相似问题识别-特征工程+预测模型(XGBoost)+评估.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "\n",
    "<a href=\"https://www.quora.com/\">Quora</a>是一个海外知名的在线问答网站(类似中国的知乎、百度知道)，Quora上有许多问题和答案，也容许用户协同编辑问题和答案.不过由于某些“你懂的”原因，在国内无法访问访问该网站。在2018年9月，据Quora报告称每个月有超过3亿人访问Quora，很多人都会问重复的问题，还有很多问题具有相同意图仅仅只是表达方式不一样。 例如，“如何进行网上购物?”和“网上购物的步骤有哪些?”类似这样的问题都是重复的问题，因为它们都有相同的意图，所以应该只回答一次就可以了。\n",
    "\n",
    "Quora正非常努力地消除重复的问题，但是这非常困难，因为对于相同的意图,不同的人可能会有不同的表达方式。如何从不同的表达方式中识别出相同的意图这是一个非常具有挑战性的工作。今天我们就来对相似问题进行研究，并通过特征工程来丰富我们的词向量表达，丰富的词向量表达方式可以帮助我们提高模型的预测准确率多人不知道什么是特征工程，为什么要搞特征工程？  \n",
    "\n",
    "在这里我就举一个形象一点的例子来帮助大家里面特征工程的重要性：大家都知道奶牛会产奶,不同的奶牛产的牛奶质量也不完全一样，有的口感好,质量高，有的口感差，质量也就差一点，那么牛奶质量的好坏取决于什么呢？牛奶质量主要取决于奶牛所吃的饲料,奶牛的饲料大致可分为粗饲料和精饲料两种，如果你只给奶牛喂粗饲料,那么奶牛所产的牛奶的质量可能就要差一点，但是如果你给奶牛喂粗饲料的时候同时配上精饲料，那么它产的奶的质量就要比只吃粗饲料的奶牛产的奶的质量要高。在这里我们可以把奶牛比作我们的算法模型,牛奶的质量比作模型的质量(预测准确率等指标),饲料就好比是我们的训练数据集(特征集)。奶牛吃饲料并产奶的过程就好比我们把训练数据喂给算法模型,模型就产生了预测的能力。此时模型预测能力的好坏就取决于我们所喂的训练数据(特征集),如果你只给模型喂当前的特征集,这就好比你只给奶牛喂粗饲料，那么模型的质量和牛奶的质量都不会高。因此我们要在当前特征集(粗饲料)的情况下，研发出更有价值的新的特征集(精饲料)，这个过程我们就称之为特征工程，只有当我们的模型同时吃了“粗饲料”和“精饲料”，它产出“奶”的质量才会更好。我举的这个奶牛的例子不一定科学,但它能比较形象的帮助大家来理解什么是特征工程。\n",
    "\n",
    "今天我们处理文本的特征工程会用到以下一些方法:\n",
    "* basic \n",
    "* BOW（词袋）\n",
    "* tf-idf\n",
    "* svd\n",
    "* fuzzywuzzy\n",
    "* distance \n",
    "* word2vec  \n",
    "虽然我们这次处理的是英文的文本,但是相信通过对上述方法的学习,对我们日后处理中文文本也会有很好的借鉴作用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据\n",
    "\n",
    "我们的数据来自于Kaggle，你可以在此<a href=\"https://www.kaggle.com/c/quora-question-pairs/data\">下载</a>,在train表中有3个主要字段:\n",
    "\n",
    "* question1和question2:对问题的完整描述\n",
    "* is_duplicate  - 目标变量，如果question1和question2具有基本相同的含义，则为1，否则为0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\fuzzywuzzy\\fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
      "  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import gensim\n",
    "from fuzzywuzzy import fuzz\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from scipy.stats import skew, kurtosis\n",
    "from scipy.spatial.distance import cosine, cityblock, jaccard, canberra, euclidean, minkowski, braycurtis\n",
    "from scipy.stats import skew, kurtosis\n",
    "import gensim\n",
    "from gensim.models import Word2Vec\n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words('english')\n",
    "import scipy\n",
    "from tqdm import tqdm_notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "404287\n"
     ]
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       "            id    qid1    qid2  \\\n",
       "124091  124092  191225   88601   \n",
       "241727  241729  353793  353794   \n",
       "74068    74068  127004   12140   \n",
       "33384    33384   61344   61345   \n",
       "272361  272363  390604  348729   \n",
       "\n",
       "                                                question1  \\\n",
       "124091  How can I get the funding for my startup witho...   \n",
       "241727  What does it mean when you hear a beeping in y...   \n",
       "74068   What will be the cut-off for KVPY SA stream in...   \n",
       "33384   What is the secret you have never share with a...   \n",
       "272361  How do I maintain a healthy relationship with ...   \n",
       "\n",
       "                                                question2  is_duplicate  \n",
       "124091  How can you get VCs to fund you if you just ha...             1  \n",
       "241727  Why do I keep hearing the beep sound in my ear...             0  \n",
       "74068      What is the expected cut off for KVPY SA 2016?             1  \n",
       "33384          What secret can you not share with anyone?             1  \n",
       "272361  What are some good ways to maintain a healthy ...             0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/quora/train.csv')\n",
    "df = df.dropna(how=\"any\").reset_index(drop=True)\n",
    "print(len(df))\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "总共有40多万条记录，下面我们看一下目标变量的分布情况:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_duplicate\n",
      "0    255024\n",
      "1    149263\n",
      "Name: id, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xca9cdd8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df.groupby(\"is_duplicate\")['id'].count())\n",
    "df.groupby(\"is_duplicate\")['id'].count().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看来大部分的问题都被标记为0，少部分被标记为1,下面我们随机抽取10条记录的question1和question2看看:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why is salt water taffy candy imported in France?\n",
      "Why is Saltwater Taffy candy imported in Greece?\n",
      "\n",
      "How should I remove dark circles from eyes?\n",
      "How do I remove dark circles below eyes naturally?\n",
      "\n",
      "How can you make money on YouTube?\n",
      "How can I make money on YouTube?\n",
      "\n",
      "Are there any China-Israel investment funds? Especially in early stage start-ups- software mobile and internet space?\n",
      "Who can we approach for funding/sale of an early stage mobile internet startup?\n",
      "\n",
      "What's the difference between knitting and crocheting?\n",
      "Is weird knitting/crochet really useful?\n",
      "\n",
      "Is kindle really worth it?\n",
      "Are Kindles really worth it?\n",
      "\n",
      "Does any university offer an online course in cyber security/ethical hacking?\n",
      "I have to make a project on \"ethical hacking\" and/or \"cyber security\". What is a good idea? What is the way to implement the idea?\n",
      "\n",
      "Why did Catelyn Stark die?\n",
      "Did you like Catelyn Stark or not?\n",
      "\n",
      "Are American gay men really this promiscuous?\n",
      "Are gay men promiscuous?\n",
      "\n",
      "Suppose a convicted person has done his sentence in jail. Is he eligible to contest in election again in India?\n",
      "Should Salman Khan's punishment be reduced because he does so much of charity and educate a number of illiterates?\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in df.sample(10).index:\n",
    "    print(df.question1[i])\n",
    "    print(df.question2[i])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 接下来我们删除不需要的字段："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程\n",
    "## basic\n",
    "基本特征工程包括计算question1和question2的长度,以及它们的长度差，计算每个question的字符数等，主要包含以下功能:\n",
    "\n",
    "* 计算question1的长度\n",
    "* 计算question2的长度\n",
    "* 计算question1和question2的长度差\n",
    "* 计算过滤掉空格后的question1的字符长度\n",
    "* 计算过滤掉空格后的question2的字符长度\n",
    "* 计算question1中的单词数量\n",
    "* 计算question2中的单词数量\n",
    "* 计算question1和question2中的常用单词数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "      <th>len_q1</th>\n",
       "      <th>len_q2</th>\n",
       "      <th>diff_len</th>\n",
       "      <th>len_char_q1</th>\n",
       "      <th>len_char_q2</th>\n",
       "      <th>len_word_q1</th>\n",
       "      <th>len_word_q2</th>\n",
       "      <th>common_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>57</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>88</td>\n",
       "      <td>-37</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
       "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>-15</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
       "      <td>Which fish would survive in salt water?</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Astrology: I am a Capricorn Sun Cap moon and c...</td>\n",
       "      <td>I'm a triple Capricorn (Sun, Moon and ascendan...</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>-4</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Should I buy tiago?</td>\n",
       "      <td>What keeps childern active and far from phone ...</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>62</td>\n",
       "      <td>-43</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>How can I be a good geologist?</td>\n",
       "      <td>What should I do to be a great geologist?</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
       "      <td>-11</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>When do you use シ instead of し?</td>\n",
       "      <td>When do you use \"&amp;\" instead of \"and\"?</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "      <td>-6</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Motorola (company): Can I hack my Charter Moto...</td>\n",
       "      <td>How do I hack Motorola DCX3400 for free internet?</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>49</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Method to find separation of slits using fresn...</td>\n",
       "      <td>What are some of the things technicians can te...</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>116</td>\n",
       "      <td>-59</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>How do I read and find my YouTube comments?</td>\n",
       "      <td>How can I see all my Youtube comments?</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>What can make Physics easy to learn?</td>\n",
       "      <td>How can you make physics easy to learn?</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>39</td>\n",
       "      <td>-3</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>What was your first sexual experience like?</td>\n",
       "      <td>What was your first sexual experience?</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>What are the laws to change your status from a...</td>\n",
       "      <td>What are the laws to change your status from a...</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>What would a Trump presidency mean for current...</td>\n",
       "      <td>How will a Trump presidency affect the student...</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>91</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>What does manipulation mean?</td>\n",
       "      <td>What does manipulation means?</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>-1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Why do girls want to be friends with the guy t...</td>\n",
       "      <td>How do guys feel after rejecting a girl?</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Why are so many Quora users posting questions ...</td>\n",
       "      <td>Why do people ask Quora questions which can be...</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>73</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Which is the best digital marketing institutio...</td>\n",
       "      <td>Which is the best digital marketing institute ...</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Why do rockets look white?</td>\n",
       "      <td>Why are rockets and boosters painted white?</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>43</td>\n",
       "      <td>-17</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>What's causing someone to be jealous?</td>\n",
       "      <td>What can I do to avoid being jealous of someone?</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>48</td>\n",
       "      <td>-11</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>What are the questions should not ask on Quora?</td>\n",
       "      <td>Which question should I ask on Quora?</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>How much is 30 kV in HP?</td>\n",
       "      <td>Where can I find a conversion chart for CC to ...</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>57</td>\n",
       "      <td>-33</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>What does it mean that every time I look at th...</td>\n",
       "      <td>How many times a day do a clock’s hands overlap?</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>48</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>What are some tips on making it through the jo...</td>\n",
       "      <td>What are some tips on making it through the jo...</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>89</td>\n",
       "      <td>-10</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>What is web application?</td>\n",
       "      <td>What is the web application framework?</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>-14</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Does society place too much importance on sports?</td>\n",
       "      <td>How do sports contribute to the society?</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>40</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>What is best way to make money online?</td>\n",
       "      <td>What is best way to ask for money online?</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>41</td>\n",
       "      <td>-3</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>How should I prepare for CA final law?</td>\n",
       "      <td>How one should know that he/she completely pre...</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>69</td>\n",
       "      <td>-31</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404257</th>\n",
       "      <td>Which phone is best under 12000?</td>\n",
       "      <td>What is the best phone to buy below 15k?</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>-8</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404258</th>\n",
       "      <td>Who is the overall most popular Game of Throne...</td>\n",
       "      <td>Who is the most popular character in the Game ...</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>-6</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404259</th>\n",
       "      <td>How do you troubleshoot a Toshiba laptop?</td>\n",
       "      <td>How do I reset a Toshiba laptop?</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404260</th>\n",
       "      <td>How does the burning of fossil fuels contribut...</td>\n",
       "      <td>Why does CO2 contribute more to global warming...</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404261</th>\n",
       "      <td>Is it safe to store an external battery power ...</td>\n",
       "      <td>How do I make a safe and cheap power bank?</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404262</th>\n",
       "      <td>How can I gain weight on my body?</td>\n",
       "      <td>What should I eat to gain weight?</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404263</th>\n",
       "      <td>What is the green dot next to the phone icon o...</td>\n",
       "      <td>My boyfriend says he deleted his Facebook Mess...</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>179</td>\n",
       "      <td>-121</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404264</th>\n",
       "      <td>What are the causes of the fall of the Roman E...</td>\n",
       "      <td>What were the most important causes and effect...</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>80</td>\n",
       "      <td>-28</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404265</th>\n",
       "      <td>Why don't we still do great music like in the ...</td>\n",
       "      <td>Should I raise my young child on 80's music?</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>44</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404266</th>\n",
       "      <td>How do you diagnose antisocial personality dis...</td>\n",
       "      <td>What Does It Feel Like to have antisocial pers...</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>63</td>\n",
       "      <td>-11</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404267</th>\n",
       "      <td>What is the difference between who and how?</td>\n",
       "      <td>What is the difference between \"&amp;\" and \"and\"?</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>-2</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404268</th>\n",
       "      <td>Does Stalin have any grandchildren that are st...</td>\n",
       "      <td>What was Joseph Stalin's 5 year plan? How did ...</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404269</th>\n",
       "      <td>What are the best new car products or inventio...</td>\n",
       "      <td>What are some mind-blowing vehicles tools that...</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>87</td>\n",
       "      <td>-4</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404270</th>\n",
       "      <td>What happens if you put milk in a coffee maker?</td>\n",
       "      <td>What would happen if I put milk instead of wat...</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>83</td>\n",
       "      <td>-36</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404271</th>\n",
       "      <td>Will the next generation of parenting change o...</td>\n",
       "      <td>What kind of parents will the next generation ...</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404272</th>\n",
       "      <td>In accounting, why do we debit expenses and cr...</td>\n",
       "      <td>What is a utilities expense in accounting? How...</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>79</td>\n",
       "      <td>-19</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404273</th>\n",
       "      <td>What is copilotsearch.com?</td>\n",
       "      <td>What is ContenVania.com?</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404274</th>\n",
       "      <td>What does analytics do?</td>\n",
       "      <td>What are analytical people like?</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>32</td>\n",
       "      <td>-9</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404275</th>\n",
       "      <td>How did you prepare for AIIMS/NEET/AIPMT?</td>\n",
       "      <td>How did you prepare for the AIIMS UG entrance ...</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>52</td>\n",
       "      <td>-11</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404276</th>\n",
       "      <td>What is the minimum time required to build a f...</td>\n",
       "      <td>What is a cheaper and quicker way to build an ...</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>77</td>\n",
       "      <td>-11</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404277</th>\n",
       "      <td>What are some outfit ideas to wear to a frat p...</td>\n",
