Skip to content

Latest commit

 

History

History
199 lines (143 loc) · 6.13 KB

CHANGELOG.md

File metadata and controls

199 lines (143 loc) · 6.13 KB

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.

[v6.0.0] - 2024-03-26

Changed

  • Updated tables dependency to v3.9.x, which fixes issues with installation of the package. This requires Python 3.9 or above, however, so we require that here as well.

[v5.1.0] - 2023-10-31

Added

  • The Boot class now has save and load methods, which uses joblib under the hood.

[v5.0.0] - 2023-10-30

Changed

  • The dataset-specific dependencies are now put in a separate datasets extra, to make the core package more lean. You can install the package with all the dependencies using pip install doubt[datasets].

[v4.5.2] - 2023-10-30

Changed

  • Now also allows pandas 2.x.x versions.

[v4.5.1] - 2023-10-25

Security

  • Updated urllib3 to 2.0.7 due to a security update.

[v4.5.0] - 2023-07-05

Added

  • Now saves the models during training with a Boot and reuses those during inference, speeding up inference. Thanks to @andrepugni for this contribution!

Fixed

  • Downgraded tables to 3.7.x to fix an installation bug.
  • Downgraded scikit-learn to >=1.1,<1.3, as the decision tree API in v1.3 is incompatible with the previous ones. This will be dealt with separately in the future.

[v4.4.1] - 2023-04-23

### Fixed

  • When return_all is specified in Boot.predict and multiple samples have been inputted, then it now returns an array of shape (num_samples, num_boots) rather than the previous (num_boots, num_samples).

[v4.4.0] - 2023-04-23

Added

  • Added a return_all argument to the Boot.predict method, which will override the uncertainty and quantiles arguments and return the raw bootstrap distribution over which the quantiles would normally be calculated. This allows other uses of the bootstrap distribution than for computing prediction intervals.

[v4.3.1] - 2023-03-20

Fixed

  • Previously, all the trees in QuantileRegressionForest were the same. This has now been fixed. Thanks to @gugerlir for noticing this!
  • The random_seed argument in QuantileRegressionTree and QuantileRegressionForest has been changed to random_state to be consistent with DecisionTreeRegressor, and to avoid an AttributeError when accessing the estimators of a QuantileRegressionForest.

[v4.3.0] - 2022-07-17

### Added

  • The QuantileRegressionForest now has a feature_importances_ attribute.

[v4.2.0] - 2022-07-17

Changed

  • Boot.fit and Boot.predict methods are now parallelised, speeding up both training and prediction time a bit.
  • Updated README to include generalised linear models, rather than only mentioning linear regression.

Fixed

  • Removed mention of PyTorch model support, as that has not been implemented yet

[v4.1.0] - 2021-07-26

Changed

  • The verbose argument to QuantileRegressionForest also displays a progress bar during inference now.

Fixed

  • Fixed QuantileRegressionForest.__repr__.

[v4.0.0] - 2021-07-26

Added

  • Added a verbose argument to QuantileRegressionForest, which displays a progress bar during training.

Changed

  • The default value of QuantileRegressionForest.min_samples_leaf has changed from 1 to 5, to ensure that the quantiles can always be computed sensibly with the default setting.

Fixed

  • The logkow feature in the FishBioconcentration dataset is now converted into a float, rather than a string.
  • Typo in example script in README

[v3.0.0] - 2021-04-25

Added

  • Added __repr__ to QuantileRegressor

[v3.0.0] - 2021-04-25

Removed

  • QuantileLinearRegression has been removed, and QuantileRegressor should be used instead

[v2.3.0] - 2021-04-25

Added

  • Added quantiles argument to QuantileRegressionTree and Boot, as an alternative to specifying uncertainty, if you want to return specific quantiles.
  • Added general QuantileRegressor, which can wrap any general linear model for quantile predictions.

Fixed

  • The predictions in Boot.predict were based on a fitting of the model to one of the bootstrapped datasets. It is now based on the entire dataset, which in particular means that the predictions will be deterministic. The intervals will still be stochastic, as they should be.

Changed

  • Updated Numpy random number generation to their new API
  • All residuals in Boot are now calculated during fitting, which should decrease the prediction times a tiny bit.

Removed

  • Package no longer relies on statsmodels

[v2.2.1] - 2021-04-16

Fixed

  • A handful of docstring style changes to yield a cleaner Sphinx documentation

[v2.2.0] - 2021-04-16

Added

  • Sphinx documentation

[v2.1.0] - 2021-04-11

Added

  • Implemented score method to QuantileLinearRegression, which either outputs the mean negative pinball loss function, or the R^2 value
  • Added more documentation to QuantileLinearRegression

Changed

  • Outputs more informative error message when a singular feature matrix is being used with QuantileLinearRegression
  • Datasets look prettier in notebooks now

Removed

  • Removed docstring comments about closing datasets after use, as this is automatic

[v2.0.2] - 2021-04-09

Fixed

  • Small mistake in the computation of the prediction intervals in Boot.predict, where the definition of generalisation should be the difference of the means of the residuals, and not the difference between the individual quantiles. Makes a very tiny difference to the prediction intervals. Thanks to Bryan Shalloway for catching this mistake.

[v2.0.1] - 2021-04-04

Fixed

  • Boot.__repr__ was not working properly

[v2.0.0] - 2021-04-04

Added

  • Added proper __repr__ descriptions to all models

Changed

  • Changed the ordering of Dataset.split to X_train, X_test, y_train and y_test, to agree with scikit-learn
  • Moved some Dataset attributes to the private API