keras_word_seg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 基于深度学习的中文分词尝试\n",
    "- 基于word2vec + 神经网络进行中文分词\n",
    "    - 步骤1：使用的是sogou的语料库建立初始的字向量。\n",
    "    - 步骤2：读入有标注的训练语料库，处理成keras需要的数据格式。\n",
    "    - 步骤3：根据训练数据建模，使用CNN方法\n",
    "    - 步骤4：读入无标注的检验语料库，用CNN模型进行分词标注\n",
    "    - 步骤5：检查最终的效果\n",
    "- 参考资料:[中文分词资源](http://www.52nlp.cn/%E4%B8%AD%E6%96%87%E5%88%86%E8%AF%8D%E5%85%A5%E9%97%A8%E4%B9%8B%E8%B5%84%E6%BA%90) \n",
    "    [中文分词标注法](http://www.52nlp.cn/the-character-based-tagging-method-of-chinese-word-segmentation) [word2vec原理](http://suanfazu.com/t/word2vec-zhong-de-shu-xue-yuan-li-xiang-jie-duo-tu-wifixia-yue-du/178) [基于word2vec的中文分词](http://blog.csdn.net/itplus/article/details/17122431)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 步骤1：先用sogou语料库生成中文的单字向量，以备后用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from os import path\n",
    "import os\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rootdir = '/SogouC.reduced/Reduced'\n",
    "dirs = os.listdir(rootdir)\n",
    "dirs = [path.join(rootdir,f) for f in dirs if f.startswith('C')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def load_txt(x):\n",
    "    with open(x) as f:\n",
    "        res = [t.decode('gbk','ignore') for t in f]\n",
    "        return ''.join(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "text_t = {}\n",
    "for i, d in enumerate(dirs):\n",
    "    files = os.listdir(d)\n",
    "    files = [path.join(d, x) for x in files if x.endswith('txt') and not x.startswith('.')]\n",
    "    text_t[i] = [load_txt(f) for f in files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# to dataframe\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "flen = [len(t) for t in text_t.values()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = np.repeat(text_t.keys(),flen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# flatter nested list\n",
    "import itertools\n",
    "merged = list(itertools.chain.from_iterable(text_t.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>txt</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>本报记者陈雪频实习记者唐翔发自上海\\r\\n　　一家刚刚成立两年的网络支付公司，它的目标是...</td>\n",
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      "text/plain": [
       "   label                                                txt\n",
       "0      0  　　本报记者陈雪频实习记者唐翔发自上海\\r\\n　　一家刚刚成立两年的网络支付公司，它的目标是...\n",
       "1      0      证券通：百联股份未来5年有能力保持高速增长\\r\\n\\r\\n    深度报告 权威内参...\n",
       "2      0      5月09日消息快评\\r\\n\\r\\n    深度报告 权威内参 来自“证券通”www....\n",
       "3      0      5月09日消息快评\\r\\n\\r\\n    深度报告 权威内参 来自“证券通”www....\n",
       "4      0      5月09日消息快评\\r\\n\\r\\n    深度报告 权威内参 来自“证券通”www...."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'label': labels, 'txt': merged})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# cut character\n",
    "def cutchar(x):\n",
    "    words = list(x)\n",
    "    return ' '.join(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['seg_word'] = df.txt.map(cutchar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from cPickle import dump,load\n",
    "#dump(df, open('df.pickle', 'wb'))\n",
    "df = load(open('df.pickle','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 探索 转成nltk需要格式,建立list \n",
    "txt = df['seg_word'].values \n",
    "txtnltk = [] \n",
    "for sent in txt: \n",
    "    temp = [w for w in sent.split()] \n",
    "    txtnltk.extend(temp) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>freq</th>\n",
       "      <th>word</th>\n",
       "      <th>idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4327816</td>\n",
       "      <td>\u0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5981</th>\n",
       "      <td>899948</td>\n",
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       "      <td>1</td>\n",
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       "      <td>417699</td>\n",
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      "text/plain": [
       "         freq word  idx\n",
       "2     4327816    \u0000    0\n",
       "5981   899948    ，    1\n",
       "2658   709992    的    2\n",
       "6288   417699    。    3\n",
       "4272   227591    一    4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nltk  \n",
    "import nltk \n",
