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<title>Google Summer of Code 2021 - Project "PyProbML"</title>
<h1>Google Summer of Code 2021 - Project "PyProbML"</h1>
<p>
<img src="figures/cover1.jpg" style="height:200">
<img src="figures/cover2.jpg" style="height:200">
<p>
This page summarizes the contributions made to the
<a href="https://github.com/probml/pyprobml">pyprobml repo</a>
by some awesome members of the
<a href="https://summerofcode.withgoogle.com/">GSOC</a>
and <a href="https://www.outreachy.org/">Outreachy</a> communities
during the summer of 2021. The students were
mentored by
<a href="https://github.com/murphyk">Kevin Murphy</a>
and <a href="https://github.com/mjsML">Mahmoud Soliman</a>.
<p>
The goal of the summer project was
to create code to accompany my new textbook, <a href="https://probml.github.io/pml-book/">Probabilistic Machine Learning</a>.
The first task was
to finish translating <a href="https://github.com/probml/pmtk3">my old Matlab code</a> to Python, using modern libraries like
<a href="https://scikit-learn.org/stable/">sklearn</a> and <a href="https://github.com/google/jax">JAX</a>.
Fortunately, the students finished this translation task quicker than expected, so they had time to make lots of interesting new content.
Below I give a little taste of some of these new examples. But first, let me introduce the students...
<h1>The students</h1>
<style>
table, th, td {
border: 1px solid black;
border-collapse: collapse;
}
</style>
</head>
<table>
<tr>
<th>Name
<th>Country of origin
<th> Current institution
<th> End of summer report
</tr>
<tr>
<td> <a href="https://github.com/karalleyna">Aleyna Kara</a>
<td> Turkey
<td> Boğaziçi University, Turkey
<td> <a href="https://medium.com/@aleynakara98/google-summer-of-code-2021-a-probabilistic-perspective-9cc5a03ec91e">Blog</a>
<tr>
<td> <a href="https://github.com/Drishttii">Drishti Patel</a>
<td> India
<td> Arizona State University, USA
<td> <a href="https://drishttii.github.io/">Blog</a>
<tr>
<td> <a href="https://github.com/gerdm">Gerardo Duran-Martin</a>
<td> Mexico
<td> Queen Mary College, London
<td> <a href="https://gerdm.github.io/2021/08/16/gsoc2021">Blog </a>
<tr>
<td> <a href="https://github.com/Neoanarika">Ming Liang Ang</a>
<td> Singapore
<td> National University of Singapore
<td> <a href="https://neoanarika.github.io/posts/2021/08/gsoc/">Blog</a>
<tr>
<td> <a href="https://github.com/always-newbie161">Srikar Reddy</a>
<td> India
<td> IIT Hyderabad
<td> <a href="https://srikarjilugu-gsoc2021.blogspot.com/">Blog</a>
</table>
<p>
<h1>Highlight reel</h1>
Below I highlight one artefact per student, although they actually produced many more -- see their reports for details.
<h2><a id="hmm">HMMs for changepoint detection</h2>
<a href="https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/hmm_poisson_changepoint_jax.ipynb">hmm_poisson_changepoint_jax.ipynb</a>, by Aleyna.
<p>
This uses an HMM with $K$ hidden states to segment a scalar time series
into different "regimes".
The observation (emission) model is Poisson.
<br>
The <a href="https://www.tensorflow.org/probability/examples/Multiple_changepoint_detection_and_Bayesian_model_selection">original notebook</a> for this demo
was written using the <a href="https://www.tensorflow.org/probability/examples/TensorFlow_Distributions_Tutorial">Tensorflow probability distributions</a> library.
<br>
Unfortunately the <a href="https://github.com/tensorflow/probability/blob/main/tensorflow_probability/python/distributions/hidden_markov_model.py#L41-L1316">TFP source code for the HMM class</a>
is somewhat hard to read (mostly due to the need to handle batches), so we decided to write a
<a href="https://github.com/deepmind/distrax/blob/master/distrax/_src/utils/hmm.py">
pure JAX implementation of HMMs</a>.
<br>
This is much simpler, due to JAX primitives such as vmap.
Our code has since been upstreamed into <a href="https://github.com/deepmind/distrax">distrax library</a>
from Deepmind. (Distrax is a JAX version of part of the TFP distributions library).
<br>
<p>
<img src="figures/hmm-changepoints.png" style="height:300">
<h2> ><a id="mixfa">Mixtures of (low-rank) Gaussians for CelebA faces</h2>
<a href="https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/mix_PPCA_celeba.ipynb">mix_PPCA_celeba.ipynb</a>, by Drishti.
<p>
This colab uses <a href="https://github.com/eitanrich/torch-mfa">this pytorch code</a> to demonstrate how to fit a mixture of low-rank Gaussians (i.e., probabilistic principal components model) to the CelebA face dataset.
<br>
After fitting, we can easily generate samples, and perform imputation
from occluded images, as shown below.
<p>
<img src="figures/mfa-samples.jpeg" style="height:200">
<img src="figures/mfa-occluded.jpeg" style="height:200">
<img src="figures/mfa-recon.jpeg" style="height:200">
<p>
<h2> <a id="ekfmlp">EKF for online training of an MLP</h2>
<a href="https://github.com/probml/pyprobml/blob/master/scripts/ekf_mlp_anim_demo.py">
ekf_mlp_anim_demo.py</a>
by Gerardo.
<p>
We fit a shallow MLP to a sequence of (x,y) pairs which arrive in a streaming fashion.
<br>
One epoch of Bayesian training trumps multiple epochs of SGD training!
</p><video width="500" controls=""> <source src="https://github.com/probml/probml-data/blob/main/data/ekf_mlp_demo.mp4?raw=true" type="video/mp4" /> Your browser does not support HTML video. </video><p>
<h2> <a id="potts">Fast blocked Gibbs sampling for the Potts model using JAX</h2>
<a href="https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/gibbs_demo_potts_jax.ipynb">gibbs_demo_potts_jax.ipynb</a>, by Ming.
<p>
Summary slide below. Note the phase transition at J=1.42, as predicted by theory.
<br>
This demo took 18 minutes with numpy+numba, but only 6s with JAX+math tricks :).
<p>
<img src="figures/potts-slide.png" style="height:400">
<h2><a id="cliplogreg">Imagenette classifier using logistic regression + CLIP preprocessing</h2>
<a href="https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/clip_imagenette_demo.ipynb">clip_imagenette_demo.ipynb</a> by Srikar.
<p>
<a href="https://www.tensorflow.org/datasets/catalog/imagenette">ImageNette</a>
is a small version of Imagenet with just 10 classes,
and ~10k training and ~4k test examples.
<br>
We used a pre-trained
<a href="https://github.com/openai/CLIP">CLIP</a> model to perform feature extraction,
and then fit a linear logistic regression model,
using
<a href="https://scikit-learn.org/stable/">sklearn</a>,
<a href="https://github.com/google/flax">flax</a>,
and <a href="https://pytorchlightning.ai/">pytorch lightning</a>.
<br>
Since the model has a convex loss,
all methods get the same test set accuracy, namely 98.98%.
Sample results below,
<p>
<img src="figures/imagenette-logreg-clip.png" style="height:300">