sg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show Gril Industry Data Insights\n",
    "This is a for-fun project by Jack Ye and Xingfan Xia\n",
    "It should be more useful if we had more data lol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.164281Z",
     "start_time": "2017-08-18T23:27:35.137838Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"SG\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "\tpath = os.path.join(PROJECT_ROOT_DIR, \"images\", fig_id + \".png\")\n",
    "\tdir_path = os.path.join(PROJECT_ROOT_DIR, \"images\")\n",
    "\tif not os.path.isdir(dir_path):\n",
    "\t\tos.makedirs(dir_path)\n",
    "\tprint(\"Saving figure\", fig_id)\n",
    "\tif tight_layout:\n",
    "\t\tplt.tight_layout()\n",
    "\tplt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T09:34:56.281266Z",
     "start_time": "2017-08-18T09:34:56.275444Z"
    }
   },
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.269804Z",
     "start_time": "2017-08-18T23:27:35.233790Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "pd.set_option('display.max_columns', None)\n",
    "sg = pd.read_csv('show_girls.tsv', sep='\\t')\n",
    "\n",
    "'''\n",
    "Cleaning Index\n",
    "'''\n",
    "sg['provider_recommend'] = sg['序号'].apply(lambda x: 1 if isinstance(x, str) and '推荐' in x else 0)\n",
    "\n",
    "# only one person for 暂时不找, temporarily ignored\n",
    "# sg['status_available'] = sg['序号'].apply(lambda x: 0 if isinstance(x, str) and '暂时不找' in x else 1)\n",
    "def clean_index(e):\n",
    "    if isinstance(e, str):\n",
    "        if '原' in e:\n",
    "            return int(e.split('原')[0][:-1])\n",
    "        elif '参考资源' in e:\n",
    "            return int(e.split('参考资源')[0][:-1])\n",
    "        elif '暂时不找' in e:\n",
    "            return int(e.split('暂时不找')[0][:-1])\n",
    "        elif '推荐' in e:\n",
    "            return int(e[2:])\n",
    "        else:\n",
    "            return int(e)\n",
    "    else:\n",
    "        return e\n",
    "\n",
    "# we don't really need index for classification, but I just leave it here for further use\n",
    "# sg['index'] = sg['序号'].apply(clean_index)\n",
    "sg = sg.drop('序号', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.293447Z",
     "start_time": "2017-08-18T23:27:35.277019Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Cleaning Age\n",
    "'''\n",
    "sg['年龄'].value_counts()\n",
    "\n",
    "sg['sugar_reject'] = sg['年龄'].apply(lambda x: 1 if x == '不想被包养' else 0)\n",
    "sg['sugar_taken'] = sg['年龄'].apply(lambda x: 1 if x == '已被包' else 0)\n",
    "\n",
    "def clean_age(e):\n",
    "\ttry:\n",
    "\t\treturn int(e)\n",
    "\texcept:\n",
    "\t\treturn np.nan\n",
    "\n",
    "sg['age'] = sg['年龄'].apply(clean_age)\n",
    "sg = sg.drop('年龄', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean Province"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.349175Z",
     "start_time": "2017-08-18T23:27:35.324654Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Cleaning Province\n",
    "'''\n",
    "# should really use the one-hot encoder in sklearn\n",
    "# but here we only consider those high frequency ones, because the event is in Shanghai\n",
    "sg['province_is_shanghai'] = sg['籍贯'].apply(lambda x: 1 if x == '上海' else 0)\n",
    "sg['province_is_anhui'] = sg['籍贯'].apply(lambda x: 1 if x == '安徽' else 0)\n",
    "sg['province_is_jiangsu'] = sg['籍贯'].apply(lambda x: 1 if x == '江苏' or x == '无锡' else 0)\n",
    "sg['province_is_zhejiang'] = sg['籍贯'].apply(lambda x: 1 if x == '浙江' or x == '宁波' else 0)\n",
    "sg['province_is_sichuan'] = sg['籍贯'].apply(lambda x: 1 if x == '四川' else 0)\n",
    "sg['province_is_shandong'] = sg['籍贯'].apply(lambda x: 1 if x == '山东' else 0)\n",
    "sg['province_is_hubei'] = sg['籍贯'].apply(lambda x: 1 if x == '武汉' or x == '湖北' else 0)\n",
    "sg['province_is_guangxi'] = sg['籍贯'].apply(lambda x: 1 if x == '广西' else 0)\n",
    "\n",
    "# some people put mixed in 学历, will add later\n",
    "sg['blood_is_mixed'] = sg['籍贯'].apply(lambda x: 1 if x == '中日混血' else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.406106Z",
     "start_time": "2017-08-18T23:27:35.377564Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Cleaning Education \n",
    "'''\n",
    "sg['blood_is_mixed'] = sg['学历'].apply(lambda x: 1 if isinstance(x, str) and '混血' in x else 0) | sg['blood_is_mixed']\n",
    "sg = sg.drop('籍贯', 1)\n",
    "# print(\"finish cleaning province\")\n",
    "\n",
    "sg['education_is_grad'] = sg['学历'].apply(lambda x: 1 if isinstance(x, str) and '硕士' in x else 0) \n",
    "sg['education_is_undergrad'] = sg['学历'].apply(lambda x: 1 if isinstance(x, str) and \n",
    "\t('本科' in x or '上师大' in x or '上戏' in x or '大学' in x) else 0) \n",
    "sg['education_is_tech_high'] = sg['学历'].apply(lambda x: 1 if isinstance(x, str) and \n",
    "\t('大专' in x or '高职' in x or '专升本' in x) else 0) \n",
    "sg['education_is_tech_mid'] = sg['学历'].apply(lambda x: 1 if isinstance(x, str) and \n",
    "\t('中专' in x or '高中' in x) else 0) \n",
    "sg['education_is_mid'] = sg['学历'].apply(lambda x: 1 if isinstance(x, str) and '初中' in x else 0) \n",
    "\n",
    "# only one person married, temporarily ignored\n",
    "# sg['status_is_married'] = sg['学历'].apply(lambda x: 1 if isinstance(x, str) and '已婚' in x else 0) \n",
    "sg = sg.drop('学历', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.435902Z",
     "start_time": "2017-08-18T23:27:35.422789Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Cleaning Job\n",
    "'''\n",
    "sg['job_is_flight'] = sg['职业'].apply(lambda x: 1 if isinstance(x, str) and '空姐' in x else 0)\n",
    "sg['job_is_student'] = sg['职业'].apply(lambda x: 1 if isinstance(x, str) and '学' in x else 0)\n",
    "sg['job_is_model'] = sg['职业'].apply(lambda x: 1 if isinstance(x, str) and '模特' in x else 0)\n",
    "sg['job_is_working'] = sg['职业'].apply(lambda x: 1 if isinstance(x, str) and\n",
    "\t('工作' in x or '在职' in x or '护士' in x or '白领' in x) else 0)\n",
    "\n",
    "sg = sg.drop('职业', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.536915Z",
     "start_time": "2017-08-18T23:27:35.492934Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Cleaning Price\n",
    "'''\n",
    "# sg['包养价元/每月（参考）'] \n",
    "def clean_price(string, split, bound_idx):\n",
    "\tvalue = string.split(split)[bound_idx].lower()\n",
    "\tif 'w' in value:\n",
    "\t\treturn float(value.split(\"w\")[0])\n",
    "\treturn float(value) / 10000\n",
    "\n",
    "def clean_price_bound(e, bound_idx):\n",
    "\tif not e:\n",
    "\t\treturn np.nan\n",
    "\ttry:\n",
    "\t\treturn float(e) / 10000\n",
    "\texcept:\n",
    "\t\tif \"~\" in e:\n",
    "\t\t\treturn clean_price(e, \"~\", bound_idx)\n",
    "\t\telif \"-\" in e:\n",
    "\t\t\treturn clean_price(e, \"-\", bound_idx)\n",
    "\t\telse:\n",
    "\t\t\treturn float(e[:-1])\n",
    "\n",
    "sg[\"price_dynamic\"] = sg['包养价元/每月（参考）'].apply(lambda x: 1 if isinstance(x, str) and\n",
    "\t(x == '经纪提供资源' or x == '价格由老板报' or '待定' in x) else 0)\n",
    "\n",
    "sg['包养价元/每月（参考）'] = sg['包养价元/每月（参考）'].apply(lambda x: np.nan if isinstance(x, str) and\n",
    "\t(x == '经纪提供资源' or x == '价格由老板报' or '待定' in x) else x)\n",
    "\n",
    "sg[\"price_lower_bound\"] = sg['包养价元/每月（参考）'].apply(lambda x: clean_price_bound(x, 0))\n",
    "sg[\"price_upper_bound\"] = sg['包养价元/每月（参考）'].apply(lambda x: clean_price_bound(x, 1))\n",
    "sg[\"price_mean\"] = (sg[\"price_lower_bound\"] + sg[\"price_upper_bound\"]) / 2\n",
    "\n",
    "sg = sg.drop('包养价元/每月（参考）', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean Miscell and Save Cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.723788Z",
     "start_time": "2017-08-18T23:27:35.554355Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Cleaning other miscellaneous data\n",
    "'''\n",
    "sg['is_virgin'] = sg['是否为处女'].apply(lambda x: 1 if x == \"处女\" else 0)\n",
    "sg = sg.drop('是否为处女', 1)\n",
    "\n",
    "sg['size_height'] = sg['身高cm'] \n",
    "sg = sg.drop('身高cm', 1)\n",
    "\n",
    "sg['size_weight'] = sg['体重KG'].apply(lambda x: x if x < 70 else x/2)\n",
    "sg = sg.drop('体重KG', 1)\n",
    "\n",
    "\n",
    "# body figure data, i.e. circumfrences of breast, waist, hip\n",
    "def clean_sizes(string, index):\n",
    "\tif not string or pd.isnull(string):\n",
    "\t\treturn np.nan\n",
    "\tstring = string.replace(\" \", \"\").replace(\",\", \"\").replace(\".\", \"\").\\\n",
    "\t\treplace(\"/\", \"\").replace(\"-\", \"\").replace(\"，\", \"\")\n",
    "\tif len(string) > 6:\n",
    "\t\tstring = string[:2] + string[5:]\n",
    "\tsize_string = string[index * 2:index * 2 + 2]\n",
    "\treturn int(size_string)\n",
    "\n",
    "sg['size_breast'] = sg['三围'].apply(lambda x: clean_sizes(x, 0))\n",
    "sg['size_waist'] = sg['三围'].apply(lambda x: clean_sizes(x, 1))\n",
    "sg['size_hip'] = sg['三围'].apply(lambda x: clean_sizes(x, 2))\n",
    "sg = sg.drop('三围', 1)\n",
    "\n",
    "# Cup Data One Hot \n",
    "sg['size_cup_a'] = sg['罩杯'].apply(lambda x: 1 if (not pd.isnull(x)) and 'a' in x.lower() else 0)\n",
    "sg['size_cup_b'] = sg['罩杯'].apply(lambda x: 1 if (not pd.isnull(x)) and 'b' in x.lower() else 0)\n",
    "sg['size_cup_c'] = sg['罩杯'].apply(lambda x: 1 if (not pd.isnull(x)) and 'c' in x.lower() else 0)\n",
    "sg['size_cup_d'] = sg['罩杯'].apply(lambda x: 1 if (not pd.isnull(x)) and 'd' in x.lower() else 0)\n",
    "sg['size_cup_e'] = sg['罩杯'].apply(lambda x: 1 if (not pd.isnull(x)) and 'e' in x.lower() else 0)\n",
    "sg = sg.drop('罩杯', 1)\n",
    "\n",
    "\n",
    "'''\n",
    "Categorizing Sugar Daddy requirements\n",
    "'''\n",
    "sg['sex_no_condom'] = sg['她的对男方的要求'].apply(lambda x: 1 if (not pd.isnull(x)) and '不带套' in x else 0)\n",
    "sg['sex_oral'] = sg['她的对男方的要求'].apply(lambda x: 1 if (not pd.isnull(x)) and '口交' in x\n",
    "\tand '不要口交' not in x and '不口交' not in x and '不接受口交' not in x and '不能口交' not in x else 0)\n",
    "sg['sex_anal'] = sg['她的对男方的要求'].apply(lambda x: 1 if (not pd.isnull(x)) and '肛交' in x\n",
    "\tand '不要肛交' not in x and '不肛交' not in x and '不接受肛交' not in x and '不能肛交' not in x else 0)\n",
    "sg = sg.drop('她的对男方的要求', 1)\n",
    "\n",
    "'''\n",
    "The following features has too few entries, ignore for now\n",
    "'''\n",
    "sg = sg.drop('第一次做爱的时间', 1)\n",
    "sg = sg.drop('最后一次做爱的时间', 1)\n",
    "sg = sg.drop('做爱次数', 1)\n",
    "\n",
    "'''\n",
    "Save Cleaned File\n",
    "'''\n",
    "sg.to_csv(\"cleaned_show_girls.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataExploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.728962Z",
