stack_mean_no_search_20_33,35-37.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from utilities import cal_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "stack_idx = '20'\n",
    "models = '33,35-37'\n",
    "use_test_kfold = set([2, 7, 8, 12, 13])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read CV predictions and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_models(exp):\n",
    "    exp_split = exp.split(',')\n",
    "    idx_models = []\n",
    "    for e in exp_split:\n",
    "        if '-' in e:\n",
    "            n0, n1 = e.split('-')\n",
    "            idx_models.extend(list(range(int(n0), int(n1)+1, 1)))\n",
    "        else:\n",
    "            idx_models.append(int(e))\n",
    "    return idx_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[33, 35, 36, 37]\n"
     ]
    }
   ],
   "source": [
    "idx_models = parse_models(models)\n",
    "print(idx_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{33: ['model-33-lgb-remove_outlier_03-test-one.csv'],\n",
       " 35: ['model-35-lgb-remove_outlier_03-2-test-one.csv'],\n",
       " 36: ['model-36-lgb-remove_outlier_03-0.001-2-test-one.csv'],\n",
       " 37: ['model-37-lgb-remove_outlier_05-test-one.csv']}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# File candidate\n",
    "files_in_output = [f for f in os.listdir('output/') if os.path.isfile('output/'+f)]\n",
    "{idx: [f for f in files_in_output \n",
    "       if f.startswith('model-%02d-' % idx) and f.endswith('test-one.csv')]\n",
    " for idx in idx_models}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# files_in_output = [f for f in os.listdir('output/') if os.path.isfile('output/'+f)]\n",
    "# # files_cv = {idx: [f for f in files_in_output \n",
    "# #                   if f.startswith('model-%02d-' % idx) and f.endswith('cv.csv')][0] \n",
    "# #             for idx in idx_models}\n",
    "# files_test_one = {idx: [f for f in files_in_output \n",
    "#                         if f.startswith('model-%02d-' % idx) and f.endswith('test-one.csv')][0]\n",
    "#                   for idx in idx_models}\n",
    "# files_test_kf = {idx: [f for f in files_in_output \n",
    "#                        if f.startswith('model-%02d-' % idx) and f.endswith('test-kfold.csv')][0]\n",
    "#                  for idx in idx_models}\n",
    "files_test_one = {\n",
    "    33: 'model-33-lgb-remove_outlier_03-test-one.csv',\n",
    "    35: 'model-35-lgb-remove_outlier_03-2-test-one-400000.csv',\n",
    "    36: 'model-36-lgb-remove_outlier_03-0.001-2-test-one.csv',\n",
    "    37: 'model-37-lgb-remove_outlier_05-test-one.csv',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = [pd.read_csv('output/'+f).rename({'total_price': 'pred_'+str(i)}, axis=1) \n",
    "       for i, f in files_test_one.items()]\n",
    "df = dfs[0]\n",
    "for df_i in dfs[1:]:\n",
    "    df = df.merge(df_i, on='building_id', how='inner')\n",
    "\n",
    "cols_pred = ['pred_'+str(i) for i in files_test_one]\n",
    "print(cols_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>building_id</th>\n",
       "      <th>pred_33</th>\n",
       "      <th>pred_35</th>\n",
       "      <th>pred_36</th>\n",
       "      <th>pred_37</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X5gsdTWGS3W7JJQB</td>\n",
       "      <td>1.334910e+07</td>\n",
       "      <td>1.313244e+07</td>\n",
       "      <td>1.298618e+07</td>\n",
       "      <td>1.304845e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTshNOJyKHnT2YIT</td>\n",
       "      <td>3.890587e+06</td>\n",
       "      <td>3.896216e+06</td>\n",
       "      <td>3.897411e+06</td>\n",
       "      <td>3.897545e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dhdymr0lV8N5kZOT</td>\n",
       "      <td>1.059390e+07</td>\n",
       "      <td>1.078764e+07</td>\n",
       "      <td>1.049745e+07</td>\n",
