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Folktables plural. Stub out basic readme
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millerjohnp committed Jun 14, 2021
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49 changes: 43 additions & 6 deletions README.md
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# Folktables

**Folktables** is a TODO: one-paragraph description.

# TODO: Add to PyPI.
To make the shared code available outside the `pkg/tablebench` directory, you can make the code accessible as a Python package by running the following command in the `pkg` directory:

## Table of Contents
1. [Basic installation instructions](#basic-installation-instructions)
2. [Quick start examples](#quick-start-examples)
3. [Datasets in folktables](#datasets-in-folktables)
5. [Frequently asked questions](#frequently-asked-questions)
6. [Citing folkTables](#citing-folktables)

Folktables is still under active development! If you find bugs or have feature
requests, please file a
[Github issue](https://github.com/zykls/folktables/issues).
We welcome all kinds of issues, especially those related to correctness, documentation, performance, and new features.


## Basic installation instructions
1. (Optionally) create a virtual environment
```
pip install -e .
python3 -m venv folkenv
source folkenv/bin/activate
```
This also installs the dependencies for our shared code listed in `setup.py` (e.g., NumPy, Pandas, etc.) if they are not available yet.
After installing the package, you can simply import the code with
2. Install via pip
```
import tablebench
pip install folktables
```
You can also install folktables directly from source.
```
git clone https://github.com/zykls/folktables.git
cd folktables
pip install -r requirements.txt
```


## Quick start examples
TODO!


## Datasets in folktables
TODO!


## Frequently asked questions
TODO!


## Citing folktables
TODO!
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3 changes: 3 additions & 0 deletions requirements.txt
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-e .

pytest
6 changes: 3 additions & 3 deletions setup.py
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from setuptools import setup, find_packages

setup(
name="folktable",
name="folktables",
version="0.0.0",
author="John Miller, Frances Ding, Ludwig Schmidt, Moritz Hardt",
author_email="miller_john@berkeley.edu",
description="New machine learning benchmarks from tabular datasets.",
packages=find_packages(),
install_requires=[
"numpy",
"numpy",
"pandas",
"pytest",
"sklearn",
],
classifiers=[
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