A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs

This toolbox supports the results in the following publication: D. Florescu and M. England. A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs. The authors are supported by EPSRC Project EP/R019622/1: Embedding Machine Learning within Quantifer Elimination Procedures. The main script is ML_test_rand/pipeline1.py. More details can be found as comments in the script. The sotd heuristic is implemented in the file data_gen_sotd_rand_test.mw. The data is already generated in the repository. The dataset of polynomials can be found in folders entitled poly_rand_dataset (for training) and poly_rand_dataset_test (for testing). The CAD data is generated by running generate_CAD_data.py. The data is already generated in the repository. The CAD routine was run in Maple 2018, with an updated version of the RegularChains Library downloaded in February 2019 from http://www.regularchains.org. The library file is also available in this repository (RegularChains_Updated.mla) This updated library contains bug fixes and additional functionality. The training and evaluation of the machine learning models was done using the scikit-learn package v0.20.2 for Python 2.7. Some data files generated by the pipeline are included in this repository for consistency and for saving time. However, they can be generated again by the user should they wish so: - the predictions with the sotd heuristic (II(d) in the supported paper) - the ML hyperparameters, resulted from 5-fold cross-validation (I(d)i in the supported paper) - the files containing CAD runtimes (in the folders comp_times_rand_dataset and comp_times_rand_dataset_test, corresponding to I(a) and II(e) in the supported paper)