analysis_using_starting_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This takes the some available categorical features in the original wildfire dataset (fire start month and state) for some visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import text\n",
    "import calendar\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_pickle('wildfire_encoded_data.pkl')\n",
    "y = pd.read_pickle('wildfire_encoded_labels.pkl')\n",
    "#Grab more features from the final dataset.\n",
    "X_1 = pd.read_pickle('data_for_features.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>STAT_CAUSE_CODE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>DISCOVERY_TIME</th>\n",
       "      <th>CONT_TIME</th>\n",
       "      <th>FIRE_YEAR</th>\n",
       "      <th>STATE_CODE</th>\n",
       "      <th>DISCOVERY_DATETIME</th>\n",
       "      <th>HOURS_TO_CONT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-121.005833</td>\n",
       "      <td>40.036944</td>\n",
       "      <td>1300</td>\n",
       "      <td>1730</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>2005-02-02 13:00:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-120.404444</td>\n",
       "      <td>38.933056</td>\n",
       "      <td>845</td>\n",
       "      <td>1530</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-05-12 08:45:00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-120.735556</td>\n",
       "      <td>38.984167</td>\n",
       "      <td>1921</td>\n",
       "      <td>2024</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-05-31 19:21:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-119.913333</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>1600</td>\n",
       "      <td>1400</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-06-28 16:00:00</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-119.933056</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>1600</td>\n",
       "      <td>1200</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-06-28 16:00:00</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-120.103611</td>\n",
       "      <td>38.635278</td>\n",
       "      <td>1800</td>\n",
       "      <td>1600</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-06-30 18:00:00</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-120.153333</td>\n",
       "      <td>38.688333</td>\n",
       "      <td>1800</td>\n",
       "      <td>1400</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-07-01 18:00:00</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-122.433889</td>\n",
       "      <td>40.968056</td>\n",
       "      <td>1300</td>\n",
       "      <td>1600</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>2005-03-08 13:00:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-122.283333</td>\n",
       "      <td>41.233611</td>\n",
       "      <td>1200</td>\n",
       "      <td>1700</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>2005-03-15 12:00:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-120.149167</td>\n",
       "      <td>38.548333</td>\n",
       "      <td>1800</td>\n",
       "      <td>1800</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-07-01 18:00:00</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-120.159722</td>\n",
       "      <td>38.691667</td>\n",
       "      <td>1830</td>\n",
       "      <td>1900</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-07-02 18:30:00</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-120.106111</td>\n",
       "      <td>38.527500</td>\n",
       "      <td>1730</td>\n",
       "      <td>2000</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-07-02 17:30:00</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-120.193333</td>\n",
       "      <td>38.786667</td>\n",
       "      <td>1600</td>\n",
       "      <td>1630</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-09-03 16:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-120.510000</td>\n",
       "      <td>38.433333</td>\n",
       "      <td>1200</td>\n",
       "      <td>1800</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-09-28 12:00:00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-120.279722</td>\n",
       "      <td>38.675833</td>\n",
       "      <td>630</td>\n",
       "      <td>1500</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-10-03 06:30:00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-120.542222</td>\n",
       "      <td>38.564167</td>\n",
       "      <td>930</td>\n",
       "      <td>1400</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-10-03 09:30:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-120.211667</td>\n",
       "      <td>38.523333</td>\n",
       "      <td>1415</td>\n",
       "      <td>1000</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-10-06 14:15:00</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-120.260000</td>\n",
       "      <td>38.780000</td>\n",
       "      <td>1618</td>\n",
       "      <td>1800</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-10-13 16:18:00</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-120.411667</td>\n",
       "      <td>38.945000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1630</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-11-20 20:00:00</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-105.720556</td>\n",
       "      <td>33.440833</td>\n",
       "      <td>1030</td>\n",
       "      <td>1400</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-06-04 10:30:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-105.628611</td>\n",
       "      <td>33.307222</td>\n",
       "      <td>1530</td>\n",
       "      <td>1940</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-06-19 15:30:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-105.768056</td>\n",
       "      <td>33.444444</td>\n",
       "      <td>1440</td>\n",
       "      <td>1100</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-06-21 14:40:00</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-105.766111</td>\n",
       "      <td>33.559444</td>\n",
       "      <td>1910</td>\n",
       "      <td>1300</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-06-25 19:10:00</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-105.526111</td>\n",
       "      <td>33.308056</td>\n",
       "      <td>1250</td>\n",
       "      <td>1110</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-07-01 12:50:00</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-105.229444</td>\n",
       "      <td>33.545278</td>\n",
       "      <td>1712</td>\n",
       "      <td>1450</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-07-01 17:12:00</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-105.512222</td>\n",
       "      <td>33.315833</td>\n",
       "      <td>1405</td>\n",
       "      <td>1600</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-07-20 14:05:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-105.631111</td>\n",
       "      <td>33.444444</td>\n",
       "      <td>1145</td>\n",
       "      <td>2030</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-03-18 11:45:00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-105.507222</td>\n",
       "      <td>33.368056</td>\n",
       "      <td>945</td>\n",
       "      <td>1815</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-08-05 09:45:00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-105.638611</td>\n",
       "      <td>33.385278</td>\n",
       "      <td>1655</td>\n",
       "      <td>2130</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-03-18 16:55:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-105.700278</td>\n",
       "      <td>33.382778</td>\n",
       "      <td>1515</td>\n",
       "      <td>1552</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-05-11 15:15:00</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891977</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-121.583650</td>\n",
       "      <td>41.077600</td>\n",
       "      <td>720</td>\n",
       "      <td>2052</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-08-18 07:20:00</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891978</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-121.101025</td>\n",
       "      <td>38.931063</td>\n",
       "      <td>1036</td>\n",
       "      <td>1115</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-08-19 10:36:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891979</th>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-118.915364</td>\n",
       "      <td>35.985512</td>\n",
       "      <td>1802</td>\n",
       "      <td>1832</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-06-19 18:02:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891980</th>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-121.468244</td>\n",
       "      <td>39.694921</td>\n",
       "      <td>654</td>\n",
       "      <td>1300</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-03-14 06:54:00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891981</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-120.717679</td>\n",
       "      <td>38.338223</td>\n",
       "      <td>1221</td>\n",
       "      <td>1347</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-03-13 12:21:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891982</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-120.405563</td>\n",
       "      <td>38.031985</td>\n",
       "      <td>1542</td>\n",
       "      <td>1557</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-09-15 15:42:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891983</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-124.136117</td>\n",
       "      <td>40.555183</td>\n",
       "      <td>1552</td>\n",
       "      <td>1808</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-08-01 15:52:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891984</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-122.949776</td>\n",
       "      <td>38.584807</td>\n",
       "      <td>1417</td>\n",
       "      <td>1450</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-30 14:17:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891985</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-117.601667</td>\n",
       "      <td>34.391667</td>\n",
       "      <td>759</td>\n",
       "      <td>930</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-04-28 07:59:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891986</th>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-121.294430</td>\n",
       "      <td>39.452856</td>\n",
       "      <td>1704</td>\n",
       "      <td>1830</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-02 17:04:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891987</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-122.454350</td>\n",
       "      <td>40.451090</td>\n",
       "      <td>1854</td>\n",
       "      <td>1921</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-06-30 18:54:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891988</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-122.057301</td>\n",
       "      <td>37.098821</td>\n",
       "      <td>948</td>\n",
       "      <td>1042</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-12-25 09:48:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891989</th>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-116.965475</td>\n",
       "      <td>32.950034</td>\n",
       "      <td>1900</td>\n",
       "      <td>2154</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-05-29 19:00:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891990</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-122.247918</td>\n",
       "      <td>40.579968</td>\n",
       "      <td>1655</td>\n",
       "      <td>1709</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-15 16:55:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891991</th>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-122.336489</td>\n",
       "      <td>40.714702</td>\n",
       "      <td>1836</td>\n",
