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Official implementation of the ICLR 2023 paper A Learning Based Hypothesis Test for Harmful Covariate Shift

Intro

We introduce the Detectron, a learning based hypothesis test for harmful covariate shift. Given a pretrained model $f: X\to Y$ and an unlabeled dataset $Q=\{x\}_{i=1}^n$ Detectron aims to automatically decide if $Q$ is similar enough to the $f(x)$'s training domain such that we can trust it to make reliable predictions.

The algorithm works in two major steps:

First, we estimate the distribution of the test statistic $\phi$ which is computed as the empirical disagreement rate of a classifier $g(x)$ trained to explicitly disagree with a pretrained model $f(x)$ on i.i.d samples from the training set. In practice, we create $g(x)$ by finetuning $f(x)$ using the diagreement cross entropy defined formally in the paper. It is also important to limit the hypothesis space for $g(x)$ by forcing it to agree with $f(x)$ on the original training set while giving it a limited compute budget to prevent overfitting. Conceptually we can interpret $\phi$ as the degree of underspecification $f(x)$ admits on its training domain.

Next, we train another classifier $g^\star(x)$ in the exact same way as $g(x)$ but we use the unlabeled data $Q$. We detect covariate shift at a significance level $\alpha$ by comparing the empirical disagreement rate of $g^\star(x)$ on $Q$ (denoted $\phi^\star$) to the estimated distribution of $\phi$.

In our paper, we further show how to boost the power of the test using emsembling and by replacing the disagreement statistic $\phi$ with the related predictive entropy.

Benchmarks

Test power at $5%$ significance level for Detectron and baselines. We use a very small sample size of $|Q|=10$. Results for other samples sizes can be found in the paper.

	CIFAR 10.1 [Recht et al.]	Camelyon 17	UCI Heart Disease
Black Box Shift Detection [Lipton et al.]	$.07\pm.03$	$.05 \pm .02$	$.12 \pm .03$
Rel. Mahalanobis Distance [Ren et al.]	$.05 \pm .02$	$.03 \pm .03$	$.04 \pm .02$
Deep Ensemble (Disagreement) [Ablation]	$.05 \pm .02$	$.03 \pm .03$	$.04 \pm .02$
Deep Ensemble (Entropy) [Ablation]	$\mathit{.33 \pm .05}$	$\mathit{.52 \pm .05}$	$.68 \pm .05$
Classifier Two Sample Test (CTST) [Lopez-Paz et al.]	$.03 \pm .02$	$.04 \pm .02$	$.04 \pm .02$
Deep Kernel MMD [Liu et al.]	$.24 \pm .04$	$.10 \pm .03$	$.05 \pm .02$
H-Divergence [Zhao et al.]	$.02\pm .01$	$.05\pm .02$	$.04\pm .02$
Detectron (Disagreement) [Ours]	$\mathbf{.37 \pm .05}$	$\underline{.54 \pm .05}$	$.83 \pm .04$
Detectron (Entropy) [Ours]	$\underline{.35 \pm .05}$	$\mathbf{.56 \pm .05}$	$\mathbf{.92 \pm .03}$

The best result for each column is bolded, results that are within 2% of the best are underlined and the best baseline method is italicized.

Setup

Environment

detectron requires a working build of pytorch with the cudatoolkit enabled. A simple environment setup using conda is provided below.

# create and activate conda environment using a python version >= 3.9
conda create -n detectron python=3.9
conda activate detectron

# install the latest stable release of pytorch (tested for >= 1.9.0)
conda install pytorch torchvision cudatoolkit=11.3 -c pytorch

# install additional dependencies with pip
pip install -r requirements.txt

Datasets

We provide a simple config system to store dataset path mappings in the file detectron/config.yml

datasets:
  default: /datasets
  cifar10_1: /datasets/cifar-10-1
  camelyon17: /datasets/camelyon17

for more information on downloading datasets see detectron/data/sample_data/README.md.

Running Detectron

There is work in progress to package Detectron in a robust and easy to deploy system. For now, all the code needed to reproduce our experiments is in located in the experiments directory and can be run like the following example.

# run the cifar experiment using the standard config
# use python experiments.detectron_cifar --help for a documented list of options
❯ python -m experiments.detectron_cifar --run_name cifar

Evaluating Detectron

The scratch files will write the output for each seed to a .pt file in a directory named results/<run_name>.

The script in experiments/analysis.py will read these files and produce a summary of the results for each test described in the paper.

❯ python -m experiments.analysis --run_name cifar
# Output
→ 600 runs loaded
→ Running Disagreement Test
N = 10, 20, 50
TPR: .37 ± .05 AUC: 0.799 | TPR: .54 ± .05 AUC: 0.902 | TPR: .83 ± .04 AUC: 0.981
→ Running Entropy Test
N = 10, 20, 50
TPR: .35 ± .05 AUC: 0.712 | TPR: .56 ± .05 AUC: 0.866 | TPR: .92 ± .03 AUC: 0.981

Citation

Please use the following citation if you use this code or methods in your own work.

@inproceedings{
    ginsberg2023a,
    title = {A Learning Based Hypothesis Test for Harmful Covariate Shift},
    author = {Tom Ginsberg and Zhongyuan Liang and Rahul G Krishnan},
    booktitle = {The Eleventh International Conference on Learning Representations },
    year = {2023},
    url = {https://openreview.net/forum?id=rdfgqiwz7lZ}
}