OCW.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Only Connect Wall (OCW) dataset"""

import json
import os

import datasets


_CITATION = """\
@article{Naeini2023LargeLM,
    title        = {Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset},
    author       = {Saeid Alavi Naeini and Raeid Saqur and Mozhgan Saeidi and John Giorgi and Babak Taati},
    year         = 2023,
    journal      = {ArXiv},
    volume       = {abs/2306.11167},
    url          = {https://api.semanticscholar.org/CorpusID:259203717}
}
"""

_DESCRIPTION = """\
The Only Connect Wall (OCW) dataset contains 618 "Connecting Walls" from the Round 3: Connecting Wall segment of the Only Connect quiz show, collected from 15 seasons' worth of episodes. Each wall contains the ground-truth groups and connections as well as recorded human performance.
"""

_HOMEPAGE_URL = "https://github.com/TaatiTeam/OCW/"

_LICENSE = "MIT"

_BASE_URL = "https://www.cs.toronto.edu/~taati/OCW/"
_URLS = {
    "ocw": _BASE_URL + "OCW.tar.gz",
    "ocw_randomized": _BASE_URL + "OCW_randomized.tar.gz",
    "ocw_wordnet": _BASE_URL + "OCW_wordnet.tar.gz"
    
}


class OCW(datasets.GeneratorBasedBuilder):
    """OCW dataset"""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="ocw", version=VERSION,
                               description="main OCW dataset"),
        datasets.BuilderConfig(name="ocw_randomized", version=VERSION,
                               description="Easy OCW dataset with randomized groups in each wall"),
        datasets.BuilderConfig(name="ocw_wordnet", version=VERSION,
                               description="Easy OCW dataset with wordnet synonyms replaced with original clues")
    ]

    DEFAULT_CONFIG_NAME = "ocw"

    def _info(self):
        features = datasets.Features(
            {
                # "total_walls_in_season": datasets.Value("int32"),
                # "season_start_date": datasets.Value("string"),
                # "season_end_date": datasets.Value("string"),
                "wall_id": datasets.Value("string"),
                "season": datasets.Value("int32"),
                "episode": datasets.Value("int32"),
                "words": datasets.features.Sequence(feature=datasets.Value("string")),
                "gt_connections": datasets.features.Sequence(feature=datasets.Value("string")),
                "group_1":
                    {
                        "group_id": datasets.Value("string"),
                        "gt_words":datasets.features.Sequence(feature=datasets.Value("string")),
                        "gt_connection": datasets.Value("string"),
                        "human_performance":
                            {
                            "grouping": datasets.Value("int32"),
                            "connection": datasets.Value("int32")
                            }
                    },
                "group_2":
                    {
                        "group_id": datasets.Value("string"),
                        "gt_words": datasets.features.Sequence(feature=datasets.Value("string")),
                        "gt_connection": datasets.Value("string"),
                        "human_performance": 
                            {
                            "grouping": datasets.Value("int32"),
                            "connection": datasets.Value("int32")
                            }
                    },
                "group_3":
                    {
                        "group_id": datasets.Value("string"),
                        "gt_words": datasets.features.Sequence(feature=datasets.Value("string")),
                        "gt_connection": datasets.Value("string"),
                        "human_performance":
                            {
                            "grouping": datasets.Value("int32"),
                            "connection": datasets.Value("int32")
                            }
                    },
                "group_4":
                    {
                        "group_id": datasets.Value("string"),
                        "gt_words": datasets.features.Sequence(feature=datasets.Value("string")),
                        "gt_connection": datasets.Value("string"),
                        "human_performance":
                            {
                            "grouping": datasets.Value("int32"),
                            "connection": datasets.Value("int32")
                            }
                    },

            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features= features,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE_URL,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
            # No default supervised_keys
            supervised_keys=None
        )

    def _split_generators(self, dl_manager):
        url = _URLS[self.config.name]
        if self.config.name == "ocw_randomized":
            url = [url, _URLS[self.DEFAULT_CONFIG_NAME]]
        path = dl_manager.download_and_extract(url)
        if self.config.name == self.DEFAULT_CONFIG_NAME:
            dir = 'dataset'
            train_filepath = os.path.join(path, dir, 'train.json')
            val_filepath = os.path.join(path, dir, 'validation.json')
            test_filepath = os.path.join(path, dir, 'test.json')
        elif self.config.name == "ocw_randomized":
            # OCW-randomized only contains a test set, we load main OCW train/validation files
            dir = 'OCW_randomized'
            dir2 = 'dataset'
            train_filepath = os.path.join(path[1], dir2, 'train.json')
            val_filepath = os.path.join(path[1], dir2, 'validation.json')
            test_filepath = os.path.join(path[0], dir, 'easy_test.json')
        else:
            dir = 'OCW_wordnet'
            train_filepath = os.path.join(path, dir, 'easy_train_wordnet.json')
            val_filepath = os.path.join(path, dir, 'easy_validation_wordnet.json')
            test_filepath = os.path.join(path, dir, 'easy_test_wordnet.json')

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_filepath}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_filepath}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_filepath}),
        ]


    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        key = 0
        with open(filepath, encoding="utf-8") as f:
            ocw = json.load(f)
            for data in ocw["dataset"]:
                wall_id = data.get("wall_id")
                season = data.get("season")
                # season_to_walls_map = ocw['season_to_walls_map'][str(season)]
                # total_walls_in_season = season_to_walls_map["num_walls"]
                # season_start_date = season_to_walls_map["start_date"]
                # season_end_date = season_to_walls_map["end_date"]
                episode = data.get("episode")
                words = data.get("words")
                gt_connections = data.get("gt_connections")
                group_1 = data['groups']['group_1']
                group_1_human_performance = group_1['human_performance']
                group_2 = data['groups']['group_2']
                group_2_human_performance = group_2['human_performance']
                group_3 = data['groups']['group_3']
                group_3_human_performance = group_3['human_performance']
                group_4 = data['groups']['group_4']
                group_4_human_performance = group_4['human_performance']
                yield key, {
                    # "total_walls_in_season": total_walls_in_season,
                    # "season_start_date": season_start_date,
                    # "season_end_date": season_end_date,
                    "wall_id": wall_id,
                    "season": season,
                    "episode": episode,
                    "words": words,
                    "gt_connections": gt_connections,
                    "group_1": {
                        "group_id": group_1.get("group_id"),
                        "gt_words": group_1.get("gt_words"),
                        "gt_connection": group_1.get("gt_connection"),
                        "human_performance": {
                            "grouping": group_1_human_performance.get("grouping"),
                            "connection": group_1_human_performance.get("connection")
                        }
                    },
                    "group_2": {
                        "group_id": group_2.get("group_id"),
                        "gt_words": group_2.get("gt_words"),
                        "gt_connection": group_2.get("gt_connection"),
                        "human_performance": {
                            "grouping": group_2_human_performance.get("grouping"),
                            "connection": group_2_human_performance.get("connection")
                        }
                    },
                    "group_3": {
                        "group_id": group_3.get("group_id"),
                        "gt_words": group_3.get("gt_words"),
                        "gt_connection": group_3.get("gt_connection"),
                        "human_performance": {
                            "grouping": group_3_human_performance.get("grouping"),
                            "connection": group_3_human_performance.get("connection")
                        }
                    },
                    "group_4": {
                        "group_id": group_4.get("group_id"),
                        "gt_words": group_4.get("gt_words"),
                        "gt_connection": group_4.get("gt_connection"),
                        "human_performance": {
                            "grouping": group_4_human_performance.get("grouping"),
                            "connection": group_4_human_performance.get("connection")
                        }
                    },
                }
                key += 1