udacity-project.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# "
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Optimizing an Machine Learning Pipeline in Azure\n",
        "## Using Hyperdrive and AutoML"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preparing the experiment"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's start by creating a new experiment in order to find out which of the two methods can find the best model."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core import Workspace, Experiment\n",
        "import os\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "exp = Experiment(workspace=ws, name=\"bankdata-hyperdrive\")\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: udacity\nAzure region: eastus2\nSubscription id: 79facb57-c974-4c43-935b-f645fdca73d9\nResource group: standard\n"
        }
      ],
      "execution_count": 1,
      "metadata": {
        "gather": {
          "logged": 1676550405741
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We also need a compute cluster to run the training jobs on."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.exceptions import ComputeTargetException\n",
        "\n",
        "\n",
        "cluster_name = \"compute-cluster\"\n",
        "\n",
        "# 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",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
        "    print(\"Found existing cpu-cluster\")\n",
        "except ComputeTargetException:\n",
        "    print(\"Creating new cpu-cluster\")\n",
        "    \n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
        "                                                           min_nodes=0,\n",
        "                                                           max_nodes=4)\n",
        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
        "    \n",
        "compute_target.wait_for_completion(show_output=True)\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Creating new cpu-cluster\nInProgress..\nSucceededProvisioning operation finished, operation \"Succeeded\"\nSucceeded\nAmlCompute wait for completion finished\n\nMinimum number of nodes requested have been provisioned\n"
        }
      ],
      "execution_count": 2,
      "metadata": {
        "gather": {
          "logged": 1676550420531
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
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        "nteract": {
          "transient": {
            "deleting": false
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperdrive"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use Hyperdrive to find the best parameters for our LogisticRegression model. Hyperdrive will run the **train.py** script with different hyperparameters in order to find the best model."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.widgets import RunDetails\n",
        "from azureml.train.hyperdrive import BanditPolicy, PrimaryMetricGoal\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, uniform\n",
        "from azureml.core import Environment, ScriptRunConfig\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "# Specify parameter sampler\n",
        "ps = RandomParameterSampling(\n",
        "    {\n",
        "        \n",
        "        '--C': choice(10, 1, .1, .01),\n",
        "        '--max_iter': choice(100, 200, 500, 1000)\n",
        "    }\n",
        ")\n",
        "\n",
        "# Specify a Policy\n",
        "policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)\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",
        "script_folder = \".\"\n",
        "src = ScriptRunConfig(source_directory=script_folder,\n",
        "                      script='train.py',\n",
        "                      environment=sklearn_env,\n",
        "                      compute_target=compute_target)\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=50,\n",
        "                             max_duration_minutes=60,\n",
        "                             max_concurrent_runs=4)"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "gather": {
          "logged": 1676550421433
        },
        "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",
        "hyperdrive_run = exp.submit(hyperdrive_config)\n",
        "\n",
        "RunDetails(hyperdrive_run).show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO'…",
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "fc2c21b22aaa483b9338d68734bc64c2"
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        {
          "output_type": "display_data",
          "data": {