    "corpus = nltk.Text(txtnltk) \n",
    "from nltk.probability import FreqDist \n",
    "# 词频 \n",
    "fdist = FreqDist(corpus) \n",
    "w = fdist.keys() \n",
    "v = fdist.values() \n",
    "freqdf = pd.DataFrame({'word':w,'freq':v}) \n",
    "freqdf.sort('freq',ascending =False, inplace=True)\n",
    "freqdf['idx'] = np.arange(len(v))\n",
    "freqdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "word2idx = dict((c, i) for c, i in zip(freqdf.word, freqdf.idx))\n",
    "idx2word = dict((i, c) for c, i in zip(freqdf.word, freqdf.idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 保持字符串，为生成词向量准备\n",
    "all_news_wv = []\n",
    "for news in txt:\n",
    "    all_news_wv.append([x for x in news.split() ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# word2vec\n",
    "from gensim.models import word2vec\n",
    "def trainW2V(corpus, epochs=50, num_features = 100,\\\n",
    "             min_word_count = 1, num_workers = 4,\\\n",
    "             context = 10, sample = 1e-5):\n",
    "    global w2v\n",
    "    w2v = word2vec.Word2Vec(workers = num_workers,\n",
    "                          sample = sample,\n",
    "                          size = num_features,\n",
    "                          min_count=min_word_count,\n",
    "                          window = context)\n",
    "    np.random.shuffle(corpus)\n",
    "    w2v.build_vocab(corpus)  \n",
    "    for epoch in range(epochs):\n",
    "        print epoch, \n",
    "        np.random.shuffle(corpus)\n",
    "        w2v.train(corpus)\n",
    "        w2v.alpha *= 0.9  \n",
    "        w2v.min_alpha = w2v.alpha  \n",
    "    print \"Done.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Done.\n"
     ]
    }
   ],
   "source": [
    "# word2vec\n",
    "trainW2V(all_news_wv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 保存词向量lookup矩阵，按idx位置存放。目的是保存词频，也可以直接使用w2v.index2word\n",
    "init_weight_wv = []\n",
    "for i in range(freqdf.shape[0]):\n",
    "    init_weight_wv.append(w2v[idx2word[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from cPickle import dump,load\n",
    "#dump(init_weight_wv, open('init_weight.pickle', 'wb'))\n",
    "init_weight_wv= load(open('init_weight.pickle','rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 步骤2：训练数据读取和转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 读取数据，将格式进行转换为带四种标签 S B M E\n",
    "input_file = 'icwb2-data/training/msr_training.utf8'\n",
    "output_file = 'icwb2-data/training/msr_training.tagging.utf8'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "# -*- coding: utf-8 -*-\n",
    "# Author: 52nlpcn@gmail.com\n",
    "# Copyright 2014 @ YuZhen Technology\n",
    "#\n",
    "# 4 tags for character tagging: B(Begin), E(End), M(Middle), S(Single)\n",
    "\n",
    "import codecs\n",
    "import sys\n",
    "\n",
    "def character_tagging(input_file, output_file):\n",
    "    input_data = codecs.open(input_file, 'r', 'utf-8')\n",
    "    output_data = codecs.open(output_file, 'w', 'utf-8')\n",
    "    for line in input_data.readlines():\n",
    "        word_list = line.strip().split()\n",
    "        for word in word_list:\n",
    "            if len(word) == 1:\n",
    "                output_data.write(word + \"/S \")\n",
    "            else:\n",
    "                output_data.write(word[0] + \"/B \")\n",
    "                for w in word[1:len(word)-1]:\n",
    "                    output_data.write(w + \"/M \")\n",
    "                output_data.write(word[len(word)-1] + \"/E \")\n",
    "        output_data.write(\"\\n\")\n",
    "    input_data.close()\n",
    "    output_data.close()\n",
    "\n",
    "character_tagging(input_file, output_file)\n",
    "# if __name__ == '__main__':\n",
    "#     if len(sys.argv) != 3:\n",
    "#         print \"Please use: python character_tagging.py input output\"\n",
    "#         sys.exit()\n",
    "#     input_file = sys.argv[1]\n",
    "#     output_file = sys.argv[2]\n",
    "#     character_tagging(input_file, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 定义'U'为未登陆新字, 空格为两头padding用途，并增加两个相应的向量表示\n",
    "char_num = len(init_weight_wv)\n",
    "idx2word[char_num] = u'U'\n",
    "word2idx[u'U'] = char_num\n",
    "idx2word[char_num+1] = u' '\n",
