06_clustering/src/py_part_6_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# License \n",
    "***\n",
    "Copyright (C) 2017 J. Patrick Hall, jphall@gwu.edu\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import h2o \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from h2o.estimators.deeplearning import H2ODeepLearningEstimator\n",
    "from h2o.estimators.kmeans import H2OKMeansEstimator\n",
    "from h2o.estimators.pca import H2OPrincipalComponentAnalysisEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# display matplotlib graphics in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: java version \"1.8.0_112\"; Java(TM) SE Runtime Environment (build 1.8.0_112-b16); Java HotSpot(TM) 64-Bit Server VM (build 25.112-b16, mixed mode)\n",
      "  Starting server from /Users/phall/anaconda/lib/python3.5/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmpwcyoy_n6\n",
      "  JVM stdout: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmpwcyoy_n6/h2o_phall_started_from_python.out\n",
      "  JVM stderr: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmpwcyoy_n6/h2o_phall_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>02 secs</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.10.4.1</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>15 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_phall_3uj259</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>3.556 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 status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.5.2 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ------------------------------\n",
       "H2O cluster uptime:         02 secs\n",
       "H2O cluster version:        3.10.4.1\n",
       "H2O cluster version age:    15 days\n",
       "H2O cluster name:           H2O_from_python_phall_3uj259\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    3.556 Gb\n",
       "H2O cluster total cores:    8\n",
       "H2O cluster allowed cores:  8\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "Python version:             3.5.2 final\n",
       "--------------------------  ------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# start and connect to h2o server\n",
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load clean data\n",
    "path = '/Users/phall/workspace/GWU_data_mining/03_regression/data/loan_clean.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define input variable measurement levels \n",
    "# strings automatically parsed as enums (nominal)\n",
    "# numbers automatically parsed as numeric\n",
    "col_types = {'bad_loan': 'enum',\n",
    "             'GRP_addr_state': 'enum',\n",
    "             'GRP_home_ownership': 'enum',\n",
    "             'GRP_verification_status': 'enum',\n",
    "             'GRP_REP_home_ownership': 'enum',\n",
    "             'GRP_purpose': 'enum'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "frame = h2o.import_file(path=path, col_types=col_types) # multi-threaded import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:163987\n",
      "Cols:18\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>       </th><th>id               </th><th>bad_loan  </th><th>GRP_REP_home_ownership  </th><th>GRP_addr_state  </th><th>GRP_home_ownership  </th><th>GRP_purpose  </th><th>GRP_verification_status  </th><th>_WARN_  </th><th>STD_IMP_REP_annual_inc  </th><th>STD_IMP_REP_delinq_2yrs  </th><th>STD_IMP_REP_dti      </th><th>STD_IMP_REP_emp_length  </th><th>STD_IMP_REP_int_rate  </th><th>STD_IMP_REP_loan_amnt  </th><th>STD_IMP_REP_longest_credit_lengt  </th><th>STD_IMP_REP_revol_util  </th><th>STD_IMP_REP_term_length  </th><th>STD_IMP_REP_total_acc  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>int              </td><td>enum      </td><td>enum                    </td><td>enum            </td><td>enum                </td><td>enum         </td><td>enum                     </td><td>int     </td><td>real                    </td><td>real                     </td><td>real                 </td><td>real                    </td><td>real                  </td><td>real                   </td><td>real                              </td><td>real                    </td><td>real                     </td><td>real                   </td></tr>\n",
       "<tr><td>mins   </td><td>10001.0          </td><td>          </td><td>                        </td><td>                </td><td>                    </td><td>             </td><td>                         </td><td>NaN     </td><td>-1.767455639            </td><td>-0.39219617              </td><td>-2.119639396         </td><td>-1.6213902740000001     </td><td>-1.907046215          </td><td>-1.587129405           </td><td>-2.22445124                       </td><td>-2.164541326            </td><td>-0.516495577             </td><td>-2.058861889           </td></tr>\n",
