Pycaret

Summary of Changes

- Fixed issues with `[full]` install by pinning `interpret<=0.2.4`
- Added support for S3 folder path in `deploy_model()` with AWS
- Enabled experimental Optuna `TPESampler` options to improve convergence (in `tune_model()`)

Summary of Changes

- Implemented PDP, MSA and PFI plots in `interpret_model` - thanks to IncubatorShokuhou (https://github.com/pycaret/pycaret/pull/1415)
- Implemented Kolmogorov-Smirnov (KS) plot in `plot_model` under `pycaret.classification` module
- Fixed a typo "RVF" to "RBF" - thanks to baturayo (https://github.com/pycaret/pycaret/pull/1220)
- Readme & license updates and improvements
- Fixed `remove_multicollinearity` considering categorical features
- Fixed keyword issues with PyCaret's cuML wrappers
- Improved performance of iterative imputation
- Fixed `gain` and `lift` plots taking wrong arguments, creating misleading plots
- `interpret_model` on LightGBM will now show a beeswarm plot
- Multiple improvements to exception handling and documentation in `pycaret.persistence` (https://github.com/pycaret/pycaret/pull/1324)
- `remove_perfect_collinearity` option will now be show in the `setup()` summary - thanks to mjkanji (https://github.com/pycaret/pycaret/pull/1342)
- Fixed `IterativeImputer` setting wrong float precision
- Fixed custom grids in `tune_model` raising an exception when composed of lists
- Improved documentation in `pycaret.clustering` - thanks to susmitpy (https://github.com/pycaret/pycaret/pull/1372)
- Added support for LightGBM CUDA version - thanks to IncubatorShokuhou (https://github.com/pycaret/pycaret/pull/1396)
- Exposed `address` in `get_data` for alternative data sources - thanks to IncubatorShokuhou (https://github.com/pycaret/pycaret/pull/1416)

Summary of Changes

- Fixed an exception with missing variables (display_container etc.) during load_config()
- Fixed exceptions when using Ridge and RF estimators with cuML (GPU mode)
- Fixed PyCaret's cuML wrappers not being pickleable
- Added an extra check to get_all_object_vars_and_properties internal method, fixing exceptions with certain estimators
- save_model() now supports kwargs, which will be passed to joblib.dump()
- Fixed an issue with load_model() from AWS (duplicate .pkl extension) - thanks to markgrujic (https://github.com/pycaret/pycaret/pull/1128)
- Fixed a typo in documentation - thanks to koorukuroo (https://github.com/pycaret/pycaret/pull/1149)
- Optimized Fix_multicollinearity transformer, drastically reducing the size of the saved pipeline
- interpret_model() now supports data passed as an argument - thanks to jbechtel (https://github.com/pycaret/pycaret/pull/1184)
- Removed `infer_signature` from MLflow logging when `log_experiment=True`.
- Fixed a rare issue where binary_multiclass_score_func was not pickleable
- Fixed edge case exceptions in feature selection
- Fixed an exception with `finalize_model` when using GroupKFold CV
- Pinned `mlxtend>=0.17.0`, `imbalanced-learn==0.7.0`, and `gensim<4.0.0`

**Modules Impacted:** `pycaret.classification` `pycaret.regression` `pycaret.clustering` `pycaret.anomaly` `pycaret.arules` `pycaret.nlp`

Summary of Changes

- Added new interactive residual plots in the `pycaret.regression` module. You can now generate interactive residual plots by using `residuals_interactive` in the `plot_model` function.
- Added plot rendering support for streamlit applications. A new parameter `display_format` is added in the `plot_model` function. To render plot in streamlit app, set this to `streamlit`.
- Revamped Boruta feature selection algorithm. (give it a try)
- `tune_model` in `pycaret.classification` and `pycaret.regression` is now compatible with custom models.
- Added low_memory and max_len support to association rules module (https://github.com/pycaret/pycaret/pull/1008)
- Increased robustness of DataFrame checks (https://github.com/pycaret/pycaret/pull/1005)
- Improved loading of models from AWS (https://github.com/pycaret/pycaret/pull/1005)
- Catboost and XGBoost are now optional dependencies. They are not automatically installed with default slim installation. To install optional dependencies use `pip install pycaret[full]`.
- Added `raw_score` argument in the `predict_model` function for `pycaret.classification` module. When set to True, scores for each class will be returned separately.
- PyCaret now returns base scikit-learn objects, whenever possible
- When `handle_unknown_categorical` is set to False in the `setup` function, an exception will be raised during prediction if the data contains unknown levels in categorical features.
- `predict_model` for multiclass classification now returns labels as an integer.
- Fixed an edge case where an IndexError would be raised in `pycaret. clustering` and `pycaret. anomaly`
- Fixed text formatting for certain plots in `pycaret.classification` and `pycaret.regression`.
- If a `logs.log` file cannot be created when `setup` is initialized, no exception will be raised now (support for more configurable logging to come in the future)
- User added metrics will not raise exceptions now and instead return 0.0
- Compatibility with tune-sklearn>=0.2.0
- Fixed an edge case for dropping NaNs in the target column.
- Fixed stacked models not being tuned correctly.
- Fixed an exception with KFold when fold_shuffle=False.

- Fixed exceptions with the `predict_model` function when data columns had non-string characters.
- Fixed a rare exception with the `remove_multicollinearity` parameter in the `setup` function`.
- Improved performance and robustness of conversion of date features to categoricals.
- Fixed an exception with the `models` function when the `type` parameter was passed.
- The data frame displayed after setup can now be accessed with the `pull` function.
- Fixed an exception with save_config
- Fixed a rare case where the target column would be treated as an ID column and thus dropped
- SHAP plots can now be saved (pass save parameter as True).
- **| CRITICAL |** Compatibility broke for catboost, pyod (other impacts unknown as of now) with sklearn=0.24 (released on Dec 22, 2020). A temporary fix is requiring 0.23.2 specifically in the `requirements.txt`.