code/sensitivity-numhomes-all.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook find the sensitivity of Gemello to the number of homes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sensitivity_numhomes_dir = \"../sensitivity-data-new/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "appliance_homes={\"hvac\":33,\"fridge\":21,\"dr\":26,\"wm\":16,\"dw\":32,\"light\":12}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No objects to concatenate",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-6a7f10661412>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m                     \u001b[0mcount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mappliance\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_homes\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/nipunbatra/anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)\u001b[0m\n\u001b[1;32m    810\u001b[0m                        \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    811\u001b[0m                        \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 812\u001b[0;31m                        copy=copy)\n\u001b[0m\u001b[1;32m    813\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    814\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/nipunbatra/anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)\u001b[0m\n\u001b[1;32m    843\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    844\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 845\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No objects to concatenate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    846\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    847\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
     ]
    }
   ],
   "source": [
    "appliances = [\"hvac\",\"fridge\",\"dr\"]\n",
    "out = {}\n",
    "count=0\n",
    "for appliance in appliances:\n",
    "    out[appliance]={}\n",
    "    for feature in [\"Monthly\",\"Static\", \"Monthly+Static\"]:\n",
    "        out[appliance][feature]={}\n",
    "        appliance_max =appliance_homes[appliance]\n",
    "        for num_homes in range(7, appliance_max, 2):\n",
    "            out[appliance][feature][num_homes] = {}\n",
    "            t = []\n",
    "            for start_seed in range(0, 100, 1):\n",
    "                stop_seed = start_seed+1\n",
    "                try:\n",
    "                    t.append(pd.read_csv(\"%s%s_%d_%d_%d_%s.csv\" %(sensitivity_numhomes_dir, appliance, \n",
    "                                                                               num_homes, \n",
    "                                                                               start_seed, \n",
    "                                                                              stop_seed,\n",
    "                                                                              feature), index_col=0,header=None, names=['a'] ))\n",
    "                except:\n",
    "                    count=count+1\n",
    "            out[appliance][feature][num_homes] = pd.concat(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "means = {}\n",
    "sems = {}\n",
    "for appliance in [\"hvac\",\"dr\",\"fridge\"]:\n",
    "    means[appliance] = {}\n",
    "    sems[appliance] = {}\n",
    "    for feature in [\"Monthly\",\"Static\", \"Monthly+Static\"]:\n",
    "        means[appliance][feature] ={}\n",
    "        sems[appliance][feature] ={}\n",
    "        for num_homes in range(7, appliance_homes[appliance], 2):\n",
    "            means[appliance][feature][num_homes] = out[appliance][feature][num_homes].mean(axis=1).mean()\n",
    "            sems[appliance][feature][num_homes] = out[appliance][feature][num_homes].mean(axis=1).sem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from common_functions import latexify, format_axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nipunbatra/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n",
      "/Users/nipunbatra/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: text.fontsize is deprecated and replaced with font.size; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "latexify()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "to_use_feature = {'dr': 'Monthly+Static',\n",
    " 'dw': 'Monthly+Static',\n",
    " 'fridge': 'Monthly+Static',\n",
    " 'hvac': 'Monthly',\n",
    " 'light': 'Static',\n",
    " 'wm': 'Static'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "appliance_name_map = {\"fridge\":\"Fridge\",\"hvac\":\"HVAC\",\"wm\":\"Washing\\nmachine\",\n",
    "                      \"dr\":\"Dryer\",\"dw\":\"Dish\\nwasher\",\"light\":\"Lights\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10f020450>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPEAAACdCAYAAABl/Z4oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "latexify(fig_height=2)\n",
    "tableau20blind =  [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),  \n",
    "             (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),  \n",
    "             (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),  \n",
    "             (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),  \n",
    "             (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n",
    "  \n",
    "# Rescale to values between 0 and 1\n",
    "for i in range(len(tableau20blind)):  \n",
    "    r, g, b = tableau20blind[i]  \n",
    "    tableau20blind[i] = (r / 255., g / 255., b / 255.)\n",
    "\n",
    "for i, appliance in enumerate([\"fridge\",\"hvac\"]):\n",
    "    means_df = pd.DataFrame(means[appliance])\n",
    "    sems_df = pd.DataFrame(sems[appliance])\n",
    "    feature = to_use_feature[appliance]\n",
    "    plt.errorbar(means_df[feature].index, means_df[feature].values, marker='.',\n",
    "                 yerr=sems_df[feature].values, label=appliance_name_map[appliance], color=tableau20blind[i*2])\n",
    "#.plot(kind='line',rot=0, colors=tableau20blind[::2])\n",
    "plt.ylim((0, 80))\n",
    "format_axes(plt.gca())\n",
    "plt.legend(loc=4)\n",
    "#plt.ylim((62, 78))\n",
    "plt.ylim((55, 80))\n",
    "plt.xlim((5, 33))\n",
    "plt.ylabel(\"Energy Accuracy (\\%)\\n(Higher is better)\")\n",
    "plt.xlabel(\"Number of submetered homes\")\n",
    "#plt.savefig(\"/Users/nipunbatra/git/monthly-disaggregation/results/sensitivity-num-homes.pdf\", bbox_inches=\"tight\")\n",
    "#plt.savefig(\"/Users/nipunbatra/git/monthly-disaggregation/results/sensitivity-num-homes.png\", bbox_inches=\"tight\")"
   ]
  }
 ],
 "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
}