Datasets:
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import os, sys
import nltk
from collections import Counter
import pickle
from datasets import load_dataset
from tqdm import tqdm
import csv
import json
import re
def tokenize(message):
"""
Text processing: Sentence tokenize, then concatenate the word_tokenize of each sentence. Then lower.
:param message:
:return:
"""
sentences = nltk.sent_tokenize(message)
tokenized = []
for sentence in sentences:
tokenized += nltk.word_tokenize(sentence)
return [word.lower() for word in tokenized]
def load_movie_mappings(path):
id2name = {}
db2id = {}
with open(path, 'r') as f:
reader = csv.reader(f)
# remove date from movie name
for row in reader:
if row[0] != "index":
id2name[int(row[0])] = row[1]
# id2name[int(row[0])] = row[1]
db2id[int(row[2])] = int(row[0])
del db2id[-1]
date_pattern = re.compile(r'\(\d{4}\)')
# get dataset characteristics
db2name = {db: date_pattern.sub('', id2name[id]).strip(" ") for db, id in db2id.items()}
n_redial_movies = len(db2id.values()) # number of movies mentioned in ReDial
# name2id = {name: int(i) for i, name in id2name.items() if name != ''}
# print("loaded {} movies from {}".format(len(name2id), path))
return id2name, db2name
def get_vocab(dataset, db2name):
"""
get the vocabulary from the train data
:return: vocabulary
"""
print(f"Loading vocabulary from {dataset} dataset")
counter = Counter()
# get vocabulary from dialogues
datasets = load_dataset(dataset, download_mode="force_redownload")
date_pattern = re.compile(r'@(\d+)')
for subset in ["train", "validation", "test"]:
for conversation in tqdm(datasets[subset]):
for message in conversation["messages"]:
# remove movie Ids
text = tokenize(date_pattern.sub(" ", message))
counter.update([word.lower() for word in text])
# get vocabulary from movie names
for movieId in db2name:
tokenized_movie = tokenize(db2name[movieId])
counter.update([word.lower() for word in tokenized_movie])
# Keep the most common words
kept_vocab = counter.most_common(15000)
vocab = [x[0] for x in kept_vocab]
print("Vocab covers {} word instances over {}".format(
sum([x[1] for x in kept_vocab]),
sum([counter[x] for x in counter])
))
# note: let the <pad> token corresponds to 0
vocab = ['<pad>', '<s>', '</s>', '<unk>', '\n'] + vocab
return vocab
if __name__ == '__main__':
import os
dataset = 'redial'
base_dir = os.path.dirname(os.path.abspath(__file__))
id2entity, db2name = load_movie_mappings(os.path.join(base_dir, "movies_merged.csv"))
with open(os.path.join(base_dir, 'id2entity.json'), 'w') as f:
json.dump(id2entity, f)
# vocab = get_vocab(dataset, db2name)
# print("vocab has length:", len(vocab))
# with open(os.path.join(base_dir, 'vocab.json'), 'w') as f:
# json.dump(vocab, f)
# |