dga_0926技术分享整理.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 文件说明"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本文件用于对2019-09-26组内技术分享--DGA内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pydot\n",
    "import pickle\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import lightgbm as lgb\n",
    "from imp import reload\n",
    "\n",
    "from collections import Counter\n",
    "from collections import defaultdict\n",
    "from itertools import groupby\n",
    "from scipy.stats import reciprocal, uniform\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.externals import joblib\n",
    "from keras.models import Sequential, Model\n",
    "from keras.models import model_from_json\n",
    "from keras.layers import Bidirectional, TimeDistributed\n",
    "from keras.layers import Dense, Dropout, LSTM, GRU, Input\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.layers import Concatenate\n",
    "from keras.utils import np_utils\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.preprocessing import sequence\n",
    "from keras.utils.vis_utils import plot_model\n",
    "\n",
    "from keras_bert import Tokenizer, get_base_dict, get_model, compile_model, gen_batch_inputs\n",
    "from keras_bert import load_trained_model_from_checkpoint\n",
    "\n",
    "import tools.attention_layer as att"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 构建数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 数据集来源"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 正样本：1199949 http://data.netlab.360.com/dga/ (该数据集每天仍在更新，当前使用的是20190717的数据)\n",
    "- 负样本：1000000 Alexa-Top sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# legit\n",
    "alexa_top_1m = 'data/domains/top-1m.csv'\n",
    "alexa = pd.read_csv(alexa_top_1m, header=None, names=['index', 'domain'])\n",
    "alexa.drop(['index'], axis=1, inplace=True)\n",
    "alexa['label'] = 'legit'\n",
    "alexa.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domain</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google.com</td>\n",
       "      <td>legit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>youtube.com</td>\n",
       "      <td>legit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>facebook.com</td>\n",
       "      <td>legit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>baidu.com</td>\n",
       "      <td>legit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wikipedia.org</td>\n",
       "      <td>legit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          domain  label\n",
       "0     google.com  legit\n",
       "1    youtube.com  legit\n",
       "2   facebook.com  legit\n",
       "3      baidu.com  legit\n",
       "4  wikipedia.org  legit"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alexa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1199949, 2)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dga\n",
    "lab360 = 'data/domains/net360_0717.txt'\n",
    "dga_360 = pd.read_csv(lab360, sep='\\t', header=None, names=['label', 'domain', 'start time', 'end time'], skiprows=18)\n",
    "dga_360.drop(['start time', 'end time'], axis=1, inplace=True)\n",
    "dga_360.drop(dga_360.index[dga_360['label'].isin(['madmax', 'ccleaner', 'xshellghost', 'blackhole'])], \n",
    "             axis=0, inplace=True)\n",
    "dga_360.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>domain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nymaim</td>\n",
       "      <td>jhggsybzi.info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nymaim</td>\n",
       "      <td>tzilvta.net</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nymaim</td>\n",
       "      <td>tztvdeg.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nymaim</td>\n",
       "      <td>lkpsowb.info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nymaim</td>\n",
       "      <td>mfkjunns.biz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label          domain\n",
       "0  nymaim  jhggsybzi.info\n",
       "1  nymaim     tzilvta.net\n",
       "2  nymaim     tztvdeg.com\n",
       "3  nymaim    lkpsowb.info\n",
       "4  nymaim    mfkjunns.biz"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dga_360.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2199949, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [alexa, dga_360]\n",
    "data = pd.concat(frames)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 数据集划分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 正负样本占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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zwFAzGw2cBDzknFvtnFsDPARMDrYNds497ZxzwM2bfFZ/jiEiIkVExSVPDTn0s7sNPfpL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\ng4skSZKktvd/rg7NoGgoX7sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['label_dga'] = data['label'].map(lambda x: 1 if not x=='legit' else 0)\n",
    "# 各个类别的dga占比\n",
    "f,ax=plt.subplots(1,2,figsize=(14,6))\n",
    "data['label_dga'].value_counts().plot.pie(autopct='%1.1f%%',ax=ax[0],shadow=True)\n",
    "ax[0].set_title('label_dga')\n",
    "data['label_dga'].value_counts().plot.bar(ax=ax[1])\n",
    "ax[1].set_title('label_dga')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 各个类别占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eMrOHzeykUce5Itn+STO7YlT7yWb222SfL5iZJe3bfQwRERk/GiG0zAAM4NAJ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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 各个类别的dga占比\n",
    "f,ax=plt.subplots(1,2,figsize=(14,6))\n",
    "data['label'].value_counts().plot.pie(autopct='%1.1f%%',ax=ax[0],shadow=True)\n",
    "ax[0].set_title('label')\n",
    "data['label'].value_counts().plot.bar(ax=ax[1])\n",
    "ax[1].set_title('label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>family</th>\n",
       "      <th>number</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>legit</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.454556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>banjori</td>\n",
       "      <td>452341</td>\n",
       "      <td>0.205614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>emotet</td>\n",
       "      <td>330562</td>\n",
       "      <td>0.150259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rovnix</td>\n",
       "      <td>179935</td>\n",
       "      <td>0.081791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tinba</td>\n",
       "      <td>72490</td>\n",
       "      <td>0.032951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pykspa_v1</td>\n",
       "      <td>44278</td>\n",
       "      <td>0.020127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>simda</td>\n",
       "      <td>23731</td>\n",
       "      <td>0.010787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ramnit</td>\n",
       "      <td>18935</td>\n",
       "      <td>0.008607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gameover</td>\n",
       "      <td>11997</td>\n",
       "      <td>0.005453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ranbyus</td>\n",
       "      <td>10903</td>\n",
       "      <td>0.004956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>virut</td>\n",
       "      <td>9714</td>\n",
       "      <td>0.004416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>murofet</td>\n",
       "      <td>8560</td>\n",
       "      <td>0.003891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>necurs</td>\n",
       "      <td>8182</td>\n",
       "      <td>0.003719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>shiotob</td>\n",
       "      <td>7968</td>\n",
       "      <td>0.003622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>symmi</td>\n",
       "      <td>4256</td>\n",
       "      <td>0.001935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>shifu</td>\n",
       "      <td>2541</td>\n",
       "      <td>0.001155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>suppobox</td>\n",
       "      <td>2100</td>\n",
       "      <td>0.000955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>qadars</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.000909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>locky</td>\n",
       "      <td>1158</td>\n",
       "      <td>0.000526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>cryptolocker</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.000455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>dyre</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.000455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>chinad</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.000455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>pykspa_v2_fake</td>\n",
       "      <td>800</td>\n",
       "      <td>0.000364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>dircrypt</td>\n",
       "      <td>758</td>\n",
       "      <td>0.000345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>vawtrak</td>\n",
       "      <td>746</td>\n",
       "      <td>0.000339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>conficker</td>\n",
       "      <td>486</td>\n",
       "      <td>0.000221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>nymaim</td>\n",
       "      <td>423</td>\n",
       "      <td>0.000192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>fobber_v2</td>\n",
       "      <td>297</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>fobber_v1</td>\n",
       "      <td>294</td>\n",
       "      <td>0.000134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>feodo</td>\n",
       "      <td>263</td>\n",
       "      <td>0.000120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>tempedreve</td>\n",
       "      <td>200</td>\n",
       "      <td>0.000091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>pykspa_v2_real</td>\n",
       "      <td>196</td>\n",
       "      <td>0.000089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>padcrypt</td>\n",
       "      <td>168</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>matsnu</td>\n",
       "      <td>128</td>\n",
       "      <td>0.000058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>bamital</td>\n",
       "      <td>104</td>\n",
       "      <td>0.000047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>proslikefan</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>vidro</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>gspy</td>\n",
       "      <td>98</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>mydoom</td>\n",
       "      <td>45</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>omexo</td>\n",
       "      <td>40</td>\n",
       "      <td>0.000018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>tinynuke</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>tofsee</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            family   number     ratio\n",
       "0            legit  1000000  0.454556\n",
       "1          banjori   452341  0.205614\n",
       "2           emotet   330562  0.150259\n",
       "3           rovnix   179935  0.081791\n",
       "4            tinba    72490  0.032951\n",
       "5        pykspa_v1    44278  0.020127\n",
       "6            simda    23731  0.010787\n",
       "7           ramnit    18935  0.008607\n",
       "8         gameover    11997  0.005453\n",
       "9          ranbyus    10903  0.004956\n",
       "10           virut     9714  0.004416\n",
       "11         murofet     8560  0.003891\n",
       "12          necurs     8182  0.003719\n",
       "13         shiotob     7968  0.003622\n",
       "14           symmi     4256  0.001935\n",
       "15           shifu     2541  0.001155\n",
       "16        suppobox     2100  0.000955\n",
       "17          qadars     2000  0.000909\n",
       "18           locky     1158  0.000526\n",
       "19    cryptolocker     1000  0.000455\n",
       "20            dyre     1000  0.000455\n",
       "21          chinad     1000  0.000455\n",
       "22  pykspa_v2_fake      800  0.000364\n",
       "23        dircrypt      758  0.000345\n",
       "24         vawtrak      746  0.000339\n",
       "25       conficker      486  0.000221\n",
       "26          nymaim      423  0.000192\n",
       "27       fobber_v2      297  0.000135\n",
       "28       fobber_v1      294  0.000134\n",
       "29           feodo      263  0.000120\n",
       "30      tempedreve      200  0.000091\n",
       "31  pykspa_v2_real      196  0.000089\n",
       "32        padcrypt      168  0.000076\n",
       "33          matsnu      128  0.000058\n",
       "34         bamital      104  0.000047\n",
       "35     proslikefan      100  0.000045\n",
       "36           vidro      100  0.000045\n",
       "37            gspy       98  0.000045\n",
       "38          mydoom       45  0.000020\n",
       "39           omexo       40  0.000018\n",
       "40        tinynuke       32  0.000015\n",
       "41          tofsee       20  0.000009"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_statis = pd.DataFrame()\n",
    "label_counts = data['label'].value_counts()\n",
    "data_statis['family'] = label_counts.index\n",
    "data_statis['number'] = label_counts.values\n",
    "data_statis['ratio'] = label_counts.values / np.sum(data_statis['number'])\n",
    "data_statis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 训练与测试集划分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "采用分层采样的原则，将数据据划分为0.8 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1759959, 2), (439990, 2))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test = train_test_split(data, test_size=0.2, stratify=data['label'])\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1759959, 49), (439990, 49))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载已经处理好的特征文件\n",
    "train_features_filepath = 'data/dataset/train_numeric.csv'\n",
    "test_features_filepath = 'data/dataset/test_numeric.csv'\n",
    "train_features_df = pd.read_csv(train_features_filepath)\n",
    "test_features_df = pd.read_csv(test_features_filepath)\n",
    "train_features_df.shape, test_features_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['domain', 'label', 'main_domain', 'tld', 'entropy', 'vowel_ratio',\n",
       "       'digit_ratio', 'repeat_letter_ratio', 'consecutive_digits_ratio',\n",
       "       'consecutive_consonant_ratio', 'gib', 'hmm_log', 'length', 'uni_ave',\n",
       "       'bi_ave', 'tri_ave', 'uni_std', 'bi_std', 'tri_std', 'tld_0', 'tld_1',\n",
       "       'tld_2', 'tld_3', 'tld_4', 'tld_5', 'tld_6', 'tld_7', 'tld_8', 'tld_9',\n",
       "       'tld_10', 'tld_11', 'tld_12', 'tld_13', 'tld_14', 'tld_15', 'tld_16',\n",
       "       'tld_17', 'tld_18', 'tld_19', 'tld_20', 'tld_21', 'tld_22', 'tld_23',\n",
       "       'tld_24', 'tld_25', 'tld_26', 'tld_27', 'tld_28', 'tld_29'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domain</th>\n",
       "      <th>label</th>\n",
       "      <th>main_domain</th>\n",
       "      <th>tld</th>\n",
       "      <th>entropy</th>\n",
       "      <th>vowel_ratio</th>\n",
       "      <th>digit_ratio</th>\n",
       "      <th>repeat_letter_ratio</th>\n",
       "      <th>consecutive_digits_ratio</th>\n",
       "      <th>consecutive_consonant_ratio</th>\n",
       "      <th>...</th>\n",
       "      <th>tld_20</th>\n",
       "      <th>tld_21</th>\n",
       "      <th>tld_22</th>\n",
       "      <th>tld_23</th>\n",
       "      <th>tld_24</th>\n",
       "      <th>tld_25</th>\n",
       "      <th>tld_26</th>\n",
       "      <th>tld_27</th>\n",
       "      <th>tld_28</th>\n",
       "      <th>tld_29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nlwpvkhlxitcjiej.eu</td>\n",
       "      <td>emotet</td>\n",
       "      <td>nlwpvkhlxitcjiej</td>\n",
       "      <td>eu</td>\n",
       "      <td>2.512659</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.723791e-16</td>\n",
       "      <td>-1.435627e-15</td>\n",
       "      <td>1.227174e-15</td>\n",
       "      <td>1.064156e-14</td>\n",
       "      <td>-5.007343e-17</td>\n",
       "      <td>5.994943e-15</td>\n",
       "      <td>4.278799e-16</td>\n",
       "      <td>-3.721696e-16</td>\n",
       "      <td>-7.395807e-15</td>\n",
       "      <td>2.791984e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lifespa.com</td>\n",
       "      <td>legit</td>\n",
       "      <td>lifespa</td>\n",
       "      <td>com</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.607818e-18</td>\n",
       "      <td>1.243457e-18</td>\n",
       "      <td>-1.974010e-18</td>\n",
       "      <td>-1.924975e-17</td>\n",
       "      <td>2.423300e-18</td>\n",
       "      <td>-1.436490e-17</td>\n",
       "      <td>3.957242e-18</td>\n",
       "      <td>3.718585e-18</td>\n",
       "      <td>-3.527445e-18</td>\n",
       "      <td>1.182715e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sedes.org.br</td>\n",
       "      <td>legit</td>\n",
       "      <td>sedes</td>\n",
       "      <td>org.br</td>\n",
       "      <td>1.054920</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.226024e-07</td>\n",
       "      <td>-3.377123e-06</td>\n",
       "      <td>-7.597923e-07</td>\n",
       "      <td>3.135754e-05</td>\n",
       "      <td>3.792294e-06</td>\n",
       "      <td>2.058674e-05</td>\n",
       "      <td>4.911968e-06</td>\n",
       "      <td>-2.102178e-05</td>\n",
       "      <td>-1.956920e-05</td>\n",
       "      <td>4.067783e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dbedestnessbiophysicalohax.com</td>\n",
       "      <td>banjori</td>\n",
       "      <td>dbedestnessbiophysicalohax</td>\n",
       "      <td>com</td>\n",
       "      <td>2.598143</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.607818e-18</td>\n",
       "      <td>1.243457e-18</td>\n",
       "      <td>-1.974010e-18</td>\n",
       "      <td>-1.924975e-17</td>\n",
       "      <td>2.423300e-18</td>\n",
       "      <td>-1.436490e-17</td>\n",
       "      <td>3.957242e-18</td>\n",
       "      <td>3.718585e-18</td>\n",
       "      <td>-3.527445e-18</td>\n",
       "      <td>1.182715e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fnnvanerraticallyqozaw.com</td>\n",
       "      <td>banjori</td>\n",
       "      <td>fnnvanerraticallyqozaw</td>\n",
       "      <td>com</td>\n",
       "      <td>2.563151</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.607818e-18</td>\n",
       "      <td>1.243457e-18</td>\n",
       "      <td>-1.974010e-18</td>\n",
       "      <td>-1.924975e-17</td>\n",
       "      <td>2.423300e-18</td>\n",
       "      <td>-1.436490e-17</td>\n",
       "      <td>3.957242e-18</td>\n",
       "      <td>3.718585e-18</td>\n",
       "      <td>-3.527445e-18</td>\n",
       "      <td>1.182715e-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           domain    label                 main_domain  \\\n",
       "0             nlwpvkhlxitcjiej.eu   emotet            nlwpvkhlxitcjiej   \n",
       "1                     lifespa.com    legit                     lifespa   \n",
       "2                    sedes.org.br    legit                       sedes   \n",
       "3  dbedestnessbiophysicalohax.com  banjori  dbedestnessbiophysicalohax   \n",
       "4      fnnvanerraticallyqozaw.com  banjori      fnnvanerraticallyqozaw   \n",
       "\n",
       "      tld   entropy  vowel_ratio  digit_ratio  repeat_letter_ratio  \\\n",
       "0      eu  2.512659     0.187500          0.0             0.187500   \n",
       "1     com  1.945910     0.428571          0.0             0.000000   \n",
       "2  org.br  1.054920     0.400000          0.0             0.400000   \n",
       "3     com  2.598143     0.346154          0.0             0.307692   \n",
       "4     com  2.563151     0.318182          0.0             0.181818   \n",
       "\n",
       "   consecutive_digits_ratio  consecutive_consonant_ratio      ...       \\\n",
       "0                       0.0                     0.750000      ...        \n",
       "1                       0.0                     0.285714      ...        \n",
       "2                       0.0                     0.000000      ...        \n",
       "3                       0.0                     0.461538      ...        \n",
       "4                       0.0                     0.454545      ...        \n",
       "\n",
       "         tld_20        tld_21        tld_22        tld_23        tld_24  \\\n",
       "0  2.723791e-16 -1.435627e-15  1.227174e-15  1.064156e-14 -5.007343e-17   \n",
       "1 -1.607818e-18  1.243457e-18 -1.974010e-18 -1.924975e-17  2.423300e-18   \n",
       "2 -4.226024e-07 -3.377123e-06 -7.597923e-07  3.135754e-05  3.792294e-06   \n",
       "3 -1.607818e-18  1.243457e-18 -1.974010e-18 -1.924975e-17  2.423300e-18   \n",
       "4 -1.607818e-18  1.243457e-18 -1.974010e-18 -1.924975e-17  2.423300e-18   \n",
       "\n",
       "         tld_25        tld_26        tld_27        tld_28        tld_29  \n",
       "0  5.994943e-15  4.278799e-16 -3.721696e-16 -7.395807e-15  2.791984e-15  \n",
       "1 -1.436490e-17  3.957242e-18  3.718585e-18 -3.527445e-18  1.182715e-17  \n",
       "2  2.058674e-05  4.911968e-06 -2.102178e-05 -1.956920e-05  4.067783e-06  \n",
       "3 -1.436490e-17  3.957242e-18  3.718585e-18 -3.527445e-18  1.182715e-17  \n",
       "4 -1.436490e-17  3.957242e-18  3.718585e-18 -3.527445e-18  1.182715e-17  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基本特征 - 随机性和熵"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 香农熵分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lili/anaconda3/envs/dga_env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd5a2dad2e8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CJph2qseVw28zSvAViiax7GiHH5RelvfEd1xJ3nbrVunETZ2kqTfv7gHsIMOv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\nOI5TJ7jhO47j1Alu+I7jOHXC/wE8V2vSOf3RbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['entropy'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['entropy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce6808d780>"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N4JiRLqgQQggxUajTc14PuNfd73f354CfAtsVZLYDzkz/TwW2MDMbuWIKIYQQEwdz9+4C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\ndNBEk4/mRs0VR4iP0QKFVk6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P9h5ryo4FFnOb10u7N7n7o8NMb3miV3gYYWa5gAjLWFpJ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\nD8IGE1nN/0/fAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "entropy_mean = train_features_df.groupby(['label']).apply(lambda x: x['entropy'].mean()).sort_values()\n",
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=-90)\n",
    "sns.barplot(x=entropy_mean.index, y=entropy_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 高级特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 合法域名一般比较好念出来，C&C域名不好念"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 元音字母比重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lili/anaconda3/envs/dga_env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd5998e0a90>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3NQ/d+N/pX36Rt3z/3/MW60ms1Uk+n38jo+5syNKy3PmXkUIb8y99kIia/tqz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\n48RtZlYz/wHHmLDgDkF1QAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['vowel_ratio'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['vowel_ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce6811d0f0>"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wQbbuNX0TYRFbn7AatT0fXelmP19SC/AL4iW/D9GCmgl8CFijwz53kvrA0vqa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\nkiQNhKEtSdJA/AtSIVOGXFCRvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vowel_ratio_mean = train_features_df.groupby(['label']).apply(lambda x: x['vowel_ratio'].mean()).sort_values()\n",
