KG-Evaluation__Bachelor-Thesis__/data/wn18.py

from typing import List, Tuple

import torch
from pykeen.datasets import WN18, WN18RR

from .kg_dataset import KGDataset  # Importing KGDataset from the same module


class WN18Dataset(KGDataset):
    """A wrapper around PyKeen's WN18 dataset that produces KGDataset instances."""

    def __init__(self, split: str = "train") -> None:
        """
        Initialize the WN18RR wrapper.

        :param split: One of "train", "valid", or "test" indicating which split to load.
        """
        # Load the PyKeen WN18 dataset
        dataset = WN18()

        # Select the appropriate TriplesFactory based on the requested split
        if split == "train":
            tf = dataset.training
        elif split in ("valid", "val", "validation"):
            tf = dataset.validation
        elif split in ("test", "testing"):
            tf = dataset.testing
        else:
            msg = f"Unrecognized split '{split}'. Must be one of 'train', 'valid', or 'test'."
            raise ValueError(
                msg,
            )

        # `tf.mapped_triples` is a NumPy array of shape (n_triples, 3) with dtype=int64
        # Convert each row into a Python tuple of ints
        triples_list: List[Tuple[int, int, int]] = [
            (int(h), int(r), int(t)) for h, r, t in tf.mapped_triples
        ]

        # Get the total number of entities and relations in the entire dataset
        num_entities = dataset.num_entities
        num_relations = dataset.num_relations

        # Initialize the base KGDataset with the selected triples
        super().__init__(
            triples_factory=tf,
            triples=triples_list,
            num_entities=num_entities,
            num_relations=num_relations,
            split=split,
        )

    def __getitem__(self, idx: int) -> torch.Tensor:
        data = super().__getitem__(idx)

        # Convert the tuple to a torch tensor
        return torch.tensor(data, dtype=torch.long)


# Assuming KGDataset is defined in the same module or already imported:
# from your_module import KGDataset


class WN18RRDataset(KGDataset):
    """A wrapper around PyKeen's WN18RR dataset that produces KGDataset instances."""

    def __init__(self, split: str = "train") -> None:
        """
        Initialize the WN18RR wrapper.

        :param split: One of "train", "valid", or "test" indicating which split to load.
        """
        # Load the PyKeen WN18RR dataset
        dataset = WN18RR()

        # Select the appropriate TriplesFactory based on the requested split
        if split == "train":
            tf = dataset.training
        elif split in ("valid", "val", "validation"):
            tf = dataset.validation
        elif split in ("test", "testing"):
            tf = dataset.testing
        else:
            msg = f"Unrecognized split '{split}'. Must be one of 'train', 'valid', or 'test'."
            raise ValueError(
                msg,
            )

        # `tf.mapped_triples` is a NumPy array of shape (n_triples, 3) with dtype=int64
        # Convert each row into a Python tuple of ints
        triples_list: List[Tuple[int, int, int]] = [
            (int(h), int(r), int(t)) for h, r, t in tf.mapped_triples
        ]

        # Get the total number of entities and relations in the entire dataset
        num_entities = dataset.num_entities
        num_relations = dataset.num_relations

        # Initialize the base KGDataset with the selected triples
        super().__init__(
            triples_factory=tf,
            triples=triples_list,
            num_entities=num_entities,
            num_relations=num_relations,
            split=split,
        )

    def __getitem__(self, idx: int) -> torch.Tensor:
        data = super().__getitem__(idx)

        # Convert the tuple to a torch tensor
        return torch.tensor(data, dtype=torch.long)