CPEN291_project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CPEN291_project.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GgFQbfDJU_oh"
      },
      "source": [
        "Data Collection Part 1: Cleaning Up and Creating Fire Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YcY-iowYXQ1M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "outputId": "ba7a382a-df22-47eb-a8cb-b236448c3917"
      },
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import numpy as np\n",
        "import requests\n",
        "#These \"RAW\" urls may require you to get you own \"RAW\" url because they stop working after some time (weird github thing)\n",
        "url = 'https://raw.githubusercontent.com/UBC-CPEN291/project-team-corythosaurus/main/data/H_FIRE_PNT.csv?token=AQTQKXKXJO3RQOQTXVLK7UTATOHA4'\n",
        "df = pd.read_csv(url, index_col=0)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (9) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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_YEAR</th>\n",
              "      <th>IGN_DATE</th>\n",
              "      <th>FIRE_CAUSE</th>\n",
              "      <th>FIRELABEL</th>\n",
              "      <th>FRCNTR</th>\n",
              "      <th>ZONE</th>\n",
              "      <th>FIRE_ID</th>\n",
              "      <th>FIRE_TYPE</th>\n",
              "      <th>GEO_DESC</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>LONGITUDE</th>\n",
              "      <th>SIZE_HA</th>\n",
              "      <th>FCODE</th>\n",
              "      <th>SHAPE</th>\n",
              "      <th>OBJECTID</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FIRE_NO</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>K30894</th>\n",
              "      <td>2001</td>\n",
              "      <td>2.001082e+13</td>\n",
              "      <td>Person</td>\n",
              "      <td>2001-K30894</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>500894</td>\n",
              "      <td>Nuisance Fire</td>\n",
              "      <td>Nielson Beach #1</td>\n",
              "      <td>51.0167</td>\n",
              "      <td>-119.0167</td>\n",
              "      <td>NaN</td>\n",
              "      <td>JA70003000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11402317</td>\n",
              "      <td>-119.016667</td>\n",
              "      <td>51.016667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V70397</th>\n",
              "      <td>2010</td>\n",
              "      <td>2.010071e+13</td>\n",
              "      <td>Person</td>\n",
              "      <td>2010-V70397</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>200397</td>\n",
              "      <td>Fire</td>\n",
              "      <td>Extension Road</td>\n",
              "      <td>49.1102</td>\n",
              "      <td>-123.9395</td>\n",
              "      <td>0.009</td>\n",
              "      <td>JA70003000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11402318</td>\n",
              "      <td>-123.939484</td>\n",
              "      <td>49.110249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K40261</th>\n",
              "      <td>2004</td>\n",
              "      <td>2.004070e+13</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>2004-K40261</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>500261</td>\n",
              "      <td>Fire</td>\n",
              "      <td>Fintry</td>\n",
              "      <td>50.1537</td>\n",
              "      <td>-119.5490</td>\n",
              "      <td>0.009</td>\n",
              "      <td>JA70003000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11402319</td>\n",
              "      <td>-119.548950</td>\n",
              "      <td>50.153733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>R50229</th>\n",
              "      <td>2019</td>\n",
              "      <td>2.019051e+13</td>\n",
              "      <td>Person</td>\n",
              "      <td>2019-R50229</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>300229</td>\n",
              "      <td>Fire</td>\n",
              "      <td>.6km south of Arbor FSR on Hwy 16</td>\n",
              "      <td>55.8015</td>\n",
              "      <td>-128.8971</td>\n",
              "      <td>0.009</td>\n",
              "      <td>JA70003000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11402320</td>\n",
              "      <td>-128.897117</td>\n",
              "      <td>55.801450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V81229</th>\n",
              "      <td>2009</td>\n",
              "      <td>2.009080e+13</td>\n",
              "      <td>Person</td>\n",
              "      <td>2009-V81229</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>201229</td>\n",
              "      <td>Nuisance Fire</td>\n",
              "      <td>Cumberland Dump</td>\n",
              "      <td>49.6422</td>\n",
              "      <td>-125.0558</td>\n",
              "      <td>0.000</td>\n",
              "      <td>JA70003000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11402321</td>\n",
              "      <td>-125.055783</td>\n",
              "      <td>49.642200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N10266</th>\n",
              "      <td>1999</td>\n",
              "      <td>1.999090e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>1999-N10266</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>266</td>\n",
              "      <td>Fire</td>\n",
              "      <td>FORT STEELE</td>\n",
              "      <td>49.4923</td>\n",
              "      <td>-115.4473</td>\n",
              "      <td>0.009</td>\n",
              "      <td>JA70003000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11579248</td>\n",
              "      <td>-115.447333</td>\n",
              "      <td>49.492333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N10267</th>\n",
              "      <td>1999</td>\n",
              "      <td>1.999081e+07</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>1999-N10267</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>267</td>\n",
              "      <td>Fire</td>\n",
              "      <td>CAVEN CREEK</td>\n",
              "      <td>49.2630</td>\n",
              "      <td>-115.3325</td>\n",
              "      <td>0.200</td>\n",
              "      <td>JA70003000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11579249</td>\n",
              "      <td>-115.332500</td>\n",
              "      <td>49.263000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N10269</th>\n",
              "      <td>1999</td>\n",
              "      <td>1.999091e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>1999-N10269</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>269</td>\n",
              "      <td>Fire</td>\n",
              "      <td>Hidden Valley</td>\n",
              "      <td>49.4748</td>\n",
              "      <td>-115.7871</td>\n",
              "      <td>0.100</td>\n",
              "      <td>JA70003000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11579250</td>\n",
              "      <td>-115.787067</td>\n",
              "      <td>49.474767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N10270</th>\n",
              "      <td>1999</td>\n",
              "      <td>1.999091e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>1999-N10270</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>270</td>\n",
              "      <td>Fire</td>\n",
              "      <td>Hidden Valley</td>\n",
              "      <td>49.4750</td>\n",
              "      <td>-115.7837</td>\n",
              "      <td>0.100</td>\n",
              "      <td>JA70003000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11579251</td>\n",
              "      <td>-115.783667</td>\n",
              "      <td>49.474950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N10277</th>\n",
              "      <td>1999</td>\n",
              "      <td>1.999092e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>1999-N10277</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>277</td>\n",
              "      <td>Fire</td>\n",
              "      <td>elkford/line creek</td>\n",
              "      <td>49.9333</td>\n",
              "      <td>-114.8500</td>\n",
              "      <td>1.000</td>\n",
              "      <td>JA70003000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11579252</td>\n",
              "      <td>-114.850000</td>\n",
              "      <td>49.933333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>177495 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         FIRE_YEAR      IGN_DATE FIRE_CAUSE  ...  OBJECTID           X          Y\n",
              "FIRE_NO                                      ...                                 \n",
              "K30894        2001  2.001082e+13     Person  ...  11402317 -119.016667  51.016667\n",
              "V70397        2010  2.010071e+13     Person  ...  11402318 -123.939484  49.110249\n",
              "K40261        2004  2.004070e+13  Lightning  ...  11402319 -119.548950  50.153733\n",
              "R50229        2019  2.019051e+13     Person  ...  11402320 -128.897117  55.801450\n",
              "V81229        2009  2.009080e+13     Person  ...  11402321 -125.055783  49.642200\n",
              "...            ...           ...        ...  ...       ...         ...        ...\n",
              "N10266        1999  1.999090e+07     Person  ...  11579248 -115.447333  49.492333\n",
              "N10267        1999  1.999081e+07  Lightning  ...  11579249 -115.332500  49.263000\n",
              "N10269        1999  1.999091e+07     Person  ...  11579250 -115.787067  49.474767\n",
              "N10270        1999  1.999091e+07     Person  ...  11579251 -115.783667  49.474950\n",
              "N10277        1999  1.999092e+07     Person  ...  11579252 -114.850000  49.933333\n",
              "\n",
              "[177495 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqyY6DTSRfe9"
      },
      "source": [
        "#Dropping features we dont need\n",
        "df.drop(columns=['FIRE_YEAR', 'FIRELABEL', 'FRCNTR', 'ZONE', 'OBJECTID', 'FIRE_ID', 'GEO_DESC', 'FCODE', 'SHAPE', 'OBJECTID', 'X', 'Y'], inplace=True, errors='ignore')\n",
        "\n",
        "#Making it so that data rows have valid SIZE_HA (fire size in hectares) and IGN_DATE (date of one row of data)\n",
        "df['IGN_DATE'] = df['IGN_DATE'].div(1000000)\n",
        "df = df[df['SIZE_HA'].notnull()]\n",
        "df = df[df['IGN_DATE'].notnull()]\n",
        "\n",
        "#Use data after 1970\n",
        "df = df[df['IGN_DATE'] >= 1970]\n",
        "\n",
        "#Group data that has one common feature: There is no fire\n",
        "df_nofire = df[df['SIZE_HA'] == 0]\n",
        "df_nofire = df_nofire.head(2500)\n",
