Metadata-Version: 2.1
Name: imbDRL
Version: 2021.1.26.1
Summary: Imbalanced Classification with Deep Reinforcement Learning.
Home-page: https://github.com/Denbergvanthijs/imbDRL
Author: Thijs van den Berg
Author-email: denbergvanthijs@gmail.com
License: UNKNOWN
Description: # imbDRL
        
        ![GitHub Workflow Status](https://img.shields.io/github/workflow/status/Denbergvanthijs/imbDRL/Build) ![License](https://img.shields.io/github/license/Denbergvanthijs/imbDRL)
        
        ***Imbalanced Classification with Deep Reinforcement Learning.***
        
        This repository contains an (Double) Deep Q-Network implementation of binary classification on unbalanced datasets using [TensorFlow 2.3+](https://www.tensorflow.org/) and [TF Agents 0.6+](https://www.tensorflow.org/agents). The Double DQN as published in [this paper](https://arxiv.org/abs/1509.06461) by *van Hasselt et al. (2015)* is using a custom environment based on [this paper](https://arxiv.org/abs/1901.01379) by *Lin, Chen & Qi (2019)*.
        
        Example scripts on the [Mnist](http://yann.lecun.com/exdb/mnist/), [Fashion Mnist](https://github.com/zalandoresearch/fashion-mnist), [Credit Card Fraud](https://www.kaggle.com/mlg-ulb/creditcardfraud) and [Titanic](https://www.tensorflow.org/datasets/catalog/titanic) datasets can be found in the `./imbDRL/examples/ddqn/` folder.
        
        ## Requirements
        
        * [Python 3.7+](https://www.python.org/)
        * The required packages as listed in: `requirements.txt`
        * Logs are by default saved in `./logs/`
        * Trained models are by default saved in `./models/`
        * Optional: `./data/` folder located at the root of this repository.
          * This folder must contain ```creditcard.csv``` downloaded from [Kaggle](https://www.kaggle.com/mlg-ulb/creditcardfraud) if you would like to use the [Credit Card Fraud](https://www.kaggle.com/mlg-ulb/creditcardfraud) dataset.
          * Note: `creditcard.csv` needs to be split in a seperate train and test file. Please use the function `imbDRL.utils.split_csv`
        
        ## Getting started
        
        Install via `pip`:
        
        * `pip install imbDRL`
        
        Run any of the following scripts:
        
        * `python .\imbDRL\examples\ddqn\train_credit.py`
        * `python .\imbDRL\examples\ddqn\train_famnist.py`
        * `python .\imbDRL\examples\ddqn\train_mnist.py`
        * `python .\imbDRL\examples\ddqn\train_titanic.py`
        
        ## TensorBoard
        
        To enable [TensorBoard](https://www.tensorflow.org/tensorboard), run ```tensorboard --logdir logs```
        
        ## Tests and linting
        
        Extra arguments are handled with the `./tox.ini` file.
        
        * Pytest: `python -m pytest`
        * Flake8: `flake8`
        * Coverage can be found in the generated `./htmlcov` folder
        
        ## Appendix
        
        The appendix can be found in the [imbDRLAppendix](https://github.com/Denbergvanthijs/imbDRLAppendix) repository.
        
Keywords: imbalanced classification,deep reinforcement learning,deep q-network,reward-function,classification,medical
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Environment :: GPU :: NVIDIA CUDA
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown
