udacity-project.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from azureml.core import Workspace, Experiment\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "exp = Experiment(workspace=ws, name=\"udacity-project\")\n",
        "\n",
        "print('Workspace name: ' + ws.name, \n",
        "      'Azure region: ' + ws.location, \n",
        "      'Subscription id: ' + ws.subscription_id, \n",
        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
        "\n",
        "run = exp.start_logging()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Workspace name: quick-starts-ws-241842\nAzure region: southcentralus\nSubscription id: 61c5c3f0-6dc7-4ed9-a7f3-c704b20e3b30\nResource group: aml-quickstarts-241842\n"
        }
      ],
      "execution_count": 1,
      "metadata": {
        "gather": {
          "logged": 1695733414465
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "cluster_name = \"ml-cluster\"\n",
        "\n",
        "# TODO: Create compute cluster\n",
        "# Use vm_size = \"Standard_D2_V2\" in your provisioning configuration.\n",
        "# max_nodes should be no greater than 4.\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        " \n",
        "try:\n",
        "    aml_compute = ComputeTarget(workspace=ws, name=cluster_name)\n",
        "    print('Found existing cluster, use it.')\n",
        "except ComputeTargetException:\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS2_V2',\n",
        "                                                           max_nodes=4)\n",
        "    aml_compute = ComputeTarget.create(ws, cluster_name, compute_config)\n",
        "\n",
        "aml_compute.wait_for_completion(show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "InProgress..\nSucceededProvisioning operation finished, operation \"Succeeded\"\nSucceeded\nAmlCompute wait for completion finished\n\nMinimum number of nodes requested have been provisioned\n"
        }
      ],
      "execution_count": 44,
      "metadata": {
        "gather": {
          "logged": 1695740001075
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n",
        "from azureml.train.hyperdrive.policy import BanditPolicy\n",
        "from azureml.train.hyperdrive.sampling import RandomParameterSampling\n",
        "from azureml.train.hyperdrive.runconfig import HyperDriveConfig\n",
        "from azureml.train.hyperdrive.parameter_expressions import choice, loguniform\n",
        "from azureml.core import Environment, ScriptRunConfig\n",
        "import os\n",
        "\n",
        "# Specify parameter sampler\n",
        "ps = RandomParameterSampling(\n",
        "    {\n",
        "        '--max_iter': choice(100, 500, 1000),\n",
        "        '--C': loguniform(1e-4, 1e4)\n",
        "    }\n",
        ") ### YOUR CODE HERE ###\n",
        "\n",
        "# Specify a Policy\n",
        "policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1) ### YOUR CODE HERE ###\n",
        "\n",
        "# Setup environment for your training run\n",
        "sklearn_env = Environment.from_conda_specification(name='sklearn-env', file_path='conda_dependencies.yml')\n",
        "\n",
        "# Create a ScriptRunConfig Object to specify the configuration details of your training job\n",
        "src = ScriptRunConfig(source_directory=\".\",\n",
        "                      script='train.py',\n",
        "                      compute_target=aml_compute,\n",
        "                      environment=sklearn_env) ### YOUR CODE HERE ###\n",
        "\n",
        "# Create a HyperDriveConfig using the src object, hyperparameter sampler, and policy.\n",
        "hyperdrive_config = HyperDriveConfig(run_config=src,\n",
        "                                     hyperparameter_sampling=ps,\n",
        "                                     policy=policy,\n",
        "                                     primary_metric_name='Accuracy',\n",
        "                                     primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
        "                                     max_total_runs=100,\n",
        "                                     max_concurrent_runs=4) ### YOUR CODE HERE ###"
      ],
      "outputs": [],
      "execution_count": 51,
      "metadata": {
        "gather": {
          "logged": 1695740551768
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Submit your hyperdrive run to the experiment and show run details with the widget.\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "hyperdrive_run = exp.submit(config=hyperdrive_config)"
      ],
      "outputs": [],
      "execution_count": 52,
      "metadata": {
        "gather": {
          "logged": 1695740560860
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hyperdrive_run.wait_for_completion(show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "RunId: HD_0f389e54-1834-412b-9f43-10e895e4cd8d\nWeb View: https://ml.azure.com/runs/HD_0f389e54-1834-412b-9f43-10e895e4cd8d?wsid=/subscriptions/61c5c3f0-6dc7-4ed9-a7f3-c704b20e3b30/resourcegroups/aml-quickstarts-241842/workspaces/quick-starts-ws-241842&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n\nStreaming azureml-logs/hyperdrive.txt\n=====================================\n\n[2023-09-26T15:02:41.252767][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\n[2023-09-26T15:02:41.8717577Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_0' \n[2023-09-26T15:02:42.0043622Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_1' \n[2023-09-26T15:02:42.1264613Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_2' \n[2023-09-26T15:02:42.2300544Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_3' \n[2023-09-26T15:02:42.2795878Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_0' \n[2023-09-26T15:02:42.160904][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:02:42.5939462Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_3' \n[2023-09-26T15:02:42.5740173Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_1' \n[2023-09-26T15:02:42.6107544Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_2' \n[2023-09-26T15:04:11.239892][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\n[2023-09-26T15:04:11.5317850Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_4' \n[2023-09-26T15:04:11.7580994Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_5' \n[2023-09-26T15:04:11.736806][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:04:11.8327180Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_4' \n[2023-09-26T15:04:12.0096135Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_6' \n[2023-09-26T15:04:12.0463288Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_5' \n[2023-09-26T15:04:12.2409871Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_6' \n[2023-09-26T15:04:41.193343][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2023-09-26T15:04:41.6372021Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_7' \n[2023-09-26T15:04:41.596234][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:04:41.8674106Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_7' \n[2023-09-26T15:05:41.236376][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\n[2023-09-26T15:05:41.6673800Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_8' \n[2023-09-26T15:05:41.7846459Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_9' \n[2023-09-26T15:05:41.8463158Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_10' \n[2023-09-26T15:05:41.760990][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:05:41.9468516Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_8' \n[2023-09-26T15:05:42.0501070Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_9' \n[2023-09-26T15:05:42