1 FastText 学习路径
FastText是 facebook 近期开源的一个词向量计算以及文本分类工具,FastText的学习路径为：
具体原理就不作解析了,详细教程见：/docs/en/support.html
2 FastText 安装
2.1 基于框架的安装
需要从github下载源码，然后生成可执行的fasttext文件
（1）命令：git clone /facebookresearch/fastText.git
（2）命令：cd fastText/ and ls
（3）命令：make
2.2 基于Python模块的安装
（1）直接pip安装：pip install fasttext
（2）源码安装：
3 FastText 实现文本分类
3.1 例子
（1）下载数据
#读取数据wget /fasttext/data/cooking.stackexchange.tar.gz#解压数据tar xvzf cooking.stackexchange.tar.gz#显示前几行head cooking.stackexchange.txt
（2）划分数据集
#查看数据wc cooking.stackexchange.txt#划分数据集head -n 12404 cooking.stackexchange.txt > cooking.traintail -n 3000 cooking.stackexchange.txt > cooking.valid
（3）训练与调参
此处是基于命令行的展示，Python的展示可参考：/docs/en/supervised-tutorial.html
fasttext的参数有：
训练：
./fasttext supervised -input cooking.train -output model_cooking
预测：
./fasttext predict model_cooking.bin -
3.2 基于新闻文本的FastText分析
import fasttextimport pandas as pdfrom sklearn.metrics import f1_scoretrain_df = pd.read_csv('data/data45216/train_set.csv',sep='\t')train_df['label_ft'] = '__label__' + train_df['label'].astype(str) train_df[['text','label_ft']].iloc[:-5000].to_csv('train.csv',index=None,header=None,sep='\t')model = fasttext.train_supervised('train.csv',lr=1.0,wordNgrams=2,verbose=2,minCount=1,epoch=25,loss='hs')val_pred = [model.predict(x)[0][0].split('__')[-1] for x in train_df.iloc[-5000:]['text']]print(f1_score(train_df['label'].values[-5000:].astype(str),val_pred,average='macro'))
输出结果为：
4 FastText调参
FastText的train_supervised参数有：
可通过以上参数进行手动设置，也可用过FastText的自动调参功能进行调参。
4.1 基于命令行
（1）验证集验证-autotune-validation
./fasttext supervised -input cooking.train -output model_cooking -autotune-validation cooking.valid
（2）设置执行时间-autotune-duration
./fasttext supervised -input cooking.train -output model_cooking -autotune-validation cooking.valid -autotune-duration 600
（3）模型大小-autotune-modelsize
./fasttext supervised -input cooking.train -output model_cooking -autotune-validation cooking.valid -autotune-modelsize 2M
（4）指标-autotune-metric
-autotune-metric f1:__label__baking-autotune-metric precisionAtRecall:30-autotune-metric precisionAtRecall:30:__label__baking-autotune-metric recallAtPrecision:30-autotune-metric recallAtPrecision:30:__label__baking
4.2 基于Python模块
（1）验证集验证autotuneValidationFile
model = fasttext.train_supervised(input='cooking.train', autotuneValidationFile='cooking.valid')
（2）设置执行时间autotuneDuration
model = fasttext.train_supervised(input='cooking.train', autotuneValidationFile='cooking.valid', autotuneDuration=600)
（3）模型大小autotuneModelSize
model = fasttext.train_supervised(input='cooking.train', autotuneValidationFile='cooking.valid', autotuneModelSize="2M")
（4）指标autotuneMetric
model = fasttext.train_supervised(input='cooking.train', autotuneValidationFile='cooking.valid', autotuneMetric="f1:__label__baking")
5 作业
使用自动调参进行训练：
import fasttextimport pandas as pdfrom sklearn.metrics import f1_scoretrain_df = pd.read_csv('data/data45216/train_set.csv',sep='\t')#将label值转成fasttext识别的格式train_df['label_ft'] = '__label__' + train_df['label'].astype(str) #划分训练集和验证集train_df[['text','label_ft']].iloc[:10000].to_csv('train.csv',index=None,header=None,sep='\t')train_df[['text','label_ft']].iloc[10000:15000].to_csv('valid.csv',index=None,header=None,sep='\t')#建立模型model = fasttext.train_supervised('train.csv',lr=1.0,wordNgrams=2,verbose=2,minCount=1,epoch=25,loss='hs',autotuneValidationFile='valid.csv',autotuneMetric="f1:__label__baking")#预测val_pred = [model.predict(x)[0][0].split('__')[-1] for x in train_df.iloc[-5000:]['text']]print(f1_score(train_df['label'].values[-5000:].astype(str),val_pred,average='macro'))
