Merge pull request #77 from thomaspinder/joss_response

Joss response

.pre-commit-config.yaml

@@ -0,0 +1,10 @@
+repos:
+  - repo: https://github.com/psf/black
+    rev: 22.3.0
+    hooks:
+      - id: black
+  - repo: https://github.com/pycqa/isort
+    rev: 5.10.1
+    hooks:
+      - id: isort
+        args: ["--profile", "black"]

README.md

@@ -13,6 +13,12 @@
 
 GPJax aims to provide a low-level interface to Gaussian process (GP) models in [Jax](https://github.com/google/jax), structured to give researchers maximum flexibility in extending the code to suit their own needs. We define a GP prior in GPJax by specifying a mean and kernel function and multiply this by a likelihood function to construct the posterior. The idea is that the code should be as close as possible to the maths we write on paper when working with GP models.
 
+## Package support
+
+GPJax was created by [Thomas Pinder](https://github.com/thomaspinder). Today, the maintenance of GPJax is undertaken by Thomas and [Daniel Dodd](https://github.com/Daniel-Dodd).
+
+We would be delighted to review pull requests (PRs) from new contributors. Before contributing, please read our [guide for contributing](https://github.com/thomaspinder/GPJax/blob/master/CONTRIBUTING.md). If you do not have the capacity to open a PR, or you would like guidance on how best to structure a PR, then please [open an issue](https://github.com/thomaspinder/GPJax/issues/new/choose). For broader discussions on best practices for fitting GPs, technical questions surrounding the mathematics of GPs, or anything else that you feel doesn't quite constitue an issue, please start a discussion thread in our [discussion tracker](https://github.com/thomaspinder/GPJax/discussions).
+
 ## Supported methods and interfaces
 
 ### Examples

docs/latex_symbols.tex

@@ -1,3 +1,16 @@
 \newcommand{\bbR}{\mathbb{R}}
 \DeclareMathOperator*{\argmax}{arg\,max}
-\DeclareMathOperator*{\argmin}{arg\,min}
+\DeclareMathOperator*{\argmin}{arg\,min}
+
+\newcommand{\x}{\mathbf{x}}
+\newcommand{\X}{\mathbf{X}}
+\newcommand{\xx}{\mathbf{x}^{\star}}
+\newcommand{\Xx}{\mathbf{X}^{\star}}
+\newcommand{\Y}{\mathbf{Y}}
+\newcommand{\y}{\mathbf{y}}
+\newcommand{\by}{\mathbf{y}}
+\newcommand{\chol}{\operatorname{chol}}
+\newcommand{\GP}{\mathcal{GP}}
+
+\newcommand{\Kff}{\mathbf{K}_{\mathbf{ff}}}
+\newcommand{\IdentityMatrix}{\mathbf{I}}

docs/nbs/classification.pct.py

@@ -68,6 +68,7 @@
 params = gpx.transform(params, unconstrainer)
 
 mll = jax.jit(posterior.marginal_log_likelihood(D, constrainer, negative=True))
+
 # %% [markdown]
 # We can obtain a MAP estimate by optimising the marginal log-likelihood with Obtax's optimisers.
 # %%
@@ -79,6 +80,9 @@
     opt,
     n_iters=500,
 )
+ps = gpx.transform(map_estimate, constrainer)
+latent_dist = posterior(D, ps)(xtest)
+predictive_dist = likelihood(latent_dist, ps)
 # %% [markdown]
 # However, as a point estimate, MAP estimation is severely limited for uncertainty quantification, providing only a single piece of information about the posterior. On the other hand, through approximate sampling, MCMC methods allow us to learn all information about the posterior distribution.
 # %% [markdown]
@@ -95,7 +99,7 @@
 # We begin by generating _sensible_ initial positions for our sampler before defining an inference loop and sampling 500 values from our Markov chain. In practice, drawing more samples will be necessary.
 # %%
 # Adapted from BlackJax's introduction notebook.
-num_adapt = 1000
+num_adapt = 500
 num_samples = 500
 
 mll = jax.jit(posterior.marginal_log_likelihood(D, constrainer, negative=False))
@@ -156,7 +160,8 @@ def one_step(state, rng_key):
     ps["latent"] = states.position["latent"][i, :, :]
     ps = gpx.transform(ps, constrainer)
 
-    predictive_dist = likelihood(posterior(D, ps)(xtest), ps)
+    latent_dist = posterior(D, ps)(xtest)
+    predictive_dist = likelihood(latent_dist, ps)
     samples.append(predictive_dist.sample(seed=key, sample_shape=(10,)))
 
 samples = jnp.vstack(samples)

requirements-dev.txt

@@ -1,3 +1,4 @@
 pytest
 networkx
-pytest-cov
+pytest-cov
+pre-commit