notebooks/mono_xgboost.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# License \n",
    "***\n",
    "Copyright 2017 J. Patrick Hall, phall@h2o.ai & Navdeep Gill, navdeep@h2o.ai\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": "markdown",
   "metadata": {},
   "source": [
    "# Monotonic Gradient Boosting using XGBoost \n",
    "***\n",
    "http://xgboost.readthedocs.io/en/latest//tutorials/monotonic.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Monotonicity is an important facet of intepretability. Monotonicity constraints ensure that the modeled relationship between inputs and the target move in only direction, i.e. as an input increases the target can only increase or as input increases the target can only decrease. Such monotonic relationships are usually easier to explain and understand than non-monotonic relationships. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries: imports, start h2o, load and clean data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import h2o\n",
    "from h2o.estimators.xgboost import H2OXGBoostEstimator\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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_181\"; Java(TM) SE Runtime Environment (build 1.8.0_181-b13); Java HotSpot(TM) 64-Bit Server VM (build 25.181-b13, mixed mode)\n",
      "  Starting server from /home/patrickh/anaconda3/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /tmp/tmpffosr2ot\n",
      "  JVM stdout: /tmp/tmpffosr2ot/h2o_patrickh_started_from_python.out\n",
      "  JVM stderr: /tmp/tmpffosr2ot/h2o_patrickh_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>01 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>America/New_York</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.20.0.4</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>1 month and 20 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_patrickh_9i4c17</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>3.422 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>H2O API Extensions:</td>\n",
       "<td>XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.6.4 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ----------------------------------------\n",
       "H2O cluster uptime:         01 secs\n",
       "H2O cluster timezone:       America/New_York\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.20.0.4\n",
       "H2O cluster version age:    1 month and 20 days\n",
       "H2O cluster name:           H2O_from_python_patrickh_9i4c17\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    3.422 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",
       "H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             3.6.4 final\n",
       "--------------------------  ----------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# start h2o\n",
    "h2o.init()\n",
    "h2o.remove_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load and prepare data for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# load clean data\n",
    "path = '../data/train.csv'\n",
    "frame = h2o.import_file(path=path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign target and inputs\n",
    "y = 'SalePrice'\n",
    "X = [name for name in frame.columns if name not in [y, 'Id']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Monotonic constraints are easier to understand for numeric inputs without missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# determine column types\n",
    "# impute\n",
    "reals, enums = [], []\n",
    "for key, val in frame.types.items():\n",
    "    if key in X:\n",
    "        if val == 'enum':\n",
    "            enums.append(key)\n",
    "        else: \n",