       "      <td>What are some outfit ideas wear to a frat them...</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>55</td>\n",
       "      <td>-4</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404278</th>\n",
       "      <td>Why is Manaphy childish in Pokémon Ranger and ...</td>\n",
       "      <td>Why is Manaphy annoying in Pokemon ranger and ...</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404279</th>\n",
       "      <td>How does a long distance relationship work?</td>\n",
       "      <td>How are long distance relationships maintained?</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "      <td>-4</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404280</th>\n",
       "      <td>What do you think of the removal of the MagSaf...</td>\n",
       "      <td>What will the CPU upgrade to the 2016 Apple Ma...</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>61</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404281</th>\n",
       "      <td>What does Jainism say about homosexuality?</td>\n",
       "      <td>What does Jainism say about Gays and Homosexua...</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>51</td>\n",
       "      <td>-9</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404282</th>\n",
       "      <td>How many keywords are there in the Racket prog...</td>\n",
       "      <td>How many keywords are there in PERL Programmin...</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>79</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404283</th>\n",
       "      <td>Do you believe there is life after death?</td>\n",
       "      <td>Is it true that there is life after death?</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>-1</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404284</th>\n",
       "      <td>What is one coin?</td>\n",
       "      <td>What's this coin?</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404285</th>\n",
       "      <td>What is the approx annual cost of living while...</td>\n",
       "      <td>I am having little hairfall problem but I want...</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>127</td>\n",
       "      <td>-33</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404286</th>\n",
       "      <td>What is like to have sex with cousin?</td>\n",
       "      <td>What is it like to have sex with your cousin?</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>45</td>\n",
       "      <td>-8</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>404287 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                question1  \\\n",
       "0       What is the step by step guide to invest in sh...   \n",
       "1       What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2       How can I increase the speed of my internet co...   \n",
       "3       Why am I mentally very lonely? How can I solve...   \n",
       "4       Which one dissolve in water quikly sugar, salt...   \n",
       "5       Astrology: I am a Capricorn Sun Cap moon and c...   \n",
       "6                                     Should I buy tiago?   \n",
       "7                          How can I be a good geologist?   \n",
       "8                         When do you use シ instead of し?   \n",
       "9       Motorola (company): Can I hack my Charter Moto...   \n",
       "10      Method to find separation of slits using fresn...   \n",
       "11            How do I read and find my YouTube comments?   \n",
       "12                   What can make Physics easy to learn?   \n",
       "13            What was your first sexual experience like?   \n",
       "14      What are the laws to change your status from a...   \n",
       "15      What would a Trump presidency mean for current...   \n",
       "16                           What does manipulation mean?   \n",
       "17      Why do girls want to be friends with the guy t...   \n",
       "18      Why are so many Quora users posting questions ...   \n",
       "19      Which is the best digital marketing institutio...   \n",
       "20                             Why do rockets look white?   \n",
       "21                  What's causing someone to be jealous?   \n",
       "22        What are the questions should not ask on Quora?   \n",
       "23                               How much is 30 kV in HP?   \n",
       "24      What does it mean that every time I look at th...   \n",
       "25      What are some tips on making it through the jo...   \n",
       "26                               What is web application?   \n",
       "27      Does society place too much importance on sports?   \n",
       "28                 What is best way to make money online?   \n",
       "29                 How should I prepare for CA final law?   \n",
       "...                                                   ...   \n",
       "404257                   Which phone is best under 12000?   \n",
       "404258  Who is the overall most popular Game of Throne...   \n",
       "404259          How do you troubleshoot a Toshiba laptop?   \n",
       "404260  How does the burning of fossil fuels contribut...   \n",
       "404261  Is it safe to store an external battery power ...   \n",
       "404262                  How can I gain weight on my body?   \n",
       "404263  What is the green dot next to the phone icon o...   \n",
       "404264  What are the causes of the fall of the Roman E...   \n",
       "404265  Why don't we still do great music like in the ...   \n",
       "404266  How do you diagnose antisocial personality dis...   \n",
       "404267        What is the difference between who and how?   \n",
       "404268  Does Stalin have any grandchildren that are st...   \n",
       "404269  What are the best new car products or inventio...   \n",
       "404270    What happens if you put milk in a coffee maker?   \n",
       "404271  Will the next generation of parenting change o...   \n",
       "404272  In accounting, why do we debit expenses and cr...   \n",
       "404273                         What is copilotsearch.com?   \n",
       "404274                            What does analytics do?   \n",
       "404275          How did you prepare for AIIMS/NEET/AIPMT?   \n",
       "404276  What is the minimum time required to build a f...   \n",
       "404277  What are some outfit ideas to wear to a frat p...   \n",
       "404278  Why is Manaphy childish in Pokémon Ranger and ...   \n",
       "404279        How does a long distance relationship work?   \n",
       "404280  What do you think of the removal of the MagSaf...   \n",
       "404281         What does Jainism say about homosexuality?   \n",
       "404282  How many keywords are there in the Racket prog...   \n",
       "404283          Do you believe there is life after death?   \n",
       "404284                                  What is one coin?   \n",
       "404285  What is the approx annual cost of living while...   \n",
       "404286              What is like to have sex with cousin?   \n",
       "\n",
       "                                                question2  is_duplicate  \\\n",
       "0       What is the step by step guide to invest in sh...             0   \n",
       "1       What would happen if the Indian government sto...             0   \n",
       "2       How can Internet speed be increased by hacking...             0   \n",
       "3       Find the remainder when [math]23^{24}[/math] i...             0   \n",
       "4                 Which fish would survive in salt water?             0   \n",
       "5       I'm a triple Capricorn (Sun, Moon and ascendan...             1   \n",
       "6       What keeps childern active and far from phone ...             0   \n",
       "7               What should I do to be a great geologist?             1   \n",
       "8                   When do you use \"&\" instead of \"and\"?             0   \n",
       "9       How do I hack Motorola DCX3400 for free internet?             0   \n",
       "10      What are some of the things technicians can te...             0   \n",
       "11                 How can I see all my Youtube comments?             1   \n",
       "12                How can you make physics easy to learn?             1   \n",
       "13                 What was your first sexual experience?             1   \n",
       "14      What are the laws to change your status from a...             0   \n",
       "15      How will a Trump presidency affect the student...             1   \n",
       "16                          What does manipulation means?             1   \n",
       "17               How do guys feel after rejecting a girl?             0   \n",
       "18      Why do people ask Quora questions which can be...             1   \n",
       "19      Which is the best digital marketing institute ...             0   \n",
       "20            Why are rockets and boosters painted white?             1   \n",
       "21       What can I do to avoid being jealous of someone?             0   \n",
       "22                  Which question should I ask on Quora?             0   \n",
       "23      Where can I find a conversion chart for CC to ...             0   \n",
       "24       How many times a day do a clock’s hands overlap?             0   \n",
       "25      What are some tips on making it through the jo...             0   \n",
       "26                 What is the web application framework?             0   \n",
       "27               How do sports contribute to the society?             0   \n",
       "28              What is best way to ask for money online?             0   \n",
       "29      How one should know that he/she completely pre...             1   \n",
       "...                                                   ...           ...   \n",
       "404257           What is the best phone to buy below 15k?             0   \n",
       "404258  Who is the most popular character in the Game ...             1   \n",
       "404259                   How do I reset a Toshiba laptop?             0   \n",
       "404260  Why does CO2 contribute more to global warming...             0   \n",
       "404261         How do I make a safe and cheap power bank?             0   \n",
       "404262                  What should I eat to gain weight?             1   \n",
       "404263  My boyfriend says he deleted his Facebook Mess...             0   \n",
       "404264  What were the most important causes and effect...             1   \n",
       "404265       Should I raise my young child on 80's music?             0   \n",
       "404266  What Does It Feel Like to have antisocial pers...             0   \n",
       "404267      What is the difference between \"&\" and \"and\"?             0   \n",
       "404268  What was Joseph Stalin's 5 year plan? How did ...             0   \n",
       "404269  What are some mind-blowing vehicles tools that...             1   \n",
       "404270  What would happen if I put milk instead of wat...             1   \n",
       "404271  What kind of parents will the next generation ...             1   \n",
       "404272  What is a utilities expense in accounting? How...             0   \n",
       "404273                           What is ContenVania.com?             0   \n",
       "404274                   What are analytical people like?             0   \n",
       "404275  How did you prepare for the AIIMS UG entrance ...             0   \n",
       "404276  What is a cheaper and quicker way to build an ...             0   \n",
       "404277  What are some outfit ideas wear to a frat them...             1   \n",
       "404278  Why is Manaphy annoying in Pokemon ranger and ...             1   \n",
       "404279    How are long distance relationships maintained?             1   \n",
       "404280  What will the CPU upgrade to the 2016 Apple Ma...             0   \n",
       "404281  What does Jainism say about Gays and Homosexua...             1   \n",
       "404282  How many keywords are there in PERL Programmin...             0   \n",
       "404283         Is it true that there is life after death?             1   \n",
       "404284                                  What's this coin?             0   \n",
       "404285  I am having little hairfall problem but I want...             0   \n",
       "404286      What is it like to have sex with your cousin?             0   \n",
       "\n",
       "        len_q1  len_q2  diff_len  len_char_q1  len_char_q2  len_word_q1  \\\n",
       "0           66      57         9           20           20           14   \n",
       "1           51      88       -37           21           29            8   \n",
       "2           73      59        14           25           24           14   \n",
       "3           50      65       -15           19           26           11   \n",
       "4           76      39        37           25           18           13   \n",
       "5           86      90        -4           26           27           16   \n",
       "6           19      62       -43           14           20            4   \n",
       "7           30      41       -11           16           16            7   \n",
       "8           31      37        -6           16           16            8   \n",
       "9           60      49        11           25           24            9   \n",
       "10          57     116       -59           19           22            9   \n",
       "11          43      38         5           21           17            9   \n",
       "12          36      39        -3           17           19            7   \n",
       "13          43      38         5           20           19            7   \n",
       "14         141     140         1           26           26           29   \n",
       "15          93      91         2           25           24           15   \n",
       "16          28      29        -1           15           15            4   \n",
       "17          57      40        17           21           19           12   \n",
       "18          82      73         9           22           24           14   \n",
       "19          60      54         6           19           19            9   \n",
       "20          26      43       -17           15           18            5   \n",
       "21          37      48       -11           18           21            6   \n",
       "22          47      37        10           17           19            9   \n",
       "23          24      57       -33           16           19            7   \n",
       "24          79      48        31           21           21           17   \n",
       "25          79      89       -10           23           24           14   \n",
       "26          24      38       -14           15           19            4   \n",
       "27          49      40         9           17           17            8   \n",
       "28          38      41        -3           16           18            8   \n",
       "29          38      69       -31           19           25            8   \n",
       "...        ...     ...       ...          ...          ...          ...   \n",
       "404257      32      40        -8           18           19            6   \n",
       "404258      58      64        -6           20           19           10   \n",
       "404259      41      32         9           18           17            7   \n",
       "404260      66      65         1           20           25           11   \n",
       "404261      78      42        36           26           18           16   \n",
       "404262      33      33         0           17           16            8   \n",
       "404263      58     179      -121           17           31           12   \n",
       "404264      52      80       -28           19           21           11   \n",
       "404265      60      44        16           24           21           13   \n",
       "404266      52      63       -11           18           22            7   \n",
       "404267      43      45        -2           16           17            8   \n",
       "404268      80      65        15           22           25           13   \n",
       "404269      83      87        -4           22           25           15   \n",
       "404270      47      83       -36           19           22           10   \n",
       "404271      62      51        11           19           20           11   \n",
       "404272      60      79       -19           22           22           10   \n",
       "404273      26      24         2           15           15            3   \n",
       "404274      23      32        -9           14           15            4   \n",
       "404275      41      52       -11           22           26            6   \n",
       "404276      66      77       -11           22           23           13   \n",
       "404277      51      55        -4           17           17           11   \n",
       "404278      68      68         0           26           21           13   \n",
       "404279      43      47        -4           18           18            7   \n",
       "404280      88      61        27           32           29           16   \n",
       "404281      42      51        -9           18           20            6   \n",
       "404282      85      79         6           24           24           14   \n",
       "404283      41      42        -1           17           13            8   \n",
       "404284      17      17         0           11           11            4   \n",
       "404285      94     127       -33           26           27           17   \n",
       "404286      37      45        -8           17           19            8   \n",
       "\n",
       "        len_word_q2  common_words  \n",
       "0                12            10  \n",
       "1                13             4  \n",
       "2                10             4  \n",
       "3                 9             0  \n",
       "4                 7             2  \n",
       "5                16             8  \n",
       "6                11             0  \n",
