examine.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm(data):\n",
    "    return (data)/(max(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72       0.363636\n",
       "73       0.365751\n",
       "74       0.365398\n",
       "75       0.387949\n",
       "76       0.372798\n",
       "           ...   \n",
       "17646    0.432699\n",
       "17647    0.416138\n",
       "17648    0.411910\n",
       "17649    0.416490\n",
       "17650    0.416490\n",
       "Name: CO, Length: 17578, dtype: float64"
      ]
     },
     "execution_count": 595,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm(appa1.CO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "appa1 = pd.read_csv(\"data/appa1_gt_clean.csv\")\n",
    "appa2 = pd.read_csv(\"data/appa2_gt_clean.csv\")\n",
    "appa1 = appa1[appa1[\"PM10\"] != \"n.d.\"]\n",
    "appa2 = appa2[appa2[\"PM10\"] != \"n.d.\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "appa1.columns = ['Time', 'PM10', 'NO2', 'SO2', 'O3']\n",
    "appa2.columns = ['Time', 'PM10', 'CO', 'NO2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "appa1.PM10 = appa1.PM10.astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Time', 'PM10', 'CO', 'NO2'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'PM10', 'NO2', 'SO2', 'O3'], dtype='object')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(appa2.columns)\n",
    "appa1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphs\n",
    "\n",
    "**CO and NO2 during a week**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "plt.plot(norm(appa1[(appa1[\"Time\"] > '2022-01-01') & (appa1[\"Time\"] < '2022-01-05')].NO2), label=\"NO2\", linewidth=5)\n",
    "plt.plot(norm(appa1[(appa1[\"Time\"] > '2022-01-01') & (appa1[\"Time\"] < '2022-01-05')].SO2), label=\"SO2\", alpha=0.5)\n",
    "plt.plot(norm(appa1[(appa1[\"Time\"] > '2022-01-01') & (appa1[\"Time\"] < '2022-01-05')].O3), label=\"O3\", alpha=0.5)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**APPA 1**\n",
    "Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_47d49_row0_col0, #T_47d49_row1_col1, #T_47d49_row2_col2, #T_47d49_row3_col3 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_47d49_row0_col1 {\n",
       "  background-color: #f59f80;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_47d49_row0_col2 {\n",
       "  background-color: #f6bfa6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_47d49_row0_col3 {\n",
       "  background-color: #536edd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_47d49_row1_col0 {\n",
       "  background-color: #f7a889;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_47d49_row1_col2 {\n",
       "  background-color: #e7745b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_47d49_row1_col3, #T_47d49_row3_col0, #T_47d49_row3_col1, #T_47d49_row3_col2 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_47d49_row2_col0 {\n",
       "  background-color: #f7b79b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_47d49_row2_col1 {\n",
       "  background-color: #e26952;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_47d49_row2_col3 {\n",
       "  background-color: #6a8bef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_47d49\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_47d49_level0_col0\" class=\"col_heading level0 col0\" >PM10</th>\n",
       "      <th id=\"T_47d49_level0_col1\" class=\"col_heading level0 col1\" >NO2</th>\n",
       "      <th id=\"T_47d49_level0_col2\" class=\"col_heading level0 col2\" >SO2</th>\n",
       "      <th id=\"T_47d49_level0_col3\" class=\"col_heading level0 col3\" >O3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_47d49_level0_row0\" class=\"row_heading level0 row0\" >PM10</th>\n",
       "      <td id=\"T_47d49_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_47d49_row0_col1\" class=\"data row0 col1\" >0.540589</td>\n",
       "      <td id=\"T_47d49_row0_col2\" class=\"data row0 col2\" >0.473543</td>\n",
       "      <td id=\"T_47d49_row0_col3\" class=\"data row0 col3\" >-0.590956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47d49_level0_row1\" class=\"row_heading level0 row1\" >NO2</th>\n",
       "      <td id=\"T_47d49_row1_col0\" class=\"data row1 col0\" >0.540589</td>\n",
       "      <td id=\"T_47d49_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_47d49_row1_col2\" class=\"data row1 col2\" >0.756000</td>\n",
       "      <td id=\"T_47d49_row1_col3\" class=\"data row1 col3\" >-0.731285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47d49_level0_row2\" class=\"row_heading level0 row2\" >SO2</th>\n",
       "      <td id=\"T_47d49_row2_col0\" class=\"data row2 col0\" >0.473543</td>\n",
       "      <td id=\"T_47d49_row2_col1\" class=\"data row2 col1\" >0.756000</td>\n",
       "      <td id=\"T_47d49_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_47d49_row2_col3\" class=\"data row2 col3\" >-0.472522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47d49_level0_row3\" class=\"row_heading level0 row3\" >O3</th>\n",
       "      <td id=\"T_47d49_row3_col0\" class=\"data row3 col0\" >-0.590956</td>\n",
       "      <td id=\"T_47d49_row3_col1\" class=\"data row3 col1\" >-0.731285</td>\n",
       "      <td id=\"T_47d49_row3_col2\" class=\"data row3 col2\" >-0.472522</td>\n",
