FeatureSelection

class FeatureSelection(method='feature-importances', n_columns=None, regressor=None, random_state=None, columns=None)[source]

Select exogenous features.

Transformer to enable tuneable feature selection of exogenous data. The FeatureSelection implements multiple methods to select features (columns). In case X is a pd.Series, then it is just passed through, unless method=”none”, then None is returned in transform().

Parameters:

methodstr, required

The method of how to select the features. Implemented methods are:

“feature-importances”: Use feature_importances_ of the regressor (meta-model) to select n_columns with highest importance values. Requires parameter n_columns.
“random”: Randomly select n_columns features. Requires parameter n_columns.
“columns”: Select features by given names.
“none”: Remove all columns, transform returns None.
“all”: Select all given features.

n_columnsint, optional

Number of features (columns) to select. n_columns must be <= number of X columns. Some methods require n_columns to be given.

regressorsklearn-like regressor, optional, default=None.

Used as meta-model for the method “feature-importances”. The given regressor must have an attribute “feature_importances_”. If None, then a GradientBoostingRegressor(max_depth=5) is used.

random_stateint, RandomState instance or None, default=None

Used to set random_state of the default regressor and to set random.seed() if method=”random”.

columnslist of str

A list of columns to select. If columns is given.

Attributes:

columns_list of str: List of columns that have been selected as features.
regressor_sklearn-like regressor: Fitted regressor (meta-model).
n_columns_: int: Derived from number of features if n_columns is None, then n_columns_ is calculated as int(math.ceil(Z.shape[1] / 2)). So taking half of given features only as default.
feature_importances_dict or None: A dictionary with column name as key and feature imporatnce value as value. The dict is sorted descending on value. This attribute is a dict if method=”feature-importances”, else None.

Examples

>>> from sktime.transformations.feature_selection import FeatureSelection
>>> from sktime.datasets import load_longley
>>> y, X = load_longley()
>>> transformer = FeatureSelection(method="feature-importances", n_columns=3)
>>> Xt = transformer.fit_transform(X, y)

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 transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`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 a dict of parameters values for this object.
`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.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`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.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`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.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.