        "\n",
        "#Group data that has one common feature: There is fire (We define \"there is fire\" as there exists fire with SIZE_HA larger than 1.9)\n",
        "df_yesfire = df[df['SIZE_HA'] > 1.9]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRZxcilRblbj"
      },
      "source": [
        "total_nofire_list = []\n",
        "#Create data for no fire (this is fine because the chances that a fire happening at a random location given random date is pretty small)\n",
        "for i in range(2500):\n",
        "  one_entry = []\n",
        "  lat = random.uniform(48, 60)\n",
        "  lon = -random.uniform(130, 115)\n",
        "  date_yyyy = random.randint(1970, 2020)\n",
        "  date_mm = random.randint(1,12)\n",
        "\n",
        "  if (date_mm <= 3 or date_mm == 5 or date_mm == 7 or date_mm == 8 or date_mm == 10 or date_mm == 12):\n",
        "    if (date_mm == 2):\n",
        "      date_dd = int(random.randint(1,28))\n",
        "    else:\n",
        "      date_dd = int(random.randint(1,31))\n",
        "  else:\n",
        "    date_dd = int(random.randint(1,30))\n",
        "  \n",
        "  one_entry.append(date_yyyy*10000+date_mm*100+date_dd)\n",
        "  one_entry.append(\"No Cause\")\n",
        "  one_entry.append(\"No Fire\")\n",
        "  one_entry.append(lat)\n",
        "  one_entry.append(lon)\n",
        "  one_entry.append(0.0)\n",
        "  total_nofire_list.append(one_entry)\n",
        "\n",
        "df2 = pd.DataFrame(total_nofire_list, columns=df.columns)\n",
        "df_nofire = df_nofire.append(df2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chpFJW1J_2EM"
      },
      "source": [
        "df_nofire.to_csv('data_nofire.csv')\n",
        "df_yesfire.to_csv('data_yesfire.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1AY_3RYsUtsP"
      },
      "source": [
        "Data Collection Part 2: Getting Climate Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NE9veDGCVD7q"
      },
      "source": [
        "import requests\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uytCqpq5_LPy"
      },
      "source": [
        "#These \"RAW\" urls may require you to get you own \"RAW\" url because they stop working after some time (weird github thing)\n",
        "url_yes = 'https://raw.githubusercontent.com/UBC-CPEN291/project-team-corythosaurus/main/data/data_yesfire.csv?token=AQTQKXO564ZNCWKO7L5FVODATOEM2'\n",
        "df_yesfire = pd.read_csv(url_yes, index_col=0)\n",
        "#These \"RAW\" urls may require you to get you own \"RAW\" url because they stop working after some time (weird github thing)\n",
        "url_no = 'https://raw.githubusercontent.com/UBC-CPEN291/project-team-corythosaurus/main/data/data_nofire.csv?token=AQTQKXMYF2ZTDLZBJUEJI3TATOEOW'\n",
        "df_nofire = pd.read_csv(url_no, index_col=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "09KfLMxujmDW",
        "outputId": "20011a1f-db24-42af-b439-72e1294a1324"
      },
      "source": [
        "df_yesfire"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th {\n",
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              "    }\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>IGN_DATE</th>\n",
              "      <th>FIRE_CAUSE</th>\n",
              "      <th>FIRE_TYPE</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>LONGITUDE</th>\n",
              "      <th>SIZE_HA</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FIRE_NO</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C30085</th>\n",
              "      <td>2.015052e+07</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Fire</td>\n",
              "      <td>52.2711</td>\n",
              "      <td>-121.5118</td>\n",
              "      <td>3.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K71366</th>\n",
              "      <td>2.017073e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>Fire</td>\n",
              "      <td>50.3299</td>\n",
              "      <td>-121.3953</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N20709</th>\n",
              "      <td>2.000081e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>Fire</td>\n",
              "      <td>49.8731</td>\n",
              "      <td>-115.6792</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G90279</th>\n",
              "      <td>2.010071e+07</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Fire</td>\n",
              "      <td>58.8575</td>\n",
              "      <td>-121.9688</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G40080</th>\n",
              "      <td>2.000051e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>Fire</td>\n",
              "      <td>53.9200</td>\n",
              "      <td>-124.0817</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G70024</th>\n",
              "      <td>2.010041e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>Fire</td>\n",
              "      <td>56.0713</td>\n",
              "      <td>-121.8325</td>\n",
              "      <td>2.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G41084</th>\n",
              "      <td>2.019072e+07</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Fire</td>\n",
              "      <td>54.0024</td>\n",
              "      <td>-124.6282</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C20014</th>\n",
              "      <td>2.016041e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>Fire</td>\n",
              "      <td>51.5929</td>\n",
              "      <td>-123.0197</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G70316</th>\n",
              "      <td>2.003080e+07</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Fire</td>\n",
              "      <td>54.8517</td>\n",
              "      <td>-120.0743</td>\n",
              "      <td>3.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V30040</th>\n",
              "      <td>2.011052e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>Fire</td>\n",
              "      <td>49.7374</td>\n",
              "      <td>-123.0413</td>\n",
              "      <td>2.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4982 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             IGN_DATE FIRE_CAUSE FIRE_TYPE  LATITUDE  LONGITUDE  SIZE_HA\n",
              "FIRE_NO                                                                 \n",
              "C30085   2.015052e+07  Lightning      Fire   52.2711  -121.5118      3.6\n",
              "K71366   2.017073e+07     Person      Fire   50.3299  -121.3953     30.0\n",
              "N20709   2.000081e+07     Person      Fire   49.8731  -115.6792      3.5\n",
              "G90279   2.010071e+07  Lightning      Fire   58.8575  -121.9688      4.7\n",
              "G40080   2.000051e+07     Person      Fire   53.9200  -124.0817     25.0\n",
              "...               ...        ...       ...       ...        ...      ...\n",
              "G70024   2.010041e+07     Person      Fire   56.0713  -121.8325      2.8\n",
              "G41084   2.019072e+07  Lightning      Fire   54.0024  -124.6282      2.0\n",
              "C20014   2.016041e+07     Person      Fire   51.5929  -123.0197     17.0\n",
              "G70316   2.003080e+07  Lightning      Fire   54.8517  -120.0743      3.9\n",
              "V30040   2.011052e+07     Person      Fire   49.7374  -123.0413      2.4\n",
              "\n",
              "[4982 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "Yblw_EVpjn7F",
        "outputId": "1556a152-e373-4b4a-d32b-c9ed56772873"
      },
      "source": [
        "df_nofire"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>IGN_DATE</th>\n",
              "      <th>FIRE_CAUSE</th>\n",
              "      <th>FIRE_TYPE</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>LONGITUDE</th>\n",
              "      <th>SIZE_HA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>V81229</th>\n",
              "      <td>2.009080e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>Nuisance Fire</td>\n",
              "      <td>49.642200</td>\n",
              "      <td>-125.055800</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V10028</th>\n",
              "      <td>2.013052e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>Nuisance Fire</td>\n",
              "      <td>49.121100</td>\n",
              "      <td>-121.591700</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V10055</th>\n",
              "      <td>2.011052e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>Nuisance Fire</td>\n",
              "      <td>49.077200</td>\n",
              "      <td>-121.707800</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V70761</th>\n",
              "      <td>2.008101e+07</td>\n",
              "      <td>Person</td>\n",
              "      <td>Nuisance Fire</td>\n",
              "      <td>49.341200</td>\n",
              "      <td>-124.420900</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>V60046</th>\n",
              "      <td>2.004043e+07</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Nuisance Fire</td>\n",
              "      <td>48.783300</td>\n",
              "      <td>-123.950000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2495</th>\n",
              "      <td>1.971071e+07</td>\n",
              "      <td>No Cause</td>\n",
              "      <td>No Fire</td>\n",
              "      <td>54.524433</td>\n",
              "      <td>-121.838602</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2496</th>\n",
              "      <td>1.999021e+07</td>\n",
              "      <td>No Cause</td>\n",
              "      <td>No Fire</td>\n",
              "      <td>48.405481</td>\n",
              "      <td>-116.337460</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2497</th>\n",
              "      <td>1.994050e+07</td>\n",
              "      <td>No Cause</td>\n",
              "      <td>No Fire</td>\n",
              "      <td>51.126953</td>\n",
              "      <td>-127.393534</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2498</th>\n",
              "      <td>1.977022e+07</td>\n",
              "      <td>No Cause</td>\n",
              "      <td>No Fire</td>\n",
              "      <td>59.242536</td>\n",
              "      <td>-125.893874</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2499</th>\n",
              "      <td>2.010102e+07</td>\n",
              "      <td>No Cause</td>\n",
              "      <td>No Fire</td>\n",
              "      <td>54.895212</td>\n",
              "      <td>-122.943954</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            IGN_DATE FIRE_CAUSE      FIRE_TYPE   LATITUDE   LONGITUDE  SIZE_HA\n",
              "V81229  2.009080e+07     Person  Nuisance Fire  49.642200 -125.055800      0.0\n",
