20180826_분류별 머신러닝작업.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import font_manager, rc\n",
    "font_name = font_manager.FontProperties(fname=\"c:/Windows/Fonts/malgun.ttf\").get_name()\n",
    "rc('font', family=font_name)\n",
    "matplotlib.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split(df, train_ratio, X, y, random = False, dtrain = False):\n",
    "    \"\"\"\n",
    "    Argument\n",
    "    df : DataFrame object\n",
    "    train_ratio : float, in range(0,1)\n",
    "    X : sequence data, such as list, tuple (Train Features)\n",
    "    y : str\n",
    "    random : boolean, you need \"numpy\" library cause we use np.random.permutation function.\n",
    "    \n",
    "    Return\n",
    "    train : 학습시킬 Feature data\n",
    "    train_idx : 지도학습의 Y value\n",
    "    test : test 검증할 Feature data\n",
    "    test_idx : test set의 Y value\n",
    "    \"\"\"\n",
    "    train_size = int(len(df)*train_ratio)\n",
    "    if random:\n",
    "        shuffle_indicies = np.random.permutation(len(df))\n",
    "        train_indicies = shuffle_indicies[:train_size]\n",
    "        test_indicies = shuffle_indicies[train_size:]\n",
    "    else:\n",
    "        normal_indicies = np.arange(len(df))\n",
    "        train_indicies = normal_indicies[:train_size]\n",
    "        test_indicies = normal_indicies[train_size:]\n",
    "    if dtrain:\n",
    "        train = df.iloc[:train_size]\n",
    "        train_idx = df.iloc[:train_size]\n",
    "        test = df.iloc[train_size:]\n",
    "        test_idx = df.iloc[train_size:]\n",
    "    else:\n",
    "        train = df.iloc[:train_size][X]\n",
    "        train_idx = df.iloc[:train_size][y]\n",
    "        test = df.iloc[train_size:][X]\n",
    "        test_idx = df.iloc[train_size:][y]\n",
    "    return train, train_idx, test, test_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "kospi200 = pd.read_csv(\"./data/data.csv\", encoding=\"cp949\")\n",
    "kospi200 = kospi200[kospi200.columns[:2]]\n",
    "kospi200[\"종목코드\"] = kospi200[\"종목코드\"].map('{:06d}'.format)\n",
    "\n",
    "name_dict = { i : j for i, j in zip(kospi200[\"종목명\"], kospi200[\"종목코드\"]) }\n",
    "code_dict = { j : i for i, j in zip(kospi200[\"종목명\"], kospi200[\"종목코드\"]) }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('raw_data_20180824.pickle','rb') as handle:\n",
    "    raw_data = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>6110.0</td>\n",
       "      <td>5660.0</td>\n",
       "      <td>6110.0</td>\n",
       "      <td>0.092131</td>\n",
       "      <td>74195000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>5800.0</td>\n",
       "      <td>6060.0</td>\n",
       "      <td>5520.0</td>\n",
       "      <td>5580.0</td>\n",
       "      <td>0.084139</td>\n",
       "      <td>74680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>5750.0</td>\n",
       "      <td>5780.0</td>\n",
       "      <td>5580.0</td>\n",
       "      <td>5620.0</td>\n",
       "      <td>0.084743</td>\n",
       "      <td>54390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>5560.0</td>\n",
       "      <td>5670.0</td>\n",
       "      <td>5360.0</td>\n",
       "      <td>5540.0</td>\n",
       "      <td>0.083536</td>\n",
       "      <td>40305000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>5600.0</td>\n",
       "      <td>5770.0</td>\n",
       "      <td>5580.0</td>\n",
       "      <td>5770.0</td>\n",
       "      <td>0.087004</td>\n",
       "      <td>46880000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close  Adj Close    Volume\n",
       "Date                                                           \n",
       "2000-01-04  6000.0  6110.0  5660.0  6110.0   0.092131  74195000\n",
       "2000-01-05  5800.0  6060.0  5520.0  5580.0   0.084139  74680000\n",
       "2000-01-06  5750.0  5780.0  5580.0  5620.0   0.084743  54390000\n",
       "2000-01-07  5560.0  5670.0  5360.0  5540.0   0.083536  40305000\n",
       "2000-01-10  5600.0  5770.0  5580.0  5770.0   0.087004  46880000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[name_dict['삼성전자']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "party_df = pd.read_csv('party_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "party_df[\"종목코드\"] = party_df[\"종목코드\"].map('{:06d}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>업종</th>\n",
       "      <th>영문코드</th>\n",
       "      <th>종목명</th>\n",
       "      <th>종목코드</th>\n",
       "      <th>시가총액</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>정보기술</td>\n",
       "      <td>SEC</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>005930</td>\n",
       "      <td>296893767375000</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>정보기술</td>\n",
       "      <td>SKH</td>\n",
       "      <td>SK하이닉스</td>\n",
       "      <td>000660</td>\n",
       "      <td>62826604099500</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>헬스케어</td>\n",
       "      <td>CTRN</td>\n",
       "      <td>셀트리온</td>\n",
       "      <td>068270</td>\n",
       "      <td>34044987664500</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>철강소재</td>\n",
       "      <td>POSCO</td>\n",
       "      <td>POSCO</td>\n",
       "      <td>005490</td>\n",
       "      <td>28815248967500</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>경기소비재</td>\n",
       "      <td>HMC</td>\n",
       "      <td>현대차</td>\n",
       "      <td>005380</td>\n",
       "      <td>28525804030500</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      업종   영문코드     종목명    종목코드             시가총액 income\n",
       "0   정보기술    SEC    삼성전자  005930  296893767375000   high\n",
       "1   정보기술    SKH  SK하이닉스  000660   62826604099500   high\n",
       "2   헬스케어   CTRN    셀트리온  068270   34044987664500   high\n",
       "3   철강소재  POSCO   POSCO  005490   28815248967500   high\n",
       "4  경기소비재    HMC     현대차  005380   28525804030500   high"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "del_list = []\n",
    "for key, df in raw_data.items():\n",
    "    if len(df) < 252*2:\n",
    "        del_list.append(key)\n",
    "for code in del_list:\n",
    "    del raw_data[code]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BGF리테일',\n",
       " 'HDC현대산업개발',\n",
       " 'SK케미칼',\n",
       " '넷마블',\n",
       " '두산밥캣',\n",
       " '삼성바이오로직스',\n",
       " '오리온',\n",
       " '현대중공업지주',\n",
       " '효성중공업']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[code_dict[i] for i in del_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "mod = sys.modules[__name__]\n",
    "for code in raw_data.keys():\n",
    "    \"\"\"\n",
    "    X\n",
    "    \"\"\"\n",
    "    raw_data[code][\"pct_change\"] = raw_data[code].Close.pct_change().shift(1)\n",
    "    for i in range(3, 30):\n",
    "        raw_data[code][\"close_ma_{}\".format(i)] = raw_data[code].Close.rolling(window = i).mean().shift(1)\n",
    "        raw_data[code][\"volume_ma_{}\".format(i)] =  raw_data[code].Volume.rolling(window = i).mean().shift(1)\n",
    "        #raw_data[code][\"close_ewm_{}\".format(i)] = raw_data[code].Close.ewm(span = i).mean()\n",
    "        #raw_data[code][\"volume_ewm_{}\".format(i)] = raw_data[code].Volume.ewm(span = i).mean()\n",
    "        if i in [7, 21, 30, 60, 90, 91, 100, 120]:\n",
    "            raw_data[code][\"his_vol_{}\".format(i)] = raw_data[code][\"pct_change\"].fillna(0).rolling(window = i).std().shift(1)/(252**0.5)\n",
    "    \"\"\"\n",
    "    Y\n",
    "    \"\"\"\n",
    "    \n",
    "    raw_data[code] = raw_data[code].assign(Y1 = raw_data[code].Close - raw_data[code].close_ma_7.shift(1))\n",
    "    raw_data[code] = raw_data[code].assign(Y2 =\n",
    "                                           raw_data[code][\"pct_change\"] - raw_data[code][\"pct_change\"].rolling(window = 7).mean().shift(1))\n",
    "    raw_data[code] = raw_data[code].assign(Y3 = raw_data[code].Close.rolling(window=7).sum().pct_change())\n",
    "    \n",
    "    raw_data[code] = raw_data[code].assign(Y1 = np.where(raw_data[code].Y1>=0, 1, 0))\n",
    "    raw_data[code] = raw_data[code].assign(Y2 = np.where(raw_data[code].Y2>=0, 1, 0))\n",
    "    raw_data[code] = raw_data[code].assign(Y3 = np.where(raw_data[code].Y3>=0, 1, 0))\n",
    "    \"\"\"\n",
    "    Drop NaN\n",
    "    \"\"\"\n",
    "    raw_data[code] = raw_data[code].dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = {}\n",
    "for code in raw_data.keys():\n",
    "    test[code] = raw_data[code][['Close', 'Y2']]\n",
    "    test[code] = test[code].assign(change = (test[code]['Y2']-test[code]['Y2'].shift(1)).fillna(0))\n",
    "    test[code] = test[code].assign(close_change = (test[code]['Close']-test[code]['Close'].shift(1)).fillna(0))\n",
