NLP and Classification - Stumbleupon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification with stumbleupon data\n",
    "\n",
    "Project 4 has been changed since scraping was untenable. The project now focuses on the stumbleupon kaggle dataset. For more information on this dataset, [check out the website here](https://www.kaggle.com/c/stumbleupon).\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load in the dataset\n",
    "\n",
    "This is the only part completed for you.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1047,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "sns.set_style('white')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1048,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "su = pd.read_csv('../dataset/evergreen.tsv', delimiter='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Clean up/examine your data\n",
    "\n",
    "Some of the columns may have values that need changing or that are of the wrong type. There could also be columns that aren't very useful.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1049,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>urlid</th>\n",
       "      <th>boilerplate</th>\n",
       "      <th>alchemy_category</th>\n",
       "      <th>alchemy_category_score</th>\n",
       "      <th>avglinksize</th>\n",
       "      <th>commonlinkratio_1</th>\n",
       "      <th>commonlinkratio_2</th>\n",
       "      <th>commonlinkratio_3</th>\n",
       "      <th>commonlinkratio_4</th>\n",
       "      <th>...</th>\n",
       "      <th>is_news</th>\n",
       "      <th>lengthyLinkDomain</th>\n",
       "      <th>linkwordscore</th>\n",
       "      <th>news_front_page</th>\n",
       "      <th>non_markup_alphanum_characters</th>\n",
       "      <th>numberOfLinks</th>\n",
       "      <th>numwords_in_url</th>\n",
       "      <th>parametrizedLinkRatio</th>\n",
       "      <th>spelling_errors_ratio</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://www.bloomberg.com/news/2010-12-23/ibm-p...</td>\n",
       "      <td>4042</td>\n",
       "      <td>{\"title\":\"IBM Sees Holographic Calls Air Breat...</td>\n",
       "      <td>business</td>\n",
       "      <td>0.789131</td>\n",
       "      <td>2.055556</td>\n",
       "      <td>0.676471</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.047059</td>\n",
       "      <td>0.023529</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>5424</td>\n",
       "      <td>170</td>\n",
       "      <td>8</td>\n",
       "      <td>0.152941</td>\n",
       "      <td>0.079130</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://www.popsci.com/technology/article/2012-...</td>\n",
       "      <td>8471</td>\n",
       "      <td>{\"title\":\"The Fully Electronic Futuristic Star...</td>\n",
       "      <td>recreation</td>\n",
       "      <td>0.574147</td>\n",
       "      <td>3.677966</td>\n",
       "      <td>0.508021</td>\n",
       "      <td>0.288770</td>\n",
       "      <td>0.213904</td>\n",
       "      <td>0.144385</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>4973</td>\n",
       "      <td>187</td>\n",
       "      <td>9</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.125448</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  urlid  \\\n",
       "0  http://www.bloomberg.com/news/2010-12-23/ibm-p...   4042   \n",
       "1  http://www.popsci.com/technology/article/2012-...   8471   \n",
       "\n",
       "                                         boilerplate alchemy_category  \\\n",
       "0  {\"title\":\"IBM Sees Holographic Calls Air Breat...         business   \n",
       "1  {\"title\":\"The Fully Electronic Futuristic Star...       recreation   \n",
       "\n",
       "  alchemy_category_score  avglinksize  commonlinkratio_1  commonlinkratio_2  \\\n",
       "0               0.789131     2.055556           0.676471           0.205882   \n",
       "1               0.574147     3.677966           0.508021           0.288770   \n",
       "\n",
       "   commonlinkratio_3  commonlinkratio_4  ...    is_news  lengthyLinkDomain  \\\n",
       "0           0.047059           0.023529  ...          1                  1   \n",
       "1           0.213904           0.144385  ...          1                  1   \n",
       "\n",
       "   linkwordscore  news_front_page  non_markup_alphanum_characters  \\\n",
       "0             24                0                            5424   \n",
       "1             40                0                            4973   \n",
       "\n",
       "   numberOfLinks  numwords_in_url parametrizedLinkRatio  \\\n",
       "0            170                8              0.152941   \n",
       "1            187                9              0.181818   \n",
       "\n",
       "   spelling_errors_ratio  label  \n",
       "0               0.079130      0  \n",
       "1               0.125448      1  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 1049,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "su.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1050,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"Global replacement ? to 0 \"\"\"\n",
    "columns = su.columns\n",
    "for col in columns:\n",
    "    try:\n",
    "        su[col] = su[col].map(lambda x:x.replace('?','0'))\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1051,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: convert_objects is deprecated.  Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
      "  from ipykernel import kernelapp as app\n",
      "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: convert_objects is deprecated.  Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
      "  app.launch_new_instance()\n",
      "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:4: FutureWarning: convert_objects is deprecated.  Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n"
     ]
    }
   ],
   "source": [
    "\"\"\" From Object to numeric \"\"\"\n",
    "su['alchemy_category_score'] = su['alchemy_category_score'].convert_objects(convert_numeric=True)\n",
    "su['news_front_page'] = su['news_front_page'].convert_objects(convert_numeric=True)\n",
    "su['is_news'] = su['is_news'].convert_objects(convert_numeric=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1052,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\" Making 0 categories pass to unknown\"\"\"\n",
    "su['alchemy_category'] = su['alchemy_category'].map(lambda x:x.replace('0','unknown'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1053,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# \"\"\"Pass possible numerical values datatype\"\"\"\n",
    "# for i, col in enumerate(columns):\n",
    "#     try:\n",
    "#         su[i] = su[i].map(lambda x:pd.to_numeric(x, errors='coerce'))\n",
    "#     except:\n",
    "#         pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1054,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\" Dropping vars\"\"\"\n",
    "## framebased all values are 0\n",
    "su = su.drop(['framebased'], axis=1)\n",
    "# url','urlid', "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1055,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## -1 values on image ratio and embeded ratio\n",
    "neg_values = ['embed_ratio', 'image_ratio']\n",
    "for i in neg_values:\n",
    "    su[i] = [t if t>=0 else 0 for t in su[i]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1056,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>embed_ratio</th>\n",
       "      <th>image_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7395.000000</td>\n",
       "      <td>7395.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000915</td>\n",
       "      <td>0.369015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.007429</td>\n",
       "      <td>1.878911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.236700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>113.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       embed_ratio  image_ratio\n",
       "count  7395.000000  7395.000000\n",
       "mean      0.000915     0.369015\n",
       "std       0.007429     1.878911\n",
       "min       0.000000     0.000000\n",
       "25%       0.000000     0.025900\n",
       "50%       0.000000     0.083051\n",
       "75%       0.000000     0.236700\n",
       "max       0.250000   113.333333"
      ]
     },
     "execution_count": 1056,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_values = su[['embed_ratio', 'image_ratio']]\n",
    "neg_values.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1057,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16ea21390>"
      ]
     },
     "execution_count": 1057,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ek71nFQ+5asyk4jHbZfRjfywfc5udG8O3ar/7P/jWol++23/oC23fL16dWJtc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VgASDoF+C/gTFKGxqWVQj9r+zOSfirpp8TzURiFYhIbQu/MTSpcMD/D1YinkAo1DBzb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\nCCGEEEIIIYwa/w8e26oDzs96sAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16e9d8590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "sns.heatmap(su.corr())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Use statsmodels' logistic regression function to look at variable significance\n",
    "\n",
    "The **`import statsmodels.formula.api as smf`** code below gives us access to a statsmodels api that can run logistic regressions using patsy-style formulas.\n",
    "\n",
    "Ex:\n",
    "\n",
    "```python\n",
    "formula = 'target ~ var1 + var2 + C(var3) -1'\n",
    "logreg = smf.logit(formula, data=data)\n",
    "logreg_results = logreg.fit()\n",
    "print logreg_results.summary()\n",
    "```\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1058,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Run a logistic regression predicting evergreen from the numeric columns\n",
    "\n",
    "And print out the results as shown in the example above.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1059,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.655944\n",
      "         Iterations 6\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                  label   No. Observations:                 7395\n",
      "Model:                          Logit   Df Residuals:                     7379\n",
      "Method:                           MLE   Df Model:                           15\n",
      "Date:                Tue, 17 May 2016   Pseudo R-squ.:                 0.05319\n",
      "Time:                        21:37:37   Log-Likelihood:                -4850.7\n",
      "converged:                       True   LL-Null:                       -5123.2\n",
      "                                        LLR p-value:                1.677e-106\n",
      "==========================================================================================\n",
      "                             coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------------------\n",
      "Intercept                  1.1925      0.177      6.754      0.000         0.846     1.539\n",
      "alchemy_category_score    -0.2033      0.076     -2.676      0.007        -0.352    -0.054\n",
      "avglinksize                0.0051      0.003      1.768      0.077        -0.001     0.011\n",
      "embed_ratio              -21.2203      5.212     -4.071      0.000       -31.436   -11.004\n",
      "frameTagRatio             -7.5641      0.764     -9.900      0.000        -9.062    -6.067\n",
      "hasDomainLink              0.0203      0.166      0.122      0.903        -0.305     0.346\n",
      "html_ratio                 0.0701      0.576      0.122      0.903        -1.060     1.200\n",
      "image_ratio               -0.0206      0.017     -1.208      0.227        -0.054     0.013\n",
      "lengthyLinkDomain          0.0527      0.057      0.921      0.357        -0.059     0.165\n",
      "linkwordscore             -0.0176      0.001    -13.245      0.000        -0.020    -0.015\n",
      "news_front_page           -0.5003      0.132     -3.780      0.000        -0.760    -0.241\n",
      "numberOfLinks              0.0008      0.000      4.076      0.000         0.000     0.001\n",
      "numwords_in_url           -0.0316      0.008     -3.818      0.000        -0.048    -0.015\n",
      "parametrizedLinkRatio     -0.0575      0.139     -0.414      0.679        -0.330     0.215\n",
      "spelling_errors_ratio     -1.6000      0.331     -4.838      0.000        -2.248    -0.952\n",
      "is_news                    0.0905      0.053      1.712      0.087        -0.013     0.194\n",
      "==========================================================================================\n"
     ]
    }
   ],
   "source": [
    "formula1 = '''\n",
    "label ~ \n",
    "alchemy_category_score,\n",
    "avglinksize,\n",
    "embed_ratio,\n",
    "frameTagRatio,\n",
    "hasDomainLink,\n",
    "html_ratio,\n",
    "image_ratio,\n",
    "lengthyLinkDomain,\n",
    "linkwordscore,\n",
    "news_front_page,\n",
    "numberOfLinks,\n",
    "numwords_in_url,\n",
    "parametrizedLinkRatio,\n",
    "spelling_errors_ratio,\n",
    "is_news,\n",
    "-1\n",
    "'''\n",
    "\n",
    "\n",
    "formula1 = formula1.replace(\",\", \"+\")\n",
    "\n",
    "# logit function\n",
    "logreg = smf.logit(formula1, data=su)\n",
    "logreg_results = logreg.fit()\n",
    "print logreg_results.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1060,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.628819\n",
      "         Iterations 17\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                  label   No. Observations:                 7395\n",
      "Model:                          Logit   Df Residuals:                     7368\n",
      "Method:                           MLE   Df Model:                           26\n",
      "Date:                Tue, 17 May 2016   Pseudo R-squ.:                 0.09234\n",
      "Time:                        21:37:40   Log-Likelihood:                -4650.1\n",
      "converged:                       True   LL-Null:                       -5123.2\n",
      "                                        LLR p-value:                9.419e-183\n",
