code/mf-short-test-features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matrix_factorisation import nmf_features, transform, transform_2, preprocess, get_static_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cvxpy as cvx\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import  os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "from features import feature_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import  os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "from create_df_larger import read_df_larger\n",
    "df, dfc, all_homes, appliance_min, national_average = read_df_larger()\n",
    "\n",
    "df = df.rename(columns={'house_num_rooms':'num_rooms',\n",
    "                        'num_occupants':'total_occupants',\n",
    "                        'difference_ratio_min_max':'ratio_difference_min_max'})\n",
    "K_min, K_max = 1,6\n",
    "F_min, F_max=1,8\n",
    "\n",
    "from all_functions import *\n",
    "from features_larger import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "feature_combinations = [['None']]\n",
    "for l in range(1,4):\n",
    "    for a in itertools.combinations(['occ','area','rooms'], l):\n",
    "        feature_combinations.append(list(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fridge\n",
      "28.3997325236 ['None'] 1\n",
      "31.9768780113 ['None'] 2\n",
      "30.8229177565 ['None'] 3\n",
      "30.9856796647 ['None'] 4\n",
      "36.7142198273 ['None'] 5\n",
      "37.069110831 ['None'] 6\n",
      "39.8378630108 ['None'] 7\n",
      "41.5939288477 ['None'] 8\n",
      "38.3064769451 ['None'] 9\n",
      "34.1280479944 ['occ'] 1\n",
      "24.7904464353 ['occ'] 2\n",
      "30.4286952912 ['occ'] 3\n",
      "28.951977106 ['occ'] 4\n",
      "33.7381230135 ['occ'] 5\n",
      "34.4363986101 ['occ'] 6\n",
      "34.9652639518 ['occ'] 7\n",
      "39.9654528172 ['occ'] 8\n",
      "37.566813555 ['occ'] 9\n",
      "28.1653388835 ['area'] 1\n",
      "28.7183355178 ['area'] 2\n",
      "31.4975226403 ['area'] 3\n",
      "30.600713927 ['area'] 4\n",
      "35.832572931 ['area'] 5\n",
      "33.9378583293 ['area'] 6\n",
      "34.677863696 ['area'] 7\n",
      "47.353092134 ['area'] 8\n",
      "41.9533444902 ['area'] 9\n",
      "29.3970658269 ['rooms'] 1\n",
      "32.4193738445 ['rooms'] 2\n",
      "30.6751040581 ['rooms'] 3\n",
      "30.3118376727 ['rooms'] 4\n",
      "35.8659054965 ['rooms'] 5\n",
      "35.986859439 ['rooms'] 6\n",
      "39.4716580698 ['rooms'] 7\n",
      "40.587136059 ['rooms'] 8\n",
      "37.4722493256 ['rooms'] 9\n",
      "24.747916968 ['occ', 'area'] 2\n",
      "24.6696879985 ['occ', 'area'] 3\n",
      "27.2969141989 ['occ', 'area'] 4\n",
      "30.8456628417 ['occ', 'area'] 5\n",
      "29.0983476263 ['occ', 'area'] 6\n",
      "30.8492898666 ['occ', 'area'] 7\n",
      "38.204025454 ['occ', 'area'] 8\n",
      "40.9743474832 ['occ', 'area'] 9\n",
      "25.9908546486 ['occ', 'rooms'] 2\n",
      "30.496838499 ['occ', 'rooms'] 3\n",
      "29.0383878227 ['occ', 'rooms'] 4\n",
      "33.4153622714 ['occ', 'rooms'] 5\n",
      "34.4189403555 ['occ', 'rooms'] 6\n",
      "34.1696267147 ['occ', 'rooms'] 7\n",
      "39.44908037 ['occ', 'rooms'] 8\n",
      "38.2673087387 ['occ', 'rooms'] 9\n",
      "29.3059060765 ['area', 'rooms'] 2\n",
      "31.6208455582 ['area', 'rooms'] 3\n",
      "31.360606985 ['area', 'rooms'] 4\n",
      "35.9365535487 ['area', 'rooms'] 5\n",
      "33.1924408383 ['area', 'rooms'] 6\n",
      "37.97107438 ['area', 'rooms'] 7\n",
      "43.422378106 ['area', 'rooms'] 8\n",
      "36.8920984029 ['area', 'rooms'] 9\n",
      "24.7112925176 ['occ', 'area', 'rooms'] 3\n",
      "30.3147147219 ['occ', 'area', 'rooms'] 4\n",
      "29.8298547078 ['occ', 'area', 'rooms'] 5\n",
      "33.2876024318 ['occ', 'area', 'rooms'] 6\n",
      "32.515264211 ['occ', 'area', 'rooms'] 7\n",
      "40.1581627682 ['occ', 'area', 'rooms'] 8\n",
      "41.3955980113 ['occ', 'area', 'rooms'] 9\n",
      "hvac\n",
      "26.8015097595 ['None'] 1\n",
      "34.2166622083 ['None'] 2\n",
      "32.5781926393 ['None'] 3\n",
      "39.3230493173 ['None'] 4\n",
      "46.8725363467 ['None'] 5\n",
      "39.1873313931 ['None'] 6\n",
      "28.9670158945 ['None'] 7\n",
      "39.4589299504 ['None'] 8\n",
      "26.2925158907 ['None'] 9\n",
