Using the AzureML R package to connect Azure ML Studio and R.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using the AzureML R package to connect Azure ML Studio and R\n",
    "\n",
    "This notebook demonstrates some of the capabilities of the `AzureML` package:\n",
    "  * Read, download, and delete data in Azure ML workspace\n",
    "  * Read intermediate data from Azure ML experiment\n",
    "  * Allow for a concise way of publishing and consuming web services\n",
    "\n",
    "The target audience should have a basic understanding of the [Azure Machine Learning studio][studio link]. Specifically, you should:\n",
    "* Know how to get the **workspace ID** and **authorization token**\n",
    "     - Note that this step is not necessary in Azure ML Jupyter notebooks, because the notebook service stores your credentials in the file system\n",
    "* Understand setting up web services on Azure\n",
    " \n",
    "If you are completely new to Azure ML, the [Tutorial for Data Scientists](tutorial link) can help you get started.\n",
    "All results as shown here are from my own Azure ML workspace.\n",
    "\n",
    "Note that you don't have to specify own workspace ID and authorization token to run the code.\n",
    "\n",
    "[AzureML link]: https://github.com/RevolutionAnalytics/AzureML\n",
    "[CRAN link]: https://cran.r-project.org/web/packages/AzureML/index.html\n",
    "[studio link]: https://studio.azureml.net/\n",
    "[tutorial link]: https://gallery.cortanaanalytics.com/Experiment/Tutorial-for-Data-Scientists-3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the package\n",
    "\n",
    "The package is already in the Jupyter service on Azure ML.\n",
    "\n",
    "Start by loading the package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      ": package 'AzureML' was built under R version 3.2.3"
     ]
    }
   ],
   "source": [
    "library(AzureML)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Work with Workspace\n",
    "\n",
    "The `AzureML` R package allows you to work with workspaces directly. Specifically, with this package users can read, download, and delete data in an Azure ML workspace.\n",
    "\n",
    "### Connect with AzureML workspace\n",
    "Start by loading the library and setting up connection with an AzureML workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Connect to the workspace\n",
    "ws <- workspace()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List datasets\n",
    "The *datasets* attribute of a workspace() variable contains information about all the datasets in the workspace, including the default datasets from Microsoft."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>\"text.preprocessing.zip\"</li>\n",
       "\t<li>\"fraudTemplateUtil.zip\"</li>\n",
       "\t<li>\"testDataSource_a5e9faf817084997981d094ef040389b\"</li>\n",
       "\t<li>\"MetaAnalytics.Test.GlobalDataset.IntegerTSVFile\"</li>\n",
       "\t<li>\"MetaAnalytics.Test.GlobalDataset.IntegerCSVFile\"</li>\n",
       "\t<li>\"testDataSource_65885ed31a854d70962a02d5641d3c92\"</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item \"text.preprocessing.zip\"\n",
       "\\item \"fraudTemplateUtil.zip\"\n",
       "\\item \"testDataSource_a5e9faf817084997981d094ef040389b\"\n",
       "\\item \"MetaAnalytics.Test.GlobalDataset.IntegerTSVFile\"\n",
       "\\item \"MetaAnalytics.Test.GlobalDataset.IntegerCSVFile\"\n",
       "\\item \"testDataSource_65885ed31a854d70962a02d5641d3c92\"\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. \"text.preprocessing.zip\"\n",
       "2. \"fraudTemplateUtil.zip\"\n",
       "3. \"testDataSource_a5e9faf817084997981d094ef040389b\"\n",
       "4. \"MetaAnalytics.Test.GlobalDataset.IntegerTSVFile\"\n",
       "5. \"MetaAnalytics.Test.GlobalDataset.IntegerCSVFile\"\n",
       "6. \"testDataSource_65885ed31a854d70962a02d5641d3c92\"\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"text.preprocessing.zip\"                         \n",
       "[2] \"fraudTemplateUtil.zip\"                          \n",
       "[3] \"testDataSource_a5e9faf817084997981d094ef040389b\"\n",
       "[4] \"MetaAnalytics.Test.GlobalDataset.IntegerTSVFile\"\n",
       "[5] \"MetaAnalytics.Test.GlobalDataset.IntegerCSVFile\"\n",
       "[6] \"testDataSource_65885ed31a854d70962a02d5641d3c92\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list first several datasets in my workspace\n",
    "head(datasets(ws, filter = \"sample\")$Name)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download a dataset\n",