       "7                 9             4  \n",
       "8                 8             6  \n",
       "9                 9             3  \n",
       "10               19             1  \n",
       "11                8             5  \n",
       "12                8             6  \n",
       "13                6             5  \n",
       "14               29            20  \n",
       "15               17             4  \n",
       "16                4             3  \n",
       "17                8             1  \n",
       "18               13             5  \n",
       "19                9             7  \n",
       "20                7             3  \n",
       "21               10             1  \n",
       "22                7             4  \n",
       "23               11             0  \n",
       "24               10             0  \n",
       "25               15            13  \n",
       "26                6             3  \n",
       "27                7             0  \n",
       "28                9             7  \n",
       "29               12             6  \n",
       "...             ...           ...  \n",
       "404257            9             3  \n",
       "404258           12             8  \n",
       "404259            7             5  \n",
       "404260           11             4  \n",
       "404261           10             3  \n",
       "404262            7             2  \n",
       "404263           33             6  \n",
       "404264           15             7  \n",
       "404265            9             0  \n",
       "404266           10             3  \n",
       "404267            8             6  \n",
       "404268           12             1  \n",
       "404269           14             8  \n",
       "404270           16             7  \n",
       "404271            9             5  \n",
       "404272           15             3  \n",
       "404273            3             2  \n",
       "404274            5             1  \n",
       "404275           10             5  \n",
       "404276           16             6  \n",
       "404277           11            10  \n",
       "404278           13            10  \n",
       "404279            6             3  \n",
       "404280           12             4  \n",
       "404281            8             6  \n",
       "404282           13            11  \n",
       "404283            9             5  \n",
       "404284            3             1  \n",
       "404285           25             1  \n",
       "404286           10             8  \n",
       "\n",
       "[404287 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算question1和question2的长度\n",
    "df['len_q1'] = df.question1.apply(lambda x: len(str(x)))\n",
    "df['len_q2'] = df.question2.apply(lambda x: len(str(x)))\n",
    "\n",
    "# 计算两个问题的长度差\n",
    "df['diff_len'] = df.len_q1 - df.len_q2\n",
    "\n",
    "# 计算两个问题的字符数量\n",
    "df['len_char_q1'] = df.question1.apply(lambda x: len(''.join(set(str(x).replace(' ', '')))))\n",
    "df['len_char_q2'] = df.question2.apply(lambda x: len(''.join(set(str(x).replace(' ', '')))))\n",
    "\n",
    "# 计算两个问题的单词数\n",
    "df['len_word_q1'] = df.question1.apply(lambda x: len(str(x).split()))\n",
    "df['len_word_q2'] = df.question2.apply(lambda x: len(str(x).split()))\n",
    "\n",
    "# 计算两个问题的常用单词数\n",
    "df['common_words'] = df.apply(lambda x: len(set(str(x['question1']).lower().split()).intersection(set(str(x['question2'])\n",
    "                                                                                                          .lower().split()))), axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了以后使用方便,我们将基本特征集记录为fs_basic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs_basic = df[['len_q1', 'len_q2', 'diff_len', 'len_char_q1', \n",
    "        'len_char_q2', 'len_word_q1', 'len_word_q2',     \n",
    "        'common_words']]\n",
    "# fs_basic.to_csv(\"fs_basic.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BOW(词袋)\n",
    "关于BOW在我之前的多篇博客中均有使用及说明,这里不再详细阐述其原理，我们在这里使用的词袋模型为sklearn的CountVectorizer,为了以后使用方便,我们将bow特征集记录为fs_bow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404287, 172708)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vect = CountVectorizer(analyzer='word', token_pattern=r'\\w{1,}')\n",
    "count_vect.fit(pd.concat((df['question1'],df['question2'])).unique())\n",
    "trainq1_trans = count_vect.transform(df['question1'].values)\n",
    "trainq2_trans = count_vect.transform(df['question2'].values)\n",
    "fs_bow = scipy.sparse.hstack((trainq1_trans,trainq2_trans))\n",
    "fs_bow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 14288)\t1\n",
      "  (0, 34376)\t1\n",
      "  (0, 38705)\t2\n",
      "  (0, 38976)\t1\n",
      "  (0, 40287)\t1\n",
      "  (0, 40598)\t1\n",
      "  (0, 47859)\t1\n",
      "  (0, 69203)\t1\n",
      "  (0, 72848)\t2\n",
      "  (0, 76405)\t1\n",
      "  (0, 77236)\t1\n",
      "  (0, 83272)\t1\n",
      "  (1, 23441)\t1\n",
      "  (1, 37728)\t1\n",
      "  (1, 40598)\t1\n",
      "  (1, 43564)\t1\n",
      "  (1, 43567)\t1\n",
      "  (1, 53740)\t1\n",
      "  (1, 54691)\t1\n",
      "  (1, 73117)\t1\n",
      "  (1, 76405)\t1\n",
      "  (1, 83272)\t1\n",
      "  (2, 4061)\t1\n",
      "  (2, 14752)\t1\n",
      "  (2, 19067)\t1\n",
      "  :\t:\n",
      "  (404285, 124082)\t3\n",
      "  (404285, 132098)\t1\n",
      "  (404285, 141045)\t1\n",
      "  (404285, 141392)\t1\n",
      "  (404285, 142166)\t1\n",
      "  (404285, 146851)\t1\n",
      "  (404285, 147333)\t1\n",
      "  (404285, 147406)\t1\n",
      "  (404285, 156048)\t1\n",
      "  (404285, 159838)\t1\n",
      "  (404285, 163590)\t1\n",
      "  (404285, 166960)\t1\n",
      "  (404285, 168942)\t1\n",
      "  (404285, 169187)\t1\n",
      "  (404285, 169713)\t1\n",
      "  (404286, 106449)\t1\n",
      "  (404286, 121781)\t1\n",
      "  (404286, 126952)\t1\n",
      "  (404286, 127166)\t1\n",
      "  (404286, 131808)\t1\n",
      "  (404286, 155320)\t1\n",
      "  (404286, 163590)\t1\n",
      "  (404286, 169626)\t1\n",
      "  (404286, 170185)\t1\n",
      "  (404286, 171405)\t1\n"
     ]
    }
   ],
   "source": [
    "print(fs_bow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们最后得到的词袋特征集fs_bow是一个稀疏矩阵，因为稀疏矩阵中存在大量的零元素，因次我们使用了scipy.sparse来存储稀疏矩阵(只存储非零元素)。  \n",
    "CountVectorizer模型所用的参数请参考我之前的几篇博客或者官方<a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\">文档</a>，这里不再详细阐述。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF-IDF\n",
    "关于TF-IDF在我之前的多篇博客中均有使用及说明,这里不再详细阐述其原理，我们在这里使用的tf-idf模型为sklearn的TfidfVectorizer,在这里我们所使用的tf-idf也可以分为两种类型:\n",
    "\n",
    "* 基于word的tf-idf\n",
    "* 基于字符的tf-idf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基于word的TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vect = TfidfVectorizer(analyzer='word',min_df=3,token_pattern=r'\\w{1,}',ngram_range=(1,2),max_features=5000)\n",
    "tfidf_vect.fit(pd.concat((df['question1'],df['question2'])).unique())\n",
    "trainq1_trans = tfidf_vect.transform(df['question1'].values)\n",
    "trainq2_trans = tfidf_vect.transform(df['question2'].values)\n",
    "fs_tfidf_word = scipy.sparse.hstack((trainq1_trans,trainq2_trans))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404287, 10000)\n",
      "  (0, 4701)\t0.10705452704845354\n",
      "  (0, 4686)\t0.07102608407800481\n",
      "  (0, 4365)\t0.28380318790374565\n",
      "  (0, 4325)\t0.09053474417047624\n",
      "  (0, 4070)\t0.07199046915353155\n",
      "  (0, 3885)\t0.6044443034571639\n",
      "  (0, 3688)\t0.26848538329582405\n",
      "  (0, 2666)\t0.24540151135699356\n",
      "  (0, 2332)\t0.11319372125290773\n",
      "  (0, 2302)\t0.07748232804955184\n",
      "  (0, 2284)\t0.28741457540684634\n",
      "  (0, 2283)\t0.26132380639065944\n",
      "  (0, 2220)\t0.16100996840725149\n",
      "  (0, 2167)\t0.1804985788305284\n",
      "  (0, 2131)\t0.17919206608579683\n",
      "  (0, 1786)\t0.31644539595632626\n",
      "  (0, 718)\t0.17733289630469834\n",
      "  (1, 4701)\t0.22431033613032303\n",
      "  (1, 4686)\t0.1488202809615606\n",
      "  (1, 4205)\t0.6674187992832605\n",
      "  (1, 4070)\t0.15084094787228883\n",
      "  (1, 3901)\t0.5443290021220754\n",
      "  (1, 2969)\t0.2047615733016986\n",
      "  (1, 2332)\t0.2371737315750311\n",
      "  (1, 2302)\t0.16234798777905576\n",
      "  :\t:\n",
      "  (404285, 6971)\t0.17209701242505268\n",
      "  (404285, 6968)\t0.2511182909643519\n",
      "  (404285, 6857)\t0.2112292700042006\n",
      "  (404285, 6805)\t0.21463625418243842\n",
      "  (404285, 5707)\t0.2226452925862841\n",
      "  (404285, 5706)\t0.1710555346581965\n",
      "  (404285, 5322)\t0.09435716353867286\n",
      "  (404285, 5287)\t0.1644935737468854\n",
      "  (404286, 9980)\t0.20324267927195552\n",
      "  (404286, 9847)\t0.37745624547545875\n",
      "  (404286, 9832)\t0.18638102071970603\n",
      "  (404286, 9701)\t0.13784560586546904\n",
      "  (404286, 9686)\t0.09145464336648759\n",
      "  (404286, 9359)\t0.29896172830264023\n",
      "  (404286, 9325)\t0.11657439443365246\n",
      "  (404286, 8683)\t0.3717972954760236\n",
      "  (404286, 8682)\t0.28930812122784305\n",
      "  (404286, 7545)\t0.26482356271182816\n",
      "  (404286, 7537)\t0.20521426579389682\n",
      "  (404286, 7367)\t0.27757613083754473\n",
      "  (404286, 7348)\t0.16027171778366725\n",
      "  (404286, 7318)\t0.20122943677684194\n",
      "  (404286, 7302)\t0.09976783559114127\n",
      "  (404286, 6851)\t0.36016440267396815\n",
      "  (404286, 6839)\t0.1845821648979351\n"
     ]
    }
   ],
   "source": [
    "print(fs_tfidf_word.shape)\n",
    "print(fs_tfidf_word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基于字符的TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vect_ngram_chars = TfidfVectorizer(analyzer='char',min_df=3, token_pattern=r'\\w{1,}',ngram_range=(1,2), max_features=5000)\n",
    "tfidf_vect_ngram_chars.fit(pd.concat((df['question1'],df['question2'])).unique())\n",
    "trainq1_trans = tfidf_vect_ngram_chars.transform(df['question1'].values)\n",
    "trainq2_trans = tfidf_vect_ngram_chars.transform(df['question2'].values)\n",
    "fs_tfidf_char = scipy.sparse.hstack((trainq1_trans,trainq2_trans))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404287, 8466)\n",
      "  (0, 3281)\t0.059408417777667824\n",
      "  (0, 3280)\t0.04691817403787812\n",
      "  (0, 3194)\t0.04661816411292095\n",
      "  (0, 3163)\t0.034010039166989955\n",
      "  (0, 3138)\t0.07510349065656595\n",
      "  (0, 3105)\t0.06246931824583835\n",
      "  (0, 3083)\t0.13696525746098426\n",
      "  (0, 3053)\t0.04318358930378903\n",
      "  (0, 3030)\t0.06648582962546076\n",
      "  (0, 3023)\t0.04872696838733591\n",
      "  (0, 3020)\t0.13313229764041307\n",
      "  (0, 2980)\t0.1260106149342917\n",
      "  (0, 2979)\t0.22730658293683764\n",
      "  (0, 2961)\t0.18604661449054205\n",
      "  (0, 2949)\t0.09538016471912954\n",
      "  (0, 2908)\t0.03944565529120923\n",
      "  (0, 2907)\t0.16315589220320564\n",
      "  (0, 2882)\t0.1257604073162725\n",
      "  (0, 2876)\t0.05246349559627378\n",
      "  (0, 2835)\t0.06760911796842292\n",
      "  (0, 2734)\t0.22273472062657\n",
      "  (0, 2733)\t0.09583233445589848\n",
      "  (0, 2675)\t0.05684459251804122\n",
      "  (0, 2674)\t0.03207201045344375\n",
      "  (0, 2659)\t0.1542406217249039\n",
      "  :\t:\n",
      "  (404286, 6573)\t0.08170765471321281\n",
      "  (404286, 6568)\t0.0693186600518891\n",
      "  (404286, 6565)\t0.18302328550227354\n",
      "  (404286, 6525)\t0.2317370681786679\n",
      "  (404286, 6491)\t0.1458068403845505\n",
      "  (404286, 6461)\t0.11756699649846782\n",
      "  (404286, 6460)\t0.13937074302082098\n",
      "  (404286, 6326)\t0.1623205919713994\n",
      "  (404286, 6267)\t0.10855101576523465\n",
      "  (404286, 6266)\t0.1364695659343145\n",
      "  (404286, 6177)\t0.11035683999169421\n",
      "  (404286, 6131)\t0.05834890644402762\n",
      "  (404286, 6049)\t0.1495681086384634\n",
      "  (404286, 6047)\t0.0685485962423401\n",
      "  (404286, 5993)\t0.09148034750986472\n",
      "  (404286, 5757)\t0.04460497785175249\n",
      "  (404286, 4297)\t0.13027073445900075\n",
      "  (404286, 4295)\t0.08814479843250117\n",
      "  (404286, 4292)\t0.06349821774725764\n",
      "  (404286, 4291)\t0.07882997168139204\n",
      "  (404286, 4284)\t0.11388868011359299\n",
      "  (404286, 4281)\t0.12290716406421548\n",
      "  (404286, 4280)\t0.10740228359301764\n",
      "  (404286, 4275)\t0.08344032550175906\n",
      "  (404286, 4234)\t0.4009356228747447\n"
     ]
    }
   ],
   "source": [
    "print(fs_tfidf_char.shape)\n",
    "print(fs_tfidf_char)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TF-IDF模型所用的参数请参考我之前的几篇博客或者官方<a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">文档</a>，这里不再详细阐述。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "svd_word = TruncatedSVD(n_components=180)\n",
    "fs_svd_word = svd_word.fit_transform(fs_tfidf_word)\n",
    "\n",
    "svd_char = TruncatedSVD(n_components=180)\n",
    "fs_svd_char = svd_word.fit_transform(fs_tfidf_char)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.98267634, -0.23215909,  0.02747439, ...,  0.01709309,\n",
       "        -0.05000904, -0.05622315],\n",
       "       [ 0.68478225,  0.05307884,  0.02009339, ..., -0.08059883,\n",
       "        -0.01186929, -0.0596837 ],\n",
       "       [ 0.99499667,  0.03737902, -0.24064721, ..., -0.01576613,\n",
       "        -0.01623998, -0.01537312],\n",
       "       ...,\n",
       "       [ 0.67501132, -0.12496267, -0.06988956, ..., -0.02438415,\n",
       "         0.00749498,  0.02127296],\n",
       "       [ 1.03160088,  0.03845409, -0.16618078, ...,  0.00311606,\n",
       "         0.03860928,  0.03075244],\n",
       "       [ 0.92788675, -0.03182806,  0.2189347 , ..., -0.00370618,\n",
       "        -0.05251763, -0.01335699]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_svd_char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.25149603, -0.11164787, -0.10707992, ...,  0.01285021,\n",
       "         0.00323745,  0.01918228],\n",
       "       [ 0.32606597, -0.08005594, -0.11615081, ..., -0.04966374,\n",
       "         0.10416991, -0.0288566 ],\n",
       "       [ 0.17020132,  0.22485091, -0.00103089, ..., -0.02316811,\n",
       "         0.00352326, -0.00137425],\n",
       "       ...,\n",
       "       [ 0.24639001, -0.14273649, -0.14588693, ...,  0.0684703 ,\n",
       "         0.00531925,  0.00179647],\n",
       "       [ 0.24953584,  0.02531906, -0.056789  , ..., -0.02984291,\n",
       "        -0.01086887,  0.02821785],\n",
       "       [ 0.25625918, -0.07755942, -0.16841639, ...,  0.04442834,\n",
       "        -0.00849257, -0.02675239]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_svd_word"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过SVD降维后，tf-idf特征集的维度降到了180维。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fuzzywuzzy\n",
    "<a href=\"https://github.com/seatgeek/fuzzywuzzy\">fuzzywuzzy</a>是python的一个用来进行模糊字符串匹配的工具,它的基本原理是基于计算两个相似字符串之间的编辑距离，fuzzywuzzy提供了多种比率(ratio)来描述两个字符串之间的相似程度。主要包含以下几种比率:\n",
    "\n",
    "* QRatio\n",
    "* WRatio\n",
    "* Partial ratio\n",
    "* Partial token set ratio\n",
    "* Partial token sort ratio\n",
    "* Token set ratio\n",
    "* Token sort ratio  \n",
    "我们看看下面几个例子:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz.QRatio(\"How can I start an online shopping (e-commerce) website?\",\n",
    "            \"Which web technology is best suitable for building a big E-Commerce website?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz.partial_ratio(\"Why did Trump win the Presidency?\", \n",
    "                   \"How did Donald Trump win the 2016 Presidential Election\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz.partial_ratio(\"How can I start an online shopping (e-commerce) website?\", \n",
    "                   \"Which web technology is best suitable for building a big E-Commerce website?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz.token_set_ratio(\"今天 天气 非常好\",\"今天 天气 很好\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据fuzzywuzzy返回的ratio值越高，说明两个字符串越相似。下面就让我们在原始数据上计算一下fuzzywuzzy的各自ritio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['fuzz_qratio'] = df.apply(lambda x: fuzz.QRatio(str(x['question1']),str(x['question2'])), axis=1)\n",
    "# df['fuzz_WRatio'] = df.apply(lambda x: fuzz.WRatio(str(x['question1']),str(x['question2'])), axis=1)\n",
    "df['fuzz_ratio'] = df.apply(lambda x: fuzz.ratio(str(x['question1']), str(x['question2'])), axis=1)\n",
    "df['fuzz_partial_ratio'] = df.apply(lambda x: fuzz.partial_ratio(str(x['question1']),str(x['question2'])), axis=1)\n",
    "df['fuzz_partial_token_set_ratio'] = df.apply(lambda x:fuzz.partial_token_set_ratio(str(x['question1']),str(x['question2'])), axis=1)\n",
    "df['fuzz_partial_token_sort_ratio'] = df.apply(lambda x:fuzz.partial_token_sort_ratio(str(x['question1']),str(x['question2'])), axis=1)\n",
    "df['fuzz_token_set_ratio'] = df.apply(lambda x:fuzz.token_set_ratio(str(x['question1']),str(x['question2'])), axis=1)\n",
    "df['fuzz_token_sort_ratio'] = df.apply(lambda x: fuzz.token_sort_ratio(str(x['question1']),str(x['question2'])), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了便于以后的使用,我们将空间距离和偏度与峰度的特征集记为fs_distance :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fuzz_ratio</th>\n",
       "      <th>fuzz_partial_ratio</th>\n",
       "      <th>fuzz_partial_token_set_ratio</th>\n",
       "      <th>fuzz_partial_token_sort_ratio</th>\n",
       "      <th>fuzz_token_set_ratio</th>\n",
       "      <th>fuzz_token_sort_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>93</td>\n",
       "      <td>98</td>\n",
       "      <td>100</td>\n",
       "      <td>88</td>\n",
       "      <td>100</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>73</td>\n",
       "      <td>100</td>\n",
       "      <td>73</td>\n",
       "      <td>86</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>41</td>\n",
       "      <td>100</td>\n",
