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IO_classification.py
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IO_classification.py
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import re
import math
import numpy as np
from collections import Counter
import enchant
from nltk.stem import SnowballStemmer
def clear(file_path):
'''
数据清洗
'''
stemmer = SnowballStemmer("english")
ecd = enchant.Dict("en_US")
train_datas = []
test_datas = []
stop_words = []
read_object = open("data/stoplist.csv", 'r', encoding='UTF-8')
for line in read_object.readlines():
stop_words.append(line.strip())
read_object.close()
step = 0
read_object = open(file_path+"/trainData.txt", 'r', encoding='UTF-8')
for line in read_object.readlines():
line = re.sub(r'<.*?>', '',line.strip())
line_words = list(filter(None, (stemmer.stem(word.lower()) for word in re.split(r'[-\'?/*&,;.:!()<>"`$#%@ ]', line))))
for i in range(len(line_words)):
if not ecd.check(line_words[i]):
line_words[i] = (ecd.suggest(line_words[i])+[line_words[i]])[0].lower()
train_datas.append([word for word in line_words if word not in stop_words])
print("train: " + str(step))
step += 1
read_object.close()
write_object = open(file_path+"/clearTrainData.txt", 'w', encoding='UTF-8')
for train_data in train_datas:
write_object.write(' '.join('%s' %s for s in train_data) + '\n')
write_object.close()
step = 0
read_object = open(file_path + "/testData.txt", 'r', encoding='UTF-8')
for line in read_object.readlines():
line = re.sub(r'<.*?>', '', line.strip())
line_words = list(filter(None, (stemmer.stem(word.lower()) for word in re.split(r'[-\'?/*&,;.:!()<>"`$#%@ ]', line))))
for i in range(len(line_words)):
if not ecd.check(line_words[i]):
line_words[i] = (ecd.suggest(line_words[i])+[line_words[i]])[0].lower()
test_datas.append([word for word in line_words if word not in stop_words])
print("test: " + str(step))
step += 1
read_object.close()
write_object = open(file_path + "/clearTestData.txt", 'w', encoding='UTF-8')
for test_data in test_datas:
write_object.write(' '.join('%s' %s for s in test_data) + '\n')
write_object.close()
def resplit(file_path):
train_datas = []
test_datas = []
read_object = open(file_path + "/trainData.txt", 'r', encoding='UTF-8')
for line in read_object.readlines():
line = re.sub(r'<.*?>', '', line.strip())
train_datas.append(list(filter(None, (word.lower() for word in re.split(r'[-\'?/*&,;.:!()<>"`$#%@ ]', line)))))
read_object.close()
write_object = open(file_path + "/unclearTrainData.txt", 'w', encoding='UTF-8')
for train_data in train_datas:
write_object.write(' '.join('%s' % s for s in train_data) + '\n')
write_object.close()
read_object = open(file_path + "/testData.txt", 'r', encoding='UTF-8')
for line in read_object.readlines():
line = re.sub(r'<.*?>', '', line.strip())
test_datas.append(list(filter(None, (word.lower() for word in re.split(r'[-\'?/*&,;.:!()<>"`$#%@ ]', line)))))
read_object.close()
write_object = open(file_path + "/unclearTestData.txt", 'w', encoding='UTF-8')
for test_data in test_datas:
write_object.write(' '.join('%s' % s for s in test_data) + '\n')
write_object.close()
def padding(file_path):
train_datas = []
test_datas = []
stop_words = []
read_object = open("data/stoplist.csv", 'r', encoding='UTF-8')
for line in read_object.readlines():
stop_words.append(line.strip())
read_object.close()
read_object = open(file_path + "/unclearTrainData.txt", 'r', encoding='UTF-8')
for line in read_object.readlines():
train_datas.append([word for word in line.strip().split() if word not in stop_words])
read_object.close()
read_object = open(file_path + "/unclearTestData.txt", 'r', encoding='UTF-8')
for line in read_object.readlines():
test_datas.append([word for word in line.strip().split() if word not in stop_words])