    "To download a dataset we can use the download.datasets() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>UserId</th><th scope=col>MovieId</th><th scope=col>Rating</th><th scope=col>Timestamp</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>68646</td><td>10</td><td>1381620027</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>113277</td><td>10</td><td>1379466669</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2</td><td>454876</td><td>8</td><td>1394818630</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>2</td><td>790636</td><td>7</td><td>1389963947</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>2</td><td>816711</td><td>8</td><td>1379963769</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>2</td><td>1091191</td><td>7</td><td>1391173869</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & UserId & MovieId & Rating & Timestamp\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 68646 & 10 & 1381620027\\\\\n",
       "\t2 & 1 & 113277 & 10 & 1379466669\\\\\n",
       "\t3 & 2 & 454876 & 8 & 1394818630\\\\\n",
       "\t4 & 2 & 790636 & 7 & 1389963947\\\\\n",
       "\t5 & 2 & 816711 & 8 & 1379963769\\\\\n",
       "\t6 & 2 & 1091191 & 7 & 1391173869\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  UserId MovieId Rating  Timestamp\n",
       "1      1   68646     10 1381620027\n",
       "2      1  113277     10 1379466669\n",
       "3      2  454876      8 1394818630\n",
       "4      2  790636      7 1389963947\n",
       "5      2  816711      8 1379963769\n",
       "6      2 1091191      7 1391173869"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# download datasets\n",
    "movies <- download.datasets(ws, name = \"Movie Ratings\")\n",
    "head(movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 119.332031 208.800781 L 135.199219 208.800781 L 135.199219 202.480469 L 119.332031 202.480469 Z M 119.332031 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 135.199219 208.800781 L 151.066406 208.800781 Z M 135.199219 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 151.066406 208.800781 L 166.933594 208.800781 L 166.933594 198.121094 L 151.066406 198.121094 Z M 151.066406 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 166.933594 208.800781 L 182.800781 208.800781 Z M 166.933594 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 182.800781 208.800781 L 198.667969 208.800781 L 198.667969 189.015625 L 182.800781 189.015625 Z M 182.800781 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 198.667969 208.800781 L 214.535156 208.800781 Z M 198.667969 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 214.535156 208.800781 L 230.402344 208.800781 L 230.402344 167.390625 L 214.535156 167.390625 Z M 214.535156 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 230.398438 208.800781 L 246.265625 208.800781 Z M 230.398438 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 246.265625 208.800781 L 262.132813 208.800781 L 262.132813 130.851563 L 246.265625 130.851563 Z M 246.265625 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 262.132813 208.800781 L 278 208.800781 Z M 262.132813 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 278 208.800781 L 293.867188 208.800781 L 293.867188 75.855469 L 278 75.855469 Z M 278 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 293.867188 208.800781 L 309.734375 208.800781 Z M 293.867188 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 309.734375 208.800781 L 325.601563 208.800781 L 325.601563 64.800781 L 309.734375 64.800781 Z M 309.734375 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 325.601563 208.800781 L 341.46875 208.800781 Z M 325.601563 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 341.464844 208.800781 L 357.332031 208.800781 L 357.332031 124.722656 L 341.464844 124.722656 Z M 341.464844 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 357.332031 208.800781 L 373.199219 208.800781 Z M 357.332031 208.800781 \"/>\n",
       "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 373.199219 208.800781 L 389.066406 208.800781 L 389.066406 137.878906 L 373.199219 137.878906 Z M 373.199219 208.800781 \"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot with title \"Rating\""
      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 6, repr.plot.height = 4)\n",
    "hist(movies$Rating, main = \"Rating\", xlab = NULL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upload a dataset\n",