      "===========================================================================================================\n",
      "                                              coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
      "-----------------------------------------------------------------------------------------------------------\n",
      "C(alchemy_category)[arts_entertainment]     0.0675      0.208      0.325      0.745        -0.339     0.474\n",
      "C(alchemy_category)[business]               1.4629      0.208      7.036      0.000         1.055     1.870\n",
      "C(alchemy_category)[computer_internet]     -0.4553      0.246     -1.850      0.064        -0.938     0.027\n",
      "C(alchemy_category)[culture_politics]       0.4095      0.221      1.853      0.064        -0.024     0.843\n",
      "C(alchemy_category)[gaming]                 0.0925      0.307      0.301      0.763        -0.509     0.694\n",
      "C(alchemy_category)[health]                 0.8969      0.225      3.982      0.000         0.455     1.338\n",
      "C(alchemy_category)[law_crime]              0.4580      0.424      1.081      0.280        -0.373     1.288\n",
      "C(alchemy_category)[recreation]             1.3497      0.198      6.828      0.000         0.962     1.737\n",
      "C(alchemy_category)[religion]               0.3216      0.307      1.048      0.295        -0.280     0.923\n",
      "C(alchemy_category)[science_technology]     0.3973      0.231      1.721      0.085        -0.055     0.850\n",
      "C(alchemy_category)[sports]                -0.7078      0.238     -2.970      0.003        -1.175    -0.241\n",
      "C(alchemy_category)[unknown]                0.4148      0.174      2.380      0.017         0.073     0.756\n",
      "C(alchemy_category)[weather]             -107.6080   2.07e+23  -5.19e-22      1.000     -4.07e+23  4.07e+23\n",
      "C(is_news)[T.1]                             0.1539      0.055      2.804      0.005         0.046     0.261\n",
      "alchemy_category_score                     -0.3575      0.154     -2.323      0.020        -0.659    -0.056\n",
      "avglinksize                                -0.0004      0.003     -0.131      0.896        -0.006     0.005\n",
      "embed_ratio                               -13.1686      5.004     -2.632      0.008       -22.977    -3.361\n",
      "frameTagRatio                             -10.2263      0.764    -13.394      0.000       -11.723    -8.730\n",
      "hasDomainLink                              -0.1425      0.169     -0.842      0.400        -0.474     0.189\n",
      "html_ratio                                  1.3167      0.583      2.260      0.024         0.175     2.458\n",
      "image_ratio                                -0.0405      0.022     -1.856      0.063        -0.083     0.002\n",
      "lengthyLinkDomain                           0.0042      0.058      0.072      0.943        -0.110     0.118\n",
      "news_front_page                            -0.5865      0.137     -4.286      0.000        -0.855    -0.318\n",
      "numberOfLinks                               0.0003      0.000      1.673      0.094     -5.18e-05     0.001\n",
      "numwords_in_url                            -0.0194      0.009     -2.275      0.023        -0.036    -0.003\n",
      "parametrizedLinkRatio                      -0.0072      0.142     -0.051      0.959        -0.285     0.270\n",
      "spelling_errors_ratio                      -1.2427      0.326     -3.814      0.000        -1.881    -0.604\n",
      "===========================================================================================================\n"
     ]
    }
   ],
   "source": [
    "formula2 = '''\n",
    "label ~ \n",
    "alchemy_category_score,\n",
    "avglinksize,\n",
    "embed_ratio,\n",
    "frameTagRatio,\n",
    "hasDomainLink,\n",
    "html_ratio,\n",
    "image_ratio,\n",
    "lengthyLinkDomain,\n",
    "news_front_page,\n",
    "numberOfLinks,\n",
    "numwords_in_url,\n",
    "parametrizedLinkRatio,\n",
    "spelling_errors_ratio,\n",
    "C(alchemy_category),\n",
    "C(is_news)\n",
    "-1\n",
    "'''\n",
    "\n",
    "formula2 = formula2.replace(\",\", \"+\")\n",
    "\n",
    "# logit function\n",
    "logreg = smf.logit(formula2, data=su)\n",
    "logreg_results = logreg.fit()\n",
    "print logreg_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1061,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>urlid</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>5305.704665</td>\n",
       "      <td>3048.384114</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2688.500000</td>\n",
       "      <td>5304.000000</td>\n",
       "      <td>7946.500000</td>\n",
       "      <td>10566.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alchemy_category_score</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.412258</td>\n",
       "      <td>0.331274</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.471444</td>\n",
       "      <td>0.708279</td>\n",
       "      <td>0.999426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avglinksize</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>2.761823</td>\n",
       "      <td>8.619793</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.602062</td>\n",
       "      <td>2.088235</td>\n",
       "      <td>2.627451</td>\n",
       "      <td>363.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commonlinkratio_1</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.468230</td>\n",
       "      <td>0.203133</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340370</td>\n",
       "      <td>0.481481</td>\n",
       "      <td>0.616604</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commonlinkratio_2</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.214080</td>\n",
       "      <td>0.146743</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.202454</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commonlinkratio_3</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.092062</td>\n",
       "      <td>0.095978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.068627</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commonlinkratio_4</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.049262</td>\n",
       "      <td>0.072629</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.065065</td>\n",
       "      <td>0.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression_ratio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>2.255103</td>\n",
       "      <td>5.704313</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.442616</td>\n",
       "      <td>0.483680</td>\n",
       "      <td>0.578227</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embed_ratio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.000915</td>\n",
       "      <td>0.007429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameTagRatio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.056423</td>\n",
       "      <td>0.041446</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028502</td>\n",
       "      <td>0.045775</td>\n",
       "      <td>0.073459</td>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hasDomainLink</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.021231</td>\n",
       "      <td>0.144162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>html_ratio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.233778</td>\n",
       "      <td>0.052487</td>\n",
       "      <td>0.045564</td>\n",
       "      <td>0.201061</td>\n",
       "      <td>0.230564</td>\n",
       "      <td>0.260770</td>\n",
       "      <td>0.716883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>image_ratio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.369015</td>\n",
       "      <td>1.878911</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025900</td>\n",
       "      <td>0.083051</td>\n",
       "      <td>0.236700</td>\n",
       "      <td>113.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_news</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.615551</td>\n",
       "      <td>0.486498</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lengthyLinkDomain</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.660311</td>\n",
       "      <td>0.473636</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linkwordscore</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>30.077079</td>\n",
       "      <td>20.393101</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>news_front_page</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.039757</td>\n",
       "      <td>0.195400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_markup_alphanum_characters</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>5716.598242</td>\n",
       "      <td>8875.432430</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1579.000000</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>6377.000000</td>\n",
       "      <td>207952.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numberOfLinks</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>178.754564</td>\n",
       "      <td>179.466198</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>4997.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numwords_in_url</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>4.960649</td>\n",
       "      <td>3.233111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parametrizedLinkRatio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.172864</td>\n",
       "      <td>0.183286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040984</td>\n",
       "      <td>0.113402</td>\n",
       "      <td>0.241299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spelling_errors_ratio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.101221</td>\n",
       "      <td>0.079231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068739</td>\n",
       "      <td>0.089312</td>\n",
       "      <td>0.112376</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>0.513320</td>\n",
       "      <td>0.499856</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 count         mean          std       min  \\\n",
       "urlid                           7395.0  5305.704665  3048.384114  1.000000   \n",
       "alchemy_category_score          7395.0     0.412258     0.331274  0.000000   \n",
       "avglinksize                     7395.0     2.761823     8.619793  0.000000   \n",
       "commonlinkratio_1               7395.0     0.468230     0.203133  0.000000   \n",
       "commonlinkratio_2               7395.0     0.214080     0.146743  0.000000   \n",
       "commonlinkratio_3               7395.0     0.092062     0.095978  0.000000   \n",
       "commonlinkratio_4               7395.0     0.049262     0.072629  0.000000   \n",
       "compression_ratio               7395.0     2.255103     5.704313  0.000000   \n",
       "embed_ratio                     7395.0     0.000915     0.007429  0.000000   \n",
       "frameTagRatio                   7395.0     0.056423     0.041446  0.000000   \n",
       "hasDomainLink                   7395.0     0.021231     0.144162  0.000000   \n",
       "html_ratio                      7395.0     0.233778     0.052487  0.045564   \n",
       "image_ratio                     7395.0     0.369015     1.878911  0.000000   \n",
       "is_news                         7395.0     0.615551     0.486498  0.000000   \n",
       "lengthyLinkDomain               7395.0     0.660311     0.473636  0.000000   \n",
       "linkwordscore                   7395.0    30.077079    20.393101  0.000000   \n",
       "news_front_page                 7395.0     0.039757     0.195400  0.000000   \n",
       "non_markup_alphanum_characters  7395.0  5716.598242  8875.432430  0.000000   \n",
       "numberOfLinks                   7395.0   178.754564   179.466198  1.000000   \n",
       "numwords_in_url                 7395.0     4.960649     3.233111  0.000000   \n",
       "parametrizedLinkRatio           7395.0     0.172864     0.183286  0.000000   \n",
       "spelling_errors_ratio           7395.0     0.101221     0.079231  0.000000   \n",
       "label                           7395.0     0.513320     0.499856  0.000000   \n",
       "\n",
       "                                        25%          50%          75%  \\\n",
       "urlid                           2688.500000  5304.000000  7946.500000   \n",
       "alchemy_category_score             0.000000     0.471444     0.708279   \n",
       "avglinksize                        1.602062     2.088235     2.627451   \n",
       "commonlinkratio_1                  0.340370     0.481481     0.616604   \n",
       "commonlinkratio_2                  0.105263     0.202454     0.300000   \n",
       "commonlinkratio_3                  0.022222     0.068627     0.133333   \n",
       "commonlinkratio_4                  0.000000     0.022222     0.065065   \n",
       "compression_ratio                  0.442616     0.483680     0.578227   \n",
       "embed_ratio                        0.000000     0.000000     0.000000   \n",
       "frameTagRatio                      0.028502     0.045775     0.073459   \n",
       "hasDomainLink                      0.000000     0.000000     0.000000   \n",
       "html_ratio                         0.201061     0.230564     0.260770   \n",
       "image_ratio                        0.025900     0.083051     0.236700   \n",
       "is_news                            0.000000     1.000000     1.000000   \n",
       "lengthyLinkDomain                  0.000000     1.000000     1.000000   \n",
       "linkwordscore                     14.000000    25.000000    43.000000   \n",
       "news_front_page                    0.000000     0.000000     0.000000   \n",
       "non_markup_alphanum_characters  1579.000000  3500.000000  6377.000000   \n",
       "numberOfLinks                     82.000000   139.000000   222.000000   \n",
       "numwords_in_url                    3.000000     5.000000     7.000000   \n",
       "parametrizedLinkRatio              0.040984     0.113402     0.241299   \n",
       "spelling_errors_ratio              0.068739     0.089312     0.112376   \n",
