RotationForest

class RotationForest(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=False, n_jobs=1, random_state=None)[source]

A rotation forest (RotF) vector classifier.

Implementation of the Rotation Forest classifier 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 classifier for transformation based approaches such as ShapeletTransformClassifier, this sktime 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.
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 in transformed_data_ for use in _get_train_probs.
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_instances_int: The number of train cases in the training set.
n_atts_int: The number of attributes in the training set.
transformed_data_list of shape (n_estimators) of ndarray: The transformed training dataset for all classifiers. Only saved when save_transformed_data is True.
estimators_list of shape (n_estimators) of BaseEstimator: The collections of estimators trained in fit.

Notes

For the Java version, see tsml.

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 sklearn.datasets import load_iris
>>> from sklearn.model_selection import train_test_split
>>> from sktime.classification.sklearn import RotationForest
>>>
>>> X, y = load_iris(return_X_y=True, as_frame=True)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y)
>>>
>>> clf = RotationForest(n_estimators=10)
>>> clf.fit(X_train, y_train)
RotationForest(...)
>>> y_pred = clf.predict(X_test)

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.
`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.