Gas consumption Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gas consumption Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Status overview & Dataframe Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gc01 = pd.read_csv(\"/Users/xinyuyangren/Documents/TU:e_Year2/Thesis/Aramis_Data_Warehouse/Consumption_Study/0300_gc.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gc01 = gc01.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_windspeed</th>\n",
       "      <th>avg_irradiation</th>\n",
       "      <th>avg_rh</th>\n",
       "      <th>avg_t</th>\n",
       "      <th>avg_wc_lr</th>\n",
       "      <th>avg_wc_br2</th>\n",
       "      <th>avg_wc_br1_br3</th>\n",
       "      <th>set_point_ventilation_unit</th>\n",
       "      <th>avg_t_lr</th>\n",
       "      <th>avg_t_br2</th>\n",
       "      <th>avg_t_br1_br3</th>\n",
       "      <th>avg_co2_lr</th>\n",
       "      <th>avg_co2_br2</th>\n",
       "      <th>avg_co2_br1_br3</th>\n",
       "      <th>gc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.919657</td>\n",
       "      <td>121.031824</td>\n",
       "      <td>78.569964</td>\n",
       "      <td>11.779555</td>\n",
       "      <td>0.999318</td>\n",
       "      <td>0.432415</td>\n",
       "      <td>0.331504</td>\n",
       "      <td>1.127298</td>\n",
       "      <td>22.704808</td>\n",
       "      <td>22.540290</td>\n",
       "      <td>21.771070</td>\n",
       "      <td>851.416450</td>\n",
       "      <td>758.468513</td>\n",
       "      <td>720.749406</td>\n",
       "      <td>0.057432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.687856</td>\n",
       "      <td>200.287064</td>\n",
       "      <td>15.736454</td>\n",
       "      <td>6.693290</td>\n",
       "      <td>0.023112</td>\n",
       "      <td>0.486629</td>\n",
       "      <td>0.464399</td>\n",
       "      <td>0.404039</td>\n",
       "      <td>1.769490</td>\n",
       "      <td>1.993261</td>\n",
       "      <td>2.367904</td>\n",
       "      <td>230.255692</td>\n",
       "      <td>102.720935</td>\n",
       "      <td>141.611734</td>\n",
       "      <td>0.093954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-576.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.400000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>14.700000</td>\n",
       "      <td>486.300000</td>\n",
       "      <td>529.400000</td>\n",
       "      <td>470.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-4.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>675.925000</td>\n",
       "      <td>695.800000</td>\n",
       "      <td>611.625000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.400000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>794.600000</td>\n",
       "      <td>748.600000</td>\n",
       "      <td>693.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>968.475000</td>\n",
       "      <td>811.500000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>0.087000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>957.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>34.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>29.300000</td>\n",
       "      <td>29.800000</td>\n",
       "      <td>3322.900000</td>\n",
       "      <td>1924.900000</td>\n",
       "      <td>1938.900000</td>\n",
       "      <td>1.066000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       avg_windspeed  avg_irradiation        avg_rh         avg_t  \\\n",
       "count   15994.000000     15994.000000  15994.000000  15994.000000   \n",
       "mean        1.919657       121.031824     78.569964     11.779555   \n",
       "std         1.687856       200.287064     15.736454      6.693290   \n",
       "min         0.000000      -576.000000      0.000000    -90.000000   \n",
       "25%         1.000000        -4.000000     69.000000      7.000000   \n",
       "50%         2.000000         3.000000     82.000000     11.800000   \n",
       "75%         3.000000       175.000000     91.000000     16.500000   \n",
       "max        13.000000       957.000000    100.000000     34.300000   \n",
       "\n",
       "          avg_wc_lr    avg_wc_br2  avg_wc_br1_br3  set_point_ventilation_unit  \\\n",
       "count  15994.000000  15994.000000    15994.000000                15994.000000   \n",
       "mean       0.999318      0.432415        0.331504                    1.127298   \n",
       "std        0.023112      0.486629        0.464399                    0.404039   \n",
       "min        0.000000      0.000000        0.000000                    1.000000   \n",
       "25%        1.000000      0.000000        0.000000                    1.000000   \n",
       "50%        1.000000      0.000000        0.000000                    1.000000   \n",
       "75%        1.000000      1.000000        1.000000                    1.000000   \n",
       "max        1.000000      1.000000        1.000000                    3.000000   \n",
       "\n",
       "           avg_t_lr     avg_t_br2  avg_t_br1_br3    avg_co2_lr   avg_co2_br2  \\\n",
       "count  15994.000000  15994.000000   15994.000000  15994.000000  15994.000000   \n",
       "mean      22.704808     22.540290      21.771070    851.416450    758.468513   \n",
       "std        1.769490      1.993261       2.367904    230.255692    102.720935   \n",
       "min       15.400000     17.000000      14.700000    486.300000    529.400000   \n",
       "25%       21.800000     21.400000      20.100000    675.925000    695.800000   \n",
