gas_types_aq_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "import punch_party_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_quality_df = punch_party_utils.make_average_aq_df()\n",
    "average_air_quality = air_quality_df[air_quality_df[\"MeasureName\"] == \"Annual average ambient concentrations of PM2.5 in micrograms per cubic meter (based on seasonal averages and daily measurement)\"]\n",
    "\n",
    "\n",
    "# For the first model, just CO2 emmisions in a given year -> air quality in a given year\n",
    "air_quality_df = air_quality_df.drop(\n",
    "    columns=[\"MeasureId\", \"MeasureName\", \"MeasureType\", \"StratificationLevel\",\n",
    "             \"StateFips\", \"StateName\", \"CountyFips\", \"Unit\",\n",
    "             \"UnitName\", \"DataOrigin\", \"MonitorOnly\"])\n",
    "\n",
    "# air_quality_df = air_quality_df.drop(\"CountyName\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_quality_df[\"County_Year\"] = air_quality_df[\"CountyName\"] + \"_\" + air_quality_df[\"ReportYear\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42621\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['ReportYear', 'Value'], dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "                Value\n",
      "ReportYear           \n",
      "1999        13.748994\n",
      "2000        13.020075\n",
      "2001        11.958253\n",
      "2002        11.235081\n",
      "2003        11.194532\n",
      "2004        10.966340\n",
      "2005        11.737703\n",
      "2006        10.923896\n",
      "2007        11.301560\n",
      "2008        10.335900\n",
      "2009         9.430018\n",
      "2010         9.596560\n",
      "2011         9.372181\n",
      "2012         9.063382\n",
      "2013         8.821925\n"
     ]
    }
   ],
   "source": [
    "print(len(air_quality_df))\n",
    "\n",
    "aq_year_only = air_quality_df.drop(columns=[\"CountyName\", \"County_Year\"])\n",
    "display(aq_year_only.columns)\n",
    "aq_by_year = aq_year_only.groupby(\"ReportYear\")\n",
    "\n",
    "print (\"\\n\")\n",
    "\n",
    "air_quality_yearly_averages = aq_by_year.agg(\"mean\")\n",
    "print(air_quality_yearly_averages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>CO2 Emission</th>\n",
       "      <th>County</th>\n",
       "      <th>Gas Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>County_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58024.000</td>\n",
       "      <td>BROWN</td>\n",
       "      <td>BIOGENIC CO2</td>\n",
       "      <td>2017</td>\n",
       "      <td>BROWN_2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>134.500</td>\n",
       "      <td>NEW</td>\n",
       "      <td>METHANE</td>\n",
       "      <td>2018</td>\n",
       "      <td>NEW_2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.854</td>\n",
       "      <td>SABINE</td>\n",
       "      <td>NITROUS OXIDE</td>\n",
       "      <td>2012</td>\n",
       "      <td>SABINE_2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53562.000</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>METHANE</td>\n",
       "      <td>2017</td>\n",
       "      <td>WASHINGTON_2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7635064.700</td>\n",
       "      <td>TRIMBLE</td>\n",
       "      <td>CARBON DIOXIDE</td>\n",
       "      <td>2018</td>\n",
       "      <td>TRIMBLE_2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CO2 Emission      County        Gas Name  Year      County_Year\n",
       "0     58024.000       BROWN    BIOGENIC CO2  2017       BROWN_2017\n",
       "1       134.500         NEW         METHANE  2018         NEW_2018\n",
       "2         6.854      SABINE   NITROUS OXIDE  2012      SABINE_2012\n",
       "3     53562.000  WASHINGTON         METHANE  2017  WASHINGTON_2017\n",
       "4   7635064.700     TRIMBLE  CARBON DIOXIDE  2018     TRIMBLE_2018"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gas_types = punch_party_utils.make_gas_types_df()\n",
    "\n",
    "gas_types = gas_types.drop(\n",
    "    columns=[\"Address Line 1\", \"City\", \n",
    "             \"Facility ID\", \"Gas Code\", \"Latitude\", \"Longitude\", \n",
    "             \"State Name\", \"Zip Code\", \"Facility Name\"])\n",
    "\n",
    "gas_types[\"County_Year\"] = gas_types[\"County\"] + \"_\" + gas_types[\"Year\"].astype(str)\n",
    "\n",