    "            reals.append(key)\n",
    "            \n",
    "_ = frame[reals].impute(method='median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into training and validation\n",
    "train, valid = frame.split_frame([0.7], seed=12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for convenience create a tuple for xgboost monotone_constraints parameter\n",
    "# in this data, most inputs increase with increasing home prices\n",
    "# xgboost does allow for positive, negative, and no constraints in a model\n",
    "mono_constraints = tuple(int(i) for i in np.ones(shape=(int(1), len(reals))).tolist()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a monotonic predictive model\n",
    "In this XGBoost GBM all the modeled relationships between the inputs and the target are forced to be monotonically increasing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Log transform for better regression results and easy RMSLE in XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  SalePrice</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">    12.2477</td></tr>\n",
       "<tr><td style=\"text-align: right;\">    12.109 </td></tr>\n",
       "<tr><td style=\"text-align: right;\">    12.3172</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f23a5afce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check log transform - looks good\n",
    "%matplotlib inline\n",
    "train['SalePrice'].log().as_data_frame().hist()\n",
    "\n",
    "# Execute log transform\n",
    "train['SalePrice'] = train['SalePrice'].log()\n",
    "valid['SalePrice'] = valid['SalePrice'].log()\n",
    "print(train[0:3, 'SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train XGBoost with monotonicity Constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:0.392336\teval-rmse:0.40992\n",
      "Multiple eval metrics have been passed: 'eval-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until eval-rmse hasn't improved in 50 rounds.\n",
      "[1]\ttrain-rmse:0.390804\teval-rmse:0.408264\n",
      "[2]\ttrain-rmse:0.389547\teval-rmse:0.406994\n",
      "[3]\ttrain-rmse:0.388241\teval-rmse:0.405793\n",
      "[4]\ttrain-rmse:0.386788\teval-rmse:0.404297\n",
      "[5]\ttrain-rmse:0.385428\teval-rmse:0.402984\n",
      "[6]\ttrain-rmse:0.384188\teval-rmse:0.401721\n",
      "[7]\ttrain-rmse:0.383222\teval-rmse:0.400734\n",
      "[8]\ttrain-rmse:0.381748\teval-rmse:0.399191\n",
      "[9]\ttrain-rmse:0.380336\teval-rmse:0.397775\n",
      "[10]\ttrain-rmse:0.379033\teval-rmse:0.396372\n",
      "[11]\ttrain-rmse:0.377589\teval-rmse:0.394849\n",
      "[12]\ttrain-rmse:0.376343\teval-rmse:0.393598\n",
      "[13]\ttrain-rmse:0.374996\teval-rmse:0.392173\n",
      "[14]\ttrain-rmse:0.373387\teval-rmse:0.390483\n",
      "[15]\ttrain-rmse:0.37208\teval-rmse:0.388907\n",
      "[16]\ttrain-rmse:0.370673\teval-rmse:0.38743\n",
      "[17]\ttrain-rmse:0.369318\teval-rmse:0.386086\n",
      "[18]\ttrain-rmse:0.367916\teval-rmse:0.384505\n",
      "[19]\ttrain-rmse:0.366619\teval-rmse:0.383301\n",
      "[20]\ttrain-rmse:0.36532\teval-rmse:0.381953\n",
      "[21]\ttrain-rmse:0.364238\teval-rmse:0.380842\n",
      "[22]\ttrain-rmse:0.363082\teval-rmse:0.379569\n",
      "[23]\ttrain-rmse:0.3617\teval-rmse:0.378076\n",
      "[24]\ttrain-rmse:0.360528\teval-rmse:0.376899\n",
      "[25]\ttrain-rmse:0.359409\teval-rmse:0.375733\n",
      "[26]\ttrain-rmse:0.358119\teval-rmse:0.374317\n",
      "[27]\ttrain-rmse:0.356996\teval-rmse:0.373147\n",
      "[28]\ttrain-rmse:0.355765\teval-rmse:0.371809\n",
      "[29]\ttrain-rmse:0.354658\teval-rmse:0.370479\n",
      "[30]\ttrain-rmse:0.353358\teval-rmse:0.369097\n",
      "[31]\ttrain-rmse:0.352107\teval-rmse:0.36776\n",
