h2o.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is part of my [Python data science curriculum](http://www.terran.us/articles/python_curriculum.html)\n",
    "\n",
    "There are two major sets of documentation which are relevant for H2O:\n",
    "\n",
    "http://docs.h2o.ai/h2o/latest-stable/h2o-docs/welcome.html  \n",
    "http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/intro.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RendererRegistry.enable('notebook')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import altair as alt\n",
    "alt.renderers.enable('notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cluster Init"
   ]
  },
  {
   "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: openjdk version \"1.8.0_111\"; OpenJDK Runtime Environment (build 1.8.0_111-8u111-b14-2~bpo8+1-b14); OpenJDK 64-Bit Server VM (build 25.111-b14, mixed mode)\n",
      "  Starting server from /opt/pyenv/versions/anaconda3-5.2.0/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /tmp/tmpz21aee3a\n",
      "  JVM stdout: /tmp/tmpz21aee3a/h2o_terran_started_from_python.out\n",
      "  JVM stderr: /tmp/tmpz21aee3a/h2o_terran_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>3.22.0.2</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>21 days, 13 hours and 13 minutes </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_terran_lnzuev</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>10.50 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>24</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>24</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>3.6.5 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:        3.22.0.2\n",
       "H2O cluster version age:    21 days, 13 hours and 13 minutes\n",
       "H2O cluster name:           H2O_from_python_terran_lnzuev\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    10.50 Gb\n",
       "H2O cluster total cores:    24\n",
       "H2O cluster allowed cores:  24\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:             3.6.5 final\n",
       "--------------------------  ----------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h2o\n",
    "h2o.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first I thought you could only load from disk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "from plotnine.data import diamonds\n",
    "diamonds.to_csv('/tmp/diamonds.csv')\n",
    "h2o_diamonds = h2o.import_file('/tmp/diamonds.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But in fact you __can__ transfer data from Python.  The key piece of information is that you use the h2o.H2OFrame __constructor__ to do it.  See references:\n",
    "\n",
    "http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/data.html#loading-data-from-a-python-object  \n",
    "http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/frame.html#h2oframe  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# The destination_frame argument is optional, but if you don't use it, you get a horrible hex name.\n",
    "h2o_diamonds2 = h2o.H2OFrame(diamonds, destination_frame='diamonds2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>diamonds.hex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>diamonds2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            key\n",
       "0  diamonds.hex\n",
       "1     diamonds2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  carat</th><th>cut      </th><th>color  </th><th>clarity  </th><th style=\"text-align: right;\">  depth</th><th style=\"text-align: right;\">  table</th><th style=\"text-align: right;\">  price</th><th style=\"text-align: right;\">   x</th><th style=\"text-align: right;\">   y</th><th style=\"text-align: right;\">   z</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">   0.23</td><td>Ideal    </td><td>E      </td><td>SI2      </td><td style=\"text-align: right;\">   61.5</td><td style=\"text-align: right;\">     55</td><td style=\"text-align: right;\">    326</td><td style=\"text-align: right;\">3.95</td><td style=\"text-align: right;\">3.98</td><td style=\"text-align: right;\">2.43</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   0.21</td><td>Premium  </td><td>E      </td><td>SI1      </td><td style=\"text-align: right;\">   59.8</td><td style=\"text-align: right;\">     61</td><td style=\"text-align: right;\">    326</td><td style=\"text-align: right;\">3.89</td><td style=\"text-align: right;\">3.84</td><td style=\"text-align: right;\">2.31</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   0.23</td><td>Good     </td><td>E      </td><td>VS1      </td><td style=\"text-align: right;\">   56.9</td><td style=\"text-align: right;\">     65</td><td style=\"text-align: right;\">    327</td><td style=\"text-align: right;\">4.05</td><td style=\"text-align: right;\">4.07</td><td style=\"text-align: right;\">2.31</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   0.29</td><td>Premium  </td><td>I      </td><td>VS2      </td><td style=\"text-align: right;\">   62.4</td><td style=\"text-align: right;\">     58</td><td style=\"text-align: right;\">    334</td><td style=\"text-align: right;\">4.2 </td><td style=\"text-align: right;\">4.23</td><td style=\"text-align: right;\">2.63</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   0.31</td><td>Good     </td><td>J      </td><td>SI2      </td><td style=\"text-align: right;\">   63.3</td><td style=\"text-align: right;\">     58</td><td style=\"text-align: right;\">    335</td><td style=\"text-align: right;\">4.34</td><td style=\"text-align: right;\">4.35</td><td style=\"text-align: right;\">2.75</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   0.24</td><td>Very Good</td><td>J      </td><td>VVS2     </td><td style=\"text-align: right;\">   62.8</td><td style=\"text-align: right;\">     57</td><td style=\"text-align: right;\">    336</td><td style=\"text-align: right;\">3.94</td><td style=\"text-align: right;\">3.96</td><td style=\"text-align: right;\">2.48</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   0.24</td><td>Very Good</td><td>I      </td><td>VVS1     </td><td style=\"text-align: right;\">   62.3</td><td style=\"text-align: right;\">     57</td><td style=\"text-align: right;\">    336</td><td style=\"text-align: right;\">3.95</td><td style=\"text-align: right;\">3.98</td><td style=\"text-align: right;\">2.47</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   0.26</td><td>Very Good</td><td>H      </td><td>SI1      </td><td style=\"text-align: right;\">   61.9</td><td style=\"text-align: right;\">     55</td><td style=\"text-align: right;\">    337</td><td style=\"text-align: right;\">4.07</td><td style=\"text-align: right;\">4.11</td><td style=\"text-align: right;\">2.53</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   0.22</td><td>Fair     </td><td>E      </td><td>VS2      </td><td style=\"text-align: right;\">   65.1</td><td style=\"text-align: right;\">     61</td><td style=\"text-align: right;\">    337</td><td style=\"text-align: right;\">3.87</td><td style=\"text-align: right;\">3.78</td><td style=\"text-align: right;\">2.49</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   0.23</td><td>Very Good</td><td>H      </td><td>VS1      </td><td style=\"text-align: right;\">   59.4</td><td style=\"text-align: right;\">     61</td><td style=\"text-align: right;\">    338</td><td style=\"text-align: right;\">4   </td><td style=\"text-align: right;\">4.05</td><td style=\"text-align: right;\">2.39</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o_diamonds2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Failed Attempt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fit a very simple model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2o_lm = h2o.estimators.H2OGeneralizedLinearEstimator(family='gaussian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "h2o_lm.train(x=['carat','cut','color','clarity'],y='price',training_frame=h2o_diamonds2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Details\n",
