MyApp_smv_example/notebooks/Advanced_Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Analytics\n",
    "\n",
    "Under this framework it is also convenient to run more advanced analytics like supervised or unsupervised learning algorithms. Given currently there is only one dataset covering limited features, we will show a few straightforward examples of advanced techniques.\n",
    "\n",
    "\n",
    "* <a href=#bookmark1>Bivariate Analysis</a>\n",
    "* <a href=#bookmark2>K-Means Clustering: A Simple Example</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import necessary packages\n",
    "import smv\n",
    "import pandas as pd\n",
    "from numpy import array\n",
    "from pyspark.sql import HiveContext, DataFrame\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Read the input data from a csv file\n",
    "raw = openCsv(\"data/input/employment/CB1200CZ11.csv\")\n",
    "raw_clean = raw.where((col(\"EMP_F\").isNull())&(col(\"ST\")!=99)).cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "<a name='bookmark1'/>\n",
    "## Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23626089869715505"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pearson correlation\n",
    "raw_clean.select(\n",
    "    (col(\"PAYANN\")/col(\"EMP\")).alias(\"PAYANN_per_EMP\"),\n",
    "    \"ESTAB\"\n",
    ").corr(\"PAYANN_per_EMP\", \"ESTAB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "<a name='bookmark2'/>\n",
    "## K-Means Clustering: A Simple Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose we are interested to see which states are relatively similar in terms of average payroll per employee and # of establishments. What we can do is to run a clustering based on these features. One of the standard clustering algorithms is K-Means, which is supported in both the scikit-learn package in Python and ml package in PySpark."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before running the clustering, we firstly create features that will be used to cluster states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_feature = raw_clean.groupBy(\"ST\").agg(\n",
    "    avg(col(\"PAYANN\")/col(\"EMP\")).alias(\"avg_PAYANN_per_EMP\"),\n",
    "    avg(col(\"ESTAB\")).alias(\"avg_ESTAB\")\n",
    ").na.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scikit-Learn Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# format model input\n",
    "feature_arrays = all_feature.drop(\"ST\").toPandas().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred = KMeans(n_clusters=5).fit_predict(feature_arrays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3, 2, 4, 4, 4, 2, 3, 2, 0, 3, 3, 3, 3, 0, 3, 3, 1, 1, 1, 2, 2,\n",
       "       4, 4, 2, 3, 3, 1, 4, 4, 4, 4, 2, 3, 4, 3, 4, 3, 1, 2, 1, 4, 3, 4, 3,\n",
       "       1, 2, 1, 3, 3], dtype=int32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x110e6cc50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvMAAAH/CAYAAADaL1IxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ddb150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.scatter(feature_arrays[:,0], feature_arrays[:,1], c=y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PySpark ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.ml.clustering import KMeans as ml_kmeans\n",
    "from pyspark.ml.feature import VectorAssembler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Format input data: vectorize all features\n",
    "feature_lst = [\"avg_PAYANN_per_EMP\", \"avg_ESTAB\"]\n",
    "vecAssembler = VectorAssembler(inputCols=feature_lst, outputCol=\"features\")\n",
    "feature_sel = vecAssembler.transform(all_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run clustering\n",
    "ml_kmeans_def = ml_kmeans(k=5, seed=1)\n",
    "model = ml_kmeans_def.fit(feature_sel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([  38.42128174,  384.66958233]),\n",
       " array([  33.32884978,  107.26749735]),\n",
       " array([  32.83855282,  161.63474717]),\n",
       " array([  38.92734204,  300.46473298]),\n",
       " array([  33.75191146,  213.36928972])]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view centers of each cluster\n",
    "model.clusterCenters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cluster = model.transform(feature_sel).cache()\n",
    "cluster_pd = cluster.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "feat1 = cluster_pd[\"avg_PAYANN_per_EMP\"].values.ravel()\n",
    "feat2 = cluster_pd[\"avg_ESTAB\"].values.ravel()\n",
    "pred = cluster_pd[\"prediction\"].values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1116e9fd0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvMAAAH/CAYAAADaL1IxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1115c4610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.scatter(feat1, feat2, c=pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further check the clusters and the states within each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ST</th>\n",
       "      <th>avg_PAYANN_per_EMP</th>\n",
       "      <th>avg_ESTAB</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>44</td>\n",
