import json
import re
from typing import List
import html
import datasets
ENTITY = 'entity'
ENTITY_PATTERN = r'{}'
logger = datasets.logging.get_logger(__name__)
class RedialConfig(datasets.BuilderConfig):
"""BuilderConfig for ReDIAL."""
def __init__(self, features,
initiator_prefix='User: ',
respondent_prefix='System: ',
**kwargs):
"""BuilderConfig for ReDIAL.
Args:
features: *list[string]*, list of the features that will appear in the
feature dict. Should not include "label".
**kwargs: keyword arguments forwarded to super.
"""
super().__init__(version=datasets.Version("0.0.1"), **kwargs)
self.features = features
self.initiator_prefix = initiator_prefix
self.respondent_prefix = respondent_prefix
_URL = "./"
_URLS = {
"train": _URL + "train.jsonl",
"valid": _URL + "valid.jsonl",
"test": _URL + "test.jsonl",
}
class ReDIAL(datasets.GeneratorBasedBuilder):
DEFAULT_CONFIG_NAME = "rec"
BUILDER_CONFIGS = [
RedialConfig(
name="SA",
description="For using the ReDIAL dataset to train sentiment analysis on movies in sentences",
features={
"movieId": datasets.Value("int32"),
"movieName": datasets.Value("string"),
"messages": datasets.features.Sequence(datasets.Value("string")),
"senders": datasets.features.Sequence(datasets.Value("int32")),
"form": datasets.features.Sequence(
datasets.Value("int32"), length=6
)
},
# certain information(e.g. movie_occurrences) is model-specific, and we leave it for Dataset.map
),
# RedialConfig(
# name="SA_debug",
# description="For using the ReDIAL dataset to train sentiment analysis on movies in sentences",
# features={
# "id": datasets.Value("int32"),
# "movieName": datasets.Value("string"),
# "messages": datasets.features.Sequence(datasets.Value("string")),
# "senders": datasets.features.Sequence(datasets.Value("int32")),
# "form": datasets.features.Sequence(
# datasets.Value("int32"), length=6
# )
# },
# ),
RedialConfig(
name="autorec",
description="For training autorec model on ReDIAL data",
features=datasets.Features({
"movieIds": datasets.Sequence(datasets.Value("int32")),
"ratings": datasets.Sequence(datasets.Value("float"))
}),
),
RedialConfig(
name="rec",
description="For using the ReDIAL dataset to train recommender",
features={
"movieIds": datasets.Sequence(datasets.Value("int32")),
"messages": datasets.features.Sequence(datasets.Value("string")),
"senders": datasets.features.Sequence(datasets.Value("int32")),
},
),
RedialConfig(
name="formatted",
description='Embed all information into a text sequence for each dialog',
features={
"messages": datasets.features.Sequence(datasets.Value("string")),
}
)
]
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.last_sender = None
def _processMessage(self, msg, initialId):
"""
msg example: {
"timeOffset": 0,
"text": "Hi I am looking for a movie like @111776",
"senderWorkerId": 956,
"messageId": 204171
},
"""
res = {
"text": msg["text"],
"sender": 1 if msg["senderWorkerId"] == initialId else -1
}
return res
def _flattenMessages(self, conversation, add_prefix=False):
messages = []
senders = []
for message in conversation["messages"]:
role = 1 if message["senderWorkerId"] == conversation["initiatorWorkerId"] else -1
text = message["text"]
if len(senders) > 0 and senders[-1] == role:
messages[-1] += "\n" + text
else:
senders.append(role)
if add_prefix:
prefix = self.config.initiator_prefix if role == 1 else self.config.respondent_prefix
text = prefix + text
messages.append(text)
return messages, senders
def _info(self):
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(self.config.features),
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
urls_to_download = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
movie_pattern = re.compile(r'@(\d+)')
default_movie_entity = ''
def _process_utt(self, utt, movieid2name, replace_movieId=True, remove_movie=False):
def convert(match):
movieid = match.group(0)[1:]
if movieid in movieid2name:
if remove_movie:
return ''
movie_name = movieid2name[movieid]
movie_name = ' '.join(movie_name.split())
return ENTITY_PATTERN.format(movie_name)
else:
return match.group(0)
if replace_movieId:
utt = re.sub(self.movie_pattern, convert, utt)
utt = ' '.join(utt.split())
utt = html.unescape(utt)
return utt
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
if self.config.name == "autorec":
with open(filepath, encoding="utf-8") as f:
idx = 0
for line in f:
conversation = json.loads(line)
movieIds = []
ratings = []
if len(conversation["initiatorQuestions"]) == 0:
continue
for id, form in conversation["initiatorQuestions"].items():
rating = int(form["liked"])
if rating < 2:
movieIds.append(id)
ratings.append(rating)
if len(movieIds) > 0:
yield idx, {
"movieIds": movieIds,
"ratings": ratings
}
idx += 1
elif "SA" in self.config.name:
Idx = 0
date_pattern = re.compile(r'\(\d{4}\)') # To match e.g. "(2009)"
with open(filepath, encoding="utf-8") as f:
for line in f:
conversation = json.loads(line)
init_q = conversation["initiatorQuestions"]
resp_q = conversation["respondentQuestions"]
msgs, senders = self._flattenMessages(conversation)
# get movies that are in both forms.
gen = [key for key in init_q if key in resp_q]
for id in gen:
# remove date from movie name
movieName = date_pattern.sub('', conversation["movieMentions"][id]).strip(" ")
if len(movieName) == 0:
continue
yield Idx, {
"movieId": int(id),
"movieName": movieName,
"messages": msgs,
"senders": senders,
"form": [init_q[id]["suggested"], init_q[id]["seen"], init_q[id]["liked"],
resp_q[id]["suggested"], resp_q[id]["seen"], resp_q[id]["liked"], ]
}
Idx += 1
if Idx > 100 and "debug" in self.config.name:
break
elif "rec" in self.config.name:
Idx = 0
with open(filepath, encoding="utf-8") as f:
for line in f:
conversation = json.loads(line)
msgs, senders = self._flattenMessages(conversation)
yield Idx, {
"messages": msgs,
"senders": senders,
"movieIds": [int(movieId) for movieId in conversation["movieMentions"]]
}
Idx += 1
elif "formatted" in self.config.name:
Idx = 0
with open(filepath, encoding="utf-8") as f:
for line in f:
dialog = json.loads(line)
msgs, senders = self._flattenMessages(dialog, add_prefix=True)
movieid2name = dialog['movieMentions']
formatted_msgs = [self._process_utt(utt, movieid2name) for utt in msgs]
yield Idx, {
"messages": formatted_msgs,
}
Idx += 1