      "=============\n",
      "H2OGeneralizedLinearEstimator :  Generalized Linear Modeling\n",
      "Model Key:  GLM_model_python_1544750361877_1\n",
      "\n",
      "\n",
      "ModelMetricsRegressionGLM: glm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 10733609.79576042\n",
      "RMSE: 3276.2188259883405\n",
      "MAE: 2430.585433525635\n",
      "RMSLE: 0.9615249847776893\n",
      "R^2: 0.3255806286420655\n",
      "Mean Residual Deviance: 10733609.79576042\n",
      "Null degrees of freedom: 53939\n",
      "Residual degrees of freedom: 53919\n",
      "Null deviance: 858473135517.3629\n",
      "Residual deviance: 578970912383.317\n",
      "AIC: 1026347.841393112\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>iterations</b></td>\n",
       "<td><b>negative_log_likelihood</b></td>\n",
       "<td><b>objective</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:30</td>\n",
       "<td> 0.000 sec</td>\n",
       "<td>0</td>\n",
       "<td>858473135517.3977051</td>\n",
       "<td>15915334.3625769</td></tr></table></div>"
      ],
      "text/plain": [
       "    timestamp            duration    iterations    negative_log_likelihood    objective\n",
       "--  -------------------  ----------  ------------  -------------------------  -----------\n",
       "    2018-12-13 20:19:30  0.000 sec   0             8.58473e+11                1.59153e+07"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o_lm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__This is not right at all.  $R^2$ should be 0.91 for this model, not 0.35!__\n",
    "\n",
    "__Aha, it appears the default model is regularized.__  This is not explicitly stated but it is implied by the available arguments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Successful Attempt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "Model Details\n",
      "=============\n",
      "H2OGeneralizedLinearEstimator :  Generalized Linear Modeling\n",
      "Model Key:  GLM_model_python_1544750361877_2\n",
      "\n",
      "\n",
      "ModelMetricsRegressionGLM: glm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 1337834.1891373708\n",
      "RMSE: 1156.6478241614302\n",
      "MAE: 803.6533760003574\n",
      "RMSLE: NaN\n",
      "R^2: 0.9159405540179452\n",
      "Mean Residual Deviance: 1337834.1891373708\n",
      "Null degrees of freedom: 53939\n",
      "Residual degrees of freedom: 53921\n",
      "Null deviance: 858473135517.3629\n",
      "Residual deviance: 72162776162.06978\n",
      "AIC: 914023.0749361591\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>iterations</b></td>\n",
       "<td><b>negative_log_likelihood</b></td>\n",
       "<td><b>objective</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:31</td>\n",
       "<td> 0.000 sec</td>\n",
       "<td>0</td>\n",
       "<td>858473135517.3977051</td>\n",
       "<td>15915334.3625769</td></tr></table></div>"
      ],
      "text/plain": [
       "    timestamp            duration    iterations    negative_log_likelihood    objective\n",
       "--  -------------------  ----------  ------------  -------------------------  -----------\n",
       "    2018-12-13 20:19:31  0.000 sec   0             8.58473e+11                1.59153e+07"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note the lambda_=0 to turn off regularization\n",
    "h2o_lm = h2o.estimators.H2OGeneralizedLinearEstimator(family='gaussian',lambda_=0)\n",
    "h2o_lm.train(x=['carat','cut','color','clarity'],y='price',training_frame=h2o_diamonds2)\n",
    "h2o_lm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's more like it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Intercept': -7362.802156302129,\n",
       " 'clarity.IF': 5419.646844614482,\n",
       " 'clarity.SI1': 3573.6879873533635,\n",
       " 'clarity.SI2': 2625.949986564772,\n",
       " 'clarity.VS1': 4534.878969577319,\n",
       " 'clarity.VS2': 4217.829101987275,\n",
       " 'clarity.VVS1': 5072.027644985769,\n",
       " 'clarity.VVS2': 4967.199410006727,\n",
       " 'color.E': -211.68248136947267,\n",
       " 'color.F': -303.3100325817252,\n",
       " 'color.G': -506.1995360406308,\n",
       " 'color.H': -978.697664842068,\n",
       " 'color.I': -1440.301901907411,\n",
       " 'color.J': -2325.2223602461086,\n",
       " 'cut.Good': 655.7674482639526,\n",
       " 'cut.Ideal': 998.254438325935,\n",
       " 'cut.Premium': 869.3959030779079,\n",
       " 'cut.Very Good': 848.7168776837254,\n",
       " 'carat': 8886.128882503353}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o_lm.coef()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GLM_model_python_1544750361877_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GLM_model_python_1544750361877_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>diamonds.hex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>diamonds2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>modelmetrics_GLM_model_python_1544750361877_1@...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>modelmetrics_GLM_model_python_1544750361877_2@...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 key\n",
       "0                   GLM_model_python_1544750361877_1\n",
       "1                   GLM_model_python_1544750361877_2\n",
       "2                                       diamonds.hex\n",
       "3                                          diamonds2\n",
       "4  modelmetrics_GLM_model_python_1544750361877_1@...\n",
       "5  modelmetrics_GLM_model_python_1544750361877_2@..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drf Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "Model Details\n",
      "=============\n",
      "H2ORandomForestEstimator :  Distributed Random Forest\n",
      "Model Key:  DRF_model_python_1544750361877_3\n",
      "\n",
      "\n",
      "ModelMetricsRegression: drf\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 2079779.0992632748\n",
      "RMSE: 1442.143924600896\n",
      "MAE: 966.3844774489775\n",
      "RMSLE: 0.5464101031548934\n",
      "Mean Residual Deviance: 2079779.0992632748\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>number_of_trees</b></td>\n",
