AIM

This repo includes codes for reproducing the experiments in the paper Additive Instance-wise Approach to Multi-class Model Interpretation accepted at ICLR 2023.

Dependencies

AIM requires Python 3.7+ and the following packages

• spacy
• nltk
• lemminflect
• numpy
• pandas
• tqdm
• torch
• torchtext

Download this to use spacy tokenizer

python -m spacy download en_core_web_sm

Replicating the experiments on baselines in this repo further requires

• lime
• tensorflow==1.15.0
• keras==2.0.0

Or you can run the following command

git clone https://github.com/isVy08/AIM
cd AIM
pip install -r requirements.txt

The following sections focus on experiments for texts. For details on experiments for MNIST dataset, please refer to mnist/.

Data

data_generator.py provides scripts for downloading datasets and training a tokenizer. IMDB and AG News are available in torchtext library while HateXplain can be obtained from HateXplain repo (Mathew et al. 2021).

For pre-processed datasets and a pre-trained tokenizer used in our experiment, refer to this Google Drive collection.
Download the datasets to data/ and the pre-trained tokenizer to model/.

Model

Configurations for black-box models and model explainers are given in config/.

Black-box Models

Pre-trained black-box models for each dataset are available in their respective folders. If you need to train them from sratch, please check blackbox.py and train_blackbox.py.

For example, to train a bidirectional GRU on IMDB dataset, run the following command

python train_blackbox.py config/WordGRU.json train

Before training model explainers, you need to obtain the black-box's predictions. Do run

python train_blackbox.py config/WordGRU.json val

and predictions will be generated in the same format as the original dataset under the name WordGRU.pickle. Again, you can directly download the predictions from Google Drive and place them inside the corresponding folder, i.e., data/imdb/.

Model Explainers

Our architecture is described in explainer.py. To train a model explainer for a dataset e.g., IMDB, do

python main.py config/imdb.json

You will find the trained models inside their respective directory model/

Inference

infer.py provides instructions on how to perform inference for AIM and other baselines. For example on IMDB test set, run

python infer.py imdb

and feature weights will be written in a text file data/imdb/aim.txt.

Evaluation

First, we need a list of stopwords and WordNet database. The Google Drive folder provides a curated list of stopwords and a shortcut dict object to Wordnet database. Download and place them inside model/.

To evaluate AIM on IMDB test set, run

python evaluate.py imdb data/imdb/aim.txt

On how to run experiments on the baseline models, please refer to baseline/.
The codes are gratefully adapted from L2X repo, LIME repo and VIBI repo.

Citation

If you use the codes or datasets in this repository, please cite our paper.

@inproceedings{vo2022additive,
  title={An Additive Instance-Wise Approach to Multi-class Model Interpretation},
  author={Vo, Vy and Nguyen, Van and Le, Trung and Tran, Quan Hung and Haf, Reza and Camtepe, Seyit and Phung, Dinh},
  booktitle={The Eleventh International Conference on Learning Representations},
  year={2022}
}