h2o-py/demos/airlines_demo_small.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/__init__.py:7: DeprecationWarning: bad escape \\s\n",
      "  from pandas import hashtable, tslib, lib\n"
     ]
    }
   ],
   "source": [
    "import h2o\n",
    "import pandas\n",
    "import pprint\n",
    "import operator\n",
    "import matplotlib\n",
    "from h2o.estimators.glm import H2OGeneralizedLinearEstimator\n",
    "from h2o.estimators.gbm import H2OGradientBoostingEstimator\n",
    "from h2o.estimators.random_forest import H2ORandomForestEstimator\n",
    "from h2o.estimators.deeplearning import H2ODeepLearningEstimator\n",
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime: </td>\n",
       "<td>11 seconds 120 milliseconds </td></tr>\n",
       "<tr><td>H2O cluster version: </td>\n",
       "<td>3.7.0.99999</td></tr>\n",
       "<tr><td>H2O cluster name: </td>\n",
       "<td>spIdea</td></tr>\n",
       "<tr><td>H2O cluster total nodes: </td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster total free memory: </td>\n",
       "<td>12.44 GB</td></tr>\n",
       "<tr><td>H2O cluster total cores: </td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster allowed cores: </td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster healthy: </td>\n",
       "<td>True</td></tr>\n",
       "<tr><td>H2O Connection ip: </td>\n",
       "<td>127.0.0.1</td></tr>\n",
       "<tr><td>H2O Connection port: </td>\n",
       "<td>54321</td></tr>\n",
       "<tr><td>H2O Connection proxy: </td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>Python Version: </td>\n",
       "<td>3.5.0</td></tr></table></div>"
      ],
      "text/plain": [
       "------------------------------  ---------------------------\n",
       "H2O cluster uptime:             11 seconds 120 milliseconds\n",
       "H2O cluster version:            3.7.0.99999\n",
       "H2O cluster name:               spIdea\n",
       "H2O cluster total nodes:        1\n",
       "H2O cluster total free memory:  12.44 GB\n",
       "H2O cluster total cores:        8\n",
       "H2O cluster allowed cores:      8\n",
       "H2O cluster healthy:            True\n",
       "H2O Connection ip:              127.0.0.1\n",
       "H2O Connection port:            54321\n",
       "H2O Connection proxy:\n",
       "Python Version:                 3.5.0\n",
       "------------------------------  ---------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Connect to a cluster\n",
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set this to True if interactive (matplotlib) plots are desired\n",
    "interactive = False\n",
    "if not interactive: matplotlib.use('Agg', warn=False)\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Import and Parse airlines data\n",
      "\n",
      "Parse Progress: [##################################################] 100%\n",
      "Rows:43,978 Cols:31\n",
      "\n",
      "Chunk compression summary: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>chunk_type</b></td>\n",
       "<td><b>chunk_name</b></td>\n",
       "<td><b>count</b></td>\n",
       "<td><b>count_percentage</b></td>\n",
       "<td><b>size</b></td>\n",
       "<td><b>size_percentage</b></td></tr>\n",
       "<tr><td>C0L</td>\n",
       "<td>Constant Integers</td>\n",
       "<td>10</td>\n",
       "<td>5.376344</td>\n",
       "<td>    800  B</td>\n",
       "<td>0.0504024</td></tr>\n",
       "<tr><td>C0D</td>\n",
       "<td>Constant Reals</td>\n",
       "<td>23</td>\n",
       "<td>12.365591</td>\n",
       "<td>    1.8 KB</td>\n",
       "<td>0.1159254</td></tr>\n",
       "<tr><td>CBS</td>\n",
       "<td>Bits</td>\n",
       "<td>2</td>\n",
       "<td>1.0752689</td>\n",
       "<td>    2.0 KB</td>\n",
       "<td>0.1272030</td></tr>\n",
       "<tr><td>CX0</td>\n",
       "<td>Sparse Bits</td>\n",
       "<td>10</td>\n",
       "<td>5.376344</td>\n",
       "<td>    1.9 KB</td>\n",
       "<td>0.1247459</td></tr>\n",
       "<tr><td>C1</td>\n",
       "<td>1-Byte Integers</td>\n",
       "<td>40</td>\n",
       "<td>21.505377</td>\n",
       "<td>  287.8 KB</td>\n",
       "<td>18.564957</td></tr>\n",
       "<tr><td>C1N</td>\n",
       "<td>1-Byte Integers (w/o NAs)</td>\n",
       "<td>19</td>\n",
       "<td>10.215054</td>\n",
       "<td>  133.1 KB</td>\n",
       "<td>8.58617</td></tr>\n",
       "<tr><td>C1S</td>\n",
       "<td>1-Byte Fractions</td>\n",
       "<td>6</td>\n",
       "<td>3.2258065</td>\n",
       "<td>   43.4 KB</td>\n",
       "<td>2.8024976</td></tr>\n",
       "<tr><td>C2</td>\n",
       "<td>2-Byte Integers</td>\n",
       "<td>76</td>\n",
       "<td>40.860214</td>\n",
       "<td>    1.1 MB</td>\n",
       "<td>69.628105</td></tr></table></div>"
      ],
      "text/plain": [
       "chunk_type    chunk_name                 count    count_percentage    size      size_percentage\n",
       "------------  -------------------------  -------  ------------------  --------  -----------------\n",
       "C0L           Constant Integers          10       5.37634             800  B    0.0504024\n",
       "C0D           Constant Reals             23       12.3656             1.8 KB    0.115925\n",
       "CBS           Bits                       2        1.07527             2.0 KB    0.127203\n",
       "CX0           Sparse Bits                10       5.37634             1.9 KB    0.124746\n",
       "C1            1-Byte Integers            40       21.5054             287.8 KB  18.565\n",
       "C1N           1-Byte Integers (w/o NAs)  19       10.2151             133.1 KB  8.58617\n",
       "C1S           1-Byte Fractions           6        3.22581             43.4 KB   2.8025\n",
       "C2            2-Byte Integers            76       40.8602             1.1 MB    69.6281"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Frame distribution summary: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>size</b></td>\n",
       "<td><b>number_of_rows</b></td>\n",
       "<td><b>number_of_chunks_per_column</b></td>\n",
       "<td><b>number_of_chunks</b></td></tr>\n",
       "<tr><td>172.16.2.84:54321</td>\n",
       "<td>    1.5 MB</td>\n",
       "<td>43978.0</td>\n",
       "<td>6.0</td>\n",
       "<td>186.0</td></tr>\n",
       "<tr><td>mean</td>\n",
       "<td>    1.5 MB</td>\n",
       "<td>43978.0</td>\n",
       "<td>6.0</td>\n",
       "<td>186.0</td></tr>\n",
       "<tr><td>min</td>\n",
       "<td>    1.5 MB</td>\n",
       "<td>43978.0</td>\n",
       "<td>6.0</td>\n",
       "<td>186.0</td></tr>\n",
       "<tr><td>max</td>\n",
       "<td>    1.5 MB</td>\n",
       "<td>43978.0</td>\n",
       "<td>6.0</td>\n",
       "<td>186.0</td></tr>\n",
       "<tr><td>stddev</td>\n",
       "<td>      0  B</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>total</td>\n",
       "<td>    1.5 MB</td>\n",
       "<td>43978.0</td>\n",
       "<td>6.0</td>\n",
       "<td>186.0</td></tr></table></div>"
      ],
      "text/plain": [
       "                   size    number_of_rows    number_of_chunks_per_column    number_of_chunks\n",
       "-----------------  ------  ----------------  -----------------------------  ------------------\n",
       "172.16.2.84:54321  1.5 MB  43978             6                              186\n",
       "mean               1.5 MB  43978             6                              186\n",
       "min                1.5 MB  43978             6                              186\n",
       "max                1.5 MB  43978             6                              186\n",
       "stddev             0  B    0                 0                              0\n",
       "total              1.5 MB  43978             6                              186"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr><th>       </th><th>Year             </th><th>Month            </th><th>DayofMonth        </th><th>DayOfWeek         </th><th>DepTime           </th><th>CRSDepTime        </th><th>ArrTime           </th><th>CRSArrTime        </th><th>UniqueCarrier  </th><th>FlightNum        </th><th>TailNum  </th><th>ActualElapsedTime  </th><th>CRSElapsedTime    </th><th>AirTime           </th><th>ArrDelay          </th><th>DepDelay          </th><th>Origin  </th><th>Dest  </th><th>Distance         </th><th>TaxiIn           </th><th>TaxiOut           </th><th>Cancelled           </th><th>CancellationCode  </th><th>Diverted             </th><th>CarrierDelay     </th><th>WeatherDelay      </th><th>NASDelay          </th><th>SecurityDelay       </th><th>LateAircraftDelay  </th><th>IsArrDelayed      </th><th>IsDepDelayed       </th></tr>\n",