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Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_1' \\n[2023-02-16T12:27:03.7288775Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_0' \\n[2023-02-16T12:27:03.8143613Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_2' \\n[2023-02-16T12:27:03.6993607Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_3' \\n[2023-02-16T12:27:03.8556552Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_3' \\n[2023-02-16T12:33:33.504946][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\n[2023-02-16T12:33:34.0667356Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_4' \\n[2023-02-16T12:33:34.081366][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\n[2023-02-16T12:33:34.2692456Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_4' \\n[2023-02-16T12:34:03.438409][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\n[2023-02-16T12:34:04.1255453Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_5' \\n[2023-02-16T12:34:04.2793043Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_6' \\n[2023-02-16T12:34:04.295159][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\n[2023-02-16T12:34:04.3833797Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_7' \\n[2023-02-16T12:34:04.4760838Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_6' \\n[2023-02-16T12:34:04.3070965Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_5' \\n[2023-02-16T12:34:04.6292197Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_7' \\n[2023-02-16T12:34:34.425734][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\n[2023-02-16T12:34:35.0730717Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_8' \\n[2023-02-16T12:34:34.993321][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\n[2023-02-16T12:34:35.2503556Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_8' \\n[2023-02-16T12:35:04.477275][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\n[2023-02-16T12:35:05.062900][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\n[2023-02-16T12:35:04.9900226Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_9' \\n[2023-02-16T12:35:05.1845532Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_10' \\n[2023-02-16T12:35:05.4504131Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_9' \\n[2023-02-16T12:35:05.4642843Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_10' \\n[2023-02-16T12:35:34.403728][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\n[2023-02-16T12:35:34.9209238Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_11' \\n[2023-02-16T12:35:34.928922][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\n[2023-02-16T12:35:35.0791712Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_11' \\n[2023-02-16T12:35:35.0007329Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_12' \\n[2023-02-16T12:35:35.2038604Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_12' \\n[2023-02-16T12:36:04.398534][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\n[2023-02-16T12:36:04.965069][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\n[2023-02-16T12:36:04.9392594Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_13' \\n[2023-02-16T12:36:05.1151318Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_13' \\n[2023-02-16T12:36:34.485879][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\n[2023-02-16T12:36:35.0621094Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_14' \\n[2023-02-16T12:36:35.212658][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\n[2023-02-16T12:36:35.2353349Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_14' \\n[2023-02-16T12:36:35.3597002Z][SCHEDULER][INFO]Scheduling job, id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_15' \\n[2023-02-16T12:36:35.6138266Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_a38f014a-1227-443a-b8f8-d78c0ef79b4d_15' \\n[2023-02-16T12:38:15.998918][CONTROLLER][INFO]Experiment was 'ExperimentStatus.RUNNING', is 'ExperimentStatus.FINISHED'.\\n\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.48.0\"}, \"loading\": false}"
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12:35:50.6153|DEBUG|Telemetry|l_ceccea70-2fd2-46e5-94e8-72e8e3429613|event: DataSourceGetFiles|metrics: {\\\"size\\\":4150422.0}, properties: {\\\"datasourcePath\\\":\\\"Vj2Pztph7VmcJK/sWnlQUqUuBbMVmpo8asfbzS0CBtE=\\\"}|\\n2023-02-16 12:35:50.7004|DEBUG|CLex|l_ceccea70-2fd2-46e5-94e8-72e8e3429613|MapReduceExecutor|Execution Succeeded|{\\n  \\\"ExecutionId\\\": \\\"99bd1679-cddc-45e8-9f8d-4ad5ffde41d3\\\",\\n  \\\"Main\\\": \\\"Kyj8eiJGMTNeGJ0sj4sXJChugeI8HOFG2CcItSPVZs4=\\\"\\n}\\n2023-02-16 12:35:51.0908|DEBUG|Telemetry|l_ceccea70-2fd2-46e5-94e8-72e8e3429613|event: StepInfo|metrics: {}, properties: {\\\"blockType\\\":\\\"Microsoft.DPrep.GetFilesBlock\\\",\\\"id\\\":\\\"0\\\"}|\\n2023-02-16 12:35:51.0908|DEBUG|Telemetry|l_ceccea70-2fd2-46e5-94e8-72e8e3429613|event: StepInfo|metrics: {}, properties: {\\\"blockType\\\":\\\"Microsoft.DPrep.ParseDelimitedBlock\\\",\\\"id\\\":\\\"1\\\"}|\\n2023-02-16 12:35:51.0908|DEBUG|Telemetry|l_ceccea70-2fd2-46e5-94e8-72e8e3429613|event: StepInfo|metrics: {}, properties: {\\\"blockType\\\":\\\"Microsoft.DPrep.DropColumnsBlock\\\",\\\"id\\\":\\\"2\\\"}|\\n2023-02-16 12:35:51.0908|DEBUG|Telemetry|l_ceccea70-2fd2-46e5-94e8-72e8e3429613|event: StepInfo|metrics: {}, properties: {\\\"blockType\\\":\\\"Microsoft.DPrep.SetColumnTypesBlock\\\",\\\"id\\\":\\\"3\\\"}|\\n2023-02-16 12:35:51.1130|DEBUG|Telemetry|l_ceccea70-2fd2-46e5-94e8-72e8e3429613|event: YamlConverter|metrics: {\\\"duration\\\":9.0}, properties: {\\\"hadException\\\":\\\"False\\\",\\\"success\\\":\\\"True\\\",\\\"conversionOperations\\\":\\\"  paths:\\\\n  - pattern: wasbs://automl-sample-notebook-data@automlsamplenotebookdata.blob.core.windows.net/REDACTED:23\\\\n\\\\ntransformations:\\\\n  - read_files:\\\\n      path_column: Path\\\\n      keep_existing_columns: false\\\\n      reader: textLines\\\\n      reader_arguments:\\\\n        encoding: utf-8\\\\n        support_multi_line: false\\\\n        delimiter: ','\\\\n\\\\n  - split_by_delimiter:\\\\n      source_column: 'Line'\\\\n      delimiter: ','\\\\n      empty_as_string: false\\\\n\\\\n  - promote_headers: all_files_same_headers\\\\n\\\\n  - convert_column_types:\\\\n    - columns: count:1\\\\n      column_type: int\\\\n    - columns: count:1\\\\n      column_type: int\\\\n    - columns: count:1\\\\n      column_type: int\\\\n    - columns: count:1\\\\n      column_type: int\\\\n    - columns: count:1\\\\n      column_type: int\\\\n    - columns: count:1\\\\n      column_type: float\\\\n    - columns: count:1\\\\n      column_type: float\\\\n    - columns: count:1\\\\n      column_type: float\\\\n    - columns: count:1\\\\n      column_type: float\\\\n    - columns: count:1\\\\n      column_type: float\\\\n\\\\n\\\"}|\\n\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.48.0\", \"display\": \"popup\"}, \"loading\": false}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1676550502309
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally we get the hyperdrive run with the best metric score and save the corresponding model."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hyperdrive_run.wait_for_completion()\n",
        "\n",
        "\n",
        "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n",
        "best_run_metrics = best_run.get_metrics()\n",
        "\n",
        "for file in best_run.get_file_names():\n",
        "    if file.startswith(\"model\"):\n",
        "        best_run.download_file(file, output_file_path=f\"./outputs/best_hyperdrive_model.pkl\")"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1676551144889
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2 AutoML"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's now train a model for the same dataset using AutoML and see what AutoML can come up with."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "exp = Experiment(workspace=ws, name=\"bankdata-automl\")\n",
        "\n",
        "exp.start_logging()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "Run(Experiment: bankdata-automl,\nId: 5c7e05d0-3065-4bda-8102-b6bbbe0d9586,\nType: None,\nStatus: Running)",
            "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>bankdata-automl</td><td>5c7e05d0-3065-4bda-8102-b6bbbe0d9586</td><td></td><td>Running</td><td><a href=\"https://ml.azure.com/runs/5c7e05d0-3065-4bda-8102-b6bbbe0d9586?wsid=/subscriptions/79facb57-c974-4c43-935b-f645fdca73d9/resourcegroups/standard/workspaces/udacity&amp;tid=418350f0-e90b-4aeb-bebe-54cdd1935db3\" 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": 6,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1676552463294
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.data.dataset_factory import TabularDatasetFactory\n",
        "\n",
        "ds = TabularDatasetFactory.from_delimited_files(\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\")"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "gather": {
          "logged": 1676552481450
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We reuse the clean_data function from our train.py script above."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from train import clean_data\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Use the clean_data function to clean your data.\n",
        "x, y = clean_data(ds)\n",
        "x[\"y\"] = y\n",
        "\n",
