INVASE: Instance-wise Variable Selection
Project description
INVASE: Instance-wise Variable Selection
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
$ pip install invase
or from source, using
$ pip install .
:boom: Sample Usage
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
pip install .[testing]
The tests can be executed using
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},
}
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