      "[32]\ttrain-rmse:0.351047\teval-rmse:0.366638\n",
      "[33]\ttrain-rmse:0.349934\teval-rmse:0.365427\n",
      "[34]\ttrain-rmse:0.348689\teval-rmse:0.364225\n",
      "[35]\ttrain-rmse:0.347352\teval-rmse:0.362893\n",
      "[36]\ttrain-rmse:0.346135\teval-rmse:0.361591\n",
      "[37]\ttrain-rmse:0.344798\teval-rmse:0.360307\n",
      "[38]\ttrain-rmse:0.343712\teval-rmse:0.359184\n",
      "[39]\ttrain-rmse:0.342525\teval-rmse:0.357928\n",
      "[40]\ttrain-rmse:0.341451\teval-rmse:0.356766\n",
      "[41]\ttrain-rmse:0.340226\teval-rmse:0.355589\n",
      "[42]\ttrain-rmse:0.338945\teval-rmse:0.354254\n",
      "[43]\ttrain-rmse:0.337603\teval-rmse:0.352686\n",
      "[44]\ttrain-rmse:0.336266\teval-rmse:0.351326\n",
      "[45]\ttrain-rmse:0.335167\teval-rmse:0.350031\n",
      "[46]\ttrain-rmse:0.333993\teval-rmse:0.348913\n",
      "[47]\ttrain-rmse:0.332767\teval-rmse:0.347648\n",
      "[48]\ttrain-rmse:0.33165\teval-rmse:0.346623\n",
      "[49]\ttrain-rmse:0.33047\teval-rmse:0.345394\n",
      "[50]\ttrain-rmse:0.329416\teval-rmse:0.344272\n",
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      "[570]\ttrain-rmse:0.132561\teval-rmse:0.126889\n",
      "[571]\ttrain-rmse:0.132505\teval-rmse:0.126828\n",
      "[572]\ttrain-rmse:0.132468\teval-rmse:0.126802\n",
      "[573]\ttrain-rmse:0.132408\teval-rmse:0.126755\n",
      "[574]\ttrain-rmse:0.132357\teval-rmse:0.126698\n",
      "[575]\ttrain-rmse:0.132313\teval-rmse:0.126636\n",
      "[576]\ttrain-rmse:0.132275\teval-rmse:0.126611\n",
      "[577]\ttrain-rmse:0.132247\teval-rmse:0.12658\n",
      "[578]\ttrain-rmse:0.132202\teval-rmse:0.126539\n",
      "[579]\ttrain-rmse:0.13215\teval-rmse:0.126498\n",
      "[580]\ttrain-rmse:0.13211\teval-rmse:0.126466\n",
      "[581]\ttrain-rmse:0.132056\teval-rmse:0.126402\n",
      "[582]\ttrain-rmse:0.132002\teval-rmse:0.126377\n",
      "[583]\ttrain-rmse:0.13197\teval-rmse:0.126362\n",
      "[584]\ttrain-rmse:0.131924\teval-rmse:0.126332\n",
      "[585]\ttrain-rmse:0.131893\teval-rmse:0.126334\n",
      "[586]\ttrain-rmse:0.131819\teval-rmse:0.126326\n",
      "[587]\ttrain-rmse:0.131739\teval-rmse:0.126277\n",
      "[588]\ttrain-rmse:0.131704\teval-rmse:0.126274\n",
      "[589]\ttrain-rmse:0.131647\teval-rmse:0.126263\n",
      "[590]\ttrain-rmse:0.131614\teval-rmse:0.126243\n",
      "[591]\ttrain-rmse:0.131552\teval-rmse:0.126207\n",
      "[592]\ttrain-rmse:0.131492\teval-rmse:0.126203\n",
      "[593]\ttrain-rmse:0.13144\teval-rmse:0.126197\n",
      "[594]\ttrain-rmse:0.131426\teval-rmse:0.126172\n",
      "[595]\ttrain-rmse:0.131405\teval-rmse:0.126125\n",
      "[596]\ttrain-rmse:0.131376\teval-rmse:0.126086\n",
      "[597]\ttrain-rmse:0.13134\teval-rmse:0.126056\n",
      "[598]\ttrain-rmse:0.131297\teval-rmse:0.126009\n",
      "[599]\ttrain-rmse:0.131244\teval-rmse:0.125948\n",
      "[600]\ttrain-rmse:0.131225\teval-rmse:0.126026\n",
      "[601]\ttrain-rmse:0.131212\teval-rmse:0.126014\n",
      "[602]\ttrain-rmse:0.131179\teval-rmse:0.126002\n",
      "[603]\ttrain-rmse:0.131105\teval-rmse:0.125956\n",
      "[604]\ttrain-rmse:0.131039\teval-rmse:0.126048\n",
      "[605]\ttrain-rmse:0.130993\teval-rmse:0.125989\n",
      "[606]\ttrain-rmse:0.130947\teval-rmse:0.125917\n",
      "[607]\ttrain-rmse:0.130928\teval-rmse:0.125883\n",
      "[608]\ttrain-rmse:0.130846\teval-rmse:0.125771\n",
      "[609]\ttrain-rmse:0.130777\teval-rmse:0.125926\n",
      "[610]\ttrain-rmse:0.130764\teval-rmse:0.125975\n",
      "[611]\ttrain-rmse:0.130685\teval-rmse:0.125881\n",
      "[612]\ttrain-rmse:0.130642\teval-rmse:0.125957\n",
      "[613]\ttrain-rmse:0.130599\teval-rmse:0.125946\n",
      "[614]\ttrain-rmse:0.130564\teval-rmse:0.125911\n",