       "50%       22.400000     21.800000      21.100000    794.600000    748.600000   \n",
       "75%       23.700000     23.800000      23.400000    968.475000    811.500000   \n",
       "max       28.000000     29.300000      29.800000   3322.900000   1924.900000   \n",
       "\n",
       "       avg_co2_br1_br3            gc  \n",
       "count     15994.000000  15994.000000  \n",
       "mean        720.749406      0.057432  \n",
       "std         141.611734      0.093954  \n",
       "min         470.700000      0.000000  \n",
       "25%         611.625000      0.000000  \n",
       "50%         693.300000      0.000000  \n",
       "75%         816.500000      0.087000  \n",
       "max        1938.900000      1.066000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc01.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Deal with the datetime attributes\n",
    "\n",
    "gc01.timestamp = pd.to_datetime(gc01.timestamp)\n",
    "t = pd.DatetimeIndex(gc01.timestamp)\n",
    "hr = t.hour\n",
    "gc01['HourOfDay'] = hr\n",
    "month = t.month\n",
    "gc01['Month'] = month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timestamp\n",
      "avg_windspeed\n",
      "avg_irradiation\n",
      "avg_rh\n",
      "avg_t\n",
      "avg_wc_lr\n",
      "avg_wc_br2\n",
      "avg_wc_br1_br3\n",
      "set_point_ventilation_unit\n",
      "avg_t_lr\n",
      "avg_t_br2\n",
      "avg_t_br1_br3\n",
      "avg_co2_lr\n",
      "avg_co2_br2\n",
      "avg_co2_br1_br3\n",
      "gc\n",
      "HourOfDay\n",
      "Month\n"
     ]
    }
   ],
   "source": [
    "for column in gc01.columns:\n",
    "    print column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1096c7390>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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gXFwGmrLqqtDsuNm3b3p5F03ZzwvKXn6UVxotDrbZRnrz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\nxjekf//bfdmqpMiR83o3mrAZQYou7/oLQHuC6p5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rpqCKIo3j7EWL0ufnhzAOIyxseJBa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\nKIqiKIqiKJH8P9DwNJzbU+x3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103ecfcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,6), dpi=1600) \n",
    "plt.plot(gc01.timestamp,gc01.gc) \n",
    "plt.ylim(0,1.2)\n",
    "plt.legend(('0300'),loc='best') \n",
    "plt.title('Type 505 Gas Consumption')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run Above here to pre-process the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gas consumption: Impact factor Study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gc = gc01.pop('gc')\n",
    "timestamp = gc01.pop('timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor(n_estimators=1000,n_jobs=-1,oob_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=1000, n_jobs=-1, oob_score=True, random_state=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(gc01,gc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xinyuyangren/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: sort is deprecated, use sort_values(inplace=True) for for INPLACE sorting\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d2aa690>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pldXrKVfAkRGxS0RMJIV/HFl+C4d6mJm12ohosNW+hK63SpqVk7EWSdpzoLKy\n0ZKukPSQpGtyOAmSlisle80HPhQRj+QhR2u9gWErpVSvRyR9IZc5Pn/Gy3PPfsuI+H1eNpo09rjv\nLDMz6yAjosEmJXTtBuxGGrJzFqmXWjINuFprJnQdDEwpV2BE/BoYKKFrK3JCF3ABcEdE7EwaTvTB\nCnWcAFwUETsCK4FPFJY9HxFTIuKaej50tlv+TDsBRxZOo78z729iRPwSQNItwLPAS8B/D2JfZmbW\nJCNl4JR2JXQdABwHkJ+FWlmhrKciYm6evgI4Cfhafv/d2j7mgG6NiBcBlDK29wGuB56MiHnFFSPi\nQKXYze/kut82cJG9hekenNZlZtbHaV2D1OaErnpOKw+UulXy8iDrV6ncAcuMiD/lMdQPo6YG28zM\nipzWNXjtTOi6jXxqOydhbVihrPGS9sjTR1M+R7uoluvY75e0cR4PfSrpM66xraT1JI3L028C/pr0\nRcbMzDrESGiwbyHd0PUg6bT16oQuUiLWVsWELqCU0HUTNSR05cliQteLhYSuTwL759Ss+cC7KtTz\nYeBESQ8BGwMXl3ZTXEnSVEm/JH3x+L6kmyt/fO4jXbNfDHyvEBRSLHc94AZJi4GFpHSyiylLFV9j\nx46vUiUzM6uXhybtp9MTutrNQ5OamdXHaV3N0+kJXWZmNgK5h12DRiV0SdqUdF27dNBLZb0vIl4Y\nQv3+Cji3X7lPRMQRgy2zwr7cwzYzq4PTuqwt3GCbmdXHaV1mZmYjiBtsMzOzLtDQBjuPyT2uwWV+\nVNKxVdbZSdJBjdxvhX1tJOnjhfebS7omT+9XbXS0/nWVdIik05pX4+qKdZB0WH5evdL6TucyM2ux\nRvewjwe2aGSBEfGNiLiiymo7kwYsaYVNKIzzHRHPRMSHC8urXeBdo64RcWNEnFdh/abrV4epwLur\nbIHTuczMWqtqgy1pXUnfz2lT90s6UtJkSXfkBKybJY2TdAQpLOMKSQslrVOmvOWSzs1lzZW0TZ4/\nXtJtOdnq1kLy1ZmlxKycOnWOpJ/ltKm9c7rUF4EP5/2+IRZSyfLiSGOSfq6UuPUWSf+dy/yZpL0K\n+7007/MxSf+QN/0ysE3e17kqk2UtaTdJcyQtkHS3pO0GqqsKmdUVjsFMSedLuifX5fAK/15r9PIl\nXSjpbwrHvjfXaYmk7fP86Xm9vYBDgfNy/d5Rbj9mZtZatfSwDwR+lbOSJwE/BC4EjsgJWDOBf4mI\na0mjeR0jJl4zAAAKi0lEQVQdEZMj4tUKZb6Qy/oP4Pw870JgZk62ujK/H8haEbEH8I9Abw7q+ALw\n3bzf7/XfIN/WfB05oUsp9OMXOXHrfOBrucwPAZcWNp0AvB/YA+iVtBbwWeDxvK/TS7sYoJ7LgH0i\nYlfgTODLFepa2r7SMRgXEXsDh5Ae4aqkUi//uVyni4HPFLeJiHtJI72dmuu3vMp+zMysRWoZOGUp\n8FVJXyYN1/kC8B7gVkkiNfr/U1i/llvXS2N3X0VfItVe9EVe/hflG6VZ+ecCoJ4xMK8hNZaXk8YP\nLyVg/SXwrvxZANaXtG6evikiXgd+I2kFMLaO/W0MfFvSdqQGtJZjXekYXAcQEcskvbWOevRXCjpZ\nwJoRo3XoLUz3DKEqZmbDT9vSuiLiUaUM5YOBfwZuBx7Ivb3BijLTtSj13P9MHSO1RcS9kraV9BbS\nddov5kUiJXm9Vlw/t9/FswSr6tkf6Vj9JCIOlzSedNyqVrPCsmJdKn0pep01z5yMKVNOXcdvTb2D\n28zMbARoW1qXpM2BP0bElcBXSaeHN5O0Z17+JqWhPAFeAiolUpVMyz+PIodxkFKkPpKnj6W+tKqV\nNe53NqlH/1ApIxr4EXDK6gKlnaqUsRLYoIZ9bQT8Kk/P6Ld9ubrOobZjUKnBfhLYUdJoSRsD76uh\nrkW1HkszM2uhWq5hTwTuk7SIdEr5DNK13nOV0p0WkU7lQjrdfHGlm86yTSQtAU4iXYsGOBmYkcs8\nhkIjWlAu2/l2UiM14E1nBdfksq8uzDsFmJJvwnoA+GiZbQMgIn4L3JNvmqt0Lfk84BxJC1jzOFeq\na7ljUCkre80FEU+TPucDpM+5sLi4Qn1LrgZOzTemlbnpzOlcZmat1vKhSSUtB3bNDZ91GXloUjOz