.2082378Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_10' \n[2023-09-26T15:06:11.230704][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2023-09-26T15:06:11.6262451Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_11' \n[2023-09-26T15:06:11.545803][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:06:11.9497742Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_11' \n[2023-09-26T15:07:11.198447][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:07:11.6288777Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_12' \n[2023-09-26T15:07:11.7159056Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_13' \n[2023-09-26T15:07:11.663488][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:07:11.8557440Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_12' \n[2023-09-26T15:07:12.0649041Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_13' \n[2023-09-26T15:07:41.169279][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2023-09-26T15:07:41.5801518Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_14' \n[2023-09-26T15:07:41.545225][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:07:41.8677422Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_14' \n[2023-09-26T15:08:11.278799][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2023-09-26T15:08:11.6355543Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_15' \n[2023-09-26T15:08:11.585260][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:08:11.8863049Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_15' \n[2023-09-26T15:08:41.172847][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:08:41.5349749Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_16' \n[2023-09-26T15:08:41.6775707Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_17' \n[2023-09-26T15:08:41.626790][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:08:41.9235465Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_16' \n[2023-09-26T15:08:41.9973294Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_17' \n[2023-09-26T15:09:11.194084][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2023-09-26T15:09:11.5051538Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_18' \n[2023-09-26T15:09:11.454743][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:09:11.7601243Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_18' \n[2023-09-26T15:09:41.214745][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:09:41.6036519Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_19' \n[2023-09-26T15:09:41.7544168Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_20' \n[2023-09-26T15:09:41.698399][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:09:41.8673618Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_19' \n[2023-09-26T15:09:42.0147297Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_20' \n[2023-09-26T15:10:11.198931][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2023-09-26T15:10:11.5381988Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_21' \n[2023-09-26T15:10:11.471098][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:10:11.7799496Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_21' \n[2023-09-26T15:10:41.213639][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2023-09-26T15:10:41.6436025Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_22' \n[2023-09-26T15:10:41.593401][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:10:42.0714634Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_22' \n[2023-09-26T15:11:11.217603][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\n[2023-09-26T15:11:11.6139430Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_23' \n[2023-09-26T15:11:11.7329377Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_24' \n[2023-09-26T15:11:11.684384][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:11:11.8302430Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_25' \n[2023-09-26T15:11:12.0527402Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_23' \n[2023-09-26T15:11:12.1346945Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_24' \n[2023-09-26T15:11:12.2511668Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_25' \n[2023-09-26T15:11:41.235094][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2023-09-26T15:11:41.6081091Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_26' \n[2023-09-26T15:11:41.549440][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:11:41.8925173Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_26' \n[2023-09-26T15:12:11.277268][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\n[2023-09-26T15:12:11.6283668Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_27' \n[2023-09-26T15:12:11.7630219Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_28' \n[2023-09-26T15:12:11.8603709Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_27' \n[2023-09-26T15:12:11.8960724Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_29' \n[2023-09-26T15:12:11.832821][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:12:12.0365142Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_28' \n[2023-09-26T15:12:12.1594220Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_29' \n[2023-09-26T15:13:11.199284][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\n[2023-09-26T15:13:11.7282089Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_30' \n[2023-09-26T15:13:11.8295996Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_31' \n[2023-09-26T15:13:11.9713924Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_32' \n[2023-09-26T15:13:11.9730659Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_33' \n[2023-09-26T15:13:11.897514][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:13:12.1110363Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_30' \n[2023-09-26T15:13:12.2097895Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_32' \n[2023-09-26T15:13:12.2739076Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_31' \n[2023-09-26T15:13:12.2860920Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_33' \n[2023-09-26T15:14:41.213215][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\n[2023-09-26T15:14:41.6741102Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_34' \n[2023-09-26T15:14:41.7364244Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_35' \n[2023-09-26T15:14:41.9688817Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_35' \n[2023-09-26T15:14:41.9757345Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_37' \n[2023-09-26T15:14:41.9383210Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_36' \n[2023-09-26T15:14:41.924992][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:14:42.0096540Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_34' \n[2023-09-26T15:14:42.2285093Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_37' \n[2023-09-26T15:14:42.2284271Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_36' \n[2023-09-26T15:15:41.253540][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\n[2023-09-26T15:15:41.7385365Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_38' \n[2023-09-26T15:15:41.8674720Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_39' \n[2023-09-26T15:15:41.9933119Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_40' \n[2023-09-26T15:15:41.911216][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:15:42.0651290Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_38' \n[2023-09-26T15:15:42.2228403Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_40' \n[2023-09-26T15:15:42.3049134Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_39' \n[2023-09-26T15:16:11.279115][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\n[2023-09-26T15:16:11.7656807Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