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=-90)\n",
    "sns.barplot(x=vowel_ratio_mean.index, y=vowel_ratio_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### gibberish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce67d986a0>"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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REdmKspmZWlUz9Vy1wFWlJQDiRlf0IO5sX73dY43GnbNa6hMRERlk2QymVmSmzlWN2SjH\nvmYwZUmAFXito1Msbymj/flEREQGWnaDKQsgCHliNtns+OpSEvTEjWxVx5kpbSkjIiKSCRkNpmrL\nS3zfmkseryommal6UAAgiJc6OsX2oTz50NQeQUREZMBlM5iqRxeCqZmQnMVMFJNlvWafqVzceRf0\n3aMlbSkjIiIy4LIZTMURNArNn5jLsbdYI7TkrSgoA533mQK0pYyIiEgGZDeYCpPM1BOz4XLxObDc\ntDOXQjClxp0iIiKDL6PBVFIzVYvh6blwufgcAAuIUtjsGLSljIiISBZkNJhKlvmemQ+J3JZ7TDXV\nwxK5+mLHp9k7XmJ6scbikrqgi4iIDKpNgykze7+ZnTKzh1a8tsPMPm1mjzcet3d3mimrRxCEfKvR\nFmFf8eJgKgpLHRegA1y1LSlmf/Z854GZiIiIbE2tZKY+ANy+6rVfAD7j7geAzzS+7h9xBGGep+ca\nwdTqzFRQSmWZ7+ptyTYyCqZEREQG16bBlLvfC5xd9fJbgD9oPP8D4PtTnld3NWqmZmvJX384jC96\nOwrLqRSgX709uTPw2XMKpkRERAZVuzVTe9z9OEDjcXd6U+qBRp+phcgYCmMCu/jtKKWaqT2jRXKB\n8ez5hY7HEhERka2p6wXoZnaXmR02s8NTU1PdPl1r4iSYmouMoZxf8nY9LBGmUDOVCwP2jpc4psyU\niIjIwGo3mDppZvsAGo+n1jvQ3e9290l3n5yYmGjzdCmLaxDmWYiM4TWCqSgsEcY1zDu/C+/qbWUt\n84mIiAywdoOpjwPvaDx/B/CxdKbTI3Edghzz62WmUuyCfvX2sgrQRUREBlgrrRHuAe4DbjKzY2b2\nTuBdwBvM7HHgDY2v+0ejAH2jzBSk0wV9/7YyJ2cq1Orx5geLiIhI38ltdoC737nOW69LeS690yhA\nn4+M8cKlQU4zmApTKEK/enuZ2OHEdIVrdgx1PJ6IiIhsLdnrgO6+3Gdqob52ZqqeZmZqexJAqQhd\nRERkMGUwmIoBb2mZL407+q7eltRfHTun9ggiIiKDKHvBVFxLHhvLfGtmphoF6GlkpvZpSxkREZGB\nlr1gqh4lj0HSGmGtu/kuFKB3HgAVcyG7R4tqjyAiIjKgNi1AHzhxEkxFlmMpXjsz5UGO2HJtt0b4\n4KGjF31dzAU8cPTcJa+/7RXXtjW+iIiIbB3Zy0w1lvmWyAOsmZmC5pYynS/zAWwfLnB+oZbKWCIi\nIrK1ZC+YaizzVTxJyq2VmYLGZscpFKADbCsXmF6sEfva5xIREZH+lb1gqpGZqlAE1s9M1YNiKn2m\nALYN5anHzlwlSmU8ERER2TqyF0zVk2BqwQvAJpmpejWVU24fSpYUzy0spTKeiIiIbB0ZDKaSgGax\nEUytm5kKSylmppJzqW5KRERk8GQwmEoCmvlNM1PpFaBva2SmziszJSIiMnCyF0w1aqbm440zU1FY\nSjqgp1A0XsyFDBVCzi0qMyUiIjJoshdMNTJTs54UoK+XmaoHZQwI43TqprYN5ZWZEhERGUAZDKaS\ngGauvvkyH6TTBR2S9giqmRIRERk8GQymkoBmOt6kNUJzs+O0GncO5Tm3sISr15SIiMhA6SiYMrOf\nNrOHzewhM7vHzEppTaxrGjVTM/UCpdAJbe3DLmSm0ipCL1CrOwtL9VTGExERka2h7WDKzK4GfgKY\ndPebgRD4obQm1jX1GgQh8/Vw3SU+WJGZSqsL+vIdfVrqExERGSSdLvPlgLKZ5YAh4LnOp9Rl9SUI\nCyxEtu4SH0AUlIH0MlPbG72m1LhTRERksLQdTLn7s8BvAUeB48C0u/9VWhPrmnoNgjzzkW2YmYqW\na6bS21IG4LzaI4iIiAyUTpb5tgNvAa4HrgKGzeztaxx3l5kdNrPDU1NT7c80LXENwjzzUcBQuH4w\nFQcFHEstM1XOhxRygdojiIiIDJhOlvleDzzp7lPuXgM+Cnzn6oPc/W53n3T3yYmJiQ5Ol5J6DcLC\nppkpzFLtgm5mbCvnVTMlIiIyYDoJpo4CrzSzITMz4HXAI+lMq4vqSxDkGzVT8caHhuXUCtAhqZtS\nzZSIiMhg6aRm6hDwEeDLwNcbY92d0ry6p95c5tskM0Vzf750aqag2QVdmSkREZFBkuvkw+7+K8Cv\npDSX3qjXoDC06d18APUgvWU+SHpNLdbqVKM6xVyY2rgiIiJy5WSvA3rcYs0Ujc2OUwymxsvJHX3T\nuqNPRERkYGQvmKrXiIM81biVYKqcamZKwZSIiMjgyWAwtURkSVCz6TJfWCSMFyGl/fSWgynVTYmI\niAyM7AVTcY2aJd3IN81MBWUCjwk8SuXUY+WkRG26omBKRERkUGQvmKrXWGoEU5tlptLugp4LAkaK\nOWWmREREBki2gimPIY5YIllu2ywz1dzsOO26KdVMiYiIDI5sBVP1JIipUAQuJzOlYEpERETWlq1g\nKm4EU95qZqoMQC5Or3GngikREZHB0lHTzq3u0JNnL/q6UJvmVuDp2SSIOnLiPJXp9bd3iYLuLPNV\no5hKrZ7amCIiInLlZCozFTQyUwueLPOVws325uvCMt+Qek2JiIgMkowFU0mLg0VP7uYrBRsHU1E3\nCtBLCqZEREQGSbaCKU8CmPkWM1NYQD0oKDMlIiIi68pWMLVimS9vMTnb/DPJljLpFaCPlfIYCqZE\nREQGRbaCqUYn8zkvbrrE1xSFJXJxepmpMDBGSjkFUyIiIgMiW8FU3AymCpTC1vbbqwelVJf5ILmj\nb0bBlIiIyEDoKJgys21m9hEz+6aZPWJmr0prYt3QrJmaqxcpXk5mqgvB1HkFUyIiIgOh08zUe4BP\nufsLgG8DHul8St3TrJma8eLmxecNUVjuSmZqerGGe2vZMREREdm62g6mzGwM+LvA+wDcfcndz6c1\nsW5oLvPN1Est10zVw1KqHdAhCaaWopjZapTquCIiItJ7nWSmbgCmgN83s6+Y2XvNbDileXVFc5lv\nJr6MAvSgRBjXME+vY/l4OWmPcPx8uhkvERER6b1Ogqkc8O3Af3f3W4F54BdWH2Rmd5nZYTM7PDU1\n1cHpOhfENRxjNi60vMzXlS7ojWDquel0M14iIiLSe50EU8eAY+5+qPH1R0iCq4u4+93uPunukxMT\nEx2crnOBR8RBjkocXkZrhMZmx10Ipk5MKzMlIiLS79oOptz9BPCMmd3UeOl1wDdSmVWXBHFEbHkq\ncXAZmamkW3qYYuPO0UbjzuPnlZkSERHpd7kOP//jwB+aWQF4AviRzqfUPYHXiIM81TigFLR2J10U\nDgGk2gU9DIzRUo7nlJkSERHpex0FU+7+IDCZ0ly6Lohr1C35K7e6zFfLJTX1+Wg+1bmMl/Na5hMR\nERkA2eqA7hGRJfVKrS7z1cJGMFVPP5hSAbqIiEj/y1YwFdeoWQFoPTMVB3nqlutKZur4+Yoad4qI\niPS57AVTXF5mCjOi3DC5aCHVuYwPFVis1ZlZVONOERGRfpatYMqjC8FUi5kpSOqmurHMB+o1JSIi\n0u+yFUzFNarNZb5WM1MkdVPdWOYD9ZoSERHpd9kKpjxiqd3MVJeCKWWmRERE+lu2gqm4RoXLK0AH\nkpqp+jykWCw+WsoRBqb9+URERPpctoIpr1GlvWW+wGPy0Ux6czFjz2iR41rmExER6WvZCabcCeKI\nRU+2h2m1AzpcaNxZqp5NdUp7x0sc1zKfiIhIX8tMMGUeYziL5AlxcnY5wVSypUxpKd1gat+2sjJT\nIiIifS4zwVTgNQAWvEApjDFr/bNRMzOVcjB11XiJ584vqnGniIhIH8tOMBU3g6niZRWfw4UtZYop\nL/M9b+cw1ShWdkpERKSPZSeY8qTT+LwXL6v4HFbUTKWcmTqwewSAx07OpjquiIiI9E52gqlGZmo+\nvvzMFBZQC8uUls6kOqeDe0YBOHJqLtVxRUREpHeyE0w1aqZm28hMQVI3lXZmavtwgV0jBWWmRERE\n+lh2gqk4WeabayczRVI3Vaqmm5kCOLB7lMeVmRIREelbHQdTZhaa2VfM7BNpTKhbmpmpuTYK0CGp\nmyqmnJkCOLBnhCMn53RHn4iISJ9KIzP1k8AjKYzTVc3M1HRcpBRefuBS68IyH8CBPaPMViNOzOiO\nPhERkX7UUTBlZvuBNwPvTWc63dMsQJ+pl9rKTEXhEMXazPI4ablwR5+W+kRERPpRp5mp/wr8PLBu\ndGJmd5nZYTM7PDU11eHp2tdsjTATt1eA3myPUFw6l+q8mnf0Pa4idBERkb7UdjBlZt8LnHL3BzY6\nzt3vdvdJd5+cmJho93Qda2aUFilSbLNmCki9bmrHcIGdwwW1RxAREelTnWSmvgu4w8yeAj4EvNbM\n/k8qs+qCZgH6IoW2C9Ah/c2OISlCV3sEERGR/tR2MOXuv+ju+939OuCHgM+6+9tTm1nKmgXoFQrt\nLfOFzS7o3WuPoDv6RERE+k92+kx5jTohTtBeAXqXtpQBOLhnhNlKxMmZaupji4iISHelEky5++fc\n/XvTGKtbgjgiCvIAlNvITNWDInXLd6Vx5427G0Xop7TUJyIi0m+yk5mKa0QkwVQ7mSnMqBR3dK1x\nJ6g9goiISD/KTjDlEZE1gqk2mnYCVAs7urLMt2ukyI7hAkeUmRIREek72Qmm4hpLVgDazEwBlS4F\nUwA37h5RZkpERKQPZSeY8hpLnSzzAZXizq60RoCkCP3xk7O6o09ERKTPZCeYiiOqJJmpdgrQASqF\nnUnNVBcCngO7R5mpRJya1R19IiIi/SQ7wZTXmPcio7mIfNBeMFQp7CAXV8nVF1Ke3YUi9Me11Cci\nItJXshNMxRHzXmBHPmp7jGpxB9ClLuiN9gjqhC4iItJfcld6Ar0SeI25uMT2QvvBVKXQCKaWzjA3\nfE3Hc/rgoaPLz92doULIXzx0glI+vOi4t73i2o7PJSIiIt2RocxUjZm4yI6Ugqm0mRm7R4ucmqmk\nPraIiIh0T4aCqYiZuMj2Dpb5msFUsUt39O0eK3FytqI7+kRERPpIZoIp84gKRXbka22PUV3OTHUn\nmLpme5lKLdYefSIiIn0kG8GUx4QeUSHfUWYqDgss5Ua72LhTe/SJiIj0m0wEU4EnAdSid1YzBY3G\nnV0KpsbLeXaPFtUeQUREpI9kI5iKkwCqQqGjzBQkdVPdqpkCOLhnlKfOzLMUtddYVERERHorG8GU\nJ3VSS+QZy9U7Gqub+/NBskdfFDtPnZnv2jlEREQkPW0HU2Z2jZn9tZk9YmYPm9lPpjmxNAVxEkwF\nYY7AOhurWthBqZp+a4Sm63cNkwuMx9W8U0REpC900rQzAn7W3b9sZqPAA2b2aXf/RkpzS01zmS+X\nCzc5cnOV4g6KtfNYHOFB+j1P82HAdbuGefyU6qZERET6QduZKXc/7u5fbjyfBR4Brk5rYmlqLvMV\nws5XNSuFHRhOsXa+47HWc2D3CKdmq5xfWOraOURERCQdqdRMmdl1wK3AoTTGS1tzma+YRmaq2Wuq\ni0t9B/YkLRKOKDslIiKy5XUcTJnZCPAnwE+5+8wa799lZofN7PDU1FSnp2tLVE+KzsuFzmPHmZEb\nANhz5osdj7WePaNFxko5LfWJiIj0gY6iCzPLkwRSf+juH13rGHe/290n3X1yYmKik9O1bbGWtBko\n5zqsPgemRw8wte3bOHj0Q+DdaV9gZty4e5Qjp+aItbWMiIjIltbJ3XwGvA94xN3/c3pTSl+1lmSm\nhlPITAE89ry3MbpwlH2n/zaV8dZyYPcIi7U6z55b7No5REREpHOdRBffBfxj4LVm9mDjz5tSmleq\nKo0GmCP5dIKpZ/a+gcXiLg4+fU8q463lxt0jGNpaRkREZKtr+95+d/8boPN1sx6oNYKpsWI648VB\nniPXvJWbj/weI/NHmRu+Np2BVxgu5rhqW1l1UyIiIltc+o2StqConvSZKuQ6++s+/+gfLz+vBSUc\n42UP/zpH977xouO+de1bOzpP0017R/nrb57iyKk5btw9ksqYIiIikq5MbCdTr9epu4Gl99et5Uc5\nN/ZCJs49SBB3px/UK2/YSSEX8Juf+mZXxhcREZHOZSKYiusRS1YAS3dV8sTOl5GLq+w8//VUx20a\nKea47eAEn/7GSb74ZPf2AxQREZH2ZSKY8rhOjXzq486Vr2G+tJe9Z78EXWph8J3P38XesRK/8clH\ncLVJEBER2XIGPphyTzqgR5aKkmvxAAAM/0lEQVR+MIUZJ3e8jKHqKUYWj6U/PlDIBfzs9xzkq8+c\n5/9+/XhXziEiIiLtG/hgaq4eUKBGPehCMAWcGXsRseXYOf1QV8YH+IFv388L9o7y7k89SjWqd+08\nIiIicvkGPpg6u5SnyBJu3blxMQ6LnBs9yM7phzHvTqATBsYvvumFHD27wB/ef7Qr5xAREZH2DHww\nda6Wo0wVwu5kpgDOjL+EfH2Bsbknu3aO2w5O8OoDu3jPZx7n5Eyla+cRERGRyzPwwdTZWo6S1bAg\n7No5zo88nygodXWpD+DX7ngxS1HMz3z4QeJYxegiIiJbwcAHU+dqOUosEYbd60/qQY6zYy9kx+w3\nCeJa185zw8QIv3rHi/jbI2f4n194omvnERERkdYNfAf0s0s5hq2KB0NdPc+ZbTez+/xX2Db7WOpj\nf/DQhTopd+fFV43xm5/6JtOLNfZvv/D3etsr0t/WRkRERDaWicxU2ZaIu3Q3X9PM0PNYyo10fanP\nzPj7t17NaCnPH33pGd3dJyIicoUNZjBVmVl+eraWp8QScZfu5ltmAWfGX8y2ucfJ16a7eqqhQo63\nTu7n7PwSf/qVZ1moRl09n4iIiKxv8IKpqUfht2+Fhz4KwLmlpGaq25kpSO7qCzzm2hP/r+vnumHX\nCK974W6+dmyad33qm3z48DMcfuqsuqSLiIj02OAFU9uvh503wsf+BYXFk0xHAYVeZKaA+dI+Fgs7\neN5znySoLzEyf5Q9p+9n39TfgMepn++1L9jDT7zuAJPXbeeR4zP84O/dx5t/+2/4y4dPKKgSEZGB\n8OTpeX7k97/Ixx58dsv+22adTMzMbgfeA4TAe939XRsdPzk56YcPH277fC2bPQl338Z8pcJts/+B\nw6Uf45nd381zE6/u+qmvPvU59k/di2MYF763T++7nfte8u+Jw2JXzluN6pTyIXff+wRPnp7nlv3j\n/PQbDvKagxNYyhs8i4iI9MLjJ2d523sPcXquijt8z4v28Bt//yVMjHbn39LVzOwBd5/c9Lh2gykz\nC4HHgDcAx4AvAXe6+zfW+0zPgimAZ75E/X1v5IH4Rl4ePMrTe97AiV2v6vppc9EcI5XjVPPbmS/v\nY758FbvOf5WXPvbbTG17Kfd++3uoFncsH19YOs/Y/FOcHX9xKkuR9dh58JlzfPabpzi3UGPXSJGx\nUo5SPqRcCBkp5njLS6/i4J5Rbtw9Qinfvf5bIiIi7Xr4uWne8d772BHM8553voF7H5viP336MYYK\nIb92x4u549uu6nqyoNVgqpO1r5cDR9z9icYJPwS8BVg3mOq2v3r4BB/84lFuvmqcm6/eT23kH/B9\ncx8G6EnNFECUG+H8yAEg2WB5dP5pqvltPL7/B3n+s3/Gm7/w/RzZ/wOUq1OUl86w98whAo+o5sd4\nZs/rObr3jUztuJXR+aNsm32MbbOPUa6eYXrkBs6N3cT50YMsFicoV08ztHic4cpxctEiC6U9LJT3\nMl/ay3dcu53JvQHfeuppzpz6Fh5VOVUZ5kQ0wterQ3z+sVOUWGLMFnj+SJ1t5YCgPE5+eDsjo+Nc\nu3OY523Lcd0IbM9VmKvUmI6LnKsVqXqO0XKB0YIxlqsxbEs4Tj0sE+fKhLkc5XxIOW8UvIbXKsxU\nIk5XYGoxYCl2dg4X2DVcYOdQQN5rxA5V8izWjdidUj6klAvImUN9CTAICxCsWpWO61CvgQVJh/vV\nP1TuEEfJ+xas/b7Ha7/XyvsiIlvNygTJlf69FcfJ7/C4BkEecsVkTu5QnaVy7jjTU8eYnT7DZ4/G\nLJV3YcN7GC0aY8c+D499ir8KvsKOeJbp/3MDr959G29+6/fxE1/I85MfepBKrc4/etnWaAnUSTB1\nNfDMiq+PAa/obDqdqUQxJ6YrfOHx09RjB76f6dwZ3p77TM+CqfWcHX8RS/kxDh79I25+8n3JfPPb\nOLHj5cyX97J99nGuf+4T3Hjsoxd9LraQSmEH1z/35xdeIyBg/Rqs2HIEvs4dfkWoW46w+X6t8adx\nA2TdjYiQol34/O4VH695SExA0dZuTlr1HGDL7xuwrfHnRqDqyXXIExFY8kMfAGWg4Eat8Z+kEYFd\nnDWNPKBGDsPJExGuer/mIREhhpOjTs4u/h4teUhEjoCYkJi8XWgrUXejTvL5td6PPGi8H+Cs/wvK\nGu8mjxees8brwYr5x37hKF9eIL7w/MKnLpxnrfOtfq9p9ed9g7HXG3+9sZvjrTX2Wue++HvVHG/9\nsVefpznWenNf7+/QyvxXz3v1322j81z6fO25rGf19/DC87Xn2cqc1rt+651/9fd2vXOlde3WGr/b\n/+2t/tlc/dO2+m+7+r+zjX52Whk7WPV7a/XP/spzxC2Mv9HYTXW3lsZf/X1a+Zg8v9R61zJPdNHv\n0KaV/4aUgBKwh+Tfh5Xfk8Cc6WCYE7v/Lke3HWTvmft5wVP/m+DJ3+dPyjv46Bvv4U3fdvWac78S\nOlnmeyvwRnf/542v/zHwcnf/8VXH3QXc1fjyJuDR9qd72XYBp3t4PtmYrsfWo2uyteh6bD26JltL\nr6/H89x9YrODOslMHQOuWfH1fuC51Qe5+93A3R2cp21mdriVtU7pDV2PrUfXZGvR9dh6dE22lq16\nPTppjfAl4ICZXW9mBeCHgI+nMy0RERGR/tB2ZsrdIzP7l8BfkrRGeL+7P5zazERERET6QEedLN39\nk8AnU5pLN1yR5UVZl67H1qNrsrXoemw9uiZby5a8Hh017RQRERHJusHbTkZERESkhwYimDKz283s\nUTM7Yma/sMb7RTP7o8b7h8zsut7PMjtauB4/Y2bfMLOvmdlnzOx5V2KeWbHZ9Vhx3A+amZvZlrtT\nZtC0ck3M7B82fk4eNrMP9nqOWdLC76xrzeyvzewrjd9bb7oS88wKM3u/mZ0ys4fWed/M7Lcb1+tr\nZvbtvZ7jan0fTDW2tfld4O8BLwLuNLMXrTrsncA5d78R+C/Ab/Z2ltnR4vX4CjDp7rcAHwHe3dtZ\nZkeL1wMzGwV+AjjU2xlmTyvXxMwOAL8IfJe7vxj4qZ5PNCNa/Bn5N8CH3f1WkjvX/1tvZ5k5HwBu\n3+D9vwccaPy5C/jvPZjThvo+mGLFtjbuvgQ0t7VZ6S3AHzSefwR4nWn3327Z9Hq4+1+7+0Ljy/tJ\nepRJd7Ty8wHw70iC2kovJ5dRrVyTHwV+193PAbj7qR7PMUtauR4OjDWej7NGT0VJj7vfC5zd4JC3\nAP/LE/cD28xsX29mt7ZBCKbW2tZmdY/55WPcPQKmgZ09mV32tHI9Vnon8BddnVG2bXo9zOxW4Bp3\n/0QvJ5ZhrfyMHAQOmtnfmtn9ZrbR/6VLZ1q5Hr8KvN3MjpHcwf7jyJV0uf/OdF1HrRG2iPW2C7rc\nYyQdLX+vzeztwCRwW1dnlG0bXg8zC0iWvv9pryYkLf2M5EiWMF5Dkrn9gpnd7O7nuzy3LGrletwJ\nfMDd/5OZvQr4343rsf4mqdJNW+7f9EHITLWyrc3yMWaWI0nTbpRClPa1tM2Qmb0e+CXgDnev9mhu\nWbTZ9RgFbgY+Z2ZPAa8EPq4i9K5q9XfWx9y95u5PkuxpeqBH88uaVq7HO4EPA7j7fST78+7qyexk\nLS39O9NLgxBMtbKtzceBdzSe/yDwWVeDrW7Z9Ho0lpX+B0kgpVqQ7trwerj7tLvvcvfr3P06khq2\nO9z98JWZbia08jvrz4DvBjCzXSTLfk/0dJbZ0cr1OAq8DsDMXkgSTE31dJay0seBf9K4q++VwLS7\nH7+SE+r7Zb71trUxs18HDrv7x4H3kaRlj5BkpH7oys14sLV4Pf4jMAL8ceM+gKPufscVm/QAa/F6\nSA+1eE3+EvgeM/sGUAd+zt3PXLlZD64Wr8fPAv/TzH6aZDnpn+p/yLvHzO4hWeLe1ahT+xUgD+Du\nv0dSt/Ym4AiwAPzIlZnpBeqALiIiItKBQVjmExEREbliFEyJiIiIdEDBlIiIiEgHFEyJiIiIdEDB\nlIiIiEgHFEyJSF8zs19vNIHFzJ5q9GUSEekZtUYQkYHR6OI+6e6nr/RcRCQ7+r5pp4hkh5n9W+CH\nSTY5PQ08QLIdzifc/SONw37OzL678fxt7n6k9zMVkSzRMp+I9IXGfoH/ALgV+AGSTbLXMuPuLwd+\nB/ivPZqeiGSYgikR6Rd/h2Tz30V3nwX+fJ3j7lnx+KqezExEMk3BlIj0C2vxOF/nuYhIVyiYEpF+\n8TfA95lZycxGgDevc9w/WvF4X09mJiKZpgJ0EekL7v4lM/s48FXgaeAwML3GoUUzO0TyP4t39nCK\nIpJRao0gIn3DzEbcfc7MhoB7gbvc/ctXel4ikm3KTIlIP7nbzF4ElIA/UCAlIluBMlMiIiIiHVAB\nuoiIiEgHFEyJiIiIdEDBlIiIiEgHFEyJiIiIdEDBlIiIiEgHFEyJiIiIdOD/A/u6Z8LNlVxrAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(10,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['gib'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['gib'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce682d82e8>"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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63fv5RFfRZiW5nvMHfgbslf7fQnhP/yxXH6nxwizI300ozecDN6RtOQfH2s9p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\nYWlLktQIS1uSpEb8A/GyaNpPyjsHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gib_mean = train_features_df.groupby(['label']).apply(lambda x: x['gib'].mean()).sort_values()\n",
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=-90)\n",
    "sns.barplot(x=gib_mean.index, y=gib_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 连续vs分散"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 数字字母占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce67c5deb8>"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R8IXfhhe9qXgDs3l1z7e7bhFccTr84B09e4Mmqd/q9jnjiGgCrgDOBhYC90fE9Mxs0GXC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\nJEkl+//Ksz0wGncYQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['digit_ratio'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['digit_ratio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 重复字母占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce67b3e9e8>"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1+/j8PSv4y+s7SHXaubqwjhuKa7iyoJ6TMtt4ozGdP+wqpCc4yIftXwvAK12z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\n4cTvtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['repeat_letter_ratio'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['repeat_letter_ratio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 连续数字占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce679f1c18>"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gh+A//wm+9Cp46iG4bwW1y95VLB+LWo0t3/4AXPl++MYZcMsXx7a/BvUODLOlb3BS3kvS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\nQfkdFS5GNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['consecutive_digits_ratio'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['consecutive_digits_ratio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 连续辅音占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce678b80f0>"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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56nPJPVRV5aNDTZwyMRmD/uRf46lWO41uEw2uIfbwP34M2itgxc/h+KQxigLJE6G5VFtk\nJUZP/T6ISft0ODlrLsRmwLH1Y9suERSD9nRVVV0LhEXKpg6HF5fXP+ge3R5WsxGb0xOyD0oxet47\n0IDX7+ecqem9Hr91WRHPbK3kwbXl/Pi8KSe9bsC51eN65ne/e4gOh4dVtyyhKDW213HFaVbOn5nJ\nKztrWH2wkeWT0wav7MKKwd/UAI40dVHd5uBrZxT1+fw0axcA+2wW0qLaAz/xx09AxkwoPPPk55KL\ntAU+jhawJA+n2ScJ9uiI2dWEzu8BpwGiYkEZhSH/znqo3QnOdm3O1WiGWVcG51peF7SUQsHpnz6m\nKNoQf+W4Xb8qhiCiMlL19GZSh9DT9atgd/tGs1lilDXaXGw92sLCCckkn1DOcUJKDBfMzOLxDce4\nbdnEYc3tHqyz8cSmCq5ZnM+kDGufxyyekERFcxfv7a/XUpCOzhquT3x0qBGAZSWpsO9l2PMCzL5K\n29up6Mg1u7EatHndM1ICDLotR6BmO5z9m9693B5JE7XvzaVBC7oDDhEDLPpewOcyemzMKLsPneqD\nI2hBsGgFTL5gZI080a5noKUMjBYwRIGjVQuSCbkjP3fTQW0hW9oJqRDyFmsrym11YM0Y+XXEmImo\noFvfoSXGGEpPFyRBRrhbc7gRvU7Reph9uO2MIl7ZWcOjG47yrRXFfR7TH1VV+c2r+4iNMvCds0r6\nPU5RFC6ek0Ntu5NVWypZOt/EWVmjV1BjzeEmCpIt5Bra4OVvaNt69r3EbGM8tcmLqU9ayNRYO/s6\nLahqdwwdrPdtb9a+T7u47+et6WCK0YJu7qKgvp9gyGjZjKL6KcteSVGiERr2Qem7kD0veIGq9ZgW\ncCedD8Wf037u7/xMC5bBCLr1+8BghqTC3o/3/LwrN8HUlSO/jhgzEVVlqKenG+icbk+CjA5JkBG2\nulxedlW3MzM7gdiovu8hp2TGsWJyGg+tK6driAvn3t3fwNrSJr5zVjGJMQOvHDUZdFyzOJ+YKAM3\nrU/gK2vjOdapp9WlsK3GxYZt29i1eTX79u1CqdqM4mobUlt6eH1+Npe3sLQoGV77Hvjc8PVNcNkj\nuIzxFNS9RVxXOdOsdprcRhrdAfbu97wAuYv7Dx6KDpKKtKA7zuh8LtJattESN5mmhFlQcCrMvlrr\niR54NXgX2vWM9j17vvY9ygrWLGg8OPJzq6p2o5A6WVs1fryMmVowrpAh5nAXYUG3OwWk9HQ/M17b\nXYvb62d+wcAr1r++fCJtdg9PbDoW8LmdHh93vLaP4rRYrl6cH9BrUmKj+NbyYn4608bmJj3/9/Yh\ntr/5CDO3/YQltY8xs+Elph55iCt338hF75/NhPKnh5zub3d1O50uL5eYt8DB1+HMn0LqJJh2MQfy\nr8Gjt5DespWp3ft1A1rFbKvVVs1O/+LAxyVP1IZT7S1DavNoS23bgcHvpDb5uJXVphhteLl+jzZ0\nPlKqCjuf0n4GluNWR6dOgtYj2s3PSHRUgavj5KFl0LYKZc2Ved0IEGHDy05iTPp+ezwn6il60CEr\nmMPWqi2VpMRGkZc0cGCZm5fIspJU7nmvlJWzswOagrjjtX0ca7bzxE2LMPaxQrjf+UhVZVF8Pdcn\nvIqxs4ZOQyK1yWdizpuD25xGW3s7a5osTK98itMO/JYJDW+zefqv6IwJbHhyw5FmErAxZ8+dkDkb\nFt/26aV1BhoT55DZtJ4JGY3E6PM51GUefF635mOtJzvY0GVy97xuSylYFgbU3lGn+sls3kiHJZcu\nS07v5yacDkfXaL3dJd/se646UFVbtaHlWVf2fjylBI58oG2nGome3nLayQv+AMhbBOvvBY8DjANX\nURPjV0QF3QabK+BeLoBRryPaqJeebpgqbbCx7Vgr503PQAngw/RXF03jc3/5iF+/so+/Xz13wGP3\nVLfz5OYKbj5tAksnBp4JyORup6j6JbAfw2hJhjnXEps1h9jjVrbmxKfQ5WvhRceNvNKwl1+3PsYF\na1ZyJPvztMRPH3SP6YbSJv5sfRKdsw2uewn0vX+N6xPnkdm0nozWbUyMmcbhrkE+oFVVC7rJE7We\n80CsGdoCouZSyBkfQTepYz9RnnaOZZx78pOGKCg5B3Y/q/Xk00ewX3vX02CIhoxZvR9PLtKGg5sO\nDf/cAB3VEJ2oDVn3JXcR+O/W/q3yTxnZtcSYiaig29jhCnjlcg/Zqxu+Vm2pxKBTmBNgMpSClBi+\neeZE/vTOIS492MCZk/peeNVmd/PCx1XMzInnf86ZHHB74jqPMLHqBRTVB9Mv1Vac6vr/FftCZgt/\ndsxnedsMXoj7E8VVL1DjqIX8S0+e0+vm9voprHiW5boPtWHljBknH2NKoM1aTFrrdiZbL+aZugzs\nvgFmktqroKsRipYP/iY/mdcdQq9u0O1TI6CqZDZvwGFKotXaz0K33MVwZDUceF1LODGc3q7XBXue\n11ZCn5gGU2+CxEJtMdVItFdDXE7/z/fc5FRslKAbxiJqTrfe5gx45XKPuO69uiK8uL1+XthezYop\naQFPJwDcsqyQotQYfvbSHhx9bBXz+VVWba1EVeFvV84JLLWjqpLZtI7Jx57AY4hhb+FN2kKeAQIu\naJ/9txXUopitXGD/BdUJ88lq3gCb7wNH34usDu/4iJ8oj9CYfiqc9v1+z12XtACjz845us2oKJR2\nDfB7UbMdFL22WCcQyUXaSmdHa2DH98Xvg44aUtp2klv/LmZX07BOY3HVE+uooS55Uf/BVKeHiWeB\nrQaaDw+vvYff1t7viUPLPVJLtMIEnQ3DO7+7S7vxiR+ggFtMMiQXQ+Xm4V1DjAsR09MdajaqHlaz\ngaYm1yi1KvIMlsxgpFRVxenxD5pO8f0D9TR3ubliQR617YFnm4oy6PntxTO44v6N/PTF3RSlxZLY\nXaHncION13bV0mBzcfn83IALGWQ3riancQ3NcVM5knURfn3g+XGj9X5uya/jFwfzud9wNV/OyqSw\n7k1Y/TuYdB4UnPZpr9fdRe6HX6WJeGIv+zfo+r8h6IgpxGFKZp5jHQoXdQ8x95EExuvSFuekT9MW\nHgWiZ163uQxy5gf8Xj/haIU1fwJ3Jz2pPUzudspyB1nE1Ye4Tm2BVKt1kBGJrLmw/xUo/1Cbgx2q\nnU9DbDoUnqFl7TpRyiTgNTjyIcy8bOjnr98HqBA3SNXUvEVaj/2TfWAi3ERM0O10eXF4fMMKujan\nF1VVA5oXFMHn9PjYUdlGaYONww2d2Jxe5uQmcMGMTCx99GJtTg93v3OYzHgzp5eksmpL5ZCut7gw\nmZtPm8ADa8oBLYGGSa/jYL2NpBgT1y7OZ0pmXEDnSm39mJzGNTQkzKY86/PD+iCcHOtgSqydV+qT\nOHv6XAonzdKGMve9pPVq4rO14NhZh8XVws/j7uIvKYNk31AU6pPmU1D3FivMBzjUmQvYTz6uYiN4\n7FDURwaq/sRladtXWo8ML+hWbdF6drOuYmdXAhnNG0lp243O5x7SDQtAfNdRHKYUPMZ+5kF76I3a\nkOzhd7Qe5VC0VWhz3ad866T5808bkqPNdR/5YHhBt26n9n2woJu7CD5+XJtTTxnannNAC9bNpZCY\nrw2Li5CLmOHloSbG6GE1G/GpKq12GWIeCxXNdi7+x3qe2VrJ/lobBckxLClKZmdVG3e/e4hdVW29\n6tZ6fH5ue2I7ZY2d/OHSmeh1w7tR+ukFU1nzgzM5a0o6HQ4P5c1dnDMtg2+vKA444MZ3ljKh5lXa\nYgo5mnXBiHoeF2c20eIx8lFznJZzd+EtMO8GbTtRcyl0NeI3xPA/vttInhTYfF5Twmw8+mi+rX+O\nw13R+E/s6Pp9WpBIKtKq2QRK0UFCPrQeDfw1PVQVqrdpyR9yF+KMSqEpfiZ61UOibWhzoorfh9V+\njPbYANuev1Rre/maobV58/2AAgtvHqAxOq0HXfbB8HJT1+3Wgnb0IOsTchdr34e7daj2Y9j4d+2G\nToyJiOnpDjUxRo+ebUMNNidJgyQ/EMG1+mADtz+9A1VVuXpRHlMy49B1B675+Ym8sL2ap7dUsr6s\nmazEaM4oSeUnL+xmzeEm/u/SmZxWnDqi6+cmWVg+OY0zJ6WiwifXDkjdboorn8NuTqM09zJUZWTV\nhWZa7RRaHLxcn8wP/G0YdApkztK+um1qMPLiR4k8WBRYCkafPoqalFOZXv8OM9UDHLFlMDHuuHns\nmm3gbIMZlw+9wYn5Wq/RO/jUzKbyT/f0Wpx1zOispzxzPg3dj3dacnEZ40hu30NzwskLw/oT46xG\n7/fQERNg0DXHa9WTqjZpOZPNAdxcuTph23+0rVTxAyxyAm2IuXYHNB3W5niHom631ssd7P9g8kSI\nToJjG2DONUO7hrMDdj+n3SBUbITC5VKjdwxETE+3oWNoiTF69CTI6Hm9CA6fX6XD4aGmzUGHw/NJ\nb9Xr87P2cBM/en4XNzyyhcx4M69881SmZcX3CnqZ8dF8dVkRF83Kot3h4YaHt3DqXR/w7LYqbl9R\nzGXzg5Byr5uiKEMLuJ0N8OQV+HRRHMy7Ep9+aDd6fbcBLs5opt5l4rWqvs+3odGEToEFQyhbV5+0\nALs+jh8anmZ783H32KofSt8Ha2b/+0IHklgAqNrQ6xAkt+3Bj46WuOOuqSg0x00nvrMMg7ePIfB+\nxHeWowIdlsASlwDavl2vSyuTF4idT4Grvdde6H4ld6durNoSeHsAfF6taH1c1uDH6nTaMPmxtUO7\nhqrC7me0BB6LvqYtnDv81tDOIYIi8nq6cUP7AOxJBVnfMbLSb0ILtJvKm1lb2kS73dNr2Y7ZqOPZ\nbVUca+6i1e4h2qj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D69eGnPs02gt1ehZTVW7SyiCeqKtZS7945k9Gdp2YZFj5D3jii/De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\nOI6TJf4Hs6jUgBA8YXAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['consecutive_consonant_ratio'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['consecutive_consonant_ratio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### n-gram平均排名"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### uni-ave、uni-std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce6777f400>"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XFCnJdn4RmQ1K2hVgdGKVjjz2aTdl1tDO13Kc07mseZCBlI+njpbQXORj+UNw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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['uni_ave'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['uni_ave'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce6763b4a8>"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dzOOOuTFzlrZWNqi9WkraQliQ9Z6aYlYYHE2Q73GcSMxKKe5eM4+PrJnH5TUB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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['uni_std'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['uni_std'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### bi-ave、uni-std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce674852b0>"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sxv6mdxxL27K7gy27O3jy1Q5WVfTxnwdjx9K27J7k42Yl82DN78HRPbDnVzku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\nwVxERKTIKZiLiIgUOQVzERGRIvf/Axz0Q8tJyMbhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['bi_ave'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['bi_ave'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce6734a668>"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7tNjzZsO1T0PuDPj9Fda0NaWUQpO5Lfr9IbxuB4543mc7jiycnkVbn5/0/c9A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\nf0CSuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['bi_std'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['bi_std'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### tri-ave、tri-std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce67273d30>"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CCfmW+hRFB+0n+8MoloiILGBlhbyZXWtmu8xsj5ndNMbzaTO7N3j+YTNbN+K5m4Ptu8zs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\nScxwF/DdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['tri_ave'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['tri_ave'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce670ea630>"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U5gVYOLWC13YfpaktnO3qGGOMOQUWAGTA8TEAo+/t/rPZ40gklIf+sCPbVTHG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\nSVKDDAAkSWqQAYAkSQ36G1S7VAC/iKL8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['tri_std'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['tri_std'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HMM概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce29a9fd68>"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VGOfuj7i7A78ALuzz3YiIyLAWDIA7dClXUDAvZRqwNWe/OUwrmMfdU8AuYHJeno8Aj7t7\nZ5i/uUSZIiIiB0mmMgVfsgK5febVNwAuESFPob7s/CfVbR4zO56g6f2cHpSZPXcZQXM8M2fOLHWt\nIiIyjCVTmYKvPwUwM2rjMdXMi2gGZuTsTwe2F8tjZglgPNAa7k8H7gE+6e7P5+SfXqJMANz9Rnef\n7+7z6+vrI1yuiIgMV8l08dHsENTO9aKVwtYAc81stpnVAouBhrw8DQQD3AAuAh5wdzezCcBvgK+5\n+1+ymd39ZaDNzE4LR7F/Evh1H+9FRESGsWQqQzrjRQfAQTDXXDXzAsI+8CuBVcDTwB3uvtHMrjGz\nC8JsNwGTzawJ+AqQnb52JTAH+LqZrQs/h4XHrgB+CjQBzwO/LddNiYjI8NPemQJgRLc185j6zItx\n95XAyry0q3O2O4CLC5z3LeBbRcpsBE7oycWKiEj1au8IgnldTeHR7ID6zEVERIayts4uoFTN3BTM\nRUREhqooNfMa1cxFRESGrmyfefej2WNazlVERGSoOjAArpuaeaI6B8ApmIuISEVo29/MXjx01Wqe\nuYiIyNB1oJm9m9HsmmcuIiIydLV3pIjZgTXYC9EAOBERkSGsvTNFXSJOsHBoYTXxGJ1qZhcRERma\n2jpS3faXQzDSPamauYiIyNDU3tnV7Uh2CPrTO7sUzEVERIakoJm9RM28Rs3sIiIiQ1bUZvbOVHqA\nrmjoUDAXEZGK0N6R6nZaGgRT01QzFxERGaLaOlOMKFkzj5NMZXCvrlXgFMxFRKQiRKmZZ/vUq612\nrmAuIiJDXiqdYV9XOlKfOVRfME8M9gWIiIjQ+LPC6fMvB2BPZzCoreTUtPD1qNX25rRINXMzW2hm\nm8ysycyuKnC8zsxuD4+vNrNZYfpkM/ujmbWb2fV55zwYlrku/BxWjhsSEZHhp62zC+j+9ae5x6tt\nRHvJmrmZxYEbgLOBZmCNmTW4+1M52ZYCO919jpktBq4FLgE6gK8DJ4SffJe5e2Mf70FERIa5/S9Z\nqVGfeSFRauYLgCZ33+zuSWAFsCgvzyLglnD7LuBMMzN33+PuDxMEdRERkV5p78i+y7z0aHag6laB\nixLMpwFbc/abw7SCedw9BewCJkco+2dhE/vXrbuV80VEpKq19bhmXl3N7FGCeaEgmz+BL0qefJe5\n+1uAM8LPJwr+cLNlZtZoZo0tLS0lL1ZERIafbM08ep+5aub5moEZOfvTge3F8phZAhgPtHZXqLtv\nC/9sA24jaM4vlO9Gd5/v7vPr6+sjXK6IiAw32T7zEaVq5jUK5sWsAeaa2WwzqwUWAw15eRqAJeH2\nRcAD3s3yO2aWMLMp4XYNcD6woacXLyIi1aHnfebV1cxecjS7u6fM7EpgFRAHbnb3jWZ2DdDo7g3A\nTcByM2siqJEvzp5vZi8A44BaM7sQOAd4EVgVBvI4cD/wk7LemYiIDBttnSnMoCZiM3u1vdM80qIx\n7r4SWJmXdnXOdgdwcZFzZxUp9uRolygiItWuvSPFmNoEsRJjpTWaXUREZIhq7+xizIjS9U/1mYuI\niAxR7Z0pxtRFCOZVOjVNa7MPtGLrD8P+NYhFRORgbR2pSDXzWk1NExERGZqi1sxr42EwV5+5iIjI\n0NLekWJshJp5Ih4jEbOqa2ZXMBcRkSEvas0cgn5zNbOLiIgMMe0dKcbU1UTKW1cT1/vMRUREhpJM\nxmlPRhsAB9maeXU1s2s0u4iIDGl7kincYWyJZvbbVr8EQDKVYdMrbfv3AT526sx+vcbBppq5iIgM\nadmXrEStmSfiRipT6sWdw4uCuYiIDGnZl6xEHQCXiMVIpRXMRUREhoy2ntbMY0ZXRgPgREREhoxs\nzbxUn3lWIm6qmYuIiAwlPe4zj8VIqWYuIiIydPS4z7wKa+aamiYiIgOjuxdNdSPbZz424qIxNfGY\nRrOLiIgMJdma+ei6eKT8iZiRSquZ/RBmttDMNplZk5ldVeB4nZndHh5fbWazwvTJZvZHM2s3s+vz\nzjnZzNaH53zfzKwcNyQiIsNLe2cXI2viJOLR6p+aZ16AmcWBG4DzgHnApWY2Ly/bUmCnu88BrgOu\nDdM7gK8DXy1Q9A+BZcDc8LOwNzcgIiLDW3tn9KVcQfPMi1kANLn7ZndPAiuARXl5FgG3hNt3AWea\nmbn7Hnd/mCCo72dmU4Fx7v6IuzvwC+DCvtyIiIgMT20dqcjT0iBsZtdo9kNMA7bm7DeHaQXzuHsK\n2AVMLlFmc4kyRUREel4zjxsZh3QVNbVHCeaF+rLzn1CUPL3Kb2bLzKzRzBpbWlq6KVJERIajto7o\n7zKHoJkdqKraeZRg3gzMyNmfDmwvlsfMEsB4oLVEmdNLlAmAu9/o7vPdfX59fX2EyxURkeGkpa2T\nKWPqIudPxIP6YrqK+s2jBPM1wFwzm21mtcBioCEvTwOwJNy+CHgg7AsvyN1fBtrM7LRwFPsngV/3\n+OpFRGRYy2ScV3Z1MHXCiMjn1IQ1864qamYv2W7h7ikzuxJYBcSBm919o5ldAzS6ewNwE7DczJoI\nauSLs+eb2QvAOKDWzC4EznH3p4ArgJ8DI4Hfhh8REalGyT2w/k7o2gtT3gT1x8K4abTuTZJMZ5g6\nLnowz9bMq2mueaROCHdfCazMS7s6Z7sDuLjIubOKpDcCJ0S9UBERGab27YTVP4K9O2B0PTxzb/CZ\nOItX6s8HYOqEkZGLy85Hr6a55lrOVUREBs/u7bD6x5DuhAWfhSlzoWMXbF8HT93DuD9/E/ggU8f3\noGYey9bMFcxFRET6V8dueOQ/IV4L7/wCjDsySB8xHo5+D3S8wcznb+UDsclMHX9W5GL3B3ONZhcR\nEelnz62CVCec9rkDgTzXceezbcwJfKfmJ0zu3Ba52Gwze1cV1cwVzEVEZODtaYGXHoGZ74AxhxfO\nE4vzk8P/lYzFif3qcsikIxVdjTVzNbOLiMjA27QSYgmYe2632Z56tYOf1X2cL778I7j3yxzTedT+\nY8/PLDjuOmc0u2rmIiIi/eONrbD9cZj9HhgxrtusL++Ls2XsfBhzGDT9HoovYbJfTaz6RrMrmIuI\nyMB65l6oGQ3HvL/bbBmHV/fFOHyUwzFnwe7tjG9vKll8Nc4zVzAXEZGB07oZdmyCuWdBTfdzx1s7\njWTGOHJUBqadDCMnMq3lzyVr59U4z1zBXEREBs4Lfw6C+FGnl8z68r44AEeMTEMsDse8n7H7mhm7\n98Vuzzswz7x6auYaACciIgOjsw1efhJmnR7MLS9i9ZbgPV1r3hgDTKJ1505WJzuxzJs4MTGaaS0P\n88zoWUXPPzCaXTVzERGR8tq6GjwNM98ZKfvryaC+Oak2BYDHanhl8mmM37OZUfteLnpePAzmmmcu\nIiJSTplMMK980jEw9ohIp7Qma0hYhnGJA/PLX5t4Mmmr4fDWNUXPMzMSMauqeeYK5iIi0v+efwD2\nvh40sUe0oyvBpJoUYUUbgHR8BDsmvIUpuzZQm9xV9NxE3NTMLiIiUlaNN0PtGDjirZFPaU3WMDls\nYs/12qT5xDzF0dv+u+i5NbGYFo0REREpm13b4NnfwoxTg1XfIno9mWBSbdch6XtHHEHbqBnMfel2\n8MJN6Ym4VdVodgVzERHpX48vD+aGHxVt4BsEC8a0diWYUnNozRzg1UmnMHbvVqbu+GvB44lYTM3s\n+cxsoZltMrMmM7uqwPE6M7s9PL7azGblHPtamL7JzM7NSX/BzNab2TozayzHzYiIyBCTScPjv4Sj\n3wujJkc+bXcqTspjBWvmAK1j38y+2km86cUVBY+rZp7HzOLADcB5wDzgUjObl5dtKbDT3ecA1wHX\nhufOAxYDxwMLgR+E5WW9z91PdPf5fb4TEREZejY/CLu2wkmf7NFpreG0tEJ95gAei/P8jI9wZMtD\njN576OtRg9HsqpnnWgA0uftmd08CK4BFeXkWAbeE23cBZ5qZhekr3L3T3bcATWF5IiJSDf72Cxg5\nCY77YI9Oe72rBoDJNYVr5gBNMz6KY8zZeschxxLxmOaZ55kGbM3Zbw7TCuZx9xSwC5hc4lwH7jOz\ntWa2rOeXLiIiQ9qe1+GZ38BbL4FEXY9Ofb1EzRxg78gj2Hb4+zhm693E0p0HHdM880NZgbT8/+4U\ny9Pduae7+0kEzfefN7N3F/zhZsvMrNHMGltaWiJcroiIDAlP3g6ZLjjpEz0+dUe4YMzYnAVjCnl2\n5mJGdL3BzFfuOyg9EY+RVjP7QZqBGTn704HtxfKYWQIYD7R2d667Z/98DbiHIs3v7n6ju8939/n1\n9fURLldERAade9DEPu1kOPz4Hp/evK+OI0ckD1owppBXJ5/KrtGzDhkIl4iZmtnzrAHmmtlsM6sl\nGNDWkJenAVgSbl8EPODuHqYvDke7zwbmAo+Z2WgzGwtgZqOBc4ANfb8dEREZErathZanezzwLeul\nfXUcNbKzdEYznpu5mCm7nmTiro37k2viamY/SNgHfiWwCngauMPdN5rZNWZ2QZjtJmCymTUBXwGu\nCs/dCNwBPAX8Dvi8u6eBw4GHzewJ4DHgN+7+u/LemoiIDJq1P4eaUXD8h3t8ansqxutdNcyMEsyB\nLdMuoCs+8qDaeaLKVoCLtBSPu68EVualXZ2z3QFcXOTcfwf+PS9tM/C2nl6siIhUgD2vw/o7g4Fv\nI8b1+PSX9gWD5aIG866asbxw5PnM3tbA48d9lWTt+HBtdtXMpT+0PAt7W4O+JBGR4epvP4dUB5z6\n2V6d/uK+EQAcNaoj8jnPzbyERKaTo5vvAcLR7KqZS9mku+CpX8PqH0PzY0FaYgSMOxKOPAmOOh2s\nxAgPEZFKke6Cx34Ks98Dh+evLxbNS/vqGJtIMaHESPZcb4w7llcmLeC4F5bz7FGXkogHy7m6O1YF\nv2NVM+9PzWvheyfCr5bCnhY49//AWy4ORnemkrDhLmi8CZJ7BvtKRUTK4+kGaNsOp32u10W8tDcY\n/NbTGLxhzmcY1fkaxzTfQyIcBl8tq8CpZt5fnn8AVnwcxtTDpbfD3HMgFoPGnwXH3WHLQ8EX/6Hv\nwklLui9PRKQSPPojmHR08DuvF9IOWzvqOHPKGz0+97VJp/DaxLczb/NPGTEtWLoknXFq4iVOHAZU\nM+8PG++BWz8Kk2bDp1fBsQuDQJ7LDI5+D5z+RYjF4dEfwIuPDM71ioiUQ/PaoDtxwWcO/Z0X0Uvt\ncTozsciD3w5ixoZjPsvojld5Z3uwiExXlbxsRcG83NbfBXdeDtPnw6d+A2OP6D7/hJlw+pdh5AT4\nr0vg1Y3d5xcRGapW/xBqx8KJH+t1Ec/sChqMjxoZffBbrlemvIMd49/KOa23kiBVNc3sCubl9MLD\n8N9XBIPaPn53EKCjqBsDp14RzMlc/mHY+UK/XqaISNm9uhE2/ApOXtKr6WhZT+9KYDjTRyZ7V4AZ\nG+Z8hkldr/Ch+MNVM6JdwbxcWjbBio/BxFmw+JdQO6pn54+aBJ+4J5jOsfxDwTxNEZFK8furoW4s\nnPEPfSrm6V0Jpo5IUhvrfRDeXn8GzSOP40uJX2Fd7X26nkqhYF4O7a/BrRdBvBYuuxNGTuxdOYe9\nGT52B+zaBrdfBqle9BmJiAy0pj9A0/3w7n8MKiZ98MyuRLRlXLtjxu+mf4mptPLOF3/Yt7IqhIJ5\nXyX3wn8thvaWYNT6xFl9K2/mqfChH8JLj0DDF7TAjIgMbZl0UCufOAsW/H2fimrrMrbuifdu8Fue\nl8e/jV+mz+LkV+5g8htP9rm8oU7BvC8yabj772Hb3+AjP4XpJ5en3BM+Au/7V3hyBTz0H+UpU0Sk\nPzzxX/DqBjjrGz1+Z3m+A4Pf+h7MEzHjO6lLaKuZwoIN3wgWsxnGNM+8L+77V3jmXlj4bXjz+eUt\n+91fhdeb4I/fgnFT4e0fL2/5IiJ9tbcV/vBvMP0UmHdhn4v7ybOjGBl35o7eFyn/MS/dWfTYtnHn\n084o/mf6P3DZlqvgL98Lfq8OU6qZ99YjPwjmhp96BZx2RfnLN4MLvg/HvB9+fWUw5U1EZKhIp+DO\nT8G+nfCB/+jzstR/fLmW+7bX8YU372FcTfRlXIvJrgC3fszpvHjEOfDgt2Hzg30ud6hSMO+Nh6+D\nVV+D486Hc/+9dP7eStTBJbcGU93uXgZP5b9GXkRkkNz/v2HLn+D86+DIE/tUVEcavrFuDEePTbH0\nTXvLcnk18SC8pTLOYydcDVMGyLkaAAAMKUlEQVTmworLYPvjZSl/qFEw7wl3uP8bweeEj8DFPw9W\nb+tPtaPgYyuC9dzv+nQwj1NEZDA9eSc8cn2w0tvbL+tzcTduGsWLexJcc2IbtWWKSvvXZk87XTXj\nw7U/JsEvL4LXny/PDxlCFMyj6uqAe78U1MpPvhw+/BOI1wzMz64bG0x5m3ZSENB/98/DfjCHiAxR\nm34LDVfCUe8qS8vkM7vi3PDMaD44vYN3HV6+32uJePZFK+FyruOmBmt54LD8Qnj1qbL9rKFAwTyK\nrWvgx2fA2p/Du74cNCv1d40838gJsOReWLAMHr0BbrkAdr88sNcgItUrnYL7vxlMxa0/NmiZ7EOF\nZvWWVv7w3BsseWgso+Ip/m7SVlZvaWX1ltayXG4iXBv+oBXgpsyBy+6Crn1w43vhkRsgMzzWbo8U\nzM1soZltMrMmM7uqwPE6M7s9PL7azGblHPtamL7JzM6NWuaQ0P4arPoXuPmc4C//43cH0y8G6924\niVr4wHeDVoHtj8N/nhTM79RqcSLSn15/PqjNPvz/4ORPwafvC94I2QcZh+u3HMmOZA1fPno7E8sw\n6C1XPGxm78oP1tNOgisegTlnwqp/Du7rlfVl/dmDoeTUNDOLAzcAZwPNwBoza3D33DaKpcBOd59j\nZouBa4FLzGwesBg4HjgSuN/M3hSeU6rMwZFOBQu2NN4ET98LmS6Y/2k465t9Wm+4rN760aAP/cH/\nC3/5Pqy5KXiF6nEfgBmnDlzzv4gMX5kMbP4jrP4xPHdfMCB30Q/61Ef+RtJY11rDK/ti3PvCVB7f\nPYalM1/h2DHRpqL1RDxmxCyomb+8ax9fuX0dXzhzLrOmjA7+I7L4NvjbLUG35Y/eBdMXwClL4bgP\nBl2bFSbKPPMFQJO7bwYwsxXAIiA38C4CvhFu3wVcb2YWpq9w905gi5k1heURocz+1dkObS/D7u3B\n5/XnYOtjwQIwXXtgxISgSXv+5cEoyDJJO7y8N0ZrZ4wjR6WZXOe9q+hPPiZYqOaMr8Kfvg2P/Tho\nfq8bD7PPCJaGnTwneK/wqMnBErMjxg9894CIDF2ZNHTthT07oP1VaHsFXnsKmhth21roeANGHwbv\n+SeYfzk+5nDyf111ptK4w4jwpeGdqTTPvtLO5h3tjB9Zw7QJI9mbTHPr6hf59d+m0JkJSjCc8w5r\n5exevLc8qkQ8xmNbWvnTsy2Mqo1z1rzDg2AOQQvryZ+CN18QLHzTeDPc8xmwGBw2L5g7f/jxMO7I\n4DO6HhIjg//UJEZAfGgt0xLlaqYBW3P2m4FTi+Vx95SZ7QImh+mP5p07LdwuVWb/+sUi2NZ4YN/i\ncMRbgv91zjwNjv0A1Iws24/b3NLO0lsaaX69ni4/8M9hVDzDxDonlYHkyvtwglGYMTPisQMfd8i4\n4w7ujsOBND7O6NiHOTX2JKd3/Y23b1rL1GdWkuDQvqAuEqSJkSJBijgp4qSJkyYGuf9MC2/i4Z4f\nSCgswn9QPDdTfjm97MmwohcU5dzylZ/N1ZPr6ekt9/Res/mjnmU9/Lvt7fUU1Mu/xoMvzYsdKFh+\nz/6uDvwNd1dwsVwHl3VoCT1/lgfOLnVlBsRwaklSx6EDztLEaPLpPOknsS7xVv6afiftf42z54/r\n2JtMM6o2zui6BImYsWtfF3uTQfP4yJo440YmeL2tg5Qfesd1sQxnTNrFOye1cVhtkok1KRL9PGpr\nwsga9iXTnDvvcL5z0dsYP6pAq+WoSfCOz8Npn4MX/wJb/hy8k33D3bD2Z8ULt3gQ1BO1wS9jzwSf\nU5bC2df0300VESWYF/oeRvnV692kF/orLPjtNbNlwLJwt93MNhW5zjJ4KPz0qynAjv4o+E/9UejA\n6rdnU+H0XIrTsymuD8/mDWAD8N9lvBx4FriprCV258BqbxuAG5fs3+3n78y/hZ+yOSpKpijBvBmY\nkbM/HdheJE+zmSWA8UBriXNLlQmAu98I3BjhOiuCmTW6+/zBvo6hSM+mMD2X4vRsitOzKWy4Ppco\njRxrgLlmNtvMagkGtOUvRdYAZP/fcxHwgLt7mL44HO0+G5gLPBaxTBEREYmgZM087AO/ElgFxIGb\n3X2jmV0DNLp7A0HLyfJwgFsrQXAmzHcHwcC2FPB5d08DFCqz/LcnIiIy/JnrfdkDysyWhV0HkkfP\npjA9l+L0bIrTsylsuD4XBXMREZEKp+VcRUREKpyCeRmZ2cVmttHMMmY2P+9Yj5a1DQcHrjaz58Kl\ncmsH8l76k5mdaGaPmtk6M2s0swVhupnZ98Nn8aSZnZRzzpLwWTxnZkuKl175zOx/hd+JjWb2nZz0\nyl0auYzM7Ktm5mY2Jdyv6u+NmX3XzJ4J7/0eM5uQc0zfmRzD+r7dXZ8yfYA3A8cCDwLzc9LnAU8A\ndcBs4HmCgX/xcPtooDbMMy885w5gcbj9I+CKwb6/Mj6n+4Dzwu0PAA/mbP+WYH2C04DVYfokYHP4\n58Rwe+Jg30c/PZv3AfcDdeH+Yb39Dg3HD8GU1lXAi8AUfW8c4BwgEW5fC1yr70zB5zSs71s18zJy\n96fdvdCiNvuXtXX3LUB2Wdv9S+W6exJYASwKl8J9P8HSuAC3ABf2/x0MGAeyC92P58AaA4uAX3jg\nUWCCmU0FzgV+7+6t7r4T+D2wcKAveoBcAXzbgyWQcffXwvQefYcG4boHynXAP3LwIlNV/b1x9/vc\nPRXuPkqwbgfoO5NvWN+3gvnAKLQk7rRu0icDb+T8A81dBnc4+BLwXTPbCvwH8LUwvafPaTh6E3BG\n2MXyJzM7JUyv+mdjZhcA29z9ibxDVf9scnyaoJUC9FzyDev7HlorxVcAM7sfOKLAoX9x918XO61A\nWnfL2kZZQndI6+45AWcCX3b3X5nZRwnWKTiLni8LXJFKPJsEQZPwacApwB1mdjRlWBq5EpR4Nv9M\n0KR8yGkF0obV9ybK7x0z+xeC9TxuzZ5WIP+w+870wLD5PhSiYN5D7n5WL07r6bK2OwiaChNh7bzo\ncrdDVXfPycx+AXwx3L0T+Gm4Xew5NQPvzUt/sEyXOuBKPJsrgLs96OR7zMwyBGtJ93lp5EpQ7NmY\n2VsI+n2fCHqhmA78LRw8Oey/N6V+74SD+84Hzgy/O1Al35keiLI0eeUa7E774fjh0AFwx3PwQJTN\nBIMxEuH2bA4MyDg+POdODh4A97nBvq8yPp+ngfeG22cCa8PtD3LwQKbHwvRJwBaCGuvEcHvSYN9H\nPz2bzwLXhNtvImgWtN58h4bzB3iBAwPgqvp7QzAO4CmgPi9d35mDn8ewvm/VzMvIzD4E/CdQD/zG\nzNa5+7neu2Vt/wlYYWbfAh5nIF821P/+HvieBS/l6eDAW/FWEoxMbgL2ApcDuHurmf0bwZr+EAS7\n1oG95AFzM3CzmW0AksASD34TaWnk4qr9e3M9QcD+fdhq8ai7f7aXv3eGLS+yNPkgX1bZaAU4ERGR\nCqfR7CIiIhVOwVxERKTCKZiLiIhUOAVzERGRCqdgLiIiUuEUzEVERCqcgrnIMGJms8I56gP9cz9l\nZtcP9M8VkYCCuYiISIVTMBcZfuJm9hMz22hm95nZSDN70MyuM7OHzOxpMzvFzO42s+fCVQaztfpn\nzOynZrbBzG41s7PM7C9hvgVRfriZHWVmfzCzJ8M/Z4bpx5jZo2a2xsyuMbP2/nwIItVEwVxk+JkL\n3ODuxwNvAB8J05Pu/m6Ctf5/DXweOAH4lJlNDvPMAb4HvBU4DvgY8C7gqwRvLYvieoL3i7+V4A1e\n3w/Tvwd8z91PYTi94EJkCFAwFxl+trj7unB7LTAr3G4I/1wPbHT3l929k+DlEzNyzl3v7hlgI/CH\ncG349TnllPIO4LZweznBfway6XeG27flnyQivadgLjL8dOZspznwquNseiYvT6ZAnvx8uXl6Si+A\nEOlnCuYiUm5/BRaH25cBD4fbj3KgyX9x/kki0nsK5iJSbl8ALjezJ4FPAF8M078EfMXMHgOmArsG\n6fpEhh29AlVEBoSZjQL2ubub2WLgUndfNNjXJTIc9LYPTESkp04GrjczIxhl/+lBvh6RYUM1cxGJ\nzMwu50CzedZf3P3zg3E9IhJQMBcREalwGgAnIiJS4RTMRUREKpyCuYiISIVTMBcREalwCuYiIiIV\n7v8DUK1YTis95d8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(8,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['hmm_log'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['hmm_log'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 领域特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ccTLD特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们知道多数情况下.com的域名申请又贵又要审核，所以现在很多C&C不会选择.com，反而会选一些审核不严的比如.biz .info .ru .ws以及最近爆发的.xyz之类的ccTLD都是C&C重灾区。中国的白帽专家也可能知道.cn现在申请都得备案，所以C&C也不太可能用.cn的根域名当作C&C。值得提醒的是，这些情况并非100%确定，比如C&C可能找到一个cn域名的下级域名当C&C而主域名已备案，这些需要机器综合考虑其他特征来判断。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>legit</th>\n",
       "      <th>dga</th>\n",
       "      <th>legit_ratio</th>\n",
       "      <th>dga_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>com</th>\n",
       "      <td>375750</td>\n",
       "      <td>458675.0</td>\n",
       "      <td>0.469687</td>\n",
       "      <td>0.477920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>org</th>\n",
       "      <td>39527</td>\n",
       "      <td>10786.0</td>\n",
       "      <td>0.049409</td>\n",
       "      <td>0.011239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>net</th>\n",
       "      <td>33656</td>\n",
       "      <td>57059.0</td>\n",
       "      <td>0.042070</td>\n",
       "      <td>0.059453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ru</th>\n",
       "      <td>33606</td>\n",
       "      <td>38192.0</td>\n",
       "      <td>0.042008</td>\n",
       "      <td>0.039794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de</th>\n",
       "      <td>32594</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.040743</td>\n",
       "      <td>0.000152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>co.uk</th>\n",
       "      <td>15822</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.019777</td>\n",
       "      <td>0.000125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>it</th>\n",
       "      <td>12102</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.015128</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>com.br</th>\n",
       "      <td>11964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014955</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ir</th>\n",
       "      <td>11903</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0.014879</td>\n",
       "      <td>0.000174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fr</th>\n",
       "      <td>10340</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.012925</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         legit       dga  legit_ratio  dga_ratio\n",
       "com     375750  458675.0     0.469687   0.477920\n",
       "org      39527   10786.0     0.049409   0.011239\n",
       "net      33656   57059.0     0.042070   0.059453\n",
       "ru       33606   38192.0     0.042008   0.039794\n",
       "de       32594     146.0     0.040743   0.000152\n",
       "co.uk    15822     120.0     0.019777   0.000125\n",
       "it       12102       4.0     0.015128   0.000004\n",
       "com.br   11964       NaN     0.014955        NaN\n",
       "ir       11903     167.0     0.014879   0.000174\n",
       "fr       10340       4.0     0.012925   0.000004"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tld_df = pd.DataFrame()\n",
    "legit_tld = train_features_df.loc[train_features_df['label']=='legit']['tld'].value_counts()\n",
    "dga_tld = train_features_df.loc[train_features_df['label']!='legit']['tld'].value_counts()\n",
    "tld_df['legit'] = legit_tld\n",
    "tld_df['legit_ratio'] = tld_df['legit'] / np.sum(tld_df['legit'])\n",
    "tld_df['dga'] = dga_tld\n",
    "tld_df['dga_ratio'] = tld_df['dga'] / np.sum(tld_df['dga'])\n",
    "tld_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 域名长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lili/anaconda3/envs/dga_env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd5da928d68>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ABwD8cyHEc/q1dwD8AyjB+zSAj+nHCMHtgRJcrx42v4m/NZjCdQQudgNc6qtI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vfxutl/oOmxr/HEjg/hNa8lYni0Mk/7+MOEjtyJWHu1jCb3XwHaWfy+uC5oNerCray8X73G62nn\nwajxs2vn5N/SSvfheTD2NAztg003Qdf2KteRAQSYVdxr15FjiVoHyp+H6ePwlTfL/e/5kbyGL79R\nCtb3/Kj8fI4N08egfVP1x+N58l+158zOwfgz0LlNuu2VmB8HKw3tGyvvb4R0HHLz0La+8v5sAk4+\nCN07z+08jgWaUf1vfTou0wCdW8/t/4N6P4f5tPygodoa15V14KHKs8sBSE3LsVFGla70VhbmR2Wa\nQUXvFYoVI5WzsRyX1khj73mSOZtnTycIB3R29DYTMGr3MknmbA6cjLPv+DSvu6SXPQMrOXrwwtDI\nu7ZrgGOe5w0CCCG+CbwVKBWrbwU+Wvj6O8BnhFRcbwW+6XleDjghhDhWuL+Hz8/lrw7D8TR3Hxrn\np4cm2Dc4je16tEVMdve38P3HT/Mf+4cImzqv2NrJa3b2cPP2Lg6NzfPNR4b4yXPj2K7H3vVt/J+v\n3MKGjgjfeXSE2548w38eHGFbT4x3Xj3Aho4IH/z643TEArzv+o10W8O8+sAHsLUQY53XcbrrRs50\n30jAmqd/4l7WTN5L58yTaLjMRTdwuutGTnffxHxkgC3D32Hr0H8SsmaYj6yjO36QHSe/ylDvL3B4\nw68zFx2gdf44Lclj9E0+gO7ZTLVcXvGx54IdHB74FQbG72bryPdIhR5kou0qeuIHmGi7slwAC8Gp\n3tdy6fHPsfPkV/AQDHe/csVeF6cgQuo1VzoXkpG1tM8/T7pKc6Xzha2Hz7q5kk861EM4P0XerPym\ncTkx4JWuWQVx4UbXrOB5sgVHe6WFt+9013osvpAd7XwZA6M/LtuXCXTgaiY3H/htIrkJ5iMDnOm+\nkatm7mTn7hAHZ97Jy8a/zkcTf07QSnAk8hpmSoRwEHhlPyQya2jJnmZMdHJqzmNtKMU1zXn6Q3n6\ngzn6QhZBzeVgx1s4k8gyOpshPR7C9PKQmeAd+Y/w5A9PlVxZU9nXn+SPuS34Yfqe/Tx/bH2At2gP\n81b9QULCYsjtYuDInfCzv2aeCE9qu7C1EKabJeDlML0c424rz3obGYtsY651N90tEa7mWXZkn6B/\n9iDh+VNMNe/iWGQPTxiXsj+/icjcca60n+AG7Wm25p/D1YLMdl+Du/FmQttfzaTbRObIPQSH7qdr\nah/tuREG9Y08G9zDeOe12GuuZWdwim3JA3RNPow+sh9P00l0X8uplmt4KriHk0mNreN3clP2Z/Tn\nTy68rl2Xol9xK+x+G/HxYawjPyU4dC/tU4/iITgevpTh9uuYX3Mjsa4Bts4/Qs/4fYRO/RyRieOE\n2pjtv5HhjhsY0tZw8+N/gG6nuW3NH2He9zg9YYe1236D9U9/GvFvryG3532k5mYQE8/SNPMcppMm\np0cZar2Oyf6byW94NV1mmr74QVrGHkIbehCcPNm+65juvpbhlr2M0Uls5Odsmb6PtfGHMO0UrhbA\nGngFxq43ou14Pcn4GPah2wkcv4vo1FMAzEbWM9pzM3MDr8FYu4ee1BE6ph8lNHYQxp7GaVlPsmcv\nU+17OBO7hFz8DJ2jP2dg6n7aZ55AeC5W62bY9lqMna/H6dxF5rk70A79F+Ghe9Fc2Qk73ryLyYHX\nk9ryZppjYbrijxEdfxRjZD9eJk6u9yrmuvcy2XYl4+Za9NFH6ZneT9/0IzTHn8aO9uJsuBFzy81o\nm24il0qQf+5HGMfuIjx2AOG5pEO9TPS/kuT6X0Csu5aO1FFaJ/YTHHkIRg7gNfWRWfNyZrquYaxt\nL8lsjtjoQ/TFD9A5uR8zO4XVdxVi442YW27C67mU9MlHcY79DPPUvYSnnsIym5novI65/huwNtxE\nUzhI5/iDREfuQz9xL2Ti2N2XkFx7I9M91zMW240YfZKu8fvpm3yQ2OwhXCOMPXAD+rbXoG+9hRwG\nuUN3oR/7MaGRB9DtDOlwL5O9N5EaeDXuhhtpam6mJWzSFDLRBGQsh0TGIpGxmMvYJDIWyZxF2NRp\nDpu0lPyLBQv/J+dsEmn/GIvZjEUm79AUMuTayMIxYVPH9SiuS2QsZtN5EhkL2/GK61v980RMgoZO\n3naZzeRJpOVxs4XzaQJaIyYt4QCtJcfpmiBjOcymLWbS8riZwjEhU5NrIwHaIgFawybNYfn457I2\ns+k8M4XjZlJ5kjmbWNCgLRKgLRqgrXBsc8jA9SiszxNPWcRTOeIpi4zl0BYxaYsGaI8EaI/Kf5GA\nTt5xiafyTCfzxFPy33Qqj+t6cl0sQEc0QEcsSEc0QNDQSOcdppI5ppJ5pktuTUOjMxakIxagq3Db\nEQ1i6oLZtMVkMsfk/MK/6VSeppBBV1NQ/osF6W4K0h6V/6dNJfNMzGcZn8sxPpdlYj7HXMaiPRqg\npzlId3OI7qYgPc0h2iMB8o7LWCLL2FyWsUSW0USWsUSGrOXS0xyktyVMX0uI3pYQvc0hWiMmc1mb\n0zMZzsxmOJPIcHomw+nZDJoQ9LeGWdMaor81XPzXFDQYn88yHM8wHE8zFE8zPJPm9EyGppDB2rYI\nA+0R1rVHWNceZl1bBFPXGIqnOTmV4uR0ihOF29FElp6mEBs6I2zoiLKhM8rGzigD7REsx+XYRJKj\nE0mOF26PTSSZTuZY3xFla0+MLV0xedsdY31HlMn5HIdG5zg0Osdzo3McGp3n5HQKz4Pe5hA7+5rY\n1d/Mzr5mdvU1s7YtwrGJJE8Mz/LE8AxPDic4OjFfbKwaMDR29zdzxbrW4r+OWJCDJ+M8PDjNvsE4\nz5xO4Lgepi4Y6Ii8KMSq8BZPnF+8QIh3AK/zPO83C9//OnCt53kfLFnzTGHNSOH748C1SAG7z/O8\nfy9s/wJwh+d536l2vr1793oHDx48pwe1knzgqwf58XOyodGW7hiv3tnNLTt72DPQhq4JspbDvsFp\nfnpogp8eGudMIls8ti1i8vYr13LrNevY0l0uIJI5mx8+eYZvHhjmieFZADZ1RXnn3nXyPwknT3f8\nIGsm72XNxL3EMqfLjo837+R0903kzFb6J++nZ/oRdE92AvUQnO6+iec3vIum5AkCVoLe+CN0zTyO\n4ebK7sfRAkw37+ZE/5tqf7rquXQknmXt5D2E8jPYWognt34Q24gsWTowehd98f2MdL2C0ysoVref\n+g/CuSme2Fa5xs1vBNUIlWLAAE2pU+w6+RVO9L2Bifa9Z3WdjbDx9G20Jo/x+PYPnfV9hHLTRLLj\nxFsqx/0MO8VVh/+BoZ7XMNr58rJ9/nN12ZFPs+v4F/jWax/Fq+BIVnuelsve5/6G8fa959RFuR7d\n8UfZOPojHtv2h1hVBPxyqfQz9daf38Jk2x4euuITxW3n63nyaU88w9aR7/HMxvdVTFIA7B78AsF8\nnCe2/v6SObsAaybuYe3kfcyH1/D8+l9bsqY98RxbRr6LrYd5fNsfFpttldI18xibzvyQo2vfTrwk\nRr+Y0udpx4mvcOXzf8/+zf+dR9e+C00IhABNCDaf/i80IRtuZR2NnCsIZ8Z41ejnMT0LS5g8HdrL\ng8FXcFKshXyajdZRttlH2OkeQxMeeUxsEcAWBh1enG53csn1pLwgB9wdHPf6uUI7xmViEFOUB36O\niI3c71xC0E1zg/YMG7Txsv3zXpgD3k5OGRu4Qhxjt3OIAFbZmkPuOh50LyGAzQ3a02zSxsr2P8l2\nvmtfzwPOLm7SnuQX9Qe5XBtcdB8D3OdeiqnBK7Sn2Ep5qce018S97uU87m7hCu0YN2tP0iHmAZjx\nYvym82c8ape7i5vFab4V+Gs6xRwAU14z97hXcPD/b+/eY6wozziOf3+zF0EWWbkpF+WiK4INLLTi\nUolVbNWqqU2q0aZNbGti2tjWprWN9g+tJKRpmvTyh2lqq9Y0tdVgbUkl9QampiUIyCICEgGxoJRV\ngcqiLOyep3/MoLvLUhY9u+ecOb9PstmZd96Z85zz7M45z5mZdwrnMFNbWVDTyuna22Od12MUK+M8\nDhZqaUk2HvVc3owRPN01hxWFGcxMtvGZZA2Tkrb3lxdCrIuzeKprDu0M5dJkLfOSDdT3et23FMaz\nISYzSbs5T9uPysuLhSksK8xmbwxnQbKWlmQjJ6nz/eVvxEiWdl3A8kIzM/QaV9WspDnZ2mMb7TGE\n1mhibwxjTvIKE9RzjILOSFgXZ7GqMI1J2s28ZCON6nm7pY2FSTxdmM2uGMUlSSvzk5c4WT3fTzcV\nzuSFOIfTeZvzk5c5Re/1WL43GlhZmM6uGMn5yWZm6DUSffCZrDMSWuNsVhRmME57mJ+sPyovbdHI\nP2Mm27vGcEGyiU8km3u8poejhtWFaTxX+BhjtY9PJeuY0utveUdhDMsKzWyN8Xwy2cj8ZD0NOkhH\n1PHdw9/g8UILUnpmxOGu//+ZsbuaREQEhf6vQl3NiT0GQH1NwqGuwgmtc6KPc2Qf1XUCT6YmEYUI\njvMx+yPF9WHXqU1EZx/P5VjtUvp1b+9FEjTU17K/o/OodWqSvl+v4UNqGVJXw9vtHUdtr6916msS\nxjWmZ5Xs2nfwqFwn6hmXlBaCExqH0t7RyY4973LgUM/9iNTjroWMGFrH5NHDGD9iCLvfOcj2t99l\nz4FDx+xfX5Mwdcwwzh7bwOiGk3j1rQNsaWvn9X3vHXOdSaNOZvrppzBj/CkMqUvYtGs/m3a9w5a2\n9j5f81NPrmNWVpDOmtjIu4e63i9gX3x9HwcP93wd6mpE8xmNtEwdRcvUUcw581SG1n/4M/QGg6Q1\nEXHcD9P9KVavAy7vVazOjYhvdeuzIevTvVidCywEVvQqVpdGxKO9HuNm4OZsdhqwuV/PsnKMBt4q\ndRA2KJzr6uFcVw/nuno419XF+a4eznX5mRQRx71Je39OA94JdL8IcCLQe5SfI312SqoFRgB7+rku\nEXEvcG8/YqlIklb355sDq3zOdfVwrquHc109nOvq4nxXD+e6cvXnorhVQJOkKZLqSQdMWtKrzxLg\nxmz6WmBZpIdslwA3SDpJ0hSgCXi+OKGbmZmZmZlZXh33yGpEdEr6JvAE6a1r7o+IDZIWAqsjYglw\nH/D7bAClPaQFLVm/R0gHY+oEbsnTSMBmZmZmZmY2MPp1D4eIWAos7dV2Z7fpg0Cfo9dExCJg0UeI\nMQ9ye4qzHcW5rh7OdfVwrquHc11dnO/q4VxXqOMOsGRmZmZmZmY22D7ajRzNzMzMzMzMBoCL1QEk\n6QpJmyVtkXR7qeOx4pJ0v6S27D7DR9pGSnpK0ivZ78q/G3OVk3SGpOWSNknaIOnWrN25ziFJQyQ9\nL2ldlu+7s/YpklZm+X44G3DQckBSjaS1kv6WzTvXOSRpu6T1klolrc7avB/PIUmNkhZLejl7757n\nXFcuF6sDRFINcA/wWWAG8EVJM0oblRXZ74ArerXdDjwTEU3AM9m8VbZO4HsRMR1oAW7J/ped63zq\nABZExCygGbhCUgvwE+DnWb73AjeVMEYrrluBTd3mnev8uiQimrvdwsT78Xz6JfD3iDgXmEX6/+1c\nVygXqwNnLrAlIrZFxCHgT8A1JY7Jiigi/kE6+nV31wAPZtMPAp8f1KCs6CJiV0S8kE3vJ33Tm4Bz\nnUuRas9m67KfABYAi7N25zsnJE0ErgJ+m80L57qaeD+eM5JOAS4ivVMJEXEoIvbhXFcsF6sDZwKw\no9v8zqzN8u20iNgFaZEDjC1xPFZEkiYDs4GVONe5lZ0W2gq0AU8BW4F9EdGZdfH+PD9+AfwAKGTz\no3Cu8yqAJyWtkXRz1ub9eP5MBd4EHshO7/+tpGE41xXLxerAUR9tHnrZrEJJagAeBb4TEe+UOh4b\nOBHRFRHNwETSs2Sm99VtcKOyYpN0NdAWEWu6N/fR1bnOhwsjYg7p5Vm3SLqo1AHZgKgF5gC/iojZ\nwAF8ym9Fc7E6cHYCZ3Sbnwi8UaJYbPDsljQOIPvdVuJ4rAgk1ZEWqn+IiD9nzc51zmWnjj1Leq1y\no6Qj9yb3/jwfLgQ+J2k76aU6C0iPtDrXORQRb2S/24DHSL+I8n48f3YCOyNiZTa/mLR4da4rlIvV\ngbMKaMpGFawHbgCWlDgmG3hLgBuz6RuBv5YwFiuC7Bq2+4BNEfGzbouc6xySNEZSYzY9FPg06XXK\ny5oBN4gAAAL5SURBVIFrs27Odw5ExB0RMTEiJpO+Ry+LiC/hXOeOpGGShh+ZBi4DXsL78dyJiP8A\nOyRNy5ouBTbiXFcsRfjsloEi6UrSb2lrgPsjYlGJQ7IikvRH4GJgNLAbuAv4C/AIcCbwb+C6iOg9\nCJNVEEnzgeeA9XxwXdsPSa9bda5zRtJM0sE3aki/0H0kIhZKmkp69G0ksBb4ckR0lC5SKyZJFwO3\nRcTVznX+ZDl9LJutBR6KiEWSRuH9eO5IaiYdNK0e2AZ8lWx/jnNdcVysmpmZmZmZWdnxacBmZmZm\nZmZWdlysmpmZmZmZWdlxsWpmZmZmZmZlx8WqmZmZmZmZlR0Xq2ZmZmZmZlZ2XKyamZkVgaT2Adhm\nc3YbtCPzP5J0W7Efx8zMrBy5WDUzMytfzcCVx+1lZmaWQy5WzczMikzS9yWtkvSipLuztsmSNkn6\njaQNkp6UNDRbdn7Wd4Wkn0p6SVI9sBC4XlKrpOuzzc+Q9KykbZK+XaKnaGZmNuBcrJqZmRWRpMuA\nJmAu6ZHRj0u6KFvcBNwTEecB+4AvZO0PAF+PiHlAF0BEHALuBB6OiOaIeDjrey5webb9uyTVDcLT\nMjMzG3QuVs3MzIrrsuxnLfACaXHZlC17NSJas+k1wGRJjcDwiPhX1v7Qcbb/eER0RMRbQBtwWlGj\nNzMzKxO1pQ7AzMwsZwT8OCJ+3aNRmgx0dGvqAoZm/U9E7234vdzMzHLJR1bNzMyK6wnga5IaACRN\nkDT2WJ0jYi+wX1JL1nRDt8X7geEDFqmZmVkZc7FqZmZWRBHxJOmpvCskrQcWc/yC8ybgXkkrSI+0\n/jdrX046oFL3AZbMzMyqgiKi1DGYmZlVNUkNEdGeTd8OjIuIW0sclpmZWUn5OhczM7PSu0rSHaTv\ny68BXyltOGZmZqXnI6tmZmZmZmZWdnzNqpmZmZmZmZUdF6tmZmZmZmZWdlysmpmZmZmZWdlxsWpm\nZmZmZmZlx8WqmZmZmZmZlR0Xq2ZmZmZmZlZ2/genigEPWy9ahAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,1,figsize=(16,4))\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']=='legit']['length'])\n",
    "sns.distplot(train_features_df.loc[train_features_df['label']!='legit']['length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 模型验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "useful_features_name = ['entropy', 'vowel_ratio',\n",
    "       'digit_ratio', 'repeat_letter_ratio', 'consecutive_digits_ratio',\n",
    "       'consecutive_consonant_ratio', 'gib', 'hmm_log', 'length', 'uni_ave',\n",
    "       'bi_ave', 'tri_ave', 'uni_std', 'bi_std', 'tri_std', 'tld_0', 'tld_1',\n",
    "       'tld_2', 'tld_3', 'tld_4', 'tld_5', 'tld_6', 'tld_7', 'tld_8', 'tld_9',\n",
    "       'tld_10', 'tld_11', 'tld_12', 'tld_13', 'tld_14', 'tld_15', 'tld_16',\n",
    "       'tld_17', 'tld_18', 'tld_19', 'tld_20', 'tld_21', 'tld_22', 'tld_23',\n",
    "       'tld_24', 'tld_25', 'tld_26', 'tld_27', 'tld_28', 'tld_29']\n",
    "label = ['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1759959, 45), (1759959,), (439990, 45), (439990,))"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_features_df[useful_features_name]\n",
    "y_train = train_features_df['label'].map(lambda x: 1 if x != 'legit' else 0)\n",
    "X_test = test_features_df[useful_features_name]\n",
    "y_test = test_features_df['label'].map(lambda x: 1 if x != 'legit' else 0)\n",
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1759959,), (439990,), 42, (1759959, 42), (439990, 42))"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes_ = train_features_df['label'].unique()\n",
    "family_dict = {classes_[idx]:idx for idx in range(classes_.shape[0])}\n",
    "y_train_mul = train_features_df['label'].map(family_dict)  # label encoder\n",
    "y_test_mul = test_features_df['label'].map(family_dict)\n",
    "y_train_multi = np_utils.to_categorical(y_train_mul)  # one-hot encoder\n",
    "y_test_multi = np_utils.to_categorical(y_test_mul)\n",
    "y_train_mul.shape, y_test_mul.shape, len(family_dict), y_train_multi.shape, y_test_multi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数字特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1759959, 45), (439990, 45))"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_max_scaler = MinMaxScaler().fit(X_train.values)\n",
    "X_train_min_max_scaler = min_max_scaler.transform(X_train.values)\n",
    "X_test_min_max_scaler = min_max_scaler.transform(X_test.values)\n",
    "X_train_min_max_scaler.shape, X_test_min_max_scaler.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 选取部分数据训练与测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100000, 45), (100000,), (20000, 45), (20000,))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_size, test_size = 100000, 20000\n",
    "X_train_10w = X_train_min_max_scaler[:train_size]\n",
    "y_train_10w = y_train.values[:train_size]\n",
    "X_test_10w = X_test_min_max_scaler[:test_size]\n",
