klinker

Installation

Clone the repo and change into the directory:

git clone https://github.com/dobraczka/klinker.git
cd klinker

For usage with GPU create a micromamba environment:

micromamba env create -n klinker-conda --file=klinker-conda.yaml

Activate it and install the remaining dependencies:

mamba activate klinker-conda
pip install -e .

Alternatively if you don't intend to utilize a GPU you can install it in a virtual environment:

python -m venv klinker-env
source klinker-env/bin/activate
pip install -e .[all]

or via poetry:

poetry install

Usage

Load a dataset:

from sylloge import MovieGraphBenchmark
from klinker.data import KlinkerDataset

ds = KlinkerDataset.from_sylloge(MovieGraphBenchmark(graph_pair="tmdb-tvdb"))

Create blocks and write to parquet:

from klinker.blockers import SimpleRelationalTokenBlocker

blocker = SimpleRelationalTokenBlocker()
blocks = blocker.assign(left=ds.left, right=ds.right, left_rel=ds.left_rel, right_rel=ds.right_rel)
blocks.to_parquet("tmdb-tvdb-tokenblocked")

Read blocks from parquet and evaluate:

from klinker import KlinkerBlockManager
from klinker.eval_metrics import Evaluation

kbm = KlinkerBlockManager.read_parqet("tmdb-tvdb-tokenblocked")
ev = Evaluation.from_dataset(blocks=kbm, dataset=ds)

Reproduce Experiments

The experiment.py has commands for datasets and blockers. You can use python experiment.py --help to show the available commands. Subcommands can also offer help e.g. python experiment.py gcn-blocker --help.

You have to use a dataset command before a blocker command.

For example if you used micromamba for installation:

micromamba run -n klinker-conda python experiment.py movie-graph-benchmark-dataset --graph-pair "tmdb-tvdb" relational-token-blocker

This would be similar to the steps described in the above usage section.

In order to precisely reproduce the results from the paper we provide (adapted) run scripts from our SLURM batch scripts in the run_scripts folder. Please consult the run_scripts/README.md for further information. For archival purposes the experiment artifacts and the source code are stored in Zenodo.