week1_iris_automl.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Week 1 - Iris Auto ML\n",
    "\n",
    "Now let's look at this again, but using H2O's Auto ML feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h2o"
   ]
  },
  {
   "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_232\"; OpenJDK Runtime Environment (build 1.8.0_232-8u232-b09-0ubuntu1~19.04.1-b09); OpenJDK 64-Bit Server VM (build 25.232-b09, mixed mode)\n",
      "  Starting server from /home/megan/Projects/h2oclass/lib/python3.7/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /tmp/tmp69mjwbd9\n",
      "  JVM stdout: /tmp/tmp69mjwbd9/h2o_megan_started_from_python.out\n",
      "  JVM stderr: /tmp/tmp69mjwbd9/h2o_megan_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/Chicago</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.28.0.3</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>4 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_megan_3qz755</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>1.520 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>3</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>3</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': None, 'https': None}</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>3.7.3 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ------------------------------------------------------------------\n",
       "H2O cluster uptime:         01 secs\n",
       "H2O cluster timezone:       America/Chicago\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.28.0.3\n",
       "H2O cluster version age:    4 days\n",
       "H2O cluster name:           H2O_from_python_megan_3qz755\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    1.520 Gb\n",
       "H2O cluster total cores:    3\n",
       "H2O cluster allowed cores:  3\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:       {'http': None, 'https': None}\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
       "Python version:             3.7.3 final\n",
       "--------------------------  ------------------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check to see if H2O is running already and connect, or start it up\n",
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# import the iris dataset\n",
    "url = 'http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv'\n",
    "iris = h2o.import_file(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data into 80% train and 20% test\n",
    "train, test = iris.split_frame([0.8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>       </th><th>sepal_len         </th><th>sepal_wid         </th><th>petal_len         </th><th>petal_wid        </th><th>class      </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>real              </td><td>real              </td><td>real              </td><td>real             </td><td>enum       </td></tr>\n",
       "<tr><td>mins   </td><td>4.3               </td><td>2.0               </td><td>1.0               </td><td>0.1              </td><td>           </td></tr>\n",
       "<tr><td>mean   </td><td>5.8280701754385955</td><td>3.077192982456141 </td><td>3.671052631578949 </td><td>1.16140350877193 </td><td>           </td></tr>\n",
       "<tr><td>maxs   </td><td>7.9               </td><td>4.4               </td><td>6.9               </td><td>2.5              </td><td>           </td></tr>\n",
       "<tr><td>sigma  </td><td>0.8261766906875108</td><td>0.4337600755442564</td><td>1.7841347861802646</td><td>0.776024446737654</td><td>           </td></tr>\n",
       "<tr><td>zeros  </td><td>0                 </td><td>0                 </td><td>0                 </td><td>0                </td><td>           </td></tr>\n",
       "<tr><td>missing</td><td>0                 </td><td>0                 </td><td>0                 </td><td>0                </td><td>0          </td></tr>\n",
       "<tr><td>0      </td><td>5.1               </td><td>3.5               </td><td>1.4               </td><td>0.2              </td><td>Iris-setosa</td></tr>\n",
       "<tr><td>1      </td><td>4.6               </td><td>3.1               </td><td>1.5               </td><td>0.2              </td><td>Iris-setosa</td></tr>\n",
       "<tr><td>2      </td><td>5.0               </td><td>3.6               </td><td>1.4               </td><td>0.2              </td><td>Iris-setosa</td></tr>\n",
       "<tr><td>3      </td><td>5.4               </td><td>3.9               </td><td>1.7               </td><td>0.4              </td><td>Iris-setosa</td></tr>\n",
       "<tr><td>4      </td><td>4.6               </td><td>3.4               </td><td>1.4               </td><td>0.3              </td><td>Iris-setosa</td></tr>\n",
       "<tr><td>5      </td><td>5.0               </td><td>3.4               </td><td>1.5               </td><td>0.2              </td><td>Iris-setosa</td></tr>\n",
       "<tr><td>6      </td><td>4.4               </td><td>2.9               </td><td>1.4               </td><td>0.2              </td><td>Iris-setosa</td></tr>\n",
       "<tr><td>7      </td><td>4.9               </td><td>3.1               </td><td>1.5               </td><td>0.1              </td><td>Iris-setosa</td></tr>\n",
       "<tr><td>8      </td><td>5.4               </td><td>3.7               </td><td>1.5               </td><td>0.2              </td><td>Iris-setosa</td></tr>\n",