    "y_test_10w = y_test.values[:test_size]\n",
    "X_train_10w.shape, y_train_10w.shape, X_test_10w.shape, y_test_10w.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100000,), (20000,), (1759959, 42), (439990, 42))"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_mul_10w = y_train_mul.values[:train_size]\n",
    "y_test_mul_10w = y_test_mul.values[:test_size]\n",
    "y_train_multi_10w = y_train_multi[:train_size]\n",
    "y_test_multi_10w = y_test_multi[:test_size]\n",
    "y_train_mul_10w.shape, y_test_mul_10w.shape, y_train_multi.shape, y_test_multi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 文本特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_domain = train_features_df['main_domain']\n",
    "X_test_domain = test_features_df['main_domain']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_deep, domain_max_features, domain_maxlen= get_feature_vectors('model/nlp/', \n",
    "                                                                      'nlp_valid_chars_API.pkl', \n",
    "                                                                      X_train_domain.fillna('').tolist())  # 编码特征\n",
    "X_test_deep, *rst = get_feature_vectors('model/nlp/', \n",
    "                                        'nlp_valid_chars_API.pkl', \n",
    "                                        X_test_domain.fillna('').tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100000, 64), (100000,), (20000, 64), (20000,))"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_size, test_size = 100000, 20000\n",
    "\n",
    "X_train_deep_10w = X_train_deep[:train_size]\n",
    "y_train_deep_10w = y_train.values[:train_size]\n",
    "X_test_deep_10w = X_test_deep[:test_size]\n",
    "y_test_deep_10w = y_test.values[:test_size]\n",
    "X_train_deep_10w.shape, y_train_deep_10w.shape, X_test_deep_10w.shape, y_test_deep_10w.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100000,), (20000,), (100000, 42), (20000, 42))"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_deep_mul_10w = y_train_mul.values[:train_size]\n",
    "y_test_deep_mul_10w = y_test_mul.values[:test_size]\n",
    "y_train_deep_multi_10w = y_train_multi[:train_size]\n",
    "y_test_deep_multi_10w = y_test_multi[:test_size]\n",
    "y_train_deep_mul_10w.shape, y_test_deep_mul_10w.shape, y_train_deep_multi_10w.shape, y_test_deep_multi_10w.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一些函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 评估函数\n",
    "def evaluate_result(label, y_pred=None, y_proba=None):\n",
    "    if y_pred is None:\n",
    "        y_pred = np.where(y_proba>0.5, 1, 0)\n",
    "    \n",
    "    accuracy = accuracy_score(label, y_pred)\n",
    "    precision = precision_score(label, y_pred)\n",
    "    recall = recall_score(label, y_pred)\n",
    "    f1 = 2*(precision*recall)/(precision+recall)\n",
    "   \n",
    "    if y_proba is None: \n",
    "        roc = 0\n",
    "    else:\n",
    "        roc = roc_auc_score(label, y_proba)\n",
    "        \n",
    "    confusion = confusion_matrix(label, y_pred)\n",
    "    print(\"Accuracy: {:.4f}% \\t Precision: {:.4f}% \\t Recall: {:.4f}% \\t Roc: {:.4f}\\tF1: {:.4f}\".format(accuracy*100, \n",
    "                                                                                                         precision*100, \n",
    "                                                                                                         recall*100, \n",
    "                                                                                                         roc,\n",
    "                                                                                                         f1))\n",
    "    print(\"Confusion:\")\n",
    "    print(confusion)\n",
    "    \n",
    "# 加载模型\n",
    "def load_model(name):\n",
    "    architecture_path = \"model/{}/{}_model_architecture.json\".format(name, name)\n",
    "    weight_path = \"model/{}/{}_model_weights.best.h5\".format(name, name)\n",
    "    model = model_from_json(open(architecture_path).read(),\n",
    "                            custom_objects={'AttentionLayer': att.AttentionLayer})\n",
    "    model.load_weights(weight_path)\n",
    "    return model\n",
    "\n",
    "\n",
    "'''\n",
    "    序列化main_domain特征\n",
    "    save_dir：保存位置\n",
    "    X：\n",
    "'''\n",
    "def get_feature_vectors(save_dir, filename, X):\n",
    "    if not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "    \n",
    "    file_path = save_dir + filename\n",
    "    if not os.path.exists(file_path):\n",
    "        valid_chars = {x: idx + 1 for idx, x in enumerate(set(''.join(X)))}  # 统计有效字符出现的次数\n",
    "        max_features = len(valid_chars) + 1  # 有效字符长度+1\n",
    "        maxlen = np.max([len(x) for x in X])  # 最长域名的长度\n",
    "        # save the dictionary of valid characters\n",
    "        valid_chars['maxlen'] = maxlen\n",
    "        pickle.dump(valid_chars, open(file_path, 'wb'))\n",
    "        \n",
    "    valid_chars = pickle.load(open(file_path, 'rb'))\n",
    "    max_features = len(valid_chars) # 有效字符长度\n",
    "    maxlen = valid_chars['maxlen']\n",
    "    del (valid_chars['maxlen'])\n",
    "    \n",
    "    X = [[valid_chars.get(y, 0) for y in x] for x in X]\n",
    "    X = sequence.pad_sequences(X, maxlen=maxlen)  # 形成长度相同的序列\n",
    "    return X, max_features, maxlen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### logstic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model/logstic/logstic_10w.model']"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg = LogisticRegression(solver=\"liblinear\", random_state=42)\n",
    "log_reg.fit(X_train_10w, y_train_10w)\n",
    "joblib.dump(log_reg, 'model/logstic/logstic_10w.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 95.1960% \t Precision: 96.0374% \t Recall: 95.0949% \t Roc: 0.9888\tF1: 0.9556\n",
      "Confusion:\n",
      "[[43452  2135]\n",
      " [ 2669 51744]]\n",
      "Accuracy: 95.2900% \t Precision: 96.1861% \t Recall: 95.1864% \t Roc: 0.9893\tF1: 0.9568\n",
      "Confusion:\n",
      "[[ 8617   414]\n",
      " [  528 10441]]\n"
     ]
    }
   ],
   "source": [
    "evaluate_result(y_train_10w, log_reg.predict(X_train_10w), log_reg.predict_proba(X_train_10w)[:,1])\n",
    "evaluate_result(y_test_10w, log_reg.predict(X_test_10w), log_reg.predict_proba(X_test_10w)[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看权重系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_weight = { useful_features_name[i]:log_reg.coef_[0, i] for i in range(45)}\n",
    "linear_weight = pd.Series(linear_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_weight_df = pd.DataFrame()\n",
    "linear_weight_df['feature'] = linear_weight.index\n",
    "linear_weight_df['weight'] = linear_weight.values\n",
    "linear_weight_df['absolute_weight'] = np.abs(linear_weight.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JHt+DmJIkSdJAOEVQ3ToPWJpkhDH7VyVZCGwAltNe2n3hZAGqag9wWXN8e5Jt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\nSZIkSX3y/wEQwWTe6LSiQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(x=\"absolute_weight\", y=\"feature\", data=linear_weight_df.sort_values(by=\"absolute_weight\",ascending=False))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### svm 调参至最佳参数\n",
    "- 由于SVM训练实在缓慢，故此在小数据集上寻找出一组参数后，再应用与大数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
      "[CV] C=4.24023234172, gamma=0.027864182986 ...........................\n",
      "[CV] ............ C=4.24023234172, gamma=0.027864182986, total=   0.4s\n",
      "[CV] C=4.24023234172, gamma=0.027864182986 ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............ C=4.24023234172, gamma=0.027864182986, total=   0.4s\n",
      "[CV] C=4.24023234172, gamma=0.027864182986 ...........................\n",
      "[CV] ............ C=4.24023234172, gamma=0.027864182986, total=   0.4s\n",
      "[CV] C=2.82394459439, gamma=0.00563384128224 .........................\n",
      "[CV] .......... C=2.82394459439, gamma=0.00563384128224, total=   0.5s\n",
      "[CV] C=2.82394459439, gamma=0.00563384128224 .........................\n",
      "[CV] .......... C=2.82394459439, gamma=0.00563384128224, total=   0.5s\n",
      "[CV] C=2.82394459439, gamma=0.00563384128224 .........................\n",
      "[CV] .......... C=2.82394459439, gamma=0.00563384128224, total=   0.5s\n",
      "[CV] C=5.96597597003, gamma=0.00522424540472 .........................\n",
      "[CV] .......... C=5.96597597003, gamma=0.00522424540472, total=   0.4s\n",
      "[CV] C=5.96597597003, gamma=0.00522424540472 .........................\n",
      "[CV] .......... C=5.96597597003, gamma=0.00522424540472, total=   0.4s\n",
      "[CV] C=5.96597597003, gamma=0.00522424540472 .........................\n",
      "[CV] .......... C=5.96597597003, gamma=0.00522424540472, total=   0.4s\n",
      "[CV] C=8.03648083881, gamma=0.00883815461567 .........................\n",
      "[CV] .......... C=8.03648083881, gamma=0.00883815461567, total=   0.4s\n",
      "[CV] C=8.03648083881, gamma=0.00883815461567 .........................\n",
      "[CV] .......... C=8.03648083881, gamma=0.00883815461567, total=   0.4s\n",
      "[CV] C=8.03648083881, gamma=0.00883815461567 .........................\n",
      "[CV] .......... C=8.03648083881, gamma=0.00883815461567, total=   0.4s\n",
      "[CV] C=5.06645375849, gamma=0.0142220459373 ..........................\n",
      "[CV] ........... C=5.06645375849, gamma=0.0142220459373, total=   0.4s\n",
      "[CV] C=5.06645375849, gamma=0.0142220459373 ..........................\n",
      "[CV] ........... C=5.06645375849, gamma=0.0142220459373, total=   0.4s\n",
      "[CV] C=5.06645375849, gamma=0.0142220459373 ..........................\n",
      "[CV] ........... C=5.06645375849, gamma=0.0142220459373, total=   0.4s\n",
      "[CV] C=10.8925218606, gamma=0.0894004684768 ..........................\n",
      "[CV] ........... C=10.8925218606, gamma=0.0894004684768, total=   0.2s\n",
      "[CV] C=10.8925218606, gamma=0.0894004684768 ..........................\n",
      "[CV] ........... C=10.8925218606, gamma=0.0894004684768, total=   0.2s\n",
      "[CV] C=10.8925218606, gamma=0.0894004684768 ..........................\n",
      "[CV] ........... C=10.8925218606, gamma=0.0894004684768, total=   0.2s\n",
      "[CV] C=4.31768660578, gamma=0.00452054402954 .........................\n",
      "[CV] .......... C=4.31768660578, gamma=0.00452054402954, total=   0.5s\n",
      "[CV] C=4.31768660578, gamma=0.00452054402954 .........................\n",
      "[CV] .......... C=4.31768660578, gamma=0.00452054402954, total=   0.5s\n",
      "[CV] C=4.31768660578, gamma=0.00452054402954 .........................\n",
      "[CV] .......... C=4.31768660578, gamma=0.00452054402954, total=   0.5s\n",
      "[CV] C=3.2634673684, gamma=0.00826912251042 ..........................\n",
      "[CV] ........... C=3.2634673684, gamma=0.00826912251042, total=   0.4s\n",
      "[CV] C=3.2634673684, gamma=0.00826912251042 ..........................\n",
      "[CV] ........... C=3.2634673684, gamma=0.00826912251042, total=   0.5s\n",
      "[CV] C=3.2634673684, gamma=0.00826912251042 ..........................\n",
      "[CV] ........... C=3.2634673684, gamma=0.00826912251042, total=   0.4s\n",
      "[CV] C=10.2439365814, gamma=0.0647840353081 ..........................\n",
      "[CV] ........... C=10.2439365814, gamma=0.0647840353081, total=   0.3s\n",
      "[CV] C=10.2439365814, gamma=0.0647840353081 ..........................\n",
      "[CV] ........... C=10.2439365814, gamma=0.0647840353081, total=   0.3s\n",
      "[CV] C=10.2439365814, gamma=0.0647840353081 ..........................\n",
      "[CV] ........... C=10.2439365814, gamma=0.0647840353081, total=   0.2s\n",
      "[CV] C=3.10537782111, gamma=0.0146441293077 ..........................\n",
      "[CV] ........... C=3.10537782111, gamma=0.0146441293077, total=   0.4s\n",
      "[CV] C=3.10537782111, gamma=0.0146441293077 ..........................\n",
      "[CV] ........... C=3.10537782111, gamma=0.0146441293077, total=   0.4s\n",
      "[CV] C=3.10537782111, gamma=0.0146441293077 ..........................\n",
      "[CV] ........... C=3.10537782111, gamma=0.0146441293077, total=   0.4s\n",
      "[CV] C=4.77778993148, gamma=0.00753667862206 .........................\n",
      "[CV] .......... C=4.77778993148, gamma=0.00753667862206, total=   0.4s\n",
      "[CV] C=4.77778993148, gamma=0.00753667862206 .........................\n",
      "[CV] .......... C=4.77778993148, gamma=0.00753667862206, total=   0.4s\n",
      "[CV] C=4.77778993148, gamma=0.00753667862206 .........................\n",
      "[CV] .......... C=4.77778993148, gamma=0.00753667862206, total=   0.4s\n",
      "[CV] C=1.42997743622, gamma=0.0355334840598 ..........................\n",
      "[CV] ........... C=1.42997743622, gamma=0.0355334840598, total=   0.4s\n",
      "[CV] C=1.42997743622, gamma=0.0355334840598 ..........................\n",
      "[CV] ........... C=1.42997743622, gamma=0.0355334840598, total=   0.4s\n",
      "[CV] C=1.42997743622, gamma=0.0355334840598 ..........................\n",
      "[CV] ........... C=1.42997743622, gamma=0.0355334840598, total=   0.4s\n",
      "[CV] C=3.50603866037, gamma=0.00466699459088 .........................\n",
      "[CV] .......... C=3.50603866037, gamma=0.00466699459088, total=   0.5s\n",
      "[CV] C=3.50603866037, gamma=0.00466699459088 .........................\n",
      "[CV] .......... C=3.50603866037, gamma=0.00466699459088, total=   0.5s\n",
      "[CV] C=3.50603866037, gamma=0.00466699459088 .........................\n",
      "[CV] .......... C=3.50603866037, gamma=0.00466699459088, total=   0.5s\n",
      "[CV] C=10.9202322172, gamma=0.00383600166344 .........................\n",
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      "[CV] C=10.9202322172, gamma=0.00383600166344 .........................\n",
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      "[CV] C=10.9202322172, gamma=0.00383600166344 .........................\n",
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      "[CV] C=2.9648706739, gamma=0.0153965945954 ...........................\n",
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      "[CV] C=2.9648706739, gamma=0.0153965945954 ...........................\n",
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      "[CV] C=2.9648706739, gamma=0.0153965945954 ...........................\n",
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      "[CV] C=3.7611574942, gamma=0.00681051172093 ..........................\n",
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      "[CV] C=3.7611574942, gamma=0.00681051172093 ..........................\n",
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      "[CV] C=3.7611574942, gamma=0.00681051172093 ..........................\n",
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      "[CV] C=2.41619705387, gamma=0.0666932362515 ..........................\n",
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      "[CV] C=2.41619705387, gamma=0.0666932362515 ..........................\n",
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      "[CV] C=2.41619705387, gamma=0.0666932362515 ..........................\n",
      "[CV] ........... C=2.41619705387, gamma=0.0666932362515, total=   0.3s\n",
      "[CV] C=4.86248271175, gamma=0.0138347456622 ..........................\n",
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      "[CV] C=4.86248271175, gamma=0.0138347456622 ..........................\n",
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      "[CV] C=4.86248271175, gamma=0.0138347456622 ..........................\n",
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      "[CV] C=10.0278618012, gamma=0.0104043136181 ..........................\n",
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      "[CV] C=10.0278618012, gamma=0.0104043136181 ..........................\n",
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      "[CV] C=10.0278618012, gamma=0.0104043136181 ..........................\n",
      "[CV] ........... C=10.0278618012, gamma=0.0104043136181, total=   0.3s\n",
      "[CV] C=1.5864626082, gamma=0.0477832637476 ...........................\n",
      "[CV] ............ C=1.5864626082, gamma=0.0477832637476, total=   0.4s\n",
      "[CV] C=1.5864626082, gamma=0.0477832637476 ...........................\n",
      "[CV] ............ C=1.5864626082, gamma=0.0477832637476, total=   0.4s\n",
      "[CV] C=1.5864626082, gamma=0.0477832637476 ...........................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............ C=1.5864626082, gamma=0.0477832637476, total=   0.4s\n",
      "[CV] C=2.44256396435, gamma=0.00436003144247 .........................\n",
      "[CV] .......... C=2.44256396435, gamma=0.00436003144247, total=   0.6s\n",
      "[CV] C=2.44256396435, gamma=0.00436003144247 .........................\n",
      "[CV] .......... C=2.44256396435, gamma=0.00436003144247, total=   0.6s\n",
      "[CV] C=2.44256396435, gamma=0.00436003144247 .........................\n",
      "[CV] .......... C=2.44256396435, gamma=0.00436003144247, total=   0.6s\n",
      "[CV] C=8.70588598327, gamma=0.0260224683207 ..........................\n",
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      "[CV] C=8.70588598327, gamma=0.0260224683207 ..........................\n",
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      "[CV] C=8.70588598327, gamma=0.0260224683207 ..........................\n",
      "[CV] ........... C=8.70588598327, gamma=0.0260224683207, total=   0.3s\n",
      "[CV] C=4.7481943668, gamma=0.00612768607146 ..........................\n",
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      "[CV] C=4.7481943668, gamma=0.00612768607146 ..........................\n",
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      "[CV] C=4.7481943668, gamma=0.00612768607146 ..........................\n",
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      "[CV] C=8.21047072817, gamma=0.0189296958208 ..........................\n",
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      "[CV] C=8.21047072817, gamma=0.0189296958208 ..........................\n",
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      "[CV] C=8.21047072817, gamma=0.0189296958208 ..........................\n",
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      "[CV] C=10.6466447347, gamma=0.00129981336694 .........................\n",
      "[CV] .......... C=10.6466447347, gamma=0.00129981336694, total=   0.5s\n",
      "[CV] C=10.6466447347, gamma=0.00129981336694 .........................\n",
      "[CV] .......... C=10.6466447347, gamma=0.00129981336694, total=   0.5s\n",
      "[CV] C=10.6466447347, gamma=0.00129981336694 .........................\n",
      "[CV] .......... C=10.6466447347, gamma=0.00129981336694, total=   0.5s\n",
      "[CV] C=9.16112619319, gamma=0.00330949119859 .........................\n",
      "[CV] .......... C=9.16112619319, gamma=0.00330949119859, total=   0.4s\n",
      "[CV] C=9.16112619319, gamma=0.00330949119859 .........................\n",
      "[CV] .......... C=9.16112619319, gamma=0.00330949119859, total=   0.4s\n",
      "[CV] C=9.16112619319, gamma=0.00330949119859 .........................\n",
      "[CV] .......... C=9.16112619319, gamma=0.00330949119859, total=   0.4s\n",
      "[CV] C=8.72390803269, gamma=0.00607593308978 .........................\n",
      "[CV] .......... C=8.72390803269, gamma=0.00607593308978, total=   0.4s\n",
      "[CV] C=8.72390803269, gamma=0.00607593308978 .........................\n",
      "[CV] .......... C=8.72390803269, gamma=0.00607593308978, total=   0.4s\n",
      "[CV] C=8.72390803269, gamma=0.00607593308978 .........................\n",
      "[CV] .......... C=8.72390803269, gamma=0.00607593308978, total=   0.4s\n",
      "[CV] C=1.12120075451, gamma=0.0956214426662 ..........................\n",
      "[CV] ........... C=1.12120075451, gamma=0.0956214426662, total=   0.3s\n",
      "[CV] C=1.12120075451, gamma=0.0956214426662 ..........................\n",
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      "[CV] C=1.12120075451, gamma=0.0956214426662 ..........................\n",
      "[CV] ........... C=1.12120075451, gamma=0.0956214426662, total=   0.3s\n",
      "[CV] C=2.11682618346, gamma=0.0253004222702 ..........................\n",
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      "[CV] C=2.11682618346, gamma=0.0253004222702 ..........................\n",
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      "[CV] C=2.11682618346, gamma=0.0253004222702 ..........................\n",
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      "[CV] C=7.55957588507, gamma=0.00173531312533 .........................\n",
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      "[CV] C=7.55957588507, gamma=0.00173531312533 .........................\n",
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      "[CV] C=7.55957588507, gamma=0.00173531312533 .........................\n",
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      "[CV] C=9.66828568493, gamma=0.00229086873208 .........................\n",
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      "[CV] C=9.66828568493, gamma=0.00229086873208 .........................\n",
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      "[CV] C=9.66828568493, gamma=0.00229086873208 .........................\n",
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      "[CV] C=4.47412978699, gamma=0.00333390403347 .........................\n",
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      "[CV] C=4.47412978699, gamma=0.00333390403347 .........................\n",
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      "[CV] C=4.47412978699, gamma=0.00333390403347 .........................\n",
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      "[CV] C=7.3801718232, gamma=0.0681845892769 ...........................\n",
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      "[CV] C=7.3801718232, gamma=0.0681845892769 ...........................\n",
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      "[CV] C=7.3801718232, gamma=0.0681845892769 ...........................\n",
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      "[CV] C=10.629035425, gamma=0.00177319602339 ..........................\n",
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      "[CV] C=2.09358133556, gamma=0.00126225556705 .........................\n",
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      "[CV] C=2.09358133556, gamma=0.00126225556705 .........................\n",
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      "[CV] C=2.09358133556, gamma=0.00126225556705 .........................\n",
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      "[CV] C=2.50447265798, gamma=0.00949979622347 .........................\n",
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      "[CV] C=2.50447265798, gamma=0.00949979622347 .........................\n",
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      "[CV] C=9.11027298957, gamma=0.0782812206479 ..........................\n",
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      "[CV] C=2.9237902031, gamma=0.00141560753673 ..........................\n",
