AutoML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "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 \"11.0.22\" 2024-01-16 LTS; Java(TM) SE Runtime Environment 18.9 (build 11.0.22+9-LTS-219); Java HotSpot(TM) 64-Bit Server VM 18.9 (build 11.0.22+9-LTS-219, mixed mode)\n",
      "  Starting server from /Users/yvankammelu/anaconda3/envs/causalml-py38/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /var/folders/8y/9ttyks_n5f7_6c4hyq6_g50r0000gn/T/tmp9uw3fhjx\n",
      "  JVM stdout: /var/folders/8y/9ttyks_n5f7_6c4hyq6_g50r0000gn/T/tmp9uw3fhjx/h2o_yvankammelu_started_from_python.out\n",
      "  JVM stderr: /var/folders/8y/9ttyks_n5f7_6c4hyq6_g50r0000gn/T/tmp9uw3fhjx/h2o_yvankammelu_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": [
       "\n",
       "<style>\n",
       "\n",
       "#h2o-table-5.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-5 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-5 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-5 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-5 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-5 .h2o-table th,\n",
       "#h2o-table-5 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-5 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-5\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption></caption>\n",
       "    <thead></thead>\n",
       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>01 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>America/Toronto</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.46.0.1</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 month and 12 days</td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_yvankammelu_8qleq0</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>8 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>12</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>12</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://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>Python_version:</td>\n",
       "<td>3.10.13 final</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n"
      ],
      "text/plain": [
       "--------------------------  ----------------------------------\n",
       "H2O_cluster_uptime:         01 secs\n",
       "H2O_cluster_timezone:       America/Toronto\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.46.0.1\n",
       "H2O_cluster_version_age:    1 month and 12 days\n",
       "H2O_cluster_name:           H2O_from_python_yvankammelu_8qleq0\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    8 Gb\n",
       "H2O_cluster_total_cores:    12\n",
       "H2O_cluster_allowed_cores:  12\n",
       "H2O_cluster_status:         locked, 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",
       "Python_version:             3.10.13 final\n",
       "--------------------------  ----------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h2o\n",
    "from h2o.automl import H2OAutoML\n",
    "h2o.init()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "\n",
       "#h2o-table-4.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-4 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-4 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-4 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-4 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-4 .h2o-table th,\n",
       "#h2o-table-4 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-4 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-4\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption></caption>\n",
       "    <thead></thead>\n",
       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>1 day 6 hours 16 mins</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>America/Toronto</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.46.0.1</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 month and 12 days</td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_yvankammelu_1ku70n</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>7.947 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>12</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>12</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.10.13 final</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n"
      ],
      "text/plain": [
       "--------------------------  ----------------------------------\n",
       "H2O_cluster_uptime:         1 day 6 hours 16 mins\n",
       "H2O_cluster_timezone:       America/Toronto\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.46.0.1\n",
       "H2O_cluster_version_age:    1 month and 12 days\n",
