# 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"], }