hyperparameter_tuning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Hyperparameter Tuning using HyperDrive\n",
        "\n",
        "Import Dependencies. In the cell below, import all the dependencies that I will need to complete the project."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core import Workspace, Experiment, ScriptRunConfig, Environment\n",
        "\n",
        "from azureml.core.dataset import Dataset\n",
        "\n",
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy,\\\n",
        "                                     choice, randint, PrimaryMetricGoal, HyperDriveConfig\n",
        "\n",
        "\n",
        "from azureml.widgets import RunDetails"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "gather": {
          "logged": 1708168206280
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset\n",
        "\n",
        "Get data. In the cell below, I write code to access the data I will be using in this project."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "ws = Workspace.from_config(path=\"/\")\n",
        "\n",
        "experiment_name = 'HyperTune'\n",
        "\n",
        "experiment=Experiment(ws, experiment_name)"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "gather": {
          "logged": 1708168206731
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "found = False\n",
        "key = \"Employee Attrition\"\n",
        "description_text = \"IBM HR Analytics Employee Attrition & Performance\"\n",
        "\n",
        "if key in ws.datasets.keys(): \n",
        "        found = True\n",
        "        dataset = ws.datasets[key] \n",
        "\n",
        "if not found:\n",
        "        url = \"https://raw.githubusercontent.com/eljandoubi/Azure-Machine-Learning-Engineer/main/attrition-dataset.csv\"\n",
        "        dataset = Dataset.Tabular.from_delimited_files(url)        \n",
        "        dataset = dataset.register(workspace=ws,\n",
        "                                   name=key,description=description_text)\n",
        "\n",
        "\n",
        "df = dataset.to_pandas_dataframe()\n",
        "df.describe()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "               Age    DailyRate  DistanceFromHome    Education  EmployeeCount  \\\ncount  1470.000000  1470.000000       1470.000000  1470.000000         1470.0   \nmean     36.923810   802.485714          9.192517     2.912925            1.0   \nstd       9.135373   403.509100          8.106864     1.024165            0.0   \nmin      18.000000   102.000000          1.000000     1.000000            1.0   \n25%      30.000000   465.000000          2.000000     2.000000            1.0   \n50%      36.000000   802.000000          7.000000     3.000000            1.0   \n75%      43.000000  1157.000000         14.000000     4.000000            1.0   \nmax      60.000000  1499.000000         29.000000     5.000000            1.0   \n\n       EmployeeNumber  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\ncount     1470.000000              1470.000000  1470.000000     1470.000000   \nmean      1024.865306                 2.721769    65.891156        2.729932   \nstd        602.024335                 1.093082    20.329428        0.711561   \nmin          1.000000                 1.000000    30.000000        1.000000   \n25%        491.250000                 2.000000    48.000000        2.000000   \n50%       1020.500000                 3.000000    66.000000        3.000000   \n75%       1555.750000                 4.000000    83.750000        3.000000   \nmax       2068.000000                 4.000000   100.000000        4.000000   \n\n          JobLevel  ...  RelationshipSatisfaction  StandardHours  \\\ncount  1470.000000  ...               1470.000000         1470.0   \nmean      2.063946  ...                  2.712245           80.0   \nstd       1.106940  ...                  1.081209            0.0   \nmin       1.000000  ...                  1.000000           80.0   \n25%       1.000000  ...                  2.000000           80.0   \n50%       2.000000  ...                  3.000000           80.0   \n75%       3.000000  ...                  4.000000           80.0   \nmax       5.000000  ...                  4.000000           80.0   \n\n       StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\ncount       1470.000000        1470.000000            1470.000000   \nmean           0.793878          11.279592               2.799320   \nstd            0.852077           7.780782               1.289271   \nmin            0.000000           0.000000               0.000000   \n25%            0.000000           6.000000               2.000000   \n50%            1.000000          10.000000               3.000000   \n75%            1.000000          15.000000               3.000000   \nmax            3.000000          40.000000               6.000000   \n\n       WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\ncount      1470.000000     1470.000000         1470.000000   \nmean          2.761224        7.008163            4.229252   \nstd           0.706476        6.126525            3.623137   \nmin           1.000000        0.000000            0.000000   \n25%           2.000000        3.000000            2.000000   \n50%           3.000000        5.000000            3.000000   \n75%           3.000000        9.000000            7.000000   \nmax           4.000000       40.000000           18.000000   \n\n       YearsSinceLastPromotion  YearsWithCurrManager  \ncount              1470.000000           1470.000000  \nmean                  2.187755              4.123129  \nstd                   3.222430              3.568136  \nmin                   0.000000              0.000000  \n25%                   0.000000              2.000000  \n50%                   1.000000              3.000000  \n75%                   3.000000              7.000000  \nmax                  15.000000             17.000000  \n\n[8 rows x 26 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age</th>\n      <th>DailyRate</th>\n      <th>DistanceFromHome</th>\n      <th>Education</th>\n      <th>EmployeeCount</th>\n      <th>EmployeeNumber</th>\n      <th>EnvironmentSatisfaction</th>\n      <th>HourlyRate</th>\n      <th>JobInvolvement</th>\n      <th>JobLevel</th>\n      <th>...