\nuqiLhyb1//ZmZmZ1alq8pqRZwNalt6SG+vSI2KZZ+8z7PZ50Krn4xeCeiDipmfttNUnvId1JXkzo\neiUi9iq/lZmZdSundVldfErczKw+3XxK3MzMzOrkBtvMzKwLuMEepsqNcV5m3fMkLctjmF9bHHO9\nzPpO6jIzazE32MNb1YvNkkaRBo95dx7D/FHgn6oX66QuM7NWcoM9SJJmK6WVLZV0glJu93mF5dMl\nXZCnz1BKF7tT0pXK6WMDlLmZpPl5eidJqwqJXY9JGiPprZJm5d7wotKIc2WMlnSFpIckXSNpTC5r\nuVLq2XzgQxHx44hYlbeZC2w59CNkZmaN5AZ78GbktLLdSCOUzWLNMI1pwNWSpuT5E0njsU8pV2BO\nD1tH0vrAPsA84L2StgJWRMQrwAXAHbk3PBl4sEIdJwAXRcSOpCFHP1FY9nxETImIa/pt87fAzZU/\nupmZtVrTnsMeAT4paWqe3hLYBng8R3c+BkyIiDmSTgGuz+EirxWzqsuYQ2qs9wW+BBxE+mJVGlf8\nAOA4WB0burJCWU9FxNw8fQVpKNhSOtp3+68s6XPAa3nc+Ap6q3wEM7ORq21pXfZGkvYjNZx7RMSr\nkm4H1iGNwz0NeJi+GMt63QW8F9gqIq6X9FlSUthNeXk9D0FXGoP85eKCPODMwaTPVUVvYboxKTRm\nZsNF29K6bEAbAS/kxnoHoHQd+TrgMFIKWSlg5B7gEEmlU90fqFL2XaSkrkfz+9+SGtK78/vbyKe2\nJY2qckf3eEl75OmjKZP+JelA4FTg0Ih4daB1zMysvdxgD84tpBu6HiSdtr4XIEd2LiP1jufnefOB\nG4AlpF7y/cDvyhUcEaXbrn+af94NvBgRpW0+Cewv6X5gPvCuCvV8GDhR0kPAxsDFpd30W+9CYH3g\n1pwi9vUKZeKkLjOz1vPQpC0gab2IeFnSm4E7gRMiYnG76zUYHprUzKw+jRqa1NewW+MSSTuSrnNf\n1q2NtZmZtY972G0i6SJgb9Lp6VKa2fkRcXmd5WxKuq5dTO0K4H0R8ULjarx6f+5hm5nVoVE9bDfY\nVhc32GZm9XFal5mZ2QjiBtvMzKwLuME2JFUaLc3MzDqAG2yDAUZPk7RWOypiZmYDc4PdZF2S6lUq\nd7+87+upHCpiZmYt5uewm29GRLyYoy3nkcbqngOclpdPA87ul+q1DrCQNJLZG0TErwtDnRZTve4h\np3pJuoyU6nW4JJFGMqvFLqRs7KcG82HNzKw53GA3XzekehXdV62x7u3tXT3df5B7M7ORrllpXX4O\nu4lyqtc/A+8vpHqdSWq0J5LG+p4QEZ/JDfbGEXFW3vZfgV9FxNfKlH0saRzxAyJiL0n3AouAmyLi\nJkkrgC3zF4Bq9XwpIjbM9f10RBxaYV0/h21mVgc/h90duiXVa8i/SGZm1lxusJurW1K9RkSXuRmn\nqFrJ9W8v17+9ur3+jeAGu4ki4k8RcXBEvDsiDo+IAyLizrzskIjYrt8m/xoROwAHAlsDC6qUPz4i\nLs3TX46InQvLnouIqRExKSImR8TPKpSzYf7500qnw7tdt//Bu/7t5fq3V7fXvxF801lncaqXmZkN\nyA12B4mIY/rP69ZULzMzayzfJW51keRfGDOzOjle08zMbITwTWdmZmZdwA22mZlZF3CDbQBIOjAH\nj/xc0ull1rlA0qM5UGTnerZttkHUf5fC/F9IWpJDUu5rXa3XqFvF+kuaIGmOpFf6h8K0+/gPse7d\ncOyPznVcIuluSZNq3bYVhlj/bjj+hxbrKGnvWrdthSHWv77jHxF+jfAX6YvbY8B4YDSwGNih3zoH\nkYY9BdgDmFvrtp1c//z+CWCTDj/+bwF2JQ11+6l6tu3UunfRsd8T2ChPH9iFv/sD1r+Ljv+6hemJ\nwLIuO/4D1n8wx989bAPYHXg0Ip6MNPb41aShU4sOA74NEGkQlo0kja1x22YbSv0hPeLWzr+FqvWP\niOcjYgHwer3bNtlQ6g7dceznRt8IgnOBLWrdtgWGUn/ojuP/h8Lb9YFVtW7bAkOpP9R5/N1gG6Q/\n4F8W3j/Nmn/UldapZdtmG0z9f1VYJ4BblXLLT2haLcsbyjFs9/Ef6v677dj/PXDzILdthqHUH7rk\n+EuaKmkZcCPwt/Vs22RDqT/Uefw9cIoN1nAKDNk7Ip6RtBnpj2dZRNxddStrhK459pL2B2aQYm27\nTpn6d8Xxj4jrgOsk7QOcDby/zVWqS4X613X83cM2SL3NrQrvt8zz+q/z9gHWqWXbZhtK/YmIZ/LP\nXwOzSae5Wmkox7Ddx39I+++WY59v1LoEODT6RgZs97GvuQ5l6t81x78kN2bbKI3c2DXHv6Rf/es/\n/q28QO9XZ76Atei7cWJt0o0T7+q3zsH03bS1J3033lTdtsPrvy6wfp5ejxRz+ledVv/CumeSMsvr\n3rYD694Vx570H/KjwJ6D/ewdWv9uOf7bFqYnA7/ssuNfrv51H/+WfTC/OvtFunv0kfyH/dk876PA\n/ymsc1H+5VwCTK60bbfUH3hH/iNbBCzt1PoDY0nXyl4kZZ8/Vfhjb+vxH2zdu+jYfxP4DbAw1/W+\nbvrdL1f/Ljr+pwEP5PrfA+zVZcd/wPoP5vh7aFIzM7Mu4GvYZmZmXcANtpmZWRdwg21mZtYF3GCb\nmZl1ATfYZmZmXcANtpmZWRdwg21mZtYF3GCbmZl1gf8Piq/TcH1h0QIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d0fb850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances = pd.Series(rf.feature_importances_,index = gc01.columns)\n",
    "feature_importances.sort(inplace = True)\n",
    "feature_importances.plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = rf.predict(gc01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x14443b310>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RkVfq7wsvpG+zbl24rhODB6cHIlA4jv8Awoiirhg7Vmqomvb1yRfq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\nfbJ8Bh/6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1096fb210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,6), dpi=1600) \n",
    "plt.plot(timestamp,gc) \n",
    "plt.plot(timestamp,pred) \n",
    "plt.ylim(0,1.0)\n",
    "plt.legend(('real','prediction'),loc='best') \n",
    "plt.title('Type 505 Gas Consumption Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1446a7c50>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pekkHSRos6fvGmG181rtc0lNRFS4NTScbTaUDUEZ5dw4AklbpMev5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\nJ4oYVicTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1445ed2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predoob = rf.oob_prediction_\n",
    "fig = plt.figure(figsize=(18,6), dpi=1600) \n",
    "plt.plot(timestamp,gc) \n",
    "plt.plot(timestamp,predoob) \n",
    "plt.ylim(0,1.0)\n",
    "plt.legend(('real','prediction'),loc='best') \n",
    "plt.title('Type 505 Gas Consumption OOB Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.937604432165\n"
     ]
    }
   ],
   "source": [
    "print rf.score(gc01,gc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.541850296307\n"
     ]
    }
   ],
   "source": [
    "print rf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.639664423775 0.15022787441 0.210107701815\n"
     ]
    },
    {
     "data": {
      "image/png": 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z3ijcxivUWg833YbpF/DLfWqrAkOm/C2RnycOB8CAUVbY4nR2ANO11hKpcDNp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\nAC5H/Z8EnS79wMY1vEtwE3z/8v2BgM/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mrva2Qavx+zrwwRs8d8cit2YwvjYmafaik/mH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\noasoijKI/g9MZPTi6hY1TgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1445f74d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Motivation breakdown\n",
    "\n",
    "weather_g = feature_importances.avg_t + feature_importances.avg_rh\\\n",
    "+ feature_importances.avg_windspeed + feature_importances.avg_irradiation\n",
    "\n",
    "time_g = feature_importances.Month + feature_importances.HourOfDay\n",
    "\n",
