Skip to content

Latest commit

 

History

History
112 lines (85 loc) · 4.26 KB

CHANGELOG.md

File metadata and controls

112 lines (85 loc) · 4.26 KB
Raw

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Changelogs for this project are recorded in this file since v0.2.0.

[Towards v0.3.0]

Changed

  • dtw_barycenter_averaging is made faster by using vectorized computations
  • dtw_barycenter_averaging can be restarted several times to reach better local optima using a parameter n_init set to 1 by default
  • Functions load_timeseries_txt and save_timeseries_txt from the utils module have changed their names to load_time_series_txt and save_time_series_txt. Old names can still be used but considered deprecated and removed from the public API documentation for the sake of harmonization
  • Default value for the maximum number of iterations to train ShapeletModel and SerializableShapeletModel is now set to 10,000 (used to be 100)
  • TimeSeriesScalerMeanVariance and TimeSeriesScalerMinMax now ignore any NaNs when calling their respective transform methods in order to better mirror scikit-learn's handling of missing data in preprocessing.

Added

  • KNeighborsTimeSeriesRegressor is a new regressor based on k-nearest-neighbors that accepts the same metrics as KNeighborsTimeSeriesClassifier
  • A set_weights method is added to the ShapeletModel and
    SerializableShapeletModel estimators
  • subsequence_path and subsequence_cost_matrix are now part of the public API and properly documented as such with an example use case in which more than one path could be of interest (cf. plot_sdtw.py)
  • verbose levels can be set for all functions / classes that use joblib for parallel computations and joblib levels are used;
  • conversion functions are provided in the utils module to interact with other Python time series packages (pyts, sktime, cesium, seglearn, tsfresh, stumpy, pyflux)
  • dtw_barycenter_averaging_subgradient is now available to compute DTW barycenter based on subgradient descent
  • dtw_limited_warping_length is provided as a way to compute DTW under upper bound constraint on warping path length

Changed

  • When constrained DTW is used, if the name of the constraint is not given but its parameter is set, that is now considered sufficient to identify the constraint.

[v0.2.4]

Fixed

  • The tests subdirectory is now made a python package and hence included in wheels

[v0.2.2]

Fixed

  • The way version number is retrieved in setup.py was not working properly on Python 3.4 (and made the install script fail), switched back to the previous version

[v0.2.1]

Added

  • A RuntimeWarning is raised when an 'itakura' constraint is set that is unfeasible given the provided shapes.

Fixed

  • 'itakura' and 'sakoe_chiba' were swapped in metrics.compute_mask

[v0.2.0]

Added

  • tslearn estimators are now automatically tested to match sklearn requirements "as much as possible" (cf. tslearn needs in terms of data format, etc.)
  • cdist_dtw and cdist_gak now have a n_jobs parameter to parallelize distance computations using joblib.Parallel
  • n_jobs is also available as a prameter in silhouette_score, TimeSeriesKMeans, KNeighborsTimeSeries, KNeighborsTimeSeriesClassifier, TimeSeriesSVC, TimeSeriesSVR and GlobalAlignmentKernelKMeans

Changed

  • Faster DTW computations using numba
  • tslearn estimators can be used in conjunction with sklearn pipelines and cross-validation tools, even (for those concerned) with variable-length data
  • doctests have been reduced to those necessary for documentation purposes, the other tests being moved to tests/*.py
  • The list of authors for the tslearn bibliographic reference has been updated to include Johann Faouzi and Gilles Van de Wiele
  • In TimeSeriesScalerMinMax, min and max parameters are now deprecated in favor of value_range. Will be removed in v0.4
  • In TimeSeriesKMeans and silhouette_score, 'gamma_sdtw' is now deprecated as a key for metric_params in favor of gamma. Will be removed in v0.4

Removed

  • Barycenter methods implemented as estimators are no longer provided: use dedicated functions from the tslearn.barycenters module instead