ShapeletTransform

class sktime.transformations.panel.shapelets.ShapeletTransform(min_shapelet_length=3, max_shapelet_length=inf, max_shapelets_to_store_per_class=200, random_state=None, verbose=0, remove_self_similar=True)[source]

Shapelet Transform.

Original journal publication: @article{hills2014classification,

title={Classification of time series by shapelet transformation}, author={Hills, Jon and Lines, Jason and Baranauskas, Edgaras and Mapp, James and Bagnall, Anthony}, journal={Data Mining and Knowledge Discovery}, volume={28}, number={4}, pages={851–881}, year={2014}, publisher={Springer}

}

Parameters
  • min_shapelet_length (int, lower bound on candidate) –

  • lengths (default = 3) (shapelet) –

  • max_shapelet_length (int, upper bound on candidate) –

  • lengths (default = inf or series length) (shapelet) –

  • max_shapelets_to_store_per_class (int, upper bound on number of) –

  • to retain from each distinct class (default = 200) (shapelets) –

  • random_state (RandomState, int, or none: to) –

  • random state objects for deterministic results (default = None) (control) –

  • verbose (int, level of output printed to) –

  • console (for information only) (default = 0) (the) –

  • remove_self_similar (boolean, remove overlapping) –

  • shapelets from the final transform (default = True) ("self-similar") –

predefined_ig_rejection_level[source]
Type

float, minimum information gain

required to keep a shapelet (default = 0.05)
self.shapelets[source]
Type

list of Shapelet objects,

the stored shapelets after a dataset has been processed
__init__(min_shapelet_length=3, max_shapelet_length=inf, max_shapelets_to_store_per_class=200, random_state=None, verbose=0, remove_self_similar=True)[source]

Initialize self. See help(type(self)) for accurate signature.