      "48.0679152021 ['occ'] 1\n",
      "26.7302416609 ['occ'] 2\n",
      "33.8678439088 ['occ'] 3\n",
      "34.3535430228 ['occ'] 4\n",
      "47.7644297823 ['occ'] 5\n",
      "44.7438647076 ['occ'] 6\n",
      "51.3710000019 ['occ'] 7\n",
      "38.1545102141 ['occ'] 8\n",
      "25.6367941453 ['occ'] 9\n",
      "34.976306054 ['area'] 1\n",
      "26.2292631924 ['area'] 2\n",
      "33.3521243243 ['area'] 3\n",
      "32.6141604609 ['area'] 4\n",
      "50.7410583932 ['area'] 5\n",
      "46.0322881245 ['area'] 6\n",
      "47.6067265263 ['area'] 7\n",
      "37.5914985458 ['area'] 8\n",
      "25.4748652895 ['area'] 9\n",
      "28.0262562492 ['rooms'] 1\n",
      "31.9043232528 ['rooms'] 2\n",
      "31.4071141175 ['rooms'] 3\n",
      "52.0781756263 ['rooms'] 4\n",
      "49.5484917695 ['rooms'] 5\n",
      "41.2122342682 ['rooms'] 6\n",
      "28.7225150032 ['rooms'] 7\n",
      "38.3449694186 ['rooms'] 8\n",
      "27.2858312619 ['rooms'] 9\n",
      "36.8514185467 ['occ', 'area'] 2\n",
      "26.2894539333 ['occ', 'area'] 3\n",
      "31.1563361366 ['occ', 'area'] 4\n",
      "31.0465683332 ['occ', 'area'] 5\n",
      "39.1251503759 ['occ', 'area'] 6\n",
      "50.2762398644 ['occ', 'area'] 7\n",
      "36.8045135735 ['occ', 'area'] 8\n",
      "26.8100014366 ['occ', 'area'] 9\n",
      "27.6762656005 ['occ', 'rooms'] 2\n",
      "32.51866173 ['occ', 'rooms'] 3\n",
      "33.302750008 ['occ', 'rooms'] 4\n",
      "47.5692790653 ['occ', 'rooms'] 5\n",
      "46.0987991011 ['occ', 'rooms'] 6\n",
      "49.8382219547 ['occ', 'rooms'] 7\n",
      "34.1766654993 ['occ', 'rooms'] 8\n",
      "26.2228416187 ['occ', 'rooms'] 9\n",
      "26.8148630145 ['area', 'rooms'] 2\n",
      "31.8296541378 ['area', 'rooms'] 3\n",
      "37.948026252 ['area', 'rooms'] 4\n",
      "40.2719565946 ['area', 'rooms'] 5\n",
      "40.6475128431 ['area', 'rooms'] 6\n",
      "44.0772668706 ['area', 'rooms'] 7\n",
      "26.3119006897 ['area', 'rooms'] 8\n",
      "26.785309575 ['area', 'rooms'] 9\n",
      "28.2263846884 ['occ', 'area', 'rooms'] 3\n",
      "33.295804452 ['occ', 'area', 'rooms'] 4\n",
      "38.2304365869 ['occ', 'area', 'rooms'] 5\n",
      "38.6119233811 ['occ', 'area', 'rooms'] 6\n",
      "50.07863319 ['occ', 'area', 'rooms'] 7\n",
      "37.7088364612 ['occ', 'area', 'rooms'] 8\n",
      "22.0407512899 ['occ', 'area', 'rooms'] 9\n",
      "mw\n",
      "44.3055725024 ['None'] 1\n",
      "46.8336703914 ['None'] 2\n",
      "49.2192962767 ['None'] 3\n",
      "49.5144968661 ['None'] 4\n",
      "45.908175048 ['None'] 5\n",
      "51.5855836107 ['None'] 6\n",
      "52.4324579881 ['None'] 7\n",
      "52.7158050856 ['None'] 8\n",
      "52.5120655618 ['None'] 9\n",
      "49.3625026165 ['occ'] 1\n",
      "44.5581401762 ['occ'] 2\n",
      "47.0629319369 ['occ'] 3\n",
      "48.0421376593 ['occ'] 4\n",
      "47.4709149924 ['occ'] 5\n",
      "45.8454134734 ['occ'] 6\n",
      "51.3476688371 ['occ'] 7\n",
      "52.3839479216 ['occ'] 8\n",
      "48.1395878663 ['occ'] 9\n",
      "45.3916644009 ['area'] 1\n",
      "43.9291882918 ['area'] 2\n",
      "46.3988882861 ['area'] 3\n",
      "49.8730260039 ['area'] 4\n",
      "50.0027632699 ['area'] 5\n",
      "47.3858625841 ['area'] 6\n",
      "51.0029425729 ['area'] 7\n",
      "51.1804556983 ['area'] 8\n",
      "47.8157323401 ['area'] 9\n",
      "44.5447183423 ['rooms'] 1\n",
      "46.1550333321 ['rooms'] 2\n",
      "49.3459484278 ['rooms'] 3\n",
      "49.5387520942 ['rooms'] 4\n",
      "46.5193275037 ['rooms'] 5\n",
      "50.2054342846 ['rooms'] 6\n",
      "51.7544072512 ['rooms'] 7\n",
      "54.2667295895 ['rooms'] 8\n",
      "53.3388642555 ['rooms'] 9\n",
      "45.3212038461 ['occ', 'area'] 2\n",
      "43.8601331881 ['occ', 'area'] 3\n",
      "45.8953888081 ['occ', 'area'] 4\n",
      "48.0321295156 ['occ', 'area'] 5\n",
      "48.3676985878 ['occ', 'area'] 6\n",
      "46.6815704415 ['occ', 'area'] 7\n",
      "49.0931216491 ['occ', 'area'] 8\n",
      "50.3330531942 ['occ', 'area'] 9\n",
      "45.1821288491 ['occ', 'rooms'] 2\n",
      "46.9225176379 ['occ', 'rooms'] 3\n",
      "47.6171296341 ['occ', 'rooms'] 4\n",
      "47.4512284231 ['occ', 'rooms'] 5\n",
      "44.7203088021 ['occ', 'rooms'] 6\n",
      "50.958894197 ['occ', 'rooms'] 7\n",
