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 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 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 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 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 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 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 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 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 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 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 train(self, vecSize, winSize, epochs, minCount, lossFunction,
sampleThreshold, learnRate, ngrams, wordGrams, bucket):
self.model = ft.train_supervised(
input=self.trainingFile,
lr=learnRate,
dim=vecSize,
ws=winSize,
epoch=epochs,
minCount=minCount,
loss=("ns" if lossFunction < 0 else
("softmax" if lossFunction == 0 else "hs")),
neg=(-lossFunction if lossFunction < 0 else 0),
t=sampleThreshold,
minn=ngrams // 2,
maxn=ngrams,
wordNgrams=wordGrams,
bucket=bucket,
verbose=0,
thread=4 #8
)
class TCFastText(object):
texts, labels = read("./data/cut_data.txt")
labels = list(map(lambda x: "__label__" + str(x), labels))
data = zip(texts, labels)
data = list(map(lambda x: " ".join(x), data))
data = np.array(data)
kf = KFold(n_splits=8)
for train_index, test_index in kf.split(data):
print("Train:", train_index, "Test:", test_index)
train = data[train_index]
test = data[test_index]
with open("data/ft_train", "w") as f:
f.write("\n".join(train))
with open("data/ft_test", "w") as f:
f.write("\n".join(test))
# ft = FastText.train_supervised("data/ft_train", dim=128, epoch=60, minCount=4, wordNgrams=5, label="__label__")
ft = FastText.train_supervised("data/ft_train", dim=128, epoch=60, minCount=5, wordNgrams=3, label="__label__")
# ft = FastText.train_supervised("data/ft_train", dim=80, epoch=60, minCount=5, wordNgrams=3, label="__label__")
result = ft.test("data/ft_test")
ft.save_model("fastText")
print(result)
break
#十折交叉验证法检验最终模型的准确率
precision=[]
for i in range(0,len(result)):
f_train = open(os.path.join(filename, "original/train.tsv"), 'w', encoding="utf-8")#将训练集分成训练文件与测试文件
f_test = open(os.path.join(filename, "original/test.tsv"), 'w', encoding="utf-8")#此处自动生成
a=result[i]
list_train=random.sample(list(set(resultList)-set(a)),(len(resultList)-len(a)))
for x in list_train:
f_train.write(linecache.getline(train_new,x))
for y in a:
f_test.write(linecache.getline(train_new,y))
f_train.close()
f_test.close()
start=time.time()
classifier = ff.train_supervised(os.path.join(filename, 'original/train.tsv'), dim=64, lr=0.7, wordNgrams=2,
minCount=2,bucket=10000000,label = '__label__',thread = 20,epoch=7)#训练代码
model=classifier.save_model(os.path.join(filename,'original/model/model'+str(i+1)+'.model')) # 保存模型
test = classifier.test(os.path.join(filename, 'original/test.tsv'), k=1)#测试
end=time.time()
precision.append((test[1],end-start))
print('模型预测准确率：', test[1])
print("训练时间为：",end-start)
sum_precision=0
sum_time=0
for i,t in precision:
sum_precision+=i
sum_time+=t
#用于删除本程序中生成的训练文件，测试文件，这两文件每次运行均会生成，最终删除
os.remove(os.path.join(filename,"original/train.tsv"))
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv("D:\\new_data\\new_data\\train_set.csv")
data = data.loc[:, ['word_seg', 'class']]
data['label'] = data.apply(lambda x: 'label' + str(x[1]), axis=1)
x_train, x_test = train_test_split(data, test_size=0.3, random_state=42)
x_train.loc[:, ['word_seg', 'label']].to_csv(
"D:\\new_data\\new_data\\train_set1.txt",
index=False,
header=None,
sep='\t')
x_test.loc[:, ['word_seg', 'label']].to_csv(
"D:\\new_data\\new_data\\test_set1.txt",
index=False,
header=None,
sep='\t')
import fastText.FastText as ff
classifier = ff.train_supervised('D:\\new_data\\new_data\\train_set1.txt',
label="label")
result = classifier.test("D:\\new_data\\new_data\\test_set1.txt")
# _*_coding:utf-8 _*_
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