    "    test[code] = test[code].assign(return_price = np.where(test[code]['change']==0, 0, test[code]['close_change']))\n",
    "    test[code] = test[code].assign(balance = test[code]['return_price'].cumsum())\n",
    "    test[code] = test[code].assign(compare = test[code]['Close']-test[code]['Close'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c3f7de1828>"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IllHyaIZIzUOtViKSbZQ8msFU8xCRLKXk0QwWZg9NUSIi2aZRycPMbjSzV81stpk9bWbdLDDFzBaY2VwzuzZq/wFm9pKZzTGzWWZ2ZNS2CWb2Tnish82sQ7i+wMweNLN54fEmRZUZHh5ntplNN7OBLfkmNFWkz0O5Q0SyTWNrHh+7+6nuPhZYBFwPnAcMBkqA44EJZjYm3P8e4E53HwP8AHgcwMz6ApOBceGxtgLXhGWuAra4+2jgdOBGM9sv3PYI8P2wzN3A/U18vS1KfR4ikq0alTzc/YmoxXlAX2ASQYJwd68g+IIfb2Y9gKHu/nxYdgGw08wOBSYAT7r7lvBYU4Hx4eNJwB/CMluAZ4BzzGwEsNnd3wv3+yswwsw6NekVtyCNthKRbJVUn4eZ5QNXA38GBgHLojavBPoD+wMrYopGtiUqA9Df3Vc1VMaDDobVBAksNr7LzWy+mc0vLS1N5qU1idXUPJQ8RCS7NDp5mNnBwCvAX8JaRSEQPSNgFVAdZ3192yLrAfIaWSa2XA13n+ruJe5eUlxc3MhX1nSRSwSVO0Qk2zS2w/xcgmap77l7pL/hc4JaRsRAghpH7Pr6tkXWA2wwsz4NlbHg535fgtpHRuWo5iEiWarB5GFm+wD/C5zl7gujNj0FXBnuUwhcBDzm7muALWZ2crhtJFDh7suBp4ELzKwoPMYVwKNxjtcDOAd4FngbGGxmQ8L9xgMzw36WjJr1yQYArv3L+xmOREQkvWKbiuI5CugN/M32zuW0iaDze5iZzQFygfvdfUm4/ULgATObApQBlwG4+2IzuwOYYWbVBJ3v08IytwBTzWx2GNd1kY51M5sITAtrHRuBS5vxmlvMyo07AViybkeGIxERSa8Gk4e7/xPolWDz1QnKfAKckmDbQ8BDcdbvBCYmKDMXGNtQrOmmiRFFJFvpCnMREUmakkcLmbF4faZDEBFJGyWPZhh/VP+axxdPm6ebQolI1lDyaIbB+3SutVyp5CEiWULJoxmM2h3mldV1rlsUEWmXlDyaISdmsJVqHiKSLZQ8mqF358Jay5VVSh4ikh2UPJqhR1FBrWU1W4lItlDyaEGqeYhItlDyaEFV6vMQkSyh5NGC3lq2MdMhiIikRWMmRpRG+ulTH7B+WxknHlLM8P26ZzocEZGUUc2jmaZeMKrW8u0vL+Hbf5qfoWhERNJDyaOZDuhdVGfd9rLYGx+KiLQvSh7NNHifLvz2P0bUWqc7C4pIe6fk0QK+Pmo/Rg3sUbOsyz1EpL1T8mgh+/XoWPO4SjUPEWnnlDxaSH7u3rdSzVYi0t4pebSQgry9b6Vyh4i0d0oeLaQgV2+liGQPfeO1kOiaB4Cr+iEi7ZiSRwvJz619c4/ySg25EpH2S8mjheTl1H4ry/coeYhI+9Wo5GFmx5nZbDM7Mlw2M5tiZgvMbK6ZXRu17wAze8nM5pjZrEiZcNsEM3snPNbDZtYhXF9gZg+a2bzweJOiygwPjzPbzKab2cCWe/ktJ8dq1zx2VOgqcxFpvxpMHmb2J+BaoGvU6vOAwUAJcDwwwczGhNvuAe509zHAD4DHw+P0BSYD49x9LLAVuCYscxWwxd1HA6cDN5rZfuG2R4Dvh2XuBu5v2ktNrdj+8jtfXZqZQERE0qAxNY/vuvvXgA1R6yYRJAh39wqCL/jxZtYDGOruzwO4+wJgp5kdCkwAnnT3LeExpgLjo473h7DMFuAZ4BwzGwFsdvf3wv3+Cowws05NfL0pkxNzQ/M5mp5dRNqxBpOHu++Ks3oQsCxqeSXQH9gfWBGzb2RbojIA/d19VUNlPBjCtBroGy9WM7vczOab2fzS0tJ6XlXLyw2brUYPCqYpOemQ4pptd722lHG3z+DRt1akNSYRyQ6ZGN3Z1A7zQiC6Ub8KqI6zvr5tkfVQ974ijTleHe4+1d1L3L2kuLg43i4pE+nz6FiQR99uHdhZUVWz7faXl7Bsw07+528L0xqTiLR/M5eUcsB1/+CZdz5P6/M2NXl8TlDLiBhIUOOIXV/ftsh6gA1m1qehMmZmBLWO1U2MO2UizVY9OuXTs6iATTsrMhyRiLRl7p7w1tZL123numc+wN1Zun4HAD/68/vMXFLKx2u3pSW+piaPp4ArAcysELgIeMzd1wBbzOzkcNtIoMLdlwNPAxeYWeQGGFcAj8Y5Xg/gHOBZ4G1gsJkNCfcbD8wM+1lalcqqoDLUu3MhPYsK2Bgmjx3lGnUlIsn71fMfcdD1/2DdtjI+Wb+91rZv3PcWT8z9jP+47y1+9fyimvUX/nEuZ97xRlria+ptaP8IDDOzOUAucL+7Lwm3XQg8YGZTgDLgMgB3X2xmdwAzzKwamAdMC8vcAkw1s9lhTNdFOtbNbCIwLax1bAQubWLMKbVhRzkQJI+Fa7by/mfBuIDpi9bV2q+isrrO1egiItEWrtnKH99cDsAxU14FYPmtZ2Fh83hRQS5bd+9h/srNccu/9elGjj2oV0pjbHTycPeTox5XAlcn2O8T4JQE2x4CHoqzficwMUGZucDYxsaZKZt37QGCZqs5yzYBsHXXHgb0rD0wbFdFJQV5BWmPT0Tajvkr6iaFGUtKOWVI0Lq/ZmtZveV7FqX+O0Y/gVvI7rCDvKgwj5H7dwdg0669rWvnDA8GiOkWtSLSkKfjdH5/a9o8jrrpZWYsXl9r/cF9OrP45jP5YPLpNaM8h+zbJeUxNrXZSmLsDK8oLyrM5bsnHcTljy5gy64KKsI5rrp1zK+1n4hIIpGRt10K8xjYuxP/Xh10gm/etYeLp80DoH/3jtx/wSiG9e8GQGFeLn+8eHTNd06qqebRQi4aOwiAEft1JzJA4mv3zOaqJ94B4KDizgDsVAe6iMSxdmsZg372AjMWr2fjjnLOPqIvH954Bn///vG17lQaMfOnp9QkjojcHKNjQW5a4lXyaCGnDOnDitvOplfnQvbpWlizfsOOoOkqUo1sqNlq1cZdNSO3RCQ7bN5ZwZhbg47xi6fNY83WMgbvE/zgNDNm/fc4nvv+cTX7/+SMIeTGzGqRbmq2SoGj9u9RZ10koazblrija/32Mk7833/RuTCPf994RsriE5HW5Q+v1Z0L7/B+tWsVw/frzr9vPIN/fPhFTR9qJil5pMkBvYNfEas37064z87yoNNd14aIZJe8mFpEbo7xpcP2qbNf58I8JpQMSFdY9VKzVYpcfergmsffPuGAmipm5GrQWLOWbuCU22ekIzQRaUWqqp0H3ljOvl078IuzhwJw+38Mz3BUDVPySJEffemQmsc9i/b2gbz477Vx95/00Nu1lqsTTEsgIu3H9EXrOOj6fwCwdlsZl51wIK//5GS+dtR+DZTMPCWPFPr2CQcAsHZr0FR1cJ/OdW5Xm8iwyS+lLC6R9mbNlt0ZmVk2GWV7qhj0sxcY9LMXANi4o5zL/jS/Zvu9E0cCMLBXUdzyrY36PFJo7EG9eeCN5TVDd086pJgn5ibu84i2K2pWXhGJr7yyiiG/+GfN8orbzq55XFFZzZufbuDT9TtYuGYb28v28Pv/PJIuHfKprnZycozJzy3k4dkreP+G02uuxUqVQ/9nb5yRBBLxr2tP5oDebSNpRCh5pNDJQ4q56auH89Ujg9uW9CwqYFdFFWV7quiQ3/BY7N0VVWkbsy3SFk154aNay0N+8SJXjTuY219eEnf/Iya/DAQd1JVRTcN3TF/CDV85PCUxVlRWM+nBtxNuX3TTGXQqaHtfxWq2SiEz48JjB9X8oomMqFi+YWet/WKnXT6wOPgFEm/4nogEKquqeeStlbXWlVdWJ0wctcrG/M1Ne3MF5ZUtW9v/eO02Xv1oHV+/dzZzVwTz3U0o2duXcdrQfXjjp6e0ycQBqnmk1efhMN2XF65jaN+9t4RfvLb2dMsXjx3EL/+2kHtnfMp/n3loWmNsixas3EyH/Jw64+IbsnZrGT2K8inIzamZrVRap/LKKhau2ca85ZuYOGYgr328nqufeBeAq8cdzLdPPLCmVhFt+o9O4uA+wTD50u3lzFxSyo//8n7N9vNHD+CVRevYuLOCIb/4Z62Za5vjhr/9u05iO/7g3kz52hH8avwwqqq9zSaNCGvtnUxNVVJS4vPnz294xzRaum47X/r9TA7sXcST3xlDny4dgNrtnw9dVEJVtXP5owsAWuxkbkvmLt/EhPvf4vB+XXn6irH1NvF9+PlWvnLXLCAYHn3ZCQcw+5MNPDx7BScMLuZ7pxwct9wtLyzigTeW1yxHt5W3Z+7Oc++v4UuH7dMmvrw+37yL43/9r3r3iW72+eiLbbyyaB37du3AYf261pm+I+LdVZvpXJjH4H261Ok3uf0/RtAxP5f/m/8ZD188uuZGb4018cE5vPnJxlrrfjV+GBeMGZjUcTLFzBa4e0mD+yl5pM/67WUcfcurNcuRxBBJHk9991hKBvXk36u3cs6dwRfitItHc8qhfeIer72KTqa5OcaHk09n1tINTH5uIb27FDLt4tH06lzI9EXrao1Wiefv3z+eft078MqidSxZt4NvHj2Abh3zOXrKq7X2e+d/vpSWaawzLfo9e+3HJ3FgOOdapkQ6rqNt3b2Hb9w7m/17duLVj9cnKAnfOelArvvy0BaJY8HKTXz93rfqrL/uy4dyyfEHkJdjDf6IW7hmK2f/YVbN8v936TGUDOrBll172LdbhxaJMx2UPFph8oDaX4yv/+RkBvYq4ptT5/DWso01v36jf00DzP/FafTuXFjnWO2Ru3PAdf+otW7UwB4sSHDTG4ChfbvSr1uHer9oGuOhi0o4dWjdq3qjY9uyaw/nT51Dh4Jcnvz2mDYxoGFHeSVL122nV1EhJ/5v7V/xydS45q3YxOrNu8nPzeHsZkyPUV3tODDtzeXcHHZ4P3/V8Qzr342yPVW1RiVF5OUYg3oX8coPT+TNTzaSn2scc2DL3uzo+mc/5PG3VyXcft5R/fnpmYcy5tZXg/v2XH8qhXnB5x87eurJy8cwpoXjSxcljzaQPCD44z38l/9k1KCe/OmSo4H4X6DZ0qzy3PtruPqJd+lcmNeoaVrOHz2A274eXI27flsZhXm5dOuUn/BLKNrCG8+gY34uB16/972e+/NT2bprDxVV1bX6UD7btIsTflP7i/e4g3vx2GVjknl5zfLKonV8O6w1PPXdY3nx32s5dWgfxh7UO2GZVRt31UkYsR68sITT4kyF0dAxXvzBCbX67mLtLK/ECa5zKszLpWNBLiU3T683llgnHVLMI+HfRTr87uXFLN+4i5vHD2PEjXX7UBqjrY6eilDyaCPJ4+krxvL1e2cDtRNE2Z4q5izbWDN3f3vr+1i7tYzH567ikuMGccf0pYwY0I1R+/es+ZJ66ZoTKcjL4fpnPuStZUH78QeTT6dTfi4H//xFAJbe8mXyc5MbMPjY2yv5+/truG/SKLp3CpqptpXt4VvT5tVbu0nkN98YzmF9E7etN8eGHeU1X7bLppxVK8lFO2d4X+76r+ACs9Lt5Tz3/hruem1pzd0tYy2/9Sxmf7qRiVHDR489sBdPXB4kwsVrt3PtX97noYtLcN97G9R4zh3Rjy+27uaJb48hLzeHddvKWL+tvFbNOZHjDu7F7yYcGff4835+GsVdMl/brqp2pn+0jr+/v4bnP/ii3n3f+Okpde4c2hYpebSR5BFxxuH7cP8FdT+v6575kCfmruKt68bRt1vdOf3boq279jDipvp/1UUnyyXrttO5MI9+3YPXv3X3HnIMunRo2Yu6En02EQf2LuLW845g5MAeXPnYO7wSdX/6X40fxjdG7tekZqwd5ZX81wNz+ODzrTz8rdGcOLiY7eWVcX/5Hntgr5pk2lh9uhTy8g9PpKra6RXV/PndRxfwz4V7p8uZ/JXDmPz3RQmPE2nW27KrgiNveiWpGCLOOmJf7vrmyDr9HBt3lPPsu6v58hF92adLIXlJ/ihIh2fe+ZyBvYoYsV83csx497PNTJ25jLI91Tz8rdHt5sedkkcrTR6VVdWs2rSLcb99vdb6p68Yy6iBdadyf+3jdVzy8HyKCnL5cPIZSY/8aI2Ou+01Vm9JfKV9Q80oqbJ47XYuf3Q+xZ0LueP8Izn+1//iN18fzqIvttEMCKKwAAAO4klEQVSlQx4/+tIhNV8QVdVeMydRtKvHHcyPTh/S6Od877MtjL/7zYTbj9q/O++u2lKzvPzWswB49aP1nHJoH15auJYrH3snbtn9enTk4rGDuPT4AxJ+se2qqOSwX9Y/Fc7h/brywtUn1Fq3eO12zrhjZr3llk05C7PgeqdVG3dRWV2d8Q56aZiSRytNHgB7qqoZHDa9RCTq09hZXsnhNwR/3OePHsB3TzqImUtLufDYQakOs0ZkksZEiWvt1jJ2lFfWjKeP+N5j7/DyorW8+8vTMeCDz7fyzQfm1Gx/9ccn8buXl/C7/xzB+m3l/OalxUz+ymG1fh23dvH6QhI1Ma7fVlYzymvOdafyxtJSfvLUBwAc0b8bH67eWmv/H552CD84bTDuzvbySroU5iVMAjvKK3lszkomjhlI58Lk2ttj+9jumzSK0w/bh5wc49PSHQzqVVTvjYeWrtvOKx+t45LjDmB3RRU9smDUWnum5NGKkwfAzCWldOuYz4gB3RvcN9KcYrb33sYQXKF65zeP4tf//JjjD+6dkl/rd722tNYVuycdUswXW3dz63lHMGpgzzpfnjePH8b0j9YxY3Fpvcd96ZoTa+6u2NZt2FHOqx+t47+f/hCABy4sqXMvhsqq6pq+mlgXjx3E5HODqTEqKqvZsquCnkUF5DZieGhLuWP6Ev61uJQHLyxpFX0NkjlKHq08eSTj7n99wv++tLhR+0aGPCayo7ySnz71Pm8s3UD3Tvnc+rXhHLl/d3ZVVNZctFhRWc3vpy/h3hmfNjv22DmEnr7iWEYN7Nns47ZG0U1Q900aRZ+uhazauItr/u+9hGXu/q+RzRr2KtLS2l3yMLNTgF8DlcB64BJ335Ro//aUPGBv30fE0YN61syXE8+lxx/Afx2zPwcVd6aispr8XOPlRev4TnjleiKH9+vKwjXbaq2besEovthaxg3PLQTgl+ccxk3PBx2rZnBY36785bvHsmjNNh5/exU9igq4etxgunVK7Sylrc2mnRWM/FX9Hcmf3PJlzIwdZZVZ9/5I29CukoeZdQDeB77k7qvM7IfAEHf/bqIy7S15RHfQvvLDExm8TxfcnR/+33tUVFVz5zdH8p1H5zP9o9oXyg3ZpwuL19WeO2vEgO6cftg+fLZpFzvKK2uGIB5UXMSnpcGkjRcdO5AfnHZIwquuI+dNexlh0lLi9YHcPH4Y+bnGV4/s36jZlEUyqb0lj68CX3X3S8LlLsBid++XqEx7Sx4Q3PBm/srNnDsi4csGgl/A973+KctKd7Jg5aaa8f7jDu3DV4/sVzNFfDxle6oor6xO+b0NRKR1amzyaCuXQQ4ClkUW3H27meWZWb6711wJZWaXA5cD7L///mkPMtX6de/Iud0bvtajZ1EB15/VtDl/OuTn6texiDSo9V2JE18hQV9HtEqgVrXJ3ae6e4m7lxQXF6ctOBGRbNNWksfnQE1VwsyKgDJ3b3jyIxERaXFtJXm8CJxpZpHB85cDj2cwHhGRrNYm+jzcfbOZXQX83cyqCfo/vp3hsEREslabSB4A7v4CUP/MdSIikhZtpdlKRERaESUPERFJWpu4SLApzKwUWJlksd7AhhSEoxgUg2JQDG0lhoHu3uC1Du02eTSFmc1vzJWVikExKAbFkK0xRKjZSkREkqbkISIiSVPyqG1qpgNAMUQohoBiCCiGQGuIAVCfh4iINIFqHiIikjQlDxERSZqSh4iIJE3JI4tZhu8hm+nnl9Yn0+dEpp+/Lcmq5GFmp5rZcWbWMVxO+4kSxnC0mWXkPq9mdpaZfS9czMjnb2bnm9ltwIBMPH8Yw1fM7FwzGxAuZ+pcyOrzMYxB5ySt45xMRlaMtjKz/sDDQBmwHdgK/N7dl5iZeRreBDM7FJgM9AB2Af8G7nb3tal+7qgYCoBHgHOBfu6+1cxy3L06Dc9tQAfg98CBwGR3n53q540TR1/g3nBxEXAAcK27r05jDDof98ahc7IVnJNNkS01j3HALHf/CvAj4DPgToA0/aEWAN8BXnT3M4CbgCKgX6qfO5q7VwBLgQ9J83jx8H0uChe/7O6zzawwnTGETgbmuPt44G5gE+mfr0jnY0jnJAAnkflzMmntNnmYWXczi7y+g4Du4eP17j4F6GNm3wz3Tcn7EMaQG/6BPAH8FcDd3wWOBapS8bwxz2/h43wz6wYMAs4ETjazgyK/8FJVRY6OATge2Nfdq8zsB8CfzOyHZnZcuG8qP4fIsYcCXwsffws4DfhmGmLIjVocSGbOxzyo+cL+E2k+H6NjiFrOxDkZHcMxZOacjD4fDgHGh4/Tdk42V6sMqjnC9tNZwP8DHgtXLwbWmlmfqOrwLwjvRtjSVeSYGB4Nn2NuWCXPNbMDgabM+tuU54+8B5VAZ6Dc3bcQNFlMN7M/m1lRS//ijYkhcsvg2UC5md1N0LZ8D5ALPAgp/xweC5/jl8C/zOwt4ASCc6ATwZeGpSCG/czs18DpUauXA+vMrDhN52Mkhi9FrX4v0kSU6vMxUQzhl2IXoCxN52S892EBUGFmd5Gec7LO+eDuNwGvm9ls0nBOtpR2lTzCP4KfADcQZPCeZvZjYDfQFzgi3C+HoI33YzNr0ap6nBh6mNlkM+sB4O5VBL/y/hH+wWBmncP/m/1LK8Hz3wQYwR/HqrBD7gSCX79fuPvOluwwTRDDL4F9CN73we5+rbu/7u63A4vN7PTER2yRGLqb2c1m1gm4GfjM3c9w95nufg9B09FFLRlDaAwwGhgZtm0DfE7wWaT8fIwTw76wt3ks/GJK2fnYQAzVBO37KT8nE8UA5AMfk4Zzsp4YAG4jfedki2jzySPmBD+KoO3w1fDEvAI4miB57ASONbPR4bYeQAd3X5PiGK4EDgeOC2sdvYDzgCfNrKuZ3QhMMbOCpv7SasTzHwacAZxI8EX6Z+ClMI4rANx9T1OeO4kYhgH7Efy63WFmp4bl+hG08b7dnOdvZAyHEvQ3dAXKzKwkLNeDoDYwoyVjMLNhwPnA6wT3YTgJwN1fBzYSnBMpPR8TxRC1vTctfD4mGcPRwKXA/5HCczJODCeHz/EZQcvELjMbF+6bknOygfehgKAG1OLnZKq06eRhwfC+O8zs4nDVCuCrke3uvoygqeREgjbeXcBUM/sJQefcuxZKYQzLCU6WM8NaRzeCE+fLBF/iOcDPwnboVD3/TOA44BOCP9BT3f0Rd/8X8EMzK0jxe7AMmAWcArwKPAvcHn4OjxP8kexI0+cwDigMt99iZtcATwNrCGoETRYVw0UWtKt/DFzi7jcSvMbhZnZ4uPufCEZapep8TBTDCAtGWkV0pgXPxyRiiLwPnwD/AE5L0TnZmM/iVeAp4LcpOifrex+Ghrt3AD6lhc/JlHL3NvWP4OTuDNwFPEnQ0fYScAtBR+RdwNVR+3cC3gOGhMunAd8DjkhjDB2Bdwk6a3sAc4FngKFpfg8OjVpXkKHPYWi4fDRwITAsAzEcHC6fBfwYODwFMdwMHBC131DgdoJRTrlR68el8HxMFMPlQE64biAwpznnY0u8Dyk+Jxv7WZSk8JxsMAaCGvGXm3tOputfxgNI8oPpGv7fheDXa69w+XCCtu3bCGoZLxOMoIiU+y1wTIZj+B1BU0pHgvbVdD//7ZH3IPLFkaWfQzpiOCyMYUrM/hcAvwaOT8PfRKNiIEioTT4fmxnDceGypfCcbDOfRVv71yaarcK+gl8Az5rZjwjaRT8CupiZuftC4BWgmGCkxEzgYTM7NCw3CliS4RhGAivcfbe7L83A85dE3gNv4uiNdvI5pCOGRQRJq6+Fwy1D/yT48XC0BddaZDqGQnff1ZTzsYViOCbSt5LCc7KtfBbNiiETWn3yMLMDCIa7FgI/B5ygancCQfNDpFNvSfivk7vfTNDR9FOCET5fd/fNbTWGTD+/YmhyDB8TJDHCL5FS4AHgPm9en0JLxVDeCmJoDe9Dm44hU/Ia3iXjdgFz3f0OAAvmAepF0E57nZm9DlS5+wYzG0wwXh13vy38VdMSH0qmY8j08yuG5GPYGMYQme7DglD8Q8WgGFo4hoxo9TUPd19HeKFdqBzoHH5Yu4BrCTqfIBjutiqqbItk80zHkOnnVwzNimF1WK7FLvRSDIqhNWj1yQPA3TdGLX6LYMQMBKNGnGBc+iyCPoXp7TGGTD+/YlAMiqH1xpAR3gp67Rv7j2AOmFeils8iuEq3L1CcDTFk+vkVg2JQDK03hnT+axM1jyjDgb+YWQ8ze4zgStRqd//Cg86nbIgh08+vGBSDYmi9MaRPprNXkpn9cqCaYNjbxGyMIdPPrxgUg2JovTGk81+buhmUmQ0hmBHzAW/GMMO2HEOmn18xKAbF0HpjSKc2lTxERKR1aGt9HiIi0gooeYiISNKUPEREJGlKHiIikjQlDxERSZqSh0gzmNnDZrbZzFaE/z41sz+Z2fBMxyaSSkoeIs33W3cf5O6DgBHAm8BrZnZ2QwXNbLiZvZHqAEVaWluYkl2kzXD3HcD9ZrYBuM/MDnT3PfUU6Ukw95FIm6Kah0gKuPvTBH9fo8zsfDN7z8xWmtkyM/sWgJldQ3Cf64Fhk9c94foOZvY7M1tiZsvN7D4z65S5VyNSl5KHSOosAQYS3PjnLHcfCIwH7jaz3h7c8+F8YGXY7HVlWO5+oA/BjKyHENxc6Ia0Ry9SDyUPkdTJAyrc/Qmg1MyGAvsDuwmSQh1m1hv4T+AKdy8Pm7x+A3wjTTGLNIr6PERSwMwKCGoO75nZbwnua/0hsAKoIrirXDwHALnAh2YWWZdDcI9skVZDyUMkNa4GZhEkg68Ah7l7JUCkzyOBNUAZcKC3g1uVSvulZiuRFmRmxWb2K+AC4BKCGkMhUBRuvxboFlVkE9DHzDqbWZ67rwbeAm41s/ywzAAzG5PO1yHSECUPkeb7cThaajnwEkGfxlh3Xx8uvwQsNrMlQAWwPFLQ3T8AniPoXP9NuHoisB/wqZl9AjyO/lalldH9PEREJGn6NSMiIklT8hARkaQpeYiISNKUPEREJGlKHiIikjQlDxERSZqSh4iIJE3JQ0REkqbkISIiSfv/Ab1bsr94U8Q8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f7c22eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_data[name_dict['SK하이닉스']].Close.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c3ea0a1390>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3e3da8390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "test[name_dict['SK하이닉스']].compare.plot()\n",
    "plt.legend(fontsize=12)\n",
    "plt.subplot(122)\n",
    "test[name_dict['SK하이닉스']].balance.plot()\n",
    "plt.legend(fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c3e46803c8>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3e18dc940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "test[name_dict['삼성전자']].compare.plot()\n",
    "plt.legend(fontsize=12)\n",
    "plt.subplot(122)\n",
    "test[name_dict['삼성전자']].balance.plot()\n",
    "plt.legend(fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c3e369c0f0>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3e85580b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "test[name_dict['동양']].compare.plot()\n",
    "plt.legend(fontsize=12)\n",
    "plt.subplot(122)\n",
    "test[name_dict['동양']].balance.plot()\n",
    "plt.legend(fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c3e853b240>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3e1f6c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "test[name_dict['BGF']].compare.plot()\n",
    "plt.legend(fontsize=12)\n",
    "plt.subplot(122)\n",
    "test[name_dict['BGF']].balance.plot()\n",
    "plt.legend(fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c3e5919e48>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3e3206f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(121)\n",
    "test[name_dict['대우조선해양']].compare.plot()\n",
    "plt.legend(fontsize=12)\n",
    "plt.subplot(122)\n",
    "test[name_dict['대우조선해양']].balance.plot()\n",
    "plt.legend(fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modelfit(alg, dtrain, predictors, target, useTrainCV = True, \n",
    "             cv_folds = 5, early_stopping_rounds = 50, top = 10, \n",
    "             figure = True, model_report = True):\n",
    "    \n",
    "    start = time.time()\n",
    "    \n",
    "    if useTrainCV:\n",
    "        xgb_param = alg.get_xgb_params()\n",
    "        xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values)\n",
    "        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n",
    "            metrics='auc', early_stopping_rounds=early_stopping_rounds, show_stdv=False)\n",
    "        alg.set_params(n_estimators=cvresult.shape[0])\n",
    "    \n",
    "    #Fit the algorithm on the data\n",
    "    alg.fit(dtrain[predictors], dtrain[target], eval_metric='auc')\n",
    "        \n",
    "    #Predict training set:\n",
    "    dtrain_predictions = alg.predict(dtrain[predictors])\n",
    "    dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]\n",
    "        \n",
    "    if model_report:\n",
    "        #Print model report:\n",
    "        print(\"Model Report\")\n",
    "        print(\"Accuracy : %.4g\" % metrics.accuracy_score(dtrain[target].values, dtrain_predictions))\n",
    "        print(\"AUC Score (Train): %f\" % metrics.roc_auc_score(dtrain[target], dtrain_predprob))\n",
    "                    \n",
    "    imp_fear_name = pd.Series(predictors)[alg.feature_importances_ > 0.01]\n",
    "    imp_fear_name.index = range(len(imp_fear_name))\n",
    "    imp_fear = pd.concat((imp_fear_name, \n",
    "               pd.Series(alg.feature_importances_[alg.feature_importances_ > 0.01])),axis=1).sort_values(by=1,ascending=True)\n",
    "    imp_fear.index = range(len(imp_fear))\n",
    "    imp_fear.columns = [\"feature_name\", \"feature_importance\"]\n",
    "#     {i:j for i,j in zip(imp_fear.index, imp_fear[\"feature_name\"])}\n",
    "    if figure:\n",
    "        if top:\n",
    "            imp_fear.iloc[-top:].plot(y='feature_importance', x='feature_name', kind='barh', legend=False, figsize=(8,5))\n",
    "        else:\n",
    "            imp_fear.plot(y='feature_importance', x='feature_name', kind='barh', legend=False, figsize=(8,5))\n",
    "    \n",
    "#     feat_imp = pd.Series(alg.feature_importances_).sort_values(ascending=False)\n",
    "#     feat_imp.plot(kind='bar', title='Feature Importances', figsize=(200,5))\n",
    "#     plt.ylabel('Feature Importance Score')\n",
    "    \n",
    "    elapse = time.time() - start\n",
    "    print(\"elapse time : {} sec\".format(round(elapse,2)))\n",
    "    \n",
    "    return alg, imp_fear, elapse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost.sklearn import XGBClassifier\n",
    "#from sklearn.model_selection import cross_validation, metrics   #Additional scklearn functions\n",
    "from sklearn.model_selection import GridSearchCV   #Perforing grid search\n",
    "\n",
    "xgb_clf = XGBClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "alg = XGBClassifier(\n",
    " learning_rate =0.1,\n",
    " n_estimators=1000,\n",
    " max_depth=5,\n",
    " min_child_weight=1,\n",
    " gamma=0,\n",
    " subsample=0.8,\n",
    " colsample_bytree=0.8,\n",
    " objective= 'binary:logistic',\n",
    " nthread=4,\n",
    " scale_pos_weight=1,\n",
    " seed=27)\n",
    "top10 = ['삼성전자', 'SK하이닉스', '셀트리온', 'POSCO', \n",
    "         '현대차', 'LG화학', 'NAVER', '삼성물산', \n",
    "         '현대모비스']\n",
    "predictors = [x for x in df.columns if x not in ['Y1','Y2','Y3']]\n",
    "target = \"Y2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Y2'"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {}\n",
    "for code in raw_data.keys():\n",
    "    data[code] = raw_data[code].loc['2012-01-01':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = {}\n",
    "train_y = {}\n",
    "test = {}\n",
    "test_y = {}\n",
    "for code, df in data.items():\n",
    "    X_train, y_train, X_test, y_test = train_test_split(df, 0.8, predictors, target, dtrain=True)\n",