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      "[CV] C=2.9237902031, gamma=0.00141560753673 ..........................\n",
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      "[CV] C=7.28776609839, gamma=0.00125598574902 .........................\n",
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      "[CV] C=7.28776609839, gamma=0.00125598574902 .........................\n",
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      "[CV] C=7.28776609839, gamma=0.00125598574902 .........................\n",
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      "[CV] C=10.7167056104, gamma=0.0245614812226 ..........................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........... C=10.7167056104, gamma=0.0245614812226, total=   0.3s\n",
      "[CV] C=10.7167056104, gamma=0.0245614812226 ..........................\n",
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      "[CV] C=10.7167056104, gamma=0.0245614812226 ..........................\n",
      "[CV] ........... C=10.7167056104, gamma=0.0245614812226, total=   0.3s\n",
      "[CV] C=8.28915202608, gamma=0.0158768369968 ..........................\n",
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      "[CV] C=8.28915202608, gamma=0.0158768369968 ..........................\n",
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      "[CV] C=8.28915202608, gamma=0.0158768369968 ..........................\n",
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      "[CV] C=2.99106819276, gamma=0.0802837477644 ..........................\n",
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      "[CV] C=2.99106819276, gamma=0.0802837477644 ..........................\n",
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      "[CV] C=2.99106819276, gamma=0.0802837477644 ..........................\n",
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      "[CV] C=4.50175459889, gamma=0.0659400803527 ..........................\n",
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      "[CV] C=4.50175459889, gamma=0.0659400803527 ..........................\n",
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      "[CV] C=4.50175459889, gamma=0.0659400803527 ..........................\n",
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      "[CV] C=4.00942781051, gamma=0.00242514057534 .........................\n",
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      "[CV] C=4.00942781051, gamma=0.00242514057534 .........................\n",
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      "[CV] C=4.00942781051, gamma=0.00242514057534 .........................\n",
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      "[CV] C=5.80241891467, gamma=0.0506997568282 ..........................\n",
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      "[CV] C=5.80241891467, gamma=0.0506997568282 ..........................\n",
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      "[CV] C=10.4535892792, gamma=0.0390065121394 ..........................\n",
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      "[CV] C=10.4535892792, gamma=0.0390065121394 ..........................\n",
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      "[CV] C=10.4535892792, gamma=0.0390065121394 ..........................\n",
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      "[CV] C=3.55348681331, gamma=0.00313280028064 .........................\n",
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      "[CV] C=3.55348681331, gamma=0.00313280028064 .........................\n",
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      "[CV] C=3.55348681331, gamma=0.00313280028064 .........................\n",
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      "[CV] C=1.21433813587, gamma=0.0499758341476 ..........................\n",
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      "[CV] C=1.21433813587, gamma=0.0499758341476 ..........................\n",
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      "[CV] C=7.54230514101, gamma=0.0121075759264 ..........................\n",
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      "[CV] C=7.54230514101, gamma=0.0121075759264 ..........................\n",
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      "[CV] C=7.54230514101, gamma=0.0121075759264 ..........................\n",
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      "[CV] C=7.92886725214, gamma=0.00228931832368 .........................\n",
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      "[CV] C=7.92886725214, gamma=0.00228931832368 .........................\n",
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      "[CV] C=7.92886725214, gamma=0.00228931832368 .........................\n",
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      "[CV] C=10.757404811, gamma=0.0416117801467 ...........................\n",
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      "[CV] C=10.757404811, gamma=0.0416117801467 ...........................\n",
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      "[CV] C=10.757404811, gamma=0.0416117801467 ...........................\n",
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      "[CV] C=9.01647017928, gamma=0.00375596951224 .........................\n",
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      "[CV] C=6.48551450542, gamma=0.026622367294 ...........................\n",
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      "[CV] C=4.53056193044, gamma=0.0190117669544 ..........................\n",
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      "[CV] C=4.45797775231, gamma=0.00425331170659 .........................\n",
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      "[CV] C=4.45797775231, gamma=0.00425331170659 .........................\n",
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      "[CV] C=4.45797775231, gamma=0.00425331170659 .........................\n",
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      "[CV] C=6.66747301666, gamma=0.0248707494615 ..........................\n",
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      "[CV] C=7.26688212896, gamma=0.0797728944428 ..........................\n",
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      "[CV] C=8.57786528786, gamma=0.00123376249603 .........................\n",
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      "[CV] C=8.57786528786, gamma=0.00123376249603 .........................\n",
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      "[CV] C=8.57786528786, gamma=0.00123376249603 .........................\n",
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      "[CV] C=3.11680445944, gamma=0.0135441864665 ..........................\n",
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      "[CV] C=3.11680445944, gamma=0.0135441864665 ..........................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........... C=3.11680445944, gamma=0.0135441864665, total=   0.4s\n",
      "[CV] C=3.11680445944, gamma=0.0135441864665 ..........................\n",
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      "[CV] C=6.53178783083, gamma=0.0359555694885 ..........................\n",
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      "[CV] C=9.08598751026, gamma=0.057376039859 ...........................\n",
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      "[CV] C=9.08598751026, gamma=0.057376039859 ...........................\n",
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      "[CV] C=9.08598751026, gamma=0.057376039859 ...........................\n",
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      "[CV] C=1.80800454113, gamma=0.0146234309525 ..........................\n",
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      "[CV] C=8.31769894277, gamma=0.00279840394818 .........................\n",
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      "[CV] C=8.31769894277, gamma=0.00279840394818 .........................\n",
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      "[CV] C=8.31769894277, gamma=0.00279840394818 .........................\n",
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      "[CV] C=2.00500834016, gamma=0.0153275706555 ..........................\n",
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      "[CV] C=2.82402768037, gamma=0.00370838044635 .........................\n",
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      "[CV] C=2.82402768037, gamma=0.00370838044635 .........................\n",
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      "[CV] C=2.82402768037, gamma=0.00370838044635 .........................\n",
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      "[CV] C=3.15214064969, gamma=0.00431589993199 .........................\n",
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      "[CV] C=3.15214064969, gamma=0.00431589993199 .........................\n",
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      "[CV] C=3.15214064969, gamma=0.00431589993199 .........................\n",
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      "[CV] C=4.31936540635, gamma=0.0388933103341 ..........................\n",
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      "[CV] C=9.11476145586, gamma=0.00247308902945 .........................\n",
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      "[CV] C=2.41669186418, gamma=0.00636579484223 .........................\n",
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      "[CV] C=6.12749067581, gamma=0.0470771627384 ..........................\n",
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      "[CV] C=4.28109698582, gamma=0.00591802581779 .........................\n",
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      "[CV] C=3.15707699335, gamma=0.00370498401723 .........................\n",
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      "[CV] C=4.23595261153, gamma=0.00397978257086 .........................\n",
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      "[CV] C=5.80797797595, gamma=0.00322350090748 .........................\n",
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      "[CV] C=3.63567244681, gamma=0.025969635427 ...........................\n",
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      "[CV] C=5.64536064064, gamma=0.0208251584319 ..........................\n",
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      "[CV] C=5.64536064064, gamma=0.0208251584319 ..........................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........... C=5.64536064064, gamma=0.0208251584319, total=   0.3s\n",
      "[CV] C=9.87691099873, gamma=0.00170217261314 .........................\n",
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      "[CV] C=9.87691099873, gamma=0.00170217261314 .........................\n",
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      "[CV] C=9.87691099873, gamma=0.00170217261314 .........................\n",
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      "[CV] C=1.04028371476, gamma=0.00323654917749 .........................\n",
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      "[CV] C=1.04028371476, gamma=0.00323654917749 .........................\n",
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      "[CV] C=1.04028371476, gamma=0.00323654917749 .........................\n",
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      "[CV] C=2.05044656099, gamma=0.00831220156027 .........................\n",
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      "[CV] C=2.10197874578, gamma=0.0788105004441 ..........................\n",
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      "[CV] C=8.86910850745, gamma=0.00107839622318 .........................\n",
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      "[CV] C=8.49451248316, gamma=0.00671506738073 .........................\n",
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      "[CV] C=2.09590610166, gamma=0.0768446308811 ..........................\n",
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      "[CV] C=4.41682808166, gamma=0.0112595477473 ..........................\n",
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      "[CV] C=1.73612473624, gamma=0.0664130569689 ..........................\n",
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      "[CV] ........... C=1.73612473624, gamma=0.0664130569689, total=   0.3s\n",
      "[CV] C=1.73612473624, gamma=0.0664130569689 ..........................\n",
      "[CV] ........... C=1.73612473624, gamma=0.0664130569689, total=   0.3s\n",
      "[CV] C=3.70238793439, gamma=0.00103341922754 .........................\n",
      "[CV] .......... C=3.70238793439, gamma=0.00103341922754, total=   0.7s\n",
      "[CV] C=3.70238793439, gamma=0.00103341922754 .........................\n",
      "[CV] .......... C=3.70238793439, gamma=0.00103341922754, total=   0.7s\n",
      "[CV] C=3.70238793439, gamma=0.00103341922754 .........................\n",
      "[CV] .......... C=3.70238793439, gamma=0.00103341922754, total=   0.7s\n",
      "[CV] C=2.18234899015, gamma=0.0504484323906 ..........................\n",
      "[CV] ........... C=2.18234899015, gamma=0.0504484323906, total=   0.3s\n",
      "[CV] C=2.18234899015, gamma=0.0504484323906 ..........................\n",
      "[CV] ........... C=2.18234899015, gamma=0.0504484323906, total=   0.3s\n",
      "[CV] C=2.18234899015, gamma=0.0504484323906 ..........................\n",
      "[CV] ........... C=2.18234899015, gamma=0.0504484323906, total=   0.3s\n",
      "[CV] C=8.8363487993, gamma=0.00512461622928 ..........................\n",
      "[CV] ........... C=8.8363487993, gamma=0.00512461622928, total=   0.4s\n",
      "[CV] C=8.8363487993, gamma=0.00512461622928 ..........................\n",
      "[CV] ........... C=8.8363487993, gamma=0.00512461622928, total=   0.4s\n",
      "[CV] C=8.8363487993, gamma=0.00512461622928 ..........................\n",
      "[CV] ........... C=8.8363487993, gamma=0.00512461622928, total=   0.4s\n",
      "[CV] C=8.61291066755, gamma=0.0234804227131 ..........................\n",
      "[CV] ........... C=8.61291066755, gamma=0.0234804227131, total=   0.3s\n",
      "[CV] C=8.61291066755, gamma=0.0234804227131 ..........................\n",
      "[CV] ........... C=8.61291066755, gamma=0.0234804227131, total=   0.3s\n",
      "[CV] C=8.61291066755, gamma=0.0234804227131 ..........................\n",
      "[CV] ........... C=8.61291066755, gamma=0.0234804227131, total=   0.3s\n",
      "[CV] C=4.6041680293, gamma=0.0081375142861 ...........................\n",
      "[CV] ............ C=4.6041680293, gamma=0.0081375142861, total=   0.4s\n",
      "[CV] C=4.6041680293, gamma=0.0081375142861 ...........................\n",
      "[CV] ............ C=4.6041680293, gamma=0.0081375142861, total=   0.4s\n",
      "[CV] C=4.6041680293, gamma=0.0081375142861 ...........................\n",
      "[CV] ............ C=4.6041680293, gamma=0.0081375142861, total=   0.4s\n",
      "[CV] C=6.25728447417, gamma=0.0030688674436 ..........................\n",
      "[CV] ........... C=6.25728447417, gamma=0.0030688674436, total=   0.5s\n",
      "[CV] C=6.25728447417, gamma=0.0030688674436 ..........................\n",
      "[CV] ........... C=6.25728447417, gamma=0.0030688674436, total=   0.5s\n",
      "[CV] C=6.25728447417, gamma=0.0030688674436 ..........................\n",
      "[CV] ........... C=6.25728447417, gamma=0.0030688674436, total=   0.5s\n",
      "[CV] C=8.63990742571, gamma=0.0452952002371 ..........................\n",
      "[CV] ........... C=8.63990742571, gamma=0.0452952002371, total=   0.3s\n",
      "[CV] C=8.63990742571, gamma=0.0452952002371 ..........................\n",
      "[CV] ........... C=8.63990742571, gamma=0.0452952002371, total=   0.3s\n",
      "[CV] C=8.63990742571, gamma=0.0452952002371 ..........................\n",
      "[CV] ........... C=8.63990742571, gamma=0.0452952002371, total=   0.3s\n",
      "[CV] C=1.5022451977, gamma=0.00342478798791 ..........................\n",
      "[CV] ........... C=1.5022451977, gamma=0.00342478798791, total=   0.7s\n",
      "[CV] C=1.5022451977, gamma=0.00342478798791 ..........................\n",
      "[CV] ........... C=1.5022451977, gamma=0.00342478798791, total=   0.7s\n",
      "[CV] C=1.5022451977, gamma=0.00342478798791 ..........................\n",
      "[CV] ........... C=1.5022451977, gamma=0.00342478798791, total=   0.7s\n",
      "[CV] C=6.81033749598, gamma=0.00343392837294 .........................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .......... C=6.81033749598, gamma=0.00343392837294, total=   0.5s\n",
      "[CV] C=6.81033749598, gamma=0.00343392837294 .........................\n",
      "[CV] .......... C=6.81033749598, gamma=0.00343392837294, total=   0.5s\n",
      "[CV] C=6.81033749598, gamma=0.00343392837294 .........................\n",
      "[CV] .......... C=6.81033749598, gamma=0.00343392837294, total=   0.5s\n",
      "[CV] C=6.88275525112, gamma=0.0532227200286 ..........................\n",
      "[CV] ........... C=6.88275525112, gamma=0.0532227200286, total=   0.3s\n",
      "[CV] C=6.88275525112, gamma=0.0532227200286 ..........................\n",
      "[CV] ........... C=6.88275525112, gamma=0.0532227200286, total=   0.3s\n",
      "[CV] C=6.88275525112, gamma=0.0532227200286 ..........................\n",
      "[CV] ........... C=6.88275525112, gamma=0.0532227200286, total=   0.3s\n",
      "[CV] C=6.68881962627, gamma=0.0121829495276 ..........................\n",
      "[CV] ........... C=6.68881962627, gamma=0.0121829495276, total=   0.3s\n",
      "[CV] C=6.68881962627, gamma=0.0121829495276 ..........................\n",
      "[CV] ........... C=6.68881962627, gamma=0.0121829495276, total=   0.4s\n",
      "[CV] C=6.68881962627, gamma=0.0121829495276 ..........................\n",
      "[CV] ........... C=6.68881962627, gamma=0.0121829495276, total=   0.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed:  3.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(SVC(C=10.892521860571078, cache_size=200, class_weight=None, coef0=0.0,\n",
       "   decision_function_shape='ovr', degree=3, gamma=0.089400468476754411,\n",
       "   kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "   shrinking=True, tol=0.001, verbose=False), 0.94720000000000004)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \n",
    "                       \"C\": uniform(1, 10)}\n",
    "\n",
    "rnd_search_cv = RandomizedSearchCV(SVC(), \n",
    "                                   param_distributions, \n",
    "                                   n_iter=100, \n",
    "                                   verbose=2, \n",
    "                                   cv=3)\n",
    "\n",
    "rnd_search_cv.fit(X_train_10w[:5000], y_train_10w[:5000])\n",
    "rnd_search_cv.best_estimator_,rnd_search_cv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rbf核\n",
    "svm_clf = SVC(C=10.892521860571078,\n",
    "              gamma=0.089400468476754411)\n",
    "svm_clf.fit(X_train_10w, y_train_10w)\n",
    "joblib.dump(svm_clf, 'model/svm/svm_rbf_10w.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 96.4030% \t Precision: 97.0170% \t Recall: 96.3520% \t Roc: 0.0000\tF1: 0.9668\n",
      "Confusion:\n",
      "[[43975  1612]\n",
      " [ 1985 52428]]\n",
      "Accuracy: 96.5300% \t Precision: 97.2327% \t Recall: 96.4172% \t Roc: 0.0000\tF1: 0.9682\n",
      "Confusion:\n",
      "[[ 8730   301]\n",
      " [  393 10576]]\n"
     ]
    }
   ],
   "source": [
    "svm_clf = joblib.load('model/svm/svm_rbf_10w.model')\n",
    "evaluate_result(y_train_10w, svm_clf.predict(X_train_10w))\n",
    "evaluate_result(y_test_10w, svm_clf.predict(X_test_10w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 60 rounds.\n",
      "[20]\ttraining's binary_logloss: 0.362421\ttraining's auc: 0.991977\tvalid_1's binary_logloss: 0.371994\tvalid_1's auc: 0.990606\n",
      "[40]\ttraining's binary_logloss: 0.222215\ttraining's auc: 0.993058\tvalid_1's binary_logloss: 0.240586\tvalid_1's auc: 0.991275\n",
      "[60]\ttraining's binary_logloss: 0.153177\ttraining's auc: 0.994028\tvalid_1's binary_logloss: 0.169953\tvalid_1's auc: 0.992666\n",
      "[80]\ttraining's binary_logloss: 0.115946\ttraining's auc: 0.995036\tvalid_1's binary_logloss: 0.130251\tvalid_1's auc: 0.993968\n",
      "[100]\ttraining's binary_logloss: 0.0941161\ttraining's auc: 0.995782\tvalid_1's binary_logloss: 0.108418\tvalid_1's auc: 0.994653\n",
      "[120]\ttraining's binary_logloss: 0.0810619\ttraining's auc: 0.996436\tvalid_1's binary_logloss: 0.0945204\tvalid_1's auc: 0.995465\n",
      "[140]\ttraining's binary_logloss: 0.071739\ttraining's auc: 0.99719\tvalid_1's binary_logloss: 0.0837593\tvalid_1's auc: 0.996227\n",
      "[160]\ttraining's binary_logloss: 0.0650052\ttraining's auc: 0.997646\tvalid_1's binary_logloss: 0.0774918\tvalid_1's auc: 0.996967\n",
      "[180]\ttraining's binary_logloss: 0.0599603\ttraining's auc: 0.997952\tvalid_1's binary_logloss: 0.0742997\tvalid_1's auc: 0.997332\n",
      "[200]\ttraining's binary_logloss: 0.0563349\ttraining's auc: 0.998151\tvalid_1's binary_logloss: 0.0737827\tvalid_1's auc: 0.997492\n",
      "[220]\ttraining's binary_logloss: 0.0532791\ttraining's auc: 0.998327\tvalid_1's binary_logloss: 0.0749273\tvalid_1's auc: 0.997633\n",
      "[240]\ttraining's binary_logloss: 0.0508059\ttraining's auc: 0.998468\tvalid_1's binary_logloss: 0.0738429\tvalid_1's auc: 0.997782\n",
      "[260]\ttraining's binary_logloss: 0.0487624\ttraining's auc: 0.998585\tvalid_1's binary_logloss: 0.0746742\tvalid_1's auc: 0.997859\n",
      "[280]\ttraining's binary_logloss: 0.0469452\ttraining's auc: 0.998692\tvalid_1's binary_logloss: 0.0742017\tvalid_1's auc: 0.997949\n",
      "Early stopping, best iteration is:\n",
      "[237]\ttraining's binary_logloss: 0.0511273\ttraining's auc: 0.998452\tvalid_1's binary_logloss: 0.0720834\tvalid_1's auc: 0.997788\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "          'num_leaves': 31,\n",
    "          'min_data_in_leaf': 20,\n",
    "          'objective': 'binary', # 定义的目标函数\n",
    "          'max_depth': -1,\n",
    "          'learning_rate': 0.03,\n",
    "          \"min_sum_hessian_in_leaf\": 6,\n",
    "          \"boosting\": \"gbdt\", \n",
    "          \"feature_fraction\": 0.9,  # 提取的特征比率\n",
    "          \"bagging_freq\": 1,\n",
    "          \"bagging_fraction\": 0.8,\n",
    "          \"bagging_seed\": 11,\n",
    "          \"lambda_l1\": 0.1,       # l1正则\n",
    "          'lambda_l2': 0.001,     # l2正则\n",
    "          \"verbosity\": -1,\n",
    "          \"nthread\": 16,          # 线程数量，-1表示全部线程，线程越多，运行的速度越快\n",
    "          'metric': {'binary_logloss', 'auc'},  ##评价函数选择\n",
    "          \"random_state\": 42,     #随机数种子，可以防止每次运行的结果不一致\n",
    "          }\n",
    "\n",
    "trn_data = lgb.Dataset(X_train_10w, label=y_train_10w)\n",
    "tst_data = lgb.Dataset(X_test_10w, label=y_test_10w)\n",
    "lgb_clf = lgb.train(params, \n",
    "                    trn_data,\n",
    "                    10000,\n",
    "                    valid_sets=[trn_data, tst_data],\n",
    "                    verbose_eval=20,\n",
    "                    early_stopping_rounds=60)\n",