       "<td><b>training_rmse</b></td>\n",
       "<td><b>training_mae</b></td>\n",
       "<td><b>training_deviance</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:31</td>\n",
       "<td> 0.099 sec</td>\n",
       "<td>0.0</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:32</td>\n",
       "<td> 0.599 sec</td>\n",
       "<td>1.0</td>\n",
       "<td>1572.8323757</td>\n",
       "<td>936.4122636</td>\n",
       "<td>2473801.6821759</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:32</td>\n",
       "<td> 0.750 sec</td>\n",
       "<td>2.0</td>\n",
       "<td>1511.6251593</td>\n",
       "<td>980.5250812</td>\n",
       "<td>2285010.6223487</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:32</td>\n",
       "<td> 0.861 sec</td>\n",
       "<td>3.0</td>\n",
       "<td>1689.9269547</td>\n",
       "<td>1047.6371095</td>\n",
       "<td>2855853.1122827</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:32</td>\n",
       "<td> 1.000 sec</td>\n",
       "<td>4.0</td>\n",
       "<td>1492.0456369</td>\n",
       "<td>896.8322222</td>\n",
       "<td>2226200.1827169</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:34</td>\n",
       "<td> 2.717 sec</td>\n",
       "<td>46.0</td>\n",
       "<td>1440.3415218</td>\n",
       "<td>966.2892714</td>\n",
       "<td>2074583.6993443</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:34</td>\n",
       "<td> 2.741 sec</td>\n",
       "<td>47.0</td>\n",
       "<td>1462.3473237</td>\n",
       "<td>981.7449913</td>\n",
       "<td>2138459.6951667</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:34</td>\n",
       "<td> 2.766 sec</td>\n",
       "<td>48.0</td>\n",
       "<td>1465.3928902</td>\n",
       "<td>983.4891060</td>\n",
       "<td>2147376.3225565</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:34</td>\n",
       "<td> 2.796 sec</td>\n",
       "<td>49.0</td>\n",
       "<td>1447.4702824</td>\n",
       "<td>972.0903774</td>\n",
       "<td>2095170.2184324</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:34</td>\n",
       "<td> 2.825 sec</td>\n",
       "<td>50.0</td>\n",
       "<td>1442.1439246</td>\n",
       "<td>966.3844774</td>\n",
       "<td>2079779.0992633</td></tr></table></div>"
      ],
      "text/plain": [
       "     timestamp            duration    number_of_trees    training_rmse       training_mae        training_deviance\n",
       "---  -------------------  ----------  -----------------  ------------------  ------------------  -------------------\n",
       "     2018-12-13 20:19:31  0.099 sec   0.0                nan                 nan                 nan\n",
       "     2018-12-13 20:19:32  0.599 sec   1.0                1572.8323757399799  936.412263596716    2473801.682175869\n",
       "     2018-12-13 20:19:32  0.750 sec   2.0                1511.6251593396855  980.5250812429891   2285010.62234873\n",
       "     2018-12-13 20:19:32  0.861 sec   3.0                1689.9269547180704  1047.6371094907183  2855853.1122826915\n",
       "     2018-12-13 20:19:32  1.000 sec   4.0                1492.0456369417573  896.8322222088989   2226200.182716934\n",
       "---  ---                  ---         ---                ---                 ---                 ---\n",
       "     2018-12-13 20:19:34  2.717 sec   46.0               1440.341521773313   966.2892713705475   2074583.6993442627\n",
       "     2018-12-13 20:19:34  2.741 sec   47.0               1462.347323711689   981.7449913222137   2138459.695166739\n",
       "     2018-12-13 20:19:34  2.766 sec   48.0               1465.3928901685279  983.489105994372    2147376.322556471\n",
       "     2018-12-13 20:19:34  2.796 sec   49.0               1447.4702824004373  972.0903773725037   2095170.218432402\n",
       "     2018-12-13 20:19:34  2.825 sec   50.0               1442.143924600896   966.3844774489775   2079779.0992632748"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n",
      "Variable Importances: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>variable</b></td>\n",
       "<td><b>relative_importance</b></td>\n",
       "<td><b>scaled_importance</b></td>\n",
       "<td><b>percentage</b></td></tr>\n",
       "<tr><td>carat</td>\n",
       "<td>18256318431232.0000000</td>\n",
       "<td>1.0</td>\n",
       "<td>0.9027340</td></tr>\n",
       "<tr><td>clarity</td>\n",
       "<td>931802841088.0000000</td>\n",
       "<td>0.0510400</td>\n",
       "<td>0.0460756</td></tr>\n",
       "<tr><td>color</td>\n",
       "<td>737371488256.0000000</td>\n",
       "<td>0.0403899</td>\n",
       "<td>0.0364614</td></tr>\n",
       "<tr><td>cut</td>\n",
       "<td>297871540224.0000000</td>\n",
       "<td>0.0163161</td>\n",
       "<td>0.0147291</td></tr></table></div>"
      ],
      "text/plain": [
       "variable    relative_importance    scaled_importance    percentage\n",
       "----------  ---------------------  -------------------  ------------\n",
       "carat       1.82563e+13            1                    0.902734\n",
       "clarity     9.31803e+11            0.05104              0.0460756\n",
       "color       7.37371e+11            0.0403899            0.0364614\n",
       "cut         2.97872e+11            0.0163161            0.0147291"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o_rf = h2o.estimators.random_forest.H2ORandomForestEstimator()\n",
    "h2o_rf.train(x=['carat','cut','color','clarity'],y='price',training_frame=h2o_diamonds2)\n",
    "h2o_rf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('carat', 18256318431232.0, 1.0, 0.9027339941905617),\n",
       " ('clarity', 931802841088.0, 0.05104001908149883, 0.04607556028900392),\n",
       " ('color', 737371488256.0, 0.04038993354731048, 0.03646136603625495),\n",
       " ('cut', 297871540224.0, 0.01631607935334959, 0.014729079484179432)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o_rf.varimp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will split the data into training and test, use cross-validation on the training data to tune the hyperparameters, and then evaluate on the test data.  This is a standard workflow for high-variance ML models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First split the data\n",
    "\n",
    "http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-munging/splitting-datasets.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds_split = h2o_diamonds2.split_frame(ratios=[0.75], destination_frames=['diamonds_train','diamonds_test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[40444, 10]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_split[0].dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then fit a model with cross-validation by specifying nfolds  \n",
    "http://docs.h2o.ai/h2o/latest-stable/h2o-docs/cross-validation.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2o_gb = h2o.estimators.gbm.H2OGradientBoostingEstimator(nfolds=5)                                                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "h2o_gb.train(x=['carat','cut','color','clarity'],y='price',training_frame=diamonds_split[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Details\n",