       "      <td>71</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>54</td>\n",
       "      <td>100</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>66</td>\n",
       "      <td>67</td>\n",
       "      <td>100</td>\n",
       "      <td>71</td>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>59</td>\n",
       "      <td>67</td>\n",
       "      <td>100</td>\n",
       "      <td>69</td>\n",
       "      <td>71</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50</td>\n",
       "      <td>57</td>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>65</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>39</td>\n",
       "      <td>100</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>74</td>\n",
       "      <td>71</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>81</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>83</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>89</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>94</td>\n",
       "      <td>97</td>\n",
       "      <td>100</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>95</td>\n",
       "      <td>97</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>100</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>98</td>\n",
       "      <td>96</td>\n",
       "      <td>100</td>\n",
       "      <td>96</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>39</td>\n",
       "      <td>48</td>\n",
       "      <td>100</td>\n",
       "      <td>59</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>59</td>\n",
       "      <td>55</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>71</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>100</td>\n",
       "      <td>91</td>\n",
       "      <td>86</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>100</td>\n",
       "      <td>76</td>\n",
       "      <td>81</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>45</td>\n",
       "      <td>65</td>\n",
       "      <td>100</td>\n",
       "      <td>81</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>81</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>86</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>43</td>\n",
       "      <td>46</td>\n",
       "      <td>100</td>\n",
       "      <td>47</td>\n",
       "      <td>45</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>93</td>\n",
       "      <td>91</td>\n",
       "      <td>100</td>\n",
       "      <td>94</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>77</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>61</td>\n",
       "      <td>100</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "      <td>100</td>\n",
       "      <td>63</td>\n",
       "      <td>57</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>91</td>\n",
       "      <td>87</td>\n",
       "      <td>100</td>\n",
       "      <td>88</td>\n",
       "      <td>93</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>91</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404257</th>\n",
       "      <td>47</td>\n",
       "      <td>46</td>\n",
       "      <td>100</td>\n",
       "      <td>62</td>\n",
       "      <td>60</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404258</th>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "      <td>100</td>\n",
       "      <td>75</td>\n",
       "      <td>92</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404259</th>\n",
       "      <td>82</td>\n",
       "      <td>69</td>\n",
       "      <td>100</td>\n",
       "      <td>74</td>\n",
       "      <td>85</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404260</th>\n",
       "      <td>55</td>\n",
       "      <td>62</td>\n",
       "      <td>100</td>\n",
       "      <td>59</td>\n",
       "      <td>68</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404261</th>\n",
       "      <td>42</td>\n",
       "      <td>52</td>\n",
       "      <td>100</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404262</th>\n",
       "      <td>48</td>\n",
       "      <td>55</td>\n",
       "      <td>100</td>\n",
       "      <td>56</td>\n",
       "      <td>58</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404263</th>\n",
       "      <td>14</td>\n",
       "      <td>33</td>\n",
       "      <td>100</td>\n",
       "      <td>53</td>\n",
       "      <td>78</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404264</th>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>100</td>\n",
       "      <td>67</td>\n",
       "      <td>95</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404265</th>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "      <td>100</td>\n",
       "      <td>44</td>\n",
       "      <td>52</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404266</th>\n",
       "      <td>68</td>\n",
       "      <td>79</td>\n",
       "      <td>100</td>\n",
       "      <td>65</td>\n",
       "      <td>76</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404267</th>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>100</td>\n",
       "      <td>89</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404268</th>\n",
       "      <td>43</td>\n",
       "      <td>44</td>\n",
       "      <td>100</td>\n",
       "      <td>52</td>\n",
       "      <td>48</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404269</th>\n",
       "      <td>58</td>\n",
       "      <td>63</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404270</th>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>100</td>\n",
       "      <td>65</td>\n",
       "      <td>82</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404271</th>\n",
       "      <td>51</td>\n",
       "      <td>55</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>79</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404272</th>\n",
       "      <td>45</td>\n",
       "      <td>56</td>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404273</th>\n",
       "      <td>64</td>\n",
       "      <td>62</td>\n",
       "      <td>100</td>\n",
       "      <td>65</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404274</th>\n",
       "      <td>65</td>\n",
       "      <td>74</td>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404275</th>\n",
       "      <td>65</td>\n",
       "      <td>71</td>\n",
       "      <td>100</td>\n",
       "      <td>62</td>\n",
       "      <td>84</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404276</th>\n",
       "      <td>55</td>\n",
       "      <td>52</td>\n",
       "      <td>100</td>\n",
       "      <td>52</td>\n",
       "      <td>59</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404277</th>\n",
       "      <td>91</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404278</th>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>100</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404279</th>\n",
       "      <td>76</td>\n",
       "      <td>77</td>\n",
       "      <td>100</td>\n",
       "      <td>71</td>\n",
       "      <td>77</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404280</th>\n",
       "      <td>51</td>\n",
       "      <td>55</td>\n",
       "      <td>100</td>\n",
       "      <td>52</td>\n",
       "      <td>71</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404281</th>\n",
       "      <td>88</td>\n",
       "      <td>76</td>\n",
       "      <td>100</td>\n",
       "      <td>88</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404282</th>\n",
       "      <td>87</td>\n",
       "      <td>84</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>97</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404283</th>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404284</th>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>100</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404285</th>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>100</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404286</th>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>92</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>404287 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        fuzz_ratio  fuzz_partial_ratio  fuzz_partial_token_set_ratio  \\\n",
       "0               93                  98                           100   \n",
       "1               65                  73                           100   \n",
       "2               45                  41                           100   \n",
       "3                7                  20                            32   \n",
       "4               37                  54                           100   \n",
       "5               66                  67                           100   \n",
       "6               17                  32                            39   \n",
       "7               59                  67                           100   \n",
       "8               85                  90                           100   \n",
       "9               50                  57                           100   \n",
       "10              21                  39                           100   \n",
       "11              74                  71                           100   \n",
       "12              83                  86                           100   \n",
       "13              94                  97                           100   \n",
       "14              96                  96                           100   \n",
       "15              51                  52                           100   \n",
       "16              98                  96                           100   \n",
       "17              39                  48                           100   \n",
       "18              59                  55                           100   \n",
       "19              89                  91                           100   \n",
       "20              64                  65                           100   \n",
       "21              45                  65                           100   \n",
       "22              81                  86                           100   \n",
       "23               7                  20                            39   \n",
       "24              43                  46                           100   \n",
       "25              93                  91                           100   \n",
       "26              77                  83                           100   \n",
       "27              25                  28                           100   \n",
       "28              91                  87                           100   \n",
       "29              65                  68                           100   \n",
       "...            ...                 ...                           ...   \n",
       "404257          47                  46                           100   \n",
       "404258          70                  69                           100   \n",
       "404259          82                  69                           100   \n",
       "404260          55                  62                           100   \n",
       "404261          42                  52                           100   \n",
       "404262          48                  55                           100   \n",
       "404263          14                  33                           100   \n",
       "404264          77                  77                           100   \n",
       "404265          29                  34                           100   \n",
       "404266          68                  79                           100   \n",
       "404267          84                  84                           100   \n",
       "404268          43                  44                           100   \n",
       "404269          58                  63                           100   \n",
       "404270          66                  66                           100   \n",
       "404271          51                  55                           100   \n",
       "404272          45                  56                           100   \n",
       "404273          64                  62                           100   \n",
       "404274          65                  74                           100   \n",
       "404275          65                  71                           100   \n",
       "404276          55                  52                           100   \n",
       "404277          91                  86                           100   \n",
       "404278          84                  84                           100   \n",
       "404279          76                  77                           100   \n",
       "404280          51                  55                           100   \n",
       "404281          88                  76                           100   \n",
       "404282          87                  84                           100   \n",
       "404283          72                  76                           100   \n",
       "404284          76                  76                           100   \n",
       "404285          30                  34                           100   \n",
       "404286          90                  86                           100   \n",
       "\n",
       "        fuzz_partial_token_sort_ratio  fuzz_token_set_ratio  \\\n",
       "0                                  88                   100   \n",
       "1                                  73                    86   \n",
       "2                                  71                    63   \n",
       "3                                  30                    28   \n",
       "4                                  67                    67   \n",
       "5                                  71                    78   \n",
       "6                                  39                    24   \n",
       "7                                  69                    71   \n",
       "8                                  93                    93   \n",
       "9                                  55                    65   \n",
       "10                                 39                    36   \n",
       "11                                 70                    81   \n",
       "12                                 89                    92   \n",
       "13                                 86                   100   \n",
       "14                                 95                    97   \n",
       "15                                 52                    52   \n",
       "16                                 96                    98   \n",
       "17                                 59                    51   \n",
       "18                                 60                    71   \n",
       "19                                 91                    86   \n",
       "20                                 76                    81   \n",
       "21                                 81                    78   \n",
       "22                                 86                    78   \n",
       "23                                 39                    23   \n",
       "24                                 47                    45   \n",
       "25                                 94                    93   \n",
       "26                                 61                   100   \n",
       "27                                 63                    57   \n",
       "28                                 88                    93   \n",
       "29                                 70                    91   \n",
       "...                               ...                   ...   \n",
       "404257                             62                    60   \n",
       "404258                             75                    92   \n",
       "404259                             74                    85   \n",
       "404260                             59                    68   \n",
       "404261                             49                    59   \n",
       "404262                             56                    58   \n",
       "404263                             53                    78   \n",
       "404264                             67                    95   \n",
       "404265                             44                    52   \n",
       "404266                             65                    76   \n",
       "404267                             89                   100   \n",
       "404268                             52                    48   \n",
       "404269                             60                    68   \n",
       "404270                             65                    82   \n",
       "404271                             66                    79   \n",
       "404272                             55                    64   \n",
       "404273                             65                    67   \n",
       "404274                             55                    60   \n",
       "404275                             62                    84   \n",
       "404276                             52                    59   \n",
       "404277                             90                   100   \n",
       "404278                             88                    88   \n",
       "404279                             71                    77   \n",
       "404280                             52                    71   \n",
       "404281                             88                   100   \n",
       "404282                             83                    97   \n",
       "404283                             70                    79   \n",
       "404284                             75                    75   \n",
       "404285                             36                    37   \n",
       "404286                             92                   100   \n",
       "\n",
       "        fuzz_token_sort_ratio  \n",
       "0                          93  \n",
       "1                          63  \n",