_41' \n[2023-09-26T15:16:11.689857][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:16:12.0996247Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_41' \n[2023-09-26T15:16:41.255246][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:16:41.7182643Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_42' \n[2023-09-26T15:16:41.777325][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:16:41.9219791Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_43' \n[2023-09-26T15:16:41.9864038Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_42' \n[2023-09-26T15:16:42.2285786Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_43' \n[2023-09-26T15:17:11.217587][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:17:11.686846][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:17:11.8024158Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_44' \n[2023-09-26T15:17:11.8011596Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_45' \n[2023-09-26T15:17:12.0274842Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_45' \n[2023-09-26T15:17:12.1460996Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_44' \n[2023-09-26T15:17:41.256059][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:17:41.6854626Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_46' \n[2023-09-26T15:17:41.761962][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:17:41.9183088Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_47' \n[2023-09-26T15:17:41.9963050Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_46' \n[2023-09-26T15:17:42.3652925Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_47' \n[2023-09-26T15:18:11.300020][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:18:11.8087668Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_48' \n[2023-09-26T15:18:11.9426707Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_49' \n[2023-09-26T15:18:11.882063][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:18:12.1556604Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_48' \n[2023-09-26T15:18:12.2549805Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_49' \n[2023-09-26T15:18:41.268820][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:18:41.9296018Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_50' \n[2023-09-26T15:18:41.894495][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:18:41.9822412Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_51' \n[2023-09-26T15:18:42.2903621Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_50' \n[2023-09-26T15:18:42.3877811Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_51' \n[2023-09-26T15:19:11.336865][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:19:11.8179651Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_52' \n[2023-09-26T15:19:11.9825793Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_53' \n[2023-09-26T15:19:11.872891][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:19:12.0942970Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_52' \n[2023-09-26T15:19:12.2455061Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_53' \n[2023-09-26T15:19:41.272013][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:19:41.7860578Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_54' \n[2023-09-26T15:19:41.791561][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:19:41.9007144Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_55' \n[2023-09-26T15:19:42.0538367Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_54' \n[2023-09-26T15:19:42.1777166Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_55' \n[2023-09-26T15:20:11.244347][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:20:11.7507457Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_56' \n[2023-09-26T15:20:11.783848][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:20:11.8920664Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_57' \n[2023-09-26T15:20:12.0128561Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_56' \n[2023-09-26T15:20:12.1839462Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_57' \n[2023-09-26T15:20:41.255420][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:20:41.7698148Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_58' \n[2023-09-26T15:20:41.9418324Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_59' \n[2023-09-26T15:20:41.844154][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:20:42.0086632Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_58' \n[2023-09-26T15:20:42.3347463Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_59' \n[2023-09-26T15:21:11.260521][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:21:11.7984743Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_60' \n[2023-09-26T15:21:11.744526][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:21:11.8600571Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_61' \n[2023-09-26T15:21:12.0505017Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_60' \n[2023-09-26T15:21:12.2023994Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_61' \n[2023-09-26T15:21:41.249695][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:21:41.7686621Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_62' \n[2023-09-26T15:21:41.9241284Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_63' \n[2023-09-26T15:21:41.842724][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:21:41.9972637Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_62' \n[2023-09-26T15:21:42.2071698Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_63' \n[2023-09-26T15:22:11.248544][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:22:11.8317943Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_64' \n[2023-09-26T15:22:11.894666][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:22:12.0283193Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_65' \n[2023-09-26T15:22:12.3443379Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_64' \n[2023-09-26T15:22:12.4418028Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_65' \n[2023-09-26T15:22:41.258450][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:22:41.8189960Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_66' \n[2023-09-26T15:22:41.9057527Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_67' \n[2023-09-26T15:22:41.862938][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:22:42.1315998Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_66' \n[2023-09-26T15:22:42.1531750Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_67' \n[2023-09-26T15:23:11.242755][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:23:11.7813435Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_68' \n[2023-09-26T15:23:11.852847][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:23:11.9115954Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_69' \n[2023-09-26T15:23:12.0529116Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_68' \n[2023-09-26T15:23:12.2821133Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_69' \n[2023-09-26T15:23:41.410701][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:23:41.9396801Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_70' \n[2023-09-26T15:23:42.0558211Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_71' \n[2023-09-26T15:23:42.007551][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:23:42.2167119Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_70' \n[2023-09-26T15:23:42.2891952Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_71' \n[2023-09-26T15:24:11.291793][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:24:11.8779107Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_72' \n[2023-09-26T15:24:11.867142][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\n[2023-09-26T15:24:12.1044111Z][SCHEDULER][INFO]Scheduling job, id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_73' \n[2023-09-26T15:24:12.2848805Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_72' \n[2023-09-26T15:24:12.3548413Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0f389e54-1834-412b-9f43-10e895e4cd8d_73' \n[2023-09-26T15:24:41.301365][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\n[2023-09-26T15:24:41.608633][GENERATOR][WARNING]Could not sample any more jobs from the space.\n[2023-09-26T15:25:20.733417][CONTROLLER][INFO]Experiment was 'ExperimentStatus.RUNNING', is 'ExperimentStatus.FINISHED'.\n\nExecution Summary\n=================\nRunId: HD_0f389e54-1834-412b-9f43-10e895e4cd8d\nWeb View: https://ml.azure.com/runs/HD_0f389e54-1834-412b-9f43-10e895e4cd8d?wsid=/subscriptions/61c5c3f0-6dc7-4ed9-a7f3-c704b20e3b30/resourcegroups/aml-quickstarts-241842/workspaces/quick-starts-ws-241842&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n\n"
