diff --git a/CHANGELOG.md b/CHANGELOG.md index 266cb33ec8666099ffcb638ff85d814d7e2cf184..988e3e5a7863868cead1a2fec7c7b6d1c750b8d8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,29 @@ # Changelog All notable changes to this project will be documented in this file. +## v2.1.0 - 2022-06-07 - new evaluation metrics and improved training + +### general: +* new evaluation metrics, IOA and MNMB +* advanced train options for early stopping +* reduced execution time by refactoring + +### new features: +* uncertainty estimation of MSE is now applied for each season separately (#374) +* added different configurations of early stopping to use either last trained or best epoch (#378) +* train monitoring plots now add a star for best epoch when using early stopping (#367) +* new evaluation metric index of agreement, IOA (#376) +* new evaluation metric modified normalised mean bias, MNMB (#380) +* new plot available that shows temporal evolution of MSE for each station (#381) + +### technical: +* reduced loading of forecast path from data store (#328) +* bug fix for not catched error during transformation (#385) +* bug fix for data handler with climate and fir filter leading to calculate transformation always with fir filter (#387) +* improved duration for latex report creation at end of preprocessing (#388) +* enhanced speed for make prediction in postprocessing (#389) +* fix to always create version badge from version and not from tag name (#382) + ## v2.0.0 - 2022-04-08 - tf2 usage, new model classes, and improved uncertainty estimate ### general: diff --git a/README.md b/README.md index 8decf00b29f91e0a3a014bbf57e92aff12c5e035..792c6d4a06564eb050467f271f660761ec4d3d71 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,7 @@ HPC systems, see [here](#special-instructions-for-installation-on-jülich-hpc-sy * Installation of **MLAir**: * Either clone MLAir from the [gitlab repository](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair.git) and use it without installation (beside the requirements) - * or download the distribution file ([current version](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-2.0.0-py3-none-any.whl)) + * or download the distribution file ([current version](https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-2.1.0-py3-none-any.whl)) and install it via `pip install <dist_file>.whl`. In this case, you can simply import MLAir in any python script inside your virtual environment using `import mlair`. diff --git a/dist/mlair-2.1.0-py3-none-any.whl b/dist/mlair-2.1.0-py3-none-any.whl new file mode 100644 index 0000000000000000000000000000000000000000..b5069f2ae900ff7bf43428d3adba8a50be742588 Binary files /dev/null and b/dist/mlair-2.1.0-py3-none-any.whl differ diff --git a/docs/_source/installation.rst b/docs/_source/installation.rst index 6ac4937e6a729c12e54007aa32f0e59635289fdd..6cbf8c424bdd29470c23eb95a9b5d3a5071cf39f 100644 --- a/docs/_source/installation.rst +++ b/docs/_source/installation.rst @@ -27,7 +27,7 @@ Installation of MLAir * Install all requirements from `requirements.txt <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/requirements.txt>`_ preferably in a virtual environment * Either clone MLAir from the `gitlab repository <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair.git>`_ -* or download the distribution file (`current version <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-2.0.0-py3-none-any.whl>`_) +* or download the distribution file (`current version <https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/blob/master/dist/mlair-2.1.0-py3-none-any.whl>`_) and install it via :py:`pip install <dist_file>.whl`. In this case, you can simply import MLAir in any python script inside your virtual environment using :py:`import mlair`. diff --git a/mlair/__init__.py b/mlair/__init__.py index 2ca5c3ab96fb3f96fa2343efab02860d465db870..901947e5313a183e3909687b1fea0096075f836c 100644 --- a/mlair/__init__.py +++ b/mlair/__init__.py @@ -1,6 +1,6 @@ __version_info__ = { 'major': 2, - 'minor': 0, + 'minor': 1, 'micro': 0, }