    "display(gas_types.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CO2 Emission\n",
      "Year              \n",
      "2010  3.229325e+09\n",
      "2011  3.333042e+09\n",
      "2012  3.194602e+09\n",
      "2013  3.210963e+09\n",
      "2014  3.222917e+09\n",
      "2015  3.078839e+09\n",
      "2016  3.019157e+09\n",
      "2017  2.947673e+09\n",
      "2018  3.037238e+09\n",
      "2019  2.898178e+09\n"
     ]
    }
   ],
   "source": [
    "raw_emmisions_year_only = gas_types.drop(columns=[\"Gas Name\", \"County\", \"County_Year\"])\n",
    "yearly_raw_emmisions = raw_emmisions_year_only.groupby(\"Year\").agg(\"sum\")\n",
    "print(yearly_raw_emmisions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(type(yearly_raw_emmisions))\n",
    "\n",
    "plt.plot(yearly_raw_emmisions.index, yearly_raw_emmisions['CO2 Emission'], marker='o')\n",
    "plt.title('CO2 Emissions Over Years')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('CO2 Emissions')\n",
    "plt.savefig('CO2_emmisions_by_year.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CO2 Emission     Value\n",
      "2010  3.229325e+09  9.596560\n",
      "2011  3.333042e+09  9.372181\n",
      "2012  3.194602e+09  9.063382\n",
      "2013  3.210963e+09  8.821925\n"
     ]
    }
   ],
   "source": [
    "co2_emmisions_with_aq = yearly_raw_emmisions.merge(\n",
    "    air_quality_yearly_averages, left_index=True, right_index=True, how=\"inner\")\n",
    "print(co2_emmisions_with_aq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>CO2 Emission</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.968882</td>\n",
       "      <td>9.596560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.372181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.958464</td>\n",
       "      <td>9.063382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.963373</td>\n",
       "      <td>8.821925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CO2 Emission     Value\n",
       "2010      0.968882  9.596560\n",
       "2011      1.000000  9.372181\n",
       "2012      0.958464  9.063382\n",
       "2013      0.963373  8.821925"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalize\n",
    "max_co2_emission = np.max(co2_emmisions_with_aq[\"CO2 Emission\"])\n",
    "max_co2_emission\n",
    "co2_emmisions_with_aq[\"CO2 Emission\"] = co2_emmisions_with_aq[\"CO2 Emission\"] / max_co2_emission\n",
    "\n",
    "display(co2_emmisions_with_aq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>CO2 Emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.968882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.958464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.963373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CO2 Emission\n",
       "2010      0.968882\n",
       "2011      1.000000\n",
       "2012      0.958464\n",
       "2013      0.963373"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2010    9.596560\n",
       "2011    9.372181\n",
       "2012    9.063382\n",
       "2013    8.821925\n",
       "Name: Value, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_aq_model = LinearRegression(fit_intercept=True)\n",
    "\n",
    "independent_var = co2_emmisions_with_aq[[\"CO2 Emission\"]]\n",
    "dependent_var = co2_emmisions_with_aq[\"Value\"]\n",
    "\n",
    "display(independent_var)\n",
    "display(dependent_var)\n",
    "\n",
    "co2_aq_model.fit(independent_var, dependent_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.10959159]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sean/miniconda3/envs/msse-python/lib/python3.11/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Divide value to predict by max emission from training for normalization consistency\n",
    "value_to_predict = (3.2 * 10**9) / max_co2_emission\n",
    "\n",
    "print(co2_aq_model.predict(np.array([value_to_predict]).reshape(-1, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Gas Name</th>\n",
       "      <th>BIOGENIC CO2</th>\n",
       "      <th>CARBON DIOXIDE</th>\n",
       "      <th>HFCS</th>\n",
       "      <th>HFES</th>\n",
       "      <th>METHANE</th>\n",
       "      <th>NITROGEN TRIFLOURIDE</th>\n",
       "      <th>NITROUS OXIDE</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>OTHER FULLY FLUORINATED GHGS</th>\n",
       "      <th>PFCS</th>\n",
       "      <th>SULFUR HEXAFLUORIDE</th>\n",
