Metadata-Version: 2.1
Name: groufi
Version: 0.0.3
Summary: A small library to compute group feature importance
Home-page: https://github.com/BorealisAI/group-feature-importance
License: Attribution-NonCommercial-ShareAlike 4.0 International
Keywords: ML,explainability,sklearn,feature importance
Author: Borealis AI
Maintainer: Thibaut Durand
Maintainer-email: durand.tibo+gh@gmail.com
Requires-Python: >=3.8,<4.0
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: Free for non-commercial use
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: numpy (>=1.22.2,<2.0.0)
Requires-Dist: pandas (>=1.0.0,<2.0.0)
Requires-Dist: scikit-learn (>=1.0.0,<2.0.0)
Project-URL: Repository, https://github.com/BorealisAI/group-feature-importance
Description-Content-Type: text/markdown

# Group feature importance

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This release looks at the role of joint feature importance for explainability in instances where
features may be highly correlated when providing an output. Specifically, the method operates by
regrouping the correlated features and then looking at the group-level impact of imputation. Doing
so allows us to consider the impact of a joint permutation of the correlated features.

## Examples

Some examples are available in [`examples`](examples)

## Installation

### Installing with `pip`

This repository is tested on Python 3.9, and Linux systems.
It is recommended to install in a virtual environment to keep your system in order.
The following command installs the latest version of the library:

```shell
pip install groufi
```

### Installing from source

To install `groufi` from source, you can follow the steps below. First, you will need to
install [`poetry`](https://python-poetry.org/docs/master/). `poetry` is used to manage and install
the dependencies.
If `poetry` is already installed on your machine, you can skip this step. There are several ways to
install `poetry` so
you can use the one that you prefer. You can check the `poetry` installation by running the
following command:

```shell
poetry --version
```

Then, you can clone the git repository:

```shell
git clone git@github.com:BorealisAI/group-feature-importance.git
```

Then, it is recommended to create a Python 3.8+ virtual environment. This step is optional so you
can skip it. To create
a virtual environment, you can use the following command:

```shell
make conda
```

It automatically creates a conda virtual environment. When the virtual environment is created, you
can activate it with
the following command:

```shell
conda activate groufi
```

This example uses `conda` to create a virtual environment, but you can use other tools or
configurations. Then, you
should install the required package to use `groufi` with the following command:

```shell
make install
```

This command will install all the required packages. You can also use this command to update the
required packages. This
command will check if there is a more recent package available and will install it. Finally, you can
test the
installation with the following command:

```shell
make test
```

## License

This repository is released under the Attribution-NonCommercial-ShareAlike 4.0 International license
as found in
the [LICENSE](LICENSE) file.