        },
        {
          "output_type": "execute_result",
          "execution_count": 53,
          "data": {
            "text/plain": "{'runId': 'HD_0f389e54-1834-412b-9f43-10e895e4cd8d',\n 'target': 'ml-cluster',\n 'status': 'Completed',\n 'startTimeUtc': '2023-09-26T15:02:40.421206Z',\n 'endTimeUtc': '2023-09-26T15:25:20.220766Z',\n 'services': {},\n 'properties': {'primary_metric_config': '{\"name\":\"Accuracy\",\"goal\":\"maximize\"}',\n  'resume_from': 'null',\n  'runTemplate': 'HyperDrive',\n  'azureml.runsource': 'hyperdrive',\n  'platform': 'AML',\n  'ContentSnapshotId': '3d78c111-761e-4738-9f21-e23201ec3bc9',\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  'score': '0.9059237679048313',\n  'best_child_run_id': 'HD_0f389e54-1834-412b-9f43-10e895e4cd8d_11',\n  'best_metric_status': 'Succeeded',\n  'best_data_container_id': 'dcid.HD_0f389e54-1834-412b-9f43-10e895e4cd8d_11'},\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': '4b44264d-0071-478e-ac7b-e86b016457d6',\n   'amlClientSessionId': 'fb4b85b8-bdc4-4c08-8ab8-6829474a1211',\n   'subscriptionId': '61c5c3f0-6dc7-4ed9-a7f3-c704b20e3b30',\n   'estimator': 'NoneType',\n   'samplingMethod': 'RANDOM',\n   'terminationPolicy': 'Bandit',\n   'primaryMetricGoal': 'maximize',\n   'maxTotalRuns': 100,\n   'maxConcurrentRuns': 4,\n   'maxDurationMinutes': 10080,\n   'vmSize': None},\n  'snapshotId': '3d78c111-761e-4738-9f21-e23201ec3bc9',\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://mlstrg241842.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_0f389e54-1834-412b-9f43-10e895e4cd8d/azureml-logs/hyperdrive.txt?sv=2019-07-07&sr=b&sig=Mm8Z%2B909JJsUGGXI%2FXC494bLTgb%2FkAOvl5QijM4ozfE%3D&skoid=15be4cce-8688-4fbc-87f7-b9dccb22e085&sktid=660b3398-b80e-49d2-bc5b-ac1dc93b5254&skt=2023-09-26T12%3A53%3A51Z&ske=2023-09-27T21%3A03%3A51Z&sks=b&skv=2019-07-07&st=2023-09-26T15%3A15%3A42Z&se=2023-09-26T23%3A25%3A42Z&sp=r'},\n 'submittedBy': 'ODL_User 241842'}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 53,
      "metadata": {
        "gather": {
          "logged": 1695741943365
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "assert(hyperdrive_run.get_status() == \"Completed\")"
      ],
      "outputs": [],
      "execution_count": 54,
      "metadata": {
        "gather": {
          "logged": 1695742039362
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "# Get your best run and save the model from that run.\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "best_run = hyperdrive_run.get_best_run_by_primary_metric()"
      ],
      "outputs": [],
      "execution_count": 55,
      "metadata": {
        "gather": {
          "logged": 1695742042404
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_run.get_metrics()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 56,
          "data": {
            "text/plain": "{'Regularization Strength:': 3122262070046.714,\n 'Max iterations:': 500,\n 'Accuracy': 0.9059237679048313}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 56,
      "metadata": {
        "gather": {
          "logged": 1695742046064
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_run.get_file_names()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 57,
          "data": {
            "text/plain": "['logs/azureml/dataprep/0/backgroundProcess.log',\n 'logs/azureml/dataprep/0/backgroundProcess_Telemetry.log',\n 'logs/azureml/dataprep/0/rslex.log.2023-09-26-15',\n 'system_logs/cs_capability/cs-capability.log',\n 'system_logs/hosttools_capability/hosttools-capability.log',\n 'system_logs/lifecycler/execution-wrapper.log',\n 'system_logs/lifecycler/lifecycler.log',\n 'system_logs/metrics_capability/metrics-capability.log',\n 'system_logs/snapshot_capability/snapshot-capability.log',\n 'user_logs/std_log.txt']"
          },
          "metadata": {}
        }
      ],
      "execution_count": 57,
      "metadata": {
        "gather": {
          "logged": 1695742051814
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.data.dataset_factory import TabularDatasetFactory\n",
        "\n",
        "# Create TabularDataset using TabularDatasetFactory\n",
        "# Data is available at: \n",
        "# \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "data_link = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
        "ds = TabularDatasetFactory.from_delimited_files(path=data_link)"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "gather": {
          "logged": 1695734404028
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from train import clean_data\n",
        "\n",
        "# Use the clean_data function to clean your data.\n",
        "x, y = clean_data(ds)"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "gather": {
          "logged": 1695734411705
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.train.automl import AutoMLConfig\n",
        "\n",
        "# Set parameters for AutoMLConfig\n",
        "# NOTE: DO NOT CHANGE THE experiment_timeout_minutes PARAMETER OR YOUR INSTANCE WILL TIME OUT.\n",
        "# If you wish to run the experiment longer, you will need to run this notebook in your own\n",
        "# Azure tenant, which will incur personal costs.\n",
        "automl_config = AutoMLConfig(\n",
        "    experiment_timeout_minutes=100,\n",
        "    task=\"classification\",\n",
        "    primary_metric='accuracy',\n",
        "    X=x,\n",
        "    y=y,\n",
        "    n_cross_validations=5)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": "WARNING:root:The AutoMLConfig parameters, X and y, will soon be deprecated. Please refer to our documentation for the latest interface: https://aka.ms/AutoMLConfig\n"
        }
      ],
      "execution_count": 12,
      "metadata": {
        "gather": {
          "logged": 1695734411912
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Submit your automl run\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "automl_run = exp.submit(config=automl_config)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": "WARNING:root:Running on local machine. Note that local runs always run synchronously even if you use the parameter 'show_output=False'\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>udacity-project</td><td>AutoML_8fb93061-3da9-4b5b-b10e-b2bb1df0fabe</td><td>automl</td><td>Preparing</td><td><a href=\"https://ml.azure.com/runs/AutoML_8fb93061-3da9-4b5b-b10e-b2bb1df0fabe?wsid=/subscriptions/61c5c3f0-6dc7-4ed9-a7f3-c704b20e3b30/resourcegroups/aml-quickstarts-241842/workspaces/quick-starts-ws-241842&amp;tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": "2023/09/26 13:22:10 WARNING mlflow.sklearn: Model was missing function: predict. Not logging python_function flavor!\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nWARNING:azureml._base_sdk_common._docstring_wrapper:Class StackEnsembleClassifier: This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.\nINFO:interpret_community.common.explanation_utils:Using default datastore for uploads\n"
