ejemplo DSX

Ejemplo DSX Churn.ipynb

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        {
            "source": "## Predict Customer Churn Use Case Implementation\nThe objective is to follow the CRISP-DM methodology to build a model to predict customer churn\n![CRISP-DM](https://raw.githubusercontent.com/yfphoon/dsx_demo/master/crisp_dm.png)\n\n### Step 1: Connect to Object Storage\n\nWe start with connecting to Object Storage. Object Storage is the Bluemix environment for storing flat files. \n\nThe following code has been generated by DSX.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 1, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "cell_type": "code", 
            "source": "# The code was removed by DSX for sharing."
        }, 
        {
            "source": "### Step 2: Load files", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 2, 
            "metadata": {}, 
            "outputs": [], 
            "cell_type": "code", 
            "source": "# Customer Information\ncustomer = spark.read.format('csv')\\\n  .options(header='true', inferschema='true')\\\n  .load('swift://PredictChurn.' + name + '/customer.csv')\n  \n#Churn information    \ncustomer_churn = spark.read.format('csv')\\\n  .options(header='true', inferschema='true')\\\n  .load('swift://PredictChurn.' + name + '/churn.csv')"
        }, 
        {
            "source": "### Step 3: Merge Files\n", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 3, 
            "metadata": {
                "scrolled": false
            }, 
            "outputs": [
                {
                    "data": {
                        "text/plain": "   ID Gender Status  Children  Est Income Car Owner        Age  LongDistance  \\\n0   1      F      S         1    38000.00         N  24.393333         23.56   \n1   6      M      M         2    29616.00         N  49.426667         29.78   \n2   8      M      M         0    19732.80         N  50.673333         24.81   \n3  11      M      S         2       96.33         N  56.473333         26.13   \n4  14      F      M         2    52004.80         N  25.140000          5.03   \n\n   International   Local  Dropped Paymethod LocalBilltype  \\\n0              0  206.08        0        CC        Budget   \n1              0   45.50        0        CH     FreeLocal   \n2              0   22.44        0        CC     FreeLocal   \n3              0   32.88        1        CC        Budget   \n4              0   23.11        0        CH        Budget   \n\n  LongDistanceBilltype   Usage  RatePlan CHURN  \n0       Intnl_discount  229.64         3     T  \n1             Standard   75.29         2     F  \n2             Standard   47.25         3     F  \n3             Standard   59.01         1     F  \n4       Intnl_discount   28.14         1     F  ", 
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>Gender</th>\n      <th>Status</th>\n      <th>Children</th>\n      <th>Est Income</th>\n      <th>Car Owner</th>\n      <th>Age</th>\n      <th>LongDistance</th>\n      <th>International</th>\n      <th>Local</th>\n      <th>Dropped</th>\n      <th>Paymethod</th>\n      <th>LocalBilltype</th>\n      <th>LongDistanceBilltype</th>\n      <th>Usage</th>\n      <th>RatePlan</th>\n      <th>CHURN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>F</td>\n      <td>S</td>\n      <td>1</td>\n      <td>38000.00</td>\n      <td>N</td>\n      <td>24.393333</td>\n      <td>23.56</td>\n      <td>0</td>\n      <td>206.08</td>\n      <td>0</td>\n      <td>CC</td>\n      <td>Budget</td>\n      <td>Intnl_discount</td>\n      <td>229.64</td>\n      <td>3</td>\n      <td>T</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6</td>\n      <td>M</td>\n      <td>M</td>\n      <td>2</td>\n      <td>29616.00</td>\n      <td>N</td>\n      <td>49.426667</td>\n      <td>29.78</td>\n      <td>0</td>\n      <td>45.50</td>\n      <td>0</td>\n      <td>CH</td>\n      <td>FreeLocal</td>\n      <td>Standard</td>\n      <td>75.29</td>\n      <td>2</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>M</td>\n      <td>M</td>\n      <td>0</td>\n      <td>19732.80</td>\n      <td>N</td>\n      <td>50.673333</td>\n      <td>24.81</td>\n      <td>0</td>\n      <td>22.44</td>\n      <td>0</td>\n      <td>CC</td>\n      <td>FreeLocal</td>\n      <td>Standard</td>\n      <td>47.25</td>\n      <td>3</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11</td>\n      <td>M</td>\n      <td>S</td>\n      <td>2</td>\n      <td>96.33</td>\n      <td>N</td>\n      <td>56.473333</td>\n      <td>26.13</td>\n      <td>0</td>\n      <td>32.88</td>\n      <td>1</td>\n      <td>CC</td>\n      <td>Budget</td>\n      <td>Standard</td>\n      <td>59.01</td>\n      <td>1</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>14</td>\n      <td>F</td>\n      <td>M</td>\n      <td>2</td>\n      <td>52004.80</td>\n      <td>N</td>\n      <td>25.140000</td>\n      <td>5.03</td>\n      <td>0</td>\n      <td>23.11</td>\n      <td>0</td>\n      <td>CH</td>\n      <td>Budget</td>\n      <td>Intnl_discount</td>\n      <td>28.14</td>\n      <td>1</td>\n      <td>F</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 3, 
                    "output_type": "execute_result", 
                    "metadata": {}
                }
            ], 
            "cell_type": "code", 
            "source": "merged=customer.join(customer_churn,customer['ID']==customer_churn['ID']).select(customer['*'],customer_churn['CHURN'])\nmerged.toPandas().head()"
        }, 
        {
            "source": "### Step 4: Rename some columns\nThis step is not a requirement, it just makes some columns names simpler to type with no spaces", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 4, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/plain": "   ID Gender Status  Children  EstIncome CarOwner        Age  LongDistance  \\\n0   1      F      S         1   38000.00        N  24.393333         23.56   \n1   6      M      M         2   29616.00        N  49.426667         29.78   \n2   8      M      M         0   19732.80        N  50.673333         24.81   \n3  11      M      S         2      96.33        N  56.473333         26.13   \n4  14      F      M         2   52004.80        N  25.140000          5.03   \n\n   International   Local  Dropped Paymethod LocalBilltype  \\\n0              0  206.08        0        CC        Budget   \n1              0   45.50        0        CH     FreeLocal   \n2              0   22.44        0        CC     FreeLocal   \n3              0   32.88        1        CC        Budget   \n4              0   23.11        0        CH        Budget   \n\n  LongDistanceBilltype   Usage  RatePlan CHURN  \n0       Intnl_discount  229.64         3     T  \n1             Standard   75.29         2     F  \n2             Standard   47.25         3     F  \n3             Standard   59.01         1     F  \n4       Intnl_discount   28.14         1     F  ", 
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>Gender</th>\n      <th>Status</th>\n      <th>Children</th>\n      <th>EstIncome</th>\n      <th>CarOwner</th>\n      <th>Age</th>\n      <th>LongDistance</th>\n      <th>International</th>\n      <th>Local</th>\n      <th>Dropped</th>\n      <th>Paymethod</th>\n      <th>LocalBilltype</th>\n      <th>LongDistanceBilltype</th>\n      <th>Usage</th>\n      <th>RatePlan</th>\n      <th>CHURN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>F</td>\n      <td>S</td>\n      <td>1</td>\n      <td>38000.00</td>\n      <td>N</td>\n      <td>24.393333</td>\n      <td>23.56</td>\n      <td>0</td>\n      <td>206.08</td>\n      <td>0</td>\n      <td>CC</td>\n      <td>Budget</td>\n      <td>Intnl_discount</td>\n      <td>229.64</td>\n      <td>3</td>\n      <td>T</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6</td>\n      <td>M</td>\n      <td>M</td>\n      <td>2</td>\n      <td>29616.00</td>\n      <td>N</td>\n      <td>49.426667</td>\n      <td>29.78</td>\n      <td>0</td>\n      <td>45.50</td>\n      <td>0</td>\n      <td>CH</td>\n      <td>FreeLocal</td>\n      <td>Standard</td>\n      <td>75.29</td>\n      <td>2</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>M</td>\n      <td>M</td>\n      <td>0</td>\n      <td>19732.80</td>\n      <td>N</td>\n      <td>50.673333</td>\n      <td>24.81</td>\n      <td>0</td>\n      <td>22.44</td>\n      <td>0</td>\n      <td>CC</td>\n      <td>FreeLocal</td>\n      <td>Standard</td>\n      <td>47.25</td>\n      <td>3</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11</td>\n      <td>M</td>\n      <td>S</td>\n      <td>2</td>\n      <td>96.33</td>\n      <td>N</td>\n      <td>56.473333</td>\n      <td>26.13</td>\n      <td>0</td>\n      <td>32.88</td>\n      <td>1</td>\n      <td>CC</td>\n      <td>Budget</td>\n      <td>Standard</td>\n      <td>59.01</td>\n      <td>1</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>14</td>\n      <td>F</td>\n      <td>M</td>\n      <td>2</td>\n      <td>52004.80</td>\n      <td>N</td>\n      <td>25.140000</td>\n      <td>5.03</td>\n      <td>0</td>\n      <td>23.11</td>\n      <td>0</td>\n      <td>CH</td>\n      <td>Budget</td>\n      <td>Intnl_discount</td>\n      <td>28.14</td>\n      <td>1</td>\n      <td>F</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 4, 
                    "output_type": "execute_result", 
                    "metadata": {}
                }
            ], 
            "cell_type": "code", 
            "source": "merged = merged.withColumnRenamed(\"Est Income\", \"EstIncome\").withColumnRenamed(\"Car Owner\",\"CarOwner\")\nmerged.toPandas().head()"
        }, 
        {
            "source": "### Step 5: Data understanding", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "The Brunel Visualization Language is a highly succinct and novel language that defines interactive data visualizations based on tabular data. The language is well suited for both data scientists and more aggressive business users. The system interprets the language and produces visualizations using the user's choice of existing lower-level visualization technologies typically used by application engineers such as RAVE or D3. \n\nMore information about Brunel Visualization: https://github.com/Brunel-Visualization/Brunel/wiki\n\nTry Brunel visualization here:  http://brunel.mybluemix.net/gallery_app/renderer", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 5, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<IPython.core.display.HTML object>", 