      "52.377107082 ['occ', 'rooms'] 8\n",
      "50.3728565522 ['occ', 'rooms'] 9\n",
      "44.0231475355 ['area', 'rooms'] 2\n",
      "43.9928964457 ['area', 'rooms'] 3\n",
      "49.8479400579 ['area', 'rooms'] 4\n",
      "50.6279565964 ['area', 'rooms'] 5\n",
      "49.7456596499 ['area', 'rooms'] 6\n",
      "50.09983332 ['area', 'rooms'] 7\n",
      "48.4363723025 ['area', 'rooms'] 8\n",
      "52.6920016637 ['area', 'rooms'] 9\n",
      "44.1968767483 ['occ', 'area', 'rooms'] 3\n",
      "45.2231915688 ['occ', 'area', 'rooms'] 4\n",
      "48.1910742798 ['occ', 'area', 'rooms'] 5\n",
      "50.0992928211 ['occ', 'area', 'rooms'] 6\n",
      "51.0617988442 ['occ', 'area', 'rooms'] 7\n",
      "51.317020939 ['occ', 'area', 'rooms'] 8\n",
      "48.309942498 ['occ', 'area', 'rooms'] 9\n",
      "wm\n",
      "44.6968900864 ['None'] 1\n",
      "41.8377411106 ['None'] 2\n",
      "43.975973673 ['None'] 3\n",
      "55.1150462886 ['None'] 4\n",
      "53.6600638564 ['None'] 5\n",
      "48.9832988042 ['None'] 6\n",
      "48.8507837408 ['None'] 7\n",
      "55.2932614958 ['None'] 8\n",
      "53.4134612326 ['None'] 9\n",
      "44.2390003584 ['occ'] 1\n",
      "46.5305761694 ['occ'] 2\n",
      "42.5950520844 ['occ'] 3\n",
      "46.4502078252 ['occ'] 4\n",
      "55.704995218 ['occ'] 5\n",
      "54.8356360296 ['occ'] 6\n",
      "43.1333233797 ['occ'] 7\n",
      "60.2130893929 ['occ'] 8\n",
      "53.4927508473 ['occ'] 9\n",
      "49.6413098181 ['area'] 1\n",
      "44.973261166 ['area'] 2\n",
      "40.9971439003 ['area'] 3\n",
      "44.4861736755 ['area'] 4\n",
      "50.6596148944 ['area'] 5\n",
      "54.3233863892 ['area'] 6\n",
      "43.0115759694 ['area'] 7\n",
      "58.6484769327 ['area'] 8\n",
      "55.0741485995 ['area'] 9\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "zero-size array to reduction operation",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-4a48d99009a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     52\u001b[0m                 \u001b[0;31m# Ensure repeatably random problem data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_home\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnmf_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0midx_user\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_user\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m                 \u001b[0mpred_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/nipunbatra/git/monthly-disaggregation/code/matrix_factorisation.py\u001b[0m in \u001b[0;36mnmf_features\u001b[0;34m(A, k, idx_user, data_user, idx_item, data_item, MAX_ITERS)\u001b[0m\n\u001b[1;32m     65\u001b[0m                 \u001b[0;31m#print num_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mindex_feature\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfe_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx_user\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m                     \u001b[0mconstraint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex_feature\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx_user\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfe_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mdata_user\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfe_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m         \u001b[0;31m#print constraint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m         \u001b[0;31m#return constraint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/nipunbatra/anaconda/lib/python2.7/site-packages/cvxpy-0.4.2-py2.7.egg/cvxpy/expressions/expression.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    211\u001b[0m         \u001b[0;31m# the built-in sum to hang.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mku\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_special_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 213\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcvxtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_special_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    214\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mcvxtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/nipunbatra/anaconda/lib/python2.7/site-packages/cvxpy-0.4.2-py2.7.egg/cvxpy/atoms/affine/index.pyc\u001b[0m in \u001b[0;36mget_special_slice\u001b[0;34m(expr, key)\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0;31m# Select the chosen entries from expr.