     "start_time": "2017-08-18T23:27:35.725520Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.size'] = 8.0\n",
    "cols = list(sg.axes[1])\n",
    "total_girls = sg.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.735838Z",
     "start_time": "2017-08-18T23:27:35.730621Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['provider_recommend',\n",
       " 'sugar_reject',\n",
       " 'sugar_taken',\n",
       " 'age',\n",
       " 'province_is_shanghai',\n",
       " 'province_is_anhui',\n",
       " 'province_is_jiangsu',\n",
       " 'province_is_zhejiang',\n",
       " 'province_is_sichuan',\n",
       " 'province_is_shandong',\n",
       " 'province_is_hubei',\n",
       " 'province_is_guangxi',\n",
       " 'blood_is_mixed',\n",
       " 'education_is_grad',\n",
       " 'education_is_undergrad',\n",
       " 'education_is_tech_high',\n",
       " 'education_is_tech_mid',\n",
       " 'education_is_mid',\n",
       " 'job_is_flight',\n",
       " 'job_is_student',\n",
       " 'job_is_model',\n",
       " 'job_is_working',\n",
       " 'price_dynamic',\n",
       " 'price_lower_bound',\n",
       " 'price_upper_bound',\n",
       " 'price_mean',\n",
       " 'is_virgin',\n",
       " 'size_height',\n",
       " 'size_weight',\n",
       " 'size_breast',\n",
       " 'size_waist',\n",
       " 'size_hip',\n",
       " 'size_cup_a',\n",
       " 'size_cup_b',\n",
       " 'size_cup_c',\n",
       " 'size_cup_d',\n",
       " 'size_cup_e',\n",
       " 'sex_no_condom',\n",
       " 'sex_oral',\n",
       " 'sex_anal']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(sg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.831738Z",
     "start_time": "2017-08-18T23:27:35.737514Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>provider_recommend</th>\n",
       "      <th>sugar_reject</th>\n",
       "      <th>sugar_taken</th>\n",
       "      <th>age</th>\n",
       "      <th>province_is_shanghai</th>\n",
       "      <th>province_is_anhui</th>\n",
       "      <th>province_is_jiangsu</th>\n",
       "      <th>province_is_zhejiang</th>\n",
       "      <th>province_is_sichuan</th>\n",
       "      <th>province_is_shandong</th>\n",
       "      <th>province_is_hubei</th>\n",
       "      <th>province_is_guangxi</th>\n",
       "      <th>blood_is_mixed</th>\n",
       "      <th>education_is_grad</th>\n",
       "      <th>education_is_undergrad</th>\n",
       "      <th>education_is_tech_high</th>\n",
       "      <th>education_is_tech_mid</th>\n",
       "      <th>education_is_mid</th>\n",
       "      <th>job_is_flight</th>\n",
       "      <th>job_is_student</th>\n",
       "      <th>job_is_model</th>\n",
       "      <th>job_is_working</th>\n",
       "      <th>price_dynamic</th>\n",
       "      <th>price_lower_bound</th>\n",
       "      <th>price_upper_bound</th>\n",
       "      <th>price_mean</th>\n",
       "      <th>is_virgin</th>\n",
       "      <th>size_height</th>\n",
       "      <th>size_weight</th>\n",
       "      <th>size_breast</th>\n",
       "      <th>size_waist</th>\n",
       "      <th>size_hip</th>\n",
       "      <th>size_cup_a</th>\n",
       "      <th>size_cup_b</th>\n",
       "      <th>size_cup_c</th>\n",
       "      <th>size_cup_d</th>\n",
       "      <th>size_cup_e</th>\n",
       "      <th>sex_no_condom</th>\n",
       "      <th>sex_oral</th>\n",
       "      <th>sex_anal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.033835</td>\n",
       "      <td>0.481203</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>21.081818</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.048872</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.022556</td>\n",
       "      <td>0.022556</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.011278</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.112782</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.323308</td>\n",
       "      <td>0.281955</td>\n",
       "      <td>0.180451</td>\n",
       "      <td>0.056391</td>\n",
       "      <td>5.087046</td>\n",
       "      <td>6.905061</td>\n",
       "      <td>5.996053</td>\n",
       "      <td>0.030075</td>\n",
       "      <td>166.626214</td>\n",
       "      <td>48.816489</td>\n",
       "      <td>84.453488</td>\n",
       "      <td>63.906977</td>\n",
       "      <td>87.825581</td>\n",
       "      <td>0.048872</td>\n",
       "      <td>0.330827</td>\n",
       "      <td>0.203008</td>\n",
       "      <td>0.063910</td>\n",
       "      <td>0.003759</td>\n",
       "      <td>0.048872</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.041353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.181144</td>\n",
       "      <td>0.500588</td>\n",
       "      <td>0.307471</td>\n",
       "      <td>2.081512</td>\n",
       "      <td>0.425946</td>\n",
       "      <td>0.216007</td>\n",
       "      <td>0.223718</td>\n",
       "      <td>0.121932</td>\n",
       "      <td>0.121932</td>\n",
       "      <td>0.148764</td>\n",
       "      <td>0.148764</td>\n",
       "      <td>0.121932</td>\n",
       "      <td>0.105797</td>\n",
       "      <td>0.086547</td>\n",
       "      <td>0.454373</td>\n",
       "      <td>0.408451</td>\n",
       "      <td>0.316922</td>\n",
       "      <td>0.086547</td>\n",
       "      <td>0.160374</td>\n",
       "      <td>0.468621</td>\n",
       "      <td>0.450800</td>\n",
       "      <td>0.385288</td>\n",
       "      <td>0.231110</td>\n",
       "      <td>3.266395</td>\n",
       "      <td>4.150314</td>\n",
       "      <td>3.556986</td>\n",
       "      <td>0.171116</td>\n",
       "      <td>4.310067</td>\n",
       "      <td>4.241644</td>\n",
       "      <td>3.493379</td>\n",
       "      <td>6.024710</td>\n",
       "      <td>4.892231</td>\n",
       "      <td>0.216007</td>\n",
       "      <td>0.471398</td>\n",
       "      <td>0.402996</td>\n",
       "      <td>0.245053</td>\n",
       "      <td>0.061314</td>\n",
       "      <td>0.216007</td>\n",
       "      <td>0.452606</td>\n",
       "      <td>0.199482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "      <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",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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": [
       "       provider_recommend  sugar_reject  sugar_taken         age  \\\n",
       "count          266.000000    266.000000   266.000000  110.000000   \n",
       "mean             0.033835      0.481203     0.105263   21.081818   \n",
       "std              0.181144      0.500588     0.307471    2.081512   \n",
       "min              0.000000      0.000000     0.000000   17.000000   \n",
       "25%              0.000000      0.000000     0.000000   20.000000   \n",
       "50%              0.000000      0.000000     0.000000   21.000000   \n",
       "75%              0.000000      1.000000     0.000000   23.000000   \n",
       "max              1.000000      1.000000     1.000000   26.000000   \n",
       "\n",
       "       province_is_shanghai  province_is_anhui  province_is_jiangsu  \\\n",
       "count            266.000000         266.000000           266.000000   \n",
       "mean               0.236842           0.048872             0.052632   \n",
       "std                0.425946           0.216007             0.223718   \n",
       "min                0.000000           0.000000             0.000000   \n",
       "25%                0.000000           0.000000             0.000000   \n",
       "50%                0.000000           0.000000             0.000000   \n",
       "75%                0.000000           0.000000             0.000000   \n",
       "max                1.000000           1.000000             1.000000   \n",
       "\n",
       "       province_is_zhejiang  province_is_sichuan  province_is_shandong  \\\n",
       "count            266.000000           266.000000            266.000000   \n",
       "mean               0.015038             0.015038              0.022556   \n",
       "std                0.121932             0.121932              0.148764   \n",
       "min                0.000000             0.000000              0.000000   \n",
       "25%                0.000000             0.000000              0.000000   \n",
       "50%                0.000000             0.000000              0.000000   \n",
       "75%                0.000000             0.000000              0.000000   \n",
       "max                1.000000             1.000000              1.000000   \n",
       "\n",
       "       province_is_hubei  province_is_guangxi  blood_is_mixed  \\\n",
       "count         266.000000           266.000000      266.000000   \n",
       "mean            0.022556             0.015038        0.011278   \n",
       "std             0.148764             0.121932        0.105797   \n",
       "min             0.000000             0.000000        0.000000   \n",
       "25%             0.000000             0.000000        0.000000   \n",
       "50%             0.000000             0.000000        0.000000   \n",
       "75%             0.000000             0.000000        0.000000   \n",
       "max             1.000000             1.000000        1.000000   \n",
       "\n",
       "       education_is_grad  education_is_undergrad  education_is_tech_high  \\\n",
       "count         266.000000              266.000000              266.000000   \n",
       "mean            0.007519                0.289474                0.210526   \n",
       "std             0.086547                0.454373                0.408451   \n",
       "min             0.000000                0.000000                0.000000   \n",
       "25%             0.000000                0.000000                0.000000   \n",
       "50%             0.000000                0.000000                0.000000   \n",
       "75%             0.000000                1.000000                0.000000   \n",
       "max             1.000000                1.000000                1.000000   \n",
       "\n",
       "       education_is_tech_mid  education_is_mid  job_is_flight  job_is_student  \\\n",
       "count             266.000000        266.000000     266.000000      266.000000   \n",
       "mean                0.112782          0.007519       0.026316        0.323308   \n",
       "std                 0.316922          0.086547       0.160374        0.468621   \n",