       "<tr><td>type   </td><td>int              </td><td>int              </td><td>int               </td><td>int               </td><td>int               </td><td>int               </td><td>int               </td><td>int               </td><td>enum           </td><td>int              </td><td>enum     </td><td>int                </td><td>int               </td><td>int               </td><td>int               </td><td>int               </td><td>enum    </td><td>enum  </td><td>int              </td><td>int              </td><td>int               </td><td>int                 </td><td>enum              </td><td>int                  </td><td>int              </td><td>int               </td><td>int               </td><td>int                 </td><td>int                </td><td>enum              </td><td>enum               </td></tr>\n",
       "<tr><td>mins   </td><td>1987.0           </td><td>1.0              </td><td>1.0               </td><td>1.0               </td><td>1.0               </td><td>0.0               </td><td>1.0               </td><td>0.0               </td><td>0.0            </td><td>1.0              </td><td>0.0      </td><td>16.0               </td><td>17.0              </td><td>14.0              </td><td>-63.0             </td><td>-16.0             </td><td>0.0     </td><td>0.0   </td><td>11.0             </td><td>0.0              </td><td>0.0               </td><td>0.0                 </td><td>0.0               </td><td>0.0                  </td><td>0.0              </td><td>0.0               </td><td>0.0               </td><td>0.0                 </td><td>0.0                </td><td>0.0               </td><td>0.0                </td></tr>\n",
       "<tr><td>mean   </td><td>1997.5           </td><td>1.409090909090909</td><td>14.601073263904679</td><td>3.820614852880991 </td><td>1345.8466613820763</td><td>1313.2228614307164</td><td>1504.6341303788884</td><td>1485.289167310927 </td><td>NaN            </td><td>818.8429896766577</td><td>NaN      </td><td>124.8145291354043  </td><td>125.02156260661899</td><td>114.31611109078277</td><td>9.317111936984313 </td><td>10.0073906556001  </td><td>NaN     </td><td>NaN   </td><td>730.1821905650501</td><td>5.381368059530628</td><td>14.168634184732056</td><td>0.024694165264450407</td><td>NaN               </td><td>0.0024785119832643593</td><td>4.047800291055627</td><td>0.2893764692712417</td><td>4.855031904175534 </td><td>0.017015560282100096</td><td>7.620060450016789  </td><td>0.555755150302424 </td><td>0.5250579835372226 </td></tr>\n",
       "<tr><td>maxs   </td><td>2008.0           </td><td>10.0             </td><td>31.0              </td><td>7.0               </td><td>2400.0            </td><td>2359.0            </td><td>2400.0            </td><td>2359.0            </td><td>9.0            </td><td>3949.0           </td><td>3500.0   </td><td>475.0              </td><td>437.0             </td><td>402.0             </td><td>475.0             </td><td>473.0             </td><td>131.0   </td><td>133.0 </td><td>3365.0           </td><td>128.0            </td><td>254.0             </td><td>1.0                 </td><td>3.0               </td><td>1.0                  </td><td>369.0            </td><td>201.0             </td><td>323.0             </td><td>14.0                </td><td>373.0              </td><td>1.0               </td><td>1.0                </td></tr>\n",
       "<tr><td>sigma  </td><td>6.344360901711177</td><td>1.874711371343963</td><td>9.175790425861443 </td><td>1.9050131191328936</td><td>465.340899124234  </td><td>476.25113999259946</td><td>484.34748790351614</td><td>492.75043412270094</td><td>NaN            </td><td>777.4043691636349</td><td>NaN      </td><td>73.97444166059017  </td><td>73.4015946300093  </td><td>69.63632951506109 </td><td>29.840221962414848</td><td>26.438809042916454</td><td>NaN     </td><td>NaN   </td><td>578.438008230424 </td><td>4.201979939864828</td><td>9.905085747204327 </td><td>0.15519314135784237 </td><td>NaN               </td><td>0.049723487218862286 </td><td>16.20572990448423</td><td>4.416779898734124 </td><td>18.619776221475682</td><td>0.40394018210151184 </td><td>23.487565874106213 </td><td>0.4968872883428837</td><td>0.49937738031758017</td></tr>\n",
       "<tr><td>zeros  </td><td>0                </td><td>0                </td><td>0                 </td><td>0                 </td><td>0                 </td><td>569               </td><td>0                 </td><td>569               </td><td>724            </td><td>0                </td><td>2        </td><td>0                  </td><td>0                 </td><td>-8878             </td><td>1514              </td><td>6393              </td><td>59      </td><td>172   </td><td>0                </td><td>-8255            </td><td>-8321             </td><td>42892               </td><td>81                </td><td>43869                </td><td>-23296           </td><td>-21800            </td><td>-23252            </td><td>-21726              </td><td>-23500             </td><td>19537             </td><td>20887              </td></tr>\n",
       "<tr><td>missing</td><td>0                </td><td>0                </td><td>0                 </td><td>0                 </td><td>1086              </td><td>0                 </td><td>1195              </td><td>0                 </td><td>0              </td><td>0                </td><td>32       </td><td>1195               </td><td>13                </td><td>16649             </td><td>1195              </td><td>1086              </td><td>0       </td><td>0     </td><td>35               </td><td>16026            </td><td>16024             </td><td>0                   </td><td>9774              </td><td>0                    </td><td>35045            </td><td>35045             </td><td>35045             </td><td>35045               </td><td>35045              </td><td>0                 </td><td>0                  </td></tr>\n",
       "<tr><td>0      </td><td>1987.0           </td><td>10.0             </td><td>14.0              </td><td>3.0               </td><td>741.0             </td><td>730.0             </td><td>912.0             </td><td>849.0             </td><td>PS             </td><td>1451.0           </td><td>NA       </td><td>91.0               </td><td>79.0              </td><td>nan               </td><td>23.0              </td><td>11.0              </td><td>SAN     </td><td>SFO   </td><td>447.0            </td><td>nan              </td><td>nan               </td><td>0.0                 </td><td>NA                </td><td>0.0                  </td><td>nan              </td><td>nan               </td><td>nan               </td><td>nan                 </td><td>nan                </td><td>YES               </td><td>YES                </td></tr>\n",
       "<tr><td>1      </td><td>1987.0           </td><td>10.0             </td><td>15.0              </td><td>4.0               </td><td>729.0             </td><td>730.0             </td><td>903.0             </td><td>849.0             </td><td>PS             </td><td>1451.0           </td><td>NA       </td><td>94.0               </td><td>79.0              </td><td>nan               </td><td>14.0              </td><td>-1.0              </td><td>SAN     </td><td>SFO   </td><td>447.0            </td><td>nan              </td><td>nan               </td><td>0.0                 </td><td>NA                </td><td>0.0                  </td><td>nan              </td><td>nan               </td><td>nan               </td><td>nan                 </td><td>nan                </td><td>YES               </td><td>NO                 </td></tr>\n",
       "<tr><td>2      </td><td>1987.0           </td><td>10.0             </td><td>17.0              </td><td>6.0               </td><td>741.0             </td><td>730.0             </td><td>918.0             </td><td>849.0             </td><td>PS             </td><td>1451.0           </td><td>NA       </td><td>97.0               </td><td>79.0              </td><td>nan               </td><td>29.0              </td><td>11.0              </td><td>SAN     </td><td>SFO   </td><td>447.0            </td><td>nan              </td><td>nan               </td><td>0.0                 </td><td>NA                </td><td>0.0                  </td><td>nan              </td><td>nan               </td><td>nan               </td><td>nan                 </td><td>nan                </td><td>YES               </td><td>YES                </td></tr>\n",
       "<tr><td>3      </td><td>1987.0           </td><td>10.0             </td><td>18.0              </td><td>7.0               </td><td>729.0             </td><td>730.0             </td><td>847.0             </td><td>849.0             </td><td>PS             </td><td>1451.0           </td><td>NA       </td><td>78.0               </td><td>79.0              </td><td>nan               </td><td>-2.0              </td><td>-1.0              </td><td>SAN     </td><td>SFO   </td><td>447.0            </td><td>nan              </td><td>nan               </td><td>0.0                 </td><td>NA                </td><td>0.0                  </td><td>nan              </td><td>nan               </td><td>nan               </td><td>nan                 </td><td>nan                </td><td>NO                </td><td>NO                 </td></tr>\n",
