Segmentation Generation from Census and PUMAS

Segmentation.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from sqlalchemy import create_engine\n",
    "engine = create_engine('postgresql://stuartlynn@localhost:5432/stuartlynn')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Census segmentation Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Census produces summary tables of a large number of variables such as number of individuals in a given age bracket, rent range etc. While these are often sufficent for producing segments the joint probabilities for say vision problems by household income are not often avaliable. \n",
    "\n",
    "Public use microdata however contain the individual responses of a number of individual at PUMA tract levels which allow any conditional probability distribution of any set of variables to be computed. \n",
    "\n",
    "This document is a test to see if we can train a machine learning model to predict custom probability distributions for a given set of microdata variables and then map these to other census geometries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a given breakdown of variables we use the [Data Ferret](http://dataferrett.census.gov/)  tool provided by the census. This is not ideal for general use but it allows us to perform some easy tests. \n",
    "\n",
    "From there we extract the variables $DEYE$, $DEAR$ and $age$ brackets   \n",
    "\n",
    "\n",
    "|   | DEYE               | DEAR                |\n",
    "|---|--------------------|---------------------|\n",
    "| 1 | Has Vision problem | Has Hearing problem |\n",
    "| 2 | No Vision problem  | No Hearing problem  |\n",
    "\n",
    "The datafile pums_age_vision_hearing.csv extracted from Data Ferret has an entry per survey respondant with their age and their responses to the eye and ear questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "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>AGEP</th>\n",
       "      <th>DEAR</th>\n",
       "      <th>DEYE</th>\n",
       "      <th>PUMA10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGEP  DEAR  DEYE  PUMA10\n",
       "0    42     2     2      -9\n",
       "1    42     2     2      -9\n",
       "2     4     2     2      -9\n",
       "3     3     2     2      -9\n",
       "4    58     2     2      -9"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/pums_age_vision_hearing.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x5b89c1190>"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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fV5+o4/4LgF1xD7Z/92AnPQMIAAAAgM9zCqACN/JwJuYk+75kD1DPLu5L9j3JHdYUQAAA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\nC+aVNVwy25LWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x618737150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure( figsize= (20,10))\n",
    "plt.subplot(121)\n",
    "labels  = ['> 50 problem', '>50 no problem', '<50 problem', '<50 no problem']\n",
    "ax = data.groupby([data['AGEP'] < 50, 'DEAR']).count()['AGEP'].plot(kind='bar')\n",
    "ax.set_xticklabels(labels)\n",
    "ax.set_title(\"EAR\")\n",
    "plt.subplot(122)\n",
    "labels  = ['> 50 problem', '>50 no problem', '<50 problem', '<50 no problem']\n",
    "ax = data.groupby([data['AGEP'] < 50, 'DEYE']).count()['AGEP'].plot(kind='bar')\n",
    "ax.set_xticklabels(labels)\n",
    "ax.set_title(\"EYE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are also going to read in the pumus summary table which contains the same columns as the other census geometries. This will give us the training data we need to train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pumas = pd.read_sql_query('select * from pumas_joined', engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Generate the target distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal is going to be to calculate the joint distribution of a person having eye and ear problems per ceunsus geometry. To do this we group by PUMA and count up the various different variations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "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>eye_ear</th>\n",
       "      <th>eye_noear</th>\n",
       "      <th>noeye_ear</th>\n",
       "      <th>noeye_no_ear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUMA10</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-9</th>\n",
       "      <td>52160</td>\n",
       "      <td>100221</td>\n",
       "      <td>203987</td>\n",
       "      <td>5817341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2221</td>\n",
       "      <td>3774</td>\n",
       "      <td>9436</td>\n",
       "      <td>188686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>398</td>\n",
       "      <td>924</td>\n",
       "      <td>1786</td>\n",
       "      <td>53645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>422</td>\n",
       "      <td>955</td>\n",
       "      <td>1920</td>\n",
       "      <td>57291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>296</td>\n",
       "      <td>734</td>\n",
       "      <td>1266</td>\n",
       "      <td>44842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        eye_ear  eye_noear  noeye_ear  noeye_no_ear\n",
       "PUMA10                                             \n",
       "-9        52160     100221     203987       5817341\n",
       " 100       2221       3774       9436        188686\n",
       " 101        398        924       1786         53645\n",
       " 102        422        955       1920         57291\n",
       " 103        296        734       1266         44842"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eye_ear = data[data['DEYE'] ==1][data['DEAR']==1].groupby(\"PUMA10\").count()['AGEP']\n",
    "eye_noear = data[data['DEYE']==1][data['DEAR']==2].groupby(\"PUMA10\").count()['AGEP']\n",
    "noeye_ear = data[data['DEYE']==2][data['DEAR']==1].groupby(\"PUMA10\").count()['AGEP']\n",
    "noeye_noear = data[data['DEYE']==2][data['DEAR']==2].groupby(\"PUMA10\").count()['AGEP']\n",
    "\n",
    "summary = pd.DataFrame({ \"eye_ear\": eye_ear, \n",
    "                         \"eye_noear\": eye_noear,\n",
    "                         \"noeye_ear\": noeye_ear,\n",
    "                         \"noeye_no_ear\":noeye_noear})\n",
    "summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot out some of the breakdown of our counts across a few PUMAS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x5d1353b50>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x6352e28d0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x64f1c5650>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x64eb193d0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x10f184050>], dtype=object)"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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tjtVNp46sX/GRI/VVTe2pysaDdZWNuxpqmh9JpVJH8z0fAACwMLz2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\nWq7ESgWrOZPBg0FlEEJcDeDDWJl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/quY2pZQ/AnDhy7j/0vVLs3b4wbeKWU6F1S6JiDag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\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mk/miTbh+EXQXzdp08H3f0AjPoAAAl688u+7izTOW/zDoKXyIQRmAURngqrntvu1GrXm7\n357dsOXQaq76AQA+kUFnVB5qeWJXeXbdNaGM/I2yewAAQOwRQoyk6FJfCHmLQyfOHsk91X2Cq34A\ngE9UEKjovXPul5/P8RZ/TlXVEdk9AAAg9qRZHV0mg+3lHG/x7I0H3nOPRIZlJwEAYpxG0Si3zFx5\npK5g7oNledU/lt0DAABik9eR/oYqXNGR6Ej4cNcek+weAEDsc1i8kZWLnn6/JHNSo6qqp2X3AACA\n2COE6LUaHS/k+EpmbGn7MDAw1C87CQAwAUwrXnhmSd3d/1BZUPeIEEJ2DhATGJUBRoEzzdMmtOZ3\nuOoHAPgkZqNVWbno6U1VOdMaPS7fIdk9AAAgdgkhokJnfjHdmaNe6rtQxFU/AMBfUxma2n3j9If+\nLeQpWspVBQAA8NdYzNbzaor7Fzne4pmbD37gGxwekJ0EAIhR2iSdcn/zY/vD2VNuLAyWvSW7BwAA\nxDavI/ChJTntjDCYJu09tjVVdg8AIHZlOHMHH1j4rVfz/OWtqqr2yO4BAACxSwjRn5qc9nyur3TK\ntkNrMvsGezSymwAAsWtx7Z3H54SXPBrOn/xd2S1ALGFUBhglH1/1eynXVzp708H33cMjQ7KTAAAx\nxp7qiaxc/PT7xVxVAAAAl0kIofic6a/bUlwRJaqUt3Xt5qofAOC/aShqOru47s6/ryqs56oCAAC4\nLEKIHovR/nyur6xh++E1GTy+BAD8KaMhRVnRump7OFi/MCsQ2iG7BwAATAxuu39zit5y0GHx1G8/\nstYquwcAEHtKMiddumPOI8+FvMU3qao6LLsHAADEPiHEoNlgey7XV1azp2Nz5qX+C0mymwAAsUWj\nSVLumPM3hybnzb6vJLfyBdk9QKxhVAYYRRaz9YIqXD/L8ZZM33podWBgqE92EgAgRmQ4c4YeXPDt\n3+f5y1u4qgAAAK6U1xFYnWpQT6cKW83ujk1c9QMA/D9zK6492VR1wxMVBbVPyW4BAAATixBiwGyw\nPZvnL6/e27E562Ifjy8BAH9gNdmVlYueXhsOTpnvcriPye4BAAATiyvNu1dozRsCjuD0zW0fpilK\nVHYSACBGTClsPPvZKff9Y1Vh/XIhBP9AAACAyyaEGDbprc/l+koLjp05FDxzsVMvuwkAEBv0umTl\nwYXf2VMRnHJtblbRh7J7gFjEqAwwyoQQvZbktGfz/OWTdx3dkNkzcJHHlwCQ4IrSq3runPuVF0Le\n4hu4qgAAAD4tt92/RejMezy2jCnbDn1kk90DAJDv2ob7O6YWL/hKOL/mn2W3AACAiUkIMZyitzyf\n4ystPty1N3Tu0imd7CYAgFweNWN4WcuTbxWlVzWrqnpBdg8AAJiYHKrrqNClvp3tLpi16eD7jkg0\nIjsJACDZgkmfOzG/8rpvVeRzLAMAAHw6QoiI0KX+MsuVr/YOXCrsOH1QyG4CAMhlNlqVlYue3lSW\nXdsY8GYckN0DxCpGZYAxIIQYMumtz+b6S8vaOncHz/ec4fElACSo2vw5566b+vl/CXoKH1BVlasK\nAADgqrjSvPuTtaaPstwFMzYdfN8e5fElACQknVav3N/82P6q0NTbinMqfiO7BwAATGwfP778eba7\nMON094m8rvMdybKbAABy5HhL+u9rfPTFXF/pElVVB2T3AACAiU212E+a9LaXcr2lszYd/MA9PDIo\nOwkAIIFG0Sg3z1hxpL5w/kNludU/kt0DAAAmNiGE4nWmv6amuHqNhpTwvmNbU2U3AQDkcFp9kRWL\nnnqvJHNSo8PuPC27B4hljMoAY0QIEUnRWX4W9BRldp3vyDt54ZhBdhMAYHw1Vt7Q1VR946qKgtpv\nCcEAMgAAGB12m/OYSW95NddXOntz24cuHl8CQGJJFTZl5aKnN1cGpy7I9Ae3ye4BAADxQQih+Jzp\nLznMAb1G0RS3de4yyW4CAIyvytC0CzfPWPbvIW/xXaqqsmYNAABGhcVsvaCmuJ7P95fX72rfmN47\ncFEjuwkAMH60STrl/qZv7q8MTb2pMFj6puweAAAQPzx2/zqTwbrPq2bWbT28xqYo3IAGE6IJBwAA\nIABJREFUgESS7S7sX9r82Mt5/vJFqqr2yu4BYh2jMsAYEkJEfc703zhNvpRINFJ0uGtPiuwmAMD4\nuH7q59unlSx4uCxv0g9ktwAAgPhjTbWdTUtxP5/vL6/f07HZ39PfnSS7CQAw9nxpWUPLWp98pzhj\nUpPD7jgpuwcAAMQfryPwrsVoP+qy+Wu2H15rk90DABgfs8quOdUy+dbvVhXWf5VjGQAAYLQJIfrN\nBvUnef6ywo4zh7LPXOzSy24CAIw9oyFFWdG6ans4OGVhlj+0Q3YPAACIP640774UvfXdHG/RjM0H\nP3SMRIZlJwEAxkFFsKH7lllf+FHIW3ybqqojsnuAiYBRGWAceJ3pb1uM9uMui696x5F1Vtk9AICx\nY9AZlaXNj+2rCE65vShU/orsHgAAEL+EEH1mg/qTPF9ZXtf59uxTF44bZDcBAMZOWVbtpTvmPvKz\nXF/pdaqq9snuAQAA8cuV5t1p0lnfz/GVTtvS9gGPLwEgjmkUjXJdw9KOaSULv1KRP/n/l90DAADi\nlxBiJEVn+XmWO982MNRXcPTUAZbsACCOqWZndEXr0+vCwfpGl8N9THYPAACIX2lWe6fD7P9lrq90\n2rbDa/39Q72ykwAAY2hW2TWnFtXe/r3KgrpHOJYBXD5GZYBx4rb7tpv0tvdzfKVTt7R94OTxJQDE\nH3uqO7pi0dPrqnOnNwe8mTtl9wAAgPgnhBgRutRfZjrzDBpNUlFb5y6T7CYAwOibG7725KLaO/6u\nqqBupRAiIrsHAADEvzSb44TD7PtFrq9s+vYja/z9gzy+BIB4k6wXyucXPL4nHGr4XHEo/LLsHgAA\nEP+EEIrXkf66KlznrKa0il3tGy2ymwAAoy/PX957f9M3fluYXtGqquoF2T0AACD+CSEuWZLtP8kP\nlFe2de7OvNBzRiu7CQAw+q5rWNoxvaTlkXB+DccygCvEqAwwjj5+fPnzPF9Zw44j6/x9gz0a2U0A\ngNGR7w/33tf0jVcKAhUtqqqek90DAAASxx8eXwbesRjth3xpmTXbDq+1RZWo7CwAwCjQaJKUW2au\nONJQ3PSF8rxJ35fdAwAAEssfHl+m/STPVx4+fHJv5vme0zrZTQCA0eG0eCPLFz21pipnWlPAm7FH\ndg8AAEgsbrt/k0ln3RT0FE7Z0rbaHomOyE4CAIyS2eVLTn2m/p5/CnmL71NVdUh2DwAASBxCiKEU\nvfXZkKfY3917Nvf42cNG2U0AgNGRrBfKAwu+vaciNOWWolD4Fdk9wETEqAwwzoQQPanJaT/J84dL\n208dyDp76SSPLwFggptdvuTUkvq7vx/yFN2vquqw7B4AAJCYXGne3Sa97c2CQPm0zW2rXcMjg7KT\nAABXIVlvVB5c+J3dFcEp1+Vnl7wpuwcAACQmIcSQSW99LugpcvQN9uS2nz4oZDcBAK5OScaknrvn\nf/XXBYHwIlVVu2X3AACAxORQ3UfSUty/zguUT91+eJ2vf6hXdhIA4CokabTKbbMfPtRQ1LSyPK/m\nH4Tgz4gAAGD8CSGiPmf6y2kpnkFhMJXtO7Y1VXYTAODquG2BkeWtT31UmTOVYxnAVWBUBpBACDGc\norc8n+0utI1EhvMOn9ybIrsJAHDlkjRa5dbZXzzcUNT0BT6CAQCAWKBa7SfTUjzPFwTCdbvbNwd6\n+ruTZDcBAK6canZGVy56Zn1laGqTzx04ILsHAAAkto8fX/5OFe6zaamuil1HN1hlNwEAPp2mqps6\nWyff9r2gp2i5qqojsnsAAEBiM5nM3Vaj46f5gXD5kZP7Ms5dOsWRRgCYgMxGq7K8ddW28mD9krys\nondl9wAAAHgcgY9MBuvuTFd+7bZDa9IiUf4cDgATUUWwofuOOY/8Ij9QvkRV1Yuye4CJjFEZQBIh\nhOJ1pr9mMzqPedSM6u2H19miSlR2FgDgMpmSU5XlrU9uDwenLMnLKnpHdg8AAMB/EEL0mQ22n+T5\nynJPXTieffLCMYPsJgDA5Qt5igaWNj/+WkF6RbOqqudl9wAAAPwHj92/yWxQV+d4Sxq2HPrQMRIZ\nlp0EALhM2iSdcve8rxycnD/n82V51f/KsQwAABArhBBDKXrLs9nuQnv/YG9e++kD/IcKAEwgGc6c\noQdbnni7NKumyePydcjuAQAA+A+uNO9+h8n36/xAuGF3+0Zf78AljewmAMDlu6buruONVTesqiys\n+5IQIiK7B5joGJUBJHPbfdtTk9U38wPhqVvbVruGRgZlJwEAPoHfnj30UMsT75Vl1TZ6XL522T0A\nAAB/SggR8Tkzfuk0B3Q6rb7owIkdZtlNAIBPNr/y+pNL6u/5l5C3+C5VVYdk9wAAAPwpu815zGn2\n/zzXV9aw8+gGf98gjy8BINZZU9KU5a1PbaoITmkJZeStk90DAADwp4QQis+Z/ntVuE6npboqdx3d\nYJXdBAD4ZLX5c87dPGPFj3J9JTepqtonuwcAAOBPmUzmCxaj/Ud5/nDemYtdGV3n25NlNwEA/rpk\nvVH5fPPje6tzp99enFPxnOweIF4wKgPEANViP5mW4n42PxCefOD4jkB33zmt7CYAwJ9XnTO9+9bZ\nX3wuz1d2HR/BAABArPM6Au9ZjGkH/WnZNdsOr1WjUUa6ASAWGXRG5d75X98/OX/2A2V5k/5RCBGV\n3QQAAPCXCCF6LMlpP873l+efvXQqo/Mcjy8BIFZluwv7H1j4rdeLM6oXuJzuk7J7AAAA/hq33b/Z\npLd+UJheWbf98FrX4PCA7CQAwJ+hUTTKdQ1LO2aHlzxWUVD7ON82AQBALBNCDPudGT93mv3D1pS0\noj3tmy2ymwAAf57T6ossb31qbVXOtKaAN2On7B4gnjAqA8QIIUS/2WD7cY6vJNA70BPqOH3QKLsJ\nAPBfXVN31/H5ldc9UVlQ91U+ggEAgInClebdYxXOV4vSq+p3Hl3v7R/s5Yo8AMQQj5oxsqz1ybXl\nwfrm7EDORtk9AAAAl0MIMeJ3Zf7clRrotae6i3e2b7QqCn82B4BYMrWo+cwN0x74t5C35HZVVflF\nNgAAmBDsNtfxNJPnpwWBivKOM22BsxdP6mU3AQD+U7JeKA8s/PaeytDUm4pC4Rdl9wAAAFwujyOw\nOjXZvjYvUF639dAa5/DIoOwkAMAfKcuqvXT3vK/8Kj8QXqSq6gXZPUC8YVQGiCFCiKjPmfFSmslz\nIt0RCm8/slaNcEUeAKQTBrOytPmxPZWhqXeW5FQ+J7sHAADgSllTbWesRsePCgIVhae7OzO6zncY\nZDcBABSlNn/O+VtmrnwhPxD+jMPu5CMYAACYcDx2/1qL0fF2cXpV/Y4j69wDQ32ykwAg4SVptMpN\nM5YdmV7a+uVw/uRnOJYBAAAmGiHEgElv/Wm2u8AgDKb8fce3mWU3AQAUxW0LRJa1rvqoMjS10e9J\n3ye7BwAA4ErZbc52h8n3fEGgoubgiV3+7t6zWtlNAABFaam59cTCSZ/7bmVh/QohxIjsHiAeMSoD\nxCC33bc1Tbh/U5xRXbf32FZPT393kuwmAEhUIW/xwOcXfOudylBDc7o3c7fsHgAAgE9LCDHsd2a8\n4LakD1lN9qLd7ZssspsAIFElabTKzTOXH51ZuujRioK6bwghWJYGAAATVprV3qmmuH5cEKgo6TrX\nHjjVfYIhUwCQJM3sii5rfXJzeXbd9QXZpa/L7gEAAPi0hBCK15H+jtXo2JjnK6vddnitgyvyACBP\nQ1Hzuc/NWP58fiB8raqq3bJ7AAAAPi0hRK/ZYPtxjrfEPTg8EDp6ar+Q3QQAiUqvS1bub/zG/pq8\nWXeX5FT+H9k9QDxjVAaIUalmy3lLsv3/5PnK0vuHerPaTx/kf1AAYJwtrLmlc3HtHd/P9ZXcoapq\nr+weAACA0eCx+1fbjM4PCgLhum2H17iGhgdkJwFAQrGa7Mrylie3VgSnfiY/u/h3snsAAABGgxBi\nMEVveT7Lna+kCrVwb8fmVNlNAJBoJuXOuHDH3C/9qiijerHX7T8muwcAAGA0OFT3YafZ/3xBIFx9\nqGuP/0LPGa7IA8A4MuiMyp1zv3ywoajx4YqC2ie4GA8AAOKBECLqc6b/VhWu4760rMrtR9bZolFu\nggHAeEp35Aw9tPCJNWXZdQuz/KGtsnuAeMeoDBDDhBAjPmfGi/YU7/GAI1S+/chaNcL/oADAmDMZ\nLcoDC761qzpn+m2luVU/FIJdLwAAEF/SrI7jaSbPs4WByopjZw/7z1zs0stuAoBEUBio7L2v6Ruv\nFmdOWuhxefmBHwAAiCtCCMXjCLyfmpz2UUEgXLv9yFqGTAFgHGiTdMqts75waFbZ4kcrCuq+JoQY\nlt0EAAAwmv7fFXlPsU2jScpp69xlkt0EAIkgYA8OPdTyxJqK0JSFwfS81bJ7AAAARpvb7tumCtfv\nCgIVU7YfWecdGOqTnQQACaGx8oauaxvu+7c8f9lNDrvzguweIBEwKgNMAG67b1uacL9UnDGpdu+x\nLd6e/u4k2U0AEK/y/eG+pc3ffLM8u7454M3cJ7sHAABgrAgh+k1660+yPQW6VGHL29uxhSvyADCG\nWmpuPdFSc+v/F/IWfV5V1UHZPQAAAGPFYXN12E3enxYEKio6Th8MnL10Uie7CQDildsWGFneumpD\nZWja4pyMgjdl9wAAAIwVIUTU60x/zWp07A55iyZtP7zGPhJhSw8Axsqc8GdOXTd16b/n+ctv4Ad+\nAAAgnllSbWdswvnjgkC4+Ex3Z6DrfIdBdhMAxCtTcqqytPnxXTX/l737/o+rvBM9ftQ1aqNneh/1\nLlnuFXdKMC2hl9CSUNJII5tk00PKZjckF7KQZJcSQgmETmixwdjYuGDZKlbv0mg0/Rk1q0v3B8jN\nvXtDQrF9VD7vv+DzwrzmpXOe83y/hVtvKs9ffq9Go5lVuwlYLBgqA8wT6WkZ0Ywk3UMFtgrH+ORo\nVm+oTaN2EwAsJDFKjPLxtZ/ynrfyk7/KtZbeLIQYU7sJAADgVNNoNIrV4NiTmWw6UORYuvp492HT\nxBR/BgHAyZSWrFVuPff7jcvzNt1Ynr/89xoNr/UAAMDC97dBpsXJyQkpBa3e2jS1mwBgodlQcm7k\nmi1f+mORo/ISk8EUUrsHAADgdDDprC3mDNeThfbKNa3eOtvQaJQljQBwEmkSU5Wbz/lu8+rCbZ9d\nUrDqLi74AQCAxUCj0UymJGQ87jYVDluEq7ih90jmzOyM2lkAsKAUOZaO3nru916rzFm/w2l1N6jd\nAyw2DJUB5hGNRjNtM7qe16WY+5yGvMrj3YfFzOy02lkAMO+lazKVz59/R92y3I3XlOYtfZQLfgAA\nYLHRZRo8ulTLI8XOpeV+6XEEB/sT1G4CgIWgMmf90KfP+tZfKnPWnee0upvU7gEAADid3hlk6nxd\nqzHU5NsqVtV1HTRMTU+qnQUA815ifLJy45nfaNtQcs7Xlhau/olGo+HDEQAAsKhoNJrh9CTxUL6t\nwjo5NZHdHWxJUbsJABaCHEvJ+Od2/HDv8ryNO1y27Gq1ewAAAE4njUajWPT2Q4Y02wtlrpWrOvyN\n1sETkkGmAPARxcTEKpdtuMVz7oqrf5FrLblVCDGqdhOwGDFUBpiHzHpbrTHN8VyRc+ma5r5a68jY\nIA8oAPAhlbpWjNx0znderchac57d4uxQuwcAAEAtGo1mPCVB+0e3uXBan24ubPIc086yaQEAPpSE\n+CTl2q1f7dy25BM/WF687qsajWZM7SYAAAC1GIS5w5hmf6LIsbQyEO2zMsgUAD48hyF36gvn/fit\npbnrz8txFhxQuwcAAEAtGo1mxmZ0/lmfaunIs5ZXNPQcMUxOT6idBQDz1nmrPum7aM2N9+bbym8Q\nQoyo3QMAAKCW9LQMmZ6k+32+rTwzKSE5u9Vbl6Z2EwDMV7o00+wXz/9x9fK8jVcU5ZQ/qdFo1E4C\nFi2GygDzVGpq2kBGkv6hAluFLTY21t3ha2TTAgB8ADExscol62/ynLviqn/PtZZ8QQgxrnYTAACA\n2t7ZIu/Yp0uxvFKetXpFp6/RPDgq49TuAoD5JMtcNPGF8+44sCJv0wU5zoLX1O4BAACYCzQazYm0\nxMyHsy1Fo1adq7ChpypzZnZa7SwAmFfOrLwkePmGzz5YYK+40qA3DqjdAwAAMBeYdNYGU7rzsSLn\n0jI5HLT6o71JajcBwHySlqxVPrfjRw0r8zdfX5a37H6NRjOrdhMAAIDaNBrNjNXgfFWXYj5c4lq+\noqG3yjw+Oap2FgDMKyvzt0RvOPPrz5a6VlxoNds8avcAi13M7CzvfID5rrnz+Fldgeb/uH/nv5VH\nR0Jq5wDAnGfTuaeu23Z7tdOY95lcZ0G12j0AAABzkZQyvtPX+G8Hm3dd+fzhh6yzszNqJwHAnBYT\nE6tctOZG7+qCrb/PMhd+WwjBDycAAMDfEQj63a3euof/sPuXKzv9jVz2A4B/Ii1Zq9xw5tebs83F\nXy3NrXxR7R4AAIC5qrblyC2NnqO3P/LGr3LGJ8fUzgGAOa/cvXrk0g037ymwL7laCBFVuwcAAGAu\nklKmtXrrHnj+0INnvt36hlbtHgCY6xLiEpVrtny5o8y98o6yvGUPqN0D4B0MlQEWCCllWlv/8d/s\nPPanj+05/med2j0AMBfFxMQq56/8ZP+aojMfz7EUf10IMal2EwAAwFzX3tO8sifY+pv7dv6sMjjg\njVW7BwDmIkOGdebTZ32zxmnMuynPVXRE7R4AAIC5TkoZ2xVo/kFV297rn9r/X46Z2Wm1kwBgTlpd\nuH1gx4qrXy+wV3xKCCHV7gEAAJjrpJT2xt6jf3hsz91rW7y1yWr3AMBclJSgUa7Z8qX2IvvSXzqN\nufcIIbhUBAAA8E/Uth65odlT/a8PvX5n7sQUg0wB4O9xGvImr99++9sFtoorTUZzj9o9AP6GoTLA\nAlPfXn1Bt7/5pw+89vOSwRN8TwQAf2XOdE7fsP3r1U5j7i1c8AMAAPhgpJRJHb6Gu9+oe/6ivxz7\nk1HtHgCYSzaVnR8+a9llL+RaSm4VQvDFAAAAwAfQ4+0s7/Q33ffgrp8v9Ua649XuAYC5Ii1Zq1y3\n7WtNOZbiH5blLXtM7R4AAID5REoZ0x1o+VZ1x/6bntj3G9f0zJTaSQAwZ5S7V49cvP6mt4ocldcK\nIXxq9wAAAMwn/qDP3t5f/8hDr9+5ptPfmKR2DwDMFXGx8crF6z7Tuyz3jIezzIXfEUKwWQiYYxgq\nAyxAUsqMVm/dfS++/cj2g807M9XuAQA1xSgxyo6VV/vWF5/zZLal+KtCiAm1mwAAAOarps667T3B\n1jvv+8vPyqMjIbVzAEBVqUnpyo1nfqMpx1L8jZLcyufU7gEAAJivpJQJHb7G/9jf8PLlLx151Dyr\n8A0DgMVtdcG2gR0rr3m9wF7xKSEE24QAAAA+JE9/d2Gnv+nBB1/7jxWeUDuDTAEsakkJGuWTW77c\nVmiv/JXTmHuPEIKXcAAAAB+ClDK2y9/8w0Mtr13/7MEH7LOzM2onAYCqss3F49dsvq0qy1x4o8Pq\nbla7B8Dfx1AZYAGrbzt2ebuv4QcPvvbvhSNjg2rnAMBpZ9LaZ27Y/vVqpzHvc/nu4oNq9wAAACwE\nUsq0tv7jv3ul6vFz9jW8JNTuAQA1LM1ZP/TxtZ/eW2CvuFYIEVG7BwAAYCFo6WpY3xNs/c/7d/6s\nIjzkj1G7BwBOt7RkrXLdtq815ViKf1iWt+wxtXsAAAAWAillfKev6WcHm3de9fzhh6xc9gOwGJW7\nV49cvP6m/UWOyuuEED61ewAAABaCjt6Wpb3B9t88/MYvl/YE2xLU7gGA0y0hLlG5dMMt3Uuy1z7g\nNhX8SAjBizdgDmOoDLDASSl1LX019z936MEtVW17M9TuAYDTIUaJUc5ZfoX/jNJzn86xlHxZCDGu\ndhMAAMBCU99e/YlOX+MdD+z6efHw2IDaOQBwWmSkCOWTW77SmGMp/ne7PvtBNvgBAACcXFJKTXt/\n/b07q58874265/Vq9wDA6bK6YNvAjpXXvF5gr/iUEEKq3QMAALDQtPc0r+wJtv7uvr/8tCI42B+r\ndg8AnA7JiSnKNZu/1FZgX/JLlzHvXs42AQAATi4pZVxXoPn7tZ0Hr/3Tvt+4Jqcn1E4CgNOiwFYx\ndsXGzx/OthTdYDM7OtTuAfDPMVQGWCTqWquub/XWffOh1+8sGJ0YVjsHAE4ZQ4Z15obtX691G/O/\nkJ9Vsk/tHgAAgIVMSila+mofePHII5sPNe/Sqt0DAKfS1iUfD20pv3BXvq38FiEE07QAAABOoYb2\n6vN6gm0/fej1X5SGh/wxavcAwKmSlqxVrtv2taYcS/GPyvKWPap2DwAAwEImpUzu8DXcdaBp5/kv\nvv2IZWZ2Wu0kADhlyt2rRy5ef9P+IkfldUIIn9o9AAAAC5nX58nuDDTd99T+362u7zmSonYPAJwq\nifHJypUbP99Z6l75W5cx7+cMLwXmD4bKAIuIlNLY5Kn+/Z/f/sP6I61vZKjdAwAn21lLLw1sKrvg\nuVxryReFEGNq9wAAACwWDe3VF/cE2777h913locGfVz2A7CgWIRr+tqtX6lzGHK/VphV+praPQAA\nAIuFlDKlw9dw56Hm1y944fBDVi77AVhoVhdsG9ix8prXC+wVnxJCSLV7AAAAFouO3palnnDnPX/c\n++ul7f31SWr3AMDJlJyYolyz+UtthY7KO52G3N9wwQ8AAOD0kFLG9AbbP9vWX3fbw7t/lT8yPqR2\nEgCcVCXO5Scu3XDLwTxr6fUmo6VX7R4AHwxDZYBFqL792BU9wbZ/fXj3L0u57AdgIXAacqeu2vTF\naoch52sFWaV71O4BAABYjKSUmg5fw7+/3brn488fetA2PTOldhIAfCRxsfHKRWtu7FuRv+lP2eai\nfxFCTKjdBAAAsBh1edqW9Iba73nizXuXtXhrk9XuAYCPSpuiU67Z8uXGHEvxHWV5yx5VuwcAAGAx\nklLG9gTbbm/2HPvMo3t+nTs6Max2EgB8ZJXZ64YvWnvj/iLH0uuEEH61ewAAABYjKaWhua/mv16t\nenzzW02vZqrdAwAfVVKCRrlm823tRc5ldzkNuXczvBSYnxgqAyxS7172+0VV294Lnj34gJ3LfgDm\no+TEFOXyDZ/tKHEt/4PbVHCHEIIfMwAAAJV193WW9gZb7/3T/t+uaPIc06jdAwAfRoGtYuzyjZ+r\nyjIVfsZhdTWq3QMAALDYvbvZ78st3ppbHn3jLjb7AZiXYmPilHNXXOVbXbjt1Xxb+ReFEINqNwEA\nACx2UkpTs6f6dzurn9y4r+FloXYPAHwY+nTL7DVbbqt3GQt+btdnPcwFPwAAAPU1dtR8vCvQ8sPf\nv/YfZXI4qHYOAHwoS3PWD1245sYDRY7K64UQ/Wr3APjwGCoDLHKe/p6i7kDzb58+cN/y492HU9Xu\nAYD364zSc+X2JRfvLXRU3iKE8KndAwAAgL+RUsb0htq/0OY9/rlH3vhfBcNjA2onAcD7oklMVa7a\n9IX2Isey3ziNuXcKIWbUbgIAAMDfSCkNLX01v32t5pnNe46/oFO7BwDeryLH0tFL1t901GnM+0KW\nPfeY2j0AAAD4fzW015zvCXf86OHdvyz3Rz2xavcAwPsRH5egXLTmBs+y3DOey7GUfF0IcULtJgAA\nAPyNlDK1vb/+rn0NL5/3StUfTbMKd7kBzA8mrX3m6s231TuNeT+36dyPMLwUmP8YKgNAkVLGeEId\nn+n0N33p4Td+VTwwElY7CQDek12fPXX15tvq7PrsbxVll7+idg8AAADem5RS1+KtvfeNuue3vl7z\njEHtHgD4R1YXbBs4d8VV+wodlZ8SQvjV7gEAAMB7a+48fnZvqP2nD+/+ZUW/7IlTuwcA3os2Radc\nvfm2phxLya8dhpx7+OASAABg7pJSJnX4Gn9+tP3Ni585cJ99emZK7SQAeE9LczYMn7/62sM55uJb\nrGZ7q9o9AAAAeG9t3U1rPeH2u/6w+1eVnlB7vNo9APBeEuOTlYvXfbqnInvt09nmom8JIUbVbgJw\ncjBUBsD/IaXMaOs/fteBxr+c81LVY+bZWRYxA5g7khKSlUs33NJV5lr1WJa58HtCiEm1mwAAAPD+\ntHQ1bPaEO/7j4d2/XNIX7uRADMCcYs50zFy16Yv1LmPeHSW5lU+o3QMAAID3R0qZ2Olv+nF1x/7L\nn37rv52T0xNqJwHA/xEbE6ecu/Iq35qC7a/m2cq+KIQYVLsJAAAA74+nv7uwO9Dyu6fe+q8V9T1H\nUtTuAYD/mzHDOnv15tvqXab8n9l0WY8yvBQAAGB+kFLGdwVavtvaV3vV42/ekzs8NqB2EgD8P9YV\nnx09q/LSA7nW0ltNRnO32j0ATi6GygD4/3T2ti7vDbXf/fib9yxr6z+epHYPAKwrOit61rLL9hc5\nlt4shOhTuwcAAAAfnJQyocvf/IParoNXPbn/d+6JqTG1kwAscsmJKcol627qKnWtfCrbUvRtIQQ/\nTAAAAPOQ19+X2+lr/N2zBx9YXdt1IFXtHgAodi4bvXjdZ6qchrwvZDlyq9XuAQAAwAcnpYzpDbXf\n0t5ff9sjb/yvwqHRqNpJABa5hLhE5aK1N3qW5mx4LsdSfDvb4gEAAOYnKaW+1Vt31+GW17e+dORR\ny/TMlNpJABY5hyF36qpNX6iz67K/W5RT/me1ewCcGgyVAfB3SSlje4NtX2nx1t706J6780fGWJoF\n4PSzCNf0NZu/VOcw5Hy/OKfiObV7AAAA8NEFgn5XW//xe/bWv7jmjdrn9LMK76YAnF4xSoyypeLC\n8May8/cVOSo/K4Twqt0EAACAj0ZKGeMNd13XG2r/8uNv3lPqjXTFqd0EYPHRpuqVqzd9sTHHUvJr\nhyHnXrbFAwAAzH9Sysw27/FfHevYt/35ww/ZJ6fG1U4CsAgtz9s4dN7KTx7ONhfN/s4RAAAgAElE\nQVTdYjXb29TuAQAAwEfX4+0s7wm23fXSkUeWV7XtTVe7B8Dik5KUplx+xmc7Ch1LH8kyFfxICDGp\ndhOAU4ehMgD+ob9Ovzzatnfzn99+2DY5PaF2EoBFQJOYqnxi3ae7y9yr/pRtLvq2EILTeAAAgAWm\nvad5Q1+482fPH36osq7rIJvkAZwW+baK8UvX31xt12f/S567aI/aPQAAADi5pJSJ3YGWb7d66654\nYt+9+YMnpNpJABaB2Jg4ZcfKq/tXF257Nc9adpsQgq09AAAAC4zX35fb5W+6a+/xP695o+55HYsz\nAJwORq1t9prNX6p3GfN+WpJb+ajaPQAAADj56turL+0Ld37r8b3/WeYJd8Sr3QNg4YtRYpRtlZ8I\nbyzdsafAvuRWIURA7SYApx5DZQC8L/3+voJOf9Ov9h7/82oOxACcKnGx8crZyy4LrC7c/mahfckX\n2RYPAACwsL27Sf6TfeHOrzy+795ST6idAzEAp4Qxwzp7xaYvNLiN+b91GHL+Uwgxo3YTAAAATp13\nN8n/orrzrbOeP/R7x8TUmNpJABaoVQVbB89eetlRhyH3y1mO3Gq1ewAAAHBqtXU3beoLd/7k+cO/\nrzzefThF7R4AC1NKUppy8brPdBU7lz+fYyn+hhBiVO0mAAAAnDpSyoSuQMt3WvpqrnzizXvzhscG\n1E4CsEDlWcvGLzvj1hqbLuv2fHfxXrV7AJw+DJUB8IG09zSf0Rfu/OmLbz9cWd35FpvkAZw0qwu3\nDZ619LIqhyHnq1n23GNq9wAAAOD0kVIm9ARav9nWf/zqx/fdWzAwElY7CcACkZKUplyy/ubOYuey\nZ7PNRd8WQpxQuwkAAACnTyDoz2rvr797b/2La3fXPaefnWW2IICTI99WPv6JtZ+uteuzf1yYXfac\n2j0AAAA4fd5dnHFdb6j9S0/su7e0L9zJ4gwAJ0V8XIJy7oqrfCvyNu8psFd8SQjhU7sJAAAAp4+U\nUt/qrbvrUMtrW18+8phlemZK7SQAC4RRa5u9bMOtjVmmggecxrw7WcwILD4MlQHwgUkpY3yy53JP\nuPP2J/f/rrzL35SgdhOA+euvH1za9Fk/Ksouf0HtHgAAAKhHSqlt72/495rOt8557tADzvFJNskD\n+HDiYuOVc5Zd7l9duH1Pgb3ii0IIv9pNAAAAUE97T/P6vkjXv71w6KHK2q4DLM4A8KFZhHPmsg23\nNrhNBb+167Pv4YNLAACAxUtKmdgVaPl2m7fuiif23Zs/eEKqnQRgnopRYpQzynbILeUXHnYZ87/s\nsLoa1W4CAACAenq8neU9wba7XjryyPKqtr3pavcAmL8yUoRyyfqb2/Jt5c9mm4u+x2JGYPFiqAyA\nD01KGd8bbP9SV6DphifevLc4ONgfo3YTgPnDrs+evmT9zQ1OQ+5/OQw5/8kHlwAAAPirQNDvau+v\nv3tf48vrXqt5xsAmeQDvV0xMrLKh5GNyc/n5x9zGgq84bVk1ajcBAABgbpBSxngj3Vf3hTu/+sSb\n95T1htrZJA/gfdOm6pVL1n2mNd9W8WSWufCHQgimIQMAAEBRFEWRUma2eY//orrzrbOeP/R7x8QU\nfyoCeP8qstaMnLfqk7U2nftf890lu9XuAQAAwNxR3159qS/S8/UX3n6otLH3qEbtHgDzR3JiinLh\n6ht6yrNW7cqzlt0uhIio3QRAXQyVAfCRSSlTOn2NP2rqq77oyf2/yxkZG1Q7CcAcpk+3zF664ebm\nbHPRH92mgp8KISbUbgIAAMDc1NHbsqYv3PnzP7/98NLqjv1pavcAmLtilBhlXfHZ0S0VF1bb9dnf\ny3EW7FW7CQAAAHOTlDKhJ9j6jfb+hquf2HdPoRwOqZ0EYA5LTUpXLlr7qa4S57JXcq2l3xRCRNVu\nAgAAwNwUCPqz2vqP//rN+pfW7q57TsfiDAD/SIF9yfiFq6+vs+uzfmERrseFEFzsAQAAwP9HShnr\nDXdd2xfp+vwLhx8qa+mrSVK7CcDcFR+XoJyz7Ar/yoIt+wvtS24TQnjUbgIwNzBUBsBJI6U0tnrr\n7jzWvm/rC2//wTY5Na52EoA5JF2TqVy8/qb2QlvFC9mW4u8IIYbVbgIAAMDcJ6WM8cneS/tl95df\nPfpEeXXH/lS1mwDMLWsKtw9sW/KJGqvO/cN8d/FravcAAABgfpBSZnT4Gn7S6j1+9rMH78+Tw0G1\nkwDMIUkJGuX8Vdd6lmSv3ZNvK/+qEMKvdhMAAADmh7aepnX9ke4f7m98demeuhd0M7PTaicBmENc\nxvypi9d9pt5hyP2tXZ/1WyEEE6gAAADwT707XObTnnDnLc8deqC0vb8+Ue0mAHNHXGy8sr3y4uCa\nwu1vu435t9utrga1mwDMLQyVAXDSef19ud2B5p/Xdh1a89KRR2xjEyfUTgKgonRNpnL+qmu7S5zL\nX8uzlX1dCBFWuwkAAADzj5Qyxh/1XNQf6b59Z/VT5VVte9LUbgKgrlUFW4e2V15cY9Nl3ZHvLn5V\n7R4AAADMT1LKzA5f4w86fA0fe+bA/fnhIZ/aSQBU9O72Pt+K/M1v5VpKvmwymnvUbgIAAMD81OVp\nX+0Jtf/oQPPOZbtrn9NPz0ypnQRAReZM58yl629qdJkK/uAy5t0phJhUuwkAAADzj5Qyri/ceasn\n1PGpZw7eX9rlb0pQuwmAemJiYpUt5RdG1pecU+U05H7Dbc85qnYTgLmJoTIAThkppbulr+Zn9T1V\n6/58+CHXyPiQ2kkATiORZlQuWnNDW661dHeetezbQoiA2k0AAACY/6SUMcEB73n9ke5/2VXzdMXh\nltfT1W4CcHotz904fNayy2qtwvUTU6b9JSEEL7kBAADwkUkpMzr9Td/r9DfteOat/y4IDvbHqN0E\n4PRJTkxRPrbsCm9F9tq3Xcb8bzqsrka1mwAAALAwdPd1rvCE2u842Lxr+Ws1TxsYLgMsLi5j/tQF\nq65tchhzn80yFf5YCDGmdhMAAADmPyllfG+o/fOeUMcNzxy4r6Qn2BqvdhOA0ydGiVHWl5wzsLn8\ngqN2fc6/5jjzD6jdBGBuY6gMgFNOSmlt9db9pLmvZtPzhx7MHjwh1U4CcAqZMx2zF665oTnbVPhK\ntqX4B0KIqNpNAAAAWHjeHS5zTn+k55u7655dcrBpV8aswnsuYCGrzF43cs7yK+usOte/mTMdzzFM\nBgAAAKeClDKty9/87a5A84XPHLi/wB/tjVW7CcCpk5asVc5ffW13sWPpW9nm4m+ajOZutZsAAACw\nMPV6u5b0BNt++nbr7uU7q580TU1Pqp0E4BQqtC8ZP3fFVQ02ffYjTkPu3UKICbWbAAAAsPBIKRN6\ng21f7gm1X/PMgfuKPaF2hssAC1h8XIKypfyi8KqCLdVWnfsn+e7i19VuAjA/MFQGwGkjpTS09zfc\n0dZ/fNuzB+/Pk8NBtZMAnEROQ+7UBauvb3IZ855zmwp+KoQYUbsJAAAAi0NbT9OZ3nDXt/ccf2HJ\n/sZXtbOzM2onATiJlmSvO3HO8svrbLqs/zBnOp5imAwAAABOByllSneg5Vs9wdaPP33gvsL+SHec\n2k0ATh5dmkm5cM31bbmW0t15trLvCCH8ajcBAABgcejz9ZZ2B1p+dqRtz8q/HH3CPDnNnAlgIVma\ns2Fke+XF9Vad+7c2nfv3QohptZsAAACw8EkpE3uCbbf3BFuvfPbg/UWeUAdnm8ACoklMUz624gpv\nuXt1ldOQ+x2nLatG7SYA8wtDZQCcdlJKbaev8fud/qaPPXPw/oLggDdG7SYAH16utXTi/JWfbLTr\ncx5zGnN/JYQYV7sJAAAAi1NbT/Nmb7jre2/Wv1j5ZsNLmQyXAeav+LgEZVPZ+ZFVBVtrbTr33Uat\n7RmGyQAAAEANUsrknmDrv/QE2y559uD9xXyACcxvVuGauWD19S1Z5sKXs81FPxBCDKjdBAAAgMWp\n3+8t7PQ3/tuxjn2rX6l63DIxNaZ2EoAPKSYmVllffPbAGaU7ai3Ceac50/EcZ5sAAABQg5QyyRPq\n+EJ/pPuKXTVPFdd0HkhRuwnAh5eZalDOX3VtZ76tfH+BveJfhRA9ajcBmJ8YKgNANVLK1C5/87/2\nBFsvfPbgA4XeSBcfYALzSJl75dg5y66st+rc99n1Wf8lhJhSuwkAAABQFEVp72ne4I10f6e280DF\nzuonLaMTI2onAXifUpMzlI8tv9JT6lpR5TTm/cBpdR9TuwkAAABQlHc+wOwJtn6lL9x5+V+O/qmo\n0XM0Se0mAO9flrlw6oJV1zXa9dlPuU0FPxdCjKrdBAAAACiKogRCgdz2/uP/1uSpXvnykUddAyci\naicBeJ/i4xKUrRUfD60q2Fpt12f9KMdZsFftJgAAAEBRFEVKGRMc8F7UH+m57VDLa+V7jr+gm5qe\nVDsLwPv07qKMVrcp/y85lpLvCyF4YQTgI2GoDADVvbvd73ZvuPvjr9c+U1TbdVCjdhOAvy8mJlZZ\nlb9laEvFhfXmTOevrTrXY0KIGbW7AAAAgL8nEPRnd/qbftDlb1r74pFHcv1RT4zaTQD+PnOmc/b8\nVZ9szTIX7smzln1fCOFVuwkAAAD4e6SUcX7pucIX7bnpcMvusr3H/6ybnJ5QOwvAeyhxLh/72PIr\nG6z6rN879Nn3CiH4YhoAAABzkpTS1OFr+LYn1Ln1larHCjv8jfFqNwH4+5ISNMo5y6/wVWavO+I0\n5H7bacuqUbsJAAAAeC+93u7K3lDb9xt6q5a/fOQxx/DYgNpJAN5DnrVsYsfKa5rs+qynXcb8fxdC\nnFC7CcDCwFAZAHOGlDI2OOC9sF/2fK6m80DZazVPm8cm+JsHmAvSNZnK2csu7yt2Lqux6dx3ijTj\n60II/ogAAADAvCClTO8OtH61P9J1wa6ap4uPdx9OVrsJwDuKHEvHz152eaNdl/WMy5T/CyHEiNpN\nAAAAwPvl9XmWdAdbv9vSV7Ps5apHs+RwSO0kAIqiJMQnKRtLd0RW5G1qsAjXf1l1rodZlAEAAID5\nQkqZ6A13fdoX7b1mf8MrJQeadmpnZqfVzgKgKIo50zF7zvIrOnIsJUdyzMXfNBnNnWo3AQAAAO+X\nlNLa1n/8B92Blo0vHH4o3yd7Y9VuAvCOZblnjGxb8okGq851n02XdZ8QYkrtJgALC0NlAMxJgVAg\nt9PX+L3uQMual6oeze2PdPOQAqggz1o2edbSy1rs+uw9udaSO4QQ/Wo3AQAAAB+WlDLWH/Vc7JO9\nnz3StqfsjbrnDZNT42pnAYtObEycsq747IH1JWcft2Q6f2vVuR8VQvA1NAAAAOYtKaW+09f4rb5I\n11mvVj1e0Npfl6h2E7AY/fVyX7a5uMptzP+xzeKoVbsJAAAA+Cg6PW0bvOGub9T3HKl89ejjdrbJ\nA6dfTEyssjxv48jG0h1NFuF8wWXM/6UQYlDtLgAAAODDklKm9gRbv9IX7vrEzmN/KmrorWJRI6CC\n1KR0ZVvlxb4y98oGi3DebdLanxNCMPQBwCnBUBkAc9o7Dyltt/lk78f3Hn+huKr9zdTZWRaIAadS\nQlyisrFsR2RF/uYGS6bzQavO/ZAQYlLtLgAAAOBk6vP1lvUE277X6q1b8dKRh9kmD5wGGSlC2V55\nibfMtaLGps/6cbYjf7/aTQAAAMDJJKVM6I90X+uL9t5wsGlX6b6GlzOnZ1ggBpxKXO4DAADAYiCl\ntLf313+3J9i28aUjj+T3htrj1G4CFrqMFKGcvezyviJ7Za1F5/qlPt28i8t9AAAAWEiklLE+2XOV\nT3puOdi8q2R/4yuCRY3AqZdvK5/cXnlxq12fvT/PWvZjIUS32k0AFj6GygCYF6SUMZGhwFneSPeX\n67sPl//l2J9sI+NDamcBC4oxw6p8bMVVHbmWkqMuY/4dNoujRu0mAAAA4FSTUuo6fU3f8oQ7ztpV\n/WRhc18N2+SBkygmJlapzF534ozSc1stwvVGjqX450IIr9pdAAAAwKnW4+1a7Qm1f6vJc2zpy1WP\nOQdPSLWTgAWFy30AAABYjKSUyZ5Qx+d80d7L99S9UHykbU8aixqBk6vQXjlxZuXFLTZ91hs5lpIf\nCyF8ajcBAAAAp5rX71naE2j9em+ofdmu6qdyugLN8Wo3AQtJcmKKsqX8osCS7LUN5kzHQ1ad62Eh\nxKTaXQAWD4bKAJh3pJTOtv7j3+0Ntq1/9egT+TykAB9ejBKjVGSvPbG14sJmi3C/6Dbl/0IIEVW7\nCwAAADjdpJQJ/qjncn+077qWvpqi12qecsjhkNpZwLylT7coZy27tDvPWnbcIpz36NPNrwgh+KoZ\nAAAAi46U0tzha/y2T/acsb/xlbxj7ftTZ2an1c4C5q1C+5KJ7ZWXtNj0WbtzLSU/4XIfAAAAFqN3\nFzWe2S97vtDpbyrdVf1klk/2xqjdBcxXSQkaZUv5hcHKnPX1FuG83yKcjwkhptTuAgAAAE43KaXG\nG+66wR/1XFbXfbhkd+1zxtGJYbWzgHkry1Q4ddayy9ochpxDOebiO4wGU5vaTQAWJ4bKAJi3pJTJ\n/ZGe6/1Rz6Ut3tqi3bXP2LjwB7w/Jq1d2brk4z251tImS6bzP41a65+53AcAAAC8Q0pp7vI3f8Uf\n9Ww93PJ6waGW1zKmphkGD/wzcbHxysqCLYNrC89ssQjnq25TwS+FEGG1uwAAAIC5QEoZOzAS3uqN\ndH++O9ha9lr1U9m