                        "text/html": "<!--\n  ~ Copyright (c) 2015 IBM Corporation and others.\n  ~\n  ~ Licensed under the Apache License, Version 2.0 (the \"License\");\n  ~ You may not use this file except in compliance with the License.\n  ~ You may obtain a copy of the License at\n  ~\n  ~     http://www.apache.org/licenses/LICENSE-2.0\n  ~\n  ~ Unless required by applicable law or agreed to in writing, software\n  ~ distributed under the License is distributed on an \"AS IS\" BASIS,\n  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n  ~ See the License for the specific language governing permissions and\n  ~ limitations under the License.\n  -->\n\n\n<link rel=\"stylesheet\" type=\"text/css\" href=\"/data/jupyter2/e1f377e3-d279-45fc-8191-4754435a94b5/nbextensions/brunel_ext/brunel.2.3.css\">\n<link rel=\"stylesheet\" type=\"text/css\" href=\"/data/jupyter2/e1f377e3-d279-45fc-8191-4754435a94b5/nbextensions/brunel_ext/sumoselect.css\">\n\n<style>\n    \n</style>\n\n<div id=\"controlsidfe34191e-52cd-11e7-9ed8-002590fb6500\" class=\"brunel\"/>\n<svg id=\"visidfe341676-52cd-11e7-9ed8-002590fb6500\" width=\"1100\" height=\"400\"></svg>"
                    }, 
                    "output_type": "display_data", 
                    "metadata": {}
                }, 
                {
                    "data": {
                        "application/javascript": "/*\n * Copyright (c) 2015 IBM Corporation and others.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * You may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *     http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nrequire.config({\n    waitSeconds: 60,\n    paths: {\n        'd3': '//cdnjs.cloudflare.com/ajax/libs/d3/4.2.1/d3.min',\n        'topojson': '//cdnjs.cloudflare.com/ajax/libs/topojson/1.6.20/topojson.min',\n        'brunel' : '/data/jupyter2/e1f377e3-d279-45fc-8191-4754435a94b5/nbextensions/brunel_ext/brunel.2.3.min',\n        'brunelControls' : '/data/jupyter2/e1f377e3-d279-45fc-8191-4754435a94b5/nbextensions/brunel_ext/brunel.controls.2.3.min'\n    },\n    shim: {\n       'brunel' : {\n            exports: 'BrunelD3',\n            deps: ['d3', 'topojson'],\n            init: function() {\n               return {\n                 BrunelD3 : BrunelD3,\n                 BrunelData : BrunelData\n              }\n            }\n        },\n       'brunelControls' : {\n            exports: 'BrunelEventHandlers',\n            init: function() {\n               return {\n                 BrunelEventHandlers: BrunelEventHandlers,\n                 BrunelJQueryControlFactory: BrunelJQueryControlFactory\n              }\n            }\n        }\n\n    }\n\n});\n\nrequire([\"d3\"], function(d3) {\n    require([\"brunel\", \"brunelControls\"], function(brunel, brunelControls) {\n        function  BrunelVis(visId) {\n  \"use strict\";                                                                       // strict mode\n  var datasets = [],                                      // array of datasets for the original data\n      pre = function(d, i) { return d },                         // default pre-process does nothing\n      post = function(d, i) { return d },                       // default post-process does nothing\n      transitionTime = 200,                                        // transition time for animations\n      charts = [],                                                       // the charts in the system\n      vis = d3.select('#' + visId).attr('class', 'brunel');                     // the SVG container\n\n  BrunelD3.addDefinitions(vis);                                   // ensure standard symbols present\n\n  // Define chart #1 in the visualization //////////////////////////////////////////////////////////\n\n  charts[0] = function(parentNode, filterRows) {\n    var geom = BrunelD3.geometry(parentNode || vis.node(), 0, 0, 1, 0.5, 5, 65, 37, 104),\n      elements = [];                                              // array of elements in this chart\n\n    // Define groups for the chart parts ///////////////////////////////////////////////////////////\n\n    var chart =  vis.append('g').attr('class', 'chart1')\n      .attr('transform','translate(' + geom.chart_left + ',' + geom.chart_top + ')');\n    var overlay = chart.append('g').attr('class', 'element').attr('class', 'overlay');\n    var zoom = d3.zoom().scaleExtent([1/3,3]);\n    var zoomNode = overlay.append('rect').attr('class', 'overlay')\n      .attr('x', geom.inner_left).attr('y', geom.inner_top)\n      .attr('width', geom.inner_rawWidth).attr('height', geom.inner_rawHeight)\n      .style('cursor', 'move').call(zoom)\n      .node();\n    zoomNode.__zoom = d3.zoomIdentity;\n    chart.append('rect').attr('class', 'background').attr('width', geom.chart_right-geom.chart_left).attr('height', geom.chart_bottom-geom.chart_top);\n    var interior = chart.append('g').attr('class', 'interior zoomNone')\n      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')')\n      .attr('clip-path', 'url(#clip_visidfe341676-52cd-11e7-9ed8-002590fb6500_chart1_inner)');\n    interior.append('rect').attr('class', 'inner').attr('width', geom.inner_width).attr('height', geom.inner_height);\n    var gridGroup = interior.append('g').attr('class', 'grid');\n    var axes = chart.append('g').attr('class', 'axis')\n      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')');\n    var legends = chart.append('g').attr('class', 'legend')\n      .attr('transform','translate(' + (geom.chart_right-geom.chart_left - 3) + ',' + 0 + ')');\n    vis.append('clipPath').attr('id', 'clip_visidfe341676-52cd-11e7-9ed8-002590fb6500_chart1_inner').append('rect')\n      .attr('x', 0).attr('y', 0)\n      .attr('width', geom.inner_rawWidth+1).attr('height', geom.inner_rawHeight+1);\n\n    // Scales //////////////////////////////////////////////////////////////////////////////////////\n\n    var scale_x = d3.scalePoint().padding(0.5)\n      .domain(['F', 'T'])\n      .range([0, geom.inner_width]);\n    var scale_inner = d3.scaleLinear().domain([0,1])\n      .range([-0.5, 0.5]);\n    var scale_y = d3.scaleLinear().domain([0, 120000.01])\n      .range([geom.inner_height, 0]);\n    var base_scales = [scale_x, scale_y];                           // untransformed original scales\n\n    // Axes ////////////////////////////////////////////////////////////////////////////////////////\n\n    axes.append('g').attr('class', 'x axis')\n      .attr('transform','translate(0,' + geom.inner_rawHeight + ')')\n      .attr('clip-path', 'url(#clip_visidfe341676-52cd-11e7-9ed8-002590fb6500_chart1_haxis)');\n    vis.append('clipPath').attr('id', 'clip_visidfe341676-52cd-11e7-9ed8-002590fb6500_chart1_haxis').append('polyline')\n      .attr('points', '-1,-1000, -1,-1 -5,5, -1000,5, -100,1000, 10000,1000 10000,-1000');\n    axes.select('g.axis.x').append('text').attr('class', 'title').text('CHURN').style('text-anchor', 'middle')\n      .attr('x',geom.inner_rawWidth/2)\n      .attr('y', geom.inner_bottom - 2.0).attr('dy','-0.27em');\n    axes.append('g').attr('class', 'y axis')\n      .attr('clip-path', 'url(#clip_visidfe341676-52cd-11e7-9ed8-002590fb6500_chart1_vaxis)');\n    vis.append('clipPath').attr('id', 'clip_visidfe341676-52cd-11e7-9ed8-002590fb6500_chart1_vaxis').append('polyline')\n      .attr('points', '-1000,-10000, 10000,-10000, 10000,' + (geom.inner_rawHeight+1) + ', -1,' + (geom.inner_rawHeight+1) + ', -1,' + (geom.inner_rawHeight+5) + ', -1000,' + (geom.inner_rawHeight+5) );\n    axes.select('g.axis.y').append('text').attr('class', 'title').text('Mean(Est Income)').style('text-anchor', 'middle')\n      .attr('x',-geom.inner_rawHeight/2)\n      .attr('y', 4-geom.inner_left).attr('dy', '0.7em').attr('transform', 'rotate(270)');\n\n    var axis_bottom = d3.axisBottom(scale_x).ticks(Math.min(10, Math.round(geom.inner_width / 16.5)));\n    var axis_left = d3.axisLeft(scale_y).ticks(Math.min(10, Math.round(geom.inner_width / 20)));\n\n    function buildAxes(time) {\n      axis_bottom.tickValues(BrunelD3.filterTicks(scale_x))\n      var axis_x = axes.select('g.axis.x');\n      BrunelD3.transition(axis_x, time).call(axis_bottom.scale(scale_x));\n      var axis_y = axes.select('g.axis.y');\n      BrunelD3.transition(axis_y, time).call(axis_left.scale(scale_y));\n    }\n    zoom.on('zoom', function(t, time) {\n        t = t ||BrunelD3.restrictZoom(d3.event.transform, geom, this);\n        scale_y = t.rescaleY(base_scales[1]);\n        zoomNode.__zoom = t;\n        interior.attr('class', 'interior ' + BrunelD3.zoomLabel(t.k));;\n        build(time || -1);\n    });\n\n    // Define element #1 ///////////////////////////////////////////////////////////////////////////\n\n    elements[0] = function() {\n      var original, processed,                           // data sets passed in and then transformed\n        element, data,                                 // brunel element information and brunel data\n        selection, merged;                                      // d3 selection and merged selection\n      var elementGroup = interior.append('g').attr('class', 'element1'),\n        main = elementGroup.append('g').attr('class', 'main'),\n        labels = BrunelD3.undoTransform(elementGroup.append('g').attr('class', 'labels').attr('aria-hidden', 'true'), elementGroup);\n\n      function makeData() {\n        original = datasets[0];\n        if (filterRows) original = original.retainRows(filterRows);\n        processed = pre(original, 0)\n          .summarize('EstIncome=EstIncome:mean; CHURN=CHURN:base; LocalBilltype=LocalBilltype')\n          .stack('EstIncome; CHURN; LocalBilltype; false');\n        processed = post(processed, 0);\n        var f0 = processed.field('EstIncome$lower'),\n          f1 = processed.field('EstIncome$upper'),\n          f2 = processed.field('CHURN'),\n          f3 = processed.field('EstIncome'),\n          f4 = processed.field('LocalBilltype'),\n          f5 = processed.field('#row'),\n          f6 = processed.field('#selection');\n        var keyFunc = function(d) { return f2.value(d)+ '|' + f4.value(d) };\n        data = {\n          EstIncome$lower:function(d) { return f0.value(d.row) },\n          EstIncome$upper:function(d) { return f1.value(d.row) },\n          CHURN:        function(d) { return f2.value(d.row) },\n          EstIncome:    function(d) { return f3.value(d.row) },\n          LocalBilltype:function(d) { return f4.value(d.row) },\n          $row:         function(d) { return f5.value(d.row) },\n          $selection:   function(d) { return f6.value(d.row) },\n          EstIncome$lower_f:function(d) { return f0.valueFormatted(d.row) },\n          EstIncome$upper_f:function(d) { return f1.valueFormatted(d.row) },\n          CHURN_f:      function(d) { return