Release Notes
=============

This is the list of changes to MLCompare between each release. For full details,
see the `commit logs <https://github.com/MitchMedeiros/MLCompare/commits/>`_.

Version 1.2.0
-------------

### Pipelines
- Created a `data_pipeline` function for performing only data retrieval and processing
- Expanded the generated model performance metrics and added a required argument to `full_pipeline` for specifying whether the pipeline is being used for regression or classification tasks

### DatasetProcessor
- Refactored the class to store the train-test split data for easier processing
- Added a `handle_nan` method which can drop, forward-fill, and backward-fill missing values
- Added label encoding ordinal encoding, and target encoding methods
- Added several scaling and transformation methods from sklearn: StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler, PowerTransformer, QuantileTransformer, and Normalize

### Documentation
- Created a new homepage
- Updated the layout of the API Reference page
- Added content to the Release Notes page
- Improved various docstrings
- Made multiple updates to the README including adding a "Planned Additions" section

### Other
- Added a `ResultsWriter` class, responsible for directory and file naming and creation throughout pipelines
    - Implemented directory and file name incrementing to prevent overwrites
    - Changed the default directory name to use the current timestamp to ensure uniqueness
    - Improved how saving model results is handled
- Removed the `DataProcessor` class in favor of pipelines
- Migrated several high-level functions being used within pipelines to a new module: `processing.py`
- Improved unit test coverage

Version 1.1.0
-------------

- Refactored `DatasetProcessor`, moving `save_directory` from a class attribute to a method argument
- Added type validation to several methods within `DatasetProcessor`
- Updated docstrings for the `dataset_processor` module
- Updated unit tests for `DatasetProcessor`
- Added optimal device selection for PyTorch models as default behavior
- Corrected a logging issue with model processing

Version 1.0.1
-------------

- Updated the project versioning to dynamically use the version in `mlcompare/__init__.py`
- Modified the package attributes displayed on PyPi including adding links to documentation
- Added a link to the documentation to the library `__init__`
- Created a GitHub action for publishing newly tagged versions to PyPi

Version 1.0.0
-------------

Initial Release