Update from unit8co/darts. #11
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…2218) * add support for more lr scheduler config parameters to torch models * update changelog
* update dev requirements with new pre commit hook lint dependency versions * black reformatting * fix flake8 checks
* fix: append/prepend correctul retain components names and hierarchy * updated changelog * fix: revert unecessary change * update changelog --------- Co-authored-by: dennisbader <dennis.bader@gmx.ch>
* fix: update hierarchy for single transform window_transform * update changelog * update changelog * fix: using set to check overlap * fix: corrected logic to update the hierarchy after window_transform * fix: hierarchy can be conserved when applying non-overlapping transforms * feat: add new argument, improve logic * feat: adding tests * fix: expected argument match docstring in resample() * fix: addressing review comments * fix: linting issue * fix: linting * linting * update changelog and remane keep_old_names to keep_names --------- Co-authored-by: dennisbader <dennis.bader@gmx.ch>
* remove pytorch lightning upper version cap * fix failing unit test and update changelog
…rror (#2222) * fix: arguments must be provided to model cls in order to check presence of the fitted_values attribute * fix: added a check that parameters is indeed a dict * updated changelog * fix: update test to pass the new sanity checks * fix: addressing review comments --------- Co-authored-by: Dennis Bader <dennis.bader@gmx.ch>
* Change default kwarg * Update CHANGELOG.md --------- Co-authored-by: Dennis Bader <dennis.bader@gmx.ch>
* add output chunk shift to lightning modeuls * training torch model with shifted output * first shifted output inference works for mixed covariates models * full covariateds support for shifted mixed covariates dataset * add shift support to all torch models * update torch model extreme lags with shift * update torch model encoder settings with shift * update torch model encoder settings with shift * add unit test for shifted torch mmodel with encoders * add unit tests for tft model * add unit tests for all torch models * update output_chunk_shift description * apply suggestions from PR review * add output chunk shift to extreme lags * udpate historical forecasts to work with shifted output * update historical forecasts start description for shifted output * apply suggestions from PR review * prepare regression models for output chunk shift * fix failing unit tests * prepare regression models for output chunk shift part 2 * update hist fc for regression models with output shift * update tabularization * add test for comparing results between output shift and normal multi models * historical forecasts for shifted regression models * update tabularization training tests * update tabulirazion get feature times tests * update tabularization get shared times tests * update tabularization get shared bounds tests * update tabularization get lagged prediction data tests * add tests for tabularization without target lags but only covariate lags * update n_steps_between docs * update changelog * add unit tests for inference datasets * add unit tests for sequential training datasts * update changelog * make ocs property non optional * skip output_chunk_shift checks when loading weights since not relevant for parameter shape * apply suggestions from PR review
…#2242) * fix: datetime_attribute account for 0 or 1-indexing of the attributes * feat: 1-indexed date attribute are shifted to enforce 0-indexing for all the generated encodings * updated changelog * fix: remove commented lines * fix: typo in comment * make ONE_INDEXED_FREQS a constant * fix: simplified test by using year 2001 * feat: better handling of years with 53 weeks or 366 days * fix: properly take the index length when adding the extra week * fix: simplifying test * fix: update tests to account for the forced 0-indexing of the datetime attributes encoding * fix: passing lmbda parameter as BoxCox doesn't converge when encodings contains a 0 --------- Co-authored-by: Dennis Bader <dennis.bader@gmx.ch>
* feat: adding docstring and check to get_multioutput_estimator * fix: added lowbound check * fix: update docstring, indexing account for multi_models param * feat: added corresponding test * feat: added tests for estimator getter * feat: store and expose the lagged label names (for each model estimator) * fix: rephrasing docstring * update changelog * fix: linting * fix: replaced ocl with hrz in naming of the lagged label * fix: update error messages * feat: simplify test, overfit XGB on only one training example * feat: added a method to get estimator for models supporting multi-output natively * feat: added corresponding test * update changelog * fix: linting * Update CHANGELOG.md --------- Co-authored-by: Dennis Bader <dennis.bader@gmx.ch>
* update changelog * bump u8darts 0.27.2 to 0.28.0 * update changelog
* update code owners * udpated PR template
