train_model.py

# _*_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])


predict_labels=[]
for predict_label in labels_predict:
    predict_labels.append(predict_label[0])

A = dict.fromkeys(labels_right,0)  #预测正确的各个类的数目
B = dict.fromkeys(labels_right,0)   #测试数据集中各个类的数目
C = dict.fromkeys(predict_labels,0) #预测结果中各个类的数目
for i in range(0,len(labels_right)):
    B[labels_right[i]] += 1
    C[predict_labels[i]] += 1
    if labels_right[i] == predict_labels[i]:
        A[labels_right[i]] += 1
print A
print B
print C
#计算准确率，召回率，F值
for key in B:
    try:
        r = float(A[key]) / float(B[key])
        p = float(A[key]) / float(C[key])
        f = p * r * 2 / (p + r)
        print "%s:\t precision:%f\t recall:%f\t f:%f" % (key,p,r,f)
    except:
        print "error:", key, "right:", A.get(key,0), "real:", B.get(key,0), "predict:",C.get(key,0)