f2.valueFormatted(d.row) },\n          EstIncome_f:  function(d) { return f3.valueFormatted(d.row) },\n          LocalBilltype_f:function(d) { return f4.valueFormatted(d.row) },\n          $row_f:       function(d) { return f5.valueFormatted(d.row) },\n          $selection_f: function(d) { return f6.valueFormatted(d.row) },\n          _split:       function(d) { return f4.value(d.row) },\n          _key:         keyFunc,\n          _rows:        BrunelD3.makeRowsWithKeys(keyFunc, processed.rowCount())\n        };\n      }\n      // Aesthetic Functions\n      var scale_color = d3.scaleOrdinal()\n        .domain(['Budget', 'FreeLocal'])\n        .range([ '#347DAD', '#D43F58', '#F7D84A', '#31A461', '#A66A9C', '#FF954D', \n          '#A7978E', '#FFCA4D', '#F99EAF', '#B1C43B', '#7E64A2', '#FFB04D', '#CA5C7C', \n          '#DDBC8C', '#FFA28D', '#A5473D', '#8B6141', '#F57357', '#5C6B46']);\n      var color = function(d) { return scale_color(data.LocalBilltype(d)) };\n      legends._legend = legends._legend || { title: ['Local Billtype'], \n        ticks: scale_color.domain()};\n      legends._legend.color = scale_color;\n\n      // Build element from data ///////////////////////////////////////////////////////////////////\n\n      function build(transitionMillis) {\n        element = elements[0];\n        var w = 0.9 * Math.abs(scale_x(scale_x.domain()[1]) - scale_x(scale_x.domain()[0]) );\n        var x = function(d) { return scale_x(data.CHURN(d))};\n        var h = Math.abs( scale_y(scale_y.domain()[0] + 11157.974178469827) - scale_y.range()[0] );\n        var y1 = function(d) { return scale_y(data.EstIncome$lower(d))};\n        var y2 = function(d) { return scale_y(data.EstIncome$upper(d))};\n        var y = function(d) { return scale_y( (data.EstIncome$upper(d) + data.EstIncome$lower(d) )/2)};\n\n        // Define selection entry operations\n        function initialState(selection) {\n          selection\n            .attr('class', 'element bar filled')\n        }\n\n        // Define selection update operations on merged data\n        function updateState(selection) {\n          selection\n            .each(function(d) {\n              var width = w, left = x(d) - width/2, \n              c = y1(d), d = y2(d), top = Math.min(c,d), height = Math.max(1e-6, Math.abs(c-d));\n              this.r = {x:left, y:top, w:width, h:height};\n            })\n            .attr('x', function(d) { return this.r.x })\n            .attr('y', function(d) { return this.r.y })\n            .attr('width', function(d) { return this.r.w })\n            .attr('height', function(d) { return this.r.h })\n            .filter(BrunelD3.hasData)                     // following only performed for data items\n            .style('fill', color);\n        }\n\n        // Define labeling for the selection\n        function label(selection, transitionMillis) {\n\n          var tooltipLabeling  = {\n            index: -1, method: 'box', location: ['center', 'top'], inside: true, align: 'middle', pad: 0, dy: 0.7,\n            fit: true, granularity: 0,\n            content: function(d) {\n              return d.row == null ? null : '<span class=\"field\">' + data.EstIncome_f(d) + '</span>'\n            }\n          };\n          BrunelD3.addTooltip(selection, tooltipLabeling, geom);\n        }\n        // Create selections, set the initial state and transition updates\n        selection = main.selectAll('.element').data(data._rows, function(d) { return d.key });\n        var added = selection.enter().append('rect');\n        merged = selection.merge(added);\n        initialState(added);\n        selection.filter(BrunelD3.hasData)\n          .classed('selected', BrunelD3.isSelected(data))\n          .filter(BrunelD3.isSelected(data)).raise();\n        updateState(BrunelD3.transition(merged, transitionMillis));\n        label(merged, transitionMillis);\n\n        BrunelD3.transition(selection.exit(), transitionMillis/3)\n          .style('opacity', 0.5).each( function() {\n            this.remove(); BrunelD3.removeLabels(this); \n        });\n      }\n\n      return {\n        data:           function() { return processed },\n        original:       function() { return original },\n        internal:       function() { return data },\n        selection:      function() { return merged },\n        makeData:       makeData,\n        build:          build,\n        chart:          function() { return charts[0] },\n        group:          function() { return elementGroup },\n        fields: {\n          x:            ['CHURN'],\n          y:            ['EstIncome'],\n          key:          ['CHURN', 'LocalBilltype'],\n          color:        ['LocalBilltype']\n        }\n      };\n    }();\n\n    function build(time, noData) {\n      var first = elements[0].data() == null;\n      if (first) time = 0;                                           // no transition for first call\n      buildAxes(time);\n      if ((first || time > -1) && !noData) {\n        elements[0].makeData();\n        BrunelD3.addLegend(legends, legends._legend);\n      }\n      elements[0].build(time);\n    }\n\n    // Expose the following components of the chart\n    return {\n      elements : elements,\n      interior : interior,\n      scales: {x:scale_x, y:scale_y},\n      zoom: function(params, time) {\n          if (params) zoom.on('zoom').call(zoomNode, params, time);\n          return d3.zoomTransform(zoomNode);\n      },\n      build : build\n    };\n    }();\n\n  // Define chart #2 in the visualization //////////////////////////////////////////////////////////\n\n  charts[1] = function(parentNode, filterRows) {\n    var geom = BrunelD3.geometry(parentNode || vis.node(), 0, 0.5, 1, 1, 5, 43, 37, 69),\n      elements = [];                                              // array of elements in this chart\n\n    // Define groups for the chart parts ///////////////////////////////////////////////////////////\n\n    var chart =  vis.append('g').attr('class', 'chart2')\n      .attr('transform','translate(' + geom.chart_left + ',' + geom.chart_top + ')');\n    var overlay = chart.append('g').attr('class', 'element').attr('class', 'overlay');\n    var zoom = d3.zoom().scaleExtent([1/3,3]);\n    var zoomNode = overlay.append('rect').attr('class', 'overlay')\n      .attr('x', geom.inner_left).attr('y', geom.inner_top)\n      .attr('width', geom.inner_rawWidth).attr('height', geom.inner_rawHeight)\n      .style('cursor', 'move').call(zoom)\n      .node();\n    zoomNode.__zoom = d3.zoomIdentity;\n    chart.append('rect').attr('class', 'background').attr('width', geom.chart_right-geom.chart_left).attr('height', geom.chart_bottom-geom.chart_top);\n    var interior = chart.append('g').attr('class', 'interior zoomNone')\n      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')')\n      .attr('clip-path', 'url(#clip_visidfe341676-52cd-11e7-9ed8-002590fb6500_chart2_inner)');\n    interior.append('rect').attr('class', 'inner').attr('width', geom.inner_width).attr('height', geom.inner_height);\n    var gridGroup = interior.append('g').attr('class', 'grid');\n    var axes = chart.append('g').attr('class', 'axis')\n      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')');\n    var legends = chart.append('g').attr('class', 'legend')\n      .attr('transform','translate(' + (geom.chart_right-geom.chart_left - 3) + ',' + 0 + ')');\n    vis.append('clipPath').attr('id', 'clip_visidfe341676-52cd-11e7-9ed8-002590fb6500_chart2_inner').append('rect')\n      .attr('x', 0).attr('y', 0)\n      .attr('width', geom.inner_rawWidth+1).attr('height', geom.inner_rawHeight+1);\n\n    // Scales //////////////////////////////////////////////////////////////////////////////////////\n\n    var scale_x = d3.scaleLinear().domain([-10, 70.000008])\n      .range([0, geom.inner_width]);\n    var scale_inner = d3.scaleLinear().domain([0,1])\n      .range([-0.5, 0.5]);\n    var scale_y = d3.scaleSqrt().domain([0, 400.00004])\n      .range([geom.inner_height, 0]);\n    var base_scales = [scale_x, scale_y];                           // untransformed original scales\n\n    // Axes ////////////////////////////////////////////////////////////////////////////////////////\n\n    axes.append('g').attr('class', 'x axis')\n      .attr('transform','translate(0,' + geom.inner_rawHeight + ')')\n      .attr('clip-path', 'url(#clip_visidfe341676-52cd-11e7-9ed8-002590fb6500_chart2_haxis)');\n    vis.append('clipPath').attr('id', 'clip_visidfe341676-52cd-11e7-9ed8-002590fb6500_chart2_haxis').append('polyline')\n      .attr('points', '-1,-1000, -1,-1 -5,5, -1000,5, -100,1000, 10000,1000 10000,-1000');\n    axes.select('g.axis.x').append('text').attr('class', 'title').text('Long Distance').style('text-anchor', 'middle')\n      .attr('x',geom.inner_rawWidth/2)\n      .attr('y', geom.inner_bottom - 2.0).attr('dy','-0.27em');\n    axes.append('g').attr('class', 'y axis')\n      .attr('clip-path', 'url(#clip_visidfe341676-52cd-11e7-9ed8-002590fb6500_chart2_vaxis)');\n    vis.append('clipPath').attr('id', 'clip_visidfe341676-52cd-11e7-9ed8-002590fb6500_chart2_vaxis').append('polyline')\n      .attr('points', '-1000,-10000, 10000,-10000, 10000,' + (geom.inner_rawHeight+1) + ', -1,' + (geom.inner_rawHeight+1) + ', -1,' + (geom.inner_rawHeight+5) + ', -1000,' + (geom.inner_rawHeight+5) );\n    axes.select('g.axis.y').append('text').attr('class', 'title').text('Usage').style('text-anchor', 'middle')\n      .attr('x',-geom.inner_rawHeight/2)\n      .attr('y', 4-geom.inner_left).attr('dy', '0.7em').attr('transform', 'rotate(270)');\n\n    var axis_bottom = d3.axisBottom(scale_x).ticks(Math.min(10, Math.round(geom.inner_width / 33.0)));\n    var axis_left = d3.axisLeft(scale_y).ticks(Math.min(10, Math.round(geom.inner_width / 20)));\n\n    function buildAxes(time) {\n      var axis_x = axes.select('g.axis.x');\n      BrunelD3.transition(axis_x, time).call(axis_bottom.scale(scale_x));\n      var axis_y = axes.select('g.axis.y');\n      BrunelD3.transition(axis_y, time).call(axis_left.scale(scale_y));\n    }\n    zoom.on('zoom', function(t, time) {\n        t = t ||BrunelD3.restrictZoom(d3.event.transform, geom, this);\n        scale_x = t.rescaleX(base_scales[0]);\n        scale_y = t.rescaleY(base_scales[1]);\n        zoomNode.