       "      <td id=\"T_47d49_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe9a288b790>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = appa1[(appa1[\"Time\"] > '2022-01-01') & (appa1[\"Time\"] < '2022-01-31')][[\"PM10\", \"NO2\", \"SO2\", \"O3\"]].corr()\n",
    "corr.style.background_gradient(cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**APPA 2**\n",
    "Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4aad7_row0_col0, #T_4aad7_row1_col1, #T_4aad7_row2_col2 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4aad7_row0_col1 {\n",
       "  background-color: #799cf8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4aad7_row0_col2, #T_4aad7_row2_col0, #T_4aad7_row2_col1 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4aad7_row1_col0 {\n",
       "  background-color: #f0cdbb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4aad7_row1_col2 {\n",
       "  background-color: #dadce0;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4aad7\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4aad7_level0_col0\" class=\"col_heading level0 col0\" >PM10</th>\n",
       "      <th id=\"T_4aad7_level0_col1\" class=\"col_heading level0 col1\" >CO</th>\n",
       "      <th id=\"T_4aad7_level0_col2\" class=\"col_heading level0 col2\" >NO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4aad7_level0_row0\" class=\"row_heading level0 row0\" >PM10</th>\n",
       "      <td id=\"T_4aad7_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_4aad7_row0_col1\" class=\"data row0 col1\" >0.851578</td>\n",
       "      <td id=\"T_4aad7_row0_col2\" class=\"data row0 col2\" >0.638649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4aad7_level0_row1\" class=\"row_heading level0 row1\" >CO</th>\n",
       "      <td id=\"T_4aad7_row1_col0\" class=\"data row1 col0\" >0.851578</td>\n",
       "      <td id=\"T_4aad7_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_4aad7_row1_col2\" class=\"data row1 col2\" >0.816147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4aad7_level0_row2\" class=\"row_heading level0 row2\" >NO2</th>\n",
       "      <td id=\"T_4aad7_row2_col0\" class=\"data row2 col0\" >0.638649</td>\n",
       "      <td id=\"T_4aad7_row2_col1\" class=\"data row2 col1\" >0.816147</td>\n",
       "      <td id=\"T_4aad7_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe9a2895070>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = appa2[(appa2[\"Time\"] > '2022-01-01') & (appa2[\"Time\"] < '2022-01-31')][[\"PM10\", \"CO\", \"NO2\"]].corr()\n",
    "corr.style.background_gradient(cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error,median_absolute_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [],
   "source": [
    "appa2.Time = pd.to_datetime(appa2.Time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "dfx[\"time\"] = dfx[\"Time\"].dt.hour\n",
    "dfx[\"month\"] = dfx[\"Time\"].dt.month\n",
    "\n",
    "\n",
    "dfx[\"weekend\"] = (\n",
    "    dfx[\"Time\"].dt.dayofweek >= 4\n",
    ").astype(\"int\")\n",
    "\n",
    "dfx[\"sin_time\"] = dfx[\"time\"].apply(\n",
    "    lambda x: math.sin(x) * 2 * (math.pi / 24)\n",
    ")\n",
    "dfx[\"cos_time\"] = dfx[\"time\"].apply(\n",
    "    lambda x: math.cos(x) * 2 * (math.pi / 24)\n",
    ")\n",
    "\n",
    "dfx[\"sin_month\"] = dfx[\"month\"].apply(\n",
    "    lambda x: math.sin(x) * 2 * (math.pi / 12)\n",
    ")\n",
    "\n",
    "dfx[\"cos_month\"] = dfx[\"month\"].apply(\n",
    "    lambda x: math.cos(x) * 2 * (math.pi / 12)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>Time</th>\n",
       "      <th>PM10</th>\n",
       "      <th>CO</th>\n",
       "      <th>NO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-31 01:00:00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-31 02:00:00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-31 03:00:00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-31 04:00:00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-31 05:00:00</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17063</th>\n",
       "      <td>2022-07-25 20:00:00</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17064</th>\n",
       "      <td>2022-07-25 21:00:00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17065</th>\n",
       "      <td>2022-07-25 22:00:00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17066</th>\n",
       "      <td>2022-07-25 23:00:00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17067</th>\n",
       "      <td>2022-07-26 00:00:00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17068 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Time  PM10   CO   NO2\n",
       "0     2020-07-31 01:00:00  16.0  0.6   8.0\n",
       "1     2020-07-31 02:00:00  16.0  0.6   7.0\n",
       "2     2020-07-31 03:00:00  16.0  0.6   8.0\n",
       "3     2020-07-31 04:00:00  17.0  0.5  11.0\n",
       "4     2020-07-31 05:00:00  18.0  0.6  15.0\n",
       "...                   ...   ...  ...   ...\n",
       "17063 2022-07-25 20:00:00  31.0  0.8  13.0\n",
       "17064 2022-07-25 21:00:00  30.0  0.8  12.0\n",
       "17065 2022-07-25 22:00:00  22.0  0.8   6.0\n",
       "17066 2022-07-25 23:00:00  30.0  0.8  10.0\n",