       "      <td>37.246753</td>\n",
       "      <td>360.376623</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>34</td>\n",
       "      <td>45.247649</td>\n",
       "      <td>355.394984</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>32</td>\n",
       "      <td>36.076471</td>\n",
       "      <td>343.611765</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12</td>\n",
       "      <td>33.391833</td>\n",
       "      <td>432.604692</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>06</td>\n",
       "      <td>40.995754</td>\n",
       "      <td>455.475584</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>37.569231</td>\n",
       "      <td>360.553846</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>33.106838</td>\n",
       "      <td>85.675214</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>46</td>\n",
       "      <td>30.968504</td>\n",
       "      <td>98.035433</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>38</td>\n",
       "      <td>38.149813</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>31</td>\n",
       "      <td>30.689732</td>\n",
       "      <td>113.323661</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20</td>\n",
       "      <td>32.567416</td>\n",
       "      <td>134.340824</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19</td>\n",
       "      <td>32.039242</td>\n",
       "      <td>105.266576</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23</td>\n",
       "      <td>32.594366</td>\n",
       "      <td>108.081461</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>05</td>\n",
       "      <td>29.594080</td>\n",
       "      <td>132.794926</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>31.526559</td>\n",
       "      <td>83.085450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>02</td>\n",
       "      <td>42.051948</td>\n",
       "      <td>128.071429</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>29</td>\n",
       "      <td>28.863065</td>\n",
       "      <td>179.663317</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>40</td>\n",
       "      <td>33.703237</td>\n",
       "      <td>160.305755</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>35</td>\n",
       "      <td>32.299145</td>\n",
       "      <td>182.461538</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>33</td>\n",
       "      <td>36.665179</td>\n",
       "      <td>162.209821</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21</td>\n",
       "      <td>30.848101</td>\n",
       "      <td>158.942134</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56</td>\n",
       "      <td>40.406250</td>\n",
       "      <td>157.664062</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01</td>\n",
       "      <td>32.594915</td>\n",
       "      <td>162.645763</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>30</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>147.373950</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>28</td>\n",
       "      <td>30.167082</td>\n",
       "      <td>143.446384</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>37.187387</td>\n",
       "      <td>313.118919</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>41</td>\n",
       "      <td>33.346154</td>\n",
       "      <td>290.778082</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>36</td>\n",
       "      <td>39.767498</td>\n",
       "      <td>317.178332</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>09</td>\n",
       "      <td>46.757576</td>\n",
       "      <td>293.831650</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>08</td>\n",
       "      <td>38.406780</td>\n",
       "      <td>320.889831</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>49</td>\n",
       "      <td>34.454167</td>\n",
       "      <td>288.037500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>25</td>\n",
       "      <td>43.566607</td>\n",
       "      <td>301.506217</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>24</td>\n",
       "      <td>37.460137</td>\n",
       "      <td>302.938497</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>48</td>\n",
       "      <td>37.763130</td>\n",
       "      <td>285.016604</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>04</td>\n",
       "      <td>33.647208</td>\n",
       "      <td>328.114213</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17</td>\n",
       "      <td>35.616509</td>\n",
       "      <td>265.566638</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11</td>\n",
       "      <td>60.449275</td>\n",
       "      <td>304.710145</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13</td>\n",
       "      <td>32.727027</td>\n",
       "      <td>289.260811</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15</td>\n",
       "      <td>33.833333</td>\n",
       "      <td>305.558824</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>47</td>\n",
       "      <td>32.632013</td>\n",
       "      <td>212.397690</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51</td>\n",
