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Gaussian Process Regression for Python/Numpy
Python C++
Branch: master
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experiments
src_c expose distance derivatives to enable GPy MaternLLD kernel
test split compiled product trees into multiple files, to mollify gcc
.gitignore full experimental pipeline for training, evaluating predictive accura…
LICENSE reorganize into a genuine python package
README rename sparsegp to treegp
TODO
__init__.py
aistats data munging.ipynb catching up
distributions.py combined mechanism for parametric/FIC additive kernels. also some hyp…
features.py move from explicit basis functions to just passing in text that selec…
gp.py optimizations for multiBCM gradients
jointgp.py --amend
munge.py
plot.py no need for a separate directory
setup.py
util.py split compiled product trees into multiple files, to mollify gcc

README

This is very preliminary documentation, will be improved in a final release.

To install: set up a Python virtualenv with the packages specified in requirements.txt.

To compile the C++ components (requires boost_python): python setup.py build_ext --inplace

To test: python test/test_sgp.py

To train hyperparameters on a subset of 5000 training points, for a model with 20 inducing points learned during the optimization:
python experiments/code/train_hyperparams.py seismic_tt_ASAR --n-hyper=5000 -f 20 --optimize-xu

To train hyperparameters for an SE model on a subset of 5000 training points:
python experiments/code/train_hyperparams.py seismic_tt_ASAR --n-hyper=5000 --se

(details of initial values, priors, and so on are hard-coded in train_hyperparams.py)

The trained hyperparameters are saved to experiments/models/<dataset_name>/<model_type>/hyperparams_5000.pkl.

Given learned hyperparameters, to train a model and compute predictive performance:
python experiments/code/prediction.py seismic_tt_ASAR csfic20 5000

To measure runtimes for posterior variances:
python experiments/code/timing.py seismic_tt_ASAR csfic20 5000
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