https://github.com/vanderschaarlab/invase

INVASE: Instance-wise Variable Selection . For more details, read the paper "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019.
https://github.com/vanderschaarlab/invase

interpretability machine-learning machine-learning-algorithms machine-learning-interpretability pytorch xai

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INVASE: Instance-wise Variable Selection . For more details, read the paper "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019.

• Host: GitHub
• URL: https://github.com/vanderschaarlab/invase
• Owner: vanderschaarlab
• License: mit
• Created: 2022-08-30T14:56:34.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-08-30T16:35:50.000Z (over 3 years ago)
• Last Synced: 2025-04-05T07:23:04.036Z (about 1 year ago)
• Topics: interpretability, machine-learning, machine-learning-algorithms, machine-learning-interpretability, pytorch, xai
• Language: Python
• Homepage:
• Size: 110 KB
• Stars: 5
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: docs/contributing.rst
  • License: LICENSE

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README

          
# INVASE: Instance-wise Variable Selection

[![Tests](https://github.com/vanderschaarlab/INVASE/actions/workflows/test.yml/badge.svg)](https://github.com/vanderschaarlab/INVASE/actions/workflows/test.yml)

[![Downloads](https://img.shields.io/pypi/dd/invase)](https://pypi.org/project/invase/)

[![arXiv](https://img.shields.io/badge/arXiv-2206.07769-b31b1b.svg)](https://openreview.net/pdf?id=BJg_roAcK7)

[![Test In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/11PZ6gk46lprhoDR30ZCpdLVB7WNn3pFj?usp=sharing)

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

![image](https://github.com/vanderschaarlab/invase/raw/main/docs/arch.png "INVASE")

Authors: Jinsung Yoon, James Jordon, Mihaela van der Schaar

Paper: Jinsung Yoon, James Jordon, Mihaela van der Schaar, "IINVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019. (https://openreview.net/forum?id=BJg_roAcK7)

## :rocket: Installation

The library can be installed from PyPI using

```bash

$ pip install invase

```

or from source, using

```bash

$ pip install .

```

## :boom: Sample Usage

```python

import pandas as pd

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

from invase import INVASE

X, y = load_iris(return_X_y=True, as_frame = True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

## Load the model

model = LogisticRegression()

model.fit(X_train, y_train)

## Load INVASE

explainer = INVASE(

    model, 

    X_train, 

    y_train, 

    n_epoch=1000, 

    prefit = True, # the model is already trained

)

## Explain

explainer.explain(X_test.head(5))

```

## :hammer: Tests

Install the testing dependencies using

```bash

pip install .[testing]

```

The tests can be executed using

```bash

pytest -vsx

```

## Citing

If you use this code, please cite the associated paper:

```

@inproceedings{

    yoon2018invase,

    title={{INVASE}: Instance-wise Variable Selection using Neural Networks},

    author={Jinsung Yoon and James Jordon and Mihaela van der Schaar},

    booktitle={International Conference on Learning Representations},

    year={2019},

    url={https://openreview.net/forum?id=BJg_roAcK7},

}

```