sportsett_basketball.py

# coding=utf-8
# 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.
"""TODO: Add a description here."""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{thomson-etal-2020-sportsett,
    title = "{S}port{S}ett:Basketball - A robust and maintainable data-set for Natural Language Generation",
    author = "Thomson, Craig  and
      Reiter, Ehud  and
      Sripada, Somayajulu",
    booktitle = "Proceedings of the Workshop on Intelligent Information Processing and Natural Language Generation",
    month = sep,
    year = "2020",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Lingustics",
    url = "https://aclanthology.org/2020.intellang-1.4",
    pages = "32--40",
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
SportSett:Basketball dataset for Data-to-Text Generation contains NBA games stats aligned with their human written summaries.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/nlgcat/sport_sett_basketball"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLs = {
    "train": "train.json",
    "validation": "validation.json",
    "test": "test.json"
}


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class SportSettBasketball(datasets.GeneratorBasedBuilder):
    """SportSett:Basketball datatset for Data-to-Text Generation."""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        features = datasets.Features(
            {
                "sportsett_id": datasets.Value("string"),
                "gem_id": datasets.Value("string"),
                "game": {
                    "day": datasets.Value("string"),
                    "month": datasets.Value("string"),
                    "year": datasets.Value("string"),
                    "dayname": datasets.Value("string"),
                    "season": datasets.Value("string"),
                    "stadium": datasets.Value("string"),
                    "city": datasets.Value("string"),
                    "state": datasets.Value("string"),
                    "attendance": datasets.Value("string"),
                    "capacity": datasets.Value("string")
                },
                "teams": {
                    "home": {
                        "name": datasets.Value("string"),
                        "place": datasets.Value("string"),
                        "conference": datasets.Value("string"),
                        "division": datasets.Value("string"),
                        "wins": datasets.Value("string"),
                        "losses": datasets.Value("string"),
                        "conference_standing": datasets.Value("string"),
                        "division_standing": datasets.Value("string"),
                        "streak_num": datasets.Value("string"),
                        "game_number": datasets.Value("string"),
                        "previous_game_id": datasets.Value("string"),
                        "next_game_id": datasets.Value("string"),
                        "line_score": {
                            "game": {
                                "FG3A": datasets.Value("string"),
                                "FG3M": datasets.Value("string"),
                                "FGA": datasets.Value("string"),
                                "FGM": datasets.Value("string"),
                                "FTA": datasets.Value("string"),
                                "FTM": datasets.Value("string"),
                                "PF": datasets.Value("string"),
                                "DREB": datasets.Value("string"),
                                "OREB": datasets.Value("string"),
                                "BLK": datasets.Value("string"),
                                "AST": datasets.Value("string"),
                                "STL": datasets.Value("string"),
                                "TOV": datasets.Value("string"),
                                "PTS": datasets.Value("string"),
                                "SEC": datasets.Value("string")
                            }, 
                            "H1": {}, 
                            "H2": {},
                            "Q1": {},
                            "Q2": {},
                            "Q3": {},
                            "Q4": {},
                            "OT": {}
                        }, 
                        "box_score": [                            {
                                "first_name": datasets.Value("string"),
                                "last_name": datasets.Value("string"),
                                "name": datasets.Value("string"),
                                "starter": datasets.Value("string"),
                                "MIN": datasets.Value("string"),
                                "FGM": datasets.Value("string"),
                                "FGA": datasets.Value("string"),
                                "FG3M": datasets.Value("string"),
                                "FG3A": datasets.Value("string"),
                                "FTM": datasets.Value("string"),
                                "FTA": datasets.Value("string"),
                                "OREB": datasets.Value("string"),
                                "DREB": datasets.Value("string"),
                                "AST": datasets.Value("string"),
                                "STL": datasets.Value("string"),
                                "BLK": datasets.Value("string"),
                                "TOV": datasets.Value("string"),
                                "PF": datasets.Value("string"),
                                "PTS": datasets.Value("string"),
                                "+/-": datasets.Value("string"),
                            }
                        ], 
                        "next_game": {
                            "day": datasets.Value("string"),