    "    train[code] = X_train\n",
    "    train_y[code] = y_train\n",
    "    test[code] = X_test\n",
    "    test_y[code] = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "Accuracy : 0.9985\n",
      "AUC Score (Train): 1.000000\n",
      "elapse time : 1.94 sec\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3f72ac470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model, fear_importance, elapse = modelfit(alg, train[\"005930\"], predictors, target, cv_folds=5, early_stopping_rounds=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapse time : 1.84 sec\n"
     ]
    }
   ],
   "source": [
    "model, fear_importance, elapse = modelfit(alg, train[\"005930\"], predictors, target, cv_folds=5, early_stopping_rounds=5, \n",
    "                                  figure=False, model_report=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapse time : 1.9 sec\n",
      "elapse time : 1.3 sec\n",
      "elapse time : 1.92 sec\n",
      "elapse time : 1.73 sec\n",
      "elapse time : 1.72 sec\n",
      "elapse time : 1.7 sec\n",
      "elapse time : 1.7 sec\n",
      "elapse time : 1.7 sec\n",
      "elapse time : 1.75 sec\n",
      "elapse time : 1.91 sec\n",
      "elapse time : 1.71 sec\n",
      "elapse time : 1.72 sec\n",
      "elapse time : 1.71 sec\n",
      "elapse time : 1.67 sec\n",
      "elapse time : 1.72 sec\n",
      "elapse time : 1.82 sec\n",
      "elapse time : 1.75 sec\n",
      "elapse time : 1.69 sec\n",
      "elapse time : 1.71 sec\n",
      "elapse time : 1.79 sec\n",
      "elapse time : 1.81 sec\n",
      "elapse time : 1.68 sec\n",
      "elapse time : 1.68 sec\n",
      "elapse time : 1.77 sec\n",
      "elapse time : 1.72 sec\n",
      "elapse time : 1.75 sec\n",
      "elapse time : 1.7 sec\n",
      "elapse time : 1.71 sec\n",
      "elapse time : 1.68 sec\n",
      "elapse time : 1.6 sec\n",
      "elapse time : 0.62 sec\n",
      "elapse time : 0.85 sec\n",
      "elapse time : 0.83 sec\n",
      "elapse time : 0.89 sec\n",
      "elapse time : 0.75 sec\n",
      "elapse time : 0.8 sec\n",
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      "elapse time : 0.81 sec\n",
      "elapse time : 0.78 sec\n",
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      "elapse time : 0.82 sec\n",
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      "elapse time : 0.77 sec\n",
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      "elapse time : 0.81 sec\n",
      "elapse time : 0.76 sec\n",
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      "elapse time : 0.67 sec\n",
      "elapse time : 0.81 sec\n",
      "elapse time : 0.82 sec\n",
      "elapse time : 0.76 sec\n",
      "elapse time : 0.78 sec\n",
      "elapse time : 0.75 sec\n",
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      "elapse time : 0.85 sec\n",
      "elapse time : 0.92 sec\n",
      "elapse time : 0.88 sec\n",
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      "elapse time : 0.91 sec\n",
      "elapse time : 0.87 sec\n",
      "elapse time : 0.87 sec\n",
      "elapse time : 0.5 sec\n",
      "elapse time : 0.76 sec\n",
      "elapse time : 0.55 sec\n",
      "elapse time : 0.76 sec\n",
      "elapse time : 0.78 sec\n",
      "elapse time : 0.77 sec\n",
      "elapse time : 0.78 sec\n",
      "elapse time : 0.8 sec\n",
      "elapse time : 0.8 sec\n",
      "elapse time : 0.76 sec\n",
      "elapse time : 0.8 sec\n",
      "elapse time : 0.79 sec\n",
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      "elapse time : 0.77 sec\n",
      "elapse time : 0.82 sec\n",
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      "elapse time : 0.79 sec\n",
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      "elapse time : 0.79 sec\n",
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      "elapse time : 0.81 sec\n",
      "elapse time : 0.45 sec\n",
      "elapse time : 0.77 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.75 sec\n",
      "elapse time : 0.58 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.57 sec\n",
      "elapse time : 0.77 sec\n",
      "elapse time : 0.73 sec\n",
      "elapse time : 0.53 sec\n",
      "elapse time : 0.8 sec\n",
      "elapse time : 0.78 sec\n",
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      "elapse time : 0.77 sec\n",
      "elapse time : 0.76 sec\n",
      "elapse time : 0.8 sec\n",
      "elapse time : 0.76 sec\n",
      "elapse time : 0.77 sec\n",
      "elapse time : 0.75 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.8 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.88 sec\n",
      "elapse time : 0.68 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.8 sec\n",
      "elapse time : 0.81 sec\n",
      "elapse time : 0.77 sec\n",
      "elapse time : 0.78 sec\n",
      "elapse time : 0.77 sec\n",
      "elapse time : 0.78 sec\n",
      "elapse time : 0.76 sec\n",
      "elapse time : 0.77 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.76 sec\n",
      "elapse time : 0.75 sec\n",
      "elapse time : 0.82 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.82 sec\n",
      "elapse time : 0.75 sec\n",
      "elapse time : 0.82 sec\n",
      "elapse time : 0.78 sec\n",
      "elapse time : 0.78 sec\n",
      "elapse time : 0.8 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.62 sec\n",
      "elapse time : 0.82 sec\n",
      "elapse time : 0.8 sec\n",
      "elapse time : 0.75 sec\n",
      "elapse time : 0.78 sec\n",
      "elapse time : 0.82 sec\n",
      "elapse time : 0.84 sec\n",
      "elapse time : 0.78 sec\n",
      "elapse time : 0.77 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.78 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.79 sec\n",
      "elapse time : 0.89 sec\n",
      "elapse time : 0.89 sec\n",
      "elapse time : 0.85 sec\n",
      "\n",
      "total time : 3.0 minute\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(\"ignore\", DeprecationWarning)\n",
    "\n",
    "total_time = 0\n",
    "test_result = {}\n",
    "fi_dict = {}\n",
    "for i in raw_data.keys():\n",
    "    model, fear_importance, elapse = modelfit(alg, train[i], predictors, target, \n",
    "                                              cv_folds=5, early_stopping_rounds=20,\n",
    "                                              figure=False, model_report=False)\n",
    "    start = time.time()\n",
    "    pred = model.predict(test[i][predictors])\n",
    "    # Calculating Score\n",
    "    test_result[i] = np.array([\n",
    "        metrics.accuracy_score(pred, test[i][target]),\n",
    "        metrics.f1_score(pred, test[i][target]),\n",
    "        metrics.precision_score(pred, test[i][target]),\n",
    "        metrics.recall_score(pred, test[i][target]),\n",
    "        metrics.roc_auc_score(pred, test[i][target])\n",
    "    ])\n",
    "    fi_dict[i] = {feature:value for feature,value in fear_importance.iloc[-10:].values}\n",
    "    #print(\"test accuracy {} : {}\".format(code_dict[i], accuracy[i]))\n",
    "    total_time += elapse + time.time() - start\n",
    "print(\"\\ntotal time : {} minute\".format(round(total_time / 60,2)))\n",
    "\n",
    "result = pd.DataFrame(test_result, index = score_li).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_li = ['accuracy_score', 'f1_score', 'precision_score', 'recall_score', 'roc_auc_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c3f5f06550>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3e8012a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_data['000030'].Close.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'우리은행'"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "code_dict['000030']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "accuracy_score     0.876270\n",
       "f1_score           0.871108\n",
       "precision_score    0.861949\n",
       "recall_score       0.885164\n",
       "roc_auc_score      0.879649\n",
       "dtype: float64"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.apply(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>업종</th>\n",
       "      <th>영문코드</th>\n",
       "      <th>종목명</th>\n",
       "      <th>종목코드</th>\n",
       "      <th>시가총액</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>정보기술</td>\n",
       "      <td>SEC</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>005930</td>\n",
       "      <td>296893767375000</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>정보기술</td>\n",
       "      <td>SKH</td>\n",
       "      <td>SK하이닉스</td>\n",
       "      <td>000660</td>\n",
       "      <td>62826604099500</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>헬스케어</td>\n",
       "      <td>CTRN</td>\n",
       "      <td>셀트리온</td>\n",