        "x.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "   age  marital  default  housing  loan  month  day_of_week  duration  \\\n0   57        1        0        0     1      5            1       371   \n1   55        1        0        1     0      5            4       285   \n2   33        1        0        0     0      5            5        52   \n3   36        1        0        0     0      6            5       355   \n4   27        1        0        1     0      7            5       189   \n\n   campaign  pdays  ...  contact_telephone  education_basic.4y  \\\n0         1    999  ...                  0                   0   \n1         2    999  ...                  1                   0   \n2         1    999  ...                  0                   0   \n3         4    999  ...                  1                   0   \n4         2    999  ...                  0                   0   \n\n   education_basic.6y  education_basic.9y  education_high.school  \\\n0                   0                   0                      1   \n1                   0                   0                      0   \n2                   0                   1                      0   \n3                   0                   0                      1   \n4                   0                   0                      1   \n\n   education_illiterate  education_professional.course  \\\n0                     0                              0   \n1                     0                              0   \n2                     0                              0   \n3                     0                              0   \n4                     0                              0   \n\n   education_university.degree  education_unknown  y  \n0                            0                  0  0  \n1                            0                  1  0  \n2                            0                  0  0  \n3                            0                  0  0  \n4                            0                  0  0  \n\n[5 rows x 40 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>marital</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>month</th>\n      <th>day_of_week</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>...</th>\n      <th>contact_telephone</th>\n      <th>education_basic.4y</th>\n      <th>education_basic.6y</th>\n      <th>education_basic.9y</th>\n      <th>education_high.school</th>\n      <th>education_illiterate</th>\n      <th>education_professional.course</th>\n      <th>education_university.degree</th>\n      <th>education_unknown</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>57</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>371</td>\n      <td>1</td>\n      <td>999</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>55</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>4</td>\n      <td>285</td>\n      <td>2</td>\n      <td>999</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>5</td>\n      <td>52</td>\n      <td>1</td>\n      <td>999</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>36</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>5</td>\n      <td>355</td>\n      <td>4</td>\n      <td>999</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7</td>\n      <td>5</td>\n      <td>189</td>\n      <td>2</td>\n      <td>999</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 40 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "gather": {
          "logged": 1676552486862
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we setup an automl job with cross validation and a holdout test data set."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.core import Dataset\n",
        "\n",
        "datastore = ws.get_default_datastore()\n",
        "train_data = Dataset.Tabular.register_pandas_dataframe(\n",
        "        dataframe=x, \n",
        "        name='bank-data', \n",
        "        target=datastore\n",
        "    )\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=30,\n",
        "    task=\"classification\",\n",
        "    primary_metric=\"accuracy\",\n",
        "    training_data=train_data,\n",
        "    label_column_name=\"y\",\n",
        "    debug_log=\"automl_errors.log\",\n",
        "    compute_target=compute_target,\n",
        "    n_cross_validations=5,\n",
        "    test_size=.2\n",
