Stochastic Deep Gaussian Processes over Graphs

code and results for a NeurIPS2020 paper submission

Prerequests

our implementation is mainly based on following packages:

python 3.7
pip install keras==2.3.1
pip install gpuinfo
pip install tensorflow-gpu==1.15
pip install gpflow==1.5

Besides, some basic packages like numpy are also needed.

Specification

Source code and experiment result are both provided.

Files

• src/
  • doubly_stochastic_dgp: codes from repository DGP
  • compatible: codes to make the DGP source codes compatible with gpflow1.5.
  • gpflow_monitor: monitoring tool for gpflow models, from this repo.
  • data: datasets.
  • dgp_graph: implemetation of our model.
  • *.ipynb: jupyter notebooks for experiments.
  • run_toy.sh: shell script to run additional experiment.
  • toy_main.py: code for additional experiment (Traditional ML methods and DGPG with linear kernel).
• results/: contains results of experiments
  • *.html: experiment results demonstrated by static HTML files.

Experiments

The experiments are demonstrated by jupyter notebooks. The source is under directory src/ and the corresponding result is exported as a static HTML file stored in the directory results/. They are organized by dataset names:

1. Synthetic Datasets
   • demo_toy_run1.ipynb
   • demo_toy_run2.ipynb
   • demo_toy_run3.ipynb
   • demo_toy_run4.ipynb
   • demo_toy_run5.ipynb
2. Small Datasets
   • demo_city45.ipynb
   • demo_city45_linear.ipynb (linear kernel)
   • demo_city45_baseline.ipynb (traditional regression methods)
   • demo_etex.ipynb
   • demo_etex_linear.ipynb
   • demo_etex_baseline.ipynb
   • demo_fmri.ipynb
   • demo_fmri_linear.ipynb
   • demo_fmri_baseline.ipynb
3. Large Datasets (traffic flow)
   • LA
     • demo_la_15min.ipynb
     • demo_la_30min.ipynb
     • demo_la_60min.ipynb
   • BAY
     • demo_bay_15min.ipynb
     • demo_bay_30min.ipynb
     • demo_bay_60min.ipynb