       "H2O_cluster_name:           H2O_from_python_yvankammelu_1ku70n\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    7.947 Gb\n",
       "H2O_cluster_total_cores:    12\n",
       "H2O_cluster_allowed_cores:  12\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.10.13 final\n",
       "--------------------------  ----------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yvankammelu/anaconda3/envs/causalml-py38/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3553: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "AutoML progress: |\n",
      "17:39:01.10: Project: AutoML_1_20240425_173900\n",
      "17:39:01.11: 5-fold cross-validation will be used.\n",
      "17:39:01.11: Setting stopping tolerance adaptively based on the training frame: 0.0013274583189194754\n",
      "17:39:01.11: Build control seed: 1\n",
      "17:39:01.11: training frame: Frame key: AutoML_1_20240425_173900_training_Key_Frame__upload_ba5a5d408c1a36cd20595b82dad84866.hex    cols: 29    rows: 567490  chunks: 25    size: 28389269  checksum: -4111174395153498966\n",
      "17:39:01.11: validation frame: NULL\n",
      "17:39:01.11: leaderboard frame: NULL\n",
      "17:39:01.11: blending frame: NULL\n",
      "17:39:01.11: response column: at_least_one_conversion\n",
      "17:39:01.11: fold column: null\n",
      "17:39:01.11: weights column: null\n",
      "17:39:01.17: AutoML: XGBoost is not available; skipping it.\n",
      "17:39:01.20: Loading execution steps: [{XGBoost : [def_2 (1g, 10w), def_1 (2g, 10w), def_3 (3g, 10w), grid_1 (4g, 90w), lr_search (7g, 30w)]}, {GLM : [def_1 (1g, 10w)]}, {DRF : [def_1 (2g, 10w), XRT (3g, 10w)]}, {GBM : [def_5 (1g, 10w), def_2 (2g, 10w), def_3 (2g, 10w), def_4 (2g, 10w), def_1 (3g, 10w), grid_1 (4g, 60w), lr_annealing (7g, 10w)]}, {DeepLearning : [def_1 (3g, 10w), grid_1 (4g, 30w), grid_2 (5g, 30w), grid_3 (5g, 30w)]}, {completion : [resume_best_grids (6g, 60w)]}, {StackedEnsemble : [monotonic (9g, 10w), best_of_family_xglm (10g, 10w), all_xglm (10g, 10w)]}]\n",
      "17:39:01.31: Disabling Algo: DeepLearning as requested by the user.\n",
      "17:39:01.31: Disabling Algo: XGBoost as requested by the user.\n",
      "17:39:01.31: AutoML job created: 2024.04.25 17:39:00.987\n",
      "17:39:01.31: AutoML build started: 2024.04.25 17:39:01.31\n",
      "17:39:01.42: AutoML: starting GLM_1_AutoML_1_20240425_173900 model training\n",
      "17:39:01.43: _response param, We have detected that your response column has only 2 unique values (0/1). If you wish to train a binary model instead of a regression model, convert your target column to categorical before training.\n",
      "\n",
      "█████\n",
      "17:39:08.40: New leader: GLM_1_AutoML_1_20240425_173900, rmse: 0.10049591223216876\n",
      "17:39:08.44: AutoML: starting GBM_1_AutoML_1_20240425_173900 model training\n",
      "17:39:08.44: _response param, We have detected that your response column has only 2 unique values (0/1). If you wish to train a binary model instead of a regression model, convert your target column to categorical before training.\n",
      "\n",
      "██\n",
      "17:39:36.493: New leader: GBM_1_AutoML_1_20240425_173900, rmse: 0.09129511904934498\n",
      "17:39:36.495: AutoML: starting DRF_1_AutoML_1_20240425_173900 model training\n",
      "17:39:36.495: _response param, We have detected that your response column has only 2 unique values (0/1). If you wish to train a binary model instead of a regression model, convert your target column to categorical before training.\n",
      "\n",
      "███\n",
      "17:40:18.738: AutoML: starting GBM_2_AutoML_1_20240425_173900 model training\n",
      "17:40:18.738: _response param, We have detected that your response column has only 2 unique values (0/1). If you wish to train a binary model instead of a regression model, convert your target column to categorical before training.\n",
      "\n",
      "█\n",
      "17:40:34.261: AutoML: starting GBM_3_AutoML_1_20240425_173900 model training\n",
      "17:40:34.262: _response param, We have detected that your response column has only 2 unique values (0/1). If you wish to train a binary model instead of a regression model, convert your target column to categorical before training.\n",
      "\n",
      "███\n",
      "17:40:50.567: AutoML: starting GBM_4_AutoML_1_20240425_173900 model training\n",
      "17:40:50.567: _response param, We have detected that your response column has only 2 unique values (0/1). If you wish to train a binary model instead of a regression model, convert your target column to categorical before training.\n",
      "\n",
      "███\n",