       "<tr><td>9      </td><td>4.8               </td><td>3.4               </td><td>1.6               </td><td>0.2              </td><td>Iris-setosa</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# review a summary of our training data\n",
    "train.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many rows in training - not exactly 80%\n",
    "train.nrows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many rows in testing - not exactly 20%\n",
    "test.nrows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import auto ml\n",
    "from h2o.automl import H2OAutoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoML progress: |██████████████\n",
      "19:52:51.915: Skipping training of model GBM_5_AutoML_20200209_195234 due to exception: water.exceptions.H2OModelBuilderIllegalArgumentException: Illegal argument(s) for GBM model: GBM_5_AutoML_20200209_195234.  Details: ERRR on field: _min_rows: The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 114.0.\n",
      "\n",
      "\n",
      "███████████████████Failed polling AutoML progress log: HTTP 500 Server Error:\n",
      "Server error java.lang.ArrayIndexOutOfBoundsException:\n",
      "  Error: 68\n",
      "  Request: None\n",
      "\n",
      "███████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# train the model\n",
    "mA = H2OAutoML(max_runtime_secs=60)\n",
    "mA.train(['sepal_len', 'sepal_wid', 'petal_len', 'petal_wid'], 'class', train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Details\n",
      "=============\n",
      "H2OGeneralizedLinearEstimator :  Generalized Linear Modeling\n",
      "Model Key:  GLM_1_AutoML_20200209_195234\n",
      "\n",
      "\n",
      "GLM Model: summary\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>family</th>\n",
       "      <th>link</th>\n",
       "      <th>regularization</th>\n",
       "      <th>lambda_search</th>\n",
       "      <th>number_of_predictors_total</th>\n",
       "      <th>number_of_active_predictors</th>\n",
       "      <th>number_of_iterations</th>\n",
       "      <th>training_frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>multinomial</td>\n",
       "      <td>multinomial</td>\n",
       "      <td>Ridge ( lambda = 7.15E-5 )</td>\n",
       "      <td>nlambda = 30, lambda.max = 44.404, lambda.min = 7.15E-5, lambda.1s...</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>191</td>\n",
       "      <td>automl_training_py_3_sid_83d2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          family         link              regularization  \\\n",
       "0    multinomial  multinomial  Ridge ( lambda = 7.15E-5 )   \n",
       "\n",
       "                                                           lambda_search  \\\n",
       "0  nlambda = 30, lambda.max = 44.404, lambda.min = 7.15E-5, lambda.1s...   \n",
       "\n",
       "   number_of_predictors_total number_of_active_predictors  \\\n",
       "0                          15                          12   \n",
       "\n",
       "   number_of_iterations                 training_frame  \n",
       "0                   191  automl_training_py_3_sid_83d2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ModelMetricsMultinomialGLM: glm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.004326081850443802\n",
      "RMSE: 0.06577295683214951\n",
      "\n",
      "ModelMetricsMultinomialGLM: glm\n",
      "** Reported on cross-validation data. **\n",
      "\n",
      "MSE: 0.013469083212128271\n",
      "RMSE: 0.11605637945467828\n",
      "\n",
      "Cross-Validation Metrics Summary: \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</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>accuracy</td>\n",
       "      <td>0.99130434</td>\n",
       "      <td>0.019444069</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.95652175</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>err</td>\n",
       "      <td>0.008695652</td>\n",
       "      <td>0.019444069</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.04347826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>err_count</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4472136</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>logloss</td>\n",
       "      <td>0.040125437</td>\n",
       "      <td>0.042201582</td>\n",
       "      <td>3.8147127E-4</td>\n",
       "      <td>0.015059838</td>\n",
       "      <td>0.10869362</td>\n",
       "      <td>0.028063606</td>\n",
       "      <td>0.048428655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>max_per_class_error</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.0559017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mean_per_class_accuracy</td>\n",
       "      <td>0.9916667</td>\n",
       "      <td>0.0186339</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9583333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mean_per_class_error</td>\n",
       "      <td>0.008333334</td>\n",
       "      <td>0.0186339</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041666668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mse</td>\n",
       "      <td>0.011813632</td>\n",
       "      <td>0.014728339</td>\n",
       "      <td>5.3972985E-7</td>\n",
       "      <td>0.0016182398</td>\n",
       "      <td>0.036627512</td>\n",
       "      <td>0.008904781</td>\n",