* Remove unnessesary `pass` statements * Rename ForecastingModel_is_probabilistic to supports_probabilistic_prediction, rearrange some documentation * Remove redundant overrides * Reformat * Add CHANGELOG entry --------- Co-authored-by: Dennis Bader <dennis.bader@gmx.ch>
* fix type hinting for _with_sanity_checks * update changelog
* Add optional inverse transform in historical forecast * Update variables names and docstrings * Move the inverse transform to InvertibleDataTransformer * Fix single element list * Update docstrings * Move the inverse transform of list of lists to inverse_transform method * make invertible transformers act on list of lists of series * add tests * update changelog --------- Co-authored-by: dennisbader <dennis.bader@gmx.ch>
* lxml_html_clean for nbshinx * update changelog
* fix lighgbm segmentation fualt * update changelog * parameterize unit tests
* fix lighgbm segmentation fualt * update changelog * parameterize unit tests * make metric_kwargs metric specific rather than infereing which kwarg belongs to which metric * update hierarchical reconciliation notebook * fix failing residuals tests
* use pytest to skip torch tests * fix some mistakes in tsmixer notebook
* add TimesSeries.from_group_dataframe parallel mode * remove code mess * add doc string for new parameters * update CHANGELOG.md * add miss dtype * fix static covariates * make parallel function as local and fix tests * fix parallel utils imports * update changelog * Update CHANGELOG.md --------- Co-authored-by: Bohdan Bilonoh <bilonogo@rozetka.ua> Co-authored-by: dennisbader <dennis.bader@gmx.ch>
* bump black[jupyter] 24.1.1 to 24.3.0 * update changeloig
* add gaussian likelihood * refactor f models * general statsforecast model * update statsforecastmodel * add transferable series support * update docs * simplify model_args and kwargs names * fix hist fc test with new autoarima * add tests * apply suggestions from PR review * add tests * update docs * final adaptions * docs upadte * update examples in docs * update changelog
* change tbats backend from tbats to statsforecast * fix failing tests * update changelog * update changelog * fix failing test * update docs
* relexnumpy v2 upper cap * remove bats and tbats * run macos only * run on macos-14 (silicon) * run develop workflow on macos-14 (silicon) * test * run all macos tests * run failing tests * change cp test * further adapt tests * another failing test * fix last test * fix elec zurich ds for arm processors * check dataset loading * complete macos tests * fix last failing macos tests * another test fix for macos * run failing ubuntu tests * check explicit failing tests * try setting locale sorting * try fix sorting * run all tabularization tests * update workflows * remove macos uv install step * try removing libomp installation step since new macos-14 build * remove unused limbomp istall scripts * update changelog
* add back libomp for python 3.11 checks * lightweight 3.11 tests * simplify command * make libomp script executable * fix type * fix typo * add libomp installation to all workflows
* bump u8darts 0.34.0 to 0.35.0 * update changelog * update changelog * update readme * update docs
…restModel (#2774) * Rename RegressionModel into SKLearnModel * Rename RandomForest to RandomForestModel * Rename RandomForest to RandomForestModel * Log deprecation warning when old names are used * Revert file renaming * Rename regression_model.py to sklearn_model.py * Re-apply changes after file renaming * Move RegressionModel into sklearn_model.py * Add test for RegressionModelWithCategoricalCovariates * Update CHANGELOG.md * Remove RegressionModelWithCategoricalCovariates * Fix link * Revert images changes in example notebook * update docs --------- Co-authored-by: dennisbader <dennis.bader@gmx.ch>
* update readme * try to fix failing optuna test * run only unit tests * install darts locally for ray * fix ray darts import
* Regardless of whether self.n_targets > 1, reshape the target and predict * Add test for R2Score * Update CHANGELOG.md --------- Co-authored-by: Dennis Bader <dennis.bader@gmx.ch>
* fix clone bug with shared_weigths * add normalization for past covars * change the default for the normalization * add note about Linear model and encoders * add tests for nlinear with normalization * fix test for normalize=False * add NLinearModel fix to CHANGELOG as breaking * rm unused past_cov (linter error) * lint changelog * fix global forecasting model tests * fix weight test by adding sine because nlinear with normalize=True does not learn weight for linear time sereies by design * fix test for test_predict_likelihood_parameter by setting explicit normalize=False because normalize=True is the new default * fix linting * Update darts/models/forecasting/nlinear.py Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * fix nlinear auto-regression with norm * update changelog * final updates --------- Co-authored-by: dennisbader <dennis.bader@gmx.ch>
* codecov ignore tests * fix type