      "17:41:09.275: AutoML: starting XRT_1_AutoML_1_20240425_173900 model training\n",
      "17:41:09.275: _response param, We have detected that your response column has only 2 unique values (0/1). If you wish to train a binary model instead of a regression model, convert your target column to categorical before training.\n",
      "\n",
      "███\n",
      "17:41:50.721: AutoML: starting GBM_5_AutoML_1_20240425_173900 model training\n",
      "17:41:50.722: _response param, We have detected that your response column has only 2 unique values (0/1). If you wish to train a binary model instead of a regression model, convert your target column to categorical before training.\n",
      "\n",
      "██\n",
      "17:42:05.169: New leader: GBM_5_AutoML_1_20240425_173900, rmse: 0.0912178021685691\n",
      "17:42:05.170: AutoML: starting GBM_grid_1_AutoML_1_20240425_173900 hyperparameter search\n",
      "\n",
      "████████████████████\n",
      "17:42:37.326: New leader: GBM_grid_1_AutoML_1_20240425_173900_model_2, rmse: 0.09117706008891874\n",
      "17:45:39.110: No base models, due to timeouts or the exclude_algos option. Skipping StackedEnsemble 'monotonic'.\n",
      "17:45:39.117: AutoML: starting StackedEnsemble_BestOfFamily_1_AutoML_1_20240425_173900 model training\n",
      "17:45:39.117: _response param, We have detected that your response column has only 2 unique values (0/1). If you wish to train a binary model instead of a regression model, convert your target column to categorical before training.\n",
      "\n",
      "██████████\n",
      "17:45:41.472: New leader: StackedEnsemble_BestOfFamily_1_AutoML_1_20240425_173900, rmse: 0.09117309194820582\n",
      "17:45:41.473: AutoML: starting StackedEnsemble_AllModels_1_AutoML_1_20240425_173900 model training\n",
      "17:45:41.473: _response param, We have detected that your response column has only 2 unique values (0/1). If you wish to train a binary model instead of a regression model, convert your target column to categorical before training.\n",
      "\n",
      "███████████| (done) 100%\n",
      "\n",
      "17:45:45.4: New leader: StackedEnsemble_AllModels_1_AutoML_1_20240425_173900, rmse: 0.09099896506616917\n",
      "17:45:45.4: Actual modeling steps: [{GLM : [def_1 (1g, 10w)]}, {GBM : [def_5 (1g, 10w)]}, {DRF : [def_1 (2g, 10w)]}, {GBM : [def_2 (2g, 10w), def_3 (2g, 10w), def_4 (2g, 10w)]}, {DRF : [XRT (3g, 10w)]}, {GBM : [def_1 (3g, 10w), grid_1 (4g, 60w)]}, {StackedEnsemble : [best_of_family_xglm (10g, 10w), all_xglm (10g, 10w)]}]\n",
      "17:45:45.4: AutoML build stopped: 2024.04.25 17:45:45.4\n",
      "17:45:45.4: AutoML build done: built 20 models\n",
      "17:45:45.4: AutoML duration:  6 min 43.973 sec\n",
      "\n",
      "model_id                                                      rmse         mse        mae      rmsle    mean_residual_deviance\n",
      "StackedEnsemble_AllModels_1_AutoML_1_20240425_173900     0.090999   0.00828081  0.0168121  0.0636856                0.00828081\n",
      "StackedEnsemble_BestOfFamily_1_AutoML_1_20240425_173900  0.0911731  0.00831253  0.0170901  0.0638183                0.00831253\n",
      "GBM_grid_1_AutoML_1_20240425_173900_model_2              0.0911771  0.00831326  0.0168853  0.0638495                0.00831326\n",
      "GBM_5_AutoML_1_20240425_173900                           0.0912178  0.00832069  0.0168035  0.0638668                0.00832069\n",
      "GBM_grid_1_AutoML_1_20240425_173900_model_6              0.0912359  0.00832399  0.0172738  0.0638638                0.00832399\n",
      "GBM_grid_1_AutoML_1_20240425_173900_model_12             0.0912445  0.00832556  0.0167794  0.0638656                0.00832556\n",
      "GBM_1_AutoML_1_20240425_173900                           0.0912951  0.0083348   0.0168547  0.0640042                0.0083348\n",
      "GBM_2_AutoML_1_20240425_173900                           0.0913186  0.00833908  0.0168433  0.0639104                0.00833908\n",
      "GBM_3_AutoML_1_20240425_173900                           0.091319   0.00833916  0.0167509  0.063973                 0.00833916\n",
      "GBM_grid_1_AutoML_1_20240425_173900_model_3              0.0914206  0.00835772  0.0173173  0.0639769                0.00835772\n",
      "GBM_grid_1_AutoML_1_20240425_173900_model_9              0.0914677  0.00836634  0.0174533  0.0639271                0.00836634\n",
      "GBM_4_AutoML_1_20240425_173900                           0.0917091  0.00841055  0.0167922  0.06431                  0.00841055\n",
      "GBM_grid_1_AutoML_1_20240425_173900_model_7              0.0917963  0.00842657  0.0183527  0.0642373                0.00842657\n",