      "=============\n",
      "H2OGradientBoostingEstimator :  Gradient Boosting Machine\n",
      "Model Key:  GBM_model_python_1544750361877_4\n",
      "\n",
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 291289.0591014654\n",
      "RMSE: 539.7120149685992\n",
      "MAE: 291.15811039439154\n",
      "RMSLE: 0.11403758077295047\n",
      "Mean Residual Deviance: 291289.0591014654\n",
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on cross-validation data. **\n",
      "\n",
      "MSE: 318952.6407328581\n",
      "RMSE: 564.7589226677682\n",
      "MAE: 300.514499690523\n",
      "RMSLE: 0.11638301817429214\n",
      "Mean Residual Deviance: 318952.6407328581\n",
      "Cross-Validation Metrics Summary: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>mean</b></td>\n",
       "<td><b>sd</b></td>\n",
       "<td><b>cv_1_valid</b></td>\n",
       "<td><b>cv_2_valid</b></td>\n",
       "<td><b>cv_3_valid</b></td>\n",
       "<td><b>cv_4_valid</b></td>\n",
       "<td><b>cv_5_valid</b></td></tr>\n",
       "<tr><td>mae</td>\n",
       "<td>300.5128</td>\n",
       "<td>3.677444</td>\n",
       "<td>306.593</td>\n",
       "<td>302.6331</td>\n",
       "<td>299.6562</td>\n",
       "<td>291.09317</td>\n",
       "<td>302.58844</td></tr>\n",
       "<tr><td>mean_residual_deviance</td>\n",
       "<td>318975.66</td>\n",
       "<td>9070.769</td>\n",
       "<td>337070.2</td>\n",
       "<td>326859.25</td>\n",
       "<td>310532.75</td>\n",
       "<td>300005.06</td>\n",
       "<td>320411.0</td></tr>\n",
       "<tr><td>mse</td>\n",
       "<td>318975.66</td>\n",
       "<td>9070.769</td>\n",
       "<td>337070.2</td>\n",
       "<td>326859.25</td>\n",
       "<td>310532.75</td>\n",
       "<td>300005.06</td>\n",
       "<td>320411.0</td></tr>\n",
       "<tr><td>r2</td>\n",
       "<td>0.9800078</td>\n",
       "<td>0.0004752</td>\n",
       "<td>0.9794658</td>\n",
       "<td>0.9795043</td>\n",
       "<td>0.9807221</td>\n",
       "<td>0.9809305</td>\n",
       "<td>0.9794166</td></tr>\n",
       "<tr><td>residual_deviance</td>\n",
       "<td>318975.66</td>\n",
       "<td>9070.769</td>\n",
       "<td>337070.2</td>\n",
       "<td>326859.25</td>\n",
       "<td>310532.75</td>\n",
       "<td>300005.06</td>\n",
       "<td>320411.0</td></tr>\n",
       "<tr><td>rmse</td>\n",
       "<td>564.6648</td>\n",
       "<td>8.041395</td>\n",
       "<td>580.57745</td>\n",
       "<td>571.71606</td>\n",
       "<td>557.25464</td>\n",
       "<td>547.7272</td>\n",
       "<td>566.0486</td></tr>\n",
       "<tr><td>rmsle</td>\n",
       "<td>0.1163747</td>\n",
       "<td>0.0008166</td>\n",
       "<td>0.1151984</td>\n",
       "<td>0.1153711</td>\n",
       "<td>0.1163005</td>\n",
       "<td>0.1165711</td>\n",
       "<td>0.1184322</td></tr></table></div>"
      ],
      "text/plain": [
       "                        mean      sd           cv_1_valid    cv_2_valid    cv_3_valid    cv_4_valid    cv_5_valid\n",
       "----------------------  --------  -----------  ------------  ------------  ------------  ------------  ------------\n",
       "mae                     300.513   3.67744      306.593       302.633       299.656       291.093       302.588\n",
       "mean_residual_deviance  318976    9070.77      337070        326859        310533        300005        320411\n",
       "mse                     318976    9070.77      337070        326859        310533        300005        320411\n",
       "r2                      0.980008  0.000475228  0.979466      0.979504      0.980722      0.98093       0.979417\n",
       "residual_deviance       318976    9070.77      337070        326859        310533        300005        320411\n",
       "rmse                    564.665   8.04139      580.577       571.716       557.255       547.727       566.049\n",
       "rmsle                   0.116375  0.000816584  0.115198      0.115371      0.116301      0.116571      0.118432"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scoring History: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>number_of_trees</b></td>\n",
       "<td><b>training_rmse</b></td>\n",
       "<td><b>training_mae</b></td>\n",
       "<td><b>training_deviance</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:43</td>\n",
       "<td> 6.711 sec</td>\n",
       "<td>0.0</td>\n",
       "<td>3994.3964930</td>\n",
       "<td>3033.6648252</td>\n",
       "<td>15955203.3430623</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:43</td>\n",
       "<td> 6.769 sec</td>\n",
       "<td>1.0</td>\n",
       "<td>3619.8152636</td>\n",
       "<td>2739.5361668</td>\n",
       "<td>13103062.5423542</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:43</td>\n",
       "<td> 6.827 sec</td>\n",
       "<td>2.0</td>\n",
       "<td>3283.5998197</td>\n",
       "<td>2476.0971627</td>\n",
       "<td>10782027.7762236</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:43</td>\n",
       "<td> 6.884 sec</td>\n",
       "<td>3.0</td>\n",
       "<td>2983.6510957</td>\n",
       "<td>2238.4966037</td>\n",
       "<td>8902173.8607495</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:43</td>\n",
       "<td> 6.928 sec</td>\n",
       "<td>4.0</td>\n",
       "<td>2715.2776594</td>\n",
       "<td>2026.2305075</td>\n",
       "<td>7372732.7677644</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:44</td>\n",
       "<td> 7.437 sec</td>\n",
       "<td>46.0</td>\n",
       "<td>547.1342616</td>\n",
       "<td>296.8247327</td>\n",
       "<td>299355.9001715</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:44</td>\n",
       "<td> 7.449 sec</td>\n",
       "<td>47.0</td>\n",
       "<td>544.7315694</td>\n",
       "<td>295.1905176</td>\n",
       "<td>296732.4826661</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:44</td>\n",
       "<td> 7.460 sec</td>\n",
       "<td>48.0</td>\n",
       "<td>543.2680914</td>\n",
       "<td>293.7298975</td>\n",
       "<td>295140.2191057</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:44</td>\n",
       "<td> 7.470 sec</td>\n",
       "<td>49.0</td>\n",
       "<td>541.3042959</td>\n",
       "<td>292.4713330</td>\n",
       "<td>293010.3407167</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:19:44</td>\n",
       "<td> 7.479 sec</td>\n",
       "<td>50.0</td>\n",
       "<td>539.7120150</td>\n",
       "<td>291.1581104</td>\n",
       "<td>291289.0591015</td></tr></table></div>"
      ],
      "text/plain": [
       "     timestamp            duration    number_of_trees    training_rmse       training_mae        training_deviance\n",
       "---  -------------------  ----------  -----------------  ------------------  ------------------  -------------------\n",
       "     2018-12-13 20:19:43  6.711 sec   0.0                3994.396492971414   3033.6648251877127  15955203.34306233\n",
       "     2018-12-13 20:19:43  6.769 sec   1.0                3619.8152635672122  2739.5361668161627  13103062.542354165\n",