       "<tr><td>mean   </td><td>91994.0          </td><td>          </td><td>                        </td><td>                </td><td>                    </td><td>             </td><td>                         </td><td>0.0     </td><td>2.38744452882879e-11    </td><td>2.2959296297769782e-12   </td><td>6.807013811211564e-11</td><td>-3.566867876239133e-11  </td><td>-8.948753565861857e-12</td><td>8.311927579716105e-11  </td><td>5.0612534090153816e-11            </td><td>-1.4734128080190765e-11 </td><td>-1.5009542966560638e-10  </td><td>8.060924856225354e-13  </td></tr>\n",
       "<tr><td>maxs   </td><td>173987.0         </td><td>          </td><td>                        </td><td>                </td><td>                    </td><td>             </td><td>                         </td><td>NaN     </td><td>4.6180619798            </td><td>4.1566950661             </td><td>3.0371487270000004   </td><td>1.2288169612            </td><td>2.8376799992          </td><td>2.7671323946           </td><td>3.1431598296                      </td><td>3.0363495275            </td><td>1.9718787627             </td><td>3.0684672884           </td></tr>\n",
       "<tr><td>sigma  </td><td>47339.11363414683</td><td>          </td><td>                        </td><td>                </td><td>                    </td><td>             </td><td>                         </td><td>-0.0    </td><td>0.9999999999982868      </td><td>0.9999999999212518       </td><td>1.0000000000037712   </td><td>1.0000000000339833      </td><td>1.0000000000199503    </td><td>0.999999999985285      </td><td>0.9999999999850594                </td><td>1.000000000017688       </td><td>1.0000000000642086       </td><td>1.0000000000331841     </td></tr>\n",
       "<tr><td>zeros  </td><td>0                </td><td>          </td><td>                        </td><td>                </td><td>                    </td><td>             </td><td>                         </td><td>0       </td><td>0                       </td><td>0                        </td><td>0                    </td><td>0                       </td><td>0                     </td><td>0                      </td><td>0                                 </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><td>0               </td><td>0                   </td><td>0            </td><td>0                        </td><td>163987  </td><td>0                       </td><td>0                        </td><td>0                    </td><td>0                       </td><td>0                     </td><td>0                      </td><td>0                                 </td><td>0                       </td><td>0                        </td><td>0                      </td></tr>\n",
       "<tr><td>0      </td><td>10001.0          </td><td>0         </td><td>3                       </td><td>14              </td><td>3                   </td><td>3            </td><td>2                        </td><td>nan     </td><td>-1.1992995020000001     </td><td>-0.39219617              </td><td>1.5712460425         </td><td>1.2288169612            </td><td>-0.7047730510000001   </td><td>-1.019182214           </td><td>1.6839024850000002                </td><td>1.1858716502            </td><td>-0.516495577             </td><td>-1.359278248           </td></tr>\n",
       "<tr><td>1      </td><td>10002.0          </td><td>1         </td><td>3                       </td><td>10              </td><td>3                   </td><td>8            </td><td>2                        </td><td>nan     </td><td>-1.04507688             </td><td>-0.39219617              </td><td>-1.9861534850000002  </td><td>-1.6213902740000001     </td><td>0.3572732234          </td><td>-1.3347084310000001    </td><td>-0.42059567400000003              </td><td>-1.7882703350000002     </td><td>1.9718787627             </td><td>-1.7965180230000002    </td></tr>\n",
       "<tr><td>2      </td><td>10003.0          </td><td>0         </td><td>3                       </td><td>7               </td><td>3                   </td><td>7            </td><td>3                        </td><td>nan     </td><td>-1.501267394            </td><td>-0.39219617              </td><td>-0.9556422520000001  </td><td>1.2288169612            </td><td>0.5158905241          </td><td>-1.34732948            </td><td>-0.7212382690000001               </td><td>1.7782983174            </td><td>-0.516495577             </td><td>-1.271830292           </td></tr>\n",
       "<tr><td>3      </td><td>10004.0          </td><td>0         </td><td>3                       </td><td>2               </td><td>3                   </td><td>4            </td><td>2                        </td><td>nan     </td><td>-0.303921333            </td><td>-0.39219617              </td><td>0.5500788236         </td><td>1.2288169612            </td><td>-0.051913437          </td><td>-0.388129779           </td><td>0.0303682169                      </td><td>0.0325652593            </td><td>-0.516495577             </td><td>1.089264497            </td></tr>\n",
