uqdeepnn 0.1.2

PyTorch uncertainty quantification toolkit with Bayes-by-Backprop VI, Laplace, SGLD, MC Dropout, and Gaussian Processes.

deepuq

Unified deep learning uncertainty quantification (UQ) toolkit in PyTorch.

Implements five widely used methods:

1. Variational Inference (VI) — Bayes by Backprop with BayesianLinear layers.
2. Laplace Approximation — native diagonal-family backends (diag, fisher_diag, lowrank_diag, block_diag) plus laplace-torch backends (kron, full).
3. MCMC (SGLD) — Stochastic Gradient Langevin Dynamics sampler for NN posteriors.
4. MC Dropout — Keep dropout active at test-time and aggregate Monte Carlo predictions.
5. Gaussian Processes (GPs) — Exact regression and sparse inducing-point approximations with RBF kernels.

Examples and tutorials focus on a synthetic Euler-Bernoulli beam deflection regression task to illustrate confidence bounds.

Method Summary

Method Family	Implemented Variants	Main Wrapper / Class	Tutorial
Variational Inference	Bayes by Backprop	BayesianLinear, vi_elbo_step	notebooks/BayesByBackprop_Tutorial.ipynb
Laplace Approximation	diag, fisher_diag, lowrank_diag, block_diag, kron, full	LaplaceWrapper	notebooks/laplace/Laplace_HessianComparison_Tutorial.ipynb
MCMC	Stochastic Gradient Langevin Dynamics	SGLDSampler	notebooks/SGLD_Tutorial.ipynb
MC Dropout	Monte Carlo dropout inference	MCDropoutWrapper	notebooks/MC_Dropout_Tutorial.ipynb
Gaussian Process	Exact GP (RBFKernel)	GaussianProcessRegressor	notebooks/GaussianProcess_Tutorial.ipynb
Sparse GP	Variational inducing-point GP	SparseGaussianProcessRegressor	notebooks/SparseGaussianProcess_Tutorial.ipynb

Install (local)

git clone https://github.com/Vispikarkaria/Deep-UQ.git
cd Deep-UQ
pip install -e .

Install (PyPI)

pip install uqdeepnn

For LaplaceWrapper structures kron and full, install the optional backend:

pip install "laplace-torch>=0.1.7"

Publish / Update PyPI Release

Use this flow whenever you want to publish a new pip version.

1. Bump version in pyproject.toml:

[project]
version = "0.1.2"

2. Commit and push the version bump:

git add pyproject.toml
git commit -m "Bump version to 0.1.2"
git push

3. Build distributions:

python -m pip install --upgrade build twine
python -m build

4. Validate package metadata:

python -m twine check dist/*

5. Upload to TestPyPI (recommended first):

python -m twine upload --repository testpypi dist/*

6. Upload to PyPI:

python -m twine upload dist/*

7. Verify installation:

pip install -U uqdeepnn
python -c "import deepuq; print('deepuq import ok')"

Notes:

• Prefer API tokens over passwords for Twine auth.
• Revoke and rotate any token immediately if it is ever exposed.

Quickstart

import torch
from deepuq.models import MLP
from deepuq.methods import MCDropoutWrapper

# Beam deflection regression input grid
L = 2.0
x = torch.linspace(0.0, L, 200).unsqueeze(-1)

# After training an MLP, enable MC Dropout for uncertainty estimates
model = MLP(input_dim=1, hidden_dims=[128, 128], output_dim=1, p_drop=0.15)
uq = MCDropoutWrapper(model, n_mc=200, apply_softmax=False)
mean, var = uq.predict(x)
print(mean.shape, var.shape)

See the examples/ folder for end-to-end regression scripts on the Euler-Bernoulli beam deflection problem.

Methods

• VI: Place Gaussian posteriors over weights with reparameterization trick and KL regularization.
• Laplace: Fit a Gaussian around a MAP solution using one of multiple curvature structures (diag, fisher_diag, lowrank_diag, block_diag, kron, full) and calibrate with a prior precision.
• MCMC (SGLD): Inject Gaussian noise into SGD steps to sample from the posterior.
• MC Dropout: Use dropout at inference; Monte Carlo average for mean and variance.
• Gaussian Processes: Closed-form posterior inference with RBF kernels for regression and uncertainty-aware interpolation.

For Laplace users:

• Native backends (diag, fisher_diag, lowrank_diag, block_diag) work without extra runtime dependencies.
• kron and full use laplace-torch under the hood.

Tutorials

• notebooks/BayesByBackprop_Tutorial.ipynb: Variational Inference (Bayes by Backprop) for regression with predictive uncertainty.
• notebooks/MC_Dropout_Tutorial.ipynb: MC Dropout tutorial on a nonlinear beam-style regression case.
• notebooks/laplace/Laplace_Tutorial.ipynb: Core Laplace workflow around a MAP model.
• notebooks/laplace/Laplace_FullHessian_Tutorial.ipynb: Full-Hessian Laplace example (requires laplace-torch).
• notebooks/laplace/Laplace_HessianComparison_Tutorial.ipynb: Side-by-side comparison of all Hessian structures (diag, fisher_diag, lowrank_diag, block_diag, kron, full) using shared MAP weights and common metrics (RMSE, NLL, coverage, interval width, ID/OOD uncertainty ratio).
• notebooks/SGLD_Tutorial.ipynb: MCMC posterior sampling with SGLD.
• notebooks/GaussianProcess_Tutorial.ipynb: Exact Gaussian Process regression.
• notebooks/SparseGaussianProcess_Tutorial.ipynb: Sparse variational GP with inducing points.

Gaussian Processes

The module deepuq.models.gaussian_process provides lightweight GP utilities implemented entirely in PyTorch so everything can run on CPU or GPU.

Exact GP

GaussianProcessRegressor implements closed-form inference with an RBF kernel. The API mirrors scikit-learn while keeping tensors on the chosen device.

import torch
from deepuq.models import GaussianProcessRegressor, RBFKernel

# Training data
x = torch.linspace(-1.0, 1.0, 40).unsqueeze(-1)
y = torch.sin(2 * torch.pi * x) + 0.05 * torch.randn_like(x)

# Model setup
kernel = RBFKernel(lengthscale=0.5, outputscale=1.0)
gp = GaussianProcessRegressor(kernel=kernel, noise=0.02)
gp.fit(x, y)

# Posterior predictions
x_star = torch.linspace(-1.5, 1.5, 200).unsqueeze(-1)
mean, var = gp.predict(x_star)
samples = gp.posterior_samples(x_star, n_samples=5)

Sparse Variational GP

SparseGaussianProcessRegressor follows the variational inducing-point approach of Titsias (2009), optimising kernel hyperparameters and inducing locations with Adam for scalability.

import torch
from deepuq.models import SparseGaussianProcessRegressor

x = torch.linspace(-2.0, 2.0, 500).unsqueeze(-1)
y = torch.sin(2 * torch.pi * x) + 0.1 * torch.randn_like(x)

sparse_gp = SparseGaussianProcessRegressor(num_inducing=40, num_iterations=800)
sparse_gp.fit(x, y)
mean, var = sparse_gp.predict(x[:50])

Explore both flavours in the notebooks notebooks/GaussianProcess_Tutorial.ipynb (exact GP) and notebooks/SparseGaussianProcess_Tutorial.ipynb (sparse GP), each of which visualises posterior means, credible intervals, and posterior samples on toy datasets.

Documentation

• API docs are in each module and the README sections below.
• Run pydoc deepuq.methods.vi etc., or open the examples.

Contributing

PRs welcome. Please add tests under tests/ and run pytest.

License

MIT