{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "85ba94ff-4a8b-4579-a8e9-81cc0743cb39",
   "metadata": {},
   "source": [
    "# Synthetic Control Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "50fb738b-969b-4ebe-b181-fbbb4c0c6f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import normet as nm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c03fc71",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('data/AQ_Weekly.csv',parse_dates=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00b0d4ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.query(f\"date>='2015-05-01'\").query(f\"date<'2016-04-30'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "96a1a1a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "control_pool=[\"Dongguan\", \"Zhongshan\" , \"Foshan\", \"Beihai\"\n",
    "               , \"Nanning\",\"Nanchang\" , \"Xiamen\", \"Taizhou\" \n",
    "               , \"Ningbo\",\"Guangzhou\" , \"Huizhou\", \"Hangzhou\" \n",
    "               , \"Liuzhou\", \"Shantou\", \"Jiangmen\", \"Heyuan\", \"Quanzhou\",\"Haikou\" , \"Shenzhen\", \"Wenzhou\", \"Huzhou\"\n",
    "               , \"Zhuhai\", \"Fuzhou\", \"Shaoxing\", \"Zhaoqing\",\"Zhoushan\"  \n",
    "               , \"Quzhou\", \"Jinhua\", \"Shaoguan\" , \"Sanya\"\n",
    "               , \"Jieyang\" , \"Meizhou\", \"Shanwei\" \n",
    "               , \"Zhanjiang\" , \"Chaozhou\", \"Maoming\" , \"Yangjiang\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12989a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[df['ID'].isin(control_pool+[\"2+26 cities\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b54a709d",
   "metadata": {
    "scrolled": true
   },
   "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>date</th>\n",
       "      <th>ID</th>\n",
       "      <th>CO</th>\n",
       "      <th>COwn</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NO2wn</th>\n",
       "      <th>O3</th>\n",
       "      <th>O3_8h</th>\n",
       "      <th>O3_8hwn</th>\n",
       "      <th>O3wn</th>\n",
       "      <th>Ox</th>\n",
       "      <th>Oxwn</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM10wn</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM2.5wn</th>\n",
       "      <th>SO2</th>\n",
       "      <th>SO2wn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>2015-05-03</td>\n",
       "      <td>2+26 cities</td>\n",
       "      <td>1.277812</td>\n",
       "      <td>1.328918</td>\n",
       "      <td>38.484623</td>\n",
       "      <td>45.068304</td>\n",
       "      <td>83.690318</td>\n",
       "      <td>133.060175</td>\n",
       "      <td>90.980089</td>\n",
       "      <td>65.825884</td>\n",
       "      <td>60.984249</td>\n",
       "      <td>54.415501</td>\n",
       "      <td>134.523758</td>\n",
       "      <td>139.447383</td>\n",
       "      <td>82.221006</td>\n",
       "      <td>73.876656</td>\n",
       "      <td>32.971551</td>\n",
       "      <td>36.822724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>2015-05-10</td>\n",
       "      <td>2+26 cities</td>\n",
       "      <td>1.113243</td>\n",
       "      <td>1.298979</td>\n",
       "      <td>35.289414</td>\n",
       "      <td>44.352582</td>\n",
       "      <td>70.075957</td>\n",
       "      <td>103.363985</td>\n",
       "      <td>90.875789</td>\n",
       "      <td>66.100641</td>\n",
       "      <td>52.659234</td>\n",
       "      <td>54.337364</td>\n",
       "      <td>111.771975</td>\n",
       "      <td>134.412595</td>\n",
       "      <td>58.313419</td>\n",
       "      <td>69.386309</td>\n",
       "      <td>27.898696</td>\n",
       "      <td>35.284317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>2015-05-17</td>\n",
       "      <td>2+26 cities</td>\n",
       "      <td>0.996762</td>\n",
       "      <td>1.274902</td>\n",
       "      <td>35.810778</td>\n",
       "      <td>43.981644</td>\n",
       "      <td>83.598553</td>\n",
       "      <td>125.082867</td>\n",
       "      <td>91.245896</td>\n",
       "      <td>66.418891</td>\n",
       "      <td>59.588514</td>\n",
       "      <td>54.389203</td>\n",
       "      <td>117.075504</td>\n",
       "      <td>129.474025</td>\n",
       "      <td>52.802738</td>\n",
       "      <td>67.146002</td>\n",
       "      <td>29.642007</td>\n",
       "      <td>34.742172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>2015-05-24</td>\n",
       "      <td>2+26 cities</td>\n",
       "      <td>1.031094</td>\n",
       "      <td>1.267058</td>\n",
       "      <td>39.838150</td>\n",
       "      <td>43.398110</td>\n",
       "      <td>96.838000</td>\n",
       "      <td>149.709652</td>\n",
       "      <td>92.792728</td>\n",
       "      <td>66.695864</td>\n",
       "      <td>68.180477</td>\n",
       "      <td>54.529002</td>\n",
       "      <td>117.650986</td>\n",
       "      <td>121.537391</td>\n",
       "      <td>61.278043</td>\n",
       "      <td>65.876502</td>\n",
       "      <td>34.972942</td>\n",
       "      <td>34.482513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>2+26 cities</td>\n",
       "      <td>1.297053</td>\n",
       "      <td>1.285556</td>\n",
       "      <td>37.988483</td>\n",
       "      <td>43.548340</td>\n",
