{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "An example of machine learning techniques -- principal component analysis and k-means clustering -- for visual analysis of high-dimensional disaster recovery data\n",
      "=================================================================================================\n",
      "**Data is simulated using ResilUS ([Miles and Chang, 2011](http://www.ingentaconnect.com/content/cagis/cagis/2011/00000038/00000001/art00003)).**  \n",
      "*Coded by* [*Scott Miles*](http://resilscience.com/?page_id=2)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Import some cool stuff for doing the analysis: [Scikit Learn](http://scikit-learn.org/stable/), [Matplotlib](http://matplotlib.org), and [Pandas](http://pandas.pydata.org).\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "from sklearn import decomposition    # allows you to do PCA\n",
      "from sklearn import cluster # allows you to do k means clustering\n",
      "from matplotlib import cm, colors # helps with creating a legend\n",
      "import pandas as pd # awesome data handling\n",
      "import plotly as py\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Set display options for pandas for easier viewing of tables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pandas.set_option('display.height', 500)\n",
      "pandas.set_option('display.max_rows', 500)\n",
      "pandas.set_option('display.max_columns', 500)\n",
      "pandas.set_option('display.width', 500)\n",
      "pandas.set_option('display.mpl_style', False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "username='geomando'\n",
      "api_key = '83orx28wm4'\n",
      "py = plotly.plotly(username=username, key=api_key)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Read data for PCA and pull out columns and rows for labeling later"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rec_data = pd.read_csv('sample_data.csv') # our recovery data table\n",
      "row_labels = rec_data.index.astype('int')\n",
      "column_labels = rec_data.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Print out recovery data. Notice how pandas permits pretty printing of tables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rec_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>% No Mitigation</th>\n",
        "      <th>% High Income</th>\n",
        "      <th>% Low Income</th>\n",
        "      <th>% MFR</th>\n",
        "      <th>% SFR</th>\n",
        "      <th>% Owner</th>\n",
        "      <th>% Renter</th>\n",
        "      <th>% Damaged</th>\n",
        "      <th>% Low Damage</th>\n",
        "      <th>% Medium Damage</th>\n",
        "      <th>% High Damage</th>\n",
        "      <th>% MFR Damaged</th>\n",
        "      <th>% Injured</th>\n",
        "      <th>Health Recovery Wks</th>\n",
        "      <th># Residents Left</th>\n",
        "      <th>% Residents Left</th>\n",
        "      <th>Household Debt Wk 13</th>\n",
        "      <th>Household Debt Wk 208</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 88.902861</td>\n",
        "      <td> 14.397988</td>\n",
        "      <td> 85.602012</td>\n",
        "      <td> 29.424709</td>\n",
        "      <td> 70.575291</td>\n",
        "      <td> 48.066646</td>\n",
        "      <td> 51.933354</td>\n",
        "      <td>  0.125747</td>\n",
        "      <td> 25.000000</td>\n",
        "      <td> 50.000000</td>\n",
        "      <td>  25.000000</td>\n",
        "      <td>  0.427350</td>\n",
        "      <td> 100.000000</td>\n",
        "      <td>   4</td>\n",
        "      <td>  64</td>\n",
        "      <td> 0.1981</td>\n",
        "      <td> 0.0011</td>\n",
        "      <td> 0.0007</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 80.258398</td>\n",
        "      <td>  6.201550</td>\n",
        "      <td> 93.798450</td>\n",
        "      <td> 56.227390</td>\n",
        "      <td> 43.772610</td>\n",
        "      <td> 21.705426</td>\n",
        "      <td> 78.294574</td>\n",
        "      <td>  0.671835</td>\n",
        "      <td>  7.692308</td>\n",
        "      <td> 61.538462</td>\n",
        "      <td>  30.769231</td>\n",