       "      <th>VERY SHORT-LIVED COMPOUNDS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>219854.886983</td>\n",
       "      <td>462302.634066</td>\n",
       "      <td>196118.872666</td>\n",
       "      <td>888.608834</td>\n",
       "      <td>31507.831677</td>\n",
       "      <td>22648.793747</td>\n",
       "      <td>6002.174766</td>\n",
       "      <td>6747.611722</td>\n",
       "      <td>28001.296085</td>\n",
       "      <td>113693.707661</td>\n",
       "      <td>34794.461450</td>\n",
       "      <td>7.803089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>221692.959997</td>\n",
       "      <td>428138.510385</td>\n",
       "      <td>153791.590090</td>\n",
       "      <td>680.194925</td>\n",
       "      <td>29956.920238</td>\n",
       "      <td>20703.412830</td>\n",
       "      <td>4755.363164</td>\n",
       "      <td>6425.133333</td>\n",
       "      <td>28315.847044</td>\n",
       "      <td>104668.101817</td>\n",
       "      <td>28516.478182</td>\n",
       "      <td>5.895594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>215409.963821</td>\n",
       "      <td>427731.424518</td>\n",
       "      <td>129820.451666</td>\n",
       "      <td>623.485861</td>\n",
       "      <td>29092.144228</td>\n",
       "      <td>16215.268148</td>\n",
       "      <td>4291.312665</td>\n",
       "      <td>5294.825375</td>\n",
       "      <td>32481.755893</td>\n",
       "      <td>103245.245113</td>\n",
       "      <td>27478.328136</td>\n",
       "      <td>8.221518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>211993.601611</td>\n",
       "      <td>420014.630570</td>\n",
       "      <td>121274.558525</td>\n",
       "      <td>1409.273671</td>\n",
       "      <td>28052.491027</td>\n",
       "      <td>15063.658824</td>\n",
       "      <td>4523.518900</td>\n",
       "      <td>4466.333333</td>\n",
       "      <td>27371.955091</td>\n",
       "      <td>93473.825614</td>\n",
       "      <td>27592.388072</td>\n",
       "      <td>13.654441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>214321.305711</td>\n",
       "      <td>405575.849913</td>\n",
       "      <td>100378.183108</td>\n",
       "      <td>4111.709119</td>\n",
       "      <td>28259.379143</td>\n",
       "      <td>16324.279200</td>\n",
       "      <td>4282.749662</td>\n",
       "      <td>4692.033333</td>\n",
       "      <td>25813.783808</td>\n",
       "      <td>87343.614173</td>\n",
       "      <td>24874.326846</td>\n",
       "      <td>10.037224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gas Name   BIOGENIC CO2  CARBON DIOXIDE           HFCS         HFES  \\\n",
       "Year                                                                  \n",
       "2011      219854.886983   462302.634066  196118.872666   888.608834   \n",
       "2012      221692.959997   428138.510385  153791.590090   680.194925   \n",
       "2013      215409.963821   427731.424518  129820.451666   623.485861   \n",
       "2014      211993.601611   420014.630570  121274.558525  1409.273671   \n",
       "2015      214321.305711   405575.849913  100378.183108  4111.709119   \n",
       "\n",
       "Gas Name       METHANE  NITROGEN TRIFLOURIDE  NITROUS OXIDE        OTHER  \\\n",
       "Year                                                                       \n",
       "2011      31507.831677          22648.793747    6002.174766  6747.611722   \n",
       "2012      29956.920238          20703.412830    4755.363164  6425.133333   \n",
       "2013      29092.144228          16215.268148    4291.312665  5294.825375   \n",
       "2014      28052.491027          15063.658824    4523.518900  4466.333333   \n",
       "2015      28259.379143          16324.279200    4282.749662  4692.033333   \n",
       "\n",
       "Gas Name  OTHER FULLY FLUORINATED GHGS           PFCS  SULFUR HEXAFLUORIDE  \\\n",
       "Year                                                                         \n",
       "2011                      28001.296085  113693.707661         34794.461450   \n",
       "2012                      28315.847044  104668.101817         28516.478182   \n",
       "2013                      32481.755893  103245.245113         27478.328136   \n",
       "2014                      27371.955091   93473.825614         27592.388072   \n",