       "      <td>109.770206</td>\n",
       "      <td>165.029314</td>\n",
       "      <td>92.427553</td>\n",
       "      <td>66.419975</td>\n",
       "      <td>73.600167</td>\n",
       "      <td>54.475052</td>\n",
       "      <td>143.115653</td>\n",
       "      <td>126.681810</td>\n",
       "      <td>88.963408</td>\n",
       "      <td>67.239277</td>\n",
       "      <td>33.903061</td>\n",
       "      <td>34.490734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date           ID        CO      COwn        NO2      NO2wn  \\\n",
       "552 2015-05-03  2+26 cities  1.277812  1.328918  38.484623  45.068304   \n",
       "560 2015-05-10  2+26 cities  1.113243  1.298979  35.289414  44.352582   \n",
       "568 2015-05-17  2+26 cities  0.996762  1.274902  35.810778  43.981644   \n",
       "576 2015-05-24  2+26 cities  1.031094  1.267058  39.838150  43.398110   \n",
       "584 2015-05-31  2+26 cities  1.297053  1.285556  37.988483  43.548340   \n",
       "\n",
       "             O3       O3_8h    O3_8hwn       O3wn         Ox       Oxwn  \\\n",
       "552   83.690318  133.060175  90.980089  65.825884  60.984249  54.415501   \n",
       "560   70.075957  103.363985  90.875789  66.100641  52.659234  54.337364   \n",
       "568   83.598553  125.082867  91.245896  66.418891  59.588514  54.389203   \n",
       "576   96.838000  149.709652  92.792728  66.695864  68.180477  54.529002   \n",
       "584  109.770206  165.029314  92.427553  66.419975  73.600167  54.475052   \n",
       "\n",
       "           PM10      PM10wn      PM2.5    PM2.5wn        SO2      SO2wn  \n",
       "552  134.523758  139.447383  82.221006  73.876656  32.971551  36.822724  \n",
       "560  111.771975  134.412595  58.313419  69.386309  27.898696  35.284317  \n",
       "568  117.075504  129.474025  52.802738  67.146002  29.642007  34.742172  \n",
       "576  117.650986  121.537391  61.278043  65.876502  34.972942  34.482513  \n",
       "584  143.115653  126.681810  88.963408  67.239277  33.903061  34.490734  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1692472",
   "metadata": {},
   "outputs": [],
   "source": [
    "xx=nm.scm(df,'SO2wn','ID',\"2+26 cities\",control_pool,'2015-10-23')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3581197e",
   "metadata": {},
   "outputs": [],
   "source": [
    "xy=nm.scm_all(df,'SO2wn','ID',control_pool,'2015-10-23')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a785ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx[['SO2wn','synthetic']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e2f35ac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "for i,city in enumerate(list(xy['ID'].unique())):\n",
    "    xy[xy['ID']==city]['effects'].plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8026e801",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-09-24 21:10:59 : Training AutoML...\n",
      "2024-09-24 21:12:29 : Best model is lgbm with best model parameters of {'n_estimators': 4, 'num_leaves': 6, 'min_child_samples': 2, 'learning_rate': 0.7148544310430003, 'log_max_bin': 4, 'colsample_bytree': 0.1939065104493952, 'reg_alpha': 0.01427319308925257, 'reg_lambda': 0.009361671309532844}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "mlscdata1=nm.mlsc(df,'SO2wn','ID',\"2+26 cities\",control_pool,'2015-10-23',automl_pkg='flaml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b6dc5b23-108f-4a27-bc16-ecce92f875ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H2O is not running. Starting H2O...\n",
      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "\n",
       "#h2o-table-1.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-1 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-1 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-1 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-1 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-1 .h2o-table th,\n",
       "#h2o-table-1 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-1 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-1\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption></caption>\n",
       "    <thead></thead>\n",
       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>8 hours 50 mins</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Europe/London</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.46.0.5</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>26 days</td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_n94921cs_5qrqdn</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>6.829 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.12.2 final</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n"
      ],
      "text/plain": [
       "--------------------------  -------------------------------\n",
       "H2O_cluster_uptime:         8 hours 50 mins\n",
       "H2O_cluster_timezone:       Europe/London\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.46.0.5\n",
       "H2O_cluster_version_age:    26 days\n",
       "H2O_cluster_name:           H2O_from_python_n94921cs_5qrqdn\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    6.829 Gb\n",
       "H2O_cluster_total_cores:    8\n",
       "H2O_cluster_allowed_cores:  1\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.12.2 final\n",