       "2                          63  \n",
       "3                          24  \n",
       "4                          47  \n",
       "5                          74  \n",
       "6                          23  \n",
       "7                          61  \n",
       "8                          87  \n",
       "9                          44  \n",
       "10                         35  \n",
       "11                         76  \n",
       "12                         85  \n",
       "13                         94  \n",
       "14                         95  \n",
       "15                         46  \n",
       "16                         98  \n",
       "17                         51  \n",
       "18                         61  \n",
       "19                         84  \n",
       "20                         66  \n",
       "21                         75  \n",
       "22                         78  \n",
       "23                         23  \n",
       "24                         42  \n",
       "25                         93  \n",
       "26                         77  \n",
       "27                         51  \n",
       "28                         83  \n",
       "29                         63  \n",
       "...                       ...  \n",
       "404257                     57  \n",
       "404258                     82  \n",
       "404259                     65  \n",
       "404260                     60  \n",
       "404261                     50  \n",
       "404262                     56  \n",
       "404263                     40  \n",
       "404264                     75  \n",
       "404265                     41  \n",
       "404266                     67  \n",
       "404267                     85  \n",
       "404268                     39  \n",
       "404269                     63  \n",
       "404270                     69  \n",
       "404271                     63  \n",
       "404272                     56  \n",
       "404273                     67  \n",
       "404274                     60  \n",
       "404275                     64  \n",
       "404276                     50  \n",
       "404277                     94  \n",
       "404278                     89  \n",
       "404279                     73  \n",
       "404280                     59  \n",
       "404281                     90  \n",
       "404282                     88  \n",
       "404283                     69  \n",
       "404284                     75  \n",
       "404285                     35  \n",
       "404286                     90  \n",
       "\n",
       "[404287 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_fuzz = df[['fuzz_ratio', 'fuzz_partial_ratio', \n",
    "       'fuzz_partial_token_set_ratio', 'fuzz_partial_token_sort_ratio',\n",
    "       'fuzz_token_set_ratio', 'fuzz_token_sort_ratio']]\n",
    "fs_fuzz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance\n",
    "接下来我们将另外一个距离特征集,我们将计算question1和question2之间的各种空间距离,主要包括:\n",
    "\n",
    "* 余弦距离(Cosine distance) \n",
    "* 曼哈顿距离（Manhattan distance）\n",
    "* Jaccard 距离\n",
    "* 堪培拉距离(Canberra distance)\n",
    "* 欧式距离( Euclidean distance)\n",
    "* 闵可夫斯基的距离（Minkowski distance）\n",
    "* 布雷柯蒂斯距离(Bray-Curtis distance)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在计算距离之前我们要先将question1和question2中的问题句子进行向量化处理,因此我们要定义一个将句子向量化的函数,然后再将question1和question2进行向量化:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = gensim.models.KeyedVectors.load_word2vec_format('./data/GoogleNews-vectors-negative300.bin.gz', binary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01f8a5b076cc444481ae6aa17b668b4b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=404287), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3bb186e3e3054909b7f985a185720586",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=404287), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def sent2vec(s,model):\n",
    "    words = str(s).lower()\n",
    "    words = word_tokenize(words)\n",
    "    words = [w for w in words if not w in stop_words]\n",
    "    words = [w for w in words if w.isalpha()]\n",
    "    M = []\n",
    "    for w in words:\n",
    "        try:\n",
    "            M.append(model[w])\n",
    "        except:\n",
    "            continue\n",
    "    M = np.array(M)\n",
    "    v = M.sum(axis=0)\n",
    "    return v / np.sqrt((v ** 2).sum())\n",
    "\n",
    "#创建question1的句向量\n",
    "question1_vectors = np.zeros((df.shape[0], 300))\n",
    "for i, q in enumerate(tqdm_notebook(df.question1.values)):\n",
    "    question1_vectors[i, :] = sent2vec(q,model)\n",
    "    \n",
    "#创建question2的句向量    \n",
    "question2_vectors  = np.zeros((df.shape[0], 300))\n",
    "for i, q in enumerate(tqdm_notebook(df.question2.values)):\n",
    "    question2_vectors[i, :] = sent2vec(q,model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.08091219,  0.0077042 , -0.01682285, ...,  0.07126812,\n",
       "         0.03986768, -0.01777058],\n",
       "       [-0.07044965,  0.06259938, -0.02236313, ..., -0.03841253,\n",
       "         0.04433751, -0.05794364],\n",
       "       [ 0.04230251, -0.00322384,  0.03679858, ..., -0.01732291,\n",
       "        -0.08087941, -0.02825323],\n",
       "       ...,\n",
       "       [-0.00126756,  0.00785884,  0.00709831, ...,  0.01347446,\n",
       "         0.02799114, -0.00032104],\n",
       "       [-0.0082281 ,  0.02625634,  0.04778542, ...,  0.04280505,\n",
       "        -0.0033227 , -0.00439151],\n",
       "       [ 0.0253418 ,  0.00810537,  0.02050422, ..., -0.04502985,\n",
       "        -0.0505335 ,  0.09045997]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_w2v = np.hstack((question1_vectors, question2_vectors))\n",
    "fs_w2v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "完成了question1和question2的向量化以后,我们就可以开始计算question1和question2之间的空间距离了:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\spatial\\distance.py:698: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist = 1.0 - uv / np.sqrt(uu * vv)\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\spatial\\distance.py:853: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dist = np.double(unequal_nonzero.sum()) / np.double(nonzero.sum())\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\spatial\\distance.py:1138: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return l1_diff.sum() / l1_sum.sum()\n"
     ]
    }
   ],
   "source": [
    "#计算question1和question2空间距离\n",
    "df['cosine_distance'] = [cosine(x, y) for (x, y) in zip(np.nan_to_num(question1_vectors), np.nan_to_num(question2_vectors))]\n",
    "df['cityblock_distance'] = [cityblock(x, y) for (x, y) in zip(np.nan_to_num(question1_vectors), np.nan_to_num(question2_vectors))]\n",
    "df['jaccard_distance'] = [jaccard(x, y) for (x, y) in zip(np.nan_to_num(question1_vectors), np.nan_to_num(question2_vectors))]\n",
    "df['canberra_distance'] = [canberra(x, y) for (x, y) in zip(np.nan_to_num(question1_vectors), np.nan_to_num(question2_vectors))]\n",
    "df['euclidean_distance'] = [euclidean(x, y) for (x, y) in zip(np.nan_to_num(question1_vectors), np.nan_to_num(question2_vectors))]\n",
    "df['minkowski_distance'] = [minkowski(x, y, 3) for (x, y) in zip(np.nan_to_num(question1_vectors), np.nan_to_num(question2_vectors))]\n",
    "df['braycurtis_distance'] = [braycurtis(x, y) for (x, y) in zip(np.nan_to_num(question1_vectors), np.nan_to_num(question2_vectors))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#计算偏度和峰度\n",
    "df['skew_q1vec'] = [skew(x) for x in np.nan_to_num(question1_vectors)]\n",
    "df['skew_q2vec'] = [skew(x) for x in np.nan_to_num(question2_vectors)]\n",
    "df['kur_q1vec'] = [kurtosis(x) for x in np.nan_to_num(question1_vectors)]\n",
    "df['kur_q2vec'] = [kurtosis(x) for x in np.nan_to_num(question2_vectors)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了便于以后的使用,我们将距离的特征集记为fs_distance :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cosine_distance</th>\n",
       "      <th>cityblock_distance</th>\n",
       "      <th>jaccard_distance</th>\n",
       "      <th>canberra_distance</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>minkowski_distance</th>\n",
       "      <th>braycurtis_distance</th>\n",
       "      <th>skew_q1vec</th>\n",
       "      <th>skew_q2vec</th>\n",
       "      <th>kur_q1vec</th>\n",
       "      <th>kur_q2vec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.068972</td>\n",
       "      <td>5.081614</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.023324</td>\n",
       "      <td>0.371408</td>\n",
       "      <td>0.168999</td>\n",
       "      <td>0.186557</td>\n",
       "      <td>0.031817</td>\n",
       "      <td>-0.091902</td>\n",
       "      <td>0.050416</td>\n",
       "      <td>0.337301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.512164</td>\n",
       "      <td>14.195119</td>\n",
       "      <td>1.0</td>\n",
       "      <td>177.588090</td>\n",
       "      <td>1.012091</td>\n",
       "      <td>0.455910</td>\n",
       "      <td>0.592655</td>\n",
       "      <td>0.008735</td>\n",
       "      <td>0.094704</td>\n",
       "      <td>0.284010</td>\n",
       "      <td>-0.034444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.222009</td>\n",
       "      <td>9.055989</td>\n",
       "      <td>1.0</td>\n",
       "      <td>135.988707</td>\n",
       "      <td>0.666346</td>\n",
       "      <td>0.307828</td>\n",
       "      <td>0.342306</td>\n",
       "      <td>0.239752</td>\n",
       "      <td>0.144554</td>\n",
       "      <td>0.026759</td>\n",
       "      <td>-0.474131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.650411</td>\n",
       "      <td>15.987437</td>\n",
       "      <td>1.0</td>\n",
       "      <td>192.237828</td>\n",
       "      <td>1.140536</td>\n",
       "      <td>0.506028</td>\n",
       "      <td>0.692421</td>\n",
       "      <td>-0.002527</td>\n",
       "      <td>0.069649</td>\n",
       "      <td>-0.244560</td>\n",
       "      <td>-0.265568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.369993</td>\n",
       "      <td>12.103178</td>\n",
       "      <td>1.0</td>\n",
       "      <td>161.408435</td>\n",
       "      <td>0.860225</td>\n",
       "      <td>0.382770</td>\n",
       "      <td>0.480633</td>\n",
       "      <td>-0.133849</td>\n",
       "      <td>0.114777</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>-0.338876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.275242</td>\n",
       "      <td>10.254048</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150.335589</td>\n",
       "      <td>0.741946</td>\n",
       "      <td>0.337470</td>\n",
       "      <td>0.400061</td>\n",
       "      <td>0.102946</td>\n",
       "      <td>-0.077741</td>\n",
       "      <td>-0.335505</td>\n",
       "      <td>-0.057847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.820637</td>\n",
       "      <td>17.414754</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.598725</td>\n",
       "      <td>1.281122</td>\n",
       "      <td>0.597082</td>\n",
       "      <td>0.816676</td>\n",
       "      <td>-0.090366</td>\n",
       "      <td>-0.000266</td>\n",
       "      <td>0.296892</td>\n",
       "      <td>0.130717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.064615</td>\n",
       "      <td>5.008762</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.395891</td>\n",
       "      <td>0.359486</td>\n",
       "      <td>0.161125</td>\n",
       "      <td>0.187150</td>\n",
       "      <td>0.346824</td>\n",
       "      <td>0.368469</td>\n",
       "      <td>0.635773</td>\n",
       "      <td>0.865446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.202336</td>\n",
       "      <td>0.202336</td>\n",
       "      <td>-0.434927</td>\n",
       "      <td>-0.434927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.284710</td>\n",
       "      <td>10.634987</td>\n",
       "      <td>1.0</td>\n",
       "      <td>153.647611</td>\n",
       "      <td>0.754599</td>\n",
       "      <td>0.337939</td>\n",
       "      <td>0.409126</td>\n",
       "      <td>0.120050</td>\n",
       "      <td>0.204449</td>\n",
       "      <td>-0.214573</td>\n",
       "      <td>-0.126814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.675558</td>\n",
       "      <td>16.322558</td>\n",
       "      <td>1.0</td>\n",
       "      <td>198.223503</td>\n",
       "      <td>1.162375</td>\n",
       "      <td>0.524994</td>\n",
       "      <td>0.719706</td>\n",
       "      <td>0.043137</td>\n",
       "      <td>-0.063559</td>\n",
       "      <td>-0.200786</td>\n",
       "      <td>-0.102609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.092988</td>\n",
       "      <td>6.070114</td>\n",
       "      <td>1.0</td>\n",
       "      <td>104.809370</td>\n",
       "      <td>0.431250</td>\n",
       "      <td>0.192676</td>\n",
       "      <td>0.222229</td>\n",
       "      <td>0.054940</td>\n",
       "      <td>0.016346</td>\n",
       "      <td>0.102659</td>\n",
       "      <td>-0.034264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160086</td>\n",
       "      <td>0.160086</td>\n",
       "      <td>-0.199056</td>\n",
       "      <td>-0.199056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.054033</td>\n",
       "      <td>4.479045</td>\n",
       "      <td>1.0</td>\n",
       "      <td>88.368786</td>\n",
       "      <td>0.328734</td>\n",
       "      <td>0.150588</td>\n",
       "      <td>0.165923</td>\n",
       "      <td>-0.297042</td>\n",
       "      <td>-0.305001</td>\n",
       "      <td>-0.072469</td>\n",
       "      <td>0.238834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.029337</td>\n",
       "      <td>3.384729</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.531623</td>\n",
       "      <td>0.242227</td>\n",
       "      <td>0.108029</td>\n",
       "      <td>0.121229</td>\n",
       "      <td>0.204837</td>\n",
       "      <td>0.200392</td>\n",
       "      <td>-0.187159</td>\n",
       "      <td>-0.131375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.298026</td>\n",
       "      <td>10.389965</td>\n",
       "      <td>1.0</td>\n",
       "      <td>143.751566</td>\n",
       "      <td>0.772044</td>\n",
       "      <td>0.352782</td>\n",
       "      <td>0.403146</td>\n",
       "      <td>0.189724</td>\n",
       "      <td>0.253101</td>\n",
       "      <td>-0.082265</td>\n",
       "      <td>0.033484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.105008</td>\n",
       "      <td>6.153397</td>\n",
       "      <td>1.0</td>\n",
       "      <td>114.436569</td>\n",
       "      <td>0.458274</td>\n",
       "      <td>0.211844</td>\n",
       "      <td>0.235470</td>\n",
       "      <td>0.109077</td>\n",
       "      <td>0.245433</td>\n",
       "      <td>0.480246</td>\n",
       "      <td>0.272194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.210927</td>\n",
       "      <td>8.973471</td>\n",
       "      <td>1.0</td>\n",
       "      <td>137.218990</td>\n",
       "      <td>0.649503</td>\n",
       "      <td>0.292234</td>\n",
       "      <td>0.344382</td>\n",
       "      <td>0.196464</td>\n",
       "      <td>0.117629</td>\n",
       "      <td>-0.132388</td>\n",
       "      <td>-0.102870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.158718</td>\n",
       "      <td>7.644780</td>\n",
       "      <td>1.0</td>\n",
       "      <td>131.118310</td>\n",
       "      <td>0.563414</td>\n",
       "      <td>0.254905</td>\n",
       "      <td>0.296686</td>\n",
       "      <td>0.067047</td>\n",
       "      <td>-0.109934</td>\n",
       "      <td>0.097776</td>\n",
       "      <td>0.576989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.187632</td>\n",
       "      <td>8.386141</td>\n",
       "      <td>1.0</td>\n",
       "      <td>123.157630</td>\n",
       "      <td>0.612588</td>\n",
       "      <td>0.277683</td>\n",
       "      <td>0.310737</td>\n",
       "      <td>-0.151435</td>\n",
       "      <td>0.136743</td>\n",
       "      <td>0.060241</td>\n",
       "      <td>-0.292946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.223924</td>\n",
       "      <td>9.360889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>144.318097</td>\n",
       "      <td>0.669214</td>\n",
       "      <td>0.301769</td>\n",
       "      <td>0.364169</td>\n",
       "      <td>0.152604</td>\n",
       "      <td>-0.116710</td>\n",
       "      <td>0.107042</td>\n",
       "      <td>-0.042110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.193987</td>\n",
       "      <td>8.742956</td>\n",
       "      <td>1.0</td>\n",
       "      <td>137.219357</td>\n",
       "      <td>0.622876</td>\n",
       "      <td>0.279148</td>\n",
       "      <td>0.331540</td>\n",
       "      <td>0.129072</td>\n",
       "      <td>0.255839</td>\n",
       "      <td>0.003395</td>\n",
       "      <td>0.014641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.097223</td>\n",
       "      <td>6.151494</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113.315077</td>\n",
       "      <td>0.440960</td>\n",
       "      <td>0.198423</td>\n",
       "      <td>0.234526</td>\n",
       "      <td>-0.131105</td>\n",
       "      <td>0.100397</td>\n",
       "      <td>0.711873</td>\n",
       "      <td>0.409838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.465562</td>\n",
       "      <td>13.505689</td>\n",
       "      <td>1.0</td>\n",
       "      <td>172.788971</td>\n",
       "      <td>0.964947</td>\n",
       "      <td>0.428098</td>\n",
       "      <td>0.561693</td>\n",
       "      <td>-0.170487</td>\n",
       "      <td>-0.211725</td>\n",
       "      <td>-0.219191</td>\n",
       "      <td>-0.102034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.309113</td>\n",
       "      <td>10.923952</td>\n",
       "      <td>1.0</td>\n",
       "      <td>151.516323</td>\n",
       "      <td>0.786274</td>\n",
       "      <td>0.353257</td>\n",
       "      <td>0.429385</td>\n",
       "      <td>-0.038043</td>\n",
