H2O_AutoML_IPYNB/rulefit_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RuleFit Analysis\n",
    "\n",
    "The goal of the notebook is to use machine learning to automatically create a set of rules to predict a target column.  The rules provide an explainable solution.\n",
    "\n",
    "## Steps\n",
    "\n",
    "1. Import and clean data\n",
    "2. Train rule-fit model\n",
    "3. Examine rules\n",
    "4. Customizations\n",
    "\n",
    "## Step 1. Import and Clean Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 \"12.0.2\" 2019-07-16; Java(TM) SE Runtime Environment (build 12.0.2+10); Java HotSpot(TM) 64-Bit Server VM (build 12.0.2+10, mixed mode, sharing)\n",
      "  Starting server from /Users/megankurka/env2/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /var/folders/fk/z2fjbsq163scfcsq9fhsw7r00000gn/T/tmp9tuw0k38\n",
      "  JVM stdout: /var/folders/fk/z2fjbsq163scfcsq9fhsw7r00000gn/T/tmp9tuw0k38/h2o_megankurka_started_from_python.out\n",
      "  JVM stderr: /var/folders/fk/z2fjbsq163scfcsq9fhsw7r00000gn/T/tmp9tuw0k38/h2o_megankurka_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>3.32.0.2</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 day </td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_megankurka_gr81uf</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>4 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>16</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>16</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>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O_API_Extensions:</td>\n",
       "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.6.8 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:        3.32.0.2\n",
       "H2O_cluster_version_age:    1 day\n",
       "H2O_cluster_name:           H2O_from_python_megankurka_gr81uf\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    4 Gb\n",
       "H2O_cluster_total_cores:    16\n",
       "H2O_cluster_allowed_cores:  16\n",
       "H2O_cluster_status:         accepting new members, healthy\n",
       "H2O_connection_url:         http://127.0.0.1:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "H2O_API_Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
       "Python_version:             3.6.8 final\n",
       "--------------------------  ------------------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h2o\n",
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  pclass</th><th style=\"text-align: right;\">  survived</th><th>name                                           </th><th>sex   </th><th style=\"text-align: right;\">    age</th><th style=\"text-align: right;\">  sibsp</th><th style=\"text-align: right;\">  parch</th><th style=\"text-align: right;\">  ticket</th><th style=\"text-align: right;\">    fare</th><th>cabin  </th><th>embarked  </th><th style=\"text-align: right;\">  boat</th><th style=\"text-align: right;\">  body</th><th>home.dest                      </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">         1</td><td>Allen  Miss. Elisabeth Walton                  </td><td>female</td><td style=\"text-align: right;\">29     </td><td style=\"text-align: right;\">      0</td><td style=\"text-align: right;\">      0</td><td style=\"text-align: right;\">   24160</td><td style=\"text-align: right;\">211.338 </td><td>B5     </td><td>S         </td><td style=\"text-align: right;\">     2</td><td style=\"text-align: right;\">   nan</td><td>St Louis  MO                   </td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">         1</td><td>Allison  Master. Hudson Trevor                 </td><td>male  </td><td style=\"text-align: right;\"> 0.9167</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">  113781</td><td style=\"text-align: right;\">151.55  </td><td>C22 C26</td><td>S         </td><td style=\"text-align: right;\">    11</td><td style=\"text-align: right;\">   nan</td><td>Montreal  PQ / Chesterville  ON</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">         0</td><td>Allison  Miss. Helen Loraine                   </td><td>female</td><td style=\"text-align: right;\"> 2     </td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">  113781</td><td style=\"text-align: right;\">151.55  </td><td>C22 C26</td><td>S         </td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td>Montreal  PQ / Chesterville  ON</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">         0</td><td>Allison  Mr. Hudson Joshua Creighton           </td><td>male  </td><td style=\"text-align: right;\">30     </td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">  113781</td><td style=\"text-align: right;\">151.55  </td><td>C22 C26</td><td>S         </td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   135</td><td>Montreal  PQ / Chesterville  ON</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">         0</td><td>Allison  Mrs. Hudson J C (Bessie Waldo Daniels)</td><td>female</td><td style=\"text-align: right;\">25     </td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">  113781</td><td style=\"text-align: right;\">151.55  </td><td>C22 C26</td><td>S         </td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td>Montreal  PQ / Chesterville  ON</td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">         