    "pickle.dump(lgb_clf, open('model/lgb/lgb_10w.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mAsdsTIAkmwEfBJ5TVQuBQeAvu5CbJEmS1BduNKzJOgnYJclqYO2GxiSzaS+3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\nS5IkSZK6xAJLkiRJkrrk/wdLyAT9jT7e9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgb_importance_df = pd.DataFrame()\n",
    "lgb_importance_df[\"feature\"] = useful_features_name\n",
    "lgb_importance_df[\"importance\"] = lgb_clf.feature_importance()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(x=\"importance\", y=\"feature\", data=lgb_importance_df.sort_values(by=\"importance\",ascending=False))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.2690% \t Precision: 98.4869% \t Recall: 98.3294% \t Roc: 0.9985\tF1: 0.9841\n",
      "Confusion:\n",
      "[[44765   822]\n",
      " [  909 53504]]\n",
      "Accuracy: 97.5250% \t Precision: 98.9348% \t Recall: 96.5266% \t Roc: 0.9978\tF1: 0.9772\n",
      "Confusion:\n",
      "[[ 8917   114]\n",
      " [  381 10588]]\n"
     ]
    }
   ],
   "source": [
    "# lgb_clf = pickle.load(open('model/lgb/lgb_10w.pkl', 'rb'))\n",
    "evaluate_result(y_train_10w, None, lgb_clf.predict(X_train_10w))\n",
    "evaluate_result(y_test_10w, None, lgb_clf.predict(X_test_10w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Deep Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dnn_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(128, activation='relu', input_dim=45))\n",
    "    model.add(Dropout(rate=0.5))\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dropout(rate=0.5))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', \n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dnn_model = create_dnn_model()\n",
    "\n",
    "filepath = 'model/dnn/dnn_model_weights.best.h5'\n",
    "checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=0,\n",
    "                            save_best_only=True, mode='max')\n",
    "early_stopping = EarlyStopping(monitor='val_acc', patience=5)\n",
    "callback_list = [checkpoint, early_stopping]\n",
    "\n",
    "dnn_history=dnn_model.fit(X_train_10w, y_train_10w, epochs=500, batch_size=256, shuffle=True,\n",
    "              validation_split=0.1, callbacks=callback_list, verbose=1)  # 训练模型\n",
    "\n",
    "json_string = dnn_model.to_json()  # 保存模型结构\n",
    "open('model/dnn/dnn_model_architecture.json', 'w').write(json_string)\n",
    "\n",
    "pickle.dump(dnn_history.history, open('model/dnn/dnn_history.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.1040% \t Precision: 97.5644% \t Recall: 97.1018% \t Roc: 0.9962\tF1: 0.9733\n",
      "Confusion:\n",
      "[[44268  1319]\n",
      " [ 1577 52836]]\n",
      "Accuracy: 97.2550% \t Precision: 97.7019% \t Recall: 97.2833% \t Roc: 0.9964\tF1: 0.9749\n",
      "Confusion:\n",
      "[[ 8780   251]\n",
      " [  298 10671]]\n"
     ]
    }
   ],
   "source": [
    "dnn_model = load_model('dnn')\n",
    "evaluate_result(y_train_10w, None, dnn_model.predict(X_train_10w))\n",
    "evaluate_result(y_test_10w, None, dnn_model.predict(X_test_10w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fce293a22e8>"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YYw5kycJTX7MorbSahTHGNBXWZCEi00XkcxHJFpHbmnn/FhFZJyKrROQNERkW9N4VIrLB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\nYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dnn_history.history['loss'])\n",
    "plt.plot(dnn_history.history['val_loss'])\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Binary Crossentropy')\n",
    "plt.title('Loss Over Time')\n",
    "plt.legend(['Train','Valid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_lstm_model():\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(domain_max_features, \n",
    "                        128, \n",
    "                        input_length=domain_maxlen))\n",
    "    model.add(LSTM(128))\n",
    "    model.add(Dropout(rate=0.5))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', \n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 90000 samples, validate on 10000 samples\n",
      "Epoch 1/500\n",
      "90000/90000 [==============================] - 37s - loss: 0.2458 - acc: 0.9053 - val_loss: 0.1972 - val_acc: 0.9244\n",
      "Epoch 2/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.1689 - acc: 0.9387 - val_loss: 0.1483 - val_acc: 0.9487\n",
      "Epoch 3/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.1364 - acc: 0.9518 - val_loss: 0.1242 - val_acc: 0.9563\n",
      "Epoch 4/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.1236 - acc: 0.9559 - val_loss: 0.1133 - val_acc: 0.9614\n",
      "Epoch 5/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0994 - acc: 0.9659 - val_loss: 0.0930 - val_acc: 0.9687\n",
      "Epoch 6/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0859 - acc: 0.9709 - val_loss: 0.0878 - val_acc: 0.9703\n",
      "Epoch 7/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0793 - acc: 0.9730 - val_loss: 0.0801 - val_acc: 0.9728\n",
      "Epoch 8/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0738 - acc: 0.9750 - val_loss: 0.0780 - val_acc: 0.9735\n",
      "Epoch 9/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0700 - acc: 0.9763 - val_loss: 0.0743 - val_acc: 0.9754\n",
      "Epoch 10/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0649 - acc: 0.9780 - val_loss: 0.0702 - val_acc: 0.9772\n",
      "Epoch 11/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0607 - acc: 0.9797 - val_loss: 0.0753 - val_acc: 0.9756\n",
      "Epoch 12/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0567 - acc: 0.9812 - val_loss: 0.0641 - val_acc: 0.9784\n",
      "Epoch 13/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0531 - acc: 0.9822 - val_loss: 0.0608 - val_acc: 0.9808\n",
      "Epoch 14/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0506 - acc: 0.9831 - val_loss: 0.0586 - val_acc: 0.9811\n",
      "Epoch 15/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0472 - acc: 0.9846 - val_loss: 0.0587 - val_acc: 0.9813\n",
      "Epoch 16/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0448 - acc: 0.9850 - val_loss: 0.0537 - val_acc: 0.9822\n",
      "Epoch 17/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0426 - acc: 0.9857 - val_loss: 0.0574 - val_acc: 0.9820\n",
      "Epoch 18/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0391 - acc: 0.9870 - val_loss: 0.0515 - val_acc: 0.9837\n",
      "Epoch 19/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0380 - acc: 0.9874 - val_loss: 0.0508 - val_acc: 0.9828\n",
      "Epoch 20/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0353 - acc: 0.9881 - val_loss: 0.0495 - val_acc: 0.9842\n",
      "Epoch 21/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0337 - acc: 0.9889 - val_loss: 0.0534 - val_acc: 0.9833\n",
      "Epoch 22/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0320 - acc: 0.9895 - val_loss: 0.0513 - val_acc: 0.9829\n",
      "Epoch 23/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0294 - acc: 0.9903 - val_loss: 0.0525 - val_acc: 0.9832\n",
      "Epoch 24/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0270 - acc: 0.9914 - val_loss: 0.0607 - val_acc: 0.9839\n",
      "Epoch 25/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0261 - acc: 0.9912 - val_loss: 0.0517 - val_acc: 0.9850\n",
      "Epoch 26/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0240 - acc: 0.9920 - val_loss: 0.0543 - val_acc: 0.9846\n",
      "Epoch 27/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0226 - acc: 0.9926 - val_loss: 0.0570 - val_acc: 0.9844\n",
      "Epoch 28/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0214 - acc: 0.9929 - val_loss: 0.0603 - val_acc: 0.9845\n",
      "Epoch 29/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0204 - acc: 0.9932 - val_loss: 0.0544 - val_acc: 0.9852\n",
      "Epoch 30/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0181 - acc: 0.9940 - val_loss: 0.0560 - val_acc: 0.9851\n",
      "Epoch 31/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0158 - acc: 0.9950 - val_loss: 0.0586 - val_acc: 0.9853\n",
      "Epoch 32/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0151 - acc: 0.9950 - val_loss: 0.0615 - val_acc: 0.9844\n",
      "Epoch 33/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0148 - acc: 0.9950 - val_loss: 0.0630 - val_acc: 0.9852\n",
      "Epoch 34/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0141 - acc: 0.9954 - val_loss: 0.0719 - val_acc: 0.9818\n",
      "Epoch 35/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0129 - acc: 0.9954 - val_loss: 0.0627 - val_acc: 0.9848\n",
      "Epoch 36/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0128 - acc: 0.9957 - val_loss: 0.0624 - val_acc: 0.9855\n",
      "Epoch 37/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0107 - acc: 0.9964 - val_loss: 0.0703 - val_acc: 0.9850\n",
      "Epoch 38/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0109 - acc: 0.9965 - val_loss: 0.0637 - val_acc: 0.9855\n",
      "Epoch 39/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0078 - acc: 0.9974 - val_loss: 0.0703 - val_acc: 0.9853\n",
      "Epoch 40/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0070 - acc: 0.9978 - val_loss: 0.0698 - val_acc: 0.9854\n",
      "Epoch 41/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0087 - acc: 0.9970 - val_loss: 0.0724 - val_acc: 0.9852\n",
      "Epoch 42/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0075 - acc: 0.9974 - val_loss: 0.0700 - val_acc: 0.9860\n",
      "Epoch 43/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0067 - acc: 0.9976 - val_loss: 0.0869 - val_acc: 0.9831\n",
      "Epoch 44/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0056 - acc: 0.9982 - val_loss: 0.0788 - val_acc: 0.9843\n",
      "Epoch 45/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0056 - acc: 0.9982 - val_loss: 0.0857 - val_acc: 0.9835\n",
      "Epoch 46/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0054 - acc: 0.9983 - val_loss: 0.0818 - val_acc: 0.9837\n",
      "Epoch 47/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0053 - acc: 0.9982 - val_loss: 0.0861 - val_acc: 0.9846\n",
      "Epoch 48/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0039 - acc: 0.9987 - val_loss: 0.0887 - val_acc: 0.9844\n"
     ]
    }
   ],
   "source": [
    "lstm_model = create_lstm_model()\n",
    "\n",
    "filepath = 'model/lstm/lstm_model_weights.best.h5'\n",
    "checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=0,\n",
    "                            save_best_only=True, mode='max')\n",
    "early_stopping = EarlyStopping(monitor='val_acc', patience=5)\n",
    "callback_list = [checkpoint, early_stopping]\n",
    "\n",
    "lstm_history = lstm_model.fit(X_train_deep_10w, y_train_deep_10w, \n",
    "              epochs=500, batch_size=256, shuffle=True,\n",
    "              validation_split=0.1, callbacks=callback_list, verbose=1)  # 训练模型\n",
    "\n",
    "json_string = lstm_model.to_json()  # 保存模型结构\n",
    "open('model/lstm/lstm_model_architecture.json', 'w').write(json_string)\n",
    "\n",
    "pickle.dump(lstm_history.history, open('model/lstm/lstm_history.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.7540% \t Precision: 99.8307% \t Recall: 99.7170% \t Roc: 0.9998\tF1: 0.9977\n",
      "Confusion:\n",
      "[[45495    92]\n",
      " [  154 54259]]\n",
      "Accuracy: 98.4750% \t Precision: 98.7921% \t Recall: 98.4228% \t Roc: 0.9977\tF1: 0.9861\n",
      "Confusion:\n",
      "[[ 8899   132]\n",
      " [  173 10796]]\n"
     ]
    }
   ],
   "source": [
    "lstm_model = load_model('lstm')\n",
    "evaluate_result(y_train_deep_10w, None, lstm_model.predict(X_train_deep_10w))\n",
    "evaluate_result(y_test_deep_10w, None, lstm_model.predict(X_test_deep_10w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fce283d5ba8>"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N2xd4+9peVCz0GAglG4/syk6Js2ooY4wJELKJBFW1XkTuxP2RjwQeV9W1IjIbyFfVubhq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\nJQtjjDEtsmRhjDGmRZYsjDHGtOj/A/WBGYsAFJP9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lstm_history.history['loss'])\n",
    "plt.plot(lstm_history.history['val_loss'])\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Binary Crossentropy')\n",
    "plt.title('Loss Over Time')\n",
    "plt.legend(['Train','Valid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### biLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_bilstm_model():\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(domain_max_features, \n",
    "                        128, \n",
    "                        input_length=domain_maxlen))\n",
    "    model.add(Bidirectional(LSTM(128, return_sequences=True)))\n",
    "    model.add(att.AttentionLayer())\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', \n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 90000 samples, validate on 10000 samples\n",
      "Epoch 1/500\n",
      "90000/90000 [==============================] - 66s - loss: 0.2707 - acc: 0.8849 - val_loss: 0.2077 - val_acc: 0.9191\n",
      "Epoch 2/500\n",
      "90000/90000 [==============================] - 63s - loss: 0.1867 - acc: 0.9279 - val_loss: 0.1749 - val_acc: 0.9344\n",
      "Epoch 3/500\n",
      "90000/90000 [==============================] - 62s - loss: 0.1559 - acc: 0.9416 - val_loss: 0.1521 - val_acc: 0.9443\n",
      "Epoch 4/500\n",
      "90000/90000 [==============================] - 62s - loss: 0.1339 - acc: 0.9505 - val_loss: 0.1296 - val_acc: 0.9527\n",
      "Epoch 5/500\n",
      "90000/90000 [==============================] - 62s - loss: 0.1239 - acc: 0.9547 - val_loss: 0.1160 - val_acc: 0.9586\n",
      "Epoch 6/500\n",
      "90000/90000 [==============================] - 63s - loss: 0.0973 - acc: 0.9649 - val_loss: 0.0981 - val_acc: 0.9644\n",
      "Epoch 7/500\n",
      "90000/90000 [==============================] - 63s - loss: 0.0849 - acc: 0.9696 - val_loss: 0.0811 - val_acc: 0.9717\n",
      "Epoch 8/500\n",
      "90000/90000 [==============================] - 61s - loss: 0.0696 - acc: 0.9756 - val_loss: 0.0721 - val_acc: 0.9750\n",
      "Epoch 9/500\n",
      "90000/90000 [==============================] - 62s - loss: 0.0627 - acc: 0.9785 - val_loss: 0.0766 - val_acc: 0.9740\n",
      "Epoch 10/500\n",
      "90000/90000 [==============================] - 62s - loss: 0.0552 - acc: 0.9813 - val_loss: 0.0647 - val_acc: 0.9787\n",
      "Epoch 11/500\n",
      "90000/90000 [==============================] - 62s - loss: 0.0514 - acc: 0.9830 - val_loss: 0.0581 - val_acc: 0.9803\n",
      "Epoch 12/500\n",
      "90000/90000 [==============================] - 61s - loss: 0.0471 - acc: 0.9842 - val_loss: 0.0554 - val_acc: 0.9821\n",
      "Epoch 13/500\n",
      "90000/90000 [==============================] - 61s - loss: 0.0429 - acc: 0.9858 - val_loss: 0.0546 - val_acc: 0.9832\n",
      "Epoch 14/500\n",
      "90000/90000 [==============================] - 61s - loss: 0.0395 - acc: 0.9871 - val_loss: 0.0523 - val_acc: 0.9828\n",
      "Epoch 15/500\n",
      "90000/90000 [==============================] - 61s - loss: 0.0368 - acc: 0.9879 - val_loss: 0.0543 - val_acc: 0.9822\n",
      "Epoch 16/500\n",
      "90000/90000 [==============================] - 61s - loss: 0.0342 - acc: 0.9892 - val_loss: 0.0520 - val_acc: 0.9826\n",
      "Epoch 17/500\n",
      "90000/90000 [==============================] - 60s - loss: 0.0314 - acc: 0.9902 - val_loss: 0.0584 - val_acc: 0.9825\n",
      "Epoch 18/500\n",
      "90000/90000 [==============================] - 62s - loss: 0.0302 - acc: 0.9902 - val_loss: 0.0517 - val_acc: 0.9831\n",
      "Epoch 19/500\n",
      "90000/90000 [==============================] - 61s - loss: 0.0278 - acc: 0.9912 - val_loss: 0.0521 - val_acc: 0.9840\n",
      "Epoch 20/500\n",
      "90000/90000 [==============================] - 60s - loss: 0.0242 - acc: 0.9924 - val_loss: 0.0509 - val_acc: 0.9845\n",
      "Epoch 21/500\n",
      "90000/90000 [==============================] - 60s - loss: 0.0221 - acc: 0.9933 - val_loss: 0.0530 - val_acc: 0.9848\n",
      "Epoch 22/500\n",
      "90000/90000 [==============================] - 60s - loss: 0.0203 - acc: 0.9936 - val_loss: 0.0513 - val_acc: 0.9858\n",
      "Epoch 23/500\n",
      "90000/90000 [==============================] - 61s - loss: 0.0194 - acc: 0.9941 - val_loss: 0.0552 - val_acc: 0.9847\n",
      "Epoch 24/500\n",
      "90000/90000 [==============================] - 60s - loss: 0.0170 - acc: 0.9949 - val_loss: 0.0554 - val_acc: 0.9842\n",
      "Epoch 25/500\n",
      "90000/90000 [==============================] - 60s - loss: 0.0159 - acc: 0.9951 - val_loss: 0.0558 - val_acc: 0.9852\n",
      "Epoch 26/500\n",
      "90000/90000 [==============================] - 61s - loss: 0.0136 - acc: 0.9960 - val_loss: 0.0632 - val_acc: 0.9830\n",
      "Epoch 27/500\n",
      "90000/90000 [==============================] - 61s - loss: 0.0131 - acc: 0.9962 - val_loss: 0.0631 - val_acc: 0.9834\n",
      "Epoch 28/500\n",
      "90000/90000 [==============================] - 60s - loss: 0.0128 - acc: 0.9960 - val_loss: 0.0579 - val_acc: 0.9857\n"
     ]
    }
   ],
   "source": [
    "reload(att)\n",
    "bilstm_model = create_bilstm_model()\n",
    "\n",
    "filepath = 'model/bilstm/bilstm_model_weights.best.h5'\n",
    "checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=0,\n",
    "                            save_best_only=True, mode='max')\n",
    "early_stopping = EarlyStopping(monitor='val_acc', patience=5)\n",
    "callback_list = [checkpoint, early_stopping]\n",
    "\n",
    "bilstm_history = bilstm_model.fit(X_train_deep_10w, y_train_deep_10w, \n",
    "              epochs=500, batch_size=256, shuffle=True,\n",
    "              validation_split=0.1, callbacks=callback_list, verbose=1)  # 训练模型\n",
    "\n",
    "json_string = bilstm_model.to_json()  # 保存模型结构\n",
    "open('model/bilstm/bilstm_model_architecture.json', 'w').write(json_string)\n",
    "\n",
    "pickle.dump(bilstm_history.history, open('model/bilstm/bilstm_history.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.4350% \t Precision: 99.6240% \t Recall: 99.3366% \t Roc: 0.9994\tF1: 0.9948\n",
      "Confusion:\n",
      "[[45383   204]\n",
      " [  361 54052]]\n",
      "Accuracy: 98.4350% \t Precision: 98.8538% \t Recall: 98.2861% \t Roc: 0.9974\tF1: 0.9857\n",
      "Confusion:\n",
      "[[ 8906   125]\n",
      " [  188 10781]]\n"
     ]
    }
   ],
   "source": [
    "bilstm_model = load_model('bilstm')\n",
    "evaluate_result(y_train_deep_10w, None, bilstm_model.predict(X_train_deep_10w))\n",
    "evaluate_result(y_test_deep_10w, None, bilstm_model.predict(X_test_deep_10w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fce261f4eb8>"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LyGoRWS4iH4nIYK/XrhWRDe5ybUfet8OGTIW+Y2HB404bRjNNPaKskdsYE6x8SRbDgaeB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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bilstm_history.history['loss'])\n",
    "plt.plot(bilstm_history.history['val_loss'])\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Binary Crossentropy')\n",
    "plt.title('Loss Over Time')\n",
    "plt.legend(['Train','Valid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_gru_model():\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(domain_max_features, \n",
    "                        128, \n",
    "                        input_length=domain_maxlen))\n",
    "    model.add(GRU(128))\n",
    "    model.add(Dropout(rate=0.5))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', \n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 90000 samples, validate on 10000 samples\n",
      "Epoch 1/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.2324 - acc: 0.9128 - val_loss: 0.1801 - val_acc: 0.9341\n",
      "Epoch 2/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.1565 - acc: 0.9437 - val_loss: 0.1451 - val_acc: 0.9490\n",
      "Epoch 3/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.1257 - acc: 0.9560 - val_loss: 0.1122 - val_acc: 0.9601\n",
      "Epoch 4/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0982 - acc: 0.9659 - val_loss: 0.0876 - val_acc: 0.9699\n",
      "Epoch 5/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0781 - acc: 0.9734 - val_loss: 0.0747 - val_acc: 0.9751\n",
      "Epoch 6/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0654 - acc: 0.9780 - val_loss: 0.0659 - val_acc: 0.9764\n",
      "Epoch 7/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0578 - acc: 0.9808 - val_loss: 0.0616 - val_acc: 0.9797\n",
      "Epoch 8/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0522 - acc: 0.9829 - val_loss: 0.0583 - val_acc: 0.9805\n",
      "Epoch 9/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0491 - acc: 0.9834 - val_loss: 0.0602 - val_acc: 0.9805\n",
      "Epoch 10/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0458 - acc: 0.9852 - val_loss: 0.0555 - val_acc: 0.9821\n",
      "Epoch 11/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0428 - acc: 0.9858 - val_loss: 0.0514 - val_acc: 0.9824\n",
      "Epoch 12/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0402 - acc: 0.9871 - val_loss: 0.0495 - val_acc: 0.9844\n",
      "Epoch 13/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0369 - acc: 0.9882 - val_loss: 0.0522 - val_acc: 0.9827\n",
      "Epoch 14/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0349 - acc: 0.9885 - val_loss: 0.0511 - val_acc: 0.9824\n",
      "Epoch 15/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0314 - acc: 0.9898 - val_loss: 0.0555 - val_acc: 0.9821\n",
      "Epoch 16/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0299 - acc: 0.9902 - val_loss: 0.0465 - val_acc: 0.9844\n",
      "Epoch 17/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0267 - acc: 0.9918 - val_loss: 0.0481 - val_acc: 0.9848\n",
      "Epoch 18/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0245 - acc: 0.9922 - val_loss: 0.0501 - val_acc: 0.9844\n",
      "Epoch 19/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0224 - acc: 0.9929 - val_loss: 0.0506 - val_acc: 0.9848\n",
      "Epoch 20/500\n",
      "90000/90000 [==============================] - 27s - loss: 0.0203 - acc: 0.9934 - val_loss: 0.0519 - val_acc: 0.9838\n",
      "Epoch 21/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0206 - acc: 0.9938 - val_loss: 0.0593 - val_acc: 0.9825\n",
      "Epoch 22/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0170 - acc: 0.9945 - val_loss: 0.0555 - val_acc: 0.9839\n",
      "Epoch 23/500\n",
      "90000/90000 [==============================] - 27s - loss: 0.0157 - acc: 0.9951 - val_loss: 0.0512 - val_acc: 0.9864\n",
      "Epoch 24/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0147 - acc: 0.9952 - val_loss: 0.0566 - val_acc: 0.9849\n",
      "Epoch 25/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0127 - acc: 0.9963 - val_loss: 0.0586 - val_acc: 0.9856\n",
      "Epoch 26/500\n",
      "90000/90000 [==============================] - 28s - loss: 0.0109 - acc: 0.9963 - val_loss: 0.0628 - val_acc: 0.9848\n",
      "Epoch 27/500\n",
      "90000/90000 [==============================] - 29s - loss: 0.0095 - acc: 0.9970 - val_loss: 0.0665 - val_acc: 0.9836\n",
      "Epoch 28/500\n",
      "90000/90000 [==============================] - 29s - loss: 0.0088 - acc: 0.9971 - val_loss: 0.0665 - val_acc: 0.9847\n",
      "Epoch 29/500\n",
      "90000/90000 [==============================] - 29s - loss: 0.0089 - acc: 0.9972 - val_loss: 0.0726 - val_acc: 0.9845\n"
     ]
    }
   ],
   "source": [
