OpenML-R-Short .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Open Machine Learning in R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Setup\n",
    "* Register on OpenML.org to get your API key (on profile page)\n",
    "* Install OpenML and set *your* key (only once)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: mlr\n",
      "Loading required package: BBmisc\n",
      "Loading required package: ggplot2\n",
      "Loading required package: ParamHelpers\n"
     ]
    }
   ],
   "source": [
    "devtools::install_github(\"openml/r\")\n",
    "library(\"OpenML\")\n",
    "setOMLConfig(apikey = hdigafkj8h2rn2iz27hn27f8uh5dn82zfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Exploring datasets and tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### List known datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading from 'http://www.openml.org/api/v1/data/list/' to '<mem>'.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>did</th><th scope=col>name</th><th scope=col>NumberOfInstances</th><th scope=col>NumberOfFeatures</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td> 1                 </td><td>anneal             </td><td>  898              </td><td> 39                </td></tr>\n",
       "\t<tr><th scope=row>2</th><td> 2                 </td><td>anneal             </td><td>  898              </td><td> 39                </td></tr>\n",
       "\t<tr><th scope=row>3</th><td> 3                 </td><td>kr-vs-kp           </td><td> 3196              </td><td> 37                </td></tr>\n",
       "\t<tr><th scope=row>4</th><td> 4                 </td><td>labor              </td><td>   57              </td><td> 17                </td></tr>\n",
       "\t<tr><th scope=row>5</th><td> 5                 </td><td>arrhythmia         </td><td>  452              </td><td>280                </td></tr>\n",
       "\t<tr><th scope=row>6</th><td> 6                 </td><td>letter             </td><td>20000              </td><td> 17                </td></tr>\n",
       "\t<tr><th scope=row>7</th><td> 7                 </td><td>audiology          </td><td>  226              </td><td> 70                </td></tr>\n",
       "\t<tr><th scope=row>8</th><td> 8                 </td><td>liver-disorders    </td><td>  345              </td><td>  7                </td></tr>\n",
       "\t<tr><th scope=row>9</th><td> 9                 </td><td>autos              </td><td>  205              </td><td> 26                </td></tr>\n",
       "\t<tr><th scope=row>10</th><td>10                 </td><td>lymph              </td><td>  148              </td><td> 19                </td></tr>\n",
       "\t<tr><th scope=row>11</th><td>11                 </td><td>balance-scale      </td><td>  625              </td><td>  5                </td></tr>\n",
       "\t<tr><th scope=row>12</th><td>12                 </td><td>mfeat-factors      </td><td> 2000              </td><td>217                </td></tr>\n",
       "\t<tr><th scope=row>13</th><td>13                 </td><td>breast-cancer      </td><td>  286              </td><td> 10                </td></tr>\n",
       "\t<tr><th scope=row>14</th><td>14                 </td><td>mfeat-fourier      </td><td> 2000              </td><td> 77                </td></tr>\n",
       "\t<tr><th scope=row>15</th><td>15                 </td><td>breast-w           </td><td>  699              </td><td> 10                </td></tr>\n",
       "\t<tr><th scope=row>16</th><td>16                 </td><td>mfeat-karhunen     </td><td> 2000              </td><td> 65                </td></tr>\n",
       "\t<tr><th scope=row>17</th><td>18                 </td><td>mfeat-morphological</td><td> 2000              </td><td>  7                </td></tr>\n",
       "\t<tr><th scope=row>18</th><td>20                 </td><td>mfeat-pixel        </td><td> 2000              </td><td>241                </td></tr>\n",
       "\t<tr><th scope=row>19</th><td>21                 </td><td>car                </td><td> 1728              </td><td>  7                </td></tr>\n",
       "\t<tr><th scope=row>20</th><td>22                 </td><td>mfeat-zernike      </td><td> 2000              </td><td> 48                </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & did & name & NumberOfInstances & NumberOfFeatures\\\\\n",
       "\\hline\n",
       "\t1 &  1                  & anneal              &   898               &  39                \\\\\n",
       "\t2 &  2                  & anneal              &   898               &  39                \\\\\n",
       "\t3 &  3                  & kr-vs-kp            &  3196               &  37                \\\\\n",
       "\t4 &  4                  & labor               &    57               &  17                \\\\\n",
       "\t5 &  5                  & arrhythmia          &   452               & 280                \\\\\n",
       "\t6 &  6                  & letter              & 20000               &  17                \\\\\n",
       "\t7 &  7                  & audiology           &   226               &  70                \\\\\n",
       "\t8 &  8                  & liver-disorders     &   345               &   7                \\\\\n",
       "\t9 &  9                  & autos               &   205               &  26                \\\\\n",
       "\t10 & 10                  & lymph               &   148               &  19                \\\\\n",
       "\t11 & 11                  & balance-scale       &   625               &   5                \\\\\n",
       "\t12 & 12                  & mfeat-factors       &  2000               & 217                \\\\\n",
       "\t13 & 13                  & breast-cancer       &   286               &  10                \\\\\n",
       "\t14 & 14                  & mfeat-fourier       &  2000               &  77                \\\\\n",
       "\t15 & 15                  & breast-w            &   699               &  10                \\\\\n",
       "\t16 & 16                  & mfeat-karhunen      &  2000               &  65                \\\\\n",
       "\t17 & 18                  & mfeat-morphological &  2000               &   7                \\\\\n",
       "\t18 & 20                  & mfeat-pixel         &  2000               & 241                \\\\\n",
       "\t19 & 21                  & car                 &  1728               &   7                \\\\\n",
       "\t20 & 22                  & mfeat-zernike       &  2000               &  48                \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "   did name                NumberOfInstances NumberOfFeatures\n",
       "1   1  anneal                898              39             \n",
       "2   2  anneal                898              39             \n",
       "3   3  kr-vs-kp             3196              37             \n",
       "4   4  labor                  57              17             \n",
       "5   5  arrhythmia            452             280             \n",
       "6   6  letter              20000              17             \n",
       "7   7  audiology             226              70             \n",
       "8   8  liver-disorders       345               7             \n",
       "9   9  autos                 205              26             \n",
       "10 10  lymph                 148              19             \n",
       "11 11  balance-scale         625               5             \n",
       "12 12  mfeat-factors        2000             217             \n",
       "13 13  breast-cancer         286              10             \n",
       "14 14  mfeat-fourier        2000              77             \n",
       "15 15  breast-w              699              10             \n",
       "16 16  mfeat-karhunen       2000              65             \n",
       "17 18  mfeat-morphological  2000               7             \n",
       "18 20  mfeat-pixel          2000             241             \n",
       "19 21  car                  1728               7             \n",
       "20 22  mfeat-zernike        2000              48             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasets = listOMLDataSets()  # fetches all data sets\n",
    "datasets[1:20, c(\"did\", \"name\", \"NumberOfInstances\", \"NumberOfFeatures\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Search for specific datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>did</th><th scope=col>status</th><th scope=col>format</th><th scope=col>name</th><th scope=col>MajorityClassSize</th><th scope=col>MaxNominalAttDistinctValues</th><th scope=col>MinorityClassSize</th><th scope=col>NumberOfClasses</th><th scope=col>NumberOfFeatures</th><th scope=col>NumberOfInstances</th><th scope=col>NumberOfInstancesWithMissingValues</th><th scope=col>NumberOfMissingValues</th><th scope=col>NumberOfNumericFeatures</th><th scope=col>NumberOfSymbolicFeatures</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1177</th><td>1471         </td><td>active       </td><td>ARFF         </td><td>eeg-eye-state</td><td>8257         </td><td>-1           </td><td>6723         </td><td>2            </td><td>15           </td><td>14980        </td><td>0            </td><td>0            </td><td>14           </td><td>1            </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       "  & did & status & format & name & MajorityClassSize & MaxNominalAttDistinctValues & MinorityClassSize & NumberOfClasses & NumberOfFeatures & NumberOfInstances & NumberOfInstancesWithMissingValues & NumberOfMissingValues & NumberOfNumericFeatures & NumberOfSymbolicFeatures\\\\\n",
       "\\hline\n",
       "\t1177 & 1471          & active        & ARFF          & eeg-eye-state & 8257          & -1            & 6723          & 2             & 15            & 14980         & 0             & 0             & 14            & 1            \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "     did  status format name          MajorityClassSize\n",
       "1177 1471 active ARFF   eeg-eye-state 8257             \n",
       "     MaxNominalAttDistinctValues MinorityClassSize NumberOfClasses\n",
       "1177 -1                          6723              2              \n",
       "     NumberOfFeatures NumberOfInstances NumberOfInstancesWithMissingValues\n",
       "1177 15               14980             0                                 \n",
       "     NumberOfMissingValues NumberOfNumericFeatures NumberOfSymbolicFeatures\n",
       "1177 0                     14                      1                       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Search by name\n",