       "label                              0.000000     1.000000     1.000000   \n",
       "\n",
       "                                          max  \n",
       "urlid                            10566.000000  \n",
       "alchemy_category_score               0.999426  \n",
       "avglinksize                        363.000000  \n",
       "commonlinkratio_1                    1.000000  \n",
       "commonlinkratio_2                    1.000000  \n",
       "commonlinkratio_3                    0.980392  \n",
       "commonlinkratio_4                    0.980392  \n",
       "compression_ratio                   21.000000  \n",
       "embed_ratio                          0.250000  \n",
       "frameTagRatio                        0.444444  \n",
       "hasDomainLink                        1.000000  \n",
       "html_ratio                           0.716883  \n",
       "image_ratio                        113.333333  \n",
       "is_news                              1.000000  \n",
       "lengthyLinkDomain                    1.000000  \n",
       "linkwordscore                      100.000000  \n",
       "news_front_page                      1.000000  \n",
       "non_markup_alphanum_characters  207952.000000  \n",
       "numberOfLinks                     4997.000000  \n",
       "numwords_in_url                     22.000000  \n",
       "parametrizedLinkRatio                1.000000  \n",
       "spelling_errors_ratio                1.000000  \n",
       "label                                1.000000  "
      ]
     },
     "execution_count": 1061,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "su.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Use sklearn to cross-validate the accuracy of the model above\n",
    "\n",
    "Normalize the numeric and categorical columns of the predictor matrix.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1062,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Standarizing the dataset\n",
    "nc = [x for x in su if x not in ['url','urlid', 'boilerplate', 'alchemy_category', 'label', 'is_news', \n",
    "                                'hasDomainLink', 'lengthyLinkDomain', 'news_front_page']]\n",
    "su.ix[:, nc] = (su.ix[:, nc] - su.ix[:, nc].mean()) / su.ix[:, nc].std() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1063,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sub_list = su[[\n",
    "'label', \n",
    "'alchemy_category_score',\n",
    "'avglinksize',\n",
    "'embed_ratio',\n",
    "'frameTagRatio',\n",
    "'hasDomainLink',\n",
    "'html_ratio',\n",
    "'image_ratio',\n",
    "'lengthyLinkDomain',\n",
    "'news_front_page',\n",
    "'numberOfLinks',\n",
    "'numwords_in_url',\n",
    "'parametrizedLinkRatio',\n",
    "'spelling_errors_ratio',\n",
    "'is_news']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x12ee48710>"
      ]
     },
     "execution_count": 626,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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NQ12ZX9TwYE79ffk7zwQAAHBA+L6vlbWizlxZ1SuX13Txeknnrq2pXHU3nefw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\nvHO1WpPfSiZtBGFz/rnVFdMwTIoYAS17lX3xYUmvzeVylyX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8t3n+5BSomUMu7j62pP0NmeRAAThSonBqfi/Rs6DI2VSIVi0vp7CGbPYHcpUq66y3IcKcHQBQlT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CbAum6sDzmyoByxVUCsUGi/4BgFt7e3tPd3d3/zmAPQB+o8RpOtrd3X1n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\n1+Ljv/E6Y+Rk2SgJyA4JP9h3DodPj1udZ2rcDty8ox3vedPVRlD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RHBUIrDTF62bamjLaqIbWtbW1QT0wtKZ/Pq3tayrvFgPY+lWs4XM/ccXu1tMXVtiyudzSs1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\n8vQJdfePauPaRl1/aYd7fc42tiwr9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S+dbQGiiSy6rhHw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\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Y+H06uzqDKOB836yblqSt2bfqwvB6Fgu8Xp0vfOauVX1+vRREnQVhp2Ur7LzAUymVd20t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\n+2CzTP2NVw0Si7RnN7csC8uK4gG5ss9UdgrbgkjIJhGLEI1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dfn2Od8x7OYz3Uhx5rZUv3vvX7fF/C5LC3wma35/wbWHGWk2bHkcrbe+9arGjeyCn7j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\nL9+6IvWYtMOj5/WHn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ZhIwjALloCcZiIymXaiFSSIBr9P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\n3cYOmNy/rYX7t7Xwted2MjaVZ2i4nnX0sws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nhNl27z+W\nU+VdzentxcgrVts3XXFh7tvy7MzY4ZuuuLDdIZ2Td37ogXz2wSeTZOYbTfOdALdq+ZjJycmMjJzM\nydHxjE82pmcMnbuMfH1waNFl5McnGjl4+LRC0MPTS8UfGsmJ0aUtt1BL8qJNa/PizWtfKASd9fOm\nDcOp1bp7eaZWGx4eyOUXX5BdT06NvR7bdyQnRidz7WVnfgB7agyxacPUNxtvuf6Ks+ZTLy6RRfvs\nOXB80W3odoOD9bz1B1+Rb7l2z8wMCMn852yzz5v/4vFDeeuvfTrfc8t1mXo3BGAhv/UnX83RE2Np\nNpOjJ8byW3/y1fzUD72i3WGxiGsv25JHdz8/Z7vfGRfTrf7rH92fT3/liZkl6P/KlVvy977rurOO\nfU+ZGi/P/xmKa8i02m9+5Kt55tBIms3kmUMj+c2PfDVv/cGXLznXFnr8Uq+Z9eKMb9pt51jp/Frp\nYz1fe2rl822/Z28+eNc3cvjYWCYbjZS9z+eRXQfzlu+/YcF2u9h71vnSdub65L2P56HHpj7TO3Zi\nPN963Yvn3L9t66Zzes3me8xiffWn7ns8ew4cSb1Wy54DR/Kp+x7v6uMir9pj9/5jOTUvTqN59roJ\n2q9er+Vbrn1xNkzPUtwJn3kqFp3HwzsP5uj0MvRj4408vPNgXnfTNW2OisXsfPJwJhuNmQsGO588\n3O6QVsQD33g6E5PNOdurYanfCunFpS0UrrTX408dXXQb2m18vDEza+VAvZbx8cbZf6kD7Np/+Izt\nVvXh4+PjOX7i5Mwy8uMTjTSatQwND6deX3gZ+UazmSPHxmYVgZ76dzIHD4/k0NHRLG1u0GT92sHp\nItB1efGWtVPLxE/PEvrizWszOLDys6n2mlNjr1PjkieeOpb33PFQvvbYs/lnP/DymffI2WOIqy/Z\nmGYzec8dDy36XtqL4wjaxxiCfnEu52w7nzw0p+/e9/SxvO/PHs7oeCODA/VsXD+UZjP57u/Q5wLM\n9uVHDsx8ENNsTm3T2W5+2eX50PZvzMzsdrblfvuBcTHd6vQ++LMPPpkN64dz1SUbc/DIydRSS7M5\ndf1+9jUG1/Nphwf/4pk5n+E9+BfPtHT/C10zWyjfV3LGt25vY90ef7s1Gs00m8kF64aTWjM3v+zy\nrptR8FR7aib54kP7c/cDT+SW66+Y+TvOJz8ajWbufuCJHDo2momJRhrNpNmczL2PPpXt9+x1rbsD\n7HhgXw4fG0uSnBydyDf2HsqLN69LM41ctHlddu0/kk98ac+cY3+u/cZ8ffVtN16V7ffszZ99bmeO\nnRjPxvVDMzNOw1I9/dyJRbfpPB//0p787kcfmZl0arLRzN+6aVtbY1IsOo9TM2zN3v7pH25TMJyT\nNUMDc07A1gwNLPLo7nXk+Nii2ytlqd8KmT2waTanBsTzDZy66WRsqSfhtNbxk5OLbkO7vf+jX5tZ\n3nyy0cz7P/q1/J2br21zVGd3zaWbZ2YUPbW91OVjms1mRkZOZmR0bLoodHL6PbmeoeEXlpGf/dY8\ncnJibiHo4alC0FNLxY9PLK3YdnCgNqf48+Itp35el4u3rMu6tYa8rbZ2eGAm55OpWZ9HRifz6fuf\nSK1Wyzdfc2F2HziSEyMTWb9uKNdcuinNZjN3fuHsRaC9uEQW7WMMQb84/Zyt0WjmE1/aM+c8Ze3w\n4Jzz5kYzOTYyNSP3qffwHQ/sy2u//SrnOACzjI1PLrpN53n7b38u+w9OfWC2/+CJvP23P5f/9JZb\n2xxVexkX021OXXcfGT2tD55o5NP3PZ6x8YnUavWsGR7Ik88eS9LMd3/HtpnH+SIq7TDRaMy7vdDn\nSEv96YxzOgAAIABJREFUfGmha2Z3fXlPPnjXNzI6PpkvDg2k2ZxqDys541u3t7Fuj7/dtt+zN3d+\nYVeazWaOnhjPjgeeTK1W67jrB4u1sVPt5+jxsRw+NjY9w+TU3/TIrudy76NPZc3QQB5+7NkkS8uP\n7ffszZPPHE+j0Zz5wkOtNlW/0MnXuvvpM+9mGjMTkTWbzew/eDyHj49mbLyRb9QOZeP64Tyyc+6x\nX6ivPd18ffWpPufYifGZItVNG4Z7YsZnVt+xkbFFt+k8f3r3Yzk2MjVh5fhEI39692OKRTvR+KwP\nT+bbpvMMDdZTq2VmZtGhwd6cIez0TOzUzJy99MDRE+NT/46PTX0z6/59ueWGy2e+QdMJJ2PzDX6T\nud8aO332vVMDvU75G4D2OjnWXHS7U/3k912fZGpG0Wsu3Zyf/L7r86n7Hl9w+ZjJycmcODGS0bGJ\nF5aRn2hkYGg4g4ODObWM/ECtkeeOnMyz+5+fNTPoC/+On1zaUvFJsvmCNbnotFlBL9q8Nhe9aF02\nX7AmdUvFr6rrrtiSJ54+NjMb/ymNRvL5h/bnoemLaIePjWXThqF8+r7HMz7ZyNBAPRdvWZt6vb7g\nhbFeXCILYLXNd57yTVdsyV8+cXjBL2VMTDaz75mj+Y0P3p/d+w+nVqvl4Z0H8/DOgzNL5PTyBwUA\nC2k2m4tu03l27z+66DbQ+U6NZ+v12pwvqyaZdS1iMmMTjRzLeP7k7sfy2m+/+owioFM6uTiH3nHB\nuqEcOjo2ZztZ+HOkpU5SstA1sx0Pzp4hbzI7Htw3bwHT2SylUKzb21i3x99uM4WW04Vvo+OHZ4qV\nOukz0sU+wz3Vnkanvwh2aiKqHQ/uy64nj+T4yHiOnRjPyP/P3p3HyXHXd/5/V1V3zz26b8k6bNOA\nD9kYG+QDQzAxkBBCQkIC+yCbEJYsIRuy/PJYskk2m7BkN9l1fgkhBEIgJL/NJpwGHDbYsvAhH7Js\nLMk6W0gaaTSjkTT30XdVfX9/VHdNd0/3HNL0XHo9H5anq7q66tsz367+1rc+38836064R+z7Ro/v\nP6e9h7olY+m+2zbpgbvKA1HbWmKSpMHRrHxjtKKtQW2FvpWF6lq6572qvamQ8TXIkms8o1TGlesZ\nWZbk+1mlMnn9n8dO6OkD3brvtk16+mDXtM611c7Vxc9MsV60Nkf1zru3L7qMvFgYcq6ZdBkLTyqb\nV2lXUiqbr73xHCFYFEvC0GhWUhAoWrq81LQ1xzRY8t7ammNzXobpXCyWTm3R2TOq0XSuZGTW+AXD\nQrkYq9b4lVS2bmtF473YmJ/ue7iWRmMBmDtXe26JRGx9/BdfV7aueA7/UWefNqxs1C3b29R9sb9s\nGnnLcjSa8tQ3lC3JDppW/1BGvUNpDY5kZjygobHB0epCJtBVxUDQ5ePZQqORqbOG+77R84cvqLt3\nTJvWtGrXLRvn9Fzr+748z5MxvozvyzJGlm3Jti05tq3U8KXMnBWmToKOsE4dOtmrvOvLsSWvIubI\n9fywk02ShsZycj1fMlJGnvKur41rWpRM5atOSV/PKbIA4FrR0TOikWQunNqmo2dE2zYsU2PMkev5\nqhbnZEmyLCvMntHeEtNoMqeXTlzSmuVNzLAA4JpVGWM/w4kQMA8qv5P4jsJi5Lq+Pvv1g2UDfCNL\nNElGNWcvjsgYI8e2NNmtVL8QSHqxP6k/+Jvnw0QVDERFPdW+Hqr8vgmWOy4M69JAavz67EIQeFbr\n/lKtgK2afWam4riVy9Ms/0wCxRb7Z2yxl38++b5RMpVX71BaubwnY0wYaFmcunuh9BVMdg+3+Pl5\n+kC3LvSNqa05CO6WCbL+FgcqpDKuUuny5BeP7+/UV753LEyKkegc1LGOfv2H991eFtjd3hJTWyF7\nZOkg3KKF1reyUO7bz4WBkaxsSzKy5JvxWQOlIDlZzvWVzfvK5DyNJvM63T0kY4w835dj2/J8X92X\nxyZMVS9Vv7+x58VOHTvTL0sKgoYLs/ztebFz3v/uWHwq+3UZz7rwrV7WpP7hbNnyfCNYFEtCW3Os\n7CQ4H0GUc6GyL6iefUNXcrFY+ZoPvevmcPti0Eg256nz0qgefvKUfupN11e9GJvrxnGxsVucLuFf\nnj2j9paYjDGyChHIzY0RbS1kGN2+YZnecseWsMzTuaC8lkZjzRXfNzSesahN91zn+0aPvXBO3336\ntNI5V7dev1q/8fO3KxKxZ+XcMmEa+bynGzY267p112ko6erlU8NhQGj/cCYMDM3lZ3aH1LYtrWov\nBoA2htPEr14eBIg2N0bCc+6Vev7wBT31cpck6dT5IUnSPTs3zXg/xhh5niffD4I+gy4DybYl2wqC\nP23bqngsRRxH0UKWVcdxJryfMy8/MnhVb3AB2PNip/559wkNjGbl1ci+n3d9eZ6vpoaIMllPfmF0\nblE272lwNKuxVJ/aWmJVs9bxHQlcu671m+IzUdqW2LquXZLRuUuj2ra+Xal0rizbQSqdl+f7Smfd\nmh2IkYglU9jv0FjQeVW8mVnEDAsArkXciFl85rIPFaiXz379oJ45dEGS1H05KUkTBvwuZdvWt2vf\n4Z6ywaiTybtGp7sHdaFvLMwA9o5d28L2MQNRF4/iYGzP85TPu3I9Lwjm8Y08z5cxwbTOd97/023d\nJ56el9TRNa+HajQafnR+MMyIm8v7+tH5oItw67rxet4QdQrXddUH/0nBtdrRM/3q6BlWMpXXW+7Y\nItsOshpe6BsLt7/vtqA/tNb19eP7O/XVxxMlUylLP/7GrTWPW818DfaerfuHDFa/co/v79Qrp/o0\nlsrL940cxwrvy6fS+QXVVzDZPVzbtsIg7NL7LxHHDj67wX+y7CDRRam9B7uVyrjhgIVMztVLJy5p\nz4udetsbtur+2zdr9/5z6u5NatOaFr1663Kd7w2+y0vrcDKV19meEY2mcnrq5S4dPTMecDqZet1H\nv5aCqD3fk1tyf6Pyt1c8nXtecK8ol/dk21bhXocvzzfqHcroCw+/ooefPKWVyxq1qr1RLc0xbd8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NCXfuR0nyxLOtU1JM8z8iSNpfKamLAIU2lrcjRaJbvobHE9o77hrJ480K0Xj1/SupXNZefE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\n1YJXbduqek1X6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kjs6sW2bQyj/NNqgYfLxHwRs+6E44ET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Krl4Q88CTczzw5BzwMFOjJW6uTvHC6/dx47WTVEpSNl4I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sfHRtMMxTNlAYlatDWH2L2thT3bWti9rZk921vYuaWZUFDHMAz+7N+uwJtY\nQrI5k8m0My/hXMPrwzF1PdMZS8xY3ohUzltUlhWrDzVnrFCsXk5eiJX1DWZza64tCm2tEIW2lvao\n7G/N3P+ar3OpGr8pFkNlhta7Dm0BHKGoe1+419dSX2/VjlU5B7Ic89HuPSulJJU1pglil/o8tcqK\n5cWdbysYFpZtUzQlUjrxvuPnY7wRHSWbN7FtG9uWpLJF7r59K0/cv3fO51BifcVSIBAzlhWrj3/8\n7lvki05cNV+0+MfvvrV2xKLRaPSJ5axIBfcCzZFI5HuADvxuNBo9vVInt2w5Y1mx+ugbSXtDJVkq\nr0fevDmGXboepZS8eXNs0cdczk6JvxObyhoUjDzhoO4ISHZt5b0P7lvw+Wd73UqsJFsI1Tr6q7Wu\nqwlZpRmeyfV5putDdcQVy4HfVdRF3cuK9cit/skylzA/toRwSKet2Um62twUVO2rou5Uts/V2mvF\n6kJNjM9OZSDkifv38sKpHk5e6CeezLO5tYHHDu/h5378HRw718srbw6SrhA2OfeCTSpreC4gB3a2\ncaUzTqrknpfJGnN281ffm6IS12WjbJvlrJ5vCGtzOsZqG7slU2lSmQJFE0LhMKGGulVlSckXTQZG\nM6XU8Wn6R5y/mfzsgshgQGP31mY6trfQsa30b3sLrU2hFaj5ytHUGKS9JeQJOZoa15s8eP1RNOWM\n5Y2I6hevPdScsUKxihGyarlW/7VW1kBbQq5gUjAsbFt6mVG6h8qzolSWK5mvc+ladk1fbWOEjciJ\nC7GybJ/XeyfYuqmRyXSBtuYgrU1OX9m93qpdh7YtOXqmh5MXYyCdrCfveWAvL73eN+N3W+1YmiaW\nXezs/ia7jpNQLohd6vP4y0uFadp8+qsX6Bqc5OCudj76ofsIBOY2Nl+vVLYnrujzxIV+oj0JzMLU\nmNi0JIl0AeeSFJ4u5dKtMT72iZfY0tZYlr2nFssp1lft48ZhU2sD/SV9lCiVFaubZEVctbJcD+bj\nLApAJBJ5FPg4sMm/fYldQLPAn0Wj0acjkcgPAN+JRCJvi0ajKzKFIW1rxrJi9ZHMFGcsrxcmUvkZ\nywthOTsl/k6tO+isFJAcOd2zoPPPVu+VWEk2V/wd8HApJYko9c38dVsNdV2t6BpUNsUzpRCb6fpQ\nq2YVK4W6lxXrkZcvxmZ8viGke49VIEuhUCwEFRSfncpAyJHTPTxzNMp4Mo9lSwbHMgzGM1zpHOPV\nS4PequVKbCkxzCmXx/c+uI8rnXHOXRsmFNB48+Yov/83r/HY4Y5ZJ5jV96aopJosS0pnbqAzNjcx\n8WoYuxmGQSKZIZMz0AIhdD2Mr7uzprBtychEtswpNDaaYSwxe7oyAWzd1FhyCp0ShW67rXFDBJ8O\n7mrjra54WVmhUCiWGzVnrFCsXh69t4OB0YwX93r03g6gdv+1VtbAVy4OUDRsBIKiYfPKxQF+9N0H\nSGeLjE3mkVIihCCdnTnm2TUwyfB4dkr8OeCIP2uN09aya/pqGCNsdMYn857RlrRheCJHQziAKAU+\nhRBIKclkDT77zUtksoYnaALnOjx6pocvfPsq2byJEHCtZ5zPP3cZIQTbNzXV/G7rNffg/ga7jqKt\nTSGSmeKSO4wu52//p796gZcvDgAQG3Ec5j/+kXcu2fHXIpXtiRu/37ujleF4llyhvG2U0hH5azro\nQhDQNaetLs3F+bP31GK2xdZzFXxW20+1jxuH4YlMmZHe8ITKGrHaqTRGq2aUttLMWywKfAEnJX3P\n0laljOvATYBoNHojEonEgV3AzNHpJaKi3Z9WVqw+Ks1f16sZ7HKsAF9OBxh/pzaTNegeSiJxUhH0\nDiU5crrHc7GZ7/lnq3dlANW2JUdO99RlNY3bAbdKtvQ7NjfxwNt3cnDXVD1UZ21mzCrx7WIVpxqX\nma4P5XqkWCk2QtBUsfHI5mde7RYOBbCl5OCu9nmlX1EoFAqXJ+7fy5XOuOd0oNqS2ekeSlIwLG+S\nyRXknbk6XFMoKgQEdEF7c5ixRI7PfvMSB3a20dQQIBzUSWWLGKZN0bRJ55wg4kxjFiVmUMwVXRM1\nr8tK6jl2S6czJFI5DAtC4QaC4YVModaPZKZYIQpNMziWqer4WklzY5A9JTFoRymN/O6tLYTXqkp2\nCVBtnEKhqAdqzlihWL08+dB+hBDT+gZdg8kKx06n//rmrYqsgbecrIESiWXbnkBJlqQfN3onyva/\n0TsxY31u9idIlxYBGqbNzX7HaKNWH2Ytu6ar+E792dzewGA84wjnkARKcZDW5hCtjSH27mzlZt8E\n564NEw7qtDQFObirneamoHcd/v5nX3XSeUuJtMG0LEqGjUCWHZubqn631TKtLDT2Ox8XRv9v8vOv\ndpHMFJfFYXQ5f/srHYTXkqPwclF5jZ28EPPmwNK5IuGgRsEoH0NrmmBTaxhNCFKljDzgZCpOlbQQ\nMzGb4Hmugs9q+6n2ceMwOpGbsaxQzIWFzHTGotHo3y95Tcr5eeBu4NcjkchuoBUYXOZzKtYwugBL\nlpcVc2M5V2H5O7VOSoFevnH8JqlskdiI5NuvdHJgV/u0+ixHveu5mqZrcBLTsjxx70gix4GdrbOe\nX9nFT1FN/x2cIT3CTNeH/zn/6saN/hkrFArFbNi2JKBrGFZtgUdsJE0gIEhmihw718tTpbRaCoVC\nMVdeer2PnqEkmhD0DCV56fU+FSSvgn+skMkahAKak72gFGQMB3WyVnWBv6A0hrYk8ck8E6k8sdEU\nV26NEQrqTKaLmJaNLaU36T3bBLMSMyjmgiYEuqZxaHf77Duz8q4xlmUxkUiRyRsIPYQeaCC0yjWi\nRcNicMxJIe+mj4+NpknNIZ1VQBfs2uKkkN+9rYU9JXFoW3PIcwVSOKg2TqFQKBQKhZ9afYNszqhw\n7HT6ZJZVHuFwy1vaytPGuuW8YeHvjuUNZy4unzf52CdeIj6ZZ0t7A5/6rSdoaAiQL5romvBEp/mi\nOWM917JruspqUX8eu6+DwTHHWde2pZdpSgCPHXZcdk+c7ydfsMgXTM+d8QM/dGgqBieFN4fh4gim\nncWvAPt3tHpC0P07WgFBz7ATL/2Fn7hrUdkrYWFx4+kOo47Qei2I8g7uavccRd3yRo5D27YTHx5N\n5AgHdee7FP62WlRdbGnZkntu38pdt2/lSy9co1A0y0zMZmuTZluIOFfBZ7X9VPu4cdgoRnqK5WUh\nU56fikQiXwSOAZ7n5hILSJ8GPh+JRE4CNvDzK5WCXrE2aWrQSeWssvJ6ZNttYUYThbLyYllOdwRH\nINrDyYsxkIIt7Q0ks0VsG5IZAyEETY0B3v/IwbLzz6Vz+sT9e7l8a4w3b43RGApg2U4gs5YV+4kL\n/WUdvlqdq/l0jN19uwaTZHMGTY0BDu5qn/aag7va6RqYOp+UcPJibFYBjbKLn5n25to/YTNd19Uc\nbwXqM1YoFIrZOHK6B6Oa1bMPCRimZCyR5/j5fiUWVSgU80atgp8b/rGCBO65YxvxyRxjkzlAsLW9\nkXzR4HpvYvqEYWmxpetEatkwNlkgVzDZtqmR9pYQqUyRgmlTMCySmSL7d6gJZsXi2dzWwD13bOWj\nH7pvTvuvlJtjOp0hmSlQMGxC4QYCq1AhaktJPJHzBKH9o2kGRjOMTGTnlLpqS3uDlzq+o5RGfvvm\nRnSt9iJMhUKhUNQX07T59FcveI77H/3QfQRmWDyvUChWjmLR4vf++hVioxk6tjXzx7/8Q4RCOo0N\nOqGg5jl2NpZilXu3t5BIFbxU3Hu3twDQ1BigMRzwOXw6/dC7D23h+PmYJ/68+9AWAH7jEy8xFM8C\nMBjP8hufeInP/s77OLT7NgZGs179Du2+bcb6z6efXastqpfITDm+15/3PLCPK51xzlwdxrYtCgaM\nTOTo2NbM44f38HffuUo4qJMvWFi2JJs3SWcNbw7jvQ/uY0t7A8GAhrBsTzytawIJ7NjUxPsfOYiU\njmunBI6d7aVg2AR0QUtjiMu3xmhpDtE7mCpLcT+flN4LmX+qdBh1ma8orx73z0c/dJ/nbNwYCnDn\ngc0cPdPLd17bmHFo9/MPB3UKhsVdO7cgBHTFkthSks0ZNQV4nQMJ7rp9Kx9+MsIzR6Oes/Mjd++c\ntU2abSHiXAWf1fZbj9maNrKgeSZCAUHRlGVlhWK+LGT289dKfx/