       "2015                      25813.783808   87343.614173         24874.326846   \n",
       "\n",
       "Gas Name  VERY SHORT-LIVED COMPOUNDS  \n",
       "Year                                  \n",
       "2011                        7.803089  \n",
       "2012                        5.895594  \n",
       "2013                        8.221518  \n",
       "2014                       13.654441  \n",
       "2015                       10.037224  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "emissions_per_year_per_gas = gas_types.drop(columns=[\"County\", \"County_Year\"]).groupby(['Year', 'Gas Name']).mean()\n",
    "\n",
    "emissions_per_year_per_gas = emissions_per_year_per_gas.reset_index()\n",
    "\n",
    "emissions_per_year_per_gas = emissions_per_year_per_gas.pivot(index='Year', columns='Gas Name', values='CO2 Emission').dropna()\n",
    "\n",
    "display(emissions_per_year_per_gas.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>BIOGENIC CO2</th>\n",
       "      <th>CARBON DIOXIDE</th>\n",
       "      <th>HFCS</th>\n",
       "      <th>HFES</th>\n",
       "      <th>METHANE</th>\n",
       "      <th>NITROGEN TRIFLOURIDE</th>\n",
       "      <th>NITROUS OXIDE</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>OTHER FULLY FLUORINATED GHGS</th>\n",
       "      <th>PFCS</th>\n",
       "      <th>SULFUR HEXAFLUORIDE</th>\n",
       "      <th>VERY SHORT-LIVED COMPOUNDS</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>219854.886983</td>\n",
       "      <td>462302.634066</td>\n",
       "      <td>196118.872666</td>\n",
       "      <td>888.608834</td>\n",
       "      <td>31507.831677</td>\n",
       "      <td>22648.793747</td>\n",
       "      <td>6002.174766</td>\n",
       "      <td>6747.611722</td>\n",
       "      <td>28001.296085</td>\n",
       "      <td>113693.707661</td>\n",
       "      <td>34794.461450</td>\n",
       "      <td>7.803089</td>\n",
       "      <td>9.372181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>221692.959997</td>\n",
       "      <td>428138.510385</td>\n",
       "      <td>153791.590090</td>\n",
       "      <td>680.194925</td>\n",
       "      <td>29956.920238</td>\n",
       "      <td>20703.412830</td>\n",
       "      <td>4755.363164</td>\n",
       "      <td>6425.133333</td>\n",
       "      <td>28315.847044</td>\n",
       "      <td>104668.101817</td>\n",
       "      <td>28516.478182</td>\n",
       "      <td>5.895594</td>\n",
       "      <td>9.063382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>215409.963821</td>\n",
       "      <td>427731.424518</td>\n",
       "      <td>129820.451666</td>\n",
       "      <td>623.485861</td>\n",
       "      <td>29092.144228</td>\n",
       "      <td>16215.268148</td>\n",
       "      <td>4291.312665</td>\n",
       "      <td>5294.825375</td>\n",
       "      <td>32481.755893</td>\n",
       "      <td>103245.245113</td>\n",
       "      <td>27478.328136</td>\n",
       "      <td>8.221518</td>\n",
       "      <td>8.821925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BIOGENIC CO2  CARBON DIOXIDE           HFCS        HFES       METHANE  \\\n",
       "2011  219854.886983   462302.634066  196118.872666  888.608834  31507.831677   \n",
       "2012  221692.959997   428138.510385  153791.590090  680.194925  29956.920238   \n",
       "2013  215409.963821   427731.424518  129820.451666  623.485861  29092.144228   \n",
       "\n",
       "      NITROGEN TRIFLOURIDE  NITROUS OXIDE        OTHER  \\\n",
       "2011          22648.793747    6002.174766  6747.611722   \n",
       "2012          20703.412830    4755.363164  6425.133333   \n",
       "2013          16215.268148    4291.312665  5294.825375   \n",
       "\n",
       "      OTHER FULLY FLUORINATED GHGS           PFCS  SULFUR HEXAFLUORIDE  \\\n",
       "2011                  28001.296085  113693.707661         34794.461450   \n",
       "2012                  28315.847044  104668.101817         28516.478182   \n",
       "2013                  32481.755893  103245.245113         27478.328136   \n",
       "\n",
       "      VERY SHORT-LIVED COMPOUNDS     Value  \n",
       "2011                    7.803089  9.372181  \n",
       "2012                    5.895594  9.063382  \n",
       "2013                    8.221518  8.821925  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_gasses_with_air_quality = emissions_per_year_per_gas.merge(\n",