       "min                 0.000000          0.000000       0.000000        0.000000   \n",
       "25%                 0.000000          0.000000       0.000000        0.000000   \n",
       "50%                 0.000000          0.000000       0.000000        0.000000   \n",
       "75%                 0.000000          0.000000       0.000000        1.000000   \n",
       "max                 1.000000          1.000000       1.000000        1.000000   \n",
       "\n",
       "       job_is_model  job_is_working  price_dynamic  price_lower_bound  \\\n",
       "count    266.000000      266.000000     266.000000         247.000000   \n",
       "mean       0.281955        0.180451       0.056391           5.087046   \n",
       "std        0.450800        0.385288       0.231110           3.266395   \n",
       "min        0.000000        0.000000       0.000000           0.000250   \n",
       "25%        0.000000        0.000000       0.000000           3.000000   \n",
       "50%        0.000000        0.000000       0.000000           4.000000   \n",
       "75%        1.000000        0.000000       0.000000           8.000000   \n",
       "max        1.000000        1.000000       1.000000          20.000000   \n",
       "\n",
       "       price_upper_bound  price_mean   is_virgin  size_height  size_weight  \\\n",
       "count         247.000000  247.000000  266.000000   206.000000   188.000000   \n",
       "mean            6.905061    5.996053    0.030075   166.626214    48.816489   \n",
       "std             4.150314    3.556986    0.171116     4.310067     4.241644   \n",
       "min             0.350000    0.275000    0.000000   156.000000    40.000000   \n",
       "25%             4.000000    3.500000    0.000000   163.000000    46.000000   \n",
       "50%             6.000000    5.000000    0.000000   167.000000    48.000000   \n",
       "75%            10.000000    9.000000    0.000000   170.000000    52.000000   \n",
       "max            30.000000   20.000000    1.000000   176.000000    60.000000   \n",
       "\n",
       "       size_breast  size_waist   size_hip  size_cup_a  size_cup_b  size_cup_c  \\\n",
       "count    86.000000   86.000000  86.000000  266.000000  266.000000  266.000000   \n",
       "mean     84.453488   63.906977  87.825581    0.048872    0.330827    0.203008   \n",
       "std       3.493379    6.024710   4.892231    0.216007    0.471398    0.402996   \n",
       "min      73.000000   58.000000  60.000000    0.000000    0.000000    0.000000   \n",
       "25%      83.000000   60.000000  86.000000    0.000000    0.000000    0.000000   \n",
       "50%      84.000000   62.000000  88.000000    0.000000    0.000000    0.000000   \n",
       "75%      86.000000   65.000000  90.000000    0.000000    1.000000    0.000000   \n",
       "max      93.000000   95.000000  95.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "       size_cup_d  size_cup_e  sex_no_condom    sex_oral    sex_anal  \n",
       "count  266.000000  266.000000     266.000000  266.000000  266.000000  \n",
       "mean     0.063910    0.003759       0.048872    0.285714    0.041353  \n",
       "std      0.245053    0.061314       0.216007    0.452606    0.199482  \n",
       "min      0.000000    0.000000       0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000       0.000000    0.000000    0.000000  \n",
       "50%      0.000000    0.000000       0.000000    0.000000    0.000000  \n",
       "75%      0.000000    0.000000       0.000000    1.000000    0.000000  \n",
       "max      1.000000    1.000000       1.000000    1.000000    1.000000  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.842088Z",
     "start_time": "2017-08-18T23:27:35.833535Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 266 entries, 0 to 265\n",
      "Data columns (total 40 columns):\n",
      "provider_recommend        266 non-null int64\n",
      "sugar_reject              266 non-null int64\n",
      "sugar_taken               266 non-null int64\n",
      "age                       110 non-null float64\n",
      "province_is_shanghai      266 non-null int64\n",
      "province_is_anhui         266 non-null int64\n",
      "province_is_jiangsu       266 non-null int64\n",
      "province_is_zhejiang      266 non-null int64\n",
      "province_is_sichuan       266 non-null int64\n",
      "province_is_shandong      266 non-null int64\n",
      "province_is_hubei         266 non-null int64\n",
      "province_is_guangxi       266 non-null int64\n",
      "blood_is_mixed            266 non-null int64\n",
      "education_is_grad         266 non-null int64\n",
      "education_is_undergrad    266 non-null int64\n",
      "education_is_tech_high    266 non-null int64\n",
      "education_is_tech_mid     266 non-null int64\n",
      "education_is_mid          266 non-null int64\n",
      "job_is_flight             266 non-null int64\n",
      "job_is_student            266 non-null int64\n",
      "job_is_model              266 non-null int64\n",
      "job_is_working            266 non-null int64\n",
      "price_dynamic             266 non-null int64\n",
      "price_lower_bound         247 non-null float64\n",
      "price_upper_bound         247 non-null float64\n",
      "price_mean                247 non-null float64\n",
      "is_virgin                 266 non-null int64\n",
      "size_height               206 non-null float64\n",
      "size_weight               188 non-null float64\n",
      "size_breast               86 non-null float64\n",
      "size_waist                86 non-null float64\n",
      "size_hip                  86 non-null float64\n",
      "size_cup_a                266 non-null int64\n",
      "size_cup_b                266 non-null int64\n",
      "size_cup_c                266 non-null int64\n",
      "size_cup_d                266 non-null int64\n",
      "size_cup_e                266 non-null int64\n",
      "sex_no_condom             266 non-null int64\n",
      "sex_oral                  266 non-null int64\n",
      "sex_anal                  266 non-null int64\n",
      "dtypes: float64(9), int64(31)\n",
      "memory usage: 83.2 KB\n"
     ]
    }
   ],
   "source": [
    "sg.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.867635Z",
     "start_time": "2017-08-18T23:27:35.843796Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# visualize prop\n",
    "def visualize_property(keyword, most_frequent):\n",
    "\tprop, prop_count, explode = list(), list(), list()\n",
    "\tfor col in cols:\n",
    "\t\tif keyword in col:\n",
    "\t\t\tname = col.split(\"_\")[-1]\n",
    "\t\t\tif most_frequent in name:\n",
    "\t\t\t\texplode.append(0.05)\n",
    "\t\t\telse:\n",
    "\t\t\t\texplode.append(0)\n",
    "\t\t\tprop.append(name)\n",
    "\t\t\tprop_count.append(sg[col].sum())\n",
    "\n",
    "\tprop.append(\"unknown\")\n",
    "\tprop_count.append(total_girls-sum(prop_count))\n",
    "\texplode.append(0)\n",
    "\n",
    "\tfig1, ax1 = plt.subplots()\n",
    "\tax1.pie(prop_count, labels=prop, explode=explode, autopct='%1.1f%%',\n",
    "\t\t\tshadow=False, startangle=90)\n",
    "\tax1.axis('equal')\n",
    "\tsave_fig(keyword)\n",
    "\tplt.show()\n",
    "\tplt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:35.876828Z",
     "start_time": "2017-08-18T23:27:35.869375Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# visualize dist\n",
    "def visualize_dist(lst, name):\n",
    "\n",
    "\tplt.figure()\n",
    "\tplt.hist(lst, bins=len(lst)//5)\n",
    "\tplt.title(\"{}\\n mean {}, median {}, sd {}\".format(name, \n",
    "\t\tround(np.mean(lst), 4), \n",
    "\t\tround(np.median(lst), 4),\n",
    "\t\tround(np.std(lst), 4)))\n",
    "\tsave_fig(name)\n",
    "\tplt.show()\n",
    "\tplt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:36.207206Z",
     "start_time": "2017-08-18T23:27:35.878550Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure province\n"
     ]
    },
    {
     "data": {
      "image/png": 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jmBRMtNSojf6VP3jf3z7Gx4k1rqpiEo8sG/Ho1qrY1AkAxvhrqo6/vtT4D5/Y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\n33e4sLLopaozTVid4np+fkHquGavHlw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ln1NEyZBbABwcR+kVSskkfaE0TruQbKv8C8DH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\nckNBS0REckNBS0REcuP/vIo/RF8hbwwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda8a442b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_property(\"province\", \"shanghai\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:36.576416Z",
     "start_time": "2017-08-18T23:27:36.209482Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure job\n"
     ]
    },
    {
     "data": {
      "image/png": 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h5TF95++tgO0CVFqNBebstG0+yQcf76CDgP1MQ8tjGlre0pZWdhtH8tA6K9nO\nOgg4K2hoeUxDy1va0speS4EPk2xv3RnDmIYetV/M22/hU8X91//nIHQCZb/T0PKYhpa3tKWVnR4D\nLsGJRpM8Nh90EHAW09DymIaWtzS0sstW4Aqc6P1t7VAy5rrifR4bP2v/xXMm6yDgrNTNdgG5Rv+I\neqvedgEqZeYB43cXWKGK0GUizXNvP33uETXdjP1Z31U61NkuINdoaHlrs+0CVJcZ4HfA0TjRBcl2\nCFWE+oUqQs8AYaD71h7S55LLA6M2lDDPy0KVJ2ptF5BrNLS85EQb0XdmfrYamIwTvSb+f7mLUEVo\nEvAxcHri9oYC6TH1ssBBK/rxlgd1Ku9oaHlMQ8t72tryp+eAA3GiryV7MFQRKghVhG4DXgEGJ9sn\nFpCCq34UOPqTfXkjjXUqb2loeUxDy3saWv5SB0zDiZ6OE12XbIdQRWg/4C3g5+zpd0pEnPPzJ745\nVmamulBlhYaWxzS0vKeh5R//AQ7DiYbb2iFUEfo+8AFweEcOfNcZgROeOUpmGWjuWonKMg0tj2mX\nd+9paPlDGPgpTjTpPchQRSgITAfO6ewJHj4xcPz6ns3vXPhK86Gis//7lYaWxzS0vLfJdgFqt9YB\nF+JEn29rh1BF6GjgYWB4V0/20mF5EzaW8P5VTzXvL1DS1eMpz2loeUwvD3pvle0CVJtexe1skTSw\nQhWhQKgidCMwixQEVos5Y/LGO+cFljXD+lQd05a1TY18a8liDv7sU5qMoba5mfIVyylbtpSpK1fQ\n0Nz6auiC+nrOX7aU85Yu5dM6t1F717ovOXvpEmZu3QrAPzZtYl5NjeevpZ2S3udU6aOh5b0VtgtQ\nu2gErgFOxomuTrZDqCI0FHgduJk0zCFZNVTG/uwHgc0xYWWqj+2lYF6A+/YdykFFRQC8uW0bBxZ1\np2LoMEJFRczetq3V/net+5Lf7T2YOwcP5q717t//hfX1PDR0GM9tjtLQ3MzHdbUc1iNj11r09f+X\nH2loeU9DK7MsACbgRH+HEzXJdghVhM4CPgLSOhP78gEyYtqlgbyGAAvTeZ506paXRzCwI9P3LSig\n1ritqy2xZnoHWuf95liMvQsKGFhQwJZYDABBaDSGQhEej27irGBv715Ax+nvs8c0tLy33HYBarv7\ngUNwou8lezBUESoOVYTuBR4HPPnLuT4oe5dPC/Td1o3/eHG+dBtWWMiHtbWctngR8+vqOKR76/ll\nEy8WtrzDq43mAAAWV0lEQVRjmFRSwvVrVnN27z4sqG9gZWMjzpo1fFafcePyY8Aa20XkGg0t7y22\nXYBiE3AOTvQinOi2ZDuEKkLjgfeBizytDNjaQ/qUTwuMyIZpn56JRjmhpITnRozk+JJintvcuvOs\nJHzd8sfo9GCQPwwewvu1NXy3d29mb9vG9QMH8vDGjOvD9EVpdVXMdhG5RkPLeytw76EoO94EDsaJ\nPpbswVBFSEIVoZ8C7wD7e1pZgvpCKc6GaZ8M0DvPvSTYJxBgS3Prv/HBQIA1jY2sbWqkJG/HpcNt\nzTFWNDYypqho+3N2fm4G0EuDFmhoec2JxoBltsvIQTHAAU7AiS5NtkOoIjQI+BfuhLjWx035cdqn\nRmO4aPkyPq