1bZPAkolFo9GoAfzsUh1Psf7x\nC0WrldcLuYrUr5XlhbCc7ggvnu3lmaPXvZWUmgYBXfPSaqSyRQ7sbJt2/morwd774L6yzoCUcLkz\nTipjkMoYfPXF6+g13suLZ3sZGM2QL5hkcoZz3tKKqcoOxXwEmu6+yUyR8WSeYECjtSmElLJMHPPR\nD93HG9ERJlIFBM7gPp7Ie26Wjx/ew2e+fnHaYFsFymdmaLxQ87mZrmv/c5/95iX8V4D6jBVLSS0B\nu0KxVvnmiVvMIXOpR99wevkqo1Ao1i1qFfzc8PdbBdDcFOTjH3knL5zq4ZmjUboGJwkFdFoagyQr\nHAZrCcsyeYvWgkVrUxApJcVkHilhPJnni999i5MX+nn0vj08+ZCamFUsjHBI5wcPbJ7z9bOc8xWW\nZTExmSKTMxBaED0QIrT49bhLQjpnECsThTrCUNdhZyYawwGfKLS5lEa+hcZw/QWwUkpM08QsFhCa\nrnwvFAqFYhY+9ZXzJbGYpHswhS0lv/Uz99e7WgqFAvid//Ey0V4n1XsyU+R3/sfL/Pm/fpxc3nFR\nlBJytkku73gubW5v8AwUZakMkM2V75/NOfu//dBWzl0b8USkbz+0FYDh8WxZPdzyr/3UvQyMpT3x\n6q/91L0z1r9WP7uaKOfTX73AyxcHADxXwo9/5J11MztRju/159i5Xk5e6McbnhScCeNC0eJ3/+oV\nMjmDZKaAlNIRggqYTDvlrsFJ7xprbQpRMCzeGdmOwJnncGOkmib4/b95jdFEznGAzDv3iWlBwcjz\n2uVBOra1kMw48ee25hASpmUSnOk6Xej8k21LpISWxhAIyaP3dsxbtFyP+ycQ0Ljr9q30DqcA+N6p\nbuc9+NhIMVL/ey0aNmeuDoEQM4pEvdcOpvnH717l3h/YRse2Vu86ePKh/d58x0JFjnMVxFfb78Wz\nvesuW5My1qqOYcoZywrFXFjITOGuaDT6g0teE4VCMSvpvD1jebXRPZScFswwLYktnSCBlI5du5tG\nwO3MVBNJVnYGWhpDZccuGNaMVuytzSFyBZNsaXDuHrOyQzGbQNO/itIwbAIBwUSqgGlJLNvCsgrT\nXEMDAY3Db9vG90uTe7YtmUgXuNoZ52pnnBdOd3OzfxIpoXcohZSS3/yZ+1WgfAVQn7FiOTl6pke5\nKirWFW4Kq7lSNK2yyTklLFIoFHNBuYTMDbcfa9k2g2NZYqNpbvROENQ1JtNFLNtm0irO+7jDEzk6\nAjoSR3SWL1qYlmQiVSCVLTIwlkEINTGrWBj9I2n+6n+9yVvd43zsw4dn7Bssl5taJpsjmc6RK1iE\nGxrr6iJqmDbD8Qz9o2liI076+Nho2ltwOxOaJti5pclzCXUFoptaw3VLIW9ZFqZpgJTomlPHgK6h\nawJd1wjoGg3hJoLBdjKJwfk3UHWkVspXhUKhWE7OXBnCLqkVpJScuTJU5xopFAqX6yWhaGV5ZDyL\n6Us5P1ISc168MVa2v1senSjff3TC2b93OEVjOICmCcJB3RN3VS78c8vff6OP+GTeMSqZzPP9N/oW\nNC9dTZTTOTDpGcAI4ZRh/q6Myh1u/XD8fIxq69gM0+J67wSaJsquayQUDJvxZIGbfQmudMZJZYrO\nBQWMJ3M8dt9eWppD7N/RVhKjDnArlqBQdNxJ/de+9BVam4K0NofYt7PNySQ4OIkQwrt+K6/LrsGk\nF5Peu72VvdtbuNQZnzWDpZ8Xz065cQIIIeZ9Lde6f5bbVXzafSrKG5WNFCPdt72VF071kC8uzIAs\nnixy/PwATQ0BhIAtbY08+dDUXJnbnkopOXVpkBPn+9nS3lgzS6lLpSDerqKj0DRRVTjfNThJMlP0\nFhp0DU4u6L2tJpSxVnUqpaFKKqpYCAuZ1ToZiUQ+AHw3Go3OL2KsUCg2FAd2tnGqlGoAoKUxSCZv\nepNchmnx7MlOwiEnFYfbea8m4HN//CWQyhRJpAsUihamZSMEhIOhWa3Y3UBFa1PIW6VWa19/2T+I\nvd4z7gk7pZQI4aRtBGdgbksJcqpz5772zVtjICWi9B5MnzVb92AKy12mJHH2RQXKVwL1GSuWk0rh\nuEKx1rnnjq28eK5/zvublvQWRoASFikUirmhXELmhttv/Yfnr3oT2291T9AY1jEs2xtzzRcpIT6Z\no7UpNC0gY9mSRLrAifMxFdhTLBjTkpy7Nlx18aafWg5GC8G2bcYTSWfxqAigB0KEGxZ0qAUhpWQ8\nmSc2mimJQlPERjMMx7POHMIsbGoNs9snCO3Y1szOLc0E9JVLRey6gtqWiUB64k9NE+iaIBjQCAZD\nhEPNBALrT0T5G584xlA8B7gpX4/x2d95qs61UigU651cwZyxrFAo6kctkcbNWLkwxy3Xup+jFaJT\nt5zNGd4ConzBIpubOcvg8fP9jCVynqDz+Pl+nnr4QE2BZi1BWjVRTkNILxP+NZTiefM14lDucOuH\n/pFU1e126forE4pWEBtJY0tJOmciAF0XRHsSXO+dJKALb34joAssWxIO6ZiWjVGKqUoJwYBGS2MQ\n27YZTeRJZgy2tDXS2BAoWzjnXvf+6zSTLfLlI1EKhoVVOq5pSRKiwFdevM617nGam4LTBM3+e6l3\nMIUEL9574nxs3iLoWvfPUo6D53LeR+/tQAixIWOkV7viFBYoFHWxbUk6Z6BrYto8h9ueprJOe57K\nGtj2BO0tId7qGgfm1gbOp+3M5syK347afce1IuBXpk8KxfKxkNm7nwB+ESASibjbZDQa1ZeqUgqF\nYnGs5A+8e66uwUkyWYN4Mo9A8Oh9Hbzngb1IKTl+vp/+kTS5guU5e4IjspxI5dm5pRmY6lTv3dnC\n/p1tNDUGSynnJb1DSZKZIhKYSOad11uSQMARgN51aDNSUuZgBnh127ejhfhkHtO2yeYNWkqdfduW\nHD3Tw/Hz/fQNpykaJqGATnNTkEzW4G+/dYm//dZlDNMmoGvTJ+VKgx83viOAzW1hrx5Swnde6yKd\nnbKtF5SvfGtqCDguqdL5DPIFixdOdQOCnuHV3UmrJ0sRFlNiBMVyEhvJcOR0j7p/FeuGX/up++Yl\nFhW+qXO14lOhUCgWR7Ux3vvetZ+/+ealsv0KhoW9yAQUuYIFFLFt0DSwbWec4qR8k8TG0rMK/RSK\nmSgULb7w7atc6YzXdEqpdMBYiCNGNpcnmcqSK9oEQ2H04PKLGHN5k5gvfXz/SJqBsbS3iHYmwiG9\n5BLa7LmF7t7eQnNDcNnrXc0VNBjQ0QRomsbLF2IMxHPcvmcz73vXgQ03vhkez81YVigUiuVA0wS2\nT3Cz0dpehWIt4jcJ8ZeDAUHe56seDDj3s2GV7++WwyGBZTsitoAuCIec/VsbA6R84p/WRqd/2zec\n9uJPUjplgBdOdfH0s1cpmhahgI5lWfzoI4f45D+d5cRFx624M5bEMAx++/94F7u3NPHcyU5sCZqA\nJx/cS1eFANZdsPT44T0cOdNDbDRDx7ZmHj+8B6gdn6zlDrdWBEuKKWplMtDEdPdbP1ZJWOfzzsG0\nJKY1faxkljbpmmDn5kbCoQBjk3lsW7Jneytb2xuI9o6TyRmkcwbHXu9jx6ZGbInnqpjJGnQOJAgG\nNPJFi0O72xmZyHjCareqoqT6TKQKnLs2zLbbGqeJ8vyCvWTGuZnbmkOkMkUmU3nHBdWwOHKmhz/9\n1R+e1Q20lpHNUoyD53vejXq/vXlrbPFujKWPTkoIB/Wyds4VOTruuLZnPFUoWtA893jJfJw1mxqD\ntLeEvHugqbF8LsHf3mayBt1DSQSrW8CvTJ8UiuVj3rOk0Wh013JURKFQLB3+TuuVzjhXOuNVV0L5\nWciAzLYln3rmPOeuDWNZNunSIFUIuBVLAJKnHj7A1a5xoj0Jiub0Dr9h2iQzRVqbgqSyBsl0gYGx\nNAXD4oE7dwCS77zWjW3bZAsmubyBf/ysa4KObS2Mpwo8/1oXqUyR42/089UXr2OYNvmiSUNIJ5Eu\nYFmAgHTOJJDMI6UjFP3y0eve4AAgg8VElbRvhlk96iqE8FxGdV3jUmec9uYQVzvjtDSGpu0vgU1t\nDbz90BYO7GzDtGz+4TtvkSuY2NJJ9/jM0euAM9hYzZ20ehIKrpyLikKxIAReW6zuX8V64Pj5uQtF\nAYpG6Te+ecr9W00AKxSKjcIn/+kNDt+TIrJvE2/bt4n2lvCijlfLSUCvDNKUgnoLNBb1yBcshJhK\np+ZmQhAAUqpFAIpF4WYJcR1TPvbhw9P6Bwd3tXtOKgAHd7XP6di2bZNIpslki9joBIIhQou7/api\nWTZD49kpQehomthohvHS4taZ0IRg++bGsvTxHdta2NzegLYMKeSruYK6qeFdV9BAIEhDuLor6JHT\nPRy/OALAjf4UmqZtvPGNyvGmUCjqwO0d7WWug7d3zO23UKFQ1I9QUMewzLIyMG1Bn+3LVufHLZ+7\nOuK5M5qW5NzVEfgpyFaYmbjlbL7cedQt/70vE0W+aPH3z1/lRx855AlFXU5cHOK3gX96IeqNJW3p\nlHWtPA7TP+IIUT/z9Yvc6EsgJdzoS/CZr1/k4x95J0fP9PLMUce58VRQR0p46uH9Nd3hao111Rzi\n6iWoV/8eZGk+YgZj0TnPVbj3gmFaDI/nKFo20pLYQNGw6BoQpHNG2T00PJEjHNSxpaRQtHjz5igA\n48k8Qgh6h5JYNRbXOotjnSwtrmto91DSuw6fe6WTdNagtTlEa1OQ1uYQ+3a20TuU5GZfgkzJKOl6\nb4JPf/XCrG6gtYxsFjoOnivKQGcKq0bMfz7opfmyYMDJaup3vXRFjf/r+E0ypTbZsiWW7cRMegdT\nntkMULO9m4+z5oGdbZy+PFhzX397O5pw7pe2ZkfHsFrn+dQ1q1AsH/MWi0Yikf9UbXs0Gv3DxVdH\noVAsBf4f9FSmWHMllJ+FpIB48Wwv564Nky9YjjNmCSkhnTO8FMxdg5NYtl11RZnb2SkaNtm8Sb7o\ndKh1TePVS4NcvDnqBZIKRWvaQCJftLkVm0TgBDJFKTA66RN7ZvLm1LlLf5OZIs8cvY4tJRPJwoyr\n3WZDSokmoLkxiBCQzhYpllbt5IsW8cm8NyB3sSybX/rg3c57L1q89HofN/sn0ZAUiiaGZRMsrTwr\nGBYnLvSrwXAFecNWro2KVU1radXeah1kKRTzZb7Xsi1hZCJHJmd4k20q5ZRCodgoXOmKc314apCx\nfVMjb9u3icj+TfzA3k3cvqedhtDcp2RqOQkc6mjn0q2pSWNd17BdK9BF4DqJVg6UbClJZQ3272hd\n3AkUGxpdd1KXg+OUUq1/8NEP3ec976bGnIlcLs9kOkcubxIMN6AFG5YkG4WUkkS6UEofnyE2miY2\nkmYonvFE1DPR1hxiz/aWqTTy21rYtbWJYGDpEjRVcwUN+AShAV2jIdxEMBhE0+b/qczHyWS90hjW\nyfrcYRvDKsGWQqFYfp58cC89QynPEfDJB/fWu0oKhWIWDnW0c7kz7qWDP+SKvCvDF2Lqj5y+mZFE\n+QIkt1wpdHPLtda1pHLlcanKciXJjDGt3BAqr3yu4MTe3rw5NtUflk4Z4MT5Ps+cRQin/NTD+3ni\n/r1c6Yx7/fsn7nfatFp9zaNnenjm6HWf6NQxp1HUn4Zw9bkMycxC0YVgWkxzHk3XSK0tpSOKdu+r\ngmH5pjRqV8y9VkMBjWzeYGA0TWtTiH3bW/iLL7/Ba5cHMU0b05YkMwXamsO8/5GDPPXwAY6c7uFq\nKaU4OMdZjBvofMfBioXT3Bisahw1H0zLGYNLaVM0LaSU2LZE04QncuwcmCSRKlAwLAK6RjjkjCVT\nuaI3DwLUjJnMz1lz5lWO/vY2HNTLtB0qvbtCsfFYSP4lf68wCPwocHppqqNQKJYC/yoT12rcpdak\n/kIm/7uHkoQCGuns9M6UlOQijmMAACAASURBVPDmjTH+rz894q2s8eO61IAgVzBJZYsUDEdIAlOd\n/2lp36vgpQuoofistllKZ9XMDC+bM65QNV+0sCxHFFs0bFKyiK5BtcVJk+kixaJFKKTzma9f5GZf\nAqM0ikpmDXRNICUUis5nOzCaUakeq6BcGxWrmZ6hFJvbwuzfMfsgS62UVqwFFjphkMmbfO7ZK2gC\nXn5zwFu12toU3JBiA4VCMTOmafPpr14omxifLX3XaiSol9d5ZCLHyETOc1LUNMH+na28reQ8Gtm3\niT07Wj0BXSW1nAR+5J17uNmfoGjYvingypDj0jE1dlL9FMXCMS1ZupYk4aA+LZjWPZQkENBmdWOR\nUpKYTJHOFbGkRjAYIrSIlO35gsnA2JQgNFZKJe+6xMxEKKixe2spdbybRn57C61N07ONzIdqrqAB\nXUPTxJxcQZeC+TiZrFdaGgNlYtGWxuX5rBUKhcJP10AKw7CxJRiGTddAqt5VUigUs/D44Q66B5Ne\nXO7xwx0AtDQEyfgEbi2lPutSmZeHdCga5eWlopYrakPF4hm33DuSnkozLp0ywLFzfVy6NUbBsEhn\nDY6d65vRcfTkxZhnCpMvWJ45jaL+NNYQi64WPO+gedxQAV1gl1KF5woWplXg6Nk+eoaS5ItTolM3\njnu1K07PcIr9O9q4vaOd670JJ/ukpi3KDXQu42BQ8aSlYHN7I7GxzKI1ApbtOIaOJXJ8+5VOrnTG\nGU/lQQoeva+DbM4gV3BMrWwhaQwHaQjNrNvwb5uPs2bPcMpzCnXLfvztbWtTkLt3bS3LTKtQKDYW\nC0lD/wf+ciQS+SPghSWrkUKhWDT+FXq7tzZTKE4NQg/sbKvaiTyws40rnXFSmSIFwyKTNbBtZwWM\nG6w9sLONOw9s5pWLA/QOpzBMC8OUNW37bQmD8WzNegrhpIjLGk5PzF5srsR5sNjOnx+75LjjF8S6\nj+0aCzWLps1H//wYD/zgDl6+GPMGGFP1kwR0jaJplQQ1ISWoqYJUKTAVqxjLlowm8rxwuov3PLB3\nRqGLcltUrAXe++A+PvP1i14arPmQL1p8/ttX0YSgaFjkS8H2jSg2UCgUM/MXXz7H9887KZM6Y0ks\ny+Tf/OxDda7V/PnLf/cecnYjV7vGuNk3ya2BJANjWW8cYtuSroEkXQNJvneqB4CGkM4de2/jbXs3\n8bb9m3jb3k1sva0BIURNJ4H3PLCPo2d7udGXwLYkxgqMqQzTpnsRThkKBTh95YaQ7mQZqXCGma1/\nkM8XmExlyRZM9ECYU1fixEbTdGxr4d137541SGbZNqMTOV/6eMc1dKy0oHQmBLBtU6OXOt4VhW69\nrXFBKeQty8KyTKRtT3MF1TRBMKAvyhV0KZifk8n6ZDxZmLGsUCgUy8Hzr3V7jy0pef61bn71f7u3\nXtVRKBRzwDQl2bzppBq2JKbpjM8SqfJ+ZmV5saTz9ozluVLN6bTSCMYtf/DRQ/zd8295wtgPPnoI\nAL2iT+yWT16oEH9eiPHUw/t5/PAejpzpITaaoWNbM48f3uO8UOJlLBTLtx5SsQAO7b6Nt7on6l2N\nJcUwZUXZ5lrP9PcYCmjkCiYnLw5wW0uYK51xfuzh/eze2rKibqBHz/TyzNGoz3kXnnpYxZPmQ75g\nLKlWoGDY9I2k6R12BPK2hGs94+gaWJZEApoApCRZ0mKEgzr7d7QhhBMXlDgZY3uHkmWZNW1bcvRM\nLycvxEBIHr23gycf2j9t7mO2hZ7VxvZKZKxQbFyWYulHC7DxZgkVilWMf4VeOKhz9+1baG4KeT/8\nlaIkKSUgKBYtUtkiLY1Bukup4K50xjl+vt8JZsaSHD8fw7blosdlUjoBUickVFtwup4ZjGd54XQP\nBWP6m7cl5IsmuqY5LqXZohLUVGFgLMOBXe2erX81/OJox+FR0jOcUh1hxYoR7Z3kY//9Jf7Fj9xR\n85pTqR2XBrWidgVYxAxKJmcSCmpoQmBLx0nMTTmlUCgULq5Q1F/+Nz9bp8osAk0TRPZtJXJgq7ct\nXzB5q2uUq11xbvZP0hlLMZ6aEhzlixaXb8W57Esrv6k17LmPvm3fbTx81y5OXR7k6Wcvc2BnWymr\ngYUQgsWP0uaGZUteON1DNm+uWedXxerAtGwGxjK0NoX4sXcfKBunVSKlZDKZIpUpYkpBKBQmFA7y\nysUYx9/oB+BmXwKAH7q3w3tdMlOgfyTtpZEfGE0zMJbBnMMkREtjcJoodNeWZi9t3GxUuoIGAron\nAtU1QUAXBJuW1xV0KZiPk8l6pTJjTLUMMgqFQqFQKBR/9/wVT0xp2ZK/e/4KP/7oISoT6M0hoV5d\nqOZ0WtltdsvvfXA/b3VPeAK59z7o9BfvuWMr3z8fQ0qJEIJ77iiNiUXF0Uvlz3z9Ip0xZy68M5bk\nM1+/yMc/8k62tDeW7V5ZVtSPzoFEvatQF4QAXddI5xyRoZtB5nPPXeWJd+7h4K52mhoDvPR6X9W4\nxFLGLhzxdQHLlqSzBt84fpMnH1p8LGQjxVduxZZ2EbRjwFW+LV8sd5SSEuKpvNfYhgIaIL3288T5\nGKlMkVSmWJZZ88WzjjjYFdwPjGYQYvo4fbaFnmtxbL+RrkmFYqWZ90xkJBLpYqq/KIBNwJ8tZaUU\nCsXiOHG+n7HJvDcYG0vk+fhH7veed0VIUkpSWYMvHYmiCUEqa1A0bPK6RWuT5Pgb/VzujFd1zFwK\npHRW02xkqglFXWxb0hh20svt3tbME/fv5cjpHtUhqsAVNld2cN0O5InzMW7FnBQQUkJDOEB7c0i5\nNypWlOHxbNngDsoHOZms4bXZoNwWF4pyaHX4k8+f5o7bhx1Rw1ZH2LB7azMNi0wR9OLZ3kUHxouG\njRAQCugUDIuXXu/bkN+RQqHYmDSEAxy+cxeH79zlbZtI5bnWNcaVzjg3+hJ0D6bKUh1PpAqcvjLE\n6StD3jZXbBYO6jQ3BMgVVz7SWDBsjp3rY2AszX/99UfVuESxIExLYtkmsdEUQgh+6YN3T9unWCyS\nSGbI5E0CwTBasAF/YvfYqOvaITFMm7NvDXui0NhomlTWmHbMSgK6xq6tzXT40sd3bGuhrTnk9c+r\nYVkWlmkipY0mQNenu4KGQ42EQqG6uYIqFAqFQqFQKFaOXNGesbye+O6rXRw714cEumJJ7uho5wOP\n3U5k/yZOXIhh2qBrENm/CYB337WLy7fimJYkoAvefZczLu4cmCxzEO0ccARcjQ0BGsMBzxSnsWFh\n85pKaLT0jIzXzii5FtG16aLoSoSA21rCaAKylAurs3mT51/rRghBc0OAq5uqxyXc2IUETl0a5MSF\nfh67b48n6Kt0jnzPA/t46fU+ugaTZHNFxiZzCDQeva8DW9oYlvQydo4mclXjpLWoZbKTyRp0DyUR\nrP/4Sj1MrCSOs7ImBO5UQ9fgJJ965jxv3hwjVzCqpqjvHkpSMKbmCguGtWEMZ46e6eGZo9d9LrqS\npx4+UO9qKRTrgoX0rJ4C/hmwuVROlP4pFIpVQu9Q0usgSinpregwuDbkqaxBIlVAwpSrhoRU1iCT\nM7BlaoVrrvBjS8jkDEJBnS1tDRw718t3XusG1n8nfa60NjnBu2qdYnfgFRvNlFxaBVI613p7sxNe\n3Cid6dWElBLbtr1/lmU7QVa7tL3kdCylREpnf8uWPPWR/3Dg8rG/6a53/ReKYdkkM0W6Bp1rzrYl\nn3rmPOeuDRMO6rQ2hTiwq53mpuCCUjuupkmvetZFObQ6dA0k6U/0T9u+pb2hTADh/tu+qRFdn13A\nsFSfp3Nv20gpee6VTgA1UatQKDYsm1obePc9e3j3PU6qPSklA2MZ3uoacxxI+ybpG0ljWuUL+Czb\nEcalc7ML4ZYLCdzsT8wrIKFQVCIljCbyfO7ZK9i2zfvedQAhIJnKkMrkMW2NYChEKBwEHFHoWCJX\ncgpNc7NvsuQU6twjw+M5rs2QEnFrewO7K/pD2zc3oleIOStdQfWSCNT9GwxoBAKr3xVUoVAoFAqF\nQqFYDj77rcueYE6Wyh947Ha+efym1zc3Lck3j9/k/T90iKNnesq2Hz3Tw/t/6BChgFY23g2VMlfk\n8qaz6BxB0bDJ5Z1FkqZp8+mvXihL+T1Ttota5gKraT59rZFdrda4C6TSDbIaUsJkukBbc4hqnkpu\nLCuTM0iFA948uv866x1MeWnGJ9NOGvJUppMrnXHiyRy3+icpGhYgiI1mOHq2l4GxDIWiRcGwEEAg\noHErlkDXhacDcM4v+dILUU5c6J+Worzate6/L05dcjL8tDWHGE3kCAd12lQMdclxDY3AmddAQjpn\ncKt/kht9CSzbiYlm8yaaptHWHPJMZQ7sbONUUCdfWlweDupVDWdW2kxlJdrRkxdjnqNqvmBx8mJM\niUUViiViIbOZfwLsB96Csn7g3y9VpRQKxeIomtaM5Sfu38uVzjhn3xpG0wSGaU3LKruEBqKKRWCX\n0kq+eXOMeDJf9pzqpENLo/MzdmBn27ROqSvKc3FXpvpZre6Nq2WiwhV2un9N05ou7HT+8wbDtvvX\nS7djAwK79NfZKhBCIDQNrfTPceypkc5Rg+0H39m8Eu95uZASxibzXLg+wpHTPUgJ564Nky9Y3gCv\nuSlY1U1pJvwOugNjaVqbgnWf9Kqnu6e7GMJf3ohsag2RsaanjopP5olP5nnz5ljZ9oAu2LmluSzF\nase2FnZva+a2lrDnqLV329LdhkVTMppw6vP5565gWRa6rte93VMoFIp6I4Tw2uMnHzoAgGFadA0k\nudYd5ytHokxmiiBWh0uhYUpeer1XtduKRZPOGXzu2UukUmkeePtO9GCYbFEjNuqkj3fFoQNjaYoz\nZOhwaQoHHHf1bS10bGtmz/bWMqd10zSxLBMhJdIsIEsi0ICueYLQhnATwWBQuYIqFAqFQqFQKDYs\nAV2UiTkDekmAViOmOBDPlW13yzf6y+M1bnkoninb7pabGgO0t4Q8Z9GmUizo01+9wMsXBwCIjTj7\nfvwj76xZ/67BSZKZonecrkHHubSaW92TD+1fFXGZ1U6+YM2+0xpiruFwW+KJ1mbaJ5Utsn9HK+DE\nSr79SieprEEqWySgO+Jo07KxbY1k1uDctWHAEQk60/CSeCLHWCKHYOrekjhZuxwRdTkFw2Y0kWMi\nlad/JM3VrnGaGgNkcybxyRwDYxkvdiSl5OTFmCcMzRdNb/4/HNTLHCxni6/4s+UpZkYTAoRPXCUl\njeEA+WK5RsONt/7Yuw/y3gf3YdsSKSW7tjRjWk7Wts1tYboGJjlyuqesnVoqM5W5xhVXIhZo247O\nxY3xz0XcrVAo5sZCxKL3RKPRO5e8Jj4ikYgAPgPcC+SBX4xGo53LeU6FYj2xua2RTD7tlYXQvA4D\nOIOpc9eGkVJSNJ2VeXPvDitWGrvktJLMGGgatDQGaWkKcqN3gn/9iZfmtHpyvdLWEvbSNFR2SveX\nBjGtTUEM0yIY0GhpDHL37VtpKa3Imsm9calEdgs5zkI62LMJOyUgbVkm7JSAZdk+oeeUm6crAkVo\nZcJOIUQpWFpD2Cmcf65+QXnsTGHbkkQqz/OvdtHSGCIU0MjkDGTFBEKta2amFaCjiZw3SdPWHPIG\ngfUQbtbT3dO9p/2f0Ubkzz72ONt37GIw7ggr+oaT9A2niI2mGYpnSefKV3+blqR/JE3/SHrasZob\nAp771uUKkelSYJcczf/y65ecVMoFEwl871QX73toP70jaTU5q1AoNjzBgM7b9m3ibfs20RAK8oVv\nX/FSa0vbBqTXZ6sHV7snlLuoYlFYRgGJJGMHOHZhhDPRBN2DSXJzcKzRtdKil+0tXhr53VubaW7Q\nQDpBtKn08BJdMwkENELNYcLhVnS9xrhGoVAoFAqFQqFQlAlFq5UXSzJrVC3v39HK91/vp2BY2Lb0\n5s5vxRKeAY4QThlqz6lnc2aZK122NC9aza1OCFE3E4S1xEaOJs/lvecLFkdO99A9lKRvKE0ya5BM\nF5FSYpim5yCZyhpk8wYBXcMw7eqOpfOsh2lJJpJ5Xr00QEDXyOZNz+RlbDJPQ1Djy0ejpLMGBcMm\nJZ24s5R4ddQ0gWFYtDQF+daJW3z9pRuYpo1pOf+MktjVsiS6JnjxCx8PJIZvrS+72WXALn22GmBZ\nTnw2kzPYvqnRcR0tiSAFTtsmBGia4MjpHr7zWjfJTJFMziQU1OgaSDGeLPBW9zgw1U7t39HGqUuD\nnjh+/47pYt+5xKznGldciVjgcDztiWmldMoKhWJpWIiG461IJLIrGo0OLnltpvjnQDgajT4SiUTe\nBXyitE2hUMyBH3/kIJ/91mWsUs+yIazz7Ve7PCv7aM+Et/IiFNAwzPW1Cmy94q7myhUsxhJ5EBDQ\nxJxWT65XJpJ5rnTG+f7rfXQOTGLZkpbGIG3NIZoaA7z/kYN0DSbJ5gyaGgMc3NU+Z8HRUonsqh3n\nvQ/u9USdnqCz9FdKyeWbAxRyWW/Ad+nGAHcfasOyplw63XTttmPlOU3Y6Yo6ZxR24jxV0ney8eTG\nK4+brmQonvEmuhBO8JrSetBa11617e7gK1xKQeG2E+6Kz3oIN+vp7qlpQk3glQgFdfbvbCsJ53eX\nPZfKFh2XrpE0sZEUvSUh6fB4DsMsX5qZyZvc6Etwoy+x7HXO5KfmlKK9k8RGr7JzS7OanFUoFAof\nTz60j5MX+3nz5hi2DaKK46GUthM5EGJFBKSWJfnLr13kc89e4cG37+AdBzcrsb9iVmzbdsScQkML\nhLxrtW+49sT/5rYGdm9rZteWJnZuaaRjSyM7tzQSCuolMaggENAJ6hrhcIhgMKhcThSKdY5tSyzb\nxjBtz6XJNKvbzVy8PorhBrlNG8sf8PYFwE3TLu03dTxnv6nHv/tz71rhd6pQOOSLJkjKFnyXZ/yR\nZc+X7efbZ8/21nq/FYVCoSBfNKdlPZQSRsaznL486C2SLBo2r1wc4GDHbaQyxSmnxZLT49mrQ7x+\nbYTXSyY5py8P8lb3OG8/uJnRRBaQmJYkoAsGxzJ87dgNhuJZTGuqzzAwmuG5V7qIT+Y8I41vnLjF\nla54qb8hS8Yccqps2YwlcmQLJuGgTmtTCFtO7WfZdu3X2pIv//H7V+aDVqwoErjWm+B6f2JWF0TL\ndrPzVTnOAl07LduJI0N57N+2JdmCRbbCGdb2F4XAlvj2K894Of1ckmBDSwhQYtFZkBKwJdLnLlow\nbAZHnYyBEylHvK5r0Nyge7E81x05kS5g25J80UYTmhcL9Mf8TMtiPJn32jvTmq7/qBV/9ItIe4eS\nZddfrbjiSsQC48nCjGWFQrFwFiIWbQKikUjkMr5fiGg0+p4lqxX8MPDd0nFPRyKRB5bw2AvCKolx\n7LIBeMVAu9q2in39g3UoH8T7968+4K88NkjKjyuRSLv0Wt9rqKjDjMf1tpVvt0sVs0uzD36XOttX\nJ5DYdpXjl+pa5mxH5XuqqCNT7nbSlr73Vf6aavz5F1+v/vmXvWf3XFOfe+W+mib4o19+ZAmvptoM\njmWwA84PrvT+N/WdVONmf2JqpxLffq3TE4oCJFIFLEvSP5zCtG38fQPlKbo2ca8Pw5II2+L1a8P8\nw3fecp7zXSvuw//zx9++IvW6eGOUgcnpPy21rt/Zrr0aL/PoGUrTM1QeTCwYFgXDWdXW3BjkrkNb\nvDq8eXOMVy8NsH1TE3ffvhVRGbj2ne/VSwOks0apnZKcvNBHQJNTTp2ytNKvol11DyNtCQJeuThA\nPJFxVroCL5y6xWQqW9IFltKxC4EQGu64r2gFyBpTgX/DDnDqanxOn0nNp2d5Ye3XLex8tb7z2Vjg\ny5aM//iZl2nbtJ1QUCcU0AgFdcJB3SkHK8ql50PBubsR5QpmyTmxJPQFdAGmZfON4zc5eSFG/2gK\ngeMQKoTwBmLVhJ/uYKy1OQTA7m3Nntsu1Ee4qdw9Vz+tTSHu3L+ZO/dvLttu25KxRM5J8Tqapm8k\nTf9wkoGxDGOJ/Ir3F9I5k5v9TmqoT33lAn/5tQs0hgM89Pad/MZPH0bTBEfP9HLyQj/xZJ7NrQ08\ndngPTz6khElrnRt9CSaLjeV99NK4wf+4vL9cMe6Z4fivvjlQ9bj+4zjP+37Xy8ZvpT3l1O9gtW3u\n+LHycdkYx/cmqh27sl837bW+8dH0Y/uPW3mcKtsq369/bOh9XlOfcbVj+8e5VLy2+nuoeL7GN/dH\nT5/2nqt27GrHkRXnlNJxIPyTX/2hqudYK2ia4LH79nCzLzHNKdpFCI3KnGTStShgeQSkli1J5wxe\ner2fkxdi7Pf1QVw39O6hZMkNRtAzrNL6bXScRW3Vl6vpmkATTuMspc2uzY381s/cz20tIccVNBgk\nFAoSCKg8BgrFciClM/dhmjamLTFMC9OUU2JKn+DSEVPKGtsdIaZVsX1KdCkrxJnl2/3iTP9z/n39\n85+z8Xt//eqSfUZCiICUctmD4kPxDDKYqioA9PdlqwsE/eLA6sd/7dJg1RiBFwOpEh9wt7sxArvs\nHNWOUas8PR4Avjk2u7xe1fb36m77jul/jV1xTG//6TEId3//51H5HsuOIX0xkNIxpu3nHaPWd+K+\npnx/f0ynkg/9x28vybV19ht/0j5068zkkhxMoVDMSC2zltGJXNXtN2ss2H69lKq6kmPneqtu/58v\n3ay6/fPPXqm6/ZNfeqPq9mr82784UXX7L/7Jkarbf/p3nqu6vVab9gtVjnP+xhjnq5x3IlXgD58+\nPW37kTO9HDlT/tlYtuRq9zhXS258fkYmcoxUfCfJTJHeoVTVOlYnM4994c4f/tm9117+Yt+8XqRY\nMyw2XfZKLnysFs9raghwW2uYgCa8bB0BXaBrzt+ALmgIBXj5n/LLnhj8zZujDCanxv81RwA1nlho\nvHKpsUr9Qj+T2fIhhWFB30ia5sYw//mzr3Gzb4LJTLkDMzjGMfmCye0dt3HyQgwkfOG5K577s2lJ\nvvDcFVqaQmWvfOXNAVLZIhLIF0y+fCRKtHcCJFy4MQLgZXlpDDufeaFg8f03+qnsoOq6xqHd7Vzr\nmQAheat7HF0TXuy99sde/Ylq+1d+d1JKjp7p9Y7hn2v27z81/1wq21P72+5zpX1/7l/+ZDjef3nV\nqVD92q3yGED1zy+bd66TMi2X7326461qsYOp8WXFvH/lPL5/Xn7qcNPGSmVxF8q3V+rkpu3ve1zz\nWL7HZePQWY8lq7wX/xiv8li+7f7P1Bv3et9Wqc2fHtfxPscqfPG7b1X/3MteW+M5d7uEX/7Je6oe\nfzYWMqv6pws60/xoA/wDVTMSiWjRaLTmj82//8uXCTdvLpswmD7RMTXoLtsOJSEi1Lq5/vlvf2vp\n3p1iRTh+vn/JjrVl7116vO/ysttv/t9//SrBps2z7+jjNz95fNZ9TEsykar+G7c6ukeKxSAlJNJF\nvnL0es19/tUH3qHJqQjxsvH/feUCwabqkxMrhW07g/jnX+3m+Ve7a+4303PVGJnIc/56fPYdZ2Ei\nk+Ja73wmGODliwO8fHFg0edWzM7oRI5EYX7fz3yYWqwsfdskuYJF34gjznOnAJIZg1BQozEc5Olv\nXWZwNMN4Mu+4xgrI5gxCQZ07929mLJnjvh/Yxrvv3k1DWGdkIksoqPPQ23dy6eYY3UOTHNrdjm1L\nPvvNS8sqzlDunmsXTRNs39zE9s1NHI5sL3uuaFj86ede4/XrrnDdndlYGdc6cO6fdM7k2Ov9HHu9\nn9taQqX7x0nfMziWYTCeQYiN5UI6l9Qxa43/5+/PzrtPPB/+y9+dXbZjK5aHM1eHluxYHXc+1hq7\ndmL5fuyXkMr7+4n793LsXC8nLsSYpgadBSGqOZDK0nNL22aYluRWzJnS+dtvXeJ7p7oZGs8SDuq8\n9uYAQgjamkMLco5ej23eWscwbWKjaboHk/QMJuku/ZsLsjRZJ6WNlBIhJG/bextPPrSfr73USSJd\nRKKRyEpuDWTQtFzdFiSpa0+xFNi2nCaodP9argjSrBRJynIB5jSnS1m2fUpI6TuXT1jpCj+9/SoE\nmUbpdYrpCPAC5dsPPdAALHsOxN/7q/nPFc+HP/3CmWU7tmJ1c+DwB3ZTHoNTKBQ1+MOnT9HQsqXk\nJCmnxBYVfPh3qwsff/LfVxdK/vwfv1B1+2/+v9Vjfv/5s6eqbv/kl85X3f7556qLQv/n96uLSI+d\nm7tmMdo7UXX78Hi26vZcYf1kNxTgLGALaBRNC8uSnglIKKhzW0sITROlBXACTXPmTvWS4E4Tzj9d\nF1z5frL6B7bEXLkVZ2AygOH2Kf39St82o6zP6e83li8cUsyP6UJFWaVYS0okK3YvCZxE+S5lO7lC\nIjH1vPBbR3lzQAJdh61tDYwkCt5zAggGNH7hJ97B+9613zO8qcV/+FfRme1Hl4BPPVP/2PNKYlpw\nuXP2eHTBsPne6R6+d7qn6vO5os2fffH1GY+RzBh871T11ydLItWZzuGndyg9TaS/1Fg2/MUz1X/3\nFsruO394O7Dswv1f/2/HCJb0bFVFixXl+fLh331+iWqqWCmeOVJb2zMffuWn7l2QDmjeYtFoNDq7\nMm3xJAF/HowZhaIA8USOYLH6KizFxkNK6axcEMLr87jiGvexEKXUy+7jUgcI4d9XEAwIrGJORQAU\na5qOH3x8G1B96alCofDYv7OFYFMLRcOZGCmaFoY5ldJuJXDP4rjkWly6NcalW2PT9jt6to+jZ8vH\nL1+vsWIcoGsgxbFz/Wia8zv3xe++xabWMGOJHAXDoqUxyJ37NxMK6YyOZ8nkTTa3NXDngc2Eghrh\nYMD5GwqU3FU1ggHde1zpwhrQtZqTCKs12L5a67UaCAV1Bsen+trOd1v9s/FPgC2nkDSRLpaVTUsy\nMpHjU1+5wN9+8xJ37L2Nxw/v4cmH9q/r77FW6hiFYrXgjbG8DVPjMqco3M0AFIrTg0hNDYGp8Zp3\nvKnXiYpteGM9p+A+dCRJWgAAIABJREFUr6+xtsB/f1/pjHPkTA/dg0mMGul150u1NnqpBaTZvEW0\nd8oZJxwUNISDXrl7KDmv31/V5tUPKSXxybwnBu0eSNIzlKR/JDWnfrIsiUIdBJpW6isKgfA5jPaN\n5vni925i2bYXeM3mTf7xhSibW8MIIbjaGUdK5x6vvG7819NSOtmqa2/tMTKRxQ4kZxBbyunOlVZ5\nOnP3NdX4g789NV28WSHw9AScpe3zMMHcUOgVbkXlfzUCAY2grhEMaOi6IBjQCZTKAd0Ra+i6Mz51\n96kWdPnNjxwmqOvOuUrHdM/hvs5/XP/2oO6cw+O/fGDZhaKKuVF1jl+AVhEXKN9n6vmpOEF5jKDs\nObf/6n8OgVa6JLxzlfZz+1GaKD+3Ox+D7xhufURJPOQvOwvH/eIPeM8De8veo7/fW/le3Oe94/o+\ngz/8/b9Z/Ip4hWKD0DuUItgUnHW/bH7tZ2Gu7b5Xvn3XlmYGx0oumj4NWuTAJq51j0+NzUtPvOuu\nXZy6PMjUCxx+9N37+d5r3WXbEPDhJyM8cyRatg3gV37yXo6d7eF636TXnv7g/tv4icfu4JNfeqNs\nrBwMaPzBL72bp791mc6BSW/88AN7buNj//thTl8aLC3EdLY/+eA+fuT+vei6hiYcxzxX8Pn0s5fL\nsne9/dAWfumDd3PkdI83RgB4/yMH5zVG+KNf+eEVaYs/+eU3lnXxSb2ZUYzpE1LOpSvuu5xxLzwx\n9XBq2zzmTKbvK6oWl3vGSggIaALDkl5Z1wQTGRtNn5IMaQIeO7yH973rwLqeU1coXDQtsCIj9YJh\nYVeZ91YoFsuO29/VwQIEz6s1X9MrwAeAr0UikYeBS7O94PaONprbN5UP9KkY8LsreMALJGkCRGmb\nVhqJn7ww3ZHyyYf2Tw3q3QG/d67aEwvub6i7csg9t+Y9Lk0STJuMqDxm+aRE+eSEWyfn4M65mDq+\nGzQrmwSZmoDAd3z/RIf/Nd5f/JMfM3/WM03UQPl7LZ9YmZrImfq+RPnkilb+Of2Lf/ds2fclhOCb\nf/7B2S6bOfP0791akVHeTz5xB5u37vQ6hO536j7+XJX0EL/0wbt8HUmBlJLPfvPySlRXscr43O89\n5ZsEndouhGDzf//gighFP/zeQ2zZtrO8baF0zwpnmKWVXI10DYSmoWuaFyh02+lyxLycDvyDuYaQ\nxg/s3cTwRJbxyRyGYXk7BAMatrSxLUk4pNPWHMS2JZpwnEmLhk0oqNHSHKJjWwvvuX8vti25fCvO\na1eGKBQNwsEAjQ0BOrY28/g79yKRXLkV582bo1597r59K0LA2GSeLSXhnTfEFODNUiA4eTHG0FjG\ne273thYeO7zXa2cRU+0BMO1c99yxjXf8/+zdeXwkZ33v+29Vdbf2bWa0a0bSzHjaY7DHHjPe94Ud\nHE4OAcJJLoQQSAIEEnISAuTkRSDbheSEEE4ICTdwk5wQ39yEEMgF2xgMNrbHxuN9euwZLaN9mZFa\n6m71VnX/6EW9SSNpJPWiz/v1mteoqqurn249evqp5/nV8zuwO+vzcBzphYFZzc6FtKe5Rpf1706n\nACj02W1EZn177vSMnn5puUxHLmnVKw+0XvB5FyrHp764OWm2LuST77lePT09BR+L246iyQDOSDQR\nSBpJbv/m53+46nnTqzA6+XeIpr5vM/ekf1rHoMNaOEqtburonD+sc/7lVaeXImH96JnxrOPPjPn1\nxMmpDb2WocTfWepfIrg0MYEXWopqbjGc7it860cva297g9wuSx6XIXfq2OTEn8fjktsy0kGp6f1u\nK31sldtK3iiS+NwSq1Nlf5aZn6YjJ93uOI4j0zD0/SfP6v7HhyRJJ16QAouLuu1V+5b7Jam/wWTH\nJfOcqbbLNJcnEHPvunW73YZTKjlPNmBsZm03u69Wb7crkDQYjuuZl2f1zMuz+ot7n5YkuS3pQE+z\nbr96rx55dlzn5pfSqSRkSLsaqtXaUqvaGrf6O8snWHhwwr/qdjn6+ddfpj1tHZKWryek5WuY9M/J\nHzL7zqk2NfXzH30tfxXRj7/7muXzGtmvkXee5D7TWN7IfTzzuWZG+QqXOd1xz7qeWX7tjEntnPee\nfjz9/PwJ8NQT88uRH2iZ+Vii7MtPMAo918g+T+paJeOjyS7/Bv/G3/Qb38jb989/8MYNnaugT76m\nLFYVlbL/nhcCEc0thtMr2ZiG5LKMTb+ZZasDSMNRR+FoRPPJgP+XR+b1zYfOJNaqNhLfp/c9PqQ/\n+OWb5HLlr4ZaqM3jZo+tcXpkXs+ejaVXCx0a92sxlJv6LJ/HZapzd60GxuYkw0z3jRLtg3XB54ej\ntsLRxCRv6trOcRwtBCJyW6Ya6xIp1H54YlSLoUQ9ygzezAzqfPTZRN92oyvZZqrE79tK9ztffHhL\nJ8WfeLG078dNBTkkUkUmgh1druS2Zaa3U9domcGZ6X0ZAZWpgMnUc9KBnOkAy+Q5cgItU8e4XVbB\nYEyXtTWZCgoFi97xqu1dkXgzvOW2g9q1pz0rsHG1+Y3Mx5fH3BPHfO4f89ML/867jhUcbzcz5kuM\nnHkCw5ByAy3zxvRzHzeNdP8zL4AxZ84gcx4l8/HETT8ZcwTG5vRNSlmhfvFv/LdrNuXc737ztzY2\n2APsQO27alXXVJcOHLRMQ6eG81PFX/OKjkRWjNQlWrKJevW1ffpuxopsqbbrZ+46VDBb3Ht/6pX6\n8r/lz/F99J1X67P/kL9C3Kfff4M+8VeP5O3/i4/erg9+9sG8/V/7vdfo53/vO3n7//2zb9abP7pS\nls3s9vbLH391dhuVfPhzv3Zbwbbrk++5vuD+D/7MUX33sfzYhp97/WX65wdeytv/xpv269sPD2S1\n/4uhmG6+slt/ee+JrGBRj8vU5Qf3yOM2s1LNWpah3o5GffexIbkzrjmn5kJqqq8q+O77OhqzgkX7\nOholKZ3toFjZD7aDaRpymUZWP292PrmgwGrDERmDUFv9fb1qMGbhH1c/38UWqEQ5jlRX49ZSJLFA\nSmKuKHsVQ8uQ2nbVamB8Xp//+lP6wFuvLDg2s91+6adeqT1tHRnjqsv91jwr/AJzd5dT5qnlOZWM\nxkwZf4JO4vovc+Xr1E9XHNilqfOh9Ps/emm7jhzco3/8zosanFxIJY9TQ7VLhmkqFovLZZk6uK9Z\nr76mL/HahrGcb9tIvX7ijM+8NK2nfFPp17zS26ojl7Sly20kn1twQdz0+8t8CSf9e3bk6LN//2TO\n6xv6jZ89mj5fzleupMTnYMjImvvJe9HkueQ4+sCP/2lbKvmVh/aoobk1Y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tDVW655YDK86lPvrseHr//GJETfUevThwTtLmt9U74ftgJ7zHi/HA8WENjs+rym1p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V8JYuu1++jMYiHqyHBcn3MzG5ybqScqDA+km9xHj/anr1unxHZx944DxaqPly/g+zqVwzB0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\n/7/UPlOr1a59N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nvka+eCLkNz22It98uV2gW306/+4ctc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Wpf8t8T3zX2fzZBrZ75n57Ove/3+X5O+6Z/+g9vSlsoGg6WDQEyPzH9EsNxg0wzCywWfZ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\nL4whNzjRdR299m9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GdEbhcntQugtlmauqESntCdEMpByL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LQojnYNibGSYN78H396vGZZLKKCEKBQMhvGggkhiEVV/KtH93iGO/6xYKxJw772DRiZLHFBqGUD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\nl4GwLamnMyXX9zVanF0p4blwbCPbsuR6voL2eeY5b4ykRMJSb09G9956g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nY/r1qk2lYdt2GWwfIgF64MYZZlaq1UvCgTIqSUPBCAVEQYzWd+jUYmKBmnhA1GW2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ee48650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.pairplot(sub_list, kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1064,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>urlid</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>5.305705e+03</td>\n",
       "      <td>3048.384114</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2688.500000</td>\n",
       "      <td>5304.000000</td>\n",
       "      <td>7946.500000</td>\n",
       "      <td>10566.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alchemy_category_score</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>1.920873e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.244463</td>\n",
       "      <td>-1.244463</td>\n",
       "      <td>0.178662</td>\n",
       "      <td>0.893583</td>\n",
       "      <td>1.772454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avglinksize</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>1.259067e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.320405</td>\n",
       "      <td>-0.134546</td>\n",
       "      <td>-0.078144</td>\n",
       "      <td>-0.015589</td>\n",
       "      <td>41.791977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commonlinkratio_1</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>4.503459e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.305040</td>\n",
       "      <td>-0.629442</td>\n",
       "      <td>0.065233</td>\n",
       "      <td>0.730424</td>\n",
       "      <td>2.617834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commonlinkratio_2</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>-6.260307e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.458879</td>\n",
       "      <td>-0.741548</td>\n",
       "      <td>-0.079227</td>\n",
       "      <td>0.585515</td>\n",
       "      <td>5.355767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commonlinkratio_3</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>-2.861358e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.959201</td>\n",
       "      <td>-0.727667</td>\n",
       "      <td>-0.244169</td>\n",
       "      <td>0.430004</td>\n",
       "      <td>9.255536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commonlinkratio_4</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>1.110914e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.678274</td>\n",
       "      <td>-0.678274</td>\n",
       "      <td>-0.372304</td>\n",
       "      <td>0.217580</td>\n",
       "      <td>12.820407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression_ratio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>-3.957468e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.395333</td>\n",
       "      <td>-0.317740</td>\n",
       "      <td>-0.310541</td>\n",
       "      <td>-0.293966</td>\n",
       "      <td>3.286092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embed_ratio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>-3.773107e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.123154</td>\n",
       "      <td>-0.123154</td>\n",
       "      <td>-0.123154</td>\n",
       "      <td>-0.123154</td>\n",
       "      <td>33.529674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frameTagRatio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>2.165273e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.361367</td>\n",
       "      <td>-0.673669</td>\n",
       "      <td>-0.256917</td>\n",
       "      <td>0.411039</td>\n",
       "      <td>9.362184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hasDomainLink</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>2.123056e-02</td>\n",
       "      <td>0.144162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>html_ratio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>-7.055163e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-3.585945</td>\n",
       "      <td>-0.623339</td>\n",
       "      <td>-0.061231</td>\n",
       "      <td>0.514255</td>\n",
       "      <td>9.204354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>image_ratio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>2.571454e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.196399</td>\n",
       "      <td>-0.182614</td>\n",
       "      <td>-0.152197</td>\n",
       "      <td>-0.070421</td>\n",
       "      <td>60.122242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_news</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>6.155510e-01</td>\n",
       "      <td>0.486498</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lengthyLinkDomain</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>6.603110e-01</td>\n",
       "      <td>0.473636</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linkwordscore</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>3.915432e-17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.474865</td>\n",
       "      <td>-0.788359</td>\n",
       "      <td>-0.248961</td>\n",
       "      <td>0.633691</td>\n",
       "      <td>3.428754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>news_front_page</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>3.975659e-02</td>\n",
       "      <td>0.195400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_markup_alphanum_characters</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>1.001378e-17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.644092</td>\n",
       "      <td>-0.466186</td>\n",
       "      <td>-0.249745</td>\n",
       "      <td>0.074408</td>\n",
       "      <td>22.785977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numberOfLinks</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>-1.926188e-17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.990463</td>\n",
       "      <td>-0.539124</td>\n",
       "      <td>-0.221516</td>\n",
       "      <td>0.240967</td>\n",
       "      <td>26.847649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numwords_in_url</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>-1.954488e-16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.534327</td>\n",
       "      <td>-0.606428</td>\n",
       "      <td>0.012171</td>\n",
       "      <td>0.630770</td>\n",
       "      <td>5.270264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parametrizedLinkRatio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>-1.176341e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.943140</td>\n",
       "      <td>-0.719535</td>\n",
       "      <td>-0.324422</td>\n",
       "      <td>0.373376</td>\n",
       "      <td>4.512824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spelling_errors_ratio</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>-4.673964e-15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.277542</td>\n",
