code/sensitivity-numhomes.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": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Vendor:  Continuum Analytics, Inc.\n",
      "Package: mkl\n",
      "Message: trial mode expires in 24 days\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sensitivity_numhomes_dir = \"../data/output/sensitivity-numhomes/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "appliance_homes={\"hvac\":33,\"fridge\":21,\"dr\":26,\"wm\":16,\"dw\":32,\"light\":12}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "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": {},
   "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": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from common_functions import latexify, format_axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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": {},
   "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": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10f020450>"
      ]
     },
     "execution_count": 12,
     "output_type": "execute_result",
     "metadata": {}
    },
    {
     "data": {
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fBwS3x9nsOHCEy651TqTT1Eicv7sujyIy3/GF9l23XEQ2dee4vREr4jCjqgVA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\nYII=\n"
     },
     "output_type": "display_data",
     "metadata": {}
    }
   ],
   "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\")"
   ]
  }
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