    "We'll use the air quality dataset that comes with base R to show how a dataset can be uploaded. Note that if dataset with the same name already exists in the workspace an error will be reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Ozone</th><th scope=col>Solar.R</th><th scope=col>Wind</th><th scope=col>Temp</th><th scope=col>Month</th><th scope=col>Day</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>41</td><td>190</td><td>7.4</td><td>67</td><td>5</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>36</td><td>118</td><td>8</td><td>72</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>12</td><td>149</td><td>12.6</td><td>74</td><td>5</td><td>3</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>18</td><td>313</td><td>11.5</td><td>62</td><td>5</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>NA</td><td>NA</td><td>14.3</td><td>56</td><td>5</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>28</td><td>NA</td><td>14.9</td><td>66</td><td>5</td><td>6</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>23</td><td>299</td><td>8.6</td><td>65</td><td>5</td><td>7</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>19</td><td>99</td><td>13.8</td><td>59</td><td>5</td><td>8</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>8</td><td>19</td><td>20.1</td><td>61</td><td>5</td><td>9</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>NA</td><td>194</td><td>8.6</td><td>69</td><td>5</td><td>10</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & Ozone & Solar.R & Wind & Temp & Month & Day\\\\\n",
       "\\hline\n",
       "\t1 & 41 & 190 & 7.4 & 67 & 5 & 1\\\\\n",
       "\t2 & 36 & 118 & 8 & 72 & 5 & 2\\\\\n",
       "\t3 & 12 & 149 & 12.6 & 74 & 5 & 3\\\\\n",
       "\t4 & 18 & 313 & 11.5 & 62 & 5 & 4\\\\\n",
       "\t5 & NA & NA & 14.3 & 56 & 5 & 5\\\\\n",
       "\t6 & 28 & NA & 14.9 & 66 & 5 & 6\\\\\n",
       "\t7 & 23 & 299 & 8.6 & 65 & 5 & 7\\\\\n",
       "\t8 & 19 & 99 & 13.8 & 59 & 5 & 8\\\\\n",
       "\t9 & 8 & 19 & 20.1 & 61 & 5 & 9\\\\\n",
       "\t10 & NA & 194 & 8.6 & 69 & 5 & 10\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "   Ozone Solar.R Wind Temp Month Day\n",
       "1     41     190  7.4   67     5   1\n",
       "2     36     118  8.0   72     5   2\n",
       "3     12     149 12.6   74     5   3\n",
       "4     18     313 11.5   62     5   4\n",
       "5     NA      NA 14.3   56     5   5\n",
       "6     28      NA 14.9   66     5   6\n",
       "7     23     299  8.6   65     5   7\n",
       "8     19      99 13.8   59     5   8\n",
       "9      8      19 20.1   61     5   9\n",
       "10    NA     194  8.6   69     5  10"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airquality[1:10,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>VisualizeEndPoint</th><th scope=col>SchemaEndPoint</th><th scope=col>SchemaStatus</th><th scope=col>Id</th><th scope=col>DataTypeId</th><th scope=col>Name</th><th scope=col>Description</th><th scope=col>FamilyId</th><th scope=col>ResourceUploadId</th><th scope=col>SourceOrigin</th><th scope=col>ellip.h</th><th scope=col>PromotedFrom</th><th scope=col>UploadedFromFilename</th><th scope=col>ServiceVersion</th><th scope=col>IsLatest</th><th scope=col>Category</th><th scope=col>DownloadLocation</th><th scope=col>IsDeprecated</th><th scope=col>Culture</th><th scope=col>Batch</th><th scope=col>CreatedDateTicks</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>NANA</td><td>NANANA</td><td>Pending</td><td>a2aba0dafad8436788401bbc8c22fe36.6faa388f56e6415492bec335192d2d00.v1-default-106</td><td>GenericTSV</td><td>my air quality</td><td></td><td>6faa388f56e6415492bec335192d2d00</td><td>f5004e84bfef4733b8cdc63bf1db717e</td><td>FromResourceUpload</td><td><e2><8b><af></td><td>NA</td><td></td><td>0</td><td>TRUE</td><td>NA</td><td>https://esintussouthsus.blob.core.windows.net/uploadedresources/C7FDC_a2aba0dafad8436788401bbc8c22fe36_f5004e84bfef4733b8cdc63bf1db717e.tsv?sv=2015-02-21&sr=b&sig=tkHy%2BvjbJ5dDQgCVY%2Fv6b9mz%2BgwMOK5GPFbq79U%2Bhsk%3D&st=2016-03-13T18%3A21%3A10Z&se=2016-03-14T18%3A26%3A10Z&sp=r&rscd=attachment%3B%20filename%3D%22my%20air%20quality.tsv%22</td><td>FALSE</td><td>default</td><td>106</td><td>6.359349e+17</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllll}\n",
       "  & VisualizeEndPoint & SchemaEndPoint & SchemaStatus & Id & DataTypeId & Name & Description & FamilyId & ResourceUploadId & SourceOrigin & ellip.h & PromotedFrom & UploadedFromFilename & ServiceVersion & IsLatest & Category & DownloadLocation & IsDeprecated & Culture & Batch & CreatedDateTicks\\\\\n",
       "\\hline\n",
