This is code for the NIPS 2015 paper by David Moore and Stuart Russell. Aside from the usual dependencies (numpy, scipy, matplotlib), it depends on:

• the treegp package, which contains C++ implementations of several distance functions, kernel functions and their derivatives. In particular, it implements the great-circle distance used in the seismic experiments. In case compatibility is broken at some future point, commit a0aa7ae65a4b9144a499016bbf0ccaf0c611cc0d is known to work with this code.
• GPy, for comparisons to sparse GP-LVM. Experiments were run using version 0.6.0.

Individual synthetic experiments from the paper can be reproduced by running gprfopt.py with appropriate options. For example,

python gprfopt.py --n=10000 --seed=0 --yd=50 --lscale=0.06 --obs_std=0.02 --noise_var=0.01 --method=l-bfgs-b  --local_dist=1.0 --nblocks=100 --task=x --maxsec=18000

will sample a synthetic problem with 10000 points, random seed 0, 50-dimensional output, SE kernel lengthscale 0.06 (note a small difference from the paper: this implementation scales the world to always lie within the unit square, so larger problems correspond to smaller lengthscales) and positional noise stddev 0.02, and output noise variance 0.01, and then attempt to solve this problem by running L-BFGS in a local GP model (local_dist=1.0 specifies a purely local GP, local_dist < 1.0 defines a GPRF and the specific value does not matter) with 100 blocks, solving only for the X locations (not kernel params), and running for a maximum time of 18000 seconds. The results will be saved under the home directory in ~/gprf_experiments/. A subdirectory is created for each experiment, and the file results.txt contains the objective value and mean location error at each step (along with other quantities).

After running a synthetic experiment, you can visualize the results, e.g., for the previous example,

python gprfopt_analyze.py vis ~/gprf_experiments/10000_10500_100_0.060000_0.020000_1.0000_50_l-bfgs-b_x_-1_0.0100_s0_gprf0/ ~/gprf_experiments/synthetic_datasets/10500_10000_0.060000_0.020000_50_0.pkl 0

will generate a series of images, one for each optimization step, and attempt to stitch them into a video.

The seismic dataset is stored in sorted_isc.npy as an array of (lon, lat, depth) values. Individual seismic experiments can be reproduced by run_seismic.py. For example,

python run_seismic.py --obs_std=20.0 --rpc_blocksize=210  --task=xcov --threshold=0.6  --maxsec=345600

will generate a set of noisy location observations with stddev 20km, partitioned into blocks of at most 210 points (allowing some leeway since the principle axis tree recursively splits the dataset and may not obtain the precise block size requested), and run inference to recover both X locations and kernel hyperparameters, using a GPRF containing an edge between any two blocks for which some initial cross-covariance value is at least 0.6 (corresponding to one kernel lengthscale), and running for at most 345600 seconds (four days). The results will be saved under the home directory in ~/seismic_experiments/.

To automatically generate the full set of synthetic experiments from the paper, run python gprfopt_analyze.py with no arguments: this will generate Bash scripts run_truegp.sh, run_fitc.sh, and run_eighty.sh. The iPython notebook gprf_camera_plot.ipynb was used to generate the plots in the paper, based on results which are included in the tarballs gprf_experiments.tgz and seismic_experiments.tgz.

For questions about the paper, or if you have difficulty running the code or reproducing the experimental results, please contact Dave Moore at dmoore@cs.berkeley.edu.