udacity_project.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from azureml.core import Workspace, Experiment\n",
        "\n",
        "ws = Workspace.get(name=\"quick-starts-ws-135824\")\n",
        "exp = Experiment(workspace=ws, name=\"quick-starts-ws-135824\")\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": [
            "Performing interactive authentication. Please follow the instructions on the terminal.\n",
            "To sign in, use a web browser to open the page https://microsoft.com/devicelogin and enter the code FVATXGVLS to authenticate.\n",
            "You have logged in. Now let us find all the subscriptions to which you have access...\n",
            "Interactive authentication successfully completed.\n",
            "Workspace name: quick-starts-ws-135824\n",
            "Azure region: southcentralus\n",
            "Subscription id: 5a4ab2ba-6c51-4805-8155-58759ad589d8\n",
            "Resource group: aml-quickstarts-135824\n"
          ]
        }
      ],
      "execution_count": 1,
      "metadata": {
        "gather": {
          "logged": 1611434942216
        },
        "id": "a1dqucoVWsfB",
        "outputId": "68a7e3d7-8db3-492c-ee65-33d472c427ea"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\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",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "cpu_cluster_name = \"hyperdriveml\"\n",
        "cpu_cluster = ComputeTarget(ws,cpu_cluster_name)\n",
        "\n",
        "try:\n",
        "       cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
        "       print('Found existing cluster, use it.')\n",
        "except ComputeTargetException:\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
        "                                                          max_nodes=4)\n",
        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
        "\n",
        "cpu_cluster.wait_for_completion(show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing cluster, use it.\n",
            "\n",
            "Running\n"
          ]
        }
      ],
      "execution_count": 2,
      "metadata": {
        "gather": {
          "logged": 1611434960950
        },
        "id": "kuel1xhkWsfL",
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile conda_dependencies.yml\n",
        "\n",
        "dependencies:\n",
        "  - python=3.6.2\n",
        "  - scikit-learn\n",
        "  - numpy\n",
        "  - pip:\n",
        "    - azureml-defaults"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing conda_dependencies.yml\n"
          ]
        }
      ],
      "execution_count": 3,
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core import Environment\n",
        "sklearn_env = Environment.from_conda_specification(name='sklearn-env', file_path='conda_dependencies.yml')"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1611434966028
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.widgets import RunDetails\n",
        "from azureml.train.sklearn import SKLearn\n",
        "from azureml.core import ScriptRunConfig\n",
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "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 uniform,choice\n",
        "import os\n",
        "\n",
        "# Specify parameter sampler\n",
        "ps =  RandomParameterSampling({\"max_iter\" : choice(100,150,200,250),\n",
        "                                \"C\" : choice(0.01,0.05,0.2,1,5,10)\n",
        "                                })\n",
        "\n",
        "# Specify a Policy\n",
        "policy = BanditPolicy(evaluation_interval=1, slack_factor=0.2, delay_evaluation=0)\n",
        "\n",
        "if \"training\" not in os.listdir():\n",
        "    os.mkdir(\"./training\")\n",
        "\n",
        "\n",
        "# Create a SKLearn estimator for use with train.py\n",
        "src = ScriptRunConfig(source_directory='./',\n",
        "                      script='train.py',\n",
        "                      compute_target=cpu_cluster,                      \n",
        "                      environment=sklearn_env)\n",
        "\n",
        "\n",
        "# est = SKLearn(source_directory='./',               \n",
        "#             compute_target=cpu_cluster,\n",
        "#             entry_script='train.py')\n",
        "\n",
        "# Create a HyperDriveConfig using the estimator, hyperparameter sampler, and policy.\n",
        "hd_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=20,\n",
        "                                max_concurrent_runs=4)"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "gather": {
          "logged": 1611434972302
        },
        "id": "fpEp9Su2WsfM",
        "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",
        "#experimnt = Experiment(ws,exp)\n",
        "hd_run = exp.submit(hd_config)\n",
        "hd_run.wait_for_completion(show_output=True)\n",
        "RunDetails(hd_run).show()\n",
        "### YOUR CODE HERE ###"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RunId: HD_9f812997-0631-461f-978b-844a8a428b1f\n",