       "17067 2022-07-26 00:00:00  22.0  0.8  10.0\n",
       "\n",
       "[17068 rows x 4 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appa2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [],
   "source": [
    "[c for c in cols[:2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|█████████▉| 16972/17068 [03:40<00:01, 77.01it/s]\n"
     ]
    }
   ],
   "source": [
    "sliding = 96\n",
    "cols = [\"PM10\", \"NO2\"]\n",
    "start = len(cols)\n",
    "for i in range(sliding):\n",
    "\tfor x in range(start):\n",
    "\t\tcols.append(f\"{cols[x]}_{i}\")\n",
    "cols.append(\"Time\")\n",
    "dfx = pd.DataFrame(columns=cols)\n",
    "buff = [[r[c] for c in cols[:start]] for i,r in appa2.iloc[:sliding].iterrows()]\n",
    "for i, row in tqdm(appa2.iloc[sliding:].iterrows(), total=len(appa2)):\n",
    "\trow_to_add = [row[c] for c in cols[:start]]\n",
    "\tfor slid in range(sliding):\n",
    "\t\trow_to_add.extend(buff[slid][c] for c in range(len(cols[:start])))\n",
    "\tbuff.insert(0,[row[c] for c in cols[:start]])\n",
    "\tbuff.pop(-1)\n",
    "\trow_to_add.append(row[\"Time\"])\n",
    "\tdfx.loc[i - sliding] = row_to_add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "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>PM10</th>\n",
       "      <th>NO2</th>\n",
       "      <th>PM10_0</th>\n",
       "      <th>NO2_0</th>\n",
       "      <th>PM10_1</th>\n",
       "      <th>NO2_1</th>\n",
       "      <th>PM10_2</th>\n",
       "      <th>NO2_2</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020-07-31 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2020-07-31 05:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020-07-31 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2020-07-31 07:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2020-07-31 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2020-10-13 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1765</th>\n",
       "      <td>15.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2020-10-13 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1766</th>\n",
       "      <td>18.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2020-10-13 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2020-10-13 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768</th>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2020-10-14 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1769 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PM10   NO2  PM10_0  NO2_0  PM10_1  NO2_1  PM10_2  NO2_2  \\\n",
       "0     17.0  11.0    16.0    8.0    16.0    7.0    16.0    8.0   \n",
       "1     18.0  15.0    17.0   11.0    16.0    8.0    16.0    7.0   \n",
       "2     19.0  31.0    18.0   15.0    17.0   11.0    16.0    8.0   \n",
       "3     20.0  37.0    19.0   31.0    18.0   15.0    17.0   11.0   \n",
       "4     20.0  30.0    20.0   37.0    19.0   31.0    18.0   15.0   \n",
       "...    ...   ...     ...    ...     ...    ...     ...    ...   \n",
       "1764   9.0  67.0     5.0   46.0     5.0   44.0     5.0   58.0   \n",
       "1765  15.0  57.0     9.0   67.0     5.0   46.0     5.0   44.0   \n",
       "1766  18.0  50.0    15.0   57.0     9.0   67.0     5.0   46.0   \n",
       "1767  11.0  27.0    18.0   50.0    15.0   57.0     9.0   67.0   \n",
       "1768  10.0  19.0    11.0   27.0    18.0   50.0    15.0   57.0   \n",
       "\n",
       "                    Time  \n",
       "0    2020-07-31 04:00:00  \n",
       "1    2020-07-31 05:00:00  \n",
       "2    2020-07-31 06:00:00  \n",
       "3    2020-07-31 07:00:00  \n",
       "4    2020-07-31 08:00:00  \n",
       "...                  ...  \n",
       "1764 2020-10-13 20:00:00  \n",
       "1765 2020-10-13 21:00:00  \n",
       "1766 2020-10-13 22:00:00  \n",
       "1767 2020-10-13 23:00:00  \n",
       "1768 2020-10-14 00:00:00  \n",
       "\n",
       "[1769 rows x 9 columns]"
      ]
     },
     "execution_count": 535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols.remove(\"Time\")\n",
    "#cols.extend(['time', 'month', 'weekend', 'sin_time', 'cos_time', 'sin_month', 'cos_month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {},
   "outputs": [],
   "source": [
    "vsplit = round(len(appa2) * 0.3)\n",
    "\n",
    "X_train = dfx.loc[vsplit:][cols]\n",
    "X_test = dfx.loc[:vsplit][cols]\n",
    "\n",
    "Y_train = appa2.loc[sliding + vsplit:][[\"CO\"]]\n",
    "Y_test = appa2.loc[sliding:vsplit+sliding][[\"CO\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>PM10</th>\n",
       "      <th>NO2</th>\n",
       "      <th>PM10_1</th>\n",
       "      <th>NO2_1</th>\n",
       "      <th>PM10_2</th>\n",
       "      <th>NO2_2</th>\n",
       "      <th>PM10_3</th>\n",
       "      <th>NO2_3</th>\n",
       "      <th>PM10_4</th>\n",
       "      <th>NO2_4</th>\n",
       "      <th>...</th>\n",
       "      <th>PM10_67</th>\n",
       "      <th>NO2_67</th>\n",
       "      <th>PM10_68</th>\n",
       "      <th>NO2_68</th>\n",
       "      <th>PM10_69</th>\n",
       "      <th>NO2_69</th>\n",
       "      <th>PM10_70</th>\n",
       "      <th>NO2_70</th>\n",
       "      <th>PM10_71</th>\n",