__zoom = t;\n        interior.attr('class', 'interior ' + BrunelD3.zoomLabel(t.k));;\n        build(time || -1);\n    });\n\n    // Define element #1 ///////////////////////////////////////////////////////////////////////////\n\n    elements[0] = function() {\n      var original, processed,                           // data sets passed in and then transformed\n        element, data,                                 // brunel element information and brunel data\n        selection, merged;                                      // d3 selection and merged selection\n      var elementGroup = interior.append('g').attr('class', 'element1'),\n        main = elementGroup.append('g').attr('class', 'main'),\n        labels = BrunelD3.undoTransform(elementGroup.append('g').attr('class', 'labels').attr('aria-hidden', 'true'), elementGroup);\n\n      function makeData() {\n        original = datasets[0];\n        if (filterRows) original = original.retainRows(filterRows);\n        processed = pre(original, 0);\n        processed = post(processed, 0);\n        var f0 = processed.field('LongDistance'),\n          f1 = processed.field('Usage'),\n          f2 = processed.field('Paymethod'),\n          f3 = processed.field('#row'),\n          f4 = processed.field('#selection');\n        var keyFunc = function(d) { return f3.value(d) };\n        data = {\n          LongDistance: function(d) { return f0.value(d.row) },\n          Usage:        function(d) { return f1.value(d.row) },\n          Paymethod:    function(d) { return f2.value(d.row) },\n          $row:         function(d) { return f3.value(d.row) },\n          $selection:   function(d) { return f4.value(d.row) },\n          LongDistance_f:function(d) { return f0.valueFormatted(d.row) },\n          Usage_f:      function(d) { return f1.valueFormatted(d.row) },\n          Paymethod_f:  function(d) { return f2.valueFormatted(d.row) },\n          $row_f:       function(d) { return f3.valueFormatted(d.row) },\n          $selection_f: function(d) { return f4.valueFormatted(d.row) },\n          _split:       function(d) { return f2.value(d.row) },\n          _key:         keyFunc,\n          _rows:        BrunelD3.makeRowsWithKeys(keyFunc, processed.rowCount())\n        };\n      }\n      // Aesthetic Functions\n      var scale_color = d3.scaleOrdinal()\n        .domain(['Auto', 'CC', 'CH'])\n        .range([ '#00538A', '#C10020', '#F4C800', '#007D34', '#803E75', '#FF6800', \n          '#817066', '#FFB300', '#F6768E', '#93AA00', '#53377A', '#FF8E00', '#B32851', \n          '#CEA262', '#FF7A5C', '#7F180D', '#593315', '#F13A13', '#232C16']);\n      var color = function(d) { return scale_color(data.Paymethod(d)) };\n      legends._legend = legends._legend || { title: ['Paymethod'], \n        ticks: scale_color.domain()};\n      legends._legend.color = scale_color;\n\n      // Build element from data ///////////////////////////////////////////////////////////////////\n\n      function build(transitionMillis) {\n        element = elements[0];\n        var w = geom.default_point_size;\n        var x = function(d) { return scale_x(data.LongDistance(d))};\n        var h = geom.default_point_size;\n        var y = function(d) { return scale_y(data.Usage(d))};\n\n        // Define selection entry operations\n        function initialState(selection) {\n          selection\n            .attr('class', 'element point filled')\n        }\n\n        // Define selection update operations on merged data\n        function updateState(selection) {\n          selection\n            .attr('cx',function(d) { return scale_x(data.LongDistance(d))})\n            .attr('cy',function(d) { return scale_y(data.Usage(d))})\n            .attr('r',geom.default_point_size / 2)\n            .filter(BrunelD3.hasData)                     // following only performed for data items\n            .style('fill', color);\n        }\n\n        // Define labeling for the selection\n        function label(selection, transitionMillis) {\n\n          var tooltipLabeling  = {\n            index: -1, method: 'box', location: ['center', 'top'], inside: true, align: 'middle', pad: 0, dy: 0.7,\n            fit: true, granularity: 0,\n            content: function(d) {\n              return d.row == null ? null : '<span class=\"title\">Long Distance: </span>'\n\t\t\t+ '<span class=\"field\">' + data.LongDistance_f(d) + '</span>'\n\t\t\t+ '<br/>'\n\t\t\t+ '<span class=\"title\">Usage: </span>'\n\t\t\t+ '<span class=\"field\">' + data.Usage_f(d) + '</span>'\n            }\n          };\n          BrunelD3.addTooltip(selection, tooltipLabeling, geom);\n        }\n        // Create selections, set the initial state and transition updates\n        selection = main.selectAll('.element').data(data._rows, function(d) { return d.key });\n        var added = selection.enter().append('circle');\n        merged = selection.merge(added);\n        initialState(added);\n        selection.filter(BrunelD3.hasData)\n          .classed('selected', BrunelD3.isSelected(data))\n          .filter(BrunelD3.isSelected(data)).raise();\n        updateState(BrunelD3.transition(merged, transitionMillis));\n        label(merged, transitionMillis);\n\n        BrunelD3.transition(selection.exit(), transitionMillis/3)\n          .style('opacity', 0.5).each( function() {\n            this.remove(); BrunelD3.removeLabels(this); \n        });\n      }\n\n      return {\n        data:           function() { return processed },\n        original:       function() { return original },\n        internal:       function() { return data },\n        selection:      function() { return merged },\n        makeData:       makeData,\n        build:          build,\n        chart:          function() { return charts[1] },\n        group:          function() { return elementGroup },\n        fields: {\n          x:            ['LongDistance'],\n          y:            ['Usage'],\n          key:          ['#row'],\n          color:        ['Paymethod']\n        }\n      };\n    }();\n\n    function build(time, noData) {\n      var first = elements[0].data() == null;\n      if (first) time = 0;                                           // no transition for first call\n      buildAxes(time);\n      if ((first || time > -1) && !noData) {\n        elements[0].makeData();\n        BrunelD3.addLegend(legends, legends._legend);\n      }\n      elements[0].build(time);\n    }\n\n    // Expose the following components of the chart\n    return {\n      elements : elements,\n      interior : interior,\n      scales: {x:scale_x, y:scale_y},\n      zoom: function(params, time) {\n          if (params) zoom.on('zoom').call(zoomNode, params, time);\n          return d3.zoomTransform(zoomNode);\n      },\n      build : build\n    };\n    }();\n\n  function setData(rowData, i) { datasets[i||0] = BrunelD3.makeData(rowData) }\n  function updateAll(time) { charts.forEach(function(x) {x.build(time || 0)}) }\n  function buildAll() {\n    for (var i=0;i<arguments.length;i++) setData(arguments[i], i);\n    updateAll(transitionTime);\n  }\n\n  return {\n    dataPreProcess:     function(f) { if (f) pre = f; return pre },\n    dataPostProcess:    function(f) { if (f) post = f; return post },\n    data:               function(d,i) { if (d) setData(d,i); return datasets[i||0] },\n    visId:              visId,\n    build:              buildAll,\n    rebuild:            updateAll,\n    charts:             charts\n  }\n}\n\n// Data Tables /////////////////////////////////////////////////////////////////////////////////////\n\nvar table1 = {\n   summarized: false,\n   names: ['CHURN', 'EstIncome', 'LocalBilltype', 'LongDistance', 'Usage', 'Paymethod'], \n   options: ['string', 'numeric', 'string', 'numeric', 'numeric', 'string'], \n   rows: [['T', 38000, 'Budget', 23.56, 229.64, 'CC'], ['F', 29616, 'FreeLocal', 29.78, 75.29, 'CH'],\n  ['F', 19732.8, 'FreeLocal', 24.81, 47.25, 'CC'], ['F', 96.33, 'Budget', 26.13, 59.01, 'CC'],\n  ['F', 52004.8, 'Budget', 5.03, 28.14, 'CH'], ['F', 53010.8, 'FreeLocal', 12.45, 58.87, 'CC'],\n  ['F', 75004.5, 'Budget', 26.52, 58.72, 'CC'], ['F', 19749.3, 'Budget', 20.22, 34.17, 'CC'],\n  ['F', 57626.9, 'Budget', 9.38, 48.35, 'CC'], ['F', 20078, 'Budget', 9.65, 15.98, 'CC'],\n  ['F', 47902, 'FreeLocal', 17.44, 72.31, 'Auto'], ['T', 7545.96, 'Budget', 22.39, 200.75, 'CC'],\n  ['T', 78851.3, 'FreeLocal', 0.37, 29.04, 'CC'], ['T', 17540.7, 'Budget', 22.17, 36.2, 'Auto'],\n  ['T', 83891.9, 'Budget', 28.92, 74.4, 'CH'], ['T', 28220.8, 'FreeLocal', 26.49, 38.95, 'CC'],\n  ['T', 28589.1, 'FreeLocal', 13.19, 100.28, 'CC'], ['F', 5237.63, 'Budget', 13.32, 78.02, 'CC'],\n  ['T', 89459.9, 'FreeLocal', 11.54, 36.05, 'CC'], ['T', 13576.5, 'Budget', 14.83, 40.49, 'CC'],\n  ['T', 67388, 'Budget', 4.79, 96.33, 'CC'], ['F', 57063, 'Budget', 16.79, 98.1, 'CH'],\n  ['T', 84166.1, 'Budget', 3.28, 15.02, 'CC'], ['T', 7737.94, 'FreeLocal', 11.05, 61.9, 'CC'],\n  ['T', 100020, 'Budget', 21.37, 314.62, 'CH'], ['F', 45287.6, 'Budget', 0, 2.97, 'CC'],\n  ['T', 59613.1, 'Budget', 12.37, 175.78, 'CC'], ['T', 16326.7, 'FreeLocal', 0, 1.34, 'CC'],\n  ['T', 9559.78, 'FreeLocal', 18.31, 77.91, 'CC'], ['F', 42031.8, 'Budget', 20.45, 59.78, 'CH'],\n  ['F', 83284.1, 'FreeLocal', 26.91, 35.96, 'Auto'], ['F', 39229.1, 'Budget', 28.82, 47.1, 'CC'],\n  ['F', 68427.4, 'FreeLocal', 23.76, 73.81, 'Auto'], ['T', 3601.71, 'Budget', 9.57, 125.6, 'Auto'],\n  ['F', 72642.6, 'FreeLocal', 18.66, 39, 'CH'], ['T', 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 ['T', 540.4, 'Budget', 12.34, 20.1, 'Auto'], ['F', 80087.7, 'FreeLocal', 8.74, 31.56, 'CC'],\n  ['F', 59691.3, 'Budget', 21.94, 278.65, 'CC'], ['T', 11251.1, 'FreeLocal', 28.42, 141.91, 'CC'],\n  ['F', 93559.1, 'Budget', 1.52, 31.42, 'CC'], ['T', 3416.62, 'Budget', 18.97, 123.7, 'CC'],\n  ['F', 93454.3, 'FreeLocal', 18.94, 44.13, 'Auto'], ['T', 5745, 'FreeLocal', 26.4, 44.11, 'CC'],\n  ['F', 11234.7, 'Budget', 0.69, 113.42, 'CC'], ['F', 82931.3, 'FreeLocal', 19.24, 137.25, 'Auto'],\n  ['F', 57483.9, 'Budget', 17.01, 44.3, 'CH'], ['T', 21274, 'FreeLocal', 10.87, 43.31, 'Auto'],\n  ['F', 28267, 'FreeLocal', 21.87, 214.41, 'CC'], ['F', 36725.1, 'Budget', 8.65, 94.88, 'CH'],\n  ['T', 94188.3, 'Budget', 20.86, 41.51, 'CC'], ['F', 91861, 'Budget', 15.32, 22.06, 'CC'],\n  ['T', 12374.9, 'FreeLocal', 17.49, 123.59, 'Auto'], ['F', 69133.9, 'FreeLocal', 8.58, 46.32, 'CH'],\n  ['F', 84948, 'FreeLocal', 11.14, 23.73, 'CH'], ['F', 30998.8, 'Budget', 2.4, 52.91, 'Auto'],\n  ['F', 21416.5, 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433.74, 'FreeLocal', 3.3, 44.22, 'CC'],\n  ['F', 16962.2, 'Budget', 18.6, 27.57, 'CC'], ['F', 10708.3, 'Budget', 20.83, 142.95, 'CH'],\n  ['T', 2031.61, 'Budget', 21.64, 109.04, 'CC'], ['F', 16938.5, 'FreeLocal', 13.35, 22.11, 'CH'],\n  ['F', 55221, 'FreeLocal', 0.49, 15.69, 'CC'], ['F', 8073.11, 'FreeLocal', 28.7, 89.05, 'Auto'],\n  ['F', 95448.6, 'Budget', 24.24, 29.46, 'CH'], ['T', 24141.5, 'FreeLocal', 28.82, 125.87, 'CC'],\n  ['F', 63152.6, 'Budget', 29.93, 70.01, 