       "<tr><td>4      </td><td>1987.0           </td><td>10.0             </td><td>19.0              </td><td>1.0               </td><td>749.0             </td><td>730.0             </td><td>922.0             </td><td>849.0             </td><td>PS             </td><td>1451.0           </td><td>NA       </td><td>93.0               </td><td>79.0              </td><td>nan               </td><td>33.0              </td><td>19.0              </td><td>SAN     </td><td>SFO   </td><td>447.0            </td><td>nan              </td><td>nan               </td><td>0.0                 </td><td>NA                </td><td>0.0                  </td><td>nan              </td><td>nan               </td><td>nan               </td><td>nan                 </td><td>nan                </td><td>YES               </td><td>YES                </td></tr>\n",
       "<tr><td>5      </td><td>1987.0           </td><td>10.0             </td><td>21.0              </td><td>3.0               </td><td>728.0             </td><td>730.0             </td><td>848.0             </td><td>849.0             </td><td>PS             </td><td>1451.0           </td><td>NA       </td><td>80.0               </td><td>79.0              </td><td>nan               </td><td>-1.0              </td><td>-2.0              </td><td>SAN     </td><td>SFO   </td><td>447.0            </td><td>nan              </td><td>nan               </td><td>0.0                 </td><td>NA                </td><td>0.0                  </td><td>nan              </td><td>nan               </td><td>nan               </td><td>nan                 </td><td>nan                </td><td>NO                </td><td>NO                 </td></tr>\n",
       "<tr><td>6      </td><td>1987.0           </td><td>10.0             </td><td>22.0              </td><td>4.0               </td><td>728.0             </td><td>730.0             </td><td>852.0             </td><td>849.0             </td><td>PS             </td><td>1451.0           </td><td>NA       </td><td>84.0               </td><td>79.0              </td><td>nan               </td><td>3.0               </td><td>-2.0              </td><td>SAN     </td><td>SFO   </td><td>447.0            </td><td>nan              </td><td>nan               </td><td>0.0                 </td><td>NA                </td><td>0.0                  </td><td>nan              </td><td>nan               </td><td>nan               </td><td>nan                 </td><td>nan                </td><td>YES               </td><td>NO                 </td></tr>\n",
       "<tr><td>7      </td><td>1987.0           </td><td>10.0             </td><td>23.0              </td><td>5.0               </td><td>731.0             </td><td>730.0             </td><td>902.0             </td><td>849.0             </td><td>PS             </td><td>1451.0           </td><td>NA       </td><td>91.0               </td><td>79.0              </td><td>nan               </td><td>13.0              </td><td>1.0               </td><td>SAN     </td><td>SFO   </td><td>447.0            </td><td>nan              </td><td>nan               </td><td>0.0                 </td><td>NA                </td><td>0.0                  </td><td>nan              </td><td>nan               </td><td>nan               </td><td>nan                 </td><td>nan                </td><td>YES               </td><td>YES                </td></tr>\n",
       "<tr><td>8      </td><td>1987.0           </td><td>10.0             </td><td>24.0              </td><td>6.0               </td><td>744.0             </td><td>730.0             </td><td>908.0             </td><td>849.0             </td><td>PS             </td><td>1451.0           </td><td>NA       </td><td>84.0               </td><td>79.0              </td><td>nan               </td><td>19.0              </td><td>14.0              </td><td>SAN     </td><td>SFO   </td><td>447.0            </td><td>nan              </td><td>nan               </td><td>0.0                 </td><td>NA                </td><td>0.0                  </td><td>nan              </td><td>nan               </td><td>nan               </td><td>nan                 </td><td>nan                </td><td>YES               </td><td>YES                </td></tr>\n",
       "<tr><td>9      </td><td>1987.0           </td><td>10.0             </td><td>25.0              </td><td>7.0               </td><td>729.0             </td><td>730.0             </td><td>851.0             </td><td>849.0             </td><td>PS             </td><td>1451.0           </td><td>NA       </td><td>82.0               </td><td>79.0              </td><td>nan               </td><td>2.0               </td><td>-1.0              </td><td>SAN     </td><td>SFO   </td><td>447.0            </td><td>nan              </td><td>nan               </td><td>0.0                 </td><td>NA                </td><td>0.0                  </td><td>nan              </td><td>nan               </td><td>nan               </td><td>nan                 </td><td>nan                </td><td>YES               </td><td>NO                 </td></tr>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from h2o.utils.shared_utils import _locate # private function. used to find files within h2o git project directory.\n",
    "# air_path = [_locate(\"bigdata/laptop/airlines_all.05p.csv\")]\n",
    "# air_path = [_locate(\"bigdata/laptop/flights-nyc/flights14.csv.zip\")]\n",
    "air_path = [_locate(\"smalldata/airlines/allyears2k_headers.zip\")]\n",
    "\n",
    "# ----------\n",
    "\n",
    "# 1- Load data - 1 row per flight.  Has columns showing the origin,\n",
    "# destination, departure and arrival time, carrier information, and\n",
    "# whether the flight was delayed.\n",
    "print(\"Import and Parse airlines data\")\n",
    "data = h2o.import_file(path=air_path)\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "glm Model Build Progress: [##################################################] 100%\n"
     ]
    }
   ],
   "source": [
    "# ----------\n",
    "\n",
    "# 2- Data exploration and munging. Generate scatter plots \n",
    "# of various columns and plot fitted GLM model.\n",
    "\n",
    "# Function to fit a GLM model and plot the fitted (x,y) values\n",
    "def scatter_plot(data, x, y, max_points = 1000, fit = True):\n",
    "    if(fit):\n",
    "        lr = H2OGeneralizedLinearEstimator(family = \"gaussian\")\n",
    "        lr.train(x=x, y=y, training_frame=data)\n",
    "        coeff = lr.coef()\n",
    "    df = data[[x,y]]\n",
    "    runif = df[y].runif()\n",
    "    df_subset = df[runif < float(max_points)/data.nrow]\n",
    "    df_py = h2o.as_list(df_subset)\n",
    "    \n",
    "    if(fit): h2o.remove(lr._id)\n",
    "\n",
    "    # If x variable is string, generate box-and-whisker plot\n",
    "    if(df_py[x].dtype == \"object\"):\n",
    "        if interactive: df_py.boxplot(column = y, by = x)\n",
    "    # Otherwise, generate a scatter plot\n",
    "    else:\n",
    "        if interactive: df_py.plot(x = x, y = y, kind = \"scatter\")\n",
    "    \n",
    "    if(fit):\n",
    "        x_min = min(df_py[x])\n",
    "        x_max = max(df_py[x])\n",
    "        y_min = coeff[\"Intercept\"] + coeff[x]*x_min\n",
    "        y_max = coeff[\"Intercept\"] + coeff[x]*x_max\n",
    "        plt.plot([x_min, x_max], [y_min, y_max], \"k-\")\n",
    "    if interactive: plt.show()\n",
    "\n",
    "scatter_plot(data, \"Distance\", \"AirTime\", fit = True)\n",
    "scatter_plot(data, \"UniqueCarrier\", \"ArrDelay\", max_points = 5000, fit = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr><th style=\"text-align: right;\">  Month</th><th style=\"text-align: right;\">  sum_Cancelled</th><th style=\"text-align: right;\">  nrow_Year</th></tr>\n",
       "<tr><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">           1067</td><td style=\"text-align: right;\">      41979</td></tr>\n",
       "<tr><td style=\"text-align: right;\">     10</td><td style=\"text-align: right;\">             19</td><td style=\"text-align: right;\">       1999</td></tr>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:2 Cols:3\n",
      "\n",
      "Chunk compression summary: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>chunk_type</b></td>\n",
       "<td><b>chunk_name</b></td>\n",
       "<td><b>count</b></td>\n",
       "<td><b>count_percentage</b></td>\n",
       "<td><b>size</b></td>\n",
       "<td><b>size_percentage</b></td></tr>\n",
       "<tr><td>C1N</td>\n",
       "<td>1-Byte Integers (w/o NAs)</td>\n",
       "<td>1</td>\n",
       "<td>33.333336</td>\n",
       "<td>     70  B</td>\n",
       "<td>30.434782</td></tr>\n",
       "<tr><td>C2</td>\n",
       "<td>2-Byte Integers</td>\n",
       "<td>1</td>\n",
       "<td>33.333336</td>\n",
       "<td>     72  B</td>\n",
       "<td>31.304348</td></tr>\n",
       "<tr><td>C2S</td>\n",
       "<td>2-Byte Fractions</td>\n",
       "<td>1</td>\n",
       "<td>33.333336</td>\n",
       "<td>     88  B</td>\n",
       "<td>38.260868</td></tr></table></div>"
      ],
      "text/plain": [
       "chunk_type    chunk_name                 count    count_percentage    size    size_percentage\n",