       "      <td>0.011917091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>null_deviance</td>\n",
       "      <td>50.061905</td>\n",
       "      <td>1.0143474</td>\n",
       "      <td>50.357388</td>\n",
       "      <td>50.53654</td>\n",
       "      <td>50.53654</td>\n",
       "      <td>50.623383</td>\n",
       "      <td>48.255672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>r2</td>\n",
       "      <td>0.98217064</td>\n",
       "      <td>0.022696504</td>\n",
       "      <td>0.9999992</td>\n",
       "      <td>0.9975115</td>\n",
       "      <td>0.94367456</td>\n",
       "      <td>0.98719937</td>\n",
       "      <td>0.9824685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>residual_deviance</td>\n",
       "      <td>1.8263987</td>\n",
       "      <td>1.9369872</td>\n",
       "      <td>0.017547678</td>\n",
       "      <td>0.69275254</td>\n",
       "      <td>4.9999065</td>\n",
       "      <td>1.2909259</td>\n",
       "      <td>2.1308608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>rmse</td>\n",
       "      <td>0.087175146</td>\n",
       "      <td>0.07257863</td>\n",
       "      <td>7.346631E-4</td>\n",
       "      <td>0.04022735</td>\n",
       "      <td>0.19138315</td>\n",
       "      <td>0.09436515</td>\n",
       "      <td>0.10916543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    mean           sd    cv_1_valid  \\\n",
       "0                  accuracy   0.99130434  0.019444069           1.0   \n",
       "1                       err  0.008695652  0.019444069           0.0   \n",
       "2                 err_count          0.2    0.4472136           0.0   \n",
       "3                   logloss  0.040125437  0.042201582  3.8147127E-4   \n",
       "4       max_per_class_error        0.025    0.0559017           0.0   \n",
       "5   mean_per_class_accuracy    0.9916667    0.0186339           1.0   \n",
       "6      mean_per_class_error  0.008333334    0.0186339           0.0   \n",
       "7                       mse  0.011813632  0.014728339  5.3972985E-7   \n",
       "8             null_deviance    50.061905    1.0143474     50.357388   \n",
       "9                        r2   0.98217064  0.022696504     0.9999992   \n",
       "10        residual_deviance    1.8263987    1.9369872   0.017547678   \n",
       "11                     rmse  0.087175146   0.07257863   7.346631E-4   \n",
       "\n",
       "      cv_2_valid   cv_3_valid   cv_4_valid   cv_5_valid  \n",
       "0            1.0   0.95652175          1.0          1.0  \n",
       "1            0.0   0.04347826          0.0          0.0  \n",
       "2            0.0          1.0          0.0          0.0  \n",
       "3    0.015059838   0.10869362  0.028063606  0.048428655  \n",
       "4            0.0        0.125          0.0          0.0  \n",
       "5            1.0    0.9583333          1.0          1.0  \n",
       "6            0.0  0.041666668          0.0          0.0  \n",
       "7   0.0016182398  0.036627512  0.008904781  0.011917091  \n",
       "8       50.53654     50.53654    50.623383    48.255672  \n",
       "9      0.9975115   0.94367456   0.98719937    0.9824685  \n",
       "10    0.69275254    4.9999065    1.2909259    2.1308608  \n",
       "11    0.04022735   0.19138315   0.09436515   0.10916543  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>duration</th>\n",
       "      <th>iteration</th>\n",
       "      <th>lambda</th>\n",
       "      <th>predictors</th>\n",
       "      <th>deviance_train</th>\n",
       "      <th>deviance_test</th>\n",
       "      <th>deviance_xval</th>\n",
       "      <th>deviance_se</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.000 sec</td>\n",
       "      <td>2</td>\n",
       "      <td>.44E2</td>\n",
       "      <td>15</td>\n",
       "      <td>2.150848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.160953</td>\n",
       "      <td>0.002924</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.022 sec</td>\n",
       "      <td>4</td>\n",
       "      <td>.28E2</td>\n",
       "      <td>15</td>\n",
       "      <td>2.125808</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.140740</td>\n",
       "      <td>0.003550</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.049 sec</td>\n",
       "      <td>6</td>\n",
       "      <td>.17E2</td>\n",
       "      <td>15</td>\n",
       "      <td>2.087434</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.109373</td>\n",
       "      <td>0.004515</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.056 sec</td>\n",
       "      <td>8</td>\n",
       "      <td>.11E2</td>\n",
       "      <td>15</td>\n",
       "      <td>2.030258</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.061918</td>\n",
       "      <td>0.005987</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.070 sec</td>\n",
       "      <td>10</td>\n",
       "      <td>.66E1</td>\n",
       "      <td>15</td>\n",
       "      <td>1.948499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.992616</td>\n",
       "      <td>0.008119</td>\n",
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       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.082 sec</td>\n",
       "      <td>12</td>\n",
       "      <td>.41E1</td>\n",
       "      <td>15</td>\n",