       "     2018-12-13 20:19:43  6.827 sec   2.0                3283.5998197441195  2476.0971627230224  10782027.776223615\n",
       "     2018-12-13 20:19:43  6.884 sec   3.0                2983.6510956794987  2238.4966036962855  8902173.860749472\n",
       "     2018-12-13 20:19:43  6.928 sec   4.0                2715.277659423504   2026.230507450068   7372732.76776438\n",
       "---  ---                  ---         ---                ---                 ---                 ---\n",
       "     2018-12-13 20:19:44  7.437 sec   46.0               547.1342615588293   296.8247327203744   299355.9001715254\n",
       "     2018-12-13 20:19:44  7.449 sec   47.0               544.7315693679818   295.1905175738089   296732.4826661044\n",
       "     2018-12-13 20:19:44  7.460 sec   48.0               543.2680913744997   293.7298975444526   295140.2191056917\n",
       "     2018-12-13 20:19:44  7.470 sec   49.0               541.3042958602255   292.4713330190102   293010.34071673447\n",
       "     2018-12-13 20:19:44  7.479 sec   50.0               539.7120149685992   291.15811039439154  291289.0591014654"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n",
      "Variable Importances: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>variable</b></td>\n",
       "<td><b>relative_importance</b></td>\n",
       "<td><b>scaled_importance</b></td>\n",
       "<td><b>percentage</b></td></tr>\n",
       "<tr><td>carat</td>\n",
       "<td>2981761384448.0000000</td>\n",
       "<td>1.0</td>\n",
       "<td>0.8942768</td></tr>\n",
       "<tr><td>clarity</td>\n",
       "<td>226965176320.0000000</td>\n",
       "<td>0.0761178</td>\n",
       "<td>0.0680704</td></tr>\n",
       "<tr><td>color</td>\n",
       "<td>115138985984.0000000</td>\n",
       "<td>0.0386144</td>\n",
       "<td>0.0345320</td></tr>\n",
       "<tr><td>cut</td>\n",
       "<td>10405691392.0000000</td>\n",
       "<td>0.0034898</td>\n",
       "<td>0.0031208</td></tr></table></div>"
      ],
      "text/plain": [
       "variable    relative_importance    scaled_importance    percentage\n",
       "----------  ---------------------  -------------------  ------------\n",
       "carat       2.98176e+12            1                    0.894277\n",
       "clarity     2.26965e+11            0.0761178            0.0680704\n",
       "color       1.15139e+11            0.0386144            0.034532\n",
       "cut         1.04057e+10            0.00348978           0.00312083"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o_gb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That didn't print well; let's try this instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>cv_1_valid</th>\n",
       "      <th>cv_2_valid</th>\n",
       "      <th>cv_3_valid</th>\n",
       "      <th>cv_4_valid</th>\n",
       "      <th>cv_5_valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mae</td>\n",
       "      <td>300.5128</td>\n",
       "      <td>3.677444</td>\n",
       "      <td>306.593</td>\n",
       "      <td>302.6331</td>\n",
       "      <td>299.6562</td>\n",
       "      <td>291.09317</td>\n",
       "      <td>302.58844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean_residual_deviance</td>\n",
       "      <td>318975.66</td>\n",
       "      <td>9070.769</td>\n",
       "      <td>337070.2</td>\n",
       "      <td>326859.25</td>\n",
       "      <td>310532.75</td>\n",
       "      <td>300005.06</td>\n",
       "      <td>320411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mse</td>\n",
       "      <td>318975.66</td>\n",
       "      <td>9070.769</td>\n",
       "      <td>337070.2</td>\n",
       "      <td>326859.25</td>\n",
       "      <td>310532.75</td>\n",
       "      <td>300005.06</td>\n",
       "      <td>320411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>r2</td>\n",
       "      <td>0.9800078</td>\n",
       "      <td>4.7522815E-4</td>\n",
       "      <td>0.9794658</td>\n",
       "      <td>0.9795043</td>\n",
       "      <td>0.9807221</td>\n",
       "      <td>0.98093045</td>\n",
       "      <td>0.97941655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>residual_deviance</td>\n",
       "      <td>318975.66</td>\n",
       "      <td>9070.769</td>\n",
       "      <td>337070.2</td>\n",
       "      <td>326859.25</td>\n",
       "      <td>310532.75</td>\n",
       "      <td>300005.06</td>\n",
       "      <td>320411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rmse</td>\n",
       "      <td>564.6648</td>\n",
       "      <td>8.041395</td>\n",
       "      <td>580.57745</td>\n",
       "      <td>571.71606</td>\n",
       "      <td>557.25464</td>\n",
       "      <td>547.7272</td>\n",
       "      <td>566.0486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>rmsle</td>\n",
       "      <td>0.11637469</td>\n",
       "      <td>8.165836E-4</td>\n",
       "      <td>0.11519845</td>\n",
       "      <td>0.115371145</td>\n",
       "      <td>0.11630051</td>\n",
       "      <td>0.116571106</td>\n",
       "      <td>0.11843221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 mean            sd  cv_1_valid   cv_2_valid  \\\n",
       "0                     mae    300.5128      3.677444     306.593     302.6331   \n",
       "1  mean_residual_deviance   318975.66      9070.769    337070.2    326859.25   \n",
       "2                     mse   318975.66      9070.769    337070.2    326859.25   \n",
       "3                      r2   0.9800078  4.7522815E-4   0.9794658    0.9795043   \n",
       "4       residual_deviance   318975.66      9070.769    337070.2    326859.25   \n",
       "5                    rmse    564.6648      8.041395   580.57745    571.71606   \n",
       "6                   rmsle  0.11637469   8.165836E-4  0.11519845  0.115371145   \n",
       "\n",
       "   cv_3_valid   cv_4_valid  cv_5_valid  \n",
       "0    299.6562    291.09317   302.58844  \n",
       "1   310532.75    300005.06    320411.0  \n",
       "2   310532.75    300005.06    320411.0  \n",
       "3   0.9807221   0.98093045  0.97941655  \n",
       "4   310532.75    300005.06    320411.0  \n",
       "5   557.25464     547.7272    566.0486  \n",
       "6  0.11630051  0.116571106  0.11843221  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o_gb.cross_validation_metrics_summary().as_data_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "results=pd.DataFrame()\n",
    "for lr in [0.02,.05,0.1,0.2,0.5]:\n",
    "    for ntrees in [5,50,500]:\n",
    "        h2o_gb = h2o.estimators.gbm.H2OGradientBoostingEstimator(nfolds=5,learn_rate=lr,ntrees=ntrees)\n",
    "        h2o_gb.train(x=['carat','cut','color','clarity','x','y','z','depth','table'],y='price',training_frame=diamonds_split[0])\n",
    "        tmp=h2o_gb.cross_validation_metrics_summary().as_data_frame()\n",
    "        tmp['lr']=lr\n",
    "        tmp['ntrees']=ntrees\n",
    "        results=pd.concat([results,tmp])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>cv_1_valid</th>\n",