level=logging.INFO)
import fastText.FastText as fasttext
#训练模型
classifier = fasttext.train_supervised("data/train/fastText_train.txt",
label="__label__")
#保存模型
classifier.save_model('models/fasttext_train.model.bin')
labels_right = []
texts = []
labels_predict = []
with open("data/train/fastText_train.txt") as fr:
for line in fr:
line = line.decode("utf-8").rstrip()
label_right = line.split("\t")[1]
labels_right.append(label_right)
text = line.split("\t")[0]
texts.append(text)
label_predict = classifier.predict(text)
labels_predict.append(label_predict[0])
print("文本")
print(line)
print("真实label")
print(label_right)
print("预测label")
print(label_predict[0])
maxn # max length of char ngram [0]
neg # number of negatives sampled [5]
wordNgrams # max length of word ngram [1]
loss # 损失函数 {ns, hs, softmax, ova} [softmax]
bucket # number of buckets [2000000]
thread # 线程数 [number of cpus]
lrUpdateRate # 学习率更新速率 [100]
t # sampling threshold [0.0001]
label # 标签前缀 ['__label__']
verbose # verbose [2]
pretrainedVectors # pretrained word vectors (.vec file) for supervised learning []
"""
model = ft.train_supervised("train.txt",
lr=1,
dim=300,
epoch=5,
wordNgrams=4,
loss='hs')
model.save_model("model_file.bin")
def print_results(N, p, r):
print("N\t" + str(N)) # 预测错的例子
# P: 准确率 R： 召回率
print("P@{}\t{:.3f}".format(1, p))
print("R@{}\t{:.3f}".format(1, r))
print_results(*model.test('test.txt'))
import csv
#def transfercsv_to_fastText(csv_path,fastText_file):
path = r"data\Chinese\Chinese raw data\seg_test.tsv"
with open(r'data\Chinese\Chinese fasttext data\seg_test',
'w',
encoding='utf_8') as t:
with open(path, 'r', encoding='utf_8') as f:
lines = csv.reader(f, delimiter='\t')
for line in lines:
target = line[2]
content = line[1]
t.write(content + '\t' + '_label_' + target + '\n')
#训练模型
classifier = ff.train_supervised(
r'data\Chinese\Chinese fasttext data\seg_train', label='_label_')
#储存模型
classifier.save_model(
r'data\Chinese\Chinese fasttext data\fastText_model1') #保存模型
#加载模型
classifier = ff.load_model(
r'data\Chinese\Chinese fasttext data\fastText_model1')
#测试模型
correct = 0
total_count = 0
with open(r'data\Chinese\Chinese fasttext data\seg_test',
'r',
encoding='utf_8') as t:
lines = t.readlines()
total_count = len(lines)
print(total_count)
def fast_text_train(data_file, model_file, test_file):
classifier = ff.train_supervised(data_file)
classifier.save_model(model_file) # 保存模型
test = classifier.test(test_file, 1) # 输出测试结果
b = test.precision
a = 1
def train_model():
start_time = time.time()
all_marco_precision = []
all_marco_recall = []
all_marco_f1 = []
all_micro_precision = []
all_micro_recall = []
all_micro_f1 = []
for i in range(5, 51):
classifier = ff.train_supervised("fastText/train_data",
epoch=i,
lr=0.5)
classifier.save_model("fastText/model/train")
print("模型构建时间：%s s" % str(time.time() - start_time))
# 因为fasttext中设计的是针对多标签的精确率与召回率，对于单标签，计算结果一致，不具有参考价值
# print("积极数据测试：")
# test = classifier.test('fastText/test_data_positive')
# print("测试数据数量：%d\t准确率：%f\t召回率：%f" % (test[0], test[1], test[2]))
# print("中立数据测试：")
# test = classifier.test('fastText/test_data_neutral')
# print("测试数据数量：%d\t准确率：%f\t召回率：%f" % (test[0], test[1], test[2]))
# print("消极数据测试：")
# test = classifier.test('fastText/test_data_negative')
# print("测试数据数量：%d\t准确率：%f\t召回率：%f" % (test[0], test[1], test[2]))
correct_labels = [
line.strip().split(" , ")[0] for line in open(
'fastText/test_data', "r", encoding="utf-8").readlines()
]
texts = [
line.strip().split(" , ")[1] for line in open(
'fastText/test_data', "r", encoding="utf-8").readlines()
]
predict_labels = classifier.predict(texts)[0]
true_positive = 0
false_positive = 0
false_negative = 0
evaluation_parameters = []
labels = {"__label__-1": "消极", "__label__0": "中立", "__label__1": "积极"}
for label, name in labels.items():
evaluate_p = {}
print("%s标签测试结果：" % name)
evaluate_p["name"] = name
evaluate_p["nexample"] = len(texts)
