{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This tutorial is losely based on https://xiaoxiaowang87.github.io/monotonicity_constraint/\n",
    "\n",
    "import h2o\n",
    "from h2o.estimators import H2OXGBoostEstimator\n",
    "from h2o.estimators import H2OGradientBoostingEstimator\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.datasets.california_housing import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cal_housing = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>45 mins 51 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>America/Los_Angeles</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.22.0.99999</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>1 hour and 57 minutes </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>mkurka</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>11.25 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>locked, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://localhost: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>H2O API Extensions:</td>\n",
       "<td>XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>2.7.14 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ----------------------------------------\n",
       "H2O cluster uptime:         45 mins 51 secs\n",
       "H2O cluster timezone:       America/Los_Angeles\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.22.0.99999\n",
       "H2O cluster version age:    1 hour and 57 minutes\n",
       "H2O cluster name:           mkurka\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    11.25 Gb\n",
       "H2O cluster total cores:    8\n",
       "H2O cluster allowed cores:  8\n",
       "H2O cluster status:         locked, healthy\n",
       "H2O connection url:         http://localhost:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             2.7.14 final\n",
       "--------------------------  ----------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "data = h2o.H2OFrame(cal_housing.data, column_names=cal_housing.feature_names)\n",
    "data[\"target\"] = h2o.H2OFrame(cal_housing.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train, test = data.split_frame([0.6], seed=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>       </th><th>MedInc       </th><th>HouseAge     </th><th>AveRooms      </th><th>AveBedrms     </th><th>Population   </th><th>AveOccup      </th><th>Latitude     </th><th>Longitude     </th><th>target       </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>real         </td><td>int          </td><td>real          </td><td>real          </td><td>int          </td><td>real          </td><td>real         </td><td>real          </td><td>real         </td></tr>\n",
       "<tr><td>mins   </td><td>0.4999       </td><td>1.0          </td><td>0.846153846154</td><td>0.375         </td><td>3.0          </td><td>0.692307692308</td><td>32.54        </td><td>-124.35       </td><td>0.14999      </td></tr>\n",
       "<tr><td>mean   </td><td>3.88080577434</td><td>28.592896835 </td><td>5.45159062686 </td><td>1.09888842652 </td><td>1426.40412338</td><td>3.1598055712  </td><td>35.6381468954</td><td>-119.572060079</td><td>2.06838035113</td></tr>\n",
       "<tr><td>maxs   </td><td>15.0001      </td><td>52.0         </td><td>132.533333333 </td><td>34.0666666667 </td><td>35682.0      </td><td>1243.33333333 </td><td>41.95        </td><td>-114.31       </td><td>5.00001      </td></tr>\n",
       "<tr><td>sigma  </td><td>1.90138273147</td><td>12.5943702314</td><td>2.51012114666 </td><td>0.503062422674</td><td>1160.40981696</td><td>13.3502637259 </td><td>2.14149825875</td><td>2.00443036004 </td><td>1.15487252367</td></tr>\n",
       "<tr><td>zeros  </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>0             </td><td>0            </td></tr>\n",
       "<tr><td>0      </td><td>8.3252       </td><td>41.0         </td><td>6.98412698413 </td><td>1.02380952381 </td><td>322.0        </td><td>2.55555555556 </td><td>37.88        </td><td>-122.23       </td><td>4.526        </td></tr>\n",
