Code for the paper: http://proceedings.mlr.press/v80/sen18a/sen18a.pdf

Please cite the above paper if using this code base for a publication.

Installation

• You first need to build the direct fortran library. For this cd into utils/direct_fortran and run bash make_direct.sh. You will need a fortran compiler such as gnu95. Once this is done, you can run simple_direct_test.py to make sure that it was installed correctly.
• Run source set_up_gittins to set up all environment variables.
• To test the installation, run bash run_all_tests.sh. Some of the tests are probabilistic and could fail at times. If this happens, run the same test several times and make sure it isn't consistently failing.
• Python packages required are numpy, pandas, scikit-learn, matplotlib, multiprocessing, brewer2mpl

Running Synthetic Examples

1. In order to run the synthetic examples, go to the file MFPDOO/experiments_synthetic.py
2. Set mfobject as the desired function. For instance it has been set as borehole.
3. Now from the root directory of the project run python MFPDOO/experiments_synthetic.py

Running SVM parameter tuning example

1. For hyper-paremeter tuning we need to convert the parameter tuning problem into a multi-fidelity black box optimization object mfobject like those of the synthetic functions.
2. An example for such an object is given in the v2_news/news_classifier.py in the context of tuning svm for the 20 news group data-set. The file is fairly self-explanatory and the user can create similar objects for other parameter tuning examples.
3. In order to run our example for the given budget run python MFPDOO/experiments_svm.py from the root directory of the project.

All the results are saved in examples/results/.