       "      <td>2045</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-11 18:36:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891992</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-122.645210</td>\n",
       "      <td>38.712955</td>\n",
       "      <td>132</td>\n",
       "      <td>154</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-23 01:32:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891993</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-121.234567</td>\n",
       "      <td>39.655817</td>\n",
       "      <td>1204</td>\n",
       "      <td>1935</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-08-06 12:04:00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891994</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-121.036533</td>\n",
       "      <td>39.143821</td>\n",
       "      <td>1138</td>\n",
       "      <td>1152</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-21 11:38:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891995</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-123.763317</td>\n",
       "      <td>41.224483</td>\n",
       "      <td>1645</td>\n",
       "      <td>2000</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-05-11 16:45:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891996</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-121.260759</td>\n",
       "      <td>39.005712</td>\n",
       "      <td>1625</td>\n",
       "      <td>1650</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-09-26 16:25:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891997</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-122.074684</td>\n",
       "      <td>37.090126</td>\n",
       "      <td>1656</td>\n",
       "      <td>1750</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-28 16:56:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891998</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-120.528000</td>\n",
       "      <td>40.297000</td>\n",
       "      <td>536</td>\n",
       "      <td>855</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-09-27 05:36:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891999</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-120.664433</td>\n",
       "      <td>40.443000</td>\n",
       "      <td>1752</td>\n",
       "      <td>2014</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-06-26 17:52:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892000</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-120.863673</td>\n",
       "      <td>38.644183</td>\n",
       "      <td>1208</td>\n",
       "      <td>1228</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-06-06 12:08:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892001</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-120.613743</td>\n",
       "      <td>37.936253</td>\n",
       "      <td>2042</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-06-28 20:42:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892002</th>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-122.391398</td>\n",
       "      <td>40.019907</td>\n",
       "      <td>1714</td>\n",
       "      <td>1913</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-06-14 17:14:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892003</th>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-123.069617</td>\n",
       "      <td>40.588583</td>\n",
       "      <td>2357</td>\n",
       "      <td>56</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-09-30 23:57:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892004</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-123.544167</td>\n",
       "      <td>40.244833</td>\n",
       "      <td>1331</td>\n",
       "      <td>1000</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-08-01 13:31:00</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892005</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-122.660044</td>\n",
       "      <td>38.415608</td>\n",
       "      <td>1420</td>\n",
       "      <td>1436</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-05-28 14:20:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892006</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-122.389375</td>\n",
       "      <td>40.481637</td>\n",
       "      <td>1726</td>\n",
       "      <td>1843</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-09-26 17:26:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891101 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        FIRE_SIZE_CLASS  STAT_CAUSE_CODE   LONGITUDE   LATITUDE  \\\n",
       "0                     1              9.0 -121.005833  40.036944   \n",
       "1                     1              1.0 -120.404444  38.933056   \n",
       "2                     1              5.0 -120.735556  38.984167   \n",
       "3                     1              1.0 -119.913333  38.559167   \n",
       "4                     1              1.0 -119.933056  38.559167   \n",
       "5                     1              1.0 -120.103611  38.635278   \n",
       "6                     1              1.0 -120.153333  38.688333   \n",
       "7                     2              5.0 -122.433889  40.968056   \n",
       "8                     2              5.0 -122.283333  41.233611   \n",
       "9                     1              1.0 -120.149167  38.548333   \n",
       "10                    1              1.0 -120.159722  38.691667   \n",
       "11                    1              1.0 -120.106111  38.527500   \n",
       "12                    1              9.0 -120.193333  38.786667   \n",
       "13                    2              4.0 -120.510000  38.433333   \n",
       "14                    1              1.0 -120.279722  38.675833   \n",
       "15                    1              1.0 -120.542222  38.564167   \n",
       "16                    7              2.0 -120.211667  38.523333   \n",
       "17                    7              2.0 -120.260000  38.780000   \n",
       "18                    1              5.0 -120.411667  38.945000   \n",
       "19                    1              1.0 -105.720556  33.440833   \n",
       "20                    1              1.0 -105.628611  33.307222   \n",
       "21                    1              1.0 -105.768056  33.444444   \n",
       "22                    1              1.0 -105.766111  33.559444   \n",
       "23                    1              1.0 -105.526111  33.308056   \n",
       "24                    2              1.0 -105.229444  33.545278   \n",
       "25                    3              1.0 -105.512222  33.315833   \n",
       "26                    2              9.0 -105.631111  33.444444   \n",
       "27                    1              1.0 -105.507222  33.368056   \n",
       "28                    1              5.0 -105.638611  33.385278   \n",
       "29                    1              5.0 -105.700278  33.382778   \n",
       "...                 ...              ...         ...        ...   \n",
       "891977                2              9.0 -121.583650  41.077600   \n",
       "891978                1             13.0 -121.101025  38.931063   \n",
       "891979                3              9.0 -118.915364  35.985512   \n",
       "891980                2             13.0 -121.468244  39.694921   \n",
       "891981                2              5.0 -120.717679  38.338223   \n",
       "891982                1             13.0 -120.405563  38.031985   \n",
       "891983                2              2.0 -124.136117  40.555183   \n",
       "891984                1              9.0 -122.949776  38.584807   \n",
       "891985                2              9.0 -117.601667  34.391667   \n",
       "891986                2             13.0 -121.294430  39.452856   \n",
       "891987                1             13.0 -122.454350  40.451090   \n",
       "891988                1              5.0 -122.057301  37.098821   \n",
       "891989                2             13.0 -116.965475  32.950034   \n",
       "891990                2              9.0 -122.247918  40.579968   \n",
       "891991                1              7.0 -122.336489  40.714702   \n",
       "891992                2              9.0 -122.645210  38.712955   \n",
       "891993                2              1.0 -121.234567  39.655817   \n",
       "891994                1              5.0 -121.036533  39.143821   \n",
       "891995                1              5.0 -123.763317  41.224483   \n",
       "891996                1              9.0 -121.260759  39.005712   \n",
       "891997                2              9.0 -122.074684  37.090126   \n",
       "891998                1              2.0 -120.528000  40.297000   \n",
       "891999                1              1.0 -120.664433  40.443000   \n",
       "892000                2              2.0 -120.863673  38.644183   \n",
       "892001                2              9.0 -120.613743  37.936253   \n",
       "892002                2             13.0 -122.391398  40.019907   \n",
       "892003                2              7.0 -123.069617  40.588583   \n",
       "892004                2              1.0 -123.544167  40.244833   \n",
       "892005                2              9.0 -122.660044  38.415608   \n",
       "892006                1             13.0 -122.389375  40.481637   \n",
       "\n",
       "        DISCOVERY_TIME  CONT_TIME  FIRE_YEAR  STATE_CODE  DISCOVERY_DATETIME  \\\n",
       "0                 1300       1730       2005           0 2005-02-02 13:00:00   \n",
       "1                  845       1530       2004           0 2004-05-12 08:45:00   \n",
       "2                 1921       2024       2004           0 2004-05-31 19:21:00   \n",
       "3                 1600       1400       2004           0 2004-06-28 16:00:00   \n",
       "4                 1600       1200       2004           0 2004-06-28 16:00:00   \n",
       "5                 1800       1600       2004           0 2004-06-30 18:00:00   \n",
       "6                 1800       1400       2004           0 2004-07-01 18:00:00   \n",
       "7                 1300       1600       2005           0 2005-03-08 13:00:00   \n",
       "8                 1200       1700       2005           0 2005-03-15 12:00:00   \n",
       "9                 1800       1800       2004           0 2004-07-01 18:00:00   \n",
       "10                1830       1900       2004           0 2004-07-02 18:30:00   \n",
       "11                1730       2000       2004           0 2004-07-02 17:30:00   \n",
       "12                1600       1630       2004           0 2004-09-03 16:00:00   \n",
       "13                1200       1800       2004           0 2004-09-28 12:00:00   \n",
       "14                 630       1500       2004           0 2004-10-03 06:30:00   \n",
       "15                 930       1400       2004           0 2004-10-03 09:30:00   \n",
       "16                1415       1000       2004           0 2004-10-06 14:15:00   \n",
       "17                1618       1800       2004           0 2004-10-13 16:18:00   \n",
       "18                2000       1630       2004           0 2004-11-20 20:00:00   \n",
       "19                1030       1400       2004           1 2004-06-04 10:30:00   \n",
       "20                1530       1940       2004           1 2004-06-19 15:30:00   \n",
       "21                1440       1100       2004           1 2004-06-21 14:40:00   \n",
       "22                1910       1300       2004           1 2004-06-25 19:10:00   \n",
       "23                1250       1110       2004           1 2004-07-01 12:50:00   \n",
       "24                1712       1450       2004           1 2004-07-01 17:12:00   \n",
       "25                1405       1600       2004           1 2004-07-20 14:05:00   \n",
       "26                1145       2030       2004           1 2004-03-18 11:45:00   \n",
       "27                 945       1815       2004           1 2004-08-05 09:45:00   \n",
       "28                1655       2130       2004           1 2004-03-18 16:55:00   \n",
       "29                1515       1552       2004           1 2004-05-11 15:15:00   \n",