       "      <td>068270</td>\n",
       "      <td>34044987664500</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>철강소재</td>\n",
       "      <td>POSCO</td>\n",
       "      <td>POSCO</td>\n",
       "      <td>005490</td>\n",
       "      <td>28815248967500</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>경기소비재</td>\n",
       "      <td>HMC</td>\n",
       "      <td>현대차</td>\n",
       "      <td>005380</td>\n",
       "      <td>28525804030500</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      업종   영문코드     종목명    종목코드             시가총액 income\n",
       "0   정보기술    SEC    삼성전자  005930  296893767375000   high\n",
       "1   정보기술    SKH  SK하이닉스  000660   62826604099500   high\n",
       "2   헬스케어   CTRN    셀트리온  068270   34044987664500   high\n",
       "3   철강소재  POSCO   POSCO  005490   28815248967500   high\n",
       "4  경기소비재    HMC     현대차  005380   28525804030500   high"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "result['income'] = np.NaN\n",
    "result['업종'] = np.NaN\n",
    "for i in result.index:\n",
    "    result.loc[i, 'income'] = party_df[party_df['종목코드'] == i]['income'].values[0]\n",
    "    result.loc[i, '업종'] = party_df[party_df['종목코드'] == i]['업종'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy_score</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>precision_score</th>\n",
       "      <th>recall_score</th>\n",
       "      <th>roc_auc_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.876270</td>\n",
       "      <td>0.871108</td>\n",
       "      <td>0.861949</td>\n",
       "      <td>0.885164</td>\n",
       "      <td>0.879649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.028807</td>\n",
       "      <td>0.031483</td>\n",
       "      <td>0.060504</td>\n",
       "      <td>0.044232</td>\n",
       "      <td>0.026927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.698980</td>\n",
       "      <td>0.697436</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>0.726744</td>\n",
       "      <td>0.725252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.861111</td>\n",
       "      <td>0.856250</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.854430</td>\n",
       "      <td>0.865326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.882716</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.869281</td>\n",
       "      <td>0.887574</td>\n",
       "      <td>0.883908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.895062</td>\n",
       "      <td>0.892617</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.898626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.932099</td>\n",
       "      <td>0.934211</td>\n",
       "      <td>0.976048</td>\n",
       "      <td>0.984252</td>\n",
       "      <td>0.931545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       accuracy_score    f1_score  precision_score  recall_score  \\\n",
       "count      193.000000  193.000000       193.000000    193.000000   \n",
       "mean         0.876270    0.871108         0.861949      0.885164   \n",
       "std          0.028807    0.031483         0.060504      0.044232   \n",
       "min          0.698980    0.697436         0.586207      0.726744   \n",
       "25%          0.861111    0.856250         0.833333      0.854430   \n",
       "50%          0.882716    0.875000         0.869281      0.887574   \n",
       "75%          0.895062    0.892617         0.900000      0.916667   \n",
       "max          0.932099    0.934211         0.976048      0.984252   \n",
       "\n",
       "       roc_auc_score  \n",
       "count     193.000000  \n",
       "mean        0.879649  \n",
       "std         0.026927  \n",
       "min         0.725252  \n",
       "25%         0.865326  \n",
       "50%         0.883908  \n",
       "75%         0.898626  \n",
       "max         0.931545  "
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "high\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       30.000000  30.000000        30.000000     30.000000      30.000000\n",
      "mean         0.880804   0.873363         0.842269      0.909818       0.885094\n",
      "std          0.016595   0.019910         0.046568      0.030339       0.015354\n",
      "min          0.839506   0.815603         0.723270      0.837989       0.846210\n",
      "25%          0.873655   0.868757         0.814691      0.894327       0.878753\n",
      "50%          0.884259   0.875462         0.848947      0.912916       0.886957\n",
      "75%          0.891204   0.882465         0.863517      0.930489       0.895241\n",
      "max          0.907407   0.908537         0.925926      0.961240       0.909398 \n",
      "\n",
      "low\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       49.000000  49.000000        49.000000     49.000000      49.000000\n",
      "mean         0.875055   0.868907         0.853212      0.890582       0.879099\n",
      "std          0.036794   0.038862         0.069531      0.045359       0.033824\n",
      "min          0.698980   0.697436         0.586207      0.802395       0.725252\n",
      "25%          0.861111   0.851613         0.833333      0.853659       0.868194\n",
      "50%          0.879630   0.875000         0.859873      0.894737       0.880125\n",
      "75%          0.898148   0.894040         0.897590      0.923077       0.900402\n",
      "max          0.932099   0.927632         0.965517      0.984252       0.931545 \n",
      "\n",
      "lower middle\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       43.000000  43.000000        43.000000     43.000000      43.000000\n",
      "mean         0.877173   0.874598         0.880555      0.871774       0.879423\n",
      "std          0.030340   0.031732         0.055807      0.039952       0.030074\n",
      "min          0.772973   0.760943         0.693252      0.766990       0.773739\n",
      "25%          0.868827   0.863184         0.853797      0.849629       0.871722\n",
      "50%          0.884793   0.877493         0.885417      0.871795       0.884656\n",
      "75%          0.898148   0.896217         0.916567      0.893542       0.900875\n",
      "max          0.916667   0.916923         0.976048      0.966387       0.916924 \n",
      "\n",
      "middle\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       49.000000  49.000000        49.000000     49.000000      49.000000\n",
      "mean         0.873283   0.868509         0.866723      0.875967       0.876778\n",
      "std          0.027474   0.031506         0.061108      0.052065       0.024459\n",
      "min          0.796296   0.791139         0.684848      0.726744       0.800872\n",
      "25%          0.854938   0.850932         0.850932      0.838926       0.859472\n",
      "50%          0.876543   0.872852         0.875776      0.882716       0.877318\n",
      "75%          0.895062   0.892617         0.901961      0.909677       0.895667\n",
      "max          0.919753   0.934211         0.961290      0.973684       0.919497 \n",
      "\n",
      "upper middle\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       22.000000  22.000000        22.000000     22.000000      22.000000\n",
      "mean         0.877682   0.871903         0.861242      0.886131       0.880287\n",
      "std          0.022106   0.026836         0.056267      0.031995       0.021205\n",
      "min          0.827160   0.816479         0.726667      0.814607       0.827559\n",
      "25%          0.864969   0.856881         0.823188      0.867543       0.866876\n",
      "50%          0.879630   0.872732         0.866196      0.887395       0.881775\n",
      "75%          0.891975   0.884793         0.896507      0.908982       0.891387\n",
      "max          0.922840   0.925816         0.962963      0.931624       0.925580 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for income in result.income.unique():\n",
    "    print(income)\n",
    "    print(result.groupby('income').get_group(income).describe(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "금융\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       17.000000  17.000000        17.000000     17.000000      17.000000\n",
      "mean         0.872186   0.864759         0.842081      0.894556       0.876773\n",
      "std          0.027865   0.032285         0.055880      0.059186       0.026158\n",
      "min          0.796296   0.791139         0.707006      0.726744       0.800872\n",
      "25%          0.854938   0.850932         0.827381      0.864516       0.866778\n",
      "50%          0.876543   0.874598         0.858974      0.917241       0.880000\n",
      "75%          0.891975   0.882943         0.871951      0.931624       0.895351\n",