9ofZYtbuA+SA1KV3ZWHZ+sDJnbYM503kfl/sAAACAv5FSantD7bcGop7zarsO\nFu49/pKBC3/A++M2Fkxvr7y43W3KP5xjKfmR0WBqUbsJAAAAmCsCoUBud6D5G95w15rddc/nN/ZW\nJandBMwHifHJysayHeFluWc0mjOdj9r1WfcLIcbV7gKwuDFUBsCCIKW0dvmbv+SPerYcbX8z/0DT\nzsyJqTG1s4A5JTUpXTmjbEewzL2qxaS1v+oy5t3D5T4AAADgvUkpY4ZHB1b3hTu/0htqX/JazdM5\nnf6meLW7gLnGqnPPnrX00o4sU2GNXZ99Z0aKeEsIwYtnAAAA4D1IKdO84a5P+aOeTzT0VhXurn3O\nPDw2oHYWMKckxCUqK/I3D64q2Npu0toO5lpL7xRCsLkPAAAA+AeCoUBBl7/56/2yZ9Wb9S/m13Uf\nTp6dZdcc8H/Tp1uUrUsu8uRZS5tNWvtTVp3790KIE2p3AQAAAHOVlDI+EO272B/1fKrJU12yq+Yp\n+8AI19GA/ynPWja1ufyCDqchryrLXPhji8lar3YTAPwVQ2UALDhev2dpb7D9K/2ye9ne43/Obeip\nSppV+K3D4hQfl6Asz9s0vKZwW5tJ6ziQay35hRCiXe0uAAAAYL6RUib3R7qv80f7Lm/srSp+vfZZ\ny9BoVO0sQDUpSWnK2qKzIktz1jdZhOs5hyHnXiHEkNpdAAAAwHwjpcxq72+43R/1rHur8ZX8Yx37\nU6dnptTOAlQRo8QoJa7l42eU7mi36ty1dl32XWmajIMMLgUAAAA+GCllXHjQd64v6rm5099Yuqv6\nKbdP9sao3QWoJTUpXTmjdEew1L2y1Zxp3+ky5t8jhAio3QUAAADMN1JKa5e/+Ws+2bNlX8PLBZxt\nYrFzGnJnNpVf0O025jeZMu2PmjMdfxJCjKvdBQD/E0NlACxYUsr44ED/hYGBvhs7/U3Fr1U/ldUv\nezgUw6JQYF8yuans/Ha7PqvWrsu+Oz0lcz8fWwIAAAAnh5TS2eFruN0f9ZxxsGlX/tH2N1MnpyfU\nzgJOOU1iqrKmcLusyF7bbtLa33ab8u8yGkxNancBAAAAC4GUMiY6Et7aH+n+fE+wtWxX9dM5vaG2\nWLW7gNPBacib2brkok6XMb/Jkum4X59heUEIMal2FwAAALAQSCm1vaH2z/qjnh11XQcL9x5/0TA6\nMaJ2FnDKJcQlKivyNw+uzN/SZs50HMy1lvxSCNGmdhcAAACwEEgpY+RwaLs/2nuzN9Jdur/h5eyG\n3qNJs7MzaqcBp5xRa1O2lF/Ym2MpbjFp7c/a9Fm/ZykjgLmOoTIAFgUppdYT6rjZH/Wc19BbVbCv\n/iXzwImI2lnASWXOdChbl3y8O8dc3GTS2v9gFo4/CSG42QoAAACcIlLKmKHR6Ib+SM/NgYG+smPt\n+7KPtO3JGJ8cVTsNOGmSE1OU1QXbopU569uNWmtVlqnoLqPBWK92FwAAALCQSSlT+8KdNwYHvBd1\nBZrz9x5/0eGNdLE8AwuKLs2kbKm4qC/fVtZs0tqfsemzHhRCDKvdBQAAACxkwVCgsMvffHtgwLus\npnN/9uGW3ZkMmMFCEqPEKCWuFeNnlJ7bbhXuGrs+6640jfYQSxkBAACAU0dKmRAZCuzwRXtv8Ia7\nivfWv5jV2lebMKvwZzgWDm2qXtlUdp6v0F7ZatTa/uIy5v1GCBFSuwsA3i+GygBYdKSUWZ3+ps+G\nBn1r2/vrc/c1vGQNDfrUzgI+FLexYGZdydm9TkNuh1Fr2+k05P5GCCHV7gIAAAAWGyllzNjEiRW9\nofZbgwP9S2q7DuQcbnk988Q4d6Ew/yQlaJRVBVsHluZsaDdl2o66DHl3JyYk1/GxJQAAAHD6SSmF\nJ9RxfWjQd25PsDXvzfoXXb2h9li1u4APQ59uVtYUnekvclR2mrT219ymgruFEH61uwAAAIDFRkoZ\nMzE5Vt4Tavt8cMC7rLbrUM6h5l2Cs03MRzExsUqBrWJybdGZ3U5jXqNFOO/Tp5tfFEJMqd0GAAAA\nLDZSyiR/tO8TwQHv1d5wZ+H+xlfdLX01DJjBvJSalK6sLzknXOZe1WrS2vdmmQvvFkJ41O4CgA+D\noTIAFjUppbU32PaZwIB3S3egJXdfw8tOb6RL7SzgPcXFxislzuXjqwu3dVuEq1WfYX7OKlxPCCEG\n1G4DAAAA8A4pZczU9FRpd6D5c8EB7/KG3qqcA0079UOjUbXTgPeUlJCsrMjbPLg8b2OHUWs75jDk\n/lqTmHKMQTIAAADA3CGlzPCGu64JDfou9IQ78t5qfNXd3l8fx0eYmMuyzcXT64rP6rXrszsMGZY3\nXcb8+4UQPWp3AQAAAPibYChQ0h1o/Xxw0Lu8yXMs+0DTTuPASFjtLOA9JSemKEtzNgxV5qzvMmnt\nLSat7TF9hvklIcSo2m0AAAAA3iGlTA4O9F8QHPBe1S+7Cw807cxu6KlKmpmdVjsNeE/6dIuypmh7\nIM9a1m3S2o+4Tfm/MhpMLWp3AcBHxVAZAHiXlFLXF+68NjzoO6df9uQebN7lavbUJPKgArVpElOV\n5XkbB5dkr+s0am1N5kz7QyLNuFMIMal2GwAAAIB/TkqZ3+Fr/FxosH9Vc19Nzv6GV8zRkZDaWYBi\nznQoqwq2+nMsJd2GDEuD05B7T3JiyhEGyQAAAABzn5QyJRDtuyg06LvMH/UUHG593V3XdShlaprj\nI6grIT5JqXCvHl2Rv6nLlOloNWRYnjZnOp4WQgyp3QYAAADgn5NS5nT5mz8TGvSt6w625OxveMXB\nskbMBfp0i7K26Exfnq2sy5BhqXXoc/6bs00AAABgfpBSJkSGAuf4o33XBaKeoprOt1zVnW+lnxgf\nVjsNi1xsTJxSYK+YXF24rccqXJ36dPObTmPegyzJALDQMFQGAP4OKWVyZChwTmDAe3VwwFtY3bE/\n62j7vvTRCR5UcHro083K2qKz/HnWsg6j1lrjNOb9LjE+qZrDLwAAAGB+k1K6ugLNt4YH/at9sjfr\n7dbXnS19dfEMNMXpkBifrJS6Vowuz9vUY8q0derSTLsdhpxHhBB9arcBAAAA+PCklHGDJ+Qmf9Rz\nYyDaV9LkqXYdbX9THx7yqZ2GRUKbolNWFWwLFzuXdhm1tnqrcD2QptG+KYTghQcAAAAwj0kpDX3h\nzmvCQ/6P+SI9OUfb33Q09B5NnpgaUzsNi0CMEqPkWEqm1xaf1WPXZXXoMyx7XMa8B4QQHrXbAAAA\nAHx4UsrYianxSk+o47rIkH9JX6Qr6+2W3Y52X0Pc7OyM2nlYBNI1mcryvI3RMveqbmOGtcWQYX1c\nn2F+SQgxqnYbAJwqDJUBgH9CShkzMTW+tDfY/qnIsL80EPU6j3XsczT31SROTo2rnYcFIilBoxQ7\nl41VZq/zmDLtPYYMy5suY/59QohetdsAAAAAnBpSypTIUODs4ID3stCgL7/VW+d6u/UNI5f+cDJZ\ndW5ldcG2/ixzYbc+3dxoFa4/pGm0+4QQk2q3AQAAADj5pJQxiqLk9ATbrpTDwXXhIX9WXddBZ23X\nobSRsUG187BAxMbEKdmWounleZu8LkNuhyHDeijLXPjfQohWtdsAAAAAnBpSysTh0YENvmjvlZGh\nQJE30u060vqGvcPXGMcCDZwsqckZSplr5Uhlzvpuc6ajxaC1PmXS2p4VQrAVFAAAAFigpJTpocH+\nHaFB3ydCA7685r4a15G2N/RyOKh2GhYQt6lgZm3RmX0OfU6nPsNS5TTk3RcfF98ghGDIAoBFgaEy\nAPABSSk1gyfkRn/Uc3lkKFDgk73Oqva99vb++rjpmSm18zBPJMYnK4WOyvGlOes9xgxrry7d3GgR\nzsdTk9MPCCEm1O4DAAAAcPpJKbN7gm1X/+3S3yFHbdfBdC794YNITkxRytyrRpblbOgxZto7dWnG\nnXZ99h+FEEwrAgAAABahdzf9Le0LdVwTGQ5WBAe87qr2Nx1NnmNJLNDA+xUbE6fkWIqnl+Zu6Lfp\n3B5dmrlTn2F63pBhfVUIIdXuAwAAAHD6SSkzggP9HwsP+T8eHvTldgWanUfa9pj7I91qp2EeSUvW\nKqXulcNl7pV9+nRzj0g11DkMuY8nxCdWCSGYVgQAAAAsQlLKvJ5g6zXhocC6YLQv+0jbXkdjb1Xy\n5DTX7fD+mTMdSnnW6nC+tdxr0Fq79Onm5y3C+RRnmwAWK4bKAMBHJKXMiMrMTFIAABltSURBVAwH\nzwwN9H8iPOTP7Qt3Oqra9ti6/M0xswq/sXhHQlyikm+vmFyWu6HPpLX36tJNzeZMx5/SNZl7hRBj\navcBAAAAmFuklLHTM1OVvcH2q8ND/iWhwX53dcdbjpa+muSR8SG18zCH6NJMSqlrxWCRY2l/Zpqh\nNzPV0GLTuR/VJKUe5ENLAAAAAP+TlDJpaDS60Sd7rwwP+Qu8kW5XVesbtg5/U9zs7IzaeZgj4mLj\nlWxz0fSy3DP6rTqXR5du7jCkm5/TZ1h28qElAAAAgL9HSmnrC3ddIoeD28JDvuzG3mOO6o79IjoS\nUjsNc0i6JlMpda0YLnOv7tOnm3pEmrHWps/6Y2J80jHONgE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x5fa82fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary.head().T.plot(kind='pie', subplots=True, figsize=(80,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Getting the model features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next step is to generate the model features. We take the PUMAS dataset and discard id's, geometries etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "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>100_000_to_124_999_in_households</th>\n",
       "      <th>10_000_to_14_999_in_households</th>\n",
       "      <th>10_to_14_years_female_pop</th>\n",
       "      <th>10_to_14_years_male_pop</th>\n",
       "      <th>125_000_to_149_999_in_households</th>\n",
       "      <th>150_000_to_199_999_in_households</th>\n",
       "      <th>15_000_to_19_999_in_households</th>\n",
       "      <th>15_to_17_years_female_pop</th>\n",
       "      <th>15_to_17_years_male_pop</th>\n",
       "      <th>18_and_19_years_female_pop</th>\n",
       "      <th>...</th>\n",
       "      <th>total_pop</th>\n",
       "      <th>under_5_years_female_pop</th>\n",
       "      <th>under_5_years_male_pop</th>\n",
       "      <th>vacant_housing_units</th>\n",
       "      <th>walked_to_work</th>\n",
       "      <th>worked_at_home</th>\n",
       "      <th>eye_ear</th>\n",
       "      <th>eye_noear</th>\n",
       "      <th>noeye_ear</th>\n",
       "      <th>noeye_no_ear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pumas_10</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2126</td>\n",
       "      <td>2678</td>\n",
       "      <td>1607</td>\n",
       "      <td>1616</td>\n",
       "      <td>1256</td>\n",
       "      <td>955</td>\n",
       "      <td>2659</td>\n",
       "      <td>1227</td>\n",
       "      <td>1347</td>\n",
       "      <td>4984</td>\n",
       "      <td>...</td>\n",
       "      <td>101906</td>\n",
       "      <td>2461</td>\n",
       "      <td>2876</td>\n",
       "      <td>6282</td>\n",
       "      <td>2506</td>\n",
       "      <td>3021</td>\n",
       "      <td>49</td>\n",
       "      <td>83</td>\n",
       "      <td>198</td>\n",
       "      <td>8091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>9015</td>\n",
       "      <td>1579</td>\n",
       "      <td>7441</td>\n",
       "      <td>7776</td>\n",
       "      <td>7193</td>\n",
       "      <td>10286</td>\n",
       "      <td>1557</td>\n",
       "      <td>4496</td>\n",
       "      <td>4541</td>\n",
       "      <td>2301</td>\n",
       "      <td>...</td>\n",
       "      <td>205918</td>\n",
       "      <td>6491</td>\n",
       "      <td>6486</td>\n",
       "      <td>3516</td>\n",
       "      <td>2026</td>\n",
       "      <td>5853</td>\n",
       "      <td>54</td>\n",
       "      <td>120</td>\n",
       "      <td>258</td>\n",
       "      <td>9374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>3986</td>\n",
       "      <td>1151</td>\n",
       "      <td>2645</td>\n",
       "      <td>2622</td>\n",
       "      <td>3676</td>\n",
       "      <td>4024</td>\n",
       "      <td>1634</td>\n",
       "      <td>2109</td>\n",
       "      <td>2044</td>\n",
       "      <td>799</td>\n",
       "      <td>...</td>\n",
       "      <td>105432</td>\n",
       "      <td>1874</td>\n",
       "      <td>2217</td>\n",
       "      <td>11680</td>\n",
       "      <td>832</td>\n",
       "      <td>5464</td>\n",
       "      <td>29</td>\n",
       "      <td>42</td>\n",
       "      <td>91</td>\n",
       "      <td>2477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>3036</td>\n",
       "      <td>2648</td>\n",
       "      <td>3765</td>\n",
       "      <td>4242</td>\n",
       "      <td>1582</td>\n",
       "      <td>1486</td>\n",
       "      <td>2309</td>\n",
       "      <td>2214</td>\n",
       "      <td>2288</td>\n",
       "      <td>2438</td>\n",
       "      <td>...</td>\n",
       "      <td>122624</td>\n",
       "      <td>4044</td>\n",
       "      <td>4452</td>\n",
       "      <td>6364</td>\n",
       "      <td>1055</td>\n",
       "      <td>1132</td>\n",
       "      <td>148</td>\n",
       "      <td>313</td>\n",
       "      <td>571</td>\n",
       "      <td>16515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>2288</td>\n",
       "      <td>3077</td>\n",
       "      <td>2764</td>\n",
