Hyperbolic Embeddings
Switch branches/tags
Nothing to show
Clone or download
Latest commit 3af3794 Aug 17, 2018

README.md

hyperbolics

Hyperbolic embedding implementations of Representation Tradeoffs for Hyperbolic Embeddings.

Hyperbolic embedding of binary tree

Setup

We use Docker to set up the environment for our code. See Docker/README.md for installation and launch instructions.

In this README, all instructions are assumed to be run inside the Docker container. All paths are relative to the /hyperbolics directory, and all commands are expected to be run from this directory.

Usage

The following programs and scripts expect the input graphs to exist in the /data/edges folder, e.g. /data/edges/phylo_tree.edges. All graphs that we report results on have been prepared and saved here.

Combinatorial construction

julia combinatorial/comb.jl --help to see options. Example usage (for better results on this dataset, raise the precision):

julia combinatorial/comb.jl -d data/edges/phylo_tree.edges -m phylo_tree.r10.emb -e 1.0 -p 64 -r 10 -a -s

Pytorch optimizer

python pytorch/pytorch_hyperbolic.py learn --help to see options. Example usage:

python pytorch/pytorch_hyperbolic.py learn data/edges/phylo_tree.edges --batch-size 64 -r 10 -l 5.0 --epochs 100 --checkpoint-freq 10 -w phylo_tree.r10.emb

Experiment scripts

  • scripts/run_exps.py runs a full set of experiments for a list of datasets. Example usage (note: the default run settings take a long time to finish):

    python scripts/run_exps.py phylo -d phylo_tree --epochs 20
    

    Currently, it executes the following experiments:

    1. The combinatorial construction with fixed precision in varying dimensions
    2. The combinatorial construction in dimension 2 (Sarkar's algorithm), with very high precision
    3. Pytorch optimizer in varying dimensions, random initialization
    4. Pytorch optimizer in varying dimensions, using the embedding produced by the combinatorial construction as initialization
  • The combinatorial constructor combinatorial/comb.jl has an option for reporting the MAP and distortion statistics. However, this can be slow on larger datasets such as wordnet

    • scripts/comb_stats.py provides an alternate method for computing stats that can leverage multiprocessing Example usage: python scripts/comb_stats.py phylo_tree -e 1.0 -r 2 -p 1024 -q 4 to run on 4 cores