2v50crljO2qIiXtmyhbNlSnt+8mVN7BfmyqYnp8U4X0/r15+pVq7hy5Sqm9e+//TgP\nbtzIeb37ADCqsBvnL1vKqb16WXlNu6GdMCwQY5Lee1bp5ASfB6bYLiOHLAXOw4m22WoJVYROBe4D\n9vKsqg644pnYzGM/MSfYrkO1ck9pddXltovINdrSsuMj2wXkkMeAg9oKrFBFqChUEbobd0LcjAws\n0GmfMtQS2wXkIg0tOzS00m8rcBFO9BycaDTZDqGK0Djg38A0TyvrpIdPDBx//+S8OQYabNeiAPjE\ndgEAIjJTRH7YxmMvikiZ1zWlk07jZIeGVnrNA85ta5FGcFcVBu4Aure1TybSaZ8ySsavRm2M+brt\nGlJNW1p2LAAydl4aH+vwqsJeFpcq2TTtk49tKa2ustqhSlw59zc8515wRnCizUDEdhlZptOrCvtR\ntkz75GMdvjQoIheKyHMJ3y8QkX8kfL9cRA4WkaNFZK6IROOfj07YZ6aI3Coib+G+8R250zn2FpGP\nReRnCfv/MP7190XkTRG5Q0Q2ishiEfl6wnNHiMgsEdkiIq+KSFhEHuro60w3DS179BJh6rRnVeHb\n2c2qwn6UDdM++dgHnXjOG8BxIpInIoNxh1VMABCRkbiXe5cBM4C7gH7AncAMEemXcJwLgIuBnrg9\nY4kfY0T8HPcYY37XRg1HAp8C/YHfAveKSMsY70dw7/H2wx0eckEnXmPaaWjZ877tArJAe1cVfht3\nQtys+3nPtmmffKTDoWWMWQRsAQ4GjscdE7hKRMYAE4HZuENhFhhjHjTGNBljHgWqgdMSDvWAMWZ+\n/PGWqwpjcSd0vskY85fdlLHUGPNXY0wMqAD2BgaKyFDchUxvNMY0GGPeBJ7t6Gv0Qtb9EvvILNsF\n+FxHVhU+zKuibGiZ9mljsf+nffKRzrS0wG0JnYAbWm8AM3EDa2L8+8EktJ7ilgJDEr5PNn/pebiD\nnZ/Yw/m3z5VojGm5r14SP++GhG1tncc6DS1bnGgV8IXtMnwqDByOE03aeytUEQqGKkKP4k6ImxM9\n7OoLpfiyqYGDVvbjbdu15IBGOn9PuiW0jot//QatQ2sVMGyn5wyl9ewbyWaEcHDX9npERDqzdM5q\noK+IJK4Bs28njpN2Glp2zbRdgM+sA07DiU7DiSad8ju+qvCHwDmeVpYBYgEpuPJHgQl+mvbJp/5d\nWl3V2Snn3wBOBLobY1bgXhI8Bfc+0gfAC8D+InKuiOSLyNm4l/7aXEk7rhE4CygG/tbRXoXGmKW4\nQ0UcESkUkQm0viSZMTS07JppuwAfsbKqsO+IiHN+/sQ3x8pM26Vksdc7+0RjzGe4A99nx7/fDCwC\n3jLGxIwx64FTgatxhzRcA5xqjNnjCsnGmAbgm8BA4L5OdIc/D7djyHrgFtzZZOo7eIy007kHbXKC\nY4Aq22VkuEbgeuCO3SzSOBR4iDQv0ug3570em3X6u+ZY0TenqXZSaXVVpe0i0k1EHgOqjTE32a4l\nkYaWbU5wFW4PHrWrBcB321qkEbavKvwXPFqk0W9Omdf8zoWvNB8qGTBrfZaoB3p34fJgxhKRw4EN\nuGv+nQw8DUwwxnS200la6DRO9s0Evmu7iAx0P3D5bhZpLMYdy+L5Io1+8tJheRM2FfP+lU/rtE8p\n8m42BlbcIOBJ3PtrK4BLMy2wQEMrEzyPhlaiTUB5W4s0wvZVhR/F4iKNfvJuad54p1g+uenh2MA8\n9w+S6rxO38/KdMaY53AH6mc0vdZt3wx01u4WvlhV2I902qeUydrQ8gsNLdvcZTOy/qbuHvhuVWE/\nWj5ARlyu0z51RS3wru0icp2GVmZ40nYBFi0FJuJEb8aJxpLtEF9V+GNgsqeVZaF1Ou1TV/yrtLpK\nr4pYpqGVGZ4mN1ekzYpVhf1Gp33qtH/seReVbtrlPVM4wTdw5yPLBVuBK3Ci97e1Q3xV4UeBkGdV\n5ZhAzDTecW9s7pD1HL3nvXNeHbBXaXXVVtuF5DptaWWOf9ouwCPzgPF7CKzLgLloYKVVy7RPVfvo\ntE/t8JIGVmbQ0MocjwNNtotIo5xYVdh3ROSmC/InvlUqGly7p5cGM4ReHswkTvAp4EzbZaTBauCC\nthZpBAhVhE4C/kYWLdLoNzrtU5vqgAGl1VVbbBei9Icz0/yf7QLSoL2rCr+MBpZVD58YOP7+yXlz\njI4b3Nm/NLAyh86IkVlewp0+ZR/bhaRAHfDTPSzSuB9uZ4usXqTRT3Tap6T00mAG0ZZWJnHHKbXZ\nQcFHdFVhH3u3NG/8zecGljW7S1Tkuk3AU7aLUDtoaGWee/H3mC1dVTgLfDJMp32KqyitrqrZ827K\nK9oRIxM5wX/hLg3gJ+uAC9tapBG2ryr8MLm8SKPP9I+a1X/8c6ymMMYo27VYMqa0uupT20WoHbSl\nlZnusl1AB+mqwlmqZdqnmkKStpyzXKUGVubR0MpML4Av5oZrxF0O/GSc6OpkO8RXFX4duBkIeFib\nSpGtPaTPJZcHhufgtE//Y7sAtSu9PJipnOAFuOOWMpWuKpxjcmzap1XAsNLqqmwe8O9L2tLKXI/i\nzoCeie4HDmkrsEIVoeJQRehe3Fk+NLCyRI5N+/RXDazMpC2tTOYErwD+ZLuMBLqqsALgx0/HZh5T\nZU6wXUeaNAIjSqurcr3nZEbS0MpkTrAHbmurv+1ScFcVPn83izQKcDVwK7pIY044vzI267Q5WTnt\n019Lq6sutl2ESi7bftiyixOtAe62XIWuKqySemhS4PgHvpp10z414r7xUhlKW1qZzgn2AhZip7W1\nFDivrUUaYfuqwvehizTmrKOqmrNp2qf/K62u+pHtIlTbNLT8wM69rceAS3Ci0WQPhipCRbgtq2me\nVqUy0til5pMbH4kNzIN+tmvpgkZg/9LqqiW2C1Ft09DyAydYAFSBJ7MS6KrCqlOGrjWLbr8vVhQw\nvp2tX1tZPqD3tPzAiTYC13lwJl1VWHXasgEy8vJLA9IQYKHtWjqhEfi17SLUnmlLy0+c4BzgiDQc\n2QB3ANfHA3IXoYpQP9x7V6en4fwqi5TUmI33/G9sVY8GxtmupQPuLa2u+qHtItSeaUvLX36WhmOu\nBibjRK/ZTWCdBHyMBpZqBx9O+1QL/Mp2Eap9tKXlN07wSeAbKTrac8BFONF1yR4MVYQKgFuAn6Jv\ncFQHBWKm8Y7/i80dsiHjp326qbS6SkPLJzS0/MYJ7gN8AvTswlF0VWHlCTGm2XkoNrt0BRNt19KG\nxcDY0uqqOtuFqPbRd89+40RXAL/swhF0VWHlGSOSd9MF+RPfKpWZtmtpw1UaWP6iLS0/coJ5wDt0\nvFNGGLeFlfSXNFQRCgLTgXO6VqBSu8rAaZ9eLq2u+prtIlTHaGj5lRM8EHgPyG/H3rqqsMoIX5/b\n/M73X20+VOxP99UIhHSRR//R0PIzJ3gb8PM97PUq8L3dLNIYwL3ceAO6SKPywISq5vd+8nTzAZan\nfbqjtLoqHb1xVZplSjNddc7N0OZAzvauKjwTd0JcDSzliXdK8w69+dzAsmZYb6mEFWgXd9/Slpbf\nOcFjgDdoHTq6qrDKeJamfTLA5NLqqtc8PKdKIQ2tbOAE/5sdPQrvBy7HiW5LtmuoIlQM3AVc5FF1\nSrWpf9Ss/uOfYzWFMU/m1QS4u7S66gqPzqXSQEMrGzjBfGAGcJ+uKqz8pmeN2XD3/8ZWezDtUzUw\nvrS6qjbN51FppKGVA3RV4a6pWVjD6kdXIyJ0H9GdQecMYsVfV9C4vhHJF/a9dF/ye+7oxLn26bVs\niWwBYOC3BlIytoTNH27my2e/pGRcCQO/NZD6VfVE34sy4LQBtl5WRunWYLbdNT1W1Wdb2sYGNgET\nSqur/DK1lGqDdsTIcrqqcNcV9CtgxDUjGHn9SJo2N1HzWQ2SL4y8biR9ju3Dpnc2tdq/9zG9GXXD\nKIZfNZy1T68FIPp2lJHXjaR+VT0A619dT7+v+nnpqdSqL5Tiy6YGDlrZl7fTdIpbNbCyg4ZW9vsD\nMNl2EX5W0LuAvEL3V0UC4m5sdj/FamLkl7QeKle4l/veQAoE4rtLvmCa3asatctqKdyrkEB37bCZ\nKBaQgqsuDhxVvQ+zUnzoubhzaKosoKGV/a4AVtkuIhvULa+jaUsTPfbvQXNDMwt+sYANr2+g16G9\nku6/9um19D2hLwD9vtaPFX9dQa/De7GhcgM99u/BqopVROckXRg6ZxmRvBsvyD/+7THyRooOuQW4\noLS6qilFx1OWaWhluUhZ5EvcaZlitmvxs6atTax6cBVDLhrC1v9sJb9nPqN/M5oBZw5g3Uu7TpK/\n+b3NxLbG6D3BHVHQfVh3hk4dSuGAQoqGFLFx9kb2Pm/v7fe+VGt//EZg4nNHyCyzvU3bKQY3sHTW\niyyioZUDImWR2XRtkt2cZmKGFX9ZwaBzBlHQuwAMBIrdS3uBkgCxmtbvB+qW17H+tfXsfcHeuxxr\n48yN9DmhD7Ft7nN2fq7a4cGTAsc/8NW8OQYaOnmI/y6trnompUUp6zS0csftwAu2i/Cj6NwotYtr\nWfPYGhb9ZhF5RXnUr65n0W8WsfaptfQ9yb0EuOpB9yrsmsfW0LS5iSV3LGHpn5ZuP862T7fRfWR3\n8gry6H10bxb9ehFFg4usvCa/ePHwvAl/PDMvYmBrB5/6HO5MLyrLaJf3HBKqCPXDvSk9wnYtSnXE\nuKXN8294pHnvPOjbjt0/BY4ora7anO66lPc0tHJMqCI0GngL2Mt2LUp1RDunfdoMHFlaXVXtVV3K\nW3p5MMdEyiILgK/j9qpSyjeWDZCRl18akIZAm5NEt3S80MDKYhpaOShSFnkP+Aadv8GtlBXrgrL3\npdMCfWoKmZ/k4WtKq6ue9bwo5SkNrRwVKYu8BlxA17oUK+W5LT2k7yWXB4ZvLCZxFYM7S6ur7rBW\nlPKMhlYOi5RFHscdfKyUr9QXSvHUqYFQfNqnh4Gf2q5JeUNDK8dFyiJh4L9t16FURzUFpPCqHwXW\nAxeWVldpj7IcoaGliJRFbgT+bLsOpTroNZMn3ymtrmq0XYjyjoaWanEZ8E/bRSjVTm8BZ0TKInW2\nC1He0nFaartQRagb8CJwou1alNqNecBJkbKIDh7OQdrSUttFyiL1wJmQ8qUhlEqVCPA1DazcpaGl\nWon/MTgZeMx2LUrtZBZwQqQsssF2IcoeDS21i3iL67uAjntRmeIRYLIGltJ7Wmq3QhWhacCf0Dc4\nyp5bgRsiZRH9Y6U0tNSehSpCZ+K+0+1uuxaVU5qASyJlkftsF6Iyh4aWapdQRego3DWK+tuuReWE\nKPDtSFnkVduFqMyioaXaLb6syYvAKNu1qKy2DJgSKYv8x3YhKvPofQrVbvFlTSYAc2zXorLWe8BR\nGliqLRpaqkMiZZEvgUmALgGhUu15YGKkLLLadiEqc2loqQ6LlEVqcNfjug7Qed9UKvwRODNSFtlm\nuxCV2fSeluqSUEVoPPAQUGq7FuVLK4GLImWRl20XovxBW1qqSyJlkfeBQ4G7cZc7V6q9HgVCGliq\nI7SlpVImVBE6GXgA2NtyKSqzbQAui5RFdKow1WHa0lIpE3/HHEKXOFFtexH4igaW6ixtaam0CFWE\nyoC7gF62a1EZYRtwdaQsoouNqi7R0FJpE6oIDQceBI61XIqy623ge5GyyELbhSj/08uDKm0iZZEl\nwETgWqDGbjXKggbcYRHHa2CpVNGWlvJEqCK0D3A77pInYrkclX5PA7+IlEWqbReisouGlvJUfOLd\nPwJH2q5FpcUs4NpIWeQd24Wo7KShpTwXqggJcC7wG2Bfy+Wo1Ijgtqxm2C5EZTcNLWVNqCJUBEzF\nveelS5740zLgRuDBSFmk2XYxKvtpaCnrQhWhnsBV8Q/tIu8P64FfA+FIWaTedjEqd2hoqYwRqgj1\nA34OTENXSc5UNbj3JH8bKYtEbRejco+Glso4oYpQf+BCoBwYabkc5doA3AfcqUuHKJs0tFTGinfY\nOBm4DJgCBOxWlJM+Au4BHo6URWptF6OUhpbyhVBFaF/gYuCHwCDL5WS7JuBJ4J5IWWS27WKUSqSh\npXwlVBEqAM4ELgVOtFxOtvkE9xLgQ5GyyBe2i1EqGQ0t5VuhitAY3PteZUBvy+X4VRT4O3B/pCwy\nx3YxSu2JhpbyvVBFqBvuHIf/BXwd2N9uRRlvNfAv3GVCntN7VcpPNLRU1glVhEayI8BORLvPNwCz\ncYPqX5GyyMeW61Gq0zS0VFaLz7pxAjtCbD+rBXlnAfASblDNjJRFtlmuR6mU0NBSOSVUEdoPN8Am\nAQcCw8mOWefXA2/ihtRLkbLIYsv1KJUWGloqp4UqQsXAWOAr8Y9x8c9DbNa1G2twe/l9AlS1fB0p\ni6y1WpVSHtHQUiqJUEWoN61D7CvAAbgT+xak+fQGWMGOcNoeUJGyyMY0n1upjKahpVQHhSpCQdzw\n6hf/3B8IAj3iH8UJn4uAWmBLwsfmnb7fedvWSFkk5t0rUso/NLSUUkr5Rp7tApRSSqn20tBSSinl\nGxpaSimlfENDSymllG9oaCmllPINDS2l4kTkARG5pZ37LhGRr6a7JqVUaxpaSimlfENDSymllG9o\naCnfiV+a+5mIfCwi20TkXhEZKCIvisgWEXlVRPrE9z1dROaLyCYRmSkipQnHOURE3o8/5zHc2SsS\nz3OqiHwYf+7bInKgxy9VKbUTDS3lV98CJuMu+Hga7oKG1wF74f5cXyEi+wOPAj+Jb38BeE5ECkWk\nEHgaeBDoC/wjfkzADTTcpecvwZ2u6c/AsyLSzZNXp5RKSkNL+dXdxpgvjDErcRc4nGOM+cAYUwc8\nBRwCnA3MMMa8YoxpBO7AXRDyaOAo3Ilv/2iMaTTGPAHMTTj+xcCfjTFzjDExY0wFUB9/nlLKknzb\nBSjVSV8kfF2b5PsSYDCwtGWjMaZZRJbjLjsSA1aa1pNvLk34ehhQJiKXJ2wrjB9TKWWJhpbKZquA\nUMs3IiLAvsBK3OU/hoiIJATXUGBh/OvlwK3GmFs9rFcptQd6eVBls8eBKSJykogUAFfjXuJ7G3gH\naMK991UgIt8Ejkh47l+BchE5UlzFIjJFRHp6/SKUUjtoaKmsZYz5FDgfuBtYh9th4zRjTIMxpgH4\nJvB9YAPu/a8nE547D/gRcA+wEfg8vq9SyiJdT0sppZRvaEtLKaWUb2hoKaWU8g0NLaWUUr6hoaWU\nUso3NLSUUkr5hoaWUkop39DQUkop5RsaWkoppXzj/wHsmXJLbhMgNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda8a56160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_property(\"job\", \"student\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:36.936585Z",
     "start_time": "2017-08-18T23:27:36.578775Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure education\n"
     ]
    },
    {
     "data": {
      "image/png": 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SWhty8jf8fdqy869fU9/43DQ6zTpUrqcjLUs0tOzZgYZWT+3B6UaxiF50o2iT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\nAAttF6F6T0MrcfwXCbDeVtW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SZybnO3QMp3dyK8qPxurr6a9IaO0FrsE5UvMasBPnmr9y\n4BlgMbCQjqG1AmdU+N/AhcDTwFMaWqq39PCgUscRKgodAY7gnCvqVt7CPMEZOWUCWZF/225+nCU8\n2m71e4dSc9kKzy7j8StjzD4AEVkC7DfGvBb5/EngMpzQInLfeJx11C43xjQCr4jIooEvWyUCDS2l\nYiBUFDK0h9KRE+5QFO+K4iq6S0Z9F5931aD5iDEmuo/kdlzS71J5i7ZxUkrF2x5gqIgMirpvvK1i\nlLdpaCml4soYsx1YC9wlImkiMhe4ynJZyqP08KBSaiBch3Oeq22a/p9wJqIo1Ss6e1AppZRn6OFB\npZRSnqGhpZRSyjM0tJRSSnmGhpZSSinP0NBSSinlGRpaSimlPENDSymllGdoaCmllPIMDS2llFKe\noaGllFLKMzS0lFJKeYaGllJKKc/Q0FJKKeUZGlpKKaU8Q0NLKaWUZ2hoKaWU8gwNLaWUUp6hoaWU\nUsoz/j8Nqu2aJ6LenQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fedaef3e320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_property(\"education\", \"undergrad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:37.255767Z",
     "start_time": "2017-08-18T23:27:36.938861Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure size_cup\n"
     ]
    },
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D3QHSzCfAmZjhjqUnDL/xZeBh7CuiePqN4Tdyo7Zvogfrqg3csnjyyYt/nZmX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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda8303978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_property(\"size_cup\", \"c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:37.524717Z",
     "start_time": "2017-08-18T23:27:37.258276Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sugar\n"
     ]
    },
    {
     "data": {
      "image/png": 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6hFSWSiv5fgg86DpEwvR7pDVuuOGRL4zkoMl1ADz/dgfN63yePHUk6zp8npnX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\nVFoiIhIbKi0REYkNlZaIiMTG/wPBRwFn8tqeiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda85a85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_property(\"sugar\", \"taken\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:38.009036Z",
     "start_time": "2017-08-18T23:27:37.526914Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure mean monthly price\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda834c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_dist(sg[~pd.isnull(sg['price_mean'])]['price_mean'], \"mean monthly price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:38.528333Z",
     "start_time": "2017-08-18T23:27:38.011508Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure height\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fedab19ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_dist(sg[~pd.isnull(sg['size_height'])]['size_height'], \"height\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:39.055844Z",
     "start_time": "2017-08-18T23:27:38.530854Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure weight\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda82e7278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_dist(sg[~pd.isnull(sg['size_weight'])]['size_weight'], \"weight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:53.037249Z",
     "start_time": "2017-08-18T23:27:39.058142Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure attribute_histogram_plots\n"
     ]
    },
    {
     "data": {
      "image/png": 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IuvaYg3GODYxvscYuvgHm7tht6xwY83B0Leaxi3fA+9yx29456FrMXYsXjLkv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6m+D05ydJK9mP1U332THEx3qu8pwyld0gKYXUmSxsNtriRJ4+E2V+qBeTudB071PZzuVN+d7XFM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda8cadc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg.hist(bins=50, figsize=(20,15))\n",
    "save_fig(\"attribute_histogram_plots\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:53.488587Z",
     "start_time": "2017-08-18T23:27:53.039222Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure correlation_height_price\n"
     ]
    },
    {
     "data": {
      "image/png": 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ctX+OC87YPrAnriycxyzEMKqi3mX/BfgCcAYwSZAC3/5MJhuayHAZhjGYaq0WzZYzXgm+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\n05iVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed926ac860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg.plot(kind=\"scatter\", x=\"size_height\", y=\"price_mean\", alpha=0.1)\n",
    "save_fig(\"correlation_height_price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:54.195056Z",
     "start_time": "2017-08-18T23:27:53.490874Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure correlation_height_price\n"
     ]
    },
    {
     "data": {
      "image/png": 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3UuYT372v4xrX/PwKn/n+A0yMlNmxZZKJkTKf+f4DkWpc9USIydESx245jMnR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\noSUiIrmhQktERHJDhZaIiOTG/wfueSVtggvgYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda9a74710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg.plot(kind=\"scatter\", x=\"size_weight\", y=\"price_mean\", alpha=0.1)\n",
    "save_fig(\"correlation_height_price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:54.650771Z",
     "start_time": "2017-08-18T23:27:54.197584Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure correlation_height_price\n"
     ]
    },
    {
     "data": {
      "image/png": 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jYmszgmlnK7u76M5nGZ0oMdCXZ3SiRHc+y8put7LMzNLSaEvri8CLmxFIu+vv6+LSs9Yx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\n0SEefmqMQrlK37I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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda888c748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg.plot(kind=\"scatter\", x=\"is_virgin\", y=\"price_mean\", alpha=0.1)\n",
    "save_fig(\"correlation_height_price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:55.603527Z",
     "start_time": "2017-08-18T23:27:54.653195Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure housing_prices_scatterplot\n"
     ]
    },
    {
     "data": {
      "image/png": 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iErXKJEzVcH82JakiIiIiUWtKwSsa7s+mJFVEREQkaqqk7kVJqoiIiEjUmlVJ7UxJqoiI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\nLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda95f26a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg.plot(kind=\"scatter\", x=\"size_height\", y=\"size_weight\", alpha=0.6,\n",
    "    s=sg[\"price_mean\"]*2, label=\"mean price\", figsize=(10,7),\n",
    "    c=\"age\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "    sharex=False)\n",
    "plt.legend()\n",
    "save_fig(\"housing_prices_scatterplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict Cup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:55.630624Z",
     "start_time": "2017-08-18T23:27:55.606092Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['provider_recommend', 'sugar_reject', 'sugar_taken', 'age',\n",
       "       'province_is_shanghai', 'province_is_anhui', 'province_is_jiangsu',\n",
       "       'province_is_zhejiang', 'province_is_sichuan', 'province_is_shandong',\n",
       "       'province_is_hubei', 'province_is_guangxi', 'blood_is_mixed',\n",
       "       'education_is_grad', 'education_is_undergrad', 'education_is_tech_high',\n",
       "       'education_is_tech_mid', 'education_is_mid', 'job_is_flight',\n",
       "       'job_is_student', 'job_is_model', 'job_is_working', 'price_dynamic',\n",
       "       'price_lower_bound', 'price_upper_bound', 'price_mean', 'is_virgin',\n",
       "       'size_height', 'size_weight', 'size_breast', 'size_waist', 'size_hip',\n",
       "       'sex_no_condom', 'sex_oral', 'sex_anal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_cup = 1 * sg['size_cup_a'] + 2 * sg['size_cup_b'] + \\\n",
    "          3 * sg['size_cup_c'] + 4 * sg['size_cup_d'] + 4 * sg['size_cup_e']\n",
    "\n",
    "sg = sg[(col_cup != 0) & (col_cup != 5)]\n",
    "\n",
    "col_cup = 1 * sg['size_cup_a'] + 2 * sg['size_cup_b'] + \\\n",
    "          3 * sg['size_cup_c'] + 4 * sg['size_cup_d'] + 4 * sg['size_cup_e']\n",
    "\n",
    "cols = [c for c in sg.columns if 'size_cup' not in c]\n",
    "\n",
    "new_sg = sg[cols]\n",
    "\n",
    "X = new_sg.as_matrix()\n",
    "y = col_cup.as_matrix()\n",
    "new_sg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:55.639316Z",
     "start_time": "2017-08-18T23:27:55.633239Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Imputer for null vals\n",
    "'''\n",
    "from sklearn.preprocessing import Imputer\n",
    "imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
    "X = imp.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SGD_CLF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:55.651858Z",
     "start_time": "2017-08-18T23:27:55.641823Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1,\n",
       "       penalty='l2', power_t=0.5, random_state=42, shuffle=True, verbose=0,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:55.667671Z",
     "start_time": "2017-08-18T23:27:55.653905Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5       ,  0.31578947,  0.10714286])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(sgd_clf, X, y, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:55.685793Z",
     "start_time": "2017-08-18T23:27:55.670144Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.39655172,  0.38596491,  0.41071429])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X.astype(np.float64))\n",
    "cross_val_score(sgd_clf, X_scaled, y, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:55.691919Z",
     "start_time": "2017-08-18T23:27:55.687706Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FOREST_CLF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:55.746865Z",
     "start_time": "2017-08-18T23:27:55.694258Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "forest_clf = RandomForestClassifier(random_state=42)\n",
    "y_probas_forest = cross_val_predict(forest_clf, X, y, cv=3,\n",
    "                                    method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:55.767945Z",
     "start_time": "2017-08-18T23:27:55.748431Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0. ,  0.1,  0.8,  0.1]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf.fit(X, y)\n",
    "forest_clf.predict_proba(X[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:27:55.816904Z",
     "start_time": "2017-08-18T23:27:55.769763Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.39655172,  0.52631579,  0.48214286])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(forest_clf, X_scaled, y, cv=3, scoring=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:28:04.276957Z",
     "start_time": "2017-08-18T23:27:55.818724Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=42,\n",
       "            verbose=0, warm_start=False),\n",
       "          fit_params={}, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fed8c1de128>, 'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fed8c1ded30>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score=True, scoring='neg_mean_squared_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low=1, high=200),\n",
    "        'max_features': randint(low=1, high=20),\n",
    "    }\n",
    "\n",
    "rnd_search = RandomizedSearchCV(forest_clf, param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
    "rnd_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:28:04.285642Z",
     "start_time": "2017-08-18T23:28:04.278765Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.830697586088 {'max_features': 7, 'n_estimators': 180}\n",
      "0.837707816583 {'max_features': 15, 'n_estimators': 107}\n",
      "0.841191024192 {'max_features': 8, 'n_estimators': 189}\n",
      "0.837707816583 {'max_features': 7, 'n_estimators': 122}\n",
      "0.837707816583 {'max_features': 19, 'n_estimators': 75}\n",
      "0.871913939634 {'max_features': 11, 'n_estimators': 88}\n",
      "0.875261030405 {'max_features': 4, 'n_estimators': 104}\n",
      "0.809302638223 {'max_features': 3, 'n_estimators': 150}\n",
      "0.851555164878 {'max_features': 2, 'n_estimators': 88}\n",
      "0.820069887194 {'max_features': 12, 'n_estimators': 158}\n"
     ]
    }
   ],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:28:04.303475Z",
     "start_time": "2017-08-18T23:28:04.287314Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.01151765,  0.02063524,  0.00633237,  0.07527622,  0.02517186,\n",
       "        0.01111315,  0.00801713,  0.00981413,  0.00481789,  0.00584374,\n",
       "        0.00945233,  0.00310811,  0.00506205,  0.00353825,  0.02322831,\n",
       "        0.0237796 ,  0.02034509,  0.00664539,  0.00903376,  0.02591417,\n",
       "        0.01338586,  0.02016013,  0.00079859,  0.07668345,  0.07869659,\n",
       "        0.10070283,  0.00479428,  0.0876086 ,  0.10317707,  0.06882413,\n",
       "        0.04364994,  0.04948948,  0.0096046 ,  0.0249793 ,  0.00879873])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = rnd_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:28:04.309178Z",
     "start_time": "2017-08-18T23:28:04.305223Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['provider_recommend', 'sugar_reject', 'sugar_taken', 'age',\n",