       "<tr><td>1      </td><td>8.3014       </td><td>21.0         </td><td>6.2381370826  </td><td>0.971880492091</td><td>2401.0       </td><td>2.10984182777 </td><td>37.86        </td><td>-122.22       </td><td>3.585        </td></tr>\n",
       "<tr><td>2      </td><td>7.2574       </td><td>52.0         </td><td>8.28813559322 </td><td>1.07344632768 </td><td>496.0        </td><td>2.80225988701 </td><td>37.85        </td><td>-122.24       </td><td>3.521        </td></tr>\n",
       "<tr><td>3      </td><td>3.8462       </td><td>52.0         </td><td>6.28185328185 </td><td>1.08108108108 </td><td>565.0        </td><td>2.18146718147 </td><td>37.85        </td><td>-122.25       </td><td>3.422        </td></tr>\n",
       "<tr><td>4      </td><td>4.0368       </td><td>52.0         </td><td>4.76165803109 </td><td>1.10362694301 </td><td>413.0        </td><td>2.13989637306 </td><td>37.85        </td><td>-122.25       </td><td>2.697        </td></tr>\n",
       "<tr><td>5      </td><td>3.12         </td><td>52.0         </td><td>4.79752704791 </td><td>1.06182380216 </td><td>1157.0       </td><td>1.78825347759 </td><td>37.84        </td><td>-122.25       </td><td>2.414        </td></tr>\n",
       "<tr><td>6      </td><td>3.2705       </td><td>52.0         </td><td>4.77247956403 </td><td>1.02452316076 </td><td>1504.0       </td><td>2.04904632153 </td><td>37.85        </td><td>-122.26       </td><td>2.418        </td></tr>\n",
       "<tr><td>7      </td><td>3.075        </td><td>52.0         </td><td>5.32264957265 </td><td>1.01282051282 </td><td>1098.0       </td><td>2.34615384615 </td><td>37.85        </td><td>-122.26       </td><td>2.135        </td></tr>\n",
       "<tr><td>8      </td><td>2.1202       </td><td>52.0         </td><td>4.05280528053 </td><td>0.96699669967 </td><td>648.0        </td><td>2.13861386139 </td><td>37.85        </td><td>-122.27       </td><td>1.555        </td></tr>\n",
       "<tr><td>9      </td><td>1.9911       </td><td>50.0         </td><td>5.34367541766 </td><td>1.08591885442 </td><td>990.0        </td><td>2.36276849642 </td><td>37.84        </td><td>-122.26       </td><td>1.587        </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>       </th><th>MedInc       </th><th>HouseAge     </th><th>AveRooms     </th><th>AveBedrms     </th><th>Population   </th><th>AveOccup     </th><th>Latitude     </th><th>Longitude     </th><th>target       </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>real         </td><td>int          </td><td>real         </td><td>real          </td><td>int          </td><td>real         </td><td>real         </td><td>real          </td><td>real         </td></tr>\n",
       "<tr><td>mins   </td><td>0.4999       </td><td>1.0          </td><td>1.0          </td><td>0.333333333333</td><td>6.0          </td><td>1.16932907348</td><td>32.56        </td><td>-124.3        </td><td>0.14999      </td></tr>\n",
       "<tr><td>mean   </td><td>3.85536716527</td><td>28.7098382585</td><td>5.39488676457</td><td>1.09333302879 </td><td>1424.07637115</td><td>2.93603511044</td><td>35.6223701812</td><td>-119.566147391</td><td>2.06882668004</td></tr>\n",
       "<tr><td>maxs   </td><td>15.0001      </td><td>52.0         </td><td>141.909090909</td><td>25.6363636364 </td><td>15507.0      </td><td>83.1714285714</td><td>41.95        </td><td>-114.56       </td><td>5.00001      </td></tr>\n",
       "<tr><td>sigma  </td><td>1.8974750829 </td><td>12.5726769447</td><td>2.41862993425</td><td>0.426136469691</td><td>1088.96995567</td><td>1.26628333493</td><td>2.12764543091</td><td>2.00229059869 </td><td>1.15264118508</td></tr>\n",
       "<tr><td>zeros  </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>0             </td><td>0            </td></tr>\n",
       "<tr><td>0      </td><td>5.6431       </td><td>52.0         </td><td>5.81735159817</td><td>1.07305936073 </td><td>558.0        </td><td>2.54794520548</td><td>37.85        </td><td>-122.25       </td><td>3.413        </td></tr>\n",
