PHOTONAI v2.4.0

What's Changed

• Fixed issue with photon registry when multiple registry objects are i… by @jernsting in #48
• Fix/save optimum pipe by @jernsting in #51
• Added multiprocessing switch for parallel computing by @jernsting in #53
• Changed versioning to pbr, resolved circular input errors, moved base… by @jernsting in #52
• Create dependabot.yml by @jernsting in #54
• Feature/new imports by @jernsting in #55
• minor features added by @RLeenings in #56
• debug imbalanced data transform unit test by @RLeenings in #57
• Debug/debug imbalanced test by @RLeenings in #58
• Added monthly matrix testing technique by @jernsting in #60
• Revert "Develop" due to squashing. by @jernsting in #62
• Develop by @jernsting in #63
• Added switch to create json output by @jernsting in #65
• Fix/permutation importances by @julblanke in #67
• add functionality to compare model and permutation feature importances by @RLeenings in #72
• Feature/model zoo by @RLeenings in #73
• Develop by @RLeenings in #74

Full Changelog: 2.3.0...2.4.0

PHOTONAI v2.3.0

Documentation: https://wwu-mmll.github.io/photonai/

What's Changed

• Develop by @RLeenings in #50

Full Changelog: 2.2.1...2.3.0

PHOTONAI v2.2.1

Documentation: https://wwu-mmll.github.io/photonai/

Changelog:

• Removed Keras requirement
• Fixed Random Search Optimizer
• Minor bugfixes

PHOTONAI v2.2.0

Documentation: https://wwu-mmll.github.io/photonai/

Changelog
tba

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

PHOTONAI v2.0.0

• removed investigator, instead we offer the Explorer, a Javascript web application to visualize and analyze the results
• moved photon.neuro module to an own package called photonai_neuro
• updated repository structure, moved tests and examples to root directory
• consistently named the repository photonai everywhere
• included continuous integration pipeline with travis-ci

1.1.0

Update version to 1.1.0

0.5.0

Cleaned Up PHOTON

Reassured all examples are working

0.4.0

Starting with this release, you should be able to install PHOTON via pip. Issues with installing the requirements have been resolved. This release includes the PHOTON Investigator to visualize the results.