       "      <th>NO2_71</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3414</th>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3415</th>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>39.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3416</th>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3417</th>\n",
       "      <td>16.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3418</th>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16991</th>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16992</th>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16993</th>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16994</th>\n",
       "      <td>30.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16995</th>\n",
       "      <td>22.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13582 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PM10   NO2  PM10_1  NO2_1  PM10_2  NO2_2  PM10_3  NO2_3  PM10_4  NO2_4  \\\n",
       "3414   23.0  23.0    24.0   23.0    29.0   25.0    26.0   26.0    27.0   28.0   \n",
       "3415   20.0  23.0    23.0   23.0    24.0   23.0    29.0   25.0    26.0   26.0   \n",
       "3416   17.0  24.0    20.0   23.0    23.0   23.0    24.0   23.0    29.0   25.0   \n",
       "3417   16.0  24.0    17.0   24.0    20.0   23.0    23.0   23.0    24.0   23.0   \n",
       "3418   16.0  22.0    16.0   24.0    17.0   24.0    20.0   23.0    23.0   23.0   \n",
       "...     ...   ...     ...    ...     ...    ...     ...    ...     ...    ...   \n",
       "16991  31.0  13.0    28.0   16.0    26.0   18.0    24.0   20.0    22.0   13.0   \n",
       "16992  30.0  12.0    31.0   13.0    28.0   16.0    26.0   18.0    24.0   20.0   \n",
       "16993  22.0   6.0    30.0   12.0    31.0   13.0    28.0   16.0    26.0   18.0   \n",
       "16994  30.0  10.0    22.0    6.0    30.0   12.0    31.0   13.0    28.0   16.0   \n",
       "16995  22.0  10.0    30.0   10.0    22.0    6.0    30.0   12.0    31.0   13.0   \n",
       "\n",
       "       ...  PM10_67  NO2_67  PM10_68  NO2_68  PM10_69  NO2_69  PM10_70  \\\n",
       "3414   ...     32.0    52.0     28.0    50.0     28.0    35.0     29.0   \n",
       "3415   ...     39.0    67.0     32.0    52.0     28.0    50.0     28.0   \n",
       "3416   ...     40.0    67.0     39.0    67.0     32.0    52.0     28.0   \n",
       "3417   ...     38.0    49.0     40.0    67.0     39.0    67.0     32.0   \n",
       "3418   ...     35.0    51.0     38.0    49.0     40.0    67.0     39.0   \n",
       "...    ...      ...     ...      ...     ...      ...     ...      ...   \n",
       "16991  ...     38.0    22.0     37.0    27.0     35.0    41.0     32.0   \n",
       "16992  ...     38.0    22.0     38.0    22.0     37.0    27.0     35.0   \n",
       "16993  ...     38.0    18.0     38.0    22.0     38.0    22.0     37.0   \n",
       "16994  ...     38.0    24.0     38.0    18.0     38.0    22.0     38.0   \n",
       "16995  ...     38.0    30.0     38.0    24.0     38.0    18.0     38.0   \n",
       "\n",
       "       NO2_70  PM10_71  NO2_71  \n",
       "3414     37.0     32.0    40.0  \n",
       "3415     35.0     29.0    37.0  \n",
       "3416     50.0     28.0    35.0  \n",
       "3417     52.0     28.0    50.0  \n",
       "3418     67.0     32.0    52.0  \n",
       "...       ...      ...     ...  \n",
       "16991    39.0     29.0    28.0  \n",
       "16992    41.0     32.0    39.0  \n",
       "16993    27.0     35.0    41.0  \n",
       "16994    22.0     37.0    27.0  \n",
       "16995    22.0     38.0    22.0  \n",
       "\n",
       "[13582 rows x 144 columns]"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gl/2h8nn4ps031g1jysgll17wz00000gn/T/ipykernel_2567/1751700509.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  model.fit(X_train, Y_train)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=150)"
      ]
     },
     "execution_count": 582,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor(n_estimators=150)\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('sgdregressor', SGDRegressor(max_iter=100000000))])"
      ]
     },
     "execution_count": 583,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "svm = make_pipeline(StandardScaler(), SGDRegressor(max_iter=100000000, tol=1e-3))\n",
    "svm.fit(X_train, Y_train[\"CO\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:494: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=0.01, max_depth=4, min_samples_split=5,\n",
       "                          n_estimators=500)"
      ]
     },
     "execution_count": 584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "params = {\n",
    "    \"n_estimators\": 500,\n",
    "    \"max_depth\": 4,\n",
    "    \"min_samples_split\": 5,\n",
    "    \"learning_rate\": 0.01,\n",
    "    \"loss\": \"squared_error\",\n",
    "}\n",
    "gbreg = GradientBoostingRegressor(**params)\n",
    "gbreg.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "Median Error: 0.28800000000000003 \n",
      " Mean error: 0.3031501659832064\n",
      "SVM\n",
      "Median Error: 0.2930692412149041 \n",
      " Mean error: 0.31339635392908094\n",
      "Gradient Boosting\n",
      "Median Error: 0.283250278640538 \n",
      " Mean error: 0.29868429996075274\n",
      "Aggregate\n",
      "Median Error: 0.29005073968147 \n",