       "...                ...        ...        ...         ...                 ...   \n",
       "891977             720       2052       2012           0 2012-08-18 07:20:00   \n",
       "891978            1036       1115       2009           0 2009-08-19 10:36:00   \n",
       "891979            1802       1832       2009           0 2009-06-19 18:02:00   \n",
       "891980             654       1300       2013           0 2013-03-14 06:54:00   \n",
       "891981            1221       1347       2014           0 2014-03-13 12:21:00   \n",
       "891982            1542       1557       2010           0 2010-09-15 15:42:00   \n",
       "891983            1552       1808       2010           0 2010-08-01 15:52:00   \n",
       "891984            1417       1450       2015           0 2015-03-30 14:17:00   \n",
       "891985             759        930       2015           0 2015-04-28 07:59:00   \n",
       "891986            1704       1830       2015           0 2015-07-02 17:04:00   \n",
       "891987            1854       1921       2015           0 2015-06-30 18:54:00   \n",
       "891988             948       1042       2013           0 2013-12-25 09:48:00   \n",
       "891989            1900       2154       2014           0 2014-05-29 19:00:00   \n",
       "891990            1655       1709       2015           0 2015-01-15 16:55:00   \n",
       "891991            1836       2045       2015           0 2015-01-11 18:36:00   \n",
       "891992             132        154       2015           0 2015-01-23 01:32:00   \n",
       "891993            1204       1935       2015           0 2015-08-06 12:04:00   \n",
       "891994            1138       1152       2015           0 2015-03-21 11:38:00   \n",
       "891995            1645       2000       2015           0 2015-05-11 16:45:00   \n",
       "891996            1625       1650       2015           0 2015-09-26 16:25:00   \n",
       "891997            1656       1750       2015           0 2015-03-28 16:56:00   \n",
       "891998             536        855       2015           0 2015-09-27 05:36:00   \n",
       "891999            1752       2014       2015           0 2015-06-26 17:52:00   \n",
       "892000            1208       1228       2015           0 2015-06-06 12:08:00   \n",
       "892001            2042          0       2015           0 2015-06-28 20:42:00   \n",
       "892002            1714       1913       2015           0 2015-06-14 17:14:00   \n",
       "892003            2357         56       2015           0 2015-09-30 23:57:00   \n",
       "892004            1331       1000       2015           0 2015-08-01 13:31:00   \n",
       "892005            1420       1436       2015           0 2015-05-28 14:20:00   \n",
       "892006            1726       1843       2015           0 2015-09-26 17:26:00   \n",
       "\n",
       "        HOURS_TO_CONT  \n",
       "0                   4  \n",
       "1                   7  \n",
       "2                   1  \n",
       "3                 118  \n",
       "4                 116  \n",
       "5                  22  \n",
       "6                  20  \n",
       "7                   3  \n",
       "8                   5  \n",
       "9                  24  \n",
       "10                 24  \n",
       "11                 26  \n",
       "12                  0  \n",
       "13                  6  \n",
       "14                  8  \n",
       "15                  4  \n",
       "16                356  \n",
       "17                 98  \n",
       "18                 20  \n",
       "19                  4  \n",
       "20                  4  \n",
       "21                 20  \n",
       "22                 18  \n",
       "23                 22  \n",
       "24                118  \n",
       "25                  2  \n",
       "26                  9  \n",
       "27                  8  \n",
       "28                  5  \n",
       "29                  1  \n",
       "...               ...  \n",
       "891977             38  \n",
       "891978              1  \n",
       "891979              0  \n",
       "891980              6  \n",
       "891981              1  \n",
       "891982              0  \n",
       "891983              2  \n",
       "891984              1  \n",
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       "891989              3  \n",
       "891990              0  \n",
       "891991              2  \n",
       "891992              0  \n",
       "891993              8  \n",
       "891994              0  \n",
       "891995              3  \n",
       "891996              0  \n",
       "891997              1  \n",
       "891998              3  \n",
       "891999              2  \n",
       "892000              0  \n",
       "892001              3  \n",
       "892002              2  \n",
       "892003              1  \n",
       "892004            116  \n",
       "892005              0  \n",
       "892006              1  \n",
       "\n",
       "[891101 rows x 10 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>STAT_CAUSE_CODE</th>\n",
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      ],
      "text/plain": [
       "   FIRE_SIZE_CLASS  STAT_CAUSE_CODE   LONGITUDE   LATITUDE  DISCOVERY_TIME  \\\n",
       "0                1              9.0 -121.005833  40.036944            1300   \n",
       "1                1              1.0 -120.404444  38.933056             845   \n",
       "2                1              5.0 -120.735556  38.984167            1921   \n",
       "3                1              1.0 -119.913333  38.559167            1600   \n",
       "4                1              1.0 -119.933056  38.559167            1600   \n",
       "\n",
       "   CONT_TIME  FIRE_YEAR  STATE_CODE  DISCOVERY_DATETIME  HOURS_TO_CONT  \n",
       "0       1730       2005           0 2005-02-02 13:00:00              4  \n",
       "1       1530       2004           0 2004-05-12 08:45:00              7  \n",
       "2       2024       2004           0 2004-05-31 19:21:00              1  \n",
       "3       1400       2004           0 2004-06-28 16:00:00            118  \n",
       "4       1200       2004           0 2004-06-28 16:00:00            116  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_1 = X_1['HOURS_TO_CONT']\n",
    "X_1 = X_1.drop(columns=['HOURS_TO_CONT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>STAT_CAUSE_CODE</th>\n",
       "      <th>LONGITUDE</th>\n",
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       "      <td>0</td>\n",
       "      <td>2004-06-28 16:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-119.933056</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>1600</td>\n",
       "      <td>1200</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-06-28 16:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIRE_SIZE_CLASS  STAT_CAUSE_CODE   LONGITUDE   LATITUDE  DISCOVERY_TIME  \\\n",
       "0                1              9.0 -121.005833  40.036944            1300   \n",
       "1                1              1.0 -120.404444  38.933056             845   \n",
       "2                1              5.0 -120.735556  38.984167            1921   \n",
       "3                1              1.0 -119.913333  38.559167            1600   \n",
       "4                1              1.0 -119.933056  38.559167            1600   \n",
       "\n",
       "   CONT_TIME  FIRE_YEAR  STATE_CODE  DISCOVERY_DATETIME  \n",
       "0       1730       2005           0 2005-02-02 13:00:00  \n",
       "1       1530       2004           0 2004-05-12 08:45:00  \n",
       "2       2024       2004           0 2004-05-31 19:21:00  \n",
       "3       1400       2004           0 2004-06-28 16:00:00  \n",
       "4       1200       2004           0 2004-06-28 16:00:00  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     22.00\n",
       "1     22.25\n",
       "2     21.45\n",
       "3    132.00\n",
       "4    128.00\n",
       "Name: TIME_TO_CONT, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a very basic, visual look at the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1_train, X1_test, y1_train, y1_test = train_test_split(X_1, y_1, test_size = .2, random_state = 20);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150737      2\n",
       "472884      0\n",
       "56259      77\n",
       "623123      1\n",
       "653476      0\n",
       "165721     62\n",
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       "172373      2\n",
       "556353      0\n",
       "633046     22\n",
       "387868      2\n",
       "575493      0\n",
       "5524        3\n",
       "736527      1\n",
       "265030      2\n",
       "348932      1\n",
       "73555       2\n",
       "231382     18\n",
       "779877      3\n",
       "407668      1\n",
       "450778      1\n",
       "768240      0\n",
       "431062      2\n",
       "18614       2\n",
       "84650      51\n",
       "217432      1\n",
       "382005      1\n",
       "33412     814\n",
       "639733      2\n",
       "         ... \n",
       "217035      0\n",
       "257677      1\n",
       "645402     48\n",
       "170978      0\n",
       "185599      0\n",
       "405989      3\n",
       "422663      3\n",
       "398480      2\n",
       "191678      2\n",
       "686822      1\n",
       "429798      2\n",
       "174845      4\n",
       "427792      1\n",
       "635859      1\n",
       "11003      28\n",
       "91930       0\n",
       "677333      2\n",
       "860712      1\n",
       "60534       0\n",
       "80864       3\n",
       "52386       1\n",
       "460359      3\n",
       "3915        1\n",
       "648596     25\n",
       "702857      1\n",
       "556250      0\n",
       "744348     41\n",
       "285919      1\n",
       "37135      21\n",
       "879066      0\n",
       "Name: HOURS_TO_CONT, Length: 713605, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7ff423d049b0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff423d042e8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff423d04198>,\n",
       "  <matplotlib.axis.XTick at 0x7ff423d21e10>,\n",
       "  <matplotlib.axis.XTick at 0x7ff423d28320>,\n",
       "  <matplotlib.axis.XTick at 0x7ff423d287f0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff423d28cc0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40f7401d0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40f7406a0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40f740b70>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40f740780>,\n",
       "  <matplotlib.axis.XTick at 0x7ff423d28940>,\n",
       "  <matplotlib.axis.XTick at 0x7ff423d21ef0>],\n",
       " <a list of 13 Text xticklabel objects>)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Cause dict based off of the two columns for cause and cause desc:\n",
    "#state_cause_dict = {9.0: 'Miscellaneous',\n",
    "# 1.0: 'Lightning',\n",
    "# 5.0: 'Debris Burning',\n",
    "# 4.0: 'Campfire',\n",
    "# 2.0: 'Equipment Use',\n",
    "# 7.0: 'Arson',\n",
    "# 8.0: 'Children',\n",
    "# 6.0: 'Railroad',\n",
    "# 3.0: 'Smoking',\n",
    "# 11.0: 'Powerline',\n",
    "# 12.0: 'Structure',\n",
    "# 10.0: 'Fireworks',\n",
    "# 13.0: 'Missing/Undefined'}\n",
    "cause_list = ['Lightning', 'Equipment Use', 'Smoking', 'Campfire', 'Debris Burning',\n",
    "              'Railroad', 'Arson', 'Children', 'Miscellaneous', 'Fireworks', 'Powerline',\n",
    "             'Structure', 'Missing or Undefined']\n",
    "plt.figure(figsize=(10,6));\n",
    "X1_train_feat = np.array(X1_train['STAT_CAUSE_CODE']).reshape(-1,1)\n",
    "plt.scatter(X1_train_feat, y1_train, alpha = 0.1)\n",
    "plt.title(\"Fire Time to Containment by Fire Cause\")\n",
    "plt.xlabel(\"Fire Cause\")\n",
    "plt.ylabel('Hours to Containment')\n",
    "plt.xticks(np.arange(1,14), cause_list, rotation=90)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7ff423e2a2e8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff423f33e10>,\n",
       "  <matplotlib.axis.XTick at 0x7ff423f33a90>,\n",
       "  <matplotlib.axis.XTick at 0x7ff43369e5f8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff43369eac8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff43369ee80>,\n",