       "       'province_is_shanghai', 'province_is_anhui', 'province_is_jiangsu',\n",
       "       'province_is_zhejiang', 'province_is_sichuan', 'province_is_shandong',\n",
       "       'province_is_hubei', 'province_is_guangxi', 'blood_is_mixed',\n",
       "       'education_is_grad', 'education_is_undergrad', 'education_is_tech_high',\n",
       "       'education_is_tech_mid', 'education_is_mid', 'job_is_flight',\n",
       "       'job_is_student', 'job_is_model', 'job_is_working', 'price_dynamic',\n",
       "       'price_lower_bound', 'price_upper_bound', 'price_mean', 'is_virgin',\n",
       "       'size_height', 'size_weight', 'size_breast', 'size_waist', 'size_hip',\n",
       "       'sex_no_condom', 'sex_oral', 'sex_anal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_sg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:28:04.316136Z",
     "start_time": "2017-08-18T23:28:04.310853Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.1031770667168115, 'size_weight'),\n",
       " (0.10070282974751772, 'price_mean'),\n",
       " (0.087608595028026773, 'size_height'),\n",
       " (0.078696589512283671, 'price_upper_bound'),\n",
       " (0.076683452017937762, 'price_lower_bound'),\n",
       " (0.075276219075180575, 'age'),\n",
       " (0.068824125905845759, 'size_breast'),\n",
       " (0.049489479416290401, 'size_hip'),\n",
       " (0.043649938896556231, 'size_waist'),\n",
       " (0.025914170616883527, 'job_is_student'),\n",
       " (0.025171861630806171, 'province_is_shanghai'),\n",
       " (0.024979297735478197, 'sex_oral'),\n",
       " (0.023779603363377134, 'education_is_tech_high'),\n",
       " (0.02322830825438844, 'education_is_undergrad'),\n",
       " (0.020635236559822422, 'sugar_reject'),\n",
       " (0.020345093608167601, 'education_is_tech_mid'),\n",
       " (0.020160133046075144, 'job_is_working'),\n",
       " (0.013385861871946906, 'job_is_model'),\n",
       " (0.011517649433084632, 'provider_recommend'),\n",
       " (0.011113149483240744, 'province_is_anhui'),\n",
       " (0.0098141272356378293, 'province_is_zhejiang'),\n",
       " (0.0096045988960084608, 'sex_no_condom'),\n",
       " (0.0094523314020827816, 'province_is_hubei'),\n",
       " (0.0090337644442290733, 'job_is_flight'),\n",
       " (0.0087987293310186915, 'sex_anal'),\n",
       " (0.0080171333754302694, 'province_is_jiangsu'),\n",
       " (0.0066453866410598004, 'education_is_mid'),\n",
       " (0.0063323691459386032, 'sugar_taken'),\n",
       " (0.0058437381936610822, 'province_is_shandong'),\n",
       " (0.0050620516942718932, 'blood_is_mixed'),\n",
       " (0.0048178850588424466, 'province_is_sichuan'),\n",
       " (0.0047942771119709538, 'is_virgin'),\n",
       " (0.0035382450219709843, 'education_is_grad'),\n",
       " (0.0031081083675577252, 'province_is_guangxi'),\n",
       " (0.00079859216059807205, 'price_dynamic')]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(feature_importances, new_sg.columns), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LINEAR_REG_CLF_JACK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:28:04.325676Z",
     "start_time": "2017-08-18T23:28:04.317899Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# from sklearn import linear_model, decomposition, datasets\n",
    "# from sklearn.pipeline import Pipeline\n",
    "# from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# logistic = linear_model.LogisticRegression()\n",
    "\n",
    "# pca = decomposition.PCA()\n",
    "# pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])\n",
    "\n",
    "# # Plot the PCA spectrum\n",
    "# pca.fit(X)\n",
    "\n",
    "# plt.figure()\n",
    "# plt.clf()\n",
    "# plt.axes([.2, .2, .7, .7])\n",
    "# plt.plot(pca.explained_variance_, linewidth=2)\n",
    "# plt.axis('tight')\n",
    "# plt.xlabel('n_components')\n",
    "# plt.ylabel('explained_variance_')\n",
    "\n",
    "# # Prediction\n",
    "# n_components = range(3, 37)\n",
    "# Cs = np.logspace(-4, 6, num=30, base=2.0)\n",
    "\n",
    "# # Parameters of pipelines can be set using ‘__’ separated parameter names:\n",
    "# estimator = GridSearchCV(pipe,\n",
    "#                          dict(pca__n_components=n_components,\n",
    "#                               logistic__C=Cs))\n",
    "# estimator.fit(X, y)\n",
    "\n",
    "# plt.axvline(estimator.best_estimator_.named_steps['pca'].n_components,\n",
    "#             linestyle=':', label='n_components chosen')\n",
    "# plt.legend(prop=dict(size=12))\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FOREST_CLF_JACK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:28:09.391487Z",
     "start_time": "2017-08-18T23:28:04.327448Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:439: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "/home/ubuntu/.local/lib/python3.5/site-packages/sklearn/ensemble/forest.py:444: RuntimeWarning: invalid value encountered in true_divide\n",
      "  predictions[k].sum(axis=1)[:, np.newaxis])\n"
     ]
    },
    {
     "data": {
      "image/png": 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QkRoRKQHuwVgn0+RSdxGZIyJDRGRITEwM/kFG/HLW1AaUutXyemBe0zTttDQb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\nK0IIUmNTySzN5FjFMTad3ERDawP3jrkXoMv9Kum56fg6+5rXT9Jz+57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N34A2Uxtj2Urg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\nTVAORaFQKBQ24f8DyGs9abxbyooAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda956c7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "\n",
    "RANDOM_STATE = 123\n",
    "\n",
    "# NOTE: Setting the `warm_start` construction parameter to `True` disables\n",
    "# support for parallelized ensembles but is necessary for tracking the OOB\n",
    "# error trajectory during training.\n",
    "ensemble_clfs = [\n",
    "    (\"RandomForestClassifier, max_features='sqrt'\",\n",
    "        RandomForestClassifier(warm_start=True, oob_score=True,\n",
    "                               max_features=\"sqrt\",\n",
    "                               random_state=RANDOM_STATE)),\n",
    "    (\"RandomForestClassifier, max_features='log2'\",\n",
    "        RandomForestClassifier(warm_start=True, max_features='log2',\n",
    "                               oob_score=True,\n",
    "                               random_state=RANDOM_STATE)),\n",
    "    (\"RandomForestClassifier, max_features=None\",\n",
    "        RandomForestClassifier(warm_start=True, max_features=None,\n",
    "                               oob_score=True,\n",
    "                               random_state=RANDOM_STATE))\n",
    "]\n",
    "\n",
    "# Map a classifier name to a list of (<n_estimators>, <error rate>) pairs.\n",
    "error_rate = OrderedDict((label, []) for label, _ in ensemble_clfs)\n",
    "\n",
    "# Range of `n_estimators` values to explore.\n",
    "min_estimators = 3\n",
    "max_estimators = 175\n",
    "\n",
    "for label, clf in ensemble_clfs:\n",
    "    for i in range(min_estimators, max_estimators + 1):\n",
    "        clf.set_params(n_estimators=i)\n",
    "        clf.fit(X, y)\n",
    "\n",
    "        # Record the OOB error for each `n_estimators=i` setting.\n",
    "        oob_error = 1 - clf.oob_score_\n",
    "        error_rate[label].append((i, oob_error))\n",
    "\n",
    "# Generate the \"OOB error rate\" vs. \"n_estimators\" plot.\n",
    "for label, clf_err in error_rate.items():\n",
    "    xs, ys = zip(*clf_err)\n",
    "    plt.plot(xs, ys, label=label)\n",
    "\n",
    "plt.xlim(min_estimators, max_estimators)\n",
    "plt.xlabel(\"n_estimators\")\n",
    "plt.ylabel(\"OOB error rate\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict Price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:28:09.420980Z",
     "start_time": "2017-08-18T23:28:09.393988Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sg = pd.read_csv('cleaned_show_girls.csv')\n",
    "cols = list(sg.axes[1])\n",
    "\n",
    "sg = sg[~pd.isnull(sg['price_mean'])]\n",
    "\n",
    "col_interest = sg['price_mean']\n",
    "\n",
    "cols = [c for c in cols if \"price\" not in c]\n",
    "\n",
    "new_sg = sg[cols]\n",
    "\n",
    "X = new_sg.as_matrix()\n",
    "y = col_interest.as_matrix()\n",
    "\n",
    "imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
    "X = imp.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:28:46.672663Z",
     "start_time": "2017-08-18T23:28:09.423377Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start random forest to get feature importance\n",
      "best estimator: RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=3,\n",
      "           max_features=0.8, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=1,\n",
      "           oob_score=False, random_state=None, verbose=0, warm_start=False)\n",
      "best score: 0.38183137347533785\n",
      "feature importance: job_is_model - 0.5721515601999725\n",
      "feature importance: size_weight - 0.15035663756066064\n",
      "feature importance: size_height - 0.05641764938209007\n",
      "feature importance: size_breast - 0.03890185096182987\n",
      "feature importance: size_waist - 0.035545705585773996\n",
      "feature importance: sex_oral - 0.03309155189340237\n",
      "feature importance: size_hip - 0.025164467123196803\n",
      "feature importance: sugar_reject - 0.01841599425843962\n",
      "feature importance: size_cup_a - 0.01768633798282575\n",
      "feature importance: education_is_tech_mid - 0.012830865678681937\n",
      "feature importance: is_virgin - 0.00897823996518897\n",
      "feature importance: size_cup_b - 0.006307928604539547\n",
      "feature importance: province_is_shanghai - 0.0040941645360065455\n",
      "feature importance: sugar_taken - 0.0036524110767317606\n",
      "feature importance: education_is_mid - 0.0030895446365004796\n",
      "feature importance: education_is_undergrad - 0.0023047378054671225\n",
      "feature importance: age - 0.002214585545332204\n",
      "feature importance: education_is_tech_high - 0.0018153428783798632\n",
      "feature importance: education_is_grad - 0.0016299411801771802\n",
      "feature importance: size_cup_c - 0.0015205895475817182\n",
      "feature importance: size_cup_d - 0.0012095371858717088\n",
      "feature importance: job_is_working - 0.0010991005874854264\n",
      "feature importance: blood_is_mixed - 0.000700512453152544\n",
      "feature importance: job_is_student - 0.00031327187099108996\n",