      " Mean error: 0.3039216890724165\n"
     ]
    }
   ],
   "source": [
    "Y_test_pred_r = model.predict(X_test)\n",
    "Y_test_pred_s = svm.predict(X_test)\n",
    "Y_test_pred_g = gbreg.predict(X_test)\n",
    "\n",
    "Y_test_pred_agg = (Y_test_pred_r + Y_test_pred_s + Y_test_pred_g) / 3\n",
    "\n",
    "print(\"Random Forest\")\n",
    "print(f\"Median Error: {median_absolute_error(Y_test, Y_test_pred_r)} \\n Mean error: {mean_absolute_error(Y_test, Y_test_pred_r)}\")\n",
    "\n",
    "print(\"SVM\")\n",
    "print(f\"Median Error: {median_absolute_error(Y_test, Y_test_pred_s)} \\n Mean error: {mean_absolute_error(Y_test, Y_test_pred_s)}\")\n",
    "\n",
    "print(\"Gradient Boosting\")\n",
    "print(f\"Median Error: {median_absolute_error(Y_test, Y_test_pred_g)} \\n Mean error: {mean_absolute_error(Y_test, Y_test_pred_g)}\")\n",
    "\n",
    "print(\"Aggregate\")\n",
    "print(f\"Median Error: {median_absolute_error(Y_test, Y_test_pred_agg)} \\n Mean error: {mean_absolute_error(Y_test, Y_test_pred_agg)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**96 hours no Time**\n",
    "```\n",
    "Random Forest\n",
    "Median Error: 0.28800000000000003 \n",
    " Mean error: 0.3031501659832064\n",
    "SVM\n",
    "Median Error: 0.2930692412149041 \n",
    " Mean error: 0.31339635392908094\n",
    "Gradient Boosting\n",
    "Median Error: 0.283250278640538 \n",
    " Mean error: 0.29868429996075274\n",
    "Aggregate\n",
    "Median Error: 0.29005073968147 \n",
    " Mean error: 0.3039216890724165\n",
    "```\n",
    "**72 hours no Time**\n",
    "```\n",
    "Random Forest\n",
    "Median Error: 0.1940000000000005 \n",
    " Mean error: 0.2287012201073694\n",
    "SVM\n",
    "Median Error: 0.1948674061157284 \n",
    " Mean error: 0.2191402999531943\n",
    "Gradient Boosting\n",
    "Median Error: 0.19372584326515763 \n",
    " Mean error: 0.22408953691916916\n",
    "Aggregate\n",
    "Median Error: 0.1944680085235101 \n",
    " Mean error: 0.22211972165913738\n",
    "```\n",
    " **48 hours no Time**\n",
    "```\n",
    "Random Forest\n",
    "Median Error: 0.27733333333333354 \n",
    " Mean error: 0.3035549046410206\n",
    "SVM\n",
    "Median Error: 0.2831773488277234 \n",
    " Mean error: 0.303885332677762\n",
    "Gradient Boosting\n",
    "Median Error: 0.2705884745504852 \n",
    " Mean error: 0.2946250474476653\n",
    "Aggregate\n",
    "Median Error: 0.27893480900930534 \n",
    " Mean error: 0.2993598397356344\n",
    " ```\n",
    "\n",
    "  **48 hours with Time**\n",
    " ```\n",
    " Random Forest\n",
    "Median Error: 0.3286666666666669 \n",
    " Mean error: 0.34165228145544485\n",
    "SVM\n",
    "Median Error: 0.2812094634256046 \n",
    " Mean error: 0.29353549682193225\n",
    "Gradient Boosting\n",
    "Median Error: 0.324785770648355 \n",
    " Mean error: 0.3340284208613547\n",
    "Aggregate\n",
    "Median Error: 0.3135715707948403 \n",
    " Mean error: 0.32123597921764274\n",
    " ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(Y_test, Y_test, label=\"True\", color=\"green\")\n",
    "#plt.scatter(Y_test, Y_test_pred_r, label=\"Random Forest\", alpha=0.1)\n",
    "plt.scatter(Y_test, Y_test_pred_s, label=\"SVM\", alpha=0.1)\n",
    "#plt.scatter(Y_test, Y_test_pred_g, label=\"Gradient Boosting\", alpha=0.1, color=\"purple\")\n",
    "plt.legend()\n",
    "plt.title(\"Regression Models for CO\")\n",
    "plt.xlabel(\"True Values (ppm)\")\n",
    "plt.ylabel(\"Predicted Values (ppm)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [],
   "source": [
    "models.append([sliding, cols, model, svm, gbreg])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RandomForestRegressor(n_estimators=150),\n",
       " Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                 ('sgdregressor', SGDRegressor(max_iter=100000000))]),\n",
       " GradientBoostingRegressor(learning_rate=0.01, max_depth=4, min_samples_split=5,\n",
       "                           n_estimators=500),\n",
       " [72,\n",
       "  ['PM10',\n",
       "   'NO2',\n",
       "   'PM10_1',\n",
       "   'NO2_1',\n",
       "   'PM10_2',\n",
       "   'NO2_2',\n",
       "   'PM10_3',\n",
       "   'NO2_3',\n",
       "   'PM10_4',\n",
       "   'NO2_4',\n",
       "   'PM10_5',\n",
       "   'NO2_5',\n",
       "   'PM10_6',\n",
       "   'NO2_6',\n",
       "   'PM10_7',\n",
       "   'NO2_7',\n",
       "   'PM10_8',\n",
       "   'NO2_8',\n",
       "   'PM10_9',\n",
       "   'NO2_9',\n",
       "   'PM10_10',\n",
       "   'NO2_10',\n",
       "   'PM10_11',\n",
       "   'NO2_11',\n",
       "   'PM10_12',\n",
       "   'NO2_12',\n",
       "   'PM10_13',\n",
       "   'NO2_13',\n",
       "   'PM10_14',\n",
       "   'NO2_14',\n",
       "   'PM10_15',\n",
       "   'NO2_15',\n",
       "   'PM10_16',\n",
       "   'NO2_16',\n",
       "   'PM10_17',\n",
       "   'NO2_17',\n",
       "   'PM10_18',\n",
       "   'NO2_18',\n",
       "   'PM10_19',\n",
       "   'NO2_19',\n",
       "   'PM10_20',\n",
       "   'NO2_20',\n",
       "   'PM10_21',\n",
       "   'NO2_21',\n",
       "   'PM10_22',\n",
       "   'NO2_22',\n",
       "   'PM10_23',\n",
       "   'NO2_23',\n",