'CC'], ['F', 72084.7, 'Budget', 29.11, 38.13, 'CC'],\n  ['F', 42760.5, 'Budget', 13.47, 53.7, 'CC'], ['F', 49072, 'Budget', 23.5, 34.25, 'Auto'],\n  ['F', 27371.8, 'FreeLocal', 16.14, 142.7, 'CC'], ['T', 71472.9, 'Budget', 16.02, 130.22, 'CH'],\n  ['T', 95405.7, 'Budget', 7.74, 13.96, 'CC'], ['T', 95786.8, 'Budget', 21.33, 108.79, 'CC'],\n  ['T', 90321.6, 'FreeLocal', 0.87, 12.39, 'CC'], ['T', 90478.6, 'FreeLocal', 18.02, 154.49, 'CH'],\n  ['F', 56187, 'Budget', 8.09, 21.83, 'CH'], ['T', 35089, 'Budget', 23.56, 229.64, 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158.22, 'CC'], ['T', 95620.3, 'Budget', 10.91, 104.53, 'CC'],\n  ['F', 85275, 'FreeLocal', 22.48, 174.09, 'Auto'], ['F', 66921.4, 'FreeLocal', 15.7, 24.67, 'CC'],\n  ['F', 98453.6, 'FreeLocal', 13.72, 25.38, 'Auto'], ['F', 57420, 'Budget', 25.18, 277.72, 'Auto'],\n  ['T', 70000, 'FreeLocal', 3.93, 34.35, 'CC'], ['F', 47639.1, 'Budget', 17.48, 57.11, 'CC'],\n  ['T', 39461, 'FreeLocal', 22.87, 126.68, 'CC'], ['F', 47371, 'Budget', 4.98, 270.97, 'CC'],\n  ['T', 16432.1, 'Budget', 20.41, 109.78, 'CH'], ['T', 67388, 'Budget', 4.79, 96.33, 'CC'],\n  ['T', 84166.1, 'Budget', 3.28, 15.02, 'CC'], ['T', 540.4, 'Budget', 12.34, 20.1, 'Auto'],\n  ['T', 59613.1, 'Budget', 12.37, 175.78, 'CC'], ['F', 80087.7, 'FreeLocal', 8.74, 31.56, 'CC'],\n  ['F', 59691.3, 'Budget', 21.94, 278.65, 'CC'], ['T', 11251.1, 'FreeLocal', 28.42, 141.91, 'CC'],\n  ['T', 45429.7, 'FreeLocal', 24.7, 97.74, 'CH'], ['T', 67388, 'Budget', 4.79, 96.33, 'CC'],\n  ['T', 84166.1, 'Budget', 3.28, 15.02, 'CC'], ['F', 93559.1, 'Budget', 1.52, 31.42, 'CC'],\n  ['T', 3416.62, 'Budget', 18.97, 123.7, 'CC'], ['F', 93454.3, 'FreeLocal', 18.94, 44.13, 'Auto'],\n  ['T', 5745, 'FreeLocal', 26.4, 44.11, 'CC'], ['T', 59613.1, 'Budget', 12.37, 175.78, 'CC'],\n  ['T', 45429.7, 'FreeLocal', 24.7, 97.74, 'CH'], ['T', 67388, 'Budget', 4.79, 96.33, 'CC'],\n  ['T', 84166.1, 'Budget', 3.28, 15.02, 'CC'], ['F', 60095.2, 'Budget', 1.71, 117.85, 'Auto'],\n  ['F', 11234.7, 'Budget', 0.69, 113.42, 'CC'], ['F', 82931.3, 'FreeLocal', 19.24, 137.25, 'Auto'],\n  ['F', 57483.9, 'Budget', 17.01, 44.3, 'CH'], ['T', 59613.1, 'Budget', 12.37, 175.78, 'CC'],\n  ['T', 21274, 'FreeLocal', 10.87, 43.31, 'Auto'], ['F', 28267, 'FreeLocal', 21.87, 214.41, 'CC'],\n  ['T', 45429.7, 'FreeLocal', 24.7, 97.74, 'CH'], ['F', 36725.1, 'Budget', 8.65, 94.88, 'CH'],\n  ['T', 67388, 'Budget', 4.79, 96.33, 'CC'], ['T', 84166.1, 'Budget', 3.28, 15.02, 'CC'],\n  ['T', 67388, 'Budget', 4.79, 96.33, 'CC'], ['T', 94188.3, 'Budget', 20.86, 41.51, 'CC'],\n  ['F', 84166.1, 'Budget', 3.28, 15.02, 'CC'], ['T', 59613.1, 'Budget', 12.37, 175.78, 'CC'],\n  ['F', 91861, 'Budget', 15.32, 22.06, 'CC'], ['T', 45429.7, 'FreeLocal', 24.7, 97.74, 'CH'],\n  ['T', 67388, 'Budget', 4.79, 96.33, 'CC'], ['T', 12374.9, 'FreeLocal', 17.49, 123.59, 'Auto'],\n  ['F', 69133.9, 'FreeLocal', 8.58, 46.32, 'CH'], ['T', 84166.1, 'Budget', 3.28, 15.02, 'CC'],\n  ['F', 84948, 'FreeLocal', 11.14, 23.73, 'CH'], ['F', 30998.8, 'Budget', 2.4, 52.91, 'Auto'],\n  ['T', 59613.1, 'Budget', 12.37, 200.5, 'CC'], ['T', 45429.7, 'FreeLocal', 24.7, 97.74, 'CH'],\n  ['T', 67388, 'Budget', 4.79, 96.33, 'CC'], ['T', 84166.1, 'Budget', 3.28, 15.02, 'CC'],\n  ['T', 59613.1, 'Budget', 12.37, 134.67, 'CC'], ['F', 21416.5, 'FreeLocal', 9.9, 27.61, 'CC'],\n  ['T', 45429.7, 'FreeLocal', 24.7, 97.74, 'CH'], ['T', 67388, 'Budget', 4.79, 96.33, 'CC'],\n  ['T', 84166.1, 'Budget', 3.28, 15.02, 'CC'], ['F', 78554.2, 'Budget', 5.59, 35.88, 'Auto'],\n  ['F', 38215.9, 'Budget', 0, 0.97, 'Auto'], ['F', 49745.3, 'Budget', 2.84, 27.28, 'CH'],\n  ['T', 59613.1, 'Budget', 12.37, 175.78, 'CC'], ['F', 88427, 'Budget', 22.46, 69.97, 'CC'],\n  ['F', 98003.7, 'FreeLocal', 1.03, 84.42, 'CC'], ['T', 89142.7, 'Budget', 3.76, 16.91, 'CC'],\n  ['F', 74530.8, 'FreeLocal', 20.89, 127.89, 'Auto'], ['F', 76289.2, 'Budget', 22.2, 104.63, 'CC'],\n  ['T', 66931.9, 'Budget', 11.72, 17.46, 'CC'], ['F', 9309.57, 'FreeLocal', 8.56, 108.3, 'CC'],\n  ['F', 77726.4, 'Budget', 14, 23.52, 'Auto'], ['T', 2800.68, 'Budget', 28.45, 134.75, 'CC'],\n  ['F', 90271.6, 'FreeLocal', 13.96, 110.4, 'CC'], ['F', 64816.6, 'Budget', 21, 27.87, 'CC'],\n  ['F', 98217.1, 'FreeLocal', 29.49, 42.74, 'CC'], ['T', 15989.3, 'Budget', 3.67, 29.17, 'CC'],\n  ['T', 84978.1, 'FreeLocal', 5.03, 88.79, 'CH'], ['F', 36647.9, 'Budget', 21.41, 107.38, 'CC'],\n  ['T', 64319.2, 'Budget', 3.01, 40.16, 'CH'], ['F', 73338, 'Budget', 20.92, 29.93, 'Auto'],\n  ['T', 433.74, 'FreeLocal', 3.3, 44.22, 'CC'], ['F', 16962.2, 'Budget', 18.6, 27.57, 'CC'],\n  ['F', 10708.3, 'Budget', 20.83, 142.95, 'CH'], ['T', 2031.61, 'Budget', 21.64, 109.04, 'CC'],\n  ['F', 16938.5, 'FreeLocal', 13.35, 22.11, 'CH'], ['F', 55221, 'FreeLocal', 0.49, 15.69, 'CC'],\n  ['F', 8073.11, 'FreeLocal', 28.7, 89.05, 'Auto'], ['F', 95448.6, 'Budget', 24.24, 29.46, 'CH'],\n  ['T', 24141.5, 'FreeLocal', 28.82, 125.87, 'CC'], ['F', 63152.6, 'Budget', 29.93, 70.01, 'CC'],\n  ['F', 72084.7, 'Budget', 29.11, 38.13, 'CC'], ['F', 42760.5, 'Budget', 13.47, 53.7, 'CC'],\n  ['F', 49072, 'Budget', 23.5, 34.25, 'Auto'], ['F', 27371.8, 'FreeLocal', 16.14, 142.7, 'CC'],\n  ['T', 71472.9, 'Budget', 16.02, 130.22, 'CH'], ['T', 95405.7, 'Budget', 7.74, 13.96, 'CC'],\n  ['T', 95786.8, 'Budget', 21.33, 108.79, 'CC'], ['F', 90321.6, 'FreeLocal', 0.87, 12.39, 'Auto'],\n  ['T', 90478.6, 'FreeLocal', 18.02, 154.49, 'CC'], ['F', 56187, 'Budget', 8.09, 21.83, 'CH'],\n  ['F', 28313.1, 'Budget', 6.89, 13.63, 'CC'], ['F', 29616, 'FreeLocal', 29.78, 75.29, 'CH'],\n  ['F', 19732.8, 'FreeLocal', 24.81, 47.25, 'CC'], ['F', 41690.5, 'FreeLocal', 2.5, 31.45, 'CC'],\n  ['F', 96.33, 'Budget', 26.13, 59.01, 'CC'], ['F', 52004.8, 'Budget', 5.03, 28.14, 'CH'],\n  ['T', 78851.3, 'FreeLocal', 0.37, 29.04, 'CC'], ['T', 17540.7, 'Budget', 22.17, 36.2, 'Auto'],\n  ['T', 83891.9, 'Budget', 28.92, 74.4, 'CH'], ['T', 28220.8, 'FreeLocal', 26.49, 38.95, 'CC'],\n  ['T', 28589.1, 'FreeLocal', 13.19, 100.28, 'CC']]\n};\n\n// Call Code to Build the system ///////////////////////////////////////////////////////////////////\n\nvar v  = new BrunelVis('visidfe341676-52cd-11e7-9ed8-002590fb6500');\nv.build(table1);\n\n    });\n});", 
                        "text/plain": "<IPython.core.display.Javascript object>"
                    }, 
                    "execution_count": 5, 
                    "output_type": "execute_result", 
                    "metadata": {}
                }
            ], 
            "cell_type": "code", 
            "source": "import brunel\nMerged = merged.toPandas()\n%brunel data('Merged') bar x(CHURN) y(EstIncome) mean(EstIncome) color(LocalBilltype) stack tooltip(EstIncome) | x(LongDistance) y(Usage) point color(Paymethod) tooltip(LongDistance, Usage) :: width=1100, height=400 "
        }, 
        {
            "execution_count": 6, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<IPython.core.display.HTML object>", 
                        "text/html": "<!--\n  ~ Copyright (c) 2015 IBM Corporation and others.\n  ~\n  ~ Licensed under the Apache License, Version 2.0 (the \"License\");\n  ~ You may not use this file except in compliance with the License.\n  ~ You may obtain a copy of the License at\n  ~\n  ~     http://www.apache.org/licenses/LICENSE-2.0\n  ~\n  ~ Unless required by applicable law or agreed to in writing, software\n  ~ distributed under the License is distributed on an \"AS IS\" BASIS,\n  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n  ~ See the License for the specific language governing permissions and\n  ~ limitations under the License.\n  -->\n\n\n<link rel=\"stylesheet\" type=\"text/css\" href=\"/data/jupyter2/e1f377e3-d279-45fc-8191-4754435a94b5/nbextensions/brunel_ext/brunel.2.3.css\">\n<link rel=\"stylesheet\" type=\"text/css\" href=\"/data/jupyter2/e1f377e3-d279-45fc-8191-4754435a94b5/nbextensions/brunel_ext/sumoselect.css\">\n\n<style>\n    #visid00dce51a-52ce-11e7-9ed8-002590fb6500.brunel .chart1 .element1 .element {\n\tsize: 100%;\n\tstroke: none;\n\tsymbol: rect;\n}\n</style>\n\n<div id=\"controlsid00dce704-52ce-11e7-9ed8-002590fb6500\" class=\"brunel\"/>\n<svg id=\"visid00dce51a-52ce-11e7-9ed8-002590fb6500\" width=\"500\" height=\"400\"></svg>"
                    }, 
                    "output_type": "display_data", 
                    "metadata": {}
                }, 
                {
                    "data": {
                        "application/javascript": "/*\n * Copyright (c) 2015 IBM Corporation and others.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * You may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *     http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nrequire.config({\n    waitSeconds: 60,\n    paths: {\n        'd3': '//cdnjs.cloudflare.com/ajax/libs/d3/4.2.1/d3.min',\n        'topojson': '//cdnjs.cloudflare.com/ajax/libs/topojson/1.6.20/topojson.min',\n        'brunel' : '/data/jupyter2/e1f377e3-d279-45fc-8191-4754435a94b5/nbextensions/brunel_ext/brunel.2.3.min',\n        'brunelControls' : '/data/jupyter2/e1f377e3-d279-45fc-8191-4754435a94b5/nbextensions/brunel_ext/brunel.controls.2.3.min'\n    },\n    shim: {\n       'brunel' : {\n            exports: 'BrunelD3',\n            deps: ['d3', 'topojson'],\n            init: function() {\n               return {\n                 BrunelD3 : BrunelD3,\n                 BrunelData : BrunelData\n              }\n            }\n        },\n       'brunelControls' : {\n            exports: 'BrunelEventHandlers',\n            init: function() {\n               return {\n                 BrunelEventHandlers: BrunelEventHandlers,\n                 BrunelJQueryControlFactory: BrunelJQueryControlFactory\n              }\n            }\n        }\n\n    }\n\n});\n\nrequire([\"d3\"], function(d3) {\n    require([\"brunel\", \"brunelControls\"], function(brunel, brunelControls) {\n        function  BrunelVis(visId) {\n  \"use strict\";                                                                       // strict mode\n  var datasets = [],                                      // array of datasets for the original data\n      pre = function(d, i) { return d },                         // default pre-process does nothing\n      post = function(d, i) { return d },                       // default post-process does nothing\n      transitionTime = 200,                                        // transition time for animations\n      charts = [],                                                       // the charts in the system\n      vis = d3.select('#' + visId).attr('class', 'brunel');                     // the SVG container\n\n  BrunelD3.addDefinitions(vis);                                   // ensure standard symbols present\n\n  // Define chart #1 in the visualization //////////////////////////////////////////////////////////\n\n  charts[0] = function(parentNode, filterRows) {\n    var geom = BrunelD3.geometry(parentNode || vis.node(), 0, 0, 1, 1, 5, 43, 37, 66),\n      elements = [];                                              // array of elements in this chart\n\n    // Define groups for the chart parts ///////////////////////////////////////////////////////////\n\n    var chart =  vis.append('g').attr('class', 'chart1')\n      .attr('transform','translate(' + geom.chart_left + ',' + geom.chart_top + ')');\n    var overlay = chart.append('g').attr('class', 'element').attr('class', 'overlay');\n    var zoom = d3.zoom().scaleExtent([1/3,3]);\n    var zoomNode = overlay.append('rect').attr('class', 'overlay')\n      .attr('x', geom.inner_left).attr('y', geom.inner_top)\n      .attr('width', geom.inner_rawWidth).attr('height', geom.inner_rawHeight)\n      .style('cursor', 'move').call(zoom)\n      .node();\n    zoomNode.