Merge pull request #18 from Ciela-Institute/contingencytable

Add unit tests, refactor for scipy contingency table

.github/workflows/ci.yml

@@ -20,7 +20,7 @@ jobs:
   build:
     runs-on: ${{matrix.os}}
     strategy:
-      fail-fast: false
+      fail-fast: true
       matrix:
         python-version: ["3.9", "3.10", "3.11"]
         os: [ubuntu-latest, windows-latest, macOS-latest]
@@ -68,4 +68,6 @@ jobs:
           pytest -vvv --cov=${{ env.PROJECT_NAME }} --cov-report=xml --cov-report=term tests/
 
       - name: Upload coverage reports to Codecov with GitHub Action
-        uses: codecov/codecov-action@v4
+        uses: codecov/codecov-action@v4
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}

README.md

@@ -4,6 +4,7 @@
 [![CI](https://github.com/Ciela-Institute/PQM/actions/workflows/ci.yml/badge.svg)](https://github.com/Ciela-Institute/PQM/actions/workflows/ci.yml)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 ![PyPI - Downloads](https://img.shields.io/pypi/dm/pqm)
+[![codecov](https://codecov.io/gh/Ciela-Institute/PQM/graph/badge.svg?token=wbkUiRkYtg)](https://codecov.io/gh/Ciela-Institute/PQM)
 [![arXiv](https://img.shields.io/badge/arXiv-2402.04355-b31b1b.svg)](https://arxiv.org/abs/2402.04355)
 
 [PQMass](https://arxiv.org/abs/2402.04355) is a new sample-based method for evaluating the quality of generative models as well as assessing distribution shifts to determine if two datasets come from the same underlying distribution.
@@ -22,9 +23,11 @@ PQMass takes in $x$ and $y$ two datasets and determines if they come from the sa
 
 ![Headline plot showing an example tessellation for PQMass](media/Voronoi.png "")
 PQMass partitions the space by taking reference points from $x$ and $y$ and creating Voronoi tessellations around the reference points. On the left is an example of one such region, which we note follows a Binomial Distribution; the samples are either inside or outside the region. On the right is the entire space partitioned, allowing us to see that this is a multinomial distribution, a given sample can be in region P or any other region. This is crucial as it allows for two metrics to be defined that can be used to determine if $x$ and $y$ come from the same underlying distribution. The first is the $\chi_{PQM}^2$
+
 $$\chi_{PQM}^2 \equiv \sum_{i = 1}^{n_R} \left[ \frac{(k({\bf x}, R_i) - \hat{N}_{x, i})^2}{\hat{N}_{x, i}} + \frac{(k({\bf y}, R_i) - \hat{N}_{y, i})^2}{\hat{N}_{y, i}} \right]$$
 
 and the second is the $\text{p-value}(\chi_{PQM}^2)$
+
 $$\text{p-value}(\chi_{PQM}^2) \equiv \int_{-\infty}^{\chi^2_{\rm {PQM}}} \chi^2_{n_R - 1}(z) dz$$
 
 For $\chi_{PQM}^2$ metric, given your two sets of samples, if they come from the same