read_object.close()
# length = 150
# for i in range(len(train_datas)):
# if len(train_datas[i]) < length:
# train_datas[i] = train_datas[i] + ["movie"] * (length - len(train_datas[i]))
# else:
# train_datas[i] = train_datas[i][:length]
# for i in range(len(test_datas)):
# if len(test_datas[i]) < length:
# test_datas[i] = test_datas[i] + ["movie"] * (length - len(test_datas[i]))
# else:
# test_datas[i] = test_datas[i][:length]
write_object = open(file_path + "/unclearStopTrainData.txt", 'w', encoding='UTF-8')
for train_data in train_datas:
write_object.write(' '.join('%s' % s for s in train_data) + '\n')
write_object.close()
write_object = open(file_path + "/unclearStopTestData.txt", 'w', encoding='UTF-8')
for test_data in test_datas:
write_object.write(' '.join('%s' % s for s in test_data) + '\n')
write_object.close()
def read(file_path, type = "clear"):
train_datas = []
train_labels = []
test_datas =[]
read_object = open(file_path + "/" + type + "TrainData.txt", 'r', encoding='UTF-8')
for line in read_object.readlines():
train_datas.append(line.strip().split(' '))
read_object.close()
read_object = open(file_path + "/trainLabel.txt", 'r', encoding='UTF-8')
for line in read_object.readlines():
train_labels.append(int(line))
read_object.close()
read_object = open(file_path + "/" + type + "TestData.txt", 'r', encoding='UTF-8')
for line in read_object.readlines():
test_datas.append(line.strip().split(' '))
read_object.close()
return train_datas, train_labels, test_datas
def write(file_path, predict_labels):
write_object = open(file_path, 'w', encoding='UTF-8')
for predict_label in predict_labels:
write_object.write(str(predict_label) + '\n')
write_object.close()
def tf_idf(file_path):
train_datas, train_labels, test_datas = read(file_path, "clear")
key_train_datas = []
key_test_datas = []
datas = []
for train_data in train_datas:
datas += list(set(train_data))
for test_data in test_datas:
datas += list(set(test_data))
datas_dict = Counter(datas)
datas_len = len(train_datas) + len(test_datas)
for train_data in train_datas:
data_tfidf = []
data_dict = Counter(train_data)
for word in train_data:
data_tfidf.append([-data_dict[word] * math.log(datas_len/datas_dict[word]), word])
key_train_datas.append([word[1] for word in sorted(data_tfidf)[:50]])
for test_data in test_datas:
data_tfidf = []
data_dict = Counter(test_data)
for word in test_data:
data_tfidf.append([-data_dict[word] * math.log(datas_len/datas_dict[word]), word])
key_test_datas.append([word[1] for word in sorted(data_tfidf)[:50]])
write_object = open(file_path + "/keyTrainData.txt", 'w', encoding='UTF-8')
for key_train_data in key_train_datas:
write_object.write(' '.join('%s' % s for s in key_train_data) + '\n')
write_object.close()
write_object = open(file_path + "/keyTestData.txt", 'w', encoding='UTF-8')
for key_test_data in key_test_datas:
write_object.write(' '.join('%s' % s for s in key_test_data) + '\n')
write_object.close()
def k_fold_cross_validation(train_datas, train_labels, k=10):
'''
:param train_datas：训练集数据, train_labels：训练集标签
:param k: K折交叉验证的k
:return 返回k个训练集和验证集
'''
length = len(train_datas)
k_train_datas = []
k_train_labels = []
k_validation_datas = []
k_validation_labels = []
for i in range(k):
k_train_datas.append(train_datas[:int(length / k * i)] + train_datas[int(length / k * (i + 1)):])
k_train_labels.append(train_labels[:int(length / k * i)] + train_labels[int(length / k * (i + 1)):])
k_validation_datas.append(train_datas[int(length / k * i):int(length / k * (i + 1))])
k_validation_labels.append(train_labels[int(length / k * i):int(length / k * (i + 1))])
return k_train_datas, k_train_labels, k_validation_datas, k_validation_labels
if __name__ == "__main__":
clear("data/5")