</th>\n      <th>RelationshipSatisfaction</th>\n      <th>StandardHours</th>\n      <th>StockOptionLevel</th>\n      <th>TotalWorkingYears</th>\n      <th>TrainingTimesLastYear</th>\n      <th>WorkLifeBalance</th>\n      <th>YearsAtCompany</th>\n      <th>YearsInCurrentRole</th>\n      <th>YearsSinceLastPromotion</th>\n      <th>YearsWithCurrManager</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>1470.0</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>...</td>\n      <td>1470.000000</td>\n      <td>1470.0</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n      <td>1470.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>36.923810</td>\n      <td>802.485714</td>\n      <td>9.192517</td>\n      <td>2.912925</td>\n      <td>1.0</td>\n      <td>1024.865306</td>\n      <td>2.721769</td>\n      <td>65.891156</td>\n      <td>2.729932</td>\n      <td>2.063946</td>\n      <td>...</td>\n      <td>2.712245</td>\n      <td>80.0</td>\n      <td>0.793878</td>\n      <td>11.279592</td>\n      <td>2.799320</td>\n      <td>2.761224</td>\n      <td>7.008163</td>\n      <td>4.229252</td>\n      <td>2.187755</td>\n      <td>4.123129</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>9.135373</td>\n      <td>403.509100</td>\n      <td>8.106864</td>\n      <td>1.024165</td>\n      <td>0.0</td>\n      <td>602.024335</td>\n      <td>1.093082</td>\n      <td>20.329428</td>\n      <td>0.711561</td>\n      <td>1.106940</td>\n      <td>...</td>\n      <td>1.081209</td>\n      <td>0.0</td>\n      <td>0.852077</td>\n      <td>7.780782</td>\n      <td>1.289271</td>\n      <td>0.706476</td>\n      <td>6.126525</td>\n      <td>3.623137</td>\n      <td>3.222430</td>\n      <td>3.568136</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>18.000000</td>\n      <td>102.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.0</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>30.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>80.0</td>\n      <td>0.000000</td>\n      <td>0.000000</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>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>30.000000</td>\n      <td>465.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>1.0</td>\n      <td>491.250000</td>\n      <td>2.000000</td>\n      <td>48.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>2.000000</td>\n      <td>80.0</td>\n      <td>0.000000</td>\n      <td>6.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>36.000000</td>\n      <td>802.000000</td>\n      <td>7.000000</td>\n      <td>3.000000</td>\n      <td>1.0</td>\n      <td>1020.500000</td>\n      <td>3.000000</td>\n      <td>66.000000</td>\n      <td>3.000000</td>\n      <td>2.000000</td>\n      <td>...</td>\n      <td>3.000000</td>\n      <td>80.0</td>\n      <td>1.000000</td>\n      <td>10.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>5.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>43.000000</td>\n      <td>1157.000000</td>\n      <td>14.000000</td>\n      <td>4.000000</td>\n      <td>1.0</td>\n      <td>1555.750000</td>\n      <td>4.000000</td>\n      <td>83.750000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>...</td>\n      <td>4.000000</td>\n      <td>80.0</td>\n      <td>1.000000</td>\n      <td>15.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>9.000000</td>\n      <td>7.000000</td>\n      <td>3.000000</td>\n      <td>7.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>60.000000</td>\n      <td>1499.000000</td>\n      <td>29.000000</td>\n      <td>5.000000</td>\n      <td>1.0</td>\n      <td>2068.000000</td>\n      <td>4.000000</td>\n      <td>100.000000</td>\n      <td>4.000000</td>\n      <td>5.000000</td>\n      <td>...</td>\n      <td>4.000000</td>\n      <td>80.0</td>\n      <td>3.000000</td>\n      <td>40.000000</td>\n      <td>6.000000</td>\n      <td>4.000000</td>\n      <td>40.000000</td>\n      <td>18.000000</td>\n      <td>15.000000</td>\n      <td>17.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 26 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "gather": {
          "logged": 1708168209787
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_name = \"automl-vs-hpyer\"\n",
        "\n",
        "try:\n",
        "    cpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n",
        "    print('Found existing compute target, using it!')\n",
        "    \n",
        "except ComputeTargetException:\n",
        "    print('Creating a new compute target!')\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',min_nodes=1, max_nodes=8)\n",
        "    cpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n",
        "    \n",
        "cpu_cluster.wait_for_completion(show_output=True)\n",
        " \n",
        "cpu_cluster.get_status().serialize()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Found existing compute target, using it!\nSucceeded\nAmlCompute wait for completion finished\n\nMinimum number of nodes requested have been provisioned\n"
        },
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "{'currentNodeCount': 1,\n 'targetNodeCount': 1,\n 'nodeStateCounts': {'preparingNodeCount': 0,\n  'runningNodeCount': 0,\n  'idleNodeCount': 1,\n  'unusableNodeCount': 0,\n  'leavingNodeCount': 0,\n  'preemptedNodeCount': 0},\n 'allocationState': 'Steady',\n 'allocationStateTransitionTime': '2024-02-17T10:45:21.420000+00:00',\n 'errors': None,\n 'creationTime': '2024-02-17T08:24:17.056201+00:00',\n 'modifiedTime': '2024-02-17T08:24:26.830859+00:00',\n 'provisioningState': 'Succeeded',\n 'provisioningStateTransitionTime': None,\n 'scaleSettings': {'minNodeCount': 1,\n  'maxNodeCount': 8,\n  'nodeIdleTimeBeforeScaleDown': 'PT1800S'},\n 'vmPriority': 'Dedicated',\n 'vmSize': 'STANDARD_D2_V2'}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1708168210300
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperdrive Configuration\n",
        "\n",
        "Here I will set configuration for different parameters for our hyperdrive run.\n",
        "\n",
        "I will use `RandomParameterSampling` as the Sampling method, `BanditPolicy` as the Termination policy, and `SKLearn estimator` with the Primary metric as `AUC_weighted`."