    "gru_model = create_gru_model()\n",
    "\n",
    "filepath = 'model/gru/gru_model_weights.best.h5'\n",
    "checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=0,\n",
    "                            save_best_only=True, mode='max')\n",
    "early_stopping = EarlyStopping(monitor='val_acc', patience=5)\n",
    "callback_list = [checkpoint, early_stopping]\n",
    "\n",
    "gru_history = gru_model.fit(X_train_deep_10w, y_train_deep_10w, \n",
    "              epochs=500, batch_size=256, shuffle=True,\n",
    "              validation_split=0.1, callbacks=callback_list, verbose=1)  # 训练模型\n",
    "\n",
    "json_string = gru_model.to_json()  # 保存模型结构\n",
    "open('model/gru/gru_model_architecture.json', 'w').write(json_string)\n",
    "\n",
    "pickle.dump(gru_history.history, open('model/gru/gru_history.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.5890% \t Precision: 99.7421% \t Recall: 99.5020% \t Roc: 0.9997\tF1: 0.9962\n",
      "Confusion:\n",
      "[[45447   140]\n",
      " [  271 54142]]\n",
      "Accuracy: 98.5050% \t Precision: 98.8732% \t Recall: 98.3955% \t Roc: 0.9975\tF1: 0.9863\n",
      "Confusion:\n",
      "[[ 8908   123]\n",
      " [  176 10793]]\n"
     ]
    }
   ],
   "source": [
    "gru_model = load_model('gru')\n",
    "evaluate_result(y_train_deep_10w, None, gru_model.predict(X_train_deep_10w))\n",
    "evaluate_result(y_test_deep_10w, None, gru_model.predict(X_test_deep_10w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fce25469ba8>"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oXnXMbhFhYnq8lSyMMSHNm2QxCXgU+BxQKCL/KSKTfBuWH0y+EiJie6yKyslItDYLY0xI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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(gru_history.history['loss'])\n",
    "plt.plot(gru_history.history['val_loss'])\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Binary Crossentropy')\n",
    "plt.title('Loss Over Time')\n",
    "plt.legend(['Train','Valid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### biGRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_bigru_model():\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(domain_max_features, \n",
    "                        128, \n",
    "                        input_length=domain_maxlen))\n",
    "    model.add(Bidirectional(GRU(128, return_sequences=True)))\n",
    "    model.add(att.AttentionLayer())\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', \n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 90000 samples, validate on 10000 samples\n",
      "Epoch 1/500\n",
      "90000/90000 [==============================] - 57s - loss: 0.2667 - acc: 0.8882 - val_loss: 0.2060 - val_acc: 0.9203\n",
      "Epoch 2/500\n",
      "90000/90000 [==============================] - 52s - loss: 0.1651 - acc: 0.9389 - val_loss: 0.1495 - val_acc: 0.9467\n",
      "Epoch 3/500\n",
      "90000/90000 [==============================] - 51s - loss: 0.1268 - acc: 0.9552 - val_loss: 0.1222 - val_acc: 0.9574\n",
      "Epoch 4/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.1048 - acc: 0.9633 - val_loss: 0.1025 - val_acc: 0.9649\n",
      "Epoch 5/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0876 - acc: 0.9692 - val_loss: 0.0863 - val_acc: 0.9687\n",
      "Epoch 6/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0709 - acc: 0.9748 - val_loss: 0.0701 - val_acc: 0.9746\n",
      "Epoch 7/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0659 - acc: 0.9771 - val_loss: 0.0666 - val_acc: 0.9781\n",
      "Epoch 8/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0549 - acc: 0.9817 - val_loss: 0.0625 - val_acc: 0.9786\n",
      "Epoch 9/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0494 - acc: 0.9836 - val_loss: 0.0588 - val_acc: 0.9801\n",
      "Epoch 10/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0456 - acc: 0.9849 - val_loss: 0.0572 - val_acc: 0.9811\n",
      "Epoch 11/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0424 - acc: 0.9860 - val_loss: 0.0509 - val_acc: 0.9828\n",
      "Epoch 12/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0391 - acc: 0.9875 - val_loss: 0.0523 - val_acc: 0.9817\n",
      "Epoch 13/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0362 - acc: 0.9882 - val_loss: 0.0482 - val_acc: 0.9834\n",
      "Epoch 14/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0321 - acc: 0.9896 - val_loss: 0.0501 - val_acc: 0.9836\n",
      "Epoch 15/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0321 - acc: 0.9895 - val_loss: 0.0513 - val_acc: 0.9834\n",
      "Epoch 16/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0270 - acc: 0.9915 - val_loss: 0.0484 - val_acc: 0.9837\n",
      "Epoch 17/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0233 - acc: 0.9926 - val_loss: 0.0490 - val_acc: 0.9848\n",
      "Epoch 18/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0210 - acc: 0.9934 - val_loss: 0.0494 - val_acc: 0.9847\n",
      "Epoch 19/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0188 - acc: 0.9940 - val_loss: 0.0542 - val_acc: 0.9834\n",
      "Epoch 20/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0157 - acc: 0.9952 - val_loss: 0.0535 - val_acc: 0.9835\n",
      "Epoch 21/500\n",
      "90000/90000 [==============================] - 51s - loss: 0.0145 - acc: 0.9954 - val_loss: 0.0523 - val_acc: 0.9853\n",
      "Epoch 22/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0128 - acc: 0.9956 - val_loss: 0.0582 - val_acc: 0.9850\n",
      "Epoch 23/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0118 - acc: 0.9962 - val_loss: 0.0579 - val_acc: 0.9846\n",
      "Epoch 24/500\n",
      "90000/90000 [==============================] - 51s - loss: 0.0094 - acc: 0.9972 - val_loss: 0.0632 - val_acc: 0.9841\n",
      "Epoch 25/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0086 - acc: 0.9973 - val_loss: 0.0676 - val_acc: 0.9831\n",
      "Epoch 26/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0072 - acc: 0.9978 - val_loss: 0.0584 - val_acc: 0.9847\n",
      "Epoch 27/500\n",
      "90000/90000 [==============================] - 50s - loss: 0.0059 - acc: 0.9984 - val_loss: 0.0675 - val_acc: 0.9840\n"
     ]
    }
   ],
   "source": [
    "reload(att)\n",
    "bigru_model = create_bigru_model()\n",
    "\n",
    "filepath = 'model/bigru/bigru_model_weights.best.h5'\n",
    "checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=0,\n",
    "                            save_best_only=True, mode='max')\n",
    "early_stopping = EarlyStopping(monitor='val_acc', patience=5)\n",
    "callback_list = [checkpoint, early_stopping]\n",
    "\n",
    "bigru_history = bigru_model.fit(X_train_deep_10w, y_train_deep_10w, \n",
    "              epochs=500, batch_size=256, shuffle=True,\n",
    "              validation_split=0.1, callbacks=callback_list, verbose=1)  # 训练模型\n",
    "\n",
    "json_string = bigru_model.to_json()  # 保存模型结构\n",
    "open('model/bigru/bigru_model_architecture.json', 'w').write(json_string)\n",
    "\n",
    "pickle.dump(bigru_history.history, open('model/bigru/bigru_history.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.5890% \t Precision: 99.7000% \t Recall: 99.5442% \t Roc: 0.9997\tF1: 0.9962\n",
      "Confusion:\n",
      "[[45424   163]\n",
      " [  248 54165]]\n",
      "Accuracy: 98.4200% \t Precision: 98.6572% \t Recall: 98.4593% \t Roc: 0.9977\tF1: 0.9856\n",
      "Confusion:\n",
      "[[ 8884   147]\n",
      " [  169 10800]]\n"
     ]
    }
   ],
   "source": [
    "bigru_model = load_model('bigru')\n",
    "evaluate_result(y_train_deep_10w, None, bigru_model.predict(X_train_deep_10w))\n",
    "evaluate_result(y_test_deep_10w, None, bigru_model.predict(X_test_deep_10w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fce23c1ec50>"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qeqaqPq6qDZ77VfVL3Zw+HdimqgWq2ogzTMglHc4vdOfzbu1w7rnAa6papqqH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\nD3QfqiU2is70AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bigru_history.history['loss'])\n",
    "plt.plot(bigru_history.history['val_loss'])\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Binary Crossentropy')\n",
    "plt.title('Loss Over Time')\n",
    "plt.legend(['Train','Valid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Merged Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM + DNN\n",
    "- 双模型融合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "artifical_num=45\n",
    "def create_dnn_lstm_model():\n",
    "    artifical_input = Input(shape=(artifical_num,))\n",
    "    domain_input = Input(shape=(domain_maxlen,))\n",
    "    \n",
    "    # dnn\n",
    "    dnn_dense_1 = Dense(128, activation='relu')(artifical_input)\n",
    "    dnn_dropout_1 = Dropout(rate=0.5)(dnn_dense_1)\n",
    "    dnn_output = Dense(1, activation='sigmoid')(dnn_dropout_1)\n",
    "    dnn_model = Model(inputs=artifical_input, output=dnn_output)\n",
    "    \n",
    "    # lstm\n",
    "    embedding_layer = Embedding(domain_max_features, \n",
    "                                128, \n",
    "                                input_length=domain_maxlen)(domain_input)\n",
    "    lstm_dense = LSTM(128)(embedding_layer)\n",
    "    lstm_dropout = Dropout(rate=0.5)(lstm_dense)\n",
    "    lstm_output = Dense(1, activation='sigmoid')(lstm_dropout)\n",
    "    lstm_model = Model(inputs=domain_input, output=lstm_output)\n",
    "    \n",
    "    # embedding\n",
    "    merged_layer = Concatenate(axis=-1)([dnn_output, lstm_output])\n",
    "    merged_output = Dense(1, activation='sigmoid')(merged_layer)\n",
    "    combined_model = Model(inputs=[dnn_model.input] + [lstm_model.input], output=merged_output)\n",
    "    \n",
    "    # final model\n",
    "    combined_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return combined_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 90000 samples, validate on 10000 samples\n",
      "Epoch 1/500\n",
      "90000/90000 [==============================] - 40s - loss: 0.4474 - acc: 0.8884 - val_loss: 0.3727 - val_acc: 0.9148\n",
      "Epoch 2/500\n",
      "90000/90000 [==============================] - 35s - loss: 0.3199 - acc: 0.9396 - val_loss: 0.2822 - val_acc: 0.9468\n",
      "Epoch 3/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.2544 - acc: 0.9519 - val_loss: 0.2322 - val_acc: 0.9530\n",
      "Epoch 4/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.2105 - acc: 0.9594 - val_loss: 0.1949 - val_acc: 0.9617\n",
      "Epoch 5/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.1774 - acc: 0.9654 - val_loss: 0.1669 - val_acc: 0.9648\n",
      "Epoch 6/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.1526 - acc: 0.9689 - val_loss: 0.1450 - val_acc: 0.9698\n",
      "Epoch 7/500\n",
      "90000/90000 [==============================] - 34s - loss: 0.1321 - acc: 0.9725 - val_loss: 0.1260 - val_acc: 0.9725\n",
      "Epoch 8/500\n",
      "90000/90000 [==============================] - 34s - loss: 0.1162 - acc: 0.9762 - val_loss: 0.1115 - val_acc: 0.9768\n",
      "Epoch 9/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.1025 - acc: 0.9796 - val_loss: 0.1015 - val_acc: 0.9757\n",
      "Epoch 10/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0906 - acc: 0.9818 - val_loss: 0.0904 - val_acc: 0.9805\n",
      "Epoch 11/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0813 - acc: 0.9836 - val_loss: 0.0817 - val_acc: 0.9824\n",
      "Epoch 12/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0742 - acc: 0.9845 - val_loss: 0.0774 - val_acc: 0.9809\n",
      "Epoch 13/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0671 - acc: 0.9864 - val_loss: 0.0683 - val_acc: 0.9840\n",
      "Epoch 14/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0611 - acc: 0.9877 - val_loss: 0.0641 - val_acc: 0.9836\n",
      "Epoch 15/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0565 - acc: 0.9879 - val_loss: 0.0579 - val_acc: 0.9855\n",
      "Epoch 16/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0519 - acc: 0.9891 - val_loss: 0.0566 - val_acc: 0.9864\n",
      "Epoch 17/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0480 - acc: 0.9898 - val_loss: 0.0529 - val_acc: 0.9852\n",
      "Epoch 18/500\n",
      "90000/90000 [==============================] - 34s - loss: 0.0446 - acc: 0.9903 - val_loss: 0.0515 - val_acc: 0.9857\n",
      "Epoch 19/500\n",
      "90000/90000 [==============================] - 34s - loss: 0.0414 - acc: 0.9909 - val_loss: 0.0480 - val_acc: 0.9875\n",
      "Epoch 20/500\n",
      "90000/90000 [==============================] - 34s - loss: 0.0390 - acc: 0.9914 - val_loss: 0.0453 - val_acc: 0.9882\n",
      "Epoch 21/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0361 - acc: 0.9919 - val_loss: 0.0452 - val_acc: 0.9871\n",
      "Epoch 22/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0336 - acc: 0.9924 - val_loss: 0.0448 - val_acc: 0.9876\n",
      "Epoch 23/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0315 - acc: 0.9928 - val_loss: 0.0427 - val_acc: 0.9873\n",
      "Epoch 24/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0300 - acc: 0.9932 - val_loss: 0.0420 - val_acc: 0.9871\n",
      "Epoch 25/500\n",
      "90000/90000 [==============================] - 34s - loss: 0.0284 - acc: 0.9936 - val_loss: 0.0388 - val_acc: 0.9878\n",
      "Epoch 26/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0262 - acc: 0.9942 - val_loss: 0.0392 - val_acc: 0.9868\n"
     ]
    }
   ],
   "source": [
    "dnn_lstm_model = create_dnn_lstm_model()\n",
    "\n",
    "filepath = 'model/dnn_lstm/dnn_lstm_model_weights.best.h5'\n",
    "checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=0,\n",
    "                            save_best_only=True, mode='max')\n",
    "early_stopping = EarlyStopping(monitor='val_acc', patience=5)\n",
    "callback_list = [checkpoint, early_stopping]\n",
    "\n",
    "dnn_lstm_history = dnn_lstm_model.fit([X_train_10w] + [X_train_deep_10w], y_train_deep_10w, \n",
    "              epochs=500, batch_size=256, shuffle=True,\n",
    "              validation_split=0.1, callbacks=callback_list, verbose=1)  # 训练模型\n",
    "\n",
    "json_string = dnn_lstm_model.to_json()  # 保存模型结构\n",
    "open('model/dnn_lstm/dnn_lstm_model_architecture.json', 'w').write(json_string)\n",
    "\n",
    "pickle.dump(dnn_lstm_history.history, open('model/dnn_lstm/dnn_lstm_history.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.2920% \t Precision: 99.6983% \t Recall: 98.9984% \t Roc: 0.9989\tF1: 0.9935\n",
      "Confusion:\n",
      "[[45424   163]\n",
      " [  545 53868]]\n",
      "Accuracy: 98.8800% \t Precision: 99.3932% \t Recall: 98.5596% \t Roc: 0.9984\tF1: 0.9897\n",
      "Confusion:\n",
      "[[ 8965    66]\n",
      " [  158 10811]]\n"
     ]
    }
   ],
   "source": [
    "dnn_lstm_model = load_model('dnn_lstm')\n",
    "evaluate_result(y_train_deep_10w, None, dnn_lstm_model.predict([X_train_10w] + [X_train_deep_10w]))\n",
    "evaluate_result(y_test_deep_10w, None, dnn_lstm_model.predict([X_test_10w] + [X_test_deep_10w]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fce229c59b0>"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rk4KIpHRGIJ1u2Axn3awKaWzT9JzW2GyMCUH+lBS+EJF/iMgFIiIBj6izpA6FlKEtqpBG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\nUjDGGONlScEYY4yXJQVjjDFe/x8y5hosiAKxRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dnn_lstm_history.history['loss'])\n",
    "plt.plot(dnn_lstm_history.history['val_loss'])\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Binary Crossentropy')\n",
    "plt.title('Loss Over Time')\n",
    "plt.legend(['Train','Valid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 五折交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1759959, 45),\n",
       " (1759959, 64),\n",
       " (439990, 45),\n",
       " (439990, 64),\n",
       " (1759959,),\n",
       " (439990,))"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_min_max_scaler.shape, X_train_deep.shape, X_test_min_max_scaler.shape, X_test_deep.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold_1\n",
      "Train on 1407967 samples, validate on 351992 samples\n",
      "Epoch 1/500\n",
      "1407967/1407967 [==============================] - 549s - loss: 0.1245 - acc: 0.9716 - val_loss: 0.0477 - val_acc: 0.9895\n",
      "Epoch 2/500\n",
      "1407967/1407967 [==============================] - 546s - loss: 0.0374 - acc: 0.9905 - val_loss: 0.0291 - val_acc: 0.9917\n",
      "Epoch 3/500\n",
      "1407967/1407967 [==============================] - 550s - loss: 0.0264 - acc: 0.9925 - val_loss: 0.0230 - val_acc: 0.9933\n",
      "Epoch 4/500\n",
      "1407967/1407967 [==============================] - 543s - loss: 0.0228 - acc: 0.9933 - val_loss: 0.0209 - val_acc: 0.9938\n",
      "Epoch 5/500\n",
      "1407967/1407967 [==============================] - 547s - loss: 0.0209 - acc: 0.9938 - val_loss: 0.0218 - val_acc: 0.9932\n",
      "Epoch 6/500\n",
      "1407967/1407967 [==============================] - 553s - loss: 0.0184 - acc: 0.9942 - val_loss: 0.0178 - val_acc: 0.9942\n",
      "Epoch 7/500\n",
      "1407967/1407967 [==============================] - 547s - loss: 0.0169 - acc: 0.9946 - val_loss: 0.0173 - val_acc: 0.9943\n",
      "Epoch 8/500\n",
      "1407967/1407967 [==============================] - 544s - loss: 0.0157 - acc: 0.9949 - val_loss: 0.0167 - val_acc: 0.9944\n",
      "Epoch 9/500\n",
      "1407967/1407967 [==============================] - 545s - loss: 0.0147 - acc: 0.9952 - val_loss: 0.0164 - val_acc: 0.9945\n",
      "Epoch 10/500\n",
      "1407967/1407967 [==============================] - 544s - loss: 0.0138 - acc: 0.9955 - val_loss: 0.0161 - val_acc: 0.9947\n",
      "Epoch 11/500\n",
      "1407967/1407967 [==============================] - 545s - loss: 0.0132 - acc: 0.9957 - val_loss: 0.0158 - val_acc: 0.9949\n",
      "Epoch 12/500\n",
      "1407967/1407967 [==============================] - 543s - loss: 0.0126 - acc: 0.9959 - val_loss: 0.0161 - val_acc: 0.9948\n",
      "Epoch 13/500\n",
      "1407967/1407967 [==============================] - 543s - loss: 0.0122 - acc: 0.9960 - val_loss: 0.0163 - val_acc: 0.9948\n",
      "Epoch 14/500\n",
      "1407967/1407967 [==============================] - 550s - loss: 0.0117 - acc: 0.9962 - val_loss: 0.0159 - val_acc: 0.9950\n",
      "Epoch 15/500\n",
      "1407967/1407967 [==============================] - 547s - loss: 0.0113 - acc: 0.9963 - val_loss: 0.0163 - val_acc: 0.9947\n",
      "Epoch 16/500\n",
      "1407967/1407967 [==============================] - 550s - loss: 0.0111 - acc: 0.9963 - val_loss: 0.0164 - val_acc: 0.9947\n",
      "Epoch 17/500\n",
      "1407967/1407967 [==============================] - 546s - loss: 0.0109 - acc: 0.9963 - val_loss: 0.0162 - val_acc: 0.9947\n",
      "Epoch 18/500\n",
      "1407967/1407967 [==============================] - 541s - loss: 0.0104 - acc: 0.9966 - val_loss: 0.0171 - val_acc: 0.9947\n",
      "Epoch 19/500\n",
      "1407967/1407967 [==============================] - 540s - loss: 0.0102 - acc: 0.9967 - val_loss: 0.0166 - val_acc: 0.9947\n",
      "Epoch 20/500\n",
      "1407967/1407967 [==============================] - 542s - loss: 0.0100 - acc: 0.9967 - val_loss: 0.0169 - val_acc: 0.9946\n",
      "Fold_2\n",
      "Train on 1407967 samples, validate on 351992 samples\n",
      "Epoch 1/500\n",
      "1407967/1407967 [==============================] - 551s - loss: 0.2178 - acc: 0.9487 - val_loss: 0.0639 - val_acc: 0.9845\n",
      "Epoch 2/500\n",
      " 526592/1407967 [==========>...................] - ETA: 322s - loss: 0.0542 - acc: 0.9862- ETA: 323s - loss: 0.0542 - acc: "
     ]
    }
   ],
   "source": [
    "kfold = StratifiedKFold(n_splits=5, random_state=42, shuffle=True)\n",
    "n_fold = 0\n",
    "dnn_lstm_models = list()\n",
    "for train_index, valid_index in kfold.split(X_train_deep, y_train):\n",
    "    n_fold += 1\n",
    "    print(\"Fold_{}\".format(n_fold))\n",
    "    \n",
    "    # 数据准备\n",
    "    X_train_artifical_fold = X_train_min_max_scaler[train_index]\n",
    "    X_train_deep_fold = X_train_deep[train_index]\n",
    "    X_valid_artifical_fold = X_train_min_max_scaler[valid_index]\n",
    "    X_valid_deep_fold = X_train_deep[valid_index]\n",
    "    y_train_fold = y_train.values[train_index]\n",
    "    y_valid_fold = y_train.values[valid_index]\n",
    "\n",
    "    # 模型构建\n",
    "    dnn_lstm_model = create_dnn_lstm_model()\n",
    "\n",
    "    # 模型训练\n",
    "    filepath = 'model/dnn_lstm/dnn_lstm_model_weights_fold_{}.best.h5'.format(n_fold)\n",
    "    checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=0,\n",
    "                            save_best_only=True, mode='max')\n",
    "    early_stopping = EarlyStopping(monitor='val_acc', patience=5)\n",
    "    callback_list = [checkpoint, early_stopping]\n",
    "\n",
    "    dnn_lstm_history = dnn_lstm_model.fit([X_train_artifical_fold] + [X_train_deep_fold], y_train_fold, \n",
    "                  epochs=500, batch_size=256, shuffle=True,\n",
    "                  validation_data=[[X_valid_artifical_fold] + [X_valid_deep_fold], y_valid_fold], \n",
    "                  callbacks=callback_list, verbose=1)  # 训练模型\n",
    "    pickle.dump(dnn_lstm_history.history, open('model/dnn_lstm/dnn_lstm_history_fold_{}.pkl'.format(n_fold), 'wb'))\n",
    "    dnn_lstm_models.append(dnn_lstm_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载五折模型\n",
    "dnn_list_models = list()\n",
    "for i in range(1,6):\n",
    "    model = create_dnn_lstm_model()\n",
    "    weight_path = 'model/dnn_lstm/dnn_lstm_model_weights_fold_{}.best.h5'.format(i)\n",
    "    model.load_weights(weight_path)\n",
    "    dnn_list_models.append(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_Fold:0\n",
      "Accuracy: 99.5214% \t Precision: 99.6792% \t Recall: 99.4425% \t Roc: 0.9997\tF1: 0.9956\n",
      "Confusion:\n",
      "[[199232    768]\n",
      " [  1338 238652]]\n",
      "n_Fold:1\n",
      "Accuracy: 99.5057% \t Precision: 99.6240% \t Recall: 99.4691% \t Roc: 0.9997\tF1: 0.9955\n",
      "Confusion:\n",
      "[[199099    901]\n",
      " [  1274 238716]]\n",
      "n_Fold:2\n",
      "Accuracy: 99.4968% \t Precision: 99.6422% \t Recall: 99.4346% \t Roc: 0.9997\tF1: 0.9954\n",
      "Confusion:\n",
      "[[199143    857]\n",
      " [  1357 238633]]\n",
      "n_Fold:3\n",
      "Accuracy: 99.5186% \t Precision: 99.6071% \t Recall: 99.5100% \t Roc: 0.9997\tF1: 0.9956\n",
      "Confusion:\n",
      "[[199058    942]\n",
      " [  1176 238814]]\n",
      "n_Fold:4\n",
      "Accuracy: 99.5195% \t Precision: 99.5794% \t Recall: 99.5396% \t Roc: 0.9998\tF1: 0.9956\n",
      "Confusion:\n",
      "[[198991   1009]\n",
      " [  1105 238885]]\n"
     ]
    }
   ],
   "source": [
    "# 评估在测试集上的结果\n",
    "y_test_preds = list()\n",
    "for i in range(5):\n",
    "    print(\"n_Fold:{}\".format(i))\n",
    "    model = dnn_list_models[i]\n",
    "    y_test_pred = model.predict([X_test_min_max_scaler] + [X_test_deep]).flatten()\n",
    "    evaluate_result(y_test, None, y_test_pred)\n",
    "    y_test_preds.append(y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 439990)"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据类型转化\n",
    "y_test_preds_array = np.array(y_test_preds)\n",
    "pickle.dump(y_test_preds_array, open('model/dnn_lstm/dnn_lstm_five_fold_result.pkl', 'wb'))\n",
    "y_test_preds_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.5859% \t Precision: 99.7032% \t Recall: 99.5371% \t Roc: 0.9998\tF1: 0.9962\n",