       "------------  -------------------------  -------  ------------------  ------  -----------------\n",
       "C1N           1-Byte Integers (w/o NAs)  1        33.3333             70  B   30.4348\n",
       "C2            2-Byte Integers            1        33.3333             72  B   31.3043\n",
       "C2S           2-Byte Fractions           1        33.3333             88  B   38.2609"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Frame distribution summary: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>size</b></td>\n",
       "<td><b>number_of_rows</b></td>\n",
       "<td><b>number_of_chunks_per_column</b></td>\n",
       "<td><b>number_of_chunks</b></td></tr>\n",
       "<tr><td>172.16.2.84:54321</td>\n",
       "<td>    230  B</td>\n",
       "<td>2.0</td>\n",
       "<td>1.0</td>\n",
       "<td>3.0</td></tr>\n",
       "<tr><td>mean</td>\n",
       "<td>    230  B</td>\n",
       "<td>2.0</td>\n",
       "<td>1.0</td>\n",
       "<td>3.0</td></tr>\n",
       "<tr><td>min</td>\n",
       "<td>    230  B</td>\n",
       "<td>2.0</td>\n",
       "<td>1.0</td>\n",
       "<td>3.0</td></tr>\n",
       "<tr><td>max</td>\n",
       "<td>    230  B</td>\n",
       "<td>2.0</td>\n",
       "<td>1.0</td>\n",
       "<td>3.0</td></tr>\n",
       "<tr><td>stddev</td>\n",
       "<td>      0  B</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>total</td>\n",
       "<td>    230  B</td>\n",
       "<td>2.0</td>\n",
       "<td>1.0</td>\n",
       "<td>3.0</td></tr></table></div>"
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       "                   size    number_of_rows    number_of_chunks_per_column    number_of_chunks\n",
       "-----------------  ------  ----------------  -----------------------------  ------------------\n",
       "172.16.2.84:54321  230  B  2                 1                              3\n",
       "mean               230  B  2                 1                              3\n",
       "min                230  B  2                 1                              3\n",
       "max                230  B  2                 1                              3\n",
       "stddev             0  B    0                 0                              0\n",
       "total              230  B  2                 1                              3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr><th>       </th><th>Month            </th><th>sum_Cancelled    </th><th>nrow_Year        </th></tr>\n",
       "<tr><td>type   </td><td>int              </td><td>int              </td><td>int              </td></tr>\n",
       "<tr><td>mins   </td><td>1.0              </td><td>19.0             </td><td>1999.0           </td></tr>\n",
       "<tr><td>mean   </td><td>5.5              </td><td>543.0            </td><td>21989.0          </td></tr>\n",
       "<tr><td>maxs   </td><td>10.0             </td><td>1067.0           </td><td>41979.0          </td></tr>\n",
       "<tr><td>sigma  </td><td>6.363961030678928</td><td>741.0479066835018</td><td>28270.12911183817</td></tr>\n",
       "<tr><td>zeros  </td><td>0                </td><td>0                </td><td>0                </td></tr>\n",
       "<tr><td>missing</td><td>0                </td><td>0                </td><td>0                </td></tr>\n",
       "<tr><td>0      </td><td>1.0              </td><td>1067.0           </td><td>41979.0          </td></tr>\n",
       "<tr><td>1      </td><td>10.0             </td><td>19.0             </td><td>1999.0           </td></tr>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group flights by month\n",
    "grouped = data.group_by(\"Month\")\n",
    "bpd = grouped.count().sum(\"Cancelled\").frame\n",
    "bpd.show()\n",
    "bpd.describe()\n",
    "bpd.dim\n",
    "\n",
    "# Convert columns to factors\n",
    "data[\"Year\"]      = data[\"Year\"]     .asfactor()\n",
    "data[\"Month\"]     = data[\"Month\"]    .asfactor()\n",
    "data[\"DayOfWeek\"] = data[\"DayOfWeek\"].asfactor()\n",
    "data[\"Cancelled\"] = data[\"Cancelled\"].asfactor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Parse Progress: [##################################################] 100%\n",
      "\n",
      "glm Model Build Progress: [##################################################] 100%\n"
     ]
    }
   ],
   "source": [
    "# Calculate and plot travel time\n",
    "hour1 = data[\"CRSArrTime\"] / 100\n",
    "mins1 = data[\"CRSArrTime\"] % 100\n",
    "arrTime = hour1*60 + mins1\n",
    "\n",
    "hour2 = data[\"CRSDepTime\"] / 100\n",
    "mins2 = data[\"CRSDepTime\"] % 100\n",
    "depTime = hour2*60 + mins2\n",
    "\n",
    "# TODO: Replace this once list comprehension is supported. See PUBDEV-1286.\n",
    "# data[\"TravelTime\"] = [x if x > 0 else None for x in (arrTime - depTime)]\n",
    "data[\"TravelTime\"] = (arrTime-depTime > 0).ifelse((arrTime-depTime), h2o.H2OFrame([[None]] * data.nrow))\n",
    "scatter_plot(data, \"Distance\", \"TravelTime\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "glm Model Build Progress: [##################################################] 100%\n"
     ]
    }
   ],
   "source": [
    "# Impute missing travel times and re-plot\n",
    "data.impute(column = \"Distance\", by = [\"Origin\", \"Dest\"])\n",
    "scatter_plot(data, \"Distance\", \"TravelTime\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "glm Model Build Progress: [##################################################] 100%\n",
      "\n",
      "gbm Model Build Progress: [##################################################] 100%\n",
      "\n",
      "gbm Model Build Progress: [##################################################] 100%\n",
      "\n",
      "drf Model Build Progress: [##################################################] 100%\n",
      "\n",
      "drf Model Build Progress: [##################################################] 100%\n",
      "\n",
      "deeplearning Model Build Progress: [##################################################] 100%\n"
     ]
    }
   ],
   "source": [
    "# ----------\n",
    "# 3- Fit a model on train; using test as validation\n",
    "\n",
    "# Create test/train split\n",
    "s = data[\"Year\"].runif()\n",
    "train = data[s <= 0.75]\n",
    "test  = data[s > 0.75]\n",
    "\n",
    "# Set predictor and response variables\n",
    "myY = \"IsDepDelayed\"\n",
    "myX = [\"Origin\", \"Dest\", \"Year\", \"UniqueCarrier\", \"DayOfWeek\", \"Month\", \"Distance\", \"FlightNum\"]\n",
    "\n",
    "# Simple GLM - Predict Delays\n",
    "data_glm = H2OGeneralizedLinearEstimator(family=\"binomial\", standardize=True)\n",
    "data_glm.train(x               =myX,\n",
    "               y               =myY,\n",
    "               training_frame  =train,\n",
    "               validation_frame=test)\n",
    "\n",
    "# Simple GBM\n",
    "data_gbm = H2OGradientBoostingEstimator(balance_classes=True,\n",
    "                                        ntrees         =3,\n",
    "                                        max_depth      =1,\n",
    "                                        distribution   =\"bernoulli\",\n",
    "                                        learn_rate     =0.1,\n",
    "                                        min_rows       =2)\n",
    "\n",
    "data_gbm.train(x               =myX,\n",
    "               y               =myY,\n",
    "               training_frame  =train,\n",
    "               validation_frame=test)\n",
    "\n",
    "# Complex GBM\n",
    "data_gbm2 = H2OGradientBoostingEstimator(balance_classes=True,\n",
    "                                         ntrees         =50,\n",
    "                                         max_depth      =5,\n",
    "                                         distribution   =\"bernoulli\",\n",
    "                                         learn_rate     =0.1,\n",
    "                                         min_rows       =2)\n",
    "\n",
    "data_gbm2.train(x               =myX,\n",
    "                y               =myY,\n",
    "                training_frame  =train,\n",
    "                validation_frame=test)\n",
    "\n",
    "# Simple Random Forest\n",
    "data_rf = H2ORandomForestEstimator(ntrees         =5,\n",
    "                                   max_depth      =2,\n",
    "                                   balance_classes=True)\n",
    "\n",
    "data_rf.train(x               =myX,\n",
    "              y               =myY,\n",
    "              training_frame  =train,\n",
    "              validation_frame=test)\n",
    "\n",
    "# Complex Random Forest\n",
    "data_rf2 = H2ORandomForestEstimator(ntrees         =10,\n",
    "                                    max_depth      =5,\n",
    "                                    balance_classes=True)\n",
    "\n",
    "data_rf2.train(x               =myX,\n",
    "               y               =myY,\n",
    "               training_frame  =train,\n",
    "               validation_frame=test)\n",
    "\n",
    "# Deep Learning with 5 epochs\n",