      ],
      "metadata": {
        "collapsed": true,
        "gather": {
          "logged": 1598531923519
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "env = Environment(name='hyper_env')\n",
        "\n",
        "# Specify Python dependencies\n",
        "env.python.conda_dependencies.add_pip_package('pandas')\n",
        "env.python.conda_dependencies.add_pip_package('scikit-learn')\n",
        "\n",
        "# Register the environment in your workspace\n",
        "env.register(workspace=ws)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "{\n    \"assetId\": \"azureml://locations/southcentralus/workspaces/4b9627bf-2c26-4525-823d-d4b80da461db/environments/hyper_env/versions/1\",\n    \"databricks\": {\n        \"eggLibraries\": [],\n        \"jarLibraries\": [],\n        \"mavenLibraries\": [],\n        \"pypiLibraries\": [],\n        \"rcranLibraries\": []\n    },\n    \"docker\": {\n        \"arguments\": [],\n        \"baseDockerfile\": null,\n        \"baseImage\": \"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:20230509.v1\",\n        \"baseImageRegistry\": {\n            \"address\": null,\n            \"password\": null,\n            \"registryIdentity\": null,\n            \"username\": null\n        },\n        \"buildContext\": null,\n        \"enabled\": false,\n        \"platform\": {\n            \"architecture\": \"amd64\",\n            \"os\": \"Linux\"\n        },\n        \"sharedVolumes\": true,\n        \"shmSize\": null\n    },\n    \"environmentVariables\": {\n        \"EXAMPLE_ENV_VAR\": \"EXAMPLE_VALUE\"\n    },\n    \"inferencingStackVersion\": null,\n    \"name\": \"hyper_env\",\n    \"python\": {\n        \"baseCondaEnvironment\": null,\n        \"condaDependencies\": {\n            \"channels\": [\n                \"anaconda\",\n                \"conda-forge\"\n            ],\n            \"dependencies\": [\n                \"python=3.8.13\",\n                {\n                    \"pip\": [\n                        \"azureml-defaults\",\n                        \"pandas\",\n                        \"scikit-learn\"\n                    ]\n                }\n            ],\n            \"name\": \"project_environment\"\n        },\n        \"condaDependenciesFile\": null,\n        \"interpreterPath\": \"python\",\n        \"userManagedDependencies\": false\n    },\n    \"r\": null,\n    \"spark\": {\n        \"packages\": [],\n        \"precachePackages\": true,\n        \"repositories\": []\n    },\n    \"version\": \"1\"\n}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1708168210508
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating an early termination policy.\n",
        "early_termination_policy = BanditPolicy(slack_factor=0.1,evaluation_interval=3)\n",
        "\n",
        "#Creating the different params that will be used during training\n",
        "param_sampling = RandomParameterSampling({\"--criterion\": choice(\"gini\", \"entropy\"),\n",
        "                                          \"--bootstrap\": choice(\"True\", \"False\"), \n",
        "                                          \"--max_depth\": randint(10)})\n",
        "\n",
        "#Creating estimator and hyperdrive config\n",
        "src = ScriptRunConfig(source_directory=\".\", script=\"train.py\",\n",
        "                            compute_target=cpu_cluster,environment=env)\n",
        "\n",
        "hyperdrive_run_config = HyperDriveConfig(hyperparameter_sampling=param_sampling,\n",
        "                                         policy=early_termination_policy, \n",
        "                                         primary_metric_name=\"AUC_weighted\", \n",
        "                                         primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n",
        "                                         max_total_runs=8, \n",
        "                                         max_concurrent_runs=2, \n",
        "                                         run_config=src)"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "gather": {
          "logged": 1708168210606
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hyperdrive_run = experiment.submit(hyperdrive_run_config)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "gather": {
          "logged": 1708168213180
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Run Details\n",
        "\n",
        "In the cell below, I use the `RunDetails` widget to show the different experiments."
      ],
      "metadata": {
        "collapsed": true,
        "gather": {
          "logged": 1598544898497
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hyperdrive_run.wait_for_completion(show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "RunId: HD_4f718995-c51b-4a70-8126-6afe5d465c0c\nWeb View: https://ml.azure.com/runs/HD_4f718995-c51b-4a70-8126-6afe5d465c0c?