ColumnEnsembleClassifier

class ColumnEnsembleClassifier(estimators, remainder='drop', verbose=False)[source]

Applies estimators to columns of an array or pandas DataFrame.

This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each transformer will be ensembled to form a single output.

Parameters:

estimatorslist of tuples

List of (name, estimator, column(s)) tuples specifying the transformer objects to be applied to subsets of the data.

namestring: Like in Pipeline and FeatureUnion, this allows the transformer and its parameters to be set using set_params and searched in grid search.
estimatoror {‘drop’}: Estimator must support fit and predict_proba. Special-cased strings ‘drop’ and ‘passthrough’ are accepted as well, to indicate to drop the columns.

column(s) : array-like of string or int, slice, boolean mask array or callable.

remainder{‘drop’, ‘passthrough’} or estimator, default ‘drop’

By default, only the specified columns in transformations are transformed and combined in the output, and the non-specified columns are dropped. (default of 'drop'). By specifying remainder='passthrough', all remaining columns that were not specified in transformations will be automatically passed through. This subset of columns is concatenated with the output of the transformations. By setting remainder to be an estimator, the remaining non-specified columns will use the remainder estimator. The estimator must support fit and transform.

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> from sktime.classification.dictionary_based import ContractableBOSS
>>> from sktime.classification.interval_based import CanonicalIntervalForest
>>> from sktime.datasets import load_basic_motions
>>> X_train, y_train = load_basic_motions(split="train")
>>> X_test, y_test = load_basic_motions(split="test")
>>> cboss = ContractableBOSS(
...     n_parameter_samples=4, max_ensemble_size=2, random_state=0
... )
>>> cif = CanonicalIntervalForest(
...     n_estimators=2, n_intervals=4, att_subsample_size=4, random_state=0
... )
>>> estimators = [("cBOSS", cboss, 5), ("CIF", cif, [3, 4])]
>>> col_ens = ColumnEnsembleClassifier(estimators=estimators)
>>> col_ens.fit(X_train, y_train)
ColumnEnsembleClassifier(...)
>>> y_pred = col_ens.predict(X_test)

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X, y)	Fit time series classifier to training data.
`fit_predict`(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
`fit_predict_proba`(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get parameters of estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composite.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts labels for sequences in X.
`predict_proba`(X)	Predicts labels probabilities for sequences in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y)	Scores predicted labels against ground truth labels on X.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**kwargs)	Set the parameters of estimator.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.