RotationForestClassifier

class RotationForestClassifier(n_estimators: int = 200, min_group: int = 3, max_group: int = 3, remove_proportion: float = 0.5, base_estimator: BaseEstimator | None = None, pca_solver: str = 'auto', time_limit_in_minutes: float = 0.0, contract_max_n_estimators: int = 500, n_jobs: int = 1, random_state: int | RandomState | None = None)

Bases: ClassifierMixin, BaseEstimator

A rotation forest (RotF) vector classifier.

Implementation of the Rotation Forest classifier described [1]. Builds a forest of trees build on random portions of the data transformed using PCA.

Intended as a benchmark for time series data and a base classifier for transformation based approaches such as ShapeletTransformClassifier, this aeon implementation only works with continuous attributes.

Parameters:

n_estimatorsint, default=200

Number of estimators to build for the ensemble.

min_groupint, default=3

The minimum size of an attribute subsample group.

max_groupint, default=3

The maximum size of an attribute subsample group.

remove_proportionfloat, default=0.5

The proportion of cases to be removed per group.

base_estimatorBaseEstimator or None, default=”None”

Base estimator for the ensemble. By default, uses the sklearn DecisionTreeClassifier using entropy as a splitting measure.

pca_solverstr, default=”auto”

Solver to use for the PCA svd_solver parameter. See the scikit-learn PCA implementation for options.

time_limit_in_minutesint, default=0

Time contract to limit build time in minutes, overriding n_estimators. Default of 0 means n_estimators is used.

contract_max_n_estimatorsint, default=500

Max number of estimators to build when time_limit_in_minutes is set.

n_jobsint, default=1

The number of jobs to run in parallel for both fit and predict. -1 means using all processors.

random_stateint, RandomState instance or None, default=None

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes:

classes_list

The unique class labels in the training set.

n_classes_int

The number of unique classes in the training set.

n_cases_int

The number of train cases in the training set.

n_atts_int

The number of attributes in the training set.

estimators_list of shape (n_estimators) of BaseEstimator

The collections of estimators trained in fit.

References

[1]

Rodriguez, Juan José, Ludmila I. Kuncheva, and Carlos J. Alonso. “Rotation forest: A new classifier ensemble method.” IEEE transactions on pattern analysis and machine intelligence 28.10 (2006).

[2]

Bagnall, A., et al. “Is rotation forest the best classifier for problems with continuous features?.” arXiv preprint arXiv:1809.06705 (2018).

Examples

>>> from aeon.classification.sklearn import RotationForestClassifier
>>> from aeon.testing.data_generation import make_example_2d_numpy_collection
>>> X, y = make_example_2d_numpy_collection(
...         n_cases=10, n_timepoints=12, random_state=0)
>>> clf = RotationForestClassifier(n_estimators=10)
>>> clf.fit(X, y)
RotationForestClassifier(n_estimators=10)
>>> clf.predict(X)
array([0, 1, 0, 1, 0, 0, 1, 1, 1, 0])

Methods

fit(X, y)	Fit a forest of trees on cases (X,y), where y is the target variable.
fit_predict(X, y)	Fit a forest of trees and estimate predictions of the input.
fit_predict_proba(X, y)	Fit a forest of trees and estimate probabilities of the input.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict for all cases in X.
predict_proba(X)	Probability estimates for each class for all cases in X.
score(X, y[, sample_weight])	Return accuracy on provided data and labels.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[, sample_weight])	Configure whether metadata should be requested to be passed to the score method.

fit(X, y)

Fit a forest of trees on cases (X,y), where y is the target variable.

Parameters:

X2d ndarray or DataFrame of shape = [n_cases, n_attributes]

The training data.

yarray-like, shape = [n_cases]

The class labels.

Returns:

self

Reference to self.

Notes

Changes state by creating a fitted model that updates attributes ending in “_”.

fit_predict(X, y) → ndarray

Fit a forest of trees and estimate predictions of the input.

fit_predict produces prediction estimates using just the train data. The output is found using out-of-bag (OOB) estimates from the forest.

Parameters:

X2d ndarray or DataFrame of shape = [n_cases, n_attributes]

The training data.

yarray-like, shape = [n_cases]

The class labels.

Returns:

yarray-like, shape = [n_cases]

Predicted class labels.

Notes

Changes state by creating a fitted model that updates attributes ending in “_”.

fit_predict_proba(X, y) → ndarray

Fit a forest of trees and estimate probabilities of the input.

fit_predict produces prediction probability estimates using just the train data. The output is found using out-of-bag (OOB) estimates from the forest.

Parameters:

X2d ndarray or DataFrame of shape = [n_cases, n_attributes]

The training data.

yarray-like, shape = [n_cases]

The class labels.

Returns:

yarray-like, shape = [n_cases, n_classes_]

Predicted probabilities using the ordering in classes_.

Notes

Changes state by creating a fitted model that updates attributes ending in “_”.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

predict(X) → ndarray

Predict for all cases in X. Built on top of predict_proba.

Parameters:

X2d ndarray or DataFrame of shape = [n_cases, n_attributes]

The data to make predictions for.

Returns:

yarray-like, shape = [n_cases]

Predicted class labels.

predict_proba(X) → ndarray

Probability estimates for each class for all cases in X.

Parameters:

X2d ndarray or DataFrame of shape = [n_cases, n_attributes]

The data to make predictions for.

Returns:

yarray-like, shape = [n_cases, n_classes_]

Predicted probabilities using the ordering in classes_.

score(X, y, sample_weight=None)

Return accuracy on provided data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy of self.predict(X) w.r.t. y.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → RotationForestClassifier

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:

selfobject

The updated object.