    "data_dl = H2ODeepLearningEstimator(hidden              =[10,10],\n",
    "                                   epochs              =5,\n",
    "                                   variable_importances=True,\n",
    "                                   balance_classes     =True,\n",
    "                                   loss                =\"Automatic\")\n",
    "\n",
    "data_dl.train(x               =myX,\n",
    "              y               =myY,\n",
    "              training_frame  =train,\n",
    "              validation_frame=test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable Importances:\n",
      "\n",
      "| Predictor        |   Normalized Coefficient |\n",
      "|------------------+--------------------------|\n",
      "| Year.2008        |               2.1663     |\n",
      "| Dest.HTS         |               1.59911    |\n",
      "| Year.2003        |               1.59565    |\n",
      "| Origin.MDW       |               1.58362    |\n",
      "| Year.2007        |               1.37479    |\n",
      "| Origin.HPN       |               1.34354    |\n",
      "| Origin.LIH       |               1.32598    |\n",
      "| Dest.LYH         |               1.29275    |\n",
      "| Origin.LBB       |               1.21984    |\n",
      "| Origin.LEX       |               1.21291    |\n",
      "| Origin.ERI       |               1.20959    |\n",
      "| Origin.TLH       |               1.17343    |\n",
      "| Origin.CAE       |               1.15044    |\n",
      "| UniqueCarrier.HP |               1.12944    |\n",
      "| Origin.PSP       |               1.11685    |\n",
      "| Origin.HNL       |               1.11194    |\n",
      "| Origin.TRI       |               1.02187    |\n",
      "| UniqueCarrier.TW |               1.0169     |\n",
      "| Year.2001        |               0.979973   |\n",
      "| Year.2002        |               0.944374   |\n",
      "| Origin.SDF       |               0.939753   |\n",
      "| Origin.ATL       |               0.935832   |\n",
      "| Origin.GRR       |               0.884671   |\n",
      "| Origin.PBI       |               0.882257   |\n",
      "| Origin.CHO       |               0.878584   |\n",
      "| Origin.OGG       |               0.864754   |\n",
      "| Origin.SRQ       |               0.856535   |\n",
      "| Year.2004        |               0.846669   |\n",
      "| Origin.MYR       |               0.835173   |\n",
      "| Origin.ACY       |               0.804102   |\n",
      "| Origin.ORD       |               0.787865   |\n",
      "| Year.1994        |               0.781128   |\n",
      "| Origin.MAF       |               0.766548   |\n",
      "| Origin.TUL       |               0.765077   |\n",
      "| Origin.MRY       |               0.759124   |\n",
      "| Year.2006        |               0.749834   |\n",
      "| Origin.STL       |               0.737706   |\n",
      "| Origin.LYH       |               0.728328   |\n",
      "| Dest.CHO         |               0.728328   |\n",
      "| Origin.CMH       |               0.703809   |\n",
      "| Dest.GSO         |               0.694797   |\n",
      "| Origin.BTV       |               0.678703   |\n",
      "| Origin.ROA       |               0.672739   |\n",
      "| Dest.ISP         |               0.666122   |\n",
      "| Dest.LIH         |               0.647256   |\n",
      "| Origin.AUS       |               0.646233   |\n",
      "| Origin.IAH       |               0.637049   |\n",
      "| Dest.FLL         |               0.624057   |\n",
      "| Origin.MLB       |               0.611271   |\n",
      "| Dest.PBI         |               0.609092   |\n",
      "| Origin.PIT       |               0.604604   |\n",
      "| Origin.PWM       |               0.603332   |\n",
      "| Dest.ICT         |               0.601697   |\n",
      "| Year.1996        |               0.601507   |\n",
      "| Origin.TYS       |               0.590041   |\n",
      "| Origin.MSY       |               0.587653   |\n",
      "| Year.1990        |               0.564752   |\n",
      "| Dest.DAY         |               0.564026   |\n",
      "| Origin.SYR       |               0.560879   |\n",
      "| Dest.IAH         |               0.553572   |\n",
      "| Dest.EUG         |               0.54793    |\n",
      "| Origin.JAX       |               0.542031   |\n",
      "| Origin.BOI       |               0.541044   |\n",
      "| Dest.TOL         |               0.528751   |\n",
      "| Dest.TPA         |               0.51248    |\n",
      "| Dest.BUF         |               0.512192   |\n",
      "| Dest.PSP         |               0.508527   |\n",
      "| Origin.ALB       |               0.506946   |\n",
      "| Origin.SAV       |               0.50483    |\n",
      "| Origin.CRW       |               0.504431   |\n",
      "| Dest.PNS         |               0.503218   |\n",
      "| UniqueCarrier.CO |               0.499991   |\n",
      "| Dest.SFO         |               0.499403   |\n",
      "| Origin.PHL       |               0.498516   |\n",
      "| Year.1997        |               0.492557   |\n",
      "| Origin.OKC       |               0.491762   |\n",
      "| Origin.LGA       |               0.488253   |\n",
      "| Origin.MIA       |               0.480325   |\n",
      "| Origin.OMA       |               0.477082   |\n",
      "| Dest.CHS         |               0.475901   |\n",
      "| Dest.CAK         |               0.473522   |\n",
      "| Origin.FLL       |               0.469294   |\n",
      "| Origin.ICT       |               0.464117   |\n",
      "| Dest.GEG         |               0.461246   |\n",
      "| Origin.EGE       |               0.461207   |\n",
      "| Dest.ABQ         |               0.461191   |\n",
      "| Dest.EYW         |               0.452089   |\n",
      "| Year.2005        |               0.45045    |\n",
      "| Dest.IND         |               0.449927   |\n",
      "| UniqueCarrier.WN |               0.446792   |\n",
      "| Origin.IND       |               0.446311   |\n",
      "| Origin.GSO       |               0.442529   |\n",
      "| Origin.MCO       |               0.434966   |\n",
      "| Origin.LAX       |               0.433672   |\n",
      "| Origin.BDL       |               0.418545   |\n",
      "| Dest.CAE         |               0.414453   |\n",
      "| Dest.SMF         |               0.409427   |\n",
      "| Origin.CRP       |               0.403216   |\n",
      "| Origin.DFW       |               0.399445   |\n",
      "| Dest.BDL         |               0.395146   |\n",
      "| Dest.CVG         |               0.391672   |\n",
      "| Dest.UCA         |               0.39075    |\n",
      "| Origin.DSM       |               0.387103   |\n",
      "| Origin.MEM       |               0.383554   |\n",
      "| Origin.EYW       |               0.375727   |\n",
      "| Dest.CLE         |               0.372843   |\n",
      "| Dest.FAT         |               0.369287   |\n",
      "| UniqueCarrier.PI |               0.366404   |\n",
      "| Origin.SLC       |               0.354344   |\n",
      "| Origin.JFK       |               0.34159    |\n",
      "| Origin.BWI       |               0.339737   |\n",
      "| Dest.MIA         |               0.338326   |\n",
      "| Origin.ROC       |               0.328992   |\n",
      "| Origin.OAK       |               0.327167   |\n",
      "| Dest.BGM         |               0.323214   |\n",
      "| Origin.IAD       |               0.320497   |\n",
      "| Dest.JAX         |               0.319508   |\n",
      "| Dest.MKE         |               0.31828    |\n",
      "| Year.1992        |               0.31714    |\n",
      "| Dest.MCO         |               0.315641   |\n",
      "| Dest.FAY         |               0.315447   |\n",
      "| Dest.COS         |               0.314929   |\n",
      "| Origin.RNO       |               0.314859   |\n",
      "| Origin.MCI       |               0.313843   |\n",
      "| Dest.SAT         |               0.305571   |\n",
      "| Year.1995        |               0.29602    |\n",
      "| Origin.SAN       |               0.292782   |\n",
      "| Dest.OGG         |               0.281564   |\n",
      "| Year.1991        |               0.274708   |\n",
      "| Dest.BUR         |               0.270584   |\n",
      "| Dest.ALB         |               0.268558   |\n",
      "| Dest.TUL         |               0.26762    |\n",
      "| Origin.DAY       |               0.264843   |\n",
      "| Origin.BUR       |               0.264689   |\n",
      "| Origin.CLT       |               0.256984   |\n",
      "| Origin.ONT       |               0.256321   |\n",
      "| Origin.MKE       |               0.254529   |\n",
      "| Origin.HRL       |               0.253809   |\n",
      "| DayOfWeek.5      |               0.244342   |\n",
      "| UniqueCarrier.US |               0.239344   |\n",
      "| Dest.BTV         |               0.23824    |\n",
