Polaris

Overview

This repository hosts the code and data for NIPS'18 paper: Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections.

Prerequisites

You can install all the required packages natively, but we recommend using conda.

1. Create an environment:

   conda create -n polaris python=3.6 tensorflow keras pandas requests
   source activate polaris
   

2. Install namedlist, jsonlines, and svgwrite:

   pip install namedlist jsonlines svgwrite
   

3. Install Gurobi. Academic licenses are free.

4. Install cleverhans:

   pip install cleverhans
   

5. Install magenta. (Required by drawing tutoring).

Running the experiments

• To run mortgage underwriting:

  python -m fanniemae.mortgage_exp ./fanniemae/data/imb_100k.test ./fanniemae/models/model_5_200 100 100
  

• To run solver performance prediction:

  python -m proof.proof_explain ./proof/models/8x100.h5 100
  

• To run drawing tutoring:

  python -m gold_cat.cat_exp ./gold_cat/model/dis/cat_model_mix-9000 ./gold_cat/model/gen/
  

Training the models

Instead of using pre-trained models, you can train your own models. (Coming soon)