Untitled29.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",
    "matplotlib.rcParams['axes.unicode_minus'] = False\n",
    "\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('raw_data_20181008.pickle', 'rb') as handle:\n",
    "    raw_data = pickle.load(handle)\n",
    "    \n",
    "copy_keys = list(raw_data.keys())\n",
    "for stock in copy_keys:\n",
    "    if len(raw_data[stock].loc[:'2013-01-01']) < 1:\n",
    "        del raw_data[stock]\n",
    "del copy_keys\n",
    "\n",
    "# stock에서 Volume==0인 index, stock에서 제외\n",
    "for key, df in raw_data.items():\n",
    "    raw_data[key] = df.drop(df.index[np.where(df.Volume==0)[0]], axis=0)\n",
    "    del df['Adj Close']\n",
    "    df.Volume = df.Volume / 1e7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "삼성전자\n"
     ]
    }
   ],
   "source": [
    "for key, df in raw_data.items():\n",
    "    if key != '삼성전자':\n",
    "        continue\n",
    "    print(key)\n",
    "    for i in range(1, 91):\n",
    "        df['Y{}'.format(i)] = df['Close'].shift(-1*i) - df['Close']\n",
    "\n",
    "    # RSI\n",
    "    U = np.where(df.Close.diff(1) > 0, df.Close.diff(1), 0)\n",
    "    D = np.where(df.Close.diff(1) < 0, df.Close.diff(1)*(-1), 0)\n",
    "    m_N = 14\n",
    "    AU = pd.DataFrame(U).rolling(window=m_N, min_periods=m_N).mean()\n",
    "    AD = pd.DataFrame(D).rolling(window=m_N, min_periods=m_N).mean()\n",
    "    RSI = AU.div(AD+AU)*100\n",
    "    df['rsi'] = RSI.values\n",
    "\n",
    "    # William%R\n",
    "    n = 14\n",
    "    W_R = -100 * ((df.High.rolling(n).max() - df.Close) / \n",
    "       (df.High.rolling(n).max() - df.Low.rolling(n).min()))\n",
    "    df['w_r'] = W_R\n",
    "\n",
    "    # Stochastic Oscillator\n",
    "    percent_K = 100 * ((df.Close - df.Low.rolling(n).min()) / \n",
    "       (df.High.rolling(n).max() - df.Low.rolling(n).min()))\n",
    "    df['sto_K'] = percent_K\n",
    "\n",
    "    # Commodity Channel Index\n",
    "    TP = (df.Close + df.Low + df.High) / 3\n",
    "    window = 20\n",
    "    dataX = []\n",
    "    for i in range(len(TP) - window+1):\n",
    "        a = TP.values[i:(i+window)]\n",
    "        dataX.append(a)\n",
    "    dataMean = [i.mean() for i in dataX]\n",
    "    AVEDEV = [abs(x - mean).mean() for x, mean \n",
    "              in zip(dataX, dataMean)]\n",
    "    for i in range(window-1):\n",
    "        AVEDEV.insert(0,np.nan)\n",
    "    AVEDEV = pd.Series(AVEDEV, index = TP.index)\n",
    "    CCI = (TP - TP.rolling(20).mean()) / (0.015 * AVEDEV)\n",
    "    df['cci'] = CCI\n",
    "\n",
    "    # Disparity Index\n",
    "    DI5 = 100 * ((df.Close - df.Close.rolling(5).mean()) / \n",
    "                df.Close.rolling(5).mean())\n",
    "    DI10 = 100 * ((df.Close - df.Close.rolling(10).mean()) / \n",
    "                df.Close.rolling(10).mean())\n",
    "    df['di5'] = DI5\n",
    "    df['di10'] = DI10\n",
    "\n",
    "    # Volume Prce Trend\n",
    "    VPT = pd.Series(index=df.index)\n",
    "    VPT.iloc[0] = 0\n",
    "    for n in range(1, len(VPT)):\n",
    "        VPT.iloc[n] = VPT.iloc[n-1] + ((df.Close.diff(1) / df.Close) * df.Volume).iloc[n]\n",
    "    df['vpt'] = VPT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, df in raw_data.items():\n",
    "    raw_data[key] = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_li = [col for col in raw_data['삼성전자'].columns if col.find('Y') != -1]\n",
    "X_li = [col for col in raw_data['삼성전자'].columns if col.find('Y') == -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "import time\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done : 삼성전자 \n",
      "\t0.0489 sec\n",
      "Done : 삼성전자 \n",
      "\t0.0678 sec\n",
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      "\t0.0379 sec\n",
      "Done : 삼성전자 \n",
      "\t0.0369 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done : 삼성전자 \n",
      "\t0.0409 sec\n",
      "Done : 삼성전자 \n",
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      "\t0.0379 sec"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Done : 삼성전자 \n",
      "\t0.0468 sec\n",
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      "Done :"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 삼성전자 \n",
      "\t0.0539 sec\n",
      "Done : 삼성전자 \n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done : 삼성전자 \n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Done : 삼성전자 \n",
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      "Done :"
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    },
    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 삼성전자 \n",
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    },
    {
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      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done : 삼성전자 \n",
      "\t0.0409 sec\n",
      "Done : 삼성전자 \n",
      "\t0.0360 sec\n",
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      "\t0.0399 sec\n",
      "Done : 삼성전자 \n",
      "\t0.0349 sec\n",
      "Done :"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 삼성전자 \n",
      "\t0.0329 sec\n",
      "Done : 삼성전자 \n",
      "\t0.0379 sec\n",
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      "\t0.0329 sec\n",
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      "\t0.0319 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done : 삼성전자 \n",
      "\t0.0329 sec\n",
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      "\t0.0319 sec\n",
      "Done :"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 삼성전자 \n",
      "\t0.0319 sec\n",
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      "\t0.0349 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done : 삼성전자 \n",
      "\t0.0369 sec\n",
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      "\t0.0330 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done : 삼성전자 \n",
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      "\t0.0389 sec\n",
      "Done :"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 삼성전자 \n",
      "\t0.0329 sec\n",
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      "\t0.0359 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done : 삼성전자 \n",
      "\t0.0349 sec\n",
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      "\t0.0319 sec\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\JinMyungHun\\Anaconda3\\envs\\ds\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "result = {}\n",
    "start = time.time()\n",
    "\n",
    "df_y = df[y_li].apply(np.sign).replace(0,-1)\n",
    "df_X = df[X_li]\n",
    "\n",
    "for y in y_li:\n",
    "    semi_start = time.time()\n",
    "    \n",
    "    train_data = df_X.iloc[:int(0.8*len(df))].values\n",
    "    train_label = df_y.iloc[:int(0.8*len(df))][y].values\n",
    "    test_data = df_X.iloc[int(0.8*len(df)):].values\n",
    "    test_label = df_y.iloc[int(0.8*len(df)):][y]\n",
    "    rf_clf = RandomForestClassifier()\n",
    "    rf_clf.fit(train_data, train_label)\n",
    "    pred = pd.Series(rf_clf.predict(test_data), index=test_label.index)    \n",
    "    accuracy = metrics.accuracy_score(test_label.values, pred.values)\n",