       "      <td>-0.409960</td>\n",
       "      <td>-0.150307</td>\n",
       "      <td>0.140790</td>\n",
       "      <td>11.343799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>7395.0</td>\n",
       "      <td>5.133198e-01</td>\n",
       "      <td>0.499856</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 count          mean          std       min  \\\n",
       "urlid                           7395.0  5.305705e+03  3048.384114  1.000000   \n",
       "alchemy_category_score          7395.0  1.920873e-15     1.000000 -1.244463   \n",
       "avglinksize                     7395.0  1.259067e-15     1.000000 -0.320405   \n",
       "commonlinkratio_1               7395.0  4.503459e-15     1.000000 -2.305040   \n",
       "commonlinkratio_2               7395.0 -6.260307e-15     1.000000 -1.458879   \n",
       "commonlinkratio_3               7395.0 -2.861358e-16     1.000000 -0.959201   \n",
       "commonlinkratio_4               7395.0  1.110914e-15     1.000000 -0.678274   \n",
       "compression_ratio               7395.0 -3.957468e-16     1.000000 -0.395333   \n",
       "embed_ratio                     7395.0 -3.773107e-16     1.000000 -0.123154   \n",
       "frameTagRatio                   7395.0  2.165273e-15     1.000000 -1.361367   \n",
       "hasDomainLink                   7395.0  2.123056e-02     0.144162  0.000000   \n",
       "html_ratio                      7395.0 -7.055163e-15     1.000000 -3.585945   \n",
       "image_ratio                     7395.0  2.571454e-16     1.000000 -0.196399   \n",
       "is_news                         7395.0  6.155510e-01     0.486498  0.000000   \n",
       "lengthyLinkDomain               7395.0  6.603110e-01     0.473636  0.000000   \n",
       "linkwordscore                   7395.0  3.915432e-17     1.000000 -1.474865   \n",
       "news_front_page                 7395.0  3.975659e-02     0.195400  0.000000   \n",
       "non_markup_alphanum_characters  7395.0  1.001378e-17     1.000000 -0.644092   \n",
       "numberOfLinks                   7395.0 -1.926188e-17     1.000000 -0.990463   \n",
       "numwords_in_url                 7395.0 -1.954488e-16     1.000000 -1.534327   \n",
       "parametrizedLinkRatio           7395.0 -1.176341e-15     1.000000 -0.943140   \n",
       "spelling_errors_ratio           7395.0 -4.673964e-15     1.000000 -1.277542   \n",
       "label                           7395.0  5.133198e-01     0.499856  0.000000   \n",
       "\n",
       "                                        25%          50%          75%  \\\n",
       "urlid                           2688.500000  5304.000000  7946.500000   \n",
       "alchemy_category_score            -1.244463     0.178662     0.893583   \n",
       "avglinksize                       -0.134546    -0.078144    -0.015589   \n",
       "commonlinkratio_1                 -0.629442     0.065233     0.730424   \n",
       "commonlinkratio_2                 -0.741548    -0.079227     0.585515   \n",
       "commonlinkratio_3                 -0.727667    -0.244169     0.430004   \n",
       "commonlinkratio_4                 -0.678274    -0.372304     0.217580   \n",
       "compression_ratio                 -0.317740    -0.310541    -0.293966   \n",
       "embed_ratio                       -0.123154    -0.123154    -0.123154   \n",
       "frameTagRatio                     -0.673669    -0.256917     0.411039   \n",
       "hasDomainLink                      0.000000     0.000000     0.000000   \n",
       "html_ratio                        -0.623339    -0.061231     0.514255   \n",
       "image_ratio                       -0.182614    -0.152197    -0.070421   \n",
       "is_news                            0.000000     1.000000     1.000000   \n",
       "lengthyLinkDomain                  0.000000     1.000000     1.000000   \n",
       "linkwordscore                     -0.788359    -0.248961     0.633691   \n",
       "news_front_page                    0.000000     0.000000     0.000000   \n",
       "non_markup_alphanum_characters    -0.466186    -0.249745     0.074408   \n",
       "numberOfLinks                     -0.539124    -0.221516     0.240967   \n",
       "numwords_in_url                   -0.606428     0.012171     0.630770   \n",
       "parametrizedLinkRatio             -0.719535    -0.324422     0.373376   \n",
       "spelling_errors_ratio             -0.409960    -0.150307     0.140790   \n",
       "label                              0.000000     1.000000     1.000000   \n",
       "\n",
       "                                         max  \n",
       "urlid                           10566.000000  \n",
       "alchemy_category_score              1.772454  \n",
       "avglinksize                        41.791977  \n",
       "commonlinkratio_1                   2.617834  \n",
       "commonlinkratio_2                   5.355767  \n",
       "commonlinkratio_3                   9.255536  \n",
       "commonlinkratio_4                  12.820407  \n",
       "compression_ratio                   3.286092  \n",
       "embed_ratio                        33.529674  \n",
       "frameTagRatio                       9.362184  \n",
       "hasDomainLink                       1.000000  \n",
       "html_ratio                          9.204354  \n",
       "image_ratio                        60.122242  \n",
       "is_news                             1.000000  \n",
       "lengthyLinkDomain                   1.000000  \n",
       "linkwordscore                       3.428754  \n",
       "news_front_page                     1.000000  \n",
       "non_markup_alphanum_characters     22.785977  \n",
       "numberOfLinks                      26.847649  \n",
       "numwords_in_url                     5.270264  \n",
       "parametrizedLinkRatio               4.512824  \n",
       "spelling_errors_ratio              11.343799  \n",
       "label                               1.000000  "
      ]
     },
     "execution_count": 1064,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "su.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1065,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import patsy\n",
    "\n",
    "formula3 = '''\n",
    "label ~ \n",
    "alchemy_category_score,\n",
    "avglinksize,\n",
    "embed_ratio,\n",
    "frameTagRatio,\n",
    "hasDomainLink,\n",
    "html_ratio,\n",
    "image_ratio,\n",
    "lengthyLinkDomain,\n",
    "news_front_page,\n",
    "numberOfLinks,\n",
    "numwords_in_url,\n",
    "parametrizedLinkRatio,\n",
    "spelling_errors_ratio,\n",
    "C(alchemy_category),\n",
    "C(is_news)\n",
    "-1\n",
    "'''\n",
    "\n",
    "formula3 = formula3.replace(\",\", \"+\")\n",
    "y_mat, x_mat = patsy.dmatrices(formula3, data=su)\n",
    "y_mat = np.array(y_mat.ravel()) ## target always an array, when doing dmatrices\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_mat, y_mat, test_size =0.3, random_state =7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1066,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2219, 27) (2219,)\n"
     ]
    }
   ],
   "source": [
    "print X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1067,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.64758900405588105"
      ]
     },
     "execution_count": 1067,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logit function\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "logreg.predict(X_test)\n",
    "logreg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kfold validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1068,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.63063063  0.65315315  0.6463964   0.66441441  0.63431151] 0.645781221402\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import cross_val_score, KFold\n",
    "\n",
    "cv = KFold(len(y_test), 5, shuffle=True, random_state = 7)\n",
    "scores = cross_val_score(logreg, X_test, y_test, cv=cv)\n",
    "print scores, scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Gridsearch regularization parameters for logistic regression\n",
    "\n",
    "Find the best regularization type (Ridge, Lasso) across a set of regularization strengths.\n",
    "\n",
    "[NOTE: C is the inverse of the regularization strength. Lower C values are stronger regularization. Having a C higher than 1 will significantly slow down the search. I'm not particularly interested in values over 1, since this is the default regularization strength in LogisticRegression.]\n",
    "\n",
    "**After you find the best set of parameters, build a Logistic Regression with those parameters and crossvalidate the score.**\n",
    "\n",
    "[NOTE 2: to run Lasso regularization the solver should be `'liblinear'`]\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1074,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Defining Parameters\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "Cs = np.logspace(-5, 1, 200)\n",
    "\n",
    "search_parameters = {\n",
    "    \"penalty\":             ['l1','l2'],   \n",
    "    \"C\":                   Cs,  \n",
    "    # \"dual\":                [True, False], \n",
    "    \"fit_intercept\":       [False, True], \n",
    "    \"class_weight\":        [None, \"balanced\"],  \n",
    "    \"intercept_scaling\":   [2, 1],       \n",
    "    \"solver\":              ['liblinear'],\n",
    "    \"warm_start\":          [False, True]\n",
    "}\n",
    "\n",
    "estimator = GridSearchCV(logreg, search_parameters, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1075,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Cs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1076,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5176, 27) (5176,)\n"
     ]
    }
   ],
   "source": [
    "print X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1077,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 6400 candidates, totalling 19200 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  49 tasks       | elapsed:    0.4s\n",
      "[Parallel(n_jobs=1)]: Done 199 tasks       | elapsed:    1.5s\n",
      "[Parallel(n_jobs=1)]: Done 449 tasks       | elapsed:    3.6s\n",
      "[Parallel(n_jobs=1)]: Done 799 tasks       | elapsed:    6.5s\n",
      "[Parallel(n_jobs=1)]: Done 1249 tasks       | elapsed:   10.2s\n",
      "[Parallel(n_jobs=1)]: Done 1799 tasks       | elapsed:   14.8s\n",
      "[Parallel(n_jobs=1)]: Done 2449 tasks       | elapsed:   20.2s\n",
      "[Parallel(n_jobs=1)]: Done 3199 tasks       | elapsed:   26.6s\n",
      "[Parallel(n_jobs=1)]: Done 4049 tasks       | elapsed:   33.8s\n",
      "[Parallel(n_jobs=1)]: Done 4999 tasks       | elapsed:   42.0s\n",
      "[Parallel(n_jobs=1)]: Done 6049 tasks       | elapsed:   51.4s\n",
      "[Parallel(n_jobs=1)]: Done 7199 tasks       | elapsed:  1.0min\n",
      "[Parallel(n_jobs=1)]: Done 8449 tasks       | elapsed:  1.2min\n",
      "[Parallel(n_jobs=1)]: Done 9799 tasks       | elapsed:  1.5min\n",
      "[Parallel(n_jobs=1)]: Done 11249 tasks       | elapsed:  1.8min\n",
      "[Parallel(n_jobs=1)]: Done 12799 tasks       | elapsed:  2.2min\n",
      "[Parallel(n_jobs=1)]: Done 14449 tasks       | elapsed:  2.8min\n",
      "[Parallel(n_jobs=1)]: Done 16199 tasks       | elapsed:  3.6min\n",
      "[Parallel(n_jobs=1)]: Done 18049 tasks       | elapsed:  4.9min\n",
      "[Parallel(n_jobs=1)]: Done 19200 out of 19200 | elapsed:  6.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'warm_start': [False, True], 'C': array([  1.00000e-05,   1.07189e-05, ...,   9.32930e+00,   1.00000e+01]), 'intercept_scaling': [2, 1], 'solver': ['liblinear'], 'fit_intercept': [False, True], 'penalty': ['l1', 'l2'], 'class_weight': [None, 'balanced']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=1)"
      ]
     },
     "execution_count": 1077,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1078,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.647411128284\n"
     ]
    }
   ],
   "source": [
    "print estimator.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1079,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'warm_start': False, 'C': 0.051114334834401656, 'intercept_scaling': 2, 'fit_intercept': False, 'solver': 'liblinear', 'penalty': 'l2', 'class_weight': 'balanced'}\n"
     ]
    }
   ],
   "source": [
    "print estimator.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1080,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "Non_evergreen       0.66      0.59      0.62      1079\n",
      "    Evergreen       0.65      0.71      0.68      1140\n",
      "\n",
      "  avg / total       0.65      0.65      0.65      2219\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "y_true, y_pred = y_test, estimator.predict(X_test)\n",
    "print classification_report(y_true, y_pred, target_names=[\"Non_evergreen\", \"Evergreen\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validating\n",
    "**After you find the best set of parameters, build a Logistic Regression with those parameters and crossvalidate the score.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1081,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.652\n"
     ]
    }
   ],
   "source": [
    "clf = estimator.best_estimator_\n",
    "clf.fit(X_train, y_train)\n",
    "print ('Test accuracy: %.3f' % clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1082,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 1082,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est_ = estimator.estimator\n",
    "est_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1083,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  1.,  0.,  0.,  0.,  1.,  0.,  1.,  0.,  1.,  1.,\n",
       "        1.,  1.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  1.,  0.,  0.])"
      ]
     },
     "execution_count": 1083,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Reegression\n",
    "lr_new = est_.fit(X_train, y_train).predict(X_test)\n",
    "lr_new[:25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Gridsearch neighbors for kNN\n",
    "\n",
    "Find the best number of neighbors with your predictors to predict the `label` target variable.\n",
    "\n",
