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# 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 re
from readline import parse_and_bind
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.jsonl",
"validation": "validation.jsonl",
"test": "test.jsonl"
}
def detokenize(text):
"""
Untokenizing a text undoes the tokenizing operation, restoring
punctuation and spaces to the places that people expect them to be.
Ideally, `untokenize(tokenize(text))` should be identical to `text`,
except for line breaks.
"""
step1 = text.replace("`` ", '"').replace(" ''", '"').replace('. . .', '...')
step2 = step1.replace(" ( ", " (").replace(" ) ", ") ")
step3 = re.sub(r' ([.,:;?!%]+)([ \'"`])', r"\1\2", step2)
step4 = re.sub(r' ([.,:;?!%]+)$', r"\1", step3)
step5 = step4.replace(" '", "'").replace(" n't", "n't").replace(
"can not", "cannot").replace(" 've", "'ve")
step6 = step5.replace(" ` ", " '")
return step6.strip()
# 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"),
"game_id": 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("int32"),
"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"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PF": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"H1": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"H2": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"Q1": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"Q2": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"Q3": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"Q4": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"OT": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
}
},
"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"),
"FG_PCT": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3A": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"OREB": datasets.Value("string"),
"DREB": datasets.Value("string"),
"TREB": 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"),
"DOUBLE": 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("int32"),
"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"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PF": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"H1": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"H2": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"Q1": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"Q2": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"Q3": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"Q4": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
},
"OT": {
"FG3A": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FGA": datasets.Value("string"),
"FGM": datasets.Value("string"),
"FG_PCT": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"DREB": datasets.Value("string"),
"OREB": datasets.Value("string"),
"TREB": datasets.Value("string"),
"BLK": datasets.Value("string"),
"AST": datasets.Value("string"),
"STL": datasets.Value("string"),
"TOV": datasets.Value("string"),
"PTS": datasets.Value("string"),
"MIN": datasets.Value("string")
}
},
"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"),
"FG_PCT": datasets.Value("string"),
"FG3M": datasets.Value("string"),
"FG3A": datasets.Value("string"),
"FG3_PCT": datasets.Value("string"),
"FTM": datasets.Value("string"),
"FTA": datasets.Value("string"),
"FT_PCT": datasets.Value("string"),
"OREB": datasets.Value("string"),
"DREB": datasets.Value("string"),
"TREB": 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"),
"DOUBLE": 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")),
"target": datasets.Value("string"),
"references": [datasets.Value("string")],
"linearized_input": 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 tokenize_initials(self, value):
attrib_value = re.sub(r"(\w)\.(\w)\.", r"\g<1>. \g<2>.", value)
return attrib_value
def sort_players_by_pts(self, entry, type='HOME'):
"""
Sort players by points and return the indices sorted by points
bs --> [{'pts': 10}, {'pts': 30}, {'pts': 35}, {'pts': 5}]
return --> [2, 1, 0, 3]
"""
all_pts = [int(item['PTS']) for item in entry['teams'][type.lower()]['box_score']]
all_pts1 = [[item, idx] for idx, item in enumerate(all_pts)]
all_pts1.sort()
all_pts1.reverse()
return [item[1] for item in all_pts1]
def get_one_player_data(self, player_stats, team_name, rank):
"""
player_line = "<PLAYER> %s <TEAM> %s <POS> %s <RANK> %s <MIN> %d <PTS> %d <FG> %d %d %d <FG3> %d %d %d \
<FT> %d %d %d <REB> %d <AST> %d <STL> %s <BLK> %d <DREB> %d <OREB> %d <TO> %d"
"""
pos = f'STARTER YES' if player_stats['starter'] == True else f'STARTER NO'
player_min = int(player_stats['MIN'])
rank = rank if player_min > 0 else f"{rank.split('-')[0]}-DIDNTPLAY"
player_line = f"<PLAYER> {self.tokenize_initials(player_stats['name'])} <TEAM> {team_name} <POS> {pos} <RANK> {rank}"
player_line = f"{player_line} <MIN> {player_stats['MIN']} <PTS> {player_stats['PTS']} <FG> {player_stats['FGM']} {player_stats['FGA']} {player_stats['FG_PCT']}"
player_line = f"{player_line} <FG3> {player_stats['FG3M']} {player_stats['FG3A']} {player_stats['FG3_PCT']}"