       "--------------------------  -------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-09-24 21:12:29: Training AutoML...\n",
      "\n",
      "21:12:29.854: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:12:43: Best model obtained! - GBM_grid_1_AutoML_84_20240924_211229_model_1\n"
     ]
    }
   ],
   "source": [
    "\n",
    "mlscdata2=nm.mlsc(df,'SO2wn','ID',\"2+26 cities\",control_pool,'2015-10-23',automl_pkg='h2o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e2794c2-f52a-4109-84cb-b10a585208d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "xx['effects'].plot(ax=ax)\n",
    "mlscdata1['effects'].plot(ax=ax)\n",
    "mlscdata2['effects'].plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8dada1a0-bb75-4b62-8ec6-1249a6116b2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-09-24 21:12:44 : Training AutoML...\n",
      "2024-09-24 21:12:54 : Best model is lgbm with best model parameters of {'n_estimators': 12, 'num_leaves': 5, 'min_child_samples': 2, 'learning_rate': 1.0, 'log_max_bin': 10, 'colsample_bytree': 0.930479165045027, 'reg_alpha': 0.00942574688215323, 'reg_lambda': 13.299403187619442}\n",
      "2024-09-24 21:12:54 : Training AutoML...\n",
      "2024-09-24 21:13:04 : Best model is lgbm with best model parameters of {'n_estimators': 6, 'num_leaves': 4, 'min_child_samples': 9, 'learning_rate': 0.2921597058142577, 'log_max_bin': 5, 'colsample_bytree': 0.783599369600188, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.46338749432065923}\n",
      "2024-09-24 21:13:04 : Training AutoML...\n",
      "2024-09-24 21:13:14 : Best model is lgbm with best model parameters of {'n_estimators': 6, 'num_leaves': 5, 'min_child_samples': 3, 'learning_rate': 1.0, 'log_max_bin': 7, 'colsample_bytree': 0.9503055774263902, 'reg_alpha': 0.489191843809506, 'reg_lambda': 1.6501523530499025}\n",
      "2024-09-24 21:13:14 : Training AutoML...\n",
      "2024-09-24 21:13:24 : Best model is lgbm with best model parameters of {'n_estimators': 6, 'num_leaves': 5, 'min_child_samples': 2, 'learning_rate': 0.41160801397294977, 'log_max_bin': 8, 'colsample_bytree': 0.9169378900537527, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0831188539939598}\n",
      "2024-09-24 21:13:24 : Training AutoML...\n",
      "2024-09-24 21:13:34 : Best model is lgbm with best model parameters of {'n_estimators': 11, 'num_leaves': 6, 'min_child_samples': 2, 'learning_rate': 0.32797464931090686, 'log_max_bin': 10, 'colsample_bytree': 0.7801126486250443, 'reg_alpha': 0.00783683094772429, 'reg_lambda': 0.0009765625}\n",
      "2024-09-24 21:13:34 : Training AutoML...\n",
      "2024-09-24 21:13:44 : Best model is lgbm with best model parameters of {'n_estimators': 8, 'num_leaves': 6, 'min_child_samples': 3, 'learning_rate': 0.1557307908422462, 'log_max_bin': 7, 'colsample_bytree': 0.42586054853597943, 'reg_alpha': 0.012954181982556719, 'reg_lambda': 0.6977141807080524}\n",
      "2024-09-24 21:13:44 : Training AutoML...\n",
      "2024-09-24 21:13:54 : Best model is lgbm with best model parameters of {'n_estimators': 6, 'num_leaves': 4, 'min_child_samples': 2, 'learning_rate': 0.7522841109457985, 'log_max_bin': 8, 'colsample_bytree': 0.7553536260637193, 'reg_alpha': 0.12884573478098568, 'reg_lambda': 0.34337932545067434}\n",
      "2024-09-24 21:13:54 : Training AutoML...\n",
      "2024-09-24 21:14:04 : Best model is lgbm with best model parameters of {'n_estimators': 11, 'num_leaves': 5, 'min_child_samples': 2, 'learning_rate': 0.7559672399847629, 'log_max_bin': 8, 'colsample_bytree': 0.5980451945038232, 'reg_alpha': 0.00118162844757217, 'reg_lambda': 0.050562689919296896}\n",
      "2024-09-24 21:14:04 : Training AutoML...\n",
      "2024-09-24 21:14:14 : Best model is lgbm with best model parameters of {'n_estimators': 10, 'num_leaves': 5, 'min_child_samples': 4, 'learning_rate': 0.8571921543782086, 'log_max_bin': 10, 'colsample_bytree': 0.8736741899841435, 'reg_alpha': 0.045055521614150676, 'reg_lambda': 1.5487343522543016}\n",
      "2024-09-24 21:14:14 : Training AutoML...\n",
      "2024-09-24 21:14:24 : Best model is lgbm with best model parameters of {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 3, 'learning_rate': 0.4526271826745481, 'log_max_bin': 8, 'colsample_bytree': 0.8175779151928938, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.20892183806515865}\n",
      "2024-09-24 21:14:24 : Training AutoML...\n",
      "2024-09-24 21:14:34 : Best model is lgbm with best model parameters of {'n_estimators': 24, 'num_leaves': 5, 'min_child_samples': 2, 'learning_rate': 0.16553917456172806, 'log_max_bin': 8, 'colsample_bytree': 0.09534165847954809, 'reg_alpha': 0.06097477862078419, 'reg_lambda': 0.0009765625}\n",
      "2024-09-24 21:14:34 : Training AutoML...\n",
      "2024-09-24 21:14:44 : Best model is lgbm with best model parameters of {'n_estimators': 15, 'num_leaves': 5, 'min_child_samples': 2, 'learning_rate': 0.38872795976511465, 'log_max_bin': 7, 'colsample_bytree': 0.7644713278102466, 'reg_alpha': 0.07759140431236793, 'reg_lambda': 0.0009765625}\n",
      "2024-09-24 21:14:44 : Training AutoML...\n",
      "2024-09-24 21:14:54 : Best model is lgbm with best model parameters of {'n_estimators': 7, 'num_leaves': 8, 'min_child_samples': 4, 'learning_rate': 0.9346059567885598, 'log_max_bin': 10, 'colsample_bytree': 0.6094519964255998, 'reg_alpha': 0.012059495812453521, 'reg_lambda': 7.339979964592403}\n",