    "Start by bulding a kNN model with a set number of neighbors, then use gridsearch to run through a series of neighbors.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1084,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import cross_val_score, KFold\n",
    "\n",
    "\"\"\" Defining the kNN\"\"\"\n",
    "cv = KFold(len(y_test), 5, shuffle=True, random_state = 7)\n",
    "scores = cross_val_score(logreg, X_test, y_test, cv=cv)\n",
    "\n",
    "## Defining Parameters\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "Cs = np.logspace(-5, 1, 200)\n",
    "\n",
    "search_parameters = {\n",
    "    \"penalty\":             ['l1','l2'],   \n",
    "    \"C\":                   Cs,  \n",
    "    # \"dual\":                [True, False], \n",
    "    \"fit_intercept\":       [False, True], \n",
    "    \"class_weight\":        [None, \"balanced\"], \n",
    "    \"intercept_scaling\":   [2, 1],        \n",
    "    \"solver\":              ['liblinear'],\n",
    "    \"warm_start\":          [False, True]\n",
    "}\n",
    "\n",
    "estimator = GridSearchCV(logreg, search_parameters, cv=cv, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1085,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6400 candidates, totalling 32000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  49 tasks       | elapsed:    0.3s\n",
      "[Parallel(n_jobs=1)]: Done 199 tasks       | elapsed:    1.1s\n",
      "[Parallel(n_jobs=1)]: Done 449 tasks       | elapsed:    2.7s\n",
      "[Parallel(n_jobs=1)]: Done 799 tasks       | elapsed:    4.8s\n",
      "[Parallel(n_jobs=1)]: Done 1249 tasks       | elapsed:    7.5s\n",
      "[Parallel(n_jobs=1)]: Done 1799 tasks       | elapsed:   10.9s\n",
      "[Parallel(n_jobs=1)]: Done 2449 tasks       | elapsed:   15.0s\n",
      "[Parallel(n_jobs=1)]: Done 3199 tasks       | elapsed:   19.5s\n",
      "[Parallel(n_jobs=1)]: Done 4049 tasks       | elapsed:   24.5s\n",
      "[Parallel(n_jobs=1)]: Done 4999 tasks       | elapsed:   30.4s\n",
      "[Parallel(n_jobs=1)]: Done 6049 tasks       | elapsed:   36.7s\n",
      "[Parallel(n_jobs=1)]: Done 7199 tasks       | elapsed:   43.9s\n",
      "[Parallel(n_jobs=1)]: Done 8449 tasks       | elapsed:   51.6s\n",
      "[Parallel(n_jobs=1)]: Done 9799 tasks       | elapsed:  1.0min\n",
      "[Parallel(n_jobs=1)]: Done 11249 tasks       | elapsed:  1.2min\n",
      "[Parallel(n_jobs=1)]: Done 12799 tasks       | elapsed:  1.3min\n",
      "[Parallel(n_jobs=1)]: Done 14449 tasks       | elapsed:  1.5min\n",
      "[Parallel(n_jobs=1)]: Done 16199 tasks       | elapsed:  1.7min\n",
      "[Parallel(n_jobs=1)]: Done 18049 tasks       | elapsed:  2.0min\n",
      "[Parallel(n_jobs=1)]: Done 19999 tasks       | elapsed:  2.2min\n",
      "[Parallel(n_jobs=1)]: Done 22049 tasks       | elapsed:  2.6min\n",
      "[Parallel(n_jobs=1)]: Done 24199 tasks       | elapsed:  3.1min\n",
      "[Parallel(n_jobs=1)]: Done 26449 tasks       | elapsed:  3.7min\n",
      "[Parallel(n_jobs=1)]: Done 28799 tasks       | elapsed:  4.4min\n",
      "[Parallel(n_jobs=1)]: Done 31249 tasks       | elapsed:  5.4min\n",
      "[Parallel(n_jobs=1)]: Done 32000 out of 32000 | elapsed:  5.8min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=sklearn.cross_validation.KFold(n=2219, n_folds=5, shuffle=True, random_state=7),\n",
       "       error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'warm_start': [False, True], 'C': array([  1.00000e-05,   1.07189e-05, ...,   9.32930e+00,   1.00000e+01]), 'intercept_scaling': [2, 1], 'solver': ['liblinear'], 'fit_intercept': [False, True], 'penalty': ['l1', 'l2'], 'class_weight': [None, 'balanced']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=1)"
      ]
     },
     "execution_count": 1085,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1086,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.645786390266\n"
     ]
    }
   ],
   "source": [
    "print estimator.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1087,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'warm_start': False, 'C': 0.062949889902218878, 'intercept_scaling': 2, 'fit_intercept': False, 'solver': 'liblinear', 'penalty': 'l2', 'class_weight': 'balanced'}\n"
     ]
    }
   ],
   "source": [
    "print estimator.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1088,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "Non_evergreen       0.66      0.59      0.62      1079\n",
      "    Evergreen       0.65      0.71      0.68      1140\n",
      "\n",
      "  avg / total       0.65      0.65      0.65      2219\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_true, y_pred = y_test, estimator.predict(X_test)\n",
    "print classification_report(y_true, y_pred, target_names=[\"Non_evergreen\", \"Evergreen\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Choose a new target from alchemy_category to predict with logistic regression\n",
    "\n",
    "**Ideally your category choice will have a small fraction of the total rows, but not TOO small!**\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1 Chose your target category, create the Y vector, and check the fraction of instances\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1089,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\"\"\" Recreation\"\"\"\n",
    "def step_function(x):\n",
    "    return 1 if x == \"recreation\" else 0\n",
    "\n",
    "## Copy of alchemy cat\n",
    "su['recreation'] = [x for x in su.alchemy_category.values]\n",
    "\n",
    "## Making it binary\n",
    "su['recreation'] = su.recreation.apply(step_function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1090,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    6166\n",
      "1    1229\n",
      "Name: recreation, dtype: int64\n",
      "0.166193373901\n",
      "7395\n",
      "0    0.833807\n",
      "1    0.166193\n",
      "Name: recreation, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "## Fraction of instances\n",
    "print su.recreation.value_counts()\n",
    "print su.recreation.mean()\n",
    "print len(su.recreation)\n",
    "print su.recreation.value_counts()/len(su.recreation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2 Use patsy to create an X matrix of the numeric predictors and all two-way interactions between them\n",
    "\n",
    "Ex:\n",
    "\n",
    "```python\n",
    "import patsy\n",
    "\n",
    "formula_interactions = '~ (var1 + var2 + var3)**2 -1'\n",
    "X_interactions = patsy.dmatrix(formula_interactions, data=data\n",
    "```\n",
    "\n",
    "Get the column names from the `design_info` property of the patsy X matrix.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # hasDomainLink,\n",
    "# # html_ratio,\n",
    "# # image_ratio,\n",
    "# # lengthyLinkDomain,\n",
    "# # news_front_page,\n",
    "# (avglinksize + numberOfLinks)**2,\n",
    "# (image_ratio + html_ratio+spelling_errors_ratio)**2\n",
    "# embed_ratio,\n",
    "# frameTagRatio,\n",
    "# numberOfLinks,\n",
    "# numwords_in_url,\n",
    "# parametrizedLinkRatio,\n",
    "# spelling_errors_ratio,\n",
    "# C(alchemy_category),\n",
    "# C(is_news),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1091,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alchemy_category_score',\n",
       " 'embed_ratio',\n",
       " 'frameTagRatio',\n",
       " 'numberOfLinks',\n",
       " 'avglinksize',\n",
       " 'avglinksize:numberOfLinks',\n",
       " 'image_ratio',\n",
       " 'html_ratio',\n",
       " 'spelling_errors_ratio',\n",
       " 'image_ratio:html_ratio',\n",
       " 'image_ratio:spelling_errors_ratio',\n",
       " 'html_ratio:spelling_errors_ratio']"
      ]
     },
     "execution_count": 1091,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula4 = '''\n",
    "recreation ~ \n",
    "alchemy_category_score,\n",
    "embed_ratio,\n",
    "frameTagRatio,\n",
    "numberOfLinks,\n",
    "(avglinksize + numberOfLinks)**2,\n",
    "(image_ratio + html_ratio + spelling_errors_ratio)**2\n",
    "-1\n",
    "'''\n",
    "\n",
    "formula4 = formula4.replace(\",\", \"+\")\n",
    "y_mat, x_mat = patsy.dmatrices(formula4, data=su)\n",
    "x_mat.design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1092,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_mat = np.array(y_mat.ravel()) ## target always an array, when doing dmatrices\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_mat, y_mat, test_size =0.3, random_state =7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1093,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2219, 12) (2219,)\n"
     ]
    }
   ],
   "source": [
    "print X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1094,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82694907616043267"
      ]
     },
     "execution_count": 1094,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logit function\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "# logreg.predict(X_test)\n",
    "logreg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.3 Normalize the predictor matrix columns\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 677,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Data is already standarized'"
      ]
     },
     "execution_count": 677,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## ASK??\n",
    "''' Data is already standarized'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.4 Gridsearch a logistic regression to predict accuracy on your new target from the interaction predictors\n",
    "\n",
    "Include Ridge and Lasso.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1095,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Defining Parameters\n",
    "\n",
    "Cs = np.logspace(-5, 1, 200)\n",
    "\n",
    "search_parameters = {\n",
    "    \"penalty\":             ['l1','l2'],  \n",
    "    \"C\":                   Cs, \n",
    "    # \"dual\":                [True, False], \n",
    "    \"class_weight\":        [None, \"balanced\"], \n",
    "    \"intercept_scaling\":   [2, 1],       \n",
    "    \"solver\":              ['liblinear'],\n",
    "    \"warm_start\":          [False, True]\n",
    "}\n",
    "\n",
    "estimator = GridSearchCV(logreg, search_parameters, scoring ='accuracy', verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1096,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 3200 candidates, totalling 9600 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  49 tasks       | elapsed:    0.4s\n",
      "[Parallel(n_jobs=1)]: Done 199 tasks       | elapsed:    1.5s\n",
      "[Parallel(n_jobs=1)]: Done 449 tasks       | elapsed:    3.3s\n",
      "[Parallel(n_jobs=1)]: Done 799 tasks       | elapsed:    6.0s\n",
      "[Parallel(n_jobs=1)]: Done 1249 tasks       | elapsed:    9.5s\n",
      "[Parallel(n_jobs=1)]: Done 1799 tasks       | elapsed:   13.8s\n",
      "[Parallel(n_jobs=1)]: Done 2449 tasks       | elapsed:   19.0s\n",
      "[Parallel(n_jobs=1)]: Done 3199 tasks       | elapsed:   25.4s\n",
      "[Parallel(n_jobs=1)]: Done 4049 tasks       | elapsed:   34.3s\n",
      "[Parallel(n_jobs=1)]: Done 4999 tasks       | elapsed:   44.0s\n",
      "[Parallel(n_jobs=1)]: Done 6049 tasks       | elapsed:   56.2s\n",
      "[Parallel(n_jobs=1)]: Done 7199 tasks       | elapsed:  1.2min\n",
      "[Parallel(n_jobs=1)]: Done 8449 tasks       | elapsed:  1.7min\n",
      "[Parallel(n_jobs=1)]: Done 9600 out of 9600 | elapsed:  2.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'warm_start': [False, True], 'C': array([  1.00000e-05,   1.07189e-05, ...,   9.32930e+00,   1.00000e+01]), 'intercept_scaling': [2, 1], 'solver': ['liblinear'], 'penalty': ['l1', 'l2'], 'class_weight': [None, 'balanced']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 1096,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1097,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.836553323029\n"
     ]
    }
   ],
   "source": [
    "print estimator.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1098,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'warm_start': False, 'C': 0.051114334834401656, 'intercept_scaling': 2, 'solver': 'liblinear', 'penalty': 'l1', 'class_weight': None}\n"
     ]
    }
   ],
   "source": [
    "print estimator.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "No Recreation Cat       0.83      1.00      0.91      1837\n",
      "   Recreation Cat       0.00      0.00      0.00       382\n",
      "\n",
      "      avg / total       0.69      0.83      0.75      2219\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_true, y_pred = y_test, estimator.predict(X_test)\n",
    "print classification_report(y_true, y_pred, target_names=[\"No Recreation Cat\", \"Recreation Cat\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.5 Build a logistic regression with the optimal parameters, and look at the coefficients\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:   1\n"
     ]
    }
   ],
   "source": [
    "## WHY??\n",
    "clf = estimator.best_estimator_\n",
    "clf.fit(X_train, y_train)\n",
    "print ('Test accuracy: %3.f' % clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## SEE refer to week4-4.1-classification-report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1108,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 1108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est_ = estimator.estimator\n",
    "est_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 1109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_new = est_.fit(X_train, y_train).predict(X_test)\n",
    "lr_new[:25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.6 Gridsearch parameters for a logistic regression with the same target and predictors, but score based on precision rather than accuracy\n",
    "\n",
    "Look at the documentation.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Defining Parameters\n",
    "\n",
    "Cs = np.logspace(-5, 1, 200)\n",
    "\n",
    "search_parameters = {\n",
    "    \"penalty\":             ['l1','l2'],  \n",
    "    \"C\":                   Cs, \n",
    "    # \"dual\":                [True, False], \n",
    "    \"class_weight\":        [None, \"balanced\"], \n",
    "    \"intercept_scaling\":   [2, 1],       \n",