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m         \u001b[0midentity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meye\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtocsc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselect_vec\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mvec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mfinal_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/nipunbatra/anaconda/lib/python2.7/site-packages/cvxpy-0.4.2-py2.7.egg/cvxpy/expressions/expression.pyc\u001b[0m in \u001b[0;36mcast_op\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     40\u001b[0m         \"\"\"A wrapped binary operator that can handle non-Expression arguments.\n\u001b[1;32m     41\u001b[0m         \"\"\"\n\u001b[0;32m---> 42\u001b[0;31m         \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast_to_const\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbinary_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcast_op\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/nipunbatra/anaconda/lib/python2.7/site-packages/cvxpy-0.4.2-py2.7.egg/cvxpy/expressions/expression.pyc\u001b[0m in \u001b[0;36mcast_to_const\u001b[0;34m(expr)\u001b[0m\n\u001b[1;32m    235\u001b[0m         \"\"\"Converts a non-Expression to a Constant.\n\u001b[1;32m    236\u001b[0m         \"\"\"\n\u001b[0;32m--> 237\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mexpr\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExpression\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mcvxtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0m_cast_other\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/nipunbatra/anaconda/lib/python2.7/site-packages/cvxpy-0.4.2-py2.7.egg/cvxpy/expressions/constants/constant.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0;31m# Set DCP attributes.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_pos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_neg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mConstant\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/nipunbatra/anaconda/lib/python2.7/site-packages/cvxpy-0.4.2-py2.7.egg/cvxpy/interface/matrix_utilities.pyc\u001b[0m in \u001b[0;36msign\u001b[0;34m(constant, tol)\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0mmin_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m         \u001b[0mmax_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstant\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    134\u001b[0m         \u001b[0mmin_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstant\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# Convert to Numpy array.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/nipunbatra/anaconda/lib/python2.7/site-packages/scipy/sparse/data.pyc\u001b[0m in \u001b[0;36mmax\u001b[0;34m(self, axis)\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0mMaximum\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \"\"\"\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_min_or_max\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaximum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/nipunbatra/anaconda/lib/python2.7/site-packages/scipy/sparse/data.pyc\u001b[0m in \u001b[0;36m_min_or_max\u001b[0;34m(self, axis, min_or_max)\u001b[0m\n\u001b[1;32m    153\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis\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[1;32m    154\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zero-size array to reduction operation\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    157\u001b[0m             \u001b[0mzero\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\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;31mValueError\u001b[0m: zero-size array to reduction operation"
     ]
    }
   ],
   "source": [
    "#out_overall = {}\n",
    "#error_overall = {}\n",
    "#for appliance in ['dw','oven']:\n",
    "for appliance in ['fridge','hvac','mw','wm']:\n",
    "\n",
    "    out = {}\n",
    "    error = {}\n",
    "    print appliance\n",
    "    if appliance==\"hvac\":\n",
    "        start, end = 5,11\n",
    "    else:\n",
    "        start, end=1,13\n",
