Machine Learning_Random Forest_EnergyGeneratedwPlot_Monthly.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Title:     SmartHome Dataset\n",
    "# Authors:   Aline J\n",
    "# Date:      01/06/2020\n",
    "# Goal:      Analyze and predict energy use efficiency \n",
    "#            in a smarthouse during a year under variable weather conditions.\n",
    "\n",
    "# Questions: \n",
    "# 1) What are the variables that control generated energy? \n",
    "# 2) What are the variables that control used energy? \n",
    "# 3) What are the variables that control energy efficiency?\n",
    "\n",
    "# Method. Unsupervised Machine learning Method, specifically random forest regression analysis.\n",
    "# Results: Used CleanData\n",
    "#         Several attempts to run the analysis were done at differen frequencies. \n",
    "#         Only Monthly frequency data resulted the highest accuracy (76%) vs Minute data (0.01%).  \n",
    "#         The minimum random state of 50, were enough to explain the variability in the dataset.  \n",
    "#         The same accuracy was yield when running random state of 1000.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Begins Here ####\n",
    "# 1. Import dependencies\n",
    "# Update sklearn to prevent version mismatches\n",
    "#!pip install sklearn --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# install joblib. This will be used to save your model. \n",
    "# Restart your kernel after installing \n",
    "#!pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Other dependencies\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt # plotting\n",
    "import numpy as np # linear algebra\n",
    "import os # accessing directory structure\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "from scipy import stats\n",
    "import string as string\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 351 rows and 26 columns\n"
     ]
    }
   ],
   "source": [
    "#csv_path = \"Data/CleanData_per_month.csv\"\n",
    "csv_path = \"Data/CleanData_per_day.csv\"\n",
    "df = pd.read_csv(csv_path,   parse_dates=True)\n",
    "nRow, nCol = df.shape\n",
    "print(f'There are {nRow} rows and {nCol} columns')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\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>month</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "      <th>gen</th>\n",
       "      <th>use</th>\n",
       "      <th>House overall</th>\n",
       "      <th>Furnace</th>\n",
       "      <th>Home office</th>\n",
       "      <th>Living room</th>\n",
       "      <th>Wine cellar</th>\n",
       "      <th>...</th>\n",
       "      <th>Dishwasher</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>ApparentTemperature</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>windBearing</th>\n",
       "      <th>Precip</th>\n",
       "      <th>DewPoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>18.026200</td>\n",
       "      <td>1366.269517</td>\n",
       "      <td>1366.269517</td>\n",
       "      <td>492.020017</td>\n",
       "      <td>64.668783</td>\n",
       "      <td>77.071667</td>\n",
       "      <td>29.093367</td>\n",
       "      <td>...</td>\n",
       "      <td>59.517171</td>\n",
       "      <td>40727.34</td>\n",
       "      <td>720.28</td>\n",
       "      <td>11125.60</td>\n",
       "      <td>33989.34</td>\n",
       "      <td>1156803.96</td>\n",
       "      <td>8420.92</td>\n",
       "      <td>300606</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>27849.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>76.051300</td>\n",
       "      <td>1851.521067</td>\n",
       "      <td>1851.521067</td>\n",
       "      <td>683.761967</td>\n",
       "      <td>145.570317</td>\n",
       "      <td>71.142950</td>\n",
       "      <td>24.096767</td>\n",
       "      <td>...</td>\n",
       "      <td>63.154404</td>\n",
       "      <td>45637.30</td>\n",
       "      <td>835.28</td>\n",
       "      <td>14333.36</td>\n",
       "      <td>35226.44</td>\n",
       "      <td>1460895.76</td>\n",
       "      <td>11733.58</td>\n",
       "      <td>380922</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>26637.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>74.081433</td>\n",
       "      <td>1651.128133</td>\n",
       "      <td>1651.128133</td>\n",
       "      <td>613.630533</td>\n",
       "      <td>196.064167</td>\n",
       "      <td>42.779250</td>\n",
       "      <td>32.989533</td>\n",
       "      <td>...</td>\n",
       "      <td>105.508668</td>\n",
       "      <td>47914.02</td>\n",
       "      <td>883.58</td>\n",