        "      <td>  1.011029</td>\n",
        "      <td>  92.307692</td>\n",
        "      <td>   1</td>\n",
        "      <td>  62</td>\n",
        "      <td> 0.3116</td>\n",
        "      <td> 0.0099</td>\n",
        "      <td> 0.0040</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 94.282311</td>\n",
        "      <td>  5.479452</td>\n",
        "      <td> 94.520548</td>\n",
        "      <td> 34.901727</td>\n",
        "      <td> 65.098273</td>\n",
        "      <td> 34.633711</td>\n",
        "      <td> 65.366289</td>\n",
        "      <td>  0.268017</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 88.888889</td>\n",
        "      <td>  11.111111</td>\n",
        "      <td>  0.597270</td>\n",
        "      <td> 100.000000</td>\n",
        "      <td>   1</td>\n",
        "      <td>  78</td>\n",
        "      <td> 0.2415</td>\n",
        "      <td> 0.0009</td>\n",
        "      <td> 0.0006</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 86.457342</td>\n",
        "      <td> 12.781478</td>\n",
        "      <td> 87.218522</td>\n",
        "      <td> 51.538218</td>\n",
        "      <td> 48.461782</td>\n",
        "      <td> 41.801459</td>\n",
        "      <td> 58.198541</td>\n",
        "      <td>  1.998097</td>\n",
        "      <td> 53.968254</td>\n",
        "      <td> 38.095238</td>\n",
        "      <td>   7.936508</td>\n",
        "      <td>  3.753846</td>\n",
        "      <td>  96.825397</td>\n",
        "      <td> 120</td>\n",
        "      <td>  69</td>\n",
        "      <td> 0.2270</td>\n",
        "      <td> 0.0347</td>\n",
        "      <td> 0.0327</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 86.523929</td>\n",
        "      <td>  9.319899</td>\n",
        "      <td> 90.680101</td>\n",
        "      <td> 45.541562</td>\n",
        "      <td> 54.458438</td>\n",
        "      <td> 37.002519</td>\n",
        "      <td> 62.997481</td>\n",
        "      <td>  0.377834</td>\n",
        "      <td> 13.333333</td>\n",
        "      <td> 40.000000</td>\n",
        "      <td>  46.666667</td>\n",
        "      <td>  0.829646</td>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 105</td>\n",
        "      <td>  91</td>\n",
        "      <td> 0.2258</td>\n",
        "      <td> 0.0111</td>\n",
        "      <td> 0.0063</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 67.575758</td>\n",
        "      <td> 17.595960</td>\n",
        "      <td> 82.404040</td>\n",
        "      <td> 79.878788</td>\n",
        "      <td> 20.121212</td>\n",
        "      <td> 22.222222</td>\n",
        "      <td> 77.777778</td>\n",
        "      <td>  1.757576</td>\n",
        "      <td> 36.781609</td>\n",
        "      <td> 44.827586</td>\n",
        "      <td>  18.390805</td>\n",
        "      <td>  2.124431</td>\n",
        "      <td>  88.505747</td>\n",
        "      <td> 120</td>\n",
        "      <td> 130</td>\n",
        "      <td> 0.2808</td>\n",
        "      <td> 0.0152</td>\n",
        "      <td> 0.0098</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 69.792439</td>\n",
        "      <td> 17.550037</td>\n",
        "      <td> 82.449963</td>\n",
        "      <td> 73.202372</td>\n",
        "      <td> 26.797628</td>\n",
        "      <td> 24.369904</td>\n",
        "      <td> 75.630096</td>\n",
        "      <td>  1.593773</td>\n",
        "      <td> 41.860465</td>\n",
        "      <td> 38.372093</td>\n",
        "      <td>  19.767442</td>\n",
        "      <td>  2.101266</td>\n",
        "      <td>  94.186047</td>\n",
        "      <td> 120</td>\n",
        "      <td> 145</td>\n",
        "      <td> 0.2599</td>\n",
        "      <td> 0.0162</td>\n",
        "      <td> 0.0090</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 72.354740</td>\n",
        "      <td>  9.816514</td>\n",
        "      <td> 90.183486</td>\n",
        "      <td> 71.376147</td>\n",
        "      <td> 28.623853</td>\n",
        "      <td> 19.143731</td>\n",
        "      <td> 80.856269</td>\n",
        "      <td>  0.550459</td>\n",
        "      <td> 22.222222</td>\n",
        "      <td> 61.111111</td>\n",