       "<tr><td>4      </td><td>10005.0          </td><td>0         </td><td>3                       </td><td>14              </td><td>3                   </td><td>10           </td><td>2                        </td><td>nan     </td><td>-0.890854259            </td><td>-0.39219617              </td><td>-0.624597193         </td><td>-0.7663281030000001     </td><td>-1.3369434530000002   </td><td>-1.019182214           </td><td>-0.8220262690000001               </td><td>-1.0317254690000002     </td><td>-0.516495577             </td><td>-1.0969343820000002    </td></tr>\n",
       "<tr><td>5      </td><td>10006.0          </td><td>0         </td><td>3                       </td><td>2               </td><td>3                   </td><td>8            </td><td>2                        </td><td>nan     </td><td>-0.5824090160000001     </td><td>-0.39219617              </td><td>-1.4054897720000001  </td><td>0.9437962377            </td><td>1.1319693155000001    </td><td>-1.271603188           </td><td>-1.623166051                      </td><td>1.3379811999            </td><td>-0.516495577             </td><td>-1.7965180230000002    </td></tr>\n",
       "<tr><td>6      </td><td>10007.0          </td><td>1         </td><td>4                       </td><td>2               </td><td>4                   </td><td>7            </td><td>2                        </td><td>nan     </td><td>-0.788039178            </td><td>-0.39219617              </td><td>-1.37879259          </td><td>-0.48130738             </td><td>1.7388529011          </td><td>-0.9434559220000001    </td><td>-1.17220216                       </td><td>-0.8596015050000001     </td><td>1.9718787627             </td><td>-1.0094864270000001    </td></tr>\n",
       "<tr><td>7      </td><td>10008.0          </td><td>1         </td><td>3                       </td><td>4               </td><td>3                   </td><td>4            </td><td>2                        </td><td>nan     </td><td>-1.430633434            </td><td>-0.39219617              </td><td>0.2937858745         </td><td>-1.6213902740000001     </td><td>-0.235817553          </td><td>-0.971853281           </td><td>-1.17220216                       </td><td>-0.703489072            </td><td>1.9718787627             </td><td>-1.883965979           </td></tr>\n",
       "<tr><td>8      </td><td>10009.0          </td><td>0         </td><td>4                       </td><td>14              </td><td>4                   </td><td>2            </td><td>3                        </td><td>nan     </td><td>0.0344814697            </td><td>-0.39219617              </td><td>0.032153489          </td><td>-0.196286656            </td><td>0.2147475328          </td><td>-0.8298664840000001    </td><td>-0.270274377                      </td><td>-1.339947451            </td><td>1.9718787627             </td><td>-0.135006875           </td></tr>\n",
       "<tr><td>9      </td><td>10010.0          </td><td>0         </td><td>4                       </td><td>2               </td><td>4                   </td><td>2            </td><td>2                        </td><td>nan     </td><td>0.1115927805            </td><td>-0.39219617              </td><td>-0.680661276         </td><td>1.2288169612            </td><td>-0.235817553          </td><td>-0.13570880500000002   </td><td>1.0826172966                      </td><td>0.5213930910000001      </td><td>-0.516495577             </td><td>0.8269206315000001     </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bad_loan\n",
      "['GRP_REP_home_ownership', 'GRP_addr_state', 'GRP_home_ownership', 'GRP_purpose', 'GRP_verification_status', 'STD_IMP_REP_annual_inc', 'STD_IMP_REP_delinq_2yrs', 'STD_IMP_REP_dti', 'STD_IMP_REP_emp_length', 'STD_IMP_REP_int_rate', 'STD_IMP_REP_loan_amnt', 'STD_IMP_REP_longest_credit_lengt', 'STD_IMP_REP_revol_util', 'STD_IMP_REP_term_length', 'STD_IMP_REP_total_acc']\n"
     ]
    }
   ],
   "source": [
    "# assign target and inputs\n",
    "y = 'bad_loan'\n",
    "X = [name for name in frame.columns if name not in ['id', '_WARN_', y]]\n",
    "print(y)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kmeans Model Build progress: |████████████████████████████████████████████| 100%\n",
      "Model Details\n",
      "=============\n",
      "H2OKMeansEstimator :  K-means\n",
      "Model Key:  KMeans_model_python_1489875889040_1\n",
      "\n",
      "\n",
      "ModelMetricsClustering: kmeans\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: NaN\n",
      "RMSE: NaN\n",
      "Total Within Cluster Sum of Square Error: 1624356.9966350778\n",
      "Total Sum of Square Error to Grand Mean: 2079508.988208857\n",
      "Between Cluster Sum of Square Error: 455151.99157377915\n",
      "Centroid Statistics: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>centroid</b></td>\n",
