Source code for pykeen.triples.deteriorate

# -*- coding: utf-8 -*-

"""Deterioration algorithm."""

import logging
import math
from itertools import zip_longest
from typing import List, Union

import click
import more_click
import torch

from pykeen.triples import TriplesFactory
from pykeen.typing import TorchRandomHint
from pykeen.utils import ensure_torch_random_state

__all__ = [

logger = logging.getLogger(__name__)

[docs]def deteriorate( reference: TriplesFactory, *others: TriplesFactory, n: Union[int, float], random_state: TorchRandomHint = None, ) -> List[TriplesFactory]: """Remove n triples from the reference set. :param reference: The reference triples factory :param others: Other triples factories to deteriorate :param n: The ratio to deteriorate. If given as a float, should be between 0 and 1. If an integer, deteriorates that many triples :param random_state: The random state :returns: A concatenated list of the processed reference and other triples factories :raises NotImplementedError: if the reference triples factory has inverse triples :raises ValueError: If a float is given for n that isn't between 0 and 1 """ # TODO: take care that triples aren't removed that are the only ones with any given entity if reference.create_inverse_triples: raise NotImplementedError if isinstance(n, float): if n < 0 or 1 <= n: raise ValueError n = int(n * reference.num_triples) generator = ensure_torch_random_state(random_state) logger.debug("random state %s", random_state) logger.debug("generator %s %s", generator, generator.get_state()) idx = torch.randperm(reference.num_triples, generator=generator) logger.debug("idx %s", idx) reference_idx, deteriorated_idx = idx.split(split_size=[reference.num_triples - n, n], dim=0) first = reference.clone_and_exchange_triples( mapped_triples=reference.mapped_triples[reference_idx], ) # distribute the deteriorated triples across the remaining factories didxs = deteriorated_idx.split(math.ceil(n / len(others)), dim=0) rest = [ tf.clone_and_exchange_triples( mapped_triples=([tf.mapped_triples, reference.mapped_triples[didx]], dim=0) if didx is not None else tf.mapped_triples # maybe just give same tf? should it be copied? ), ) for didx, tf in zip_longest(didxs, others) ] return [first, *rest]
@click.command() @more_click.verbose_option @click.option("--trials", type=int, default=15, show_default=True) def _main(trials: int): import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns from tabulate import tabulate from pykeen.constants import PYKEEN_EXPERIMENTS from pykeen.datasets import get_dataset n_comb = trials * (trials - 1) // 2"Number of combinations: {trials} n Choose 2 = {n_comb}") ns = [0.01, 0.05, 0.1, 0.2, 0.3, 0.4] rows = [] for dataset_name in [ # 'kinships', "nations", # 'umls', # 'codexsmall', # 'wn18', ]: dataset_rows = [] reference_dataset = get_dataset(dataset=dataset_name) for n in ns: similarities = [] for trial in range(trials): deteriorated_dataset = reference_dataset.deteriorate(n=n, random_state=trial) sim = reference_dataset.similarity(deteriorated_dataset) similarities.append(sim) rows.append((dataset_name, n, trial, sim)) dataset_rows.append((n, np.mean(similarities), np.std(similarities))) click.echo(tabulate(dataset_rows, headers=[f"{dataset_name} N", "Mean", "Std"])) df = pd.DataFrame(rows, columns=["name", "n", "trial", "sim"]) tsv_path = PYKEEN_EXPERIMENTS / "deteriorating.tsv" png_path = PYKEEN_EXPERIMENTS / "deteriorating.png" click.echo(f"writing to {tsv_path}") df.to_csv(tsv_path, sep="\t", index=False) sns.lineplot(data=df, x="n", y="sim", hue="name") plt.savefig(png_path, dpi=300) if __name__ == "__main__": _main()