ensemble.ipynb

{
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  {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "from sklearn import ensemble\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import pandas as pd\n",
    "import math"
   ]
  },
  {
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       "      <th></th>\n",
       "      <th>MemberID</th>\n",
       "      <th>DaysInHospital</th>\n",
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      "text/plain": [
       "   MemberID  DaysInHospital\n",
       "0  99365603             0.0\n",
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     "execution_count": 34,
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   "source": [
    "df = pd.read_csv('./5.ensemble/model3.csv')\n",
    "df.loc[df['DaysInHospital'] == 0.2, 'DaysInHospital'] = 0\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MemberID</th>\n",
       "      <th>DaysInHospital</th>\n",
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      "text/plain": [
       "   MemberID  DaysInHospital\n",
       "0  99365603             0.0\n",
       "1  99372887             0.0\n",
       "2  99407970             0.0\n",
       "3  99410604             0.0\n",
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     "execution_count": 35,
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   "source": [
    "data2 = pd.read_csv('./5.ensemble/model4.csv')\n",
    "data2.loc[data2['DaysInHospital'] == 0.2, 'DaysInHospital'] = 0\n",
    "\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MemberID</th>\n",
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      "text/plain": [
       "   MemberID  DaysInHospital\n",
       "0  99365603             0.0\n",
       "1  99372887             0.0\n",
       "2  99407970             0.0\n",
       "3  99410604             0.0\n",
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     "execution_count": 36,
     "metadata": {},
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   ],
   "source": [
    "data3 = pd.read_csv('./5.ensemble/model5.csv')\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.merge(data2, left_on = 'MemberID', right_on = 'MemberID')\n",
    "df = df.merge(data3, left_on = 'MemberID', right_on = 'MemberID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>MemberID</th>\n",
       "      <th>DIH_1</th>\n",
       "      <th>DIH_2</th>\n",
       "      <th>DIH_3</th>\n",
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      "text/plain": [
       "   MemberID  DIH_1  DIH_2  DIH_3\n",
       "0  99365603    0.0    0.0    0.0\n",
       "1  99372887    0.0    0.0    0.0\n",
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     "execution_count": 38,
     "metadata": {},
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   "source": [
    "df.columns = ['MemberID', 'DIH_1', 'DIH_2', 'DIH_3']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>MemberID</th>\n",
       "      <th>DIH_1</th>\n",
       "      <th>DIH_2</th>\n",
       "      <th>DIH_3</th>\n",
       "      <th>DaysInHospital</th>\n",
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      "text/plain": [
       "   MemberID  DIH_1  DIH_2  DIH_3  DaysInHospital\n",
       "0  99365603    0.0    0.0    0.0             0.0\n",
       "1  99372887    0.0    0.0    0.0             0.0\n",
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     "execution_count": 39,
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   ],
   "source": [
    "df['DaysInHospital'] = (df['DIH_1'] + df['DIH_2'] + df['DIH_3']) /3\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DaysInHospital\n",
       "0.000000    67174\n",
       "0.333333     2244\n",
       "0.666667     1247\n",
       "1.000000      770\n",
       "dtype: int64"
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     "execution_count": 40,
     "metadata": {},
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   "source": [
    "df.groupby(['DaysInHospital']).size()\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DaysInHospital\n",
       "0.2    67174\n",
       "1.0     4261\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['DaysInHospital'] > 0, 'DaysInHospital'] = 1\n",
    "df.loc[df['DaysInHospital'] == 0, 'DaysInHospital'] = 0.2\n",
    "# df['DIH']=df['DIH'].round(0).astype(int)\n",
    "df.groupby(['DaysInHospital']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DaysInHospital\n",
       "0.2    67174\n",
       "1.0     4261\n",
       "dtype: int64"
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     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "submit = pd.DataFrame()\n",
    "submit['MemberID']=df['MemberID']\n",
    "submit['DaysInHospital']=df['DaysInHospital']\n",
    "submit.groupby(['DaysInHospital']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit.to_csv('./5.ensemble/model.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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