      "feature importance: provider_recommend - 0.00020540631397809983\n",
      "feature importance: sex_no_condom - 0.00016900998102954614\n",
      "feature importance: province_is_sichuan - 8.549610935221208e-05\n",
      "feature importance: province_is_jiangsu - 3.1322171539865967e-05\n",
      "feature importance: province_is_shandong - 1.623692382041642e-05\n",
      "feature importance: size_cup_e - 0.0\n",
      "feature importance: sex_anal - 0.0\n",
      "feature importance: province_is_zhejiang - 0.0\n",
      "feature importance: province_is_hubei - 0.0\n",
      "feature importance: province_is_guangxi - 0.0\n",
      "feature importance: province_is_anhui - 0.0\n",
      "feature importance: job_is_flight - 0.0\n"
     ]
    }
   ],
   "source": [
    "###########################################################\n",
    "# random forest\n",
    "###########################################################\n",
    "print(\"start random forest to get feature importance\")\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "clf = RandomForestRegressor(max_features=0.8)\n",
    "\n",
    "# use a full grid over all parameters\n",
    "param_grid = {\"n_estimators\": range(20, 200),\n",
    "              \"max_depth\": [3, 10, None]}\n",
    "\n",
    "# run grid search\n",
    "search = RandomizedSearchCV(clf, param_distributions=param_grid, cv=5, n_iter=50)\n",
    "search.fit(X, y)\n",
    "\n",
    "print(\"best estimator: {}\".format(search.best_estimator_))\n",
    "print(\"best score: {}\".format(search.best_score_))\n",
    "col_importance = list(zip(search.best_estimator_.feature_importances_, cols))\n",
    "col_importance.sort(reverse=True)\n",
    "\n",
    "for score, col in col_importance:\n",
    "  print(\"feature importance: {} - {}\".format(col, score))\n",
    "\n",
    "\n",
    "###########################################################\n",
    "# random forest with important features\n",
    "###########################################################\n",
    "# print(\"start random forest with important features\")\n",
    "\n",
    "\n",
    "# cols = [c for s, c in col_importance][:10]\n",
    "# new_sg = sg[cols]\n",
    "# X = new_sg.as_matrix()\n",
    "# X = imp.fit_transform(X)\n",
    "\n",
    "# clf = RandomForestRegressor(max_features=0.8)\n",
    "\n",
    "# # use a full grid over all parameters\n",
    "# param_grid = {\"n_estimators\": range(20, 200),\n",
    "#               \"max_depth\": [3, 10, None]}\n",
    "\n",
    "# # run grid search\n",
    "# search = RandomizedSearchCV(clf, param_distributions=param_grid, cv=5, n_iter=50)\n",
    "# search.fit(X, y)\n",
    "\n",
    "# print(\"best estimator: {}\".format(search.best_estimator_))\n",
    "# print(\"best score: {}\".format(search.best_score_))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LASSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:28:46.677090Z",
     "start_time": "2017-08-18T23:28:46.674483Z"
    }
   },
   "outputs": [],
   "source": [
    "import mpl_toolkits.mplot3d\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:29:00.247242Z",
     "start_time": "2017-08-18T23:28:46.678802Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start lasso regression with important features\n",
      "best estimator: Lasso(alpha=0.042813323987193917, copy_X=True, fit_intercept=True,\n",
      "   max_iter=1000, normalize=False, positive=False, precompute=False,\n",
      "   random_state=None, selection='cyclic', tol=0.0001, warm_start=False)\n",
      "best score: 0.2422990954868984\n",
      "feature importance: job_is_model - 3.4257302167419335\n",
      "feature importance: education_is_tech_mid - 1.9394443279651725\n",
      "feature importance: size_cup_a - 1.0420822781157293\n",
      "feature importance: education_is_tech_high - 1.022616648955262\n",
      "feature importance: education_is_mid - 0.9994227843830389\n",
      "feature importance: education_is_undergrad - 0.7954777279261813\n",
      "feature importance: sex_no_condom - 0.7231011242150179\n",
      "feature importance: province_is_jiangsu - 0.47423908901151257\n",
      "feature importance: size_cup_c - 0.34587637793627896\n",
      "feature importance: size_cup_d - 0.2961324451080567\n",
      "feature importance: size_height - 0.25078636821059924\n",
      "feature importance: size_weight - 0.22951063854520323\n",
      "feature importance: sex_oral - 0.1868876432848401\n",
      "feature importance: size_breast - 0.18372490773147296\n",
      "feature importance: job_is_student - 0.12067258138669372\n",
      "feature importance: size_waist - 0.10703128363288204\n",
      "feature importance: sugar_reject - 0.10100255568166946\n",
      "feature importance: size_hip - 0.06553889260767472\n",
      "feature importance: age - 0.028944189309622954\n",
      "feature importance: sugar_taken - 0.0\n",
      "feature importance: size_cup_e - 0.0\n",
      "feature importance: size_cup_b - 0.0\n",
      "feature importance: sex_anal - 0.0\n",
      "feature importance: province_is_zhejiang - 0.0\n",
      "feature importance: province_is_sichuan - 0.0\n",
      "feature importance: province_is_shanghai - 0.0\n",
      "feature importance: province_is_shandong - 0.0\n",
      "feature importance: province_is_hubei - 0.0\n",
      "feature importance: province_is_guangxi - 0.0\n",
      "feature importance: province_is_anhui - 0.0\n",
      "feature importance: provider_recommend - 0.0\n",
      "feature importance: job_is_working - 0.0\n",
      "feature importance: job_is_flight - 0.0\n",
      "feature importance: is_virgin - 0.0\n",
      "feature importance: education_is_grad - 0.0\n",
      "feature importance: blood_is_mixed - 0.0\n",
      "start lasso regression with PCA\n",
      "best estimator: Pipeline(steps=[('pca', PCA(copy=True, iterated_power='auto', n_components=17, random_state=None,\n",
      "  svd_solver='auto', tol=0.0, whiten=False)), ('lasso', Lasso(alpha=0.042813323987193917, copy_X=True, fit_intercept=True,\n",
      "   max_iter=1000, normalize=False, positive=False, precompute=False,\n",
      "   random_state=None, selection='cyclic', tol=0.0001, warm_start=False))])\n",
      "best score: 0.23350497524886304\n",
      "start PCA projection\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pf/hD+vbty+mnn15fBObPn8/27dsZOnQoF154IXfccQdnnXVWh7d1xRVXcNVVVzFkyBAq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\nY4zxKSvQxhjjU1agjTHGp6xAG2OMT/1/n2Gk5YIJKusAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda8fcb240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ujK2PRCJ19Tppmk6hoGK1SAiigDw4RWHlDoKjG3QNPZdCHx3d/dkyMUR3L9hc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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed8c1de7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###########################################################\n",
    "# LASSO\n",
    "###########################################################\n",
    "print(\"start lasso regression with important features\")\n",
    "\n",
    "from sklearn import linear_model, decomposition\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "lm = linear_model.Lasso()\n",
    "alphas = np.logspace(-4, 6, num=20)\n",
    "\n",
    "search = GridSearchCV(lm, dict(alpha=alphas), cv=5)\n",
    "search.fit(X, y)\n",
    "\n",
    "print(\"best estimator: {}\".format(search.best_estimator_))\n",
    "print(\"best score: {}\".format(search.best_score_))\n",
    "\n",
    "col_importance = list(zip(np.abs(search.best_estimator_.coef_), cols))\n",
    "col_importance.sort(reverse=True)\n",
    "\n",
    "for score, col in col_importance:\n",
    "  print(\"feature importance: {} - {}\".format(col, score))\n",
    "\n",
    "\n",
    "###########################################################\n",
    "# PCA + LASSO\n",
    "###########################################################\n",
    "\n",
    "print(\"start lasso regression with PCA\")\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "lm = linear_model.Lasso()\n",
    "\n",
    "pca = decomposition.PCA()\n",
    "pipe = Pipeline(steps=[('pca', pca), ('lasso', lm)])\n",
    "\n",
    "pca.fit(X)\n",
    "\n",
    "plt.figure()\n",
    "plt.clf()\n",
    "plt.axes([.2, .2, .7, .7])\n",
    "plt.plot(pca.explained_variance_, linewidth=2)\n",
    "plt.axis('tight')\n",
    "plt.xlabel('n_components')\n",
    "plt.ylabel('explained_variance_')\n",
    "\n",
    "n_components = range(3, len(cols)+1)\n",
    "\n",
    "search = GridSearchCV(pipe, dict(pca__n_components=n_components, lasso__alpha=alphas), cv=5)\n",
    "search.fit(X, y)\n",
    "n_components = search.best_estimator_.named_steps['pca'].n_components\n",
    "plt.axvline(n_components, linestyle=':', label='n_components chosen')\n",
    "plt.legend(prop=dict(size=12))\n",
    "plt.title(\"PCA best n_components: {}\".format(n_components))\n",
    "plt.savefig(\"sugar_pca.png\")\n",
    "\n",
    "print(\"best estimator: {}\".format(search.best_estimator_))\n",
    "print(\"best score: {}\".format(search.best_score_))\n",
    "\n",
    "\n",
    "###########################################################\n",
    "# PCA projection\n",
    "###########################################################\n",
    "print(\"start PCA projection\")\n",
    "from sklearn import decomposition\n",
    "from sklearn import datasets\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.clf()\n",
    "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n",
    "\n",
    "plt.cla()\n",
    "pca = search.best_estimator_.named_steps['pca']\n",
    "X = pca.transform(X)\n",
    "\n",
    "# for name, label in [('Setosa', 0), ('Versicolour', 1), ('Virginica', 2)]:\n",
    "#     ax.text3D(X[y == label, 0].mean(),\n",
    "#               X[y == label, 1].mean() + 1.5,\n",
    "#               X[y == label, 2].mean(), name,\n",
    "#               horizontalalignment='center',\n",
    "#               bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))\n",
    "# # Reorder the labels to have colors matching the cluster results\n",
    "# y = np.choose(y, [1, 2, 0]).astype(np.float)\n",
    "ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=\"Dark2\")\n",
    "\n",
    "ax.w_xaxis.set_ticklabels([])\n",
    "ax.w_yaxis.set_ticklabels([])\n",
    "ax.w_zaxis.set_ticklabels([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict if(sugarDaddy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:29:00.268282Z",
