diff --git a/README.md b/README.md index f2ab9591..7823154b 100644 --- a/README.md +++ b/README.md @@ -6,8 +6,6 @@ MAST-ML is an open-source Python package designed to broaden and accelerate the PyPI - Downloads -[![Actions Status](https://github.com/uw-cmg/MAST-ML/tree/master/.github/workflows/python_package.yml/badge.svg)](https://github.com/uw-cmg/MAST-ML/actions) - Documentation Status @@ -35,38 +33,14 @@ MAST-ML is an open-source Python package designed to broaden and accelerate the * Tutorial 7: Model predictions with guide rails: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/uw-cmg/MAST-ML/blob/master/examples/MASTML_Tutorial_7_ModelPredictions_with_Guide_Rails.ipynb) -## MAST-ML version 3.1.x Major Updates from July 2022 -* Refinement of tutorials, addition of Tutorial 7, Colab links as badges added for easier use. - -* mastml_predictor module added to help streamline making predictions (with option to include error bars) on new test data. - -* Basic parallelization added, which is especially useful for speeding up nested CV runs with many inner splits. - -* EnsembleModel now handles ensembles of GPR and XGBoost models. - -* Numerous improvements to plotting, including new plots (QQ plot), better axis handling and error bars (RvE plot), plotting and stats separated per group if groups are specified. - -* Improvements to feature selection methods. EnsembleModelFeatureSelector includes dummy feature references, added SHAP-based selector - -* Added assessment of baseline tests like comparing metrics to predicting the data average or permuted data test - -* Many miscellaneous bug fixes. - - -## MAST-ML version 3.0.x Major Updates from July 2021 -* MAST-ML no longer uses an input file. The core functionality and workflow of MAST-ML has been rewritten to be more conducive to use in a Jupyter notebook environment. This major change has made the code more modular and transparent, and we believe more intuitive and easier to use in a research setting. The last version of MAST-ML to have input file support was version 2.0.20 on PyPi. - -* Each component of MAST-ML can be run in a Jupyter notebook environment, either locally or through a cloud-based service like Google Colab. As a result, we have completely reworked our use-case tutorials and examples. All of these MAST-ML tutorials are in the form of Jupyter notebooks and can be found in the mastml/examples folder on Github. - -* An active part of improving MAST-ML is to provide an automated, quantitative analysis of model domain assessement and model prediction uncertainty quantification (UQ). Version 3.x of MAST-ML includes more detailed implementation of model UQ using new and established techniques. - ## Contributors University of Wisconsin-Madison Computational Materials Group: * Prof. Dane Morgan * Dr. Ryan Jacobs +* Lane Schultz * Dr. Tam Mayeshiba -* Ben Afflerbach +* Dr. Ben Afflerbach * Dr. Henry Wu University of Kentucky contributors: @@ -84,11 +58,14 @@ https://mastmldocs.readthedocs.io/en/latest/ ## Funding -This work was and is funded by the National Science Foundation (NSF) SI2 award No. 1148011 and DMREF award number DMR-1332851 +This work was funded by the National Science Foundation (NSF) SI2 award number 1148011 +This work was funded by the National Science Foundation (NSF) DMREF award number DMR-1332851 + +This work was funded by the National Science Foundation (NSF) CSSI award number 1931298 -## Citing MAST-ML +## Citing MAST-ML If you find MAST-ML useful, please cite the following publication: @@ -96,20 +73,57 @@ Jacobs, R., Mayeshiba, T., Afflerbach, B., Miles, L., Williams, M., Turner, M., "The Materials Simulation Toolkit for Machine Learning (MAST-ML): An automated open source toolkit to accelerate data- driven materials research", Computational Materials Science 175 (2020), 109544. https://doi.org/10.1016/j.commatsci.2020.109544 +If you find the uncertainty quantification (error bar) approaches useful, please cite the following publication: +Palmer, G., Du, S., Politowicz, A., Emory, J. P., Yang, X., Gautam, A., Gupta, G., Li, Z., Jacobs, R., Morgan, D., +"Calibration after bootstrap for accurate uncertainty quantification in regression models", npj Computational Materials 8 115 (2022). https://doi.org/10.1038/s41524-022-00794-8 -## Code Repository +## Installation -MAST-ML is freely available via Github: +MAST-ML can be installed via pip: ``` -https://github.com/uw-cmg/MAST-ML - -git clone https://github.com/uw-cmg/MAST-ML +pip install mastml ``` -MAST-ML can also be installed via pip: +Clone from Github: ``` -pip install mastml +git clone https://github.com/uw-cmg/MAST-ML ``` + +# Changelog + +## MAST-ML version 3.2.x Major Updates from April 2024 +* Integration of domain of applicability approach using kernel density estimates based on MADML package: https://github.com/leschultz/materials_application_domain_machine_learning + +* Refinement of tutorials, addition of new tutorial for domains of applicability + +* Updates to plotting routines for error bar analysis + +* Many small bug fixes and updates to conform to updated versions of package dependencies + +## MAST-ML version 3.1.x Major Updates from July 2022 +* Refinement of tutorials, addition of Tutorial 7, Colab links as badges added for easier use. + +* mastml_predictor module added to help streamline making predictions (with option to include error bars) on new test data. + +* Basic parallelization added, which is especially useful for speeding up nested CV runs with many inner splits. + +* EnsembleModel now handles ensembles of GPR and XGBoost models. + +* Numerous improvements to plotting, including new plots (QQ plot), better axis handling and error bars (RvE plot), plotting and stats separated per group if groups are specified. + +* Improvements to feature selection methods. EnsembleModelFeatureSelector includes dummy feature references, added SHAP-based selector + +* Added assessment of baseline tests like comparing metrics to predicting the data average or permuted data test + +* Many miscellaneous bug fixes. + + +## MAST-ML version 3.0.x Major Updates from July 2021 +* MAST-ML no longer uses an input file. The core functionality and workflow of MAST-ML has been rewritten to be more conducive to use in a Jupyter notebook environment. This major change has made the code more modular and transparent, and we believe more intuitive and easier to use in a research setting. The last version of MAST-ML to have input file support was version 2.0.20 on PyPi. + +* Each component of MAST-ML can be run in a Jupyter notebook environment, either locally or through a cloud-based service like Google Colab. As a result, we have completely reworked our use-case tutorials and examples. All of these MAST-ML tutorials are in the form of Jupyter notebooks and can be found in the mastml/examples folder on Github. + +* An active part of improving MAST-ML is to provide an automated, quantitative analysis of model domain assessement and model prediction uncertainty quantification (UQ). Version 3.x of MAST-ML includes more detailed implementation of model UQ using new and established techniques.