        ")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Validating arguments.\nArguments validated.\nSuccessfully obtained datastore reference and path.\nUploading file to managed-dataset/3ce1532b-cad4-478f-9309-778258cd2425/\nColumn header contains '.' This period will be translated to '_' as we write the data out to parquet files: 'emp.var.rate' -> 'emp_var_rate'\nColumn header contains '.' This period will be translated to '_' as we write the data out to parquet files: 'cons.price.idx' -> 'cons_price_idx'\nColumn header contains '.' This period will be translated to '_' as we write the data out to parquet files: 'cons.conf.idx' -> 'cons_conf_idx'\nColumn header contains '.' This period will be translated to '_' as we write the data out to parquet files: 'nr.employed' -> 'nr_employed'\nColumn header contains '.' This period will be translated to '_' as we write the data out to parquet files: 'job_admin.' -> 'job_admin_'\nColumn header contains '.' This period will be translated to '_' as we write the data out to parquet files: 'education_basic.4y' -> 'education_basic_4y'\nColumn header contains '.' This period will be translated to '_' as we write the data out to parquet files: 'education_basic.6y' -> 'education_basic_6y'\nColumn header contains '.' This period will be translated to '_' as we write the data out to parquet files: 'education_basic.9y' -> 'education_basic_9y'\nColumn header contains '.' This period will be translated to '_' as we write the data out to parquet files: 'education_high.school' -> 'education_high_school'\nColumn header contains '.' This period will be translated to '_' as we write the data out to parquet files: 'education_professional.course' -> 'education_professional_course'\nColumn header contains '.' This period will be translated to '_' as we write the data out to parquet files: 'education_university.degree' -> 'education_university_degree'\nSuccessfully uploaded file to datastore.\nCreating and registering a new dataset.\nSuccessfully created and registered a new dataset.\n"
        }
      ],
      "execution_count": 9,
      "metadata": {
        "gather": {
          "logged": 1676552492504
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.widgets import RunDetails\n",
        "\n",
        "automl_run = exp.submit(automl_config, show_output=True)\n",
        "\n",
        "RunDetails(automl_run).show()\n",
        "\n",
        "automl_run.wait_for_completion()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Submitting remote run.\nNo run_configuration provided, running on compute-cluster with default configuration\nRunning on remote compute: compute-cluster\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.HTML object>",
            "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>bankdata-automl</td><td>AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222</td><td>automl</td><td>NotStarted</td><td><a href=\"https://ml.azure.com/runs/AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222?wsid=/subscriptions/79facb57-c974-4c43-935b-f645fdca73d9/resourcegroups/standard/workspaces/udacity&amp;tid=418350f0-e90b-4aeb-bebe-54cdd1935db3\" 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": "\nCurrent status: FeaturesGeneration. Generating features for the dataset.\nCurrent status: DatasetFeaturization. Beginning to fit featurizers and featurize the dataset.\nCurrent status: DatasetCrossValidationSplit. Generating individually featurized CV splits.\nCurrent status: ModelSelection. Beginning model selection.\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|2954                          |1                               |26360                                 |\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                          0:00:16             0.9159    0.9159\n    1   MaxAbsScaler XGBoostClassifier                 0:00:27             0.9132    0.9159\n    2   MaxAbsScaler ExtremeRandomTrees                0:00:17             0.7368    0.9159\n    3   SparseNormalizer XGBoostClassifier             0:00:15             0.9139    0.9159\n    4   MaxAbsScaler LightGBM                          0:00:14             0.9137    0.9159\n    5   MaxAbsScaler LightGBM                          0:00:14             0.8880    0.9159\n    6   StandardScalerWrapper XGBoostClassifier        0:00:11             0.9107    0.9159\n    7   MaxAbsScaler LogisticRegression                0:00:21             0.9097    0.9159\n    8   StandardScalerWrapper ExtremeRandomTrees       0:00:14             0.8889    0.9159\n    9   StandardScalerWrapper XGBoostClassifier        0:00:15             0.9145    0.9159\n   10   SparseNormalizer LightGBM                      0:00:14             0.9070    0.9159\n   11   StandardScalerWrapper XGBoostClassifier        0:00:17             0.9136    0.9159\n   12   MaxAbsScaler LogisticRegression                0:00:20             0.9093    0.9159\n   13   MaxAbsScaler SGD                               0:00:14             0.8374    0.9159\n   14   StandardScalerWrapper XGBoostClassifier        0:00:13             0.9136    0.9159\n   15   SparseNormalizer RandomForest                  0:00:33             0.8198    0.9159\n   16   StandardScalerWrapper LogisticRegression       