This repository describes datasets and codes to replicate our experiments in the paper (https://arxiv.org/abs/1901.03209). We describe our data sources and code very briefly in Part 1 of this document. We give more detailed description for how to run our code in Part 2 of this document.
Our first experiment is based on the COMPAS model and its criminal recidivism data, which can be found at this link: https://corels.eecs.harvard.edu/corels/run.html. For your convenience, we have uploaded compas.csv in this repository.
In our paper, we do variable importance could analysis based on two model classes: logistic regression models and decision tree models.
The file compas-logistics.R and its companion file ellipsoid.R contains the code we use for the VIC analysis based on logistic regressions. For more details, please refer to Section 3 (in particular, Section 3.2.1) of our paper.
The file compas-decisiontrees.R and its companion file findCandidates.R contains the code we use for the VIC analysis based on decision trees. For more details, please refer to Section (in particular, Section 3.2.2) of our paper.
Our second experiment uses the in-vehicle coupon recommendation data, which is also studied by Wang et al. (2017) at this link: https://www.researchgate.net/publication/318645502_in-vehicle_coupon_recommendation. The data can also be found there and for your convenience, we have uploaded coupon.csv in this repository.
We do our VIC analysis based on logistic regression models. The code can be found in the file coupon_analysis.R.
We have collected 1572 images of cats and dogs from ImageNet to do this experiment. We have provided the images here: https://www.dropbox.com/s/6gmai6p2siougea/dogcat-data.zip?dl=0.
We extract VGG16 features (Simonyan and Zisserman, 2004) and do VIC analysis on the features. The code we used to extract the features can be found in feature extraction.ipynb, which is written based on the sample code by François Chollet for the book Deep Learning with Python.
We save the extracted VGG16 features for the images in features_small.csv together with the labels of the images in outcomes_small.csv. Our code for VIC analysis can be found in vgg_vid.R.
To visualize the selected models (in model.csv), we use a sample image and modify it to maximize the likelihood of being identified as a dog/cat by the models. The code for the image modification can be found in Activate Neuron.ipynb. The code is written based on the method described in https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html.
A standard computer with enough RAM to support the in-memory operations.
R and Python.
The specific libraries and packages necessary for performing the code are specified in the beginning of each of the files.
To install R, please refer to https://www.r-project.org/ To install Python, please refer to https://www.anaconda.com/distribution/ One would also need to have tenserflow (https://www.tensorflow.org/install/) and keras (https://keras.io/#installation) installed.
Once the software is installed, one just need to run the code provided in this repository (described in Part 1 of this readme file). One should expect exactly the same output as we describe in the experiment section (Section 5) in our paper.