       "  <matplotlib.axis.XTick at 0x7ff4152234a8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff415223978>,\n",
       "  <matplotlib.axis.XTick at 0x7ff415223e80>,\n",
       "  <matplotlib.axis.XTick at 0x7ff4152293c8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff4152298d0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff415229dd8>],\n",
       " <a list of 12 Text xticklabel objects>)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6));\n",
    "X1_train['DISCOVERY_DATETIME'] = X1_train['DISCOVERY_DATETIME'].apply(lambda x: x.month)\n",
    "X1_train_feat = np.array(X1_train['DISCOVERY_DATETIME']).reshape(-1,1)\n",
    "plt.scatter(X1_train_feat, y1_train, alpha = 0.1, label=\"Training\")\n",
    "plt.title(\"Fire Time to Containment by Month\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel('Hours to Containment')\n",
    "plt.xticks(np.arange(1,13), calendar.month_name[1:13], rotation=90)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>FIRE_SIZE_CLASS</th>\n",
       "      <th>STAT_CAUSE_CODE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>DISCOVERY_TIME</th>\n",
       "      <th>CONT_TIME</th>\n",
       "      <th>FIRE_YEAR</th>\n",
       "      <th>STATE_CODE</th>\n",
       "      <th>DISCOVERY_DATETIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>422689</th>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-87.570967</td>\n",
       "      <td>33.570544</td>\n",
       "      <td>830</td>\n",
       "      <td>2220</td>\n",
       "      <td>2007</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525739</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-73.759261</td>\n",
       "      <td>42.640105</td>\n",
       "      <td>206</td>\n",
       "      <td>206</td>\n",
       "      <td>2007</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93227</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-120.451667</td>\n",
       "      <td>39.801667</td>\n",
       "      <td>1015</td>\n",
       "      <td>1400</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572988</th>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-73.242103</td>\n",
       "      <td>40.866457</td>\n",
       "      <td>1339</td>\n",
       "      <td>1339</td>\n",
       "      <td>2002</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260812</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-102.571300</td>\n",
       "      <td>43.213900</td>\n",
       "      <td>1610</td>\n",
       "      <td>1630</td>\n",
       "      <td>2002</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638316</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-104.990556</td>\n",
       "      <td>32.381111</td>\n",
       "      <td>1400</td>\n",
       "      <td>1800</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389581</th>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-88.743663</td>\n",
       "      <td>33.508352</td>\n",
       "      <td>1600</td>\n",
       "      <td>1643</td>\n",
       "      <td>1996</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155005</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-106.301111</td>\n",
       "      <td>37.266389</td>\n",
       "      <td>2100</td>\n",
       "      <td>1600</td>\n",
       "      <td>2002</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244839</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-96.871100</td>\n",
       "      <td>34.157800</td>\n",
       "      <td>1700</td>\n",
       "      <td>2359</td>\n",
       "      <td>2006</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325831</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-115.554270</td>\n",
       "      <td>43.202600</td>\n",
       "      <td>1304</td>\n",
       "      <td>1529</td>\n",
       "      <td>2009</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449247</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-111.412222</td>\n",
       "      <td>44.704722</td>\n",
       "      <td>1114</td>\n",
       "      <td>1530</td>\n",
       "      <td>2010</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165192</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-93.945556</td>\n",
       "      <td>47.325000</td>\n",
       "      <td>1500</td>\n",
       "      <td>1800</td>\n",
       "      <td>2004</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7854</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-118.541389</td>\n",
       "      <td>34.483889</td>\n",
       "      <td>1954</td>\n",
       "      <td>2015</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230918</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-104.663300</td>\n",
       "      <td>33.971700</td>\n",
       "      <td>1114</td>\n",
       "      <td>1500</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161418</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-106.611667</td>\n",
       "      <td>40.567222</td>\n",
       "      <td>1237</td>\n",
       "      <td>1330</td>\n",
       "      <td>2003</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428866</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-87.469564</td>\n",
       "      <td>34.893391</td>\n",
       "      <td>1423</td>\n",
       "      <td>1506</td>\n",
       "      <td>2004</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29772</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-90.902500</td>\n",
       "      <td>48.215833</td>\n",
       "      <td>1500</td>\n",
       "      <td>1710</td>\n",
       "      <td>2008</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77894</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-112.166667</td>\n",
       "      <td>36.366667</td>\n",
       "      <td>2310</td>\n",
       "      <td>1430</td>\n",
       "      <td>1995</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100576</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-121.155000</td>\n",
       "      <td>40.195000</td>\n",
       "      <td>700</td>\n",
       "      <td>1045</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876910</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-73.932000</td>\n",
       "      <td>40.601200</td>\n",
       "      <td>1454</td>\n",
       "      <td>1540</td>\n",
       "      <td>2015</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875030</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-95.890000</td>\n",
       "      <td>39.350000</td>\n",
       "      <td>1400</td>\n",
       "      <td>1624</td>\n",
       "      <td>2015</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777456</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-79.685867</td>\n",
       "      <td>35.005100</td>\n",
       "      <td>1055</td>\n",
       "      <td>1110</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149968</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-112.557222</td>\n",
       "      <td>38.275833</td>\n",
       "      <td>635</td>\n",
       "      <td>1030</td>\n",
       "      <td>2002</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840272</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-122.011389</td>\n",
       "      <td>45.611111</td>\n",
       "      <td>1205</td>\n",
       "      <td>1400</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267575</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-95.490800</td>\n",
       "      <td>47.393000</td>\n",
       "      <td>1450</td>\n",
       "      <td>1551</td>\n",
       "      <td>2005</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657368</th>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-75.110000</td>\n",
       "      <td>39.270000</td>\n",
       "      <td>1845</td>\n",
       "      <td>1930</td>\n",
       "      <td>2011</td>\n",
       "      <td>47</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450593</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-92.863333</td>\n",
       "      <td>31.980833</td>\n",
       "      <td>1500</td>\n",
       "      <td>1600</td>\n",
       "      <td>2010</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560324</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-73.976890</td>\n",
       "      <td>41.676277</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>2004</td>\n",
       "      <td>41</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819012</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-120.747817</td>\n",
       "      <td>38.414033</td>\n",
       "      <td>1646</td>\n",
       "      <td>1848</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841000</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-111.721667</td>\n",
       "      <td>35.162222</td>\n",
       "      <td>1300</td>\n",
       "      <td>1450</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>9</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>217124</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-116.809200</td>\n",
       "      <td>32.566700</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257780</th>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-99.441800</td>\n",
       "      <td>44.063000</td>\n",
       "      <td>2145</td>\n",
       "      <td>2155</td>\n",
       "      <td>2000</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645686</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-107.536100</td>\n",
       "      <td>32.689200</td>\n",
       "      <td>50</td>\n",
       "      <td>930</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171058</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-107.353889</td>\n",
       "      <td>39.303889</td>\n",
       "      <td>1636</td>\n",
       "      <td>1344</td>\n",
       "      <td>2004</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185683</th>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-89.889000</td>\n",
       "      <td>45.969400</td>\n",
       "      <td>1544</td>\n",
       "      <td>1618</td>\n",
       "      <td>1996</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406180</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-88.988764</td>\n",
       "      <td>45.130546</td>\n",
       "      <td>1540</td>\n",
       "      <td>1615</td>\n",
       "      <td>1996</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422861</th>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-87.103383</td>\n",
       "      <td>33.853827</td>\n",
       "      <td>1037</td>\n",
       "      <td>1259</td>\n",
       "      <td>2007</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398671</th>\n",
       "      <td>3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-95.352699</td>\n",
       "      <td>35.224601</td>\n",
       "      <td>1435</td>\n",
       "      <td>1535</td>\n",
       "      <td>1993</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191763</th>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-116.184200</td>\n",
       "      <td>33.650000</td>\n",
       "      <td>1202</td>\n",
       "      <td>1500</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687109</th>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-90.970833</td>\n",
       "      <td>37.161667</td>\n",
       "      <td>1330</td>\n",
       "      <td>1520</td>\n",
       "      <td>2011</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429997</th>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-85.268546</td>\n",
       "      <td>31.936988</td>\n",
       "      <td>1907</td>\n",
       "      <td>2030</td>\n",
       "      <td>2004</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174926</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-98.917100</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1705</td>\n",
       "      <td>1730</td>\n",
       "      <td>1992</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427991</th>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-86.788691</td>\n",
       "      <td>33.979499</td>\n",
       "      <td>1524</td>\n",
       "      <td>2040</td>\n",
       "      <td>2005</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636140</th>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-105.328611</td>\n",
       "      <td>40.110000</td>\n",
       "      <td>1035</td>\n",
       "      <td>1900</td>\n",