              "V10028  2.013052e+07     Person  Nuisance Fire  49.121100 -121.591700      0.0\n",
              "V10055  2.011052e+07     Person  Nuisance Fire  49.077200 -121.707800      0.0\n",
              "V70761  2.008101e+07     Person  Nuisance Fire  49.341200 -124.420900      0.0\n",
              "V60046  2.004043e+07    Unknown  Nuisance Fire  48.783300 -123.950000      0.0\n",
              "...              ...        ...            ...        ...         ...      ...\n",
              "2495    1.971071e+07   No Cause        No Fire  54.524433 -121.838602      0.0\n",
              "2496    1.999021e+07   No Cause        No Fire  48.405481 -116.337460      0.0\n",
              "2497    1.994050e+07   No Cause        No Fire  51.126953 -127.393534      0.0\n",
              "2498    1.977022e+07   No Cause        No Fire  59.242536 -125.893874      0.0\n",
              "2499    2.010102e+07   No Cause        No Fire  54.895212 -122.943954      0.0\n",
              "\n",
              "[5000 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pR8bu8_HwZ0l"
      },
      "source": [
        "def split(word):\n",
        "  return [char for char in word]\n",
        "\n",
        "#To get historical climate data given date, longitude and latitude from visual-crossing rapidapi (Used to add additional features for training/test data)\n",
        "def get_climate_data(ign_date, latitude, longitude):\n",
        "\n",
        "  url = \"https://visual-crossing-weather.p.rapidapi.com/history\"\n",
        "\n",
        "  ign_date_list = split(str(ign_date))\n",
        "  yyyy = ''.join(ign_date_list[0:4])\n",
        "  mm = ''.join(ign_date_list[4:6])\n",
        "  dd = ''.join(ign_date_list[6:8])\n",
        "  start_dt = f'{yyyy}-{mm}-{dd}T00:00:00'\n",
        "\n",
        "  coords = f'{latitude}, {longitude}'\n",
        "\n",
        "  querystring = {\"startDateTime\":start_dt,\"aggregateHours\":\"24\",\"location\":coords,\"endDateTime\":start_dt,\"unitGroup\":\"us\",\"dayStartTime\":\"8:00:00\",\"contentType\":\"csv\",\"dayEndTime\":\"17:00:00\",\"shortColumnNames\":\"0\"}\n",
        "\n",
        "  headers = {\n",
        "      'x-rapidapi-key': \"5416e60fd3msh25313be4cf91e39p1db2f2jsn7acc7269aacf\",\n",
        "      'x-rapidapi-host': \"visual-crossing-weather.p.rapidapi.com\"\n",
        "      }\n",
        "\n",
        "  response = requests.request(\"GET\", url, headers=headers, params=querystring)\n",
        "  response_list = response.text.split(\",\")\n",
        "\n",
        "  return response_list\n",
        "  # f.write(response.text)\n",
        "\n",
        "#To get forecasted climate data given longitude and latitude from visual-crossing rapidapi (Used in the finished website to get necessary features, given a user input, to feed into finished model)\n",
        "def get_future_climate_data(latitude, longitude):\n",
        "  url = \"https://visual-crossing-weather.p.rapidapi.com/forecast\"\n",
        "  coords = f'{latitude}, {longitude}'\n",
        "  querystring = {\"location\":coords,\"aggregateHours\":\"24\",\"shortColumnNames\":\"0\",\"unitGroup\":\"us\",\"contentType\":\"csv\"}\n",
        "\n",
        "  headers = {\n",
        "      'x-rapidapi-key': \"5416e60fd3msh25313be4cf91e39p1db2f2jsn7acc7269aacf\",\n",
        "      'x-rapidapi-host': \"visual-crossing-weather.p.rapidapi.com\"\n",
        "      }\n",
        "\n",
        "  response = requests.request(\"GET\", url, headers=headers, params=querystring)\n",
        "  response_list = response.text.split(\",\")\n",
        "\n",
        "  return response_list"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PErd1M4XHBbU",
        "outputId": "0694ad25-7f7d-4bf9-b6b3-a989d83cd3da"
      },
      "source": [
        "get_climate_data(20030705.09, 49.0707, -121.7133)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Address',\n",
              " 'Date time',\n",
              " 'Minimum Temperature',\n",
              " 'Maximum Temperature',\n",
              " 'Temperature',\n",
              " 'Dew Point',\n",
              " 'Relative Humidity',\n",
              " 'Heat Index',\n",
              " 'Wind Speed',\n",
              " 'Wind Gust',\n",
              " 'Wind Direction',\n",
              " 'Wind Chill',\n",
              " 'Precipitation',\n",
              " 'Precipitation Cover',\n",
              " 'Snow Depth',\n",
              " 'Visibility',\n",
              " 'Cloud Cover',\n",
              " 'Sea Level Pressure',\n",
              " 'Weather Type',\n",
              " 'Latitude',\n",
              " 'Longitude',\n",
              " 'Resolved Address',\n",
              " 'Name',\n",
              " 'Info',\n",
              " 'Conditions\\n\"49.0707',\n",
              " ' -121.7133\"',\n",
              " '\"07/05/2003\"',\n",
              " '54.8',\n",
              " '71.2',\n",
              " '62.2',\n",
              " '56.7',\n",
              " '83.42',\n",
              " '',\n",
              " '6.3',\n",
              " '',\n",
              " '215.87',\n",
              " '',\n",
              " '0.02',\n",
              " '4.17',\n",
              " '',\n",
              " '8.7',\n",
              " '10.7',\n",
              " '1018.8',\n",
              " '\"Mist',\n",
              " ' Heavy Rain',\n",
              " ' Light Rain\"',\n",
              " '49.0707',\n",
              " '-121.7133',\n",
              " '\"49.0707',\n",
              " ' -121.7133\"',\n",
              " '\"49.0707',\n",
              " ' -121.7133\"',\n",
              " '\"\"',\n",
              " '\"Rain\"\\n']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gXQCmFm0Npjt",
        "outputId": "ab709e77-fafa-473a-9dc9-8a254ff34a5d"
      },
      "source": [
        "get_future_climate_data(1,32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Address',\n",
              " 'Date time',\n",
              " 'Latitude',\n",
              " 'Longitude',\n",
              " 'Resolved Address',\n",
              " 'Name',\n",
              " 'Wind Direction',\n",
              " 'Minimum Temperature',\n",
              " 'Maximum Temperature',\n",
              " 'Temperature',\n",
              " 'Wind Speed',\n",
              " 'Cloud Cover',\n",
              " 'Heat Index',\n",
              " 'Chance Precipitation (%)',\n",
              " 'Precipitation',\n",
              " 'Sea Level Pressure',\n",
              " 'Snow Depth',\n",
              " 'Snow',\n",
              " 'Relative Humidity',\n",
              " 'Wind Gust',\n",
              " 'Wind Chill',\n",
              " 'Conditions\\n\"1',\n",
              " ' 32\"',\n",
              " '\"05/12/2021\"',\n",
              " '1.0',\n",
              " '32.0',\n",
              " '\"1',\n",
              " ' 32\"',\n",
              " '\"1',\n",
              " ' 32\"',\n",
              " '152.3',\n",
              " '63.4',\n",
              " '74.9',\n",
              " '69.1',\n",
              " '2.7',\n",
              " '65.7',\n",
              " '',\n",
              " '57.0',\n",
              " '0.15',\n",
              " '1014.0',\n",
              " '0.0',\n",
              " '0.0',\n",
              " '89.9',\n",
              " '7.6',\n",
              " '',\n",
              " '\"Rain',\n",
              " ' Partially cloudy\"\\n\"1',\n",
              " ' 32\"',\n",
              " '\"05/13/2021\"',\n",
              " '1.0',\n",
              " '32.0',\n",
              " '\"1',\n",
              " ' 32\"',\n",
              " '\"1',\n",
              " ' 32\"',\n",
              " '178.6',\n",
              " '60.9',\n",
              " '82.8',\n",
              " '68.7',\n",
              " '2.7',\n",
              " '62.7',\n",
              " '83.6',\n",
              " '90.5',\n",
              " '0.19',\n",
              " '1013.3',\n",
              " '0.0',\n",
              " '0.0',\n",
              " '84.2',\n",
              " '19.0',\n",
              " '',\n",
              " '\"Rain',\n",
              " ' Partially cloudy\"\\n\"1',\n",
              " ' 32\"',\n",
              " '\"05/14/2021\"',\n",
              " '1.0',\n",
              " '32.0',\n",
              " '\"1',\n",
              " ' 32\"',\n",
              " '\"1',\n",
              " ' 32\"',\n",
              " '179.7',\n",
              " '63.1',\n",
              " '80.9',\n",
              " '69.3',\n",
              " '1.7',\n",
              " '75.8',\n",
              " '83.2',\n",
              " '76.1',\n",
              " '0.63',\n",
              " '1012.6',\n",
              " '0.0',\n",
              " '0.0',\n",
              " '89.5',\n",
              " '4.7',\n",
              " '',\n",
              " '\"Rain',\n",
              " ' Overcast\"\\n\"1',\n",
              " ' 32\"',\n",
              " '\"05/15/2021\"',\n",
              " '1.0',\n",
              " '32.0',\n",
              " '\"1',\n",
              " ' 32\"',\n",
              " '\"1',\n",
              " ' 32\"',\n",
              " '212.1',\n",
              " '63.6',\n",
              " '82.7',\n",
              " '70.7',\n",
              " '2.7',\n",
              " '62.6',\n",
              " '84.7',\n",
              " '90.5',\n",
              " '0.21',\n",
              " '1011.8',\n",
              " '0.0',\n",
              " '0.0',\n",
              " '85.6',\n",
              " '10.1',\n",
              " '',\n",
              " '\"Rain',\n",
              " ' Partially cloudy\"\\n\"1',\n",
              " ' 32\"',\n",
              " '\"05/16/2021\"',\n",
              " '1.0',\n",
              " '32.0',\n",
              " '\"1',\n",
              " ' 32\"',\n",
              " '\"1',\n",
              " ' 32\"',\n",
              " '205.2',\n",
              " '62.1',\n",
              " '85.7',\n",
              " '70.9',\n",
              " '3.7',\n",
              " '49.7',\n",
              " '86.6',\n",
              " '66.6',\n",
              " '0.17',\n",
              " '1011.5',\n",
              " '0.0',\n",
              " '0.0',\n",
              " '81.7',\n",
              " '17.0',\n",
              " '',\n",
              " '\"Rain',\n",
              " ' Partially cloudy\"\\n\"1',\n",
              " ' 32\"',\n",
              " '\"05/17/2021\"',\n",
              " '1.0',\n",
              " '32.0',\n",
              " '\"1',\n",
              " ' 32\"',\n",
              " '\"1',\n",
              " ' 32\"',\n",
              " '179.7',\n",
              " '62.7',\n",
              " '84.8',\n",
              " '71.8',\n",
              " '2.2',\n",
              " '52.7',\n",
              " '86.1',\n",
              " '81.0',\n",
              " '0.01',\n",
              " '1011.5',\n",
              " '0.0',\n",
              " '0.0',\n",
              " '79.0',\n",