     "start_time": "2017-08-18T23:29:00.249150Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "sg = pd.read_csv('cleaned_show_girls.csv')\n",
    "cols = list(sg.axes[1])\n",
    "\n",
    "col_interest = sg['sugar_taken']\n",
    "\n",
    "cols = [c for c in cols if \"sugar\" not in c]\n",
    "\n",
    "new_sg = sg[cols]\n",
    "\n",
    "X = new_sg.as_matrix()\n",
    "y = col_interest.as_matrix()\n",
    "\n",
    "imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
    "X = imp.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:29:46.288464Z",
     "start_time": "2017-08-18T23:29:00.270470Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start random forest to get feature importance\n",
      "best estimator: RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=10, max_features=0.8, max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            n_estimators=41, n_jobs=1, oob_score=False, random_state=None,\n",
      "            verbose=0, warm_start=False)\n",
      "best score: 0.9022556390977443\n",
      "feature importance: price_mean - 0.2920487062802531\n",
      "feature importance: price_lower_bound - 0.1900729225163103\n",
      "feature importance: price_upper_bound - 0.13511779983807348\n",
      "feature importance: size_weight - 0.09529081814007015\n",
      "feature importance: size_hip - 0.0478240941532362\n",
      "feature importance: size_height - 0.04770452521698749\n",
      "feature importance: price_dynamic - 0.0371052805636685\n",
      "feature importance: size_waist - 0.029524896783399358\n",
      "feature importance: is_virgin - 0.020105994239105637\n",
      "feature importance: education_is_undergrad - 0.013136326633705\n",
      "feature importance: size_breast - 0.011661184619039543\n",
      "feature importance: sex_oral - 0.01143803605071145\n",
      "feature importance: job_is_working - 0.011269300233311167\n",
      "feature importance: size_cup_b - 0.010444938862807777\n",
      "feature importance: job_is_model - 0.010249507247862616\n",
      "feature importance: job_is_student - 0.008158997865913405\n",
      "feature importance: age - 0.007610553812856575\n",
      "feature importance: education_is_tech_high - 0.007055337372940195\n",
      "feature importance: province_is_jiangsu - 0.003857470133018267\n",
      "feature importance: job_is_flight - 0.0032775747539737846\n",
      "feature importance: province_is_shanghai - 0.002083558526055756\n",
      "feature importance: size_cup_d - 0.0014465369271976249\n",
      "feature importance: size_cup_c - 0.0009902254926529434\n",
      "feature importance: province_is_hubei - 0.0007931301806905598\n",
      "feature importance: sex_anal - 0.0006702623976495456\n",
      "feature importance: size_cup_a - 0.0005900435011512153\n",
      "feature importance: province_is_anhui - 0.0004719776573584155\n",
      "feature importance: size_cup_e - 0.0\n",
      "feature importance: sex_no_condom - 0.0\n",
      "feature importance: province_is_zhejiang - 0.0\n",
      "feature importance: province_is_sichuan - 0.0\n",
      "feature importance: province_is_shandong - 0.0\n",
      "feature importance: province_is_guangxi - 0.0\n",
      "feature importance: provider_recommend - 0.0\n",
      "feature importance: education_is_tech_mid - 0.0\n",
      "feature importance: education_is_mid - 0.0\n",
      "feature importance: education_is_grad - 0.0\n",
      "feature importance: blood_is_mixed - 0.0\n"
     ]
    }
   ],
   "source": [
    "###########################################################\n",
    "# random forest\n",
    "###########################################################\n",
    "print(\"start random forest to get feature importance\")\n",
    "\n",
    "clf = RandomForestClassifier(max_features=0.8)\n",
    "\n",
    "# use a full grid over all parameters\n",
    "param_grid = {\"n_estimators\": range(20, 200),\n",
    "              \"max_depth\": [3, 10, None]}\n",
    "\n",
    "# run grid search\n",
    "search = RandomizedSearchCV(clf, param_distributions=param_grid, cv=5, n_iter=50)\n",
    "search.fit(X, y)\n",
    "\n",
    "print(\"best estimator: {}\".format(search.best_estimator_))\n",
    "print(\"best score: {}\".format(search.best_score_))\n",
    "col_importance = list(zip(search.best_estimator_.feature_importances_, cols))\n",
    "col_importance.sort(reverse=True)\n",
    "\n",
    "for score, col in col_importance:\n",
    "  print(\"feature importance: {} - {}\".format(col, score))\n",
    "\n",
    "\n",
    "###########################################################\n",
    "# random forest with important features\n",
    "###########################################################\n",
    "# print(\"start random forest with important features\")\n",
    "\n",
    "\n",
    "# cols = [c for s, c in col_importance][:10]\n",
    "# new_sg = sg[cols]\n",
    "# X = new_sg.as_matrix()\n",
    "# X = imp.fit_transform(X)\n",
    "\n",
    "# clf = RandomForestClassifier(max_features=0.8)\n",
    "\n",
    "# # use a full grid over all parameters\n",
    "# param_grid = {\"n_estimators\": range(20, 200),\n",
    "#               \"max_depth\": [3, 10, None]}\n",
    "\n",
    "# # run grid search\n",
    "# search = RandomizedSearchCV(clf, param_distributions=param_grid, cv=5, n_iter=50)\n",
    "# search.fit(X, y)\n",
    "\n",
    "# print(\"best estimator: {}\".format(search.best_estimator_))\n",
    "# print(\"best score: {}\".format(search.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T22:33:21.663347Z",
     "start_time": "2017-08-18T22:32:56.700387Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-18T23:30:29.955126Z",
     "start_time": "2017-08-18T23:29:46.290364Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start logistic regression with important features\n",
      "best estimator: LogisticRegression(C=0.0001, class_weight=None, dual=False,\n",
      "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
      "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
      "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)\n",
      "best score: 0.8947368421052632\n",
      "start logistic regression with PCA\n",
      "best estimator: Pipeline(steps=[('pca', PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n",
      "  svd_solver='auto', tol=0.0, whiten=False)), ('logistic', LogisticRegression(C=0.012742749857031334, class_weight=None, dual=False,\n",
      "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
      "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
      "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False))])\n",
      "best score: 0.8947368421052632\n",
      "start PCA projection\n"
     ]
    },
    {
     "data": {
      "image/png": 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XXXcdXbp0YezYsfVJYMqUKaxevZq8vDxOPfVUbr/9do499thWH+v888/nwgsvpHfv3pSX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\nCdoYY2LU/wd+di+MJTD2wwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fede2528668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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175VKJUSjUVSrVSwtLan6EJL+Mj388tpL5cnU5Wg0isnJSdy/f59aKkiLyIuM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\ngUBwwiGCl0AgEAhOOP4/unST8De0vIEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda9f49d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###########################################################\n",
    "# logistic regression\n",
    "###########################################################\n",
    "print(\"start logistic regression with important features\")\n",
    "\n",
    "logistic = linear_model.LogisticRegression()\n",
    "Cs = np.logspace(-4, 6, num=20)\n",
    "\n",
    "search = GridSearchCV(logistic, dict(C=Cs), cv=5)\n",
    "search.fit(X, y)\n",
    "\n",
    "print(\"best estimator: {}\".format(search.best_estimator_))\n",
    "print(\"best score: {}\".format(search.best_score_))\n",
    "\n",
    "\n",
    "###########################################################\n",
    "# PCA + logistic regression\n",
    "###########################################################\n",
    "\n",
    "print(\"start logistic regression with PCA\")\n",
    "logistic = linear_model.LogisticRegression()\n",
    "\n",
    "pca = decomposition.PCA()\n",
    "pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])\n",
    "\n",
    "pca.fit(X)\n",
    "\n",
    "plt.figure()\n",
    "plt.clf()\n",
    "plt.axes([.2, .2, .7, .7])\n",
    "plt.plot(pca.explained_variance_, linewidth=2)\n",
    "plt.axis('tight')\n",
    "plt.xlabel('n_components')\n",
    "plt.ylabel('explained_variance_')\n",
    "\n",
    "n_components = range(3, len(cols)+1)\n",
    "\n",
    "search = GridSearchCV(pipe, dict(pca__n_components=n_components, logistic__C=Cs), cv=5)\n",
    "search.fit(X, y)\n",
    "n_components = search.best_estimator_.named_steps['pca'].n_components\n",
    "plt.axvline(n_components, linestyle=':', label='n_components chosen')\n",
    "plt.legend(prop=dict(size=12))\n",
    "plt.title(\"PCA best n_components: {}\".format(n_components))\n",
    "plt.savefig(\"sugar_pca.png\")\n",
    "\n",
    "print(\"best estimator: {}\".format(search.best_estimator_))\n",
    "print(\"best score: {}\".format(search.best_score_))\n",
    "\n",
    "\n",
    "###########################################################\n",
    "# PCA projection\n",
    "###########################################################\n",
    "print(\"start PCA projection\")\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.clf()\n",
    "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n",
    "\n",
    "plt.cla()\n",
    "pca = search.best_estimator_.named_steps['pca']\n",
    "X = pca.transform(X)\n",
    "\n",
    "# for name, label in [('Setosa', 0), ('Versicolour', 1), ('Virginica', 2)]:\n",
    "#     ax.text3D(X[y == label, 0].mean(),\n",
    "#               X[y == label, 1].mean() + 1.5,\n",
    "#               X[y == label, 2].mean(), name,\n",
    "#               horizontalalignment='center',\n",
    "#               bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))\n",
    "# # Reorder the labels to have colors matching the cluster results\n",
    "# y = np.choose(y, [1, 2, 0]).astype(np.float)\n",
    "ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=\"Dark2\")\n",
    "\n",
    "ax.w_xaxis.set_ticklabels([])\n",
    "ax.w_yaxis.set_ticklabels([])\n",
    "ax.w_zaxis.set_ticklabels([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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