__zoom = d3.zoomIdentity;\n    chart.append('rect').attr('class', 'background').attr('width', geom.chart_right-geom.chart_left).attr('height', geom.chart_bottom-geom.chart_top);\n    var interior = chart.append('g').attr('class', 'interior zoomNone')\n      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')')\n      .attr('clip-path', 'url(#clip_visid00dce51a-52ce-11e7-9ed8-002590fb6500_chart1_inner)');\n    interior.append('rect').attr('class', 'inner').attr('width', geom.inner_width).attr('height', geom.inner_height);\n    var gridGroup = interior.append('g').attr('class', 'grid');\n    var axes = chart.append('g').attr('class', 'axis')\n      .attr('transform','translate(' + geom.inner_left + ',' + geom.inner_top + ')');\n    var legends = chart.append('g').attr('class', 'legend')\n      .attr('transform','translate(' + (geom.chart_right-geom.chart_left - 3) + ',' + 0 + ')');\n    vis.append('clipPath').attr('id', 'clip_visid00dce51a-52ce-11e7-9ed8-002590fb6500_chart1_inner').append('rect')\n      .attr('x', 0).attr('y', 0)\n      .attr('width', geom.inner_rawWidth+1).attr('height', geom.inner_rawHeight+1);\n\n    // Scales //////////////////////////////////////////////////////////////////////////////////////\n\n    var scale_x = d3.scalePoint().padding(0.5)\n      .domain(['Budget', 'FreeLocal'])\n      .range([0, geom.inner_width]);\n    var scale_inner = d3.scaleLinear().domain([0,1])\n      .range([-0.5, 0.5]);\n    var scale_y = d3.scaleLinear().domain([-0.5, 4.5000005])\n      .range([geom.inner_height, 0]);\n    var base_scales = [scale_x, scale_y];                           // untransformed original scales\n\n    // Axes ////////////////////////////////////////////////////////////////////////////////////////\n\n    axes.append('g').attr('class', 'x axis')\n      .attr('transform','translate(0,' + geom.inner_rawHeight + ')')\n      .attr('clip-path', 'url(#clip_visid00dce51a-52ce-11e7-9ed8-002590fb6500_chart1_haxis)');\n    vis.append('clipPath').attr('id', 'clip_visid00dce51a-52ce-11e7-9ed8-002590fb6500_chart1_haxis').append('polyline')\n      .attr('points', '-1,-1000, -1,-1 -5,5, -1000,5, -100,1000, 10000,1000 10000,-1000');\n    axes.select('g.axis.x').append('text').attr('class', 'title').text('Local Billtype').style('text-anchor', 'middle')\n      .attr('x',geom.inner_rawWidth/2)\n      .attr('y', geom.inner_bottom - 2.0).attr('dy','-0.27em');\n    axes.append('g').attr('class', 'y axis')\n      .attr('clip-path', 'url(#clip_visid00dce51a-52ce-11e7-9ed8-002590fb6500_chart1_vaxis)');\n    vis.append('clipPath').attr('id', 'clip_visid00dce51a-52ce-11e7-9ed8-002590fb6500_chart1_vaxis').append('polyline')\n      .attr('points', '-1000,-10000, 10000,-10000, 10000,' + (geom.inner_rawHeight+1) + ', -1,' + (geom.inner_rawHeight+1) + ', -1,' + (geom.inner_rawHeight+5) + ', -1000,' + (geom.inner_rawHeight+5) );\n    axes.select('g.axis.y').append('text').attr('class', 'title').text('Dropped').style('text-anchor', 'middle')\n      .attr('x',-geom.inner_rawHeight/2)\n      .attr('y', 4-geom.inner_left).attr('dy', '0.7em').attr('transform', 'rotate(270)');\n\n    var axis_bottom = d3.axisBottom(scale_x).ticks(Math.min(10, Math.round(geom.inner_width / 82.5)));\n    var axis_left = d3.axisLeft(scale_y).ticks(Math.min(10, Math.round(geom.inner_width / 20)));\n\n    function buildAxes(time) {\n      axis_bottom.tickValues(BrunelD3.filterTicks(scale_x))\n      var axis_x = axes.select('g.axis.x');\n      BrunelD3.transition(axis_x, time).call(axis_bottom.scale(scale_x));\n      var axis_y = axes.select('g.axis.y');\n      BrunelD3.transition(axis_y, time).call(axis_left.scale(scale_y));\n    }\n    zoom.on('zoom', function(t, time) {\n        t = t ||BrunelD3.restrictZoom(d3.event.transform, geom, this);\n        scale_y = t.rescaleY(base_scales[1]);\n        zoomNode.__zoom = t;\n        interior.attr('class', 'interior ' + BrunelD3.zoomLabel(t.k));;\n        build(time || -1);\n    });\n\n    // Define element #1 ///////////////////////////////////////////////////////////////////////////\n\n    elements[0] = function() {\n      var original, processed,                           // data sets passed in and then transformed\n        element, data,                                 // brunel element information and brunel data\n        selection, merged;                                      // d3 selection and merged selection\n      var elementGroup = interior.append('g').attr('class', 'element1'),\n        main = elementGroup.append('g').attr('class', 'main'),\n        labels = BrunelD3.undoTransform(elementGroup.append('g').attr('class', 'labels').attr('aria-hidden', 'true'), elementGroup);\n\n      function makeData() {\n        original = datasets[0];\n        if (filterRows) original = original.retainRows(filterRows);\n        processed = pre(original, 0)\n          .summarize('#count=#count:sum; LocalBilltype=LocalBilltype:base; Dropped=Dropped');\n        processed = post(processed, 0);\n        var f0 = processed.field('LocalBilltype'),\n          f1 = processed.field('Dropped'),\n          f2 = processed.field('#count'),\n          f3 = processed.field('#row'),\n          f4 = processed.field('#selection');\n        var keyFunc = function(d) { return f3.value(d) };\n        data = {\n          LocalBilltype:function(d) { return f0.value(d.row) },\n          Dropped:      function(d) { return f1.value(d.row) },\n          $count:       function(d) { return f2.value(d.row) },\n          $row:         function(d) { return f3.value(d.row) },\n          $selection:   function(d) { return f4.value(d.row) },\n          LocalBilltype_f:function(d) { return f0.valueFormatted(d.row) },\n          Dropped_f:    function(d) { return f1.valueFormatted(d.row) },\n          $count_f:     function(d) { return f2.valueFormatted(d.row) },\n          $row_f:       function(d) { return f3.valueFormatted(d.row) },\n          $selection_f: function(d) { return f4.valueFormatted(d.row) },\n          _split:       function(d) { return f2.value(d.row) },\n          _key:         keyFunc,\n          _rows:        BrunelD3.makeRowsWithKeys(keyFunc, processed.rowCount())\n        };\n      }\n      // Aesthetic Functions\n      var scale_color = d3.scaleSqrt().domain([4, 28.61099, 75.652565, 145.12473, 237.02747, 351.36081, 488.12473, 647.31923, 828.94432, 1033])\n        .interpolate(d3.interpolateHcl)\n        .range([ '#FFFFFF', '#FFE3E3', '#FFC6C6', '#FFAAAA', '#FF8E8E', '#FF7171', \n          '#FF5555', '#FF3939', '#FF1C1C', '#FF0000']);\n      var color = function(d) { return scale_color(data.$count(d)) };\n      legends._legend = legends._legend || { title: ['Count'], \n        ticks: [1200, 1000, 800, 600, 400, 200, 0]};\n      legends._legend.color = scale_color;\n\n      // Build element from data ///////////////////////////////////////////////////////////////////\n\n      function build(transitionMillis) {\n        element = elements[0];\n        var w = 1.0 * Math.abs(scale_x(scale_x.domain()[1]) - scale_x(scale_x.domain()[0]) );\n        var x = function(d) { return scale_x(data.LocalBilltype(d))};\n        var h = 1.0 * Math.abs( scale_y(scale_y.domain()[0] + 1.0) - scale_y.range()[0] );\n        var y = function(d) { return scale_y(data.Dropped(d))};\n\n        // Define selection entry operations\n        function initialState(selection) {\n          selection\n            .attr('class', 'element point filled')\n        }\n\n        // Define selection update operations on merged data\n        function updateState(selection) {\n          selection\n            .each(function(d) {\n              var width = w, left = x(d) - width/2, \n              height = h, top = y(d) - height/2;\n              this.r = {x:left, y:top, w:width, h:height};\n            })\n            .attr('x', function(d) { return this.r.x })\n            .attr('y', function(d) { return this.r.y })\n            .attr('width', function(d) { return this.r.w })\n            .attr('height', function(d) { return this.r.h })\n            .filter(BrunelD3.hasData)                     // following only performed for data items\n            .style('fill', color);\n        }\n\n        // Define labeling for the selection\n        function label(selection, transitionMillis) {\n\n          var tooltipLabeling  = {\n            index: -1, method: 'box', location: ['center', 'top'], inside: true, align: 'middle', pad: 0, dy: 0.7,\n            fit: true, granularity: 0,\n            content: function(d) {\n              return d.row == null ? null : '<span class=\"title\">Dropped: </span>'\n\t\t\t+ '<span class=\"field\">' + data.Dropped_f(d) + '</span>'\n\t\t\t+ '<br/>'\n\t\t\t+ '<span class=\"title\">Count: </span>'\n\t\t\t+ '<span class=\"field\">' + data.