1</td><td>Anderson  Mr. Harry                            </td><td>male  </td><td style=\"text-align: right;\">48     </td><td style=\"text-align: right;\">      0</td><td style=\"text-align: right;\">      0</td><td style=\"text-align: right;\">   19952</td><td style=\"text-align: right;\"> 26.55  </td><td>E12    </td><td>S         </td><td style=\"text-align: right;\">     3</td><td style=\"text-align: right;\">   nan</td><td>New York  NY                   </td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">         1</td><td>Andrews  Miss. Kornelia Theodosia              </td><td>female</td><td style=\"text-align: right;\">63     </td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">      0</td><td style=\"text-align: right;\">   13502</td><td style=\"text-align: right;\"> 77.9583</td><td>D7     </td><td>S         </td><td style=\"text-align: right;\">    10</td><td style=\"text-align: right;\">   nan</td><td>Hudson  NY                     </td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">         0</td><td>Andrews  Mr. Thomas Jr                         </td><td>male  </td><td style=\"text-align: right;\">39     </td><td style=\"text-align: right;\">      0</td><td style=\"text-align: right;\">      0</td><td style=\"text-align: right;\">  112050</td><td style=\"text-align: right;\">  0     </td><td>A36    </td><td>S         </td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td>Belfast  NI                    </td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">         1</td><td>Appleton  Mrs. Edward Dale (Charlotte Lamson)  </td><td>female</td><td style=\"text-align: right;\">53     </td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">      0</td><td style=\"text-align: right;\">   11769</td><td style=\"text-align: right;\"> 51.4792</td><td>C101   </td><td>S         </td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td>Bayside  Queens  NY            </td></tr>\n",
       "<tr><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">         0</td><td>Artagaveytia  Mr. Ramon                        </td><td>male  </td><td style=\"text-align: right;\">71     </td><td style=\"text-align: right;\">      0</td><td style=\"text-align: right;\">      0</td><td style=\"text-align: right;\">     nan</td><td style=\"text-align: right;\"> 49.5042</td><td>       </td><td>C         </td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">    22</td><td>Montevideo  Uruguay            </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = h2o.import_file(\"https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv\",\n",
    "                    col_types={'pclass': \"enum\", 'survived': \"enum\"})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Train Rule-Fit Model\n",
    "\n",
    "We will train a rule-fit model to predict the survival.  The outcome of the rulefit model is rules defining whether or not someone will survive.  The rulefit model is done with the following steps:\n",
    "\n",
    "1. Train a series of random forest models with different depths\n",
    "2. Extract rules from the random forest models\n",
    "3. Train a GLM model with Lasso regularization using the rules to predict the target. \n",
    "4. Extract the most important rules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = df.split_frame(ratios=[0.75], destination_frames=[\"train.hex\", \"test.hex\"], seed=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rulefit Model Build progress: |███████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "x =  [\"age\", \"sibsp\", \"parch\", \"fare\", \"sex\", \"pclass\"]\n",
    "\n",
    "from h2o.estimators import H2ORuleFitEstimator\n",
    "rfit = H2ORuleFitEstimator(seed=1234)\n",
    "rfit.train(training_frame=train, x=x, y=\"survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule_importance = rfit.rule_importance()\n",
    "(table, nr, is_pandas) = rule_importance._as_show_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>coefficient</th>\n",
       "      <th>rule</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>M0T18N12</td>\n",
       "      <td>0.766079</td>\n",
       "      <td>(pclass in {1, 2}) &amp; (age &lt; 60.49951934814453 or age is NA) &amp; (sex in {female})</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>M0T44N21</td>\n",
       "      <td>-0.441729</td>\n",
       "      <td>(sex in {male} or sex is NA) &amp; (pclass in {2, 3} or pclass is NA) &amp; (age &gt;= 9.569365501403809 or age is NA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>linear.sex.female</td>\n",
       "      <td>0.408274</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>linear.sex.male</td>\n",
       "      <td>-0.171397</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>M0T28N20</td>\n",
       "      <td>-0.139191</td>\n",
       "      <td>(sex in {male} or sex is NA) &amp; (fare &lt; 51.03279113769531 or fare is NA) &amp; (age &gt;= 6.469268798828125 or age is NA)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "import pandas as pd\n",
    "pd.options.display.max_colwidth = 0\n",
    "display(HTML(table.head().to_html()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are only 5 rules that are created which are recapped below: \n",