        "      <td>  16.666667</td>\n",
        "      <td>  0.771208</td>\n",
        "      <td>  88.888889</td>\n",
        "      <td>   8</td>\n",
        "      <td> 100</td>\n",
        "      <td> 0.3226</td>\n",
        "      <td> 0.0112</td>\n",
        "      <td> 0.0052</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 75.730230</td>\n",
        "      <td> 49.465685</td>\n",
        "      <td> 50.534315</td>\n",
        "      <td> 37.117074</td>\n",
        "      <td> 62.882926</td>\n",
        "      <td> 63.452862</td>\n",
        "      <td> 36.547138</td>\n",
        "      <td>  1.733555</td>\n",
        "      <td> 38.356164</td>\n",
        "      <td> 43.835616</td>\n",
        "      <td>  17.808219</td>\n",
        "      <td>  4.094690</td>\n",
        "      <td>  78.082192</td>\n",
        "      <td>  12</td>\n",
        "      <td>  84</td>\n",
        "      <td> 0.1795</td>\n",
        "      <td> 0.0220</td>\n",
        "      <td> 0.0117</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 76.138681</td>\n",
        "      <td> 17.165194</td>\n",
        "      <td> 82.834806</td>\n",
        "      <td> 61.080897</td>\n",
        "      <td> 38.919103</td>\n",
        "      <td> 33.752549</td>\n",
        "      <td> 66.247451</td>\n",
        "      <td>  6.084296</td>\n",
        "      <td> 50.279330</td>\n",
        "      <td> 35.754190</td>\n",
        "      <td>  13.966480</td>\n",
        "      <td>  9.015025</td>\n",
        "      <td>  93.854749</td>\n",
        "      <td>  27</td>\n",
        "      <td>  81</td>\n",
        "      <td> 0.2765</td>\n",
        "      <td> 0.0340</td>\n",
        "      <td> 0.0112</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 87.480438</td>\n",
        "      <td> 19.092332</td>\n",
        "      <td> 80.907668</td>\n",
        "      <td> 20.031299</td>\n",
        "      <td> 79.968701</td>\n",
        "      <td> 64.241002</td>\n",
        "      <td> 35.758998</td>\n",
        "      <td> 11.189358</td>\n",
        "      <td> 60.839161</td>\n",
        "      <td> 27.272727</td>\n",
        "      <td>  11.888112</td>\n",
        "      <td> 19.531250</td>\n",
        "      <td>  90.209790</td>\n",
        "      <td>  31</td>\n",
        "      <td>  28</td>\n",
        "      <td> 0.1972</td>\n",
        "      <td> 0.0913</td>\n",
        "      <td> 0.0327</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 93.720336</td>\n",
        "      <td> 12.340270</td>\n",
        "      <td> 87.659730</td>\n",
        "      <td>  9.602045</td>\n",
        "      <td> 90.397955</td>\n",
        "      <td> 36.692223</td>\n",
        "      <td> 63.307777</td>\n",
        "      <td>  7.192406</td>\n",
        "      <td> 51.269036</td>\n",
        "      <td> 37.055838</td>\n",
        "      <td>  11.675127</td>\n",
        "      <td> 16.730038</td>\n",
        "      <td>  92.893401</td>\n",
        "      <td> 120</td>\n",
        "      <td>  66</td>\n",
        "      <td> 0.2661</td>\n",
        "      <td> 0.0397</td>\n",
        "      <td> 0.0255</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 95.712695</td>\n",
        "      <td> 12.193764</td>\n",
        "      <td> 87.806236</td>\n",
        "      <td>  6.737194</td>\n",
        "      <td> 93.262806</td>\n",
        "      <td> 53.229399</td>\n",
        "      <td> 46.770601</td>\n",
        "      <td> 15.367483</td>\n",
        "      <td> 60.144928</td>\n",
        "      <td> 24.637681</td>\n",
        "      <td>  15.217391</td>\n",
        "      <td> 23.966942</td>\n",
        "      <td>  90.217391</td>\n",
        "      <td> 120</td>\n",
        "      <td>  40</td>\n",
        "      <td> 0.2299</td>\n",
        "      <td> 0.0486</td>\n",
        "      <td> 0.0344</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 95.726496</td>\n",
        "      <td> 14.574899</td>\n",
        "      <td> 85.425101</td>\n",
        "      <td>  3.463788</td>\n",
        "      <td> 96.536212</td>\n",
        "      <td> 63.607737</td>\n",
        "      <td> 36.392263</td>\n",
        "      <td>  0.314890</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 71.428571</td>\n",