    "behavior_g = feature_importances.avg_t_br1_br3 + feature_importances.avg_t_lr + feature_importances.avg_co2_lr\\\n",
    "+ feature_importances.avg_co2_lr\n",
    "\n",
    "Indoor_g = 1 - time_g - weather_g\n",
    "\n",
    "print Indoor_g, time_g, weather_g\n",
    "\n",
    "# The slices will be ordered and plotted counter-clockwise.\n",
    "labels = 'Indoor_g', 'time_g', 'weather_g'\n",
    "sizes = [Indoor_g, time_g, weather_g]\n",
    "colors = ['yellowgreen', 'gold', 'lightskyblue']\n",
    "explode = (0, 0.1, 0.1) # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
    "        autopct='%1.1f%%', shadow=True, startangle=0)\n",
    "# Set aspect ratio to be equal so that pie is drawn as a circle.\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.title('Driven attributes breakdown: gas consumption')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try without behavior attributes (IAT, Window and SPV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--PREPROCESS THE DATASET AGAIN--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gc01 = pd.read_csv(\"/Users/xinyuyangren/Documents/TU:e_Year2/Thesis/Aramis_Data_Warehouse/Consumption_Study/0300_gc.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gc01 = gc01.dropna()\n",
    "gc01.timestamp = pd.to_datetime(gc01.timestamp)\n",
    "t = pd.DatetimeIndex(gc01.timestamp)\n",
    "hr = t.hour\n",
    "gc01['HourOfDay'] = hr\n",
    "month = t.month\n",
    "gc01['Month'] = month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gc = gc01.pop('gc')\n",
    "timestamp = gc01.pop('timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gc01 = gc01.drop(['avg_wc_lr','avg_wc_br2','avg_wc_br1_br3','set_point_ventilation_unit','avg_t_lr','avg_t_br2','avg_t_br1_br3'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf2 = RandomForestRegressor(n_estimators=1000,n_jobs=-1,oob_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_windspeed</th>\n",
       "      <th>avg_irradiation</th>\n",
       "      <th>avg_rh</th>\n",
       "      <th>avg_t</th>\n",
       "      <th>avg_co2_lr</th>\n",
       "      <th>avg_co2_br2</th>\n",
       "      <th>avg_co2_br1_br3</th>\n",
       "      <th>HourOfDay</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>181</td>\n",
       "      <td>72</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1024.7</td>\n",
       "      <td>717.1</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>96</td>\n",
       "      <td>72</td>\n",
       "      <td>7.2</td>\n",
       "      <td>939.1</td>\n",
       "      <td>914.2</td>\n",
       "      <td>900.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>74</td>\n",
       "      <td>6.9</td>\n",
       "      <td>863.9</td>\n",
       "      <td>823.1</td>\n",
       "      <td>736.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>77</td>\n",
       "      <td>6.2</td>\n",
       "      <td>765.4</td>\n",
       "      <td>624.5</td>\n",
       "      <td>592.9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>-3</td>\n",
       "      <td>79</td>\n",
       "      <td>5.9</td>\n",
       "      <td>646.1</td>\n",
       "      <td>577.2</td>\n",
       "      <td>589.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>84</td>\n",
       "      <td>5.5</td>\n",
       "      <td>575.5</td>\n",
       "      <td>566.9</td>\n",
       "      <td>596.6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>83</td>\n",
       "      <td>5.6</td>\n",
       "      <td>549.3</td>\n",
       "      <td>581.7</td>\n",
       "      <td>661.5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>-3</td>\n",
       "      <td>86</td>\n",
       "      <td>5.3</td>\n",
       "      <td>566.6</td>\n",
       "      <td>634.4</td>\n",
       "      <td>672.4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>-3</td>\n",
       "      <td>86</td>\n",
       "      <td>5.4</td>\n",
       "      <td>570.5</td>\n",
       "      <td>612.0</td>\n",
       "      <td>627.9</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>84</td>\n",
       "      <td>5.7</td>\n",
       "      <td>569.9</td>\n",
       "      <td>605.4</td>\n",
       "      <td>632.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>-2</td>\n",
       "      <td>82</td>\n",
       "      <td>6.1</td>\n",
       "      <td>695.1</td>\n",
       "      <td>615.9</td>\n",
       "      <td>610.6</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>-3</td>\n",
       "      <td>83</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1038.6</td>\n",
       "      <td>547.5</td>\n",
       "      <td>597.4</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>88</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1017.1</td>\n",
       "      <td>617.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>-3</td>\n",
       "      <td>86</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1228.6</td>\n",
       "      <td>538.7</td>\n",
       "      <td>596.7</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>88</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1306.8</td>\n",
       "      <td>530.8</td>\n",
       "      <td>584.7</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>90</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1330.4</td>\n",
       "      <td>681.8</td>\n",
       "      <td>798.2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>90</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1405.6</td>\n",
       "      <td>802.8</td>\n",