      "[615]\ttrain-rmse:0.13052\teval-rmse:0.12592\n",
      "[616]\ttrain-rmse:0.130465\teval-rmse:0.125876\n",
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      "[618]\ttrain-rmse:0.130411\teval-rmse:0.125856\n",
      "[619]\ttrain-rmse:0.130354\teval-rmse:0.125809\n",
      "[620]\ttrain-rmse:0.130302\teval-rmse:0.125736\n",
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      "[622]\ttrain-rmse:0.130262\teval-rmse:0.125671\n",
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      "[624]\ttrain-rmse:0.130178\teval-rmse:0.125562\n",
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      "[626]\ttrain-rmse:0.130126\teval-rmse:0.12553\n",
      "[627]\ttrain-rmse:0.130105\teval-rmse:0.125543\n",
      "[628]\ttrain-rmse:0.130061\teval-rmse:0.125524\n",
      "[629]\ttrain-rmse:0.130041\teval-rmse:0.125528\n",
      "[630]\ttrain-rmse:0.129984\teval-rmse:0.125434\n",
      "[631]\ttrain-rmse:0.129939\teval-rmse:0.125455\n",
      "[632]\ttrain-rmse:0.129885\teval-rmse:0.125392\n",
      "[633]\ttrain-rmse:0.129847\teval-rmse:0.125352\n",
      "[634]\ttrain-rmse:0.129819\teval-rmse:0.125291\n",
      "[635]\ttrain-rmse:0.129786\teval-rmse:0.125282\n",
      "[636]\ttrain-rmse:0.129774\teval-rmse:0.125224\n",
      "[637]\ttrain-rmse:0.12972\teval-rmse:0.125181\n",
      "[638]\ttrain-rmse:0.129684\teval-rmse:0.125163\n",
      "[639]\ttrain-rmse:0.129671\teval-rmse:0.125137\n",
      "[640]\ttrain-rmse:0.129652\teval-rmse:0.125127\n",
      "[641]\ttrain-rmse:0.129601\teval-rmse:0.125102\n",
      "[642]\ttrain-rmse:0.129564\teval-rmse:0.125097\n",
      "[643]\ttrain-rmse:0.129524\teval-rmse:0.125074\n",
      "[644]\ttrain-rmse:0.129519\teval-rmse:0.125287\n",
      "[645]\ttrain-rmse:0.129497\teval-rmse:0.125301\n",
      "[646]\ttrain-rmse:0.129451\teval-rmse:0.125284\n",
      "[647]\ttrain-rmse:0.129397\teval-rmse:0.12526\n",
      "[648]\ttrain-rmse:0.129373\teval-rmse:0.12552\n",
      "[649]\ttrain-rmse:0.129323\teval-rmse:0.125572\n",
      "[650]\ttrain-rmse:0.129362\teval-rmse:0.125794\n",
      "[651]\ttrain-rmse:0.129312\teval-rmse:0.12582\n",
      "[652]\ttrain-rmse:0.129268\teval-rmse:0.12577\n",
      "[653]\ttrain-rmse:0.129239\teval-rmse:0.125738\n",
      "[654]\ttrain-rmse:0.129166\teval-rmse:0.125681\n",
      "[655]\ttrain-rmse:0.129194\teval-rmse:0.125858\n",
      "[656]\ttrain-rmse:0.129194\teval-rmse:0.126025\n",
      "[657]\ttrain-rmse:0.129191\teval-rmse:0.126029\n",
      "[658]\ttrain-rmse:0.129144\teval-rmse:0.126051\n",
      "[659]\ttrain-rmse:0.129172\teval-rmse:0.126274\n",
      "[660]\ttrain-rmse:0.129154\teval-rmse:0.12631\n",
      "[661]\ttrain-rmse:0.129151\teval-rmse:0.126343\n",
      "[662]\ttrain-rmse:0.129148\teval-rmse:0.126476\n",
      "[663]\ttrain-rmse:0.129124\teval-rmse:0.126475\n",
      "[664]\ttrain-rmse:0.129092\teval-rmse:0.126495\n",
      "[665]\ttrain-rmse:0.129086\teval-rmse:0.126478\n",
      "[666]\ttrain-rmse:0.129038\teval-rmse:0.126496\n",
      "[667]\ttrain-rmse:0.128996\teval-rmse:0.126469\n",
      "[668]\ttrain-rmse:0.128982\teval-rmse:0.126438\n",
      "[669]\ttrain-rmse:0.128954\teval-rmse:0.126373\n",
      "[670]\ttrain-rmse:0.128902\teval-rmse:0.126345\n",
      "[671]\ttrain-rmse:0.128876\teval-rmse:0.12631\n",
      "[672]\ttrain-rmse:0.128815\teval-rmse:0.1263\n",
      "[673]\ttrain-rmse:0.128799\teval-rmse:0.126289\n",
      "[674]\ttrain-rmse:0.128755\teval-rmse:0.126295\n",
      "[675]\ttrain-rmse:0.128751\teval-rmse:0.126303\n",
      "[676]\ttrain-rmse:0.128718\teval-rmse:0.126315\n",
      "[677]\ttrain-rmse:0.128701\teval-rmse:0.126296\n",
      "[678]\ttrain-rmse:0.128669\teval-rmse:0.126275\n",
