Datasets:
Tasks:
Table to Text
Modalities:
Text
Languages:
English
Size:
10K - 100K
Tags:
data-to-text
License:
File size: 23,079 Bytes
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# 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_ERRORS>", "team_hits": "<TEAM_HITS>", "team_runs": "<TEAM_RUNS>"}
pitcher_verbalization_map = {"p_bb": "<PITCH-BASE-ON-BALLS>", "p_er": "<EARNED-RUN>", "p_era": "<EARNED-RUN-AVG>",
"p_h": "<PITCH-HITS>", "p_hr": "<PITCH-HOME-RUN>", "p_l": "<PITCH-LOSS>",
"p_loss": "<PITCH-LOSING-PITCHER>", "p_s": "<PITCH-STRIKES-THROWN>",
"p_np": "<PITCH-COUNT>", "p_r": "<PITCH-RUNS>", "p_save": "<PITCH-SAVING-PITCHER>",
"p_so": "<PITCH-STRIKE-OUT>", "p_bf": "<PITCH-BATTERS-FACED>",
"p_bs": "<PITCH-BLOWN-SAVE>",
"p_sv": "<PITCH-SAVE>", "p_w": "<PITCH-WIN>", "p_ip1": "<INNINGS-PITCHED-1>",
"p_ip2": "<INNINGS-PITCHED-2>", "p_win": "<PITCH-WINNING-PITCHER>",
"p_out": "<PITCH-OUT>"}
batter_verbalization_map = {"h": "<HITS>", "r": "<RUNS>", "hr": "<HOME-RUN>", "ab": "<ATBAT>", "avg": "<AVG>",
"rbi": "<RBI>", "cs": "<CAUGHT-STEAL>", "hbp": "<HIT-BY-PITCH>", "a": "<ASSIST>",
"bb": "<BASE-ON-BALL>", "e": "<ERROR>", "obp": "<ON-BASE-PCT>", "po": "<PUTOUT>",
"pos": "<POS>", "sb": "<STOLEN-BASE>", "sf": "<SAC-FLY>", "slg": "<SLUG>",
"so": "<STRIKEOUT>"
}
pbyp_verbalization_map = {"o": "<PBYP-OUTS>", "b": "<PBYP-BALLS>", "s": "<PBYP-STRIKES>", "b1": "<PBYP-B1>",
"b2": "<PBYP-B2>", "b3": "<PBYP-B3>", "batter": "<PBYP-BATTER>",
"pitcher": "<PBYP-PITCHER>",
"scorers": "<PBYP-SCORERS>", "event": "<PBYP-EVENT>", "event2": "<PBYP-EVENT2>",
"fielder_error": "<PBYP-FIELDER-ERROR>", "runs": "<PBYP-RUNS>", "rbi": "<PBYP-RBI>",
"error_runs": "<PBYP-ERROR-RUNS>", "top": "<TOP>", "bottom": "<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 <TEAM> %s <CITY> %s <TEAM-RESULT> %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 = "<INN> %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 = "<PLAYER> %s <TEAM> %s <POS> %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 = "<TEAM-SCORES> %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(
["<INNING> %s", pbyp_verbalization_map[top_bottom], "<BATTING> %s <PITCHING> %s <PLAY> %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)
}
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