    "subset(datasets, name == \"eeg-eye-state\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>did</th><th scope=col>status</th><th scope=col>format</th><th scope=col>name</th><th scope=col>MajorityClassSize</th><th scope=col>MaxNominalAttDistinctValues</th><th scope=col>MinorityClassSize</th><th scope=col>NumberOfClasses</th><th scope=col>NumberOfFeatures</th><th scope=col>NumberOfInstances</th><th scope=col>NumberOfInstancesWithMissingValues</th><th scope=col>NumberOfMissingValues</th><th scope=col>NumberOfNumericFeatures</th><th scope=col>NumberOfSymbolicFeatures</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1         </td><td>active    </td><td>ARFF      </td><td>anneal    </td><td> 684      </td><td>10        </td><td>   0      </td><td> 6        </td><td> 39       </td><td>  898     </td><td>  0       </td><td>    0     </td><td>  6       </td><td>33        </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>2         </td><td>active    </td><td>ARFF      </td><td>anneal    </td><td> 684      </td><td> 9        </td><td>   0      </td><td> 6        </td><td> 39       </td><td>  898     </td><td>898       </td><td>22175     </td><td>  6       </td><td>33        </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>3         </td><td>active    </td><td>ARFF      </td><td>kr-vs-kp  </td><td>1669      </td><td> 3        </td><td>1527      </td><td> 2        </td><td> 37       </td><td> 3196     </td><td>  0       </td><td>    0     </td><td>  0       </td><td>37        </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4         </td><td>active    </td><td>ARFF      </td><td>labor     </td><td>  37      </td><td> 3        </td><td>  20      </td><td> 2        </td><td> 17       </td><td>   57     </td><td> 56       </td><td>  326     </td><td>  8       </td><td> 9        </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>5         </td><td>active    </td><td>ARFF      </td><td>arrhythmia</td><td> 245      </td><td> 2        </td><td>   0      </td><td>16        </td><td>280       </td><td>  452     </td><td>384       </td><td>  408     </td><td>206       </td><td>74        </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>6         </td><td>active    </td><td>ARFF      </td><td>letter    </td><td> 813      </td><td>-1        </td><td> 734      </td><td>26        </td><td> 17       </td><td>20000     </td><td>  0       </td><td>    0     </td><td> 16       </td><td> 1        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       "  & did & status & format & name & MajorityClassSize & MaxNominalAttDistinctValues & MinorityClassSize & NumberOfClasses & NumberOfFeatures & NumberOfInstances & NumberOfInstancesWithMissingValues & NumberOfMissingValues & NumberOfNumericFeatures & NumberOfSymbolicFeatures\\\\\n",
       "\\hline\n",
       "\t1 & 1          & active     & ARFF       & anneal     &  684       & 10         &    0       &  6         &  39        &   898      &   0        &     0      &   6        & 33        \\\\\n",
       "\t2 & 2          & active     & ARFF       & anneal     &  684       &  9         &    0       &  6         &  39        &   898      & 898        & 22175      &   6        & 33        \\\\\n",
       "\t3 & 3          & active     & ARFF       & kr-vs-kp   & 1669       &  3         & 1527       &  2         &  37        &  3196      &   0        &     0      &   0        & 37        \\\\\n",
       "\t4 & 4          & active     & ARFF       & labor      &   37       &  3         &   20       &  2         &  17        &    57      &  56        &   326      &   8        &  9        \\\\\n",
       "\t5 & 5          & active     & ARFF       & arrhythmia &  245       &  2         &    0       & 16         & 280        &   452      & 384        &   408      & 206        & 74        \\\\\n",
       "\t6 & 6          & active     & ARFF       & letter     &  813       & -1         &  734       & 26         &  17        & 20000      &   0        &     0      &  16        &  1        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  did status format name       MajorityClassSize MaxNominalAttDistinctValues\n",
       "1 1   active ARFF   anneal      684              10                         \n",
       "2 2   active ARFF   anneal      684               9                         \n",
       "3 3   active ARFF   kr-vs-kp   1669               3                         \n",
       "4 4   active ARFF   labor        37               3                         \n",
       "5 5   active ARFF   arrhythmia  245               2                         \n",
       "6 6   active ARFF   letter      813              -1                         \n",
       "  MinorityClassSize NumberOfClasses NumberOfFeatures NumberOfInstances\n",
       "1    0               6               39                898            \n",
       "2    0               6               39                898            \n",
       "3 1527               2               37               3196            \n",
       "4   20               2               17                 57            \n",
       "5    0              16              280                452            \n",
       "6  734              26               17              20000            \n",
       "  NumberOfInstancesWithMissingValues NumberOfMissingValues\n",
       "1   0                                    0                \n",
       "2 898                                22175                \n",
       "3   0                                    0                \n",
       "4  56                                  326                \n",
       "5 384                                  408                \n",
       "6   0                                    0                \n",
       "  NumberOfNumericFeatures NumberOfSymbolicFeatures\n",
       "1   6                     33                      \n",
       "2   6                     33                      \n",
       "3   0                     37                      \n",
       "4   8                      9                      \n",
       "5 206                     74                      \n",
       "6  16                      1                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Search by property\n",
    "head(subset(datasets, NumberOfInstances > NumberOfFeatures))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Download datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Data Set \"eeg-eye-state\" :: (Version = 1, OpenML ID = 1471)\n",
       "  Default Target Attribute: Class"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>V1</th><th scope=col>V2</th><th scope=col>V3</th><th scope=col>V4</th><th scope=col>V5</th><th scope=col>V6</th><th scope=col>V7</th><th scope=col>V8</th><th scope=col>V9</th><th scope=col>V10</th><th scope=col>V11</th><th scope=col>V12</th><th scope=col>V13</th><th scope=col>V14</th><th scope=col>Class</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>0</th><td>4329.23</td><td>4009.23</td><td>4289.23</td><td>4148.21</td><td>4350.26</td><td>4586.15</td><td>4096.92</td><td>4641.03</td><td>4222.05</td><td>4238.46</td><td>4211.28</td><td>4280.51</td><td>4635.90</td><td>4393.85</td><td>1      </td></tr>\n",
       "\t<tr><th scope=row>1</th><td>4324.62</td><td>4004.62</td><td>4293.85</td><td>4148.72</td><td>4342.05</td><td>4586.67</td><td>4097.44</td><td>4638.97</td><td>4210.77</td><td>4226.67</td><td>4207.69</td><td>4279.49</td><td>4632.82</td><td>4384.10</td><td>1      </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>4327.69</td><td>4006.67</td><td>4295.38</td><td>4156.41</td><td>4336.92</td><td>4583.59</td><td>4096.92</td><td>4630.26</td><td>4207.69</td><td>4222.05</td><td>4206.67</td><td>4282.05</td><td>4628.72</td><td>4389.23</td><td>1      </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>4328.72</td><td>4011.79</td><td>4296.41</td><td>4155.90</td><td>4343.59</td><td>4582.56</td><td>4097.44</td><td>4630.77</td><td>4217.44</td><td>4235.38</td><td>4210.77</td><td>4287.69</td><td>4632.31</td><td>4396.41</td><td>1      </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4326.15</td><td>4011.79</td><td>4292.31</td><td>4151.28</td><td>4347.69</td><td>4586.67</td><td>4095.90</td><td>4627.69</td><td>4210.77</td><td>4244.10</td><td>4212.82</td><td>4288.21</td><td>4632.82</td><td>4398.46</td><td>1      </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>4321.03</td><td>4004.62</td><td>4284.10</td><td>4153.33</td><td>4345.64</td><td>4587.18</td><td>4093.33</td><td>4616.92</td><td>4202.56</td><td>4232.82</td><td>4209.74</td><td>4281.03</td><td>4628.21</td><td>4389.74</td><td>1      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllll}\n",
       "  & V1 & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10 & V11 & V12 & V13 & V14 & Class\\\\\n",
       "\\hline\n",
       "\t0 & 4329.23 & 4009.23 & 4289.23 & 4148.21 & 4350.26 & 4586.15 & 4096.92 & 4641.03 & 4222.05 & 4238.46 & 4211.28 & 4280.51 & 4635.90 & 4393.85 & 1      \\\\\n",
       "\t1 & 4324.62 & 4004.62 & 4293.85 & 4148.72 & 4342.05 & 4586.67 & 4097.44 & 4638.97 & 4210.77 & 4226.67 & 4207.69 & 4279.49 & 4632.82 & 4384.10 & 1      \\\\\n",
       "\t2 & 4327.69 & 4006.67 & 4295.38 & 4156.41 & 4336.92 & 4583.59 & 4096.92 & 4630.26 & 4207.69 & 4222.05 & 4206.67 & 4282.05 & 4628.72 & 4389.23 & 1      \\\\\n",
       "\t3 & 4328.72 & 4011.79 & 4296.41 & 4155.90 & 4343.59 & 4582.56 & 4097.44 & 4630.77 & 4217.44 & 4235.38 & 4210.77 & 4287.69 & 4632.31 & 4396.41 & 1      \\\\\n",
       "\t4 & 4326.15 & 4011.79 & 4292.31 & 4151.28 & 4347.69 & 4586.67 & 4095.90 & 4627.69 & 4210.77 & 4244.10 & 4212.82 & 4288.21 & 4632.82 & 4398.46 & 1      \\\\\n",
       "\t5 & 4321.03 & 4004.62 & 4284.10 & 4153.33 & 4345.64 & 4587.18 & 4093.33 & 4616.92 & 4202.56 & 4232.82 & 4209.74 & 4281.03 & 4628.21 & 4389.74 & 1      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  V1      V2      V3      V4      V5      V6      V7      V8      V9     \n",
       "0 4329.23 4009.23 4289.23 4148.21 4350.26 4586.15 4096.92 4641.03 4222.05\n",
       "1 4324.62 4004.62 4293.85 4148.72 4342.05 4586.67 4097.44 4638.97 4210.77\n",
       "2 4327.69 4006.67 4295.38 4156.41 4336.92 4583.59 4096.92 4630.26 4207.69\n",
       "3 4328.72 4011.79 4296.41 4155.90 4343.59 4582.56 4097.44 4630.77 4217.44\n",
       "4 4326.15 4011.79 4292.31 4151.28 4347.69 4586.67 4095.90 4627.69 4210.77\n",
       "5 4321.03 4004.62 4284.10 4153.33 4345.64 4587.18 4093.33 4616.92 4202.56\n",
       "  V10     V11     V12     V13     V14     Class\n",
       "0 4238.46 4211.28 4280.51 4635.90 4393.85 1    \n",
       "1 4226.67 4207.69 4279.49 4632.82 4384.10 1    \n",
       "2 4222.05 4206.67 4282.05 4628.72 4389.23 1    \n",
       "3 4235.38 4210.77 4287.69 4632.31 4396.41 1    \n",
       "4 4244.10 4212.82 4288.21 4632.82 4398.46 1    \n",
       "5 4232.82 4209.74 4281.03 4628.21 4389.74 1    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eeg.data = getOMLDataSet(did = 1471L)\n",
    "eeg.data\n",
    "head(eeg.data$data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### List known OpenML tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading from 'http://www.openml.org/api/v1/task/list/' to '<mem>'.\n"
     ]
    }
   ],
   "source": [
    "tasks = listOMLTasks() # Fetches all tasks\n",
    "head(tasks[, 1:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Search for specific tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in subset(tasks, name == \"eeg-eye-state\"): object 'tasks' not found\n",
     "output_type": "error",
     "traceback": [
      "Error in subset(tasks, name == \"eeg-eye-state\"): object 'tasks' not found\nTraceback:\n",
      "1. subset(tasks, name == \"eeg-eye-state\")"
     ]
    }
   ],
   "source": [
    "subset(tasks, name == \"eeg-eye-state\")[2:3,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Download OpenML tasks\n",
    "* Includes dataset(s), target feature(s)\n",
    "* Includes train/test splits (for clear comparison)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Task '14951' file 'task.xml' found in cache.\n",
      "Task '14951' file 'datasplits.arff' found in cache.\n",
      "Data '1471' file 'description.xml' found in cache.\n",
      "Data '1471' file 'dataset.arff' found in cache.\n",
      "Loading required package: readr\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "OpenML Task 14951 :: (Data ID = 1471)\n",