      "[679]\ttrain-rmse:0.128629\teval-rmse:0.126238\n",
      "[680]\ttrain-rmse:0.128572\teval-rmse:0.126206\n",
      "[681]\ttrain-rmse:0.128566\teval-rmse:0.126283\n",
      "[682]\ttrain-rmse:0.12855\teval-rmse:0.126297\n",
      "[683]\ttrain-rmse:0.12855\teval-rmse:0.126281\n",
      "[684]\ttrain-rmse:0.128544\teval-rmse:0.126253\n",
      "[685]\ttrain-rmse:0.128552\teval-rmse:0.126233\n",
      "[686]\ttrain-rmse:0.128551\teval-rmse:0.126243\n",
      "[687]\ttrain-rmse:0.128556\teval-rmse:0.126305\n",
      "[688]\ttrain-rmse:0.128544\teval-rmse:0.126313\n",
      "[689]\ttrain-rmse:0.128508\teval-rmse:0.12634\n",
      "[690]\ttrain-rmse:0.128483\teval-rmse:0.126342\n",
      "[691]\ttrain-rmse:0.128476\teval-rmse:0.126351\n",
      "[692]\ttrain-rmse:0.128455\teval-rmse:0.126349\n",
      "[693]\ttrain-rmse:0.128463\teval-rmse:0.126329\n",
      "Stopping. Best iteration:\n",
      "[643]\ttrain-rmse:0.129524\teval-rmse:0.125074\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ave_y = train['SalePrice'].mean()[0]\n",
    "\n",
    "# XGBoost uses SVMLight data structure, not Numpy arrays or Pandas data frames \n",
    "dtrain = xgb.DMatrix(train.as_data_frame()[reals],\n",
    "                      train.as_data_frame()['SalePrice'])\n",
    "dvalid = xgb.DMatrix(valid.as_data_frame()[reals],\n",
    "                      valid.as_data_frame()['SalePrice'])\n",
    "\n",
    "# tuning parameters\n",
    "params = {\n",
    "    'objective': 'reg:linear',\n",
    "    'booster': 'gbtree', \n",
    "    'eval_metric': 'rmse',\n",
    "    'eta': 0.005,\n",
    "    'subsample': 0.1, \n",
    "    'colsample_bytree': 0.8,\n",
    "    'max_depth': 5,\n",
    "    'reg_alpha' : 0.01,\n",
    "    'reg_lambda' : 0.0,\n",
    "    'monotone_constraints':mono_constraints,\n",
    "    'base_score': ave_y,\n",
    "    'silent': 0,\n",
    "    'seed': 12345,\n",
    "}\n",
    "\n",
    "# watchlist is used for early stopping\n",
    "watchlist = [(dtrain, 'train'), (dvalid, 'eval')]\n",
    "\n",
    "# train model\n",
    "xgb_model1 = xgb.train(params, \n",
    "                       dtrain, \n",
    "                       1000,\n",
    "                       evals=watchlist, \n",
    "                       early_stopping_rounds=50, \n",
    "                       verbose_eval=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot variable importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f239d4cd0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = xgb.plot_importance(xgb_model1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine monotonic behavior with partial dependence and ICE\n",
    "* Partial dependence is used to view the global, average behavior of a variable under the monotonic model.\n",
    "* ICE is used to view the local behavior of a single instance and single variable under the monotonic model.\n",
    "* Overlaying partial dependence onto ICE in a plot is a convenient way to validate and understand both global and local monotonic behavior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Helper function for calculating partial dependence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def par_dep(xs, frame, model, resolution=20, bins=None):\n",
    "    \n",
    "    \"\"\" Creates Pandas dataframe containing partial dependence for a single variable.\n",
    "    \n",
    "    Args:\n",
    "        xs: Variable for which to calculate partial dependence.\n",
    "        frame: H2OFrame for which to calculate partial dependence.\n",
    "        model: XGBoost model for which to calculate partial dependence.\n",
    "        resolution: The number of points across the domain of xs for which to calculate partial dependence.\n",
