RotationForestRegressor¶

class RotationForestRegressor(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)[source]¶

A Rotation Forest (RotF) vector regressor.

Implementation of the Rotation Forest regressor described in Rodriguez et al (2013) [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 regressor for transformation based appraoches such as FreshPRINCERegressor, 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 DecisionTreeRegressor using MSE 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:

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.regression.sklearn import RotationForestRegressor
>>> from aeon.testing.data_generation import make_example_2d_numpy_collection
>>> X, y = make_example_2d_numpy_collection(n_cases=10, n_timepoints=12,
...                              regression_target=True, random_state=0)
>>> reg = RotationForestRegressor(n_estimators=10)
>>> reg.fit(X, y)
RotationForestRegressor(n_estimators=10)
>>> reg.predict(X)
array([0.7252543 , 1.50132442, 0.95608366, 1.64399016, 0.42385504,
       0.60639322, 1.01919317, 1.30157483, 1.66017354, 0.2900776 ])

Methods

`fit`(X, y)	Fit a forest of trees on cases (X,y), where y is the target variable.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict for all cases in X.
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

fit_predict

fit(X, y)[source]¶

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 output values.

Returns:

self: Reference to self.

Notes

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

predict(X) → ndarray[source]¶

Predict 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]: Predicted output values.

get_metadata_routing()[source]¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]¶

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.

score(X, y, sample_weight=None)[source]¶

Return the coefficient of determination of the prediction.

The coefficient of determination $R^2$ is defined as $(1 - \frac{u}{v})$, where $u$ is the residual sum of squares ((y_true - y_pred)** 2).sum() and $v$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $R^2$ score of 0.0.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: $R^2$ of self.predict(X) w.r.t. y.

Notes

The $R^2$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(**params)[source]¶

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 = '$UNCHANGED$') → RotationForestRegressor[source]¶

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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.