       "      <td>14400.00</td>\n",
       "      <td>38548.42</td>\n",
       "      <td>1453861.98</td>\n",
       "      <td>10967.42</td>\n",
       "      <td>327054</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>30604.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>108.525017</td>\n",
       "      <td>1500.686017</td>\n",
       "      <td>1500.686017</td>\n",
       "      <td>605.129767</td>\n",
       "      <td>180.650867</td>\n",
       "      <td>93.077467</td>\n",
       "      <td>20.980700</td>\n",
       "      <td>...</td>\n",
       "      <td>58.880760</td>\n",
       "      <td>32108.72</td>\n",
       "      <td>861.98</td>\n",
       "      <td>14119.28</td>\n",
       "      <td>15936.88</td>\n",
       "      <td>1463662.42</td>\n",
       "      <td>16165.70</td>\n",
       "      <td>460508</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>14835.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>148.107500</td>\n",
       "      <td>1597.968867</td>\n",
       "      <td>1597.968867</td>\n",
       "      <td>817.065250</td>\n",
       "      <td>133.360867</td>\n",
       "      <td>44.483650</td>\n",
       "      <td>30.792300</td>\n",
       "      <td>...</td>\n",
       "      <td>72.973254</td>\n",
       "      <td>17208.20</td>\n",
       "      <td>770.82</td>\n",
       "      <td>14394.78</td>\n",
       "      <td>3973.98</td>\n",
       "      <td>1489091.46</td>\n",
       "      <td>9592.18</td>\n",
       "      <td>419634</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-3292.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>136.922033</td>\n",
       "      <td>1267.262700</td>\n",
       "      <td>1267.262700</td>\n",
       "      <td>685.990133</td>\n",
       "      <td>167.216517</td>\n",
       "      <td>47.648100</td>\n",
       "      <td>19.105800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140792</td>\n",
       "      <td>28765.20</td>\n",
       "      <td>825.10</td>\n",
       "      <td>14330.40</td>\n",
       "      <td>22724.60</td>\n",
       "      <td>1486863.08</td>\n",
       "      <td>5726.98</td>\n",
       "      <td>312532</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>7985.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2016</td>\n",
       "      <td>111.077217</td>\n",
       "      <td>1388.444150</td>\n",
       "      <td>1388.444150</td>\n",
       "      <td>568.106683</td>\n",
       "      <td>146.354700</td>\n",
       "      <td>75.690783</td>\n",
       "      <td>29.777033</td>\n",
       "      <td>...</td>\n",
       "      <td>68.746322</td>\n",
       "      <td>33599.04</td>\n",
       "      <td>895.74</td>\n",
       "      <td>14287.20</td>\n",
       "      <td>31265.42</td>\n",
       "      <td>1477864.54</td>\n",
       "      <td>3576.78</td>\n",
       "      <td>198706</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>15923.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>31.267217</td>\n",
       "      <td>1584.852233</td>\n",
       "      <td>1584.852233</td>\n",
       "      <td>630.874083</td>\n",
       "      <td>159.989967</td>\n",
       "      <td>69.618500</td>\n",
       "      <td>25.382883</td>\n",
       "      <td>...</td>\n",
       "      <td>62.607221</td>\n",
       "      <td>42463.28</td>\n",
       "      <td>1033.16</td>\n",
       "      <td>14205.98</td>\n",
       "      <td>35261.18</td>\n",
       "      <td>1475104.86</td>\n",
       "      <td>7083.10</td>\n",
       "      <td>52432</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>30723.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>27.691133</td>\n",
       "      <td>1303.707900</td>\n",
       "      <td>1303.707900</td>\n",
       "      <td>553.466300</td>\n",
       "      <td>113.336783</td>\n",
       "      <td>55.883617</td>\n",
       "      <td>26.653533</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094325</td>\n",
       "      <td>54431.36</td>\n",
       "      <td>1148.64</td>\n",
       "      <td>12903.56</td>\n",
       "      <td>50012.22</td>\n",
       "      <td>1472674.60</td>\n",
       "      <td>6061.94</td>\n",
       "      <td>91426</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>46180.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2016</td>\n",
       "      <td>8.630967</td>\n",
       "      <td>1638.143817</td>\n",
       "      <td>1638.143817</td>\n",
       "      <td>448.871300</td>\n",
       "      <td>159.419217</td>\n",
       "      <td>89.310450</td>\n",
       "      <td>27.882550</td>\n",
       "      <td>...</td>\n",
       "      <td>59.463244</td>\n",
       "      <td>65328.62</td>\n",
