AML_Project_Fan_AutoML_Apr18.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"n4ZGqvt57O30"},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from scipy import stats\n","pd.set_option('display.max_columns', None)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cE0D6gripnMC","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1713485115307,"user_tz":240,"elapsed":1989,"user":{"displayName":"Fan Bu","userId":"00225308341322915666"}},"outputId":"332bda00-32d2-48a0-daf0-99c073d72192"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n","/content/drive/My Drive/Applied ML/AML Group Project/data\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","import os\n","os.chdir(\"drive/My Drive/Applied ML/AML Group Project/data\")\n","!pwd"]},{"cell_type":"markdown","source":["# Load Data"],"metadata":{"id":"PCZguL_f8bE7"}},{"cell_type":"code","source":["target = \"target_status_binary\""],"metadata":{"id":"VNryVhRUDaiB"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nczUhL_7BCCb"},"outputs":[],"source":["#import the dataset\n","credit_overdue_processed_smote_dev = pd.read_csv('credit_overdue_processed_smote_dev.csv')\n","credit_overdue_processed_smote_test = pd.read_csv('credit_overdue_processed_smote_test.csv')"]},{"cell_type":"code","source":["def map_bool_to_int(df, col_name):\n","    df[col_name] = df[col_name].map({1: 'True', 0: 'False'})\n","    return df\n","\n","credit_overdue_processed_smote_dev = map_bool_to_int(credit_overdue_processed_smote_dev, target)\n","credit_overdue_processed_smote_test = map_bool_to_int(credit_overdue_processed_smote_test, target)"],"metadata":{"id":"gHGXOg8lDW4l"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["credit_overdue_processed_smote_dev"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":461},"id":"0lIYdgx7D456","executionInfo":{"status":"ok","timestamp":1713485126199,"user_tz":240,"elapsed":10,"user":{"displayName":"Fan Bu","userId":"00225308341322915666"}},"outputId":"79975498-4e61-4e73-c9d2-2b21c64837ef"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       Unnamed: 0            ID  CNT_CHILDREN  AMT_INCOME_TOTAL    DAYS_BIRTH  \\\n","0               0  5.126174e+06       0.00000      27000.000000 -14869.000000   \n","1               1  5.117643e+06       1.00000     135000.000000 -11101.000000   \n","2               2  5.113723e+06       0.00000      90000.000000 -19551.000000   \n","3               3  5.091170e+06       0.00000      81000.000000  -9796.000000   \n","4               4  5.044667e+06       0.00000      76500.000000 -22302.000000   \n","...           ...           ...           ...               ...           ...   \n","49347       49347  5.024389e+06       0.00000     117000.000000 -13494.000000   \n","49348       49348  5.023287e+06       0.00000     153873.589613 -22281.437370   \n","49349       49349  5.024022e+06       1.69826     315000.000000 -12122.000699   \n","49350       49350  5.030044e+06       0.85553     112500.000000 -10565.302490   \n","49351       49351  5.149469e+06       1.00000     135000.000000 -11376.334389   \n","\n","       DAYS_EMPLOYED  FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL  \\\n","0       -5067.000000         1.0          1.00000     0.00000     0.00000   \n","1         -96.000000         1.0          1.00000     0.00000     0.00000   \n","2       -1927.000000         1.0          0.00000     1.00000     1.00000   \n","3        -292.000000         1.0          1.00000     0.00000     0.00000   \n","4           0.000000         1.0          0.00000     0.00000     0.00000   \n","...              ...         ...              ...         ...         ...   \n","49347   -1281.000000         1.0          0.00000     0.00000     0.00000   \n","49348    -210.826293         1.0          0.00000     0.00000     0.00000   \n","49349   -1478.087007         1.0          0.00000     0.69826     0.00000   \n","49350   -3101.417656         1.0          0.14447     0.14447     0.85553   \n","49351   -2427.611483         1.0          0.00000     0.00000     0.00000   \n","\n","       CNT_FAM_MEMBERS  unemployed  CODE_GENDER_F  CODE_GENDER_M  \\\n","0              2.00000    0.000000       1.000000       0.000000   \n","1              3.00000    0.000000       0.000000       1.000000   \n","2              2.00000    0.000000       0.000000       1.000000   \n","3              1.00000    0.000000       1.000000       0.000000   \n","4              2.00000    1.000000       1.000000       0.000000   \n","...                ...         ...            ...            ...   \n","49347          2.00000    0.000000       1.000000       0.000000   \n","49348          2.00000    0.805869       0.805869       0.194131   \n","49349          3.69826    0.000000       0.000000       1.000000   \n","49350          2.85553    0.000000       1.000000       0.000000   \n","49351          3.00000    0.000000       1.000000       0.000000   \n","\n","       FLAG_OWN_CAR_N  FLAG_OWN_CAR_Y  FLAG_OWN_REALTY_N  FLAG_OWN_REALTY_Y  \\\n","0            1.000000        0.000000           1.000000           0.000000   \n","1            1.000000        0.000000           1.000000           0.000000   \n","2            1.000000        0.000000           0.000000           1.000000   \n","3            1.000000        0.000000           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     0.000000   \n","4                                   0.000000                    1.000000   \n","...                                      ...                         ...   \n","49347                               0.000000                    0.000000   \n","49348                               0.194131                    0.805869   \n","49349                               0.000000                    0.000000   \n","49350                               0.855530                    0.000000   \n","49351                               1.000000                    0.000000   \n","\n","       NAME_INCOME_TYPE_State servant  NAME_INCOME_TYPE_Student  \\\n","0                                 0.0                       0.0   \n","1                                 0.0                       0.0   \n","2                                 0.0                       0.0   \n","3                                 0.0                       0.0   \n","4                                 0.0                      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NAME_EDUCATION_TYPE_Incomplete higher  \\\n","0                                        0.0   \n","1                                        0.0   \n","2                                        0.0   \n","3                                        0.0   \n","4                                        0.0   \n","...                                      ...   \n","49347                                    0.0   \n","49348                                    0.0   \n","49349                                    0.0   \n","49350                                    0.0   \n","49351                                    0.0   \n","\n","       NAME_EDUCATION_TYPE_Lower secondary  \\\n","0                                 0.000000   \n","1                                 0.000000   \n","2                                 0.000000   \n","3                                 0.000000   \n","4                                 0.000000   \n","...                                    ...   \n","49347                      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\n","\n","       NAME_FAMILY_STATUS_Civil marriage  NAME_FAMILY_STATUS_Married  \\\n","0                               1.000000                    0.000000   \n","1                               0.000000                    1.000000   \n","2                               0.000000                    1.000000   \n","3                               0.000000                    0.000000   \n","4                               0.000000                    1.000000   \n","...                                  ...                         ...   \n","49347                           0.000000                    1.000000   \n","49348                           0.194131                    0.805869   \n","49349                           0.000000                    1.000000   \n","49350                           0.855530                    0.144470   \n","49351                           0.000000                    1.000000   \n","\n","       NAME_FAMILY_STATUS_Separated  NAME_FAMILY_STATUS_Single / not married  \\\n","0                               0.0                                      0.0   \n","1                               0.0                                      0.0   \n","2                               0.0                                      0.0   \n","3                               0.0                                      1.0   \n","4                               0.0                                      0.0   \n","...                             ...                                      ...   \n","49347                           0.0                                      0.0   \n","49348                           0.0                                      0.0   \n","49349                           0.0                                      0.0   \n","49350                           0.0                                      0.0   \n","49351                           0.0                                      0.0   \n","\n","       NAME_FAMILY_STATUS_Widow  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occupation_category_3  \\\n","0                      False                  False                  False   \n","1                      False                  False                  False   \n","2                      False                  False                  False   \n","3                      False                  False                  False   \n","4                      False                  False                  False   \n","...                      ...                    ...                    ...   \n","49347                   True                  False                  False   \n","49348                  False                   True                  False   \n","49349                  False                  False                  False   \n","49350                  False                  False                  False   \n","49351                   True                  False                  False   \n","\n","       occupation_category_4  occupation_category_5  occupation_category_6  \\\n","0                       True                  False                  False   \n","1                      False                  False                  False   \n","2                      False                  False                  False   \n","3                      False                  False                  False   \n","4                      False                  False                  False   \n","...                      ...                    ...                    ...   \n","49347                  False                  False                  False   \n","49348                  False                  False                  False   \n","49349                   True                  False                  False   \n","49350                  False                  False                  False   \n","49351                  False                  False                  False   \n","\n","       occupation_category_7  occupation_category_8 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<th>FLAG_OWN_CAR_Y</th>\n","      <th>FLAG_OWN_REALTY_N</th>\n","      <th>FLAG_OWN_REALTY_Y</th>\n","      <th>NAME_INCOME_TYPE_Commercial associate</th>\n","      <th>NAME_INCOME_TYPE_Pensioner</th>\n","      <th>NAME_INCOME_TYPE_State servant</th>\n","      <th>NAME_INCOME_TYPE_Student</th>\n","      <th>NAME_INCOME_TYPE_Working</th>\n","      <th>NAME_EDUCATION_TYPE_Academic degree</th>\n","      <th>NAME_EDUCATION_TYPE_Higher education</th>\n","      <th>NAME_EDUCATION_TYPE_Incomplete higher</th>\n","      <th>NAME_EDUCATION_TYPE_Lower secondary</th>\n","      <th>NAME_EDUCATION_TYPE_Secondary / secondary special</th>\n","      <th>NAME_FAMILY_STATUS_Civil marriage</th>\n","      <th>NAME_FAMILY_STATUS_Married</th>\n","      <th>NAME_FAMILY_STATUS_Separated</th>\n","      <th>NAME_FAMILY_STATUS_Single / not married</th>\n","      <th>NAME_FAMILY_STATUS_Widow</th>\n","      <th>NAME_HOUSING_TYPE_Co-op apartment</th>\n","      <th>NAME_HOUSING_TYPE_House / apartment</th>\n","      <th>NAME_HOUSING_TYPE_Municipal apartment</th>\n","      <th>NAME_HOUSING_TYPE_Office apartment</th>\n","      <th>NAME_HOUSING_TYPE_Rented apartment</th>\n","      <th>NAME_HOUSING_TYPE_With parents</th>\n","      <th>occupation_category_0</th>\n","      <th>occupation_category_1</th>\n","      <th>occupation_category_2</th>\n","      <th>occupation_category_3</th>\n","      <th>occupation_category_4</th>\n","      <th>occupation_category_5</th>\n","      <th>occupation_category_6</th>\n","      <th>occupation_category_7</th>\n","      <th>occupation_category_8</th>\n","      <th>target_status_binary</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>5.126174e+06</td>\n","      <td>0.00000</td>\n","      <td>27000.000000</td>\n","      <td>-14869.000000</td>\n","      <td>-5067.000000</td>\n","      <td>1.0</td>\n","      <td>1.00000</td>\n","      <td>0.00000</td>\n","      <td>0.00000</td>\n","      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</tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>5.117643e+06</td>\n","      <td>1.00000</td>\n","      <td>135000.000000</td>\n","      <td>-11101.000000</td>\n","      <td>-96.000000</td>\n","      <td>1.0</td>\n","      <td>1.00000</td>\n","      <td>0.00000</td>\n","      <td>0.00000</td>\n","      <td>3.