       "      <td>2781</td>\n",
       "      <td>1321</td>\n",
       "      <td>1702</td>\n",
       "      <td>3308</td>\n",
       "      <td>1363</td>\n",
       "      <td>1618</td>\n",
       "      <td>1550</td>\n",
       "      <td>...</td>\n",
       "      <td>100406</td>\n",
       "      <td>2889</td>\n",
       "      <td>3030</td>\n",
       "      <td>4949</td>\n",
       "      <td>2231</td>\n",
       "      <td>2226</td>\n",
       "      <td>104</td>\n",
       "      <td>194</td>\n",
       "      <td>311</td>\n",
       "      <td>10604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          100_000_to_124_999_in_households  10_000_to_14_999_in_households  \\\n",
       "pumas_10                                                                     \n",
       "109                                   2126                            2678   \n",
       "110                                   9015                            1579   \n",
       "111                                   3986                            1151   \n",
       "803                                   3036                            2648   \n",
       "804                                   2288                            3077   \n",
       "\n",
       "          10_to_14_years_female_pop  10_to_14_years_male_pop  \\\n",
       "pumas_10                                                       \n",
       "109                            1607                     1616   \n",
       "110                            7441                     7776   \n",
       "111                            2645                     2622   \n",
       "803                            3765                     4242   \n",
       "804                            2764                     2781   \n",
       "\n",
       "          125_000_to_149_999_in_households  150_000_to_199_999_in_households  \\\n",
       "pumas_10                                                                       \n",
       "109                                   1256                               955   \n",
       "110                                   7193                             10286   \n",
       "111                                   3676                              4024   \n",
       "803                                   1582                              1486   \n",
       "804                                   1321                              1702   \n",
       "\n",
       "          15_000_to_19_999_in_households  15_to_17_years_female_pop  \\\n",
       "pumas_10                                                              \n",
       "109                                 2659                       1227   \n",
       "110                                 1557                       4496   \n",
       "111                                 1634                       2109   \n",
       "803                                 2309                       2214   \n",
       "804                                 3308                       1363   \n",
       "\n",
       "          15_to_17_years_male_pop  18_and_19_years_female_pop      ...       \\\n",
       "pumas_10                                                           ...        \n",
       "109                          1347                        4984      ...        \n",
       "110                          4541                        2301      ...        \n",
       "111                          2044                         799      ...        \n",
       "803                          2288                        2438      ...        \n",
       "804                          1618                        1550      ...        \n",
       "\n",
       "          total_pop  under_5_years_female_pop  under_5_years_male_pop  \\\n",
       "pumas_10                                                                \n",
       "109          101906                      2461                    2876   \n",
       "110          205918                      6491                    6486   \n",
       "111          105432                      1874                    2217   \n",
       "803          122624                      4044                    4452   \n",
       "804          100406                      2889                    3030   \n",
       "\n",
       "          vacant_housing_units  walked_to_work  worked_at_home  eye_ear  \\\n",
       "pumas_10                                                                  \n",
       "109                       6282            2506            3021       49   \n",
       "110                       3516            2026            5853       54   \n",
       "111                      11680             832            5464       29   \n",
       "803                       6364            1055            1132      148   \n",
       "804                       4949            2231            2226      104   \n",
       "\n",
       "          eye_noear  noeye_ear  noeye_no_ear  \n",
       "pumas_10                                      \n",
       "109              83        198          8091  \n",
       "110             120        258          9374  \n",
       "111              42         91          2477  \n",
       "803             313        571         16515  \n",
       "804             194        311         10604  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "a =pumas.set_index( pumas[ 'pumas_10'])\n",
    "moe_cols  = [ f for f in a.columns if 'moe' in f ]\n",
    "data  = a[a.columns.difference(['the_geom', \n",
    "                                'ogc_fid',\n",
    "                                'wkb_geometry',\n",
    "                                'cartodb_id', \n",
    "                                'geoid',\n",
    "                                'pumas_10' ] + moe_cols)].join(summary)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We next normalize the various target counts to get a probability matrix across the different variables we are interested in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " target :  (982, 4)  features (982, 94)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eye_ear</th>\n",
       "      <th>eye_noear</th>\n",
       "      <th>noeye_ear</th>\n",
       "      <th>noeye_no_ear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pumas_10</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>0.005819</td>\n",
       "      <td>0.009856</td>\n",
       "      <td>0.023513</td>\n",
       "      <td>0.960812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>0.005507</td>\n",
       "      <td>0.012237</td>\n",
       "      <td>0.026310</td>\n",
       "      <td>0.955945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.015915</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.938613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>0.008434</td>\n",
       "      <td>0.017838</td>\n",
       "      <td>0.032541</td>\n",
       "      <td>0.941187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>0.009275</td>\n",
       "      <td>0.017301</td>\n",
       "      <td>0.027736</td>\n",
       "      <td>0.945688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           eye_ear  eye_noear  noeye_ear  noeye_no_ear\n",
       "pumas_10                                              \n",
       "109       0.005819   0.009856   0.023513      0.960812\n",
       "110       0.005507   0.012237   0.026310      0.955945\n",
       "111       0.010989   0.015915   0.034483      0.938613\n",
       "803       0.008434   0.017838   0.032541      0.941187\n",
       "804       0.009275   0.017301   0.027736      0.945688"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target= data[summary.columns].apply(lambda x: x/data[summary.columns].sum(axis=1), axis=0)\n",
    "features = data[data.columns.difference(summary.columns)]\n",
    "print ' target : ', np.shape(target.as_matrix()), \" features\", np.shape(features.as_matrix())\n",
    "target.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the machine learning algorithm to work well we need to make sure that both our features are distributed about 0 and are all scaled to roughtly the same range. to do this we first normalize any columns that are fractional counts by their universe root and then we subtract the mean and divide by the standard deviation.\n",
    "\n",
    "$$ \\frac{x- \\bar{x}}{ \\sigma(x)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def scale_features(features):\n",
    "    housing_brackets  = [ f for f in features if 'in_households' in f and 'gini' not in f]\n",
    "    housing_features = features[housing_brackets].apply(lambda x: x/features[housing_brackets].sum(axis=1))\n",
    "    other_housing = [a for a in  features.columns if 'housing' in a and 'moe' not in a ]\n",
    "    other_housing_features = features[other_housing].apply(lambda x: x/ features[other_housing].sum(axis=1), axis=0)\n",
    "    pop_brackets  = [ f for f in features if 'pop' in f and 'not_a_us_citizen_pop' not in f]\n",
    "\n",
    "    ignore = ['rn','not_a_us_citizen_pop','not_a_us_citizen_pop_moe','total_pop'] + [ r for r in features.columns if 'moe' in r]\n",
