Welcome to the code for our paper, Reliable Post hoc Explanations: Modeling Uncertainty in Explainability, published at NeurIPS 2021. We encourage you to read the full paper.
Visualizing the posteriors of BayesLIME explanations on an image of a dog and COMPAS:
If you found this work useful, please cite us:
@inproceedings{reliableposthoc:neurips21,
author = {Dylan Slack and Sophie Hilgard and Sameer Singh and Himabindu Lakkaraju},
title = { {Reliable Post hoc Explanations Modeling Uncertainty in Explainability} },
booktitle = {Neural Information Processing Systems (NeurIPS)},
year = {2021}
}
An example usage of the explainer is provided in ./visualization/image_posterior_example.py, where we visualize the posterior of a BayesLIME explanation on an image of the first author's dog.
The German Credit + COMPAS datasets are included in the ./data folder. Within experiments, the german credit data set is called as --dataset german and compas is called as --dataset compas.
The MNIST data is set to download automatically on the first run.
In places where the MNIST data is accepted, by specifying the --dataset flag, it is possible to select the digit on which to run the experiment by specifying, for example, --dataset mnist_1 for the 1 digit or --dataset mnist_3 for the 3 digit, and so on.
To download the ImageNet data, use this script, selecting the appropriate class indices (e.g., n02108915 is the French Bulldog class used in the paper). For example, to download the French Bulldog data, run:
python ./downloader.py
-data_root ./data/imagenet/frenchbulldog \
-use_class_list True \
-class_list n02108915 \
-images_per_class 100 Once the imagenet dataset is installed, it can be called with --dataset imagenet_classname where classname is the name of the folder where the data is stored (for instance frenchbulldog running the script above).
The tabular models are trained when they are called in experiments. The pre-trained MNIST model is provided in the ./data/mnist subfolder. The VGG16 IMAGENET model will be downloaded when it is called.
Code to run experiments from the paper is included in the ./experiments directory within the project.
For image experiments, GPU/TPU acceleration is recommended. I ran most of the experiments for this paper with a single NVIDIA 2080TI and a few with a NVIDIA Titan RTX.
For the tabular experiments, it's possible to run them on CPU. I tested this using a 1.4 GHz Intel Core i5 from a 2019 MacBook Pro, and it seemed to work fine. In places in the experiments where multithreading is used (--n_threads) in the experiments, be careful to use a value less than the avaliable cores on your CPU. I noticed that if I set --n_threads value too high on the MacBook, it caused it to freeze.
You can reach out to [email protected] with any questions.
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