    "    \"solver\":              ['liblinear'],\n",
    "    \"warm_start\":          [False, True]\n",
    "}\n",
    "\n",
    "estimator = GridSearchCV(logreg, search_parameters, scoring ='precision',verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 3200 candidates, totalling 9600 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  49 tasks       | elapsed:    0.4s\n",
      "[Parallel(n_jobs=1)]: Done 199 tasks       | elapsed:    1.6s\n",
      "[Parallel(n_jobs=1)]: Done 449 tasks       | elapsed:    3.7s\n",
      "[Parallel(n_jobs=1)]: Done 799 tasks       | elapsed:    6.7s\n",
      "[Parallel(n_jobs=1)]: Done 1249 tasks       | elapsed:   10.8s\n",
      "[Parallel(n_jobs=1)]: Done 1799 tasks       | elapsed:   15.7s\n",
      "[Parallel(n_jobs=1)]: Done 2449 tasks       | elapsed:   21.6s\n",
      "[Parallel(n_jobs=1)]: Done 3199 tasks       | elapsed:   29.7s\n",
      "[Parallel(n_jobs=1)]: Done 4049 tasks       | elapsed:   38.9s\n",
      "[Parallel(n_jobs=1)]: Done 4999 tasks       | elapsed:   49.8s\n",
      "[Parallel(n_jobs=1)]: Done 6049 tasks       | elapsed:  1.1min\n",
      "[Parallel(n_jobs=1)]: Done 7199 tasks       | elapsed:  1.4min\n",
      "[Parallel(n_jobs=1)]: Done 8449 tasks       | elapsed:  1.8min\n",
      "[Parallel(n_jobs=1)]: Done 9600 out of 9600 | elapsed:  2.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'warm_start': [False, True], 'C': array([  1.00000e-05,   1.07189e-05, ...,   9.32930e+00,   1.00000e+01]), 'intercept_scaling': [2, 1], 'solver': ['liblinear'], 'penalty': ['l1', 'l2'], 'class_weight': [None, 'balanced']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='precision', verbose=1)"
      ]
     },
     "execution_count": 1112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.333462132921\n"
     ]
    }
   ],
   "source": [
    "print estimator.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'warm_start': False, 'C': 0.051114334834401656, 'intercept_scaling': 2, 'solver': 'liblinear', 'penalty': 'l1', 'class_weight': None}\n"
     ]
    }
   ],
   "source": [
    "print estimator.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "No Recreation Cat       0.83      1.00      0.91      1837\n",
      "   Recreation Cat       0.00      0.00      0.00       382\n",
      "\n",
      "      avg / total       0.69      0.83      0.75      2219\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_true, y_pred = y_test, estimator.predict(X_test)\n",
    "print classification_report(y_true, y_pred, target_names=[\"No Recreation Cat\", \"Recreation Cat\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [BONUS] 8. Build models predicting from words\n",
    "\n",
    "This is a bit of the NLP we covered in the pipeline lecture!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1 Choose 'body' or 'title' from the boilerplate to be the basis of your word predictors\n",
    "\n",
    "You will need to parse the json from the boilerplate field.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "from sklearn.pipeline import Pipeline\n",
    "su_json = pd.read_csv('../dataset/evergreen.tsv', delimiter='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Colums \n",
    "su_json['title'] = su_json.boilerplate.map(lambda x:json.loads(x).get('title',''))\n",
    "su_json['body']= su_json.boilerplate.map(lambda x:json.loads(x).get('body',''))\n",
    "\n",
    "## na columns from title and body to blank strings\n",
    "titles=su_json['title'].fillna('')\n",
    "body=su_json['body'].fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7395\n"
     ]
    }
   ],
   "source": [
    "print len(titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1119,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    IBM Sees Holographic Calls Air Breathing Batte...\n",
       "1    The Fully Electronic Futuristic Starting Gun T...\n",
       "2    Fruits that Fight the Flu fruits that fight th...\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 1119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7395\n"
     ]
    }
   ],
   "source": [
    "print len(body)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1121,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A sign stands outside the International Busine...\n",
       "1    And that can be carried on a plane without the...\n",
       "2    Apples The most popular source of antioxidants...\n",
       "Name: body, dtype: object"
      ]
     },
     "execution_count": 1121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 1122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# target var\n",
    "Y = su_json['label']\n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7395\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    0.51332\n",
       "0    0.48668\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 1123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of classes in each type for target var\n",
    "print len(Y)\n",
    "Y.value_counts()/len(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2 Use CountVectorizer to create your predictor matrix from the string column\n",
    "\n",
    "It is up to you what range of ngrams and features, and whether or not you want the columns binary or counts.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "## Constructing the vectorizer\n",
    "title_vectorizer = CountVectorizer(max_features = 1000,\n",
    "                                   ngram_range=(1, 2),\n",
    "                                   stop_words='english',\n",
    "                                   binary=True)\n",
    "\n",
    "body_vectorizer = CountVectorizer(max_features = 1000,\n",
    "                                  ngram_range=(1, 2),\n",
    "                                  stop_words='english',\n",
    "                                  binary=True)\n",
    "\n",
    "# Vocabulary of the titles using fit\n",
    "title_vectorizer.fit(titles)\n",
    "\n",
    "# Vocabulary of the body using fit\n",
    "body_vectorizer.fit(body)\n",
    "\n",
    "# Sample title and body word matrix using transform\n",
    "title_X = title_vectorizer.transform(titles)\n",
    "body_X = body_vectorizer.transform(body)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.3 Gridsearch a logistic regression predicting accuracy of your chosen target category from word predictor matrix\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV scores: [ 0.75743243  0.75997295  0.75794456  0.74983097  0.76589986]\n",
      "Average CVScore: 0.758 +/- 0.005\n"
     ]
    }
   ],
   "source": [
    "title_model = LogisticRegression()\n",
    "title_scores = cross_val_score(title_model, title_X, Y, cv=5)\n",
    "## where Y is label evergreen or no evergreen\n",
    "## where title_X, are the titles\n",
    "print('CV scores: {}'.format(title_scores))\n",
    "\n",
    "print('Average CVScore: {:0.3f} +/- {:0.3f}'.format(title_scores.mean(), title_scores.std()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(title_X, Y, test_size =0.3, random_state =7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5176, 1000) (5176,)\n",
      "(2219, 1000) (2219,)\n"
     ]
    }
   ],
   "source": [
    "print X_train.shape, y_train.shape \n",
    "'\\n'\n",
    "print X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Gridsearch with cv = 5\n",
    "## Defining Parameters\n",
    "\n",
    "Cs = np.logspace(-5, 1, 200)\n",
    "\n",
    "search_parameters = {\n",
    "    \"penalty\":             ['l1','l2'],  \n",
    "    \"C\":                   Cs,  \n",
    "    \"class_weight\":        [None, \"balanced\"],     \n",
    "    \"solver\":              ['liblinear']\n",
    "}\n",
    "\n",
    "estimator = GridSearchCV(logreg, search_parameters, cv=5, scoring ='accuracy', verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 800 candidates, totalling 4000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  49 tasks       | elapsed:    0.5s\n",
      "[Parallel(n_jobs=1)]: Done 199 tasks       | elapsed:    2.2s\n",
      "[Parallel(n_jobs=1)]: Done 449 tasks       | elapsed:    4.8s\n",
      "[Parallel(n_jobs=1)]: Done 799 tasks       | elapsed:    8.4s\n",
      "[Parallel(n_jobs=1)]: Done 1249 tasks       | elapsed:   13.3s\n",
      "[Parallel(n_jobs=1)]: Done 1799 tasks       | elapsed:   19.4s\n",
      "[Parallel(n_jobs=1)]: Done 2449 tasks       | elapsed:   27.3s\n",
      "[Parallel(n_jobs=1)]: Done 3199 tasks       | elapsed:   38.2s\n",
      "[Parallel(n_jobs=1)]: Done 4000 out of 4000 | elapsed:   56.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': array([  1.00000e-05,   1.07189e-05, ...,   9.32930e+00,   1.00000e+01]), 'solver': ['liblinear'], 'class_weight': [None, 'balanced']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 1129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.76313755796\n"
     ]
    }
   ],
   "source": [
    "print estimator.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'penalty': 'l2', 'C': 5.2919787359584364e-05, 'solver': 'liblinear', 'class_weight': 'balanced'}\n"
     ]
    }
   ],
   "source": [
    "print estimator.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "No Evergreen       0.73      0.80      0.76      1079\n",
      "   Evergreen       0.79      0.72      0.75      1140\n",
      "\n",
      " avg / total       0.76      0.76      0.76      2219\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_true, y_pred = y_test, estimator.predict(X_test)\n",
    "print classification_report(y_true, y_pred, target_names=[\"No Evergreen\", \"Evergreen\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.4 Do the same as above, but score the gridsearch based on precision rather than accuracy\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Gridsearch with cv = 5\n",
    "## Defining Parameters\n",
    "\n",
    "Cs = np.logspace(-5, 1, 200)\n",
    "\n",
    "search_parameters = {\n",
    "    \"penalty\":             ['l1','l2'],  \n",
    "    \"C\":                   Cs,  \n",
    "    \"class_weight\":        [None, \"balanced\"],     \n",
    "    \"solver\":              ['liblinear']\n",
    "}\n",
    "\n",
    "\n",
    "estimator = GridSearchCV(logreg, search_parameters, cv=5, scoring='precision', verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 800 candidates, totalling 4000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  49 tasks       | elapsed:    0.5s\n",
      "[Parallel(n_jobs=1)]: Done 199 tasks       | elapsed:    2.1s\n",
      "[Parallel(n_jobs=1)]: Done 449 tasks       | elapsed:    4.8s\n",
      "[Parallel(n_jobs=1)]: Done 799 tasks       | elapsed:    8.8s\n",
      "[Parallel(n_jobs=1)]: Done 1249 tasks       | elapsed:   14.8s\n",
      "[Parallel(n_jobs=1)]: Done 1799 tasks       | elapsed:   22.7s\n",
      "[Parallel(n_jobs=1)]: Done 2449 tasks       | elapsed:   32.0s\n",
      "[Parallel(n_jobs=1)]: Done 3199 tasks       | elapsed:   44.4s\n",
      "[Parallel(n_jobs=1)]: Done 4000 out of 4000 | elapsed:  1.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': array([  1.00000e-05,   1.07189e-05, ...,   9.32930e+00,   1.00000e+01]), 'solver': ['liblinear'], 'class_weight': [None, 'balanced']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='precision', verbose=1)"
      ]
     },
     "execution_count": 1134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 1136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est_ = estimator.estimator\n",
    "est_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.945112656505\n"
     ]
    }
   ],
   "source": [
    "print estimator.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'penalty': 'l1', 'C': 0.0073168071434271918, 'solver': 'liblinear', 'class_weight': None}\n"
     ]
    }
   ],
   "source": [
    "print estimator.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "No Evergreen       0.52      0.99      0.69      1079\n",
      "   Evergreen       0.95      0.15      0.26      1140\n",
      "\n",
      " avg / total       0.74      0.56      0.47      2219\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_true, y_pred = y_test, estimator.predict(X_test)\n",
    "print classification_report(y_true, y_pred, target_names=[\"No Evergreen\", \"Evergreen\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.5 Build a logistic regression with optimal precision categories\n",
    "\n",
    "Print out the top 20 or 25 word features as ranked by their coefficients.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test precision:   1\n"
     ]
    }
   ],
   "source": [
    "## WHY???\n",
    "new_estimator = estimator.best_estimator_\n",
    "#new_estimator.fit(X_train, y_train)\n",
    "print ('Test precision: %3.f' % new_estimator.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coefs</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10 best</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10 things</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10 ways</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>101 cookbooks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coefs       features\n",
       "0     0.0            000\n",
       "1     0.0             10\n",
       "2     0.0        10 best\n",
       "3     0.0      10 things\n",
       "4     0.0        10 ways\n",
       "5     0.0            100\n",
       "6     0.0            101\n",
       "7     0.0  101 cookbooks\n",
       "8     0.0             11\n",
       "9     0.0             12\n",
       "10    0.0             13\n",
       "11    0.0             14\n",
       "12    0.0             15\n",
       "13    0.0             16\n",
       "14    0.0             17"
      ]
     },
     "execution_count": 1140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranks = pd.DataFrame({'coefs':np.abs(new_estimator.coef_[0]),\n",
    "                      'features':title_vectorizer.get_feature_names()})\n",
    "ranks.sort_values(['coefs'], ascending=False)\n",
    "ranks[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       0, 1])"
      ]
     },
     "execution_count": 1141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_new = est_.fit(X_train, y_train).predict(X_test)\n",
    "lr_new[:25]"
   ]
  }
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