    "    air_quality_yearly_averages, left_index=True, right_index=True, how=\"inner\")\n",
    "display(all_gasses_with_air_quality) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_air_quality_from_multiple_gasses(gasses, aq_values):\n",
    "    \"\"\"ArithmeticError\n",
    "    parameters\n",
    "    ----------\n",
    "    df : Dataframe\n",
    "        A dataframe with an independent variable for each gas type we wish to use as a feature for air quality prediction.\n",
    "    aq_values : Series\n",
    "        A series representing the dependent variable to train on.\n",
    "\n",
    "    returns\n",
    "    -------\n",
    "    A model that can predict air quality from a given input of gas types\n",
    "\n",
    "    \"\"\"\n",
    "    model_with_gas_types = LinearRegression(fit_intercept=True)\n",
    "    assert len(gasses) == len(aq_values), \"Length of input dataframe does not equal length of output dataframe\"\n",
    "\n",
    "    model_with_gas_types.fit(gasses, aq_values)\n",
    "\n",
    "    print(\"Intercept of the model:\")\n",
    "    print(model_with_gas_types.intercept_)\n",
    "    print(\"\\n\")\n",
    "    print(\"Features of the model:\")\n",
    "    print(gasses.columns.tolist())\n",
    "    print(\"\\n\")\n",
    "    print(\"Coefficients of the model:\")\n",
    "    print(model_with_gas_types.coef_)\n",
    "\n",
    "    return model_with_gas_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept of the model:\n",
      "7.520742140483977\n",
      "\n",
      "\n",
      "Features of the model:\n",
      "['BIOGENIC CO2', 'CARBON DIOXIDE', 'HFCS', 'HFES', 'METHANE', 'NITROGEN TRIFLOURIDE', 'NITROUS OXIDE', 'OTHER', 'OTHER FULLY FLUORINATED GHGS', 'PFCS', 'SULFUR HEXAFLUORIDE', 'VERY SHORT-LIVED COMPOUNDS']\n",
      "\n",
      "\n",
      "Coefficients of the model:\n",
      "[-1.12278356 -0.05521366 -0.80792737  2.27842992  1.35020234  0.68148472\n",
      " -0.08529045  0.05376891 -0.0440125   0.77360609 -0.96595478 -0.23206163]\n"
     ]
    }
   ],
   "source": [
    "all_gasses_per_year = all_gasses_with_air_quality.drop(columns=[\"Value\"])\n",
    "air_qualities_by_year = all_gasses_with_air_quality[\"Value\"]\n",
    "max_gas_emissions_per_year = all_gasses_per_year.max()\n",
    "normalized_max_gas_emissions = all_gasses_per_year / max_gas_emissions_per_year\n",
    "air_qualities_by_year = all_gasses_with_air_quality[\"Value\"]\n",
    "\n",
    "model_with_gas_types = model_air_quality_from_multiple_gasses(normalized_max_gas_emissions, air_qualities_by_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.82192485]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sean/miniconda3/envs/msse-python/lib/python3.11/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "example_value_to_predict = np.array([215409.963821, 427731.424518, 129820.451666, 623.485861, 29092.144228, 16215.268148, 4291.312665, 5294.825375, 32481.755893, 103245.245113, 27478.328136, 8.221518]).reshape(-1, 1)\n",
    "max_gas_emissions = all_gasses_with_air_quality.drop(columns=[\"Value\"]).max()\n",
    "normalized_values_to_predict = example_value_to_predict / np.array(max_gas_emissions).reshape(-1, 1)\n",
    "normalized_values_to_predict = normalized_values_to_predict.T\n",
    "\n",
    "prediction = model_with_gas_types.predict(normalized_values_to_predict)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Value', 'County_Year'], dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "                    Value\n",
      "County_Year              \n",
      "ABBEVILLE_2001  14.211375\n",
      "ABBEVILLE_2002  13.130120\n",
      "ABBEVILLE_2003  13.184619\n",
      "ABBEVILLE_2004  13.725566\n",
      "ABBEVILLE_2005  14.178854\n",
      "...                   ...\n",
      "ZIEBACH_2007     6.366442\n",
      "ZIEBACH_2008     6.497561\n",
      "ZIEBACH_2009     6.901008\n",
      "ZIEBACH_2010     6.692292\n",
      "ZIEBACH_2011     6.230142\n",
      "\n",
      "[21562 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "aq_county_year = air_quality_df.drop(columns=[\"CountyName\", \"ReportYear\"])\n",
    "display(aq_county_year.columns)\n",
    "aq_county_year = aq_county_year.groupby(\"County_Year\")\n",
    "\n",
    "print (\"\\n\")\n",
    "\n",
