v0.7.0

v0.7.0 introduces major changes to the API, several new model features, as well as a major overhaul of the library's backend. Changes in detail include:

• The API now reduces the number of strings arguments, and instead largely expects objects directly. See the univariate regression tutorial for details.
• Added explicit distance models, currently supporting anisotropic and isotropic. See the anisotropic tutorial for details of the anisotropic distance model.
• Added several new loss functions. See the new loss function tutorial for details.
• Added several experimental features that are not fully hardened:
  • Heteroscedastic noise model, where each training observation has a separate Gaussian noise prior.
  • Hierarchical nonstationary parameters, where a scalar hyperparameter has several (possibly optimized) knot values throughout the domain and interpolates values at new locations using a lower-level GP.
  • An alternative optimization workflow that reconstructs neighborhoods during optimization, meant to handle cases like anisotropy where neighborhoods change during optimization.
• Added experimental notebooks investigating several of these experimental features.
• Majorly changed the backend of the library. Control flow is increasingly functional. Member functions such as MuyGPS.posterior_mean() are composed at object creation depending on different model choices. The specific way in which this is done is subject to change.

What's Changed

• Refactoring math to remove global numpy dependency by @bwpriest in #95
• Housekeeping to tidy up the import interface by @bwpriest in #97
• All optimization functions now rely on kwargs form by @bwpriest in #98
• removed all *_from_indices functions from the libarary (less MuyGPyS.examples) by @bwpriest in #100
• Removing MuyGPS.regress in favor of separate mean and variance functions by @bwpriest in #102
• Making all member functions modular pure functions by @bwpriest in #104
• coalesced noise model application logic to single function by @bwpriest in #107
• Feature/heteroscedasticity by @alecmdunton in #109
• Exposing object creation to the user by @bwpriest in #118
• Refactored code so length scale lives in distortion models by @alecmdunton in #122
• Fixed bug that was fixing length_scale in optimizations by @bwpriest in #128
• Reorganize hyperparameters and init hierarchical nonstationary hyperparameter by @igoumiri in #129
• Anisotropic feature integrated into library. Tests added to gp.py and kernel.py by @alecmdunton in #127
• Added performance benchmarking script by @bwpriest in #135
• Updated format throughout to match PEP by @bwpriest in #133
• fixed tests/optimize.py to actually work by @bwpriest in #139
• removed jax cuda extras; user must now install manually by @bwpriest in #140
• Added pretty print overloads for MuyGPS classes by @bwpriest in #142
• Iss/141 by @alecmdunton in #143
• All torch tests passing by @alecmdunton in #144
• removed hardcoded float commands in examples/muygps_torch by @alecmdunton in #146
• Streamlined doc notebooks to obscure sampling and plotting code by @bwpriest in #148
• Refactored distortion class to take metric Callable as argument by @alecmdunton in #147
• Fix develop tests being skipped by @igoumiri in #151
• Fixed test harness errors introduced by merge. Updated some documentation. by @bwpriest in #152
• Feature: hierarchical RBF by @igoumiri in #145
• Added opt parameter indirection in preparation for hierarchical parameters by @bwpriest in #153
• Optimization for hierarchical GPs by @igoumiri in #154
• Fix knot optimization by @igoumiri in #156
• pulled boilerplate functions out of optimization pipelines by @bwpriest in #157
• minor nonstationary notebook cleanup [skip ci]. by @bwpriest in #159
• removed regress api tutorial [skip ci] by @bwpriest in #160
• [skip ci] refactored nb names and added a flat optimization for sanit… by @bwpriest in #161
• Adding new pseudo Huber loss function for outlier robustness. by @bwpriest in #164
• Added a variance-regularized pseudo-Huber loss function similar in fo… by @bwpriest in #166
• fixed bug to actually forward the looph function by @bwpriest in #167
• first implementation commit by @akilandrews in #165
• documentation nb updates/cleanup by @bwpriest in #169
• Update .readthedocs.yaml to supported python version [skip ci] by @bwpriest in #170
• Roll back required ipython versions in setup.py [skip ci] by @bwpriest in #171
• fixed RTD builds by @bwpriest in #172
• precomputing torch tutorial as it does not seem possible to run on RT… by @bwpriest in #173
• anisotropic tutorial and nb cleanup by @bwpriest in #175
• Added loss function tutorial. Fixed some mistakes in the documentatio… by @bwpriest in #176
• moved UnivariateSampler* into MuyGPyS._test.sampler for convenience by @bwpriest in #177
• moved mini batch tests to their own file by @bwpriest in #178
• Passing loss functions instead of strings. Unhooked experimental opti… by @bwpriest in #180
• improvements to samplers and tutorials by @bwpriest in #182
• reduced training ratio for anisotropic tutorial by @bwpriest in #183
• Optimization loop chassis by @akilandrews in #181
• partial fix to torch parameter optimization by @bwpriest in #184
• final updates for v0.7.0 by @bwpriest in #185

New Contributors

• @akilandrews made their first contribution in #165

Full Changelog: v0.6.6...v0.7.0