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process_movie_dialogue_subword.py
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process_movie_dialogue_subword.py
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import re
import time
import collections
import pickle as pkl
from collections import Counter
import byte_pair_encoding as bpe
from nltk.tokenize import wordpunct_tokenize as word_tokenizer
def get_stats(vocab):
pairs = collections.defaultdict(int)
for word, freq in vocab.items():
symbols = word.split(" ")
for m in range(len(symbols)-1):
pairs[symbols[m], symbols[m+1]] += freq
return pairs
def merge_vocab(pair, v_in):
v_out = {}
bigram = " ".join(pair)
for word in v_in:
w_out = word.replace(bigram, "".join(pair))
v_out[w_out] = v_in[word]
return v_out
def learn_subword_vocab(word_vocab, n_iter):
for m in range(n_iter):
pairs = get_stats(word_vocab)
most_freq = max(pairs, key=pairs.get)
word_vocab = merge_vocab(most_freq, word_vocab)
subword_vocab = collections.defaultdict(int)
for word, freq in pairs.items():
if freq == 0:
continue
subword_vocab[word[0]] += freq
subword_vocab[word[1]] += freq
return subword_vocab, pairs
print("Loading the data.")
tmp_path = "C:/Users/admin/Desktop/Codes/"
start_tm = time.time()
tmp_line_file = tmp_path + "movie_lines.txt"
with open(tmp_line_file, "r", encoding='utf-8', errors='ignore') as tmp_file:
tmp_lines = tmp_file.readlines()
tmp_conv_file = tmp_path + "movie_conversations.txt"
with open(tmp_conv_file, "r", encoding='utf-8', errors='ignore') as tmp_file:
tmp_convs = tmp_file.readlines()
id2line = {}
for tmp_line in tmp_lines:
tmp_split = str(tmp_line).split(" +++$+++ ")
if len(tmp_split) == 5:
id2line[tmp_split[0]] = tmp_split[4]
convs = []
for tmp_conv in tmp_convs[:-1]:
tmp_split = str(tmp_conv).replace("\\n", "").split(
" +++$+++ ")[-1][1:-1].replace("'","").replace(" ","")
tmp_split = tmp_split.replace("]", "")
tmp_ids = [str(x.encode("utf-8")).replace(
"b'", "").replace("'", "") for x in tmp_split.split(",")]
convs.append(tmp_ids)
q_len = 10
a_len = 10
w_counter = Counter()
tmp_corpus = []
tmp_data_tuple = []
for conv in convs:
for i in range(len(conv)-1):
tmp_qns = id2line[conv[i]].lower().replace(
"\\u", " ").replace("\\i", " ").replace("\n", " ").replace("\t", " ")
#tmp_qns = re.sub(r"[^\w\s]", " ", tmp_qns)
tmp_qns = [x for x in word_tokenizer(tmp_qns) if x != ""]
tmp_ans = id2line[conv[i+1]].lower().replace(
"\\u", " ").replace("\\i", " ").replace("\n", " ").replace("\t", " ")
#tmp_ans = re.sub(r"[^\w\s]", " ", tmp_ans)
tmp_ans = [x for x in word_tokenizer(tmp_ans) if x != ""]
if len(tmp_qns) == 0 or len(tmp_ans) == 0:
continue
elif len(tmp_qns) <= q_len and len(tmp_ans) <= a_len:
w_counter.update(tmp_qns)
w_counter.update(tmp_ans)
tmp_data_tuple.append((" ".join(tmp_qns), " ".join(tmp_ans)))
elapsed_tm = (time.time() - start_tm) / 60
print("Elapsed Time:", str(elapsed_tm), "mins.")
# Fit the subword vocabulary. #
print("Fitting subword vocabulary.")
start_tm = time.time()
word_counts = []
for word, count in w_counter.items():
tmp_word = "<" + word + ">"
tmp_word = "".join([x+" " for x in tmp_word]).strip()
word_counts.append((tmp_word, count))
word_counts = dict(word_counts)
n_iters = 1000
vocab_size = 8000
tuple_out = bpe.learn_subword_vocab(
word_counts, n_iters, vocab_size=vocab_size)
subword_vocab_list = tuple_out[0]
idx2subword = tuple_out[1]
subword2idx = tuple_out[2]
elapsed_tm = (time.time() - start_tm) / 60
print("Total Sub-word Vocabulary size:",
str(len(subword_vocab_list)), "sub-word tokens")
print("Elapsed Time:", str(elapsed_tm), "mins.")
# Encode the corpus to subword tokens. #
print("Encoding the corpus to subwords.")
start_tm = time.time()
new_tuple = []
for tmp_qns, tmp_ans, in tmp_data_tuple:
tmp_qns_sw = bpe.bp_encode(
tmp_qns, subword_vocab_list, subword2idx)
tmp_ans_sw = bpe.bp_encode(
tmp_ans, subword_vocab_list, subword2idx)
new_tuple.append((tmp_qns_sw, tmp_ans_sw))
elapsed_tm = (time.time() - start_tm) / 60
print("Elapsed Time:", str(elapsed_tm), "mins.")
# Save the data. #
print("Saving the file.")
tmp_pkl_file = tmp_path + "movie_dialogues_subword.pkl"
with open(tmp_pkl_file, "wb") as tmp_file_save:
pkl.dump(new_tuple, tmp_file_save)
pkl.dump(idx2subword, tmp_file_save)
pkl.dump(subword2idx, tmp_file_save)