    "word2idx[u' '] = char_num+1\n",
    "\n",
    "init_weight_wv.append(np.random.randn(100,))\n",
    "init_weight_wv.append(np.zeros(100,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 分离word 和 label\n",
    "with open(output_file) as f:\n",
    "    lines = f.readlines()\n",
    "    train_line = [[w[0] for w in line.decode('utf-8').split()] for line in lines]\n",
    "    train_label = [w[2] for line in lines for w in line.decode('utf-8').split()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 文档转数字list\n",
    "import numpy as np\n",
    "def sent2num(sentence, word2idx = word2idx, context = 7):\n",
    "    predict_word_num = []\n",
    "    for w in sentence:\n",
    "        # 文本中的字如果在词典中则转为数字，如果不在则设置为'U\n",
    "        if w in word2idx:\n",
    "            predict_word_num.append(word2idx[w])\n",
    "        else:\n",
    "            predict_word_num.append(word2idx[u'U'])\n",
    "    # 首尾padding\n",
    "    num = len(predict_word_num)\n",
    "    pad = int((context-1)*0.5)\n",
    "    for i in range(pad):\n",
    "        predict_word_num.insert(0,word2idx[u' '] )\n",
    "        predict_word_num.append(word2idx[u' '] )\n",
    "    train_x = []\n",
    "    for i in range(num):\n",
    "        train_x.append(predict_word_num[i:i+context])\n",
    "    return train_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       " [439, 98, 5, 195, 863, 110, 98],\n",
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       " [195, 863, 110, 98, 2, 1133, 32],\n",
       " [863, 110, 98, 2, 1133, 32, 2],\n",
       " [110, 98, 2, 1133, 32, 2, 22],\n",
       " [98, 2, 1133, 32, 2, 22, 35],\n",
       " [2, 1133, 32, 2, 22, 35, 1036],\n",
       " [1133, 32, 2, 22, 35, 1036, 3],\n",
       " [32, 2, 22, 35, 1036, 3, 6988],\n",
       " [2, 22, 35, 1036, 3, 6988, 6988],\n",
       " [22, 35, 1036, 3, 6988, 6988, 6988]]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输入字符list，输出数字list\n",
    "sent2num(train_line[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将所有训练文本转成数字list\n",
    "train_word_num = []\n",
    "for line in train_line:\n",
    "    train_word_num.extend(sent2num(line))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4050469\n",
      "4050469\n"
     ]
    }
   ],
   "source": [
    "print len(train_word_num)\n",
    "print len(train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dump(train_word_num, open('train_word_num.pickle', 'wb'))\n",
    "#train_word_num = load(open('train_word_num.pickle','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nb_classes = len(np.unique(train_label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 步骤3：训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import print_function\n",
    "\n",
    "from keras.preprocessing import sequence\n",
    "from keras.optimizers import SGD, RMSprop, Adagrad\n",
    "from keras.utils import np_utils\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.layers.recurrent import LSTM, GRU\n",
    "from keras.layers.core import Reshape, Flatten ,Dropout\n",
    "from keras.regularizers import l1,l2\n",
    "from keras.layers.convolutional import Convolution2D, MaxPooling2D,MaxPooling1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{u'M': 2, u'S': 3, u'B': 0, u'E': 1}\n",
      "{0: u'B', 1: u'E', 2: u'M', 3: u'S'}\n"
     ]
    }
   ],
   "source": [
    "# 建立两个字典\n",
    "label_dict = dict(zip(np.unique(train_label), range(4)))\n",
    "num_dict = {n:l  for l,n  in label_dict.iteritems()}\n",
    "print(label_dict)\n",
    "print(num_dict)\n",
    "# 将目标变量转为数字\n",
    "train_label = [label_dict[y] for y in train_label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 切分数据集\n",
    "from sklearn.cross_validation import train_test_split\n",
    "train_X, test_X, train_y, test_y = train_test_split(train_word_num, train_label , train_size=0.9, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_train = np_utils.to_categorical(train_y, nb_classes)\n",
    "Y_test = np_utils.to_categorical(test_y, nb_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3645422 train sequences\n",
      "405047 test sequences\n"
     ]
    }
   ],
   "source": [
    "print(len(train_X), 'train sequences')\n",
    "print(len(test_X), 'test sequences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 初始字向量格式准备\n",