       "      <td>1212.90</td>\n",
       "      <td>10617.86</td>\n",
       "      <td>60219.90</td>\n",
       "      <td>1442159.72</td>\n",
       "      <td>12076.34</td>\n",
       "      <td>219432</td>\n",
       "      <td>36.0238</td>\n",
       "      <td>58692.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2016</td>\n",
       "      <td>80.069550</td>\n",
       "      <td>1595.681217</td>\n",
       "      <td>1595.681217</td>\n",
       "      <td>607.909200</td>\n",
       "      <td>142.965233</td>\n",
       "      <td>148.340450</td>\n",
       "      <td>31.303817</td>\n",
       "      <td>...</td>\n",
       "      <td>51.100655</td>\n",
       "      <td>37995.20</td>\n",
       "      <td>667.44</td>\n",
       "      <td>14378.54</td>\n",
       "      <td>25174.16</td>\n",
       "      <td>1457371.62</td>\n",
       "      <td>15447.82</td>\n",
       "      <td>388552</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>11746.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2016</td>\n",
       "      <td>56.725133</td>\n",
       "      <td>1566.298367</td>\n",
       "      <td>1566.298367</td>\n",
       "      <td>699.878833</td>\n",
       "      <td>156.496467</td>\n",
       "      <td>88.442467</td>\n",
       "      <td>22.200167</td>\n",
       "      <td>...</td>\n",
       "      <td>73.340703</td>\n",
       "      <td>37019.56</td>\n",
       "      <td>986.00</td>\n",
       "      <td>11865.90</td>\n",
       "      <td>28385.74</td>\n",
       "      <td>1452407.30</td>\n",
       "      <td>9427.36</td>\n",
       "      <td>279262</td>\n",
       "      <td>1.3862</td>\n",
       "      <td>23338.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    month  day  year         gen          use  House overall     Furnace  \\\n",
       "0       1    1  2016   18.026200  1366.269517    1366.269517  492.020017   \n",
       "1       1    2  2016   76.051300  1851.521067    1851.521067  683.761967   \n",
       "2       1    3  2016   74.081433  1651.128133    1651.128133  613.630533   \n",
       "3       1    4  2016  108.525017  1500.686017    1500.686017  605.129767   \n",
       "4       1    5  2016  148.107500  1597.968867    1597.968867  817.065250   \n",
       "5       1    6  2016  136.922033  1267.262700    1267.262700  685.990133   \n",
       "6       1    7  2016  111.077217  1388.444150    1388.444150  568.106683   \n",
       "7       1    8  2016   31.267217  1584.852233    1584.852233  630.874083   \n",
       "8       1    9  2016   27.691133  1303.707900    1303.707900  553.466300   \n",
       "9       1   10  2016    8.630967  1638.143817    1638.143817  448.871300   \n",
       "10      1   11  2016   80.069550  1595.681217    1595.681217  607.909200   \n",
       "11      1   12  2016   56.725133  1566.298367    1566.298367  699.878833   \n",
       "\n",
       "    Home office  Living room  Wine cellar  ...  Dishwasher  Temperature  \\\n",
       "0     64.668783    77.071667    29.093367  ...   59.517171     40727.34   \n",
       "1    145.570317    71.142950    24.096767  ...   63.154404     45637.30   \n",
       "2    196.064167    42.779250    32.989533  ...  105.508668     47914.02   \n",
       "3    180.650867    93.077467    20.980700  ...   58.880760     32108.72   \n",
       "4    133.360867    44.483650    30.792300  ...   72.973254     17208.20   \n",
       "5    167.216517    47.648100    19.105800  ...    0.140792     28765.20   \n",
       "6    146.354700    75.690783    29.777033  ...   68.746322     33599.04   \n",
       "7    159.989967    69.618500    25.382883  ...   62.607221     42463.28   \n",
       "8    113.336783    55.883617    26.653533  ...    0.094325     54431.36   \n",
       "9    159.419217    89.310450    27.882550  ...   59.463244     65328.62   \n",
       "10   142.965233   148.340450    31.303817  ...   51.100655     37995.20   \n",
       "11   156.496467    88.442467    22.200167  ...   73.340703     37019.56   \n",
       "\n",
       "    Humidity  Visibility  ApparentTemperature    Pressure  WindSpeed  \\\n",
       "0     720.28    11125.60             33989.34  1156803.96    8420.92   \n",
       "1     835.28    14333.36             35226.44  1460895.76   11733.58   \n",
       "2     883.58    14400.00             38548.42  1453861.98   10967.42   \n",
       "3     861.98    14119.28             15936.88  1463662.42   16165.70   \n",
       "4     770.82    14394.78              3973.98  1489091.46    9592.18   \n",
       "5     825.10    14330.40             22724.60  1486863.08    5726.98   \n",
       "6     895.74    14287.20             31265.42  1477864.54    3576.78   \n",
