Python implementation of Automated Problem Identification: https://arxiv.org/abs/1707.00703
Part of Automated Machine Learning 2019/20 at Leiden University
Gideon Hanse, Yichao Li and Jaco Tetteroo
- python >= 3.6
- tensorflow >= 2.0
Note: for those with access to the LIACS DSLab, a virtual environment with these prerequisites is available:
All scripts in this repository are intended to be ran from the root directory: /API
Most scripts will import from /API/Project; this should work out of the box, but if import errors are encountered, try adding this manually to PYTHONPATH
python Project/GA.py Runs the API algorithm for Boston Housing for 5 generations only, with a small population of 10; and random search with a pool of 20 chromosomes. python Project/GA_adjusted.py Runs the adjusted algorithm for Boston Housing for 5 generations only, with a small population of 10. python Project/RandomForest.py Runs the random forest model for all datasets.
API Paper Published paper for the API algorithm, this is kept in the root directory /Project Contains all python scripts GA.py Our implementation of the API algorithm as described in the paper. GA_adjusted.py API algorithm with modified genetic operations RandomForest.py Contains the random forest model. preproc.py Preprocessing script that populated /processed_data. The original datasets are not include in this repository; please download these to /Project/datasets if you wish to repopulated /processed_data by running this script. /result_scripts Contains python scripts that were used to generate GA results over 20 runs; provided for reference, will take a long time to run. plot_results.py contains functions for extracting and plotting the results. /*res Result folders containing pickled results.