       "      <td>1.035078e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VEwyGGMcD56w5BOc</td>\n",
       "      <td>6.119229e+06</td>\n",
       "      <td>6.102227e+06</td>\n",
       "      <td>6.139949e+06</td>\n",
       "      <td>6.120593e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wmUeMoJZfsqaSX9b</td>\n",
       "      <td>1.110317e+06</td>\n",
       "      <td>1.104233e+06</td>\n",
       "      <td>1.106728e+06</td>\n",
       "      <td>1.098338e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>UEeCDaAJzPwdKKKA</td>\n",
       "      <td>1.181563e+06</td>\n",
       "      <td>1.191904e+06</td>\n",
       "      <td>1.191593e+06</td>\n",
       "      <td>1.175668e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>i0fgbPaQsDWs7Q87</td>\n",
       "      <td>4.817584e+07</td>\n",
       "      <td>4.858715e+07</td>\n",
       "      <td>4.826300e+07</td>\n",
       "      <td>4.830921e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>YunNwAhcqkf6YclI</td>\n",
       "      <td>1.094032e+06</td>\n",
       "      <td>1.108525e+06</td>\n",
       "      <td>1.114377e+06</td>\n",
       "      <td>1.142751e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>A2NotxtRY9MYoWMl</td>\n",
       "      <td>2.970882e+06</td>\n",
       "      <td>2.962741e+06</td>\n",
       "      <td>2.973267e+06</td>\n",
       "      <td>2.933060e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>kKvgBXiA50gRmQhP</td>\n",
       "      <td>3.129095e+06</td>\n",
       "      <td>3.118331e+06</td>\n",
       "      <td>3.102721e+06</td>\n",
       "      <td>3.067082e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           building_id       pred_33       pred_35       pred_36       pred_37\n",
       "0     X5gsdTWGS3W7JJQB  1.334910e+07  1.313244e+07  1.298618e+07  1.304845e+07\n",
       "1     BTshNOJyKHnT2YIT  3.890587e+06  3.896216e+06  3.897411e+06  3.897545e+06\n",
       "2     dhdymr0lV8N5kZOT  1.059390e+07  1.078764e+07  1.049745e+07  1.035078e+07\n",
       "3     VEwyGGMcD56w5BOc  6.119229e+06  6.102227e+06  6.139949e+06  6.120593e+06\n",
       "4     wmUeMoJZfsqaSX9b  1.110317e+06  1.104233e+06  1.106728e+06  1.098338e+06\n",
       "9995  UEeCDaAJzPwdKKKA  1.181563e+06  1.191904e+06  1.191593e+06  1.175668e+06\n",
       "9996  i0fgbPaQsDWs7Q87  4.817584e+07  4.858715e+07  4.826300e+07  4.830921e+07\n",
       "9997  YunNwAhcqkf6YclI  1.094032e+06  1.108525e+06  1.114377e+06  1.142751e+06\n",
       "9998  A2NotxtRY9MYoWMl  2.970882e+06  2.962741e+06  2.973267e+06  2.933060e+06\n",
       "9999  kKvgBXiA50gRmQhP  3.129095e+06  3.118331e+06  3.102721e+06  3.067082e+06"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape = (10000, 5)\n"
     ]
    }
   ],
   "source": [
    "df.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert not (df == 0).any().any()\n",
    "\n",
    "# to log\n",
    "for c in cols_pred:\n",
    "    df['log_'+c] = np.log(df[c])\n",
    "cols_log_pred = ['log_'+c for c in cols_pred]\n",
    "cols_log_pred\n",
    "\n",
    "df['log_total_price'] = df.loc[:, cols_log_pred].mean(axis=1)\n",
    "df['total_price'] = np.exp(df['log_total_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>building_id</th>\n",
       "      <th>pred_33</th>\n",
       "      <th>pred_35</th>\n",
       "      <th>pred_36</th>\n",
       "      <th>pred_37</th>\n",
       "      <th>log_pred_33</th>\n",
       "      <th>log_pred_35</th>\n",
       "      <th>log_pred_36</th>\n",
       "      <th>log_pred_37</th>\n",
       "      <th>log_total_price</th>\n",
       "      <th>total_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X5gsdTWGS3W7JJQB</td>\n",
       "      <td>1.334910e+07</td>\n",
       "      <td>1.313244e+07</td>\n",
       "      <td>1.298618e+07</td>\n",
       "      <td>1.304845e+07</td>\n",
       "      <td>16.406960</td>\n",
       "      <td>16.390596</td>\n",