       "7    1033.16    14205.98             35261.18  1475104.86    7083.10   \n",
       "8    1148.64    12903.56             50012.22  1472674.60    6061.94   \n",
       "9    1212.90    10617.86             60219.90  1442159.72   12076.34   \n",
       "10    667.44    14378.54             25174.16  1457371.62   15447.82   \n",
       "11    986.00    11865.90             28385.74  1452407.30    9427.36   \n",
       "\n",
       "    windBearing   Precip  DewPoint  \n",
       "0        300606   0.1276  27849.26  \n",
       "1        380922   0.0000  26637.98  \n",
       "2        327054   0.0000  30604.26  \n",
       "3        460508   0.0000  14835.90  \n",
       "4        419634   0.0000  -3292.16  \n",
       "5        312532   0.0000   7985.04  \n",
       "6        198706   0.0000  15923.34  \n",
       "7         52432   0.0000  30723.44  \n",
       "8         91426   0.0000  46180.16  \n",
       "9        219432  36.0238  58692.02  \n",
       "10       388552   0.0000  11746.14  \n",
       "11       279262   1.3862  23338.42  \n",
       "\n",
       "[12 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['month', 'day', 'year', 'gen', 'use', 'House overall', 'Furnace',\n",
       "       'Home office', 'Living room', 'Wine cellar', 'Garage door', 'Kitchen',\n",
       "       'Barn', 'Well', 'Fridge', 'Microwave', 'Dishwasher', 'Temperature',\n",
       "       'Humidity', 'Visibility', 'ApparentTemperature', 'Pressure',\n",
       "       'WindSpeed', 'windBearing', 'Precip', 'DewPoint'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Preparation\n",
    "df.columns\n",
    "#df.dtypes \n",
    "#CleanData.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputdf = df.filter(items=[ 'month', 'day', 'year', 'gen', 'Furnace',\n",
    "                            'Home office', 'Living room', 'Wine cellar', 'Garage door', 'Kitchen',\n",
    "                            'Barn', 'Well', 'Fridge', 'Microwave', 'Dishwasher', 'Temperature',\n",
    "                            'Humidity', 'Visibility', 'ApparentTemperature', 'Pressure',\n",
    "                            'WindSpeed', 'windBearing', 'Precip', 'DewPoint'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['month', 'day', 'year', 'gen', 'Furnace', 'Home office', 'Living room',\n",
       "       'Wine cellar', 'Garage door', 'Kitchen', 'Barn', 'Well', 'Fridge',\n",
       "       'Microwave', 'Dishwasher', 'Temperature', 'Humidity', 'Visibility',\n",
       "       'ApparentTemperature', 'Pressure', 'WindSpeed', 'windBearing', 'Precip',\n",
       "       'DewPoint'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running RF with all the data to investigate the driving variables of energy efficiency at a 1min resolution\n",
    "#my_data.dtypes \n",
    "features=inputdf\n",
    "type(features)\n",
    "#features.iloc[:,5:].head(5)\n",
    "#features.head(5)\n",
    "#features.info()\n",
    "features.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labels are the values we want to predict\n",
    "labels = np.array(features['gen'])\n",
    "# Remove the labels from the features\n",
    "# axis 1 refers to the columns\n",
    "features= features.drop('gen', axis = 1)\n",
    "# Saving feature names for later use\n",
    "feature_list = list(features.columns)\n",
    "# Convert to numpy array\n",
    "features = np.array(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training and Testing Sets\n",
    "# Using Skicit-learn to split data into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Split the data into training and testing sets\n",
    "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features Shape: (263, 23)\n",
      "Training Labels Shape: (263,)\n",
      "Testing Features Shape: (88, 23)\n",
      "Testing Labels Shape: (88,)\n"
     ]
    }
   ],
   "source": [
    "print('Training Features Shape:', train_features.shape)\n",
    "print('Training Labels Shape:', train_labels.shape)\n",
    "print('Testing Features Shape:', test_features.shape)\n",
    "print('Testing Labels Shape:', test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average baseline error:  72091.54\n"
     ]
    }
   ],
   "source": [
    "# The baseline predictions are the historical averages\n",
    "baseline_preds = test_features[:, feature_list.index('Temperature')]\n",
    "# Baseline errors, and display average baseline error\n",
    "baseline_errors = abs(baseline_preds - test_labels)\n",
    "print('Average baseline error: ', round(np.mean(baseline_errors), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the model we are using\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# Instantiate model with 10 decision trees\n",
