SklearnClustererPipeline

Pipeline of transformers and a clusterer.

The SklearnClustererPipeline chains transformers and an single clusterer.

Similar to ClustererPipeline, but uses a tabular sklearn clusterer.

The pipeline is constructed with a list of sktime transformers, plus a clusterer,

i.e., transformers following the BaseTransformer interface, clusterer follows the scikit-learn clusterer interface.

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 a clusterer clst,

the pipeline behaves as follows:

fit(X, y) - changes styte by 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], and then running clst.fit with X the output of trafo[N] converted to numpy, and y identical with the input to self.fit. X is converted to numpyflat mtype if X is of Panel scitype; X is converted to numpy2D mtype if X is of Table scitype.

predict(X) - result is of executing trafo1.transform, trafo2.transform, etc

with trafo[i].transform input = output of trafo[i-1].transform, then running clst.predict on the numpy converted output of trafoN.transform, and returning the output of clst.predict. Output of trasfoN.transform is converted to numpy, as in fit.

predict_proba(X) - result is of executing trafo1.transform, trafo2.transform,

etc, with trafo[i].transform input = output of trafo[i-1].transform, then running clst.predict_proba on the output of trafoN.transform, and returning the output of clst.predict_proba. Output of trasfoN.transform is converted to numpy, as in fit.

get_params, set_params uses sklearn compatible nesting interface

if list is unnamed, names are generated as names of classes if names are non-unique, f”_{str(i)}” is appended to each name string

where i is the total count of occurrence of a non-unique string inside the list of names leading up to it (inclusive)

SklearnClustererPipeline can also be created by using the magic multiplication

between sktime transformers and sklearn clusterers,

and my_trafo1, my_trafo2 inherit from BaseTransformer, then, for instance, my_trafo1 * my_trafo2 * my_clst will result in the same object as obtained from the constructor SklearnClustererPipeline(clusterer=my_clst, transformers=[t1, t2])

magic multiplication can also be used with (str, transformer) pairs,

as long as one element in the chain is a transformer