                            "month": datasets.Value("string"),
                            "year": datasets.Value("string"),
                            "dayname": datasets.Value("string"),
                            "stadium": datasets.Value("string"),
                            "city": datasets.Value("string"),
                            "opponent_name": datasets.Value("string"),
                            "opponent_place": datasets.Value("string"),
                            "is_home": datasets.Value("string"),
                        }
                    },
                    "vis": {
                        "name": datasets.Value("string"),
                        "place": datasets.Value("string"),
                        "conference": datasets.Value("string"),
                        "division": datasets.Value("string"),
                        "wins": datasets.Value("string"),
                        "losses": datasets.Value("string"),
                        "conference_standing": datasets.Value("string"),
                        "division_standing": datasets.Value("string"),
                        "streak_num": datasets.Value("string"),
                        "game_number": datasets.Value("string"),
                        "previous_game_id": datasets.Value("string"),
                        "next_game_id": datasets.Value("string"),
                        "line_score": {
                            "game": {
                                "FG3A": datasets.Value("string"),
                                "FG3M": datasets.Value("string"),
                                "FGA": datasets.Value("string"),
                                "FGM": datasets.Value("string"),
                                "FTA": datasets.Value("string"),
                                "FTM": datasets.Value("string"),
                                "PF": datasets.Value("string"),
                                "DREB": datasets.Value("string"),
                                "OREB": datasets.Value("string"),
                                "BLK": datasets.Value("string"),
                                "AST": datasets.Value("string"),
                                "STL": datasets.Value("string"),
                                "TOV": datasets.Value("string"),
                                "PTS": datasets.Value("string"),
                                "SEC": datasets.Value("string")
                            }, 
                            "H1": {}, 
                            "H2": {},
                            "Q1": {},
                            "Q2": {},
                            "Q3": {},
                            "Q4": {},
                            "OT": {}
                        }, 
                        "box_score": [
                            {
                                "first_name": datasets.Value("string"),
                                "last_name": datasets.Value("string"),
                                "name": datasets.Value("string"),
                                "starter": datasets.Value("string"),
                                "MIN": datasets.Value("string"),
                                "FGM": datasets.Value("string"),
                                "FGA": datasets.Value("string"),
                                "FG3M": datasets.Value("string"),
                                "FG3A": datasets.Value("string"),
                                "FTM": datasets.Value("string"),
                                "FTA": datasets.Value("string"),
                                "OREB": datasets.Value("string"),
                                "DREB": datasets.Value("string"),
                                "AST": datasets.Value("string"),
                                "STL": datasets.Value("string"),
                                "BLK": datasets.Value("string"),
                                "TOV": datasets.Value("string"),
                                "PF": datasets.Value("string"),
                                "PTS": datasets.Value("string"),
                                "+/-": datasets.Value("string"),
                            }
                        ],
                        "next_game": {
                            "day": datasets.Value("string"),
                            "month": datasets.Value("string"),
                            "year": datasets.Value("string"),
                            "dayname": datasets.Value("string"),
                            "stadium": datasets.Value("string"),
                            "city": datasets.Value("string"),
                            "opponent_name": datasets.Value("string"),
                            "opponent_place": datasets.Value("string"),
                            "is_home": datasets.Value("string"),
                        }
                    }
                },
                "summaries": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        data_dir = dl_manager.download_and_extract(_URLs)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["test"],
                    "split": "test"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["validation"],
                    "split": "validation",
                },
            ),
        ]

    def _generate_examples(
        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    ):
        """ Yields examples as (key, example) tuples. """
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.

        js = json.load(open(filepath, encoding="utf-8"))
        for id_, data in enumerate(js):
            yield id_, {
                "sportsett_id": data["sportsett_id"],
                "gem_id": data["gem_id"],
                "game": data["game"],
                "teams": data["teams"],
                "summaries": data["summaries"],
            }