PwTrafoPanelPipeline

class PwTrafoPanelPipeline(pw_trafo, transformers)[source]

Pipeline of transformers and a pairwise panel transformer.

PwTrafoPanelPipeline chains transformers and a pairwise transformer at the end. The pipeline is constructed with a list of sktime transformers (BaseTransformer),

plus a pairwise panel transformer, following BasePairwiseTransformerPanel.

The transformer list can be unnamed - a simple list of transformers -: or string named - a list of pairs of string, estimator.

For a list of transformers trafo1, trafo2, …, trafoN and an estimator est,

the pipeline behaves as follows:

transform(X) - running trafo1.fit_transform on X,

them trafo2.fit_transform on the output of trafo1.fit_transform, etc sequentially, with trafo[i] receiving the output of trafo[i-1]. Then passes output of trafo[N] to pw_trafo.transform, as X. Same chain of transformers is run on X2 and passed, if not None.

PwTrafoPanelPipeline can also be created by using the magic multiplication

on any parameter estimator: if pw_t is BasePairwiseTransformerPanel,: and my_trafo1, my_trafo2 inherit from BaseTransformer, then, for instance, my_trafo1 * my_trafo2 * pw_t will result in the same object as obtained from the constructor PwTrafoPanelPipeline(pw_trafo=pw_t, transformers=[my_trafo1, my_trafo2])
magic multiplication can also be used with (str, transformer) pairs,: as long as one element in the chain is a transformer

Parameters:

pw_trafopairwise panel transformer,: i.e., estimator inheriting from BasePairwiseTransformerPanel this is a “blueprint” estimator, state does not change when fit is called
transformerslist of sktime transformers, or: list of tuples (str, transformer) of sktime transformers these are “blueprint” transformers, states do not change when fit is called

Attributes:

is_fitted: Whether fit has been called.
steps_: Concatenated list of sktime transformers and pairwise panel transformer.

Examples

>>> from sktime.dists_kernels.compose import PwTrafoPanelPipeline
>>> from sktime.dists_kernels.dtw import DtwDist
>>> from sktime.transformations.exponent import ExponentTransformer
>>> from sktime.datasets import load_unit_test
>>>
>>> X, _ = load_unit_test()
>>> X = X[0:3]
>>> pipeline = PwTrafoPanelPipeline(DtwDist(), [ExponentTransformer()])
>>> dist_mat = pipeline.transform(X)

Methods

`__call__`(X[, X2])	Compute distance/kernel matrix, call shorthand.
`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, X2])	Fit method for interface compatibility (no logic inside).
`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 in `transformers`.
`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.
`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`(**kwargs)	Set the parameters of estimator in `transformers`.
`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[, X2])	Compute distance/kernel matrix.
`transform_diag`(X)	Compute diagonal of distance/kernel matrix.