       "   'PM10_24',\n",
       "   'NO2_24',\n",
       "   'PM10_25',\n",
       "   'NO2_25',\n",
       "   'PM10_26',\n",
       "   'NO2_26',\n",
       "   'PM10_27',\n",
       "   'NO2_27',\n",
       "   'PM10_28',\n",
       "   'NO2_28',\n",
       "   'PM10_29',\n",
       "   'NO2_29',\n",
       "   'PM10_30',\n",
       "   'NO2_30',\n",
       "   'PM10_31',\n",
       "   'NO2_31',\n",
       "   'PM10_32',\n",
       "   'NO2_32',\n",
       "   'PM10_33',\n",
       "   'NO2_33',\n",
       "   'PM10_34',\n",
       "   'NO2_34',\n",
       "   'PM10_35',\n",
       "   'NO2_35',\n",
       "   'PM10_36',\n",
       "   'NO2_36',\n",
       "   'PM10_37',\n",
       "   'NO2_37',\n",
       "   'PM10_38',\n",
       "   'NO2_38',\n",
       "   'PM10_39',\n",
       "   'NO2_39',\n",
       "   'PM10_40',\n",
       "   'NO2_40',\n",
       "   'PM10_41',\n",
       "   'NO2_41',\n",
       "   'PM10_42',\n",
       "   'NO2_42',\n",
       "   'PM10_43',\n",
       "   'NO2_43',\n",
       "   'PM10_44',\n",
       "   'NO2_44',\n",
       "   'PM10_45',\n",
       "   'NO2_45',\n",
       "   'PM10_46',\n",
       "   'NO2_46',\n",
       "   'PM10_47',\n",
       "   'NO2_47',\n",
       "   'PM10_48',\n",
       "   'NO2_48',\n",
       "   'PM10_49',\n",
       "   'NO2_49',\n",
       "   'PM10_50',\n",
       "   'NO2_50',\n",
       "   'PM10_51',\n",
       "   'NO2_51',\n",
       "   'PM10_52',\n",
       "   'NO2_52',\n",
       "   'PM10_53',\n",
       "   'NO2_53',\n",
       "   'PM10_54',\n",
       "   'NO2_54',\n",
       "   'PM10_55',\n",
       "   'NO2_55',\n",
       "   'PM10_56',\n",
       "   'NO2_56',\n",
       "   'PM10_57',\n",
       "   'NO2_57',\n",
       "   'PM10_58',\n",
       "   'NO2_58',\n",
       "   'PM10_59',\n",
       "   'NO2_59',\n",
       "   'PM10_60',\n",
       "   'NO2_60',\n",
       "   'PM10_61',\n",
       "   'NO2_61',\n",
       "   'PM10_62',\n",
       "   'NO2_62',\n",
       "   'PM10_63',\n",
       "   'NO2_63',\n",
       "   'PM10_64',\n",
       "   'NO2_64',\n",
       "   'PM10_65',\n",
       "   'NO2_65',\n",
       "   'PM10_66',\n",
       "   'NO2_66',\n",
       "   'PM10_67',\n",
       "   'NO2_67',\n",
       "   'PM10_68',\n",
       "   'NO2_68',\n",
       "   'PM10_69',\n",
       "   'NO2_69',\n",
       "   'PM10_70',\n",
       "   'NO2_70',\n",
       "   'PM10_71',\n",
       "   'NO2_71'],\n",
       "  RandomForestRegressor(n_estimators=150),\n",
       "  Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                  ('sgdregressor', SGDRegressor(max_iter=100000000))]),\n",
       "  GradientBoostingRegressor(learning_rate=0.01, max_depth=4, min_samples_split=5,\n",
       "                            n_estimators=500)]]"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 17578/17650 [11:38<00:02, 25.18it/s]\n"
     ]
    }
   ],
   "source": [
    "sliding = 72\n",
    "cols = [\"PM10\", \"NO2\"]\n",
    "start = len(cols)\n",
    "for i in range(sliding-1):\n",
    "\tfor x in range(start):\n",
    "\t\tcols.append(f\"{cols[x]}_{i+1}\")\n",
    "cols.append(\"Time\")\n",
    "dfx2 = pd.DataFrame(columns=cols)\n",
    "for i, row in tqdm(appa1.iloc[sliding:].iterrows(), total=len(appa1)):\n",
    "\trow_to_add = [row[c] for c in cols[:start]]\n",
    "\tfor slid in range(sliding-1):\n",
    "\t\trow_to_add.extend(appa1.iloc[i-slid-1][c] for c in cols[:start])\n",
    "\trow_to_add.append(row[\"Time\"])\n",
    "\tdfx2.loc[i - sliding] = row_to_add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfx2.Time = pd.to_datetime(dfx2.Time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gl/2h8nn4ps031g1jysgll17wz00000gn/T/ipykernel_2567/1273321946.py:1: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  dfx2[\"time\"] = dfx2[\"Time\"].dt.hour\n",
      "/var/folders/gl/2h8nn4ps031g1jysgll17wz00000gn/T/ipykernel_2567/1273321946.py:2: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  dfx2[\"month\"] = dfx2[\"Time\"].dt.month\n",
      "/var/folders/gl/2h8nn4ps031g1jysgll17wz00000gn/T/ipykernel_2567/1273321946.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  dfx2[\"weekend\"] = (\n",
      "/var/folders/gl/2h8nn4ps031g1jysgll17wz00000gn/T/ipykernel_2567/1273321946.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  dfx2[\"sin_time\"] = dfx2[\"time\"].apply(\n",
      "/var/folders/gl/2h8nn4ps031g1jysgll17wz00000gn/T/ipykernel_2567/1273321946.py:12: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  dfx2[\"cos_time\"] = dfx2[\"time\"].apply(\n",
      "/var/folders/gl/2h8nn4ps031g1jysgll17wz00000gn/T/ipykernel_2567/1273321946.py:16: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  dfx2[\"sin_month\"] = dfx2[\"month\"].apply(\n",
      "/var/folders/gl/2h8nn4ps031g1jysgll17wz00000gn/T/ipykernel_2567/1273321946.py:20: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  dfx2[\"cos_month\"] = dfx2[\"month\"].apply(\n"
     ]
    }
   ],
   "source": [
    "dfx2[\"time\"] = dfx2[\"Time\"].dt.hour\n",
    "dfx2[\"month\"] = dfx2[\"Time\"].dt.month\n",
    "\n",
    "\n",
    "dfx2[\"weekend\"] = (\n",
    "    dfx2[\"Time\"].dt.dayofweek >= 4\n",
    ").astype(\"int\")\n",
    "\n",
    "dfx2[\"sin_time\"] = dfx2[\"time\"].apply(\n",
