def train_fasttext_win(inputPath='news_fasttext/news_fasttext_train.txt',
savePath='model.m',
label='__label__'):
if not os.path.exists(savePath):
print('train model...')
classifier = ff.train_supervised(inputPath, label=label)
classifier.save_model(savePath) # 保存模型
else:
classifier = ff.load_model('model.m') # 读取模型
print('loaded model...')
return classifier
def train_fasttext(inputPath='train.txt',
savePath='./model.m',
label='__label__'):
if not os.path.exists(savePath):
print('train...')
classfication = ft.train_supervised(inputPath, label=label)
classfication.save_model(savePath)
else:
classfication = ft.load_model(savePath)
print('load model...')
return classfication
def create_predict(HudongItem_csv):
# 读取neo4j内容
db = Neo4j()
db.connectDB()
predict_List = readCSVbyColumn(HudongItem_csv, 'title')
file_object = open('vector.txt', 'a')
model = FastText('wiki.zh.bin')
count = 0
vis = set()
for p in predict_List:
cur = HudongItem(db.matchHudongItembyTitle(p))
count += 1
title = cur.title
if title in vis:
continue
vis.add(title)
wv_list = model[title]
strr = str(title)
print('===============')
print(strr)
for p in wv_list:
strr += ' ' + str(p)[:7]
print('===============')
print(strr)
print('===============')
file_object.write(strr + "\n")
print(str(count) + ' / ' + str(len(predict_List)))
file_object.close()
def test():
''' '''
source = 'jieba_v3_ft_model_2.ft'
target = 'jieba_test_fasttext_v3.txt'
clf = FT.load_model(os.path.join(const.MODELPATH, source))
size, precision, recall = clf.test(os.path.join(const.DATAPATH, target))
print('accuracy score : ', precision)
def get_fasttext_tweet_embedding(
tweet_word_list: List[str],
fasttext_model: fastText.FastText) -> np.ndarray:
all_embeddings = []
for word in tweet_word_list:
all_embeddings.append(fasttext_model.get_word_vector(word))
return np.mean(all_embeddings, axis=0)
def train_classifier(self):
# self.load_train_dataset()
#实验后的最佳参数之一
start_time = time.time()
classifier = ff.train_supervised(
self.data_path, lr=0.1, loss='hs', wordNgrams=2,
epoch=300) # epoch=20,0.91;epoch=50,0.93;
model = classifier.save_model(
self.model_save_path + 'level_2_fasttext_classifier_big_big.model'
) # 保存模型 all:0.91;all_2:0.93
classifier.get_labels() # 输出标签
# 测试模型
# print('加载fasttext模型--{}'.format('level_1_fasttext_classifier_big_test.model'))
# classifier = ff.load_model(self.model_save_path+'level_1_fasttext_classifier_big_test.model')
test_result = classifier.test(self.test_save_path + 'test_big.txt')
result_str = 'test precision:{}\n'.format(test_result)
print(result_str)
end_time = time.time()
load_time = round(end_time - start_time, 3)
train_time_str = 'train and test model time %fs' % load_time
print(train_time_str)
save_file(self.result_save_path + 'fasttext_result_big.txt',
result_str + train_time_str + '\n', 'a')
def mode_predict_fasttext(config, input_path):
"""使用训练好的fasttext模型预测 数据
"""
input_path = os.path.join(input_path, 'data_predict.txt')
ftmodel_path = os.path.join(config.log_root, config.model_name, "train", "ftmodel.bin")
# 最终预测值的保存路径
output_path = os.path.join("output", "predict_result.txt")
# 载入训练好的模型
ftmodel = fasttext.load_model(ftmodel_path)
print('开始预测数据')
with open(input_path, 'r', encoding='utf8') as fin:
lines = fin.readlines()
lines = [x.strip() for x in lines] # 去掉每行头尾空白
if config.is_python_package:
# python版本只有 predict_proba 方法才能给出预测概率
pred_pairs = ftmodel.predict_proba(lines)
predict_list = [x[0] for x in pred_pairs]
else:
# facebook的python版本是返回两个列表
pred_list, pred_prob_list = ftmodel.predict(lines)
predict_list = [(pred[0], pred_prob[0]) for (pred, pred_prob) in zip(pred_list, pred_prob_list)]