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-253507/workspaces/quick-starts-ws-253507&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n\nStreaming azureml-logs/hyperdrive.txt\n=====================================\n\n[2024-02-17T11:10:13.228831][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2024-02-17T11:10:13.6624043Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_0' \n[2024-02-17T11:10:13.7762138Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_1' \n[2024-02-17T11:10:13.723657][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2024-02-17T11:10:14.1013737Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_0' \n[2024-02-17T11:10:14.1231988Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_1' \n[2024-02-17T11:11:43.178147][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2024-02-17T11:11:43.6034379Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_2' \n[2024-02-17T11:11:43.521258][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2024-02-17T11:11:43.8651179Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_2' \n[2024-02-17T11:12:13.295933][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2024-02-17T11:12:13.6253213Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_3' \n[2024-02-17T11:12:13.572253][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2024-02-17T11:12:14.0675237Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_3' \n[2024-02-17T11:13:13.293492][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2024-02-17T11:13:13.6081397Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4' \n[2024-02-17T11:13:13.546252][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2024-02-17T11:13:14.0539096Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4' \n[2024-02-17T11:13:43.148342][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2024-02-17T11:13:43.5380410Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_5' \n[2024-02-17T11:13:43.474537][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2024-02-17T11:13:43.8137089Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_5' \n[2024-02-17T11:15:13.169598][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2024-02-17T11:15:13.4392922Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_6' \n[2024-02-17T11:15:13.390538][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2024-02-17T11:15:13.8125968Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_6' \n[2024-02-17T11:15:43.236533][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2024-02-17T11:15:43.6274754Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_7' \n[2024-02-17T11:15:43.569150][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2024-02-17T11:15:44.3740888Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_7' \n[2024-02-17T11:16:13.124937][GENERATOR][INFO]Max number of jobs '8' reached for experiment.\n[2024-02-17T11:16:13.279960][GENERATOR][INFO]All jobs generated.\n[2024-02-17T11:17:17.3946276Z][CONTROLLER][INFO]Changing Run Status from Running to Completed \n\nExecution Summary\n=================\nRunId: HD_4f718995-c51b-4a70-8126-6afe5d465c0c\nWeb View: https://ml.azure.com/runs/HD_4f718995-c51b-4a70-8126-6afe5d465c0c?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-253507/workspaces/quick-starts-ws-253507&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n\n"
        },
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "{'runId': 'HD_4f718995-c51b-4a70-8126-6afe5d465c0c',\n 'target': 'automl-vs-hpyer',\n 'status': 'Completed',\n 'startTimeUtc': '2024-02-17T11:10:12.395129Z',\n 'endTimeUtc': '2024-02-17T11:17:17.496927Z',\n 'services': {},\n 'properties': {'primary_metric_config': '{\"name\":\"AUC_weighted\",\"goal\":\"maximize\"}',\n  'resume_from': 'null',\n  'runTemplate': 'HyperDrive',\n  'azureml.runsource': 'hyperdrive',\n  'platform': 'AML',\n  'ContentSnapshotId': 'b7fab8c8-22b3-44c4-a114-fd73b0525375',\n  'user_agent': 'python/3.8.5 (Linux-5.15.0-1040-azure-x86_64-with-glibc2.10) msrest/0.7.1 Hyperdrive.Service/1.0.0 Hyperdrive.SDK/core.1.51.0',\n  'space_size': 'infinite_space_size',\n  'best_child_run_id': 'HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4',\n  'score': '0.7440925087983912',\n  'best_metric_status': 'Succeeded',\n  'best_data_container_id': 'dcid.HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4'},\n 'inputDatasets': [],\n 'outputDatasets': [],\n 'runDefinition': {'configuration': None,\n  'attribution': None,\n  'telemetryValues': {'amlClientType': 'azureml-sdk-train',\n   'amlClientModule': '[Scrubbed]',\n   'amlClientFunction': '[Scrubbed]',\n   'tenantId': '660b3398-b80e-49d2-bc5b-ac1dc93b5254',\n   'amlClientRequestId': '9900b74e-b425-421e-98f7-df107b699f86',\n   'amlClientSessionId': 'fd3fd4f1-f762-4f12-bb2d-c50f053030fc',\n   'subscriptionId': '9e65f93e-bdd8-437b-b1e8-0647cd6098f7',\n   'estimator': 'NoneType',\n   'samplingMethod': 'RANDOM',\n   'terminationPolicy': 'Bandit',\n   'primaryMetricGoal': 'maximize',\n   'maxTotalRuns': 8,\n   'maxConcurrentRuns': 2,\n   'maxDurationMinutes': 10080,\n   'vmSize': None},\n  'snapshotId': 'b7fab8c8-22b3-44c4-a114-fd73b0525375',\n  'snapshots': [],\n  'sourceCodeDataReference': None,\n  'parentRunId': None,\n  'dataContainerId': None,\n  'runType': None,\n  'displayName': None,\n  'environmentAssetId': None,\n  'properties': {},\n  'tags': {},\n  'aggregatedArtifactPath': None},\n 'logFiles': {'azureml-logs/hyperdrive.txt': 'https://mlstrg253507.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_4f718995-c51b-4a70-8126-6afe5d465c0c/azureml-logs/hyperdrive.txt?sv=2019-07-07&sr=b&sig=W67XZwt4uAWDEbOudXb8BvRvgQaENchZ9rBf%2F%2F1maZw%3D&skoid=ed6e39e1-3351-45d4-8999-1df201f2805b&sktid=660b3398-b80e-49d2-bc5b-ac1dc93b5254&skt=2024-02-17T09%3A28%3A32Z&ske=2024-02-18T17%3A38%3A32Z&sks=b&skv=2019-07-07&st=2024-02-17T11%3A07%3A56Z&se=2024-02-17T19%3A17%3A56Z&sp=r'},\n 'submittedBy': 'ODL_User 253507'}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "gather": {
          "logged": 1708168677619