    "    auc = metrics.roc_auc_score(test_label.values, pred.values)\n",
    "    result[y] = [accuracy, auc, pred]\n",
    "    \n",
    "    semi_elapse = time.time() - semi_start\n",
    "    print('Done :', key, '\\n\\t{:.4f} sec'.format(semi_elapse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = [data[0] for key, data in result.items()]\n",
    "auc = [data[1] for key, data in result.items()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x164e3dbb5f8>]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x164e6e66a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done : 삼성전자 \n",
      "\t0.1436 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1347 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1496 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1316 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1406 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1326 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1416 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1377 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1476 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1396 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1406 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1526 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1735 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1356 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1356 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1366 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1426 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1536 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1476 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1347 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1416 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1336 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1376 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1297 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1446 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1316 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1566 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1925 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1417 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1406 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1316 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1426 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1336 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1376 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1306 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1376 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1456 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1376 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1366 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1346 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1326 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1386 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1307 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1436 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1337 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1366 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1376 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1486 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1417 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1386 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1446 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1596 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1307 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1456 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1546 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1556 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1346 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1397 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1346 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1347 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1366 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1386 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1366 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1356 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1336 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1366 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1316 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1427 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1566 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1436 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1326 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1407 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1387 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1575 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1616 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1466 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1317 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1356 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1366 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1496 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1336 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1417 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1436 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1386 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1306 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1356 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1386 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1337 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1316 sec\n",
      "Done : 삼성전자 \n",
      "\t0.1346 sec\n"
     ]
    }
   ],
   "source": [
    "result = {}\n",
    "start = time.time()\n",
    "\n",
    "for y in y_li:\n",
    "    key = '삼성전자'\n",
    "    df = raw_data[key]\n",
    "    semi_start = time.time()\n",
    "    \n",
    "    train_data = df.iloc[:int(0.8*len(df))][X_li].values\n",
    "    train_label = df.iloc[:int(0.8*len(df))][y].values\n",
    "    test_data = df.iloc[int(0.8*len(df)):][X_li].values\n",
    "    test_label = df.iloc[int(0.8*len(df)):][y]\n",
    "    xgb_reg = XGBRegressor()\n",
    "    xgb_reg.fit(train_data, train_label)\n",
    "    pred = pd.Series(xgb_reg.predict(test_data), index=test_label.index)\n",
    "    rmse = metrics.mean_squared_error(test_label.values, pred.values) ** .5\n",
    "    accuracy = metrics.accuracy_score(np.where(test_label.values>0, 1, 0), \n",
    "                       np.where(pred.values>0, 1, 0))\n",
    "    result[y] = [rmse, accuracy, pred]\n",
    "    \n",
    "    semi_elapse = time.time() - semi_start\n",
    "    print('Done :', key, '\\n\\t{:.4f} sec'.format(semi_elapse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = [data[0] for key, data in result.items()]\n",
    "acc = [data[1] for key, data in result.items()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x164e7e5bf98>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x164e5de8eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}