       "<td><b>size</b></td>\n",
       "<td><b>within_cluster_sum_of_squares</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>1.0</td>\n",
       "<td>32540.0</td>\n",
       "<td>348350.7501940</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2.0</td>\n",
       "<td>62511.0</td>\n",
       "<td>698165.9424155</td></tr>\n",
       "<tr><td></td>\n",
       "<td>3.0</td>\n",
       "<td>68936.0</td>\n",
       "<td>577840.3040256</td></tr></table></div>"
      ],
      "text/plain": [
       "    centroid    size    within_cluster_sum_of_squares\n",
       "--  ----------  ------  -------------------------------\n",
       "    1           32540   348351\n",
       "    2           62511   698166\n",
       "    3           68936   577840"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scoring History: \n"
     ]
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    {
     "data": {
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       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>timestamp</b></td>\n",
       "<td><b>duration</b></td>\n",
       "<td><b>iteration</b></td>\n",
       "<td><b>number_of_reassigned_observations</b></td>\n",
       "<td><b>within_cluster_sum_of_squares</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-03-18 18:24:54</td>\n",
       "<td> 0.013 sec</td>\n",
       "<td>0.0</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-03-18 18:24:55</td>\n",
       "<td> 0.732 sec</td>\n",
       "<td>1.0</td>\n",
       "<td>163987.0</td>\n",
       "<td>3066962.6306723</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-03-18 18:24:55</td>\n",
       "<td> 0.929 sec</td>\n",
       "<td>2.0</td>\n",
       "<td>27188.0</td>\n",
       "<td>1685109.9572629</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-03-18 18:24:55</td>\n",
       "<td> 0.968 sec</td>\n",
       "<td>3.0</td>\n",
       "<td>12103.0</td>\n",
       "<td>1639657.0563031</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-03-18 18:24:55</td>\n",
       "<td> 1.007 sec</td>\n",
       "<td>4.0</td>\n",
       "<td>5808.0</td>\n",
       "<td>1629782.4700017</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-03-18 18:24:55</td>\n",
       "<td> 1.042 sec</td>\n",
       "<td>5.0</td>\n",
       "<td>3207.0</td>\n",
       "<td>1626920.9520747</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-03-18 18:24:55</td>\n",
       "<td> 1.069 sec</td>\n",
       "<td>6.0</td>\n",
       "<td>2227.0</td>\n",
       "<td>1625699.4497081</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-03-18 18:24:55</td>\n",
       "<td> 1.099 sec</td>\n",
       "<td>7.0</td>\n",
       "<td>1620.0</td>\n",
       "<td>1625066.3038514</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-03-18 18:24:55</td>\n",
       "<td> 1.142 sec</td>\n",
       "<td>8.0</td>\n",
       "<td>1245.0</td>\n",
       "<td>1624713.2196611</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-03-18 18:24:55</td>\n",
       "<td> 1.173 sec</td>\n",
       "<td>9.0</td>\n",
       "<td>905.0</td>\n",
       "<td>1624514.2903116</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-03-18 18:24:55</td>\n",
       "<td> 1.199 sec</td>\n",
       "<td>10.0</td>\n",
       "<td>646.0</td>\n",
       "<td>1624407.3879321</td></tr></table></div>"
      ],
      "text/plain": [
       "    timestamp            duration    iteration    number_of_reassigned_observations    within_cluster_sum_of_squares\n",
       "--  -------------------  ----------  -----------  -----------------------------------  -------------------------------\n",
       "    2017-03-18 18:24:54  0.013 sec   0            nan                                  nan\n",
       "    2017-03-18 18:24:55  0.732 sec   1            163987                               3.06696e+06\n",
       "    2017-03-18 18:24:55  0.929 sec   2            27188                                1.68511e+06\n",
       "    2017-03-18 18:24:55  0.968 sec   3            12103                                1.63966e+06\n",
       "    2017-03-18 18:24:55  1.007 sec   4            5808                                 1.62978e+06\n",
       "    2017-03-18 18:24:55  1.042 sec   5            3207                                 1.62692e+06\n",
       "    2017-03-18 18:24:55  1.069 sec   6            2227                                 1.6257e+06\n",
       "    2017-03-18 18:24:55  1.099 sec   7            1620                                 1.62507e+06\n",
       "    2017-03-18 18:24:55  1.142 sec   8            1245                                 1.62471e+06\n",
       "    2017-03-18 18:24:55  1.173 sec   9            905                                  1.62451e+06\n",
       "    2017-03-18 18:24:55  1.199 sec   10           646                                  1.62441e+06"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# train k-means cluster model\n",
    "# data is already standardized\n",