              " '10.3',\n",
              " '',\n",
              " '\"Rain',\n",
              " ' Partially cloudy\"\\n\"1',\n",
              " ' 32\"',\n",
              " '\"05/18/2021\"',\n",
              " '1.0',\n",
              " '32.0',\n",
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              " ' Overcast\"\\n\"1',\n",
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              " '\"05/27/2021\"',\n",
              " '1.0',\n",
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              " '\"1',\n",
              " ' 32\"',\n",
              " '229.3',\n",
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              " '42.9',\n",
              " '0.54',\n",
              " '1014.3',\n",
              " '0.0',\n",
              " '0.0',\n",
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              " '5.4',\n",
              " '',\n",
              " '\"Overcast\"\\n']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-3Qnp8fJ1_6",
        "outputId": "c7efa42c-928b-4f84-eb1f-11b825ea55f4"
      },
      "source": [
        "tomorrow_climate_data = get_future_climate_data(0,0)[0:46]\n",
        "tomorrow_climate_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Address',\n",
              " 'Date time',\n",
              " 'Latitude',\n",
              " 'Longitude',\n",
              " 'Resolved Address',\n",
              " 'Name',\n",
              " 'Wind Direction',\n",
              " 'Minimum Temperature',\n",
              " 'Maximum Temperature',\n",
              " 'Temperature',\n",
              " 'Wind Speed',\n",
              " 'Cloud Cover',\n",
              " 'Heat Index',\n",
              " 'Chance Precipitation (%)',\n",
              " 'Precipitation',\n",
              " 'Sea Level Pressure',\n",
              " 'Snow Depth',\n",
              " 'Snow',\n",
              " 'Relative Humidity',\n",
              " 'Wind Gust',\n",
              " 'Wind Chill',\n",
              " 'Conditions\\n\"0',\n",
              " ' 0\"',\n",
              " '\"05/12/2021\"',\n",
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              " '1011.7',\n",
              " '',\n",
              " '0.0',\n",
              " '76.4',\n",
              " '11.0',\n",
              " '',\n",
              " '\"Clear\"\\n\"0']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tIhnxXQh4XaK"
      },
      "source": [
        "#Reformat fire data to include additional climate data obtained from rapidAPI (For the purpose of training the model)\n",
        "def get_climate_df(df):\n",
        "  total_df = []\n",
        "  date = []\n",
        "  min_tmp = []\n",
        "  max_tmp = []\n",
        "  avg_tmp = []\n",
        "  dew_point = []\n",
        "  relative_humidity = []\n",
        "  heat_index = []\n",
        "  wind_speed = []\n",
        "  wind_gust = []\n",
        "  wind_dir = []\n",
        "  wind_chill = []\n",
        "  precipitation = []\n",
        "  visibility = []\n",
        "  cloud_cover = []\n",
        "  sea_level_pressure = []\n",
        "  weather_type = []\n",
        "  print(\"the lenght of the df passed into this function is\", len(df))\n",
        "  \n",
        "  for i in range (len(df)): \n",
        "    response_list = get_climate_data(df['IGN_DATE'][i], df['LATITUDE'][i], df['LONGITUDE'][i])\n",
        "    \n",
        "    if (len(response_list) < 22):\n",
        "      response_list = ['N/A'] * 52\n",
        "    date.append(response_list[26])  #date, to ensure we are obtaining the right data\n",
        "    min_tmp.append(response_list[27]) #minimum temperature\n",
        "    max_tmp.append(response_list[28]) #max\n",
        "    avg_tmp.append(response_list[29]) #average\n",
        "    dew_point.append(response_list[30]) #dew point\n",
        "    relative_humidity.append(response_list[31]) #relative humidity\n",
        "    heat_index.append(response_list[32]) #heat index\n",
        "    wind_speed.append(response_list[33]) #wind speed\n",
        "    wind_gust.append(response_list[34]) #wind gust\n",
        "    wind_dir.append(response_list[35]) #wind dir\n",
        "    wind_chill.append(response_list[36]) #wind chill\n",
        "    precipitation.append(response_list[37]) #precipitation\n",
        "    visibility.append(response_list[40]) #visibility\n",
        "    cloud_cover.append(response_list[41]) #cloud cover\n",
        "    sea_level_pressure.append(response_list[42]) #sea level pressure\n",
        "    weather_type.append(response_list[43:]) #rest of the response\n",
        "\n",
        "  df['minimum temperature'] = min_tmp\n",
        "  df['maximum temperature'] = max_tmp\n",
        "  df['average temperature'] = avg_tmp\n",
        "  df['dew point'] = dew_point\n",
        "  df['relative humidity'] = relative_humidity\n",
        "  df['heat index'] = heat_index\n",
        "  df['wind speed'] = wind_speed\n",
        "  df['wind gust'] = wind_gust\n",
        "  df['wind direction'] = wind_dir\n",
        "  df['wind chill'] = wind_chill\n",
        "  df['precipitation'] = precipitation\n",
        "  df['visibility'] = visibility\n",
        "  df['cloud cover'] = cloud_cover\n",
        "  df['sea level pressure'] = sea_level_pressure\n",
        "  df['weather_type'] = weather_type"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NuakaaKn9RU6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1dfad2b3-e418-4f95-f746-e5d96c7f0252"
      },
      "source": [
        "#Limit of api with free x-rapidapi-key is 500 requests, since we needed to process 10000 data, we needed to make 20 separate accounts to\n",
        "#get 20 free x-rapidapi-key to process everything. This grueling process is not done only using the two lines below but also through some Excel work\n",
        "\n",
        "#In any case, NEVER RUN THESE TWO LINES OF CODE AGAIN, which is why we are commenting it, as it will exhaust our current working key which is going to be used for the website\n",
        "\n",
        "# get_climate_df(df_yesfire)\n",
        "# get_climate_df(df_nofire)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "the lenght of the df passed into this function is 4982\n",
            "the lenght of the df passed into this function is 5000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUD_Nldo_Zk2"
      },
      "source": [
        "df_yesfire.to_csv('yesfire_complete_data2.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zb5ZizNqoEBc"
      },
      "source": [
        "df_nofire.to_csv('nofire_complete_data2.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XKND_uolMcy"
      },
      "source": [
        "Part 2.1: Manual Dataset Manipulation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfbHv_4elUaR"
      },
      "source": [
        "We combine the two datasets above to a single .csv file. We later observed that only using the randomly generated fires labeled as size 0 produced better results. Hence, the final file used for subsequent sections consist of:\n",
        "  1. 2500 entries from yesfire_complete_data2\n",
        "  2. 2500 entries from nofire_complete_data2 - the *randomly* generated entries only."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gidClxhGARCU"
      },
      "source": [
        "Part 3: Spliting Dataset for Testing and Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8qSt4B-h3u6"
      },
      "source": [
        "import numpy as np, pandas as pd, matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mV6oVOmriBv-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "20e1caf2-f2bf-4d35-c510-7cfe45a69400"
      },
      "source": [
        "#These \"RAW\" urls may require you to get you own \"RAW\" url because they stop working after some time (weird github thing)\n",
        "url = 'https://raw.githubusercontent.com/UBC-CPEN291/project-team-corythosaurus/main/data/test_data_milestone3.csv?token=ANIPG2KIV7PYSU63C2CCHBDATSF32'\n",
        "df_ = pd.read_csv(url, low_memory=False)\n",
        "df_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SIZE_HA</th>\n",
              "      <th>IGN_DATE</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>LONGITUDE</th>\n",
              "      <th>minimum temperature</th>\n",
              "      <th>maximum temperature</th>\n",
              "      <th>average temperature</th>\n",
              "      <th>dew point</th>\n",
              "      <th>relative humidity</th>\n",
              "      <th>heat index</th>\n",
              "      <th>wind speed</th>\n",
              "      <th>wind gust</th>\n",
              "      <th>wind direction</th>\n",
              "      <th>wind chill</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>visibility</th>\n",
              "      <th>cloud cover</th>\n",
              "      <th>sea level pressure</th>\n",
              "      <th>Unnamed: 18</th>\n",
              "      <th>Unnamed: 19</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>20140607.12</td>\n",
              "      <td>49.776300</td>\n",
              "      <td>-123.106700</td>\n",
              "      <td>51.8</td>\n",
              "      <td>72.8</td>\n",
              "      <td>62.5</td>\n",
              "      <td>50.5</td>\n",
              "      <td>67.49</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.2</td>\n",
              "      <td>20.8</td>\n",
              "      <td>193.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.01</td>\n",
              "      <td>633.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1019.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>20130820.11</td>\n",
              "      <td>49.194400</td>\n",
              "      <td>-125.391900</td>\n",
              "      <td>50.3</td>\n",
              "      <td>73.5</td>\n",
              "      <td>62.4</td>\n",
              "      <td>49.1</td>\n",
              "      <td>64.63</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>271.46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>99.0</td>\n",
              "      <td>5.9</td>\n",
              "      <td>1020.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>19920730.00</td>\n",
              "      <td>56.646412</td>\n",
              "      <td>-129.772666</td>\n",
              "      <td>46.5</td>\n",
              "      <td>71.7</td>\n",
              "      <td>60.9</td>\n",
              "      <td>49.6</td>\n",
              "      <td>70.22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>188.57</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>19.5</td>\n",
              "      <td>45.8</td>\n",
              "      <td>1019.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>20170131.00</td>\n",
              "      <td>54.956750</td>\n",
              "      <td>-126.093563</td>\n",