    "\n",
    "**Highest Likelihood of Survival**\n",
    "\n",
    "1. Women\n",
    "1. Women in class 1 or 2 who are less than 60 years old\n",
    "\n",
    "**Lowest Likelihood of Survival**\n",
    "1. Men\n",
    "2. Men age 6+ who paid less than 51 dollars for their tickets\n",
    "4. Men age 10+ in class 2 or 3\n",
    "\n",
    "Note: The rules are additive.  That means that if a passenger is described by multiple rules, their probability is added together from those rules.\n",
    "\n",
    "We will predict on our test data and see how well our rulefit model performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rulefit prediction progress: |████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  survived</th><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">      p0</th><th style=\"text-align: right;\">      p1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.629429</td><td style=\"text-align: right;\">0.370571</td></tr>\n",
       "<tr><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.675644</td><td style=\"text-align: right;\">0.324356</td></tr>\n",
       "<tr><td style=\"text-align: right;\">         1</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.678355</td><td style=\"text-align: right;\">0.321645</td></tr>\n",
       "<tr><td style=\"text-align: right;\">         1</td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.293804</td><td style=\"text-align: right;\">0.706196</td></tr>\n",
       "<tr><td style=\"text-align: right;\">         1</td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.316881</td><td style=\"text-align: right;\">0.683119</td></tr>\n",
       "<tr><td style=\"text-align: right;\">         0</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.64067 </td><td style=\"text-align: right;\">0.35933 </td></tr>\n",
       "<tr><td style=\"text-align: right;\">         1</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.643983</td><td style=\"text-align: right;\">0.356017</td></tr>\n",
       "<tr><td style=\"text-align: right;\">         1</td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.320156</td><td style=\"text-align: right;\">0.679844</td></tr>\n",
       "<tr><td style=\"text-align: right;\">         1</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.638349</td><td style=\"text-align: right;\">0.361651</td></tr>\n",
       "<tr><td style=\"text-align: right;\">         1</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.678833</td><td style=\"text-align: right;\">0.321167</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = rfit.predict(test)\n",
    "predictions = test[\"survived\"].cbind(predictions)\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How many times we correctly predicted survived: 76.00%\n",
      "How many times we correctly predicted not survived: 79.82%\n"
     ]
    }
   ],
   "source": [
    "positives = predictions[predictions[\"predict\"] == \"1\"]\n",
    "negatives = predictions[predictions[\"predict\"] == \"0\"]\n",
    "print(\"How many times we correctly predicted survived: {:.2%}\".format(positives[positives[\"survived\"] == positives[\"predict\"]].nrow/positives.nrow))\n",
    "print(\"How many times we correctly predicted not survived: {:.2%}\".format(negatives[negatives[\"survived\"] == negatives[\"predict\"]].nrow/negatives.nrow))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with RuleFit Model: 78.66%\n",
      "Accuracy with Constant Model: 62.80%\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy with RuleFit Model: {:.2%}\".format(predictions[predictions[\"survived\"] == predictions[\"predict\"]].nrow/predictions.nrow))\n",
    "print(\"Accuracy with Constant Model: {:.2%}\".format(predictions[predictions[\"survived\"] == \"0\"].nrow/predictions.nrow))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Customizations\n",
    "\n",
    "In this section, we train a new rulefit model to predict `parch`.  We customize the rulefit model so that the distribution used is `poisson` and that the tree model used is GBM.  We also restrict is so that it is limited to 10 rules and the rules cannot be longer than 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x =  [\"age\", \"pclass\", \"sibsp\", \"fare\", \"sex\", \"survived\"]\n",
    "rulefit_parch_model = H2ORuleFitEstimator(algorithm=\"GBM\", seed=1234,\n",
    "                                          max_num_rules=10,\n",
    "                                          max_rule_length=2,\n",
    "                                          distribution=\"poisson\"\n",
    "                                         )\n",
    "rulefit_parch_model.train(training_frame=train, x=x, y=\"parch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule_importance = rulefit_parch_model.rule_importance()\n",
    "(table, nr, is_pandas) = rule_importance._as_show_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display, HTML\n",
    "import pandas as pd\n",
    "pd.options.display.max_colwidth = 0\n",
    "display(HTML(table.head().to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H2O session _sid_8ad8 closed.\n"
     ]
    }
   ],
   "source": [
    "h2o.cluster().shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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