Ramsey

Probabilistic deep learning using JAX

About

Ramsey is a library for probabilistic deep learning using JAX, Flax and NumPyro. Its scope covers

• neural processes (vanilla, attentive, Markovian, convolutional, ...),
• neural Laplace and Fourier operator models,
• etc.

Example usage

You can, for instance, construct a simple neural process like this:

from flax import nnx

from ramsey import NP
from ramsey.nn import MLP  # just a flax.nnx module

def get_neural_process(in_features, out_features):
  dim = 128
  np = NP(
    latent_encoder=(
      MLP(in_features, [dim, dim], rngs=nnx.Rngs(0)),
      MLP(dim, [dim, dim * 2], rngs=nnx.Rngs(1))
    ),
    decoder=MLP(in_features, [dim, dim, out_features * 2], rngs=nnx.Rngs(2))
  )
  return np

neural_process = get_neural_process(1, 1)

The neural process above takes a decoder and a set of two latent encoders as arguments. All of these are typically flax.nnx MLPs, but Ramsey is flexible enough that you can change them, for instance, to CNNs or RNNs.

Ramsey provides a unified interface where each method implements (at least) __call__ and loss functions to transform a set of inputs and compute a training loss, respectively:

from jax import random as jr
from ramsey.data import sample_from_sine_function

data = sample_from_sine_function(jr.key(0))
x_context, y_context = data.x[:, :20, :],  data.y[:, :20, :]
x_target, y_target = data.x, data.y

# make a prediction
pred = neural_process(
  x_context=x_context,
  y_context=y_context,
  x_target=x_target,
)

# compute the loss
loss = neural_process.loss(
  x_context=x_context,
  y_context=y_context,
  x_target=x_target,
  y_target=y_target
)

Installation

To install from PyPI, call:

pip install ramsey

To install the latest GitHub , just call the following on the command line:

pip install git+https://github.com/ramsey-devs/ramsey@<RELEASE>

See also the installation instructions for JAX, if you plan to use Ramsey on GPU/TPU.

Contributing

Contributions in the form of pull requests are more than welcome. A good way to start is to check out issues labelled "good first issue".

In order to contribute:

1. Clone Ramsey and install uv from here,
2. create a new branch locally git checkout -b feature/my-new-feature or git checkout -b issue/fixes-bug,
3. install all dependencies via uv sync --all-extras,
4. implement your contribution and ideally a test case,
5. test it by calling make format, make lints and make tests on the (Unix) command line,
6. submit a PR 🙂

Why Ramsey

Just as the names of other probabilistic languages are inspired by researchers in the field (e.g., Stan, Edward, Turing), Ramsey takes its name from one of my favourite philosophers/mathematicians, Frank Ramsey.