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.00000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","      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'FLAG_OWN_CAR_N',\n"," 'FLAG_OWN_CAR_Y',\n"," 'FLAG_OWN_REALTY_N',\n"," 'FLAG_OWN_REALTY_Y',\n"," 'NAME_INCOME_TYPE_Commercial associate',\n"," 'NAME_INCOME_TYPE_Pensioner',\n"," 'NAME_INCOME_TYPE_State servant',\n"," 'NAME_INCOME_TYPE_Student',\n"," 'NAME_INCOME_TYPE_Working',\n"," 'NAME_EDUCATION_TYPE_Academic degree',\n"," 'NAME_EDUCATION_TYPE_Higher education',\n"," 'NAME_EDUCATION_TYPE_Incomplete higher',\n"," 'NAME_EDUCATION_TYPE_Lower secondary',\n"," 'NAME_EDUCATION_TYPE_Secondary / secondary special',\n"," 'NAME_FAMILY_STATUS_Civil marriage',\n"," 'NAME_FAMILY_STATUS_Married',\n"," 'NAME_FAMILY_STATUS_Separated',\n"," 'NAME_FAMILY_STATUS_Single / not married',\n"," 'NAME_FAMILY_STATUS_Widow',\n"," 'NAME_HOUSING_TYPE_Co-op apartment',\n"," 'NAME_HOUSING_TYPE_House / apartment',\n"," 'NAME_HOUSING_TYPE_Municipal apartment',\n"," 'NAME_HOUSING_TYPE_Office apartment',\n"," 'NAME_HOUSING_TYPE_Rented apartment',\n"," 'NAME_HOUSING_TYPE_With parents',\n"," 'occupation_category_0',\n"," 'occupation_category_1',\n"," 'occupation_category_2',\n"," 'occupation_category_3',\n"," 'occupation_category_4',\n"," 'occupation_category_5',\n"," 'occupation_category_6',\n"," 'occupation_category_7',\n"," 'occupation_category_8']"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["X_dev = credit_overdue_processed_smote_dev.iloc[:,2:-1]\n","y_dev = credit_overdue_processed_smote_dev['target_status_binary']"],"metadata":{"id":"XiXJMJNc7mTr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_test = credit_overdue_processed_smote_test.iloc[:,2:-1]\n","y_test = credit_overdue_processed_smote_test['target_status_binary']"],"metadata":{"id":"aV5gm0jT8GGH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_dev.shape, y_dev.shape, X_test.shape, y_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dqx_Fdpk8LMl","executionInfo":{"status":"ok","timestamp":1713485126200,"user_tz":240,"elapsed":7,"user":{"displayName":"Fan Bu","userId":"00225308341322915666"}},"outputId":"041df0cd-da0b-4fb1-ff9b-99af973c897b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((49352, 46), (49352,), (6986, 46), (6986,))"]},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","source":["# AutoML\n","\n","[H2O.ai](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html)"],"metadata":{"id":"WeSYYFDm8cwi"}},{"cell_type":"code","source":["!pip install h2o"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eitA7JC4nyzF","outputId":"d1ee8435-a9fc-4402-ae4f-cc7d671edb3a","executionInfo":{"status":"ok","timestamp":1713485144269,"user_tz":240,"elapsed":18074,"user":{"displayName":"Fan Bu","userId":"00225308341322915666"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting h2o\n","  Downloading h2o-3.46.0.1-py2.py3-none-any.whl (265.6 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.6/265.6 MB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from h2o) (2.31.0)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from h2o) (0.9.0)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->h2o) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->h2o) (3.7)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->h2o) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->h2o) (2024.2.2)\n","Installing collected packages: h2o\n","Successfully installed h2o-3.46.0.1\n"]}]},{"cell_type":"code","source":["import h2o\n","from h2o.automl import H2OAutoML\n","\n","h2o.init()\n","\n","# Convert the pandas DataFrame to an H2OFrame\n","hf = h2o.H2OFrame(credit_overdue_processed_smote_dev)\n","\n","# Specify the feature names and target variable name\n","x = features\n","y = \"target_status_binary\"\n","\n","# # Convert the response column to a factor (H2O requires the response to be categorical for classification)\n","hf[y] = hf[y].asfactor()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":556},"id":"C73osFY-n_mo","outputId":"c9087a27-7957-4ddc-89b1-243a2d1938e1","executionInfo":{"status":"ok","timestamp":1713485174928,"user_tz":240,"elapsed":30662,"user":{"displayName":"Fan Bu","userId":"00225308341322915666"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","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 \"11.0.22\" 2024-01-16; OpenJDK Runtime Environment (build 11.0.22+7-post-Ubuntu-0ubuntu222.04.1); OpenJDK 64-Bit Server VM (build 11.0.22+7-post-Ubuntu-0ubuntu222.04.1, mixed mode, sharing)\n","  Starting server from /usr/local/lib/python3.10/dist-packages/h2o/backend/bin/h2o.jar\n","  Ice root: /tmp/tmpfm9ccvpn\n","  JVM stdout: /tmp/tmpfm9ccvpn/h2o_unknownUser_started_from_python.out\n","  JVM stderr: /tmp/tmpfm9ccvpn/h2o_unknownUser_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"]},{"output_type":"display_data","data":{"text/plain":["--------------------------  -----------------------------------------------------------------------------------------\n","H2O_cluster_uptime:         06 secs\n","H2O_cluster_timezone:       Etc/UTC\n","H2O_data_parsing_timezone:  UTC\n","H2O_cluster_version:        3.46.0.1\n","H2O_cluster_version_age:    1 month and 5 days\n","H2O_cluster_name:           H2O_from_python_unknownUser_65qnnz\n","H2O_cluster_total_nodes:    1\n","H2O_cluster_free_memory:    3.170 Gb\n","H2O_cluster_total_cores:    2\n","H2O_cluster_allowed_cores:  2\n","H2O_cluster_status:         locked, healthy\n","H2O_connection_url:         http://127.0.0.1:54321\n","H2O_connection_proxy:       {\"http\": null, \"https\": null, \"colab_language_server\": \"/usr/colab/bin/language_service\"}\n","H2O_internal_security:      False\n","Python_version:             3.10.12 final\n","--------------------------  -----------------------------------------------------------------------------------------"],"text/html":["\n","<style>\n","\n","#h2o-table-1.h2o-container {\n","  overflow-x: auto;\n","}\n","#h2o-table-1 .h2o-table {\n","  /* width: 100%; */\n","  margin-top: 1em;\n","  margin-bottom: 1em;\n","}\n","#h2o-table-1 .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-1 .h2o-table thead {\n","  white-space: nowrap; \n","  position: sticky;\n","  top: 0;\n","  box-shadow: 0 -1px inset;\n","}\n","#h2o-table-1 .h2o-table tbody {\n","  overflow: auto;\n","}\n","#h2o-table-1 .h2o-table th,\n","#h2o-table-1 .h2o-table td {\n","  text-align: right;\n","  /* border: 1px solid; */\n","}\n","#h2o-table-1 .h2o-table tr:nth-child(even) {\n","  /* background: #F5F5F5 */\n","}\n","\n","</style>      \n","<div id=\"h2o-table-1\" class=\"h2o-container\">\n","  <table class=\"h2o-table\">\n","    <caption></caption>\n","    <thead></thead>\n","    <tbody><tr><td>H2O_cluster_uptime:</td>\n","<td>06 secs</td></tr>\n","<tr><td>H2O_cluster_timezone:</td>\n","<td>Etc/UTC</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 5 days</td></tr>\n","<tr><td>H2O_cluster_name:</td>\n","<td>H2O_from_python_unknownUser_65qnnz</td></tr>\n","<tr><td>H2O_cluster_total_nodes:</td>\n","<td>1</td></tr>\n","<tr><td>H2O_cluster_free_memory:</td>\n","<td>3.170 Gb</td></tr>\n","<tr><td>H2O_cluster_total_cores:</td>\n","<td>2</td></tr>\n","<tr><td>H2O_cluster_allowed_cores:</td>\n","<td>2</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, \"colab_language_server\": \"/usr/colab/bin/language_service\"}</td></tr>\n","<tr><td>H2O_internal_security:</td>\n","<td>False</td></tr>\n","<tr><td>Python_version:</td>\n","<td>3.10.12 final</td></tr></tbody>\n","  </table>\n","</div>\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"]}]},{"cell_type":"code","source":["# Initialize and train the AutoML model\n","# Here, you can specify max_runtime_secs according to your preference or max_models to limit the number of models\n","aml = H2OAutoML(max_runtime_secs=180,\n","                nfolds=5,\n","                seed=1,\n","                stopping_metric='AUTO',\n","                sort_metric = \"AUC\",\n","                project_name=\"automl_credit_card_approval\")\n","%time aml.train(x=x, y=y, training_frame=hf)\n","\n","# View the AutoML Leaderboard\n","lb = aml.leaderboard\n","print(lb.head(rows=lb.nrows))  # Print all rows instead of default (10 rows)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cK3-aSnNo3Zy","outputId":"73cd7f9a-19d6-4f1f-beea-e863852492fd","executionInfo":{"status":"ok","timestamp":1713485360398,"user_tz":240,"elapsed":185472,"user":{"displayName":"Fan Bu","userId":"00225308341322915666"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["AutoML progress: |\n","00:06:15.395: _train param, Dropping bad and constant columns: [FLAG_MOBIL]\n","\n","██████████████████\n","00:07:07.163: _train param, Dropping bad and constant columns: [FLAG_MOBIL]\n","\n","████████████████████\n","00:08:03.7: _train param, Dropping bad and constant columns: [FLAG_MOBIL]\n","\n","████████████████\n","00:08:50.205: _train param, Dropping unused columns: [FLAG_MOBIL]\n","\n","██\n","00:08:55.189: _train param, Dropping bad and constant columns: [FLAG_MOBIL]\n","\n","██\n","00:09:01.697: _train param, Dropping bad and constant columns: [FLAG_MOBIL]\n","\n","█\n","00:09:06.486: _train param, Dropping bad and constant columns: [FLAG_MOBIL]\n","\n","█\n","00:09:09.286: _train param, Dropping bad and constant columns: [FLAG_MOBIL]\n","\n","█\n","00:09:11.924: _train param, Dropping bad and constant columns: [FLAG_MOBIL]\n","\n","██| (done) 100%\n","\n","00:09:15.186: _train param, Dropping unused columns: [FLAG_MOBIL]\n","\n","CPU