# 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 import datasets _CITATION = """\ @inproceedings{puduppully-etal-2019-data, title = "Data-to-text Generation with Entity Modeling", author = "Puduppully, Ratish and Dong, Li and Lapata, Mirella", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1195", doi = "10.18653/v1/P19-1195", pages = "2023--2035", } """ _DESCRIPTION = """\ The MLB dataset for data to text generation contains Major League Baseball games statistics and their human-written summaries. """ _HOMEPAGE = "https://github.com/ratishsp/mlb-data-scripts" _LICENSE = "" _URL = "data.zip" team_verbalization_map = {"team_errors": "", "team_hits": "", "team_runs": ""} pitcher_verbalization_map = {"p_bb": "", "p_er": "", "p_era": "", "p_h": "", "p_hr": "", "p_l": "", "p_loss": "", "p_s": "", "p_np": "", "p_r": "", "p_save": "", "p_so": "", "p_bf": "", "p_bs": "", "p_sv": "", "p_w": "", "p_ip1": "", "p_ip2": "", "p_win": "", "p_out": ""} batter_verbalization_map = {"h": "", "r": "", "hr": "", "ab": "", "avg": "", "rbi": "", "cs": "", "hbp": "", "a": "", "bb": "", "e": "", "obp": "", "po": "", "pos": "", "sb": "", "sf": "", "slg": "", "so": "" } pbyp_verbalization_map = {"o": "", "b": "", "s": "", "b1": "", "b2": "", "b3": "", "batter": "", "pitcher": "", "scorers": "", "event": "", "event2": "", "fielder_error": "", "runs": "", "rbi": "", "error_runs": "", "top": "", "bottom": ""} player_verbalization_map = dict(pitcher_verbalization_map, **batter_verbalization_map) class MlbDataToText(datasets.GeneratorBasedBuilder): """MLB dataset for data to text generation""" VERSION = datasets.Version("1.1.0") def _info(self): features = datasets.Features( { "home_name": datasets.Value("string"), "box_score": [ { "p_l": datasets.Value("string"), "last_name": datasets.Value("string"), "p_h": datasets.Value("string"), "sac": datasets.Value("string"), "p_bb": datasets.Value("string"), "pos": datasets.Value("string"), "ao": datasets.Value("string"), "p_bf": datasets.Value("string"), "cs": datasets.Value("string"), "hbp": datasets.Value("string"), "ab": datasets.Value("string"), "full_name": datasets.Value("string"), "p_w": datasets.Value("string"), "go": datasets.Value("string"), "fldg": datasets.Value("string"), "p_bs": datasets.Value("string"), "avg": datasets.Value("string"), "p_r": datasets.Value("string"), "p_s": datasets.Value("string"), "lob": datasets.Value("string"), "first_name": datasets.Value("string"), "p_sv": datasets.Value("string"), "p_so": datasets.Value("string"), "p_save": datasets.Value("string"), "p_hr": datasets.Value("string"), "po": datasets.Value("string"), "p_ip1": datasets.Value("string"), "p_ip2": datasets.Value("string"), "bb": datasets.Value("string"), "ops": datasets.Value("string"), "p_hld": datasets.Value("string"), "bo": datasets.Value("string"), "p_loss": datasets.Value("string"), "e": datasets.Value("string"), "p_game_score": datasets.Value("string"), "p_win": datasets.Value("string"), "a": datasets.Value("string"), "p_era": datasets.Value("string"), "d": datasets.Value("string"), "p_out": datasets.Value("string"), "h": datasets.Value("string"), "p_er": datasets.Value("string"), "p_np": datasets.Value("string"), "hr": datasets.Value("string"), "r": datasets.Value("string"), "so": datasets.Value("string"), "t": datasets.Value("string"), "rbi": datasets.Value("string"), "team": datasets.Value("string"), "sb": datasets.Value("string"), "slg": datasets.Value("string"), "sf": datasets.Value("string"), "obp": datasets.Value("string"), } ], "home_city": datasets.Value("string"), "vis_name": datasets.Value("string"), "play_by_play": [{ "top": [{ "runs": datasets.Value("string"), "scorers": [ datasets.Value("string") ], "pitcher": datasets.Value("string"), "o": datasets.Value("string"), "b": datasets.Value("string"), "s": datasets.Value("string"), "batter": datasets.Value("string"), "b1": [ datasets.Value("string") ], "b2": [ datasets.Value("string") ], "b3": [ datasets.Value("string") ], "event": datasets.Value("string"), "event2": datasets.Value("string"), "home_team_runs": datasets.Value("string"), "away_team_runs": datasets.Value("string"), "rbi": datasets.Value("string"), "error_runs": datasets.Value("string"), "fielder_error": datasets.Value("string") } ], "bottom": [{ "runs": datasets.Value("string"), "scorers": [ datasets.Value("string") ], "pitcher": datasets.Value("string"), "o": datasets.Value("string"), "b": datasets.Value("string"), "s": datasets.Value("string"), "batter": datasets.Value("string"), "b1": [ datasets.Value("string") ], "b2": [ datasets.Value("string") ], "b3": [ datasets.Value("string") ], "event": datasets.Value("string"), "event2": datasets.Value("string"), "home_team_runs": datasets.Value("string"), "away_team_runs": datasets.Value("string"), "rbi": datasets.Value("string"), "error_runs": datasets.Value("string"), "fielder_error": datasets.Value("string") } ], "inning": datasets.Value("string") } ], "vis_line": { "innings": [{ "inn": datasets.Value("string"), "runs": datasets.Value("string") } ], "result": datasets.Value("string"), "team_runs": datasets.Value("string"), "team_hits": datasets.Value("string"), "team_errors": datasets.Value("string"), "team_name": datasets.Value("string"), "team_city": datasets.Value("string") }, "home_line": { "innings": [{ "inn": datasets.Value("string"), "runs": datasets.Value("string") } ], "result": datasets.Value("string"), "team_runs": datasets.Value("string"), "team_hits": datasets.Value("string"), "team_errors": datasets.Value("string"), "team_name": datasets.Value("string"), "team_city": datasets.Value("string") }, "vis_city": datasets.Value("string"), "day": datasets.Value("string"), "summary": [ datasets.Value("string"), ], "summary_eval": datasets.Value("string"), "gem_id": 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(_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "data", "train.jsonl"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "data", "test.jsonl"), "split": "test" }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, "data", "validation.jsonl"), "split": "validation", }, ), ] def tokenize_initials(self, value): attrib_value = re.sub(r"(\w)\.(\w)\.", r"\g<1>. \g<2>.", value) return attrib_value def get_team_line_attributes(self, entry, name): if name == entry["home_line"]["team_name"]: line = entry["home_line"] type = "home" elif name == entry["vis_line"]["team_name"]: line = entry["vis_line"] type = "vis" else: assert False city = line["team_city"] name = line["team_name"] result = line["result"] updated_type = "<" + type.upper() + ">" team_tup = (updated_type, name, city, result) team_line = "%s %s %s %s" sentence1 = team_line % (team_tup) other_attributes = [] attributes = ["team_runs", "team_hits", "team_errors"] for attrib in attributes: template_string = " ".join([team_verbalization_map[attrib], "%s"]) other_attributes.append(template_string % line[attrib]) other_attributes = " ".join(other_attributes) team_info = sentence1 if len(other_attributes) > 0: team_info = " ".join([sentence1, other_attributes]) innings = line["innings"] inning_verbalization = [] for inning in innings: inning_phrase = " %s %s" % (inning["inn"], inning["runs"]) inning_verbalization.append(inning_phrase) inning_sentence = " ".join(inning_verbalization) team_info = " ".join([team_info, inning_sentence]) return team_info def get_player_line(self, entry): players = [] for player in entry["box_score"]: if player["full_name"] == "N/A": continue player_line = " %s %s %s" player_tup = (self.tokenize_initials(player["full_name"]), player["team"], player["pos"]) player_basic_info = player_line % (player_tup) other_attributes = [] for attrib in ["r", "h", "hr", "rbi", "e", "ab", "avg", "cs", "hbp", "bb", "sb", "sf", "so", "a", "po", "p_ip1", "p_ip2", "p_w", "p_l", "p_h", "p_r", "p_er", "p_bb", "p_so", "p_hr", "p_np", "p_s", "p_era", "p_win", "p_loss", "p_save", "p_sv", "p_bf", "p_out", "p_bs"]: if player[attrib] == "N/A": continue if attrib in ['sb', 'sf', 'e', 'po', 'a', 'cs', 'hbp', 'hr', 'so', 'bb', "p_hr", "p_sv", "p_bs"] and int(player[attrib]) == 0: continue if attrib in ['avg'] and player[attrib] == ".000": continue template_string = " ".join([player_verbalization_map[attrib], "%s"]) other_attributes.append(template_string % (player[attrib])) player_other_attributes = " ".join(other_attributes) if other_attributes: player_info = " ".join([player_basic_info, player_other_attributes]) else: player_info = player_basic_info players.append(player_info) return players def get_runs_desc(self, inning_play): obs_desc = [] for attrib in ["runs", "rbi", "error_runs"]: if attrib in