        "      <td>  28.571429</td>\n",
        "      <td>  2.597403</td>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 120</td>\n",
        "      <td>  60</td>\n",
        "      <td> 0.2469</td>\n",
        "      <td> 0.0160</td>\n",
        "      <td> 0.0084</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 95.519542</td>\n",
        "      <td> 21.401335</td>\n",
        "      <td> 78.598665</td>\n",
        "      <td>  2.621544</td>\n",
        "      <td> 97.378456</td>\n",
        "      <td> 76.739752</td>\n",
        "      <td> 23.260248</td>\n",
        "      <td> 26.358437</td>\n",
        "      <td> 66.184448</td>\n",
        "      <td> 21.518987</td>\n",
        "      <td>  12.296564</td>\n",
        "      <td> 43.636364</td>\n",
        "      <td>  82.640145</td>\n",
        "      <td> 120</td>\n",
        "      <td>  42</td>\n",
        "      <td> 0.1963</td>\n",
        "      <td> 0.1018</td>\n",
        "      <td> 0.0353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 94.163424</td>\n",
        "      <td> 25.052380</td>\n",
        "      <td> 74.947620</td>\n",
        "      <td>  4.160431</td>\n",
        "      <td> 95.839569</td>\n",
        "      <td> 67.015864</td>\n",
        "      <td> 32.984136</td>\n",
        "      <td>  3.621670</td>\n",
        "      <td> 59.504132</td>\n",
        "      <td> 29.752066</td>\n",
        "      <td>  10.743802</td>\n",
        "      <td>  9.352518</td>\n",
        "      <td>  86.776860</td>\n",
        "      <td>  53</td>\n",
        "      <td>  76</td>\n",
        "      <td> 0.2190</td>\n",
        "      <td> 0.0526</td>\n",
        "      <td> 0.0354</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 94.011229</td>\n",
        "      <td> 28.820961</td>\n",
        "      <td> 71.179039</td>\n",
        "      <td>  4.179663</td>\n",
        "      <td> 95.820337</td>\n",
        "      <td> 69.931379</td>\n",
        "      <td> 30.068621</td>\n",
        "      <td> 15.127885</td>\n",
        "      <td> 61.443299</td>\n",
        "      <td> 25.360825</td>\n",
        "      <td>  13.195876</td>\n",
        "      <td> 24.626866</td>\n",
        "      <td>  79.587629</td>\n",
        "      <td> 120</td>\n",
        "      <td>  66</td>\n",
        "      <td> 0.1935</td>\n",
        "      <td> 0.0560</td>\n",
        "      <td> 0.0291</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 93.702178</td>\n",
        "      <td> 21.542084</td>\n",
        "      <td> 78.457916</td>\n",
        "      <td>  5.002943</td>\n",
        "      <td> 94.997057</td>\n",
        "      <td> 62.831077</td>\n",
        "      <td> 37.168923</td>\n",
        "      <td>  3.766922</td>\n",
        "      <td> 50.000000</td>\n",
        "      <td> 35.156250</td>\n",
        "      <td>  14.843750</td>\n",
        "      <td>  7.058824</td>\n",
        "      <td>  87.500000</td>\n",
        "      <td> 120</td>\n",
        "      <td>  76</td>\n",
        "      <td> 0.2190</td>\n",
        "      <td> 0.0508</td>\n",
        "      <td> 0.0284</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 90.293542</td>\n",
        "      <td> 24.422701</td>\n",
        "      <td> 75.577299</td>\n",
        "      <td> 12.054795</td>\n",
        "      <td> 87.945205</td>\n",
        "      <td> 40.273973</td>\n",
        "      <td> 59.726027</td>\n",
        "      <td>  1.487280</td>\n",
        "      <td> 57.894737</td>\n",
        "      <td> 23.684211</td>\n",
        "      <td>  18.421053</td>\n",
        "      <td>  6.493506</td>\n",
        "      <td>  84.210526</td>\n",
        "      <td>   5</td>\n",
        "      <td>  47</td>\n",
        "      <td> 0.1888</td>\n",
        "      <td> 0.0252</td>\n",
        "      <td> 0.0164</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> 93.202694</td>\n",
        "      <td> 17.677077</td>\n",
        "      <td> 82.322923</td>\n",
        "      <td>  9.471321</td>\n",
        "      <td> 90.528679</td>\n",
        "      <td> 40.640947</td>\n",
        "      <td> 59.359053</td>\n",