player_line = f"{player_line} <FT> {player_stats['FTM']} {player_stats['FTA']} {player_stats['FT_PCT']}"
player_line = f"{player_line} <REB> {player_stats['TREB']} <AST> {player_stats['AST']} <STL> {player_stats['STL']}"
player_line = f"{player_line} <BLK> {player_stats['BLK']} <DREB> {player_stats['DREB']} <OREB> {player_stats['OREB']} <TO> {player_stats['TOV']}"
player_line = f"{player_line} <DOUBLE> {player_stats['DOUBLE']}"
return player_line
def get_box_score(self, entry, type='HOME'):
bs = entry['teams'][type.lower()]['box_score']
team_name = f"{entry['teams'][type.lower()]['place']} {entry['teams'][type.lower()]['name']}"
sorted_idx = self.sort_players_by_pts(entry, type)
player_lines = [self.get_one_player_data(bs[idx], team_name, f'{type}-{rank}') for rank, idx in enumerate(sorted_idx)]
return ' '.join(player_lines)
def get_team_line(self, entry, type='HOME', winner='HOME'):
"""
team_line = "%s <TEAM> %s <CITY> %s <TEAM-RESULT> %s <TEAM-PTS> %d <WINS-LOSSES> %d %d <QTRS> %d %d %d %d \
<TEAM-AST> %d <3PT> %d <TEAM-FG> %d <TEAM-FT> %d <TEAM-REB> %d <TEAM-TO> %d"
"""
line_score = entry['teams'][type.lower()]['line_score']['game']
team_line = f"<TEAM> {entry['teams'][type.lower()]['name']} <CITY> {entry['teams'][type.lower()]['place']}"
if winner == type:
team_line = f"{team_line} <TEAM-RESULT> won"
else:
team_line = f"{team_line} <TEAM-RESULT> lost"
team_line = f"{team_line} <TEAM-PTS> {line_score['PTS']} <WINS-LOSSES> {entry['teams'][type.lower()]['wins']} {entry['teams'][type.lower()]['losses']}"
team_line = f"{team_line} <QTRS> {entry['teams'][type.lower()]['line_score']['Q1']['PTS']} {entry['teams'][type.lower()]['line_score']['Q2']['PTS']}"
team_line = f"{team_line} {entry['teams'][type.lower()]['line_score']['Q3']['PTS']} {entry['teams'][type.lower()]['line_score']['Q4']['PTS']}"
team_line = f"{team_line} <TEAM-AST> {line_score['AST']} <3PT> {line_score['FG3M']} <TEAM-FG> {line_score['FGM']} <TEAM-FT> {line_score['FTM']}"
team_line = f"{team_line} <TEAM-REB> {line_score['TREB']} <TEAM-TO> {line_score['TOV']}"
return team_line
def get_box_and_line_scores(self, entry):
"""Get line- & box- scores data for a single game"""
home_team_pts = entry['teams']['home']['line_score']['game']['PTS']
vis_team_pts = entry['teams']['home']['line_score']['game']['PTS']
winner = 'HOME' if int(home_team_pts) > int(vis_team_pts) else 'VIS'
home_team_line = self.get_team_line(entry, type='HOME', winner=winner)
vis_team_line = self.get_team_line(entry, type='VIS', winner=winner)
home_box_score = self.get_box_score(entry, type='HOME')
vis_box_score = self.get_box_score(entry, type='VIS')
return home_team_line, vis_team_line, home_box_score, vis_box_score
def get_game_data(self, entry):
"""Get game data for a single game"""
game_date = f"{entry['day']} {entry['month']} {entry['year']}"
game_day = entry['dayname']
game_stadium = entry['stadium']
game_city = entry['city']
return f"<DATE> {game_date} <DAY> {game_day} <STADIUM> {game_stadium} <CITY> {game_city}"
def get_next_game_data_of_a_team(self, entry):
"""
next_game_line = "<NEXT-GAME-DATE> %s <NEXT-GAME-DAY> %s <NEXT-GAME-STADIUM> %s <NEXT-GAME-CITY> %s"
"""
next_game_date = f"{entry['day']} {entry['month']} {entry['year']}"
next_game_is_home = 'yes' if entry['is_home'] == 'True' else 'no'
next_game_line = f"<NEXT-DATE> {next_game_date} <NEXT-DAY> {entry['dayname']}"
next_game_line = f"{next_game_line} <NEXT-STADIUM> {entry['stadium']} <NEXT-CITY> {entry['city']}"
next_game_line = f"{next_game_line} <NEXT-OPPONENT-PLACE> {entry['opponent_place']} <NEXT-OPPONENT-NAME> {entry['opponent_name']}"
next_game_line = f"{next_game_line} <NEXT-IS-HOME> {next_game_is_home}"
return next_game_line
def get_next_game_info(self, entry):
"""
Get next game data for both teams in a game.
In case of no next game, all values will be ''.
"""
home_next_game = self.get_next_game_data_of_a_team(entry['teams']['home']['next_game'])
vis_next_game = self.get_next_game_data_of_a_team(entry['teams']['vis']['next_game'])
return home_next_game, vis_next_game
def linearize_input(self, entry):
"""
Linearizes the input to the model.
"""
game_data = self.get_game_data(entry['game'])
home_line, vis_line, home_box_score, vis_box_score = self.get_box_and_line_scores(entry)
home_next, vis_next = self.get_next_game_info(entry)
linearized_input = f"<GAME> {game_data} <HOME> {home_line} <NEXT-HOME> {home_next} <VIS> {vis_line} <VIS-NEXT> {vis_next} {home_box_score} {vis_box_score}"
return linearized_input
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"))
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
yield id_, {
"sportsett_id": data["sportsett_id"],
"gem_id": data["gem_id"],
"game": data["game"],
"teams": data["teams"],
"summaries": data["summaries"],
"target": detokenize(data["summaries"][0]),
"references": [detokenize(s) for s in data["summaries"]],
"linearized_input": self.linearize_input(data)
}
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