       "      <td>-0.228792</td>\n",
       "      <td>0.051289</td>\n",
       "      <td>0.281200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.086345</td>\n",
       "      <td>5.654397</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.476298</td>\n",
       "      <td>0.415560</td>\n",
       "      <td>0.188640</td>\n",
       "      <td>0.202754</td>\n",
       "      <td>-0.053864</td>\n",
       "      <td>-0.114075</td>\n",
       "      <td>-0.186954</td>\n",
       "      <td>-0.082680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.128671</td>\n",
       "      <td>7.207820</td>\n",
       "      <td>1.0</td>\n",
       "      <td>125.362146</td>\n",
       "      <td>0.507289</td>\n",
       "      <td>0.226742</td>\n",
       "      <td>0.272925</td>\n",
       "      <td>0.200184</td>\n",
       "      <td>0.021718</td>\n",
       "      <td>0.256439</td>\n",
       "      <td>0.313484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.316377</td>\n",
       "      <td>10.793236</td>\n",
       "      <td>1.0</td>\n",
       "      <td>152.337145</td>\n",
       "      <td>0.795459</td>\n",
       "      <td>0.357927</td>\n",
       "      <td>0.421061</td>\n",
       "      <td>0.126639</td>\n",
       "      <td>-0.098862</td>\n",
       "      <td>-0.298192</td>\n",
       "      <td>-0.337595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.076269</td>\n",
       "      <td>5.337809</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.655876</td>\n",
       "      <td>0.390562</td>\n",
       "      <td>0.179427</td>\n",
       "      <td>0.196084</td>\n",
       "      <td>-0.136450</td>\n",
       "      <td>-0.048303</td>\n",
       "      <td>-0.251379</td>\n",
       "      <td>-0.092769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.256979</td>\n",
       "      <td>10.026108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145.123909</td>\n",
       "      <td>0.716908</td>\n",
       "      <td>0.321470</td>\n",
       "      <td>0.384539</td>\n",
       "      <td>0.105454</td>\n",
       "      <td>0.114024</td>\n",
       "      <td>-0.497704</td>\n",
       "      <td>-0.131668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404257</th>\n",
       "      <td>0.158945</td>\n",
       "      <td>7.711922</td>\n",
       "      <td>1.0</td>\n",
       "      <td>125.262396</td>\n",
       "      <td>0.563818</td>\n",
       "      <td>0.259239</td>\n",
       "      <td>0.289735</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>0.095217</td>\n",
       "      <td>-0.165039</td>\n",
       "      <td>-0.115898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404258</th>\n",
       "      <td>0.080059</td>\n",
       "      <td>5.551499</td>\n",
       "      <td>1.0</td>\n",
       "      <td>104.134608</td>\n",
       "      <td>0.400146</td>\n",
       "      <td>0.180823</td>\n",
       "      <td>0.204272</td>\n",
       "      <td>0.059090</td>\n",
       "      <td>-0.022312</td>\n",
       "      <td>-0.195370</td>\n",
       "      <td>-0.036544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404259</th>\n",
       "      <td>0.225308</td>\n",
       "      <td>9.346665</td>\n",
       "      <td>1.0</td>\n",
       "      <td>137.672973</td>\n",
       "      <td>0.671280</td>\n",
       "      <td>0.300865</td>\n",
       "      <td>0.355118</td>\n",
       "      <td>-0.001943</td>\n",
       "      <td>-0.023409</td>\n",
       "      <td>-0.025664</td>\n",
       "      <td>-0.129468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404260</th>\n",
       "      <td>0.270089</td>\n",
       "      <td>10.002866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>142.150070</td>\n",
       "      <td>0.734968</td>\n",
       "      <td>0.336385</td>\n",
       "      <td>0.388987</td>\n",
       "      <td>0.171006</td>\n",
       "      <td>-0.138546</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>-0.080651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404261</th>\n",
       "      <td>0.416854</td>\n",
       "      <td>12.576248</td>\n",
       "      <td>1.0</td>\n",
       "      <td>174.431498</td>\n",
       "      <td>0.913076</td>\n",
       "      <td>0.413361</td>\n",
       "      <td>0.531749</td>\n",
       "      <td>0.006301</td>\n",
       "      <td>-0.204710</td>\n",
       "      <td>0.228143</td>\n",
       "      <td>0.395708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404262</th>\n",
       "      <td>0.192592</td>\n",
       "      <td>8.468500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>135.843876</td>\n",
       "      <td>0.620632</td>\n",
       "      <td>0.283367</td>\n",
       "      <td>0.326147</td>\n",
       "      <td>-0.062982</td>\n",
       "      <td>0.012422</td>\n",
       "      <td>-0.058711</td>\n",
       "      <td>0.193919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404263</th>\n",
       "      <td>0.343386</td>\n",
       "      <td>11.654106</td>\n",
       "      <td>1.0</td>\n",
       "      <td>158.792365</td>\n",
       "      <td>0.828718</td>\n",
       "      <td>0.369664</td>\n",
       "      <td>0.456487</td>\n",
       "      <td>-0.089334</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.116267</td>\n",
       "      <td>-0.447346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404264</th>\n",
       "      <td>0.123028</td>\n",
       "      <td>6.980647</td>\n",
       "      <td>1.0</td>\n",
       "      <td>116.029275</td>\n",
       "      <td>0.496041</td>\n",
       "      <td>0.220784</td>\n",
       "      <td>0.257523</td>\n",
       "      <td>0.029914</td>\n",
       "      <td>0.039564</td>\n",
       "      <td>-0.294638</td>\n",
       "      <td>-0.309428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404265</th>\n",
       "      <td>0.480310</td>\n",
       "      <td>13.202722</td>\n",
       "      <td>1.0</td>\n",
       "      <td>165.569256</td>\n",
       "      <td>0.980112</td>\n",
       "      <td>0.451850</td>\n",
       "      <td>0.534788</td>\n",
       "      <td>0.374003</td>\n",
       "      <td>0.169240</td>\n",
       "      <td>-0.260147</td>\n",
       "      <td>0.050189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404266</th>\n",
       "      <td>0.164914</td>\n",
       "      <td>7.970375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>129.292760</td>\n",
       "      <td>0.574306</td>\n",
       "      <td>0.259188</td>\n",
       "      <td>0.301619</td>\n",
       "      <td>0.088116</td>\n",
       "      <td>0.170407</td>\n",
       "      <td>0.262186</td>\n",
       "      <td>0.046332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404267</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.195640</td>\n",
       "      <td>0.195640</td>\n",
       "      <td>0.121118</td>\n",
       "      <td>0.121118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404268</th>\n",
       "      <td>0.691732</td>\n",
       "      <td>16.242168</td>\n",
       "      <td>1.0</td>\n",
       "      <td>190.879315</td>\n",
       "      <td>1.176207</td>\n",
       "      <td>0.535687</td>\n",
       "      <td>0.730485</td>\n",
       "      <td>0.026037</td>\n",
       "      <td>-0.113297</td>\n",
       "      <td>-0.211788</td>\n",
       "      <td>-0.267157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404269</th>\n",
       "      <td>0.321311</td>\n",
       "      <td>11.235633</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154.039801</td>\n",
       "      <td>0.801637</td>\n",
       "      <td>0.358470</td>\n",
       "      <td>0.437148</td>\n",
       "      <td>0.008160</td>\n",
       "      <td>-0.089957</td>\n",
       "      <td>-0.117874</td>\n",
       "      <td>0.037253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404270</th>\n",
       "      <td>0.154369</td>\n",
       "      <td>7.860719</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.897970</td>\n",
       "      <td>0.555641</td>\n",
       "      <td>0.247978</td>\n",
       "      <td>0.299484</td>\n",
       "      <td>0.037436</td>\n",
       "      <td>0.031145</td>\n",
       "      <td>0.171073</td>\n",
       "      <td>0.154678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404271</th>\n",
       "      <td>0.281678</td>\n",
       "      <td>10.252761</td>\n",
       "      <td>1.0</td>\n",
       "      <td>148.717137</td>\n",
       "      <td>0.750571</td>\n",
       "      <td>0.348311</td>\n",
       "      <td>0.401471</td>\n",
       "      <td>-0.027998</td>\n",
       "      <td>-0.010656</td>\n",
       "      <td>0.027564</td>\n",
       "      <td>-0.008220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404272</th>\n",
       "      <td>0.365381</td>\n",
       "      <td>11.785725</td>\n",
       "      <td>1.0</td>\n",
       "      <td>161.675633</td>\n",
       "      <td>0.854846</td>\n",
       "      <td>0.388475</td>\n",
       "      <td>0.467679</td>\n",
       "      <td>-0.044142</td>\n",
       "      <td>0.081973</td>\n",
       "      <td>-0.247146</td>\n",
       "      <td>-0.271420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404273</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404274</th>\n",
       "      <td>0.611724</td>\n",
       "      <td>14.991529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>183.377998</td>\n",
       "      <td>1.106096</td>\n",
       "      <td>0.504446</td>\n",
       "      <td>0.655933</td>\n",
       "      <td>-0.158305</td>\n",
       "      <td>0.052832</td>\n",
       "      <td>0.039618</td>\n",
       "      <td>0.207738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404275</th>\n",
       "      <td>0.508726</td>\n",
       "      <td>13.813482</td>\n",
       "      <td>1.0</td>\n",
       "      <td>174.829687</td>\n",
       "      <td>1.008689</td>\n",
       "      <td>0.454038</td>\n",
       "      <td>0.572895</td>\n",
       "      <td>0.124765</td>\n",
       "      <td>-0.050016</td>\n",
       "      <td>-0.508909</td>\n",
       "      <td>-0.228341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404276</th>\n",
       "      <td>0.407599</td>\n",
       "      <td>12.647399</td>\n",
       "      <td>1.0</td>\n",
       "      <td>164.995411</td>\n",
       "      <td>0.902883</td>\n",
       "      <td>0.404728</td>\n",
       "      <td>0.500150</td>\n",
       "      <td>-0.046565</td>\n",
       "      <td>0.037655</td>\n",
       "      <td>-0.400729</td>\n",
       "      <td>-0.504796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404277</th>\n",
       "      <td>0.043866</td>\n",
       "      <td>4.044295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.217952</td>\n",
       "      <td>0.296197</td>\n",
       "      <td>0.133592</td>\n",
       "      <td>0.146502</td>\n",
       "      <td>0.113681</td>\n",
       "      <td>0.091682</td>\n",
       "      <td>-0.190559</td>\n",
       "      <td>-0.213306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404278</th>\n",
       "      <td>0.179617</td>\n",
       "      <td>8.331388</td>\n",
       "      <td>1.0</td>\n",
       "      <td>133.898489</td>\n",
       "      <td>0.599361</td>\n",
       "      <td>0.270188</td>\n",
       "      <td>0.318705</td>\n",
       "      <td>-0.223437</td>\n",
       "      <td>-0.140051</td>\n",
       "      <td>0.511802</td>\n",
       "      <td>0.155941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404279</th>\n",
       "      <td>0.152650</td>\n",
       "      <td>7.663510</td>\n",
       "      <td>1.0</td>\n",
       "      <td>123.035775</td>\n",
       "      <td>0.552540</td>\n",
       "      <td>0.249904</td>\n",
       "      <td>0.285544</td>\n",
       "      <td>0.067102</td>\n",
       "      <td>0.216397</td>\n",
       "      <td>-0.278108</td>\n",
       "      <td>0.091400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404280</th>\n",
       "      <td>0.214045</td>\n",
       "      <td>8.867896</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136.033166</td>\n",
       "      <td>0.654286</td>\n",
       "      <td>0.300878</td>\n",
       "      <td>0.343557</td>\n",
       "      <td>-0.105729</td>\n",
       "      <td>0.066624</td>\n",
       "      <td>0.146120</td>\n",
       "      <td>0.224053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404281</th>\n",
       "      <td>0.082504</td>\n",
       "      <td>5.685983</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.736977</td>\n",
       "      <td>0.406211</td>\n",
       "      <td>0.181447</td>\n",
       "      <td>0.208261</td>\n",
       "      <td>-0.115073</td>\n",
       "      <td>-0.031171</td>\n",
       "      <td>-0.349487</td>\n",
       "      <td>-0.315944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404282</th>\n",
       "      <td>0.131646</td>\n",
       "      <td>7.122625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>118.479153</td>\n",
       "      <td>0.513120</td>\n",
       "      <td>0.231429</td>\n",
       "      <td>0.262434</td>\n",
       "      <td>0.014864</td>\n",
       "      <td>0.134802</td>\n",
       "      <td>-0.084945</td>\n",
       "      <td>-0.477739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404283</th>\n",
       "      <td>0.129039</td>\n",
       "      <td>7.146889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>119.485637</td>\n",
       "      <td>0.508014</td>\n",
       "      <td>0.226945</td>\n",
       "      <td>0.263738</td>\n",
       "      <td>-0.002918</td>\n",
       "      <td>0.038642</td>\n",
       "      <td>-0.147208</td>\n",
       "      <td>-0.260159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404284</th>\n",
       "      <td>0.069016</td>\n",
       "      <td>5.065351</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.936365</td>\n",
       "      <td>0.371527</td>\n",
       "      <td>0.170819</td>\n",
       "      <td>0.182876</td>\n",
       "      <td>-0.193922</td>\n",
       "      <td>-0.147340</td>\n",
       "      <td>-0.279527</td>\n",
       "      <td>-0.397618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404285</th>\n",
       "      <td>0.708233</td>\n",
       "      <td>16.438430</td>\n",
       "      <td>1.0</td>\n",
       "      <td>194.867266</td>\n",
       "      <td>1.190154</td>\n",
       "      <td>0.540202</td>\n",
       "      <td>0.737343</td>\n",
       "      <td>-0.018571</td>\n",
       "      <td>-0.155503</td>\n",
       "      <td>-0.382487</td>\n",
       "      <td>-0.162977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404286</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.188143</td>\n",
       "      <td>0.188143</td>\n",
       "      <td>0.487497</td>\n",
       "      <td>0.487497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>404287 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cosine_distance  cityblock_distance  jaccard_distance  \\\n",
       "0              0.068972            5.081614               1.0   \n",
       "1              0.512164           14.195119               1.0   \n",
       "2              0.222009            9.055989               1.0   \n",
       "3              0.650411           15.987437               1.0   \n",
       "4              0.369993           12.103178               1.0   \n",
       "5              0.275242           10.254048               1.0   \n",
       "6              0.820637           17.414754               1.0   \n",
       "7              0.064615            5.008762               1.0   \n",
       "8              0.000000            0.000000               0.0   \n",
       "9              0.284710           10.634987               1.0   \n",
       "10             0.675558           16.322558               1.0   \n",
       "11             0.092988            6.070114               1.0   \n",
       "12             0.000000            0.000000               0.0   \n",
       "13             0.054033            4.479045               1.0   \n",
       "14             0.029337            3.384729               1.0   \n",
       "15             0.298026           10.389965               1.0   \n",
       "16             0.105008            6.153397               1.0   \n",
       "17             0.210927            8.973471               1.0   \n",
       "18             0.158718            7.644780               1.0   \n",
       "19             0.187632            8.386141               1.0   \n",
       "20             0.223924            9.360889               1.0   \n",
       "21             0.193987            8.742956               1.0   \n",
       "22             0.097223            6.151494               1.0   \n",
       "23             0.465562           13.505689               1.0   \n",
       "24             0.309113           10.923952               1.0   \n",
       "25             0.086345            5.654397               1.0   \n",
       "26             0.128671            7.207820               1.0   \n",
       "27             0.316377           10.793236               1.0   \n",
       "28             0.076269            5.337809               1.0   \n",
       "29             0.256979           10.026108               1.0   \n",
       "...                 ...                 ...               ...   \n",
       "404257         0.158945            7.711922               1.0   \n",
       "404258         0.080059            5.551499               1.0   \n",
       "404259         0.225308            9.346665               1.0   \n",
       "404260         0.270089           10.002866               1.0   \n",
       "404261         0.416854           12.576248               1.0   \n",
       "404262         0.192592            8.468500               1.0   \n",
       "404263         0.343386           11.654106               1.0   \n",
       "404264         0.123028            6.980647               1.0   \n",
       "404265         0.480310           13.202722               1.0   \n",
       "404266         0.164914            7.970375               1.0   \n",