for i in range(len(texts)):
# 预测属于该类，实际属于该类
if predict_labels[i] == label and correct_labels[i] == label:
true_positive += 1
# 预测属于该类，实际不属于该类
elif predict_labels[i] == label and correct_labels[i] != label:
false_positive += 1
# 预测不属于该类，实际属于该类
elif predict_labels[i] != label and correct_labels[i] == label:
false_negative += 1
evaluate_p["true_positive"] = true_positive
evaluate_p["false_positive"] = false_positive
evaluate_p["false_negative"] = false_negative
# 计算精确率、召回率、F值
precision = true_positive / (true_positive + false_positive)
evaluate_p["precision"] = precision
recall = true_positive / (true_positive + false_negative)
evaluate_p["recall"] = recall
f1 = 2 * precision * recall / (precision + recall)
evaluate_p["f1"] = f1
evaluation_parameters.append(evaluate_p)
print("测试集大小：%d\t精确率：%f\t召回率：%f\tF_1：%f" %
(len(texts), precision, recall, f1))
# 计算宏平均和微平均
sum_precision = 0
sum_recall = 0
sum_true_positive = 0
sum_false_positive = 0
sum_false_negative = 0
for p in evaluation_parameters:
sum_precision += p["precision"]
sum_recall += p["recall"]
sum_true_positive += p["true_positive"]
sum_false_positive += p["false_positive"]
sum_false_negative += p["false_negative"]
n = len(evaluation_parameters)
marco_precision = sum_precision / n
all_marco_precision.append(marco_precision)
marco_recall = sum_recall / n
all_marco_recall.append(marco_recall)
marco_f1 = 2 * marco_precision * marco_recall / (marco_precision +
marco_recall)
all_marco_f1.append(marco_f1)
print("宏平均----测试集大小：%d\t精确率：%f\t召回率：%f\tF_1：%f" %
(len(texts), marco_precision, marco_recall, marco_f1))
micro_true_positive = sum_true_positive / n
micro_false_positive = sum_false_positive / n
micro_false_negative = sum_false_negative / n
micro_precision = micro_true_positive / (micro_true_positive +
micro_false_positive)
all_micro_precision.append(micro_precision)
micro_recall = micro_true_positive / (micro_true_positive +
micro_false_negative)
all_micro_recall.append(micro_recall)
micro_f1 = 2 * micro_precision * micro_recall / (micro_precision +
micro_recall)
all_micro_f1.append(micro_f1)
print("微平均----测试集大小：%d\t精确率：%f\t召回率：%f\tF_1：%f" %
(len(texts), micro_precision, micro_recall, micro_f1))
names = [i for i in range(5, 51)]
ax1 = plt.subplot(311)
plt.plot(names, all_marco_precision, label='marco-P')
plt.plot(names, all_micro_precision, label='micro-P')
plt.legend(loc='upper left')
ax2 = plt.subplot(312, sharey=ax1)
plt.plot(names, all_marco_recall, label='marco-P')
plt.plot(names, all_micro_recall, label='micro-R')
plt.legend(loc='upper left')
plt.subplot(313, sharey=ax1)
plt.plot(names, all_marco_f1, label='marco-F1')
plt.plot(names, all_micro_f1, label='micro-F1')
plt.legend(loc='upper left')
plt.xlabel(u"训练轮数(ngram=1)")
plt.savefig('./ngram1.png')
plt.show()
r = float(A[key]) / float(B[key])
p = float(A[key]) / float(C[key])
f = p * r * 2 / (p + r)
logging.info("%s:\t p:%f\t r:%f\t f:%f" % (key, p, r, f))
except:
logging.error("error:", key, "right:", A.get(key, 0), "real:",
B.get(key, 0), "predict:", C.get(key, 0))
if __name__ == "__main__":
base_dir = data_path.metaphor_data_base_dir
filename_train = 'metaphor_recognition.fasttext.train'
filename_validation = 'metaphor_recognition.fasttext.validation'
# base_dir = r'/home/liyuncong/program/fasttext/data/'
# filename_train = 'news_fasttext_train.txt'
# filename_validation = 'news_fasttext_test.txt'
filename_model = 'metaphor_recognition.fasttext'
train_data = os.path.join(base_dir, filename_train)
valid_data = os.path.join(base_dir, filename_validation)
# train_supervised uses the same arguments and defaults as the fastText cli
model = ff.train_supervised(train_data, epoch=25, lr=1.0, wordNgrams=2)
test_result = model.test(valid_data)
print_results(*test_result)
__predict(model, valid_data)
import fastText.FastText as ff
import jieba
#使用训练数据集进行有监督的训练
classifier = ff.train_supervised("data/train.txt")
#模型进行持久化
model = classifier.save_model('data/try.model')