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cornell_movie_dialog / cornell_movie_dialog.py

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	"""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},
	}