    "\n",
    "    scaled_features = features[pop_brackets].apply(lambda x: x/features['total_pop'])\n",
    "    scaled_features = scaled_features.join(housing_features).join(other_housing_features)\n",
    "    normed_features = scaled_features.join(features[features.columns.difference(ignore+housing_brackets+pop_brackets+other_housing)])\n",
    "    normed_features = normed_features[normed_features.columns.difference(['total_pop'])]\n",
    "\n",
    "    return normed_features\n",
    "\n",
    "def norm_features(features,means,stds):             \n",
    "    normed_features = features.apply(lambda x: (x-np.mean(x))/np.std(x), axis=0) \n",
    "    return normed_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x168d860d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x168e96f90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x168ecc890>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1694b59d0>]], dtype=object)"
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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zmrosU5NuNIIAAAAAZkIjqEet7k9sNa9Wtfp6\nyWta5AVgzoCujJ3V1GWZmnSjEQQAAAAwExpBPWp1f2KrebWq1ddLXtMiLwBzBnRl7KymLsvUpJu7\nDvtmKeVbST6WvYbR52qtPyqlPLM4906SZ2qt3148djPJhcV/eqHW+r3BogYAAJihu+/cyAuv7Ywd\nxto+curuPHjqnrHDAPY5tBFUa/18kpRSPp3kfJK/kWQ3yeO11j/64HGllDuSXEyyuTj13VLK87XW\n3UGiPqG2traa7Ei2mlerWn295DUt8gIwZzCMt67fyMXnXh07jLVdeuz0kRtBxs5q6rJMTbpZd2vY\nTpKf7DveuOn7Z5K8WGu9Xmu9nuSVJKd7iA8AAACAnhx6RdA+v5bkG4uvd5J8p5TyVpIv1VpfTnJ/\nkrdLKU8tHrOd5IEkL/UZ7EnXaiey1bxa1errJa9pkReAOQO6MnZWU5dlatLNLRtBpZTPJvnDWusf\nJEmt9YuL8z+X5FKSX07yZpL7knwhe1cLfTPJG4c97/5LuD74yDfHjlcdb29vZ2q2t7eTh04lGb9+\njh07dnyr4w996EMBAGAeNnZ3D76NTynlE0l+tdb6myu+9/EkX621llLKnUm+n717BG0kuVJrffSg\n57169eru2bNnjx38STOV/Yk/3vlJXt95d+3Hb29v59577x0wosO9+977eeK7Pxrt53dx6bHTeWTR\nCLrdpvJ7eFTymhZ5Tcu1a9dy7ty5m7d9w9paXdsdV6tzxrpeeG0n5599eeww1nJh8+HJ3HdnSrEm\n3dbFcx87B1GXZWqybJ113a2uCPq9JP+slPJ8kh/WWv9mKeV3kzyYvS1iv5Ektdb3SikXk1xZ/HdP\nHityBvX6zrsd3pT/eJBY1nFh8+HRfjYAAAC05FafGvbRFeceP+Cxl5Nc7imuSdKJ5ANjfqznqY8+\ncuSfPYWP9Wx1fMlrWlrNCxiGOQO6MXZWU5dlatLNujeLBo5gDh/rCcD0lFK+leRj2fvk2M/VWn9U\nStlMcmHxkAu11u8tHrvyPAAwbet+fDxr+OCmm0D/Wh1f8pqWVvNiPmqtn6+1/kKSi0nOl1I2Fl9/\nZvHvySQppdxx8/nFYzkCcwZ0Y+yspi7L1KQbVwQBAMzPTpJ3k5xJ8mKt9XqSlFJeKaWcyd4fC/+1\n80lOJ3lppHgBgJ5oBPXI/kQYTqvjS17T0mpezNKvJflGkgeSvF1KeWpxfntxbuOA8xpBR2DOgG6M\nndXUZZmadGNrGADAjJRSPpvkD2utf5DkzST3JflKkicWX79xyPkD7b88f2try7HjTMmNGzfGDmFt\nU4o1Sba3t0/U76Njx60fr2Njd3d3rQf26erVq7tnz5697T93aFtbW5PoSL7w2k6Hj48fz4XNhyd1\n4+VkejFfeux0Hnno1NhhHGoq4+uo5DUtreZ17dq1nDt3zv1fZqCU8okkv1pr/c3F8Z1Jvp9kM3tX\nAV2ptT560PmDnrfVtd1xtTpnrGtKa84prd2mFGvSbZ0597FzEHVZpibL1lnXuSIIAGA+fi/JXy6l\nPF9K+Uat9b3s3RT6SpLLWdws+qDzAMD0uUdQj3QiYTitji95TUureTEftdaPrjh3OXvNnrXOsz5z\nBnRj7KymLsvUpBtXBAEAAADMhEZQj6Z2UzyYklbHl7ympdW8gGGYM6AbY2c1dVmmJt1oBAEAAADM\nhEZQj+xPhOG0Or7kNS2t5gUMw5wB3Rg7q6nLMjXpRiMIAAAAYCY0gnpkfyIMp9XxJa9paTUvYBjm\nDOjG2FlNXZapSTcaQQAAAAAzoRHUI/sTYTitji95TUureQHDMGdAN8bOauqyTE260QgCAAAAmAmN\noB7ZnwjDaXV8yWtaWs0LGIY5A7oxdlZTl2Vq0o1GEAAAAMBMaAT1yP5EGE6r40te09JqXsAwzBnQ\njbGzmrosU5NuNIIAAAAAZkIjqEf2J8JwWh1f8pqWVvMChmHOgG6MndXUZZmadKMRBAAAADATGkE9\nsj8RhtPq+JLXtLSaFzAMcwZ0Y+yspi7L1KQbjSAAAACAmdAI6pH9iTCcVseXvKal1byAYZgzoBtj\nZzV1WaYm3WgEAQAAAMyERlCP7E+E4bQ6vuQ1La3mBQzDnAHdGDurqcsyNelGIwgAAABgJjSCemR/\nIgyn1fElr2lpNS9gGOYM6MbYWU1dlqlJNxpBAAAAADOhEdQj+xNhOK2OL3lNS6t5AcMwZ0A3xs5q\n6rJMTbq567BvllK+leRj2WsYfa7W+qNSymaSC4uHXKi1fm/x2JXnAQAAADgZDr0iqNb6+VrrLyS5\nmOR8KWVj8fVnFv+eTJJSyh03n188dlbsT4ThtDq+5DUtreYFDMOcAd0YO6upyzI16ebQK4L22Uny\nbpIzSV6stV5PklLKK6WUM9lrKP1r55OcTvJS/yEDAAAA0MW6jaBfS/KNJA8kebuU8tTi/Pbi3MYB\n52fVCLI/EYbT6viS17S0mhcwDHMGdGPsrKYuy9Skm1veLLqU8tkkf1hr/YMkbya5L8lXkjyx+PqN\nQ84faP8lXFtbW45v4/H29nam5MaNG2OHcGRTi3n/78TYv5+OHTse7xgAgPZt7O7uHvjNUsonkvxq\nrfU3F8d3Jvl+ks3sXQV0pdb66EHnD3req1ev7p49e7a/LE6Ira2tSXQkX3htJ+effXnsMNZ2YfPh\nXHzu1bHDOJKpxXzpsdN55KFTY4dxqKmMr6OS17S0mte1a9dy7ty52d3bj/60urY7rlbnjHVNac05\npbXblGJNuq0z5z52DqIuy9Rk2TrrultdEfR7Sf5yKeX5Uso3aq3vZe+m0FeSXM7iZtEHnQcAAADg\n5Dj0HkG11o+uOHc5e82etc7PiU4kDKfV8SWvaWk1L2AY5gzoxthZTV2WqUk3t7xHEAAAAABt0Ajq\nkRtuwnBaHV/ympZW8wKGYc6Aboyd1dRlmZp0oxEEAAAAMBMaQT2yPxGG0+r4kte0tJoXMAxzBnRj\n7KymLsvUpBuNIAAAAICZ0Ajqkf2JMJxWx5e8pqXVvIBhmDOgG2NnNXVZpibdaAQBAAAAzIRGUI/s\nT4ThtDq+5DUtreYFDMOcAd0YO6upyzI16eausQMAAOD2KKV8KslvJ/n9Wuv5xblnknwsyTtJnqm1\nfntxfjPJhcV/eqHW+r3bHzEA0DdXBPXI/kQYTqvjS17T0mpezMo9Sb5207ndJI/XWn9hXxPojiQX\nk3xm8e/JUsrGbY20AeYM6MbYWU1dlqlJNxpBAAAzUWt9LslbK751c5PnTJIXa63Xa63Xk7yS5PTQ\n8QEAw7M1rEf2J8JwWh1f8pqWVvNi9naSfKeU8laSL9VaX05yf5K3SylPLR6zneSBJC+NFOMkmTOg\nG2NnNXVZpibduCIIAGDGaq1frLU+muS3klxanH4zyX1JvpLkicXXbxz2PPsvz9/a2nLsOFNy48aN\nsUNY25RiTZLt7e0T9fvo2HHrx+vY2N3dXeuBfbp69eru2bNnb/vPHdrW1tYkOpIvvLaT88++PHYY\na7uw+XAuPvfq2GEcydRivvTY6Tzy0KmxwzjUVMbXUclrWlrN69q1azl37pz7v8xEKeXnk/zSBzeL\n3nf+40m+WmstpZQ7k3w/yWb2to1dWTSLVmp1bXdcrc4Z65rSmnNKa7cpxZp0W2fOfewcRF2Wqcmy\nddZ1toYBAMxEKeXLSX4xyYOllA/XWn+9lPK7SR7M3hax30iSWut7pZSLSa4s/tMnx4gXAOifRlCP\ndCJhOK2OL3lNS6t5MR+11q8n+fpN5x4/4LGXk1y+HXG1ypwB3Rg7q6nLMjXpxj2CAAAAAGZCI6hH\nU7spHkxJq+NLXtPSal7AMMwZ0I2xs5q6LFOTbjSCAAAAAGZCI6hH9ifCcFodX/KallbzAoZhzoBu\njJ3V1GWZmnSjEQQAAAAwExpBPbI/EYbT6viS17S0mhcwDHMGdGPsrKYuy9SkG40gAAAAgJnQCOqR\n/YkwnFbHl7ympdW8gGGYM6AbY2c1dVmmJt1oBAEAAADMhEZQj+xPhOG0Or7kNS2t5gUMw5wB3Rg7\nq6nLMjXpRiMIAAAAYCY0gnpkfyIMp9XxJa9paTUvYBjmDOjG2FlNXZapSTcaQQAAAAAzoRHUI/sT\nYTitji95TUureQHDMGdAN8bOauqyTE260QgCAAAAmAmNoB7ZnwjDaXV8yWtaWs0LGIY5A7oxdlZT\nl2Vq0s1dh32zlPKpJL+d5PdrrecX555J8rEk7yR5ptb67cX5zSQXFv/phVrr94YKGgAAAICju9UV\nQfck+dpN53aTPF5r/YV9TaA7klxM8pnFvydLKRt9B3vS2Z8Iw2l1fMlrWlrNCxiGOQO6MXZWU5dl\natLNoY2gWutzSd5a8a2bmzxnkrxYa71ea72e5JUkp/sJEQAAAIA+HLo17AA7Sb5TSnkryZdqrS8n\nuT/J26WUpxaP2U7yQJKX+glzGuxPhOG0Or7kNS2t5gUMw5wB3Rg7q6nLMjXp5siNoFrrF5OklPJz\nSS4l+eUkbya5L8kXsne10DeTvHHY82xtbf3Zi/bB5VyOb8/x9vZ2puTGjRtjh3BkU4t5e3s7eehU\nkvF/Px07dnz7jz/0oQ8FAIB52Njd3T30AaWUn0/ySx/cLHrf+Y8n+WqttZRS7kzy/SSb2WsEXam1\nPnrQc169enX37Nmzx439xNnf3DrJXnhtJ+effXnsMNZ2YfPhXHzu1bHDOJKpxXzpsdN5ZNEIOqmm\nMr6OSl7T0mpe165dy7lz52Z3bz/60+ra7rhanTPWNaU155TWblOKNem2zpz72DmIuixTk2XrrOtu\n9alhX07yi0keLKV8uNb666WU303yYPa2iP1GktRa3yulXExyZfGfPnnc4AEAAADo16GNoFrr15N8\n/aZzjx/w2MtJLvcX2vToRMJwWh1f8pqWVvMChmHOgG6MndXUZZmadHOrj48HAAAAoBEaQT364Kab\nQP9aHV/ympZW8wKGYc6Aboyd1dRlmZp0oxEEAAAAMBMaQT2yPxGG0+r4kte0tJoXMAxzBnRj7Kym\nLsvUpBuNIAAAAICZ0Ajqkf2JMJxWx5e8pqXVvIBhmDOgG2NnNXVZpibdaAQBAAAAzIRGUI/sT4Th\ntDq+5DUtreYFDMOcAd0YO6upyzI16UYjCAAAAGAmNIJ6ZH8iDKfV8SWvaWk1L2AY5gzoxthZTV2W\nqUk3GkEAAAAAM6ER1CP7E2E4rY4veU1Lq3kBwzBnQDfGzmrqskxNurlr7AAAAID1/HjnJ3l9592x\nw1jLu++9P3YIAKygEdSjra0tHUkYSKvjS17T0mpewDCGmDNe33k35599udfnHMqFzYfHDoGJ8n67\nmrosU5NubA0DAAAAmAmNoB7pRMJwWh1f8pqWVvMChmHOgG6MndXUZZmadKMRBAAAADATGkE92tra\nGjsEaFar40te09JqXsxHKeVTpZR/XEq5tO/cZinlB4t/n77VedZnzoBujJ3V1GWZmnSjEQQAMB/3\nJPnaBwellDuSXEzymcW/Jw86X0rZuN3BAgD90wjqkf2JMJxWx5e8pqXVvJiPWutzSd7ad+pMkhdr\nrddrrdeTvFJKObPqfJLTtz/iaTNnQDfGzmrqskxNuvHx8QAA83V/krdLKU8tjreTPJBk44DzL93+\nEAGAPrkiqEf2J8JwWh1f8pqWVvNi1t5Mcl+SryR5YvH1G4ecP9D+8bG1teV437m+n5/+3bhxY+wQ\n1jalWJNke3v7yL/vTz/99JEeP5fjm+eWseM5CcdPP/30iYrnJByvY2N3d3etB/bp6tWru2fPnr3t\nP3doW1tbk7g07YXXdnL+2ZfHDmNtFzYfzsXnXh07jCOZWsyXHjudRx46NXYYh5rK+DoqeU1Lq3ld\nu3Yt586dc/+XmSil/HySX6q1ni+l3Jnk+0k2s3cV0JVa66MHnT/oOVtd2x3XEHPGlNZxU1oPiXU4\nXdaZrb7fHpe6LFOTZeus61wR1CO/gDCcVseXvKal1byYj1LKl7N3Q+jPllL+Xq31vezdFPpKksuL\n7+Wg8xyNOQO6MXZWU5dlatKNewQBAMxErfXrSb5+07nL2Wv23PzYlecBgGlzRVCP7N2G4bQ6vuQ1\nLa3mBQzDnAHdGDurqcsyNenGFUEAAAAM4u47N/LCaztH+m/ef+AvHvm/6cNHTt2dB0/dc9t/Ltxu\nGkE9sj8RhtPq+JLXtLSaFzAMcwYkb12/0fHm1n/ceyy3cumx0ye6EWROWaYm3dgaBgAAADATGkE9\nsj8RhtPq+JLXtLSaFzAMcwbQJ3PKMjXpRiMIAAAAYCY0gnpkfyIMp9XxJa9paTUvYBjmDKBP5pRl\natKNRhAAAADATBzaCCqlfKqU8o9LKZf2ndsspfxg8e/Ttzo/J/YnwnBaHV/ympZW8wKGYc4A+mRO\nWaYm3dzqiqB7knztg4NSyh1JLib5zOLfkwedL6VsDBAvAAAAAB0d2giqtT6X5K19p84kebHWer3W\nej3JK6WUM6vOJzk9VNAnlf2JMJxWx5e8pqXVvIBhmDOAPplTlqlJN3cd8fH3J3m7lPLU4ng7yQNJ\nNg44/1IvUQIAAABwbEe9WfSbSe5L8pUkTyy+fuOQ8wfav5dva2urieMPzp2UeA463t7ezpTcuHFj\n7BCObGox7/+dGPv3c+rj66jHTz/99ImKx+s1z9cLGIbxBfTJnLJMTbrZ2N3dPfQBpZSfT/JLtdbz\npZQ7k3w/yWb2rgK6Umt99KDzBz3n1atXd8+ePdtTCifH1tbWJC5Ne+G1nZx/9uWxw1jbhc2Hc/G5\nV8cO40imFvOlx07nkYdOjR3GoaYyvo5KXtPSal7Xrl3LuXPn3NuPzlpd2x3XEHPGlNZxU1oPiXU4\nU4r3pK+JW12HHIeaLFtnXXerTw37cvZuCP3ZUsrfq7W+l72bQl9JcnnxvRx0fm78AsJwWh1f8pqW\nVvMChmHOAPpkTlmmJt0ceo+gWuvXk3z9pnOXs9fsufmxK88DAAAAcDIc9R5BHML+RBhOq+NLXtPS\nal7AMMwZQJ/MKcvUpBuNIAAAAICZ0Ajqkf2JMJxWx5e8pqXVvIBhmDOAPplTlqlJNxpBAAAAADOh\nEdQj+xNhOK2OL3lNS6t5AcMwZwB9MqcsU5NuNIIAAAAAZuLQj49nPT/e+Ule33k3pz76SF54bWfs\ncG7p3ffeHzsEOLJW9//Ka1pazQsYhjkD6JM5ZZmadKMR1IPXd97N+WdfHjuMtV3YfHjsEAAAAIAR\n2BoGTEKr+3/lNS2t5gUMw5wB9MmcskxNutEIAgAAAJgJjSBgElrd/yuvaWk1L2AY5gygT+aUZWrS\njUYQAAAAwExoBAGT0Or+X3lNS6t5AcMwZwB9MqcsU5NuNIIAAAAAZkIjCJiEVvf/ymtaWs0LGIY5\nA+iTOWWZmnSjEQQAAAAwExpBwCS0uv9XXtPSal7AMMwZQJ/MKcvUpBuNIAAAAICZ0AgCJqHV/b/y\nmpZW8wKGYc4A+mROWaYm3WgEAQAAAMyERhAwCa3u/5XXtLSaFzAMcwbQJ3PKMjXpRiMIAAAAYCbu\nGjsAgHW0uv9XXtPSal7AMMwZMC1337mRF17bGTuMA5366CN/Ft9HTt2dB0/dM3JE4zPPdqMRBJz4\nN72beeMDAKBvb12/kYvPvTp2GGu59Nhp62E60wgCJvWml7T1xre1tdXkXzLkBdNSSnkmyceSvJPk\nf6m1/v1SymaSC4uHXKi1fm+s+KbKnAEwLPNsNxpBAADsJnm81vpHSVJKuSPJxSSbi+9/t5TyfK11\nd6wAAYB+uFk0wIha/QuGvGCSNvZ9fSbJi7XW67XW60leSXJ6nLCmy5wBMCzzbDeuCAIAYCfJd0op\nbyX5UpL7k7xdSnlq8f3tJA8keWmk+ACAnrgiCGBEW1tbY4cwCHnBtNRav1hrfTTJbyW5lOTNJPcl\n+UqSJxZfv3HYc+wfH1tbW473nev7+enfjRs3xg5hbVOKNZlevFNzUua7sY6ffvrpExXPSThex8bu\n7u3f6n316tXds2fP3vafO5QXXtvJ+WdfHjuMtV3YfHhSNwaeWrzJ9GKeWryXHjudRx46NXYYvWj1\nBnfympZr167l3LlzG7d+JK0rpXw8yVeT/EqSH2TvHkEbSa4sGkUrtba268sQc8aU1p1TWl+IdThT\nindKsba0Hj6OVtdmx7HOus7WMIARtfrGJS+YllLK7yT5mSR/kuQLtdb3SykXk1xZPOTJsWKbMnMG\nwLDMs91oBAEAzFyt9VdWnLuc5PII4QAAA+p0j6BSyjOllP+rlPJ8KeU/X5zbLKX8YPHv0/2GCdCm\nVu/5IC8AcwbA0Myz3XS9Img3yeO11j9KklLKHUkuZm8feZJ8t5TyfK319t+ACAAAAICVjvOpYftv\nPnQmyYu11uu11utJXkly+liRAcxAq/ua5QVgzgAYmnm2m65XBO0k+U4p5a0kX0pyf5K3SylPLb6/\nneSBJC8dP0QAAAAA+tDpiqBa6xcXHyH6W0kuJXkzyX1JvpLkicXXbxz2HDd/1v2Uj7e3tw9LvdLF\ncAAACTFJREFU9cS5cePG2CEcydTiTaYX89Ti3T/mxh7/xz1++umnT1Q8fR1/cO6kxNPXcauvFzAM\n4wtgWObZbjZ2d7vfxqeU8vEkX03yK0l+kL17BG0kubJoFK109erV3bNnz3b+uSfNC6/t5PyzL48d\nxtoubD6ci8+9OnYYa5tavMn0Yp5avJceO51HHjo1dhi92NraavKSVnlNy7Vr13Lu3LmNWz8SVmtt\nbdeXIeaMKa07p7S+EOtwphTvlGJtaT18HK2uzY5jnXVdp61hpZTfSfIzSf4kyRdqre+XUi4mubJ4\nyJNdnhdgblp945IXgDkDYGjm2W46NYJqrb+y4tzlJJePHREAAAAAgzjOp4YBcEyt7muWF4A5A2Bo\n5tluNIIAAAAAZkIjCGBEre5rlheAOQNgaObZbjSCAAAAAGZCIwhgRK3ua5YXgDkDYGjm2W40ggAA\nAABmQiMIYESt7muWF4A5A2Bo5tluNIIAAAAAZkIjCGBEre5rlheAOQNgaObZbjSCAAAAAGZCIwhg\nRK3ua5YXgDkDYGjm2W40ggAAAABmQiMIYESt7muWF4A5A2Bo5tluNIIAAAAAZkIjCGBEre5rlheA\nOQNgaObZbjSCAAAAAGbirrEDAJizra2tJv+SIS9gSn74z3fyzo33e3/enZ0/yalTf76357vrjo1s\n9PZsANNnbdaNRhAAALP296/9OD/8538y0LP/i96e6cE/f3f+5if/nd6eD4B5sjUMYESt/gVDXgAA\nDM3arBuNIAAAAICZsDUMYESt7muWFwDAcO6+cyMvvLYzdhhr+cipu/PgqXsGeW5rs240ggAAAGBC\n3rp+Ixefe3XsMNZy6bHTgzWC6MbWMIARtfoXDHkBADA0a7NuNIIAAAAAZsLWMGByprQnOjl8X3Sr\n+5rlBQDA0KzNutEIAiZnSnuiE/uiAQCAk8PWMIARtfoXDHkBADA0a7NuTtwVQf/y+p/m9374eq7/\n6ftjh7KWf+vD9+T0T39o7DAAAAAAbunENYLe393NlZf+ZbbfuTF2KGv59z/yUxpBQGet7muWFwAA\nybD399ze3s69997b2/Mddm/Plpy4RhAAAADQhuHv7/nHvT3TXO7t6R5BACNq9eoSeQEAwMmkEQQA\nAAAwExpBACPa2toaO4RByAsAAE6m3u8RVErZTHJhcXih1vq9vn8GAAC3h7UdAHMx5I2t+3acG1v3\n2ggqpdyR5GKSzcWp75ZSnq+17vb5cwCm5LA3lFMffeTEvdn08WkJrd5Lp9W84CDWdgDMyfA3tu7P\ncW5s3fcVQWeSvFhrvZ4kpZRXkpxO8lLPPwdgMqb0hpIk3/jsmby+8+7YYaztw3/urvx/79wYO4wj\nmctHk9IEazsAaEzfjaD7k7xdSnlqcbyd5IEcYbFw18ZG/vonHsxPbkzjD00//VP/xtghAPRqao2r\nC5sPTyreZD4fTUoTjr22m4K/9u/+dP7Dv3Bv78/77k9+krvv6W+s/9Tdd2Rjo7enA2CmNnZ3+2u4\nlFJ+NsnfTvKFJBtJvpnk79ZaX97/uKtXr06jywMAM3Hu3Dn/e8kSazsAmJ5brev6viLolSQ/u+/4\nzM0LhXWCAgDgRLC2A4DG9Prx8bXW97J3Q8ErSS4nebLP5wcA4PaxtgOA9vS6NQwAAACAk6vXK4IA\nAAAAOLk0ggAAAABmou+bRa+tlPJ3k/xHSd5P8l/WWn80Vix9KqV8K8nHstdk+1xDeX0qyW8n+f1a\n6/mx4zmuUspmkguLwwu11u+NGU8fWnuNPtDwmGpyDvxAKeWeJC8m+e9qrf/z2PEcVynlmez9Hr6T\n5Jla67fHjag/pZR/O8n/mr01wf9da/1bI4fERLU+r3XR6nvYcbW6ZumixTXpcfn9WGYuWeY952Dr\nrMNHawTVWv9OkpRSHk3y5SS/PlYsfaq1fj5JSimfTnI+yd8YN6Le3JPka9kbbJNWSrkjeze+3Fyc\n+m4p5fla69RvmNXMa7Rfq2Oq1Tlwn88n+X+STH1cfWA3yeO11j8aO5AB/PdJnqi1/p9jB8K0zWBe\nO7JW38N60OSa5agaXpMel9+Pm5hLlnnPOdQt1+EnYWvYX0ny/44dxAB2krw7dhB9qbU+l+StsePo\nyZkkL9Zar9dar2fvo3FPjxzTsTX2Gq3S1Jjap7k5sJTyoSR/Ncn/nqSlj5RuKZckSSnlziR/SROI\nnjU3r/Wg1fewTmawZllXk2vS4/L7cShzyTLvOfusuw4f/IqgUspfTfJf33T6b9Va/0kp5ftJHkzy\nqaHj6NsBef1XtdYfLr7+tSTfuL1RHd8aebXg/iRvl1KeWhxvJ3kgyUvjhcQaJjmmDjPlOfAWvpjk\nf0rykbED6dFOku+UUt5K8qVa68tjB9STfzPJnyul/G9JPpzkf6y1/sORY+KEa3VtdxytrguPaybr\nyuOwJuWoZjmXHGSu7zm3sNY6fPBGUK31SpIrB3zvPy6l/AdJvp3kPx06lj4dllcp5bNJ/rDW+ge3\nN6rjOyyvhryZ5L4kX8hel/SbSd4YNSIONeUxdZgpz4EHKaXcm+STtdb/tpTyX4wdT19qrV9MklLK\nzyW5lOSXx42oN29m7388/rMkdyb5P0op/2jxl2lYqdW13XG0ui48rpmsK4/DmpS1zXkuOchc33MO\ncpR1+EnYGvbjtHMPiZRSPpHkP6m1/g9jxzKAVrZFvJLkZ/cdn2nor/utvEZ/pvExlTQ2ByZ5NHtX\nmPyD7O1P/lwp5d8bOaY+vZPkT8cOoi+11j9N8s+SPFhrfTfJT0YOiTa0Nq91NoP3sONobs3SQctr\n0uPy+7GPueRQ3nP+f2uvwzd2d8epWSnld5P8dPYW1V+stb4ySiA9K6X8KHuL6veT/JMP/oo8daWU\nLyf5xexdevf7tdZJ34yrlPKZJP/N4vDi4i9Wk9baa/SBhsdUk3PgfqWUv57kp2qt3xw7luMqpfxO\nkp/J3hax36i1/tORQ+pNKeUvJPlWknuT1FqrS87pZA7z2lG1+h52XK2uWbpocU16XH4/lplLlnnP\nOdyt1uGjNYIAAAAAuL1OwtYwAAAAAG4DjSAAAACAmdAIAgAAAJgJjSAAAACAmdAIAgAAAJgJjSAA\nAACAmdAIAgAAAJgJjSAAAACAmfhXBcbypMKbd6AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x168d86f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "scaled_features  = scale_features(features)\n",
    "means = scaled_features.apply(lambda x: np.mean(x), axis=0)\n",
    "stds  = scaled_features.apply(lambda x: np.std(x), axis=0)\n",
    "normed_features = norm_features(scaled_features,means,stds)\n",
    "\n",
    "normed_features.head()\n",
    "normed_features[['median_age_female', \n",
    "                 'median_age',\n",
    "                 'percent_household_income_spent_on_rent',\n",
    "                 '10_to_14_years_female_pop']].hist( figsize=(20,10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scaled_features.to_csv(\"/Users/stuartlynn/scaled_pumas.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x168d303d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x16c210c90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x16c29e190>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x16c711390>]], dtype=object)"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HufuhKXbcdsIe2NDbte0c9tx+f3QYS7Z3+0YuOGNtdBiS1HgTExOMjY35j/8W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\nAAAAQDSJAAAAAEjy/wAFMKM91ZbiTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16c16d490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_means = target.apply(lambda x: np.mean(x), axis=0 ) \n",
    "target_std   = target.apply(lambda x: np.std(x), axis =0)\n",
    "target_max   = target.apply(lambda x: np.min(x), axis=0)\n",
    "target_min   = target.apply(lambda x: np.max(x), axis=0)\n",
    "normed_target = target.apply(lambda x: (x-np.min(x))/(np.max(x)-np.min(x)), axis=0 )\n",
    "normed_target.hist(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the data in good condition we try to train our model. We split the data in to training and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from sklearn.cross_validation  import train_test_split \n",
    "x_train, x_test, y_train, y_test  = train_test_split(normed_features.as_matrix(), \n",
    "                                                     normed_target.as_matrix(), \n",
    "                                                     test_size=0.30, \n",
    "                                                     random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def invert_target(t):\n",
    "    return t*target_std + target_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model is going to be a multiple layer neural network with $tanh$ activation between layers and a linear activation for the final output. We define the loss by the mean squared error and use a stochastic gradient descent method to train the network. \n",
    "\n",
    "The input dimension is the number of features we are useing and as we are predicting the probability of 4 classes our output layer is a vector or 4 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(output_dim=70, input_dim=np.shape(x_train)[1]))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Activation('tanh'))\n",
    "# model.add(Dense(20))\n",
    "# model.add(Dropout(0.2))\n",
    "# model.add(Activation('tanh'))\n",
    "\n",
    "model.add(Dense(output_dim=4))\n",
    "model.add(Activation('tanh'))\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer='sgd')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the model compiled we can fit it to our training set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "history = model.fit(x_train, y_train, \n",
    "                    verbose=0, \n",
    "                    nb_epoch=3000, \n",
    "                    batch_size=600, \n",
    "                    show_accuracy=True, \n",
    "                    validation_data=(x_test, y_test) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dzFg/g8d/fJyEKQnlPvfLXV9y+4Lb+fz6z/nDZ3+w+oiZHkOdsDocuPuA3zne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\nfEwzm/ZRfNw46i+6iLwf/Qjic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8spn17OnPPn8PaFb1P2vTL+/B9/pkeoxz5fs7yunN4z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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x302e23a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_model_perfromance(history):\n",
    "    fig   = plt.figure(figsize=(20,10))\n",
    "    plt.subplot(121)\n",
    "    plt.title('Loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.ylabel('loss')\n",
    "    \n",
    "    train = plt.plot(history.epoch, history.history['loss'], 'g-', label='train')\n",
    "    val   = plt.plot(history.epoch, history.history['val_loss'], 'r-', label='validation')\n",
    "    plt.legend( loc='upper left' )\n",
    "    plt.subplot(122)\n",
    "    plt.title('Accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.plot(history.epoch, history.history['acc'], 'g-', label='train')\n",
    "    plt.plot(history.epoch, history.history['val_acc'], 'r-',label='validation')\n",
    "    plt.legend( loc='upper left' )\n",
    "\n",
    "\n",
    "plot_model_perfromance(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "295/295 [==============================] - 0s\n",
      "error 0.009941, error in units of sd 0.045114, accuracy 0.990059 \n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, batch_size=2000)\n",