        "      <td>  0.796081</td>\n",
        "      <td> 30.769231</td>\n",
        "      <td> 56.410256</td>\n",
        "      <td>  12.820513</td>\n",
        "      <td>  4.094828</td>\n",
        "      <td>  94.871795</td>\n",
        "      <td> 120</td>\n",
        "      <td> 141</td>\n",
        "      <td> 0.2640</td>\n",
        "      <td> 0.0233</td>\n",
        "      <td> 0.0102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> 96.365173</td>\n",
        "      <td> 11.355311</td>\n",
        "      <td> 88.644689</td>\n",
        "      <td>  7.213300</td>\n",
        "      <td> 92.786700</td>\n",
        "      <td> 43.927867</td>\n",
        "      <td> 56.072133</td>\n",
        "      <td>  0.028177</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  0.390625</td>\n",
        "      <td> 100.000000</td>\n",
        "      <td>   1</td>\n",
        "      <td>  94</td>\n",
        "      <td> 0.2500</td>\n",
        "      <td> 0.0005</td>\n",
        "      <td> 0.0005</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "    % No Mitigation  % High Income  % Low Income      % MFR      % SFR    % Owner   % Renter  % Damaged  % Low Damage  % Medium Damage  % High Damage  % MFR Damaged   % Injured  Health Recovery Wks  # Residents Left  % Residents Left  Household Debt Wk 13  Household Debt Wk 208\n",
        "0         88.902861      14.397988     85.602012  29.424709  70.575291  48.066646  51.933354   0.125747     25.000000        50.000000      25.000000       0.427350  100.000000                    4                64            0.1981                0.0011                 0.0007\n",
        "1         80.258398       6.201550     93.798450  56.227390  43.772610  21.705426  78.294574   0.671835      7.692308        61.538462      30.769231       1.011029   92.307692                    1                62            0.3116                0.0099                 0.0040\n",
        "2         94.282311       5.479452     94.520548  34.901727  65.098273  34.633711  65.366289   0.268017      0.000000        88.888889      11.111111       0.597270  100.000000                    1                78            0.2415                0.0009                 0.0006\n",
        "3         86.457342      12.781478     87.218522  51.538218  48.461782  41.801459  58.198541   1.998097     53.968254        38.095238       7.936508       3.753846   96.825397                  120                69            0.2270                0.0347                 0.0327\n",
        "4         86.523929       9.319899     90.680101  45.541562  54.458438  37.002519  62.997481   0.377834     13.333333        40.000000      46.666667       0.829646  100.000000                  105                91            0.2258                0.0111                 0.0063\n",
        "5         67.575758      17.595960     82.404040  79.878788  20.121212  22.222222  77.777778   1.757576     36.781609        44.827586      18.390805       2.124431   88.505747                  120               130            0.2808                0.0152                 0.0098\n",
        "6         69.792439      17.550037     82.449963  73.202372  26.797628  24.369904  75.630096   1.593773     41.860465        38.372093      19.767442       2.101266   94.186047                  120               145            0.2599                0.0162                 0.0090\n",
        "7         72.354740       9.816514     90.183486  71.376147  28.623853  19.143731  80.856269   0.550459     22.222222        61.111111      16.666667       0.771208   88.888889                    8               100            0.3226                0.0112                 0.0052\n",
        "8         75.730230      49.465685     50.534315  37.117074  62.882926  63.452862  36.547138   1.733555     38.356164        43.835616      17.808219       4.094690   78.082192                   12                84            0.1795                0.0220                 0.0117\n",
        "9         76.138681      17.165194     82.834806  61.080897  38.919103  33.752549  66.247451   6.084296     50.279330        35.754190      13.966480       9.015025   93.854749                   27                81            0.2765                0.0340                 0.0112\n",