       "      <td>946.1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>90</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1229.1</td>\n",
       "      <td>828.6</td>\n",
       "      <td>927.8</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>93</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1114.9</td>\n",
       "      <td>868.7</td>\n",
       "      <td>926.0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>95</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>822.5</td>\n",
       "      <td>860.4</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>95</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1096.4</td>\n",
       "      <td>796.7</td>\n",
       "      <td>856.5</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>91</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1114.4</td>\n",
       "      <td>811.6</td>\n",
       "      <td>871.2</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>198</td>\n",
       "      <td>83</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>782.7</td>\n",
       "      <td>821.8</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>179</td>\n",
       "      <td>82</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1213.6</td>\n",
       "      <td>745.1</td>\n",
       "      <td>796.1</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>81</td>\n",
       "      <td>8.1</td>\n",
       "      <td>1155.5</td>\n",
       "      <td>763.2</td>\n",
       "      <td>898.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>85</td>\n",
       "      <td>7.7</td>\n",
       "      <td>915.0</td>\n",
       "      <td>760.9</td>\n",
       "      <td>961.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>87</td>\n",
       "      <td>7.5</td>\n",
       "      <td>791.0</td>\n",
       "      <td>746.5</td>\n",
       "      <td>873.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>86</td>\n",
       "      <td>7.3</td>\n",
       "      <td>731.3</td>\n",
       "      <td>736.2</td>\n",
       "      <td>825.2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2</td>\n",
       "      <td>-3</td>\n",
       "      <td>87</td>\n",
       "      <td>7.1</td>\n",
       "      <td>695.1</td>\n",
       "      <td>728.4</td>\n",
       "      <td>813.7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "      <td>-3</td>\n",
       "      <td>86</td>\n",
       "      <td>7.0</td>\n",
       "      <td>677.7</td>\n",
       "      <td>727.0</td>\n",
       "      <td>816.7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15964</th>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>92</td>\n",
       "      <td>3.2</td>\n",
       "      <td>649.2</td>\n",
       "      <td>762.1</td>\n",
       "      <td>705.3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15965</th>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>91</td>\n",
       "      <td>3.6</td>\n",
       "      <td>646.4</td>\n",
       "      <td>758.4</td>\n",
       "      <td>698.6</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15966</th>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>87</td>\n",
       "      <td>4.6</td>\n",
       "      <td>647.9</td>\n",
       "      <td>756.1</td>\n",
       "      <td>694.1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15967</th>\n",
       "      <td>1</td>\n",
       "      <td>119</td>\n",
       "      <td>86</td>\n",
       "      <td>5.1</td>\n",
       "      <td>648.0</td>\n",
       "      <td>755.7</td>\n",
       "      <td>696.8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15968</th>\n",
       "      <td>2</td>\n",
       "      <td>235</td>\n",
       "      <td>81</td>\n",
       "      <td>6.5</td>\n",
       "      <td>645.0</td>\n",
       "      <td>759.5</td>\n",
       "      <td>702.8</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15969</th>\n",
       "      <td>2</td>\n",
       "      <td>226</td>\n",
       "      <td>74</td>\n",
       "      <td>7.2</td>\n",
       "      <td>639.2</td>\n",
       "      <td>768.0</td>\n",
       "      <td>694.6</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15970</th>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>71</td>\n",
       "      <td>7.3</td>\n",
       "      <td>663.8</td>\n",
       "      <td>767.3</td>\n",
       "      <td>696.4</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15971</th>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>73</td>\n",
       "      <td>7.1</td>\n",
       "      <td>945.8</td>\n",
       "      <td>941.7</td>\n",
       "      <td>700.2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15972</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>75</td>\n",
       "      <td>6.0</td>\n",
       "      <td>977.4</td>\n",
       "      <td>959.8</td>\n",
       "      <td>851.3</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15973</th>\n",
       "      <td>1</td>\n",
       "      <td>-5</td>\n",
       "      <td>82</td>\n",
       "      <td>4.5</td>\n",
       "      <td>973.6</td>\n",
       "      <td>1025.9</td>\n",
       "      <td>701.7</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15974</th>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>89</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1553.4</td>\n",
       "      <td>1203.2</td>\n",
       "      <td>791.4</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15975</th>\n",
       "      <td>1</td>\n",
       "      <td>-5</td>\n",