    "print 'error %f, error in units of sd %f, accuracy %f ' %(score, score/np.std(y_test), 1-score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the model trained we can predict our test set results and comapre with the actual data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "295/295 [==============================] - 0s     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.25860682,  0.33563152,  0.2973173 ,  0.65109867],\n",
       "       [ 0.20303848,  0.14913028,  0.35877565,  0.76830971],\n",
       "       [ 0.20047362,  0.17980911,  0.33872399,  0.79012567],\n",
       "       ..., \n",
       "       [ 0.17843936,  0.20258643,  0.28450835,  0.75755018],\n",
       "       [ 0.1778084 ,  0.31786487,  0.32266736,  0.67827946],\n",
       "       [ 0.19241875,  0.32983372,  0.0998479 ,  0.78881645]])"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = model.predict_proba(x_test, batch_size=16)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   1.,   0.,   1.,   0.,   1.,   4.,   8.,  12.,  12.,  16.,\n",
       "         17.,  28.,  41.,  35.,  50.,  34.,  27.,   8.,   0.]),\n",
       " array([ 0.0864582,  0.1304984,  0.1745386,  0.2185788,  0.262619 ,\n",
       "         0.3066592,  0.3506994,  0.3947396,  0.4387798,  0.48282  ,\n",
       "         0.5268602,  0.5709004,  0.6149406,  0.6589808,  0.703021 ,\n",
       "         0.7470612,  0.7911014,  0.8351416,  0.8791818,  0.923222 ,\n",
       "         0.9672622]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17059a790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 20\n",
    "range = [0,1]\n",
    "alpha = 0.7\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "plt.subplot(141)\n",
    "labels = target.columns\n",
    "\n",
    "plt.xlabel('Probability')\n",
    "\n",
    "plt.title(\"Both vision and hearing disability\")\n",
    "truth      = [r[0] for r in y_test]\n",
    "prediction = [r[0] for r in result]\n",
    "range  = [np.min(truth + prediction), np.max(truth + prediction)]\n",
    "plt.hist(truth, color='red', bins=bins ,alpha =alpha, range=range )\n",
    "plt.hist(prediction, color='blue', bins=bins, alpha =alpha, range=range )\n",
    "plt.subplot(142)\n",
    "\n",
    "plt.title(\"Vision disability only\")\n",
    "plt.xlabel('Probability')\n",
    "truth      = [r[1] for r in y_test]\n",
    "prediction = [r[1] for r in result]\n",
    "range  = [np.min(truth + prediction), np.max(truth + prediction)]\n",
    "plt.hist([r[1] for r in y_test], color='red', bins=bins,  alpha =alpha, range=range )\n",
    "plt.hist([r[1] for r in result], color='blue',bins=bins,  alpha =alpha, range=range )\n",
    "plt.subplot(143)\n",
    "plt.title(\"Hearing disability only\")\n",
    "plt.xlabel('Probability')\n",
    "\n",
    "truth      = [r[2] for r in y_test]\n",
    "prediction = [r[2] for r in result]\n",
    "range  = [np.min(truth + prediction), np.max(truth + prediction)]\n",
    "plt.hist([r[2] for r in y_test], color='red',bins=bins, alpha =alpha, range=range)\n",
    "plt.hist([r[2] for r in result], color='blue',bins=bins,  alpha =alpha,range=range )\n",
    "plt.subplot(144)\n",
    "plt.title(\"No disability\")\n",
    "plt.xlabel('Probability')\n",
    "\n",
    "truth      = [r[3] for r in y_test]\n",
    "prediction = [r[3] for r in result]\n",
    "range  = [np.min(truth + prediction), np.max(truth + prediction)]\n",
    "plt.hist([r[3] for r in y_test], color='red', bins=bins,  alpha =alpha,range=range )\n",
    "plt.hist([r[3] for r in result], color='blue' ,bins=bins, alpha =alpha,range=range)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x171f97050>]"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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upn37XPz859UsX15Gbm4lEyakhp+36dAhUqdOJW3sWMqWLaPy1lsd1dDUEGls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\nkQlIEuME4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ACA13I+synXwjyzCbk30GwiSXr33TzNmBHbjz6xCJRM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\nJGLUbBIRERERERERkYj5/wF0vmTVWN8tOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3bfa7d7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "plt.title('Errors')\n",
    "\n",
    "def invert(x,xmin,xmax):\n",
    "    return x*(xmax-xmin) + xmin\n",
    "\n",
    "plt.subplot(231)\n",
    "plt.title(\"Both vision and hearing disability\")\n",
    "plt.xlabel('Truth')\n",
    "plt.ylabel('Predction')\n",
    "\n",
    "truth      = [invert(r[0],target_min[0], target_max[0]) for r in y_test]\n",
    "prediction = [invert(r[0],target_min[0], target_max[0]) for r in result]\n",
    "plt.scatter(truth , prediction)\n",
    "plt.plot( [np.min(truth), np.max(truth)], [np.min(truth),np.max(truth)], 'r' )\n",
    "\n",
    "plt.subplot(232)\n",
    "plt.title(\"Vision and hearing disability\")\n",
    "plt.xlabel('Truth')\n",
    "plt.ylabel('Predction')\n",
    "truth      = [invert(r[1],target_min[1], target_max[1]) for r in y_test]\n",
    "prediction = [invert(r[1],target_min[1], target_max[1]) for r in result]\n",
    "plt.scatter(truth , prediction)\n",
    "plt.plot( [np.min(truth), np.max(truth)], [np.min(truth),np.max(truth)], 'r' )\n",
    "\n",
    "plt.subplot(233)\n",
    "plt.title(\"Hearing disability only\")\n",
    "plt.xlabel('Truth')\n",
    "plt.ylabel('Predction')\n",
    "truth      = [invert(r[2],target_min[2], target_max[2]) for r in y_test]\n",
    "prediction = [invert(r[2],target_min[2], target_max[2]) for r in result]\n",
    "plt.scatter(truth , prediction)\n",
    "plt.plot( [np.min(truth), np.max(truth)], [np.min(truth),np.max(truth)], 'r' )\n",
    "\n",
    "plt.subplot(234)\n",
    "plt.title(\"No disability\")\n",
    "plt.xlabel('Truth')\n",
    "plt.ylabel('Predction')\n",
    "\n",
    "truth      = [invert(r[3],target_min[3], target_max[3]) for r in y_test]\n",
    "prediction = [invert(r[3],target_min[3], target_max[3]) for r in result]\n",
    "plt.scatter(truth , prediction)\n",
    "plt.plot( [np.min(truth), np.max(truth)], [np.min(truth),np.max(truth)], 'r' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Predicting the output for census blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s     \n",
      "10000/10000 [==============================] - 0s     \n",
      "10000/10000 [==============================] - 0s     \n",
      "10000/10000 [==============================] - 1s     \n",
      "10000/10000 [==============================] - 0s     \n",
      "10000/10000 [==============================] - 1s     \n",
      "10000/10000 [==============================] - 0s     \n",
      "4001/4001 [==============================] - 0s     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stuartlynn/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:11: FutureWarning: convert_objects is deprecated.  Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n"
     ]
    }
   ],
   "source": [
    "feature_list = ','.join(normed_features.columns)\n",
    "reader  = pd.read_sql_query('select * from census_tacts_human' % locals(),\n",
    "                               engine,\n",
    "                               chunksize=10000)\n",
    "results = []\n",
    "geoms   = []\n",
    "\n",
    "\n",
    "for chunk in reader:\n",
    "    data  = chunk[normed_features.columns | ['total_pop', 'geoid']]   \n",
    "    data  = data.convert_objects(convert_numeric=True)\n",
    "    data.index = data['geoid']\n",
    "    new_features = data[data.columns.difference(['geoid'])]\n",
    "\n",
    "    scaled_data = scale_features(new_features)\n",
    "    normed_data = norm_features(scaled_data, means,stds )\n",
    "    prediction = model.predict_proba(normed_data.as_matrix(), batch_size=16)\n",
    "    results.append(pd.DataFrame(prediction,\n",
    "                                columns=['eye_ear','eye_noear','noeye_ear', 'noeye_noear'],\n",
    "                                index= chunk['geoid'] ))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = pd.concat(results)\n",
    "result['eye_ear']= invert(result['eye_ear'], target_min[0], target_max[0]) \n",
    "result['eye_noear'] = invert(result['eye_noear'], target_min[1], target_max[1])\n",
    "result['noeye_ear'] = invert(result['noeye_ear'], target_min[2], target_max[2])\n",
    "result['noeye_noear'] = invert(result['noeye_noear'],target_min[3], target_max[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "geom_lookup = pd.read_sql_query('select geoid, the_geom, total_pop from census_tacts_human', engine)\n",
    "geom_lookup.index= geom_lookup['geoid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result.join(geom_lookup).to_csv(\"eye_ear_result.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pumas.index = pumas['pumas_10']\n",
    "target_with_geom = target.join(pumas[['pumas_10','total_pop','geoid']], )\n",
    "target_with_geom.to_csv(\"pumas_eye_ear.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from IPython.display import IFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Origional data on pumas"
   ]
  },
  {
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