       "  Task Type            : Supervised Classification\n",
       "  Data Set             : eeg-eye-state :: (Version = 1, OpenML ID = 1471)\n",
       "  Target Feature(s)    : Class\n",
       "  Estimation Procedure : Stratified crossvalidation (1 x 10 folds)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eeg.task = getOMLTask(task.id = 14951)  \n",
    "eeg.task "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Extract dataset from the task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>V1</th><th scope=col>V2</th><th scope=col>V3</th><th scope=col>V4</th><th scope=col>V5</th><th scope=col>V6</th><th scope=col>V7</th><th scope=col>V8</th><th scope=col>V9</th><th scope=col>V10</th><th scope=col>V11</th><th scope=col>V12</th><th scope=col>V13</th><th scope=col>V14</th><th scope=col>Class</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>0</th><td>4329.23</td><td>4009.23</td><td>4289.23</td><td>4148.21</td><td>4350.26</td><td>4586.15</td><td>4096.92</td><td>4641.03</td><td>4222.05</td><td>4238.46</td><td>4211.28</td><td>4280.51</td><td>4635.90</td><td>4393.85</td><td>1      </td></tr>\n",
       "\t<tr><th scope=row>1</th><td>4324.62</td><td>4004.62</td><td>4293.85</td><td>4148.72</td><td>4342.05</td><td>4586.67</td><td>4097.44</td><td>4638.97</td><td>4210.77</td><td>4226.67</td><td>4207.69</td><td>4279.49</td><td>4632.82</td><td>4384.10</td><td>1      </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>4327.69</td><td>4006.67</td><td>4295.38</td><td>4156.41</td><td>4336.92</td><td>4583.59</td><td>4096.92</td><td>4630.26</td><td>4207.69</td><td>4222.05</td><td>4206.67</td><td>4282.05</td><td>4628.72</td><td>4389.23</td><td>1      </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>4328.72</td><td>4011.79</td><td>4296.41</td><td>4155.90</td><td>4343.59</td><td>4582.56</td><td>4097.44</td><td>4630.77</td><td>4217.44</td><td>4235.38</td><td>4210.77</td><td>4287.69</td><td>4632.31</td><td>4396.41</td><td>1      </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4326.15</td><td>4011.79</td><td>4292.31</td><td>4151.28</td><td>4347.69</td><td>4586.67</td><td>4095.90</td><td>4627.69</td><td>4210.77</td><td>4244.10</td><td>4212.82</td><td>4288.21</td><td>4632.82</td><td>4398.46</td><td>1      </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>4321.03</td><td>4004.62</td><td>4284.10</td><td>4153.33</td><td>4345.64</td><td>4587.18</td><td>4093.33</td><td>4616.92</td><td>4202.56</td><td>4232.82</td><td>4209.74</td><td>4281.03</td><td>4628.21</td><td>4389.74</td><td>1      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllll}\n",
       "  & V1 & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10 & V11 & V12 & V13 & V14 & Class\\\\\n",
       "\\hline\n",
       "\t0 & 4329.23 & 4009.23 & 4289.23 & 4148.21 & 4350.26 & 4586.15 & 4096.92 & 4641.03 & 4222.05 & 4238.46 & 4211.28 & 4280.51 & 4635.90 & 4393.85 & 1      \\\\\n",
       "\t1 & 4324.62 & 4004.62 & 4293.85 & 4148.72 & 4342.05 & 4586.67 & 4097.44 & 4638.97 & 4210.77 & 4226.67 & 4207.69 & 4279.49 & 4632.82 & 4384.10 & 1      \\\\\n",
       "\t2 & 4327.69 & 4006.67 & 4295.38 & 4156.41 & 4336.92 & 4583.59 & 4096.92 & 4630.26 & 4207.69 & 4222.05 & 4206.67 & 4282.05 & 4628.72 & 4389.23 & 1      \\\\\n",
       "\t3 & 4328.72 & 4011.79 & 4296.41 & 4155.90 & 4343.59 & 4582.56 & 4097.44 & 4630.77 & 4217.44 & 4235.38 & 4210.77 & 4287.69 & 4632.31 & 4396.41 & 1      \\\\\n",
       "\t4 & 4326.15 & 4011.79 & 4292.31 & 4151.28 & 4347.69 & 4586.67 & 4095.90 & 4627.69 & 4210.77 & 4244.10 & 4212.82 & 4288.21 & 4632.82 & 4398.46 & 1      \\\\\n",
       "\t5 & 4321.03 & 4004.62 & 4284.10 & 4153.33 & 4345.64 & 4587.18 & 4093.33 & 4616.92 & 4202.56 & 4232.82 & 4209.74 & 4281.03 & 4628.21 & 4389.74 & 1      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  V1      V2      V3      V4      V5      V6      V7      V8      V9     \n",
       "0 4329.23 4009.23 4289.23 4148.21 4350.26 4586.15 4096.92 4641.03 4222.05\n",
       "1 4324.62 4004.62 4293.85 4148.72 4342.05 4586.67 4097.44 4638.97 4210.77\n",
       "2 4327.69 4006.67 4295.38 4156.41 4336.92 4583.59 4096.92 4630.26 4207.69\n",
       "3 4328.72 4011.79 4296.41 4155.90 4343.59 4582.56 4097.44 4630.77 4217.44\n",
       "4 4326.15 4011.79 4292.31 4151.28 4347.69 4586.67 4095.90 4627.69 4210.77\n",
       "5 4321.03 4004.62 4284.10 4153.33 4345.64 4587.18 4093.33 4616.92 4202.56\n",
       "  V10     V11     V12     V13     V14     Class\n",
       "0 4238.46 4211.28 4280.51 4635.90 4393.85 1    \n",
       "1 4226.67 4207.69 4279.49 4632.82 4384.10 1    \n",
       "2 4222.05 4206.67 4282.05 4628.72 4389.23 1    \n",
       "3 4235.38 4210.77 4287.69 4632.31 4396.41 1    \n",
       "4 4244.10 4212.82 4288.21 4632.82 4398.46 1    \n",
       "5 4232.82 4209.74 4281.03 4628.21 4389.74 1    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata = eeg.task$input$data.set$data\n",
    "head(mydata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Running machine learning algorithms (with MLR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Task: eeg-eye-state.task.14951, Learner: classif.rpart\n",
      "[Resample] cross-validation iter: 1\n",
      "[Resample] cross-validation iter: 2\n",
      "[Resample] cross-validation iter: 3\n",
      "[Resample] cross-validation iter: 4\n",
      "[Resample] cross-validation iter: 5\n",
      "[Resample] cross-validation iter: 6\n",
      "[Resample] cross-validation iter: 7\n",
      "[Resample] cross-validation iter: 8\n",
      "[Resample] cross-validation iter: 9\n",
      "[Resample] cross-validation iter: 10\n",
      "[Resample] Result: acc.test.mean=0.707,timetrain.test.sum=2.21,timepredict.test.sum=0.031\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "$run\n",
       "\n",
       "OpenML Run NA :: (Task ID = 14951, Flow ID = NA)\n",
       "\n",
       "$bmr\n",
       "                   task.id    learner.id acc.test.mean timetrain.test.sum\n",
       "1 eeg-eye-state.task.14951 classif.rpart     0.7072096              2.207\n",
       "  timepredict.test.sum\n",
       "1                0.031\n",
       "\n",
       "$flow\n",
       "\n",
       "Flow \"mlr.classif.rpart\" :: (Version = NA, Flow ID = NA)\n",
       "\tExternal Version         : R_3.3.1-v2.e6a066f2\n",
       "\tDependencies             : R_3.3.1, OpenML_1.0, mlr_2.8, rpart_4.1.10\n",
       "\tNumber of Flow Parameters: 13\n",
       "\tNumber of Flow Components: 0\n",
       "\n",
       "attr(,\"class\")\n",
       "[1] \"OMLMlrRun\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(mlr)\n",
    "lrn = makeLearner(\"classif.rpart\")\n",
    "run.mlr = runTaskMlr(eeg.task, lrn)\n",
    "run.mlr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Upload the run to OpenML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OpenML configuration:\n",
       "  server           : http://www.openml.org/api/v1\n",
       "  cachedir         : /Users/joa/.openml/cache\n",
       "  verbosity        : 0\n",
       "  arff.reader      : farff\n",
       "  confirm.upload   : FALSE\n",
       "  apikey           : ***************************0acb1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setOMLConfig(verbosity = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Flow already exists (Flow ID = 4148).\n",
      "Uploading to 'http://www.openml.org/api/v1/run'.\n",
      "Run successfully uploaded. Run ID: 1798178\n"
     ]
    }
   ],
   "source": [
    "run.id = uploadOMLRun(run.mlr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Easy benchmarking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Task '10' file 'task.xml' found in cache.\n",
      "Task '10' file 'datasplits.arff' found in cache.\n",
      "Downloading from 'http://www.openml.org/api/v1/data/10' to '/Users/joa/.openml/cache/datasets/10/description.xml'.\n",
      "Downloading from 'http://www.openml.org/data/download/10/dataset_10_lymph.arff' to '/Users/joa/.openml/cache/datasets/10/dataset.arff'\n",
      "Task: lymph.task.10, Learner: classif.rpart\n",
      "[Resample] cross-validation iter: 1\n",
      "[Resample] cross-validation iter: 2\n",
      "[Resample] cross-validation iter: 3\n",
      "[Resample] cross-validation iter: 4\n",
      "[Resample] cross-validation iter: 5\n",
      "[Resample] cross-validation iter: 6\n",
      "[Resample] cross-validation iter: 7\n",
      "[Resample] cross-validation iter: 8\n",
      "[Resample] cross-validation iter: 9\n",
      "[Resample] cross-validation iter: 10\n",
      "[Resample] Result: acc.test.mean=0.79,timetrain.test.sum=0.147,timepredict.test.sum=0.099\n",
      "Flow already exists (Flow ID = 4148).\n",
      "Uploading to 'http://www.openml.org/api/v1/run'.\n",
      "Run successfully uploaded. Run ID: 1810069\n",
      "Task '2382' file 'task.xml' found in cache.\n",
      "Task '2382' file 'datasplits.arff' found in cache.\n",
      "Downloading from 'http://www.openml.org/api/v1/data/187' to '/Users/joa/.openml/cache/datasets/187/description.xml'.\n",
      "Downloading from 'http://www.openml.org/data/download/3624/dataset_191_wine.arff' to '/Users/joa/.openml/cache/datasets/187/dataset.arff'\n",
      "Task: wine.task.2382, Learner: classif.rpart\n",
      "[Resample] cross-validation iter: 1\n",
      "[Resample] cross-validation iter: 2\n",
      "[Resample] cross-validation iter: 3\n",
      "[Resample] cross-validation iter: 4\n",
      "[Resample] cross-validation iter: 5\n",
      "[Resample] cross-validation iter: 6\n",
      "[Resample] cross-validation iter: 7\n",
      "[Resample] cross-validation iter: 8\n",
      "[Resample] cross-validation iter: 9\n",
      "[Resample] cross-validation iter: 10\n",
      "[Resample] Result: acc.test.mean=0.871,timetrain.test.sum=0.092,timepredict.test.sum=0.035\n",
      "Flow already exists (Flow ID = 4148).\n",
      "Uploading to 'http://www.openml.org/api/v1/run'.\n",
      "Run successfully uploaded. Run ID: 1810070\n",
      "Task '10' file 'task.xml' found in cache.\n",
      "Task '10' file 'datasplits.arff' found in cache.\n",
      "Data '10' file 'description.xml' found in cache.\n",
      "Data '10' file 'dataset.arff' found in cache.\n",
      "Task: lymph.task.10, Learner: classif.randomForest\n",
      "[Resample] cross-validation iter: 1\n",
      "[Resample] cross-validation iter: 2\n",
      "[Resample] cross-validation iter: 3\n",
      "[Resample] cross-validation iter: 4\n",
      "[Resample] cross-validation iter: 5\n",
      "[Resample] cross-validation iter: 6\n",
      "[Resample] cross-validation iter: 7\n",
      "[Resample] cross-validation iter: 8\n",
      "[Resample] cross-validation iter: 9\n",
      "[Resample] cross-validation iter: 10\n",
      "[Resample] Result: acc.test.mean=0.85,timetrain.test.sum=2.07,timepredict.test.sum=0.071\n",
      "Flow already exists (Flow ID = 4149).\n",
      "Uploading to 'http://www.openml.org/api/v1/run'.\n",
      "Run successfully uploaded. Run ID: 1810071\n",
      "Task '2382' file 'task.xml' found in cache.\n",
      "Task '2382' file 'datasplits.arff' found in cache.\n",
      "Data '187' file 'description.xml' found in cache.\n",
      "Data '187' file 'dataset.arff' found in cache.\n",
      "Task: wine.task.2382, Learner: classif.randomForest\n",
      "[Resample] cross-validation iter: 1\n",
      "[Resample] cross-validation iter: 2\n",
      "[Resample] cross-validation iter: 3\n",
      "[Resample] cross-validation iter: 4\n",
      "[Resample] cross-validation iter: 5\n",