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "RunDetails(hyperdrive_run).show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO'…",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45fb8bcf17394ce88975dbcb9942fbe9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/runs/HD_4f718995-c51b-4a70-8126-6afe5d465c0c?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-253507/workspaces/quick-starts-ws-253507&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\", \"run_id\": \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c\", \"run_properties\": {\"run_id\": \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c\", \"created_utc\": \"2024-02-17T11:10:12.245971Z\", \"properties\": {\"primary_metric_config\": \"{\\\"name\\\":\\\"AUC_weighted\\\",\\\"goal\\\":\\\"maximize\\\"}\", \"resume_from\": \"null\", \"runTemplate\": \"HyperDrive\", \"azureml.runsource\": \"hyperdrive\", \"platform\": \"AML\", \"ContentSnapshotId\": \"b7fab8c8-22b3-44c4-a114-fd73b0525375\", \"user_agent\": \"python/3.8.5 (Linux-5.15.0-1040-azure-x86_64-with-glibc2.10) msrest/0.7.1 Hyperdrive.Service/1.0.0 Hyperdrive.SDK/core.1.51.0\", \"space_size\": \"infinite_space_size\", \"best_child_run_id\": \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4\", \"score\": \"0.7440925087983912\", \"best_metric_status\": \"Succeeded\", \"best_data_container_id\": \"dcid.HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4\"}, \"tags\": {\"_aml_system_max_concurrent_jobs\": \"2\", \"_aml_system_max_total_jobs\": \"8\", \"_aml_system_max_duration_minutes\": \"10080\", \"_aml_system_policy_config\": \"{\\\"name\\\":\\\"Bandit\\\",\\\"properties\\\":{\\\"evaluation_interval\\\":3,\\\"delay_evaluation\\\":0,\\\"slack_factor\\\":0.1}}\", \"_aml_system_generator_config\": \"{\\\"name\\\":\\\"RANDOM\\\",\\\"parameter_space\\\":{\\\"--criterion\\\":[\\\"choice\\\",[[\\\"gini\\\",\\\"entropy\\\"]]],\\\"--bootstrap\\\":[\\\"choice\\\",[[\\\"True\\\",\\\"False\\\"]]],\\\"--max_depth\\\":[\\\"randint\\\",[10]]},\\\"properties\\\":null}\", \"_aml_system_primary_metric_config\": \"{\\\"name\\\":\\\"AUC_weighted\\\",\\\"goal\\\":\\\"maximize\\\"}\", \"_aml_system_platform_config\": \"{\\\"ServiceAddress\\\":\\\"https://southcentralus.experiments.azureml.net\\\",\\\"SubscriptionId\\\":\\\"9e65f93e-bdd8-437b-b1e8-0647cd6098f7\\\",\\\"ResourceGroupName\\\":\\\"aml-quickstarts-253507\\\",\\\"WorkspaceName\\\":\\\"quick-starts-ws-253507\\\",\\\"ExperimentName\\\":\\\"HyperTune\\\",\\\"Definition\\\":{\\\"Configuration\\\":null,\\\"Attribution\\\":null,\\\"TelemetryValues\\\":{\\\"amlClientType\\\":\\\"azureml-sdk-train\\\",\\\"amlClientModule\\\":\\\"[Scrubbed]\\\",\\\"amlClientFunction\\\":\\\"[Scrubbed]\\\",\\\"tenantId\\\":\\\"660b3398-b80e-49d2-bc5b-ac1dc93b5254\\\",\\\"amlClientRequestId\\\":\\\"9900b74e-b425-421e-98f7-df107b699f86\\\",\\\"amlClientSessionId\\\":\\\"fd3fd4f1-f762-4f12-bb2d-c50f053030fc\\\",\\\"subscriptionId\\\":\\\"9e65f93e-bdd8-437b-b1e8-0647cd6098f7\\\",\\\"estimator\\\":\\\"NoneType\\\",\\\"samplingMethod\\\":\\\"RANDOM\\\",\\\"terminationPolicy\\\":\\\"Bandit\\\",\\\"primaryMetricGoal\\\":\\\"maximize\\\",\\\"maxTotalRuns\\\":8,\\\"maxConcurrentRuns\\\":2,\\\"maxDurationMinutes\\\":10080,\\\"vmSize\\\":null},\\\"Overrides\\\":{\\\"Script\\\":\\\"train.py\\\",\\\"Command\\\":\\\"\\\",\\\"UseAbsolutePath\\\":false,\\\"Arguments\\\":[],\\\"SourceDirectoryDataStore\\\":null,\\\"Framework\\\":0,\\\"Communicator\\\":0,\\\"Target\\\":\\\"automl-vs-hpyer\\\",\\\"DataReferences\\\":{},\\\"Data\\\":{},\\\"OutputData\\\":{},\\\"Datacaches\\\":[],\\\"JobName\\\":null,\\\"MaxRunDurationSeconds\\\":2592000,\\\"NodeCount\\\":1,\\\"InstanceTypes\\\":[],\\\"Priority\\\":null,\\\"CredentialPassthrough\\\":false,\\\"Identity\\\":null,\\\"Environment\\\":{\\\"Name\\\":\\\"hyper_env\\\",\\\"AutoRebuild\\\":true,\\\"Python\\\":{\\\"InterpreterPath\\\":\\\"python\\\",\\\"UserManagedDependencies\\\":false,\\\"CondaDependencies\\\":{\\\"name\\\":\\\"project_environment\\\",\\\"dependencies\\\":[\\\"python=3.8.13\\\",{\\\"pip\\\":[\\\"azureml-defaults\\\",\\\"pandas\\\",\\\"scikit-learn\\\"]}],\\\"channels\\\":[\\\"anaconda\\\",\\\"conda-forge\\\"]},\\\"BaseCondaEnvironment\\\":null},\\\"EnvironmentVariables\\\":{\\\"EXAMPLE_ENV_VAR\\\":\\\"EXAMPLE_VALUE\\\"},\\\"Docker\\\":{\\\"BaseImage\\\":\\\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:20230509.v1\\\",\\\"Platform\\\":{\\\"Os\\\":\\\"Linux\\\",\\\"Architecture\\\":\\\"amd64\\\"},\\\"BaseDockerfile\\\":null,\\\"BaseImageRegistry\\\":{\\\"Address\\\":null,\\\"Username\\\":null,\\\"Password\\\":null},\\\"Enabled\\\":false,\\\"Arguments\\\":[]},\\\"Spark\\\":{\\\"Repositories\\\":[],\\\"Packages\\\":[],\\\"PrecachePackages\\\":true},\\\"InferencingStackVersion\\\":null},\\\"History\\\":{\\\"OutputCollection\\\":true,\\\"DirectoriesToWatch\\\":[\\\"logs\\\"],\\\"EnableMLflowTracking\\\":true,\\\"snapshotProject\\\":true},\\\"Spark\\\":{\\\"Configuration\\\":{\\\"spark.app.name\\\":\\\"Azure ML