       "404267         0.000000            0.000000               0.0   \n",
       "404268         0.691732           16.242168               1.0   \n",
       "404269         0.321311           11.235633               1.0   \n",
       "404270         0.154369            7.860719               1.0   \n",
       "404271         0.281678           10.252761               1.0   \n",
       "404272         0.365381           11.785725               1.0   \n",
       "404273              NaN            0.000000               NaN   \n",
       "404274         0.611724           14.991529               1.0   \n",
       "404275         0.508726           13.813482               1.0   \n",
       "404276         0.407599           12.647399               1.0   \n",
       "404277         0.043866            4.044295               1.0   \n",
       "404278         0.179617            8.331388               1.0   \n",
       "404279         0.152650            7.663510               1.0   \n",
       "404280         0.214045            8.867896               1.0   \n",
       "404281         0.082504            5.685983               1.0   \n",
       "404282         0.131646            7.122625               1.0   \n",
       "404283         0.129039            7.146889               1.0   \n",
       "404284         0.069016            5.065351               1.0   \n",
       "404285         0.708233           16.438430               1.0   \n",
       "404286         0.000000            0.000000               0.0   \n",
       "\n",
       "        canberra_distance  euclidean_distance  minkowski_distance  \\\n",
       "0               94.023324            0.371408            0.168999   \n",
       "1              177.588090            1.012091            0.455910   \n",
       "2              135.988707            0.666346            0.307828   \n",
       "3              192.237828            1.140536            0.506028   \n",
       "4              161.408435            0.860225            0.382770   \n",
       "5              150.335589            0.741946            0.337470   \n",
       "6              200.598725            1.281122            0.597082   \n",
       "7               95.395891            0.359486            0.161125   \n",
       "8                0.000000            0.000000            0.000000   \n",
       "9              153.647611            0.754599            0.337939   \n",
       "10             198.223503            1.162375            0.524994   \n",
       "11             104.809370            0.431250            0.192676   \n",
       "12               0.000000            0.000000            0.000000   \n",
       "13              88.368786            0.328734            0.150588   \n",
       "14              71.531623            0.242227            0.108029   \n",
       "15             143.751566            0.772044            0.352782   \n",
       "16             114.436569            0.458274            0.211844   \n",
       "17             137.218990            0.649503            0.292234   \n",
       "18             131.118310            0.563414            0.254905   \n",
       "19             123.157630            0.612588            0.277683   \n",
       "20             144.318097            0.669214            0.301769   \n",
       "21             137.219357            0.622876            0.279148   \n",
       "22             113.315077            0.440960            0.198423   \n",
       "23             172.788971            0.964947            0.428098   \n",
       "24             151.516323            0.786274            0.353257   \n",
       "25              96.476298            0.415560            0.188640   \n",
       "26             125.362146            0.507289            0.226742   \n",
       "27             152.337145            0.795459            0.357927   \n",
       "28             100.655876            0.390562            0.179427   \n",
       "29             145.123909            0.716908            0.321470   \n",
       "...                   ...                 ...                 ...   \n",
       "404257         125.262396            0.563818            0.259239   \n",
       "404258         104.134608            0.400146            0.180823   \n",
       "404259         137.672973            0.671280            0.300865   \n",
       "404260         142.150070            0.734968            0.336385   \n",
       "404261         174.431498            0.913076            0.413361   \n",
       "404262         135.843876            0.620632            0.283367   \n",
       "404263         158.792365            0.828718            0.369664   \n",
       "404264         116.029275            0.496041            0.220784   \n",
       "404265         165.569256            0.980112            0.451850   \n",
       "404266         129.292760            0.574306            0.259188   \n",
       "404267           0.000000            0.000000            0.000000   \n",
       "404268         190.879315            1.176207            0.535687   \n",
       "404269         154.039801            0.801637            0.358470   \n",
       "404270         132.897970            0.555641            0.247978   \n",
       "404271         148.717137            0.750571            0.348311   \n",
       "404272         161.675633            0.854846            0.388475   \n",
       "404273           0.000000            0.000000            0.000000   \n",
       "404274         183.377998            1.106096            0.504446   \n",
       "404275         174.829687            1.008689            0.454038   \n",
       "404276         164.995411            0.902883            0.404728   \n",
       "404277          80.217952            0.296197            0.133592   \n",
       "404278         133.898489            0.599361            0.270188   \n",
       "404279         123.035775            0.552540            0.249904   \n",
       "404280         136.033166            0.654286            0.300878   \n",
       "404281         101.736977            0.406211            0.181447   \n",
       "404282         118.479153            0.513120            0.231429   \n",
       "404283         119.485637            0.508014            0.226945   \n",
       "404284          91.936365            0.371527            0.170819   \n",
       "404285         194.867266            1.190154            0.540202   \n",
       "404286           0.000000            0.000000            0.000000   \n",
       "\n",
       "        braycurtis_distance  skew_q1vec  skew_q2vec  kur_q1vec  kur_q2vec  \n",
       "0                  0.186557    0.031817   -0.091902   0.050416   0.337301  \n",
       "1                  0.592655    0.008735    0.094704   0.284010  -0.034444  \n",
       "2                  0.342306    0.239752    0.144554   0.026759  -0.474131  \n",
       "3                  0.692421   -0.002527    0.069649  -0.244560  -0.265568  \n",
       "4                  0.480633   -0.133849    0.114777   0.217900  -0.338876  \n",
       "5                  0.400061    0.102946   -0.077741  -0.335505  -0.057847  \n",
       "6                  0.816676   -0.090366   -0.000266   0.296892   0.130717  \n",
       "7                  0.187150    0.346824    0.368469   0.635773   0.865446  \n",
       "8                  0.000000    0.202336    0.202336  -0.434927  -0.434927  \n",
       "9                  0.409126    0.120050    0.204449  -0.214573  -0.126814  \n",
       "10                 0.719706    0.043137   -0.063559  -0.200786  -0.102609  \n",
       "11                 0.222229    0.054940    0.016346   0.102659  -0.034264  \n",
       "12                 0.000000    0.160086    0.160086  -0.199056  -0.199056  \n",
       "13                 0.165923   -0.297042   -0.305001  -0.072469   0.238834  \n",
       "14                 0.121229    0.204837    0.200392  -0.187159  -0.131375  \n",
       "15                 0.403146    0.189724    0.253101  -0.082265   0.033484  \n",
       "16                 0.235470    0.109077    0.245433   0.480246   0.272194  \n",
       "17                 0.344382    0.196464    0.117629  -0.132388  -0.102870  \n",
       "18                 0.296686    0.067047   -0.109934   0.097776   0.576989  \n",
       "19                 0.310737   -0.151435    0.136743   0.060241  -0.292946  \n",
       "20                 0.364169    0.152604   -0.116710   0.107042  -0.042110  \n",
       "21                 0.331540    0.129072    0.255839   0.003395   0.014641  \n",
       "22                 0.234526   -0.131105    0.100397   0.711873   0.409838  \n",
       "23                 0.561693   -0.170487   -0.211725  -0.219191  -0.102034  \n",
       "24                 0.429385   -0.038043   -0.228792   0.051289   0.281200  \n",
       "25                 0.202754   -0.053864   -0.114075  -0.186954  -0.082680  \n",
       "26                 0.272925    0.200184    0.021718   0.256439   0.313484  \n",
       "27                 0.421061    0.126639   -0.098862  -0.298192  -0.337595  \n",
       "28                 0.196084   -0.136450   -0.048303  -0.251379  -0.092769  \n",
       "29                 0.384539    0.105454    0.114024  -0.497704  -0.131668  \n",
       "...                     ...         ...         ...        ...        ...  \n",
       "404257             0.289735    0.000344    0.095217  -0.165039  -0.115898  \n",
       "404258             0.204272    0.059090   -0.022312  -0.195370  -0.036544  \n",
       "404259             0.355118   -0.001943   -0.023409  -0.025664  -0.129468  \n",
       "404260             0.388987    0.171006   -0.138546   0.000161  -0.080651  \n",
       "404261             0.531749    0.006301   -0.204710   0.228143   0.395708  \n",
       "404262             0.326147   -0.062982    0.012422  -0.058711   0.193919  \n",
       "404263             0.456487   -0.089334    0.191953   0.116267  -0.447346  \n",
       "404264             0.257523    0.029914    0.039564  -0.294638  -0.309428  \n",
       "404265             0.534788    0.374003    0.169240  -0.260147   0.050189  \n",
       "404266             0.301619    0.088116    0.170407   0.262186   0.046332  \n",
       "404267             0.000000    0.195640    0.195640   0.121118   0.121118  \n",
       "404268             0.730485    0.026037   -0.113297  -0.211788  -0.267157  \n",
       "404269             0.437148    0.008160   -0.089957  -0.117874   0.037253  \n",
       "404270             0.299484    0.037436    0.031145   0.171073   0.154678  \n",
       "404271             0.401471   -0.027998   -0.010656   0.027564  -0.008220  \n",
       "404272             0.467679   -0.044142    0.081973  -0.247146  -0.271420  \n",
       "404273                  NaN    0.000000    0.000000  -3.000000  -3.000000  \n",
       "404274             0.655933   -0.158305    0.052832   0.039618   0.207738  \n",
       "404275             0.572895    0.124765   -0.050016  -0.508909  -0.228341  \n",
       "404276             0.500150   -0.046565    0.037655  -0.400729  -0.504796  \n",
       "404277             0.146502    0.113681    0.091682  -0.190559  -0.213306  \n",
       "404278             0.318705   -0.223437   -0.140051   0.511802   0.155941  \n",
       "404279             0.285544    0.067102    0.216397  -0.278108   0.091400  \n",
       "404280             0.343557   -0.105729    0.066624   0.146120   0.224053  \n",
       "404281             0.208261   -0.115073   -0.031171  -0.349487  -0.315944  \n",
       "404282             0.262434    0.014864    0.134802  -0.084945  -0.477739  \n",
       "404283             0.263738   -0.002918    0.038642  -0.147208  -0.260159  \n",
       "404284             0.182876   -0.193922   -0.147340  -0.279527  -0.397618  \n",
       "404285             0.737343   -0.018571   -0.155503  -0.382487  -0.162977  \n",
       "404286             0.000000    0.188143    0.188143   0.487497   0.487497  \n",
       "\n",
       "[404287 rows x 11 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_distance = df[['cosine_distance','cityblock_distance','jaccard_distance',\n",
    "                'canberra_distance','euclidean_distance','minkowski_distance','braycurtis_distance',\n",
    "                 'skew_q1vec','skew_q2vec','kur_q1vec','kur_q2vec']]\n",
    "fs_distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## word2vec\n",
    "一般来说，Word2vec模型是两层神经网络，它将文本语料库作为输入并输出该语料库中每个单词的向量。经拟合后，具有相似含义的词的向量彼此接近，也就是说，词义相近的单词之间的距离要比那些词义不同的词之间的距离要小。\n",
    "\n",
    "如今，Word2vec已成为自然语言处理问题的标准，并且通常它为信息检索任务提供了非常有用的帮助。我们将使用Google新闻媒介。这是在Google新闻语料库中的预训练Word2vec模型。你可以在这里下载这个预训练的语料库(https://pan.baidu.com/s/1UNE1W60JUN6HuaRMvwNMHg 提取码：ya5b )。 如果你使用来自Google新闻语料库的预训练向量，则其中的所有单词（如“德国”，“柏林”，“法国”和“巴黎”）都可以用300维向量表示。当我们对这些单词使用Word2vec表示时，我们从“柏林”的向量中减去“德国”的向量再加上“法国”的向量，我们将得到一个与“巴黎”的向量非常相似的向量,即:\n",
    "\n",
    "V(\"柏林\") - V(“德国”) + V(\"法国\") ≈ V(\"巴黎\")\n",
    "\n",
    "如过想对Word2vec进一步了解，阅读<a href=\"https://www.distilled.net/word2vec-examples/\">这个</a>和<a href=\"http://www.1-4-5.net/~dmm/ml/how_does_word2vec_work.pdf\">这个</a>  \n",
    "因此，Word2vec模型承载了向量中单词的含义。这些载体所携带的信息对我们的任务构成了非常有用的特征。要加载Word2vec向量，我们要使用Gensim："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WMD（Word Mover's Distance）\n",
    "<a href=\"http://proceedings.mlr.press/v37/kusnerb15.pdf\">WMD</a>是一种评估两个文档之间的“距离”的方法，它可以用于检索内容相似的问题,比如对应某些博客的推荐系统中,可以给用户推荐与用户当前所看的博客内容相似的其他博客,这时就用的WMD技术。如果你想进一步了解如何使用WMD，那么可以阅读这个文档。下面我们先定义一个测量文档之间距离的函数:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wmd(q1, q2):\n",
    "    q1 = str(q1).lower().split()\n",
    "    q2 = str(q2).lower().split()\n",
    "    stop_words = stopwords.words('english')\n",
    "    q1 = [w for w in q1 if w not in stop_words]\n",
    "    q2 = [w for w in q2 if w not in stop_words]\n",
    "    return model.wmdistance(q1, q2)\n",
    "\n",
    "def norm_wmd(q1, q2):\n",
    "    q1 = str(q1).lower().split()\n",
    "    q2 = str(q2).lower().split()\n",
    "    stop_words = stopwords.words('english')\n",
    "    q1 = [w for w in q1 if w not in stop_words]\n",
    "    q2 = [w for w in q2 if w not in stop_words]\n",
    "    return norm_model.wmdistance(q1, q2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参数norm表示是否需要对word2vec向量进行标准化处理(归一化)。那么我们将对question1和question2的word2vec向量进行两种方式处理即,不使用归一化和使用归一化。最后为了便于以后使用，我们将WMD的特征集记为fs_wmd:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = gensim.models.KeyedVectors.load_word2vec_format('./data/GoogleNews-vectors-negative300.bin.gz', binary=True)\n",
    "df['wmd'] = df.apply(lambda x: wmd(x['question1'], x['question2']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_model = gensim.models.KeyedVectors.load_word2vec_format('./data/GoogleNews-vectors-negative300.bin.gz', binary=True)\n",
    "norm_model.init_sims(replace=True)\n",
    "df['norm_wmd'] = df.apply(lambda x: norm_wmd(x['question1'], x['question2']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wmd</th>\n",
       "      <th>norm_wmd</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.564615</td>\n",
       "      <td>0.217555</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.772346</td>\n",
       "      <td>1.368796</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.639209</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.741994</td>\n",
       "      <td>1.263719</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.659165</td>\n",
       "      <td>1.240908</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.357449</td>\n",
       "      <td>0.780579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.546062</td>\n",
       "      <td>1.359521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.691323</td>\n",
       "      <td>0.736001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.258243</td>\n",
       "      <td>0.459266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.597312</td>\n",
       "      <td>1.271010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.721525</td>\n",
       "      <td>0.711170</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.195619</td>\n",
       "      <td>0.450777</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.829343</td>\n",
       "      <td>0.758924</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.938616</td>\n",
       "      <td>1.112131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.868051</td>\n",
       "      <td>0.751423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.692476</td>\n",
       "      <td>0.264229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.501525</td>\n",
       "      <td>0.860271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.649201</td>\n",
       "      <td>1.264833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.979273</td>\n",
       "      <td>0.349149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.038652</td>\n",
       "      <td>1.328771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.736491</td>\n",
       "      <td>1.209422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.603357</td>\n",
       "      <td>0.224160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.557144</td>\n",
       "      <td>0.604047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.355824</td>\n",
       "      <td>1.318154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.635397</td>\n",
       "      <td>0.286813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.557522</td>\n",
       "      <td>0.629641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>404257</th>\n",
       "      <td>1.133563</td>\n",
       "      <td>0.445024</td>\n",
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       "    <tr>\n",
       "      <th>404258</th>\n",
       "      <td>0.852982</td>\n",
       "      <td>0.342855</td>\n",
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       "    <tr>\n",
       "      <th>404259</th>\n",
       "      <td>2.226559</td>\n",
       "      <td>0.627186</td>\n",
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       "    <tr>\n",
       "      <th>404260</th>\n",
       "      <td>2.352375</td>\n",
       "      <td>0.740148</td>\n",
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       "    <tr>\n",