inning_play and inning_play[attrib] != "N/A" and int(inning_play[attrib]) > 0: desc = " ".join([pbyp_verbalization_map[attrib], "%d"]) obs_desc.append(desc % (int(inning_play[attrib]))) return obs_desc def get_obs_desc(self, inning_play): obs_desc = [] for attrib in ["o", "b", "s"]: if attrib in inning_play: desc = " ".join([pbyp_verbalization_map[attrib], "%d"]) obs_desc.append(desc % (int(inning_play[attrib]))) return obs_desc def get_name_desc(self, attrib, inning_play, obs_desc): if attrib in inning_play: desc = " ".join([pbyp_verbalization_map[attrib], "%s"]) attrib_value = self.tokenize_initials(inning_play[attrib]) obs_desc.append(desc % (attrib_value)) def get_name_desc_entity(self, attrib, entity_name, obs_desc): desc = " ".join([pbyp_verbalization_map[attrib], "%s"]) attrib_value = self.tokenize_initials(entity_name) obs_desc.append(desc % (attrib_value)) def get_team_scores_desc(self, away, home, inning_play, obs_desc): if "home_team_runs" in inning_play and "away_team_runs" in inning_play and inning_play[ "home_team_runs"] != "N/A" and inning_play["away_team_runs"] != "N/A": desc = " %s %d %s %d" % ( home, int(inning_play["home_team_runs"]), away, int(inning_play["away_team_runs"])) obs_desc.append(desc) def get_attrib_value_desc(self, attrib, inning_play, obs_desc): if attrib in inning_play and inning_play[attrib] != "N/A": desc = " ".join([pbyp_verbalization_map[attrib], "%s"]) obs_desc.append(desc % (inning_play[attrib])) def get_play_by_play_desc(self, home, away, inning, inning_play, play_index, top_bottom): inning_line = " ".join( [" %s", pbyp_verbalization_map[top_bottom], " %s %s %d"]) if top_bottom == "top": inning_attrib = (inning, away, home, play_index) else: inning_attrib = (inning, home, away, play_index) inning_desc = inning_line % (inning_attrib) other_attrib_desc = [inning_desc] other_attrib_desc.extend(self.get_runs_desc(inning_play)) other_attrib_desc.extend(self.get_obs_desc(inning_play)) for attrib in ["batter", "pitcher", "fielder_error"]: if attrib in inning_play and inning_play[attrib] != "N/A": self.get_name_desc(attrib, inning_play, other_attrib_desc) for attrib in ["scorers", "b2", "b3"]: if attrib in inning_play and len(inning_play[attrib]) > 0 and inning_play[attrib][0] != "N/A": for baserunner_instance in inning_play[attrib]: self.get_name_desc_entity(attrib, baserunner_instance, other_attrib_desc) self.get_attrib_value_desc("event", inning_play, other_attrib_desc) self.get_attrib_value_desc("event2", inning_play, other_attrib_desc) self.get_team_scores_desc(away, home, inning_play, other_attrib_desc) return other_attrib_desc def get_play_by_play_all_entities_inning(self, inning_data, home, away, inning, side): play_by_play_desc = [] play_index = 1 inning_plays = inning_data[side] for inning_play in inning_plays: other_attrib_desc = self.get_play_by_play_desc(home, away, inning, inning_play, play_index, side) other_attrib_desc = " ".join(other_attrib_desc) play_index += 1 play_by_play_desc.append(other_attrib_desc) return play_by_play_desc def linearize_input(self, entry): output = [] output.append(self.get_team_line_attributes(entry, entry["home_line"]["team_name"])) output.append(self.get_team_line_attributes(entry, entry["vis_line"]["team_name"])) output.extend(self.get_player_line(entry)) for inning_data in entry['play_by_play']: for side in ["top", "bottom"]: pbyp_desc = self.get_play_by_play_all_entities_inning(inning_data, entry["home_line"]["team_name"], entry["vis_line"]["team_name"], inning_data['inning'], side) if pbyp_desc: output.append(" ".join(pbyp_desc)) linearized_input = " ".join(output) linearized_input = linearized_input.replace(" ", " ") 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. with open(filepath, encoding="utf-8") as f: for id_, row in enumerate(f): data = json.loads(row) yield id_, { "home_name": data["home_name"], "box_score": data["box_score"], "home_city": data["home_city"], "vis_name": data["vis_name"], "play_by_play": data["play_by_play"], "vis_line": data["vis_line"], "vis_city": data["vis_city"], "day": data["day"], "home_line": data["home_line"], "summary": data["summary"], "summary_eval": data["summary_eval"], "gem_id": data["gem_id"], "target": data["summary_eval"], "references": [data["summary_eval"]], "linearized_input": self.linearize_input(data) }