# 得到的predict_list的格式：[(预测类别, 预测概率), (预测类别, 预测概率)...]
# 保存预测值到output文件夹
with open(output_path, "w", encoding="utf8") as fout:
for (pred, pred_prob) in predict_list:
pred = re.split("__", pred)[-1] # 去掉前缀__label__
fout.write("%s\t%f\n" % (pred, pred_prob))
print('预测完成，已将预测值写入输出文件：', output_path)
def load_fasttext(self, model_name):
start_time = time.time()
classifier = ff.load_model(self.model_save_path + model_name)
end_time = time.time()
pre_time = round(end_time - start_time, 3)
print('加载fasttext模型时间: %f' % pre_time)
return classifier
def pre_load_fastText_model(self):
"""
预加载fastText模型
:return:
"""
os.chdir(os.path.dirname(os.path.abspath(__file__)))
self.classifier = ff.load_model("model_w2_e24")
def train():
classifier = ft.train_supervised(train_path)
model = classifier.save_model(model_path)
test = classifier.test(test_path)
print("准确率:", test.precision)
print("回归率:", test.recall)
classifier.get_labels()
def load_model(model_path):
# 加载模型
# 加载windows模型
import fastText.FastText as ff
classifier = ff.load_model(model_path)
return classifier
def load_words_model(self, model_filename):
model_filename = self.data_dir + model_filename
self.model = fasttext.load_model(model_filename + '.bin')
self.word_dim = self.model.get_dimension()
self.words_list = list(self.model.get_words())
print('Loaded. Dictionary size:%s' %
'{:,}'.format(len(self.model.get_words())))
def trainUnSpv(path: str, n=1, model='skipgram'):
""" """
m = FT.train_unsupervised(path,
epoch=100,
dim=100,
wordNgrams=n,
model=model)
return m
def fastText_classifier(train_data,model_save_path):
classifier=ff.train_supervised(train_data+'book_sub_level_1_train.txt',lr=0.1,loss='hs',wordNgrams=2,epoch=300)
model = classifier.save_model(model_save_path+'book_sub_classifier.model') # 保存模型
classifier.get_labels() # 输出标签
result = classifier.test(train_data+'book_sub_level_1_train.txt')
print(result)
'''
def trainFT(path: str, n=1):
""" """
clf = FT.train_supervised(path,
epoch=100,
dim=100,
wordNgrams=n,
label='__label__',
loss='softmax')
return clf
def fasttext_train(self):
model_file = os.path.join(self.home_data, 'fasttext.model')
if os.path.exists(model_file):
print("Fasttext模型已经存在，直接载入")
classifier = ft.load_model(self.home_data + 'fasttext.model')
else:
print("Fasttext模型不存在，训练")
import fastText.FastText as ft
classifier = ft.train_supervised(self.home_data+"fasttext_train.txt") # 训练模型
model = classifier.save_model(self.home_data+'fasttext.model') # 保存模型
classifier = ft.load_model(self.home_data+'fasttext.model') # 导入模型
result = classifier.test(self.home_data+"fasttext_test.txt") # 输出测试结果
labels = classifier.get_labels() # 输出标签
print("测试实例数", result[0]) # 实例数
print("准确率", result[1]) # 全部的准确率
print("召回率", result[2]) # 召回率
logging.info('测试实例数 %s' % str(result[0]))
logging.info('准确率 %s' % str(result[1]))
logging.info('召回率 %s' % str(result[2]))
def load_model(model_path, os_name):
#加载模型
#加载windows模型
if os_name == 'nt':
import fastText.FastText as ff
classifier = ff.load_model(model_path)
else: #加载linux模型
import fasttext
classifier = fasttext.load_model(model_path, label_prefix='__label__')
return classifier
def fast_text_model(X_test):
'''
使用fasttext进行文本分类
'''
# 分类训练
classifier = ff.train_supervised('train.txt', label='__label__')
# 模型预测，返回预测标签和概率
label, prob = classifier.predict(X_test)
# 根据给定数据集对模型进行评价，返回样本个数、准确率、召回率
result = classifier.test('test.txt')
return label, prob, result
def mode_train_fasttext(config, _input_path):
"""训练fasttext模型并保存训练好的模型
"""
input_path = os.path.join(_input_path, 'data_train.txt')
output_path = os.path.join(config.log_root, config.model_name, "train", "ftmodel")
pretrained_path = os.path.join(config.log_root, config.model_name, "train", "word_vector.vec")
# 执行训练命令
print('开始训练fasttext模型')
if config.is_python_package:
# python版必须指定bucket, 未修复的bug.. bucket增加多少倍，保存的模型大小大致也会增加多少倍
fasttext.supervised(input_file=input_path, output=output_path,
epoch=config.train_epoch, lr=config.learning_rate, dim=config.dim, word_ngrams=config.wordNgrams,
loss=config.loss, pretrained_vectors=pretrained_path, bucket=200000)