       "      <td>34.760766</td>\n",
       "      <td>226.145933</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>45</td>\n",
       "      <td>32.030733</td>\n",
       "      <td>236.678487</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>42</td>\n",
       "      <td>34.068807</td>\n",
       "      <td>190.245740</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>26</td>\n",
       "      <td>34.775269</td>\n",
       "      <td>230.439785</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>27</td>\n",
       "      <td>34.143432</td>\n",
       "      <td>191.335121</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>37</td>\n",
       "      <td>32.203271</td>\n",
       "      <td>249.647196</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>18</td>\n",
       "      <td>33.219512</td>\n",
       "      <td>203.358680</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>33.409405</td>\n",
       "      <td>188.614108</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>22</td>\n",
       "      <td>37.508539</td>\n",
       "      <td>194.123340</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>39</td>\n",
       "      <td>34.565877</td>\n",
       "      <td>234.150711</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16</td>\n",
       "      <td>31.705314</td>\n",
       "      <td>203.294686</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ST  avg_PAYANN_per_EMP   avg_ESTAB  prediction\n",
       "45  44           37.246753  360.376623           0\n",
       "36  34           45.247649  355.394984           0\n",
       "34  32           36.076471  343.611765           0\n",
       "15  12           33.391833  432.604692           0\n",
       "10  06           40.995754  455.475584           0\n",
       "13  10           37.569231  360.553846           0\n",
       "0   50           33.106838   85.675214           1\n",
       "47  46           30.968504   98.035433           1\n",
       "40  38           38.149813   84.000000           1\n",
       "33  31           30.689732  113.323661           1\n",
       "22  20           32.567416  134.340824           1\n",
       "21  19           32.039242  105.266576           1\n",
       "25  23           32.594366  108.081461           1\n",
       "9   05           29.594080  132.794926           1\n",
       "3   54           31.526559   83.085450           1\n",
       "7   02           42.051948  128.071429           1\n",
       "31  29           28.863065  179.663317           2\n",
       "42  40           33.703237  160.305755           2\n",
       "37  35           32.299145  182.461538           2\n",
       "35  33           36.665179  162.209821           2\n",
       "23  21           30.848101  158.942134           2\n",
       "5   56           40.406250  157.664062           2\n",
       "6   01           32.594915  162.645763           2\n",
       "32  30           30.000000  147.373950           2\n",
       "30  28           30.167082  143.446384           2\n",
       "2   53           37.187387  313.118919           3\n",
       "43  41           33.346154  290.778082           3\n",
       "38  36           39.767498  317.178332           3\n",
       "12  09           46.757576  293.831650           3\n",
       "11  08           38.406780  320.889831           3\n",
       "50  49           34.454167  288.037500           3\n",
       "27  25           43.566607  301.506217           3\n",
       "26  24           37.460137  302.938497           3\n",
       "49  48           37.763130  285.016604           3\n",
       "8   04           33.647208  328.114213           3\n",
       "19  17           35.616509  265.566638           3\n",
       "14  11           60.449275  304.710145           3\n",
       "16  13           32.727027  289.260811           3\n",
       "17  15           33.833333  305.558824           3\n",
       "48  47           32.632013  212.397690           4\n",
       "1   51           34.760766  226.145933           4\n",
       "46  45           32.030733  236.678487           4\n",
       "44  42           34.068807  190.245740           4\n",
       "28  26           34.775269  230.439785           4\n",
       "29  27           34.143432  191.335121           4\n",
       "39  37           32.203271  249.647196           4\n",
       "20  18           33.219512  203.358680           4\n",
       "4   55           33.409405  188.614108           4\n",
       "24  22           37.508539  194.123340           4\n",
       "41  39           34.565877  234.150711           4\n",
       "18  16           31.705314  203.294686           4"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_pd.sort_values(by='prediction').head(51)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x1113d1a90>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAFkCAYAAAB1rtL+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1113e32d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_pd['prediction'].plot(kind='hist', xticks=[0,1,2,3,4], color='g', alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}