        "10        87.480438      19.092332     80.907668  20.031299  79.968701  64.241002  35.758998  11.189358     60.839161        27.272727      11.888112      19.531250   90.209790                   31                28            0.1972                0.0913                 0.0327\n",
        "11        93.720336      12.340270     87.659730   9.602045  90.397955  36.692223  63.307777   7.192406     51.269036        37.055838      11.675127      16.730038   92.893401                  120                66            0.2661                0.0397                 0.0255\n",
        "12        95.712695      12.193764     87.806236   6.737194  93.262806  53.229399  46.770601  15.367483     60.144928        24.637681      15.217391      23.966942   90.217391                  120                40            0.2299                0.0486                 0.0344\n",
        "13        95.726496      14.574899     85.425101   3.463788  96.536212  63.607737  36.392263   0.314890      0.000000        71.428571      28.571429       2.597403  100.000000                  120                60            0.2469                0.0160                 0.0084\n",
        "14        95.519542      21.401335     78.598665   2.621544  97.378456  76.739752  23.260248  26.358437     66.184448        21.518987      12.296564      43.636364   82.640145                  120                42            0.1963                0.1018                 0.0353\n",
        "15        94.163424      25.052380     74.947620   4.160431  95.839569  67.015864  32.984136   3.621670     59.504132        29.752066      10.743802       9.352518   86.776860                   53                76            0.2190                0.0526                 0.0354\n",
        "16        94.011229      28.820961     71.179039   4.179663  95.820337  69.931379  30.068621  15.127885     61.443299        25.360825      13.195876      24.626866   79.587629                  120                66            0.1935                0.0560                 0.0291\n",
        "17        93.702178      21.542084     78.457916   5.002943  94.997057  62.831077  37.168923   3.766922     50.000000        35.156250      14.843750       7.058824   87.500000                  120                76            0.2190                0.0508                 0.0284\n",
        "18        90.293542      24.422701     75.577299  12.054795  87.945205  40.273973  59.726027   1.487280     57.894737        23.684211      18.421053       6.493506   84.210526                    5                47            0.1888                0.0252                 0.0164\n",
        "19        93.202694      17.677077     82.322923   9.471321  90.528679  40.640947  59.359053   0.796081     30.769231        56.410256      12.820513       4.094828   94.871795                  120               141            0.2640                0.0233                 0.0102\n",
        "20        96.365173      11.355311     88.644689   7.213300  92.786700  43.927867  56.072133   0.028177      0.000000         0.000000     100.000000       0.390625  100.000000                    1                94            0.2500                0.0005                 0.0005"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "__Reduce the dimensionality of the data using principal components analysis and visualize__"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Run PCA to with neighborhoods as observations\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pca = decomposition.ProbabilisticPCA(n_components=2, whiten=True)\n",
      "myPCA = pca.fit_transform(rec_data)\n",
      "pc1 = myPCA[:, 0]\n",
      "pc2 = myPCA[:, 1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Run PCA to with recovery indicators as observations (transpose of original data)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pca_rec = decomposition.ProbabilisticPCA(n_components=2, whiten=True)\n",
      "myPCA_rec = pca.fit_transform(rec_data.T)  # took the transpose of the recovery data table\n",
      "pc1_rec = myPCA_rec[:, 0]\n",