      "Confusion:\n",
      "[[199289    711]\n",
      " [  1111 238879]]\n"
     ]
    }
   ],
   "source": [
    "y_test_ensemble = y_test_preds_array.sum(axis=0) / 5.0\n",
    "evaluate_result(y_test, None, y_test_ensemble)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 分析五折后的误差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(439990, 4)"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_analysis_df = pd.DataFrame()\n",
    "error_analysis_df['family'] = test_features_df['label'].values\n",
    "error_analysis_df['label'] = y_test.values\n",
    "error_analysis_df['proba'] = y_test_ensemble\n",
    "error_analysis_df['pred'] = np.where(y_test_ensemble>0.5, 1, 0)\n",
    "error_analysis_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>family</th>\n",
       "      <th>label</th>\n",
       "      <th>proba</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>banjori</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999654</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>legit</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004531</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>legit</td>\n",
       "      <td>0</td>\n",
       "      <td>0.010968</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>legit</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tinba</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999882</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    family  label     proba  pred\n",
       "0  banjori      1  0.999654     1\n",
       "1    legit      0  0.004531     0\n",
       "2    legit      0  0.010968     0\n",
       "3    legit      0  0.000442     0\n",
       "4    tinba      1  0.999882     1"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_analysis_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "evulation_df = pd.DataFrame()\n",
    "evulation_df['accuracy'] = error_analysis_df.groupby(['family']).apply(lambda x: accuracy_score(x['label'], x['pred']))\n",
    "evulation_df['precision'] = error_analysis_df.groupby(['family']).apply(lambda x: precision_score(x['label'], x['pred']))\n",
    "evulation_df['recall'] = error_analysis_df.groupby(['family']).apply(lambda x: recall_score(x['label'], x['pred']))\n",
    "evulation_df['F1'] = 2*(evulation_df['precision']*evulation_df['recall'])/(evulation_df['precision']+evulation_df['recall'])\n",
    "evulation_df['support'] = error_analysis_df.groupby(['family']).apply(lambda x: x.shape[0])\n",
    "evulation_df.sort_values(by='support', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>legit</th>\n",
       "      <td>0.996445</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banjori</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>90468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emotet</th>\n",
       "      <td>0.999773</td>\n",
       "      <td>66112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rovnix</th>\n",
       "      <td>0.999944</td>\n",
       "      <td>35987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tinba</th>\n",
       "      <td>0.996620</td>\n",
       "      <td>14498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pykspa_v1</th>\n",
       "      <td>0.993451</td>\n",
       "      <td>8856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simda</th>\n",
       "      <td>0.999368</td>\n",
       "      <td>4746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ramnit</th>\n",
       "      <td>0.961447</td>\n",
       "      <td>3787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gameover</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ranbyus</th>\n",
       "      <td>0.999083</td>\n",
       "      <td>2181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virut</th>\n",
       "      <td>0.783325</td>\n",
       "      <td>1943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>murofet</th>\n",
       "      <td>0.999416</td>\n",
       "      <td>1712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>necurs</th>\n",
       "      <td>0.940709</td>\n",
       "      <td>1636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shiotob</th>\n",
       "      <td>0.994354</td>\n",
       "      <td>1594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmi</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shifu</th>\n",
       "      <td>0.917323</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suppobox</th>\n",
       "      <td>0.792857</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qadars</th>\n",
       "      <td>0.985000</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>locky</th>\n",
       "      <td>0.905172</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dyre</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cryptolocker</th>\n",
       "      <td>0.990000</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chinad</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pykspa_v2_fake</th>\n",
       "      <td>0.850000</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dircrypt</th>\n",
       "      <td>0.980263</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vawtrak</th>\n",
       "      <td>0.711409</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conficker</th>\n",
       "      <td>0.773196</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nymaim</th>\n",
       "      <td>0.917647</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fobber_v1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fobber_v2</th>\n",
       "      <td>0.983051</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feodo</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tempedreve</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pykspa_v2_real</th>\n",
       "      <td>0.974359</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>padcrypt</th>\n",
       "      <td>0.941176</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matsnu</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bamital</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proslikefan</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vidro</th>\n",
       "      <td>0.650000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gspy</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mydoom</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omexo</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tinynuke</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tofsee</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                accuracy  support\n",
       "family                           \n",
       "legit           0.996445   200000\n",
       "banjori         1.000000    90468\n",
       "emotet          0.999773    66112\n",
       "rovnix          0.999944    35987\n",
       "tinba           0.996620    14498\n",
       "pykspa_v1       0.993451     8856\n",
       "simda           0.999368     4746\n",
       "ramnit          0.961447     3787\n",
       "gameover        1.000000     2399\n",
       "ranbyus         0.999083     2181\n",
       "virut           0.783325     1943\n",
       "murofet         0.999416     1712\n",
       "necurs          0.940709     1636\n",
       "shiotob         0.994354     1594\n",
       "symmi           1.000000      851\n",
       "shifu           0.917323      508\n",
       "suppobox        0.792857      420\n",
       "qadars          0.985000      400\n",
       "locky           0.905172      232\n",
       "dyre            1.000000      200\n",
       "cryptolocker    0.990000      200\n",
       "chinad          1.000000      200\n",
       "pykspa_v2_fake  0.850000      160\n",
       "dircrypt        0.980263      152\n",
       "vawtrak         0.711409      149\n",
       "conficker       0.773196       97\n",
       "nymaim          0.917647       85\n",
       "fobber_v1       1.000000       59\n",
       "fobber_v2       0.983051       59\n",
       "feodo           1.000000       53\n",
       "tempedreve      0.800000       40\n",
       "pykspa_v2_real  0.974359       39\n",
       "padcrypt        0.941176       34\n",
       "matsnu          0.000000       26\n",
       "bamital         1.000000       21\n",
       "proslikefan     0.900000       20\n",
       "vidro           0.650000       20\n",
       "gspy            1.000000       19\n",
       "mydoom          0.666667        9\n",
       "omexo           1.000000        8\n",
       "tinynuke        1.000000        6\n",
       "tofsee          1.000000        4"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evulation_df[['accuracy', 'support']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((35, 5), (27, 5), (22, 5))"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evulation_df[evulation_df['accuracy']>=0.80].shape, evulation_df[evulation_df['accuracy']>=0.95].shape, evulation_df[evulation_df['accuracy']>=0.99].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### lstm with manual DNN(lwm DNN)\n",
    "- 特征融合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "artifical_num=45\n",
    "def create_lstm_with_manual_dnn_model():\n",
    "    artifical_input = Input(shape=(artifical_num,))\n",
    "    domain_input = Input(shape=(domain_maxlen,))\n",
    "    \n",
    "    # lstm\n",
    "    embedding_layer = Embedding(domain_max_features, \n",
    "                                128, \n",
    "                                input_length=domain_maxlen)(domain_input)\n",
    "    lstm_dense = LSTM(128)(embedding_layer)\n",
    "    \n",
    "    # merged\n",
    "    merged_layer = Concatenate(axis=-1)([lstm_dense, artifical_input])\n",
    "    \n",
    "    # dnn\n",
    "    merged_dnn_dense1 = Dense(256)(merged_layer)\n",
    "    merged_dnn_dropout1 = Dropout(rate=0.5)(merged_dnn_dense1)\n",
    "    merged_dnn_dense2 = Dense(128)(merged_dnn_dropout1)\n",
    "    merged_dnn_dropout2 = Dropout(rate=0.5)(merged_dnn_dense2)\n",
    "    merged_output = Dense(1, activation='sigmoid')(merged_dnn_dropout2)\n",
    "    \n",
    "    combined_model = Model(inputs=[artifical_input] + [domain_input], output=merged_output)\n",
    "    \n",
    "    # final model\n",
    "    combined_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return combined_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 90000 samples, validate on 10000 samples\n",
      "Epoch 1/500\n",
      "90000/90000 [==============================] - 37s - loss: 0.1410 - acc: 0.9471 - val_loss: 0.0887 - val_acc: 0.9722\n",
      "Epoch 2/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0773 - acc: 0.9741 - val_loss: 0.0654 - val_acc: 0.9793\n",
      "Epoch 3/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0604 - acc: 0.9803 - val_loss: 0.0542 - val_acc: 0.9817\n",
      "Epoch 4/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0509 - acc: 0.9839 - val_loss: 0.0499 - val_acc: 0.9827\n",
      "Epoch 5/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0455 - acc: 0.9852 - val_loss: 0.0513 - val_acc: 0.9839\n",
      "Epoch 6/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0431 - acc: 0.9862 - val_loss: 0.0488 - val_acc: 0.9832\n",
      "Epoch 7/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0392 - acc: 0.9875 - val_loss: 0.0433 - val_acc: 0.9864\n",
      "Epoch 8/500\n",
      "90000/90000 [==============================] - 34s - loss: 0.0361 - acc: 0.9879 - val_loss: 0.0399 - val_acc: 0.9857\n",
      "Epoch 9/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0337 - acc: 0.9888 - val_loss: 0.0442 - val_acc: 0.9859\n",
      "Epoch 10/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0319 - acc: 0.9896 - val_loss: 0.0414 - val_acc: 0.9865.989\n",
      "Epoch 11/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0306 - acc: 0.9898 - val_loss: 0.0389 - val_acc: 0.9870\n",
      "Epoch 12/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0283 - acc: 0.9906 - val_loss: 0.0390 - val_acc: 0.9867\n",
      "Epoch 13/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0281 - acc: 0.9907 - val_loss: 0.0396 - val_acc: 0.9873\n",
      "Epoch 14/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0263 - acc: 0.9910 - val_loss: 0.0368 - val_acc: 0.9877\n",
      "Epoch 15/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0242 - acc: 0.9921 - val_loss: 0.0355 - val_acc: 0.9882\n",
      "Epoch 16/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0235 - acc: 0.9921 - val_loss: 0.0378 - val_acc: 0.9868\n",
      "Epoch 17/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0227 - acc: 0.9925 - val_loss: 0.0356 - val_acc: 0.9879\n",
      "Epoch 18/500\n",
      "90000/90000 [==============================] - 34s - loss: 0.0207 - acc: 0.9930 - val_loss: 0.0368 - val_acc: 0.9878\n",
      "Epoch 19/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0188 - acc: 0.9935 - val_loss: 0.0393 - val_acc: 0.9872\n",
      "Epoch 20/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0177 - acc: 0.9939 - val_loss: 0.0409 - val_acc: 0.9870\n",
      "Epoch 21/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.0159 - acc: 0.9944 - val_loss: 0.0398 - val_acc: 0.9864\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4620"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lwm_dnn_model = create_lstm_with_manual_dnn_model()\n",
    "\n",
    "filepath = 'model/lwm_dnn/lwm_dnn_model_weights.best.h5'\n",
    "checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=0,\n",
    "                            save_best_only=True, mode='max')\n",
    "early_stopping = EarlyStopping(monitor='val_acc', patience=5)\n",
    "callback_list = [checkpoint, early_stopping]\n",
    "\n",
    "lwm_dnn_model.fit([X_train_10w] + [X_train_deep_10w], y_train_deep_10w, \n",
    "              epochs=500, batch_size=256, shuffle=True,\n",
    "              validation_split=0.1, callbacks=callback_list, verbose=1)  # 训练模型\n",
    "\n",
    "json_string = lwm_dnn_model.to_json()  # 保存模型结构\n",
    "open('model/lwm_dnn/lwm_dnn_model_architecture.json', 'w').write(json_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.2450% \t Precision: 99.4635% \t Recall: 99.1473% \t Roc: 0.9996\n",
      "Confusion:\n",
      "[[45296   291]\n",
      " [  464 53949]]\n",
      "Accuracy: 98.8950% \t Precision: 99.1494% \t Recall: 98.8331% \t Roc: 0.9988\n",
      "Confusion:\n",
      "[[ 8938    93]\n",
      " [  128 10841]]\n"
     ]
    }
   ],
   "source": [
    "dnn_lstm_model = load_model('lwm_dnn')\n",
    "evaluate_result(y_train_deep_10w, None, dnn_lstm_model.predict([X_train_10w] + [X_train_deep_10w]))\n",
    "evaluate_result(y_test_deep_10w, None, dnn_lstm_model.predict([X_test_10w] + [X_test_deep_10w]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 多分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_multi_lstm_model():\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(domain_max_features, \n",
    "                        128, \n",
    "                        input_length=domain_maxlen))\n",
    "    model.add(LSTM(128))\n",
    "    model.add(Dense(42, activation='softmax'))\n",
    "    model.compile(loss='categorical_crossentropy', \n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 90000 samples, validate on 10000 samples\n",
      "Epoch 1/500\n",
      "90000/90000 [==============================] - 36s - loss: 0.8959 - acc: 0.7622 - val_loss: 0.4300 - val_acc: 0.8930\n",
      "Epoch 2/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.3501 - acc: 0.9075 - val_loss: 0.3029 - val_acc: 0.9216\n",
      "Epoch 3/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.2549 - acc: 0.9339 - val_loss: 0.2231 - val_acc: 0.9388\n",
      "Epoch 4/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.2014 - acc: 0.9485 - val_loss: 0.1832 - val_acc: 0.9541\n",
      "Epoch 5/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.1663 - acc: 0.9577 - val_loss: 0.1655 - val_acc: 0.9563\n",
      "Epoch 6/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.1440 - acc: 0.9628 - val_loss: 0.1433 - val_acc: 0.9630\n",
      "Epoch 7/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.1297 - acc: 0.9656 - val_loss: 0.1282 - val_acc: 0.9653\n",
      "Epoch 8/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.1195 - acc: 0.9685 - val_loss: 0.1352 - val_acc: 0.9631\n",
      "Epoch 9/500\n",
      "90000/90000 [==============================] - 33s - loss: 0.1403 - acc: 0.9632 - val_loss: 0.1328 - val_acc: 0.9622\n",
      "Epoch 10/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.1131 - acc: 0.9697 - val_loss: 0.1176 - val_acc: 0.9682\n",
      "Epoch 11/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.1036 - acc: 0.9720 - val_loss: 0.1146 - val_acc: 0.9698\n",
      "Epoch 12/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.1022 - acc: 0.9725 - val_loss: 0.1725 - val_acc: 0.9562\n",
      "Epoch 13/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.1043 - acc: 0.9715 - val_loss: 0.1138 - val_acc: 0.9700\n",
      "Epoch 14/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0934 - acc: 0.9747 - val_loss: 0.1095 - val_acc: 0.9697\n",
      "Epoch 15/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0912 - acc: 0.9752 - val_loss: 0.1093 - val_acc: 0.9687\n",
      "Epoch 16/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0956 - acc: 0.9739 - val_loss: 0.1070 - val_acc: 0.9696\n",
      "Epoch 17/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0849 - acc: 0.9771 - val_loss: 0.1020 - val_acc: 0.9717\n",
      "Epoch 18/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0823 - acc: 0.9772 - val_loss: 0.1043 - val_acc: 0.9712\n",
      "Epoch 19/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0841 - acc: 0.9766 - val_loss: 0.1000 - val_acc: 0.9726\n",
      "Epoch 20/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0783 - acc: 0.9784 - val_loss: 0.1011 - val_acc: 0.9720\n",
      "Epoch 21/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0766 - acc: 0.9788 - val_loss: 0.1068 - val_acc: 0.9699\n",
      "Epoch 22/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0730 - acc: 0.9797 - val_loss: 0.1033 - val_acc: 0.9727\n",
      "Epoch 23/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0712 - acc: 0.9804 - val_loss: 0.1038 - val_acc: 0.9711\n",
      "Epoch 24/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0712 - acc: 0.9800 - val_loss: 0.1177 - val_acc: 0.9668\n",
      "Epoch 25/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0686 - acc: 0.9805 - val_loss: 0.1038 - val_acc: 0.9706\n",
      "Epoch 26/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0643 - acc: 0.9823 - val_loss: 0.1030 - val_acc: 0.9718\n",
      "Epoch 27/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0619 - acc: 0.9827 - val_loss: 0.0975 - val_acc: 0.9734\n",
      "Epoch 28/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0612 - acc: 0.9827 - val_loss: 0.0989 - val_acc: 0.9741\n",
      "Epoch 29/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0608 - acc: 0.9829 - val_loss: 0.1008 - val_acc: 0.9730\n",
      "Epoch 30/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0552 - acc: 0.9844 - val_loss: 0.1009 - val_acc: 0.9724\n",
      "Epoch 31/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0534 - acc: 0.9849 - val_loss: 0.1007 - val_acc: 0.9730\n",
      "Epoch 32/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0540 - acc: 0.9846 - val_loss: 0.1087 - val_acc: 0.9703\n",
      "Epoch 33/500\n",
      "90000/90000 [==============================] - 32s - loss: 0.0511 - acc: 0.9854 - val_loss: 0.1016 - val_acc: 0.9730\n",
      "Epoch 34/500\n",
      "90000/90000 [==============================] - 31s - loss: 0.0479 - acc: 0.9865 - val_loss: 0.1035 - val_acc: 0.9720\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1842"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_lstm_model = create_multi_lstm_model()\n",
    "\n",
    "filepath = 'model/lstm/multi_lstm_model_weights.best.h5'\n",
    "checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_acc', verbose=0,\n",
    "                            save_best_only=True, mode='max')\n",
    "early_stopping = EarlyStopping(monitor='val_acc', patience=5)\n",
    "callback_list = [checkpoint, early_stopping]\n",
    "\n",
    "multi_lstm_model.fit(X_train_deep_10w, y_train_deep_multi_10w, \n",
    "              epochs=500, batch_size=256, shuffle=True,\n",
    "              validation_split=0.1, callbacks=callback_list, verbose=1)  # 训练模型\n",
    "\n",
    "json_string = multi_lstm_model.to_json()  # 保存模型结构\n",
    "open('model/lstm/multi_lstm_model_architecture.json', 'w').write(json_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.41%\n"
     ]
    }
   ],
   "source": [
    "scores = multi_lstm_model.evaluate(x=X_test_deep_10w, y=y_test_deep_multi_10w, verbose=0)\n",
    "print('Accuracy: %.2f%%' % (scores[1] * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BERT (尚未完成)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### bert-tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1759959,), (1759959,))"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_domain.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 训练与使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1759959, 439990)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_domain_pair(X, y):\n",
    "    domain_pair = list()\n",
    "    for i in range(len(X)):\n",
    "        pair = list()\n",
    "        domain = X[i]\n",
    "        split_domain = [x for x in domain]\n",
    "        label = ['legit'] if y[i]==0 else ['dga']\n",
    "        pair.append(split_domain)\n",
    "        pair.append(label)\n",
    "        domain_pair.append(pair)\n",
    "    return domain_pair\n",
    "\n",
    "train_pair = generate_domain_pair(X_train_domain.fillna('').tolist(), \n",
    "                                  y_train.tolist())\n",
    "test_pair = generate_domain_pair(X_test_domain.fillna('').tolist(), \n",
    "                                 y_test.tolist())\n",
    "len(train_pair), len(test_pair)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build token dictionary\n",
    "token_dict = get_base_dict()  # A dict that contains some special tokens\n",
    "for pairs in train_pair:\n",
    "    for token in pairs[0] + pairs[1]:\n",
    "        if token not in token_dict:\n",
    "            token_dict[token] = len(token_dict)\n",
    "token_list = list(token_dict.keys())  # Used for selecting a random word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build & train the model\n",
    "model = get_model(\n",
    "    token_num=len(token_dict),\n",
    "    head_num=32,\n",
    "    transformer_num=12,\n",
    "    embed_dim=128,\n",
    "    feed_forward_dim=100,\n",
    "    seq_len=64,\n",
    "    pos_num=64,\n",
    "    dropout_rate=0.05,\n",
    ")\n",
    "compile_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _generator():\n",
    "    while True:\n",
    "        yield gen_batch_inputs(\n",
    "            train_pair,\n",
    "            token_dict,\n",
    "            token_list,\n",
    "            seq_len=20,\n",
    "            mask_rate=0.3,\n",
    "            swap_sentence_rate=1.0,\n",
    "        )\n",
    "\n",
    "model.fit_generator(\n",
    "    generator=_generator(),\n",
    "    steps_per_epoch=1000,\n",
    "    epochs=100,\n",
    "    validation_data=_generator(),\n",
    "    validation_steps=100,\n",
    "    callbacks=[\n",
    "        EarlyStopping(monitor='val_loss', patience=5)\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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