    "    \n",
    "    Returns:\n",
    "        Pandas dataframe containing partial dependence values.\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    # don't show progress bars for parse\n",
    "    h2o.no_progress()\n",
    "    \n",
    "    # init empty Pandas frame w/ correct col names\n",
    "    par_dep_frame = pd.DataFrame(columns=[xs, 'partial_dependence'])\n",
    "    \n",
    "    # cache original data \n",
    "    col_cache = h2o.deep_copy(frame[xs], xid='col_cache')\n",
    "  \n",
    "    # determine values at which to calculate partial dependency\n",
    "    if bins == None:\n",
    "        min_ = frame[xs].min()\n",
    "        max_ = frame[xs].max()\n",
    "        by = (max_ - min_)/resolution\n",
    "        bins = np.arange(min_, max_, by)\n",
    "        \n",
    "    # calculate partial dependency  \n",
    "    # by setting column of interest to constant \n",
    "    for j in bins:\n",
    "        frame[xs] = j\n",
    "        dframe = xgb.DMatrix(frame.as_data_frame(),)\n",
    "        par_dep_i = h2o.H2OFrame(model.predict(dframe).tolist())\n",
    "        par_dep_j = par_dep_i.mean()[0]\n",
    "        par_dep_frame = par_dep_frame.append({xs:j,\n",
    "                                              'partial_dependence': par_dep_j}, \n",
    "                                              ignore_index=True)\n",
    "        \n",
    "    # return input frame to original cached state    \n",
    "    frame[xs] = h2o.get_frame('col_cache')\n",
    "\n",
    "    return par_dep_frame\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate partial dependence for 3 important variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "par_dep_OverallCond = par_dep('OverallCond', valid[reals], xgb_model1)\n",
    "par_dep_GrLivArea = par_dep('GrLivArea', valid[reals], xgb_model1)\n",
    "par_dep_LotArea = par_dep('LotArea', valid[reals], xgb_model1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Helper function for finding decile indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_quantile_dict(y, id_, frame):\n",
    "\n",
    "    \"\"\" Returns the percentiles of a column y as the indices for another column id_.\n",
    "    \n",
    "    Args:\n",
    "        y: Column in which to find percentiles.\n",
    "        id_: Id column that stores indices for percentiles of y.\n",
    "        frame: H2OFrame containing y and id_. \n",
    "    \n",
    "    Returns:\n",
    "        Dictionary of percentile values and index column values.\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    quantiles_df = frame.as_data_frame()\n",
    "    quantiles_df.sort_values(y, inplace=True)\n",
    "    quantiles_df.reset_index(inplace=True)\n",
    "    \n",
    "    percentiles_dict = {}\n",
    "    percentiles_dict[0] = quantiles_df.loc[0, id_]\n",
    "    percentiles_dict[99] = quantiles_df.loc[quantiles_df.shape[0]-1, id_]\n",
    "    inc = quantiles_df.shape[0]//10\n",
    "    \n",
    "    for i in range(1, 10):\n",
    "        percentiles_dict[i * 10] = quantiles_df.loc[i * inc,  id_]\n",
    "\n",
    "    return percentiles_dict\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate deciles of SaleProce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantile_dict = get_quantile_dict('SalePrice', 'Id', valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate values for ICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins_OverallCond = list(par_dep_OverallCond['OverallCond'])\n",
    "bins_GrLivArea = list(par_dep_GrLivArea['GrLivArea'])\n",
    "bins_LotArea = list(par_dep_LotArea['LotArea'])\n",
    "\n",
    "for i in sorted(quantile_dict.keys()):\n",
    "    \n",