      "| Origin.ABE       |               0.234584   |\n",
      "| Origin.TPA       |               0.22891    |\n",
      "| Dest.STT         |               0.225113   |\n",
      "| Origin.STX       |               0.223986   |\n",
      "| Dest.GSP         |               0.221914   |\n",
      "| Origin.BHM       |               0.219408   |\n",
      "| Dest.IAD         |               0.219399   |\n",
      "| Origin.BOS       |               0.21936    |\n",
      "| Origin.MDT       |               0.217089   |\n",
      "| Dest.PVD         |               0.21636    |\n",
      "| Dest.RSW         |               0.208373   |\n",
      "| Origin.ELP       |               0.207048   |\n",
      "| Origin.DEN       |               0.205402   |\n",
      "| Dest.LIT         |               0.204071   |\n",
      "| Month.10         |               0.203185   |\n",
      "| Year.1987        |               0.203185   |\n",
      "| Dest.BWI         |               0.202309   |\n",
      "| Origin.MSP       |               0.201702   |\n",
      "| Dest.PDX         |               0.201547   |\n",
      "| Dest.ROC         |               0.199012   |\n",
      "| Origin.TUS       |               0.197624   |\n",
      "| Dest.KOA         |               0.197388   |\n",
      "| Dest.CLT         |               0.191233   |\n",
      "| Dest.OAJ         |               0.188976   |\n",
      "| Year.1999        |               0.186221   |\n",
      "| Origin.SJC       |               0.182876   |\n",
      "| Dest.DAL         |               0.179589   |\n",
      "| Origin.BUF       |               0.178246   |\n",
      "| DayOfWeek.2      |               0.17761    |\n",
      "| Origin.DAL       |               0.175027   |\n",
      "| Origin.CLE       |               0.173502   |\n",
      "| Dest.GRR         |               0.169856   |\n",
      "| Dest.PWM         |               0.16768    |\n",
      "| UniqueCarrier.AA |               0.167342   |\n",
      "| Year.1993        |               0.166087   |\n",
      "| Dest.RNO         |               0.165744   |\n",
      "| Distance         |               0.163211   |\n",
      "| Dest.LBB         |               0.157175   |\n",
      "| Dest.HRL         |               0.156284   |\n",
      "| Dest.ABE         |               0.155532   |\n",
      "| Dest.CMH         |               0.154857   |\n",
      "| Dest.CRP         |               0.151555   |\n",
      "| Dest.SNA         |               0.151435   |\n",
      "| Origin.SFO       |               0.150441   |\n",
      "| Dest.SEA         |               0.149936   |\n",
      "| Dest.ROA         |               0.148303   |\n",
      "| Year.2000        |               0.146046   |\n",
      "| Dest.ORF         |               0.134053   |\n",
      "| Dest.SAN         |               0.133593   |\n",
      "| DayOfWeek.6      |               0.132748   |\n",
      "| Dest.MSP         |               0.132271   |\n",
      "| Origin.COS       |               0.128671   |\n",
      "| Dest.HOU         |               0.127342   |\n",
      "| Dest.TUS         |               0.120346   |\n",
      "| DayOfWeek.4      |               0.119748   |\n",
      "| Dest.DSM         |               0.116603   |\n",
      "| Dest.LAX         |               0.11609    |\n",
      "| Dest.SLC         |               0.114966   |\n",
      "| Dest.AVP         |               0.112227   |\n",
      "| Dest.STL         |               0.110793   |\n",
      "| Origin.ORF       |               0.108536   |\n",
      "| Dest.BHM         |               0.108348   |\n",
      "| UniqueCarrier.UA |               0.107298   |\n",
      "| Origin.DTW       |               0.105773   |\n",
      "| Dest.MDW         |               0.10405    |\n",
      "| Dest.DFW         |               0.0989164  |\n",
      "| Origin.CVG       |               0.0967693  |\n",
      "| Origin.SMF       |               0.0959796  |\n",
      "| Origin.RSW       |               0.0934595  |\n",
      "| Origin.SWF       |               0.0927228  |\n",
      "| Month.1          |               0.092347   |\n",
      "| Dest.PHL         |               0.0848795  |\n",
      "| Dest.PHX         |               0.0848389  |\n",
      "| Origin.RDU       |               0.0839633  |\n",
      "| Origin.DCA       |               0.0832363  |\n",
      "| Dest.OAK         |               0.0818515  |\n",
      "| Dest.MCI         |               0.0815358  |\n",
      "| Dest.EWR         |               0.0785491  |\n",
      "| Dest.DEN         |               0.0783454  |\n",
      "| Dest.DTW         |               0.0774459  |\n",
      "| Year.1989        |               0.0762646  |\n",
      "| Dest.LAS         |               0.0743316  |\n",
      "| Dest.MDT         |               0.0731147  |\n",
      "| Dest.RIC         |               0.0723303  |\n",
      "| Dest.OMA         |               0.0661859  |\n",
      "| UniqueCarrier.PS |               0.0645156  |\n",
      "| Year.1998        |               0.05845    |\n",
      "| Dest.MHT         |               0.0576363  |\n",
      "| Origin.BNA       |               0.0553462  |\n",
      "| Origin.PHX       |               0.0522407  |\n",
      "| Origin.GNV       |               0.0504304  |\n",
      "| Dest.MSY         |               0.0501866  |\n",
      "| Origin.PVD       |               0.0490418  |\n",
      "| Origin.MFR       |               0.0437977  |\n",
      "| Origin.SNA       |               0.0421396  |\n",
      "| FlightNum        |               0.0376186  |\n",
      "| Origin.SEA       |               0.0372322  |\n",
      "| Dest.BNA         |               0.0347007  |\n",
      "| Origin.PHF       |               0.029703   |\n",
      "| Dest.LGA         |               0.0291171  |\n",
      "| Intercept        |               0.026855   |\n",
      "| Dest.ORD         |               0.0244753  |\n",
      "| DayOfWeek.7      |               0.0234737  |\n",
      "| Dest.SJC         |               0.0177833  |\n",
      "| Dest.AVL         |               0.0172911  |\n",
      "| Dest.BOS         |               0.0162872  |\n",
      "| DayOfWeek.1      |               0.0153713  |\n",
      "| Origin.PDX       |               0.0112833  |\n",
      "| Origin.RIC       |               0.011192   |\n",
      "| Origin.SAT       |               0.0110852  |\n",
      "| Year.1988        |               0.00996483 |\n",
      "| Origin.BGM       |               0.00952641 |\n",
      "| Dest.PIT         |               0.00935131 |\n",
      "| Dest.ATL         |               0.00882664 |\n",
      "| Origin.CHS       |               0.00818887 |\n",
      "| Origin.ABQ       |               0.00803383 |\n",
      "| Dest.ILM         |               0.00255637 |\n",
      "| UniqueCarrier.DL |               0.00110988 |\n",
      "| Origin.GEG       |               0          |\n",
      "| Origin.SBN       |               0          |\n",
      "| Origin.STT       |               0          |\n",
      "| Origin.ANC       |               0          |\n",
      "| Dest.AMA         |               0          |\n",
      "| Dest.RDU         |               0          |\n",
      "| Dest.FNT         |               0          |\n",
      "| Dest.LEX         |               0          |\n",
      "| Origin.HOU       |               0          |\n",
      "| Origin.LAS       |               0          |\n",
      "| Dest.ACY         |               0          |\n",
      "| Dest.AUS         |               0          |\n",
      "| Dest.SDF         |               0          |\n",
      "| Dest.DCA         |               0          |\n",
      "| Dest.MRY         |               0          |\n",
      "| Dest.SCK         |               0          |\n",
      "| Origin.EWR       |               0          |\n",
      "| Dest.PHF         |               0          |\n",
      "| Dest.BOI         |               0          |\n",
      "| Origin.AVP       |               0          |\n",
      "| Origin.LAN       |               0          |\n",
      "| Dest.SBN         |               0          |\n",
      "| Dest.JFK         |               0          |\n",
      "| Dest.SJU         |               0          |\n",
      "| Origin.UCA       |               0          |\n",
      "| DayOfWeek.3      |               0          |\n",
      "| Dest.SYR         |               0          |\n",
      "| Origin.KOA       |               0          |\n",
      "| Origin.MHT       |               0          |\n",
      "| Origin.LIT       |               0          |\n",
      "| Dest.JAN         |               0          |\n",
      "| Origin.SCK       |               0          |\n",
      "| Dest.ERI         |               0          |\n",
      "| Dest.ELM         |               0          |\n",
      "| Dest.HNL         |               0          |\n",