      "max          0.910494   0.908517         0.920245      0.973684       0.911262 \n",
      "\n",
      "생활소비재\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       35.000000  35.000000        35.000000     35.000000      35.000000\n",
      "mean         0.868637   0.862297         0.849481      0.881873       0.872992\n",
      "std          0.039891   0.042074         0.078535      0.043705       0.035790\n",
      "min          0.698980   0.697436         0.586207      0.806818       0.725252\n",
      "25%          0.856481   0.845673         0.826713      0.850478       0.858632\n",
      "50%          0.876543   0.872131         0.862069      0.877698       0.879644\n",
      "75%          0.891975   0.886309         0.902029      0.918983       0.894851\n",
      "max          0.922840   0.920128         0.961290      0.966387       0.922848 \n",
      "\n",
      "헬스케어\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       17.000000  17.000000        17.000000     17.000000      17.000000\n",
      "mean         0.882538   0.880910         0.892416      0.872836       0.885073\n",
      "std          0.023023   0.024947         0.041259      0.049409       0.022701\n",
      "min          0.827160   0.829268         0.836735      0.739130       0.840994\n",
      "25%          0.864198   0.860681         0.865031      0.850575       0.863687\n",
      "50%          0.888889   0.892405         0.883436      0.860656       0.892063\n",
      "75%          0.898148   0.899696         0.921687      0.911111       0.901903\n",
      "max          0.913580   0.914110         0.976048      0.956204       0.914022 \n",
      "\n",
      "산업재\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       15.000000  15.000000        15.000000     15.000000      15.000000\n",
      "mean         0.886535   0.881019         0.877702      0.886821       0.888241\n",
      "std          0.019995   0.025823         0.055231      0.021738       0.018806\n",
      "min          0.854938   0.832740         0.769737      0.823529       0.860481\n",
      "25%          0.867284   0.860305         0.838109      0.880394       0.869626\n",
      "50%          0.890625   0.888158         0.882353      0.889535       0.894194\n",
      "75%          0.899691   0.898209         0.919606      0.902830       0.900769\n",
      "max          0.919753   0.917197         0.965517      0.912162       0.919497 \n",
      "\n",
      "건설\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       13.000000  13.000000        13.000000     13.000000      13.000000\n",
      "mean         0.868611   0.862919         0.859743      0.870724       0.871689\n",
      "std          0.023244   0.026330         0.055895      0.046383       0.020947\n",
      "min          0.830247   0.814286         0.716981      0.775281       0.836271\n",
      "25%          0.858025   0.851613         0.837349      0.837349       0.858629\n",
      "50%          0.862928   0.863354         0.886667      0.878049       0.863836\n",
      "75%          0.888889   0.888889         0.900000      0.898649       0.889024\n",
      "max          0.901235   0.899371         0.910828      0.942149       0.904861 \n",
      "\n",
      "경기소비재\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       38.000000  38.000000        38.000000     38.000000      38.000000\n",
      "mean         0.875422   0.872468         0.871365      0.877138       0.877871\n",
      "std          0.034035   0.035566         0.060299      0.042807       0.032856\n",
      "min          0.772973   0.782313         0.688623      0.766990       0.773739\n",
      "25%          0.864969   0.861104         0.835256      0.852679       0.866949\n",
      "50%          0.881358   0.877360         0.882183      0.884691       0.882560\n",
      "75%          0.897377   0.898602         0.912707      0.904940       0.898506\n",
      "max          0.932099   0.927632         0.962963      0.953846       0.931545 \n",
      "\n",
      "정보기술\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       15.000000  15.000000        15.000000     15.000000      15.000000\n",
      "mean         0.876781   0.866351         0.830041      0.910909       0.883060\n",
      "std          0.020437   0.023965         0.055279      0.042514       0.018641\n",
      "min          0.839506   0.812950         0.706250      0.821429       0.846210\n",
      "25%          0.865741   0.852615         0.809649      0.890366       0.878469\n",
      "50%          0.885802   0.872852         0.838509      0.913669       0.887899\n",
      "75%          0.890432   0.880367         0.868288      0.945180       0.890755\n",
      "max          0.907407   0.901961         0.900000      0.961240       0.909398 \n",
      "\n",
      "철강소재\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       10.000000  10.000000        10.000000     10.000000      10.000000\n",
      "mean         0.880556   0.871844         0.835124      0.915235       0.885913\n",
      "std          0.013179   0.015888         0.042232      0.040866       0.015598\n",
      "min          0.864198   0.846975         0.788462      0.853659       0.871452\n",
      "25%          0.874228   0.864432         0.805871      0.890351       0.877025\n",
      "50%          0.878086   0.872421         0.820673      0.921793       0.883042\n",
      "75%          0.882716   0.883436         0.864129      0.934420       0.886224\n",
      "max          0.910494   0.896057         0.905660      0.984252       0.923598 \n",
      "\n",
      "에너지화학\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       24.000000  24.000000        24.000000     24.000000      24.000000\n",
      "mean         0.883616   0.878055         0.868794      0.890973       0.886261\n",
      "std          0.019015   0.020765         0.049869      0.034967       0.016432\n",
      "min          0.839506   0.815603         0.723270      0.809783       0.844177\n",
      "25%          0.878086   0.869784         0.850556      0.882039       0.879724\n",
      "50%          0.885802   0.877336         0.870528      0.896875       0.886419\n",
      "75%          0.894290   0.890810         0.911012      0.907895       0.899631\n",
      "max          0.916667   0.916923         0.931250      0.948529       0.916924 \n",
      "\n",
      "중공업\n",
      "       accuracy_score  f1_score  precision_score  recall_score  roc_auc_score\n",
      "count        9.000000  9.000000         9.000000      9.000000       9.000000\n",
      "mean         0.874161  0.877003         0.892314      0.863676       0.875135\n",
      "std          0.029092  0.034513         0.047717      0.040764       0.029594\n",
      "min          0.827160  0.828221         0.808383      0.818966       0.827559\n",
      "25%          0.858025  0.852018         0.865385      0.838150       0.857519\n",
      "50%          0.861111  0.863095         0.887850      0.849057       0.860892\n",
      "75%          0.901235  0.903226         0.935065      0.874317       0.905128\n",
      "max          0.907407  0.934211         0.959459      0.945946       0.910473 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for income in result['업종'].unique():\n",
    "    print(income)\n",
    "    print(result.groupby('업종').get_group(income).describe(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = pd.DataFrame(fi_dict).T.notnull().sum().sort_values(ascending=False).index[:5].tolist()\n",
    "targets = 'Y2'\n",
    "\n",
    "alg = XGBClassifier(\n",
    " learning_rate =0.1,\n",
    " n_estimators=1000,\n",
    " max_depth=5,\n",
    " min_child_weight=1,\n",
    " gamma=0,\n",
    " subsample=0.8,\n",
    " colsample_bytree=0.8,\n",
    " objective= 'binary:logistic',\n",
    " nthread=4,\n",
    " scale_pos_weight=1,\n",
    " seed=27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pct_change', 'his_vol_7', 'his_vol_21', 'Volume', 'Open']"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapse time : 0.7 sec\n",
      "elapse time : 0.45 sec\n",
      "elapse time : 0.52 sec\n",
      "elapse time : 0.39 sec\n",
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      "elapse time : 0.34 sec\n",
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      "elapse time : 0.33 sec\n",
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      "elapse time : 0.32 sec\n",
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      "elapse time : 0.33 sec\n",
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      "elapse time : 0.34 sec\n",
      "elapse time : 0.33 sec\n",
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      "elapse time : 0.34 sec\n",
      "elapse time : 0.32 sec\n",
      "elapse time : 0.32 sec\n",