       "      <td>16.379396</td>\n",
       "      <td>16.384180</td>\n",
       "      <td>16.390283</td>\n",
       "      <td>1.312833e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTshNOJyKHnT2YIT</td>\n",
       "      <td>3.890587e+06</td>\n",
       "      <td>3.896216e+06</td>\n",
       "      <td>3.897411e+06</td>\n",
       "      <td>3.897545e+06</td>\n",
       "      <td>15.174070</td>\n",
       "      <td>15.175516</td>\n",
       "      <td>15.175823</td>\n",
       "      <td>15.175857</td>\n",
       "      <td>15.175317</td>\n",
       "      <td>3.895439e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dhdymr0lV8N5kZOT</td>\n",
       "      <td>1.059390e+07</td>\n",
       "      <td>1.078764e+07</td>\n",
       "      <td>1.049745e+07</td>\n",
       "      <td>1.035078e+07</td>\n",
       "      <td>16.175789</td>\n",
       "      <td>16.193911</td>\n",
       "      <td>16.166643</td>\n",
       "      <td>16.152573</td>\n",
       "      <td>16.172229</td>\n",
       "      <td>1.055625e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VEwyGGMcD56w5BOc</td>\n",
       "      <td>6.119229e+06</td>\n",
       "      <td>6.102227e+06</td>\n",
       "      <td>6.139949e+06</td>\n",
       "      <td>6.120593e+06</td>\n",
       "      <td>15.626947</td>\n",
       "      <td>15.624164</td>\n",
       "      <td>15.630327</td>\n",
       "      <td>15.627169</td>\n",
       "      <td>15.627152</td>\n",
       "      <td>6.120485e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wmUeMoJZfsqaSX9b</td>\n",
       "      <td>1.110317e+06</td>\n",
       "      <td>1.104233e+06</td>\n",
       "      <td>1.106728e+06</td>\n",
       "      <td>1.098338e+06</td>\n",
       "      <td>13.920156</td>\n",
       "      <td>13.914661</td>\n",
       "      <td>13.916918</td>\n",
       "      <td>13.909309</td>\n",
       "      <td>13.915261</td>\n",
       "      <td>1.104895e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>UEeCDaAJzPwdKKKA</td>\n",
       "      <td>1.181563e+06</td>\n",
       "      <td>1.191904e+06</td>\n",
       "      <td>1.191593e+06</td>\n",
       "      <td>1.175668e+06</td>\n",
       "      <td>13.982349</td>\n",
       "      <td>13.991063</td>\n",
       "      <td>13.990802</td>\n",
       "      <td>13.977347</td>\n",
       "      <td>13.985390</td>\n",
       "      <td>1.185162e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>i0fgbPaQsDWs7Q87</td>\n",
       "      <td>4.817584e+07</td>\n",
       "      <td>4.858715e+07</td>\n",
       "      <td>4.826300e+07</td>\n",
       "      <td>4.830921e+07</td>\n",
       "      <td>17.690368</td>\n",
       "      <td>17.698870</td>\n",
       "      <td>17.692176</td>\n",
       "      <td>17.693133</td>\n",
       "      <td>17.693637</td>\n",
       "      <td>4.833355e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>YunNwAhcqkf6YclI</td>\n",
       "      <td>1.094032e+06</td>\n",
       "      <td>1.108525e+06</td>\n",
       "      <td>1.114377e+06</td>\n",
       "      <td>1.142751e+06</td>\n",
       "      <td>13.905380</td>\n",
       "      <td>13.918541</td>\n",
       "      <td>13.923806</td>\n",
       "      <td>13.948949</td>\n",
       "      <td>13.924169</td>\n",
       "      <td>1.114782e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>A2NotxtRY9MYoWMl</td>\n",
       "      <td>2.970882e+06</td>\n",
       "      <td>2.962741e+06</td>\n",
       "      <td>2.973267e+06</td>\n",
       "      <td>2.933060e+06</td>\n",
       "      <td>14.904369</td>\n",
       "      <td>14.901625</td>\n",
       "      <td>14.905172</td>\n",
       "      <td>14.891557</td>\n",
       "      <td>14.900681</td>\n",
       "      <td>2.959944e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>kKvgBXiA50gRmQhP</td>\n",
       "      <td>3.129095e+06</td>\n",
       "      <td>3.118331e+06</td>\n",