      "2024-09-24 21:14:54 : Training AutoML...\n",
      "2024-09-24 21:15:04 : Best model is lgbm with best model parameters of {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0}\n",
      "2024-09-24 21:15:04 : Training AutoML...\n",
      "2024-09-24 21:15:14 : Best model is lgbm with best model parameters of {'n_estimators': 12, 'num_leaves': 4, 'min_child_samples': 2, 'learning_rate': 0.5337794280777547, 'log_max_bin': 4, 'colsample_bytree': 0.17853832068428674, 'reg_alpha': 0.0009819684544183096, 'reg_lambda': 0.08771896280358696}\n",
      "2024-09-24 21:15:14 : Training AutoML...\n",
      "2024-09-24 21:15:24 : Best model is lgbm with best model parameters of {'n_estimators': 5, 'num_leaves': 13, 'min_child_samples': 2, 'learning_rate': 0.9050153673020559, 'log_max_bin': 7, 'colsample_bytree': 0.7290288575714409, 'reg_alpha': 0.08233747695576485, 'reg_lambda': 0.18484473777391267}\n",
      "2024-09-24 21:15:24 : Training AutoML...\n",
      "2024-09-24 21:15:34 : Best model is lgbm with best model parameters of {'n_estimators': 20, 'num_leaves': 5, 'min_child_samples': 5, 'learning_rate': 0.40688176422506583, 'log_max_bin': 8, 'colsample_bytree': 0.49557577286151167, 'reg_alpha': 0.059826715540903304, 'reg_lambda': 0.0009765625}\n",
      "2024-09-24 21:15:34 : Training AutoML...\n",
      "2024-09-24 21:15:44 : Best model is lgbm with best model parameters of {'n_estimators': 13, 'num_leaves': 5, 'min_child_samples': 2, 'learning_rate': 0.47826857447050314, 'log_max_bin': 7, 'colsample_bytree': 0.6476932171895913, 'reg_alpha': 0.02721812430424376, 'reg_lambda': 0.0009765625}\n",
      "2024-09-24 21:15:44 : Training AutoML...\n",
      "2024-09-24 21:15:54 : Best model is lgbm with best model parameters of {'n_estimators': 7, 'num_leaves': 9, 'min_child_samples': 2, 'learning_rate': 1.0, 'log_max_bin': 5, 'colsample_bytree': 0.5293718044467112, 'reg_alpha': 0.4134848637908068, 'reg_lambda': 1.7899037923440302}\n",
      "2024-09-24 21:15:54 : Training AutoML...\n",
      "2024-09-24 21:16:04 : Best model is lgbm with best model parameters of {'n_estimators': 16, 'num_leaves': 11, 'min_child_samples': 3, 'learning_rate': 0.48941479233171103, 'log_max_bin': 6, 'colsample_bytree': 0.7590590788798283, 'reg_alpha': 0.1689275088229689, 'reg_lambda': 0.11767472345330872}\n",
      "2024-09-24 21:16:04 : Training AutoML...\n",
      "2024-09-24 21:16:14 : Best model is lgbm with best model parameters of {'n_estimators': 9, 'num_leaves': 9, 'min_child_samples': 2, 'learning_rate': 1.0, 'log_max_bin': 3, 'colsample_bytree': 0.6071906834084545, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.7759737417667425}\n",
      "2024-09-24 21:16:14 : Training AutoML...\n",
      "2024-09-24 21:16:24 : Best model is lgbm with best model parameters of {'n_estimators': 6, 'num_leaves': 5, 'min_child_samples': 2, 'learning_rate': 0.43635519694446395, 'log_max_bin': 10, 'colsample_bytree': 1.0, 'reg_alpha': 0.06219791570362574, 'reg_lambda': 0.0009765625}\n",
      "2024-09-24 21:16:24 : Training AutoML...\n",
      "2024-09-24 21:16:34 : Best model is lgbm with best model parameters of {'n_estimators': 6, 'num_leaves': 4, 'min_child_samples': 2, 'learning_rate': 0.5535497865789818, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.15838006664085513}\n",
      "2024-09-24 21:16:34 : Training AutoML...\n",
      "2024-09-24 21:16:44 : Best model is lgbm with best model parameters of {'n_estimators': 14, 'num_leaves': 11, 'min_child_samples': 2, 'learning_rate': 0.7732758849730388, 'log_max_bin': 7, 'colsample_bytree': 0.5022550826361638, 'reg_alpha': 0.05702517378903411, 'reg_lambda': 1.753568611251913}\n",
      "2024-09-24 21:16:44 : Training AutoML...\n",
      "2024-09-24 21:16:54 : Best model is lgbm with best model parameters of {'n_estimators': 13, 'num_leaves': 8, 'min_child_samples': 2, 'learning_rate': 1.0, 'log_max_bin': 10, 'colsample_bytree': 0.9100031664609475, 'reg_alpha': 0.01705324450041187, 'reg_lambda': 13.244564935289345}\n",
      "2024-09-24 21:16:54 : Training AutoML...\n",
      "2024-09-24 21:17:04 : Best model is lgbm with best model parameters of {'n_estimators': 6, 'num_leaves': 5, 'min_child_samples': 2, 'learning_rate': 1.0, 'log_max_bin': 8, 'colsample_bytree': 0.5230615301520156, 'reg_alpha': 0.002711670202936386, 'reg_lambda': 4.5486713208338525}\n",
      "2024-09-24 21:17:04 : Training AutoML...\n",
      "2024-09-24 21:17:14 : Best model is lgbm with best model parameters of {'n_estimators': 13, 'num_leaves': 8, 'min_child_samples': 2, 'learning_rate': 0.33421392046699044, 'log_max_bin': 8, 'colsample_bytree': 0.7779086875608411, 'reg_alpha': 0.052609769859006436, 'reg_lambda': 0.11082701194014714}\n",
      "2024-09-24 21:17:14 : Training AutoML...\n",
      "2024-09-24 21:17:24 : Best model is lgbm with best model parameters of {'n_estimators': 9, 'num_leaves': 5, 'min_child_samples': 2, 'learning_rate': 0.8943528427558822, 'log_max_bin': 8, 'colsample_bytree': 0.44886287043871437, 'reg_alpha': 0.10058681143507116, 'reg_lambda': 0.05530198191415701}\n",
      "2024-09-24 21:17:24 : Training AutoML...\n",