Experiment\\\",\\\"spark.yarn.maxAppAttempts\\\":\\\"1\\\"}},\\\"ParallelTask\\\":{\\\"MaxRetriesPerWorker\\\":0,\\\"WorkerCountPerNode\\\":1,\\\"TerminalExitCodes\\\":null,\\\"Configuration\\\":{}},\\\"BatchAi\\\":{\\\"NodeCount\\\":0},\\\"AmlCompute\\\":{\\\"Name\\\":null,\\\"VmSize\\\":null,\\\"RetainCluster\\\":false,\\\"ClusterMaxNodeCount\\\":null},\\\"AISuperComputer\\\":{\\\"InstanceType\\\":\\\"D2\\\",\\\"FrameworkImage\\\":null,\\\"ImageVersion\\\":null,\\\"Location\\\":null,\\\"AISuperComputerStorageData\\\":null,\\\"Interactive\\\":false,\\\"ScalePolicy\\\":null,\\\"VirtualClusterArmId\\\":null,\\\"TensorboardLogDirectory\\\":null,\\\"SSHPublicKey\\\":null,\\\"SSHPublicKeys\\\":null,\\\"EnableAzmlInt\\\":true,\\\"Priority\\\":\\\"Medium\\\",\\\"SLATier\\\":\\\"Standard\\\",\\\"UserAlias\\\":null},\\\"KubernetesCompute\\\":{\\\"InstanceType\\\":null},\\\"Tensorflow\\\":{\\\"WorkerCount\\\":1,\\\"ParameterServerCount\\\":1},\\\"Mpi\\\":{\\\"ProcessCountPerNode\\\":1},\\\"PyTorch\\\":{\\\"CommunicationBackend\\\":\\\"nccl\\\",\\\"ProcessCount\\\":null},\\\"Hdi\\\":{\\\"YarnDeployMode\\\":2},\\\"ContainerInstance\\\":{\\\"Region\\\":null,\\\"CpuCores\\\":2.0,\\\"MemoryGb\\\":3.5},\\\"ExposedPorts\\\":null,\\\"Docker\\\":{\\\"UseDocker\\\":false,\\\"SharedVolumes\\\":true,\\\"ShmSize\\\":\\\"2g\\\",\\\"Arguments\\\":[]},\\\"Cmk8sCompute\\\":{\\\"Configuration\\\":{}},\\\"CommandReturnCodeConfig\\\":{\\\"ReturnCode\\\":0,\\\"SuccessfulReturnCodes\\\":[]},\\\"EnvironmentVariables\\\":{},\\\"ApplicationEndpoints\\\":{},\\\"Parameters\\\":[]},\\\"SnapshotId\\\":\\\"b7fab8c8-22b3-44c4-a114-fd73b0525375\\\",\\\"Snapshots\\\":[],\\\"SourceCodeDataReference\\\":null,\\\"ParentRunId\\\":null,\\\"DataContainerId\\\":null,\\\"RunType\\\":null,\\\"DisplayName\\\":null,\\\"EnvironmentAssetId\\\":null,\\\"Properties\\\":{},\\\"Tags\\\":{},\\\"AggregatedArtifactPath\\\":null},\\\"ParentRunId\\\":\\\"HD_4f718995-c51b-4a70-8126-6afe5d465c0c\\\"}\", \"_aml_system_resume_child_runs\": \"null\", \"_aml_system_all_jobs_generated\": \"true\", \"_aml_system_cancellation_requested\": \"false\", \"_aml_system_samples_generated_count\": \"8\", \"_aml_system_progress_metadata_evaluation_timestamp\": \"\\\"2024-02-17T11:10:13.077371\\\"\", \"_aml_system_progress_metadata_digest\": \"\\\"a465c53ecde9866458f6ab5c86e99445194d03d92270117cdf78d1f625ae6ed7\\\"\", \"_aml_system_progress_metadata_active_timestamp\": \"\\\"2024-02-17T11:10:13.077371\\\"\", \"_aml_system_optimizer_state_artifact\": \"null\", \"_aml_system_outdated_optimizer_state_artifacts\": \"\\\"[]\\\"\", \"_aml_system_HD_4f718995-c51b-4a70-8126-6afe5d465c0c_0\": \"{\\\"--bootstrap\\\": \\\"False\\\", \\\"--criterion\\\": \\\"entropy\\\", \\\"--max_depth\\\": 5}\", \"_aml_system_HD_4f718995-c51b-4a70-8126-6afe5d465c0c_1\": \"{\\\"--bootstrap\\\": \\\"True\\\", \\\"--criterion\\\": \\\"entropy\\\", \\\"--max_depth\\\": 6}\", \"_aml_system_HD_4f718995-c51b-4a70-8126-6afe5d465c0c_2\": \"{\\\"--bootstrap\\\": \\\"True\\\", \\\"--criterion\\\": \\\"entropy\\\", \\\"--max_depth\\\": 9}\", \"_aml_system_HD_4f718995-c51b-4a70-8126-6afe5d465c0c_3\": \"{\\\"--bootstrap\\\": \\\"False\\\", \\\"--criterion\\\": \\\"gini\\\", \\\"--max_depth\\\": 2}\", \"_aml_system_HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4\": \"{\\\"--bootstrap\\\": \\\"False\\\", \\\"--criterion\\\": \\\"entropy\\\", \\\"--max_depth\\\": 8}\", \"_aml_system_HD_4f718995-c51b-4a70-8126-6afe5d465c0c_5\": \"{\\\"--bootstrap\\\": \\\"False\\\", \\\"--criterion\\\": \\\"gini\\\", \\\"--max_depth\\\": 4}\", \"_aml_system_HD_4f718995-c51b-4a70-8126-6afe5d465c0c_6\": \"{\\\"--bootstrap\\\": \\\"False\\\", \\\"--criterion\\\": \\\"gini\\\", \\\"--max_depth\\\": 8}\", \"_aml_system_HD_4f718995-c51b-4a70-8126-6afe5d465c0c_7\": \"{\\\"--bootstrap\\\": \\\"True\\\", \\\"--criterion\\\": \\\"gini\\\", \\\"--max_depth\\\": 2}\"}, \"script_name\": null, \"arguments\": null, \"end_time_utc\": \"2024-02-17T11:17:17.496927Z\", \"status\": \"Completed\", \"log_files\": {\"azureml-logs/hyperdrive.txt\": \"https://mlstrg253507.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_4f718995-c51b-4a70-8126-6afe5d465c0c/azureml-logs/hyperdrive.txt?sv=2019-07-07&sr=b&sig=IRGq7cWMTDwnlOSpXY1ACc3QWufxl%2Fxjb2sEjcZfUQg%3D&skoid=ed6e39e1-3351-45d4-8999-1df201f2805b&sktid=660b3398-b80e-49d2-bc5b-ac1dc93b5254&skt=2024-02-17T09%3A28%3A32Z&ske=2024-02-18T17%3A38%3A32Z&sks=b&skv=2019-07-07&st=2024-02-17T11%3A34%3A10Z&se=2024-02-17T19%3A44%3A10Z&sp=r\"}, \"log_groups\": [[\"azureml-logs/hyperdrive.txt\"]], \"run_duration\": \"0:07:05\", \"run_number\": \"1708168212\", \"run_queued_details\": {\"status\": \"Completed\", \"details\": null}, \"hyper_parameters\": {\"--criterion\": [\"choice\", [[\"gini\", \"entropy\"]]], \"--bootstrap\": [\"choice\", [[\"True\", \"False\"]]], \"--max_depth\": [\"randint\", [10]]}}, \"child_runs\": [{\"run_id\": \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c_0\", \"run_number\": 1708168213, \"metric\": 0.72187029, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2024-02-17T11:10:44.331763Z\", \"end_time\": \"2024-02-17T11:11:02.417205Z\", \"created_time\": \"2024-02-17T11:10:13.978637Z\", \"created_time_dt\": \"2024-02-17T11:10:13.978637Z\", \"duration\": \"0:00:48\", \"hyperdrive_id\": \"4f718995-c51b-4a70-8126-6afe5d465c0c\", \"arguments\": null, \"param_--bootstrap\": \"False\", \"param_--criterion\": \"entropy\", \"param_--max_depth\": 5, \"best_metric\": 0.72187029}, {\"run_id\": \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c_1\", \"run_number\": 1708168214, \"metric\": 0.73453997, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2024-02-17T11:11:17.952684Z\", \"end_time\": \"2024-02-17T11:11:36.86674Z\", \"created_time\": \"2024-02-17T11:10:14.02747Z\", \"created_time_dt\": 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\"metric\": 0.68898944, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2024-02-17T11:12:42.344238Z\", \"end_time\": \"2024-02-17T11:13:00.429046Z\", \"created_time\": \"2024-02-17T11:12:13.826713Z\", \"created_time_dt\": \"2024-02-17T11:12:13.826713Z\", \"duration\": \"0:00:46\", \"hyperdrive_id\": \"4f718995-c51b-4a70-8126-6afe5d465c0c\", \"arguments\": null, \"param_--bootstrap\": \"False\", \"param_--criterion\": \"gini\", \"param_--max_depth\": 