       "      <td>3.102721e+06</td>\n",
       "      <td>3.067082e+06</td>\n",
       "      <td>14.956254</td>\n",
       "      <td>14.952809</td>\n",
       "      <td>14.947790</td>\n",
       "      <td>14.936237</td>\n",
       "      <td>14.948272</td>\n",
       "      <td>3.104218e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           building_id       pred_33       pred_35       pred_36  \\\n",
       "0     X5gsdTWGS3W7JJQB  1.334910e+07  1.313244e+07  1.298618e+07   \n",
       "1     BTshNOJyKHnT2YIT  3.890587e+06  3.896216e+06  3.897411e+06   \n",
       "2     dhdymr0lV8N5kZOT  1.059390e+07  1.078764e+07  1.049745e+07   \n",
       "3     VEwyGGMcD56w5BOc  6.119229e+06  6.102227e+06  6.139949e+06   \n",
       "4     wmUeMoJZfsqaSX9b  1.110317e+06  1.104233e+06  1.106728e+06   \n",
       "9995  UEeCDaAJzPwdKKKA  1.181563e+06  1.191904e+06  1.191593e+06   \n",
       "9996  i0fgbPaQsDWs7Q87  4.817584e+07  4.858715e+07  4.826300e+07   \n",
       "9997  YunNwAhcqkf6YclI  1.094032e+06  1.108525e+06  1.114377e+06   \n",
       "9998  A2NotxtRY9MYoWMl  2.970882e+06  2.962741e+06  2.973267e+06   \n",
       "9999  kKvgBXiA50gRmQhP  3.129095e+06  3.118331e+06  3.102721e+06   \n",
       "\n",
       "           pred_37  log_pred_33  log_pred_35  log_pred_36  log_pred_37  \\\n",
       "0     1.304845e+07    16.406960    16.390596    16.379396    16.384180   \n",
       "1     3.897545e+06    15.174070    15.175516    15.175823    15.175857   \n",
       "2     1.035078e+07    16.175789    16.193911    16.166643    16.152573   \n",
       "3     6.120593e+06    15.626947    15.624164    15.630327    15.627169   \n",
       "4     1.098338e+06    13.920156    13.914661    13.916918    13.909309   \n",
       "9995  1.175668e+06    13.982349    13.991063    13.990802    13.977347   \n",
       "9996  4.830921e+07    17.690368    17.698870    17.692176    17.693133   \n",
       "9997  1.142751e+06    13.905380    13.918541    13.923806    13.948949   \n",
       "9998  2.933060e+06    14.904369    14.901625    14.905172    14.891557   \n",
       "9999  3.067082e+06    14.956254    14.952809    14.947790    14.936237   \n",
       "\n",
       "      log_total_price   total_price  \n",
       "0           16.390283  1.312833e+07  \n",
       "1           15.175317  3.895439e+06  \n",
       "2           16.172229  1.055625e+07  \n",
       "3           15.627152  6.120485e+06  \n",
       "4           13.915261  1.104895e+06  \n",
       "9995        13.985390  1.185162e+06  \n",
       "9996        17.693637  4.833355e+07  \n",
       "9997        13.924169  1.114782e+06  \n",
       "9998        14.900681  2.959944e+06  \n",
       "9999        14.948272  3.104218e+06  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape = (10000, 11)\n"
     ]
    }
   ],
   "source": [
    "df.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>building_id</th>\n",
       "      <th>total_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X5gsdTWGS3W7JJQB</td>\n",
       "      <td>1.312833e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTshNOJyKHnT2YIT</td>\n",
       "      <td>3.895439e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dhdymr0lV8N5kZOT</td>\n",
       "      <td>1.055625e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VEwyGGMcD56w5BOc</td>\n",
       "      <td>6.120485e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wmUeMoJZfsqaSX9b</td>\n",
       "      <td>1.104895e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>UEeCDaAJzPwdKKKA</td>\n",
       "      <td>1.185162e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>i0fgbPaQsDWs7Q87</td>\n",
       "      <td>4.833355e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>YunNwAhcqkf6YclI</td>\n",
       "      <td>1.114782e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>A2NotxtRY9MYoWMl</td>\n",