    "air_quality_county_yearly_averages = aq_county_year.agg(\"mean\")\n",
    "print(air_quality_county_yearly_averages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        CO2 Emission      County        Gas Name  Year      County_Year\n",
      "0          58024.000       BROWN    BIOGENIC CO2  2017       BROWN_2017\n",
      "1            134.500         NEW         METHANE  2018         NEW_2018\n",
      "2              6.854      SABINE   NITROUS OXIDE  2012      SABINE_2012\n",
      "3          53562.000  WASHINGTON         METHANE  2017  WASHINGTON_2017\n",
      "4        7635064.700     TRIMBLE  CARBON DIOXIDE  2018     TRIMBLE_2018\n",
      "...              ...         ...             ...   ...              ...\n",
      "219922    301493.000      SKAGIT    BIOGENIC CO2  2016      SKAGIT_2016\n",
      "219923      1929.800      SKAGIT  CARBON DIOXIDE  2016      SKAGIT_2016\n",
      "219924      2776.500    GUILFORD  CARBON DIOXIDE  2018    GUILFORD_2018\n",
      "219926        23.840    BRADFORD   NITROUS OXIDE  2012    BRADFORD_2012\n",
      "219927        23.000    BRADFORD         METHANE  2013    BRADFORD_2013\n",
      "\n",
      "[203601 rows x 5 columns]\n",
      "             CO2 Emission\n",
      "County_Year              \n",
      "ACADIA_2010   1448577.425\n",
      "ACADIA_2011   1572541.979\n",
      "ACADIA_2012   2376600.200\n",
      "ACADIA_2013   1839663.624\n",
      "ACADIA_2014   2254437.112\n",
      "...                   ...\n",
      "YUMA_2015      454925.722\n",
      "YUMA_2016      473769.412\n",
      "YUMA_2017      427815.434\n",
      "YUMA_2018      548792.446\n",
      "YUMA_2019      547350.080\n",
      "\n",
      "[11730 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(gas_types)\n",
    "raw_emmisions_county_year = gas_types.drop(columns=[\"Gas Name\", \"County\", \"Year\"])\n",
    "county_yearly_raw_emmisions = raw_emmisions_county_year.groupby(\"County_Year\").agg(\"sum\")\n",
    "print(county_yearly_raw_emmisions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "county_year_only = gas_types.drop(columns=[\"Year\", \"County\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11730"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "4156"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Gas Name</th>\n",
       "      <th>BIOGENIC CO2</th>\n",
       "      <th>CARBON DIOXIDE</th>\n",
       "      <th>METHANE</th>\n",
       "      <th>NITROUS OXIDE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>County_Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADAMS_2015</th>\n",
       "      <td>338.5</td>\n",
       "      <td>9.461366e+05</td>\n",
       "      <td>13728.293478</td>\n",
       "      <td>5257.448444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADAMS_2016</th>\n",
       "      <td>26.6</td>\n",
       "      <td>1.064286e+06</td>\n",
       "      <td>13979.718750</td>\n",
       "      <td>5353.520333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADAMS_2018</th>\n",
       "      <td>11.9</td>\n",
       "      <td>5.177267e+05</td>\n",
       "      <td>13150.250000</td>\n",
       "      <td>1727.270737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADAMS_2019</th>\n",
       "      <td>11.9</td>\n",
       "      <td>4.123245e+05</td>\n",
       "      <td>13830.102273</td>\n",
       "      <td>893.368941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADA_2015</th>\n",
       "      <td>12206.1</td>\n",
       "      <td>1.331350e+04</td>\n",
       "      <td>56095.500000</td>\n",
       "      <td>85.675000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gas Name     BIOGENIC CO2  CARBON DIOXIDE       METHANE  NITROUS OXIDE\n",
       "County_Year                                                           \n",
       "ADAMS_2015          338.5    9.461366e+05  13728.293478    5257.448444\n",
       "ADAMS_2016           26.6    1.064286e+06  13979.718750    5353.520333\n",
       "ADAMS_2018           11.9    5.177267e+05  13150.250000    1727.270737\n",
       "ADAMS_2019           11.9    4.123245e+05  13830.102273     893.368941\n",
       "ADA_2015          12206.1    1.331350e+04  56095.500000      85.675000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "emissions_per_county_year_per_gas = county_year_only.groupby(['County_Year', 'Gas Name']).mean()\n",
    "\n",