       "<tr><td>1      </td><td>3.6591       </td><td>52.0         </td><td>4.93190661479</td><td>0.951361867704</td><td>1094.0       </td><td>2.12840466926</td><td>37.84        </td><td>-122.25       </td><td>2.992        </td></tr>\n",
       "<tr><td>2      </td><td>2.0804       </td><td>42.0         </td><td>4.29411764706</td><td>1.11764705882 </td><td>1206.0       </td><td>2.0268907563 </td><td>37.84        </td><td>-122.26       </td><td>2.267        </td></tr>\n",
       "<tr><td>3      </td><td>3.6912       </td><td>52.0         </td><td>4.97058823529</td><td>0.990196078431</td><td>1551.0       </td><td>2.17226890756</td><td>37.84        </td><td>-122.25       </td><td>2.611        </td></tr>\n",
       "<tr><td>4      </td><td>3.2031       </td><td>52.0         </td><td>5.4776119403 </td><td>1.07960199005 </td><td>910.0        </td><td>2.26368159204</td><td>37.85        </td><td>-122.26       </td><td>2.815        </td></tr>\n",
       "<tr><td>5      </td><td>2.6736       </td><td>52.0         </td><td>4.0          </td><td>1.09770114943 </td><td>345.0        </td><td>1.98275862069</td><td>37.84        </td><td>-122.26       </td><td>1.913        </td></tr>\n",
       "<tr><td>6      </td><td>1.9167       </td><td>52.0         </td><td>4.26290322581</td><td>1.00967741935 </td><td>1212.0       </td><td>1.95483870968</td><td>37.85        </td><td>-122.26       </td><td>1.592        </td></tr>\n",
       "<tr><td>7      </td><td>2.125        </td><td>50.0         </td><td>4.24242424242</td><td>1.07196969697 </td><td>697.0        </td><td>2.64015151515</td><td>37.85        </td><td>-122.26       </td><td>1.4          </td></tr>\n",
       "<tr><td>8      </td><td>2.775        </td><td>52.0         </td><td>5.93957703927</td><td>1.04833836858 </td><td>793.0        </td><td>2.39577039275</td><td>37.85        </td><td>-122.27       </td><td>1.525        </td></tr>\n",
       "<tr><td>9      </td><td>1.808        </td><td>52.0         </td><td>4.78085642317</td><td>1.0604534005  </td><td>1102.0       </td><td>2.7758186398 </td><td>37.85        </td><td>-122.28       </td><td>1.055        </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_names = ['MedInc', 'AveOccup', 'HouseAge']\n",
    "monotone_constraints = {\"MedInc\": 1, \"AveOccup\": -1, \"HouseAge\": 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgboost Model Build progress: |███████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "xgb_mono = H2OXGBoostEstimator(monotone_constraints=monotone_constraints)\n",
    "xgb_mono.train(x=feature_names, y=\"target\", training_frame=train, validation_frame=test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ModelMetricsRegression: xgboost\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.450582394315\n",
      "RMSE: 0.671254343983\n",
      "MAE: 0.493647644764\n",
      "RMSLE: 0.217638157557\n",
      "Mean Residual Deviance: 0.450582394315\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_mono.model_performance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ModelMetricsRegression: xgboost\n",
      "** Reported on validation data. **\n",
      "\n",
      "MSE: 0.491813275045\n",
      "RMSE: 0.701294000434\n",
      "MAE: 0.518155694956\n",
      "RMSLE: 0.22897543713\n",
      "Mean Residual Deviance: 0.491813275045\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_mono.model_performance(valid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "gbm_mono = H2OGradientBoostingEstimator(monotone_constraints=monotone_constraints)\n",
    "gbm_mono.train(x=feature_names, y=\"target\", training_frame=train, validation_frame=test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.449986623705\n",
      "RMSE: 0.670810423074\n",
      "MAE: 0.494184218288\n",
      "RMSLE: 0.21741684144\n",