        }
      ],
      "execution_count": 13,
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1695738068318
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "automl_run.wait_for_completion(show_output = True)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>udacity-project</td><td>AutoML_8fb93061-3da9-4b5b-b10e-b2bb1df0fabe</td><td>automl</td><td>Completed</td><td><a href=\"https://ml.azure.com/runs/AutoML_8fb93061-3da9-4b5b-b10e-b2bb1df0fabe?wsid=/subscriptions/61c5c3f0-6dc7-4ed9-a7f3-c704b20e3b30/resourcegroups/aml-quickstarts-241842/workspaces/quick-starts-ws-241842&amp;tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "\n\n********************************************************************************************\nDATA GUARDRAILS: \n\nTYPE:         Class balancing detection\nSTATUS:       ALERTED\nDESCRIPTION:  To decrease model bias, please cancel the current run and fix balancing problem.\n              Learn more about imbalanced data: https://aka.ms/AutomatedMLImbalancedData\nDETAILS:      Imbalanced data can lead to a falsely perceived positive effect of a model's accuracy because the input data has bias towards one class.\n+------------------------------+--------------------------------+--------------------------------------+\n|Size of the smallest class    |Name/Label of the smallest class|Number of samples in the training data|\n+==============================+================================+======================================+\n|3692                          |1                               |32950                                 |\n+------------------------------+--------------------------------+--------------------------------------+\n\n********************************************************************************************\n\nTYPE:         Missing feature values imputation\nSTATUS:       PASSED\nDESCRIPTION:  No feature missing values were detected in the training data.\n              Learn more about missing value imputation: https://aka.ms/AutomatedMLFeaturization\n\n********************************************************************************************\n\nTYPE:         High cardinality feature detection\nSTATUS:       PASSED\nDESCRIPTION:  Your inputs were analyzed, and no high cardinality features were detected.\n              Learn more about high cardinality feature handling: https://aka.ms/AutomatedMLFeaturization\n\n********************************************************************************************\n\n********************************************************************************************\nITER: The iteration being evaluated.\nPIPELINE: A summary description of the pipeline being evaluated.\nDURATION: Time taken for the current iteration.\nMETRIC: The result of computing score on the fitted pipeline.\nBEST: The best observed score thus far.\n********************************************************************************************\n\n ITER   PIPELINE                                       DURATION            METRIC      BEST\n    0   MaxAbsScaler LightGBM                          0:00:37             0.9142    0.9142\n    1   MaxAbsScaler XGBoostClassifier                 0:00:55             0.9122    0.9142\n    2   MaxAbsScaler ExtremeRandomTrees                0:00:43             0.7306    0.9142\n    3   SparseNormalizer XGBoostClassifier             0:00:44             0.9140    0.9142\n    4   MaxAbsScaler LightGBM                          0:00:34             0.9122    0.9142\n    5   MaxAbsScaler LightGBM                          0:00:34             0.8881    0.9142\n    6   StandardScalerWrapper XGBoostClassifier        0:00:39             0.9092    0.9142\n    7   MaxAbsScaler LogisticRegression                0:00:42             0.9085    0.9142\n    8   StandardScalerWrapper ExtremeRandomTrees       0:00:36             0.8880    0.9142\n    9   StandardScalerWrapper XGBoostClassifier        0:00:37             0.9134    0.9142\n   10   SparseNormalizer LightGBM                      0:00:34             0.9048    0.9142\n   11   StandardScalerWrapper XGBoostClassifier        0:00:37             0.9143    0.9143\n   12   MaxAbsScaler LogisticRegression                0:00:42             0.9083    0.9143\n   13   MaxAbsScaler SGD                               0:00:34             0.8556    0.9143\n   14   StandardScalerWrapper XGBoostClassifier        0:00:40             0.9129    0.9143\n   15   SparseNormalizer RandomForest                  0:01:00             0.8149    0.9143\n   16   StandardScalerWrapper LogisticRegression       0:00:37             0.9086    0.9143\n   17   StandardScalerWrapper RandomForest             0:00:44             0.9007    0.9143\n   18   StandardScalerWrapper XGBoostClassifier        0:00:43             0.9139    0.9143\n   19   TruncatedSVDWrapper RandomForest               0:03:28             0.8188    0.9143\n   20   TruncatedSVDWrapper RandomForest               0:05:28             0.8290    0.9143\n   21   StandardScalerWrapper XGBoostClassifier        0:00:40             0.9144    0.9144\n   22   StandardScalerWrapper LightGBM                 0:00:47             0.9120    0.9144\n   23   MaxAbsScaler LightGBM                          0:00:35             0.8880    0.9144\n   24   StandardScalerWrapper XGBoostClassifier        0:01:21             0.9159    0.9159\n   25   StandardScalerWrapper XGBoostClassifier        0:00:38             0.8880    0.9159\n   26   MaxAbsScaler LightGBM                          0:00:37             0.9099    0.9159\n   27   StandardScalerWrapper XGBoostClassifier        0:01:32             0.9099    0.9159\n   28   StandardScalerWrapper LightGBM                 0:00:36             0.9088    0.9159\n   29   MaxAbsScaler LightGBM                          0:00:35             0.8880    0.9159\n   30   StandardScalerWrapper XGBoostClassifier        0:02:27             0.9161    0.9161\n   31   StandardScalerWrapper XGBoostClassifier        0:03:39             0.9094    0.9161\n   32   StandardScalerWrapper ExtremeRandomTrees       0:02:04             0.8880    0.9161\n   33   SparseNormalizer XGBoostClassifier             0:00:49             0.9073    0.9161\n   34   MaxAbsScaler LightGBM                          0:00:40             0.9101    0.9161\n   35   StandardScalerWrapper XGBoostClassifier        0:02:01             0.9077    0.9161\n   36   MaxAbsScaler LightGBM                          0:00:35             0.8880    0.9161\n   37   StandardScalerWrapper XGBoostClassifier        0:02:08             0.9068    0.9161\n   38   StandardScalerWrapper XGBoostClassifier        0:06:15             0.9096    0.9161\n   39   StandardScalerWrapper XGBoostClassifier        0:01:59             0.9059    0.9161\n   40   StandardScalerWrapper XGBoostClassifier        0:00:38             0.8880    0.9161\n   41    VotingEnsemble                                0:01:08             0.9177    0.9177\n   42    StackEnsemble                                 0:01:32             0.9157    0.9177\n"
        },