      "| Dest.OKC         |               0          |\n",
      "| Dest.HPN         |               0          |\n",
      "| Origin.BIL       |               0          |\n",
      "| Dest.ORH         |               0          |\n",
      "| Dest.MYR         |               0          |\n",
      "| Dest.SRQ         |               0          |\n",
      "| Dest.ANC         |               0          |\n",
      "| Dest.CHA         |               0          |\n",
      "| Dest.SWF         |               0          |\n",
      "| Origin.JAN       |               0          |\n",
      "| Origin.AMA       |               0          |\n",
      "| Dest.ONT         |               0          |\n",
      "| Dest.ELP         |               0          |\n",
      "| Origin.ISP       |               0          |\n",
      "| Dest.MAF         |               0          |\n",
      "| Origin.SJU       |               0          |\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('Year', 860.6602783203125, 1.0, 0.5018886676744018),\n",
       " ('Dest', 593.151123046875, 0.6891814784394192, 0.3458923739998345),\n",
       " ('UniqueCarrier', 87.23373413085938, 0.1013567563511901, 0.05086980740489776),\n",
       " ('DayOfWeek', 80.93794250488281, 0.09404168467358974, 0.04719845582668416),\n",
       " ('Distance', 65.31503295898438, 0.07588944744429815, 0.03808805366836533),\n",
       " ('FlightNum', 27.54490852355957, 0.032004391532181486, 0.01606264142581647),\n",
       " ('Month', 0.0, 0.0, 0.0),\n",
       " ('Origin', 0.0, 0.0, 0.0)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Variable importances from each algorithm\n",
    "# Calculate magnitude of normalized GLM coefficients\n",
    "from six import iteritems\n",
    "glm_varimp = data_glm.coef_norm()\n",
    "for k,v in iteritems(glm_varimp):\n",
    "    glm_varimp[k] = abs(glm_varimp[k])\n",
    "    \n",
    "# Sort in descending order by magnitude\n",
    "glm_sorted = sorted(glm_varimp.items(), key = operator.itemgetter(1), reverse = True)\n",
    "table = tabulate(glm_sorted, headers = [\"Predictor\", \"Normalized Coefficient\"], tablefmt = \"orgtbl\")\n",
    "print(\"Variable Importances:\\n\\n\" + table)\n",
    "\n",
    "data_gbm.varimp()\n",
    "data_rf.varimp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ModelMetricsBinomial: gbm\n",
      "** Reported on test data. **\n",
      "\n",
      "MSE: 0.20407778554922562\n",
      "R^2: 0.18116065189707653\n",
      "LogLoss: 0.5945117554029998\n",
      "AUC: 0.7467255149856272\n",
      "Gini: 0.49345102997125445\n",
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.3514986726263641: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>NO</b></td>\n",
       "<td><b>YES</b></td>\n",
       "<td><b>Error</b></td>\n",
       "<td><b>Rate</b></td></tr>\n",
       "<tr><td>NO</td>\n",
       "<td>1968.0</td>\n",
       "<td>3199.0</td>\n",
       "<td>0.6191</td>\n",
       "<td> (3199.0/5167.0)</td></tr>\n",
       "<tr><td>YES</td>\n",
       "<td>657.0</td>\n",
       "<td>5118.0</td>\n",
       "<td>0.1138</td>\n",
       "<td> (657.0/5775.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>2625.0</td>\n",
       "<td>8317.0</td>\n",
       "<td>0.3524</td>\n",
       "<td> (3856.0/10942.0)</td></tr></table></div>"
      ],
      "text/plain": [
       "       NO    YES    Error    Rate\n",
       "-----  ----  -----  -------  ----------------\n",
       "NO     1968  3199   0.6191   (3199.0/5167.0)\n",
       "YES    657   5118   0.1138   (657.0/5775.0)\n",
       "Total  2625  8317   0.3524   (3856.0/10942.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Maximum Metrics: Maximum metrics at their respective thresholds\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n",
       "<td><b>threshold</b></td>\n",
       "<td><b>value</b></td>\n",
       "<td><b>idx</b></td></tr>\n",
       "<tr><td>max f1</td>\n",
       "<td>0.3514987</td>\n",
       "<td>0.7263696</td>\n",
       "<td>287.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.1882069</td>\n",
       "<td>0.8505254</td>\n",
       "<td>372.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.5203289</td>\n",
       "<td>0.7060683</td>\n",
       "<td>199.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.4815086</td>\n",
       "<td>0.6868945</td>\n",
       "<td>220.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.9607084</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_MCC</td>\n",
       "<td>0.5011300</td>\n",
       "<td>0.3721374</td>\n",
       "<td>209.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.5067588</td>\n",
       "<td>0.6851171</td>\n",
       "<td>206.0</td></tr></table></div>"
      ],
      "text/plain": [
       "metric                      threshold    value     idx\n",
       "--------------------------  -----------  --------  -----\n",
       "max f1                      0.351499     0.72637   287\n",
       "max f2                      0.188207     0.850525  372\n",
       "max f0point5                0.520329     0.706068  199\n",
       "max accuracy                0.481509     0.686895  220\n",
       "max precision               0.960708     1         0\n",
       "max absolute_MCC            0.50113      0.372137  209\n",
       "max min_per_class_accuracy  0.506759     0.685117  206"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gains/Lift Table: Avg response rate: 52.78 %\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>group</b></td>\n",
       "<td><b>lower_threshold</b></td>\n",
       "<td><b>cumulative_data_fraction</b></td>\n",
       "<td><b>response_rate</b></td>\n",
       "<td><b>cumulative_response_rate</b></td>\n",
       "<td><b>capture_rate</b></td>\n",
       "<td><b>cumulative_capture_rate</b></td>\n",
       "<td><b>lift</b></td>\n",
       "<td><b>cumulative_lift</b></td>\n",
       "<td><b>gain</b></td>\n",
       "<td><b>cumulative_gain</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>1</td>\n",
       "<td>0.8528015</td>\n",
       "<td>0.0500823</td>\n",
       "<td>0.8978102</td>\n",
       "<td>0.8978102</td>\n",
       "<td>0.0851948</td>\n",
       "<td>0.0851948</td>\n",
       "<td>1.7010977</td>\n",
       "<td>1.7010977</td>\n",
       "<td>70.1097734</td>\n",
       "<td>70.1097734</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2</td>\n",
       "<td>0.7962821</td>\n",
       "<td>0.1001645</td>\n",
       "<td>0.8321168</td>\n",
       "<td>0.8649635</td>\n",
       "<td>0.0789610</td>\n",
       "<td>0.1641558</td>\n",
       "<td>1.5766272</td>\n",
       "<td>1.6388625</td>\n",
       "<td>57.6627168</td>\n",
       "<td>63.8862451</td></tr>\n",
       "<tr><td></td>\n",
       "<td>3</td>\n",
       "<td>0.7494101</td>\n",
       "<td>0.1500640</td>\n",
       "<td>0.7893773</td>\n",
       "<td>0.8398295</td>\n",
       "<td>0.0746320</td>\n",
       "<td>0.2387879</td>\n",
       "<td>1.4956478</td>\n",
       "<td>1.5912405</td>\n",
       "<td>49.5647844</td>\n",
       "<td>59.1240542</td></tr>\n",
       "<tr><td></td>\n",
       "<td>4</td>\n",
       "<td>0.7173250</td>\n",
       "<td>0.2000548</td>\n",
       "<td>0.7787934</td>\n",
       "<td>0.8245774</td>\n",
       "<td>0.0737662</td>\n",
       "<td>0.3125541</td>\n",
       "<td>1.4755944</td>\n",
       "<td>1.5623422</td>\n",
       "<td>47.5594387</td>\n",
       "<td>56.2342211</td></tr>\n",
       "<tr><td></td>\n",
       "<td>5</td>\n",
       "<td>0.6855407</td>\n",
       "<td>0.2501371</td>\n",
       "<td>0.7408759</td>\n",
       "<td>0.8078188</td>\n",
       "<td>0.0703030</td>\n",
       "<td>0.3828571</td>\n",
       "<td>1.4037514</td>\n",
       "<td>1.5305893</td>\n",
       "<td>40.3751382</td>\n",
       "<td>53.0589279</td></tr>\n",
       "<tr><td></td>\n",
       "<td>6</td>\n",
       "<td>0.6556998</td>\n",
       "<td>0.3002193</td>\n",
       "<td>0.7043796</td>\n",
       "<td>0.7905632</td>\n",
       "<td>0.0668398</td>\n",
       "<td>0.4496970</td>\n",
       "<td>1.3346011</td>\n",
       "<td>1.4978947</td>\n",
       "<td>33.4601068</td>\n",
       "<td>49.7894747</td></tr>\n",
       "<tr><td></td>\n",
       "<td>7</td>\n",
       "<td>0.6236469</td>\n",
       "<td>0.3500274</td>\n",
       "<td>0.6495413</td>\n",
       "<td>0.7704961</td>\n",
       "<td>0.0612987</td>\n",
       "<td>0.5109957</td>\n",
       "<td>1.2306980</td>\n",
       "<td>1.4598733</td>\n",
       "<td>23.0697963</td>\n",
       "<td>45.9873272</td></tr>\n",
       "<tr><td></td>\n",
       "<td>8</td>\n",
       "<td>0.5865343</td>\n",
       "<td>0.4001097</td>\n",
       "<td>0.5985401</td>\n",
       "<td>0.7489721</td>\n",
       "<td>0.0567965</td>\n",
       "<td>0.5677922</td>\n",
       "<td>1.1340652</td>\n",
       "<td>1.4190914</td>\n",
       "<td>13.4065156</td>\n",
       "<td>41.9091443</td></tr>\n",
       "<tr><td></td>\n",
       "<td>9</td>\n",
       "<td>0.5478266</td>\n",
       "<td>0.4500091</td>\n",
       "<td>0.5787546</td>\n",