      "GBM_grid_1_AutoML_1_20240425_173900_model_4              0.0920366  0.00847073  0.0179209  0.0645275                0.00847073\n",
      "GBM_grid_1_AutoML_1_20240425_173900_model_11             0.0921185  0.00848583  0.0184285  0.0643326                0.00848583\n",
      "GBM_grid_1_AutoML_1_20240425_173900_model_10             0.0923536  0.00852918  0.0174727  0.0646776                0.00852918\n",
      "GBM_grid_1_AutoML_1_20240425_173900_model_1              0.0927737  0.00860697  0.017511   0.0649612                0.00860697\n",
      "GBM_grid_1_AutoML_1_20240425_173900_model_5              0.0938661  0.00881084  0.0170714  0.0660494                0.00881084\n",
      "XRT_1_AutoML_1_20240425_173900                           0.0940099  0.00883785  0.0161068  0.0643614                0.00883785\n",
      "GBM_grid_1_AutoML_1_20240425_173900_model_8              0.0947288  0.00897354  0.0175256  0.0666251                0.00897354\n",
      "DRF_1_AutoML_1_20240425_173900                           0.0948133  0.00898957  0.0168658  0.0670722                0.00898957\n",
      "GLM_1_AutoML_1_20240425_173900                           0.100496   0.0100994   0.026321   0.0690198                0.0100994\n",
      "[22 rows x 6 columns]\n",
      "\n",
      "My best model is saved to: /Users/yvankammelu/Documents/GitHub/GitHub-Cloned/Revenue-Radar/StackedEnsemble_AllModels_1_AutoML_1_20240425_173900.zip\n",
      "H2O session _sid_b2cc closed.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8y/9ttyks_n5f7_6c4hyq6_g50r0000gn/T/ipykernel_11616/3807178908.py:51: H2ODeprecationWarning: Deprecated, use ``h2o.cluster().shutdown()``.\n",
      "  h2o.shutdown(prompt=False)\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries for data handling and machine learning\n",
    "from dataclasses import dataclass\n",
    "import pandas as pd\n",
    "import h2o\n",
    "from h2o.automl import H2OAutoML\n",
    "\n",
    "# Create a data class to store my AutoML parameters\n",
    "@dataclass\n",
    "class H2OAutoMLParams:\n",
    "    df: pd.DataFrame  # This will hold my DataFrame\n",
    "    y: str            # This is my target column that I want to predict\n",
    "    path: str = \"./\"  # I will save my model here\n",
    "\n",
    "# Start my H2O session to initialize the H2O machine learning environment\n",
    "h2o.init(max_mem_size=\"4G\")  # I allocate 4 GB of memory to H2O; adjust if needed\n",
    "\n",
    "# Load my dataset from a CSV file into a Pandas DataFrame\n",
    "# I need to replace '/path/to/train_df.csv' with the actual path to the CSV on my local machine\n",
    "train_df = pd.read_csv(\"/Users/yvankammelu/Documents/Winter '24 Semester/INSY695 Part 2 Entreprise Data Science and ML in Production/train_df.csv\")\n",
    "\n",
    "# Now I'll set up my AutoML parameters, including the path to save the model\n",
    "# I already know that my target variable for prediction is 'at_least_one_conversion'\n",
    "params = H2OAutoMLParams(df=train_df, y='at_least_one_conversion')\n",
    "\n",
    "# Convert my pandas DataFrame into an H2OFrame which is necessary for H2O to process it\n",
    "h2o_df = h2o.H2OFrame(params.df)\n",
    "\n",
    "# Identify my features and target variable\n",
    "# 'at_least_one_conversion' is my target, so I remove it from the list of feature names\n",
    "x = h2o_df.columns\n",
    "x.remove(params.y)\n",
    "\n",
    "# Now I initialize AutoML, setting a limit on the number of models to control runtime\n",
    "# I choose a seed for reproducibility\n",
    "aml = H2OAutoML(max_models=20, seed=1, exclude_algos=[\"DeepLearning\"], verbosity=\"info\")\n",
    "\n",
    "# Train the model on my dataset\n",
    "aml.train(x=x, y=params.y, training_frame=h2o_df)\n",
    "\n",
    "# After training, I can view the leaderboard of models trained by AutoML\n",
    "# This shows me the best performing models on my data\n",
    "lb = aml.leaderboard\n",
    "print(lb.head(rows=lb.nrows))  # Print the leaderboard\n",
    "\n",
    "# I want to save the best performing model to use it later for predictions\n",
    "best_model = aml.leader\n",
    "best_model_path = best_model.download_mojo(path=params.path)\n",
    "print(f\"My best model is saved to: {best_model_path}\")\n",
    "\n",
    "# It's important to close the H2O connection after I'm done\n",
    "h2o.shutdown(prompt=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aml.train(x=x, y=params.y, training_frame=train)\n"
   ]
  }
 ],
 "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": "3.10.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}