permutation-groups.py

import datasets
import json
import os
from sympy.combinatorics import Permutation
from sympy.combinatorics.named_groups import AlternatingGroup, SymmetricGroup

_DESCRIPTION = "A collection of permutation composition datasets for various symmetric and alternating groups."
_HOMEPAGE = "https://huggingface.co/datasets/BeeGass/permutation-groups"
_LICENSE = "MIT"

class PermutationGroupsConfig(datasets.BuilderConfig):
    def __init__(self, *args, group_name=None, group_degree=None, group_order=None, **kwargs):
        super().__init__(*args, **kwargs)
        self.group_name = group_name
        self.group_degree = group_degree
        self.group_order = group_order

class PermutationGroups(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        PermutationGroupsConfig(
            name="s3_data",
            description="Permutation Composition Dataset for the Symmetric Group S3.",
            group_name="S3",
            group_degree=3,
            group_order=6,
        ),
        PermutationGroupsConfig(
            name="s4_data",
            description="Permutation Composition Dataset for the Symmetric Group S4.",
            group_name="S4",
            group_degree=4,
            group_order=24,
        ),
        PermutationGroupsConfig(
            name="s5_data",
            description="Permutation Composition Dataset for the Symmetric Group S5.",
            group_name="S5",
            group_degree=5,
            group_order=120,
        ),
        PermutationGroupsConfig(
            name="s6_data",
            description="Permutation Composition Dataset for the Symmetric Group S6.",
            group_name="S6",
            group_degree=6,
            group_order=720,
        ),
        PermutationGroupsConfig(
            name="s7_data",
            description="Permutation Composition Dataset for the Symmetric Group S7.",
            group_name="S7",
            group_degree=7,
            group_order=5040,
        ),
        PermutationGroupsConfig(
            name="a5_data",
            description="Permutation Composition Dataset for the Alternating Group A5.",
            group_name="A5",
            group_degree=5,
            group_order=60,
        ),
        PermutationGroupsConfig(
            name="a6_data",
            description="Permutation Composition Dataset for the Alternating Group A6.",
            group_name="A6",
            group_degree=6,
            group_order=360,
        ),
        PermutationGroupsConfig(
            name="a7_data",
            description="Permutation Composition Dataset for the Alternating Group A7.",
            group_name="A7",
            group_degree=7,
            group_order=2520,
        ),
    ]

    DEFAULT_CONFIG_NAME = "s5_data"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "input_sequence": datasets.Value("string"),
                "target": datasets.Value("string"),
            }),
            homepage=_HOMEPAGE,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        # The data files are already uploaded to the repo in data/<config_name>/
        # We need to provide the paths relative to the dataset script
        data_dir = f"data/{self.config.name}"
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "train", "data-*.arrow"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "test", "data-*.arrow"),
                    "split": "test",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        # Load the Arrow files directly
        for i, file in enumerate(dl_manager.iter_files(filepath)):
            # Assuming each .arrow file contains a table that can be converted to a list of dicts
            # For simplicity, we'll load it as a Dataset and iterate
            dataset = datasets.Dataset.from_file(file)
            for id_, row in enumerate(dataset):
                yield f"{split}_{i}_{id_}", {
                    "input_sequence": row["input_sequence"],
                    "target": row["target"],
                }