https://github.com/rajatsen91/MFTREE_DET

Multi-Fidelity Deterministic Optimistic Optimization for Black-Box Functions
https://github.com/rajatsen91/MFTREE_DET

Last synced: 3 months ago
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Multi-Fidelity Deterministic Optimistic Optimization for Black-Box Functions

• Host: GitHub
• URL: https://github.com/rajatsen91/MFTREE_DET
• Owner: rajatsen91
• Created: 2018-06-06T03:50:39.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-08-17T05:34:05.000Z (over 6 years ago)
• Last Synced: 2024-07-02T06:03:52.456Z (7 months ago)
• Language: Python
• Size: 203 KB
• Stars: 5
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• awesome-monte-carlo-tree-search-papers - [Code

README

        
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/`._