       "      <td>2011</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11011</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-111.631667</td>\n",
       "      <td>39.863889</td>\n",
       "      <td>1516</td>\n",
       "      <td>1930</td>\n",
       "      <td>2006</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91978</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-116.891111</td>\n",
       "      <td>45.071944</td>\n",
       "      <td>810</td>\n",
       "      <td>2200</td>\n",
       "      <td>1996</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677620</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-92.909140</td>\n",
       "      <td>34.131140</td>\n",
       "      <td>1232</td>\n",
       "      <td>1420</td>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861613</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-95.140000</td>\n",
       "      <td>38.530000</td>\n",
       "      <td>1644</td>\n",
       "      <td>1729</td>\n",
       "      <td>2015</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60577</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-121.866667</td>\n",
       "      <td>44.538333</td>\n",
       "      <td>850</td>\n",
       "      <td>1000</td>\n",
       "      <td>1993</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80911</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-123.365000</td>\n",
       "      <td>40.606667</td>\n",
       "      <td>1845</td>\n",
       "      <td>2300</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52429</th>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-93.791667</td>\n",
       "      <td>35.663333</td>\n",
       "      <td>1530</td>\n",
       "      <td>2230</td>\n",
       "      <td>1992</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460572</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-120.486100</td>\n",
       "      <td>41.176700</td>\n",
       "      <td>815</td>\n",
       "      <td>1200</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3916</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-122.745000</td>\n",
       "      <td>41.252500</td>\n",
       "      <td>1330</td>\n",
       "      <td>1930</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648881</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-98.945556</td>\n",
       "      <td>26.404722</td>\n",
       "      <td>1415</td>\n",
       "      <td>1200</td>\n",
       "      <td>2011</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703154</th>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-114.321700</td>\n",
       "      <td>43.194700</td>\n",
       "      <td>1040</td>\n",
       "      <td>2100</td>\n",
       "      <td>2012</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556519</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-77.930364</td>\n",
       "      <td>42.676376</td>\n",
       "      <td>1435</td>\n",
       "      <td>1435</td>\n",
       "      <td>2003</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744661</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-108.053611</td>\n",
       "      <td>37.406111</td>\n",
       "      <td>1215</td>\n",
       "      <td>1417</td>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286025</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-106.579200</td>\n",
       "      <td>45.624200</td>\n",
       "      <td>1000</td>\n",
       "      <td>1700</td>\n",
       "      <td>2001</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37175</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-121.203611</td>\n",
       "      <td>40.790278</td>\n",
       "      <td>1200</td>\n",
       "      <td>1930</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879970</th>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-90.003333</td>\n",
       "      <td>31.076805</td>\n",
       "      <td>1610</td>\n",
       "      <td>1700</td>\n",
       "      <td>2015</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>712880 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        FIRE_SIZE_CLASS  STAT_CAUSE_CODE   LONGITUDE   LATITUDE  \\\n",
       "422689                2              7.0  -87.570967  33.570544   \n",
       "525739                2              9.0  -73.759261  42.640105   \n",
       "93227                 1              9.0 -120.451667  39.801667   \n",
       "572988                1              8.0  -73.242103  40.866457   \n",
       "260812                1              5.0 -102.571300  43.213900   \n",
       "638316                1              1.0 -104.990556  32.381111   \n",
       "389581                2              7.0  -88.743663  33.508352   \n",
       "155005                2              1.0 -106.301111  37.266389   \n",
       "244839                2              9.0  -96.871100  34.157800   \n",
       "325831                2              2.0 -115.554270  43.202600   \n",
       "449247                1              1.0 -111.412222  44.704722   \n",
       "165192                1              5.0  -93.945556  47.325000   \n",
       "7854                  1              4.0 -118.541389  34.483889   \n",
       "230918                1              9.0 -104.663300  33.971700   \n",
       "161418                1              4.0 -106.611667  40.567222   \n",
       "428866                2              5.0  -87.469564  34.893391   \n",
       "29772                 1              4.0  -90.902500  48.215833   \n",
       "77894                 1              1.0 -112.166667  36.366667   \n",
       "100576                1              5.0 -121.155000  40.195000   \n",
       "876910                1              9.0  -73.932000  40.601200   \n",
       "875030                2              9.0  -95.890000  39.350000   \n",
       "777456                2              5.0  -79.685867  35.005100   \n",
       "149968                2              9.0 -112.557222  38.275833   \n",
       "840272                1              5.0 -122.011389  45.611111   \n",
       "267575                2              9.0  -95.490800  47.393000   \n",
       "657368                2              6.0  -75.110000  39.270000   \n",
       "450593                2              9.0  -92.863333  31.980833   \n",
       "560324                2              9.0  -73.976890  41.676277   \n",
       "819012                2              5.0 -120.747817  38.414033   \n",
       "841000                1              4.0 -111.721667  35.162222   \n",
       "...                 ...              ...         ...        ...   \n",
       "217124                1             13.0 -116.809200  32.566700   \n",
       "257780                2             10.0  -99.441800  44.063000   \n",
       "645686                2              1.0 -107.536100  32.689200   \n",
       "171058                1              1.0 -107.353889  39.303889   \n",
       "185683                2              8.0  -89.889000  45.969400   \n",
       "406180                2              2.0  -88.988764  45.130546   \n",
       "422861                3              7.0  -87.103383  33.853827   \n",
       "398671                3             13.0  -95.352699  35.224601   \n",
       "191763                3              4.0 -116.184200  33.650000   \n",
       "687109                3              5.0  -90.970833  37.161667   \n",
       "429997                2              7.0  -85.268546  31.936988   \n",
       "174926                2              5.0  -98.917100  47.800000   \n",
       "427991                3              7.0  -86.788691  33.979499   \n",
       "636140                5              9.0 -105.328611  40.110000   \n",
       "11011                 2              1.0 -111.631667  39.863889   \n",
       "91978                 1              1.0 -116.891111  45.071944   \n",
       "677620                2              1.0  -92.909140  34.131140   \n",
       "861613                2              5.0  -95.140000  38.530000   \n",
       "60577                 1              9.0 -121.866667  44.538333   \n",
       "80911                 2              1.0 -123.365000  40.606667   \n",
       "52429                 1              7.0  -93.791667  35.663333   \n",
       "460572                1              1.0 -120.486100  41.176700   \n",
       "3916                  1              9.0 -122.745000  41.252500   \n",
       "648881                1              9.0  -98.945556  26.404722   \n",
       "703154                3              9.0 -114.321700  43.194700   \n",
       "556519                2              9.0  -77.930364  42.676376   \n",
       "744661                1              4.0 -108.053611  37.406111   \n",
       "286025                2              9.0 -106.579200  45.624200   \n",
       "37175                 2              1.0 -121.203611  40.790278   \n",
       "879970                2              9.0  -90.003333  31.076805   \n",
       "\n",
       "        DISCOVERY_TIME  CONT_TIME  FIRE_YEAR  STATE_CODE  DISCOVERY_DATETIME  \n",
       "422689             830       2220       2007          35                   3  \n",
       "525739             206        206       2007          41                   8  \n",
       "93227             1015       1400       1996           0                   7  \n",
       "572988            1339       1339       2002          41                   8  \n",
       "260812            1610       1630       2002          10                   2  \n",
       "638316            1400       1800       2011           1                   7  \n",
       "389581            1600       1643       1996          38                   7  \n",
       "155005            2100       1600       2002           5                   7  \n",
       "244839            1700       2359       2006          19                   1  \n",
       "325831            1304       1529       2009          13                   7  \n",
       "449247            1114       1530       2010          13                   6  \n",
       "165192            1500       1800       2004          14                   4  \n",
       "7854              1954       2015       2006           0                   5  \n",
       "230918            1114       1500       1998           1                   8  \n",
       "161418            1237       1330       2003           5                   8  \n",
       "428866            1423       1506       2004          35                   3  \n",
       "29772             1500       1710       2008          14                   8  \n",
       "77894             2310       1430       1995           9                   9  \n",
       "100576             700       1045       1997           0                   5  \n",
       "876910            1454       1540       2015          41                   5  \n",
       "875030            1400       1624       2015          20                   4  \n",
       "777456            1055       1110       2013           3                   1  \n",
       "149968             635       1030       2002           8                  11  \n",
       "840272            1205       1400       2015           2                   9  \n",
       "267575            1450       1551       2005          14                   4  \n",
       "657368            1845       1930       2011          47                   6  \n",
       "450593            1500       1600       2010          18                   8  \n",
       "560324              30         30       2004          41                   6  \n",
       "819012            1646       1848       2014           0                   4  \n",
       "841000            1300       1450       2015           9                   9  \n",
       "...                ...        ...        ...         ...                 ...  \n",
       "217124            1600       1600       1999           0                   6  \n",
       "257780            2145       2155       2000          10                   7  \n",
       "645686              50        930       2011           1                   6  \n",
       "171058            1636       1344       2004           5                   8  \n",