      "2024-09-24 21:17:34 : Best model is lgbm with best model parameters of {'n_estimators': 10, 'num_leaves': 4, 'min_child_samples': 3, 'learning_rate': 1.0, 'log_max_bin': 7, 'colsample_bytree': 0.7500876511043365, 'reg_alpha': 0.015070936475675171, 'reg_lambda': 4.393276343235792}\n",
      "2024-09-24 21:17:34 : Training AutoML...\n",
      "2024-09-24 21:17:44 : Best model is lgbm with best model parameters of {'n_estimators': 9, 'num_leaves': 6, 'min_child_samples': 2, 'learning_rate': 0.5256412928723886, 'log_max_bin': 3, 'colsample_bytree': 0.4527081429513771, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.3615431320128289}\n",
      "2024-09-24 21:17:44 : Training AutoML...\n",
      "2024-09-24 21:17:54 : Best model is lgbm with best model parameters of {'n_estimators': 7, 'num_leaves': 12, 'min_child_samples': 2, 'learning_rate': 1.0, 'log_max_bin': 3, 'colsample_bytree': 0.9160960930153217, 'reg_alpha': 0.0015946223503420392, 'reg_lambda': 10.821818492068704}\n",
      "2024-09-24 21:17:54 : Training AutoML...\n",
      "2024-09-24 21:18:04 : Best model is lgbm with best model parameters of {'n_estimators': 8, 'num_leaves': 6, 'min_child_samples': 2, 'learning_rate': 0.7078474896268241, 'log_max_bin': 3, 'colsample_bytree': 1.0, 'reg_alpha': 0.45791184097577037, 'reg_lambda': 3.6258352815438655}\n",
      "2024-09-24 21:18:04 : Training AutoML...\n",
      "2024-09-24 21:18:14 : Best model is lgbm with best model parameters of {'n_estimators': 11, 'num_leaves': 7, 'min_child_samples': 2, 'learning_rate': 0.4976474675540951, 'log_max_bin': 10, 'colsample_bytree': 0.7343521083054644, 'reg_alpha': 0.009142004060822672, 'reg_lambda': 0.2137803668920363}\n",
      "2024-09-24 21:18:14 : Training AutoML...\n",
      "2024-09-24 21:18:24 : Best model is lgbm with best model parameters of {'n_estimators': 24, 'num_leaves': 4, 'min_child_samples': 2, 'learning_rate': 0.935838222318515, 'log_max_bin': 6, 'colsample_bytree': 0.5583238173406014, 'reg_alpha': 0.01002075282296741, 'reg_lambda': 10.95638798161344}\n",
      "2024-09-24 21:18:24 : Training AutoML...\n",
      "2024-09-24 21:18:34 : Best model is lgbm with best model parameters of {'n_estimators': 5, 'num_leaves': 6, 'min_child_samples': 4, 'learning_rate': 1.0, 'log_max_bin': 9, 'colsample_bytree': 0.5359445694617821, 'reg_alpha': 0.01293007060710423, 'reg_lambda': 1.7821617657499946}\n",
      "2024-09-24 21:18:34 : Training AutoML...\n",
      "2024-09-24 21:18:44 : Best model is lgbm with best model parameters of {'n_estimators': 7, 'num_leaves': 4, 'min_child_samples': 2, 'learning_rate': 0.6838568454533844, 'log_max_bin': 8, 'colsample_bytree': 0.4298172382147114, 'reg_alpha': 0.004822916649385623, 'reg_lambda': 0.6246525947739224}\n",
      "2024-09-24 21:18:44 : Training AutoML...\n",
      "2024-09-24 21:18:54 : Best model is lgbm with best model parameters of {'n_estimators': 11, 'num_leaves': 16, 'min_child_samples': 4, 'learning_rate': 0.7105321001789935, 'log_max_bin': 6, 'colsample_bytree': 0.6361397370285701, 'reg_alpha': 0.03393538158114423, 'reg_lambda': 0.009254970952528998}\n",
      "2024-09-24 21:18:54 : Training AutoML...\n",
      "2024-09-24 21:19:04 : Best model is lgbm with best model parameters of {'n_estimators': 6, 'num_leaves': 4, 'min_child_samples': 3, 'learning_rate': 0.6490652628032392, 'log_max_bin': 10, 'colsample_bytree': 0.7260749083055879, 'reg_alpha': 0.017752941470684333, 'reg_lambda': 0.048632063423479185}\n"
     ]
    }
   ],
   "source": [
    "model_config = {\n",
    "    'time_budget': 10,                     # Total running time in seconds\n",
    "    'metric': 'r2',                        # Primary metric for regression, 'mae', 'mse', 'r2', 'mape',...\n",
    "    'estimator_list': [\"lgbm\"],            # List of ML learners: \"lgbm\", \"rf\", \"xgboost\", \"extra_tree\", \"xgb_limitdepth\"\n",
    "}\n",
    "mlscdataall_1=nm.mlsc_all(df,'SO2wn','ID',control_pool,'2015-10-23',automl_pkg='flaml', model_config=model_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "799c822d-7af9-4e8d-8240-a5383d031d4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H2O is not running. Starting H2O...\n",
      "Checking whether there is an H2O instance running at http://localhost:54321.H2O is not running. Starting H2O...\n",
      "Checking whether there is an H2O instance running at http://localhost:54321.H2O is not running. Starting H2O...\n",
      "Checking whether there is an H2O instance running at http://localhost:54321.H2O is not running. Starting H2O...\n",
      "Checking whether there is an H2O instance running at http://localhost:54321.H2O is not running. Starting H2O...\n",
      "Checking whether there is an H2O instance running at http://localhost:54321.H2O is not running. Starting H2O...\n",
      "Checking whether there is an H2O instance running at http://localhost:54321.H2O is not running. Starting H2O...\n",
      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n",
      "--------------------------  -------------------------------\n",
      "H2O_cluster_uptime:         8 hours 57 mins\n",
      "H2O_cluster_timezone:       Europe/London\n",
      "H2O_data_parsing_timezone:  UTC\n",
      "H2O_cluster_version:        3.46.0.5\n",
      "H2O_cluster_version_age:    26 days\n",