      "[Resample] cross-validation iter: 6\n",
      "[Resample] cross-validation iter: 7\n",
      "[Resample] cross-validation iter: 8\n",
      "[Resample] cross-validation iter: 9\n",
      "[Resample] cross-validation iter: 10\n",
      "[Resample] Result: acc.test.mean=0.983,timetrain.test.sum=0.855,timepredict.test.sum=0.045\n",
      "Flow already exists (Flow ID = 4149).\n",
      "Uploading to 'http://www.openml.org/api/v1/run'.\n",
      "Run successfully uploaded. Run ID: 1810072\n"
     ]
    }
   ],
   "source": [
    "task.ids = c(10,2382)  # List of OpenML task ID's\n",
    "learners = list(\n",
    "   makeLearner(\"classif.rpart\"),\n",
    "   makeLearner(\"classif.randomForest\")\n",
    ")\n",
    "for (lrn in learners) {\n",
    "    for (id in task.ids) {\n",
    "      task = getOMLTask(id)\n",
    "      res = runTaskMlr(task, lrn)\n",
    "      run.id = uploadOMLRun(res)\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Downloading and comparing evaluations\n",
    "* Download everyone's results (evaluated by the OpenML server)\n",
    "* Filter by task, flow, user, or study ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading from 'http://www.openml.org/api/v1/evaluation/list/task/14951' to '<mem>'.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>run.id</th><th scope=col>task.id</th><th scope=col>setup.id</th><th scope=col>flow.id</th><th scope=col>area.under.roc.curve</th><th scope=col>area.under.roc.curve.array</th><th scope=col>average.cost</th><th scope=col>confusion.matrix</th><th scope=col>f.measure</th><th scope=col>f.measure.array</th><th scope=col>⋯</th><th scope=col>root.mean.prior.squared.error</th><th scope=col>root.mean.squared.error</th><th scope=col>root.relative.squared.error</th><th scope=col>total.cost</th><th scope=col>usercpu.time.millis</th><th scope=col>usercpu.time.millis.testing</th><th scope=col>usercpu.time.millis.training</th><th scope=col>os.information</th><th scope=col>scimark.benchmark</th><th scope=col>scimark.benchmark.array</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>544246                                                                                            </td><td>14951                                                                                             </td><td>2961                                                                                              </td><td>2390                                                                                              </td><td>0.693537                                                                                          </td><td>[0.693537,0.693537]                                                                               </td><td>0                                                                                                 </td><td>[[6829,1428],[2958,3765]]                                                                         </td><td>0.700826                                                                                          </td><td>[0.756928,0.631923]                                                                               </td><td>⋯                                                                                                 </td><td>0.497371                                                                                          </td><td>0.541101                                                                                          </td><td>1.087921                                                                                          </td><td>0                                                                                                 </td><td>    1.743                                                                                         </td><td>   0.031                                                                                          </td><td>    1.712                                                                                         </td><td>NA                                                                                                </td><td>      NA                                                                                          </td><td>NA                                                                                                </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>544272                                                                                            </td><td>14951                                                                                             </td><td>2963                                                                                              </td><td>2393                                                                                              </td><td>0.934923                                                                                          </td><td>[0.934923,0.934923]                                                                               </td><td>0                                                                                                 </td><td>[[7918,339],[599,6124]]                                                                           </td><td>0.937251                                                                                          </td><td>[0.94408,0.928864]                                                                                </td><td>⋯                                                                                                 </td><td>0.497371                                                                                          </td><td>0.250234                                                                                          </td><td>0.503112                                                                                          </td><td>0                                                                                                 </td><td>  513.130                                                                                         </td><td>   4.780                                                                                          </td><td>  508.350                                                                                         </td><td>NA                                                                                                </td><td>      NA                                                                                          </td><td>NA                                                                                                </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>544274                                                                                            </td><td>14951                                                                                             </td><td>5538                                                                                              </td><td>3401                                                                                              </td><td>0.992349                                                                                          </td><td>[0.992349,0.992349]                                                                               </td><td>0                                                                                                 </td><td>[[8019,238],[389,6334]]                                                                           </td><td>0.958096                                                                                          </td><td>[0.962376,0.952839]                                                                               </td><td>⋯                                                                                                 </td><td>0.497371                                                                                          </td><td>0.179762                                                                                          </td><td>0.361424                                                                                          </td><td>0                                                                                                 </td><td>38875.000                                                                                         </td><td>1671.875                                                                                          </td><td>37203.125                                                                                         </td><td>[Oracle Corporation, 1.8.0_45, amd64, Windows 8.1, 6.3]                                           </td><td>1433.743                                                                                          </td><td>[772.8137632186107, 1186.9140871273323, 653.9231730770091, 1310.065014968779, 3244.99907038474]   </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>544316                                                                                            </td><td>14951                                                                                             </td><td>3526                                                                                              </td><td>2629                                                                                              </td><td>0.965265                                                                                          </td><td>[0.965265,0.965265]                                                                               </td><td>0                                                                                                 </td><td>[[7806,451],[1060,5663]]                                                                          </td><td>0.898533                                                                                          </td><td>[0.911756,0.882293]                                                                               </td><td>⋯                                                                                                 </td><td>0.497371                                                                                          </td><td>0.283324                                                                                          </td><td>0.569642                                                                                          </td><td>0                                                                                                 </td><td>       NA                                                                                         </td><td>      NA                                                                                          </td><td>       NA                                                                                         </td><td>NA                                                                                                </td><td>      NA                                                                                          </td><td>NA                                                                                                </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>544318                                                                                            </td><td>14951                                                                                             </td><td>3526                                                                                              </td><td>2629                                                                                              </td><td>0.963992                                                                                          </td><td>[0.963992,0.963992]                                                                               </td><td>0                                                                                                 </td><td>[[7821,436],[1101,5622]]                                                                          </td><td>0.896713                                                                                          </td><td>[0.91053,0.879743]                                                                                </td><td>⋯                                                                                                 </td><td>0.497371                                                                                          </td><td>0.285358                                                                                          </td><td>0.573732                                                                                          </td><td>0                                                                                                 </td><td>       NA                                                                                         </td><td>      NA                                                                                          </td><td>       NA                                                                                         </td><td>NA                                                                                                </td><td>      NA                                                                                          </td><td>NA                                                                                                </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>544325                                                                                            </td><td>14951                                                                                             </td><td>5539                                                                                              </td><td>2277                                                                                              </td><td>0.979674                                                                                          </td><td>[0.979674,0.979674]                                                                               </td><td>0                                                                                                 </td><td>[[7863,394],[723,6000]]                                                                           </td><td>0.925227                                                                                          </td><td>[0.933682,0.914843]                                                                               </td><td>⋯                                                                                                 </td><td>0.497371                                                                                          </td><td>0.267943                                                                                          </td><td>0.538718                                                                                          </td><td>0                                                                                                 </td><td> 7062.500                                                                                         </td><td> 812.500                                                                                          </td><td> 6250.000                                                                                         </td><td>[Oracle Corporation, 1.8.0_45, amd64, Windows 8.1, 6.3]                                           </td><td>1458.286                                                                                          </td><td>[863.4854734998407, 1223.4634601020418, 656.3214412156806, 1354.3993619867172, 3193.7623346357354]</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllll}\n",
       "  & run.id & task.id & setup.id & flow.id & area.under.roc.curve & area.under.roc.curve.array & average.cost & confusion.matrix & f.measure & f.measure.array & ⋯ & root.mean.prior.squared.error & root.mean.squared.error & root.relative.squared.error & total.cost & usercpu.time.millis & usercpu.time.millis.testing & usercpu.time.millis.training & os.information & scimark.benchmark & scimark.benchmark.array\\\\\n",
       "\\hline\n",
       "\t1 & 544246                                                                                             & 14951                                                                                              & 2961                                                                                               & 2390                                                                                               & 0.693537                                                                                           & {[}0.693537,0.693537{]}                                                                            & 0                                                                                                  & {[}{[}6829,1428{]},{[}2958,3765{]}{]}                                                              & 0.700826                                                                                           & {[}0.756928,0.631923{]}                                                                            & ⋯                                                                                                  & 0.497371                                                                                           & 0.541101                                                                                           & 1.087921                                                                                           & 0                                                                                                  &     1.743                                                                                          &    0.031                                                                                           &     1.712                                                                                          & NA                                                                                                 &       NA                                                                                           & NA                                                                                                \\\\\n",
       "\t2 & 544272                                                                                             & 14951                                                                                              & 2963                                                                                               & 2393                                                                                               & 0.934923                                                                                           & {[}0.934923,0.934923{]}                                                                            & 0                                                                                                  & {[}{[}7918,339{]},{[}599,6124{]}{]}                                                                & 0.937251                                                                                           & {[}0.94408,0.928864{]}                                                                             & ⋯                                                                                                  & 0.497371                                                                                           & 0.250234                                                                                           & 0.503112                                                                                           & 0                                                                                                  &   513.130                                                                                          &    4.780                                                                                           &   508.350                                                                                          & NA                                                                                                 &       NA                                                                                           & NA                                                                                                \\\\\n",
       "\t3 & 544274                                                                                                 & 14951                                                                                                  & 5538                                                                                                   & 3401                                                                                                   & 0.992349                                                                                               & {[}0.992349,0.992349{]}                                                                                & 0                                                                                                      & {[}{[}8019,238{]},{[}389,6334{]}{]}                                                                    & 0.958096                                                                                               & {[}0.962376,0.952839{]}                                                                                & ⋯                                                                                                      & 0.497371                                                                                               & 0.179762                                                                                               & 0.361424                                                                                               & 0                                                                                                      & 38875.000                                                                                              & 1671.875                                                                                               & 37203.125                                                                                              & {[}Oracle Corporation, 1.8.0\\_45, amd64, Windows 8.1, 6.3{]}                                          & 1433.743                                                                                               & {[}772.8137632186107, 1186.9140871273323, 653.9231730770091, 1310.065014968779, 3244.99907038474{]}   \\\\\n",
