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dm-gym-0.1.6b0
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مشاهده بیشترتوضیحات
DM-Gym: A set of environments for developing reinforcement learning agents for Data Mining problems.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dm-gym-0.1.6b0 |
| نام | dm-gym |
| نسخه کتابخانه | 0.1.6b0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Ashwin M Devanga |
| ایمیل نویسنده | devanga.a@northeastern.edu |
| آدرس صفحه اصلی | https://github.com/ashwin-M-D/DM-Gym |
| آدرس اینترنتی | https://pypi.org/project/dm-gym/ |
| مجوز | BSD 3-Clause |
# DM-Gym
Data Mining Gym Environment for Reinforcement Learning
### Installation
You can download the git repository directly and keep the dm_gym folder inside your project folder.
You could also use the following steps to install DM-Gym in your system to be accessible anywhere:
```bash
git clone https://github.com/ashwin-M-D/DM-Gym.git
cd DM-Gym
pip install -e
```
The package is also in the pypi repository so it can be installed using pip.
```bash
pip install dm-gym
```
### Testing
To test the environment using the test codes provided, you need to have ray installed. Please use the conda environment file provided to setup your environment. Then, install DM-Gym as mentioned above and proceed with running the python notebooks provided. All of this can be done as follows.
```bash
## Installing DM-Gym
git clone https://github.com/ashwin-M-D/DM-Gym.git
cd DM-Gym
pip install -e
## Creating the conda environment
cd testing
cd conda_envs
conda env create -f dmgym_environment.yml
## Activate conda environment and cd to the folder containing the experiment files.
conda activate myenv_dmgym_testing
cd ..
cd experiments
```
### Available Environments
1. Clustering:
All these environments involve records which arrive in a random order and they are classified into one of k clusters. The value of k is predefined similar to k-means clustering.
Basically the input / state space is a single record from the dataset and the output is a discreet variable which is an integer between 0 and k-1, each specifying a specific cluster.
- clustering-v0: Reward function is negative of log(db-index)
This is a poor performing environment.
- clustering-v1: Reward function is based on both the distance and also the db-index.
This performs better than clustering-v0. However, it is suggested to use one of the other 2 clustering environments.
- clustering-v2: Uses a different reward system which is either p-1 or p at each step. Based on the paper "A Reinforcement Learning Approach to Online Clustering" [1]. Please use a low gamma value with this environment for optimal results.
- clustering-v3: This has the best performance among all the clustering environments. It converts the problem into a classification problem internally. However, to showcase true capabilities of RL, this should not be used. Use a low gamma value with this environment.
2. Classification:
Classification is done by reading a single record at a time and checking the output of your RL agent against the class it belongs to.
* classification-v0: This has very good performance and the reward function is defined as 1 if the output of the agent and the class it actually belongs to match and -1 if they don't match. It is again recommended to use a low gamma value for this environment.
### Environments planned for the future
2. Linear Regression environments.
2. More Classification environments.
#### Notes:
1. **See Testing folder to see examples of each of the environments and their outputs**
2. **Documentation for all available functions is available in the documentation folder. This folder will be updated regularly to make sure there are no ambiguity in the usage of the environments**
### References
1. Likas, A., 1999. A reinforcement learning approach to online clustering. _Neural computation_, _11_(8), pp.1915-1932.
<a href="http://62.217.125.140/jspui/bitstream/123456789/11133/1/Likas-1999-A%20reinforcement%20learning%20approach%20to%20online%20clustering.pdf">PDF</a>
2. Hubbs, C.D., Perez, H.D., Sarwar, O., Sahinidis, N.V., Grossmann, I.E. and Wassick, J.M., 2020. OR-Gym: A Reinforcement Learning Library for Operations Research Problems. _arXiv preprint arXiv:2008.06319_. <a href="https://arxiv.org/pdf/2008.06319">PDF</a> <a href="https://github.com/hubbs5/or-gym">GitHub</a>
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.5 | Python |
نحوه نصب
نصب پکیج whl dm-gym-0.1.6b0:
pip install dm-gym-0.1.6b0.whl
نصب پکیج tar.gz dm-gym-0.1.6b0:
pip install dm-gym-0.1.6b0.tar.gz