else:
train_command = '{}fasttext supervised '.format(fasttext_path) + \
'-input {} -output {} '.format(input_path, output_path) + \
'-epoch {} -lr {} -dim {} -wordNgrams {} '.format(config.train_epoch, config.learning_rate, config.dim, config.wordNgrams)+ \
'-loss {} -verbose {} -pretrainedVectors {}'.format(config.loss, config.verbose, pretrained_path)
# '-loss {} -verbose {}'.format(config.loss, config.verbose)
os.system(train_command)
print('训练fasttext模型完毕')
def test_fasttext(train_path, test_path, model_save_path):
classifier = ff.train_supervised(train_path + 'A_train.txt',
lr=0.1,
loss='hs',
wordNgrams=2,
epoch=50)
#classifier = load_model.load_model(model_save_path+'train_level_1_classifier.model','nt')
model = classifier.save_model(model_save_path +
'A_train_classifier2.model') # 保存模型
classifier.get_labels() # 输出标签
result = classifier.test(test_path + 'A_test.txt')
print(result)
def make_model(data_path, data_file_name, save_path, save_file_name, **kwargs):
total_data_path = os.path.join(data_path, data_file_name)
total_save_path = os.path.join(save_path, save_file_name)
assert os.path.isfile(total_data_path) == True
start_time = strftime("%y%m%d-%H%M%S")
print('모델생산 시작시간 : ', start_time)
try:
if kwargs.keys() == None:
model = FastText.train_unsupervised(total_data_path)
else:
model = FastText.train_unsupervised(total_data_path, **kwargs)
model.save_vectors(total_save_path)
except Exception as e:
print(e)
finally:
end_time = strftime("%y%m%d-%H%M%S")
print('모델생산 종료시간 : ', end_time)
return total_save_path + '.vec'
def fasttext_model_train():
"""
fasttext模型训练
:return:
"""
for i in range(5, 51):
for w in range(1, 3):
start_time = time.time()
classifier = ff.train_supervised("fasttext.train",
epoch=i,
lr=0.5,
wordNgrams=w)
print("ngram=%d,训练第%d轮，用时%s" % (w, i, time.time() - start_time))
classifier.save_model("Model/model_w" + str(w) + "_e" + str(i))
def mode_evaluate_fasttext(config, input_path):
"""测试训练好的fasttext模型
"""
input_path = os.path.join(input_path, 'data_test.txt')
ftmodel_path = os.path.join(config.log_root, config.model_name, "train", "ftmodel.bin")
# 执行测试命令
if config.is_python_package:
ftmodel = fasttext.load_model(ftmodel_path)
test_log = ftmodel.test(input_path)
print('测试集的测试结果:\nN\t{}\nP@1\t{}\nR@1\t{}\n'.format(test_log.nexamples, test_log.precision, test_log.recall))
else:
test_command = '{}fasttext test '.format(fasttext_path) + \
'{} {}'.format(ftmodel_path, input_path)
test_log = os.popen(test_command)
print('测试集的测试结果:\n{}\n'.format(test_log.read()))
def load_fastText_vectors_char(model, vocab):
m = FastText.load_model(model)
word_vecs = {}
for subword in vocab:
if subword not in word_vecs:
word_vecs[subword] = m.get_word_vector(subword)
# for subword in vocab:
# if subword not in word_vecs:
# index = m.get_subword_id(subword)
# word_vecs[subword] = m.get_input_vector(index)
return word_vecs
def fastText_classifier(train_data, model_save_path, result_save_path):
classifier = ff.train_supervised(train_data + 'level_3_train.txt',
lr=0.1,
loss='hs',
wordNgrams=2,
epoch=150)
model = classifier.save_model(model_save_path +
'level_3_classifier.model') # 保存模型
classifier.get_labels() # 输出标签
result = classifier.test(train_data + 'level_3_train.txt')
print(result)
with open(result_save_path + 'train3_results.txt', 'w') as fp:
fp.write(str(result))
fp.write('\n')
'''
def pretrained_vectors(config, input_path):
"""预训练词向量
"""
input_path = os.path.join(input_path, 'data_train.txt')
output_path = os.path.join(config.log_root, config.model_name, "train", "word_vector")
# 执行训练命令
print('开始预训练词向量（仅需要训练一次。一般来说需要一些时间..） ')
if config.is_python_package:
model = fasttext.skipgram(input_file=input_path, output=output_path,
epoch=config.train_epoch, dim=config.dim)
# print(model.words)
else:
train_command = '{}fasttext skipgram '.format(fasttext_path) + \
'-input {} -output {} '.format(input_path, output_path) + \
'-epoch {} -dim {} '.format(config.train_epoch, config.dim)
os.system(train_command)
print('预训练词向量完毕')