       "\t4 & 544316                                                                                             & 14951                                                                                              & 3526                                                                                               & 2629                                                                                               & 0.965265                                                                                           & {[}0.965265,0.965265{]}                                                                            & 0                                                                                                  & {[}{[}7806,451{]},{[}1060,5663{]}{]}                                                               & 0.898533                                                                                           & {[}0.911756,0.882293{]}                                                                            & ⋯                                                                                                  & 0.497371                                                                                           & 0.283324                                                                                           & 0.569642                                                                                           & 0                                                                                                  &        NA                                                                                          &       NA                                                                                           &        NA                                                                                          & NA                                                                                                 &       NA                                                                                           & NA                                                                                                \\\\\n",
       "\t5 & 544318                                                                                             & 14951                                                                                              & 3526                                                                                               & 2629                                                                                               & 0.963992                                                                                           & {[}0.963992,0.963992{]}                                                                            & 0                                                                                                  & {[}{[}7821,436{]},{[}1101,5622{]}{]}                                                               & 0.896713                                                                                           & {[}0.91053,0.879743{]}                                                                             & ⋯                                                                                                  & 0.497371                                                                                           & 0.285358                                                                                           & 0.573732                                                                                           & 0                                                                                                  &        NA                                                                                          &       NA                                                                                           &        NA                                                                                          & NA                                                                                                 &       NA                                                                                           & NA                                                                                                \\\\\n",
       "\t6 & 544325                                                                                                 & 14951                                                                                                  & 5539                                                                                                   & 2277                                                                                                   & 0.979674                                                                                               & {[}0.979674,0.979674{]}                                                                                & 0                                                                                                      & {[}{[}7863,394{]},{[}723,6000{]}{]}                                                                    & 0.925227                                                                                               & {[}0.933682,0.914843{]}                                                                                & ⋯                                                                                                      & 0.497371                                                                                               & 0.267943                                                                                               & 0.538718                                                                                               & 0                                                                                                      &  7062.500                                                                                              &  812.500                                                                                               &  6250.000                                                                                              & {[}Oracle Corporation, 1.8.0\\_45, amd64, Windows 8.1, 6.3{]}                                          & 1458.286                                                                                               & {[}863.4854734998407, 1223.4634601020418, 656.3214412156806, 1354.3993619867172, 3193.7623346357354{]}\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  run.id task.id setup.id flow.id area.under.roc.curve\n",
       "1 544246 14951   2961     2390    0.693537            \n",
       "2 544272 14951   2963     2393    0.934923            \n",
       "3 544274 14951   5538     3401    0.992349            \n",
       "4 544316 14951   3526     2629    0.965265            \n",
       "5 544318 14951   3526     2629    0.963992            \n",
       "6 544325 14951   5539     2277    0.979674            \n",
       "  area.under.roc.curve.array average.cost confusion.matrix          f.measure\n",
       "1 [0.693537,0.693537]        0            [[6829,1428],[2958,3765]] 0.700826 \n",
       "2 [0.934923,0.934923]        0            [[7918,339],[599,6124]]   0.937251 \n",
       "3 [0.992349,0.992349]        0            [[8019,238],[389,6334]]   0.958096 \n",
       "4 [0.965265,0.965265]        0            [[7806,451],[1060,5663]]  0.898533 \n",
       "5 [0.963992,0.963992]        0            [[7821,436],[1101,5622]]  0.896713 \n",
       "6 [0.979674,0.979674]        0            [[7863,394],[723,6000]]   0.925227 \n",
       "  f.measure.array     ⋯ root.mean.prior.squared.error root.mean.squared.error\n",
       "1 [0.756928,0.631923] ⋯ 0.497371                      0.541101               \n",
       "2 [0.94408,0.928864]  ⋯ 0.497371                      0.250234               \n",
       "3 [0.962376,0.952839] ⋯ 0.497371                      0.179762               \n",
       "4 [0.911756,0.882293] ⋯ 0.497371                      0.283324               \n",
       "5 [0.91053,0.879743]  ⋯ 0.497371                      0.285358               \n",
       "6 [0.933682,0.914843] ⋯ 0.497371                      0.267943               \n",
       "  root.relative.squared.error total.cost usercpu.time.millis\n",
       "1 1.087921                    0              1.743          \n",
       "2 0.503112                    0            513.130          \n",
       "3 0.361424                    0          38875.000          \n",
       "4 0.569642                    0                 NA          \n",
       "5 0.573732                    0                 NA          \n",
       "6 0.538718                    0           7062.500          \n",
       "  usercpu.time.millis.testing usercpu.time.millis.training\n",
       "1    0.031                        1.712                   \n",
       "2    4.780                      508.350                   \n",
       "3 1671.875                    37203.125                   \n",
       "4       NA                           NA                   \n",
       "5       NA                           NA                   \n",
       "6  812.500                     6250.000                   \n",
       "  os.information                                          scimark.benchmark\n",
       "1 NA                                                            NA         \n",
       "2 NA                                                            NA         \n",
       "3 [Oracle Corporation, 1.8.0_45, amd64, Windows 8.1, 6.3] 1433.743         \n",
       "4 NA                                                            NA         \n",
       "5 NA                                                            NA         \n",
       "6 [Oracle Corporation, 1.8.0_45, amd64, Windows 8.1, 6.3] 1458.286         \n",
       "  scimark.benchmark.array                                                                           \n",
       "1 NA                                                                                                \n",
       "2 NA                                                                                                \n",
       "3 [772.8137632186107, 1186.9140871273323, 653.9231730770091, 1310.065014968779, 3244.99907038474]   \n",
       "4 NA                                                                                                \n",
       "5 NA                                                                                                \n",
       "6 [863.4854734998407, 1223.4634601020418, 656.3214412156806, 1354.3993619867172, 3193.7623346357354]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run.results = listOMLRunEvaluations(task.id = 14951)\n",
    "head(run.results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### List known flows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading from 'http://www.openml.org/api/v1/flow/list/' to '<mem>'.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>flow.id</th><th scope=col>full.name</th><th scope=col>name</th><th scope=col>version</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1750</th><td>2293                        </td><td>classif.J48(20)             </td><td>classif.J48                 </td><td>20                          </td></tr>\n",
       "\t<tr><th scope=row>1751</th><td>2294                        </td><td>classif.JRip(7)             </td><td>classif.JRip                </td><td> 7                          </td></tr>\n",
       "\t<tr><th scope=row>1752</th><td>2295                        </td><td>classif.LiblineaRL1LogReg(4)</td><td>classif.LiblineaRL1LogReg   </td><td> 4                          </td></tr>\n",
