https://docs.python.org/3/builtins/functions.html#int) | [None](https://docs.python.org/3/builtins/constants.html#None) = None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/feature/_feature_importance.py#L161)

#### cli_help()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_classifier.py#L111)

#### *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_feature_importances(normalize: [bool](https://docs.python.org/3/builtins/functions.html#bool) = True) → [list](https://docs.python.org/3/builtins/stdtypes.html#list)[[float](https://docs.python.org/3/builtins/functions.html#float)][[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/feature/_feature_importance.py#L222)

Return the current feature importance scores.

#### 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.

#### get_top_k_features(k: [int](https://docs.python.org/3/builtins/functions.html#int), normalize: [bool](https://docs.python.org/3/builtins/functions.html#bool) = True) → [list](https://docs.python.org/3/builtins/stdtypes.html#list)[[int](https://docs.python.org/3/builtins/functions.html#int)][[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/feature/_feature_importance.py#L123)

#### get_windowed_feature_importances() → [list](https://docs.python.org/3/builtins/stdtypes.html#list)[[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)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/feature/_feature_importance.py#L132)

#### 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/base/_classifier.py#L56)

Predict the label of an instance.

The base implementation calls [`predict_proba()`](#capymoa.feature.MOAFeatureImportanceClassifier.predict_proba) and returns the
label with the highest probability.

* **Parameters:**
  **instance** – The instance to predict the label for.
* **Returns:**
  The predicted label or `None` if the classifier is unable
  to make a prediction.

#### predict_proba(instance) → [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)[[tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple)[[Any](https://docs.python.org/3/library/typing.html#typing.Any), ...], [dtype](https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype)[float64]] | [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_classifier.py#L117)

Return probability estimates for each label.

* **Parameters:**
  **instance** – The instance to estimate the probabilities for.
* **Returns:**
  An array of probabilities for each label or `None` if the
  classifier is unable to make a prediction.

#### train(instance: [Any](https://docs.python.org/3/library/typing.html#typing.Any)) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/feature/_feature_importance.py#L218)

Train the classifier with a labeled instance.

* **Parameters:**
  **instance** – The labeled instance to train the classifier with.

#### feature_importances_per_window *: [list](https://docs.python.org/3/builtins/stdtypes.html#list)[[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)*

#### random_seed *: [int](https://docs.python.org/3/builtins/functions.html#int)*

The random seed for reproducibility.

When implementing a classifier ensure random number generators are seeded.

#### schema *: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema)*

The schema representing the instances.