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "{'runId': 'AutoML_8fb93061-3da9-4b5b-b10e-b2bb1df0fabe',\n 'target': 'local',\n 'status': 'Completed',\n 'startTimeUtc': '2023-09-26T13:21:19.925182Z',\n 'endTimeUtc': '2023-09-26T14:18:28.44693Z',\n 'services': {},\n 'warnings': [{'source': 'JasmineService',\n   'message': 'No scores improved over last 10 iterations, so experiment stopped early. This early stopping behavior can be disabled by setting enable_early_stopping = False in AutoMLConfig for notebook/python SDK runs.'}],\n 'properties': {'num_iterations': '1000',\n  'training_type': 'TrainFull',\n  'acquisition_function': 'EI',\n  'primary_metric': 'accuracy',\n  'train_split': '0',\n  'acquisition_parameter': '0',\n  'num_cross_validation': '5',\n  'target': 'local',\n  'AMLSettingsJsonString': '{\"path\":null,\"name\":\"udacity-project\",\"subscription_id\":\"61c5c3f0-6dc7-4ed9-a7f3-c704b20e3b30\",\"resource_group\":\"aml-quickstarts-241842\",\"workspace_name\":\"quick-starts-ws-241842\",\"region\":\"southcentralus\",\"compute_target\":\"local\",\"spark_service\":null,\"azure_service\":\"Microsoft.AzureNotebookVM\",\"many_models\":false,\"pipeline_fetch_max_batch_size\":1,\"enable_batch_run\":false,\"enable_parallel_run\":false,\"num_procs\":null,\"enable_run_restructure\":false,\"start_auxiliary_runs_before_parent_complete\":false,\"enable_code_generation\":true,\"iterations\":1000,\"primary_metric\":\"accuracy\",\"task_type\":\"classification\",\"positive_label\":null,\"data_script\":null,\"test_size\":0.0,\"test_include_predictions_only\":false,\"validation_size\":0.0,\"n_cross_validations\":5,\"y_min\":null,\"y_max\":null,\"num_classes\":null,\"featurization\":\"auto\",\"_ignore_package_version_incompatibilities\":false,\"is_timeseries\":false,\"max_cores_per_iteration\":1,\"max_concurrent_iterations\":1,\"iteration_timeout_minutes\":null,\"mem_in_mb\":null,\"enforce_time_on_windows\":false,\"experiment_timeout_minutes\":100,\"experiment_exit_score\":null,\"partition_column_names\":null,\"whitelist_models\":null,\"blacklist_algos\":[\"TensorFlowLinearClassifier\",\"TensorFlowDNN\"],\"supported_models\":[\"MultinomialNaiveBayes\",\"DecisionTree\",\"RandomForest\",\"KNN\",\"AveragedPerceptronClassifier\",\"LightGBM\",\"TensorFlowLinearClassifier\",\"GradientBoosting\",\"TensorFlowDNN\",\"SVM\",\"BernoulliNaiveBayes\",\"SGD\",\"XGBoostClassifier\",\"LogisticRegression\",\"TabnetClassifier\",\"LinearSVM\",\"ExtremeRandomTrees\"],\"private_models\":[],\"auto_blacklist\":true,\"blacklist_samples_reached\":false,\"exclude_nan_labels\":true,\"verbosity\":20,\"_debug_log\":\"automl.log\",\"show_warnings\":false,\"model_explainability\":true,\"service_url\":null,\"sdk_url\":null,\"sdk_packages\":null,\"enable_onnx_compatible_models\":false,\"enable_split_onnx_featurizer_estimator_models\":false,\"vm_type\":null,\"telemetry_verbosity\":20,\"send_telemetry\":true,\"enable_dnn\":false,\"scenario\":\"AutoML\",\"environment_label\":null,\"save_mlflow\":false,\"enable_categorical_indicators\":false,\"force_text_dnn\":false,\"enable_feature_sweeping\":true,\"enable_early_stopping\":true,\"early_stopping_n_iters\":10,\"arguments\":null,\"dataset_id\":null,\"hyperdrive_config\":null,\"validation_dataset_id\":null,\"run_source\":null,\"metrics\":null,\"enable_metric_confidence\":false,\"enable_ensembling\":true,\"enable_stack_ensembling\":true,\"ensemble_iterations\":15,\"enable_tf\":false,\"enable_subsampling\":null,\"subsample_seed\":null,\"enable_nimbusml\":false,\"enable_streaming\":false,\"force_streaming\":false,\"track_child_runs\":true,\"n_best_runs\":1,\"allowed_private_models\":[],\"label_column_name\":null,\"weight_column_name\":null,\"cv_split_column_names\":null,\"enable_local_managed\":false,\"_local_managed_run_id\":null,\"cost_mode\":1,\"lag_length\":0,\"metric_operation\":\"maximize\",\"preprocess\":true}',\n  'DataPrepJsonString': None,\n  'EnableSubsampling': None,\n  'runTemplate': 'AutoML',\n  'azureml.runsource': 'automl',\n  'display_task_type': 'classification',\n  'dependencies_versions': '{\"azureml-dataprep-native\": \"38.0.0\", \"azureml-dataprep\": \"4.10.8\", \"azureml-dataprep-rslex\": \"2.17.12\", \"azureml-train-automl-runtime\": \"1.51.0.post2\", \"azureml-train-automl-client\": \"1.51.0.post1\", \"azureml-training-tabular\": \"1.51.0.post1\", \"azureml-automl-runtime\": \"1.51.0.post1\", \"azureml-automl-core\": \"1.51.0.post1\", \"azureml-mlflow\": \"1.51.0\", \"azureml-datadrift\": \"1.51.0\", \"azureml-pipeline\": \"1.51.0\", \"azureml-contrib-dataset\": \"1.51.0\", \"azureml-contrib-notebook\": \"1.51.0\", \"azureml-accel-models\": \"1.51.0\", \"azureml-automl-dnn-nlp\": \"1.51.0\", \"azureml-pipeline-core\": \"1.51.0\", \"azureml-responsibleai\": \"1.51.0\", \"azureml-contrib-automl-pipeline-steps\": \"1.51.0\", \"azureml-pipeline-steps\": \"1.51.0\", \"azureml-core\": \"1.51.0\", \"azureml-contrib-reinforcementlearning\": \"1.51.0\", \"azureml-contrib-server\": \"1.51.0\", \"azureml-opendatasets\": \"1.51.0\", \"azureml-contrib-services\": \"1.51.0\", \"azureml-telemetry\": \"1.51.0\", \"azureml-train-restclients-hyperdrive\": \"1.51.0\", \"azureml-interpret\": \"1.51.0\", \"azureml-train-core\": \"1.51.0\", \"azureml-tensorboard\": \"1.51.0\", \"azureml-train\": \"1.51.0\", \"azureml-contrib-pipeline-steps\": \"1.51.0\", \"azureml-explain-model\": \"1.51.0\", \"azureml-cli-common\": \"1.51.0\", \"azureml-widgets\": \"1.51.0\", \"azureml-train-automl\": \"1.51.0\", \"azureml-defaults\": \"1.51.0\", \"azureml-contrib-fairness\": \"1.51.0\", \"azureml-sdk\": \"1.51.0\", \"azureml-dataset-runtime\": \"1.51.0\", \"azureml-inference-server-http\": \"0.8.4\"}',\n  '_aml_system_scenario_identification': 'Local.Parent',\n  'ClientSdkVersion': '1.51.0',\n  'ClientType': 'SDK',\n  'PlatformVersion': 'DPV1',\n  'environment_cpu_name': 'AzureML-AutoML',\n  'environment_cpu_label': 'prod',\n  'environment_gpu_name': 'AzureML-AutoML-GPU',\n  'environment_gpu_label': 'prod',\n  'root_attribution': 'automl',\n  'attribution': 'AutoML',\n  'Orchestrator': 'AutoML',\n  '_azureml.ComputeTargetType': 'local',\n  'ProblemInfoJsonString': '{\"dataset_num_categorical\": 0, \"is_sparse\": true, \"subsampling\": false, \"has_extra_col\": true, \"dataset_classes\": 2, \"dataset_features\": 122, \"dataset_samples\": 32950, \"single_frequency_class_detected\": false}'},\n 'inputDatasets': [],\n 'outputDatasets': [],\n 'logFiles': {},\n 'submittedBy': 'ODL_User 241842'}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "gather": {
          "logged": 1695738622095
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "assert(automl_run.get_status() == \"Completed\")"
      ],
      "outputs": [],
      "execution_count": 15,
      "metadata": {
        "gather": {
          "logged": 1695738622618
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve and save your best automl model.\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "best_automl_run, fitted_automl_model = automl_run.get_output()"
      ],
      "outputs": [],
      "execution_count": 16,
      "metadata": {
        "gather": {
          "logged": 1695738623454
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_automl_run"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": "Run(Experiment: udacity-project,\nId: AutoML_8fb93061-3da9-4b5b-b10e-b2bb1df0fabe_41,\nType: None,\nStatus: Completed)",
            "text/html": "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>udacity-project</td><td>AutoML_8fb93061-3da9-4b5b-b10e-b2bb1df0fabe_41</td><td></td><td>Completed</td><td><a href=\"https://ml.azure.com/runs/AutoML_8fb93061-3da9-4b5b-b10e-b2bb1df0fabe_41?wsid=/subscriptions/61c5c3f0-6dc7-4ed9-a7f3-c704b20e3b30/resourcegroups/aml-quickstarts-241842/workspaces/quick-starts-ws-241842&amp;tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run.Run?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 17,
      "metadata": {
        "gather": {
