From be746d31032c9e4a6fbd894a29f47a67b7deed75 Mon Sep 17 00:00:00 2001 From: Thomas Bury Date: Thu, 20 Apr 2023 14:48:43 +0200 Subject: [PATCH] doc: add changelog for tracking issues --- CHANGELOG.md | 143 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 143 insertions(+) create mode 100644 CHANGELOG.md diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..06a5738 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,143 @@ +# Changes + +## 1.0.6 + + - Return self in mrmr, fixing error when in scikit-learn pipeline + +## 1.0.5 + + - Patching classes where old unused argument was causing an error + +## 1.0.2 + + - Distribute a toy dataset for regression by modifying the Boston dataset adding noise and made up columns + +## 1.0.1 + + - Fix pkg data distribution + +## 1.0.0 + + - Parallelization of functions applied on pandas data frame + - Faster and more modular association measures + - Removing dependencies (e.g. dython) + - Better static and interactive visualization + - Sklearn selectors rather than a big class + - Discretization of continuous and categorical predictors + - Minimal redundancy maximal relevance feature selection added (a subset of all relevant predictors), based on Uber's MRmr flavor + - architecture closer to the scikit-learn one + +## 0.3.8 + + - Fix bug when compute shap importance for classifier in GrootCV + +## 0.3.7 + + - Add defensive check if no categorical found in the subsampling of the dataset + - Re-run the notebooks with the new version +## 0.3.6 + + - Fix clustering when plotting only strongly correlated predictors + - Remove palettable dependencies for plotting + - Add default colormap but implement the user defined option +## 0.3.5 + +- Enable clustering before plotting the correlation/association matrix, optional +- Decrease fontsize for the lables of the correlation matrix + +## 0.3.4 + + - Update requirements + +## 0.3.3 + + - Upgrade documentation + +## 0.3.2 + + - Fix typo for distributing the dataset and pinned the dependencies +## 0.3.1 + + - Update the syntax for computing associations using the latest version of dython + +## 0.3.0 + + - Fix the Boruta_py feature counts, now adds up to n_features + - Fix the boxplot colours, when only rejected and accepted (no tentative) the background color was the tentative color + - Numpy docstring style + - Implement the new lightGBM callbacks. The new lgbm version (>3.3.0) implements the early stopping using a callback rather than an argument + - Fix a bug for computing the shap importance when the estimator is lightGBM and the task is classification + - Add ranking and absolute ranking attributes for all the classes + - Fix future pandas TypeError when computing numerical values on a dataframe containing non-numerical columns + - Add housing data to the distribution + - Add "extreme" sampling methods + - Re-run the NBs + - reindex to keep the original columns order + +## 0.2.3 + + - Update syntax to stick to the new argument names in Dython + +## 0.2.2 + + - Check if no feature selected, warn rather than throw error + +## 0.2.1 + + - Fix a bug when removing collinear columns + +## 0.2.0 + + - Prefilters now support the filtering of continuous and nominal (categorical) collinear variables + +## 0.1.6 + + - improve the plot_y_vs_X function + - remove gc.collect() + +## 0.1.5 + + - fix readme (typos) + - move utilities in utils sub-package + - make unit tests lighter + +## 0.1.4 + + - fix bug when using catboost, clone estimator (avoid error and be sure to use a non-fitted estimator) + +## 0.1.3 + + - change the defaut for categorical encoding in pre-filters (pd.cat to integers as default) + - fix the unit tests with new defaults and names + +## 0.1.2 + + - change arguments name in pre-filters + +## 0.1.1 + + - remove old attribute names in unit-tests + +## 0.1.0 + + - Fix lightGBM warnings + - Typo in repr + - Provide load_data utility + - Enhance jupyter NB examples + - highlighting synthetic random predictors + - Benchmark using sklearn permutation importance + - Harmonization of the attributes and parameters + - Fix categoricals handling + +## 0.0.4 + + - setting optimal number of features (according to "Elements of statistical learning") when using lightGBM random forest boosting. + - Providing random forest, lightgbm implementation, estimators + +## 0.0.3 + + - Adding examples and expanding documentation + +## 0.0.2 + + - fix bug: relative import removed \ No newline at end of file