    "# w/ 3 clusters\n",
    "# print summary\n",
    "clusters = H2OKMeansEstimator(standardize=False, k=3, seed=12345)\n",
    "clusters.train(x=X, training_frame=frame)\n",
    "print(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kmeans prediction progress: |█████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  predict</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">        2</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        2</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        2</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        1</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        2</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        2</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        2</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        2</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        0</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        1</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join cluster labels to original data for further analysis\n",
    "labels = clusters.predict(frame)\n",
    "labeled_frame =  frame.cbind(labels)\n",
    "labeled_frame[-1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['GRP_home_ownership', 'GRP_verification_status', 'GRP_REP_home_ownership', 'GRP_addr_state', 'GRP_purpose']\n",
      "['STD_IMP_REP_loan_amnt', 'STD_IMP_REP_delinq_2yrs', 'STD_IMP_REP_dti', 'STD_IMP_REP_revol_util', 'STD_IMP_REP_term_length', 'STD_IMP_REP_total_acc', 'STD_IMP_REP_annual_inc', 'STD_IMP_REP_emp_length', 'STD_IMP_REP_longest_credit_lengt', 'STD_IMP_REP_int_rate']\n"
     ]
    }
   ],
   "source": [
    "# determine column types\n",
    "reals, enums = [], []\n",
    "for key, val in labeled_frame.types.items():\n",
    "    if key in X:\n",
    "        if val == 'enum':\n",
    "            enums.append(key)\n",
    "        else: \n",
    "            reals.append(key)\n",
    "\n",
    "print(enums)\n",
    "print(reals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">  mean_STD_IMP_REP_total_acc</th><th style=\"text-align: right;\">  mean_STD_IMP_REP_revol_util</th><th style=\"text-align: right;\">  mean_STD_IMP_REP_term_length</th><th style=\"text-align: right;\">  mean_STD_IMP_REP_int_rate</th><th style=\"text-align: right;\">  mean_STD_IMP_REP_longest_credit_lengt</th><th style=\"text-align: right;\">  mean_STD_IMP_REP_emp_length</th><th style=\"text-align: right;\">  mean_STD_IMP_REP_loan_amnt</th><th style=\"text-align: right;\">  mean_STD_IMP_REP_dti</th><th style=\"text-align: right;\">  mean_STD_IMP_REP_delinq_2yrs</th><th style=\"text-align: right;\">  mean_STD_IMP_REP_annual_inc</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">                    0.272192</td><td style=\"text-align: right;\">                   0.201244  </td><td style=\"text-align: right;\">                      1.8669  </td><td style=\"text-align: right;\">                   0.983326</td><td style=\"text-align: right;\">                               0.18154 </td><td style=\"text-align: right;\">                     0.218872</td><td style=\"text-align: right;\">                   0.958654 </td><td style=\"text-align: right;\">            0.199811  </td><td style=\"text-align: right;\">                     0.0510284</td><td style=\"text-align: right;\">                     0.284059</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">                    0.524552</td><td style=\"text-align: right;\">                  -0.114655  </td><td style=\"text-align: right;\">                     -0.492683</td><td style=\"text-align: right;\">                  -0.404329</td><td style=\"text-align: right;\">                               0.540815</td><td style=\"text-align: right;\">                     0.407858</td><td style=\"text-align: right;\">                   0.0761135</td><td style=\"text-align: right;\">            0.00485871</td><td style=\"text-align: right;\">                     0.165756 </td><td style=\"text-align: right;\">                     0.412678</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        2</td><td style=\"text-align: right;\">                   -0.604146</td><td style=\"text-align: right;\">                   0.00897536</td><td style=\"text-align: right;\">                     -0.434474</td><td style=\"text-align: right;\">                  -0.097517</td><td style=\"text-align: right;\">                              -0.576103</td><td style=\"text-align: right;\">                    -0.47316 </td><td style=\"text-align: right;\">                  -0.521535 </td><td style=\"text-align: right;\">           -0.0987232 </td><td style=\"text-align: right;\">                    -0.174395 </td><td style=\"text-align: right;\">                    -0.5083  </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# profile clusters by means\n",