              "      <td>-4.6</td>\n",
              "      <td>23.6</td>\n",
              "      <td>7.9</td>\n",
              "      <td>2.6</td>\n",
              "      <td>79.90</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>235.29</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>0.06</td>\n",
              "      <td>96.2</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1037.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>19781008.00</td>\n",
              "      <td>51.313833</td>\n",
              "      <td>-122.866839</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4993</th>\n",
              "      <td>165.0</td>\n",
              "      <td>20140728.15</td>\n",
              "      <td>50.343400</td>\n",
              "      <td>-126.882200</td>\n",
              "      <td>53.8</td>\n",
              "      <td>66.8</td>\n",
              "      <td>59.0</td>\n",
              "      <td>55.8</td>\n",
              "      <td>89.73</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.4</td>\n",
              "      <td>20.8</td>\n",
              "      <td>111.38</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>32.3</td>\n",
              "      <td>22.4</td>\n",
              "      <td>1020.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4994</th>\n",
              "      <td>2392.0</td>\n",
              "      <td>20180811.16</td>\n",
              "      <td>49.072300</td>\n",
              "      <td>-115.457100</td>\n",
              "      <td>61.5</td>\n",
              "      <td>93.8</td>\n",
              "      <td>78.7</td>\n",
              "      <td>42.3</td>\n",
              "      <td>28.50</td>\n",
              "      <td>90.1</td>\n",
              "      <td>12.2</td>\n",
              "      <td>26.0</td>\n",
              "      <td>182.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>91.8</td>\n",
              "      <td>10.4</td>\n",
              "      <td>1008.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>19226.0</td>\n",
              "      <td>20180717.17</td>\n",
              "      <td>49.084600</td>\n",
              "      <td>-119.869500</td>\n",
              "      <td>69.4</td>\n",
              "      <td>96.9</td>\n",
              "      <td>83.0</td>\n",
              "      <td>44.8</td>\n",
              "      <td>27.67</td>\n",
              "      <td>92.4</td>\n",
              "      <td>9.2</td>\n",
              "      <td>25.3</td>\n",
              "      <td>199.54</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>628.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1011.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>4.0</td>\n",
              "      <td>20030731.16</td>\n",
              "      <td>48.355000</td>\n",
              "      <td>-123.575000</td>\n",
              "      <td>55.0</td>\n",
              "      <td>67.3</td>\n",
              "      <td>60.2</td>\n",
              "      <td>55.8</td>\n",
              "      <td>85.95</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>223.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1014.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>8.0</td>\n",
              "      <td>20010515.15</td>\n",
              "      <td>52.126000</td>\n",
              "      <td>-123.264000</td>\n",
              "      <td>35.3</td>\n",
              "      <td>54.2</td>\n",
              "      <td>44.9</td>\n",
              "      <td>26.5</td>\n",
              "      <td>52.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>175.38</td>\n",
              "      <td>33.1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1006.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4998 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      SIZE_HA     IGN_DATE  ...  Unnamed: 18  Unnamed: 19\n",
              "0         0.0  20140607.12  ...          NaN          NaN\n",
              "1         0.0  20130820.11  ...          NaN          NaN\n",
              "2         0.0  19920730.00  ...          NaN          NaN\n",
              "3         0.0  20170131.00  ...          NaN          NaN\n",
              "4         0.0  19781008.00  ...          NaN          NaN\n",
              "...       ...          ...  ...          ...          ...\n",
              "4993    165.0  20140728.15  ...          NaN          NaN\n",
              "4994   2392.0  20180811.16  ...          NaN          NaN\n",
              "4995  19226.0  20180717.17  ...          NaN          NaN\n",
              "4996      4.0  20030731.16  ...          NaN          NaN\n",
              "4997      8.0  20010515.15  ...          NaN          NaN\n",
              "\n",
              "[4998 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pW_xUG_vnteL"
      },
      "source": [
        "#Cleanup data\n",
        "def cleanup_data(df):\n",
        "  #if it is a string (aka. 'N/A'), fill with nan. See part 1 for details\n",
        "  def to_nan(entry):\n",
        "    if type(entry) is not str: return entry\n",
        "    else: return np.nan\n",
        "  \n",
        "  #fixing the missing data\n",
        "\n",
        "  for column in df.columns:\n",
        "    df[column] = df[column].apply(to_nan)  \n",
        "    avg = df[column].mean()\n",
        "    print(avg)\n",
        "    df[column].fillna(avg, inplace=True)    #Replace with average\n",
        "\n",
        "   #fixing the missing data, end\n",
        "   \n",
        "\n",
        "  df['SIZE_HA'] = df['SIZE_HA'].values + 1 #Because log(0) is bad\n",
        "\n",
        "  df.drop(columns=['Unnamed: 18'], inplace = True)\n",
        "  df.drop(columns=['Unnamed: 19'], inplace = True)\n",
        "\n",
        "  #eliminating outliers\n",
        "  q25, q75 = np.percentile(df['SIZE_HA'], 25), np.percentile(df['SIZE_HA'], 75)\n",
        "  iqr = q75 - q25\n",
        "  cut_off = iqr * 1.5\n",
        "  lower, upper = q25 - cut_off, q75 + cut_off\n",
        "  df= df[df['SIZE_HA'] <= upper]\n",
        "  #eliminating outliers, end\n",
        "\n",
        "\n",
        "  return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0SEnfH8Rsqw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab564861-6be9-49d9-f222-8b718b8ab30d"
      },
      "source": [
        "#Cleanup dataset\n",
        "df_ = cleanup_data(df_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "330.48600520208055\n",
            "20026124.90970577\n",
            "53.40442305249716\n",
            "-122.14423555004001\n",
            "40.58008083140884\n",
            "63.09509237875295\n",
            "52.084209006928425\n",
            "37.187727006444\n",
            "62.41874633860559\n",
            "85.18183962264142\n",
            "12.53463329452857\n",
            "27.2919663351186\n",
            "192.44023689197786\n",
            "27.720631578947398\n",
            "0.06612954545454518\n",
            "76.20172189733584\n",
            "30.337810514153652\n",
            "1015.3026817640034\n",
            "nan\n",
            "nan\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOpmx0SPkX-4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1b45600-cffa-4e7b-a003-e03df62528ff"
      },
      "source": [
        "#Split dataset to train and test sets\n",
        "df_train = df_[df_['IGN_DATE'] < 20150000]\n",
        "df_test = df_[df_['IGN_DATE'] >= 20150000]\n",
        "print (df_train)\n",
        "print (df_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      SIZE_HA     IGN_DATE  ...  cloud cover  sea level pressure\n",
            "0         1.0  20140607.12  ...     0.000000         1019.700000\n",
            "1         1.0  20130820.11  ...     5.900000         1020.700000\n",
            "2         1.0  19920730.00  ...    45.800000         1019.900000\n",
            "4         1.0  19781008.00  ...    30.337811         1015.302682\n",
            "6         1.0  19911202.00  ...    30.337811         1015.302682\n",
            "...       ...          ...  ...          ...                 ...\n",
            "4988      6.1  20100621.18  ...    93.300000         1015.900000\n",
            "4990      8.4  20040624.16  ...    18.100000         1015.800000\n",
            "4992     13.0  20060723.23  ...     7.700000         1010.800000\n",
            "4996      5.0  20030731.16  ...     0.000000         1014.200000\n",
            "4997      9.0  20010515.15  ...     0.000000         1006.200000\n",
            "\n",
            "[3406 rows x 18 columns]\n",
            "      SIZE_HA     IGN_DATE  ...  cloud cover  sea level pressure\n",
            "3         1.0  20170131.00  ...     3.100000         1037.900000\n",
            "5         1.0  20170525.00  ...    49.000000         1007.500000\n",
            "16        1.0  20170228.00  ...    39.700000         1020.900000\n",
            "20        1.0  20170815.00  ...     0.000000         1016.400000\n",
            "25        1.0  20181212.00  ...    30.337811         1015.302682\n",
            "...       ...          ...  ...          ...                 ...\n",
            "4968      7.0  20150827.07  ...     1.900000         1012.900000\n",
            "4977      4.2  20150720.00  ...     9.800000         1007.800000\n",
            "4978      3.0  20180716.19  ...     0.000000         1013.800000\n",
            "4982      6.9  20150610.12  ...     0.000000         1015.000000\n",
            "4984      4.0  20180502.06  ...     0.200000         1016.600000\n",
            "\n",
            "[718 rows x 18 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGcnjG8-xCiH"
      },
      "source": [
        "#Split both train and test datasets into inputs and outputs\n",
        "sizeHA_train = np.log(df_train['SIZE_HA'].values)\n",
        "sizeHA_test = np.log(df_test['SIZE_HA'].values)\n",
        "feats_train = df_train.drop(columns=['SIZE_HA'])\n",
        "feats_test = df_test.drop(columns=['SIZE_HA'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ypGcSek2hz2Q"
      },
      "source": [
        "Part 4: Random Forests"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJUlBnHwf9UN",
        "outputId": "8c4cc452-7296-4488-a436-7aab0095a208"
      },
      "source": [
        "!pip install scikit-learn==0.24.1\n",
        "import datetime, re, math\n",
        "import numpy as np, pandas as pd, matplotlib.pyplot as plt\n",
        "from numpy import std\n",
        "import sklearn, sklearn.tree, sklearn.ensemble, sklearn.feature_extraction, sklearn.metrics"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting scikit-learn==0.24.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f3/74/eb899f41d55f957e2591cde5528e75871f817d9fb46d4732423ecaca736d/scikit_learn-0.24.1-cp37-cp37m-manylinux2010_x86_64.whl (22.3MB)\n",