          "logged": 1695738623917
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fitted_automl_model"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 39,
          "data": {
            "text/plain": "Pipeline(memory=None,\n         steps=[('datatransformer',\n                 DataTransformer(enable_dnn=False, enable_feature_sweeping=True, feature_sweeping_config={}, feature_sweeping_timeout=86400, featurization_config=None, force_text_dnn=False, is_cross_validation=True, is_onnx_compatible=False, observer=None, task='classification', working_dir='/mnt/batch/tasks/shared/LS_root/mount...\n                 PreFittedSoftVotingClassifier(classification_labels=array([0, 1]), estimators=[('30', Pipeline(memory=None, steps=[('standardscalerwrapper', StandardScalerWrapper(copy=True, with_mean=False, with_std=False)), ('xgboostclassifier', XGBoostClassifier(booster='gbtree', colsample_bylevel=0.6, colsample_bytree=1, eta=0.001, gamma=0, max_depth=6, max_leaves=15, n_estimators=800, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0, reg_lambda=2.5, subsample=1, tree_method='auto'))], verbose=False)), ('24', Pipeline(memory=None, steps=[('standardscalerwrapper', StandardScalerWrapper(copy=True, with_mean=False, with_std=False)), ('xgboostclassifier', XGBoostClassifier(booster='gbtree', colsample_bytree=1, eta=0.05, gamma=0, max_depth=6, max_leaves=0, n_estimators=200, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0.625, reg_lambda=0.8333333333333334, subsample=0.8, tree_method='auto'))], verbose=False)), ('21', Pipeline(memory=None, steps=[('standardscalerwrapper', StandardScalerWrapper(copy=True, with_mean=False, with_std=False)), ('xgboostclassifier', XGBoostClassifier(booster='gbtree', colsample_bytree=0.5, eta=0.2, gamma=0, max_depth=7, max_leaves=7, n_estimators=25, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0, reg_lambda=0.20833333333333334, subsample=1, tree_method='auto'))], verbose=False)), ('11', Pipeline(memory=None, steps=[('standardscalerwrapper', StandardScalerWrapper(copy=True, with_mean=False, with_std=False)), ('xgboostclassifier', XGBoostClassifier(booster='gbtree', colsample_bytree=0.6, eta=0.3, gamma=0, max_depth=6, max_leaves=0, n_estimators=10, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0.3125, reg_lambda=2.3958333333333335, subsample=1, tree_method='auto'))], verbose=False)), ('0', Pipeline(memory=None, steps=[('maxabsscaler', MaxAbsScaler(copy=True)), ('lightgbmclassifier', LightGBMClassifier(min_data_in_leaf=20, n_jobs=1, problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=None))], verbose=False)), ('13', Pipeline(memory=None, steps=[('maxabsscaler', MaxAbsScaler(copy=True)), ('sgdclassifierwrapper', SGDClassifierWrapper(alpha=7.5510448979591835, class_weight='balanced', eta0=0.001, fit_intercept=True, l1_ratio=0.42857142857142855, learning_rate='constant', loss='modified_huber', max_iter=1000, n_jobs=1, penalty='none', power_t=0.7777777777777777, random_state=None, tol=0.0001))], verbose=False)), ('3', Pipeline(memory=None, steps=[('sparsenormalizer', Normalizer(copy=True, norm='l2')), ('xgboostclassifier', XGBoostClassifier(booster='gbtree', colsample_bytree=0.7, eta=0.01, gamma=0.01, max_depth=7, max_leaves=31, n_estimators=10, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=2.1875, reg_lambda=1.0416666666666667, subsample=1, tree_method='auto'))], verbose=False)), ('22', Pipeline(memory=None, steps=[('standardscalerwrapper', StandardScalerWrapper(copy=True, with_mean=False, with_std=True)), ('lightgbmclassifier', LightGBMClassifier(boosting_type='gbdt', colsample_bytree=0.4955555555555555, learning_rate=0.05789894736842106, max_bin=210, max_depth=5, min_child_weight=0, min_data_in_leaf=0.07931241379310346, min_split_gain=0.3684210526315789, n_estimators=600, n_jobs=1, num_leaves=137, problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=None, reg_alpha=0.5789473684210527, reg_lambda=0.42105263157894735, subsample=0.05))], verbose=False)), ('12', Pipeline(memory=None, steps=[('maxabsscaler', MaxAbsScaler(copy=True)), ('logisticregression', LogisticRegression(C=719.6856730011514, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=None, max_iter=100, multi_class='multinomial', n_jobs=1, penalty='l2', random_state=None, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False))], verbose=False))], flatten_transform=None, weights=[0.35714285714285715, 0.07142857142857142, 0.07142857142857142, 0.14285714285714285, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142]))],\n         verbose=False)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 39,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1695739534075
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fitted_automl_model.steps[1][1].__dict__"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 43,
          "data": {
            "text/plain": "{'estimators': [('30',\n   Pipeline(memory=None,\n            steps=[('standardscalerwrapper',\n                    StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),\n                   ('xgboostclassifier',\n                    XGBoostClassifier(booster='gbtree', colsample_bylevel=0.6, colsample_bytree=1, eta=0.001, gamma=0, max_depth=6, max_leaves=15, n_estimators=800, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0, reg_lambda=2.5, subsample=1, tree_method='auto'))],\n            verbose=False)),\n  ('24',\n   Pipeline(memory=None,\n            steps=[('standardscalerwrapper',\n                    StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),\n                   ('xgboostclassifier',\n                    XGBoostClassifier(booster='gbtree', colsample_bytree=1, eta=0.05, gamma=0, max_depth=6, max_leaves=0, n_estimators=200, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0.625, reg_lambda=0.8333333333333334, subsample=0.8, tree_method='auto'))],\n            verbose=False)),\n  ('21',\n   Pipeline(memory=None,\n            steps=[('standardscalerwrapper',\n                    StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),\n                   ('xgboostclassifier',\n                    XGBoostClassifier(booster='gbtree', colsample_bytree=0.5, eta=0.2, gamma=0, max_depth=7, max_leaves=7, n_estimators=25, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0, reg_lambda=0.20833333333333334, subsample=1, tree_method='auto'))],\n            verbose=False)),\n  ('11',\n   Pipeline(memory=None,\n            steps=[('standardscalerwrapper',\n                    StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),\n                   ('xgboostclassifier',\n                    XGBoostClassifier(booster='gbtree', colsample_bytree=0.6, eta=0.3, gamma=0, max_depth=6, max_leaves=0, n_estimators=10, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0.3125, reg_lambda=2.3958333333333335, subsample=1, tree_method='auto'))],\n            verbose=False)),\n  ('0',\n   Pipeline(memory=None,\n            steps=[('maxabsscaler', MaxAbsScaler(copy=True)),\n                   ('lightgbmclassifier',\n                    LightGBMClassifier(min_data_in_leaf=20, n_jobs=1, problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=None))],\n            verbose=False)),\n  ('13',\n   Pipeline(memory=None,\n            steps=[('maxabsscaler', MaxAbsScaler(copy=True)),\n                   ('sgdclassifierwrapper',\n                    SGDClassifierWrapper(alpha=7.5510448979591835, class_weight='balanced', eta0=0.001, fit_intercept=True, l1_ratio=0.42857142857142855, learning_rate='constant', loss='modified_huber', max_iter=1000, n_jobs=1, penalty='none', power_t=0.7777777777777777, random_state=None, tol=0.0001))],\n            verbose=False)),\n  ('3',\n   