Skip to content

Latest commit

 

History

History
63 lines (54 loc) · 3.58 KB

README.md

File metadata and controls

63 lines (54 loc) · 3.58 KB
Raw

Generalizing Downsampling from Regular Data to Graphs

Installation

You can install the kmis environment and the required packages with Anaconda/Miniconda, by running the following commands in the project directory:

conda env create -f conda/environment_cu113.yml
# Alternatively, for CPU-only:
# conda env create -f conda/environment_cpu.yml
conda activate kmis

Available commands

We provide the following commands, all of which can be executed by running

python -m benchmark [COMMAND] [OPTIONS]

in the project directory:

Command Options (example) Description
info info --root datasets/ Prints a description of the datasets.
train train --model KMISPool --dataset DD --config "{'k':1}" --test Train a model on a dataset with a given configuration.
grid_search See scripts/grid_search.sh. Perform a grid search for a model and dataset in a given parameter space.
mnist mnist --k 1 --scorer lightness --reduction mean Plot the reduction of a diagonal grid constructed on a digit of the MNIST dataset.
dist dist --k 1 --name minnesota --group Gleich Plot the distance distortions on a graph from the Suite Sparse Matrix Collection.
profile profile --k 1 --name europe_osm --group DIMACS10 --device cuda Compute running time and reduction statistics.
export export --name luxembourg_osm --group DIMACS10 --max_k 8 Export all the power graphs (up to max_k) in DIMACS92 format of a given graph.

Reproducibility

The results showed in the paper can be reproduced as following:

  • The reductions of the MNIST digit can be obtained by running the script ./scripts/mnist.sh
  • To reproduce the length distorsions on the Minnesota road network, run python -m benchmark dist --k "[1,2,4,8]" --reduction None.
  • To reproduce the classification benchmarks, run the script ./scripts/grid_search.sh (note: it may take long).
  • To obtain the running time benchmarks, run the script ./scripts/profile.sh (note: the results may vary depending on the machine).
  • To reproduce the benchmarks on the total weight, run the script ./scripts/weight.sh. The results on the sequential greedy algorithm were computed using stablesolver (with -a greedy_gwmin and -a greedy_gwmin2 options), on power graphs previously exported using the export command (see above).

Reference

@misc{bacciu_generalizing_2022,
  author = {Bacciu, Davide and Conte, Alessio and Landolfi, Francesco},
  title = {Generalizing Downsampling from Regular Data to Graphs},
  url = {https://arxiv.org/abs/2208.03523},
  publisher = {arXiv},
  year = {2022}
}