       "      <td>1.838454</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.896439</td>\n",
       "      <td>0.011011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.101 sec</td>\n",
       "      <td>15</td>\n",
       "      <td>.25E1</td>\n",
       "      <td>15</td>\n",
       "      <td>1.700618</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.771865</td>\n",
       "      <td>0.014659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.127 sec</td>\n",
       "      <td>18</td>\n",
       "      <td>.16E1</td>\n",
       "      <td>15</td>\n",
       "      <td>1.542807</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.622577</td>\n",
       "      <td>0.019027</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.144 sec</td>\n",
       "      <td>21</td>\n",
       "      <td>.98E0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.376805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.459813</td>\n",
       "      <td>0.023768</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.167 sec</td>\n",
       "      <td>24</td>\n",
       "      <td>.61E0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.214921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.295602</td>\n",
       "      <td>0.028890</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.193 sec</td>\n",
       "      <td>27</td>\n",
       "      <td>.38E0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.066305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.140962</td>\n",
       "      <td>0.034215</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.243 sec</td>\n",
       "      <td>30</td>\n",
       "      <td>.24E0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.933976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.002429</td>\n",
       "      <td>0.039264</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:43</td>\n",
       "      <td>0.296 sec</td>\n",
       "      <td>33</td>\n",
       "      <td>.15E0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.816376</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.880045</td>\n",
       "      <td>0.043662</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:44</td>\n",
       "      <td>0.307 sec</td>\n",
       "      <td>36</td>\n",
       "      <td>.91E-1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.707824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.769974</td>\n",
       "      <td>0.046800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:44</td>\n",
       "      <td>0.322 sec</td>\n",
       "      <td>39</td>\n",
       "      <td>.56E-1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.604732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.666665</td>\n",
       "      <td>0.048290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:44</td>\n",
       "      <td>0.349 sec</td>\n",
       "      <td>42</td>\n",
       "      <td>.35E-1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.507438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.568115</td>\n",
       "      <td>0.047892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:44</td>\n",
       "      <td>0.392 sec</td>\n",
       "      <td>46</td>\n",
       "      <td>.22E-1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.417637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.474985</td>\n",
       "      <td>0.046474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:44</td>\n",
       "      <td>0.464 sec</td>\n",
       "      <td>51</td>\n",
       "      <td>.13E-1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.339926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.391941</td>\n",
       "      <td>0.044482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:44</td>\n",
       "      <td>0.499 sec</td>\n",
       "      <td>56</td>\n",
       "      <td>.84E-2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.275349</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.321414</td>\n",
       "      <td>0.042543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td></td>\n",
       "      <td>2020-02-09 19:52:44</td>\n",
       "      <td>0.521 sec</td>\n",
       "      <td>62</td>\n",
       "      <td>.52E-2</td>\n",
       "      <td>15</td>\n",
       "      <td>0.222854</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.263535</td>\n",
       "      <td>0.040746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp    duration  iteration  lambda  predictors  \\\n",
       "0     2020-02-09 19:52:43   0.000 sec          2   .44E2          15   \n",
       "1     2020-02-09 19:52:43   0.022 sec          4   .28E2          15   \n",
       "2     2020-02-09 19:52:43   0.049 sec          6   .17E2          15   \n",
       "3     2020-02-09 19:52:43   0.056 sec          8   .11E2          15   \n",
       "4     2020-02-09 19:52:43   0.070 sec         10   .66E1          15   \n",
       "5     2020-02-09 19:52:43   0.082 sec         12   .41E1          15   \n",
       "6     2020-02-09 19:52:43   0.101 sec         15   .25E1          15   \n",
       "7     2020-02-09 19:52:43   0.127 sec         18   .16E1          15   \n",
       "8     2020-02-09 19:52:43   0.144 sec         21   .98E0          15   \n",
       "9     2020-02-09 19:52:43   0.167 sec         24   .61E0          15   \n",
       "10    2020-02-09 19:52:43   0.193 sec         27   .38E0          15   \n",