       "\t<tr><th scope=row>1753</th><td>2296                        </td><td>classif.LiblineaRL2LogReg(4)</td><td>classif.LiblineaRL2LogReg   </td><td> 4                          </td></tr>\n",
       "\t<tr><th scope=row>1754</th><td>2297                        </td><td>classif.OneR(4)             </td><td>classif.OneR                </td><td> 4                          </td></tr>\n",
       "\t<tr><th scope=row>1755</th><td>2298                        </td><td>classif.PART(4)             </td><td>classif.PART                </td><td> 4                          </td></tr>\n",
       "\t<tr><th scope=row>1756</th><td>2299                        </td><td>classif.ada(5)              </td><td>classif.ada                 </td><td> 5                          </td></tr>\n",
       "\t<tr><th scope=row>1757</th><td>2300                        </td><td>classif.avNNet(5)           </td><td>classif.avNNet              </td><td> 5                          </td></tr>\n",
       "\t<tr><th scope=row>1758</th><td>2301                        </td><td>classif.bartMachine(3)      </td><td>classif.bartMachine         </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1759</th><td>2302                        </td><td>classif.bdk(3)              </td><td>classif.bdk                 </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1760</th><td>2303                        </td><td>classif.binomial(5)         </td><td>classif.binomial            </td><td> 5                          </td></tr>\n",
       "\t<tr><th scope=row>1761</th><td>2304                        </td><td>classif.boosting(4)         </td><td>classif.boosting            </td><td> 4                          </td></tr>\n",
       "\t<tr><th scope=row>1762</th><td>2305                        </td><td>classif.cforest(4)          </td><td>classif.cforest             </td><td> 4                          </td></tr>\n",
       "\t<tr><th scope=row>1763</th><td>2306                        </td><td>classif.ctree(4)            </td><td>classif.ctree               </td><td> 4                          </td></tr>\n",
       "\t<tr><th scope=row>1764</th><td>2307                        </td><td>classif.dbnDNN(3)           </td><td>classif.dbnDNN              </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1765</th><td>2308                        </td><td>classif.extraTrees(3)       </td><td>classif.extraTrees          </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1766</th><td>2309                        </td><td>classif.gbm(3)              </td><td>classif.gbm                 </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1767</th><td>2310                        </td><td>classif.glmboost(2)         </td><td>classif.glmboost            </td><td> 2                          </td></tr>\n",
       "\t<tr><th scope=row>1768</th><td>2311                        </td><td>classif.hdrda(2)            </td><td>classif.hdrda               </td><td> 2                          </td></tr>\n",
       "\t<tr><th scope=row>1769</th><td>2312                        </td><td>classif.ksvm(4)             </td><td>classif.ksvm                </td><td> 4                          </td></tr>\n",
       "\t<tr><th scope=row>1770</th><td>2313                        </td><td>classif.lda(9)              </td><td>classif.lda                 </td><td> 9                          </td></tr>\n",
       "\t<tr><th scope=row>1771</th><td>2314                        </td><td>classif.logreg(7)           </td><td>classif.logreg              </td><td> 7                          </td></tr>\n",
       "\t<tr><th scope=row>1772</th><td>2315                        </td><td>classif.lqa(5)              </td><td>classif.lqa                 </td><td> 5                          </td></tr>\n",
       "\t<tr><th scope=row>1773</th><td>2316                        </td><td>classif.mlp(5)              </td><td>classif.mlp                 </td><td> 5                          </td></tr>\n",
       "\t<tr><th scope=row>1774</th><td>2317                        </td><td>classif.multinom(5)         </td><td>classif.multinom            </td><td> 5                          </td></tr>\n",
       "\t<tr><th scope=row>1775</th><td>2318                        </td><td>classif.naiveBayes(5)       </td><td>classif.naiveBayes          </td><td> 5                          </td></tr>\n",
       "\t<tr><th scope=row>1776</th><td>2319                        </td><td>classif.neuralnet(3)        </td><td>classif.neuralnet           </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1777</th><td>2320                        </td><td>classif.nnTrain(3)          </td><td>classif.nnTrain             </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1778</th><td>2321                        </td><td>classif.nnet(3)             </td><td>classif.nnet                </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1779</th><td>2322                        </td><td>classif.nodeHarvest(3)      </td><td>classif.nodeHarvest         </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1780</th><td>2323                        </td><td>classif.pamr(3)             </td><td>classif.pamr                </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1781</th><td>2324                        </td><td>classif.plr(4)              </td><td>classif.plr                 </td><td> 4                          </td></tr>\n",
       "\t<tr><th scope=row>1782</th><td>2325                        </td><td>classif.plsdaCaret(3)       </td><td>classif.plsdaCaret          </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1783</th><td>2326                        </td><td>classif.probit(3)           </td><td>classif.probit              </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1784</th><td>2327                        </td><td>classif.qda(2)              </td><td>classif.qda                 </td><td> 2                          </td></tr>\n",
       "\t<tr><th scope=row>1785</th><td>2328                        </td><td>classif.randomForest(42)    </td><td>classif.randomForest        </td><td>42                          </td></tr>\n",
       "\t<tr><th scope=row>1786</th><td>2329                        </td><td>classif.randomForestSRC(3)  </td><td>classif.randomForestSRC     </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1787</th><td>2330                        </td><td>classif.ranger(3)           </td><td>classif.ranger              </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1788</th><td>2331                        </td><td>classif.rda(4)              </td><td>classif.rda                 </td><td> 4                          </td></tr>\n",
       "\t<tr><th scope=row>1789</th><td>2332                        </td><td>classif.rotationForest(3)   </td><td>classif.rotationForest      </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1790</th><td>2333                        </td><td>classif.rpart(38)           </td><td>classif.rpart               </td><td>38                          </td></tr>\n",
       "\t<tr><th scope=row>1791</th><td>2334                        </td><td>classif.saeDNN(3)           </td><td>classif.saeDNN              </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1792</th><td>2335                        </td><td>classif.sda(5)              </td><td>classif.sda                 </td><td> 5                          </td></tr>\n",
       "\t<tr><th scope=row>1793</th><td>2336                        </td><td>classif.svm(5)              </td><td>classif.svm                 </td><td> 5                          </td></tr>\n",
       "\t<tr><th scope=row>1794</th><td>2337                        </td><td>classif.xgboost(4)          </td><td>classif.xgboost             </td><td> 4                          </td></tr>\n",
       "\t<tr><th scope=row>1795</th><td>2338                        </td><td>classif.xyf(2)              </td><td>classif.xyf                 </td><td> 2                          </td></tr>\n",
       "\t<tr><th scope=row>1796</th><td>2390                        </td><td>classif.rpart(39)           </td><td>classif.rpart               </td><td>39                          </td></tr>\n",
       "\t<tr><th scope=row>1797</th><td>2391                        </td><td>classif.J48(21)             </td><td>classif.J48                 </td><td>21                          </td></tr>\n",
       "\t<tr><th scope=row>1798</th><td>2392                        </td><td>classif.glmboost(3)         </td><td>classif.glmboost            </td><td> 3                          </td></tr>\n",
       "\t<tr><th scope=row>1799</th><td>2393                        </td><td>classif.randomForest(43)    </td><td>classif.randomForest        </td><td>43                          </td></tr>\n",
       "\t<tr><th scope=row>1800</th><td>2408                        </td><td>classif.glmnet(1)           </td><td>classif.glmnet              </td><td> 1                          </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & flow.id & full.name & name & version\\\\\n",
       "\\hline\n",
       "\t1750 & 2293                         & classif.J48(20)              & classif.J48                  & 20                          \\\\\n",
       "\t1751 & 2294                         & classif.JRip(7)              & classif.JRip                 &  7                          \\\\\n",
       "\t1752 & 2295                         & classif.LiblineaRL1LogReg(4) & classif.LiblineaRL1LogReg    &  4                          \\\\\n",
       "\t1753 & 2296                         & classif.LiblineaRL2LogReg(4) & classif.LiblineaRL2LogReg    &  4                          \\\\\n",
       "\t1754 & 2297                         & classif.OneR(4)              & classif.OneR                 &  4                          \\\\\n",
       "\t1755 & 2298                         & classif.PART(4)              & classif.PART                 &  4                          \\\\\n",
       "\t1756 & 2299                         & classif.ada(5)               & classif.ada                  &  5                          \\\\\n",
       "\t1757 & 2300                         & classif.avNNet(5)            & classif.avNNet               &  5                          \\\\\n",
       "\t1758 & 2301                         & classif.bartMachine(3)       & classif.bartMachine          &  3                          \\\\\n",
       "\t1759 & 2302                         & classif.bdk(3)               & classif.bdk                  &  3                          \\\\\n",
       "\t1760 & 2303                         & classif.binomial(5)          & classif.binomial             &  5                          \\\\\n",
       "\t1761 & 2304                         & classif.boosting(4)          & classif.boosting             &  4                          \\\\\n",
       "\t1762 & 2305                         & classif.cforest(4)           & classif.cforest              &  4                          \\\\\n",
       "\t1763 & 2306                         & classif.ctree(4)             & classif.ctree                &  4                          \\\\\n",
       "\t1764 & 2307                         & classif.dbnDNN(3)            & classif.dbnDNN               &  3                          \\\\\n",
       "\t1765 & 2308                         & classif.extraTrees(3)        & classif.extraTrees           &  3                          \\\\\n",
       "\t1766 & 2309                         & classif.gbm(3)               & classif.gbm                  &  3                          \\\\\n",