0:00:13             0.9094    0.9159\n   17   StandardScalerWrapper RandomForest             0:00:20             0.9017    0.9159\n   18   StandardScalerWrapper XGBoostClassifier        0:00:14             0.9137    0.9159\n   19   TruncatedSVDWrapper RandomForest               0:02:48             0.8177    0.9159\n   20   TruncatedSVDWrapper RandomForest               0:03:57             0.8317    0.9159\n   21   StandardScalerWrapper XGBoostClassifier        0:00:37             0.9145    0.9159\n   22   MaxAbsScaler LightGBM                          0:00:28             0.8879    0.9159\n   23   MaxAbsScaler LightGBM                          0:00:41             0.9109    0.9159\n   24   MaxAbsScaler LightGBM                          0:00:33             0.9044    0.9159\n   25   MaxAbsScaler LightGBM                          0:00:27             0.8879    0.9159\n   26   MaxAbsScaler LightGBM                          0:00:34             0.9091    0.9159\n   27   StandardScalerWrapper XGBoostClassifier        0:01:06             0.9155    0.9159\n   28   StandardScalerWrapper XGBoostClassifier        0:02:44             0.9090    0.9159\n   29   MaxAbsScaler LightGBM                          0:00:33             0.9102    0.9159\n   30    VotingEnsemble                                0:00:58             0.9182    0.9182\n   31    StackEnsemble                                 0:01:27             0.9161    0.9182\n"
        },
        {
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          "data": {
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selection.\", \"Best run model explanations started\", \"Model explanations data setup completed\", \"Choosing LightGBM as the surrogate model for explanations\", \"Computation of engineered features started\", \"Computation of engineered features completed\", \"Computation of raw features started\", \"Computation of raw features completed\", \"Best run model explanations completed\"]}]}, {\"name\": \"AUC_weighted\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.9488033537597419]}]}, {\"name\": \"f1_score_weighted\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.91594549766002]}]}, {\"name\": \"matthews_correlation\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.5690385027866005]}]}, {\"name\": \"precision_score_weighted\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.9145754648867364]}]}, {\"name\": \"AUC_macro\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.9488033537597419]}]}, {\"name\": \"average_precision_score_macro\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.8294557303365521]}]}, {\"name\": \"precision_score_micro\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.9182094081942337]}]}, {\"name\": \"precision_score_macro\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.8006036239595116]}]}, {\"name\": \"recall_score_micro\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.9182094081942337]}]}, {\"name\": \"f1_score_micro\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.9182094081942337]}]}, {\"name\": \"balanced_accuracy\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.769621900379935]}]}, {\"name\": \"log_loss\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.26430684537963595]}]}, {\"name\": \"recall_score_weighted\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.9182094081942337]}]}, {\"name\": \"average_precision_score_micro\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.981737116519751]}]}, {\"name\": \"norm_macro_recall\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.5392438007598699]}]}, {\"name\": \"weighted_accuracy\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.9552307942035048]}]}, {\"name\": \"average_precision_score_weighted\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.9565143220160222]}]}, {\"name\": \"AUC_micro\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.9810659745648553]}]}, {\"name\": \"f1_score_macro\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.7834320963517902]}]}, {\"name\": \"recall_score_macro\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.769621900379935]}]}, {\"name\": \"accuracy\", \"run_id\": \"AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222\", \"categories\": [0], \"series\": [{\"data\": [0.9182094081942337]}]}], \"run_logs\": \"\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.48.0\"}, \"loading\": false}"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "{'runId': 'AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222',\n 'target': 'compute-cluster',\n 'status': 'Completed',\n 'startTimeUtc': '2023-02-16T13:01:47.213494Z',\n 'endTimeUtc': '2023-02-16T13:37:19.588443Z',\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': 