       "      <td>94</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1088.4</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>835.8</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15976</th>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>97</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1708.1</td>\n",
       "      <td>1338.8</td>\n",
       "      <td>833.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15977</th>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>98</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1124.5</td>\n",
       "      <td>1388.9</td>\n",
       "      <td>1151.2</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15978</th>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>100</td>\n",
       "      <td>1.4</td>\n",
       "      <td>984.5</td>\n",
       "      <td>1639.6</td>\n",
       "      <td>1396.5</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15979</th>\n",
       "      <td>0</td>\n",
       "      <td>-4</td>\n",
       "      <td>100</td>\n",
       "      <td>1.1</td>\n",
       "      <td>891.3</td>\n",
       "      <td>1794.2</td>\n",
       "      <td>1346.5</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15980</th>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1385.8</td>\n",
       "      <td>1924.9</td>\n",
       "      <td>1157.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15981</th>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1169.5</td>\n",
       "      <td>1849.6</td>\n",
       "      <td>1593.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15982</th>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>100</td>\n",
       "      <td>0.8</td>\n",
       "      <td>953.8</td>\n",
       "      <td>1580.8</td>\n",
       "      <td>1743.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15983</th>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>97</td>\n",
       "      <td>1.1</td>\n",
       "      <td>871.1</td>\n",
       "      <td>1378.6</td>\n",
       "      <td>1782.3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15984</th>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "      <td>95</td>\n",
       "      <td>1.4</td>\n",
       "      <td>852.9</td>\n",
       "      <td>1282.9</td>\n",
       "      <td>1750.6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15985</th>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>95</td>\n",
       "      <td>1.2</td>\n",
       "      <td>847.5</td>\n",
       "      <td>1244.9</td>\n",
       "      <td>1715.7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15986</th>\n",
       "      <td>0</td>\n",
       "      <td>-4</td>\n",
       "      <td>96</td>\n",
       "      <td>0.5</td>\n",
       "      <td>826.3</td>\n",
       "      <td>1232.2</td>\n",
       "      <td>1697.6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15987</th>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "      <td>100</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>821.4</td>\n",
       "      <td>1211.0</td>\n",
       "      <td>1717.1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15988</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>99</td>\n",
       "      <td>0.1</td>\n",
       "      <td>860.2</td>\n",
       "      <td>1233.4</td>\n",
       "      <td>1640.3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15989</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>99</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1060.3</td>\n",
       "      <td>1467.4</td>\n",
       "      <td>1352.2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15990</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>100</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1047.3</td>\n",
       "      <td>1247.7</td>\n",
       "      <td>1167.2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15991</th>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>99</td>\n",
       "      <td>1.7</td>\n",
       "      <td>974.9</td>\n",
       "      <td>1136.2</td>\n",
       "      <td>1060.4</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15992</th>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>96</td>\n",
       "      <td>2.6</td>\n",
       "      <td>823.2</td>\n",
       "      <td>1046.8</td>\n",
       "      <td>978.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15993</th>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>91</td>\n",
       "      <td>3.1</td>\n",
       "      <td>807.7</td>\n",
       "      <td>977.9</td>\n",
       "      <td>899.4</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15994 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       avg_windspeed  avg_irradiation  avg_rh  avg_t  avg_co2_lr  avg_co2_br2  \\\n",
       "0                  4              181      72    7.7      1024.7        717.1   \n",
       "1                  3               96      72    7.2       939.1        914.2   \n",
       "2                  2               44      74    6.9       863.9        823.1   \n",
       "3                  2               -1      77    6.2       765.4        624.5   \n",
       "4                  2               -3      79    5.9       646.1        577.2   \n",
       "5                  1               -4      84    5.5       575.5        566.9   \n",
       "6                  1               -3      83    5.6       549.3        581.7   \n",
       "7                  2               -3      86    5.3       566.6        634.4   \n",
       "8                  2               -3      86    5.4       570.5        612.0   \n",