    "emissions_per_county_year_per_gas = emissions_per_county_year_per_gas.reset_index()\n",
    "\n",
    "emissions_per_county_year_per_gas = emissions_per_county_year_per_gas.pivot(index='County_Year', columns='Gas Name', values='CO2 Emission')\n",
    "\n",
    "# Counting NaNs per column\n",
    "# nan_count = emissions_per_county_year_per_gas.isna().sum()\n",
    "\n",
    "# print(nan_count)\n",
    "\n",
    "# The following gasses have NaN in over 10000 of the ~12000 rows of the emissions_per_county_year_per_gas dataframe, dropping these columns before modelling.\n",
    "gasses_with_insufficient_measurements = [\"HFCS\", \"HFES\", \"NITROGEN TRIFLOURIDE\", \"OTHER\", \"OTHER FULLY FLUORINATED GHGS\", \"PFCS\", \"SULFUR HEXAFLUORIDE\", \"VERY SHORT-LIVED COMPOUNDS\"]\n",
    "\n",
    "display(len(emissions_per_county_year_per_gas))\n",
    "emissions_per_county_year_per_gas = emissions_per_county_year_per_gas.drop(columns=gasses_with_insufficient_measurements).dropna()\n",
    "display(len(emissions_per_county_year_per_gas))\n",
    "\n",
    "display(emissions_per_county_year_per_gas.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>BIOGENIC CO2</th>\n",
       "      <th>CARBON DIOXIDE</th>\n",
       "      <th>METHANE</th>\n",
       "      <th>NITROUS OXIDE</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>County_Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIKEN_2010</th>\n",
       "      <td>16171.000000</td>\n",
       "      <td>444627.100000</td>\n",
       "      <td>18422.875000</td>\n",
       "      <td>1329.974000</td>\n",
       "      <td>12.340038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIKEN_2011</th>\n",
       "      <td>10549.000000</td>\n",
       "      <td>429155.266667</td>\n",
       "      <td>18827.562500</td>\n",
       "      <td>1191.851000</td>\n",
       "      <td>12.658858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALACHUA_2013</th>\n",
       "      <td>64093.800000</td>\n",
       "      <td>317543.266667</td>\n",
       "      <td>495.250000</td>\n",
       "      <td>918.883000</td>\n",
       "      <td>6.914032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALAMEDA_2010</th>\n",
       "      <td>49073.996479</td>\n",
       "      <td>38720.705363</td>\n",
       "      <td>63579.865362</td>\n",
       "      <td>100.483415</td>\n",
       "      <td>8.442426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALAMEDA_2011</th>\n",
       "      <td>50407.739015</td>\n",
       "      <td>29260.805667</td>\n",
       "      <td>59862.691017</td>\n",
       "      <td>98.726208</td>\n",
       "      <td>10.494480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOLO_2013</th>\n",
       "      <td>69.900000</td>\n",
       "      <td>32644.566667</td>\n",
       "      <td>9132.125000</td>\n",
       "      <td>3400.676667</td>\n",
       "      <td>7.495000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YORK_2010</th>\n",
       "      <td>397115.233333</td>\n",
       "      <td>886716.105333</td>\n",
       "      <td>14722.633333</td>\n",
       "      <td>7798.018154</td>\n",
       "      <td>10.287617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YORK_2011</th>\n",
       "      <td>338540.600000</td>\n",
       "      <td>715022.474118</td>\n",
       "      <td>12648.367647</td>\n",
       "      <td>6235.789067</td>\n",
       "      <td>10.299324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YORK_2012</th>\n",
       "      <td>475279.380000</td>\n",
       "      <td>583744.761875</td>\n",
       "      <td>13729.750000</td>\n",
       "      <td>5260.998429</td>\n",
       "      <td>12.250877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YORK_2013</th>\n",
       "      <td>768271.766667</td>\n",
       "      <td>629840.215333</td>\n",
       "      <td>12460.911765</td>\n",
       "      <td>4426.864500</td>\n",
       "      <td>11.244676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1156 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               BIOGENIC CO2  CARBON DIOXIDE       METHANE  NITROUS OXIDE  \\\n",
       "County_Year                                                                \n",
       "AIKEN_2010     16171.000000   444627.100000  18422.875000    1329.974000   \n",
       "AIKEN_2011     10549.000000   429155.266667  18827.562500    1191.851000   \n",
