sktime.classification.hybrid

class sktime.classification.hybrid.HIVECOTEV1(stc_params=None, tsf_params=None, rise_params=None, cboss_params=None, verbose=0, n_jobs=1, random_state=None)[source]

Bases: sktime.classification.base.BaseClassifier

Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) V1 as described in [1].

An ensemble of the STC, TSF, RISE and cBOSS classifiers from different feature representations using the CAWPE structure.

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

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

  • n_jobs (int, optional (default=1)) –

  • number of jobs to run in parallel for both fit and predict. (The) –

  • means using all processors. (-1) –

  • random_state (int or None, seed for random, integer,) –

  • (default to no seed) (optional) –

n_classes[source]
Type

extracted from the data

Notes

..[1] Anthony Bagnall, Michael Flynn, James Large, Jason Lines and Matthew Middlehurst.

“On the usage and performance of the Hierarchical Vote Collective of

Transformation-based Ensembles version 1.0 (hive-cote v1. 0)”

International Workshop on Advanced Analytics and Learning on Temporal

Data 2020

Java version https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/ tsml/classifiers/hybrids/HIVE_COTE.java

capabilities = {'missing_values': False, 'multivariate': False, 'unequal_length': False}[source]
fit(X, y)[source]
predict(X)[source]
Parameters
  • X (panda dataframe) – instances of the dataset

  • ----

Returns

predictions – array of predictions of each instance (class value)

Return type

1d numpy array

predict_proba(X)[source]