       "9                  1               -2      84    5.7       569.9        605.4   \n",
       "10                 2               -2      82    6.1       695.1        615.9   \n",
       "11                 2               -3      83    5.9      1038.6        547.5   \n",
       "12                 1               -3      88    5.7      1017.1        617.0   \n",
       "13                 2               -3      86    6.1      1228.6        538.7   \n",
       "14                 1               -4      88    5.6      1306.8        530.8   \n",
       "15                 1               -3      90    5.7      1330.4        681.8   \n",
       "16                 1               -4      90    5.9      1405.6        802.8   \n",
       "17                 1               -4      90    5.5      1229.1        828.6   \n",
       "18                 1               -4      93    4.8      1114.9        868.7   \n",
       "19                 1                8      95    4.5      1140.0        822.5   \n",
       "20                 1               86      95    4.6      1096.4        796.7   \n",
       "21                 1              174      91    6.2      1114.4        811.6   \n",
       "22                 2              198      83    7.7      1218.0        782.7   \n",
       "23                 2              179      82    8.0      1213.6        745.1   \n",
       "24                 2              129      81    8.1      1155.5        763.2   \n",
       "25                 2               52      85    7.7       915.0        760.9   \n",
       "26                 2               19      87    7.5       791.0        746.5   \n",
       "27                 2               -1      86    7.3       731.3        736.2   \n",
       "28                 2               -3      87    7.1       695.1        728.4   \n",
       "29                 2               -3      86    7.0       677.7        727.0   \n",
       "...              ...              ...     ...    ...         ...          ...   \n",
       "15964              0               -2      92    3.2       649.2        762.1   \n",
       "15965              1               44      91    3.6       646.4        758.4   \n",
       "15966              1               90      87    4.6       647.9        756.1   \n",
       "15967              1              119      86    5.1       648.0        755.7   \n",
       "15968              2              235      81    6.5       645.0        759.5   \n",
       "15969              2              226      74    7.2       639.2        768.0   \n",
       "15970              2              174      71    7.3       663.8        767.3   \n",
       "15971              1               90      73    7.1       945.8        941.7   \n",
       "15972              1               16      75    6.0       977.4        959.8   \n",
       "15973              1               -5      82    4.5       973.6       1025.9   \n",
       "15974              1               -4      89    3.7      1553.4       1203.2   \n",
       "15975              1               -5      94    3.4      1088.4       1382.0   \n",
       "15976              0               -5      97    2.6      1708.1       1338.8   \n",
       "15977              0               -5      98    1.9      1124.5       1388.9   \n",
       "15978              1               -4     100    1.4       984.5       1639.6   \n",
       "15979              0               -4     100    1.1       891.3       1794.2   \n",
       "15980              1               -4     100    1.0      1385.8       1924.9   \n",
       "15981              1               -4     100    1.0      1169.5       1849.6   \n",
       "15982              1               -3     100    0.8       953.8       1580.8   \n",
       "15983              1               -3      97    1.1       871.1       1378.6   \n",
       "15984              0               -3      95    1.4       852.9       1282.9   \n",
       "15985              1               -3      95    1.2       847.5       1244.9   \n",
       "15986              0               -4      96    0.5       826.3       1232.2   \n",
       "15987              0               -3     100   -0.2       821.4       1211.0   \n",
       "15988              1               -1      99    0.1       860.2       1233.4   \n",
       "15989              2               10      99    0.7      1060.3       1467.4   \n",
       "15990              1               37     100    1.2      1047.3       1247.7   \n",
       "15991              2               61      99    1.7       974.9       1136.2   \n",
       "15992              2              128      96    2.6       823.2       1046.8   \n",
       "15993              2              104      91    3.1       807.7        977.9   \n",
       "\n",
       "       avg_co2_br1_br3  HourOfDay  Month  \n",
       "0                770.0          0      1  \n",
       "1                900.6          1      1  \n",
       "2                736.8          2      1  \n",
       "3                592.9          3      1  \n",
       "4                589.0          4      1  \n",
       "5                596.6          5      1  \n",
       "6                661.5          6      1  \n",
       "7                672.4          7      1  \n",
       "8                627.9          8      1  \n",
       "9                632.0          9      1  \n",
       "10               610.6         10      1  \n",