      "pc2_rec = myPCA_rec[:, 1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a bubble chart showing six variables:  \n",
      "1. First principal component of neighborhoods as x-axis  \n",
      "2. Second principal component of neighborhoods as y-axis  \n",
      "3. First principal component of recovery indicators as x-axis\n",
      "4. Second principal component of recovery indicators as y-axis  \n",
      "5. Recovery indicator 1 as graduated symbol (user defined)  \n",
      "6. Recovery indicator 2 as graduated color (user defined)  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "figure(figsize=(10,8))\n",
      "\n",
      "# Choose recovery indicator for graduated symbol\n",
      "# Set arbitrary scale value\n",
      "area_scale = 10\n",
      "indicator_for_area = rec_data['# Residents Left']*area_scale \n",
      "\n",
      "\n",
      "# Choose recovery indicator for graduated colors\n",
      "# Do various stuff for implementing graduated colors\n",
      "indicator_for_color = rec_data['% Renter']\n",
      "color_norm = [item/100. for item in indicator_for_color] # normalize from 0 to 1 in order to use as grayscale colormap\n",
      "color_norm_str = [str(item) for item in color_norm] # convert each entry to string for use in plot command\n",
      "\n",
      "# create scatter plot of the two neighborhood principal components with graduated symbols and colors.\n",
      "scatter(pc1, pc2, s=indicator_for_area, c=color_norm_str, alpha=1)\n",
      "\n",
      "# Plot the neighborhood labels\n",
      "# have to use parallel for loop using zip because annotate function will only label one point at a time\n",
      "for label, x, y in zip(row_labels, pc1, pc2):\n",
      "    annotate(\n",
      "        label, \n",
      "        xy = (x, y), xytext = (0, 0),\n",
      "        textcoords = 'offset points', ha = 'center', va = 'center', size='8')\n",
      "\n",
      "# label x and y axes of plot, inserting the calculated variance explained by the first and second principal components\n",
      "xlabel(\"Principal Component 1\" + \" (\" + str(round(100*pca.explained_variance_ratio_[0],2)) + \"% of variance)\") # the myPCA.fracs bit add the variance percentage to the label\n",
      "ylabel(\"Principal Component 2\"  + \" (\" + str(round(100*pca.explained_variance_ratio_[1],2)) + \"% of variance)\") # the myPCA.fracs bit add the variance percentage to the label\n",
      "\n",
      "# Create colorbar legend\n",
      "m = cm.ScalarMappable()\n",
      "m.set_array(indicator_for_color)\n",
      "m.set_cmap(cmap='gray')\n",
      "norm = colors.Normalize(vmin=0, vmax=100)\n",
      "m.set_norm(norm)\n",
      "m.set_clim(vmin=0.0, vmax=100.0)\n",
      "cbar = plt.colorbar(m) \n",
      "cbar.set_label('% Renter') # Too lazy to generalize legend label\n",
      "\n",
      "# Anyone know a good solution for making a graduated symbol legend?\n",
      "# None of my attempts have been generalizable\n",
      "annotate('Number Residents Left', xy = (1.8, 2.25), xytext = (0, 0),\n",
      "        textcoords = 'offset points', ha = 'right', va = 'center', size='12')\n",
      "annotate('as Graduated Symbols', xy = (1.8, 2.1), xytext = (0, 0),\n",
      "        textcoords = 'offset points', ha = 'right', va = 'center', size='12')\n",
      "\n",
      "# Plot the principal components -- as just labels -- of the recovery indicators\n",
      "for label, x, y in zip(column_labels, pc1_rec, pc2_rec):\n",
      "    annotate(\n",
      "        label, \n",
      "        xy = (x, y), xytext = (0, 0),\n",
      "        textcoords = 'offset points', ha = 'center', va = 'center', size='8')    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAqoAAAIeCAYAAABz3i0JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x94162f0>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "__Identify neighborhoods with similar resilience using k-means clustering and visualize__"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Run kmeans cluster on neighborhoods PCA to identify and symbolize potential clusters in pca results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmeans = cluster.KMeans(n_clusters=5)\n",