       "185683            1544       1618       1996          34                   5  \n",
       "406180            1540       1615       1996          34                   5  \n",
       "422861            1037       1259       2007          35                   4  \n",
       "398671            1435       1535       1993          19                   4  \n",
       "191763            1202       1500       1995           0                   9  \n",
       "687109            1330       1520       2011          21                   2  \n",
       "429997            1907       2030       2004          35                   3  \n",
       "174926            1705       1730       1992          32                   4  \n",
       "427991            1524       2040       2005          35                  11  \n",
       "636140            1035       1900       2011           5                   3  \n",
       "11011             1516       1930       2006           8                   7  \n",
       "91978              810       2200       1996           2                   7  \n",
       "677620            1232       1420       2011          11                   8  \n",
       "861613            1644       1729       2015          20                   3  \n",
       "60577              850       1000       1993           2                   9  \n",
       "80911             1845       2300       1995           0                   6  \n",
       "52429             1530       2230       1992          11                   3  \n",
       "460572             815       1200       2010           0                  10  \n",
       "3916              1330       1930       2005           0                   8  \n",
       "648881            1415       1200       2011          15                   2  \n",
       "703154            1040       2100       2012          13                   7  \n",
       "556519            1435       1435       2003          41                   3  \n",
       "744661            1215       1417       2013           5                   6  \n",
       "286025            1000       1700       2001           7                   5  \n",
       "37175             1200       1930       2009           0                   8  \n",
       "879970            1610       1700       2015          38                  10  \n",
       "\n",
       "[712880 rows x 9 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7ff412887a90>,\n",
       "  <matplotlib.axis.XTick at 0x7ff4128873c8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff412887278>,\n",
       "  <matplotlib.axis.XTick at 0x7ff412898d30>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40f9ff240>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40f9ff710>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40f9ffbe0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa07160>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa075f8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa07b00>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40f9ff2e8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa075c0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa0e128>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa0e5c0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa0eac8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa0ecf8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa15518>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa15a20>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa15f28>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa15e80>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa0e6a0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa1d320>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa1d7b8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa1dcc0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa27208>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa27710>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa27c18>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa30198>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa277f0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa1db38>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa30588>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa30a90>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa30e80>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa394e0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa399e8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa39ef0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa3f438>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa3f940>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa39ac8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa306d8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa3f240>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa481d0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa486d8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa48be0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa50160>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa50630>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa50b38>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa50be0>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa48ba8>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa1d898>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa57438>,\n",
       "  <matplotlib.axis.XTick at 0x7ff40fa57940>],\n",
       " <a list of 52 Text xticklabel objects>)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8));\n",
    "X1_train_feat = np.array(X1_train['STATE_CODE']).reshape(-1,1)\n",
    "plt.scatter(X1_train_feat, y1_train, alpha=0.1, label=\"Training\")\n",
    "plt.title(\"Fire Time to Containment by U.S. State\")\n",
    "plt.xlabel(\"State\")\n",
    "plt.ylabel('Hours to Containment')\n",
    "plt.xticks(np.arange(52), ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA',\n",
    "        'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME',\n",
    "        'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM',\n",
    "        'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'PR', 'RI', 'SC', 'SD', 'TN',\n",
    "        'TX', 'UT', 'VA', 'VT', 'WA', 'WI', 'WV', 'WY'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'rand_jitter' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-1ac512c29652>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mX_train_feat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'STATE'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mX_test_feat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'STATE'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrand_jitter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_feat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Training\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Fire Time to Containment by U.S. State\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"State\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'rand_jitter' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8));\n",
    "X_train_feat = np.array(X_train['STATE']).reshape(-1,1)\n",
    "X_test_feat = np.array(X_test['STATE']).reshape(-1,1)\n",
    "plt.scatter(X_train_feat, y_train, alpha=0.5, label=\"Training\")\n",
    "plt.title(\"Fire Time to Containment by U.S. State\")\n",
    "plt.xlabel(\"State\")\n",
    "plt.ylabel('Hours to Containment')\n",
    "plt.xticks(np.arange(52), ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA',\n",
    "        'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME',\n",
    "        'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM',\n",
    "        'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'PR', 'RI', 'SC', 'SD', 'TN',\n",
    "        'TX', 'UT', 'VA', 'VT', 'WA', 'WI', 'WV', 'WY'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_pickle('wildfire_area_encoded_data.pkl')\n",
    "y = pd.read_pickle('wildfire_area_encoded_labels.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>DISCOVERY_DATETIME</th>\n",
       "      <th>STATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1247730</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236076</th>\n",
       "      <td>7</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1405504</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340393</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10099</th>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DISCOVERY_DATETIME  STATE\n",
       "1247730                   7    NaN\n",
       "236076                    7   45.0\n",
       "1405504                   5    NaN\n",
       "1340393                   7    NaN\n",
       "10099                     4    5.0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x7fcbb18035f8>,\n",
       "  <matplotlib.axis.YTick at 0x7fcbb1806ef0>,\n",
       "  <matplotlib.axis.YTick at 0x7fcbb1804f98>,\n",
       "  <matplotlib.axis.YTick at 0x7fcbb18345c0>,\n",
       "  <matplotlib.axis.YTick at 0x7fcbb1834a90>,\n",
       "  <matplotlib.axis.YTick at 0x7fcbb1834f60>,\n",
       "  <matplotlib.axis.YTick at 0x7fcb948314a8>],\n",
       " <a list of 7 Text yticklabel objects>)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6));\n",
    "X_train_feat = np.array(X_train['DISCOVERY_DATETIME']).reshape(-1,1)\n",
    "plt.scatter(X_train_feat, y_train, s=150, alpha=0.005, label=\"Training\")\n",
    "plt.title(\"Fire Size Category by Month\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel('Fire Size Category')\n",
    "plt.xticks(np.arange(1,13), calendar.month_name[1:13], rotation=90)\n",
    "plt.yticks(np.arange(1, 8), ['A', 'B', 'C', 'D', 'E', 'F', 'G'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fc52db24908>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Rww4hhJpocb3rkRYFdghhbIuJMyGEUBf1GdYXO86EEEIJO840SBov6QZJv8/PXyPpGkl3S/q1pCXy+Un5+T35+jrNYldeYEuaL+nGwtE0UyGE0DEmjRJpdrTuE8AdheffAL5rewPgaeCQfP4Q4Gnb6wPfzfcNqhM17Hm2Ny8cD3QgzRBCaI1zk0izowWS1gDeAfwsPxewE3BGvuVEYO/8eK/8nHx953z/gKINO4Qw5rm1AnlFSdcVns+0PbPPPd8DPgssk5+vADxju7EH2Sxg9fx4deBhANs9kp7N988ZKAOdKLAnS7oxP77f9rv73iBpBjADYJKW6Xs5hBCq1dqwvjm2txrooqR3ArNtXy9px8bp/lJr4Vq/OlFgz7O9+WA35HepmQDLTFi5Ht21IYTRobxhfdsD75K0B7AksCypxj1N0oRcy14DeCTfPwtYE5glaQIwFRh0IfwYJRJCCCW0Ydv+nO01bK8D7A9cbPtA4BJg33zbwcDZ+fE5+Tn5+sV5J/UBRRt2CGFsq34tkSOA0yR9BbgBOD6fPx44WdI9pJr1/s0CRYEdQhjzWux0bD2efSlwaX58H7BNP/e8BOw3lLiVF9i2l646jRBCGLaYmh5CCDUSBXYIIdSAW58YM9KiwA4hjGkGPD8K7BBC6H7Rhh1CCDUSBXYIIdSD67FDWBTYIYQxLppEQgihRqKGHUIINWDjnqhhhxBCPdSjvO6+AnvK+AlsMXVa6XHHDbqPw/D19rukbfuWWWd+JXEB5tw5qZK4K6z/UiVxPbW6NdJ9x6OVxJ3Xs1IlcZ94ubo/2Zd6q/ldfh0DLiHdtllc2H4Ql7+WSFW6rsAOIYSOq0kbdtP1sCV9XNJynchMCCGMBPc2P7pBKxsYrAL8VdLpknZrtklkCCHUikk17GZHF2haYNs+CtiAtNj2vwB3S/qqpPUqzlsIIVTOBvc0P7pBS1uE5W1rHstHD7AccIakb1aYtxBC6Ai7+dENmnY6SjqctO/YHOBnwGdsvyppHHA3aUv3EEKorW5po26mlVEiKwDvsf1g8aTt3rytewgh1FejDbsGBm0SybXoffoW1g2276gkVyGE0EFljBKRtKSkayXdJOk2Scfk86+RdI2kuyX9WtIS+fyk/PyefH2dZmkMWmDb7gVukrRWC99zCCHUj8Hz1fRowcvATrY3AzYHdpO0LfAN4Lu2NwCeBg7J9x8CPG17feC7+b5BtdLpuCpwm6SLJJ3TOFrJfQghdDtTTg3byQv56cR8GNgJOCOfPxHYOz/eKz8nX9+52bDpVtqwj2nhnhBCqClhlzO9RNJ44HpgfeBHwL3AM/aCgYGzgNXz49WBhwFs90h6ltRnOGeg+E0LbNuXSVoZ2Dqfutb27CF8A/OBWwqnTrP99Va/PoQQKuWWR4msKOm6wvOZtmcuEsqeD2wuaRpwFvDa/lME6HchokEHELYyrO+9wLeAS3MCP5T0GdtnDPqFC82zvXmL94YQQse1WGDPsd3SSla2n5F0KbAtME3ShFzLXgN4JN82C1gTmCVpAjAVeGqwuK20YX8B2Nr2wbYPArYB/rOVTIcQQrczYKvp0Yyk6blmjaTJwC7AHcAlwL75toOBs/Pjc/Jz8vWL8yTFAbXShj2uTxPIk7Q4QzKbLOnGwvOv2f518QZJM4AZAMuMnzqE0CGE0CZDb2ujQJpZFTgxt2OPA063/XtJtwOnSfoKcANpmQ/y/ydLuodUs96/WQKtFNjnSfoTcGp+/j7gj0P4Jpo2ieR2oJkAK09avUsmgYYQxooyOh1t3wxs0c/5+0gtE33PvwTsN5Q0Wul0/IykfYDtSW3YM22fNZREQgihm7mizRvK1tIGBrbPBM6sOC8hhNBx3bS4UzOtjBJ5nsWHmjwLXAd8Olf3B9O3Dfs820cOLZshhFCV8sZhV62VGvZ3SMNQfkVqEtmftKnBXcDPgR0H+2Lb49vLYgghVMgwv5xOx8q1MtpjN9vH2X7e9nO5g3CPPNIjtg4LIdRaWcP6OqGVArtX0nsljcvHewvXatLyE0IIAxtNBfaBwAeB2cDj+fEH8sDwj1eYtxBC6Iheq+nRDVoZ1ncfsOcAl68oNzshhNBhVm2G9TWtYUvaMC+temt+/gZJR1WftRBCqF5qw67Hno6tNIn8FPgc8CosmM3TdAplCCHUxfzecU2PbtDKsL4ptq/ts652l2z6HkII7TF0TRt1M60U2HMkrUceESJpX+DRSnMVQgid4nLWEumEVgrsQ0kLM20s6R/A/aSRIyGEMCrUZNP0lgps295F0lKkpVafl/SaqjMWQgid0T3jrJtppcA+E9jS9ouFc2cAb6wiQ702c3vK75J9eX41nQYv9VQz837+3Oq6pcePr6Y+MW5yd3TMDIUmT6wk7hLj5lcS97lXqytYlluimt+5h/RQJXHLMirasCVtDLwOmCrpPYVLywJLVp2xEELolPk1GYc9WA17I+CdwDQWnTjzPPCRKjMVQgid4tHQ6Wj7bOBsSdvZvqqDeQohhI7q7XcD8+7TShv2DZIOJTWPLGgKsf3hynIVQggd1C0zGZtppZfoZNL6128HLiNt0/58lZkKIYROMc0XfuqWTslWCuz1bf8n8KLtE4F3AK+vNlshhNA5862mRzdopUnk1fz/M5I2BR4D1qksRyGE0EGjYlhfwUxJywFHAecASwNfrDRXIYTQQR4tnY62f5YfXg6sW212Qgihwwy9Nel0HGzizKeAZ20f3+f8YcB429+rOnMhhFC1OjWJDNbp+GHSCJG+ZuZrIYQwCgi3cHSDwZpEbPuVfk6+rD6LY/dH0nzgFmAiaf3sE4Hv2a7LwlghhDGiW0aBNDPosD5JK7dybgDzbG9u+3XArsAewJeGnsUQQqhOahJpfjQjaU1Jl0i6Q9Jtkj6Rzy8v6QJJd+f/l8vnJekHku6RdLOkLZulMViB/S3gD5J2kLRMPnYEfgcc2zz7C9meDcwAPt5K7TyEEDqppIkzPcCnbb8W2BY4VNImwJHARbY3AC7KzwF2BzbIxwzgx80SGGwtkZMkPQF8GdiU9EZ0G/Al2+e2kvs+8e6TNA5YCXi8eE3SjJxhlh4/daihQwihLWUMErH9KHk3rrxvwB3A6sBewI75thOBS4Ej8vmTbBu4WtI0SavmOP0adFhfLpiHXDgPot+3KdszSZ2ZTF9itZoMsAkhjAZ2y6NEVpR0XeH5zFx2LUbSOsAWwDXAyo1C2PajklbKt60OPFz4sln53PAK7DJJWheYD8zuVJohhNCKFkdCzLG9VbObJC1N2vjlk7afG6QVuL8Lg1ZYO1JgS5oO/AT4n1z9DyGErmDKGyUiaSKpsD7F9v/l0483mjokrcrCSussYM3Cl68BPDJY/Cr3dJos6UZJtwEXAucDx1SYXgghDEtJo0QEHA/cYfs7hUvnAAfnxwcDZxfOH5RHi2xLmqg4YHMItFDDzsP4vgqsZnv33Ou5Xd8ZkH3ZrmazwxBCKFVpE2O2Bz4I3CLpxnzu88DXgdMlHQI8BOyXr/2RNNz5HmAu8KFmCbTSJPIL4ATgC/n534Ffk95JQgih1hrjsNuOY1/BAAMrgJ37ud/AoUNJo5UmkRVtn05ul7fdQ+o8DCGEUWE0TE1veFHSCuTey0ZbS6W5CiGEDrGhpyYLZrRSYH+a1Di+nqQrgeksbIMJIYTa65YadDOtrId9vaQdgI1I7TN32X61yZeFEEJt1GU97KZt2JLuBf6f7dts32r7VUm/70DeQgihciZ10DU7ukErnY6vAm+VdIKkJfK51SvMUwghdJStpkc3aKXAnmv7fcAdwJ8lrU05a6WEEMKIq1MNu5VORwHY/qak64E/ActXmqsQQuig+TWpgrZSYC/YId32RZLezsJpliGEUGt12tNxsE14N7Z9J/CPfnZCqKzT0YZXK+iy3XRqNQNb1lm+miHpk9afUklcgIlzXq4k7rgVJlUS12uuVUlcAHFlJXGXnbTY7nqlWGFSdR/OX+ypZmmhHnX/oLKaVLAHrWF/irSpwLf7uWZgp0pyFEIIndTi4k7dYLAdZ2bk/9/aueyEEEJnNTod62DAz0CStpa0SuH5QZLOzptGRqdjCGHUsJsf3WCwRqvjgFcAJP0zaYnAk0jriPS7LU4IIdSPmO/mRzcYrA17vO2n8uP3kfYvOxM4s7DWawgh1NqoaBIBxktqFOg7AxcXrnVsL8gQQqhaGTvOdMJgBe+pwGWS5gDzgD8DSFqfWF41hDCKdEl53NRgo0T+W9JFwKrA+YXNc8cBh3UicyGEULWydpzphEGbNmxf3c+5v1eXnRBC6DCPrqnpIYQwao2aGnYIIYwFNSmvW1peFUlrS9olP54saZlWE5C0iqTTJN0r6XZJf5S04XAzHEIIZavLKJFWdpz5CHAGaSINwBrAb1sJLknAWcClttezvQnweWDl4WU3hBDKZUbXrumHAtsA1wDYvlvSSi3Gfyvwqu2fNE7Yjkk3IYSu0i016GZaKbBftv1KqixDnkzT6re3KXB9s5skzSCtDMhS46a2GDqEENpn6jNKpJU27MskfR6YLGlX4DfA78rMhO2ZtreyvdWS46pbBzqEEBbTQvt1t9TAWymwjwSeAG4BPgr8ETiqxfi3AW8cXtZCCKEz3MK/bjBogS1pPHCS7Z/a3s/2vvlxq7m/GJiUOy4bMbeWtEMbeQ4hhNI0xmHXvoZtez4wXdISwwmeC/Z3A7vmYX23AUcDjwwnXgghVMEtHN2glU7HB4ArJZ0DvNg4afs7rSRg+xHgvcPKXQghdEC31KCbaaXAfiQf44CWJ8yEEEId1GmUSNMC2/YxnchICCGMlNrXsCV9z/YnJf2OfppwbL+r0pyFEEIndNGejc0MVsM+Of9/bCcyEkIII6FOW4QNVmA/AWD7sg7lJYQQRkRdatiDDetbsMCTpDM7kJcQQug4Y+a7+dENBqthF5enWrfqjIQQwkipfacji3Y01uTbCSGEoatLATdYgb2ZpOdINe3J+TH5uW0vW3nuQgihYqNiizDb4zuZkRBCGBFdtFZIM123p6MES4wrf3eHu1+o5lt9/UvDWmalKc97oZK4AFNWml9N4FcqijtvXjVxAb/UU0nc2XMnVxK319XtfLLmlFcriTvRkyqJW6ZuWY2vma4rsEMIoZMMzK/JQOwosEMIY15v1LBDCKEeumSYdVNRYIcQxrTRMjU9hBDGhNY30RpZUWCHEMY0G3pqUmC3sglvCCGManbzoxWSfi5ptqRbC+eWl3SBpLvz/8vl85L0A0n3SLpZ0pbN4keBHUIY04zpbeFo0S+A3fqcOxK4yPYGwEX5OcDuwAb5mAH8uFnwKLBDCGNeWTVs25cDT/U5vRdwYn58IrB34fxJTq4GpkladbD40YYdQhjzWqxBryjpusLzmbZntvB1K9t+FMD2o5JWyudXBx4u3Dcrn3t0oEBRYIcQxrS0+FNLBfYc21uVmHR/6wwMmpFKC2xJ84FbSBmbD3zc9l+qTDOEEIZqfrUzHR+XtGquXa8KzM7nZwFrFu5bA3hksEBVt2HPs7257c2AzwFfqzi9EEIYkjRxprROx/6cAxycHx8MnF04f1AeLbIt8Gyj6WQgnWwSWRZ4uoPphRBCC1zaxBlJpwI7ktq7ZwFfAr4OnC7pEOAhYL98+x+BPYB7gLnAh5rFr7rAnizpRmBJYFVgp/5ukjSDNKyFpcdPrThLIYSwqLIWf7J9wACXdu7nXgOHDiV+1QX2PNubA0jaDjhJ0qbu83aWe1pnAkxfYrV6TDkKIYwKjSaROujYOGzbVwErAtM7lWYIIbSilakz3aBjbdiSNgbGA092Ks0QQmjGmJ4uKZCb6VQbNqShfQfbrmgfqRBCGJ7eKLBjI98QQh0YKwrsEELoenXqdIwCO4Qw5kWTSAgh1IAx89Uz0tloSRTYIYQxL2rYIYRQC44CO4QQ6sDQNRNjmokCO4Qwxple6jE9JArsEMKYFzXsEEKoAWN69OpIZ6MlUWCHEMY4Rw17uF7pNbPmvlRB3EmlxwT4y+zlK4m77uPV7fXw6nP9bSXXPk14pZK4EyZPriQuwDN/q+YP9ZXeahbCfPqVan52APPmT6wk7u1Pn1ZJ3LJzx4S0AAAVdElEQVSkPR2jDTuEEGogatghhFAbjlEiIYRQBzFxJoQQasGYXscokRBCqIWoYYcQQi042rBDCKEODNhRww4hhBqITscQQqgHR6djCCHUQjSJhBBCbdSn07GaBQ8KJL1bkiVtXHVaIYQwHHZv06MbVF5gAwcAVwD7dyCtEEIYsiiwAUlLA9sDhxAFdgihCzmPEmn2rxtU3Ya9N3Ce7b9LekrSlrb/VnGaIYTQOpve3nqMEqm6SeQAoLEY7mn5+WIkzZB0naTrXumdW3GWQghhUaa36dENKqthS1oB2AnYVJKB8YAlfda2i/fangnMBJg6YVUvFiyEECrjrmmjbqbKGva+wEm217a9ju01gfuBt1SYZgghDEljHPZY73Q8ADirz7kzgfdXmGYIIQzZmG8Ssb1jP+d+UFV6IYQwPPVpEomZjiGEMc7YPSOdiZZEgR1CGNsca4mEEEItGLqmjbqZKLBDCGNctGGHEEKN1GO1viiwQwhjXHQ6hhBCTRiiSSSEEOrB1GNFjCiwQwghRomEEEJNuB41bLnLMirpCeDBFm9fEZhTQTaqiltl7LrFrTJ23eJWGbtucYcae23b09tJTNJ5Oc1m5tjerZ202tV1BfZQSLrO9lZ1iVtl7LrFrTJ23eJWGbtucauOXXed2NMxhBBCCaLADiGEmqh7gT2zZnGrjF23uFXGrlvcKmPXLW7VsWut1m3YIYQwltS9hh1CCGNGFNghhFATUWCHjpE0WdJGI52PEOqqVgW2pI9LWq6i2O+UVKvXoyqSNq0g5p7AjcB5+fnmks4pKfaxkl5XRqxOkDRe0i8rivutsuPWOfZoU7cCahXgr5JOl7SbJJUYe3/gbknflPTaEuMiaXtJF0j6u6T7JN0v6b5hxlp+sKOkLP9E0rWSPiZpWkkxjwa2AZ4BsH0jsE5Jse8EZkq6RtK/SppaRlBJF7VybqhszwemS1qi3Vj9xH1jyX8XtY492tRulEj+ob4N+BCwFXA6cLzte0uIvSxwQI5t4ATgVNvPtxn3TuDfgesprJRu+8lhxLo/562/X27bXne4+eyTzgbAh4H9gGuBE2xf0Ea8a2y/SdINtrfI5262/YYy8pvjbUT62R0AXAn81PYlw4izJDAFuATYkYWv9bLAubbbfkOXdBywJXAO8GLjvO3vtBn328AGwG/6xP2/duLWOfZoUrvFn2xb0mPAY0APsBxwhqQLbH+2zdjPSToTmAx8Eng38BlJP7D9wzZCP2v73HbyVsjja8qI00I6d0s6CrgO+AGwRX6z/Pww/4hulfR+YHx+Mzgc+EtZ+ZU0Htg4H3OAm4BPSfqo7f2HGO6jpJ//aqQ32UaB/Rzwo3JyzCP5GAcsU1JMgOWBJ4GdCucMlFHw1TX2qFGrGrakw4GDSX+QPwN+a/vV3PZ8t+312oj9LlLtbD3gZOBE27MlTQHusL32MGJumR++FxhP+uV7uXHd9t+GEXNj23cWYi9iODH7SeMNpNfiHcAFpE8wf5O0GnDVMF+LKcAXSJ+OBPwJ+C/bL5WQ3+8A7wIuynm9tnDtLtvD6uiUdFibb9StpLGU7Reb3xlC/QrsY4Cf215sNT9Jr7V9RxuxTyT9sV/ez7WdbQ+57VLSYB/HbXunQa4PFHOm7RkDxB5WzH7SuBz4KXCG7Xl9rn3Q9sntplEmSR8GTrM9t59rU20/O8y4+wHn2X4+f9rYEvhKSW+K2wHHA0vbXkvSZsBHbX+szbgbAj8GVra9aX7zfZftr5SQ51rGHk1qU2DnWvTNtqsYwTAe+JPtXcqOXYX8Wmxn+8qRzkur8h/kf5A6Ghc0xbXzBjPQp4xC7LYK1kYbu6S3AF8DjiU1Cb2pnbg59jXAvsA5hTb9W9v9/ZZ0GfAZ4Lgy49Y59mhSmzZs272SbpK0lu2HSo49X9Lcdmpjg5H0VeCbtp/Jz5cDPm37qOHEy6/FscB2JWZzgdzG/DVgE2DJQrrtdGj+BvgJqSmrrC2qvz3INbNoe+hwNPL5DuDHts+WdHSbMRew/XCfgRFlvC5TbF/bJ25ZO8zWNfaoUZsCO1sVuE3StSzak/yuEmK/BNwi6YI+sQ8vIfbutj9fiPm0pD2AYRXY2fmS9gH+z+V/TDoB+BLwXeCtpPbsdodc9dj+cbsZK7L91jLj9eMfeTTHLsA3JE2ivKGwD0t6M+A8vO9wYNhNegVzJK1HesNC0r7AoyXErXPsUaM2TSIAknbo77zty0qIffAAsU8sIfbNwNa2X87PJwPX2R72ZA9JzwNLkWpl80gFqm0vW0J+r7f9Rkm32H59Pvdn2//URsyjgdnAWSza8fpUCfk9qL/ztk9qM+4UYDfgljxqZlXg9bbPbydujr0i8H3Sm8E4UifsJ4Yz1LNP3HVJq929GXgauB84sL9+n7ESezSpVYFdV5I+SxrFcAKpBvFhUtvlN0c0YwOQdCXwT8AZwMXAP4CvD3e0RY55fz+nSxk3Lqk4kmNJYGfgb7b3bTPuWv2dL7tJrgqSlgLGtTuHYDTFHg1qVWBL2hb4IfBaYAnSULkXS6pVVtFuW4y/G6k2JeB8238qIeZ7gLeQ3gT+bPu37cbMcbcmfTyfBvwXMJXUBn91GfGrpjTT8eR2m8ok3cLCSUpLAq8B7mrnk1Eh9rqkGva2OY2rgH+3PawZsIW4K5Casxq/F1cAX2635l7n2KNJ3Qrs60hTyH9DmuV4ELBBsX24jdhXsLDddk9yu63tL7UZt5IRKJL+F1gfODWfeh9wr+1Dy0ynLFU1WwyQ1kTSiKKylxjYkjT07qMlxLqaNAmn8fPbHzis3REouQ/mcqCxVsmBwI5l/P7VNfZoUrsC2/ZWKkxplvQX228uIXbp7baF2OcAHyxzBIqk24BNGx2OeajfLW22iw+6GFM7Ndaqmi1y7N+RO6tIn7o2AU63fUS7sftJ62+2Bx1O2GKca/oWzpKutr1tm3Gvt/3GPudK2dS2rrFHk7qNEpmbe9RvlPRNUi/yUiXFfikXendL+jip3XalsmJT/giUu4C1gEanzJrAzW3EgzRM8GFSre8a2h8ZsoDtw4rPG80WJYU/loUFdg/woO1/tBtU0qcKT8eRJs480WbMxgJdl0g6EjiNlPf3AX9oJ3Yh7v6kNXYgjfUuI26dY48adathr00aaTCRtJjSVOB/bd9TQuzK2m2rGIGSJxpsTVqYifz4KmBujj3k2nBuvtmVtHjSG0h/MKfavm24+RwkrbabLfJImf4WwjJpJMq9wBc8jFmqOX6xOawHeAA4021Mp1dFi3f1eS2WAnrzpXHAC+3089Q19mhUqwI7LNRniKNInTUHAB+D9oc65jHHBwDfInX+tLWmRiebLXJ644FNgVNitlwYLWpVYBdqJ4soaWjYJQPELmNtjkpGoEjaHHg/aXGp+0mTaNotWCeRZvYdQJpGfg5p/Za2mhjyG0zpzRYtpPtR28cN82v7a9N/lrSC4XFt1rTHk17ndVh0qn5by6vm2O8C/jk/vdT279uNWYj9BhbPcykr6lUZe7SoWxt2sQNiSdJazWUt2v8ffWLvQ3lTY0ubOai0Jsf+pAL1SeDXpDfetmf9KS2AtSlwLnCM7VtLiDlgs4WktpstmhluYZ3dD0xn0ZE4jwMbkhbH+mAbsX9H7ttgYTNA2yR9ndQ8dko+9QlJb7F9ZAmxf05qKruNhXkuZQnUKmOPJrWqYfdH0hW231JR7Mts9zu7cohxShuBIqkX+DNwSKPtXtJ9JX3K6GVhp2jxF6O0WZR90uvqZgtJl9v+5/7OSbqtzRE5pW7eUIwLbG67Nz8fD9xQRlqSbre9SbtxOh17NKlVDVuLrs42jlTjLmXxdy26vdY44I2kLcnKUOYIlH1INexLJJ1HGmVQymgO2x3dMs5pa6ib+gz56ybTVVhsLM98XDFfe6XN2OdKelsZ09z7MQ1oTPkvZbu07CpJm9i+vcSYnYg9atSqwGbR1dkavfbvLSn29Sz86N5D+jh8SEmxP0nacupw0giUnUgbMQyZ7bOAs/IU3r1Jo2VWlvRj4KyKCoBKtdlsUaVPA1dIupf0e/Ea4GP5tW93jZmrST/HccCrlPcp5mvADblPRqS27LYnlmUnkgrWx0ijcBp5LuOTQpWxR43aN4mUQTVcX7oofzrYD3hfGZ2kYaHcCbsxqQC5s52Oxj5x7yO94d7ikv8IlRap2pqU52tsP1ZS3HuAT9Gn3d3lLP5UWezRpFYFdv7j2YfFe5K/XELsq2yXur50lTMHQ/WqnE4v6U+kZXdL63DMcS+yvXOzc8OMfXFVFYIqY48mdWsSOZs0rOp6Ckt0lqSK9aUrmzkYOmLrwuMF0+mBMtY/eRS4VNK5LLrc7LCG9WnhTu8rKm2QUdzpfbU289pwp6RfkUa4FPNcxkiOKmOPGnUrsNewvVtFsT9FXl9aUlnrS6/CwpmD76fCmYOhfBVPp78/H0vko12d2Ol9MqkwfVvhXFlD76qMPWrUrUlkJvBD27eMdF6GquyZg6Hz8nT6W2xvXGLMUndNVwd2eg8jp6PDuErwFuB6SXdJulnSLXncaSkkvUfSdyR9W9LeJcWcpLRu9S+BQ4EfELWGWpD0O0nn5OMPpAW3ylpzfDtJt5O3BZO0mdKSue3qlTStkM5yktraib0Qa0NJF0m6NT9/g9Ju8l0dezSpWw177f7Ol9RLXfr60n1mDp5WxszB0Dl91mvpITUzHNDO70QhdlW7pt9oe/M+525opNFm7Ng1fYTVqg27UTBLWonCmhwl2YFF15c+kTTEqB0fJM0c3BA4XAt3hK5k5mAol+3LtPh6LWeWGL+KXdPHSVLh93g85bSRQ+yaPuJqVWArLWrzbVLHymxgbdJHyra3bKKC9aU7PXMwlKPK9VoKqto1/XzgdEk/IXXa/RtwXglxIXZNH3F1axK5iTRL8ELbW0h6K+kj6ow2YjaW/ZzKwvWlDbwJ+Itji6Ixp8r1WgppFHdNF6mgPdxt7iIvaTLwEdImyo24x+dlANqizu+a/gHbD7QbezSpVQ0beNX2k5LGSRpn+xJJ32gz5rGl5CyMJpWt11Kwke0DiyckbQ8Ma7atpAnAV0krQT5Myu+apIJvHOU0tzxoexdVsLO50+bDlcQeTer2kf0ZSUuTaj+nSPo+bbZz2b6scZDWJpmYH19LmiQRxhjbZ9l+H2lK+qUU1muR9LZBv7h1/Q29a2c43rdISw2va3vL3HH3GtInx7IqJffnobXbAi+UFBMASV+VNM32i7afz6NbvlJmGqNB3ZpEppDWEBbwAdIsrlPa/RiZY38EmAEsb3s9pU0HflLGlN5Qf2Wt1yJpO9LH/k+S1kdvWBZ4t+3Nhhn3bmDDvrN0c6fjnbY3GGaWi7EmA3uSPn1sCfyeNPrpihJiLzaSRSVteDya1KKGLel5Sc8BjwHPkNq4/of0EfAuSVdLardgPRTYnjQzDNt3U94mvKHmbD9l+7gS1rtYAlia1By5TOF4jjTMr40sLl77ym3XpdTKbM+zfbrt9wBbkN5k2tqKrmB8nlwGLHhzmDTI/WNSLdqwbQ+45rUKi+Dn/4frZduvNIYV5TbB+nz8CLWQm9suk/QL2w9KWjadbrvN9nZJB/VdmErSB4A724xdjLcDaY7C7sBfKW95418CF0k6gfR392HaX8J21KlVk8hg1Mbeffnrv0mqvR8EHEbazPZ2218oKYshLCBpK9LWcY3KyLPAh21fP8x4q5Nm0M5j4druW5PW6Hi3S9g/U2lP1RuB00kTfkqbUp/j70Zh1IztP5UZfzQYNQV2u5TWxD6EtPiMgD8BPyt7reIQYMFWXofa/nN+/hbgf93mgv2SdiLNSxBwm0vcK1PSsrafKyteP/HXBjawfWHurxofo0UWNeYLbBW2gAqhUyRdaXv7Zue6iaQ1SCNZtifV4K8APmF7Vgmxo9O/BbXodKzYgsV8JJU27TiEJq6VdJykHSXtkNeyuVTSllp079JucgJwDmmm8eqktatPKCl2dPq3oBadjhUrTogobSZbCE00Fmj6Up/zbybVXrtx95XptosF9C8kfbKk2NHp34IosBf9pYhfkNARJa9L0ilz8qiTxoqWjbVWynCZpM8DkyXtSur0/11JsUeNaMOW5pNW1BOpR31u4xKxol6oiKSVSfMIVrO9u6RNSBtBHz/CWRuQpLVI8x+2I1Vu/kJqwy5jLZHo9G/BmC+wQxgJSns5ngB8wfZmuQngBtuvH+GsjRhJ0wFsPzHSeelW0SQSwshY0fbpkj4HYLsnf9rrOpK+OMhl2/6vNmKL1I7/cVLNWvl1+KHtLw837mgVo0RCGBkvSlqBhes/b0uaPNONXuzngNSEcUSbsT9JGh2yte0VbC9PWtp4e0n/3mbsUSeaREIYAXno3g9JyyncCkwH9rVd2h6lVZC0DPAJUmF9OvBt27PbiHcDsKvtOX3OTyfNdmx7a7PRJGrYIXSQpK0lrWL7b6Rt6T4PvEzaaKDtCShVkbR8Xu70ZlJT6pa2j2insM4m9i2sYUE79sQ2Y486UWCH0FnHAa/kx28GvgD8iLQC5cyRytRgJH2LtNDT88DrbR9t++mSwr8yzGtjUjSJhNBBkm5qrHkt6UfAE7aPzs8X2/G8G+Qt014mbRZSLDDaHvpaGFa72CVgSdtRyy6IUSIhdNZ4SRNs9wA7k9bPaOjKv8cqN5O2Pb6q2KNRV/6ChDCKnUqa1TeHtBRqY7W+9eneUSKhS0STSAgdlofwrUoaBfFiPrchsHTujAyhX1FghxBCTcQokRBCqIkosEMIoSaiwA4dI8mSTi48nyDpCUm/H2a8aZI+Vni+43BjhVAHUWCHTnoR2FTS5Px8V6CdzWGnkdZNDmFMiAI7dNq5wDvy4wNYuBh+Y/rzbyXdLOlqSW/I54+W9HNJl0q6T9Lh+Uu+Dqwn6cY8Gw9gaUlnSLpT0ilqbGESwigQBXbotNOA/SUtCbwBuKZw7RjSmtBvIK2xcVLh2sbA24FtgC9JmggcCdxre3Pbn8n3bUFaAW4T0pZvXbupbQhDFQV26Ki8Gt06pNr1H/tcfgtwcr7vYmAFSVPztT/YfjkvFDQbWHmAJK61Pct2L3BjTiuEUSFmOoaRcA5wLLAjsELhfH/NF42JAi8Xzs1n4N/dVu8LoXaihh1Gws+BL9u+pc/5y4EDIY34AObYfm6QOM8Dy1SSwxC6UNQ+QsfZngV8v59LRwMnSLqZtBnywU3iPCnpSkm3kjoz/1B2XkPoJjE1PYQQaiKaREIIoSaiwA4hhJqIAjuEEGoiCuwQQqiJKLBDCKEmosAOIYSaiAI7hBBqIgrsEEKoif8PTEmlsKxvOOwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10));\n",
    "fig, ax = plt.subplots()\n",
    "plt.set_cmap('magma')\n",
    "import matplotlib.colors as mcolors\n",
    "gamma = 0.5\n",
    "X_train_feat = np.array(X_train['DISCOVERY_DATETIME']).reshape(-1,1)\n",
    "\n",
    "h = plt.hist2d(X_train_feat.transpose()[0], np.array(y_train),  norm=mcolors.PowerNorm(gamma), bins=[np.arange(1, 14), np.arange(1,9)])\n",
    "\n",
    "plt.title(\"Fire Size Category by Month (Log)\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel('Fire Size Category')\n",
    "plt.xticks(np.arange(1,13) + .5, calendar.month_name[1:13], rotation=90)\n",
    "plt.yticks(np.arange(1, 8) + .5, ['A', 'B', 'C', 'D', 'E', 'F', 'G'])\n",
    "fig.colorbar(h[3], ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fc52bcb15c0>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10));\n",
    "fig, ax = plt.subplots()\n",
    "plt.set_cmap('magma')\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "h = plt.hist2d(X_train_feat.transpose()[0], np.array(y_train), bins=[np.arange(1, 14), np.arange(1,9)])\n",
    "\n",
    "plt.title(\"Fire Size Category by Month\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel('Fire Size Category')\n",