      "Mean Residual Deviance: 0.449986623705\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_mono.model_performance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on validation data. **\n",
      "\n",
      "MSE: 0.481829630737\n",
      "RMSE: 0.694139489395\n",
      "MAE: 0.515524158621\n",
      "RMSLE: 0.226177098203\n",
      "Mean Residual Deviance: 0.481829630737\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_mono.model_performance(valid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YsKrq3VV176lp3lJV+1fVdlX10qr6YlUdX1WPHSd5SJLPttaOSJLW2llJnpDk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f753190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_mono.varimp_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VZLHPgjo/w1a2syfG97gMuxMd0Vo7aY7HPjTDFpc/S3JQVZ2Y4TiUW2Q4QcQ+SX47w/Fn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a16b39890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_mono.varimp_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Train RMSE</th>\n",
       "      <th>Test RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>H2O XGBoost</th>\n",
       "      <td>0.671254</td>\n",
       "      <td>0.701294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H2O GBM</th>\n",
       "      <td>0.670810</td>\n",
       "      <td>0.694139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Train RMSE  Test RMSE\n",
       "H2O XGBoost    0.671254   0.701294\n",
       "H2O GBM        0.670810   0.694139"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_items(\n",
    "    [('H2O XGBoost', [xgb_mono.rmse(), xgb_mono.rmse(valid=True)]),\n",
    "     ('H2O GBM', [gbm_mono.rmse(), gbm_mono.rmse(valid=True)])],\n",
    "    columns=['Train RMSE', 'Test RMSE'], orient=\"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |█████████████████████████████████████████| 100%\n",
      "PartialDependence: Partial Dependence Plot of model XGBoost_model_python_1543517619333_7 on column 'MedInc'\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>medinc</b></td>\n",
       "<td><b>mean_response</b></td>\n",
       "<td><b>stddev_response</b></td>\n",
       "<td><b>std_error_mean_response</b></td></tr>\n",
       "<tr><td>0.4999</td>\n",
       "<td>0.9042832</td>\n",
       "<td>0.4050025</td>\n",
       "<td>0.0036345</td></tr>\n",
       "<tr><td>0.6463667</td>\n",
       "<td>1.0116680</td>\n",
       "<td>0.3172816</td>\n",
       "<td>0.0028473</td></tr>\n",
       "<tr><td>0.7928333</td>\n",
       "<td>1.0117064</td>\n",
       "<td>0.3174680</td>\n",
       "<td>0.0028490</td></tr>\n",
       "<tr><td>0.9393</td>\n",
       "<td>1.0137833</td>\n",
       "<td>0.3192623</td>\n",
       "<td>0.0028651</td></tr>\n",
       "<tr><td>1.0857667</td>\n",
       "<td>1.0144802</td>\n",
       "<td>0.3185222</td>\n",
       "<td>0.0028585</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td>14.4142333</td>\n",
       "<td>4.8378433</td>\n",
       "<td>0.4268355</td>\n",
       "<td>0.0038305</td></tr>\n",
       "<tr><td>14.5607</td>\n",
       "<td>4.8378433</td>\n",
       "<td>0.4268355</td>\n",
       "<td>0.0038305</td></tr>\n",
       "<tr><td>14.7071667</td>\n",
       "<td>4.8378433</td>\n",
       "<td>0.4268355</td>\n",
       "<td>0.0038305</td></tr>\n",
       "<tr><td>14.8536333</td>\n",
       "<td>4.8379129</td>\n",
       "<td>0.4268688</td>\n",
       "<td>0.0038308</td></tr>\n",
       "<tr><td>15.0001</td>\n",
       "<td>4.8379129</td>\n",
       "<td>0.4268688</td>\n",
       "<td>0.0038308</td></tr></table></div>"
      ],
      "text/plain": [
       "medinc          mean_response    stddev_response    std_error_mean_response\n",
       "--------------  ---------------  -----------------  -------------------------\n",
       "0.4999          0.904283157418   0.405002543263     0.00363453964024\n",
       "0.646366666667  1.01166800888    0.31728160376      0.00284732178889\n",
       "0.792833333333  1.01170635115    0.317468030436     0.00284899480343\n",