            "\u001b[K     |████████████████████████████████| 22.3MB 4.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.24.1) (1.0.1)\n",
            "Collecting threadpoolctl>=2.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/f7/12/ec3f2e203afa394a149911729357aa48affc59c20e2c1c8297a60f33f133/threadpoolctl-2.1.0-py3-none-any.whl\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.24.1) (1.19.5)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.24.1) (1.4.1)\n",
            "Installing collected packages: threadpoolctl, scikit-learn\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "Successfully installed scikit-learn-0.24.1 threadpoolctl-2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhPapxtQzJzA"
      },
      "source": [
        "#Getting features\n",
        "dvr = sklearn.feature_extraction.DictVectorizer()\n",
        "enc_feats_train = dvr.fit_transform(feats_train.T.to_dict().values())\n",
        "enc_feats_test = dvr.transform(feats_test.T.to_dict().values())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2PNhJVh41OBm",
        "outputId": "cc3fa73f-b0e9-4e7c-db9a-fc7882a242f2"
      },
      "source": [
        "dvr.get_feature_names()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['IGN_DATE',\n",
              " 'LATITUDE',\n",
              " 'LONGITUDE',\n",
              " 'average temperature',\n",
              " 'cloud cover',\n",
              " 'dew point',\n",
              " 'heat index',\n",
              " 'maximum temperature',\n",
              " 'minimum temperature',\n",
              " 'precipitation',\n",
              " 'relative humidity',\n",
              " 'sea level pressure',\n",
              " 'visibility',\n",
              " 'wind chill',\n",
              " 'wind direction',\n",
              " 'wind gust',\n",
              " 'wind speed']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EghStjt2X-u"
      },
      "source": [
        "#To evaluate how good RF model is \n",
        "def eval_model(model):\n",
        "    preds_train = model.predict(enc_feats_train)\n",
        "    preds_test = model.predict(enc_feats_test)\n",
        "    for i in range (len(preds_test)):\n",
        "      print(f\"the labels: {sizeHA_test[i]}: the prediction {preds_test[i]}\")\n",
        "    tbl = [[sklearn.metrics.mean_absolute_error(*vecs),\n",
        "            sklearn.metrics.median_absolute_error(*vecs),\n",
        "            math.sqrt(sklearn.metrics.mean_squared_error(*vecs))]\n",
        "           for vecs in [(sizeHA_train, preds_train), (sizeHA_test, preds_test)]]\n",
        "    df = pd.DataFrame(tbl, columns=['MAE','MdAE', 'RMSE'], index=['training', 'testing'])\n",
        "    return df.T.round(2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2fbmD040oPa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d61c6488-a492-4aeb-a79c-fa92a54f7620"
      },
      "source": [
        "# Random forests!!!\n",
        "rf = sklearn.ensemble.RandomForestRegressor(n_estimators=80, max_samples=3406, max_features=0.7, min_samples_leaf=4, random_state=291, n_jobs=-1)\n",
        "rf.fit(enc_feats_train, sizeHA_train)\n",
        "# Testing Random Forest\n",
        "eval_model(rf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "the labels: 0.0: the prediction 0.23997708619427294\n",
            "the labels: 0.0: the prediction 1.8812911752300774\n",
            "the labels: 0.0: the prediction 0.24498561887859632\n",
            "the labels: 0.0: the prediction 0.5330358457990255\n",
            "the labels: 0.0: the prediction 1.1106028984859613\n",
            "the labels: 0.0: the prediction 1.214884089641989\n",
            "the labels: 0.0: the prediction 1.8322745267787997\n",
            "the labels: 0.0: the prediction 0.04143006544828498\n",
            "the labels: 0.0: the prediction 1.3023289549816988\n",
            "the labels: 0.0: the prediction 0.9823263073091228\n",
            "the labels: 0.0: the prediction 1.0338428404674862\n",
            "the labels: 0.0: the prediction 0.7674760381332186\n",
            "the labels: 0.0: the prediction 0.14933955779457658\n",
            "the labels: 0.0: the prediction 1.43211192002276\n",
            "the labels: 0.0: the prediction 1.012474401527372\n",
            "the labels: 0.0: the prediction 0.1750600858299724\n",
            "the labels: 0.0: the prediction 1.3603556447141005\n",
            "the labels: 0.0: the prediction 0.8579518801871219\n",
            "the labels: 0.0: the prediction 0.25676819553908575\n",
            "the labels: 0.0: the prediction 1.2830971207834125\n",
            "the labels: 0.0: the prediction 1.0070424564402651\n",
            "the labels: 0.0: the prediction 1.1404542103481337\n",
            "the labels: 0.0: the prediction 0.6236040276445461\n",
            "the labels: 0.0: the prediction 0.7299361266350036\n",
            "the labels: 0.0: the prediction 0.9370808618264757\n",
            "the labels: 0.0: the prediction 0.22354093626846566\n",
            "the labels: 0.0: the prediction 0.9328788265242854\n",
            "the labels: 0.0: the prediction 1.4067070733627687\n",
            "the labels: 0.0: the prediction 0.20043447327146163\n",
            "the labels: 0.0: the prediction 0.8524717860156772\n",
            "the labels: 0.0: the prediction 0.560992610113429\n",
            "the labels: 0.0: the prediction 0.07388055900372488\n",
            "the labels: 0.0: the prediction 0.4345699879671009\n",
            "the labels: 0.0: the prediction 0.8030838212367832\n",
            "the labels: 0.0: the prediction 0.22572374868616044\n",
            "the labels: 0.0: the prediction 0.8163040132642166\n",
            "the labels: 0.0: the prediction 0.6256832904437022\n",
            "the labels: 0.0: the prediction 1.1725058910618782\n",
            "the labels: 0.0: the prediction 0.5877950954204928\n",
            "the labels: 0.0: the prediction 0.4588577885978973\n",
            "the labels: 0.0: the prediction 0.7201567828634408\n",
            "the labels: 0.0: the prediction 0.3055928481513819\n",
            "the labels: 0.0: the prediction 1.0153689804696313\n",
            "the labels: 0.0: the prediction 0.07041482310092514\n",
            "the labels: 0.0: the prediction 0.34324259311683514\n",
            "the labels: 0.0: the prediction 0.3262391740856322\n",
            "the labels: 0.0: the prediction 0.5353225820198365\n",
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            "the labels: 2.0918640616783932: the prediction 1.8137936028465418\n",
            "the labels: 3.044522437723423: the prediction 1.3030034258449106\n",
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            "the labels: 1.8870696490323797: the prediction 1.5477423260322545\n",
            "the labels: 1.916922612182061: the prediction 1.938526481383299\n",
            "the labels: 2.8273136219290276: the prediction 1.3964775792261321\n",
            "the labels: 2.6741486494265287: the prediction 1.9243792291854074\n",
            "the labels: 2.4423470353692043: the prediction 1.7502146719272371\n",
            "the labels: 3.091042453358316: the prediction 1.5247028621466137\n",
            "the labels: 1.6094379124341003: the prediction 2.006196493938255\n",
            "the labels: 3.258096538021482: the prediction 0.9286132764579456\n",
            "the labels: 1.9459101490553132: the prediction 1.8405945015581537\n",
            "the labels: 1.3609765531356006: the prediction 1.7717996739438746\n",
            "the labels: 1.9459101490553132: the prediction 1.9350866050906745\n",
            "the labels: 1.4350845252893227: the prediction 2.15909737437594\n",
            "the labels: 1.0986122886681098: the prediction 2.145935631999474\n",
            "the labels: 1.9315214116032138: the prediction 1.3929637497597136\n",
            "the labels: 1.3862943611198906: the prediction 1.7205249454864304\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>training</th>\n",
              "      <th>testing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MAE</th>\n",
              "      <td>0.22</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MdAE</th>\n",
              "      <td>0.05</td>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RMSE</th>\n",
              "      <td>0.39</td>\n",
              "      <td>0.89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      training  testing\n",
              "MAE       0.22     0.70\n",
              "MdAE      0.05     0.59\n",
              "RMSE      0.39     0.89"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUyFsx-_pNnN"
      },
      "source": [
        "Part 5: Multiple Regression Model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u44Za7YHpcL5"
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import pandas as pd\n",
        "from sklearn import linear_model\n",
        "import math\n",
        "from scipy.stats import zscore\n",
        "from sklearn.model_selection import train_test_split\n",
        "from matplotlib import pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zR0mRRTQ8Lu3",
        "outputId": "76090ba4-7cf0-448b-c5cf-d0c765fa4eba"
      },
      "source": [
        "#This is a multiple Regression Model\n",
        "model = linear_model.LinearRegression()\n",
        "model.fit(feats_train, sizeHA_train)\n",
        "\n",
        "df_test = pd.DataFrame(sizeHA_test)\n",
        "print(df_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            0\n",
            "0    0.000000\n",
            "1    0.000000\n",
            "2    0.000000\n",
            "3    0.000000\n",
            "4    0.000000\n",
            "..        ...\n",
            "713  1.945910\n",
            "714  1.435085\n",
            "715  1.098612\n",
            "716  1.931521\n",
            "717  1.386294\n",
            "\n",
            "[718 rows x 1 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cs1r5NuJg36K"
      },
      "source": [
        "# Testing Multiple Regression Model\n",
        "\n",
        "def eval_lr(model):\n",
        "  preds_train = model.predict(feats_train)\n",
        "  preds_test = model.predict(feats_test) \n",
        "  tbl = [[sklearn.metrics.mean_absolute_error(*vecs),\n",
        "            sklearn.metrics.median_absolute_error(*vecs),\n",
        "            math.sqrt(sklearn.metrics.mean_squared_error(*vecs))]\n",
        "           for vecs in [(sizeHA_train, preds_train), (sizeHA_test, preds_test)]]\n",
        "  df = pd.DataFrame(tbl, columns=['MAE','MdAE', 'RMSE'], index=['training', 'testing'])\n",
        "  return df.T.round(2)\n",
        "#Computing the loss for half the data in the dataset"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "id": "HNw4Hbmt3IgR",
        "outputId": "306ca21f-3ef6-4ea3-eeb3-30bb8c5d0efd"
      },