      "H2O_cluster_name:           H2O_from_python_n94921cs_5qrqdn\n",
      "H2O_cluster_total_nodes:    1\n",
      "H2O_cluster_free_memory:    6.827 Gb\n",
      "H2O_cluster_total_cores:    8\n",
      "H2O_cluster_allowed_cores:  1\n",
      "H2O_cluster_status:         locked, healthy\n",
      "H2O_connection_url:         http://localhost:54321\n",
      "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
      "H2O_internal_security:      False\n",
      "Python_version:             3.12.2 final\n",
      "--------------------------  -------------------------------\n",
      " connected.\n",
      "--------------------------  -------------------------------\n",
      "H2O_cluster_uptime:         8 hours 57 mins\n",
      "H2O_cluster_timezone:       Europe/London\n",
      "H2O_data_parsing_timezone:  UTC\n",
      "H2O_cluster_version:        3.46.0.5\n",
      "H2O_cluster_version_age:    26 days\n",
      "H2O_cluster_name:           H2O_from_python_n94921cs_5qrqdn\n",
      "H2O_cluster_total_nodes:    1\n",
      "H2O_cluster_free_memory:    6.827 Gb\n",
      "H2O_cluster_total_cores:    8\n",
      "H2O_cluster_allowed_cores:  1\n",
      "H2O_cluster_status:         locked, healthy\n",
      "H2O_connection_url:         http://localhost:54321\n",
      "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
      "H2O_internal_security:      False\n",
      "Python_version:             3.12.2 final\n",
      "--------------------------  -------------------------------\n",
      " connected.\n",
      "--------------------------  -------------------------------\n",
      "H2O_cluster_uptime:         8 hours 57 mins\n",
      "H2O_cluster_timezone:       Europe/London\n",
      "H2O_data_parsing_timezone:  UTC\n",
      "H2O_cluster_version:        3.46.0.5\n",
      "H2O_cluster_version_age:    26 days\n",
      "H2O_cluster_name:           H2O_from_python_n94921cs_5qrqdn\n",
      "H2O_cluster_total_nodes:    1\n",
      "H2O_cluster_free_memory:    6.827 Gb\n",
      "H2O_cluster_total_cores:    8\n",
      "H2O_cluster_allowed_cores:  1\n",
      "H2O_cluster_status:         locked, healthy\n",
      "H2O_connection_url:         http://localhost:54321\n",
      "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
      "H2O_internal_security:      False\n",
      "Python_version:             3.12.2 final\n",
      "--------------------------  -------------------------------\n",
      " connected.\n",
      "--------------------------  -------------------------------\n",
      "H2O_cluster_uptime:         8 hours 57 mins\n",
      "H2O_cluster_timezone:       Europe/London\n",
      "H2O_data_parsing_timezone:  UTC\n",
      "H2O_cluster_version:        3.46.0.5\n",
      "H2O_cluster_version_age:    26 days\n",
      "H2O_cluster_name:           H2O_from_python_n94921cs_5qrqdn\n",
      "H2O_cluster_total_nodes:    1\n",
      "H2O_cluster_free_memory:    6.827 Gb\n",
      "H2O_cluster_total_cores:    8\n",
      "H2O_cluster_allowed_cores:  1\n",
      "H2O_cluster_status:         locked, healthy\n",
      "H2O_connection_url:         http://localhost:54321\n",
      "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
      "H2O_internal_security:      False\n",
      "Python_version:             3.12.2 final\n",
      "--------------------------  -------------------------------\n",
      " connected.\n",
      "--------------------------  -------------------------------\n",
      "H2O_cluster_uptime:         8 hours 57 mins\n",
      "H2O_cluster_timezone:       Europe/London\n",
      "H2O_data_parsing_timezone:  UTC\n",
      "H2O_cluster_version:        3.46.0.5\n",
      "H2O_cluster_version_age:    26 days\n",
      "H2O_cluster_name:           H2O_from_python_n94921cs_5qrqdn\n",
      "H2O_cluster_total_nodes:    1\n",
      "H2O_cluster_free_memory:    6.827 Gb\n",
      "H2O_cluster_total_cores:    8\n",
      "H2O_cluster_allowed_cores:  1\n",
      "H2O_cluster_status:         locked, healthy\n",
      "H2O_connection_url:         http://localhost:54321\n",
      "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
      "H2O_internal_security:      False\n",
      "Python_version:             3.12.2 final\n",
      "--------------------------  -------------------------------\n",
      " connected.\n",
      "--------------------------  -------------------------------\n",
      "H2O_cluster_uptime:         8 hours 57 mins\n",
      "H2O_cluster_timezone:       Europe/London\n",
      "H2O_data_parsing_timezone:  UTC\n",
      "H2O_cluster_version:        3.46.0.5\n",
      "H2O_cluster_version_age:    26 days\n",
      "H2O_cluster_name:           H2O_from_python_n94921cs_5qrqdn\n",
      "H2O_cluster_total_nodes:    1\n",
      "H2O_cluster_free_memory:    6.827 Gb\n",
      "H2O_cluster_total_cores:    8\n",
      "H2O_cluster_allowed_cores:  1\n",
      "H2O_cluster_status:         locked, healthy\n",
      "H2O_connection_url:         http://localhost:54321\n",
      "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
      "H2O_internal_security:      False\n",
      "Python_version:             3.12.2 final\n",
      "--------------------------  -------------------------------\n",
      " connected.\n",
