TemporalDictionaryEnsemble

class sktime.classification.dictionary_based.TemporalDictionaryEnsemble(n_parameter_samples=250, max_ensemble_size=50, time_limit=0.0, max_win_len_prop=1, min_window=10, randomly_selected_params=50, bigrams=None, dim_threshold=0.85, max_dims=20, n_jobs=1, random_state=None)[source]

Temporal Dictionary Ensemble (TDE) as described in [1].

Overview: Input n series length m with d dimensions TDE searches k parameter values selected using a Gaussian processes regressor, evaluating each with a LOOCV. It then retains s ensemble members. There are six primary parameters for individual classifiers:

alpha: alphabet size w: window length l: word length p: normalise/no normalise h: levels b: MCB/IGB

for any combination, an individual TDE classifier slides a window of length w along the series. The w length window is shortened to an l length word through taking a Fourier transform and keeping the first l/2 complex coefficients. These lcoefficients are then discretised into alpha possible values, to form a word length l using breakpoints found using b. A histogram of words for each series is formed and stored, using a spatial pyramid of h levels. For multivariate series, accuracy from a reduced histogram is used to select dimensions.

fit involves finding n histograms. predict uses 1 nearest neighbour with a the histogram intersection distance function.

For the original Java version, see https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/ tsml/classifiers/dictionary_based/TDE.java

Parameters
  • n_parameter_samples (int, number of parameter combos to try) –

  • (default=250)

  • max_ensemble_size (int, maximum number of classifiers) –

  • (default=50)

  • time_limit (int, time contract to limit build time in) –

  • (default=0 (minutes) –

  • limit) (no) –

  • max_win_len_prop (float between 0 and 1, maximum window length) –

  • a proportion of series length (default=1) (as) –

  • min_window (int, minimum window size (default=10)) –

  • randomly_selected_params (int, number of parameters randomly selected) –

  • GP is used (default=50) (before) –

  • bigrams (boolean or None, whether to use bigrams) –

  • (default=None

  • for univariate (true) –

  • for multivariate) (false) –

  • dim_threshold (float between 0 and 1, dimension accuracy) –

  • for multivariate (default=0.85) (threshold) –

  • max_dims (int, max number of dimensions for multivariate) –

  • (default=20)

  • 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

n_instances[source]
Type

extracted from the data

n_dims[source]
Type

extracted from the data

n_estimators[source]
Type

The final number of classifiers used

(<=max_ensemble_size)
series_length[source]
Type

length of all series (assumed equal)

classifiers[source]
Type

array of IndividualTDE classifiers

weights[source]
Type

weight of each classifier in the ensemble

weight_sum[source]
Type

sum of all weights

prev_parameters_x[source]
Type

parameter value of previous classifiers for GP

prev_parameters_y[source]
Type

accuracy of previous classifiers for GP

Notes

..[1] Matthew Middlehurst, James Large, Gavin Cawley and Anthony Bagnall

“The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification”,

in proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020

https://ueaeprints.uea.ac.uk/id/eprint/75490/

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

__init__(n_parameter_samples=250, max_ensemble_size=50, time_limit=0.0, max_win_len_prop=1, min_window=10, randomly_selected_params=50, bigrams=None, dim_threshold=0.85, max_dims=20, n_jobs=1, random_state=None)[source]

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