       "      <th>cv_2_valid</th>\n",
       "      <th>cv_3_valid</th>\n",
       "      <th>cv_4_valid</th>\n",
       "      <th>cv_5_valid</th>\n",
       "      <th>lr</th>\n",
       "      <th>ntrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rmse</td>\n",
       "      <td>531.83060</td>\n",
       "      <td>10.007129</td>\n",
       "      <td>540.29706</td>\n",
       "      <td>535.80707</td>\n",
       "      <td>516.2322</td>\n",
       "      <td>551.65186</td>\n",
       "      <td>515.16504</td>\n",
       "      <td>0.10</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rmse</td>\n",
       "      <td>531.89950</td>\n",
       "      <td>7.004916</td>\n",
       "      <td>533.2855</td>\n",
       "      <td>548.17737</td>\n",
       "      <td>533.898</td>\n",
       "      <td>518.42834</td>\n",
       "      <td>525.70825</td>\n",
       "      <td>0.05</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rmse</td>\n",
       "      <td>540.66590</td>\n",
       "      <td>3.400645</td>\n",
       "      <td>546.65967</td>\n",
       "      <td>545.441</td>\n",
       "      <td>540.56244</td>\n",
       "      <td>535.2106</td>\n",
       "      <td>535.45575</td>\n",
       "      <td>0.02</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rmse</td>\n",
       "      <td>546.05880</td>\n",
       "      <td>11.197579</td>\n",
       "      <td>538.4264</td>\n",
       "      <td>552.35156</td>\n",
       "      <td>547.7864</td>\n",
       "      <td>521.8549</td>\n",
       "      <td>569.87463</td>\n",
       "      <td>0.20</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rmse</td>\n",
       "      <td>550.65186</td>\n",
       "      <td>14.800012</td>\n",
       "      <td>583.1655</td>\n",
       "      <td>560.83276</td>\n",
       "      <td>547.0235</td>\n",
       "      <td>542.37396</td>\n",
       "      <td>519.86346</td>\n",
       "      <td>0.20</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean         sd cv_1_valid cv_2_valid cv_3_valid cv_4_valid  \\\n",
       "5  rmse  531.83060  10.007129  540.29706  535.80707   516.2322  551.65186   \n",
       "5  rmse  531.89950   7.004916   533.2855  548.17737    533.898  518.42834   \n",
       "5  rmse  540.66590   3.400645  546.65967    545.441  540.56244   535.2106   \n",
       "5  rmse  546.05880  11.197579   538.4264  552.35156   547.7864   521.8549   \n",
       "5  rmse  550.65186  14.800012   583.1655  560.83276   547.0235  542.37396   \n",
       "\n",
       "  cv_5_valid    lr  ntrees  \n",
       "5  515.16504  0.10     500  \n",
       "5  525.70825  0.05     500  \n",
       "5  535.45575  0.02     500  \n",
       "5  569.87463  0.20     500  \n",
       "5  519.86346  0.20      50  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp=results[lambda x: x.iloc[:,0]=='rmse'].copy()\n",
    "# For some reason all the data is strings\n",
    "tmp['mean']=tmp['mean'].astype('double')\n",
    "\n",
    "tmp.sort_values('mean').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"name\": \"data-c5a3ea4c81b97b3c5eb01ff026d06443\"}, \"mark\": \"point\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"ntrees\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"lr\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"mean\"}}, \"selection\": {\"selector001\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\", \"y\"]}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-c5a3ea4c81b97b3c5eb01ff026d06443\": [{\"\": \"rmse\", \"mean\": 3635.3438, \"sd\": \"39.68693\", \"cv_1_valid\": \"3586.019\", \"cv_2_valid\": \"3662.3457\", \"cv_3_valid\": \"3620.5728\", \"cv_4_valid\": \"3577.463\", \"cv_5_valid\": \"3730.318\", \"lr\": 0.02, \"ntrees\": 5}, {\"\": \"rmse\", \"mean\": 1657.2518, \"sd\": \"18.66671\", \"cv_1_valid\": \"1696.8447\", \"cv_2_valid\": \"1615.7197\", \"cv_3_valid\": \"1669.15\", \"cv_4_valid\": \"1652.8997\", \"cv_5_valid\": \"1651.6453\", \"lr\": 0.02, \"ntrees\": 50}, {\"\": \"rmse\", \"mean\": 540.6659, \"sd\": \"3.400645\", \"cv_1_valid\": \"546.65967\", \"cv_2_valid\": \"545.441\", \"cv_3_valid\": \"540.56244\", \"cv_4_valid\": \"535.2106\", \"cv_5_valid\": \"535.45575\", \"lr\": 0.02, \"ntrees\": 500}, {\"\": \"rmse\", \"mean\": 3153.1628, \"sd\": \"21.952799\", \"cv_1_valid\": \"3212.7288\", \"cv_2_valid\": \"3128.7449\", \"cv_3_valid\": \"3154.6409\", \"cv_4_valid\": \"3133.8528\", \"cv_5_valid\": \"3135.8464\", \"lr\": 0.05, \"ntrees\": 5}, {\"\": \"rmse\", \"mean\": 716.6177, \"sd\": \"6.9464097\", \"cv_1_valid\": \"708.68677\", \"cv_2_valid\": \"724.70624\", \"cv_3_valid\": \"701.2694\", \"cv_4_valid\": \"722.93756\", \"cv_5_valid\": \"725.4886\", \"lr\": 0.05, \"ntrees\": 50}, {\"\": \"rmse\", \"mean\": 531.8995, \"sd\": \"7.004916\", \"cv_1_valid\": \"533.2855\", \"cv_2_valid\": \"548.17737\", \"cv_3_valid\": \"533.898\", \"cv_4_valid\": \"518.42834\", \"cv_5_valid\": \"525.70825\", \"lr\": 0.05, \"ntrees\": 500}, {\"\": \"rmse\", \"mean\": 2477.3313, \"sd\": \"18.71351\", \"cv_1_valid\": \"2488.8528\", \"cv_2_valid\": \"2513.6484\", \"cv_3_valid\": \"2490.6804\", \"cv_4_valid\": \"2446.9724\", \"cv_5_valid\": \"2446.5024\", \"lr\": 0.1, \"ntrees\": 5}, {\"\": \"rmse\", \"mean\": 565.0766, \"sd\": \"8.956516\", \"cv_1_valid\": \"561.61664\", \"cv_2_valid\": \"564.6479\", \"cv_3_valid\": \"544.11255\", \"cv_4_valid\": \"572.93604\", \"cv_5_valid\": \"582.06995\", \"lr\": 0.1, \"ntrees\": 50}, {\"\": \"rmse\", \"mean\": 531.8306, \"sd\": \"10.007129\", \"cv_1_valid\": \"540.29706\", \"cv_2_valid\": \"535.80707\", \"cv_3_valid\": \"516.2322\", \"cv_4_valid\": \"551.65186\", \"cv_5_valid\": \"515.16504\", \"lr\": 0.1, \"ntrees\": 500}, {\"\": \"rmse\", \"mean\": 1536.56, \"sd\": \"15.477462\", \"cv_1_valid\": \"1514.1921\", \"cv_2_valid\": \"1549.8633\", \"cv_3_valid\": \"1570.8384\", \"cv_4_valid\": \"1534.5679\", \"cv_5_valid\": \"1513.3384\", \"lr\": 0.2, \"ntrees\": 5}, {\"\": \"rmse\", \"mean\": 550.65186, \"sd\": \"14.800012\", \"cv_1_valid\": \"583.1655\", \"cv_2_valid\": \"560.83276\", \"cv_3_valid\": \"547.0235\", \"cv_4_valid\": \"542.37396\", \"cv_5_valid\": \"519.86346\", \"lr\": 0.2, \"ntrees\": 50}, {\"\": \"rmse\", \"mean\": 546.0588, \"sd\": \"11.197579\", \"cv_1_valid\": \"538.4264\", \"cv_2_valid\": \"552.35156\", \"cv_3_valid\": \"547.7864\", \"cv_4_valid\": \"521.8549\", \"cv_5_valid\": \"569.87463\", \"lr\": 0.2, \"ntrees\": 500}, {\"\": \"rmse\", \"mean\": 685.9752, \"sd\": \"21.071066\", \"cv_1_valid\": \"694.2111\", \"cv_2_valid\": \"699.37805\", \"cv_3_valid\": \"630.75226\", \"cv_4_valid\": \"719.7807\", \"cv_5_valid\": \"685.75385\", \"lr\": 0.5, \"ntrees\": 5}, {\"\": \"rmse\", \"mean\": 574.26886, \"sd\": \"9.783495\", \"cv_1_valid\": \"589.31525\", \"cv_2_valid\": \"558.8676\", \"cv_3_valid\": \"588.0991\", \"cv_4_valid\": \"577.81476\", \"cv_5_valid\": \"557.2475\", \"lr\": 0.5, \"ntrees\": 50}, {\"\": \"rmse\", \"mean\": 569.99304, \"sd\": \"7.875081\", \"cv_1_valid\": \"564.31213\", \"cv_2_valid\": \"576.54767\", \"cv_3_valid\": \"562.4053\", \"cv_4_valid\": \"558.10504\", \"cv_5_valid\": \"588.59503\", \"lr\": 0.5, \"ntrees\": 500}]}};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"00a59a30-ca2a-4623-a591-99a9dad62aec\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x7f90d7b0cba8>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#00a59a30-ca2a-4623-a591-99a9dad62aec"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#00a59a30-ca2a-4623-a591-99a9dad62aec"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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"