'compute-cluster',\n  'AMLSettingsJsonString': '{\"path\":null,\"name\":\"bankdata-automl\",\"subscription_id\":\"79facb57-c974-4c43-935b-f645fdca73d9\",\"resource_group\":\"standard\",\"workspace_name\":\"udacity\",\"region\":\"eastus2\",\"compute_target\":\"compute-cluster\",\"spark_service\":null,\"azure_service\":\"remote\",\"many_models\":false,\"pipeline_fetch_max_batch_size\":1,\"enable_batch_run\":true,\"enable_parallel_run\":false,\"num_procs\":null,\"enable_run_restructure\":false,\"start_auxiliary_runs_before_parent_complete\":true,\"enable_code_generation\":true,\"iterations\":1000,\"primary_metric\":\"accuracy\",\"task_type\":\"classification\",\"positive_label\":null,\"data_script\":null,\"test_size\":0.2,\"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\":30,\"experiment_exit_score\":null,\"partition_column_names\":null,\"whitelist_models\":null,\"blacklist_algos\":[\"TensorFlowLinearClassifier\",\"TensorFlowDNN\"],\"supported_models\":[\"LinearSVM\",\"BernoulliNaiveBayes\",\"KNN\",\"SVM\",\"DecisionTree\",\"SGD\",\"RandomForest\",\"GradientBoosting\",\"TensorFlowDNN\",\"TensorFlowLinearClassifier\",\"TabnetClassifier\",\"XGBoostClassifier\",\"ExtremeRandomTrees\",\"LightGBM\",\"AveragedPerceptronClassifier\",\"MultinomialNaiveBayes\",\"LogisticRegression\"],\"private_models\":[],\"auto_blacklist\":true,\"blacklist_samples_reached\":false,\"exclude_nan_labels\":true,\"verbosity\":20,\"_debug_log\":\"azureml_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\":\"STANDARD_D2_V2\",\"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\":\"fc2f3911-0762-40f3-a2a4-b0e3992a5912\",\"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\":\"y\",\"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': '{\\\\\"training_data\\\\\": {\\\\\"datasetId\\\\\": \\\\\"fc2f3911-0762-40f3-a2a4-b0e3992a5912\\\\\"}, \\\\\"datasets\\\\\": 0}',\n  'EnableSubsampling': None,\n  'runTemplate': 'AutoML',\n  'azureml.runsource': 'automl',\n  'display_task_type': 'classification',\n  'dependencies_versions': '{\"azureml-widgets\": \"1.48.0\", \"azureml-training-tabular\": \"1.48.0\", \"azureml-train\": \"1.48.0\", \"azureml-train-restclients-hyperdrive\": \"1.48.0\", \"azureml-train-core\": \"1.48.0\", \"azureml-train-automl\": \"1.48.0\", \"azureml-train-automl-runtime\": \"1.48.0\", \"azureml-train-automl-client\": \"1.48.0\", \"azureml-tensorboard\": \"1.48.0\", \"azureml-telemetry\": \"1.48.0\", \"azureml-sdk\": \"1.48.0\", \"azureml-samples\": \"0+unknown\", \"azureml-responsibleai\": \"1.48.0\", \"azureml-pipeline\": \"1.48.0\", \"azureml-pipeline-steps\": \"1.48.0\", \"azureml-pipeline-core\": \"1.48.0\", \"azureml-opendatasets\": \"1.48.0\", \"azureml-mlflow\": \"1.48.0\", \"azureml-interpret\": \"1.48.0\", \"azureml-inference-server-http\": \"0.7.7\", \"azureml-explain-model\": \"1.48.0\", \"azureml-defaults\": \"1.48.0\", \"azureml-dataset-runtime\": \"1.48.0\", \"azureml-dataprep\": \"4.8.3\", \"azureml-dataprep-rslex\": \"2.15.1\", \"azureml-dataprep-native\": \"38.0.0\", \"azureml-datadrift\": \"1.48.0\", \"azureml-core\": \"1.48.0\", \"azureml-contrib-services\": \"1.48.0\", \"azureml-contrib-server\": \"1.48.0\", \"azureml-contrib-reinforcementlearning\": \"1.48.0\", \"azureml-contrib-pipeline-steps\": \"1.48.0\", \"azureml-contrib-notebook\": \"1.48.0\", \"azureml-contrib-fairness\": \"1.48.0\", \"azureml-contrib-dataset\": \"1.48.0\", \"azureml-contrib-automl-pipeline-steps\": \"1.48.0\", \"azureml-cli-common\": \"1.48.0\", \"azureml-automl-runtime\": \"1.48.0.post1\", \"azureml-automl-dnn-nlp\": \"1.48.0\", \"azureml-automl-core\": \"1.48.0\", \"azureml-accel-models\": \"1.48.0\"}',\n  '_aml_system_scenario_identification': 'Remote.Parent',\n  'ClientType': 'SDK',\n  'environment_cpu_name': 'AzureML-AutoML',\n  'environment_cpu_label': 'nimbus-deprecation',\n  'environment_gpu_name': 'AzureML-AutoML-GPU',\n  'environment_gpu_label': 'prod',\n  'root_attribution': 'automl',\n  'attribution': 'AutoML',\n  'Orchestrator': 'AutoML',\n  'CancelUri': 'https://eastus2.api.azureml.ms/jasmine/v1.0/subscriptions/79facb57-c974-4c43-935b-f645fdca73d9/resourceGroups/standard/providers/Microsoft.MachineLearningServices/workspaces/udacity/experimentids/b975a4ae-f816-484a-a775-33c1338a6eec/cancel/AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222',\n  'ClientSdkVersion': '1.48.0.post1',\n  'snapshotId': '00000000-0000-0000-0000-000000000000',\n  'SetupRunId': 'AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222_setup',\n  'SetupRunContainerId': 'dcid.AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222_setup',\n  'FeaturizationRunJsonPath': 'featurizer_container.json',\n  'FeaturizationRunId': 'AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222_featurize',\n  'ProblemInfoJsonString': '{\"dataset_num_categorical\": 0, \"is_sparse\": true, \"subsampling\": false, \"has_extra_col\": true, \"dataset_classes\": 2, \"dataset_features\": 121, \"dataset_samples\": 26360, \"single_frequency_class_detected\": false}',\n  'ModelExplainRunId': 'AutoML_25d6258e-45a2-49a0-a217-a9274d5fc222_ModelExplain'},\n 'inputDatasets': [{'dataset': {'id': 'fc2f3911-0762-40f3-a2a4-b0e3992a5912'}, 'consumptionDetails': {'type': 'RunInput', 'inputName': 'training_data', 'mechanism': 'Direct'}}],\n 'outputDatasets': [],\n 'logFiles': {},\n 'submittedBy': 'stefan huber'}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1676554661669