       "\t1 & NANA & NANANA & Pending & a2aba0dafad8436788401bbc8c22fe36.6faa388f56e6415492bec335192d2d00.v1-default-106 & GenericTSV & my air quality &  & 6faa388f56e6415492bec335192d2d00 & f5004e84bfef4733b8cdc63bf1db717e & FromResourceUpload & <e2><8b><af> & NA &  & 0 & TRUE & NA & https://esintussouthsus.blob.core.windows.net/uploadedresources/C7FDC_a2aba0dafad8436788401bbc8c22fe36_f5004e84bfef4733b8cdc63bf1db717e.tsv?sv=2015-02-21&sr=b&sig=tkHy%2BvjbJ5dDQgCVY%2Fv6b9mz%2BgwMOK5GPFbq79U%2Bhsk%3D&st=2016-03-13T18%3A21%3A10Z&se=2016-03-14T18%3A26%3A10Z&sp=r&rscd=attachment%3B%20filename%3D%22my%20air%20quality.tsv%22 & FALSE & default & 106 & 6.359349e+17\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "            Name DataTypeId Size ...\n",
       "1 my air quality GenericTSV  225 ...\n",
       "----------------------------------------------\n",
       "AzureML datasets data.frame variables include:\n",
       " [1] \"VisualizeEndPoint\"    \"SchemaEndPoint\"       \"SchemaStatus\"        \n",
       " [4] \"Id\"                   \"DataTypeId\"           \"Name\"                \n",
       " [7] \"Description\"          \"FamilyId\"             \"ResourceUploadId\"    \n",
       "[10] \"SourceOrigin\"         \"Size\"                 \"CreatedDate\"         \n",
       "[13] \"Owner\"                \"ExperimentId\"         \"ClientVersion\"       \n",
       "[16] \"PromotedFrom\"         \"UploadedFromFilename\" \"ServiceVersion\"      \n",
       "[19] \"IsLatest\"             \"Category\"             \"DownloadLocation\"    \n",
       "[22] \"IsDeprecated\"         \"Culture\"              \"Batch\"               \n",
       "[25] \"CreatedDateTicks\"     \n",
       "            Name DataTypeId Size ...\n",
       "1 my air quality GenericTSV  225 ..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uploading R data frame to Azure ML workspace\n",
    "mydata <- airquality[1:10,]\n",
    "# information about the uploaded dataset in the workspace will be returned\n",
    "upload.dataset(mydata, ws, name = \"my air quality\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Ozone</th><th scope=col>Solar.R</th><th scope=col>Wind</th><th scope=col>Temp</th><th scope=col>Month</th><th scope=col>Day</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>41</td><td>190</td><td>7.4</td><td>67</td><td>5</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>36</td><td>118</td><td>8</td><td>72</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>12</td><td>149</td><td>12.6</td><td>74</td><td>5</td><td>3</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>18</td><td>313</td><td>11.5</td><td>62</td><td>5</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>NA</td><td>NA</td><td>14.3</td><td>56</td><td>5</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>28</td><td>NA</td><td>14.9</td><td>66</td><td>5</td><td>6</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & Ozone & Solar.R & Wind & Temp & Month & Day\\\\\n",
       "\\hline\n",
       "\t1 & 41 & 190 & 7.4 & 67 & 5 & 1\\\\\n",
       "\t2 & 36 & 118 & 8 & 72 & 5 & 2\\\\\n",
       "\t3 & 12 & 149 & 12.6 & 74 & 5 & 3\\\\\n",
       "\t4 & 18 & 313 & 11.5 & 62 & 5 & 4\\\\\n",
       "\t5 & NA & NA & 14.3 & 56 & 5 & 5\\\\\n",
       "\t6 & 28 & NA & 14.9 & 66 & 5 & 6\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  Ozone Solar.R Wind Temp Month Day\n",
       "1    41     190  7.4   67     5   1\n",
       "2    36     118  8.0   72     5   2\n",
       "3    12     149 12.6   74     5   3\n",
       "4    18     313 11.5   62     5   4\n",
       "5    NA      NA 14.3   56     5   5\n",
       "6    28      NA 14.9   66     5   6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# download to check its content\n",
    "head(download.datasets(ws, name = \"my air quality\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete a dataset\n",
    "If the delete action is successful the returned status value for Deleted should be *TRUE*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Name</th><th scope=col>Deleted</th><th scope=col>status_code</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>my air quality</td><td>TRUE</td><td>204</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       "  & Name & Deleted & status_code\\\\\n",
       "\\hline\n",
       "\t1 & my air quality & TRUE & 204\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "            Name Deleted status_code\n",
       "1 my air quality    TRUE         204"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delete dataset\n",
