ColumnSelect

class ColumnSelect(columns=None, integer_treatment='col', index_treatment='remove')[source]

Column selection transformer.

In transform, subsets X to columns provided as hyper-parameters.

Sequence of columns in Xt=transform(X) is as in columns hyper-parameter. Caveat: this means that transform may change sequence of columns,

even if no columns are removed from X in transform(X).

Parameters:

columnspandas compatible index or index coercible, optional, default = None: columns to which X in transform is to be subset
integer_treatmentstr, optional, one of “col” (default) and “coerce”: determines how integer index columns are treated “col” = subsets by column iloc index, even if columns is not in X.columns “coerce” = coerces to integer pandas.Index and attempts to subset
index_treatmentstr, optional, one of “remove” (default) or “keep”: determines which column are kept in Xt = transform(X, y) “remove” = only indices that appear in both X and columns are present in Xt. “keep” = all indices in columns appear in Xt. If not present in X, NA is filled.

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> from sktime.transformations.subset import ColumnSelect
>>> from sktime.datasets import load_longley
>>> X = load_longley()[1]
>>> transformer =  ColumnSelect(columns=["GNPDEFL", "POP", "FOO"])
>>> X_subset = transformer.fit_transform(X=X)

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.