DistanceFeatures

class DistanceFeatures(distance=None, distance_mtype=None, flatten_hierarchy=False)[source]

Use distances to training series as features.

In transform, returns tabular features as follows: for i-th series in X, returns all distances to series seen in fit j th column of i-th row is distance between i-th series in transform, and j-the series in fit. Column index is instance index in fit. If fit series was Hierarchical, hierarchy index is preserved.

Parameters:

distance: sktime pairwise panel transform, str, or callable, optional, default=None: if panel transform, will be used directly as the distance in the algorithm default None = euclidean distance on flattened series, FlatDist(ScipyDist()) if str, will behave as FlatDist(ScipyDist(distance)) = scipy dist on flat series if callable, must be distance_mtype x distance_mtype -> 2D float np.array
distance_mtypestr, or list of str optional. default = None.: mtype that distance expects for X and X2, if a callable only set this if distance is not BasePairwiseTransformerPanel descendant
flatten_hierarchybool, optional, default=False.: whether column hierarchy in transform return is flattened (using __ concat), in case of a hierarchical series index seen in fit.

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> from sktime.datasets import load_unit_test
>>> from sktime.transformations.compose_distance import DistanceFeatures
>>> X_train, _ = load_unit_test(return_X_y=True, split="train")
>>> X, _ = load_unit_test(return_X_y=True, split="test")
>>> trafo = DistanceFeatures()
>>> trafo.fit(X_train)
DistanceFeatures(...)
>>> Xt = trafo.transform(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 skbase object.
`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.