       "11    2020-02-09 19:52:43   0.243 sec         30   .24E0          15   \n",
       "12    2020-02-09 19:52:43   0.296 sec         33   .15E0          15   \n",
       "13    2020-02-09 19:52:44   0.307 sec         36  .91E-1          15   \n",
       "14    2020-02-09 19:52:44   0.322 sec         39  .56E-1          15   \n",
       "15    2020-02-09 19:52:44   0.349 sec         42  .35E-1          15   \n",
       "16    2020-02-09 19:52:44   0.392 sec         46  .22E-1          15   \n",
       "17    2020-02-09 19:52:44   0.464 sec         51  .13E-1          15   \n",
       "18    2020-02-09 19:52:44   0.499 sec         56  .84E-2          15   \n",
       "19    2020-02-09 19:52:44   0.521 sec         62  .52E-2          15   \n",
       "\n",
       "    deviance_train  deviance_test  deviance_xval  deviance_se  \n",
       "0         2.150848            NaN       2.160953     0.002924  \n",
       "1         2.125808            NaN       2.140740     0.003550  \n",
       "2         2.087434            NaN       2.109373     0.004515  \n",
       "3         2.030258            NaN       2.061918     0.005987  \n",
       "4         1.948499            NaN       1.992616     0.008119  \n",
       "5         1.838454            NaN       1.896439     0.011011  \n",
       "6         1.700618            NaN       1.771865     0.014659  \n",
       "7         1.542807            NaN       1.622577     0.019027  \n",
       "8         1.376805            NaN       1.459813     0.023768  \n",
       "9         1.214921            NaN       1.295602     0.028890  \n",
       "10        1.066305            NaN       1.140962     0.034215  \n",
       "11        0.933976            NaN       1.002429     0.039264  \n",
       "12        0.816376            NaN       0.880045     0.043662  \n",
       "13        0.707824            NaN       0.769974     0.046800  \n",
       "14        0.604732            NaN       0.666665     0.048290  \n",
       "15        0.507438            NaN       0.568115     0.047892  \n",
       "16        0.417637            NaN       0.474985     0.046474  \n",
       "17        0.339926            NaN       0.391941     0.044482  \n",
       "18        0.275349            NaN       0.321414     0.042543  \n",
       "19        0.222854            NaN       0.263535     0.040746  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the leader model\n",
    "mA.leader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# predict on the test set\n",
    "p = mA.leader.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>predict        </th><th style=\"text-align: right;\">  Iris-setosa</th><th style=\"text-align: right;\">  Iris-versicolor</th><th style=\"text-align: right;\">  Iris-virginica</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>Iris-setosa    </td><td style=\"text-align: right;\">  0.995562   </td><td style=\"text-align: right;\">      0.00443822 </td><td style=\"text-align: right;\">     5.27285e-26</td></tr>\n",
       "<tr><td>Iris-setosa    </td><td style=\"text-align: right;\">  0.999814   </td><td style=\"text-align: right;\">      0.000185944</td><td style=\"text-align: right;\">     4.58444e-28</td></tr>\n",
       "<tr><td>Iris-setosa    </td><td style=\"text-align: right;\">  0.999954   </td><td style=\"text-align: right;\">      4.61818e-05</td><td style=\"text-align: right;\">     2.29827e-28</td></tr>\n",
       "<tr><td>Iris-setosa    </td><td style=\"text-align: right;\">  0.999998   </td><td style=\"text-align: right;\">      2.34739e-06</td><td style=\"text-align: right;\">     1.7193e-33 </td></tr>\n",
       "<tr><td>Iris-setosa    </td><td style=\"text-align: right;\">  0.998537   </td><td style=\"text-align: right;\">      0.00146288 </td><td style=\"text-align: right;\">     4.59221e-29</td></tr>\n",
       "<tr><td>Iris-setosa    </td><td style=\"text-align: right;\">  0.999806   </td><td style=\"text-align: right;\">      0.000193999</td><td style=\"text-align: right;\">     4.37153e-27</td></tr>\n",
       "<tr><td>Iris-setosa    </td><td style=\"text-align: right;\">  0.747049   </td><td style=\"text-align: right;\">      0.252951   </td><td style=\"text-align: right;\">     2.4273e-21 </td></tr>\n",
       "<tr><td>Iris-setosa    </td><td style=\"text-align: right;\">  0.999973   </td><td style=\"text-align: right;\">      2.65606e-05</td><td style=\"text-align: right;\">     2.0686e-27 </td></tr>\n",
       "<tr><td>Iris-setosa    </td><td style=\"text-align: right;\">  0.99999    </td><td style=\"text-align: right;\">      1.04211e-05</td><td style=\"text-align: right;\">     1.93e-30   </td></tr>\n",
       "<tr><td>Iris-versicolor</td><td style=\"text-align: right;\">  0.000121819</td><td style=\"text-align: right;\">      0.999804   </td><td style=\"text-align: right;\">     7.40484e-05</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review predictions\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ModelMetricsMultinomialGLM: glm\n",
      "** Reported on test data. **\n",
      "\n",
      "MSE: 0.04091297151480545\n",
      "RMSE: 0.2022695516255609\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review model performance on the test set\n",
    "mA.leader.model_performance(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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