$count_f(d) + '</span>'\n            }\n          };\n          BrunelD3.addTooltip(selection, tooltipLabeling, geom);\n        }\n        // Create selections, set the initial state and transition updates\n        selection = main.selectAll('.element').data(data._rows, function(d) { return d.key });\n        var added = selection.enter().append('rect');\n        merged = selection.merge(added);\n        initialState(added);\n        selection.filter(BrunelD3.hasData)\n          .classed('selected', BrunelD3.isSelected(data))\n          .filter(BrunelD3.isSelected(data)).raise();\n        updateState(BrunelD3.transition(merged, transitionMillis));\n        label(merged, transitionMillis);\n\n        BrunelD3.transition(selection.exit(), transitionMillis/3)\n          .style('opacity', 0.5).each( function() {\n            this.remove(); BrunelD3.removeLabels(this); \n        });\n      }\n\n      return {\n        data:           function() { return processed },\n        original:       function() { return original },\n        internal:       function() { return data },\n        selection:      function() { return merged },\n        makeData:       makeData,\n        build:          build,\n        chart:          function() { return charts[0] },\n        group:          function() { return elementGroup },\n        fields: {\n          x:            ['LocalBilltype'],\n          y:            ['Dropped'],\n          key:          ['#row'],\n          color:        ['#count']\n        }\n      };\n    }();\n\n    function build(time, noData) {\n      var first = elements[0].data() == null;\n      if (first) time = 0;                                           // no transition for first call\n      buildAxes(time);\n      if ((first || time > -1) && !noData) {\n        elements[0].makeData();\n        BrunelD3.addLegend(legends, legends._legend);\n      }\n      elements[0].build(time);\n    }\n\n    // Expose the following components of the chart\n    return {\n      elements : elements,\n      interior : interior,\n      scales: {x:scale_x, y:scale_y},\n      zoom: function(params, time) {\n          if (params) zoom.on('zoom').call(zoomNode, params, time);\n          return d3.zoomTransform(zoomNode);\n      },\n      build : build\n    };\n    }();\n\n  function setData(rowData, i) { datasets[i||0] = BrunelD3.makeData(rowData) }\n  function updateAll(time) { charts.forEach(function(x) {x.build(time || 0)}) }\n  function buildAll() {\n    for (var i=0;i<arguments.length;i++) setData(arguments[i], i);\n    updateAll(transitionTime);\n  }\n\n  return {\n    dataPreProcess:     function(f) { if (f) pre = f; return pre },\n    dataPostProcess:    function(f) { if (f) post = f; return post },\n    data:               function(d,i) { if (d) setData(d,i); return datasets[i||0] },\n    visId:              visId,\n    build:              buildAll,\n    rebuild:            updateAll,\n    charts:             charts\n  }\n}\n\n// Data Tables /////////////////////////////////////////////////////////////////////////////////////\n\nvar table1 = {\n   summarized: true,\n   names: ['LocalBilltype', 'Dropped', '#count'], \n   options: ['string', 'numeric', 'numeric'], \n   rows: [['Budget', 0, 1033], ['FreeLocal', 0, 844], ['Budget', 1, 83], ['FreeLocal', 1, 64],\n  ['Budget', 2, 6], ['FreeLocal', 2, 9], ['Budget', 3, 4], ['FreeLocal', 4, 23]]\n};\n\n// Call Code to Build the system ///////////////////////////////////////////////////////////////////\n\nvar v  = new BrunelVis('visid00dce51a-52ce-11e7-9ed8-002590fb6500');\nv.build(table1);\n\n    });\n});", 
                        "text/plain": "<IPython.core.display.Javascript object>"
                    }, 
                    "execution_count": 6, 
                    "output_type": "execute_result", 
                    "metadata": {}
                }
            ], 
            "cell_type": "code", 
            "source": "# Heat map\n%brunel data('Merged') x(LocalBilltype) y(Dropped) color(#count:red) style('symbol:rect; size:100%; stroke:none') tooltip(Dropped,#count)"
        }, 
        {
            "source": "**If you haven't already installed it, run the following cell to install the pixiedust Python library in your notebook environment. Then uncomment it and run it once.**\n", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 7, 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Collecting pixiedust\n  Downloading pixiedust-1.0.6.tar.gz (113kB)\n\u001b[K    100% |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 122kB 2.4MB/s \n\u001b[?25hRequirement already up-to-date: mpld3 in /gpfs/global_fs01/sym_shared/YPProdSpark/user/s191-4754435a94b541-9fb5ca908bcc/.local/lib/python2.7/site-packages (from pixiedust)\nCollecting lxml (from pixiedust)\n  Downloading lxml-3.8.0-cp27-cp27m-manylinux1_x86_64.whl (6.8MB)\n\u001b[K    100% |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6.8MB 178kB/s \n\u001b[?25hCollecting geojson (from pixiedust)\n  Downloading geojson-1.3.5-py2.py3-none-any.whl\nBuilding wheels for collected packages: pixiedust\n  Running setup.py bdist_wheel for pixiedust ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \bdone\n\u001b[?25h  Stored in directory: /gpfs/fs01/user/s191-4754435a94b541-9fb5ca908bcc/.cache/pip/wheels/1c/d2/ee/eeca50b5b09e6783cb3a5caa917129629fc55f2b6e63dcd509\nSuccessfully built pixiedust\nInstalling collected packages: lxml, geojson, pixiedust\n  Found existing installation: lxml 3.7.3\n    Uninstalling lxml-3.7.3:\n      Successfully uninstalled lxml-3.7.3\n  Found existing installation: pixiedust 1.0.4\n    Uninstalling pixiedust-1.0.4:\n      Successfully uninstalled pixiedust-1.0.4\nSuccessfully installed geojson-1.3.5 lxml-3.8.0 pixiedust-1.0.6\n"
                }
            ], 
            "cell_type": "code", 
            "source": "#!pip install --user --upgrade pixiedust"
        }, 
        {
            "execution_count": 8, 
            "metadata": {
                "pixiedust": {
                    "displayParams": {
                        "aggregation": "AVG", 
                        "keyFields": "Paymethod", 
                        "handlerId": "barChart", 
                        "valueFields": "Usage", 
                        "rowCount": "500", 
                        "chartsize": "55"
                    }
                }
            }, 
            "outputs": [
                {
                    "data": {
                        "text/plain": "<IPython.core.display.HTML object>", 
                        "text/html": "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div><img style=\"max-width:initial !important\" 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\" class=\"pd_save is-viewer-good\">"
                    }, 
                    "output_type": "display_data", 
                    "metadata": {}
                }
            ], 
            "cell_type": "code", 
            "source": "from pixiedust.display import *\ndisplay(merged)"
        }, 
        {
            "execution_count": 9, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/plain": "    ID Gender Status  Children  EstIncome CarOwner        Age  LongDistance  \\\n0   61      M      S         2   100020.0        N  50.000000         21.37   \n1   79      F      S         0    98716.3        N  55.660000         20.97   \n2   87      M      M         1    97180.4        N  50.600000          8.21   \n3  109      M      S         1    92647.5        N  56.046667         16.04   \n4  148      M      M         2    91272.2        Y  25.033333         26.99   \n\n   International   Local  Dropped Paymethod LocalBilltype  \\\n0           0.00  293.24        0        CH        Budget   \n1           8.23   13.92        0        CC     FreeLocal   \n2           9.63   45.66        0        CC     FreeLocal   \n3           5.74   10.34        0        CC        Budget   \n4           0.00   13.01        0        CC     FreeLocal   \n\n  LongDistanceBilltype   Usage  RatePlan CHURN  \n0             Standard  314.62         4     T  \n1             Standard   43.13         4     T  \n2             Standard   63.51         4     T  \n3             Standard   32.13         4     T  \n4             Standard   40.00         3     F  ", 
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>Gender</th>\n      <th>Status</th>\n      <th>Children</th>\n      <th>EstIncome</th>\n      <th>CarOwner</th>\n      <th>Age</th>\n      <th>LongDistance</th>\n      <th>International</th>\n      <th>Local</th>\n      <th>Dropped</th>\n      <th>Paymethod</th>\n      <th>LocalBilltype</th>\n      <th>LongDistanceBilltype</th>\n      <th>Usage</th>\n      <th>RatePlan</th>\n      <th>CHURN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>61</td>\n      <td>M</td>\n      <td>S</td>\n      <td>2</td>\n      <td>100020.0</td>\n      <td>N</td>\n      <td>50.000000</td>\n      <td>21.37</td>\n      <td>0.00</td>\n      <td>293.24</td>\n      <td>0</td>\n      <td>CH</td>\n      <td>Budget</td>\n      <td>Standard</td>\n      <td>314.62</td>\n      <td>4</td>\n      <td>T</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>79</td>\n      <td>F</td>\n      <td>S</td>\n      <td>0</td>\n      <td>98716.3</td>\n      <td>N</td>\n      <td>55.660000</td>\n      <td>20.97</td>\n      <td>8.23</td>\n      <td>13.92</td>\n      <td>0</td>\n      <td>CC</td>\n      <td>FreeLocal</td>\n      <td>Standard</td>\n      <td>43.13</td>\n      <td>4</td>\n      <td>T</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>87</td>\n      <td>M</td>\n      <td>M</td>\n      <td>1</td>\n      <td>97180.4</td>\n      <td>N</td>\n      <td>50.600000</td>\n      <td>8.21</td>\n      <td>9.63</td>\n      <td>45.66</td>\n      <td>0</td>\n      <td>CC</td>\n      <td>FreeLocal</td>\n      <td>Standard</td>\n      <td>63.51</td>\n      <td>4</td>\n      <td>T</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>109</td>\n      <td>M</td>\n      <td>S</td>\n      <td>1</td>\n      <td>92647.5</td>\n      <td>N</td>\n      <td>56.046667</td>\n      <td>16.04</td>\n      <td>5.74</td>\n      <td>10.34</td>\n      <td>0</td>\n      <td>CC</td>\n      <td>Budget</td>\n      <td>Standard</td>\n      <td>32.13</td>\n      <td>4</td>\n      <td>T</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>148</td>\n      <td>M</td>\n      <td>M</td>\n      <td>2</td>\n      <td>91272.2</td>\n      <td>Y</td>\n      <td>25.033333</td>\n      <td>26.99</td>\n      <td>0.00</td>\n      <td>13.01</td>\n      <td>0</td>\n      <td>CC</td>\n      <td>FreeLocal</td>\n      <td>Standard</td>\n      <td>40.00</td>\n      <td>3</td>\n      <td>F</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 9, 
                    "output_type": "execute_result", 
                    "metadata": {}
                }
            ], 
            "cell_type": "code", 
            "source": "# Spark SQL also allow you to use standard SQL\nmerged.createOrReplaceTempView(\"merged\")\nsql = \"\"\"\nSELECT c.*\nFROM merged c\nWHERE c.EstIncome>90000\n\n\"\"\"\nspark.sql(sql).toPandas().head()"
        }, 
        {
            "source": "### Step 6: Build the Spark pipeline and the Random Forest Classifier model\n\"Pipeline\" is an API in SparkML that's used for building models.\nAdditional information on SparkML: http://spark.apache.org/docs/latest/ml-guide.html", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 10, 