      "--------------------------  -------------------------------\n",
      "H2O_cluster_uptime:         8 hours 57 mins\n",
      "H2O_cluster_timezone:       Europe/London\n",
      "H2O_data_parsing_timezone:  UTC\n",
      "H2O_cluster_version:        3.46.0.5\n",
      "H2O_cluster_version_age:    26 days\n",
      "H2O_cluster_name:           H2O_from_python_n94921cs_5qrqdn\n",
      "H2O_cluster_total_nodes:    1\n",
      "H2O_cluster_free_memory:    6.827 Gb\n",
      "H2O_cluster_total_cores:    8\n",
      "H2O_cluster_allowed_cores:  1\n",
      "H2O_cluster_status:         locked, healthy\n",
      "H2O_connection_url:         http://localhost:54321\n",
      "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
      "H2O_internal_security:      False\n",
      "Python_version:             3.12.2 final\n",
      "--------------------------  -------------------------------\n",
      "2024-09-24 21:19:08: Training AutoML...\n",
      "2024-09-24 21:19:08: Training AutoML...\n",
      "2024-09-24 21:19:08: Training AutoML...\n",
      "2024-09-24 21:19:08: Training AutoML...\n",
      "2024-09-24 21:19:08: Training AutoML...\n",
      "2024-09-24 21:19:08: Training AutoML...\n",
      "2024-09-24 21:19:08: Training AutoML...\n",
      "\n",
      "21:19:08.260: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:19:08.259: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:19:08.260: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:19:08.259: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:19:08.259: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:19:08.263: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:19:08.263: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:19:19: Best model obtained! - GBM_3_AutoML_86_20240924_211908\n",
      "2024-09-24 21:19:19: Best model obtained! - GBM_3_AutoML_87_20240924_211908\n",
      "2024-09-24 21:19:19: Best model obtained! - GBM_2_AutoML_91_20240924_211908\n",
      "2024-09-24 21:19:19: Best model obtained! - GBM_3_AutoML_90_20240924_211908\n",
      "2024-09-24 21:19:19: Best model obtained! - GBM_3_AutoML_85_20240924_211908\n",
      "2024-09-24 21:19:19: Best model obtained! - GBM_3_AutoML_88_20240924_211908\n",
      "2024-09-24 21:19:19: Best model obtained! - GBM_2_AutoML_89_20240924_211908\n",
      "2024-09-24 21:19:19: Training AutoML...\n",
      "\n",
      "21:19:19.987: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:19:20: Training AutoML...\n",
      "\n",
      "21:19:21.723: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:19:31: Training AutoML...\n",
      "2024-09-24 21:19:31: Training AutoML...\n",
      "2024-09-24 21:19:31: Training AutoML...\n",
      "\n",
      "21:19:32.77: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:19:32.735: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:19:32.735: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:19:43: Training AutoML...\n",
      "\n",
      "21:19:43.490: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:19:43: Best model obtained! - GBM_5_AutoML_93_20240924_211920\n",
      "2024-09-24 21:19:44: Training AutoML...\n",
      "2024-09-24 21:19:43: Best model obtained! - GBM_5_AutoML_96_20240924_211931\n",
      "\n",
      "21:19:45.241: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:19:55: Training AutoML...\n",
      "\n",
      "21:19:55.541: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:19:55: Training AutoML...\n",
      "\n",
      "21:19:57.263: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:19:55: Best model obtained! - GBM_3_AutoML_94_20240924_211931\n",
      "2024-09-24 21:19:55: Best model obtained! - GBM_5_AutoML_95_20240924_211931\n",
      "2024-09-24 21:19:55: Best model obtained! - GBM_grid_1_AutoML_92_20240924_211919_model_2\n",
      "2024-09-24 21:20:07: Best model obtained! - GBM_2_AutoML_97_20240924_211943\n",
      "2024-09-24 21:20:07: Best model obtained! - GBM_grid_1_AutoML_98_20240924_211944_model_2\n",
      "2024-09-24 21:20:08: Training AutoML...\n",
      "\n",
      "21:20:08.104: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:20:08: Training AutoML...\n",
      "\n",
      "21:20:09.948: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:20:18: Training AutoML...\n",
      "2024-09-24 21:20:18: Training AutoML...\n",
      "\n",
      "21:20:20.473: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:20:21.551: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:20:32: Training AutoML...\n",
      "\n",
      "21:20:32.395: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:20:32: Best model obtained! - GBM_5_AutoML_99_20240924_211955\n",
      "2024-09-24 21:20:32: Best model obtained! - GBM_5_AutoML_100_20240924_211955\n",
      "2024-09-24 21:20:36: Best model obtained! - GBM_5_AutoML_102_20240924_212008\n",
      "2024-09-24 21:20:40: Best model obtained! - GBM_5_AutoML_103_20240924_212018\n",
      "2024-09-24 21:20:41: Best model obtained! - GBM_3_AutoML_101_20240924_212008\n",
      "2024-09-24 21:20:41: Best model obtained! - GBM_5_AutoML_104_20240924_212018\n",
      "2024-09-24 21:20:42: Training AutoML...\n",
      "\n",
      "21:20:42.15: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:20:43: Training AutoML...\n",
      "\n",