       "      <th>404261</th>\n",
       "      <td>2.738930</td>\n",
       "      <td>0.939160</td>\n",
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       "    <tr>\n",
       "      <th>404262</th>\n",
       "      <td>1.842916</td>\n",
       "      <td>0.618375</td>\n",
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       "    <tr>\n",
       "      <th>404263</th>\n",
       "      <td>3.022479</td>\n",
       "      <td>1.118414</td>\n",
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       "    <tr>\n",
       "      <th>404264</th>\n",
       "      <td>1.471374</td>\n",
       "      <td>0.533855</td>\n",
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       "    <tr>\n",
       "      <th>404265</th>\n",
       "      <td>3.002797</td>\n",
       "      <td>1.277923</td>\n",
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       "    <tr>\n",
       "      <th>404266</th>\n",
       "      <td>1.925015</td>\n",
       "      <td>0.668625</td>\n",
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       "    <tr>\n",
       "      <th>404267</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>404268</th>\n",
       "      <td>3.262711</td>\n",
       "      <td>1.254767</td>\n",
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       "    <tr>\n",
       "      <th>404269</th>\n",
       "      <td>2.203434</td>\n",
       "      <td>0.850759</td>\n",
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       "    <tr>\n",
       "      <th>404270</th>\n",
       "      <td>1.999002</td>\n",
       "      <td>0.760858</td>\n",
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       "    <tr>\n",
       "      <th>404271</th>\n",
       "      <td>1.838207</td>\n",
       "      <td>0.713652</td>\n",
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       "    <tr>\n",
       "      <th>404272</th>\n",
       "      <td>3.690159</td>\n",
       "      <td>1.175376</td>\n",
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       "    <tr>\n",
       "      <th>404273</th>\n",
       "      <td>inf</td>\n",
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       "    <tr>\n",
       "      <th>404274</th>\n",
       "      <td>3.889464</td>\n",
       "      <td>1.186220</td>\n",
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       "    <tr>\n",
       "      <th>404275</th>\n",
       "      <td>2.806163</td>\n",
       "      <td>0.934291</td>\n",
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       "    <tr>\n",
       "      <th>404276</th>\n",
       "      <td>2.195275</td>\n",
       "      <td>0.843901</td>\n",
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       "    <tr>\n",
       "      <th>404277</th>\n",
       "      <td>0.781488</td>\n",
       "      <td>0.254498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404278</th>\n",
       "      <td>1.929065</td>\n",
       "      <td>0.588564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404279</th>\n",
       "      <td>0.644241</td>\n",
       "      <td>0.203484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404280</th>\n",
       "      <td>3.112035</td>\n",
       "      <td>1.018480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404281</th>\n",
       "      <td>2.050545</td>\n",
       "      <td>0.662293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404282</th>\n",
       "      <td>0.904016</td>\n",
       "      <td>0.227233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404283</th>\n",
       "      <td>1.306167</td>\n",
       "      <td>0.590033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404284</th>\n",
       "      <td>3.656829</td>\n",
       "      <td>1.382975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404285</th>\n",
       "      <td>3.575980</td>\n",
       "      <td>1.313263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404286</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>404287 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             wmd  norm_wmd\n",
       "0       0.564615  0.217555\n",
       "1       3.772346  1.368796\n",
       "2       1.780585  0.639209\n",
       "3       3.741994  1.263719\n",
       "4       3.659165  1.240908\n",
       "5       2.357449  0.780579\n",
       "6       3.546062  1.359521\n",
       "7       1.691323  0.736001\n",
       "8       0.000000  0.000000\n",
       "9       1.258243  0.459266\n",
       "10      3.597312  1.271010\n",
       "11      1.721525  0.711170\n",
       "12      0.000000  0.000000\n",
       "13      1.195619  0.450777\n",
       "14      0.000000  0.000000\n",
       "15      1.829343  0.758924\n",
       "16      0.000000  0.000000\n",
       "17      2.938616  1.112131\n",
       "18      1.868051  0.751423\n",
       "19      0.692476  0.264229\n",
       "20      2.501525  0.860271\n",
       "21      3.649201  1.264833\n",
       "22      0.979273  0.349149\n",
       "23      4.038652  1.328771\n",
       "24      2.736491  1.209422\n",
       "25      0.603357  0.224160\n",
       "26      1.557144  0.604047\n",
       "27      3.355824  1.318154\n",
       "28      0.635397  0.286813\n",
       "29      1.557522  0.629641\n",
       "...          ...       ...\n",
       "404257  1.133563  0.445024\n",
       "404258  0.852982  0.342855\n",
       "404259  2.226559  0.627186\n",
       "404260  2.352375  0.740148\n",
       "404261  2.738930  0.939160\n",
       "404262  1.842916  0.618375\n",
       "404263  3.022479  1.118414\n",
       "404264  1.471374  0.533855\n",
       "404265  3.002797  1.277923\n",
       "404266  1.925015  0.668625\n",
       "404267  0.000000  0.000000\n",
       "404268  3.262711  1.254767\n",
       "404269  2.203434  0.850759\n",
       "404270  1.999002  0.760858\n",
       "404271  1.838207  0.713652\n",
       "404272  3.690159  1.175376\n",
       "404273       inf       inf\n",
       "404274  3.889464  1.186220\n",
       "404275  2.806163  0.934291\n",
       "404276  2.195275  0.843901\n",
       "404277  0.781488  0.254498\n",
       "404278  1.929065  0.588564\n",
       "404279  0.644241  0.203484\n",
       "404280  3.112035  1.018480\n",
       "404281  2.050545  0.662293\n",
       "404282  0.904016  0.227233\n",
       "404283  1.306167  0.590033\n",
       "404284  3.656829  1.382975\n",
       "404285  3.575980  1.313263\n",
       "404286  0.000000  0.000000\n",
       "\n",
       "[404287 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs_wmd=df[['wmd','norm_wmd']]\n",
    "fs_wmd"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结\n",
    "到目前为止我们已经为我们的“奶牛”准备了以下几种“饲料”:\n",
    "\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "\n",
    "总共有7种类的特征向量,9个特征向量集。接下来我们要将这些特征向量集进行各种组合，然后将他们“喂”给我们的算法模型(XGBoost或者逻辑回归)，并评估我们的模型在“吃”了这些“饲料”后的表现会如何?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix  \n",
    "from sklearn.metrics import accuracy_score\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fs_basic "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = fs_basic\n",
    "y = df.loc[:, df.columns == 'is_duplicate']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[58701 17946]\n",
      " [15449 29191]]\n",
      "Accuracy 0.7246613404569328\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.77      0.78     76647\n",
      "           1       0.62      0.65      0.64     44640\n",
      "\n",
      "   micro avg       0.72      0.72      0.72    121287\n",
      "   macro avg       0.71      0.71      0.71    121287\n",
      "weighted avg       0.73      0.72      0.73    121287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = xgb.XGBClassifier(max_depth=50, n_estimators=80, learning_rate=0.1, \n",
    "                          colsample_bytree=.7, gamma=0, reg_alpha=4, objective='binary:logistic', \n",
    "                          eta=0.3, silent=1, subsample=0.8).fit(X_train, y_train.values.ravel()) \n",
    "prediction = model.predict(X_test)\n",
    "cm = confusion_matrix(y_test, prediction)  \n",
    "print(cm)  \n",
    "print('Accuracy', accuracy_score(y_test, prediction))\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fs_basic + fs_fuzz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[60456 16191]\n",
      " [14952 29688]]\n",
      "Accuracy 0.7432288703653318\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.79      0.80     76647\n",
      "           1       0.65      0.67      0.66     44640\n",
      "\n",
      "   micro avg       0.74      0.74      0.74    121287\n",
      "   macro avg       0.72      0.73      0.73    121287\n",
      "weighted avg       0.74      0.74      0.74    121287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = np.hstack((fs_basic, fs_fuzz))\n",
    "y = df.loc[:, df.columns == 'is_duplicate']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "model = xgb.XGBClassifier(max_depth=50, n_estimators=80, learning_rate=0.1, \n",
    "                          colsample_bytree=.7, gamma=0, reg_alpha=4, objective='binary:logistic', \n",
    "                          eta=0.3, silent=1, subsample=0.8).fit(X_train, y_train.values.ravel()) \n",
    "prediction = model.predict(X_test)\n",
    "cm = confusion_matrix(y_test, prediction)  \n",
    "print(cm)  \n",
    "print('Accuracy', accuracy_score(y_test, prediction))\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fs_basic + fs_fuzz + fs_distance+ fs_wmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[61696 14951]\n",
      " [12166 32474]]\n",
      "Accuracy 0.7764228647752851\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.80      0.82     76647\n",
      "           1       0.68      0.73      0.71     44640\n",
      "\n",
      "   micro avg       0.78      0.78      0.78    121287\n",
      "   macro avg       0.76      0.77      0.76    121287\n",
      "weighted avg       0.78      0.78      0.78    121287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = np.hstack((fs_basic, fs_fuzz,fs_distance,fs_wmd))\n",
    "y = df.loc[:, df.columns == 'is_duplicate']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "model = xgb.XGBClassifier(max_depth=50, n_estimators=80, learning_rate=0.1, \n",
    "                          colsample_bytree=.7, gamma=0, reg_alpha=4, objective='binary:logistic', \n",
    "                          eta=0.3, silent=1, subsample=0.8).fit(X_train, y_train.values.ravel()) \n",
    "prediction = model.predict(X_test)\n",
    "cm = confusion_matrix(y_test, prediction)  \n",
    "print(cm)  \n",
    "print('Accuracy', accuracy_score(y_test, prediction))\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fs_w2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = fs_w2v\n",
    "# y = df.loc[:, df.columns == 'is_duplicate']\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "# model = xgb.XGBClassifier(max_depth=50, n_estimators=80, learning_rate=0.1, \n",
    "#                           colsample_bytree=.7, gamma=0, reg_alpha=4, objective='binary:logistic', \n",
    "#                           eta=0.3, silent=1, subsample=0.8).fit(X_train, y_train.values.ravel()) \n",
    "# prediction = model.predict(X_test)\n",
    "# cm = confusion_matrix(y_test, prediction)  \n",
    "# print(cm)  \n",
    "# print('Accuracy', accuracy_score(y_test, prediction))\n",
    "# print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fs_basic + fs_fuzz + fs_distance+ fs_wmd + fs_w2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = np.hstack((fs_basic, fs_fuzz,fs_distance,fs_wmd,fs_w2v))\n",
    "# y = df.loc[:, df.columns == 'is_duplicate']\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "# model = xgb.XGBClassifier(max_depth=50, n_estimators=80, learning_rate=0.1, \n",
    "#                           colsample_bytree=.7, gamma=0, reg_alpha=4, objective='binary:logistic', \n",
    "#                           eta=0.3, silent=1, subsample=0.8).fit(X_train, y_train.values.ravel()) \n",
    "# prediction = model.predict(X_test)\n",
    "# cm = confusion_matrix(y_test, prediction)  \n",
    "# print(cm)  \n",
    "# print('Accuracy', accuracy_score(y_test, prediction))\n",
    "# print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fs_bow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[68806  7841]\n",
      " [17829 26811]]\n",
      "Accuracy 0.7883532447830353\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.90      0.84     76647\n",
      "           1       0.77      0.60      0.68     44640\n",
      "\n",
      "   micro avg       0.79      0.79      0.79    121287\n",
      "   macro avg       0.78      0.75      0.76    121287\n",
      "weighted avg       0.79      0.79      0.78    121287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = fs_bow\n",
    "y = df.loc[:, df.columns == 'is_duplicate']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "model = xgb.XGBClassifier(max_depth=50, n_estimators=80, learning_rate=0.1, \n",
    "                          colsample_bytree=.7, gamma=0, reg_alpha=4, objective='binary:logistic', \n",
    "                          eta=0.3, silent=1, subsample=0.8).fit(X_train, y_train.values.ravel()) \n",
    "prediction = model.predict(X_test)\n",
    "cm = confusion_matrix(y_test, prediction)  \n",
    "print(cm)  \n",
    "print('Accuracy', accuracy_score(y_test, prediction))\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fs_tfidf_word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[68615  8032]\n",
      " [17508 27132]]\n",
      "Accuracy 0.7894250826551897\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.90      0.84     76647\n",
      "           1       0.77      0.61      0.68     44640\n",
      "\n",
      "   micro avg       0.79      0.79      0.79    121287\n",
      "   macro avg       0.78      0.75      0.76    121287\n",
      "weighted avg       0.79      0.79      0.78    121287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = fs_tfidf_word\n",
    "y = df.loc[:, df.columns == 'is_duplicate']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "model = xgb.XGBClassifier(max_depth=50, n_estimators=80, learning_rate=0.1, \n",
    "                          colsample_bytree=.7, gamma=0, reg_alpha=4, objective='binary:logistic', \n",
    "                          eta=0.3, silent=1, subsample=0.8).fit(X_train, y_train.values.ravel()) \n",
    "prediction = model.predict(X_test)\n",
    "cm = confusion_matrix(y_test, prediction)  \n",
    "print(cm)  \n",
    "print('Accuracy', accuracy_score(y_test, prediction))\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fs_tfidf_char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[70087  6560]\n",
      " [15373 29267]]\n",
      "Accuracy 0.819164461154122\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.91      0.86     76647\n",
      "           1       0.82      0.66      0.73     44640\n",
      "\n",
      "   micro avg       0.82      0.82      0.82    121287\n",
      "   macro avg       0.82      0.79      0.80    121287\n",
      "weighted avg       0.82      0.82      0.81    121287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = fs_tfidf_char\n",
    "y = df.loc[:, df.columns == 'is_duplicate']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "model = xgb.XGBClassifier(max_depth=50, n_estimators=80, learning_rate=0.1, \n",
    "                          colsample_bytree=.7, gamma=0, reg_alpha=4, objective='binary:logistic', \n",
    "                          eta=0.3, silent=1, subsample=0.8).fit(X_train, y_train.values.ravel()) \n",
    "prediction = model.predict(X_test)\n",
    "cm = confusion_matrix(y_test, prediction)  \n",
    "print(cm)  \n",
    "print('Accuracy', accuracy_score(y_test, prediction))\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fs_svd_word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[67945  8702]\n",
      " [19365 25275]]\n",
      "Accuracy 0.7685902034018485\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.89      0.83     76647\n",
      "           1       0.74      0.57      0.64     44640\n",
      "\n",
      "   micro avg       0.77      0.77      0.77    121287\n",
      "   macro avg       0.76      0.73      0.74    121287\n",
      "weighted avg       0.77      0.77      0.76    121287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = fs_svd_word\n",
    "y = df.loc[:, df.columns == 'is_duplicate']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "model = xgb.XGBClassifier(max_depth=50, n_estimators=80, learning_rate=0.1, \n",
    "                          colsample_bytree=.7, gamma=0, reg_alpha=4, objective='binary:logistic', \n",
    "                          eta=0.3, silent=1, subsample=0.8).fit(X_train, y_train.values.ravel()) \n",
    "prediction = model.predict(X_test)\n",
    "cm = confusion_matrix(y_test, prediction)  \n",
    "print(cm)  \n",
    "print('Accuracy', accuracy_score(y_test, prediction))\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fs_svd_char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[68733  7914]\n",
      " [19015 25625]]\n",
      "Accuracy 0.7779729072365547\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.90      0.84     76647\n",
      "           1       0.76      0.57      0.66     44640\n",
      "\n",
      "   micro avg       0.78      0.78      0.78    121287\n",
      "   macro avg       0.77      0.74      0.75    121287\n",
      "weighted avg       0.78      0.78      0.77    121287\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = fs_svd_char\n",
    "y = df.loc[:, df.columns == 'is_duplicate']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "model = xgb.XGBClassifier(max_depth=50, n_estimators=80, learning_rate=0.1, \n",
    "                          colsample_bytree=.7, gamma=0, reg_alpha=4, objective='binary:logistic', \n",
    "                          eta=0.3, silent=1, subsample=0.8).fit(X_train, y_train.values.ravel()) \n",
    "prediction = model.predict(X_test)\n",
    "cm = confusion_matrix(y_test, prediction)  \n",
    "print(cm)  \n",
    "print('Accuracy', accuracy_score(y_test, prediction))\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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