       "11               597.4         11      1  \n",
       "12               684.0         12      1  \n",
       "13               596.7         13      1  \n",
       "14               584.7         14      1  \n",
       "15               798.2         15      1  \n",
       "16               946.1         16      1  \n",
       "17               927.8         17      1  \n",
       "18               926.0         18      1  \n",
       "19               860.4         19      1  \n",
       "20               856.5         20      1  \n",
       "21               871.2         21      1  \n",
       "22               821.8         22      1  \n",
       "23               796.1         23      1  \n",
       "24               898.7          0      1  \n",
       "25               961.6          1      1  \n",
       "26               873.5          2      1  \n",
       "27               825.2          3      1  \n",
       "28               813.7          4      1  \n",
       "29               816.7          5      1  \n",
       "...                ...        ...    ...  \n",
       "15964            705.3          8      1  \n",
       "15965            698.6          9      1  \n",
       "15966            694.1         10      1  \n",
       "15967            696.8         11      1  \n",
       "15968            702.8         12      1  \n",
       "15969            694.6         13      1  \n",
       "15970            696.4         14      1  \n",
       "15971            700.2         15      1  \n",
       "15972            851.3         16      1  \n",
       "15973            701.7         17      1  \n",
       "15974            791.4         18      1  \n",
       "15975            835.8         19      1  \n",
       "15976            833.0         20      1  \n",
       "15977           1151.2         21      1  \n",
       "15978           1396.5         22      1  \n",
       "15979           1346.5         23      1  \n",
       "15980           1157.7          0      1  \n",
       "15981           1593.6          1      1  \n",
       "15982           1743.0          2      1  \n",
       "15983           1782.3          3      1  \n",
       "15984           1750.6          4      1  \n",
       "15985           1715.7          5      1  \n",
       "15986           1697.6          6      1  \n",
       "15987           1717.1          7      1  \n",
       "15988           1640.3          8      1  \n",
       "15989           1352.2          9      1  \n",
       "15990           1167.2         10      1  \n",
       "15991           1060.4         11      1  \n",
       "15992            978.0         12      1  \n",
       "15993            899.4         13      1  \n",
       "\n",
       "[15994 rows x 9 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=1000, n_jobs=-1, oob_score=True, random_state=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf2.fit(gc01,gc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred2 = rf2.predict(gc01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2a5aa3410>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D8pR+V/rvf0uTJsVRhiZ06M81s9sbsSVHcAFAvcp3/KrWjeeuu0pP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Wwl5ZVQt4ux/uua4fGdylXUCVdozndWKaNmqmtXvTr5gw/qnRzLCxaEENTUUO3j77+3L7PeSutc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\ntVwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2984c9650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,6), dpi=1600) \n",
    "plt.plot(timestamp,gc) \n",
    "plt.plot(timestamp,pred) \n",
    "plt.ylim(0,1.0)\n",
    "plt.legend(('real','prediction'),loc='best') \n",
    "plt.title('Type 505 Gas Consumption Regression without Behavior Attributes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.934029682382\n"
     ]
    }
   ],
   "source": [
    "print rf2.score(gc01,gc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.512505175797\n"
     ]
    }
   ],
   "source": [
    "print rf2.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xinyuyangren/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: sort is deprecated, use sort_values(inplace=True) for for INPLACE sorting\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x299026390>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CKibpHOEmYFREtKZlrZIWAuuAp4D1wDOdxL4MmCPpE8ALZBfGLJD05hRjD3BtOix6Cdmh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x299030c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances = pd.Series(rf2.feature_importances_,index = gc01.columns)\n",
    "feature_importances.sort(inplace = True)\n",
    "feature_importances.plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred2oob = rf2.oob_prediction_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x29d515e10>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d3XOPtMMO6ZUFALLu/POlq69OuxTJCTtGRz2ujhol3XtvbcoR/fyB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IVdLQhenz52TBW0lJZGfhxUVkb8/8jI5c+da3yO/v3iq/TAa/y4915tZvIYU59HURE9HQzTjREC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\nuhUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x296ca9790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,6), dpi=1600) \n",
    "plt.plot(timestamp,gc) \n",
    "plt.plot(timestamp,pred2oob) \n",
    "plt.ylim(0,1.0)\n",
    "plt.legend(('real','prediction'),loc='best') \n",
    "plt.title('Type 505 Gas Consumption OOB Regression without Behavior Attributes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}