    "delete.datasets(ws, name = \"my air quality\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"Airport Codes Dataset\" is one of the dafault datasets in Azure ML. This example shows that the default datasets cannot be deleted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# delete Azure sample dataset: not allowed\n",
    "# Uncomment the following line to see the failure report\n",
    "\n",
    "# delete.datasets(ws, name = \"Airport Codes Dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Work with experiments\n",
    "The `AzureML` package allows you to get a summary of the existing experiments and to download the intermediate datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List existing experiments\n",
    "Information for all experiments in the workspace, including the default ones from Microsoft, can be returned. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Description</th><th scope=col>ExperimentId</th><th scope=col>Creator</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Simple convert to TSV</td><td>a2aba0dafad8436788401bbc8c22fe36.f-id.2237184cf35e42c4a41c0307f623a3f3</td><td>adevries</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Experiment created on â€Ž2â€Ž/â€Ž4â€Ž/â€Ž2016</td><td>a2aba0dafad8436788401bbc8c22fe36.f-id.26730c0bfd0948afb8eb8b405bd264ba</td><td>adevries</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Experiment created on â€Ž12â€Ž/â€Ž18â€Ž/â€Ž2015</td><td>a2aba0dafad8436788401bbc8c22fe36.f-id.3292fcc8bcd14c29b75242f384190a41</td><td>adevries</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Experiment created on â€Ž2â€Ž/â€Ž9â€Ž/â€Ž2016</td><td>a2aba0dafad8436788401bbc8c22fe36.f-id.63199eaa2e8e4da1beb4105b79b74988</td><td>adevries</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Experiment created on â€Ž2â€Ž/â€Ž23â€Ž/â€Ž2016</td><td>a2aba0dafad8436788401bbc8c22fe36.f-id.9c98fd354ff2471bb6d3d5798ce35d16</td><td>adevries</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Simple experiment to test Jupyter</td><td>a2aba0dafad8436788401bbc8c22fe36.f-id.a0fbd6ccad204e65825b5b7e574c9be8</td><td>adevries</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       "  & Description & ExperimentId & Creator\\\\\n",
       "\\hline\n",
       "\t1 & Simple convert to TSV & a2aba0dafad8436788401bbc8c22fe36.f-id.2237184cf35e42c4a41c0307f623a3f3 & adevries\\\\\n",
       "\t2 & Experiment created on â€Ž2â€Ž/â€Ž4â€Ž/â€Ž2016 & a2aba0dafad8436788401bbc8c22fe36.f-id.26730c0bfd0948afb8eb8b405bd264ba & adevries\\\\\n",
       "\t3 & Experiment created on â€Ž12â€Ž/â€Ž18â€Ž/â€Ž2015 & a2aba0dafad8436788401bbc8c22fe36.f-id.3292fcc8bcd14c29b75242f384190a41 & adevries\\\\\n",
       "\t4 & Experiment created on â€Ž2â€Ž/â€Ž9â€Ž/â€Ž2016 & a2aba0dafad8436788401bbc8c22fe36.f-id.63199eaa2e8e4da1beb4105b79b74988 & adevries\\\\\n",
       "\t5 & Experiment created on â€Ž2â€Ž/â€Ž23â€Ž/â€Ž2016 & a2aba0dafad8436788401bbc8c22fe36.f-id.9c98fd354ff2471bb6d3d5798ce35d16 & adevries\\\\\n",
       "\t6 & Simple experiment to test Jupyter & a2aba0dafad8436788401bbc8c22fe36.f-id.a0fbd6ccad204e65825b5b7e574c9be8 & adevries\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "                                      Description\n",
       "1                           Simple convert to TSV\n",
       "2   Experiment created on â2â/â4â/â2016\n",
       "3 Experiment created on â12â/â18â/â2015\n",
       "4   Experiment created on â2â/â9â/â2016\n",
       "5  Experiment created on â2â/â23â/â2016\n",
       "6               Simple experiment to test Jupyter\n",
       "                                                            ExperimentId\n",
       "1 a2aba0dafad8436788401bbc8c22fe36.f-id.2237184cf35e42c4a41c0307f623a3f3\n",
       "2 a2aba0dafad8436788401bbc8c22fe36.f-id.26730c0bfd0948afb8eb8b405bd264ba\n",
       "3 a2aba0dafad8436788401bbc8c22fe36.f-id.3292fcc8bcd14c29b75242f384190a41\n",
       "4 a2aba0dafad8436788401bbc8c22fe36.f-id.63199eaa2e8e4da1beb4105b79b74988\n",
       "5 a2aba0dafad8436788401bbc8c22fe36.f-id.9c98fd354ff2471bb6d3d5798ce35d16\n",
       "6 a2aba0dafad8436788401bbc8c22fe36.f-id.a0fbd6ccad204e65825b5b7e574c9be8\n",
       "   Creator\n",
       "1 adevries\n",
       "2 adevries\n",
       "3 adevries\n",
       "4 adevries\n",
       "5 adevries\n",
       "6 adevries"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# experiments\n",
    "exps <- experiments(ws)\n",
    "head(\n",
    "    with(exps, data.frame(Description, ExperimentId, Creator, stringsAsFactors = FALSE))\n",