2, \"best_metric\": 0.7372549}, {\"run_id\": \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4\", \"run_number\": 1708168393, \"metric\": 0.74409251, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2024-02-17T11:13:38.669503Z\", \"end_time\": \"2024-02-17T11:15:13.911981Z\", \"created_time\": \"2024-02-17T11:13:13.957281Z\", \"created_time_dt\": \"2024-02-17T11:13:13.957281Z\", \"duration\": \"0:01:59\", \"hyperdrive_id\": \"4f718995-c51b-4a70-8126-6afe5d465c0c\", \"arguments\": null, \"param_--bootstrap\": \"False\", \"param_--criterion\": \"entropy\", \"param_--max_depth\": 8, \"best_metric\": 0.74409251}, {\"run_id\": \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c_5\", \"run_number\": 1708168423, \"metric\": 0.71181498, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2024-02-17T11:14:06.864644Z\", \"end_time\": \"2024-02-17T11:14:25.246931Z\", \"created_time\": \"2024-02-17T11:13:43.709387Z\", \"created_time_dt\": \"2024-02-17T11:13:43.709387Z\", \"duration\": \"0:00:41\", \"hyperdrive_id\": \"4f718995-c51b-4a70-8126-6afe5d465c0c\", \"arguments\": null, \"param_--bootstrap\": \"False\", \"param_--criterion\": \"gini\", \"param_--max_depth\": 4, \"best_metric\": 0.74409251}, {\"run_id\": \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c_6\", \"run_number\": 1708168513, \"metric\": 0.73092006, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2024-02-17T11:15:38.366097Z\", \"end_time\": \"2024-02-17T11:15:56.685228Z\", \"created_time\": \"2024-02-17T11:15:13.711549Z\", \"created_time_dt\": \"2024-02-17T11:15:13.711549Z\", \"duration\": \"0:00:42\", \"hyperdrive_id\": \"4f718995-c51b-4a70-8126-6afe5d465c0c\", \"arguments\": null, \"param_--bootstrap\": \"False\", \"param_--criterion\": \"gini\", \"param_--max_depth\": 8, \"best_metric\": 0.74409251}, {\"run_id\": \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c_7\", \"run_number\": 1708168544, \"metric\": 0.71171443, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2024-02-17T11:16:11.208534Z\", \"end_time\": \"2024-02-17T11:16:29.03553Z\", \"created_time\": \"2024-02-17T11:15:44.182564Z\", \"created_time_dt\": \"2024-02-17T11:15:44.182564Z\", \"duration\": \"0:00:44\", \"hyperdrive_id\": \"4f718995-c51b-4a70-8126-6afe5d465c0c\", \"arguments\": null, \"param_--bootstrap\": \"True\", \"param_--criterion\": \"gini\", \"param_--max_depth\": 2, \"best_metric\": 0.74409251}], \"children_metrics\": {\"categories\": [0], \"series\": {\"Attribute selection measure:\": [{\"categories\": [1708168213, 1708168214, 1708168303, 1708168333, 1708168393, 1708168423, 1708168513, 1708168544], \"mode\": \"markers\", \"name\": \"Attribute selection measure:\", \"stepped\": false, \"type\": \"scatter\", \"data\": [\"entropy\", \"entropy\", \"entropy\", \"gini\", \"entropy\", \"gini\", \"gini\", \"gini\"]}, {\"categories\": [1708168213, 1708168214, 1708168303, 1708168333, 1708168393, 1708168423, 1708168513, 1708168544], \"mode\": \"lines\", \"name\": \"Attribute selection measure:_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": []}], \"Maximum Depth of a Tree:\": [{\"categories\": [1708168213, 1708168214, 1708168303, 1708168333, 1708168393, 1708168423, 1708168513, 1708168544], \"mode\": \"markers\", \"name\": \"Maximum Depth of a Tree:\", \"stepped\": false, \"type\": \"scatter\", \"data\": [6, 7, 10, 3, 9, 5, 9, 3]}, {\"categories\": [1708168213, 1708168214, 1708168303, 1708168333, 1708168393, 1708168423, 1708168513, 1708168544], \"mode\": \"lines\", \"name\": \"Maximum Depth of a Tree:_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [6, 7, 10, 10, 10, 10, 10, 10]}], \"Bootstrap Strategy:\": [{\"categories\": [1708168213, 1708168214, 1708168303, 1708168333, 1708168393, 1708168423, 1708168513, 1708168544], \"mode\": \"markers\", \"name\": \"Bootstrap Strategy:\", \"stepped\": false, \"type\": \"scatter\", \"data\": [false, true, true, false, false, false, false, true]}, {\"categories\": [1708168213, 1708168214, 1708168303, 1708168333, 1708168393, 1708168423, 1708168513, 1708168544], \"mode\": \"lines\", \"name\": \"Bootstrap Strategy:_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [false, true, true, true, true, true, true, true]}], \"AUC_weighted\": [{\"categories\": [1708168213, 1708168214, 1708168303, 1708168333, 1708168393, 1708168423, 1708168513, 1708168544], \"mode\": \"markers\", \"name\": \"AUC_weighted\", \"stepped\": false, \"type\": \"scatter\", \"data\": [0.7218702865761689, 0.7345399698340875, 0.7372549019607844, 0.6889894419306184, 0.7440925087983912, 0.7118149824032177, 0.7309200603318251, 0.7117144293614881]}, {\"categories\": [1708168213, 1708168214, 1708168303, 1708168333, 1708168393, 1708168423, 1708168513, 1708168544], \"mode\": \"lines\", \"name\": \"AUC_weighted_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [0.7218702865761689, 0.7345399698340875, 0.7372549019607844, 0.7372549019607844, 0.7440925087983912, 0.7440925087983912, 0.7440925087983912, 0.7440925087983912]}]}, \"metricName\": null, \"primaryMetricName\": \"AUC_weighted\", \"showLegend\": false}, \"run_metrics\": [{\"name\": \"best_child_by_primary_metric\", \"run_id\": \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c\", \"categories\": [0], \"series\": [{\"data\": [{\"run_id\": [\"HD_4f718995-c51b-4a70-8126-6afe5d465c0c_0\", \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c_1\", \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c_2\", \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4\", \"HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4\"], \"metric_value\": [0.7218702865761689, 