This repository contains a PyTorch and GPyTorch implementation of the paper "Conditioning Sparse Variational Gaussian Processes for Online Decision-making."
Online variational conditioning (OVC) provides closed form conditioning (e.g. updating a model's posterior predictive distribution after having observed new data points) for stochastic variational Gaussian processes. OVC enables the development of ``fantasization" (predicting on data and then conditioning on a random posterior sample) for variational GPs, thereby enabling SVGPs to be used for the first time in advanced, look-ahead acquisitions such as the batch knowledge gradient, entropy search, and look-ahead Thompson sampling (which we introduce).
In this repo, we provide an implementation of a SVGP model with OVC hooked up as the get_fantasy_model function, allowing
it to be natively used with any advanced acquisition function in BoTorch (see the experiments in the experiments/std_bayesopt folder).
python setup.py developSee requirements.txt for our setup. We require Pytorch >= 1.8.0 and used the master versions of GPyTorch and BoTorch installed from source.
.
+-- volatilitygp/
| +-- likelihoods/
| | +-- _one_dimensional_likelihood.py (Implementation of Newton iteration and the base class for the others)
| | +-- bernoulli_likelihood.py
| | +-- binomial_likelihood.py
| | +-- fixed_noise_gaussian_likelihood.py
| | +-- multivariate_normal_likelihood.py
| | +-- poisson_likelihood.py
| | +-- volatility_likelihood.py
| +-- mlls/
| | +-- patched_variational_elbo.py (patched version of elbo to allow sumMLL training)
| +-- models/
| | +-- model_list_gp.py (patched version of ModelListGP to allow for SVGP models)
| | +-- single_task_variational_gp.py (Our basic model class for SVGPs)
| +-- utils/
| | +-- pivoted_cholesky.py (our pivoted cholesky implementation for inducing point init)
+-- experiments/
| +-- active_learning/ (malaria experiment)
| | +-- qnIPV_experiment.py (main script)
| +-- highd_bo/ (rover experiments)
| | +-- run_trbo.py (turbo script)
| | +-- run_gibbon.py (global model script, Fig 10c)
| | +-- rover_conditioning_experiment.ipynb (Fig 10b)
| | +-- trbo.py (turbo implementation)
| +-- hotspots/ (schistomiasis experiment)
| | +-- hotspots.py (main script)
| +-- mujoco/ (mujoco experiments on swimmer and hopper)
| | +-- functions/ (mujoco functions)
| | +-- lamcts/ (LA-MCTS implementation)
| | +-- turbo_1/ (TurBO implementation)
| | run.py (main script)
| +-- pref_learning/ (preference learning experiment)
| | +-- run_pref_learning_exp.py (main script)
| +-- std_bayesopt/ (bayes opt experiments)
| | +-- hartmann6.py (constrained hartmann6)
| | +-- lcls_optimization.py (laser)
| | +-- poisson_hartmann6.py (poisson constrained hartmann6)
| | +-- utils.py (model definition helpers)
| | +-- weighted_gp_benchmark/ (python 3 version of WOGP)
| | | +-- lcls_opt_script.py (main script)
+-- tests/ (assorted unit tests for the volatilitygp package)
Please see each experiment folder for the larger scale experiments.
The understanding experiments can be found in:
- Figure 1a-b:
notebooks/svgp_fantasization_plotting.ipynb - Figure 1c:
notebooks/SABR_vol_plotting.ipynb - Figure 2b-d:
experiments/std_bayesopt/knowledge_gradient_branin_plotting.ipynb - Figure 6:
notebooks/ssgp_port.ipynb - Figure 7:
notebooks/ssgp_time_series_testing_pivcholesky.ipynb - Figure 8:
notebooks/streaming_bananas_plots.ipynb - Figure 10b:
experiments/highd_bo/rover_conditioning_experiment.ipynb
- BoTorch (https://botorch.org). Throughout, many examples were inspired by assorted BoTorch tutorials, while we directly compare to Botorch single task GPs.
- GPyTorch (https://gpytorch.ai). Our implementation of SVGPs rests on this implementation.
- LA-MCTS code comes from here
- laser WOGP code comes from here
- hotspots data comes from here
- malaria active learning script comes from here. Data can be downloaded from here.