Metadata-Version: 2.1
Name: improvelib
Version: 0.0.3b1
Summary: Open-source package for model standardization and comparison in Python
Author-email: Alex Partin <apartin@anl.gov>
Project-URL: Homepage, https://github.com/JDACS4C-IMPROVE/IMPROVE
Project-URL: Issues, https://github.com/JDACS4C-IMPROVE/IMPROVE/issues
Project-URL: Documentation, https://jdacs4c-improve.github.io/docs/
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: requests
Requires-Dist: tqdm
Requires-Dist: typing-extensions
Requires-Dist: pyyaml
Requires-Dist: scikit-learn

# IMPROVE
Libraries and scripts for the IMPROVE project.

## Purpose

## Installation
Clone the `IMPROVE library` repository to a directory of your preference (outside of your drug response prediction (DRP) model's directory).

```bash
git clone https://github.com/JDACS4C-IMPROVE/IMPROVE
cd IMPROVE
git checkout develop
```

## Download data
Download the [cross-study analysis (CSA) benchmark data](https://web.cels.anl.gov/projects/IMPROVE_FTP/candle/public/improve/benchmarks/single_drug_drp/benchmark-data-pilot1/) into your model's directory. For example:
```bash
./scripts/get-benchmarks $DESTINATION/csa_data/raw_data
```

The directory structure should look like this after the above steps have been completed:

```
IMPROVE
DRP_model
└── csa_data
```

## Set environment variables

Specify the full path to the `IMPROVE library` with $PYTHONPATH and the path to the CSA data with $IMPROVE_DATA_DIR.
```bash
cd DRP_model
export PYTHONPATH=$PYTHONPATH:/your/path/to/IMPROVE
export IMPROVE_DATA_DIR="./csa_data/"
```

## Tutorial
For a detailed guide on how to use the `IMPROVE library` using an example model, LightGBM, see https://jdacs4c-improve.github.io/docs/content/unified_interface.html.

## Examples
Two repositories demonstrating the use of the `IMPROVE library` for drug response prediction:
* https://github.com/JDACS4C-IMPROVE/GraphDRP/tree/develop -- GraphDRP (deep learning model based on graph neural network)
* https://github.com/JDACS4C-IMPROVE/LGBM/tree/develop -- LightGBM model