0.7345399698340875, 0.7372549019607844, 0.7440925087983912, 0.7440925087983912], \"final\": [false, false, false, false, true], \"metric_name\": [\"AUC_weighted\", \"AUC_weighted\", \"AUC_weighted\", \"AUC_weighted\", \"AUC_weighted\"], \"time_elapse\": [53, 49, 30, 44, 135]}]}]}], \"run_logs\": \"[2024-02-17T11:10:13.228831][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\n[2024-02-17T11:10:13.6624043Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_0' \\n[2024-02-17T11:10:13.7762138Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_1' \\n[2024-02-17T11:10:13.723657][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\n[2024-02-17T11:10:14.1013737Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_0' \\n[2024-02-17T11:10:14.1231988Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_1' \\n[2024-02-17T11:11:43.178147][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\n[2024-02-17T11:11:43.6034379Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_2' \\n[2024-02-17T11:11:43.521258][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\n[2024-02-17T11:11:43.8651179Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_2' \\n[2024-02-17T11:12:13.295933][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\n[2024-02-17T11:12:13.6253213Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_3' \\n[2024-02-17T11:12:13.572253][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\n[2024-02-17T11:12:14.0675237Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_3' \\n[2024-02-17T11:13:13.293492][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\n[2024-02-17T11:13:13.6081397Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4' \\n[2024-02-17T11:13:13.546252][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\n[2024-02-17T11:13:14.0539096Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4' \\n[2024-02-17T11:13:43.148342][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\n[2024-02-17T11:13:43.5380410Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_5' \\n[2024-02-17T11:13:43.474537][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\n[2024-02-17T11:13:43.8137089Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_5' \\n[2024-02-17T11:15:13.169598][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\n[2024-02-17T11:15:13.4392922Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_6' \\n[2024-02-17T11:15:13.390538][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\n[2024-02-17T11:15:13.8125968Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_6' \\n[2024-02-17T11:15:43.236533][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\n[2024-02-17T11:15:43.6274754Z][SCHEDULER][INFO]Scheduling job, id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_7' \\n[2024-02-17T11:15:43.569150][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\n[2024-02-17T11:15:44.3740888Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_4f718995-c51b-4a70-8126-6afe5d465c0c_7' \\n[2024-02-17T11:16:13.124937][GENERATOR][INFO]Max number of jobs '8' reached for experiment.\\n[2024-02-17T11:16:13.279960][GENERATOR][INFO]All jobs generated.\\n[2024-02-17T11:17:17.3946276Z][CONTROLLER][INFO]Changing Run Status from Running to Completed \\n\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.51.0\"}, \"loading\": false}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "gather": {
          "logged": 1708168693418
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Best Model\n",
        "\n",
        "In the cell below, I get the best model from the hyperdrive experiments and display all the properties of the model."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n",
        "best_run_metrics = best_run.get_metrics()\n",
        " \n",
        "print('Best Run Id: ', best_run.id)\n",
        "print('AUC_weighted of Best Run is:', best_run_metrics['AUC_weighted'])\n",
        "print('Parameter Values are:',best_run.get_details()['runDefinition']['arguments'])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Best Run Id:  HD_4f718995-c51b-4a70-8126-6afe5d465c0c_4\nAUC_weighted of Best Run is: 0.7440925087983912\nParameter Values are: ['--bootstrap', 'False', '--criterion', 'entropy', '--max_depth', '8']\n"
        }
      ],
      "execution_count": 10,
      "metadata": {
        "gather": {
          "logged": 1708168696172
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = best_run.register_model(model_name='hyperdrive-model', \n",
        "                                model_path='outputs/model.joblib', \n",
        "                                tags={'Method':'Hyperdrive'}, \n",
        "                                properties={'AUC_weighted': best_run_metrics['AUC_weighted']})"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "gather": {
          "logged": 1708168698172
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {}
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python38-azureml"
    },
    "kernelspec": {
      "name": "python38-azureml",
      "language": "python",
      "display_name": "Python 3.8 - AzureML"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    },
    "microsoft": {
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
      },
      "ms_spell_check": {
        "ms_spell_check_language": "en"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}