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally we find the best run and save the corresponding model."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_run = automl_run.get_best_child()\n",
        "best_run.download_file(\"outputs/model.pkl\", \"./outputs/best_automl_model.pkl\")"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "gather": {
          "logged": 1676554662576
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Delete the compute cluster"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "instance = ComputeTarget(workspace=ws, name=cluster_name)\n",
        "\n",
        "try:\n",
        "  instance.delete()\n",
        "  instance.wait_for_completion(show_output=True)\n",
        "  print('Deleted compute resource')\n",
        "except ComputeTargetException:\n",
        "  print('Already deleted!')"
      ],
      "outputs": [
        {
          "output_type": "error",
          "ename": "ComputeTargetException",
          "evalue": "ComputeTargetException:\n\tMessage: ComputeTargetNotFound: Compute Target with name compute-cluster not found in provided workspace\n\tInnerException None\n\tErrorResponse \n{\n    \"error\": {\n        \"message\": \"ComputeTargetNotFound: Compute Target with name compute-cluster not found in provided workspace\"\n    }\n}",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mComputeTargetException\u001b[0m                    Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m instance \u001b[38;5;241m=\u001b[39m \u001b[43mComputeTarget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mworkspace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mws\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcluster_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m      4\u001b[0m   instance\u001b[38;5;241m.\u001b[39mdelete()\n",
            "File \u001b[0;32m/anaconda/envs/azureml_py38/lib/python3.8/site-packages/azureml/core/compute/compute.py:90\u001b[0m, in \u001b[0;36mComputeTarget.__new__\u001b[0;34m(cls, workspace, name)\u001b[0m\n\u001b[1;32m     88\u001b[0m                 \u001b[38;5;28;01mreturn\u001b[39;00m compute_target\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 90\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ComputeTargetException(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mComputeTargetNotFound: Compute Target with name \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m not found in \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     91\u001b[0m                                      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprovided workspace\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(name))\n\u001b[1;32m     92\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     93\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m(ComputeTarget, \u001b[38;5;28mcls\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__new__\u001b[39m(\u001b[38;5;28mcls\u001b[39m)\n",
            "\u001b[0;31mComputeTargetException\u001b[0m: ComputeTargetException:\n\tMessage: ComputeTargetNotFound: Compute Target with name compute-cluster not found in provided workspace\n\tInnerException None\n\tErrorResponse \n{\n    \"error\": {\n        \"message\": \"ComputeTargetNotFound: Compute Target with name compute-cluster not found in provided workspace\"\n    }\n}"
          ]
        }
      ],
      "execution_count": 14,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1676558997154
        }
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python38-azureml"
    },
    "kernelspec": {
      "name": "python38-azureml",
      "language": "python",
      "display_name": "Python 3.8 - AzureML"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.10",
      "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"
    },
    "vscode": {
      "interpreter": {
        "hash": "a78d07d5c4f852dc5da4164b5b8419e5f5df177e09376445272249b5ffefc6e9"
      }
    },
    "microsoft": {
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}