       "\t1767 & 2310                         & classif.glmboost(2)          & classif.glmboost             &  2                          \\\\\n",
       "\t1768 & 2311                         & classif.hdrda(2)             & classif.hdrda                &  2                          \\\\\n",
       "\t1769 & 2312                         & classif.ksvm(4)              & classif.ksvm                 &  4                          \\\\\n",
       "\t1770 & 2313                         & classif.lda(9)               & classif.lda                  &  9                          \\\\\n",
       "\t1771 & 2314                         & classif.logreg(7)            & classif.logreg               &  7                          \\\\\n",
       "\t1772 & 2315                         & classif.lqa(5)               & classif.lqa                  &  5                          \\\\\n",
       "\t1773 & 2316                         & classif.mlp(5)               & classif.mlp                  &  5                          \\\\\n",
       "\t1774 & 2317                         & classif.multinom(5)          & classif.multinom             &  5                          \\\\\n",
       "\t1775 & 2318                         & classif.naiveBayes(5)        & classif.naiveBayes           &  5                          \\\\\n",
       "\t1776 & 2319                         & classif.neuralnet(3)         & classif.neuralnet            &  3                          \\\\\n",
       "\t1777 & 2320                         & classif.nnTrain(3)           & classif.nnTrain              &  3                          \\\\\n",
       "\t1778 & 2321                         & classif.nnet(3)              & classif.nnet                 &  3                          \\\\\n",
       "\t1779 & 2322                         & classif.nodeHarvest(3)       & classif.nodeHarvest          &  3                          \\\\\n",
       "\t1780 & 2323                         & classif.pamr(3)              & classif.pamr                 &  3                          \\\\\n",
       "\t1781 & 2324                         & classif.plr(4)               & classif.plr                  &  4                          \\\\\n",
       "\t1782 & 2325                         & classif.plsdaCaret(3)        & classif.plsdaCaret           &  3                          \\\\\n",
       "\t1783 & 2326                         & classif.probit(3)            & classif.probit               &  3                          \\\\\n",
       "\t1784 & 2327                         & classif.qda(2)               & classif.qda                  &  2                          \\\\\n",
       "\t1785 & 2328                         & classif.randomForest(42)     & classif.randomForest         & 42                          \\\\\n",
       "\t1786 & 2329                         & classif.randomForestSRC(3)   & classif.randomForestSRC      &  3                          \\\\\n",
       "\t1787 & 2330                         & classif.ranger(3)            & classif.ranger               &  3                          \\\\\n",
       "\t1788 & 2331                         & classif.rda(4)               & classif.rda                  &  4                          \\\\\n",
       "\t1789 & 2332                         & classif.rotationForest(3)    & classif.rotationForest       &  3                          \\\\\n",
       "\t1790 & 2333                         & classif.rpart(38)            & classif.rpart                & 38                          \\\\\n",
       "\t1791 & 2334                         & classif.saeDNN(3)            & classif.saeDNN               &  3                          \\\\\n",
       "\t1792 & 2335                         & classif.sda(5)               & classif.sda                  &  5                          \\\\\n",
       "\t1793 & 2336                         & classif.svm(5)               & classif.svm                  &  5                          \\\\\n",
       "\t1794 & 2337                         & classif.xgboost(4)           & classif.xgboost              &  4                          \\\\\n",
       "\t1795 & 2338                         & classif.xyf(2)               & classif.xyf                  &  2                          \\\\\n",
       "\t1796 & 2390                         & classif.rpart(39)            & classif.rpart                & 39                          \\\\\n",
       "\t1797 & 2391                         & classif.J48(21)              & classif.J48                  & 21                          \\\\\n",
       "\t1798 & 2392                         & classif.glmboost(3)          & classif.glmboost             &  3                          \\\\\n",
       "\t1799 & 2393                         & classif.randomForest(43)     & classif.randomForest         & 43                          \\\\\n",
       "\t1800 & 2408                         & classif.glmnet(1)            & classif.glmnet               &  1                          \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "     flow.id full.name                    name                      version\n",
       "1750 2293    classif.J48(20)              classif.J48               20     \n",
       "1751 2294    classif.JRip(7)              classif.JRip               7     \n",
       "1752 2295    classif.LiblineaRL1LogReg(4) classif.LiblineaRL1LogReg  4     \n",
       "1753 2296    classif.LiblineaRL2LogReg(4) classif.LiblineaRL2LogReg  4     \n",
       "1754 2297    classif.OneR(4)              classif.OneR               4     \n",
       "1755 2298    classif.PART(4)              classif.PART               4     \n",
       "1756 2299    classif.ada(5)               classif.ada                5     \n",
       "1757 2300    classif.avNNet(5)            classif.avNNet             5     \n",
       "1758 2301    classif.bartMachine(3)       classif.bartMachine        3     \n",
       "1759 2302    classif.bdk(3)               classif.bdk                3     \n",
       "1760 2303    classif.binomial(5)          classif.binomial           5     \n",
       "1761 2304    classif.boosting(4)          classif.boosting           4     \n",
       "1762 2305    classif.cforest(4)           classif.cforest            4     \n",
       "1763 2306    classif.ctree(4)             classif.ctree              4     \n",
       "1764 2307    classif.dbnDNN(3)            classif.dbnDNN             3     \n",
       "1765 2308    classif.extraTrees(3)        classif.extraTrees         3     \n",
       "1766 2309    classif.gbm(3)               classif.gbm                3     \n",
       "1767 2310    classif.glmboost(2)          classif.glmboost           2     \n",
       "1768 2311    classif.hdrda(2)             classif.hdrda              2     \n",
       "1769 2312    classif.ksvm(4)              classif.ksvm               4     \n",
       "1770 2313    classif.lda(9)               classif.lda                9     \n",
       "1771 2314    classif.logreg(7)            classif.logreg             7     \n",
       "1772 2315    classif.lqa(5)               classif.lqa                5     \n",
       "1773 2316    classif.mlp(5)               classif.mlp                5     \n",
       "1774 2317    classif.multinom(5)          classif.multinom           5     \n",
       "1775 2318    classif.naiveBayes(5)        classif.naiveBayes         5     \n",
       "1776 2319    classif.neuralnet(3)         classif.neuralnet          3     \n",
       "1777 2320    classif.nnTrain(3)           classif.nnTrain            3     \n",
       "1778 2321    classif.nnet(3)              classif.nnet               3     \n",
       "1779 2322    classif.nodeHarvest(3)       classif.nodeHarvest        3     \n",
       "1780 2323    classif.pamr(3)              classif.pamr               3     \n",
       "1781 2324    classif.plr(4)               classif.plr                4     \n",
       "1782 2325    classif.plsdaCaret(3)        classif.plsdaCaret         3     \n",
       "1783 2326    classif.probit(3)            classif.probit             3     \n",
       "1784 2327    classif.qda(2)               classif.qda                2     \n",
       "1785 2328    classif.randomForest(42)     classif.randomForest      42     \n",
       "1786 2329    classif.randomForestSRC(3)   classif.randomForestSRC    3     \n",
       "1787 2330    classif.ranger(3)            classif.ranger             3     \n",
       "1788 2331    classif.rda(4)               classif.rda                4     \n",
       "1789 2332    classif.rotationForest(3)    classif.rotationForest     3     \n",
       "1790 2333    classif.rpart(38)            classif.rpart             38     \n",
       "1791 2334    classif.saeDNN(3)            classif.saeDNN             3     \n",
       "1792 2335    classif.sda(5)               classif.sda                5     \n",
       "1793 2336    classif.svm(5)               classif.svm                5     \n",
       "1794 2337    classif.xgboost(4)           classif.xgboost            4     \n",
       "1795 2338    classif.xyf(2)               classif.xyf                2     \n",
       "1796 2390    classif.rpart(39)            classif.rpart             39     \n",
       "1797 2391    classif.J48(21)              classif.J48               21     \n",
       "1798 2392    classif.glmboost(3)          classif.glmboost           3     \n",
       "1799 2393    classif.randomForest(43)     classif.randomForest      43     \n",
       "1800 2408    classif.glmnet(1)            classif.glmnet             1     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flows = listOMLFlows()\n",
    "flows[1750:1800, 1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Compare against the state-of-the-art"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9Fu8fekFeWR7ScSQbnVsU7AkJLBg1dNZBF5tt8qK6+WrDp47WTwKmvMFhbHl0Wv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DEUccEb75zW9qUmXhcX5556DoXXzxxbo/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\n+8c1SWyHeIVALctAfLlnrrjiirwGHCvHC/9CwUh+cHUCfc0NzzVTSP4k7kp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VI92iVGyGcqQtLlrzCDZgkIfclG5eKCdDlaOh4+aqRxif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\nsyF8HVJmUn8m7WfJCeCKNQq4st35nXTu0P0UQ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YSkw3mgr2WZQF9rYwW2DRS5NmJ3skzELLXxUh50PNDusr5wa8vyEV/UhpKT2+XfC1/4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\npY7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XPeeNIol/a5T9tXfvvK6dI883ue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\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h2XO9x1YD0U0gvNLSXEFVl8lI2lqt+d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\n0UcfrfWZomP3pa/0NRViYNigQ2pBHYjFQHH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   "source": [
    "plotdata = run.results[,c(\"flow.id\", \"predictive.accuracy\")]\n",
    "plotdata$flow.id = flows$name[match(plotdata$flow.id, flows$flow.id)] # Replace IDs by names\n",
    "colnames(plotdata) = c(\"algo\",\"ACC\")\n",
    "ggplot(data = plotdata, mapping = aes(x = reorder(as.factor(algo), ACC, max), y = ACC, fill = algo)) +\n",
    "    geom_violin(trim = TRUE, scale = \"width\") + geom_boxplot(width = 0.1) + scale_y_continuous(limits = c(0.45, 1)) +\n",
    "    theme_bw() + theme(legend.position=\"none\") + ylab(\"ACC\") + xlab(\"Flows\") + coord_flip()"
   ]
  },
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   "source": []
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