     },
     "metadata": {
      "jupyter-vega": "#00a59a30-ca2a-4623-a591-99a9dad62aec"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c=alt.Chart(tmp[tmp.iloc[:,0]=='rmse'])\n",
    "c.mark_point().encode(x='lr:Q',color='ntrees:N',y='mean').interactive()\n",
    "\n",
    "# I didn't end up using this because it was too dense to see on the chart\n",
    "#tmp['sd']=tmp['sd'].astype('double')\n",
    "#tmp['low']=tmp['mean'] - 2*tmp.sd\n",
    "#tmp['high']=tmp['mean'] + 2*tmp.sd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "Model Details\n",
      "=============\n",
      "H2OGradientBoostingEstimator :  Gradient Boosting Machine\n",
      "Model Key:  GBM_model_python_1544750361877_20\n",
      "\n",
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 199491.27996210408\n",
      "RMSE: 446.64446706760407\n",
      "MAE: 245.20373751829243\n",
      "RMSLE: 0.09238790883975073\n",
      "Mean Residual Deviance: 199491.27996210408\n",
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on validation data. **\n",
      "\n",
      "MSE: 282715.38209518313\n",
      "RMSE: 531.7098664640173\n",
      "MAE: 272.9169159379671\n",
      "RMSLE: 0.10024026821592041\n",
      "Mean Residual Deviance: 282715.38209518313\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>number_of_trees</b></td>\n",
       "<td><b>training_rmse</b></td>\n",
       "<td><b>training_mae</b></td>\n",
       "<td><b>training_deviance</b></td>\n",
       "<td><b>validation_rmse</b></td>\n",
       "<td><b>validation_mae</b></td>\n",
       "<td><b>validation_deviance</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:22:13</td>\n",
       "<td> 0.012 sec</td>\n",
       "<td>0.0</td>\n",
       "<td>3994.3964930</td>\n",
       "<td>3033.6648252</td>\n",
       "<td>15955203.3430623</td>\n",
       "<td>3974.4125294</td>\n",
       "<td>3030.7853958</td>\n",
       "<td>15795954.9541093</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:22:13</td>\n",
       "<td> 0.046 sec</td>\n",
       "<td>1.0</td>\n",
       "<td>3807.5820000</td>\n",
       "<td>2886.4265493</td>\n",
       "<td>14497680.6866169</td>\n",
       "<td>3788.0669152</td>\n",
       "<td>2883.1127535</td>\n",
       "<td>14349450.9542388</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:22:13</td>\n",
       "<td> 0.080 sec</td>\n",
       "<td>2.0</td>\n",
       "<td>3630.4535985</td>\n",
       "<td>2746.4540468</td>\n",
       "<td>13180193.3309136</td>\n",
       "<td>3611.2379138</td>\n",
       "<td>2742.5377061</td>\n",
       "<td>13041039.2699111</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:22:13</td>\n",
       "<td> 0.114 sec</td>\n",
       "<td>3.0</td>\n",
       "<td>3461.9263567</td>\n",
       "<td>2613.9418380</td>\n",
       "<td>11984934.0991557</td>\n",
       "<td>3442.9996808</td>\n",
       "<td>2609.8667122</td>\n",
       "<td>11854246.8019348</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:22:13</td>\n",
       "<td> 0.148 sec</td>\n",
       "<td>4.0</td>\n",
       "<td>3302.6116250</td>\n",
       "<td>2488.2785488</td>\n",
       "<td>10907243.5457967</td>\n",
       "<td>3283.7466748</td>\n",
       "<td>2484.0030167</td>\n",
       "<td>10782992.2244687</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:22:17</td>\n",
       "<td> 3.911 sec</td>\n",
       "<td>136.0</td>\n",
       "<td>509.3181942</td>\n",
       "<td>274.0300863</td>\n",
       "<td>259405.0229416</td>\n",
       "<td>548.3043298</td>\n",
       "<td>285.9402525</td>\n",
       "<td>300637.6381091</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:22:17</td>\n",
       "<td> 3.937 sec</td>\n",
       "<td>137.0</td>\n",
       "<td>508.9517086</td>\n",
       "<td>273.7642643</td>\n",
       "<td>259031.8416455</td>\n",
       "<td>548.1878262</td>\n",
       "<td>285.7466014</td>\n",
       "<td>300509.8927846</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:22:17</td>\n",
       "<td> 3.967 sec</td>\n",
       "<td>138.0</td>\n",
       "<td>508.5194376</td>\n",
       "<td>273.4900699</td>\n",
       "<td>258592.0184162</td>\n",
       "<td>548.0704907</td>\n",
       "<td>285.5630409</td>\n",
       "<td>300381.2628092</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:22:17</td>\n",
       "<td> 3.995 sec</td>\n",
       "<td>139.0</td>\n",
       "<td>507.9360878</td>\n",
       "<td>273.2626132</td>\n",
       "<td>257999.0692650</td>\n",
       "<td>547.7861315</td>\n",
       "<td>285.3970975</td>\n",
       "<td>300069.6458349</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2018-12-13 20:22:21</td>\n",
       "<td> 7.678 sec</td>\n",
       "<td>500.0</td>\n",
       "<td>446.6444671</td>\n",
       "<td>245.2037375</td>\n",
       "<td>199491.2799621</td>\n",
       "<td>531.7098665</td>\n",
       "<td>272.9169159</td>\n",
       "<td>282715.3820952</td></tr></table></div>"
      ],
      "text/plain": [
       "     timestamp            duration    number_of_trees    training_rmse       training_mae        training_deviance    validation_rmse     validation_mae      validation_deviance\n",
       "---  -------------------  ----------  -----------------  ------------------  ------------------  -------------------  ------------------  ------------------  ---------------------\n",
       "     2018-12-13 20:22:13  0.012 sec   0.0                3994.396492971414   3033.6648251877127  15955203.34306233    3974.4125294324067  3030.7853957997095  15795954.954109302\n",
       "     2018-12-13 20:22:13  0.046 sec   1.0                3807.5819999859423  2886.426549251067   14497680.68661695    3788.066915227183   2883.1127534798256  14349450.954238785\n",
       "     2018-12-13 20:22:13  0.080 sec   2.0                3630.4535985071607  2746.454046835497   13180193.330913592   3611.2379137784733  2742.53770613293    13041039.2699111\n",
       "     2018-12-13 20:22:13  0.114 sec   3.0                3461.926356691562   2613.941837981221   11984934.099155711   3442.9996807921466  2609.866712234026   11854246.801934823\n",
       "     2018-12-13 20:22:13  0.148 sec   4.0                3302.6116250320283  2488.278548758198   10907243.545796694   3283.746674831774   2484.0030167165005  10782992.22446873\n",
       "---  ---                  ---         ---                ---                 ---                 ---                  ---                 ---                 ---\n",
       "     2018-12-13 20:22:17  3.911 sec   136.0              509.3181941985041   274.03008625965043  259405.02294162515   548.3043298288841   285.940252512957    300637.6381091017\n",
