| | import json |
| | import re |
| | from typing import List |
| | import html |
| | import datasets |
| |
|
| | ENTITY = 'entity' |
| | ENTITY_PATTERN = r'<entity>{}</entity>' |
| |
|
| | 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 |
| | ) |
| | }, |
| | |
| | ), |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | 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 = '<movie>' |
| |
|
| | 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>' |
| | 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'\(\d{4}\)') |
| | 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) |
| | |
| | gen = [key for key in init_q if key in resp_q] |
| | for id in gen: |
| | |
| | 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 |
| |
|