    "    X_matrix, X_normalised, col_max, col_min, appliance_cols, aggregate_cols = preprocess(df, dfc, appliance)\n",
    "    static_features= get_static_features(dfc, X_normalised)\n",
    "    \n",
    "    \n",
    "    \n",
    "    for feature_comb in np.array(feature_combinations)[:]:\n",
    "        out[tuple(feature_comb)]={}\n",
    "        error[tuple(feature_comb)]={}\n",
    "        \n",
    "        if 'None' in feature_comb:\n",
    "            idx_user=None\n",
    "            data_user=None\n",
    "        else:\n",
    "            idx_user = {}\n",
    "            data_user = {}\n",
    "            dictionary_static = {}\n",
    "            for feature in feature_comb:\n",
    "                dictionary_static[feature]=static_features[feature]\n",
    "            static_features_df = pd.DataFrame(dictionary_static, index=range(len(X_normalised.index)))\n",
    "\n",
    "\n",
    "            for fe in static_features_df.columns:\n",
    "                idx_user[fe]=np.where(static_features_df[fe].notnull())[0]\n",
    "                data_user[fe]=static_features_df[fe].dropna().values\n",
    "            \n",
    "        for lat in range(1,10):\n",
    "            \n",
    "            if lat<len(feature_comb):\n",
    "                continue\n",
    "            out[tuple(feature_comb)][lat]={}\n",
    "\n",
    "\n",
    "            for home in X_normalised.index:\n",
    "                \n",
    "\n",
    "                X_home = X_normalised.copy()\n",
    "                for month in range(start, end):\n",
    "                    X_home.loc[home, '%s_%d' %(appliance, month)] = np.NAN\n",
    "                mask = X_home.notnull().values\n",
    "                # Ensure repeatably random problem data.\n",
    "                A = X_home.copy()\n",
    "                X, Y, res = nmf_features(A, lat,idx_user, data_user, 10)\n",
    "\n",
    "                pred_df = pd.DataFrame(Y*X)\n",
    "                pred_df.columns = X_normalised.columns\n",
    "                pred_df.index = X_normalised.index\n",
    "                out[tuple(feature_comb)][lat][home] = transform_2(pred_df.ix[home], appliance, col_max, col_min)[appliance_cols]\n",
    "\n",
    "            # Evaluation\n",
    "\n",
    "            gt = X_matrix[appliance_cols]\n",
    "\n",
    "            pr = pd.DataFrame(out[tuple(feature_comb)][lat]).T\n",
    "\n",
    "            error_df = (pr-gt).abs()\n",
    "            error[tuple(feature_comb)][lat] = error_df.div(gt).mul(100).median().mean()\n",
    "            print error[tuple(feature_comb)][lat], feature_comb, lat\n",
    "    out_overall[appliance]=out\n",
    "    error_overall[appliance]=error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>None</th>\n",
       "      <th colspan=\"2\" halign=\"left\">area</th>\n",
       "      <th colspan=\"4\" halign=\"left\">occ</th>\n",
       "      <th>rooms</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>rooms</th>\n",
       "      <th>NaN</th>\n",
       "      <th colspan=\"2\" halign=\"left\">area</th>\n",
       "      <th>rooms</th>\n",
       "      <th>NaN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>rooms</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.399733</td>\n",
       "      <td>28.165339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.128048</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.397066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.976878</td>\n",
       "      <td>28.718336</td>\n",
       "      <td>29.305906</td>\n",
       "      <td>24.790446</td>\n",
       "      <td>24.747917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.990855</td>\n",
       "      <td>32.419374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.822918</td>\n",
       "      <td>31.497523</td>\n",
       "      <td>31.620846</td>\n",
       "      <td>30.428695</td>\n",
       "      <td>24.669688</td>\n",
       "      <td>24.711293</td>\n",
       "      <td>30.496838</td>\n",
       "      <td>30.675104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.985680</td>\n",
       "      <td>30.600714</td>\n",
       "      <td>31.360607</td>\n",
       "      <td>28.951977</td>\n",
       "      <td>27.296914</td>\n",
       "      <td>30.314715</td>\n",