       "0.9393          1.01378326514    0.319262286792     0.00286509666738\n",
       "1.08576666667   1.01448024791    0.318522217655     0.00285845520139\n",
       "---             ---              ---                ---\n",
       "14.4142333333   4.83784330237    0.426835520607     0.00383047130275\n",
       "14.5607         4.83784330237    0.426835520607     0.00383047130275\n",
       "14.7071666667   4.83784330237    0.426835520607     0.00383047130275\n",
       "14.8536333333   4.83791292194    0.426868837521     0.00383077029259\n",
       "15.0001         4.83791292194    0.426868837521     0.00383077029259"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n",
      "PartialDependence: Partial Dependence Plot of model XGBoost_model_python_1543517619333_7 on column 'AveOccup'\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>aveoccup</b></td>\n",
       "<td><b>mean_response</b></td>\n",
       "<td><b>stddev_response</b></td>\n",
       "<td><b>std_error_mean_response</b></td></tr>\n",
       "<tr><td>0.6923077</td>\n",
       "<td>4.7145734</td>\n",
       "<td>1.6517448</td>\n",
       "<td>0.0148229</td></tr>\n",
       "<tr><td>13.2442372</td>\n",
       "<td>1.5843531</td>\n",
       "<td>0.8072629</td>\n",
       "<td>0.0072445</td></tr>\n",
       "<tr><td>25.7961668</td>\n",
       "<td>1.5843531</td>\n",
       "<td>0.8072629</td>\n",
       "<td>0.0072445</td></tr>\n",
       "<tr><td>38.3480963</td>\n",
       "<td>1.5843531</td>\n",
       "<td>0.8072629</td>\n",
       "<td>0.0072445</td></tr>\n",
       "<tr><td>50.9000259</td>\n",
       "<td>1.5843531</td>\n",
       "<td>0.8072629</td>\n",
       "<td>0.0072445</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td>1193.1256151</td>\n",
       "<td>-1.1011310</td>\n",
       "<td>0.7744430</td>\n",
       "<td>0.0069499</td></tr>\n",
       "<tr><td>1205.6775447</td>\n",
       "<td>-1.1011310</td>\n",
       "<td>0.7744430</td>\n",
       "<td>0.0069499</td></tr>\n",
       "<tr><td>1218.2294742</td>\n",
       "<td>-1.1011310</td>\n",
       "<td>0.7744430</td>\n",
       "<td>0.0069499</td></tr>\n",
       "<tr><td>1230.7814038</td>\n",
       "<td>-1.1011310</td>\n",
       "<td>0.7744430</td>\n",
       "<td>0.0069499</td></tr>\n",
       "<tr><td>1243.3333333</td>\n",
       "<td>-1.1011310</td>\n",
       "<td>0.7744430</td>\n",
       "<td>0.0069499</td></tr></table></div>"
      ],
      "text/plain": [
       "aveoccup        mean_response    stddev_response    std_error_mean_response\n",
       "--------------  ---------------  -----------------  -------------------------\n",
       "0.692307692308  4.71457340546    1.65174484764      0.0148229491991\n",
       "13.2442372442   1.58435312864    0.807262935789     0.0072444709028\n",
       "25.7961667962   1.58435312864    0.807262935789     0.0072444709028\n",
       "38.3480963481   1.58435312864    0.807262935789     0.0072444709028\n",
       "50.9000259      1.58435312864    0.807262935789     0.0072444709028\n",
       "---             ---              ---                ---\n",
       "1193.12561513   -1.10113102543   0.774442978037     0.00694994080804\n",
       "1205.67754468   -1.10113102543   0.774442978037     0.00694994080804\n",
       "1218.22947423   -1.10113102543   0.774442978037     0.00694994080804\n",
       "1230.78140378   -1.10113102543   0.774442978037     0.00694994080804\n",
       "1243.33333333   -1.10113102543   0.774442978037     0.00694994080804"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n",
      "PartialDependence: Partial Dependence Plot of model XGBoost_model_python_1543517619333_7 on column 'HouseAge'\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>houseage</b></td>\n",
       "<td><b>mean_response</b></td>\n",
       "<td><b>stddev_response</b></td>\n",
       "<td><b>std_error_mean_response</b></td></tr>\n",
       "<tr><td>1.0</td>\n",
       "<td>1.4583174</td>\n",
       "<td>0.8679680</td>\n",
       "<td>0.0077892</td></tr>\n",
