转载自：
http://blog.csdn.net/lxg0807/article/details/52960072#comments
训练数据和测试数据来自网盘：
https://pan.baidu.com/s/1jH7wyOY
https://pan.baidu.com/s/1slGlPgx
训练以上数据
# _*_coding:utf-8 _*_ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) import fasttext #训练模型 classifier = fasttext.supervised("news_fasttext_train.txt","news_fasttext.model",label_prefix="__label__")
进行测试：
# -*- coding:utf-8 -*- import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) import fasttext #load训练好的模型 classifier = fasttext.load_model('news_fasttext.model.bin', label_prefix='__label__') result = classifier.test("news_fasttext_test.txt") print result.precision print result.recall注意每次训练的模型都有不同，所以测试的结果大概是0.87~0.92左右
进行最终评价：
# -*- coding:utf-8 -*- import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) import fasttext #load训练好的模型 classifier = fasttext.load_model('news_fasttext.model.bin', label_prefix='__label__') result = classifier.test("news_fasttext_test.txt") print result.precision print result.recall labels_right = [] texts = [] with open("news_fasttext_test.txt") as fr: lines = fr.readlines() for line in lines: labels_right.append(line.split("\t")[1].rstrip().replace("__label__","")) texts.append(line.split("\t")[0].decode("utf-8")) # print labels # print texts # break labels_predict = [e[0] for e in classifier.predict(texts)] #预测输出结果为二维形式 # print labels_predict text_labels = list(set(labels_right)) text_predict_labels = list(set(labels_predict)) print text_predict_labels print text_labels A = dict.fromkeys(text_labels,0) #预测正确的各个类的数目 B = dict.fromkeys(text_labels,0) #测试数据集中各个类的数目 C = dict.fromkeys(text_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[labels_predict[i]] += 1 if labels_right[i] == labels_predict[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: p = float(A[key]) / float(B[key]) r = float(A[key]) / float(C[key]) f = p * r * 2 / (p + r) print "%s:\tp:%f\t%fr:\t%f" % (key,p,r,f)之所以搞这么一出，是因为fasttext提供的p值（准确率）和r值（召回率）只是针对所有结果的，而不是针对各个类别分别进行计算p值（准确率）和r值（召回率）的，所以该作者自己写了计算方法。
输出结果：
[u'affairs', u'fashion', u'lottery', u'house', u'sports', u'game', u'economic', u'ent', u'edu', u'home', u'stock', u'constellation', u'science'] ['affairs', 'fashion', 'house', 'sports', 'game', 'economic', 'ent', 'edu', 'home', 'stock', 'science'] {'science': 8921, 'affairs': 8544, 'fashion': 2148, 'house': 9572, 'sports': 9814, 'game': 9389, 'economic': 9492, 'ent': 9660, 'edu': 9671, 'home': 8027, 'stock': 8525} {'science': 10000, 'affairs': 10000, 'fashion': 3369, 'house': 10000, 'sports': 10000, 'game': 10000, 'economic': 10000, 'ent': 10000, 'edu': 10000, 'home': 10000, 'stock': 10000} {u'science': 10311, u'affairs': 8953, u'fashion': 2176, u'lottery': 28, u'house': 10502, u'sports': 10288, u'game': 10182, u'economic': 11087, u'ent': 10940, u'edu': 10991, u'home': 8171, u'constellation': 466, u'stock': 9274} science: p:0.892100 0.865193r: 0.878440 affairs: p:0.854400 0.954317r: 0.901599 fashion: p:0.637578 0.987132r: 0.774752 house: p:0.957200 0.911445r: 0.933763 sports: p:0.981400 0.953927r: 0.967468 game: p:0.938900 0.922117r: 0.930433 economic: p:0.949200 0.856138r: 0.900270 ent: p:0.966000 0.882998r: 0.922636 edu: p:0.967100 0.879902r: 0.921443 home: p:0.802700 0.982377r: 0.883496 stock: p:0.852500 0.919237r: 0.884611