times: user 2.77 s, sys: 276 ms, total: 3.05 s\n","Wall time: 3min 5s\n","model_id                                                     auc    logloss     aucpr    mean_per_class_error      rmse        mse\n","StackedEnsemble_BestOfFamily_2_AutoML_1_20240419_00615  0.961276   0.229974  0.97127                0.0838669  0.258137  0.0666348\n","XGBoost_1_AutoML_1_20240419_00615                       0.959449   0.239431  0.97003                0.0864403  0.263914  0.0696506\n","StackedEnsemble_BestOfFamily_1_AutoML_1_20240419_00615  0.959444   0.232567  0.970027               0.0863187  0.260058  0.06763\n","GBM_1_AutoML_1_20240419_00615                           0.937279   0.397381  0.952836               0.120259   0.343987  0.118327\n","DRF_1_AutoML_1_20240419_00615                           0.928979   1.13075   0.931074               0.130653   0.312709  0.0977872\n","XGBoost_2_AutoML_1_20240419_00615                       0.894942   0.492458  0.921445               0.168909   0.39693   0.157553\n","GBM_4_AutoML_1_20240419_00615                           0.865923   0.64847   0.898546               0.202626   0.4772    0.22772\n","GBM_3_AutoML_1_20240419_00615                           0.84171    0.64081   0.88422                0.216324   0.473318  0.22403\n","GBM_2_AutoML_1_20240419_00615                           0.839253   0.63245   0.881526               0.221572   0.468945  0.219909\n","GLM_1_AutoML_1_20240419_00615                           0.760871   0.571466  0.81564                0.388231   0.440156  0.193737\n","[10 rows x 7 columns]\n","\n"]}]},{"cell_type":"code","source":["# Get the best model\n","best_model = aml.leader\n","\n","# Convert the pandas DataFrame to an H2OFrame\n","val_hf = h2o.H2OFrame(X_test)\n","\n","# Predict on the validation set\n","val_preds = best_model.predict(val_hf)\n","\n","# Convert predictions and true labels to pandas DataFrame for evaluation with sklearn\n","predctions_df = val_preds.as_data_frame()\n","y_pred = predctions_df.values[:,0].astype(int)\n","y_true = y_test.apply(lambda x: 1 if x == \"True\" else 0).values\n","\n","from sklearn.metrics import accuracy_score\n","\n","acc = accuracy_score(y_pred, y_true)\n","print(\"ACCURACY: \", acc)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"53F2z1IcJZEE","executionInfo":{"status":"ok","timestamp":1713485531192,"user_tz":240,"elapsed":1046,"user":{"displayName":"Fan Bu","userId":"00225308341322915666"}},"outputId":"73114842-8d17-43c5-fb9f-a883b31314ce"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n","stackedensemble prediction progress: |███████████████████████████████████████████| (done) 100%\n","ACCURACY:  0.8660177497852849\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using:\n","\n","with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True):\n","    pandas_df = h2o_df.as_data_frame()\n","\n","  warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using\"\n"]}]},{"cell_type":"markdown","source":["## best model"],"metadata":{"id":"6-6osVFMrcN9"}},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, classification_report\n","\n","cm = confusion_matrix(y_true, y_pred)\n","\n","report = classification_report(y_true, y_pred)\n","\n","# Print the classification report\n","print(report)\n","\n","# Print accuracy, precision, recall, and F1 score\n","print(f'Accuracy: {accuracy_score(y_true, y_pred)}')\n","print(f'Precision: {precision_score(y_true, y_pred)}')\n","print(f'Recall: {recall_score(y_true, y_pred)}')\n","print(f'F1 Score: {f1_score(y_true, y_pred)}')\n","\n","\n","fig, ax = plt.subplots(figsize=(8, 6))\n","sns.heatmap(cm, annot=True, ax=ax, cmap='Blues', fmt='.2f')\n","ax.set_title('Confusion Matrix')\n","ax.set_xlabel('Predicted Labels')\n","ax.set_ylabel('True Labels')\n","ax.xaxis.set_ticklabels(['False', 'True'])\n","ax.yaxis.set_ticklabels(['False', 'True'])\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":790},"id":"Mti_v2oGrbOy","executionInfo":{"status":"ok","timestamp":1713485571765,"user_tz":240,"elapsed":658,"user":{"displayName":"Fan Bu","userId":"00225308341322915666"}},"outputId":"4b20405b-6b9f-4cbd-a5d6-1eaa5717393a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.91      0.94      0.93      6169\n","           1       0.41      0.31      0.35       817\n","\n","    accuracy                           0.87      6986\n","   macro avg       0.66      0.63      0.64      6986\n","weighted avg       0.85      0.87      0.86      6986\n","\n","Accuracy: 0.8660177497852849\n","Precision: 0.4060031595576619\n","Recall: 0.31456548347613217\n","F1 Score: 0.3544827586206896\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]}]}