    "plt.xticks(np.arange(1,13) + .5, calendar.month_name[1:13], rotation=90)\n",
    "plt.yticks(np.arange(1, 8) + .5, ['A', 'B', 'C', 'D', 'E', 'F', 'G'])\n",
    "fig.colorbar(h[3], ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7fcbad97d1d0>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbad975ac8>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbad975828>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbadffd3c8>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbadffd898>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbadffdd68>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae003278>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae003748>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbadffd978>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae0031d0>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae003f60>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae00c4a8>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae00c9b0>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae00ceb8>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae015400>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae015908>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae015e10>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae0159e8>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae00c470>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae01b240>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae01b710>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae01bc18>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae026198>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae026668>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae026b70>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae02f128>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae026748>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae01ba20>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae01b0b8>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae02f978>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae02fe80>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae0363c8>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae0368d0>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae036dd8>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba638b320>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba638b828>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae0369b0>,\n",
       "  <matplotlib.axis.XTick at 0x7fcbae015c18>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba638b128>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba6395128>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba63955c0>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba6395ac8>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba6395cf8>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba639c518>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba639ca20>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba639cf28>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba6395b70>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba638b2e8>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba63a3320>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba63a3828>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba63a3d30>,\n",
       "  <matplotlib.axis.XTick at 0x7fcba63af278>],\n",
       " <a list of 52 Text xticklabel objects>)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8));\n",
    "X_train_feat = np.array(X_train['STATE']).reshape(-1,1)\n",
    "plt.scatter(X_train_feat, y_train, alpha=0.005, label=\"Training\")\n",
    "plt.title(\"Fire Size Category by U.S. State\")\n",
    "plt.xlabel(\"State\")\n",
    "plt.ylabel('Size Category')\n",
    "plt.xticks(np.arange(52), ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA',\n",
    "        'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME',\n",
    "        'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM',\n",
    "        'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'PR', 'RI', 'SC', 'SD', 'TN',\n",
    "        'TX', 'UT', 'VA', 'VT', 'WA', 'WI', 'WV', 'WY'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We do have to remove nans for this visualization\n",
    "\n",
    "X_train_feat = np.array(X_train['STATE']).reshape(-1,1)\n",
    "xp = np.logical_not(np.isnan(X_train_feat))\n",
    "X_train_feat = X_train_feat[xp]\n",
    "y_train = np.array(y_train).transpose()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train[xp.transpose()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "713605"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "713605"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fc52d57c240>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(18,8))\n",
    "X_train_feat = np.array(X_train['STATE']).reshape(-1,1)\n",
    "xp = np.logical_not(np.isnan(X_train_feat))\n",
    "X_train_feat = X_train_feat[xp]\n",
    "y_train = np.array(y_train).transpose()\n",
    "h = plt.hist2d(X_train_feat, y_train, bins=[np.arange(1, 53), np.arange(1,9)])\n",
    "\n",
    "plt.title(\"Fire Size Category by U.S. State\")\n",
    "plt.xlabel(\"State\")\n",
    "plt.ylabel('Size Category')\n",
    "plt.xticks(np.arange(1,52) + .5, ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA',\n",
    "        'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME',\n",
    "        'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM',\n",
    "        'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'PR', 'RI', 'SC', 'SD', 'TN',\n",
    "        'TX', 'UT', 'VA', 'VT', 'WA', 'WI', 'WV', 'WY'])\n",
    "plt.yticks(np.arange(1, 8) + .5, ['A', 'B', 'C', 'D', 'E', 'F', 'G'])\n",
    "\n",
    "fig.colorbar(h[3], ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fc5406a9908>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma = 0.5\n",
    "fig, ax = plt.subplots(figsize=(18,8))\n",
    "\n",
    "\n",
    "X_train_feat = np.array(X_train['STATE']).reshape(-1,1)\n",
    "xp = np.logical_not(np.isnan(X_train_feat))\n",
    "X_train_feat = X_train_feat[xp]\n",
    "y_train = np.array(y_train).transpose()\n",
    "h = plt.hist2d(X_train_feat, y_train, bins=[np.arange(1, 53), np.arange(1,9)], norm=mcolors.PowerNorm(gamma))\n",
    "\n",
    "plt.title(\"Fire Size Category by U.S. State (Log)\")\n",
    "plt.xlabel(\"State\")\n",
    "plt.ylabel('Size Category')\n",
    "plt.xticks(np.arange(1,52) + .5, ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA',\n",
    "        'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME',\n",
    "        'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM',\n",
    "        'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'PR', 'RI', 'SC', 'SD', 'TN',\n",
    "        'TX', 'UT', 'VA', 'VT', 'WA', 'WI', 'WV', 'WY'])\n",
    "plt.yticks(np.arange(1, 8) + .5, ['A', 'B', 'C', 'D', 'E', 'F', 'G'])\n",
    "\n",
    "fig.colorbar(h[3], ax=ax)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_1 = pd.read_pickle('final_dataset_1.pkl')\n",
    "y_1 = X_1['FIRE_SIZE_CLASS']\n",
    "X_1 = X_1.drop(columns=['FIRE_SIZE_CLASS'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1_train, X1_test, y1_train, y1_test = train_test_split(X_1, y_1, test_size = .2, random_state = 20);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fc52ca87828>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Cause dict based off of the two columns for cause and cause desc:\n",
    "#state_cause_dict = {9.0: 'Miscellaneous',\n",
    "# 1.0: 'Lightning',\n",
    "# 5.0: 'Debris Burning',\n",
    "# 4.0: 'Campfire',\n",
    "# 2.0: 'Equipment Use',\n",
    "# 7.0: 'Arson',\n",
    "# 8.0: 'Children',\n",
    "# 6.0: 'Railroad',\n",
    "# 3.0: 'Smoking',\n",
    "# 11.0: 'Powerline',\n",
    "# 12.0: 'Structure',\n",
    "# 10.0: 'Fireworks',\n",
    "# 13.0: 'Missing/Undefined'}\n",
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "plt.set_cmap('magma')\n",
    "import matplotlib.colors as mcolors\n",
    "gamma = 0.5\n",
    "X1_train_feat = np.array(X1_train['STAT_CAUSE_CODE'])\n",
    "h = plt.hist2d(X1_train_feat, y1_train, norm=mcolors.PowerNorm(gamma), bins=[np.arange(1, 15), np.arange(1,9)])\n",
    "cause_list = ['Lightning', 'Equipment Use', 'Smoking', 'Campfire', 'Debris Burning',\n",
    "              'Railroad', 'Arson', 'Children', 'Miscellaneous', 'Fireworks', 'Powerline',\n",
    "             'Structure', 'Missing or Undefined']\n",
    "\n",
    "plt.title(\"Fire Size Category by Fire Cause Code (Log)\")\n",
    "plt.xlabel(\"Fire Cause Code\")\n",
    "plt.ylabel('Size Category')\n",
    "plt.xticks(np.arange(1,14) + .5, cause_list, rotation=90);\n",
    "plt.yticks(np.arange(1, 8) + .5, ['A', 'B', 'C', 'D', 'E', 'F', 'G'])\n",
    "\n",
    "fig.colorbar(h[3], ax=ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7ff443180b38>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "plt.set_cmap('magma')\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "X1_train_feat = np.array(X1_train['STAT_CAUSE_CODE'])\n",
    "h = plt.hist2d(X1_train_feat, y1_train, bins=[np.arange(1, 15), np.arange(1,9)])\n",
    "cause_list = ['Lightning', 'Equipment Use', 'Smoking', 'Campfire', 'Debris Burning',\n",
    "              'Railroad', 'Arson', 'Children', 'Miscellaneous', 'Fireworks', 'Powerline',\n",
    "             'Structure', 'Missing or Undefined']\n",
    "\n",
    "plt.title(\"Fire Size Category by Fire Cause Code\")\n",
    "plt.xlabel(\"Fire Cause Code\")\n",
    "plt.ylabel('Size Category')\n",
    "plt.xticks(np.arange(1,14) + .5, cause_list, rotation=90);\n",
    "plt.yticks(np.arange(1, 8) + .5, ['A', 'B', 'C', 'D', 'E', 'F', 'G'])\n",
    "\n",
    "fig.colorbar(h[3], ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret input 'size'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-69-36bf76664551>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m sns.scatterplot(x=X1_train_feat, y=y1_train, hue=\"size\", size=\"size\",\n\u001b[0;32m----> 2\u001b[0;31m     sizes=(20, 200), hue_norm=(0, 7), legend=\"full\")\n\u001b[0m",
      "\u001b[0;32m/Applications/anaconda3/lib/python3.6/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mscatterplot\u001b[0;34m(x, y, hue, style, size, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, style_order, x_bins, y_bins, units, estimator, ci, n_boot, alpha, x_jitter, y_jitter, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1333\u001b[0m         \u001b[0mx_bins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx_bins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_bins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_bins\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1334\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1335\u001b[0;31m         \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_jitter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx_jitter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_jitter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_jitter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1336\u001b[0m     )\n\u001b[1;32m   1337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Applications/anaconda3/lib/python3.6/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, x_bins, y_bins, units, estimator, ci, n_boot, alpha, x_jitter, y_jitter, legend)\u001b[0m\n\u001b[1;32m    850\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    851\u001b[0m         plot_data = self.establish_variables(\n\u001b[0;32m--> 852\u001b[0;31m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    853\u001b[0m         )\n\u001b[1;32m    854\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Applications/anaconda3/lib/python3.6/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, size, style, units, data)\u001b[0m\n\u001b[1;32m    140\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m                     \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Could not interpret input '{}'\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0;31m# Extract variable names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Could not interpret input 'size'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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