      "source": [
        "eval_lr(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>training</th>\n",
              "      <th>testing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MAE</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MdAE</th>\n",
              "      <td>0.44</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RMSE</th>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      training  testing\n",
              "MAE       0.57     0.85\n",
              "MdAE      0.44     0.82\n",
              "RMSE      0.75     1.00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UFbvpI-qiEim"
      },
      "source": [
        "Part 6: the ensemble"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9eF7a1rcbdj"
      },
      "source": [
        "def eval_ensemble(rf_model, mlp_model, entries, display_ensemble_loss):\n",
        "  #evaluate the performance of the ensemble\n",
        "  #entries must be in the form of: IGN_DATE\tLATITUDE\tLONGITUDE\tminimum temperature\tmaximum temperature\taverage temperature\tdew point\trelative humidity\theat index\twind speed\twind gust\twind direction\twind chill\tprecipitation\tvisibility\tcloud cover\tsea level pressure\n",
        "  rf_entries = dvr.fit_transform(entries.T.to_dict().values()) \n",
        "  rf_preds = rf_model.predict(rf_entries)\n",
        "  mlp_preds = mlp_model.predict(entries)\n",
        "  combined_preds = [] #return predicted size.\n",
        "  \n",
        "  for i in range (len(entries)):\n",
        "    combined_prediction = (rf_preds[i] + mlp_preds[i]) / 2.0            \n",
        "    combined_preds.append(combined_prediction)\n",
        "  \n",
        "  for i in range (len(entries)):\n",
        "    print(\"[\" , i , \"]: size_label:\", sizeHA_test[i], \" rf_preds \", rf_preds[i], \"mlp_preds: \", mlp_preds[i], \" combined_preds: \", combined_preds[i])\n",
        "\n",
        "  if (display_ensemble_loss):\n",
        "    tbl = [[sklearn.metrics.mean_absolute_error(*vecs),\n",
        "            sklearn.metrics.median_absolute_error(*vecs),\n",
        "            math.sqrt(sklearn.metrics.mean_squared_error(*vecs))]\n",
        "           for vecs in [(sizeHA_test, combined_preds)]]                         \n",
        "    df = pd.DataFrame(tbl, columns=['MAE','MdAE', 'RMSE'], index=['testing'])\n",
        "    return df.T.round(2)\n",
        "\n",
        "  return combined_preds \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3IB5ufsiyMf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ef99b16-78f1-4348-90a7-0eff76bfb50f"
      },
      "source": [
        "final_preds = eval_ensemble(rf, model, feats_test, 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0 ]: size_label: 0.0  rf_preds  0.23997708619427294 mlp_preds:  0.87683074252773  combined_preds:  0.5584039143610015\n",
            "[ 1 ]: size_label: 0.0  rf_preds  1.8812911752300774 mlp_preds:  1.6264277192288574  combined_preds:  1.7538594472294675\n",
            "[ 2 ]: size_label: 0.0  rf_preds  0.24498561887859632 mlp_preds:  0.9739518154141393  combined_preds:  0.6094687171463679\n",
            "[ 3 ]: size_label: 0.0  rf_preds  0.5330358457990255 mlp_preds:  0.23316299389217932  combined_preds:  0.3830994198456024\n",
            "[ 4 ]: size_label: 0.0  rf_preds  1.1106028984859613 mlp_preds:  1.207563262957109  combined_preds:  1.159083080721535\n",
            "[ 5 ]: size_label: 0.0  rf_preds  1.214884089641989 mlp_preds:  1.3042359450069654  combined_preds:  1.2595600173244772\n",
            "[ 6 ]: size_label: 0.0  rf_preds  1.8322745267787997 mlp_preds:  1.462928820594442  combined_preds:  1.6476016736866208\n",
            "[ 7 ]: size_label: 0.0  rf_preds  0.04143006544828498 mlp_preds:  1.1386176573889628  combined_preds:  0.590023861418624\n",
            "[ 8 ]: size_label: 0.0  rf_preds  1.3023289549816988 mlp_preds:  1.2687383318317558  combined_preds:  1.2855336434067273\n",
            "[ 9 ]: size_label: 0.0  rf_preds  0.9823263073091228 mlp_preds:  0.8182840899371797  combined_preds:  0.9003051986231513\n",
            "[ 10 ]: size_label: 0.0  rf_preds  1.0338428404674862 mlp_preds:  0.8123550644050823  combined_preds:  0.9230989524362843\n",
            "[ 11 ]: size_label: 0.0  rf_preds  0.7674760381332186 mlp_preds:  1.1431963544054469  combined_preds:  0.9553361962693327\n",
            "[ 12 ]: size_label: 0.0  rf_preds  0.14933955779457658 mlp_preds:  0.8923962840900685  combined_preds:  0.5208679209423226\n",
            "[ 13 ]: size_label: 0.0  rf_preds  1.43211192002276 mlp_preds:  1.056648432065117  combined_preds:  1.2443801760439386\n",
            "[ 14 ]: size_label: 0.0  rf_preds  1.012474401527372 mlp_preds:  0.799222519797965  combined_preds:  0.9058484606626684\n",
            "[ 15 ]: size_label: 0.0  rf_preds  0.1750600858299724 mlp_preds:  0.5630111920246605  combined_preds:  0.3690356389273165\n",
            "[ 16 ]: size_label: 0.0  rf_preds  1.3603556447141005 mlp_preds:  1.1352621180516138  combined_preds:  1.2478088813828572\n",
            "[ 17 ]: size_label: 0.0  rf_preds  0.8579518801871219 mlp_preds:  1.0980100824597514  combined_preds:  0.9779809813234366\n",
            "[ 18 ]: size_label: 0.0  rf_preds  0.25676819553908575 mlp_preds:  0.5350506943120337  combined_preds:  0.3959094449255597\n",
            "[ 19 ]: size_label: 0.0  rf_preds  1.2830971207834125 mlp_preds:  0.6199289436836608  combined_preds:  0.9515130322335367\n",
            "[ 20 ]: size_label: 0.0  rf_preds  1.0070424564402651 mlp_preds:  1.3182245118705396  combined_preds:  1.1626334841554025\n",
            "[ 21 ]: size_label: 0.0  rf_preds  1.1404542103481337 mlp_preds:  1.5130652112920444  combined_preds:  1.326759710820089\n",
            "[ 22 ]: size_label: 0.0  rf_preds  0.6236040276445461 mlp_preds:  0.5299291223919624  combined_preds:  0.5767665750182542\n",
            "[ 23 ]: size_label: 0.0  rf_preds  0.7299361266350036 mlp_preds:  0.6107267215175369  combined_preds:  0.6703314240762703\n",
            "[ 24 ]: size_label: 0.0  rf_preds  0.9370808618264755 mlp_preds:  0.5138847579974026  combined_preds:  0.725482809911939\n",
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            "[ 619 ]: size_label: 1.62924053973028  rf_preds  0.5676263644084447 mlp_preds:  1.4515245186664174  combined_preds:  1.009575441537431\n",
            "[ 620 ]: size_label: 2.0412203288596382  rf_preds  2.236505112921516 mlp_preds:  2.391898154165972  combined_preds:  2.314201633543744\n",
            "[ 621 ]: size_label: 1.791759469228055  rf_preds  1.25831104095109 mlp_preds:  1.2865980338466727  combined_preds:  1.2724545373988814\n",
            "[ 622 ]: size_label: 1.0986122886681098  rf_preds  2.092224660371265 mlp_preds:  2.437581883312248  combined_preds:  2.2649032718417565\n",
            "[ 623 ]: size_label: 3.1780538303479458  rf_preds  2.067475695411326 mlp_preds:  2.132208017646718  combined_preds:  2.099841856529022\n",
            "[ 624 ]: size_label: 2.9014215940827497  rf_preds  0.8981144953123736 mlp_preds:  0.8512670898642511  combined_preds:  0.8746907925883124\n",
            "[ 625 ]: size_label: 1.6094379124341003  rf_preds  2.0090456048634016 mlp_preds:  1.645862075933863  combined_preds:  1.8274538403986322\n",
            "[ 626 ]: size_label: 1.9459101490553132  rf_preds  1.4433041151119088 mlp_preds:  1.7880403322361644  combined_preds:  1.6156722236740366\n",
            "[ 627 ]: size_label: 1.0986122886681098  rf_preds  1.3040329768552603 mlp_preds:  1.325877754444619  combined_preds:  1.3149553656499395\n",
            "[ 628 ]: size_label: 1.9459101490553132  rf_preds  1.576751658150012 mlp_preds:  1.3779683233917126  combined_preds:  1.4773599907708623\n",
            "[ 629 ]: size_label: 2.1400661634962708  rf_preds  1.037576142727366 mlp_preds:  1.4564131785202505  combined_preds:  1.2469946606238083\n",
            "[ 630 ]: size_label: 2.1972245773362196  rf_preds  1.8218400321545665 mlp_preds:  1.65003892544442  combined_preds:  1.7359394787994933\n",
            "[ 631 ]: size_label: 2.3978952727983707  rf_preds  1.6822245641631093 mlp_preds:  1.356082374641744  combined_preds:  1.5191534694024267\n",
            "[ 632 ]: size_label: 2.1972245773362196  rf_preds  1.8409575823452493 mlp_preds:  1.4495338711340366  combined_preds:  1.645245726739643\n",
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            "[ 634 ]: size_label: 1.252762968495368  rf_preds  1.985733391527448 mlp_preds:  1.368377559481388  combined_preds:  1.677055475504418\n",
            "[ 635 ]: size_label: 2.2300144001592104  rf_preds  0.7832472206538195 mlp_preds:  1.1230842817333127  combined_preds:  0.9531657511935661\n",
            "[ 636 ]: size_label: 1.1314021114911006  rf_preds  1.9522837694497883 mlp_preds:  1.9959164672658574  combined_preds:  1.9741001183578228\n",
            "[ 637 ]: size_label: 1.9459101490553132  rf_preds  1.883782968730889 mlp_preds:  2.1676537909820723  combined_preds:  2.025718379856481\n",
            "[ 638 ]: size_label: 1.9021075263969205  rf_preds  2.1289188433073485 mlp_preds:  2.1959545534578666  combined_preds:  2.1624366983826073\n",
            "[ 639 ]: size_label: 2.772588722239781  rf_preds  1.6765744371796774 mlp_preds:  1.6114604170990958  combined_preds:  1.6440174271393866\n",
            "[ 640 ]: size_label: 1.7047480922384253  rf_preds  0.4762592820267127 mlp_preds:  0.6905434778278448  combined_preds:  0.5834013799272787\n",
            "[ 641 ]: size_label: 1.1631508098056809  rf_preds  2.011566489766774 mlp_preds:  1.5861574478238936  combined_preds:  1.7988619687953338\n",
            "[ 642 ]: size_label: 1.3609765531356006  rf_preds  1.760785723019636 mlp_preds:  1.6621639566999278  combined_preds:  1.7114748398597819\n",
            "[ 643 ]: size_label: 1.1314021114911006  rf_preds  1.1424253740564427 mlp_preds:  1.3554501699262573  combined_preds:  1.2489377719913501\n",
            "[ 644 ]: size_label: 1.3862943611198906  rf_preds  1.42341550576295 mlp_preds:  1.805177844396539  combined_preds:  1.6142966750797445\n",
            "[ 645 ]: size_label: 1.1314021114911006  rf_preds  0.9555679132242784 mlp_preds:  0.9724312104356301  combined_preds:  0.9639995618299542\n",
            "[ 646 ]: size_label: 1.0986122886681098  rf_preds  1.1109380557580484 mlp_preds:  1.60165645045727  combined_preds:  1.3562972531076591\n",
            "[ 647 ]: size_label: 2.341805806147327  rf_preds  1.9627635305796585 mlp_preds:  1.6018667558333277  combined_preds:  1.782315143206493\n",
            "[ 648 ]: size_label: 2.9444389791664403  rf_preds  1.1979705262842735 mlp_preds:  1.6299216604069358  combined_preds:  1.4139460933456047\n",
            "[ 649 ]: size_label: 2.5494451709255714  rf_preds  1.1322944786760414 mlp_preds:  1.23958304781506  combined_preds:  1.1859387632455507\n",
