Model-tuner

- Pushed fixes for the `get_feature_selection_pipeline` method.
- Updated scoring blocks for calibrated KFold models and folded confusion matrix metrics.
- Moved import statements to the top for unit tests.
- Added unittests for edge cases, including `test_rfe_calibrate_model()` and validating confusion matrix alignment.
- Fixed mismatches between confusion matrix and classification report.
- Provided fixes for all pipeline getter methods.
- Integrated `verify_imb_sampler` prints into KFold logic.
- Resolved issues in KFold confusion matrix output and updated corresponding unit tests.
- Added test coverage for selected best features and threshold-based F-beta tuning tests.
- Enhanced tests for KFold splits and removed unnecessary utilities.
- Introduced tests for `conf_mat_class_kfold` and initiated randomized search tests.
- Finished randomized grid tests and improved GroupKFold support.
- Resolved typos in group split configurations and refined nested KFold bug fixes.
- Adjusted fold metric calculations in `report_model_metrics`.
- Refined `RFE` logic with additional prints and threshold adjustment tests.
- Implemented bootstrap regression tests and probability predictions with KFold scenarios.
- Moved optimal threshold logic into prediction functionality.
- Updated tests to accommodate new `return_metrics_kfold` logic.
- Enhanced return metrics dictionary logic to handle all cases and added multilabel classification tests.
- Addressed Brier score calculation issues and optimized regression reports for KFold.
- Introduced threshold print updates for clearer reporting.
- Developed unittests for CatBoost best iteration logic and improved test coverage for feature selection post-calibration.
- Implemented SHAP scripts and tests for model explainability.
- Removed outdated calibration reports from documentation and codebase.
- Added tests for early stopping during fitting processes with and without scoring mechanisms.
- Fixed bugs in regression metric calculations and refined KFold metric aggregation.

Model Tuner Version 0.0.24a Changelog

- Updated `.gitignore` to incl. doctrees
- Added `pickleObjects` tests and updated reqs, tests passed
- Added boostrapper test and tests passed
- Adding multi class test script
- Updated Metrics Output
- Added optl' threshold print inside `return_metrics`
- KFold metric printing:
- Added new input `per_fold_print` to allow user to return per fold metrics, otherwise average
- Added `tqdm` output for KFold metric printing
- Fixed KFold avg output in report_model_metrics

- Added a regression test and updating `report_model_metrics` to work with regression and multi class
- Augmented predict_proba test, and `train_val_test_split`
- Fixed pipeline_steps arg in model definition
- Refactored `metrics_df` in `report_model_metrics` for aesthetics

- Unit Tests
- XGB early stopping multi class test
- Added fit method tests
- Added early stopping test
- Added `get_best_score_params()` tests
- Added `return_bootstrap_metrics()` tests
- Added tests for `get_preprocessing_and_feature_selection_pipeline` and `get_feature_selection_pipeline`
- Tested init, passed tests
- Tested `get_preprocessing_pipeline`

- Imbalance Sampler
- Addded `process_imbalance_sampler()` tests, passed
- Renamed `process_imbalance_sampler()` to `verify_imbalance_sampler`

- Made `return_dict` optional in return_metrics
- Added `openpyxl` versions for all python versions in `requirements.txt`
- Refactor metrics, foldwise metrics and foldwise `con_mat`, `class_labels`
- Cleaned notebooks dir
- Renamed notebooks to `py_example_scripts`, linted files, cleaned code
- Added `model_tuner` version print to scripts
- Added fix for sort of pipelinesteps now optional:
- Added required model_tuner import to `xgb_multi.py`
- Added requisite model_tuner import to `multi_class_test.py`

- Added `catboost_multi_class.py` script
- Removed `pip` dependency from requirements

- Fixed a bug found when calibrating early stopping models
- Fixed early stopping in Column Transformer application

- Fixed an issue where the feature selection name was not referenced correctly, causing a bug when printing selected feature names with the updated pipeline.
- Removed resolved print statements from April, 2024.

- Specified the pipeline class otherwise the method just returned a list
- Removed need to specify `self.estimator` when its called
- Generalized (renamed) `"K Best Features"` to just `"Best Features"` inside returns of `return_metrics()`
- Generalized (renamed) `k_best_features` to `best_features`

- Added flexibility between `boolean` and `None` for stratification inputs
- Added custom exception for non pandas inputs in `return_bootstrap_metrics`
- Enforced required `model_type` input to be specified as `"classification"` or `"regression"`
- Removed extraneous ` "="` print below `pipeline_steps`
- Handled missing `pipeline_steps` when using `imbalance_sampler`
- Updated requirements for `python==3.11`
- Fixed SMOTE for early stopping
- Removed extra `model_type` input from `xgb_early_test.py`