       "<tr><td>2.0</td>\n",
       "<td>1.4583174</td>\n",
       "<td>0.8679680</td>\n",
       "<td>0.0077892</td></tr>\n",
       "<tr><td>3.0</td>\n",
       "<td>1.6582169</td>\n",
       "<td>0.8473530</td>\n",
       "<td>0.0076042</td></tr>\n",
       "<tr><td>4.0</td>\n",
       "<td>1.7134339</td>\n",
       "<td>0.8369214</td>\n",
       "<td>0.0075106</td></tr>\n",
       "<tr><td>5.0</td>\n",
       "<td>1.7160654</td>\n",
       "<td>0.8339999</td>\n",
       "<td>0.0074844</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td>48.0</td>\n",
       "<td>2.3312915</td>\n",
       "<td>0.9915394</td>\n",
       "<td>0.0088982</td></tr>\n",
       "<tr><td>49.0</td>\n",
       "<td>2.3327401</td>\n",
       "<td>0.9897468</td>\n",
       "<td>0.0088821</td></tr>\n",
       "<tr><td>50.0</td>\n",
       "<td>2.3413390</td>\n",
       "<td>0.9806541</td>\n",
       "<td>0.0088005</td></tr>\n",
       "<tr><td>51.0</td>\n",
       "<td>2.4553383</td>\n",
       "<td>0.9887533</td>\n",
       "<td>0.0088732</td></tr>\n",
       "<tr><td>52.0</td>\n",
       "<td>2.5113788</td>\n",
       "<td>0.9656211</td>\n",
       "<td>0.0086656</td></tr></table></div>"
      ],
      "text/plain": [
       "houseage    mean_response    stddev_response    std_error_mean_response\n",
       "----------  ---------------  -----------------  -------------------------\n",
       "1.0         1.45831738348    0.867967966492     0.00778924486563\n",
       "2.0         1.45831738348    0.867967966492     0.00778924486563\n",
       "3.0         1.65821687972    0.84735304869      0.00760424421025\n",
       "4.0         1.71343394366    0.836921379682     0.00751062920671\n",
       "5.0         1.71606544807    0.833999901828     0.00748441152674\n",
       "---         ---              ---                ---\n",
       "48.0        2.331291457      0.991539423426     0.00889818940463\n",
       "49.0        2.33274008164    0.989746819547     0.00888210237019\n",
       "50.0        2.34133903773    0.980654079907     0.00880050307357\n",
       "51.0        2.45533834221    0.988753310879     0.00887318650856\n",
       "52.0        2.51137877797    0.965621058778     0.00866559500419"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[, , ]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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4kj/m3I4rO9sC+RVX2FKrFqSmfldgetbzzrMlLl57DV55BQYNshJ/XJ8jFLRl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\nESPg4otthLw1a+xncSX4Vq3gyy8rdEr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SoYRjLwZ2A2YWsn68qh5TjvQWZxkWJAEIHik0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\nq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a169d1d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_mono.partial_plot(data=train, cols=feature_names, nbins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |█████████████████████████████████████████| 100%\n",
      "PartialDependence: Partial Dependence Plot of model GBM_model_python_1543517619333_8 on column 'MedInc'\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>medinc</b></td>\n",
       "<td><b>mean_response</b></td>\n",
       "<td><b>stddev_response</b></td>\n",
       "<td><b>std_error_mean_response</b></td></tr>\n",
       "<tr><td>0.4999</td>\n",
       "<td>1.0842446</td>\n",
       "<td>0.2019811</td>\n",
       "<td>0.0018126</td></tr>\n",
       "<tr><td>0.6463667</td>\n",
       "<td>1.0842446</td>\n",
       "<td>0.2019811</td>\n",
       "<td>0.0018126</td></tr>\n",
       "<tr><td>0.7928333</td>\n",
       "<td>1.0842446</td>\n",
       "<td>0.2019811</td>\n",
       "<td>0.0018126</td></tr>\n",
       "<tr><td>0.9393</td>\n",
       "<td>1.0842446</td>\n",
       "<td>0.2019811</td>\n",
       "<td>0.0018126</td></tr>\n",
       "<tr><td>1.0857667</td>\n",
       "<td>1.0842446</td>\n",
       "<td>0.2019811</td>\n",
       "<td>0.0018126</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td>14.4142333</td>\n",