            "[ 650 ]: size_label: 1.0986122886681098  rf_preds  1.8954259604977022 mlp_preds:  1.9031743559534675  combined_preds:  1.8993001582255848\n",
            "[ 651 ]: size_label: 1.7047480922384253  rf_preds  1.2511419313273633 mlp_preds:  0.9071204048658785  combined_preds:  1.079131168096621\n",
            "[ 652 ]: size_label: 1.3862943611198906  rf_preds  2.2990300135545025 mlp_preds:  2.3760173696114038  combined_preds:  2.337523691582953\n",
            "[ 653 ]: size_label: 3.044522437723423  rf_preds  1.4419406175487233 mlp_preds:  1.3299716218084114  combined_preds:  1.3859561196785672\n",
            "[ 654 ]: size_label: 2.302585092994046  rf_preds  1.6907779506182066 mlp_preds:  1.4278109056290162  combined_preds:  1.5592944281236114\n",
            "[ 655 ]: size_label: 2.451005098112319  rf_preds  1.751596787191725 mlp_preds:  1.2593838266386825  combined_preds:  1.505490306915204\n",
            "[ 656 ]: size_label: 2.928523523860541  rf_preds  2.33785278729074 mlp_preds:  2.301790800314805  combined_preds:  2.3198217938027725\n",
            "[ 657 ]: size_label: 1.0986122886681098  rf_preds  1.8620433743468034 mlp_preds:  2.1844135046792275  combined_preds:  2.0232284395130153\n",
            "[ 658 ]: size_label: 2.995732273553991  rf_preds  1.7068378766391075 mlp_preds:  1.8642555768780227  combined_preds:  1.785546726758565\n",
            "[ 659 ]: size_label: 3.044522437723423  rf_preds  1.5058875064008164 mlp_preds:  1.1656157110286074  combined_preds:  1.3357516087147119\n",
            "[ 660 ]: size_label: 1.891604804197771  rf_preds  1.6616140397827963 mlp_preds:  1.4606225772320158  combined_preds:  1.561118308507406\n",
            "[ 661 ]: size_label: 2.766319109226186  rf_preds  1.9075468747516453 mlp_preds:  1.3480298454856836  combined_preds:  1.6277883601186645\n",
            "[ 662 ]: size_label: 2.6390573296152584  rf_preds  1.0114917974279667 mlp_preds:  1.3662319802686795  combined_preds:  1.1888618888483231\n",
            "[ 663 ]: size_label: 1.791759469228055  rf_preds  1.6714718115151475 mlp_preds:  1.685962931597004  combined_preds:  1.6787173715560757\n",
            "[ 664 ]: size_label: 3.258096538021482  rf_preds  1.7671247007077373 mlp_preds:  1.4981984214791169  combined_preds:  1.632661561093427\n",
            "[ 665 ]: size_label: 2.272125885509337  rf_preds  1.795282187074638 mlp_preds:  0.8386893149818206  combined_preds:  1.3169857510282292\n",
            "[ 666 ]: size_label: 2.6741486494265287  rf_preds  1.5166771623488406 mlp_preds:  1.1384300475742748  combined_preds:  1.3275536049615577\n",
            "[ 667 ]: size_label: 3.044522437723423  rf_preds  0.3262391740856321 mlp_preds:  1.2028810466456434  combined_preds:  0.7645601103656378\n",
            "[ 668 ]: size_label: 1.6094379124341003  rf_preds  1.4643938489665753 mlp_preds:  1.6259949070947783  combined_preds:  1.545194378030677\n",
            "[ 669 ]: size_label: 1.0986122886681098  rf_preds  0.9011891371793679 mlp_preds:  1.1460528820267086  combined_preds:  1.0236210096030383\n",
            "[ 670 ]: size_label: 2.856470206220483  rf_preds  1.8743213879503209 mlp_preds:  1.5382145504831612  combined_preds:  1.706267969216741\n",
            "[ 671 ]: size_label: 1.9459101490553132  rf_preds  1.5686188452004828 mlp_preds:  1.3426183701235175  combined_preds:  1.4556186076620001\n",
            "[ 672 ]: size_label: 2.681021528714291  rf_preds  1.6616140397827963 mlp_preds:  1.4613670546229258  combined_preds:  1.561490547202861\n",
            "[ 673 ]: size_label: 1.0986122886681098  rf_preds  1.804616631172826 mlp_preds:  1.7294233129441636  combined_preds:  1.767019972058495\n",
            "[ 674 ]: size_label: 2.379546134130174  rf_preds  1.841769381037776 mlp_preds:  1.4366731670096584  combined_preds:  1.639221274023717\n",
            "[ 675 ]: size_label: 1.3862943611198906  rf_preds  1.4081064374409766 mlp_preds:  1.25124064914273  combined_preds:  1.3296735432918534\n",
            "[ 676 ]: size_label: 1.6094379124341003  rf_preds  1.820294199981051 mlp_preds:  1.927774879792338  combined_preds:  1.8740345398866944\n",
            "[ 677 ]: size_label: 2.248128907197987  rf_preds  1.4706428015254052 mlp_preds:  1.4635722131642837  combined_preds:  1.4671075073448443\n",
            "[ 678 ]: size_label: 2.9231615807191558  rf_preds  1.9945227932132397 mlp_preds:  2.0353463312316364  combined_preds:  2.014934562222438\n",
            "[ 679 ]: size_label: 2.9444389791664403  rf_preds  1.9187364648841871 mlp_preds:  1.9708915849327724  combined_preds:  1.9448140249084798\n",
            "[ 680 ]: size_label: 1.1314021114911006  rf_preds  0.7844564911000604 mlp_preds:  0.9563208023650986  combined_preds:  0.8703886467325795\n",
            "[ 681 ]: size_label: 1.0986122886681098  rf_preds  1.8781809681815465 mlp_preds:  1.8194044897640396  combined_preds:  1.848792728972793\n",
            "[ 682 ]: size_label: 1.6863989535702288  rf_preds  1.864301506991716 mlp_preds:  1.3612294082097236  combined_preds:  1.6127654576007198\n",
            "[ 683 ]: size_label: 1.3862943611198906  rf_preds  1.4045578692232341 mlp_preds:  0.6074933170588253  combined_preds:  1.0060255931410298\n",
            "[ 684 ]: size_label: 2.5649493574615367  rf_preds  1.384258464796925 mlp_preds:  1.4580925831042748  combined_preds:  1.4211755239505999\n",
            "[ 685 ]: size_label: 1.7227665977411035  rf_preds  1.6380450643026887 mlp_preds:  1.4233650235273245  combined_preds:  1.5307050439150065\n",
            "[ 686 ]: size_label: 1.0986122886681098  rf_preds  2.021668271819343 mlp_preds:  1.6862951736286647  combined_preds:  1.8539817227240039\n",
            "[ 687 ]: size_label: 3.1780538303479458  rf_preds  0.7606035399623454 mlp_preds:  0.6467183972356167  combined_preds:  0.7036609685989811\n",
            "[ 688 ]: size_label: 1.252762968495368  rf_preds  1.3749752532946489 mlp_preds:  1.1824130208449048  combined_preds:  1.2786941370697769\n",
            "[ 689 ]: size_label: 1.1939224684724346  rf_preds  1.2148168545123588 mlp_preds:  1.2277646687948192  combined_preds:  1.2212907616535889\n",
            "[ 690 ]: size_label: 1.62924053973028  rf_preds  1.6690220189895295 mlp_preds:  1.6178197151394897  combined_preds:  1.6434208670645096\n",
            "[ 691 ]: size_label: 1.1052568313867783  rf_preds  1.6090835341544687 mlp_preds:  1.4639918794015188  combined_preds:  1.5365377067779937\n",
            "[ 692 ]: size_label: 3.044522437723423  rf_preds  1.3248401682942152 mlp_preds:  1.2484889158081103  combined_preds:  1.2866645420511627\n",
            "[ 693 ]: size_label: 1.1249295969854831  rf_preds  1.9111347240830068 mlp_preds:  2.2589497065428503  combined_preds:  2.0850422153129284\n",
            "[ 694 ]: size_label: 1.1631508098056809  rf_preds  0.9107694946221342 mlp_preds:  1.2884326231234127  combined_preds:  1.0996010588727734\n",
            "[ 695 ]: size_label: 1.308332819650179  rf_preds  2.053106937937334 mlp_preds:  1.6713550036281504  combined_preds:  1.8622309707827422\n",
            "[ 696 ]: size_label: 2.0918640616783932  rf_preds  1.8137936028465418 mlp_preds:  1.6704573344185007  combined_preds:  1.7421254686325214\n",
            "[ 697 ]: size_label: 3.044522437723423  rf_preds  1.3030034258449106 mlp_preds:  1.2470057529838385  combined_preds:  1.2750045894143747\n",
            "[ 698 ]: size_label: 1.62924053973028  rf_preds  1.1189643107267893 mlp_preds:  1.2263077204434012  combined_preds:  1.1726360155850952\n",
            "[ 699 ]: size_label: 1.7047480922384253  rf_preds  1.7195255451915827 mlp_preds:  1.5254263170179883  combined_preds:  1.6224759311047854\n",
            "[ 700 ]: size_label: 2.0794415416798357  rf_preds  1.885422043720999 mlp_preds:  1.4335350950709937  combined_preds:  1.6594785693959964\n",
            "[ 701 ]: size_label: 1.1939224684724346  rf_preds  1.6646087887568288 mlp_preds:  1.1542224813349264  combined_preds:  1.4094156350458777\n",
            "[ 702 ]: size_label: 1.7404661748405046  rf_preds  1.171146222539694 mlp_preds:  1.1226353411026224  combined_preds:  1.1468907818211582\n",
            "[ 703 ]: size_label: 1.8870696490323797  rf_preds  1.5477423260322545 mlp_preds:  1.2692996647629116  combined_preds:  1.408520995397583\n",
            "[ 704 ]: size_label: 1.916922612182061  rf_preds  1.938526481383299 mlp_preds:  1.6215503814562027  combined_preds:  1.7800384314197508\n",
            "[ 705 ]: size_label: 2.8273136219290276  rf_preds  1.3964775792261321 mlp_preds:  0.9617243198827907  combined_preds:  1.1791009495544613\n",
            "[ 706 ]: size_label: 2.6741486494265287  rf_preds  1.9243792291854074 mlp_preds:  1.6800414149892902  combined_preds:  1.8022103220873489\n",
            "[ 707 ]: size_label: 2.4423470353692043  rf_preds  1.7502146719272371 mlp_preds:  1.3589982975137929  combined_preds:  1.554606484720515\n",
            "[ 708 ]: size_label: 3.091042453358316  rf_preds  1.5247028621466139 mlp_preds:  1.6332500275705968  combined_preds:  1.5789764448586054\n",
            "[ 709 ]: size_label: 1.6094379124341003  rf_preds  2.0061964939382553 mlp_preds:  1.62213209395874  combined_preds:  1.8141642939484977\n",
            "[ 710 ]: size_label: 3.258096538021482  rf_preds  0.9286132764579456 mlp_preds:  1.394456300169736  combined_preds:  1.161534788313841\n",
            "[ 711 ]: size_label: 1.9459101490553132  rf_preds  1.8405945015581537 mlp_preds:  1.4401883149784425  combined_preds:  1.640391408268298\n",
            "[ 712 ]: size_label: 1.3609765531356006  rf_preds  1.7717996739438746 mlp_preds:  1.9043631200790117  combined_preds:  1.8380813970114431\n",
            "[ 713 ]: size_label: 1.9459101490553132  rf_preds  1.9350866050906745 mlp_preds:  2.0889474670139805  combined_preds:  2.0120170360523275\n",
            "[ 714 ]: size_label: 1.4350845252893227  rf_preds  2.15909737437594 mlp_preds:  1.9653758263545598  combined_preds:  2.06223660036525\n",
            "[ 715 ]: size_label: 1.0986122886681098  rf_preds  2.145935631999474 mlp_preds:  2.1065880925762315  combined_preds:  2.1262618622878526\n",
            "[ 716 ]: size_label: 1.9315214116032138  rf_preds  1.3929637497597136 mlp_preds:  0.9183637602540813  combined_preds:  1.1556637550068976\n",
            "[ 717 ]: size_label: 1.3862943611198906  rf_preds  1.7205249454864302 mlp_preds:  1.6448253000458521  combined_preds:  1.6826751227661412\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4lgpnePZkUa4",
        "outputId": "ceeed4ca-7cd2-4ed6-f7ce-dcc3b5bcdf78"
      },
      "source": [
        "print(final_preds)\n",
        "#..."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      testing\n",
            "MAE      0.77\n",
            "MdAE     0.70\n",
            "RMSE     0.91\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}