Formulate trained predictors in Gurobi models
APACHE-2.0 License
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.5.0...v1.5.1
Published by pobonomo 8 months ago
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.4.0...v1.4.1
Published by pobonomo 8 months ago
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.4.0...v1.5.0a1
Published by pobonomo 11 months ago
The main update of this release is to use the new nonlinear capabilities in Gurobi 11 for modeling
the logistic regression. Namely, if the user has Gurobi 11, the attribute
FuncNonLinear is set for
the nonlinear constraints with the logistic function created by the package.
In our tests this results in better results (smaller errors in the solution and sometimes faster).
The corresponding parts of the documentation have been updated. In particular the
Student admission example
uses this and there is no more discussion on tuning the piecewise-linear approximation.
Other smaller updates regard the documentation that has been reorganized. In particular with the goal of documenting
better the internal objects that the package constructs and uses.
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.3.3...v1.4.0
Published by pobonomo 11 months ago
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.4.0b1...v1.4.0b2
Published by pobonomo 11 months ago
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.3.3...v1.4.0b1
Published by pobonomo about 1 year ago
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.3.2...v1.3.3
Published by pobonomo over 1 year ago
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.3.1...v1.3.2
Published by pobonomo over 1 year ago
Small changes for compatibility with newer version of tensorflow (2.13.0).
Update dependencies for testing and with the new tensorflow python 3.11 can now be tested.
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.3.0...v1.3.1
Published by pobonomo over 1 year ago
Also updated dependencies
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.2.3...v1.3.0
Published by pobonomo over 1 year ago
Add support for XGBoost's gradient boosting tree based regressors
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.2.2...v1.3.0rc1
Published by pobonomo over 1 year ago
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.2.2...v1.2.3
Published by pobonomo over 1 year ago
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.2.1...v1.2.2
Published by pobonomo over 1 year ago
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.2.0...v1.2.1
Published by pobonomo over 1 year ago
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.1.1...v1.2.0
Published by pobonomo over 1 year ago
Fixed bug with logistic regression and binary variables
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.1.0...v1.1.1
Published by pobonomo almost 2 years ago
This release adds the possibility of using pandas dataframe as input and output for inserting regression models. Those dataframes may contain columns of Gurobi variables or constants (fixed features). This is particularly convenient when used in conjunction with gurobipy-pandas.
We also add the possibility of handling Scikit Learn column transformers. In conjunction with pandas input, this makes it much more easier to handle variables that are indexed by categorical features.
Those two features are illustrated in the student enrollment example and the price optimization example.
This release also introduces the ability to use Scikit Learn PLS Regression. Thanks to @DavidWalz for contributing it!
The formulation of the decision tree has also been improved so that if should be faster to generate the models.
Finally, the documentation has been updated to include summary explanations on the MIP formulations used to represent the various regression models, the potential sources of differences with the original regression models and how to remedy them. The new page can be found here.
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/v1.0.1...v1.1.0
Published by pobonomo almost 2 years ago
Initial release!
Full Changelog: https://github.com/Gurobi/gurobi-machinelearning/compare/initial_commit...v1.0.1