       "<td>0.7300975</td>\n",
       "<td>0.0547186</td>\n",
       "<td>0.6225108</td>\n",
       "<td>1.0965771</td>\n",
       "<td>1.3833293</td>\n",
       "<td>9.6577074</td>\n",
       "<td>38.3329289</td></tr>\n",
       "<tr><td></td>\n",
       "<td>10</td>\n",
       "<td>0.5128311</td>\n",
       "<td>0.5</td>\n",
       "<td>0.5557587</td>\n",
       "<td>0.7126668</td>\n",
       "<td>0.0526407</td>\n",
       "<td>0.6751515</td>\n",
       "<td>1.0530063</td>\n",
       "<td>1.3503030</td>\n",
       "<td>5.3006323</td>\n",
       "<td>35.0303030</td></tr>\n",
       "<tr><td></td>\n",
       "<td>11</td>\n",
       "<td>0.4815168</td>\n",
       "<td>0.5508134</td>\n",
       "<td>0.5161871</td>\n",
       "<td>0.6945412</td>\n",
       "<td>0.0496970</td>\n",
       "<td>0.7248485</td>\n",
       "<td>0.9780292</td>\n",
       "<td>1.3159602</td>\n",
       "<td>-2.1970787</td>\n",
       "<td>31.5960199</td></tr>\n",
       "<tr><td></td>\n",
       "<td>12</td>\n",
       "<td>0.4483592</td>\n",
       "<td>0.6001645</td>\n",
       "<td>0.45</td>\n",
       "<td>0.6744328</td>\n",
       "<td>0.0420779</td>\n",
       "<td>0.7669264</td>\n",
       "<td>0.8526234</td>\n",
       "<td>1.2778603</td>\n",
       "<td>-14.7376623</td>\n",
       "<td>27.7860324</td></tr>\n",
       "<tr><td></td>\n",
       "<td>13</td>\n",
       "<td>0.4159386</td>\n",
       "<td>0.6501554</td>\n",
       "<td>0.4223035</td>\n",
       "<td>0.6550464</td>\n",
       "<td>0.04</td>\n",
       "<td>0.8069264</td>\n",
       "<td>0.8001463</td>\n",
       "<td>1.2411286</td>\n",
       "<td>-19.9853748</td>\n",
       "<td>24.1128584</td></tr>\n",
       "<tr><td></td>\n",
       "<td>14</td>\n",
       "<td>0.3884948</td>\n",
       "<td>0.7000548</td>\n",
       "<td>0.3736264</td>\n",
       "<td>0.6349869</td>\n",
       "<td>0.0353247</td>\n",
       "<td>0.8422511</td>\n",
       "<td>0.7079168</td>\n",
       "<td>1.2031216</td>\n",
       "<td>-29.2083155</td>\n",
       "<td>20.3121585</td></tr>\n",
       "<tr><td></td>\n",
       "<td>15</td>\n",
       "<td>0.3570956</td>\n",
       "<td>0.7499543</td>\n",
       "<td>0.3864469</td>\n",
       "<td>0.6184499</td>\n",
       "<td>0.0365368</td>\n",
       "<td>0.8787879</td>\n",
       "<td>0.7322081</td>\n",
       "<td>1.1717886</td>\n",
       "<td>-26.7791891</td>\n",
       "<td>17.1788566</td></tr>\n",
       "<tr><td></td>\n",
       "<td>16</td>\n",
       "<td>0.3277598</td>\n",
       "<td>0.8000366</td>\n",
       "<td>0.3485401</td>\n",
       "<td>0.6015536</td>\n",
       "<td>0.0330736</td>\n",
       "<td>0.9118615</td>\n",
       "<td>0.6603855</td>\n",
       "<td>1.1397748</td>\n",
       "<td>-33.9614497</td>\n",
       "<td>13.9774757</td></tr>\n",
       "<tr><td></td>\n",
       "<td>17</td>\n",
       "<td>0.2981756</td>\n",
       "<td>0.8500274</td>\n",
       "<td>0.2888483</td>\n",
       "<td>0.5831631</td>\n",
       "<td>0.0273593</td>\n",
       "<td>0.9392208</td>\n",
       "<td>0.5472862</td>\n",
       "<td>1.1049300</td>\n",
       "<td>-45.2713819</td>\n",
       "<td>10.4929982</td></tr>\n",
       "<tr><td></td>\n",
       "<td>18</td>\n",
       "<td>0.2699267</td>\n",
       "<td>0.8999269</td>\n",
       "<td>0.2637363</td>\n",
       "<td>0.5654514</td>\n",
       "<td>0.0249351</td>\n",
       "<td>0.9641558</td>\n",
       "<td>0.4997060</td>\n",
       "<td>1.0713713</td>\n",
       "<td>-50.0293992</td>\n",
       "<td>7.1371306</td></tr>\n",
       "<tr><td></td>\n",
       "<td>19</td>\n",
       "<td>0.2239691</td>\n",
       "<td>0.9499177</td>\n",
       "<td>0.2230347</td>\n",
       "<td>0.5474312</td>\n",
       "<td>0.0211255</td>\n",
       "<td>0.9852814</td>\n",
       "<td>0.4225881</td>\n",
       "<td>1.0372281</td>\n",
       "<td>-57.7411936</td>\n",
       "<td>3.7228104</td></tr>\n",
       "<tr><td></td>\n",
       "<td>20</td>\n",
       "<td>0.0694869</td>\n",
       "<td>1.0</td>\n",
       "<td>0.1551095</td>\n",
       "<td>0.5277829</td>\n",
       "<td>0.0147186</td>\n",
       "<td>1.0</td>\n",
       "<td>0.2938888</td>\n",
       "<td>1.0</td>\n",
       "<td>-70.6111164</td>\n",
       "<td>0.0</td></tr></table></div>"
      ],
      "text/plain": [
       "    group    lower_threshold    cumulative_data_fraction    response_rate    cumulative_response_rate    capture_rate    cumulative_capture_rate    lift      cumulative_lift    gain      cumulative_gain\n",
       "--  -------  -----------------  --------------------------  ---------------  --------------------------  --------------  -------------------------  --------  -----------------  --------  -----------------\n",
       "    1        0.852802           0.0500823                   0.89781          0.89781                     0.0851948       0.0851948                  1.7011    1.7011             70.1098   70.1098\n",
       "    2        0.796282           0.100165                    0.832117         0.864964                    0.078961        0.164156                   1.57663   1.63886            57.6627   63.8862\n",
       "    3        0.74941            0.150064                    0.789377         0.839829                    0.074632        0.238788                   1.49565   1.59124            49.5648   59.1241\n",
       "    4        0.717325           0.200055                    0.778793         0.824577                    0.0737662       0.312554                   1.47559   1.56234            47.5594   56.2342\n",
       "    5        0.685541           0.250137                    0.740876         0.807819                    0.070303        0.382857                   1.40375   1.53059            40.3751   53.0589\n",
       "    6        0.6557             0.300219                    0.70438          0.790563                    0.0668398       0.449697                   1.3346    1.49789            33.4601   49.7895\n",
       "    7        0.623647           0.350027                    0.649541         0.770496                    0.0612987       0.510996                   1.2307    1.45987            23.0698   45.9873\n",
       "    8        0.586534           0.40011                     0.59854          0.748972                    0.0567965       0.567792                   1.13407   1.41909            13.4065   41.9091\n",
       "    9        0.547827           0.450009                    0.578755         0.730097                    0.0547186       0.622511                   1.09658   1.38333            9.65771   38.3329\n",
       "    10       0.512831           0.5                         0.555759         0.712667                    0.0526407       0.675152                   1.05301   1.3503             5.30063   35.0303\n",
       "    11       0.481517           0.550813                    0.516187         0.694541                    0.049697        0.724848                   0.978029  1.31596            -2.19708  31.596\n",
       "    12       0.448359           0.600165                    0.45             0.674433                    0.0420779       0.766926                   0.852623  1.27786            -14.7377  27.786\n",
       "    13       0.415939           0.650155                    0.422303         0.655046                    0.04            0.806926                   0.800146  1.24113            -19.9854  24.1129\n",
       "    14       0.388495           0.700055                    0.373626         0.634987                    0.0353247       0.842251                   0.707917  1.20312            -29.2083  20.3122\n",
       "    15       0.357096           0.749954                    0.386447         0.61845                     0.0365368       0.878788                   0.732208  1.17179            -26.7792  17.1789\n",
       "    16       0.32776            0.800037                    0.34854          0.601554                    0.0330736       0.911861                   0.660386  1.13977            -33.9614  13.9775\n",
       "    17       0.298176           0.850027                    0.288848         0.583163                    0.0273593       0.939221                   0.547286  1.10493            -45.2714  10.493\n",
       "    18       0.269927           0.899927                    0.263736         0.565451                    0.0249351       0.964156                   0.499706  1.07137            -50.0294  7.13713\n",
       "    19       0.223969           0.949918                    0.223035         0.547431                    0.0211255       0.985281                   0.422588  1.03723            -57.7412  3.72281\n",
       "    20       0.0694869          1                           0.155109         0.527783                    0.0147186       1                          0.293889  1                  -70.6111  0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model performance of GBM model on test data\n",
    "data_gbm2.model_performance(test)"
   ]
  }
 ],
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