def fastText_classifier(train_data,test_data,model_save_path,result_save_path):
files = []
results = []
if not os.path.exists(train_data):
os.makedirs(train_data)
if not os.path.exists(model_save_path):
os.makedirs(model_save_path)
if not os.path.exists(result_save_path):
os.makedirs(result_save_path)
for level_one,test_l1 in zip(os.listdir(train_data),os.listdir(test_data)):
print(level_one+'-->'+test_l1)
if '.txt' in level_one and '.txt' in test_l1:
classifier=ff.train_supervised(train_data+level_one,、lr=0.1,loss='hs',wordNgrams=2,epoch=100)
#classifier = load_model.load_model(model_save_path+'train_level_1_classifier.model','nt')
model = classifier.save_model(model_save_path+level_one+'_classifier.model') # 保存模型
classifier.get_labels() # 输出标签
result = classifier.test(test_data+test_l1)
files.append(level_one)
results.append(result)
print(result)
else:
data_list = os.listdir(train_data+level_one+'/')
test_list = os.listdir(test_data+test_l1+'/')
if not len(data_list) or not len(test_data):
continue
classifier=ff.train_supervised(train_data+level_one+'/'+data_list[0],lr=0.1,loss='hs',wordNgrams=2,epoch=50)
#classifier = load_model.load_model(model_save_path+'train_level_1_classifier.model','nt')
model = classifier.save_model(model_save_path+level_one+'_classifier.model') # 保存模型
classifier.get_labels() # 输出标签
result = classifier.test(test_data+test_l1+'/'+test_list[0])
files.append(data_list[0])
results.append(result)
print(result)
print(files)
print(results)
with open(result_save_path+'train_results.txt','w') as fp:
for i,j in zip(files,results):
fp.write(str(i)+'-->'+str(j))
fp.write('\n')
'''
def sentence_input(sentence):
classifier = ff.load_model("fastText/model/train")
stop_words = load_stop_words()
seg_line = jieba.cut(sentence)
positive_emoticons = load_positive_emoticons()
negative_emoticons = load_negative_emoticons()
punctuation_words = load_punctuation_words()
stop_words = load_stop_words()
positive_words = load_positive_words()
negative_words = load_negative_words()
# print(sentence)
# 识别其中的表情符，并替换成相应的字符
for p_word in positive_emoticons:
if p_word in sentence:
sentence = sentence.replace(p_word, " 积极 ")
for p_word in negative_emoticons:
if p_word in sentence:
sentence = sentence.replace(p_word, " 消极 ")
# print(sentence)
# 识别其中的标点符号，替换成空格
for p_word in punctuation_words:
if p_word in sentence:
sentence = sentence.replace(p_word, " ")
# print(sentence)
# 对句子进行分词,去除停用词，识别情感词，并为情感词打上特殊标记
line = sentence.replace("蒙牛", "")
seg_line = jieba.cut(line)
# print(seg_line)
add_str = ""
for word in seg_line:
if word not in stop_words and word != "" and " " not in word:
if word in positive_words:
add_str += "# " + word + " # "
elif word in negative_words:
add_str += "* " + word + " * "
else:
add_str += word + " "
# print(add_str.strip())
label = classifier.predict([add_str.strip()], k=3)
result = [(str(i), j) for i, j in zip(label[0], label[1])]
print(result)
return result
def get_predict(attrs, model_path, string):
# str_list = ['我 要 一款 内存 大 运行 快 的 苹果 电脑']
str_list = []
result = ''
for j in jieba.lcut(string):
result = result + j + ' '
result = result.strip()
str_list.append(result)
print(str_list)
attrs_predict = []
for attr in attrs:
print(attr, '预测：')
classifier = ff.load_model(model_path + attr + "_model")
lab = classifier.predict(str_list, k=1)
# print(lab) # (['__label__2'], array([0.95485353]))
# print(lab[0][0].split('__')[2])
attrs_predict.append(lab[0][0].split('__')[2])
print(attrs_predict)
return attrs_predict
def train_words_model(self,
corpus_filename,
model_filename,
model='skpigram',
min_count=5):
corpus_filename = self.data_dir + corpus_filename
model_filename = self.data_dir + model_filename
print('Training for [%s] Model=%s Dim=%d MinCount=%d...' %
(corpus_filename, model, self.word_dim, min_count))
self.model = fasttext.train_unsupervised(input=corpus_filename,
model=model,
dim=self.word_dim,
minCount=min_count)
self.model.save_model(model_filename)
self.words_list = list(self.model.get_words())
print('Finished. Dictionary size:%s' %
'{:,}'.format(len(self.model.get_words())))