       "      <td>2.959944e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>kKvgBXiA50gRmQhP</td>\n",
       "      <td>3.104218e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           building_id   total_price\n",
       "0     X5gsdTWGS3W7JJQB  1.312833e+07\n",
       "1     BTshNOJyKHnT2YIT  3.895439e+06\n",
       "2     dhdymr0lV8N5kZOT  1.055625e+07\n",
       "3     VEwyGGMcD56w5BOc  6.120485e+06\n",
       "4     wmUeMoJZfsqaSX9b  1.104895e+06\n",
       "9995  UEeCDaAJzPwdKKKA  1.185162e+06\n",
       "9996  i0fgbPaQsDWs7Q87  4.833355e+07\n",
       "9997  YunNwAhcqkf6YclI  1.114782e+06\n",
       "9998  A2NotxtRY9MYoWMl  2.959944e+06\n",
       "9999  kKvgBXiA50gRmQhP  3.104218e+06"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape = (10000, 2)\n"
     ]
    }
   ],
   "source": [
    "df[['building_id', 'total_price']].check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output file to: output/stack_meanlog_no_search_20_33,35-37.csv\n"
     ]
    }
   ],
   "source": [
    "# Output\n",
    "output_filename = 'output/stack_meanlog_no_search_{}_{}.csv'.format(stack_idx, models)\n",
    "print('Output file to:', output_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['building_id', 'total_price']].to_csv(output_filename, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "building_id        False\n",
       "pred_33            False\n",
       "pred_35            False\n",
       "pred_36            False\n",
       "pred_37            False\n",
       "log_pred_33        False\n",
       "log_pred_35        False\n",
       "log_pred_36        False\n",
       "log_pred_37        False\n",
       "log_total_price    False\n",
       "total_price        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check null\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('dataset/train.csv')\n",
    "df_test = pd.read_csv('dataset/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6.000000e+04\n",
       "mean     1.293727e+07\n",
       "std      5.522463e+07\n",
       "min      2.261495e+05\n",
       "25%      2.433114e+06\n",
       "50%      5.240482e+06\n",
       "75%      1.123932e+07\n",
       "max      5.088279e+09\n",
       "Name: total_price, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['total_price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.000000e+04\n",
       "mean     1.289628e+07\n",
       "std      5.464583e+07\n",
       "min      2.052908e+05\n",
       "25%      2.443947e+06\n",
       "50%      5.151208e+06\n",
       "75%      1.082656e+07\n",
       "max      3.024236e+09\n",
       "Name: total_price, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['total_price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test.merge(df, on='building_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['log_total_price'] = np.log(df_train['total_price'])\n",
    "df_train['parea_total_price'] = df_train['total_price'] / df_train['building_area']\n",
    "df_train['log_parea_total_price'] = np.log(df_train['parea_total_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_train['log_parea_total_price'], bins=100, label='train true', normed=True)\n",
    "plt.hist(np.log(df_test['total_price'] / df_test['building_area']), bins=100, label='test',\n",
    "         normed=True, alpha=0.7)\n",
    "plt.xlabel('log(price/area)'); plt.ylabel('ratio')\n",
    "plt.legend(); plt.grid(); plt.show()\n",
    "\n",
    "plt.hist(df_train['log_total_price'], bins=100, label='train true', normed=True)\n",
    "plt.hist(np.log(df_test['total_price']), bins=100, label='test', normed=True, alpha=0.7)\n",
    "plt.xlabel('log(price)'); plt.ylabel('ratio')\n",
    "plt.legend(); plt.grid(); plt.show()"
   ]
  },
  {
   "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": "3.6.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "247px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}