    "init_weight = [np.array(init_weight_wv)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "batch_size = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maxfeatures = init_weight[0].shape[0] # 词典大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Build model...\n"
     ]
    }
   ],
   "source": [
    "# 一个普通的单隐层神经网络，输入层700，隐藏层100，输出层4\n",
    "# 迭代时同时更新神经网络权重，以及词向量\n",
    "print('Build model...')\n",
    "model = Sequential()\n",
    "# 词向量初始化，输入维度：词典大小，输出维度：词向量100\n",
    "model.add(Embedding(maxfeatures, 100,weights=init_weight)) # 使用初使词向量可以增加准确率\n",
    "model.add(Flatten())\n",
    "model.add(Dense(700, 100))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(100, nb_classes))\n",
    "model.add(Activation('softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.callbacks import Callback\n",
    "class EarlyStopping(Callback):\n",
    "    def __init__(self, patience=0, verbose=0):\n",
    "        super(Callback, self).__init__()\n",
    "\n",
    "        self.patience = patience\n",
    "        self.verbose = verbose\n",
    "        self.best_val_loss = np.Inf\n",
    "        self.wait = 0\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        if not self.params['do_validation']:\n",
    "            warnings.warn(\"Early stopping requires validation data!\", RuntimeWarning)\n",
    "\n",
    "        cur_val_loss = logs.get('val_loss')\n",
    "        if cur_val_loss < self.best_val_loss:\n",
    "            self.best_val_loss = cur_val_loss\n",
    "            self.wait = 0\n",
    "        else:\n",
    "            if self.wait >= self.patience:\n",
    "                if self.verbose > 0:\n",
    "                    print(\"Epoch %05d: early stopping\" % (epoch))\n",
    "                self.model.stop_training = True\n",
    "            self.wait += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train...\n",
      "Train on 3280879 samples, validate on 364543 samples\n",
      "Epoch 0\n",
      "3280879/3280879 [==============================] - 504s - loss: 0.1770 - acc.: 0.9387 - val. loss: 0.1594 - val. acc.: 0.9485\n"
     ]
    }
   ],
   "source": [
    "# train_X, test_X, Y_train, Y_test\n",
    "print(\"Train...\")\n",
    "earlystop = EarlyStopping(patience=0, verbose=1)\n",
    "result = model.fit(train_X, Y_train, batch_size=batch_size, nb_epoch=1, \n",
    "          validation_split=0.1, show_accuracy=True,callbacks=[earlystop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "405047/405047 [==============================] - 5s - loss: 0.1609     \n",
      "Test score: 0.160891216656\n"
     ]
    }
   ],
   "source": [
    "score = earlystop.model.evaluate(test_X, Y_test, batch_size=batch_size)\n",
    "print('Test score:', score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "405047/405047 [==============================] - 5s     \n",
      "Test accuracy: 0.948813347587\n"
     ]
    }
   ],
   "source": [
    "# test数据集，准确率0.94\n",
    "classes = earlystop.model.predict_classes(test_X, batch_size=batch_size)\n",
    "acc = np_utils.accuracy(classes, test_y) # 要用没有转换前的y\n",
    "print('Test accuracy:', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from cPickle import dump, load\n",
    "#dump(model, open('model.pickle', 'wb'))\n",
    "model = load(open('model.pickle','rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 步骤4：用test文本进行预测，评估效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_txt = u'国家食药监总局发布通知称，酮康唑口服制剂因存在严重肝毒性不良反应，即日起停止生产销售使用。'\n",
    "temp_txt = list(temp_txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[6988, 6988, 6988, 16, 63, 609, 364],\n",
       " [6988, 6988, 16, 63, 609, 364, 731],\n",
       " [6988, 16, 63, 609, 364, 731, 254],\n",
       " [16, 63, 609, 364, 731, 254, 451],\n",
       " [63, 609, 364, 731, 254, 451, 50]]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_num = sent2num(temp_txt)\n",
    "temp_num[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 根据输入得到标注推断\n",
    "def predict_num(input_num,input_txt, \\\n",
    "                model = model,\\\n",
    "                label_dict=label_dict,\\\n",
    "                num_dict=num_dict):\n",
    "    input_num = np.array(input_num)\n",