       "     2018-12-13 20:22:17  3.937 sec   137.0              508.95170855933     273.7642643381186   259031.84164546116   548.1878261915048   285.746601429158    300509.8927845675\n",
       "     2018-12-13 20:22:17  3.967 sec   138.0              508.5194375991771   273.490069877829    258592.01841618336   548.0704907301554   285.5630408757036   300381.2628091933\n",
       "     2018-12-13 20:22:17  3.995 sec   139.0              507.9360877758028   273.2626131654033   257999.06926498804   547.7861314736491   285.39709747711527  300069.64583486604\n",
       "     2018-12-13 20:22:21  7.678 sec   500.0              446.64446706760407  245.20373751829243  199491.27996210408   531.7098664640173   272.9169159379671   282715.38209518313"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n",
      "Variable Importances: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>variable</b></td>\n",
       "<td><b>relative_importance</b></td>\n",
       "<td><b>scaled_importance</b></td>\n",
       "<td><b>percentage</b></td></tr>\n",
       "<tr><td>y</td>\n",
       "<td>3249070145536.0000000</td>\n",
       "<td>1.0</td>\n",
       "<td>0.4971292</td></tr>\n",
       "<tr><td>carat</td>\n",
       "<td>2369863811072.0000000</td>\n",
       "<td>0.7293976</td>\n",
       "<td>0.3626048</td></tr>\n",
       "<tr><td>clarity</td>\n",
       "<td>426141483008.0000000</td>\n",
       "<td>0.1311580</td>\n",
       "<td>0.0652025</td></tr>\n",
       "<tr><td>color</td>\n",
       "<td>226389131264.0000000</td>\n",
       "<td>0.0696781</td>\n",
       "<td>0.0346390</td></tr>\n",
       "<tr><td>x</td>\n",
       "<td>138247340032.0000000</td>\n",
       "<td>0.0425498</td>\n",
       "<td>0.0211528</td></tr>\n",
       "<tr><td>z</td>\n",
       "<td>106740916224.0000000</td>\n",
       "<td>0.0328528</td>\n",
       "<td>0.0163321</td></tr>\n",
       "<tr><td>cut</td>\n",
       "<td>10334444544.0000000</td>\n",
       "<td>0.0031807</td>\n",
       "<td>0.0015812</td></tr>\n",
       "<tr><td>depth</td>\n",
       "<td>6291180544.0000000</td>\n",
       "<td>0.0019363</td>\n",
       "<td>0.0009626</td></tr>\n",
       "<tr><td>table</td>\n",
       "<td>2587166464.0000000</td>\n",
       "<td>0.0007963</td>\n",
       "<td>0.0003959</td></tr></table></div>"
      ],
      "text/plain": [
       "variable    relative_importance    scaled_importance    percentage\n",
       "----------  ---------------------  -------------------  ------------\n",
       "y           3.24907e+12            1                    0.497129\n",
       "carat       2.36986e+12            0.729398             0.362605\n",
       "clarity     4.26141e+11            0.131158             0.0652025\n",
       "color       2.26389e+11            0.0696781            0.034639\n",
       "x           1.38247e+11            0.0425498            0.0211528\n",
       "z           1.06741e+11            0.0328528            0.0163321\n",
       "cut         1.03344e+10            0.00318074           0.00158124\n",
       "depth       6.29118e+09            0.0019363            0.000962592\n",
       "table       2.58717e+09            0.000796279          0.000395854"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2o_gb_best = h2o.estimators.gbm.H2OGradientBoostingEstimator(learn_rate=0.05,ntrees=500)\n",
    "h2o_gb_best.train(\n",
    "    x=['carat','cut','color','clarity','x','y','z','depth','table'],y='price',\n",
    "    training_frame=diamonds_split[0],validation_frame=diamonds_split[1])\n",
    "h2o_gb_best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you could try H2OGridSearch  \n",
    "http://docs.h2o.ai/h2o/latest-stable/h2o-docs/grid-search.html#grid-search-example-in-python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions\n",
    "\n",
    "We can also run the models and get the results back in Pandas objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n",
      "glm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "gb_predictions=h2o_gb_best.predict(diamonds_split[1])\n",
    "lm_predictions=h2o_lm.predict(diamonds_split[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
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\"table\": 58.0, \"price\": 1799, \"x\": 5.68, \"y\": 5.65, \"z\": 3.63, \"model\": \"lm\", \"pred\": 1338.2304090869432}, {\"carat\": 0.38, \"cut\": \"Ideal\", \"color\": \"E\", \"clarity\": \"SI1\", \"depth\": 61.4, \"table\": 57.0, \"price\": 653, \"x\": 4.64, \"y\": 4.67, \"z\": 2.86, \"model\": \"lm\", \"pred\": 374.1867633589709}, {\"carat\": 0.38, \"cut\": \"Premium\", \"color\": \"I\", \"clarity\": \"SI1\", \"depth\": 60.5, \"table\": 58.0, \"price\": 746, \"x\": 4.72, \"y\": 4.67, \"z\": 2.84, \"model\": \"gb\", \"pred\": 577.0848728107726}, {\"carat\": 1.0, \"cut\": \"Fair\", \"color\": \"I\", \"clarity\": \"I1\", \"depth\": 66.9, \"table\": 57.0, \"price\": 1997, \"x\": 6.15, \"y\": 6.03, \"z\": 4.09, \"model\": \"gb\", \"pred\": 2160.0228431046908}, {\"carat\": 0.3, \"cut\": \"Premium\", \"color\": \"G\", \"clarity\": \"VVS1\", \"depth\": 61.8, \"table\": 59.0, \"price\": 1013, \"x\": 4.33, \"y\": 4.28, \"z\": 2.66, \"model\": \"lm\", \"pred\": 738.2605204719231}, {\"carat\": 0.53, \"cut\": \"Fair\", \"color\": \"E\", \"clarity\": \"SI1\", \"depth\": 56.2, \"table\": 64.0, \"price\": 1286, \"x\": 5.43, \"y\": 5.41, \"z\": 3.05, \"model\": \"lm\", \"pred\": 708.8516574085397}, {\"carat\": 1.01, \"cut\": \"Fair\", \"color\": \"E\", \"clarity\": \"VS2\", \"depth\": 64.8, \"table\": 59.0, \"price\": 4972, \"x\": 6.34, \"y\": 6.26, \"z\": 4.08, \"model\": \"gb\", \"pred\": 5760.855965845423}, {\"carat\": 1.24, \"cut\": \"Ideal\", \"color\": \"I\", \"clarity\": \"SI1\", \"depth\": 61.3, \"table\": 56.0, \"price\": 5714, \"x\": 6.9, \"y\": 6.93, \"z\": 4.24, \"model\": \"gb\", \"pred\": 5974.525840793407}, {\"carat\": 0.72, \"cut\": \"Ideal\", \"color\": \"D\", \"clarity\": \"SI1\", \"depth\": 61.5, \"table\": 54.0, \"price\": 2804, \"x\": 5.77, \"y\": 5.8, \"z\": 3.56, \"model\": \"gb\", \"pred\": 3017.721426180468}, {\"carat\": 0.3, \"cut\": \"Premium\", \"color\": \"E\", \"clarity\": \"VS2\", \"depth\": 62.2, \"table\": 60.0, \"price\": 844, \"x\": 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       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"32e5e754-c2b3-4d1c-b51f-6dd49412adce\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x7f90d750bd30>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#32e5e754-c2b3-4d1c-b51f-6dd49412adce"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#32e5e754-c2b3-4d1c-b51f-6dd49412adce"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=diamonds_split[1].as_data_frame().assign(\n",
    "  model='gb',pred=gb_predictions.as_data_frame()).append(\n",
    "  diamonds_split[1].as_data_frame().assign(\n",
    "  model='lm',pred=lm_predictions.as_data_frame())).sample(1000)\n",
    "    \n",
    "alt.Chart(pred).mark_circle(opacity=0.25).encode(\n",
    "    x='price',y='pred',color='model'\n",
    ").interactive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}