      "clust = kmeans.fit(myPCA)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Run kmeans cluster on recovery indicators PCA to identify and symbolize potential clusters in pca results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmeans_rec = cluster.KMeans(n_clusters=5)\n",
      "clusters_rec = kmeans_rec.fit(myPCA_rec)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(10,8))\n",
      "\n",
      "# Plot two principal components of neigbhorhoods with color determined by which of the five k-means clusters assigned to components\n",
      "scatter(pc1, pc2, s=200, c=clust.labels_, alpha=.5)\n",
      "\n",
      "# label x and y axes\n",
      "xlabel(\"Principal Component 1\" + \" (\" + str(round(100*pca.explained_variance_ratio_[0],2)) + \"% of variance)\") # the myPCA.fracs bit add the variance percentage to the label\n",
      "ylabel(\"Principal Component 2\"  + \" (\" + str(round(100*pca.explained_variance_ratio_[1],2)) + \"% of variance)\") # the myPCA.fracs bit add the variance percentage to the label\n",
      "\n",
      "\n",
      "# plot labels of PCA recovery indicators\n",
      "# have to use parallel for loop using zip because annotate function will only label one point at a time\n",
      "for label, x, y in zip(column_labels, pc1_rec, pc2_rec):\n",
      "    annotate(\n",
      "        label,\n",
      "        xy = (x, y), xytext = (0, 0),\n",
      "        textcoords = 'offset points', ha = 'center', va = 'center', size='8')\n",
      "\n",
      "# plot labels of neighborhood IDs \n",
      "for label, x, y in zip(row_labels, pc1, pc2):\n",
      "    annotate(\n",
      "        label, \n",
      "        xy = (x, y), xytext = (0, 0),\n",
      "        textcoords = 'offset points', ha = 'center', va = 'center', size='8')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAIeCAYAAACmzPlEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x94023f0>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using plot.ly can make the bubble chart interactive...the size scale is different between the two plots and I'm too lazy to figure out the different units..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "plotly_color_scale = ['hsl('+ str(200) + ',' + str(100) + ',' + str(indicator_for_color.values[i]) + ')' for i in indicator_for_color.index]\n",
      "\n",
      "data = [\n",
      "        {\n",
      "         'x': pc1,\n",
      "         'y': pc2,\n",
      "         'marker':\n",
      "             {'size': indicator_for_area.values / 30.0,\n",
      "              'color': plotly_color_scale, \n",
      "              'line':{'width':2}\n",
      "              },\n",
      "          'type':'scatter',\n",
      "          'mode': 'markers'\n",
      "          },\n",
      "         {\n",
      "          \"x\": pc1_rec, \"y\": pc2_rec,\n",
      "          \"text\": list(column_labels),\n",
      "          \"type\": \"scatter\",\n",
      "          \"mode\": \"text\",\n",
      "          \"textposition\": \"top\",\n",
      "        }\n",
      "         ]\n",
      "\n",
      "xlabel = \"Principal Component 1\" + \" (\" + str(round(100*pca.explained_variance_ratio_[0],2)) + \"% of variance)\"\n",
      "ylabel = \"Principal Component 2\" + \" (\" + str(round(100*pca.explained_variance_ratio_[1],2)) + \"% of variance)\"\n",
      "\n",
      "layout = {\n",
      "          'autosize': False,\n",
      "          'height': 750,\n",
      "          'width': 750,\n",
      "          'xaxis':{'title': xlabel},\n",
      "          'yaxis':{'title': ylabel},\n",
      "          'showlegend':False\n",
      "          }\n",
      "            \n",
      "            \n",
      "\n",
      "filename = 'resilus_pca_bubble'\n",
      "py.iplot(data, layout=layout, filename=filename, fileopt='overwrite')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "\n"
       ]
      },
      {
       "html": [
        "<iframe height=\"650\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~geomando/2/600/600\" width=\"650\"></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "<IPython.core.display.HTML at 0x948d8d0>"
       ]
      }
     ],
     "prompt_number": 54
    }
   ],
   "metadata": {}
  }
 ]
}