    "    lambda x: math.sin(x) * 2 * (math.pi / 24)\n",
    ")\n",
    "dfx2[\"cos_time\"] = dfx2[\"time\"].apply(\n",
    "    lambda x: math.cos(x) * 2 * (math.pi / 24)\n",
    ")\n",
    "\n",
    "dfx2[\"sin_month\"] = dfx2[\"month\"].apply(\n",
    "    lambda x: math.sin(x) * 2 * (math.pi / 12)\n",
    ")\n",
    "\n",
    "dfx2[\"cos_month\"] = dfx2[\"month\"].apply(\n",
    "    lambda x: math.cos(x) * 2 * (math.pi / 12)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "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>PM10</th>\n",
       "      <th>NO2</th>\n",
       "      <th>PM10_1</th>\n",
       "      <th>NO2_1</th>\n",
       "      <th>PM10_2</th>\n",
       "      <th>NO2_2</th>\n",
       "      <th>PM10_3</th>\n",
       "      <th>NO2_3</th>\n",
       "      <th>PM10_4</th>\n",
       "      <th>NO2_4</th>\n",
       "      <th>...</th>\n",
       "      <th>PM10_71</th>\n",
       "      <th>NO2_71</th>\n",
       "      <th>Time</th>\n",
       "      <th>time</th>\n",
       "      <th>month</th>\n",
       "      <th>weekend</th>\n",
       "      <th>sin_time</th>\n",
       "      <th>cos_time</th>\n",
       "      <th>sin_month</th>\n",
       "      <th>cos_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2020-08-03 04:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.198130</td>\n",
       "      <td>-0.171123</td>\n",
       "      <td>0.518027</td>\n",
       "      <td>-0.076184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020-08-03 05:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.251046</td>\n",
       "      <td>0.074263</td>\n",
       "      <td>0.518027</td>\n",
       "      <td>-0.076184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2020-08-03 06:00:00</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.073151</td>\n",
       "      <td>0.251372</td>\n",
       "      <td>0.518027</td>\n",
       "      <td>-0.076184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2020-08-03 07:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.171999</td>\n",
       "      <td>0.197371</td>\n",
       "      <td>0.518027</td>\n",
       "      <td>-0.076184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2020-08-03 08:00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.259013</td>\n",
       "      <td>-0.038092</td>\n",
       "      <td>0.518027</td>\n",
       "      <td>-0.076184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17574</th>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2022-07-25 20:00:00</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.239009</td>\n",
       "      <td>0.106836</td>\n",
       "      <td>0.343997</td>\n",
       "      <td>0.394742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17575</th>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2022-07-25 21:00:00</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.219036</td>\n",
       "      <td>-0.143395</td>\n",
       "      <td>0.343997</td>\n",
       "      <td>0.394742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17576</th>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2022-07-25 22:00:00</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.002317</td>\n",
       "      <td>-0.261789</td>\n",
       "      <td>0.343997</td>\n",
       "      <td>0.394742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17577</th>\n",
       "      <td>29.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>39.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2022-07-25 23:00:00</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.221540</td>\n",
       "      <td>-0.139495</td>\n",
       "      <td>0.343997</td>\n",
       "      <td>0.394742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17578</th>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>41.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2022-07-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.261799</td>\n",
       "      <td>0.343997</td>\n",
       "      <td>0.394742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17578 rows × 152 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PM10   NO2  PM10_1  NO2_1  PM10_2  NO2_2  PM10_3  NO2_3  PM10_4  NO2_4  \\\n",
       "0       9.0   7.0     9.0    5.0    11.0    5.0    10.0    5.0    12.0   10.0   \n",
       "1       9.0  12.0     9.0    7.0     9.0    5.0    11.0    5.0    10.0    5.0   \n",
       "2       8.0  15.0     9.0   12.0     9.0    7.0     9.0    5.0    11.0    5.0   \n",
       "3       8.0  26.0     8.0   15.0     9.0   12.0     9.0    7.0     9.0    5.0   \n",
       "4       8.0  21.0     8.0   26.0     8.0   15.0     9.0   12.0     9.0    7.0   \n",
       "...     ...   ...     ...    ...     ...    ...     ...    ...     ...    ...   \n",
       "17574  27.0  15.0    27.0   15.0    25.0   16.0    23.0   14.0    20.0   14.0   \n",
       "17575  32.0  11.0    32.0   11.0    27.0   15.0    25.0   16.0    23.0   14.0   \n",
       "17576  28.0   7.0    28.0    7.0    32.0   11.0    27.0   15.0    25.0   16.0   \n",
       "17577  29.0   8.0    29.0    8.0    28.0    7.0    32.0   11.0    27.0   15.0   \n",
       "17578  29.0  10.0    29.0   10.0    29.0    8.0    28.0    7.0    32.0   11.0   \n",
       "\n",