       "ALACHUA_2013   64093.800000   317543.266667    495.250000     918.883000   \n",
       "ALAMEDA_2010   49073.996479    38720.705363  63579.865362     100.483415   \n",
       "ALAMEDA_2011   50407.739015    29260.805667  59862.691017      98.726208   \n",
       "...                     ...             ...           ...            ...   \n",
       "YOLO_2013         69.900000    32644.566667   9132.125000    3400.676667   \n",
       "YORK_2010     397115.233333   886716.105333  14722.633333    7798.018154   \n",
       "YORK_2011     338540.600000   715022.474118  12648.367647    6235.789067   \n",
       "YORK_2012     475279.380000   583744.761875  13729.750000    5260.998429   \n",
       "YORK_2013     768271.766667   629840.215333  12460.911765    4426.864500   \n",
       "\n",
       "                  Value  \n",
       "County_Year              \n",
       "AIKEN_2010    12.340038  \n",
       "AIKEN_2011    12.658858  \n",
       "ALACHUA_2013   6.914032  \n",
       "ALAMEDA_2010   8.442426  \n",
       "ALAMEDA_2011  10.494480  \n",
       "...                 ...  \n",
       "YOLO_2013      7.495000  \n",
       "YORK_2010     10.287617  \n",
       "YORK_2011     10.299324  \n",
       "YORK_2012     12.250877  \n",
       "YORK_2013     11.244676  \n",
       "\n",
       "[1156 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1156"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gasses_with_air_quality_and_counties = emissions_per_county_year_per_gas.merge(\n",
    "    air_quality_county_yearly_averages, left_index=True, right_index=True, how=\"inner\")\n",
    "display(gasses_with_air_quality_and_counties) \n",
    "display(len(gasses_with_air_quality_and_counties)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept of the model:\n",
      "9.52210153191177\n",
      "\n",
      "\n",
      "Features of the model:\n",
      "['BIOGENIC CO2', 'CARBON DIOXIDE', 'METHANE', 'NITROUS OXIDE']\n",
      "\n",
      "\n",
      "Coefficients of the model:\n",
      "[-0.12875852  1.22941789 -0.25566719 -2.17853407]\n"
     ]
    }
   ],
   "source": [
    "gasses_with_air_quality_per_year_and_county = gasses_with_air_quality_and_counties.drop(columns=[\"Value\"])\n",
    "max_gas_emissions_by_county = gasses_with_air_quality_per_year_and_county.max()\n",
    "normalized_max_gas_emissions_by_county = gasses_with_air_quality_per_year_and_county / max_gas_emissions_by_county\n",
    "air_qualities_by_year_and_county = gasses_with_air_quality_and_counties[\"Value\"]\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(normalized_max_gas_emissions_by_county, air_qualities_by_year_and_county)\n",
    "\n",
    "model_with_counties_and_gas_types = model_air_quality_from_multiple_gasses(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.57063165]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sean/miniconda3/envs/msse-python/lib/python3.11/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "example_point_to_predict = np.array([16171.000000, 444627.100000, 18422.875000, 1329.974000]).reshape(-1, 1)\n",
    "\n",
    "max_gas_emissions_per_county_per_year = gasses_with_air_quality_and_counties.drop(columns=[\"Value\"]).max()\n",
    "normalized_values_to_predict = example_point_to_predict / np.array(max_gas_emissions_by_county).reshape(-1, 1)\n",
    "normalized_values_to_predict = normalized_values_to_predict.T\n",
    "\n",
    "prediction = model_with_counties_and_gas_types.predict(normalized_values_to_predict)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train_pred_county_year =  model_with_counties_and_gas_types.predict(x_train)\n",
    "y_test_pred_county_year =  model_with_counties_and_gas_types.predict(x_test)\n",
    "\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train, y_train_pred_county_year))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test, y_test_pred_county_year))\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.bar(['Train RMSE', 'Test RMSE'], [rmse_train, rmse_test], color=['blue', 'red'])\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('Training and Test RMSE: air quality vs gas types prediction (no location version)')\n",
    "plt.savefig(\"rmse_quality_vs_gas_county_predictions.png\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "msse-python",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}