    "    predict_prob = model.predict_proba(input_num)\n",
    "    predict_lable = model.predict_classes(input_num)\n",
    "    for i , lable in enumerate(predict_lable[:-1]):\n",
    "        # 如果是首字 ，不可为E, M\n",
    "        if i==0:\n",
    "            predict_prob[i, label_dict[u'E']] = 0\n",
    "            predict_prob[i, label_dict[u'M']] = 0      \n",
    "        # 前字为B，后字不可为B,S\n",
    "        if lable == label_dict[u'B']:\n",
    "            predict_prob[i+1,label_dict[u'B']] = 0\n",
    "            predict_prob[i+1,label_dict[u'S']] = 0\n",
    "        # 前字为E，后字不可为M,E\n",
    "        if lable == label_dict[u'E']:\n",
    "            predict_prob[i+1,label_dict[u'M']] = 0\n",
    "            predict_prob[i+1,label_dict[u'E']] = 0\n",
    "        # 前字为M，后字不可为B,S\n",
    "        if lable == label_dict[u'M']:\n",
    "            predict_prob[i+1,label_dict[u'B']] = 0\n",
    "            predict_prob[i+1,label_dict[u'S']] = 0\n",
    "        # 前字为S，后字不可为M,E\n",
    "        if lable == label_dict[u'S']:\n",
    "            predict_prob[i+1,label_dict[u'M']] = 0\n",
    "            predict_prob[i+1,label_dict[u'E']] = 0\n",
    "        predict_lable[i+1] = predict_prob[i+1].argmax()\n",
    "    predict_lable_new = [num_dict[x]  for x in predict_lable]\n",
    "    result =  [w+'/' +l  for w, l in zip(input_txt,predict_lable_new)]\n",
    "    return ' '.join(result) + '\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45/45 [==============================] - 0s\n",
      "45/45 [==============================] - 0s\n",
      "国/B 家/M 食/M 药/M 监/M 总/M 局/E 发/B 布/E 通/B 知/E 称/S ，/S 酮/S 康/S 唑/B 口/E 服/B 制/M 剂/E 因/S 存/B 在/E 严/B 重/E 肝/B 毒/E 性/S 不/B 良/E 反/B 应/E ，/S 即/B 日/E 起/S 停/B 止/E 生/B 产/E 销/B 售/E 使/B 用/E 。/S\n",
      "\n"
     ]
    }
   ],
   "source": [
    "temp = predict_num(temp_num,temp_txt)\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_file = 'icwb2-data/testing/msr_test.utf8'\n",
    "with open(test_file,'r') as f:\n",
    "    lines = f.readlines()\n",
    "    test_texts = [list(line.decode('utf-8').strip()) for line in lines]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "test_output = []\n",
    "for line in test_texts:\n",
    "    test_num = sent2num(line)\n",
    "    output_line = predict_num(test_num,input_txt=line)\n",
    "    test_output.append(output_line.encode('utf-8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('icwb2-data/testing/msr_test_output.utf8','w') as f:\n",
    "    f.writelines(test_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_file = 'icwb2-data/testing/msr_test_output.utf8'\n",
    "output_file = 'icwb2-data/testing/msr_test.split.tag2word.utf8'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import codecs\n",
    "import sys\n",
    "\n",
    "def character_2_word(input_file, output_file):\n",
    "    input_data = codecs.open(input_file, 'r', 'utf-8')\n",
    "    output_data = codecs.open(output_file, 'w', 'utf-8')\n",
    "    # 4 tags for character tagging: B(Begin), E(End), M(Middle), S(Single)\n",
    "    for line in input_data.readlines():\n",
    "        char_tag_list = line.strip().split()\n",
    "        for char_tag in char_tag_list:\n",
    "            char_tag_pair = char_tag.split('/')\n",
    "            char = char_tag_pair[0]\n",
    "            tag = char_tag_pair[1]\n",
    "            if tag == 'B':\n",
    "                output_data.write(' ' + char)\n",
    "            elif tag == 'M':\n",
    "                output_data.write(char)\n",
    "            elif tag == 'E':\n",
    "                output_data.write(char + ' ')\n",
    "            else: # tag == 'S'\n",
    "                output_data.write(' ' + char + ' ')\n",
    "        output_data.write(\"\\n\")\n",
    "    input_data.close()\n",
    "    output_data.close()\n",
    "\n",
    "character_2_word(input_file, output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 最终使用perl脚本检验的F值为0.913"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "! ./icwb2-data/scripts/score ./icwb2-data/gold/msr_training_words.utf8 ./icwb2-data/gold/msr_test_gold.utf8 msr_test.split.tag2word.utf8 > deep.score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
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