PHOTONAI v2.1.0
Documentation: https://wwu-mmll.github.io/photonai/
Changelog
Features:
- enable integration of custom metrics
- integrate automatic generation of learning curves
- integrate nevergrad hyperparameter optimization strategy
- add new hyperparameter optimizer designed to compare different (learning) algorithms in a Switch (OR-element)
- add functionality to automatically find, analyze and compare the best config for each estimator (Switch) per outer fold
- added scorer method to hyperpipe that scores with best_config_metric, therefore the Hyperpipe object can be used with scikit-learn functions.
- integrated sklearn permutation feature importances into the workflow
- disable usage of test samples with the parameter
use_test_set
in hyperpipe - removed the need to import Output Settings class to declare the project_folder -> moved to Hyperpipe constructor
- added inverse_transform methods to several PHOTONAI algorithm implementations
Development:
- integrate documentation into github repo based on mkdocs and material theme: https://wwu-mmll.github.io/photonai/
- switch continuos integration protocol to github actions: https://github.com/wwu-mmll/photonai/actions
- code clean ups