RotationForestRegressor¶

class RotationForestRegressor(n_estimators=200, min_group=3, max_group=3, remove_proportion=0.5, base_estimator=None, time_limit_in_minutes=0.0, contract_max_n_estimators=500, save_transformed_data='deprecated', n_jobs=1, random_state=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.
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.
save_transformed_databool, default=False: Save the data transformed in fit. Deprecated and will be removed in v0.10.0. Use fit_predict to generate train estimates instead. transformed_data_ will also be removed.
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.utils.data_gen import make_example_2d_numpy
>>> X, y = make_example_2d_numpy(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.
`set_params`(**params)	Set the parameters of this estimator.

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.

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.