cornell_movie_dialog / cornell_movie_dialog.py
"""TODO(cornell_movie_dialog): Add a description here."""
import ast
import os
import datasets
# TODO(cornell_movie_dialog): BibTeX citation
_CITATION = """\
@InProceedings{Danescu-Niculescu-Mizil+Lee:11a,
author={Cristian Danescu-Niculescu-Mizil and Lillian Lee},
title={Chameleons in imagined conversations:
A new approach to understanding coordination of linguistic style in dialogs.},
booktitle={Proceedings of the
Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011},
year={2011}
}
"""
# TODO(cornell_movie_dialog):
_DESCRIPTION = """\
This corpus contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts:
- 220,579 conversational exchanges between 10,292 pairs of movie characters
- involves 9,035 characters from 617 movies
- in total 304,713 utterances
- movie metadata included:
- genres
- release year
- IMDB rating
- number of IMDB votes
- IMDB rating
- character metadata included:
- gender (for 3,774 characters)
- position on movie credits (3,321 characters)
"""
_URL = "https://www.cs.cornell.edu/~cristian/data/cornell_movie_dialogs_corpus.zip"
class CornellMovieDialog(datasets.GeneratorBasedBuilder):
"""TODO(cornell_movie_dialog): Short description of my dataset."""
# TODO(cornell_movie_dialog): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(cornell_movie_dialog): Specifies the datasets.DatasetInfo object
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
"movieID": datasets.Value("string"),
"movieTitle": datasets.Value("string"),
"movieYear": datasets.Value("string"),
"movieIMDBRating": datasets.Value("string"),
"movieNoIMDBVotes": datasets.Value("string"),
"movieGenres": datasets.features.Sequence(datasets.Value("string")),
"characterID1": datasets.Value("string"),
"characterID2": datasets.Value("string"),
"characterName1": datasets.Value("string"),
"characterName2": datasets.Value("string"),
"utterance": datasets.features.Sequence(
{"text": datasets.Value("string"), "LineID": datasets.Value("string")}
)
# These are the features of your dataset like images, labels ...
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="http://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(cornell_movie_dialog): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
dl_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepaths": os.path.join(dl_dir, "cornell movie-dialogs corpus")},
),
]
def _generate_examples(self, filepaths):
"""Yields examples."""
# TODO(cornell_movie_dialog): Yields (key, example) tuples from the dataset
movie_char_file = os.path.join(filepaths, "movie_characters_metadata.txt")
movie_conv_file = os.path.join(filepaths, "movie_conversations.txt")
movie_lines_file = os.path.join(filepaths, "movie_lines.txt")
movie_titles_file = os.path.join(filepaths, "movie_titles_metadata.txt")
with open(movie_char_file, "rb") as f:
movie_char_data = [x.decode("latin").split("+++$+++") for x in f.readlines()]
with open(movie_conv_file, "rb") as f:
movie_conv_data = [x.decode("latin").split("+++$+++") for x in f.readlines()]
with open(movie_lines_file, "rb") as f:
movie_lines_data = [x.decode("latin").split("+++$+++") for x in f.readlines()]
with open(movie_titles_file, "rb") as f:
movie_titles_data = [x.decode("latin").split("+++$+++") for x in f.readlines()]
# looping over movie conversation file
for id_, conv in enumerate(movie_conv_data):
char_id_1 = conv[0]
char_id_2 = conv[1]
movie_id = conv[2]
line_ids = conv[-1].replace("\n", "")
line_ids = ast.literal_eval(line_ids.strip())
lines_texts = []
# searching text corresponding to each lineID in line_ids in movie lines file
for line_id in line_ids:
i = 0
while i < len(movie_lines_data) and movie_lines_data[i][0].strip() != line_id:
i += 1
lines_texts.append(movie_lines_data[i][0]) # if i < len(movie_lines_data) else '')
# look for char names in movie character file
j = 0
while j < len(movie_char_data) and movie_char_data[j][0].strip() != char_id_1.strip():
j += 1
char_name_1 = movie_char_data[j][1] # if j < len(movie_char_data) else ''
movie_title = movie_char_data[j][3] # if j < len(movie_char_data) else ''
k = 0
while k < len(movie_char_data) and movie_char_data[k][0].strip() != char_id_2.strip():
k += 1
char_name_2 = movie_char_data[k][1]
# look for movie year, IMDBRating, genre, no_imdb_voting in movie tiles file
li = 0
while li < len(movie_titles_data) and movie_titles_data[li][0].strip() != movie_id.strip():
li += 1
movie_year = movie_titles_data[li][2]
imdb_rating = movie_titles_data[li][3]
no_imdb_vote = movie_titles_data[li][4]
genre = movie_titles_data[li][5].replace("\n", "").strip()
movie_genres = ast.literal_eval(genre)
yield id_, {
"movieID": movie_id,
"movieTitle": movie_title,
"movieYear": movie_year,
"movieIMDBRating": imdb_rating,
"movieNoIMDBVotes": no_imdb_vote,
"movieGenres": movie_genres,
"characterID1": char_id_1,
"characterID2": char_id_2,
"characterName1": char_name_1,
"characterName2": char_name_2,
"utterance": {"text": lines_texts, "LineID": line_ids},
}