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utils.py
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utils.py
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from sklearn.utils import shuffle
import pickle
import re
import numpy as np
import os
def clean_str(string):
"""
Tokenization/string cleaning for all datasets except for SST.
Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
"""
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\'s", " \'s", string)
string = re.sub(r"\'ve", " \'ve", string)
string = re.sub(r"n\'t", " n\'t", string)
string = re.sub(r"\'re", " \'re", string)
string = re.sub(r"\'d", " \'d", string)
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(r"\(", " \( ", string)
string = re.sub(r"\)", " \) ", string)
string = re.sub(r"\?", " \? ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip().lower().split()
def read_test_data(dir="SST"):
data = {}
test_file_0 = "sentiment.test.0.cbert"
test_file_1 = "sentiment.test.1.cbert"
x, y = [], []
with open(os.path.join(dir,test_file_0), "r", encoding="utf-8") as f:
for line in f:
if line[-1] == "\n":
_, content, label = line.split('\t')
y.append(1 - int(label))
content = clean_str(content)
x.append(content)
with open(os.path.join(dir,test_file_1), "r", encoding="utf-8") as f:
for line in f:
if line[-1] == "\n":
_, content, label = line.split('\t')
y.append(1 - int(label))
content = clean_str(content)
x.append(content)
x, y = shuffle(x, y)
data["test_x"], data["test_y"] = x, y
return data
def read_data(dir="SST", train=None, dev=None, test=None):
def clean_str(string):
"""
Tokenization/string cleaning for all datasets except for SST.
Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
"""
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\'s", " \'s", string)
string = re.sub(r"\'ve", " \'ve", string)
string = re.sub(r"n\'t", " n\'t", string)
string = re.sub(r"\'re", " \'re", string)
string = re.sub(r"\'d", " \'d", string)
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(r"\(", " \( ", string)
string = re.sub(r"\)", " \) ", string)
string = re.sub(r"\?", " \? ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip().lower().split()
data = {}
label_cnt = [0,0]
if train:
train_file_0 = "sentiment.train.0"
train_file_1 = "sentiment.train.1"
x, y = [], []
with open(os.path.join(dir,train_file_0), "r", encoding="utf-8") as f:
for line in f:
if line[-1] == "\n":
y.append(0)
label_cnt[0] += 1
line = clean_str(line)
x.append(line)
with open(os.path.join(dir,train_file_1), "r", encoding="utf-8") as f:
for line in f:
if line[-1] == "\n":
y.append(1)
label_cnt[1] += 1
line = clean_str(line)
x.append(line)
x, y = shuffle(x, y)
data["train_x"], data["train_y"] = x, y
if dev:
dev_file_0 = "sentiment.dev.0"
dev_file_1 = "sentiment.dev.1"
x, y = [], []
with open(os.path.join(dir, dev_file_0), "r", encoding="utf-8") as f:
for line in f:
if line[-1] == "\n":
y.append(0)
line = clean_str(line)
x.append(line)
with open(os.path.join(dir, dev_file_1), "r", encoding="utf-8") as f:
for line in f:
if line[-1] == "\n":
y.append(1)
line = clean_str(line)
x.append(line)
x, y = shuffle(x, y)
data["dev_x"], data["dev_y"] = x, y
if test:
test_file_0 = "sentiment.test.0"
test_file_1 = "sentiment.test.1"
x, y = [], []
with open(os.path.join(dir,test_file_0), "r", encoding="utf-8") as f:
for line in f:
if line[-1] == "\n":
y.append(0)
line = clean_str(line)
x.append(line)
with open(os.path.join(dir,test_file_1), "r", encoding="utf-8") as f:
for line in f:
if line[-1] == "\n":
y.append(1)
line = clean_str(line)
x.append(line)
x, y = shuffle(x, y)
data["test_x"], data["test_y"] = x, y
return data, label_cnt
def save_cls(model, dataset, model_name):
path = "pytorch_pretrained_cls/{}.{}.pkl".format(dataset, model_name)
pickle.dump(model, open(path, "wb"))
print("A model is saved successfully as {}!".format(path))
def save_vocab(vocab, dataset, model_name):
path = "pytorch_pretrained_cls/{}_vocab.{}.pkl".format(dataset, m)
pickle.dump(vocab, open(path, "wb"))
print("A vocab is saved successfully as {}!".format(path))
def load_cls(dataset, model_name):
path = "pytorch_pretrained_cls/{}.{}.pkl".format(dataset, model_name)
try:
model = pickle.load(open(path, "rb"))
print("Model pytorch_pretrained_cls/{}.{}.pkl loaded successfully!".format(dataset, model_name))
return model
except:
print("No available model such as {}!".format(path))
exit()
def load_vocab(dataset, model_name):
path = "pytorch_pretrained_cls/{}_vocab.{}.pkl".format(dataset, model_name)
try:
vocab = pickle.load(open(path, "rb"))
print("Model pytorch_pretrained_cls/{}_vocab.{}.pkl loaded successfully!".format(dataset, model_name))
return vocab
except:
print("No available vocab such as {}!".format(path))
exit()