Nicer docs, Will try to host it on Github Pages

docs/api.md

@@ -2,7 +2,7 @@
 
 ## Metrics
 
-### Gaussian Kernel
+### Gaussian KL divergence
 
 ::: labproject.metrics.gaussian_kl
 
@@ -30,4 +30,8 @@
 
 ### Run
 
-::: labproject.run
+::: labproject.run_default
+
+### Utils
+
+::: labproject.utils

docs/index.md

@@ -1,17 +1,5 @@
-# Welcome to MkDocs
+# A Practical Guide to Statistical Distances for Evaluating Generative Models in Science
 
-For full documentation visit [mkdocs.org](https://www.mkdocs.org).
+Generative models are highly useful in many disciplines of science. How do we evaluate them? As the data generated by these models is often high-dimensional and/or non-parametric, we can typically not resort to classical statistical tests. This paper aims to provide an accessible entry point to a understanding popular statistical distances proposed and adopted by the machine learning in science community, requiring only foundational knowledge in mathematics or statistics. We focus on four commonly used classes of statistical distances: obtaining a distance using classifiers (e.g. classifier two-sample tests), using embeddings through kernels (e.g. Maximum Mean Discrepancy) or neural networks (e.g. Frechet Inception Distance), and slicing (e.g. sliced Wasserstein). We highlight their merits, scalability, complexity and pitfalls, which are all illustrated in accompanying notebooks. We then apply each metric to multiple examples generative models in scientific applications, spanning image generation, neuroscience... . We this aim to empower researchers to use, critically assess and interpret statistical distances for generative models in science.
 
-## Commands
-
-* `mkdocs new [dir-name]` - Create a new project.
-* `mkdocs serve` - Start the live-reloading docs server.
-* `mkdocs build` - Build the documentation site.
-* `mkdocs -h` - Print help message and exit.
-
-## Project layout
-
-    mkdocs.yml    # The configuration file.
-    docs/
-        index.md  # The documentation homepage.
-        ...       # Other markdown pages, images and other files.
+{% include "../README.md" %}

mkdocs.yml

@@ -1,19 +1,35 @@
-site_name: Distribution Metrics for Evaluating Generative Models in Science
+site_name: Mackathon
 
 nav:
   - Home: index.md
-  - Example notebook: notebooks/example.ipynb
-  - FID notebook: notebooks/fid.ipynb
-  - API Reference: api.md
+  - API: api.md
+  - Notebook: 
+    - Example Notebook: notebooks/example.ipynb
+    - FID notebook: notebooks/fid.ipynb
 
 
+theme:
+  name: material
+  palette:
+    primary: 'indigo'
+    accent: 'indigo'
+  collapse_navigation: False
+
 plugins:
   - search
   - mkdocs-jupyter
+  - include-markdown
   - mkdocstrings:
       watch:
         - labproject
       handlers:
         python:
           rendering:
-            show_source: true
+            show_source: true
+
+markdown_extensions:
+    - mdx_math:
+        enable_dollar_delimiter: True
+
+extra_javascript: 
+    - https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.4/MathJax.js?config=TeX-AMS-MML_HTMLorMML

pyproject.toml

@@ -19,7 +19,8 @@ docs = [
     "mkdocs",
     # Newer versions currently produce a "'mermaid.js' is undefined" error, see https://github.com/danielfrg/mkdocs-jupyter/issues/176
     "mkdocs-jupyter==0.24.2",
-    "mkdocstrings[python]>=0.18"
+    "mkdocstrings[python]>=0.18",
+    "mkdocs-include-markdown-plugin"
 ]
 dev = [
     "black",