* refactor torch datasets * rename to as everywhere else * adapt TorchForecastingModel * remove pl forecasting module covarites subclasses * remove redundant datasets * fix tests * simplify horizon based dataset * update dataset reference in example * update docs and changelog * update docs * remove noqa * make codecov ignore tests * fix some missing lines * update changelog * further simplify training dataset * improve traning set code coverage * improve tfm code coverage * improve inference dataset code coverage * unify memory indexer * add output chunk shift test * apply suggestions from pr review * move tabularization and torch dataset modules for improved repo structure * improve code coverage * fix typo
* feat: adding stride to shifted_dataset * feat: adding stride to the sequential dataset * feat: add striding to tabularization * feat: add stride to RegressioModel fit API * fix: bug in striding implementation * feat: updating the tabularization tests * fix: bug * feat: adding test for stride in torch datasets * update changelog * fix: missing test and small bug * update changelog * doc: update some docstrings * feat: stride is now applied from the end of the series * feat: added stride support to the horizon based dataset * feat: updated unit tests * feat: adding support of stride in the fit method of torch models * fix: update the test so that the stride is applied from the end * fix: add missing defualt value * fix: test * fix tfm tests * fix regression model tests * add further tests --------- Co-authored-by: dennisbader <dennis.bader@gmx.ch>
* move forecast creation out of PLForecastingModule into TorchForecastingModel * optimize historical forecasts * update changelog * fix failing dataset tests * improve code coverage * change tqdm iterator title * add time series schema test
* Deterministic sampling for all probabilistic models * Updates after code review * Updates after code review * Update darts/models/forecasting/forecasting_model.py Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * Updates after code review * minor updates --------- Co-authored-by: madtoinou <32447896+madtoinou@users.noreply.github.com> Co-authored-by: Dennis Bader <dennis.bader@gmx.ch>
* random state added in historical forecasting * test historical forecast modified * Update darts/models/forecasting/conformal_models.py Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * Update darts/models/forecasting/sf_model.py Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * Update darts/models/forecasting/sf_model.py Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * Update darts/tests/models/forecasting/test_probabilistic_models.py Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * Update darts/tests/models/forecasting/test_probabilistic_models.py Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * Update darts/tests/models/forecasting/test_probabilistic_models.py Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * Update darts/tests/models/forecasting/test_probabilistic_models.py Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * Update darts/tests/models/forecasting/test_probabilistic_models.py Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * Update darts/tests/models/forecasting/test_probabilistic_models.py Co-authored-by: Dennis Bader <dennis.bader@gmx.ch> * Fixed end of line error * Changed description * update changelog and random_state description --------- Co-authored-by: Dennis Bader <dennis.bader@gmx.ch>
…ls (#2821) * change default shap method of extra trees and random forest models * added changelog entry * add back that ExtraTreeRegressor and change method for HistGradientBoostingRegressor * update changelog --------- Co-authored-by: Rijk van der Meulen <rijk.vandermeulen@eyeon.nl>
…F models (#2824) * do not use static covs for OLS in SF models * update changelog
* improve quantile methods for TimeSeries * update changelog
* let lightning handle moving preds to cpu * update changelog * update type annotation
* refactor factory and __init__ methods to use numpy instead of xarray * update arithmetic operands * refactor methods * add back repr methods * fix tests * update dtw * avoid copying when building forecast series * adapt further copy usage * handle time zone in TimeSeries constructor * remove raise_if and raise_if_not * add mutability tests * remove old timeseries * update changelog * minor updates * fix test * apply no-copy timeseries creation to more modules * further data transformer improvements * further improvements * add mutabitlity tests * blabla * add more mutability tests * last mutability tests * refactor from_times_and_values occurences * update docs * update timeseries docs * apply suggestions from PR review * do not mutate time index and components * fix midas static covs * update docs * final updates * update changelog
* improve likelihood handling for sklearn models * update tests * update changelog
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