       "      <td>29.038388</td>\n",
       "      <td>30.311838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36.714220</td>\n",
       "      <td>35.832573</td>\n",
       "      <td>35.936554</td>\n",
       "      <td>33.738123</td>\n",
       "      <td>30.845663</td>\n",
       "      <td>29.829855</td>\n",
       "      <td>33.415362</td>\n",
       "      <td>35.865905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>37.069111</td>\n",
       "      <td>33.937858</td>\n",
       "      <td>33.192441</td>\n",
       "      <td>34.436399</td>\n",
       "      <td>29.098348</td>\n",
       "      <td>33.287602</td>\n",
       "      <td>34.418940</td>\n",
       "      <td>35.986859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>39.837863</td>\n",
       "      <td>34.677864</td>\n",
       "      <td>37.971074</td>\n",
       "      <td>34.965264</td>\n",
       "      <td>30.849290</td>\n",
       "      <td>32.515264</td>\n",
       "      <td>34.169627</td>\n",
       "      <td>39.471658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>41.593929</td>\n",
       "      <td>47.353092</td>\n",
       "      <td>43.422378</td>\n",
       "      <td>39.965453</td>\n",
       "      <td>38.204025</td>\n",
       "      <td>40.158163</td>\n",
       "      <td>39.449080</td>\n",
       "      <td>40.587136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38.306477</td>\n",
       "      <td>41.953344</td>\n",
       "      <td>36.892098</td>\n",
       "      <td>37.566814</td>\n",
       "      <td>40.974347</td>\n",
       "      <td>41.395598</td>\n",
       "      <td>38.267309</td>\n",
       "      <td>37.472249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        None       area                   occ                        \\\n",
       "         NaN        NaN      rooms        NaN       area              \n",
       "         NaN        NaN        NaN        NaN        NaN      rooms   \n",
       "1  28.399733  28.165339        NaN  34.128048        NaN        NaN   \n",
       "2  31.976878  28.718336  29.305906  24.790446  24.747917        NaN   \n",
       "3  30.822918  31.497523  31.620846  30.428695  24.669688  24.711293   \n",
       "4  30.985680  30.600714  31.360607  28.951977  27.296914  30.314715   \n",
       "5  36.714220  35.832573  35.936554  33.738123  30.845663  29.829855   \n",
       "6  37.069111  33.937858  33.192441  34.436399  29.098348  33.287602   \n",
       "7  39.837863  34.677864  37.971074  34.965264  30.849290  32.515264   \n",
       "8  41.593929  47.353092  43.422378  39.965453  38.204025  40.158163   \n",
       "9  38.306477  41.953344  36.892098  37.566814  40.974347  41.395598   \n",
       "\n",
       "                  rooms  \n",
       "       rooms        NaN  \n",
       "         NaN        NaN  \n",
       "1        NaN  29.397066  \n",
       "2  25.990855  32.419374  \n",
       "3  30.496838  30.675104  \n",
       "4  29.038388  30.311838  \n",
       "5  33.415362  35.865905  \n",
       "6  34.418940  35.986859  \n",
       "7  34.169627  39.471658  \n",
       "8  39.449080  40.587136  \n",
       "9  38.267309  37.472249  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(error_overall['fridge'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.568681550176741"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "pd.DataFrame(error).T.min().min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'fridge_1', u'fridge_2', u'fridge_3', u'fridge_4', u'fridge_5',\n",
       "       u'fridge_6', u'fridge_7', u'fridge_8', u'fridge_9', u'fridge_10',\n",
       "       u'fridge_11', u'fridge_12', u'aggregate_1', u'aggregate_2',\n",
       "       u'aggregate_3', u'aggregate_4', u'aggregate_5', u'aggregate_6',\n",
       "       u'aggregate_7', u'aggregate_8', u'aggregate_9', u'aggregate_10',\n",
       "       u'aggregate_11', u'aggregate_12'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_matrix.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['None'],\n",
       " ['occ'],\n",
       " ['area'],\n",
       " ['rooms'],\n",
       " ['occ', 'area'],\n",
       " ['occ', 'rooms'],\n",
       " ['area', 'rooms'],\n",
       " ['occ', 'area', 'rooms']]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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