    "    )\n",
    "#head(cbind(Description = exps$Description, ExperimentId = exps$ExperimentId, Creator = exps$Creator))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also filter by using the experiments() function with the \"filter\" argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>\"Microsoft Corporation\"</li>\n",
       "\t<li>\"Microsoft Corporation\"</li>\n",
       "\t<li>\"Microsoft Corporation\"</li>\n",
       "\t<li>\"Microsoft Corporation\"</li>\n",
       "\t<li>\"Microsoft Corporation\"</li>\n",
       "\t<li>\"Microsoft Corporation\"</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item \"Microsoft Corporation\"\n",
       "\\item \"Microsoft Corporation\"\n",
       "\\item \"Microsoft Corporation\"\n",
       "\\item \"Microsoft Corporation\"\n",
       "\\item \"Microsoft Corporation\"\n",
       "\\item \"Microsoft Corporation\"\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. \"Microsoft Corporation\"\n",
       "2. \"Microsoft Corporation\"\n",
       "3. \"Microsoft Corporation\"\n",
       "4. \"Microsoft Corporation\"\n",
       "5. \"Microsoft Corporation\"\n",
       "6. \"Microsoft Corporation\"\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Microsoft Corporation\" \"Microsoft Corporation\" \"Microsoft Corporation\"\n",
       "[4] \"Microsoft Corporation\" \"Microsoft Corporation\" \"Microsoft Corporation\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>Binary Classification: Network intrusion detection                    </td><td>506153734175476c4f62416c57734963.f-id.02556bd24faf4f099dc88c1d21262f36</td></tr>\n",
       "\t<tr><td>Text Classification: Step 2 of 5, text preprocessing                  </td><td>506153734175476c4f62416c57734963.f-id.07811aadbdca4669a2d129abb083d2f0</td></tr>\n",
       "\t<tr><td>Learning with Counts: Binary Classification                           </td><td>506153734175476c4f62416c57734963.f-id.08b6ef9574de46e7a9fdc74dc5221fb2</td></tr>\n",
       "\t<tr><td>Clustering: Color quantization                                        </td><td>506153734175476c4f62416c57734963.f-id.0a4458138dc140329a934393d7bbf2ed</td></tr>\n",
       "\t<tr><td>Retail Forecasting: Step 4 of 6, train regression models              </td><td>506153734175476c4f62416c57734963.f-id.0ec7e45f59244b63a994b86ae81d7f93</td></tr>\n",
       "\t<tr><td>Binary Classification: Direct marketing                               </td><td>506153734175476c4f62416c57734963.f-id.11e8958b4571436d8848f2a75b348fdc</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t Binary Classification: Network intrusion detection                     & 506153734175476c4f62416c57734963.f-id.02556bd24faf4f099dc88c1d21262f36\\\\\n",
       "\t Text Classification: Step 2 of 5, text preprocessing                   & 506153734175476c4f62416c57734963.f-id.07811aadbdca4669a2d129abb083d2f0\\\\\n",
       "\t Learning with Counts: Binary Classification                            & 506153734175476c4f62416c57734963.f-id.08b6ef9574de46e7a9fdc74dc5221fb2\\\\\n",
       "\t Clustering: Color quantization                                         & 506153734175476c4f62416c57734963.f-id.0a4458138dc140329a934393d7bbf2ed\\\\\n",
       "\t Retail Forecasting: Step 4 of 6, train regression models               & 506153734175476c4f62416c57734963.f-id.0ec7e45f59244b63a994b86ae81d7f93\\\\\n",
       "\t Binary Classification: Direct marketing                                & 506153734175476c4f62416c57734963.f-id.11e8958b4571436d8848f2a75b348fdc\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "1. \"Binary Classification: Network intrusion detection\"\n",
       "2. \"Text Classification: Step 2 of 5, text preprocessing\"\n",
       "3. \"Learning with Counts: Binary Classification\"\n",
       "4. \"Clustering: Color quantization\"\n",
       "5. \"Retail Forecasting: Step 4 of 6, train regression models\"\n",
       "6. \"Binary Classification: Direct marketing\"\n",
       "7. \"506153734175476c4f62416c57734963.f-id.02556bd24faf4f099dc88c1d21262f36\"\n",
       "8. \"506153734175476c4f62416c57734963.f-id.07811aadbdca4669a2d129abb083d2f0\"\n",
       "9. \"506153734175476c4f62416c57734963.f-id.08b6ef9574de46e7a9fdc74dc5221fb2\"\n",
       "10. \"506153734175476c4f62416c57734963.f-id.0a4458138dc140329a934393d7bbf2ed\"\n",
       "11. \"506153734175476c4f62416c57734963.f-id.0ec7e45f59244b63a994b86ae81d7f93\"\n",
       "12. \"506153734175476c4f62416c57734963.f-id.11e8958b4571436d8848f2a75b348fdc\"\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     [,1]                                                      \n",