    "#rf = RandomForestRegressor(n_estimators = 100, random_state = 100)\n",
    "rf = RandomForestRegressor(n_estimators = 100, random_state = 100)\n",
    "# Train the model on training data\n",
    "rf.fit(train_features, train_labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 42.08 degrees.\n"
     ]
    }
   ],
   "source": [
    "# Use the forest's predict method on the test data\n",
    "predictions = rf.predict(test_features)\n",
    "# Calculate the absolute errors\n",
    "errors = abs(predictions - test_labels)\n",
    "# Print out the mean absolute error (mae)\n",
    "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')\n",
    "#Mean Absolute Error: 8453.46 degrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.62 %.\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean absolute percentage error (MAPE)\n",
    "mape = 100 * (errors / test_labels)\n",
    "# Calculate and display accuracy\n",
    "accuracy = 100 - np.mean(mape)\n",
    "print('Accuracy:', round(accuracy, 2), '%.')\n",
    "#Accuracy: 95.96 %.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What are the top predictor variables that regulate power generated in a smarthome?\n",
    "# Import tools needed for visualization\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydot\n",
    "# Pull out one tree from the forest\n",
    "tree = rf.estimators_[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import tools needed for visualization\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydot\n",
    "# Pull out one tree from the forest\n",
    "tree = rf.estimators_[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: Temperature          Importance: 0.11\n",
      "Variable: ApparentTemperature  Importance: 0.09\n",
      "Variable: Garage door          Importance: 0.08\n",
      "Variable: Wine cellar          Importance: 0.07\n",
      "Variable: Humidity             Importance: 0.06\n",
      "Variable: Pressure             Importance: 0.06\n",
      "Variable: month                Importance: 0.05\n",
      "Variable: Home office          Importance: 0.05\n",
      "Variable: day                  Importance: 0.04\n",
      "Variable: Furnace              Importance: 0.04\n",
      "Variable: Well                 Importance: 0.04\n",
      "Variable: Visibility           Importance: 0.04\n",
      "Variable: Living room          Importance: 0.03\n",
      "Variable: Barn                 Importance: 0.03\n",
      "Variable: Fridge               Importance: 0.03\n",
      "Variable: Microwave            Importance: 0.03\n",
      "Variable: Dishwasher           Importance: 0.03\n",
      "Variable: windBearing          Importance: 0.03\n",
      "Variable: DewPoint             Importance: 0.03\n",
      "Variable: Kitchen              Importance: 0.02\n",
      "Variable: WindSpeed            Importance: 0.02\n",
      "Variable: Precip               Importance: 0.02\n",
      "Variable: year                 Importance: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Get numerical feature importances\n",
    "importances = list(rf.feature_importances_)\n",
    "# List of tuples with variable and importance\n",
    "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]\n",
    "# Sort the feature importances by most important first\n",
    "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
    "# Print out the feature and importances \n",
    "[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 49.96 degrees.\n",
      "Accuracy: -6.1 %.\n"
     ]
    }
   ],
   "source": [
    "# New random forest with only the two most important variables\n",
    "rf_most_important = RandomForestRegressor(n_estimators= 1000, random_state=1000)\n",
    "# Extract the two most important features\n",
    "important_indices = [feature_list.index('ApparentTemperature'), feature_list.index('Temperature')]\n",
    "train_important = train_features[:, important_indices]\n",
    "test_important = test_features[:, important_indices]\n",
    "# Train the random forest\n",
    "rf_most_important.fit(train_important, train_labels)\n",
    "# Make predictions and determine the error\n",
    "predictions = rf_most_important.predict(test_important)\n",
    "errors = abs(predictions - test_labels)\n",
    "# Display the performance metrics\n",
    "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')\n",
    "mape = np.mean(100 * (errors / test_labels))\n",
    "accuracy = 100 - mape\n",
    "print('Accuracy:', round(accuracy, 2), '%.')\n",
    "\n",
    "#Note:\n",