       "<td>4.7717826</td>\n",
       "<td>0.2258950</td>\n",
       "<td>0.0020272</td></tr>\n",
       "<tr><td>14.5607</td>\n",
       "<td>4.7717826</td>\n",
       "<td>0.2258950</td>\n",
       "<td>0.0020272</td></tr>\n",
       "<tr><td>14.7071667</td>\n",
       "<td>4.7717826</td>\n",
       "<td>0.2258950</td>\n",
       "<td>0.0020272</td></tr>\n",
       "<tr><td>14.8536333</td>\n",
       "<td>4.7717826</td>\n",
       "<td>0.2258950</td>\n",
       "<td>0.0020272</td></tr>\n",
       "<tr><td>15.0001</td>\n",
       "<td>4.7717826</td>\n",
       "<td>0.2258950</td>\n",
       "<td>0.0020272</td></tr></table></div>"
      ],
      "text/plain": [
       "medinc          mean_response    stddev_response    std_error_mean_response\n",
       "--------------  ---------------  -----------------  -------------------------\n",
       "0.4999          1.08424463923    0.201981054756     0.00181260133375\n",
       "0.646366666667  1.08424463923    0.201981054756     0.00181260133375\n",
       "0.792833333333  1.08424463923    0.201981054756     0.00181260133375\n",
       "0.9393          1.08424463923    0.201981054756     0.00181260133375\n",
       "1.08576666667   1.08424463923    0.201981054756     0.00181260133375\n",
       "---             ---              ---                ---\n",
       "14.4142333333   4.77178258484    0.225894985984     0.00202720771696\n",
       "14.5607         4.77178258484    0.225894985984     0.00202720771696\n",
       "14.7071666667   4.77178258484    0.225894985984     0.00202720771696\n",
       "14.8536333333   4.77178258484    0.225894985984     0.00202720771696\n",
       "15.0001         4.77178258484    0.225894985984     0.00202720771696"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n",
      "PartialDependence: Partial Dependence Plot of model GBM_model_python_1543517619333_8 on column 'AveOccup'\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>aveoccup</b></td>\n",
       "<td><b>mean_response</b></td>\n",
       "<td><b>stddev_response</b></td>\n",
       "<td><b>std_error_mean_response</b></td></tr>\n",
       "<tr><td>0.6923077</td>\n",
       "<td>3.1477957</td>\n",
       "<td>0.9659091</td>\n",
       "<td>0.0086682</td></tr>\n",
       "<tr><td>13.2442372</td>\n",
       "<td>1.6114909</td>\n",
       "<td>0.7182429</td>\n",
       "<td>0.0064456</td></tr>\n",
       "<tr><td>25.7961668</td>\n",
       "<td>1.6114909</td>\n",
       "<td>0.7182429</td>\n",
       "<td>0.0064456</td></tr>\n",
       "<tr><td>38.3480963</td>\n",
       "<td>1.6114909</td>\n",
       "<td>0.7182429</td>\n",
       "<td>0.0064456</td></tr>\n",
       "<tr><td>50.9000259</td>\n",
       "<td>1.6114909</td>\n",
       "<td>0.7182429</td>\n",
       "<td>0.0064456</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td>1193.1256151</td>\n",
       "<td>1.6114909</td>\n",
       "<td>0.7182429</td>\n",
       "<td>0.0064456</td></tr>\n",
       "<tr><td>1205.6775447</td>\n",
       "<td>1.6114909</td>\n",
       "<td>0.7182429</td>\n",
       "<td>0.0064456</td></tr>\n",
       "<tr><td>1218.2294742</td>\n",
       "<td>1.6114909</td>\n",
       "<td>0.7182429</td>\n",
       "<td>0.0064456</td></tr>\n",
       "<tr><td>1230.7814038</td>\n",
       "<td>1.6114909</td>\n",
       "<td>0.7182429</td>\n",
       "<td>0.0064456</td></tr>\n",
       "<tr><td>1243.3333333</td>\n",
       "<td>1.6114909</td>\n",
       "<td>0.7182429</td>\n",
       "<td>0.0064456</td></tr></table></div>"
      ],
      "text/plain": [
       "aveoccup        mean_response    stddev_response    std_error_mean_response\n",
       "--------------  ---------------  -----------------  -------------------------\n",
       "0.692307692308  3.14779573025    0.965909120689     0.00866818010507\n",
       "13.2442372442   1.61149093453    0.718242942928     0.00644559519643\n",
       "25.7961667962   1.61149093453    0.718242942928     0.00644559519643\n",