       "[1,] \"Binary Classification: Network intrusion detection\"      \n",
       "[2,] \"Text Classification: Step 2 of 5, text preprocessing\"    \n",
       "[3,] \"Learning with Counts: Binary Classification\"             \n",
       "[4,] \"Clustering: Color quantization\"                          \n",
       "[5,] \"Retail Forecasting: Step 4 of 6, train regression models\"\n",
       "[6,] \"Binary Classification: Direct marketing\"                 \n",
       "     [,2]                                                                    \n",
       "[1,] \"506153734175476c4f62416c57734963.f-id.02556bd24faf4f099dc88c1d21262f36\"\n",
       "[2,] \"506153734175476c4f62416c57734963.f-id.07811aadbdca4669a2d129abb083d2f0\"\n",
       "[3,] \"506153734175476c4f62416c57734963.f-id.08b6ef9574de46e7a9fdc74dc5221fb2\"\n",
       "[4,] \"506153734175476c4f62416c57734963.f-id.0a4458138dc140329a934393d7bbf2ed\"\n",
       "[5,] \"506153734175476c4f62416c57734963.f-id.0ec7e45f59244b63a994b86ae81d7f93\"\n",
       "[6,] \"506153734175476c4f62416c57734963.f-id.11e8958b4571436d8848f2a75b348fdc\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check sample experiments\n",
    "e <- experiments(ws, filter = \"samples\")\n",
    "head(e$Creator)\n",
    "head(cbind(e$Description, e$ExperimentId))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download intermediate data\n",
    "\n",
    "You can also download intermediate data from an experiment. To do this you need information for four variables:\n",
    "* experiment\n",
    "* node_id\n",
    "* port_name\n",
    "* data_type_id.\n",
    "\n",
    "To obtain this information, follow these simple steps\n",
    "\n",
    "1. Click the output port of the \"Convert to CSV\" module in an experiment you get: [Figure 1][figure1 link]\n",
    "2. Next, click \"Generate Data Access Code...\", to get the information in [Figure 2][figure2 link]\n",
    "3. Copy and paste the code from the \"R\" tab to your R session\n",
    "\n",
    "\n",
    "\n",
    "#### Step 1: Click on Convert to CSV module output\n",
    "\n",
    "[![Figure 1][figure1 link]][figure1 link]\n",
    "[figure1 link]: https://raw.githubusercontent.com/andrie/jupyter-notebook-samples/master/images/data%20access/6-download-intermediate-dataset.PNG\n",
    "\n",
    "\n",
    "#### Step 2: Click Generate data access code, and \"R\" tab\n",
    "\n",
    "[![Figure 2][figure2 link]][figure2 link]\n",
    "[figure2 link]: https://raw.githubusercontent.com/andrie/jupyter-notebook-samples/master/images/data%20access/6-generate-data-access-code.PNG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copying the information to your local R session\n",
    "\n",
    "You can copy and paste the code in the \"R\" tab to a local R session. When you have evaluate the code, the data gets downloaded from the Azure ML Studio to your local session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# download intermediate data\n",
    "\n",
    "# Replace the code below with the snipped provided by the AzureML Studio\n",
    "#exp_data <- download.intermediate.dataset(ws = ws, \n",
    "#            experiment  = \"xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\",\n",
    "#            node_id = \"xxxxxxxx-xxx-xxx-xxxx-xxxxxxxxxxxx-xxx\",\n",
    "#            port_name = \"Results dataset\",\n",
    "#            data_type_id = \"GenericCSV\")\n",
    "\n",
    "#head(exp_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A concise way of consuming web service\n",
    "The `AzureML`package also allows a very concise way of consuming the web service. All you need is to provide the web service ID and the workspace information. Then use `consume()` to consume the service from any R terminal (as long as you have internet access).\n",
    "\n",
    "For illustration purpose, fit a linear model and deploy a web service based on the model.\n",
    "\n",
    "If you encounter the error `Requires external zip utility. Please install zip, ensure it's on your path and try again` while running this on Windows, you can install [RTools][rtools link] and add the install directory to the system path. For example, if it's installed in `C:\\Tools`, you should add `C:\\Tools\\bin` to your system path and then restart R.\n",
    " \n",