    "#This tells us that temperature and dew point are the two most important variables to regulate generated energy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import matplotlib for plotting and use magic command for Jupyter Notebooks\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# Set the style\n",
    "plt.style.use('fivethirtyeight')\n",
    "# list of x locations for plotting\n",
    "x=sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
    "x_values = sorted(list(range(len(x))))\n",
    "\n",
    "#x_values = list(range(len(importances)))\n",
    "# Make a bar chart\n",
    "plt.bar(x_values, importances, orientation = 'vertical')\n",
    "# Tick labels for x axis\n",
    "plt.xticks(x_values, feature_list, rotation='vertical')\n",
    "# Axis labels and title\n",
    "plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variables of Importance');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model\n",
    "#model.save(\"EnergyGenerated_trained.h5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dates of training values\n",
    "# Use datetime for creating date objects for plotting\n",
    "import datetime\n",
    "# Dates of training values\n",
    "months = features[:, feature_list.index('month')]\n",
    "days = features[:, feature_list.index('day')]\n",
    "years = features[:, feature_list.index('year')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List and then convert to datetime object\n",
    "dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n",
    "dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataframe with true values and dates\n",
    "true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dates of predictions\n",
    "months = test_features[:, feature_list.index('month')]\n",
    "days = test_features[:, feature_list.index('day')]\n",
    "years = test_features[:, feature_list.index('year')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Column of dates\n",
    "test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to datetime objects\n",
    "test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataframe with predictions and dates\n",
    "predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predictions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the actual values\n",
    "plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual')\n",
    "# Plot the predicted values\n",
    "plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'prediction')\n",
    "plt.xticks(rotation = '60'); \n",
    "plt.legend()\n",
    "# Graph labels\n",
    "plt.xlabel('Date'); plt.ylabel('Generated Energy [Kw]'); plt.title('Actual and Predicted Values');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function list.index(value, start=0, stop=9223372036854775807, /)>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_list.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'gen' is not in list",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-45c5ef136790>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Make the data accessible for plotting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtrue_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'gen'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeature_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'gen'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;31m#true_data['Temperature'] = features[:, feature_list.index('Temperature')]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#true_data['friend'] = features[:, feature_list.index('friend')]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# Plot all the data as lines\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: 'gen' is not in list"
     ]
    }
   ],
   "source": [
    "# Make the data accessible for plotting\n",
    "true_data['gen'] = features[:, feature_list.index('gen')]\n",
    "#true_data['Temperature'] = features[:, feature_list.index('Temperature')]\n",
    "#true_data['friend'] = features[:, feature_list.index('friend')]\n",
    "# Plot all the data as lines\n",
    "plt.plot(true_data['date'], true_data['actual'], 'b-', label  = 'actual', alpha = 1.0)\n",
    "#plt.plot(true_data['date'], true_data[''], 'y-', label  = 'temp_1', alpha = 1.0)\n",
    "#plt.plot(true_data['date'], true_data['Temperature'], 'k-', label = 'Temperature', alpha = 0.8)\n",
    "#plt.plot(true_data['date'], true_data['Humidity'], 'r-', label = 'Humidity', alpha = 0.3)\n",
    "# Formatting plot\n",
    "plt.legend(); plt.xticks(rotation = '60');\n",
    "# Lables and title\n",
    "plt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual Max Temp and Variables');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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