       "       ...  PM10_71  NO2_71                Time  time  month  weekend  \\\n",
       "0      ...     18.0     7.0 2020-08-03 04:00:00     4      8        0   \n",
       "1      ...     19.0     8.0 2020-08-03 05:00:00     5      8        0   \n",
       "2      ...     18.0    10.0 2020-08-03 06:00:00     6      8        0   \n",
       "3      ...     16.0    13.0 2020-08-03 07:00:00     7      8        0   \n",
       "4      ...     16.0    22.0 2020-08-03 08:00:00     8      8        0   \n",
       "...    ...      ...     ...                 ...   ...    ...      ...   \n",
       "17574  ...     24.0    14.0 2022-07-25 20:00:00    20      7        0   \n",
       "17575  ...     27.0    15.0 2022-07-25 21:00:00    21      7        0   \n",
       "17576  ...     33.0    24.0 2022-07-25 22:00:00    22      7        0   \n",
       "17577  ...     39.0    22.0 2022-07-25 23:00:00    23      7        0   \n",
       "17578  ...     41.0    16.0 2022-07-26 00:00:00     0      7        0   \n",
       "\n",
       "       sin_time  cos_time  sin_month  cos_month  \n",
       "0     -0.198130 -0.171123   0.518027  -0.076184  \n",
       "1     -0.251046  0.074263   0.518027  -0.076184  \n",
       "2     -0.073151  0.251372   0.518027  -0.076184  \n",
       "3      0.171999  0.197371   0.518027  -0.076184  \n",
       "4      0.259013 -0.038092   0.518027  -0.076184  \n",
       "...         ...       ...        ...        ...  \n",
       "17574  0.239009  0.106836   0.343997   0.394742  \n",
       "17575  0.219036 -0.143395   0.343997   0.394742  \n",
       "17576 -0.002317 -0.261789   0.343997   0.394742  \n",
       "17577 -0.221540 -0.139495   0.343997   0.394742  \n",
       "17578  0.000000  0.261799   0.343997   0.394742  \n",
       "\n",
       "[17578 rows x 152 columns]"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gl/2h8nn4ps031g1jysgll17wz00000gn/T/ipykernel_2567/3793368453.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  appa1.CO = model.predict(dfx2[cols])\n"
     ]
    }
   ],
   "source": [
    "appa1.CO = model.predict(dfx2[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [],
   "source": [
    "appa1 = appa1.iloc[sliding:].assign(CO=model.predict(dfx2[cols]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
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       "      <th id=\"T_477d1_level0_col1\" class=\"col_heading level0 col1\" >NO2</th>\n",
       "      <th id=\"T_477d1_level0_col2\" class=\"col_heading level0 col2\" >SO2</th>\n",
       "      <th id=\"T_477d1_level0_col3\" class=\"col_heading level0 col3\" >O3</th>\n",
       "      <th id=\"T_477d1_level0_col4\" class=\"col_heading level0 col4\" >CO</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_477d1_level0_row0\" class=\"row_heading level0 row0\" >PM10</th>\n",
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       "      <td id=\"T_477d1_row0_col4\" class=\"data row0 col4\" >0.807473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_477d1_level0_row1\" class=\"row_heading level0 row1\" >NO2</th>\n",
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       "    <tr>\n",
       "      <th id=\"T_477d1_level0_row2\" class=\"row_heading level0 row2\" >SO2</th>\n",
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       "      <td id=\"T_477d1_row2_col3\" class=\"data row2 col3\" >-0.472522</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_477d1_level0_row3\" class=\"row_heading level0 row3\" >O3</th>\n",
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       "    <tr>\n",
       "      <th id=\"T_477d1_level0_row4\" class=\"row_heading level0 row4\" >CO</th>\n",
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     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = appa1[(appa1[\"Time\"] > '2022-01-01') & (appa1[\"Time\"] < '2022-01-31')][[\"PM10\", \"NO2\", \"SO2\", \"O3\", \"CO\"]].corr()\n",
    "corr.style.background_gradient(cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [],
   "source": [
    "appa1.to_csv(\"exports/appa1_gt_clean_withCO.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "plt.plot(norm(appa1[(appa1[\"Time\"] > '2022-01-01') & (appa1[\"Time\"] < '2022-01-05')].NO2.to_numpy()), label=\"NO2\", alpha=0.5)\n",
    "#plt.plot(norm(appa1[(appa1[\"Time\"] > '2022-01-01') & (appa1[\"Time\"] < '2022-01-05')].SO2.to_numpy()), label=\"SO2\", alpha=0.5)\n",
    "#plt.plot(norm(appa1[(appa1[\"Time\"] > '2022-01-01') & (appa1[\"Time\"] < '2022-01-05')].O3.to_numpy()), label=\"O3\", alpha=0.5)\n",
    "plt.plot(norm(appa1[(appa1[\"Time\"] > '2022-01-01') & (appa1[\"Time\"] < '2022-01-05')].CO.to_numpy()), label=\"CO\", linewidth=4)\n",
    "#plt.plot(norm(appa2[(appa2[\"Time\"] > '2022-01-01') & (appa2[\"Time\"] < '2022-01-05')].CO.to_numpy()), label=\"CO A2\", linewidth=4)\n",
    "plt.plot(norm(appa2[(appa2[\"Time\"] > '2022-01-01') & (appa2[\"Time\"] < '2022-01-05')].NO2.to_numpy()), label=\"NO2 A2\", linewidth=4)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "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": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "3067ead486e059ec00ffe7555bdb889e6e264a24dc711bf108106cc7baee8d5d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}