            "metadata": {}, 
            "outputs": [], 
            "cell_type": "code", 
            "source": "from pyspark.ml.feature import StringIndexer, VectorIndexer\nfrom pyspark.ml import Pipeline\nfrom pyspark.ml.feature import VectorAssembler\nfrom pyspark.ml.classification import RandomForestClassifier\n\n# Prepare string variables so that they can be used by the decision tree algorithm\nstringIndexer1 = StringIndexer(inputCol='Gender', outputCol='GenderEncoded')\nstringIndexer2 = StringIndexer(inputCol='Status',outputCol='StatusEncoded')\nstringIndexer3 = StringIndexer(inputCol='CarOwner',outputCol='CarOwnerEncoded')\nstringIndexer4 = StringIndexer(inputCol='Paymethod',outputCol='PaymethodEncoded')\nstringIndexer5 = StringIndexer(inputCol='LocalBilltype',outputCol='LocalBilltypeEncoded')\nstringIndexer6 = StringIndexer(inputCol='LongDistanceBilltype',outputCol='LongDistanceBilltypeEncoded')\nstringIndexer7 = StringIndexer(inputCol='CHURN', outputCol='label')\n\n# Pipelines API requires that input variables are passed in  a vector\nassembler = VectorAssembler(inputCols=[\"GenderEncoded\", \"StatusEncoded\", \"CarOwnerEncoded\", \"PaymethodEncoded\", \"LocalBilltypeEncoded\", \\\n                                       \"LongDistanceBilltypeEncoded\", \"Children\", \"EstIncome\", \"Age\", \"LongDistance\", \"International\", \"Local\",\\\n                                      \"Dropped\",\"Usage\",\"RatePlan\"], outputCol=\"features\")\n\n\n# instantiate the algorithm, take the default settings\nrf=RandomForestClassifier(labelCol=\"label\", featuresCol=\"features\")\n\n#pipeline = Pipeline(stages=[stringIndexer1, stringIndexer2, stringIndexer3, assembler, rf])\npipeline = Pipeline(stages=[stringIndexer1,stringIndexer2,stringIndexer3,stringIndexer4,stringIndexer5,stringIndexer6,stringIndexer7, assembler, rf])\n"
        }, 
        {
            "execution_count": 11, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/plain": "DataFrame[ID: int, Gender: string, Status: string, Children: double, EstIncome: double, CarOwner: string, Age: double, LongDistance: double, International: double, Local: double, Dropped: double, Paymethod: string, LocalBilltype: string, LongDistanceBilltype: string, Usage: double, RatePlan: double, CHURN: string]"
                    }, 
                    "execution_count": 11, 
                    "output_type": "execute_result", 
                    "metadata": {}
                }
            ], 
            "cell_type": "code", 
            "source": "# Split data into train and test datasets\n(trainingData, testingData) = merged.randomSplit([0.7, 0.3],seed=9)\ntrainingData.cache()\ntestingData.cache()"
        }, 
        {
            "execution_count": 12, 
            "metadata": {}, 
            "outputs": [], 
            "cell_type": "code", 
            "source": "# Build model\nmodel = pipeline.fit(trainingData)"
        }, 
        {
            "source": "### Step 7: Score the test data set", 
            "cell_type": "markdown", 
            "metadata": {
                "collapsed": true
            }
        }, 
        {
            "execution_count": 13, 
            "metadata": {}, 
            "outputs": [
                {
                    "data": {
                        "text/plain": "   ID CHURN  Label  prediction                        probability\n0   1     T      1           1  [0.0390066830438, 0.960993316956]\n1  18     F      0           0   [0.604568153513, 0.395431846487]\n2  22     F      0           0   [0.743416529489, 0.256583470511]\n3  23     F      0           0   [0.92480094066, 0.0751990593396]\n4  29     T      1           1   [0.134435036135, 0.865564963865]\n5  40     T      1           1   [0.222010772111, 0.777989227889]", 
                        "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>CHURN</th>\n      <th>Label</th>\n      <th>prediction</th>\n      <th>probability</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>T</td>\n      <td>1</td>\n      <td>1</td>\n      <td>[0.0390066830438, 0.960993316956]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>F</td>\n      <td>0</td>\n      <td>0</td>\n      <td>[0.604568153513, 0.395431846487]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>22</td>\n      <td>F</td>\n      <td>0</td>\n      <td>0</td>\n      <td>[0.743416529489, 0.256583470511]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>23</td>\n      <td>F</td>\n      <td>0</td>\n      <td>0</td>\n      <td>[0.92480094066, 0.0751990593396]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>29</td>\n      <td>T</td>\n      <td>1</td>\n      <td>1</td>\n      <td>[0.134435036135, 0.865564963865]</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>40</td>\n      <td>T</td>\n      <td>1</td>\n      <td>1</td>\n      <td>[0.222010772111, 0.777989227889]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 13, 
                    "output_type": "execute_result", 
                    "metadata": {}
                }
            ], 
            "cell_type": "code", 
            "source": "result=model.transform(testingData)\nresult_display=result.select(result[\"ID\"],result[\"CHURN\"],result[\"Label\"],result[\"prediction\"],result[\"probability\"])\nresult_display.toPandas().head(6)"
        }, 
        {
            "source": "### Step 8: Model Evaluation\nFind accuracy of the models and the Area Under the ROC Curve ", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 14, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Model Accuracy = 0.92.\n"
                }
            ], 
            "cell_type": "code", 
            "source": "print 'Model Accuracy = {:.2f}.'.format(result.filter(result.label == result.prediction).count() / float(result.count()))"
        }, 
        {
            "execution_count": 15, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Area under ROC curve = 0.91.\n"
                }
            ], 
            "cell_type": "code", 
            "source": "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n\n# Evaluate model\nevaluator = BinaryClassificationEvaluator(rawPredictionCol=\"prediction\", labelCol=\"label\", metricName=\"areaUnderROC\")\nprint 'Area under ROC curve = {:.2f}.'.format(evaluator.evaluate(result))"
        }, 
        {
            "source": "###  Step 9:  Tune the model to find the best model", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "#### Build a Parameter Grid specifying the parameters to be evaluated to determine the best combination", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 16, 
            "metadata": {}, 
            "outputs": [], 
            "cell_type": "code", 
            "source": "# set different levels for the maxDepth\nfrom pyspark.ml.tuning import ParamGridBuilder, CrossValidator\nparamGrid = (ParamGridBuilder().addGrid(rf.maxDepth,[4,6,8]).build())"
        }, 
        {
            "source": "#### Create a cross validator to tune the pipeline with the generated parameter grid\nCross-validation attempts to fit the underlying estimator with user-specified combinations of parameters, cross-evaluate the fitted models, and output the best one.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 17, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "cell_type": "code", 
            "source": "# perform 3 fold cross validation\ncv = CrossValidator().setEstimator(pipeline).setEvaluator(evaluator).setEstimatorParamMaps(paramGrid).setNumFolds(3)"
        }, 
        {
            "execution_count": 18, 
            "metadata": {}, 
            "outputs": [], 
            "cell_type": "code", 
            "source": "# train the model\ncvModel = cv.fit(trainingData)\n\n# pick the best model\nbest_rfModel = cvModel.bestModel\n\n# score the test data set\ncvresult=best_rfModel.transform(testingData)"
        }, 
        {
            "execution_count": 19, 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Model Accuracy of the best fitted model = 0.97.\nModel Accuracy of the default model = 0.92.\n   \nArea under the ROC curve of best fitted model = 0.96.\nArea under the ROC curve of the default model = 0.91.\n"
                }
            ], 
            "cell_type": "code", 
            "source": "\nprint 'Model Accuracy of the best fitted model = {:.2f}.'.format(cvresult.filter(cvresult.label == cvresult.prediction).count()/ float(cvresult.count()))\nprint 'Model Accuracy of the default model = {:.2f}.'.format(result.filter(result.label == result.prediction).count() / float(result.count()))\nprint '   '\nprint('Area under the ROC curve of best fitted model = {:.2f}.'.format(evaluator.evaluate(cvresult)))\nprint 'Area under the ROC curve of the default model = {:.2f}.'.format(evaluator.evaluate(result))"
        }, 
        {
            "source": "### Step 10: Save Model\nSave the best model in Object Storage. \n\nA separate notebook has been created for \"batch scoring deployment\". This deployment notebook retrieves the model from object storage and applies it to a new dataset. The notebook can be scheduled to run via the Notebook scheduler (the clock icon on the menu bar) or through the deployment interface in IBM ML (currently in beta).", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 20, 
            "metadata": {}, 
            "outputs": [], 
            "cell_type": "code", 
            "source": "# Overwrite any existing saved model in the specified path\nbest_rfModel.write().overwrite().save(\"PredictChurn.churnModel\")"
        }, 
        {
            "source": "You have come to the end of this notebook", 
            "cell_type": "markdown", 
            "metadata": {
                "collapsed": true
            }
        }, 
        {
            "source": "**Sidney Phoon**<br/>\nyfphoon@us.ibm.com <br/>\nMay 4th, 2017", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": null, 
            "metadata": {
                "collapsed": true
            }, 
            "outputs": [], 
            "cell_type": "code", 
            "source": ""
        }
    ], 
    "metadata": {
        "language_info": {
            "pygments_lexer": "ipython2", 
            "mimetype": "text/x-python", 
            "version": "2.7.11", 
            "codemirror_mode": {
                "name": "ipython", 
                "version": 2
            }, 
            "name": "python", 
            "file_extension": ".py", 
            "nbconvert_exporter": "python"
        }, 
        "kernelspec": {
            "language": "python", 
            "display_name": "Python 2 with Spark 2.0", 
            "name": "python2-spark20"
        }
    }
}