Pipeline(memory=None,\n            steps=[('sparsenormalizer', Normalizer(copy=True, norm='l2')),\n                   ('xgboostclassifier',\n                    XGBoostClassifier(booster='gbtree', colsample_bytree=0.7, eta=0.01, gamma=0.01, max_depth=7, max_leaves=31, n_estimators=10, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=2.1875, reg_lambda=1.0416666666666667, subsample=1, tree_method='auto'))],\n            verbose=False)),\n  ('22',\n   Pipeline(memory=None,\n            steps=[('standardscalerwrapper',\n                    StandardScalerWrapper(copy=True, with_mean=False, with_std=True)),\n                   ('lightgbmclassifier',\n                    LightGBMClassifier(boosting_type='gbdt', colsample_bytree=0.4955555555555555, learning_rate=0.05789894736842106, max_bin=210, max_depth=5, min_child_weight=0, min_data_in_leaf=0.07931241379310346, min_split_gain=0.3684210526315789, n_estimators=600, n_jobs=1, num_leaves=137, problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=None, reg_alpha=0.5789473684210527, reg_lambda=0.42105263157894735, subsample=0.05))],\n            verbose=False)),\n  ('12',\n   Pipeline(memory=None,\n            steps=[('maxabsscaler', MaxAbsScaler(copy=True)),\n                   ('logisticregression',\n                    LogisticRegression(C=719.6856730011514, class_weight=None,\n                                       dual=False, fit_intercept=True,\n                                       intercept_scaling=1, l1_ratio=None,\n                                       max_iter=100, multi_class='multinomial',\n                                       n_jobs=1, penalty='l2', random_state=None,\n                                       solver='lbfgs', tol=0.0001, verbose=0,\n                                       warm_start=False))],\n            verbose=False))],\n 'voting': 'soft',\n 'weights': [0.35714285714285715,\n  0.07142857142857142,\n  0.07142857142857142,\n  0.14285714285714285,\n  0.07142857142857142,\n  0.07142857142857142,\n  0.07142857142857142,\n  0.07142857142857142,\n  0.07142857142857142],\n 'n_jobs': None,\n 'flatten_transform': None,\n 'estimators_': [Pipeline(memory=None,\n           steps=[('standardscalerwrapper',\n                   StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),\n                  ('xgboostclassifier',\n                   XGBoostClassifier(booster='gbtree', colsample_bylevel=0.6, colsample_bytree=1, eta=0.001, gamma=0, max_depth=6, max_leaves=15, n_estimators=800, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0, reg_lambda=2.5, subsample=1, tree_method='auto'))],\n           verbose=False),\n  Pipeline(memory=None,\n           steps=[('standardscalerwrapper',\n                   StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),\n                  ('xgboostclassifier',\n                   XGBoostClassifier(booster='gbtree', colsample_bytree=1, eta=0.05, gamma=0, max_depth=6, max_leaves=0, n_estimators=200, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0.625, reg_lambda=0.8333333333333334, subsample=0.8, tree_method='auto'))],\n           verbose=False),\n  Pipeline(memory=None,\n           steps=[('standardscalerwrapper',\n                   StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),\n                  ('xgboostclassifier',\n                   XGBoostClassifier(booster='gbtree', colsample_bytree=0.5, eta=0.2, gamma=0, max_depth=7, max_leaves=7, n_estimators=25, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0, reg_lambda=0.20833333333333334, subsample=1, tree_method='auto'))],\n           verbose=False),\n  Pipeline(memory=None,\n           steps=[('standardscalerwrapper',\n                   StandardScalerWrapper(copy=True, with_mean=False, with_std=False)),\n                  ('xgboostclassifier',\n                   XGBoostClassifier(booster='gbtree', colsample_bytree=0.6, eta=0.3, gamma=0, max_depth=6, max_leaves=0, n_estimators=10, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=0.3125, reg_lambda=2.3958333333333335, subsample=1, tree_method='auto'))],\n           verbose=False),\n  Pipeline(memory=None,\n           steps=[('maxabsscaler', MaxAbsScaler(copy=True)),\n                  ('lightgbmclassifier',\n                   LightGBMClassifier(min_data_in_leaf=20, n_jobs=1, problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=None))],\n           verbose=False),\n  Pipeline(memory=None,\n           steps=[('maxabsscaler', MaxAbsScaler(copy=True)),\n                  ('sgdclassifierwrapper',\n                   SGDClassifierWrapper(alpha=7.5510448979591835, class_weight='balanced', eta0=0.001, fit_intercept=True, l1_ratio=0.42857142857142855, learning_rate='constant', loss='modified_huber', max_iter=1000, n_jobs=1, penalty='none', power_t=0.7777777777777777, random_state=None, tol=0.0001))],\n           verbose=False),\n  Pipeline(memory=None,\n           steps=[('sparsenormalizer', Normalizer(copy=True, norm='l2')),\n                  ('xgboostclassifier',\n                   XGBoostClassifier(booster='gbtree', colsample_bytree=0.7, eta=0.01, gamma=0.01, max_depth=7, max_leaves=31, n_estimators=10, n_jobs=1, objective='reg:logistic', problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=0, reg_alpha=2.1875, reg_lambda=1.0416666666666667, subsample=1, tree_method='auto'))],\n           verbose=False),\n  Pipeline(memory=None,\n           steps=[('standardscalerwrapper',\n                   StandardScalerWrapper(copy=True, with_mean=False, with_std=True)),\n                  ('lightgbmclassifier',\n                   LightGBMClassifier(boosting_type='gbdt', colsample_bytree=0.4955555555555555, learning_rate=0.05789894736842106, max_bin=210, max_depth=5, min_child_weight=0, min_data_in_leaf=0.07931241379310346, min_split_gain=0.3684210526315789, n_estimators=600, n_jobs=1, num_leaves=137, problem_info=ProblemInfo(gpu_training_param_dict={'processing_unit_type': 'cpu'}), random_state=None, reg_alpha=0.5789473684210527, reg_lambda=0.42105263157894735, subsample=0.05))],\n           verbose=False),\n  Pipeline(memory=None,\n           steps=[('maxabsscaler', MaxAbsScaler(copy=True)),\n                  ('logisticregression',\n                   LogisticRegression(C=719.6856730011514, class_weight=None,\n                                      dual=False, fit_intercept=True,\n                                      intercept_scaling=1, l1_ratio=None,\n                                      max_iter=100, multi_class='multinomial',\n                                      n_jobs=1, penalty='l2', random_state=None,\n                                      solver='lbfgs', tol=0.0001, verbose=0,\n                                      warm_start=False))],\n           verbose=False)],\n '_labels': array([0, 1]),\n 'le_': LabelEncoder(),\n 'classes_': array([0, 1])}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 43,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1695739674544
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(fitted_automl_model, 'automl.joblib')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "['automl.joblib']"
          },
          "metadata": {}
        }
      ],
      "execution_count": 21,
      "metadata": {
        "gather": {
          "logged": 1695738625375
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aml_compute.delete()"
      ],
      "outputs": [],
      "execution_count": 58,
      "metadata": {
        "gather": {
          "logged": 1695742079090
        }
      }
    }
  ],
  "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": {
      "ms_spell_check": {
        "ms_spell_check_language": "en"
      },
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}