    "    col_name = 'Percentile_' + str(i)\n",
    "    \n",
    "    par_dep_OverallCond[col_name] = par_dep('OverallCond', \n",
    "                                            valid[valid['Id'] == int(quantile_dict[i])][reals], \n",
    "                                            xgb_model1, \n",
    "                                            bins=bins_OverallCond)['partial_dependence']\n",
    "    \n",
    "    par_dep_GrLivArea[col_name] = par_dep('GrLivArea', \n",
    "                                          valid[valid['Id'] == int(quantile_dict[i])][reals], \n",
    "                                          xgb_model1, \n",
    "                                          bins=bins_GrLivArea)['partial_dependence']\n",
    "    \n",
    "    par_dep_LotArea[col_name] = par_dep('LotArea', \n",
    "                                         valid[valid['Id'] == int(quantile_dict[i])][reals], \n",
    "                                         xgb_model1, \n",
    "                                         bins=bins_LotArea)['partial_dependence']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Partial Dependence and ICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f23a5aa5f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# OverallCond\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "par_dep_OverallCond.drop('partial_dependence', axis=1).plot(x='OverallCond', colormap='gnuplot', ax=ax)\n",
    "\n",
    "par_dep_OverallCond.plot(title='Partial Dependence and ICE for OverallCond',\n",
    "                         x='OverallCond', \n",
    "                         y='partial_dependence',\n",
    "                         style='r-', \n",
    "                         linewidth=3, \n",
    "                         ax=ax)\n",
    "\n",
    "_ = plt.legend(bbox_to_anchor=(1.05, 0),\n",
    "               loc=3, \n",
    "               borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f23975cecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GrLivArea\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "par_dep_GrLivArea.drop('partial_dependence', axis=1).plot(x='GrLivArea', colormap='gnuplot', ax=ax)\n",
    "\n",
    "par_dep_GrLivArea.plot(title='Partial Dependence and ICE for GrLivArea',\n",
    "                         x='GrLivArea', \n",
    "                         y='partial_dependence',\n",
    "                         style='r-', \n",
    "                         linewidth=3, \n",
    "                         ax=ax)\n",
    "\n",
    "_ = plt.legend(bbox_to_anchor=(1.05, 0),\n",
    "               loc=3, \n",
    "               borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f239d45f080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LotArea\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "par_dep_LotArea.drop('partial_dependence', axis=1).plot(x='LotArea', colormap='gnuplot', ax=ax)\n",
    "\n",
    "par_dep_LotArea.plot(title='Partial Dependence and ICE for LotArea',\n",
    "                     x='LotArea', \n",
    "                     y='partial_dependence',\n",
    "                     style='r-', \n",
    "                     linewidth=3, \n",
    "                     ax=ax)\n",
    "\n",
    "_ = plt.legend(bbox_to_anchor=(1.05, 0),\n",
    "               loc=3, \n",
    "               borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shutdown H2O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are you sure you want to shutdown the H2O instance running at http://127.0.0.1:54321 (Y/N)? y\n",
      "H2O session _sid_910a closed.\n"
     ]
    }
   ],
   "source": [
    "h2o.cluster().shutdown(prompt=True)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}