    "[rtools link]: https://cran.r-project.org/bin/windows/Rtools/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Publishing the web service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      ": package 'MASS' was built under R version 3.2.3"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes 'Endpoint' and 'data.frame':\t1 obs. of  14 variables:\n",
      " $ Name                 : chr \"default\"\n",
      " $ Description          : chr \"\"\n",
      " $ CreationTime         : chr \"2016-03-13T18:26:32.567Z\"\n",
      " $ WorkspaceId          : chr \"a2aba0dafad8436788401bbc8c22fe36\"\n",
      " $ WebServiceId         : chr \"126375f0e94911e58e50518e3763ef03\"\n",
      " $ HelpLocation         : chr \"https://studio.azureml-int.net/apihelp/workspaces/a2aba0dafad8436788401bbc8c22fe36/webservices/126375f0e94911e58e50518e3763ef03\"| __truncated__\n",
      " $ PrimaryKey           : chr \"rhEGPgds8G+Hptp8OC0sYFH7CqyyQx8j3nG1Tj6kocWKBaoa52rr4zJ+zAwj7DeCwMiAiLydGZXfPlcIF4uv2A==\"\n",
      " $ SecondaryKey         : chr \"lth3osRs3BjpoWrEn7urQ5F0Lxd4qnOl8TdUHw58PuBWImpC+DiZm1Ua9LoFS+u9ZsoyVyefM6dlO/BFcRMTBQ==\"\n",
      " $ ApiLocation          : chr \"https://ussouthcentral.services.azureml-int.net/workspaces/a2aba0dafad8436788401bbc8c22fe36/services/a150c8f086974477a6d9d63e12\"| __truncated__\n",
      " $ PreventUpdate        : logi FALSE\n",
      " $ GlobalParameters     :List of 1\n",
      "  ..$ : list()\n",
      " $ MaxConcurrentCalls   : int 4\n",
      " $ DiagnosticsTraceLevel: chr \"None\"\n",
      " $ ThrottleLevel        : chr \"Low\"\n"
     ]
    }
   ],
   "source": [
    "# load the package\n",
    "library(MASS)\n",
    "\n",
    "# fit a model using all variables except medv as predictors\n",
    "lm1 <- lm(medv ~ ., data = Boston)\n",
    "\n",
    "# define predict function\n",
    "mypredict <- function(newdata){\n",
    "  predict(lm1, newdata)\n",
    "}\n",
    "\n",
    "# test the prediction function\n",
    "newdata <- Boston[1:10, ]\n",
    "\n",
    "# Publish the service\n",
    "ep <- publishWebService(ws = ws, \n",
    "                        fun = mypredict, \n",
    "                        name = \"HousePricePrediction\", \n",
    "                        inputSchema = newdata)\n",
    "str(ep)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Consuming the web service\n",
    "\n",
    "Now you are ready to consume the web service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Request failed with status 401. Waiting 1.7 seconds before retry\n",
      ".."
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>ans</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>30.00384</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>25.02556</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>30.5676</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>28.60704</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>27.94352</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>25.25628</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>23.00181</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>19.53599</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>11.52364</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>18.92026</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "  & ans\\\\\n",
       "\\hline\n",
       "\t1 & 30.00384\\\\\n",
       "\t2 & 25.02556\\\\\n",
       "\t3 & 30.5676\\\\\n",
       "\t4 & 28.60704\\\\\n",
       "\t5 & 27.94352\\\\\n",
       "\t6 & 25.25628\\\\\n",
       "\t7 & 23.00181\\\\\n",
       "\t8 & 19.53599\\\\\n",
       "\t9 & 11.52364\\\\\n",
       "\t10 & 18.92026\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "        ans\n",
       "1  30.00384\n",
       "2  25.02556\n",
       "3  30.56760\n",
       "4  28.60704\n",
       "5  27.94352\n",
       "6  25.25628\n",
       "7  23.00181\n",
       "8  19.53599\n",
       "9  11.52364\n",
       "10 18.92026"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# consume\n",
    "consume(ep, newdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional resources\n",
    "\n",
    "The `AzureML` package also has a vignette [Getting Started with the AzureML Package](https://htmlpreview.github.io/?https://github.com/RevolutionAnalytics/AzureML/blob/master/vignettes/getting_started.html) that covers a wider range of examples.\n",
    "\n",
    "You can also take a look at the help for `?publishWebservice`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---  \n",
    "Created by a Microsoft Employee.  \n",
    "Copyright © Microsoft. All Rights Reserved."
   ]
  }
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