       "38.3480963481   1.61149093453    0.718242942928     0.00644559519643\n",
       "50.9000259      1.61149093453    0.718242942928     0.00644559519643\n",
       "---             ---              ---                ---\n",
       "1193.12561513   1.61149093453    0.718242942928     0.00644559519643\n",
       "1205.67754468   1.61149093453    0.718242942928     0.00644559519643\n",
       "1218.22947423   1.61149093453    0.718242942928     0.00644559519643\n",
       "1230.78140378   1.61149093453    0.718242942928     0.00644559519643\n",
       "1243.33333333   1.61149093453    0.718242942928     0.00644559519643"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n",
      "PartialDependence: Partial Dependence Plot of model GBM_model_python_1543517619333_8 on column 'HouseAge'\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>houseage</b></td>\n",
       "<td><b>mean_response</b></td>\n",
       "<td><b>stddev_response</b></td>\n",
       "<td><b>std_error_mean_response</b></td></tr>\n",
       "<tr><td>1.0</td>\n",
       "<td>1.7299145</td>\n",
       "<td>0.7237023</td>\n",
       "<td>0.0064946</td></tr>\n",
       "<tr><td>2.0</td>\n",
       "<td>1.7299145</td>\n",
       "<td>0.7237023</td>\n",
       "<td>0.0064946</td></tr>\n",
       "<tr><td>3.0</td>\n",
       "<td>1.7659286</td>\n",
       "<td>0.7296043</td>\n",
       "<td>0.0065476</td></tr>\n",
       "<tr><td>4.0</td>\n",
       "<td>1.7807129</td>\n",
       "<td>0.7446737</td>\n",
       "<td>0.0066828</td></tr>\n",
       "<tr><td>5.0</td>\n",
       "<td>1.7826570</td>\n",
       "<td>0.7470610</td>\n",
       "<td>0.0067042</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td>48.0</td>\n",
       "<td>2.3636941</td>\n",
       "<td>0.9871769</td>\n",
       "<td>0.0088590</td></tr>\n",
       "<tr><td>49.0</td>\n",
       "<td>2.3636941</td>\n",
       "<td>0.9871769</td>\n",
       "<td>0.0088590</td></tr>\n",
       "<tr><td>50.0</td>\n",
       "<td>2.3636941</td>\n",
       "<td>0.9871769</td>\n",
       "<td>0.0088590</td></tr>\n",
       "<tr><td>51.0</td>\n",
       "<td>2.4644215</td>\n",
       "<td>0.9782047</td>\n",
       "<td>0.0087785</td></tr>\n",
       "<tr><td>52.0</td>\n",
       "<td>2.5585947</td>\n",
       "<td>0.9338781</td>\n",
       "<td>0.0083807</td></tr></table></div>"
      ],
      "text/plain": [
       "houseage    mean_response    stddev_response    std_error_mean_response\n",
       "----------  ---------------  -----------------  -------------------------\n",
       "1.0         1.72991453816    0.723702251019     0.00649458765832\n",
       "2.0         1.72991453816    0.723702251019     0.00649458765832\n",
       "3.0         1.76592856006    0.729604250096     0.00654755288029\n",
       "4.0         1.7807129499     0.744673745195     0.00668278827129\n",
       "5.0         1.78265695856    0.74706097738      0.00670421156887\n",
       "---         ---              ---                ---\n",
       "48.0        2.36369414221    0.987176942411     0.0088590399957\n",
       "49.0        2.36369414221    0.987176942411     0.0088590399957\n",
       "50.0        2.36369414221    0.987176942411     0.0088590399957\n",
       "51.0        2.46442145681    0.978204724748     0.00877852227723\n",
       "52.0        2.558594724      0.933878147945     0.00838073045299"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[, , ]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6L7kkgiZ+gBEj4Kef4I034p6BMmVsJb2nn4arr47rpZxzcRDmcL4/RaQj0DXY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Pv2jdSnT+p1cr97d2ZMmcLYdtSjXbIT+D0L9xARkXSVlRHg33wTZs6M7bvvpoaiFRVFTfzYY2Hk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\n/CIiIh2IAr+IiEgH8v8BZmI91D2GbKIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fa91550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_mono.partial_plot(data=train, cols=feature_names, nbins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}