      "21:20:49.257: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:20:51: Training AutoML...\n",
      "2024-09-24 21:20:51: Training AutoML...\n",
      "2024-09-24 21:20:51: Training AutoML...\n",
      "2024-09-24 21:20:51: Training AutoML...\n",
      "\n",
      "21:20:53.162: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:20:53.162: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:20:59.570: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:21:08: Best model obtained! - GBM_grid_1_AutoML_105_20240924_212032_model_5\n",
      "2024-09-24 21:21:14: Best model obtained! - GBM_grid_1_AutoML_106_20240924_212042_model_4\n",
      "2024-09-24 21:21:14: Best model obtained! - GBM_5_AutoML_109_20240924_212051\n",
      "2024-09-24 21:21:14: Best model obtained! - GBM_5_AutoML_107_20240924_212043\n",
      "2024-09-24 21:21:14: Best model obtained! - GBM_2_AutoML_111_20240924_212051\n",
      "2024-09-24 21:21:14: Best model obtained! - GBM_5_AutoML_110_20240924_212051\n",
      "\n",
      "21:21:03.828: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:21:15: Best model obtained! - GBM_grid_1_AutoML_108_20240924_212051_model_2\n",
      "2024-09-24 21:21:15: Training AutoML...\n",
      "\n",
      "21:21:15.484: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:21:15: Training AutoML...\n",
      "2024-09-24 21:21:15: Training AutoML...\n",
      "2024-09-24 21:21:15: Training AutoML...\n",
      "\n",
      "21:21:16.728: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:21:26.158: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:21:26.158: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:21:28: Training AutoML...\n",
      "2024-09-24 21:21:28: Training AutoML...\n",
      "\n",
      "21:21:37.71: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:21:37.71: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:21:37: Training AutoML...\n",
      "\n",
      "21:21:47.951: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:21:57: Best model obtained! - GBM_grid_1_AutoML_114_20240924_212115_model_2\n",
      "2024-09-24 21:21:58: Best model obtained! - GBM_5_AutoML_112_20240924_212115\n",
      "2024-09-24 21:21:58: Best model obtained! - GBM_5_AutoML_113_20240924_212115\n",
      "2024-09-24 21:21:58: Best model obtained! - GBM_5_AutoML_116_20240924_212128\n",
      "2024-09-24 21:21:58: Best model obtained! - GBM_5_AutoML_115_20240924_212115\n",
      "2024-09-24 21:21:58: Best model obtained! - GBM_5_AutoML_117_20240924_212128\n",
      "2024-09-24 21:21:58: Best model obtained! - GBM_grid_1_AutoML_118_20240924_212137_model_4\n",
      "2024-09-24 21:21:58: Training AutoML...\n",
      "\n",
      "21:21:58.779: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:21:58: Training AutoML...\n",
      "2024-09-24 21:21:58: Training AutoML...\n",
      "2024-09-24 21:21:58: Training AutoML...\n",
      "\n",
      "21:22:00.851: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:22:05.148: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "\n",
      "21:22:05.148: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 25.0.\n",
      "\n",
      "2024-09-24 21:22:16: Best model obtained! - GBM_grid_1_AutoML_122_20240924_212158_model_2\n",
      "2024-09-24 21:22:16: Best model obtained! - GBM_5_AutoML_120_20240924_212158\n",
      "2024-09-24 21:22:16: Best model obtained! - GBM_5_AutoML_119_20240924_212158\n",
      "2024-09-24 21:22:16: Best model obtained! - GBM_5_AutoML_121_20240924_212158\n"
     ]
    }
   ],
   "source": [
    "model_config = {\n",
    "        'time_budget': 10,\n",
    "        #'max_models': 10,              # Maximum number of models to train\n",
    "        #'max_mem_size': '12g',         # Maximum memory size for H2O\n",
    "        'estimator_list': ['GBM'],     # List of algorithms to use in AutoML\n",
    "}\n",
    "mlscdataall_2=nm.mlsc_all(df,'SO2wn','ID',control_pool,'2015-10-23',automl_pkg='h2o', model_config=model_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0b5f9760-ab89-425e-aee2-71080882c477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "for i,city in enumerate(list(mlscdataall_1['ID'].unique())):\n",
    "    mlscdataall_1[mlscdataall_1['ID']==city]['effects'].plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "675af89d-8ac9-4577-a9d2-3a93d7dce9f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Closing connection _sid_8dbb at exit\n",
      "H2O session _sid_8dbb closed.\n",
      "Closing connection _sid_8531 at exit\n",
      "H2O session _sid_8531 closed.\n",
      "Closing connection _sid_ac4f at exit\n",
      "H2O session _sid_ac4f closed.\n",
      "Closing connection _sid_b025 at exit\n",
      "H2O session _sid_b025 closed.\n",
      "Closing connection _sid_8597 at exit\n",
      "H2O session _sid_8597 closed.\n",
      "Closing connection _sid_90c2 at exit\n",
      "H2O session _sid_90c2 closed.\n",
      "Closing connection _sid_b4d2 at exit\n",
      "H2O session _sid_b4d2 closed.\n"
     ]
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "for i,city in enumerate(list(mlscdataall_2['ID'].unique())):\n",
    "    mlscdataall_2[mlscdataall_2['ID']==city]['effects'].plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6861850-6bca-438c-9ae5-79206ede9f89",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}