https://github.com/changebo/Vine_MCTS

Vine copula structure learning via Monte Carlo tree search
https://github.com/changebo/Vine_MCTS

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Vine copula structure learning via Monte Carlo tree search

• Host: GitHub
• URL: https://github.com/changebo/Vine_MCTS
• Owner: changebo
• Created: 2019-07-04T14:34:41.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-12-16T19:39:48.000Z (almost 5 years ago)
• Last Synced: 2024-07-02T04:46:00.831Z (5 months ago)
• Language: Python
• Homepage:
• Size: 1.44 MB
• 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

        
# Vine_MCTS

Code for the paper

> Chang, B., Pan, S. & Joe, H.. (2019). Vine copula structure learning via Monte Carlo tree search. International Conference on Artificial Intelligence and Statistics.



  

   

   



## Install Python packages

Using [pip](https://pip.pypa.io/en/stable/installing/):

```

pip install -r requirements.txt

```

See [python-igraph website](https://igraph.org/python/) for detailed information on installing `python-igraph` package.

## Usage

### Generate correlation matrices

Use the following code to generate the correlation matrices, which are stored as `.csv` files in `data/corr` folder and used as input. For example, the following code will generate `data/corr/abalone.csv`.

```

cd data/raw_data/

python abalone.py

cd ../..

```

Customized input `csv` files can be created and saved in `data/corr` folder. The first line of the the `csv` files is the number of samples; the following lines are the correlation matrix. See `data/corr/abalone.csv` as an example.

### Run MCTS

```

python main.py -f abalone -ntrunc 4,5 -max_iter 5000

```

Other arguments:

```

usage: main.py [-h] [-f FILE_PREFIX] [-nw NUM_WORKER] [-fpu FPU] [-pb PB]

               [-log_freq LOG_FREQ] [-ntrunc NTRUNC] [-seed SEED]

               [-max_iter MAX_ITER]

optional arguments:

  -h, --help          show this help message and exit

  -f FILE_PREFIX      File prefix

  -nw NUM_WORKER      Number of workers to run in parallel (default: 1)

  -fpu FPU            First play urgency (default: 1.0)

  -pb PB              Progressive bias (default: 0.1)

  -log_freq LOG_FREQ  Log Frequency (default: 100)

  -ntrunc NTRUNC      A list of truncation level. For example, "2,3,4". By

                      default, levels from 1 to d-1.

  -seed SEED          Seed (default: 1)

  -max_iter MAX_ITER  Maximum number of iterations (default: 5000)

```

`FILE_PREFIX` is the filename of the input `csv` file. Results are saved in `output` folder.

## Interactive Jupyter Notebook example

Install [Jupyter Notebook](https://jupyter.org/install) and run the following:

```

jupyter notebook misc/visualization.ipynb

```

## BibTeX

```

@inproceedings{chang2019vine, 

  title={Vine copula structure learning via {M}onte {C}arlo tree search}, 

  author={Chang, Bo and Pan, Shenyi and Joe, Harry}, 

  booktitle={International Conference on Artificial Intelligence and Statistics}, 

  year={2019} 

}

```