    "grouped = labeled_frame.group_by(by=['predict'])\n",
    "means = grouped.mean(col=reals).get_frame()\n",
    "print(means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">  mode_GRP_purpose</th><th style=\"text-align: right;\">  mode_GRP_home_ownership</th><th style=\"text-align: right;\">  mode_GRP_verification_status</th><th style=\"text-align: right;\">  mode_GRP_addr_state</th><th style=\"text-align: right;\">  mode_GRP_REP_home_ownership</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">                 6</td><td style=\"text-align: right;\">                        1</td><td style=\"text-align: right;\">                             1</td><td style=\"text-align: right;\">                   11</td><td style=\"text-align: right;\">                            1</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">                 6</td><td style=\"text-align: right;\">                        1</td><td style=\"text-align: right;\">                             1</td><td style=\"text-align: right;\">                   11</td><td style=\"text-align: right;\">                            1</td></tr>\n",
       "<tr><td style=\"text-align: right;\">        2</td><td style=\"text-align: right;\">                 6</td><td style=\"text-align: right;\">                        2</td><td style=\"text-align: right;\">                             1</td><td style=\"text-align: right;\">                   11</td><td style=\"text-align: right;\">                            2</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# profile clusters by modes\n",
    "grouped = labeled_frame.group_by(by=['predict'])\n",
    "modes = grouped.mode(col=enums).get_frame()\n",
    "print(modes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# define a function for plotting clusters in 2-d\n",
    "def plot(_2d_labeled_frame):\n",
    "\n",
    "    _0 = plt.scatter(features_pandas[_2d_labeled_frame.label == 0].iloc[0:750, 0], \n",
    "                     features_pandas[_2d_labeled_frame.label == 0].iloc[0:750, 1],\n",
    "                     color='m', marker='^', alpha=.15)\n",
    "\n",
    "    _1 = plt.scatter(features_pandas[_2d_labeled_frame.label == 1].iloc[0:750, 0], \n",
    "                     features_pandas[_2d_labeled_frame.label == 1].iloc[0:750, 1],\n",
    "                     color='c', alpha=.15)\n",
    "\n",
    "    _2 = plt.scatter(features_pandas[_2d_labeled_frame.label == 2].iloc[0:750, 0], \n",
    "                     features_pandas[_2d_labeled_frame.label == 2].iloc[0:750, 1],\n",
    "                     color='g', marker='s', alpha=.15)               \n",
    "                     \n",
    "    plt.legend([_0, _1, _2], \n",
    "               ['Cluster 0', 'Cluster 1', 'Cluster 2'],\n",
    "               bbox_to_anchor=(1.05, 0.0), \n",
    "               loc=3, borderaxespad=0.)\n",
    "    \n",
    "    plt.xlabel('Dimension 1')\n",
    "    plt.ylabel('Dimension 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pca Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/phall/anaconda/lib/python3.5/site-packages/h2o/job.py:65: UserWarning: _train: Dataset used may contain fewer number of rows due to removal of rows with NA/missing values.  If this is not desirable, set impute_missing argument in pca call to TRUE/True/true/... depending on the client language.\n",
      "  warnings.warn(w)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pca prediction progress: |████████████████████████████████████████████████| 100%\n",
      "        PC1       PC2  label\n",
      "0 -0.512426 -1.499444      2\n",
      "1 -1.953714 -1.687145      2\n",
      "2 -1.760159 -1.681276      2\n",
      "3  0.306453 -1.133484      1\n",
      "4 -2.389103 -1.432739      2\n"
     ]
    },
    {
     "data": {
      "image/png": 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Oh4613K7VAJ6vGpclhffPVY2HbQzWe+51u3y70yGonrsYBFyMIjw94bppDH+0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\nPmA4uSQ7vwU8gdS8W0/cg2Vb1FXWMaZmDFuPbP26SUbcwopabNq7iYAnkJr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RwvE4gUS1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1183c2dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# project training data onto 2-D using principal components\n",
    "# join with clusters labels\n",
    "# plot\n",
    "pca = H2OPrincipalComponentAnalysisEstimator(k=2) # project onto 2 PCs\n",
    "pca.train(x=X, training_frame=labeled_frame)\n",
    "features = pca.predict(labeled_frame)\n",
    "features_pandas = features.as_data_frame()\n",
    "features_pandas['label'] = labeled_frame[-1].as_data_frame()\n",
    "print(features_pandas.head())\n",
    "plot(features_pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H2O session _sid_b862 closed.\n"
     ]
    }
   ],
   "source": [
    "# shutdown h2o\n",
    "h2o.cluster().shutdown(prompt=False)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}