      "elapse time : 0.34 sec\n",
      "elapse time : 0.32 sec\n",
      "\n",
      "total time : 1.13 minute\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(\"ignore\", DeprecationWarning)\n",
    "\n",
    "total_time = 0\n",
    "test_result = {}\n",
    "fi_dict = {}\n",
    "for i in raw_data.keys():\n",
    "    model, fear_importance, elapse = modelfit(alg, train[i], predictors, target, \n",
    "                                              cv_folds=5, early_stopping_rounds=20,\n",
    "                                              figure=False, model_report=False)\n",
    "    start = time.time()\n",
    "    pred = model.predict(test[i][predictors])\n",
    "    # Calculating Score\n",
    "    test_result[i] = np.array([\n",
    "        metrics.accuracy_score(pred, test[i][target]),\n",
    "        metrics.f1_score(pred, test[i][target]),\n",
    "        metrics.precision_score(pred, test[i][target]),\n",
    "        metrics.recall_score(pred, test[i][target]),\n",
    "        metrics.roc_auc_score(pred, test[i][target])\n",
    "    ])\n",
    "    #fi_dict[i] = {feature:value for feature,value in fear_importance.iloc[-10:].values}\n",
    "    #print(\"test accuracy {} : {}\".format(code_dict[i], accuracy[i]))\n",
    "    total_time += elapse + time.time() - start\n",
    "print(\"\\ntotal time : {} minute\".format(round(total_time / 60,2)))\n",
    "\n",
    "result = pd.DataFrame(test_result, index = score_li).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "result['income'] = np.NaN\n",
    "result['업종'] = np.NaN\n",
    "for i in result.index:\n",
    "    result.loc[i, 'income'] = party_df[party_df['종목코드'] == i]['income'].values[0]\n",
    "    result.loc[i, '업종'] = party_df[party_df['종목코드'] == i]['업종'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy_score</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>precision_score</th>\n",
       "      <th>recall_score</th>\n",
       "      <th>roc_auc_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.863834</td>\n",
       "      <td>0.857158</td>\n",
       "      <td>0.843037</td>\n",
       "      <td>0.876806</td>\n",
       "      <td>0.867663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.030262</td>\n",
       "      <td>0.033660</td>\n",
       "      <td>0.065427</td>\n",
       "      <td>0.042256</td>\n",
       "      <td>0.028726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.698980</td>\n",
       "      <td>0.691099</td>\n",
       "      <td>0.568966</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.725146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.854938</td>\n",
       "      <td>0.843972</td>\n",
       "      <td>0.805195</td>\n",
       "      <td>0.847826</td>\n",
       "      <td>0.856985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.867284</td>\n",
       "      <td>0.861538</td>\n",
       "      <td>0.848101</td>\n",
       "      <td>0.877301</td>\n",
       "      <td>0.871035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.879630</td>\n",
       "      <td>0.875776</td>\n",
       "      <td>0.884956</td>\n",
       "      <td>0.910448</td>\n",
       "      <td>0.884496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.922840</td>\n",
       "      <td>0.937634</td>\n",
       "      <td>0.981982</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>0.925215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       accuracy_score    f1_score  precision_score  recall_score  \\\n",
       "count      193.000000  193.000000       193.000000    193.000000   \n",
       "mean         0.863834    0.857158         0.843037      0.876806   \n",
       "std          0.030262    0.033660         0.065427      0.042256   \n",
       "min          0.698980    0.691099         0.568966      0.755556   \n",
       "25%          0.854938    0.843972         0.805195      0.847826   \n",
       "50%          0.867284    0.861538         0.848101      0.877301   \n",
       "75%          0.879630    0.875776         0.884956      0.910448   \n",
       "max          0.922840    0.937634         0.981982      0.965517   \n",
       "\n",
       "       roc_auc_score  \n",
       "count     193.000000  \n",
       "mean        0.867663  \n",
       "std         0.028726  \n",
       "min         0.725146  \n",
       "25%         0.856985  \n",
       "50%         0.871035  \n",
       "75%         0.884496  \n",
       "max         0.925215  "
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low             49\n",
       "middle          49\n",
       "lower middle    43\n",
       "high            30\n",
       "upper middle    22\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.income.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "경기소비재    38\n",
       "생활소비재    35\n",
       "에너지화학    24\n",
       "금융       17\n",
       "헬스케어     17\n",
       "정보기술     15\n",
       "산업재      15\n",
       "건설       13\n",
       "철강소재     10\n",
       "중공업       9\n",
       "Name: 업종, dtype: int64"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['업종'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "high\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       30.000000  30.000000        30.000000     30.000000      30.000000\n",
      "mean         0.864030   0.854350         0.816809      0.898398       0.869018\n",
      "std          0.018202   0.021372         0.046747      0.029614       0.017316\n",
      "min          0.820988   0.804054         0.734568      0.813725       0.830872\n",
      "25%          0.858025   0.841694         0.786631      0.882792       0.867250\n",
      "50%          0.870370   0.859498         0.816319      0.896678       0.873039\n",
      "75%          0.875772   0.866879         0.847859      0.921392       0.878708\n",
      "max          0.885802   0.893372         0.945122      0.945736       0.891582 \n",
      "\n",
      "low\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       49.000000  49.000000        49.000000     49.000000      49.000000\n",
      "mean         0.857658   0.848789         0.826271      0.879674       0.863067\n",
      "std          0.038864   0.043387         0.080258      0.044239       0.034199\n",
      "min          0.698980   0.691099         0.568966      0.792593       0.733388\n",
      "25%          0.845679   0.841379         0.791139      0.857143       0.850275\n",
      "50%          0.864198   0.849315         0.836601      0.885714       0.867336\n",
      "75%          0.873457   0.871951         0.878049      0.915493       0.878183\n",
      "max          0.922840   0.926686         0.957576      0.951613       0.925215 \n",
      "\n",
      "lower middle\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       43.000000  43.000000        43.000000     43.000000      43.000000\n",
      "mean         0.862685   0.859740         0.865212      0.857793       0.865058\n",
      "std          0.033570   0.034777         0.061597      0.041556       0.033927\n",
      "min          0.724324   0.727273         0.701031      0.755556       0.725146\n",
      "25%          0.858919   0.854767         0.846620      0.826852       0.860211\n",
      "50%          0.870056   0.863222         0.881250      0.860759       0.871514\n",
      "75%          0.879630   0.878033         0.897203      0.877753       0.885322\n",
      "max          0.907407   0.905063         0.975000      0.957143       0.907594 \n",
      "\n",
      "middle\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       49.000000  49.000000        49.000000     49.000000      49.000000\n",
      "mean         0.868725   0.862506         0.852524      0.877393       0.872213\n",
      "std          0.024131   0.027904         0.059557      0.042666       0.024030\n",
      "min          0.802469   0.804878         0.713376      0.785714       0.803114\n",
      "25%          0.854938   0.845679         0.825000      0.845714       0.856510\n",
      "50%          0.867284   0.862974         0.850932      0.869565       0.869754\n",
      "75%          0.879630   0.870968         0.889610      0.907285       0.883630\n",
      "max          0.922840   0.937634         0.981982      0.965517       0.924798 \n",
      "\n",
      "upper middle\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       22.000000  22.000000        22.000000     22.000000      22.000000\n",
      "mean         0.868673   0.862673         0.851670      0.876833       0.871006\n",
      "std          0.026571   0.030652         0.052611      0.039998       0.026641\n",
      "min          0.811728   0.795699         0.740000      0.806818       0.814598\n",
      "25%          0.858025   0.843519         0.826844      0.848842       0.860317\n",
      "50%          0.870370   0.864367         0.845798      0.864873       0.871563\n",
      "75%          0.891204   0.885617         0.890242      0.904916       0.892871\n",
      "max          0.916364   0.911197         0.952096      0.951613       0.919515 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for income in result.income.unique():\n",
    "    print(income)\n",
    "    print(result.groupby('income').get_group(income).describe(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "금융\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       17.000000  17.000000        17.000000     17.000000      17.000000\n",
      "mean         0.860748   0.851594         0.821904      0.887005       0.864736\n",
      "std          0.018418   0.023218         0.049552      0.036783       0.018581\n",
      "min          0.824074   0.795699         0.713376      0.836478       0.830233\n",
      "25%          0.851852   0.844595         0.785276      0.860465       0.855743\n",
      "50%          0.864198   0.858108         0.831081      0.885135       0.869754\n",
      "75%          0.873457   0.865031         0.850932      0.917197       0.874586\n",
      "max          0.885802   0.886154         0.892617      0.965517       0.894853 \n",
      "\n",
      "생활소비재\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       35.000000  35.000000        35.000000     35.000000      35.000000\n",
      "mean         0.857498   0.849388         0.829684      0.875419       0.861459\n",
      "std          0.037946   0.040208         0.074160      0.036450       0.033833\n",
      "min          0.698980   0.691099         0.568966      0.798611       0.733388\n",
      "25%          0.858025   0.843225         0.796305      0.854430       0.858504\n",
      "50%          0.864198   0.856250         0.830065      0.870748       0.867260\n",
      "75%          0.875000   0.869122         0.883117      0.896701       0.876721\n",
      "max          0.898148   0.899083         0.954839      0.952381       0.898285 \n",
      "\n",
      "헬스케어\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       17.000000  17.000000        17.000000     17.000000      17.000000\n",
      "mean         0.869184   0.865153         0.863099      0.869419       0.870696\n",
      "std          0.018662   0.022626         0.043807      0.031119       0.017725\n",
      "min          0.828000   0.815603         0.737179      0.792593       0.831079\n",
      "25%          0.861111   0.854305         0.846626      0.854054       0.859237\n",
      "50%          0.870968   0.867925         0.866242      0.878205       0.871197\n",
      "75%          0.882716   0.878205         0.884354      0.890323       0.884278\n",
      "max          0.891975   0.897727         0.946108      0.912698       0.894653 \n",
      "\n",
      "산업재\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       15.000000  15.000000        15.000000     15.000000      15.000000\n",
      "mean         0.869226   0.861896         0.849521      0.877497       0.871742\n",
      "std          0.024617   0.028006         0.050652      0.039575       0.025341\n",
      "min          0.802469   0.804878         0.763514      0.785714       0.803114\n",
      "25%          0.863329   0.848948         0.821305      0.858301       0.862943\n",
      "50%          0.873457   0.864353         0.845679      0.870588       0.878076\n",
      "75%          0.884259   0.878840         0.875817      0.905465       0.886088\n",
      "max          0.898148   0.903409         0.952096      0.944056       0.900953 \n",
      "\n",
      "건설\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       13.000000  13.000000        13.000000     13.000000      13.000000\n",
      "mean         0.851277   0.842576         0.832846      0.858307       0.854953\n",
      "std          0.040783   0.047885         0.085442      0.037861       0.037360\n",
      "min          0.753086   0.735099         0.654088      0.809524       0.761812\n",
      "25%          0.842593   0.839117         0.822785      0.831325       0.842949\n",
      "50%          0.859813   0.854489         0.853333      0.851064       0.859958\n",
      "75%          0.867284   0.865031         0.888889      0.882353       0.866997\n",
      "max          0.907407   0.909091         0.937500      0.927536       0.908709 \n",
      "\n",
      "경기소비재\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       38.000000  38.000000        38.000000     38.000000      38.000000\n",
      "mean         0.866425   0.863919         0.868590      0.865496       0.870502\n",
      "std          0.037692   0.040024         0.074699      0.052435       0.036330\n",
      "min          0.724324   0.727273         0.628743      0.755556       0.725146\n",
      "25%          0.845679   0.846890         0.840592      0.821863       0.848340\n",
      "50%          0.867284   0.862532         0.876524      0.865723       0.873771\n",
      "75%          0.891204   0.888889         0.922106      0.904054       0.895312\n",
      "max          0.922840   0.926686         0.975000      0.957143       0.925215 \n",
      "\n",
      "정보기술\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       15.000000  15.000000        15.000000     15.000000      15.000000\n",
      "mean         0.868274   0.856004         0.815443      0.905946       0.875453\n",
      "std          0.018296   0.023344         0.059075      0.037990       0.016398\n",
      "min          0.829670   0.818505         0.725000      0.813725       0.831863\n",
      "25%          0.858025   0.837405         0.775189      0.887417       0.871000\n",
      "50%          0.873457   0.864686         0.829114      0.904110       0.876264\n",
      "75%          0.881173   0.868588         0.846411      0.932665       0.887845\n",
      "max          0.891975   0.892857         0.937500      0.958678       0.895529 \n",
      "\n",
      "철강소재\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       10.000000  10.000000        10.000000     10.000000      10.000000\n",
      "mean         0.859568   0.849762         0.816774      0.889178       0.864444\n",
      "std          0.015277   0.017681         0.045414      0.040208       0.014880\n",
      "min          0.833333   0.817568         0.746914      0.804734       0.834625\n",
      "25%          0.858025   0.841201         0.788185      0.888249       0.861700\n",
      "50%          0.862654   0.846306         0.806362      0.898861       0.869148\n",
      "75%          0.869599   0.862801         0.843755      0.916346       0.872344\n",
      "max          0.879630   0.876972         0.899371      0.926667       0.882874 \n",
      "\n",
      "에너지화학\n",
      "       accuracy_score   f1_score  precision_score  recall_score  roc_auc_score\n",
      "count       24.000000  24.000000        24.000000     24.000000      24.000000\n",
      "mean         0.867413   0.859552         0.839565      0.884287       0.870674\n",
      "std          0.023602   0.023906         0.049219      0.042342       0.023110\n",
      "min          0.811728   0.804054         0.734568      0.796610       0.813271\n",
      "25%          0.861111   0.845688         0.802183      0.854773       0.862810\n",
      "50%          0.871914   0.862069         0.849869      0.886705       0.872148\n",
      "75%          0.882716   0.873413         0.880123      0.919860       0.886479\n",
      "max          0.910494   0.906149         0.915584      0.951613       0.911108 \n",
      "\n",
      "중공업\n",
      "       accuracy_score  f1_score  precision_score  recall_score  roc_auc_score\n",
      "count        9.000000  9.000000         9.000000      9.000000       9.000000\n",
      "mean         0.870201  0.871165         0.877437      0.867957       0.873720\n",
      "std          0.034128  0.040862         0.066216      0.043686       0.036207\n",
      "min          0.811728  0.807571         0.766467      0.809917       0.814598\n",
      "25%          0.854938  0.853147         0.852564      0.840491       0.854938\n",
      "50%          0.867284  0.859649         0.888889      0.858025       0.867450\n",
      "75%          0.891975  0.900850         0.915888      0.897119       0.896351\n",
      "max          0.922840  0.937634         0.981982      0.953642       0.924798 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for income in result['업종'].unique():\n",
    "    print(income)\n",
    "    print(result.groupby('업종').get_group(income).describe(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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