            "Web View: https://ml.azure.com/experiments/quick-starts-ws-135824/runs/HD_9f812997-0631-461f-978b-844a8a428b1f?wsid=/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourcegroups/aml-quickstarts-135824/workspaces/quick-starts-ws-135824\n",
            "\n",
            "Streaming azureml-logs/hyperdrive.txt\n",
            "=====================================\n",
            "\n",
            "\"<START>[2021-01-23T20:49:35.463927][API][INFO]Experiment created<END>\\n\"\"<START>[2021-01-23T20:49:36.469092][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.<END>\\n\"<START>[2021-01-23T20:49:36.5967650Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.<END>\"<START>[2021-01-23T20:49:36.130582][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space<END>\\n\"\n",
            "\n",
            "Execution Summary\n",
            "=================\n",
            "RunId: HD_9f812997-0631-461f-978b-844a8a428b1f\n",
            "Web View: https://ml.azure.com/experiments/quick-starts-ws-135824/runs/HD_9f812997-0631-461f-978b-844a8a428b1f?wsid=/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourcegroups/aml-quickstarts-135824/workspaces/quick-starts-ws-135824\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO'…",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d762e9184a61416cbbe592943ff2217e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/quick-starts-ws-135824/runs/HD_9f812997-0631-461f-978b-844a8a428b1f?wsid=/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourcegroups/aml-quickstarts-135824/workspaces/quick-starts-ws-135824\", \"run_id\": \"HD_9f812997-0631-461f-978b-844a8a428b1f\", \"run_properties\": {\"run_id\": \"HD_9f812997-0631-461f-978b-844a8a428b1f\", \"created_utc\": \"2021-01-23T20:49:35.105971Z\", \"properties\": {\"primary_metric_config\": \"{\\\"name\\\": \\\"Accuracy\\\", \\\"goal\\\": \\\"maximize\\\"}\", \"resume_from\": \"null\", \"runTemplate\": \"HyperDrive\", \"azureml.runsource\": \"hyperdrive\", \"platform\": \"AML\", \"ContentSnapshotId\": \"2272f6f3-2b5a-4023-995c-e0dea799876f\", \"score\": \"0.9088012139605463\", \"best_child_run_id\": \"HD_9f812997-0631-461f-978b-844a8a428b1f_1\", \"best_metric_status\": \"Succeeded\"}, \"tags\": {\"_aml_system_max_concurrent_jobs\": \"4\", \"max_concurrent_jobs\": \"4\", \"_aml_system_max_total_jobs\": \"20\", \"max_total_jobs\": \"20\", \"_aml_system_max_duration_minutes\": \"10080\", \"max_duration_minutes\": \"10080\", \"_aml_system_policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 1, \\\"delay_evaluation\\\": 0, \\\"slack_factor\\\": 0.2}}\", \"policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 1, \\\"delay_evaluation\\\": 0, \\\"slack_factor\\\": 0.2}}\", \"_aml_system_generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"max_iter\\\": [\\\"choice\\\", [[100, 150, 200, 250]]], \\\"C\\\": [\\\"choice\\\", [[0.01, 0.05, 0.2, 1, 5, 10]]]}}\", \"generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"max_iter\\\": [\\\"choice\\\", [[100, 150, 200, 250]]], \\\"C\\\": [\\\"choice\\\", [[0.01, 0.05, 0.2, 1, 5, 10]]]}}\", \"_aml_system_primary_metric_config\": \"{\\\"name\\\": \\\"Accuracy\\\", \\\"goal\\\": \\\"maximize\\\"}\", \"primary_metric_config\": \"{\\\"name\\\": \\\"Accuracy\\\", \\\"goal\\\": \\\"maximize\\\"}\", \"_aml_system_platform_config\": \"{\\\"ServiceAddress\\\": \\\"https://southcentralus.experiments.azureml.net\\\", \\\"ServiceArmScope\\\": \\\"subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/quick-starts-ws-135824\\\", \\\"SubscriptionId\\\": \\\"5a4ab2ba-6c51-4805-8155-58759ad589d8\\\", \\\"ResourceGroupName\\\": \\\"aml-quickstarts-135824\\\", \\\"WorkspaceName\\\": \\\"quick-starts-ws-135824\\\", \\\"ExperimentName\\\": \\\"quick-starts-ws-135824\\\", \\\"Definition\\\": {\\\"Overrides\\\": {\\\"script\\\": \\\"train.py\\\", \\\"arguments\\\": [], \\\"target\\\": \\\"hyperdriveml\\\", \\\"framework\\\": \\\"Python\\\", \\\"communicator\\\": 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\\\"architecture\\\": \\\"amd64\\\"}}, \\\"spark\\\": {\\\"repositories\\\": [], \\\"packages\\\": [], \\\"precachePackages\\\": true}, \\\"databricks\\\": {\\\"mavenLibraries\\\": [], \\\"pypiLibraries\\\": [], \\\"rcranLibraries\\\": [], \\\"jarLibraries\\\": [], \\\"eggLibraries\\\": []}, \\\"r\\\": null, \\\"inferencingStackVersion\\\": null}, \\\"history\\\": {\\\"outputCollection\\\": true, \\\"snapshotProject\\\": true, \\\"directoriesToWatch\\\": [\\\"logs\\\"]}, \\\"spark\\\": {\\\"configuration\\\": {\\\"spark.app.name\\\": \\\"Azure ML Experiment\\\", \\\"spark.yarn.maxAppAttempts\\\": 1}}, \\\"hdi\\\": {\\\"yarnDeployMode\\\": \\\"cluster\\\"}, \\\"tensorflow\\\": {\\\"workerCount\\\": 1, \\\"parameterServerCount\\\": 1}, \\\"mpi\\\": {\\\"processCountPerNode\\\": 1, \\\"nodeCount\\\": 1}, \\\"paralleltask\\\": {\\\"maxRetriesPerWorker\\\": 0, \\\"workerCountPerNode\\\": 1, \\\"terminalExitCodes\\\": null}, \\\"dataReferences\\\": {}, \\\"data\\\": {}, \\\"outputData\\\": 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\\\"maxDurationMinutes\\\": 10080, \\\"vmSize\\\": null}}}\", \"platform_config\": \"{\\\"ServiceAddress\\\": \\\"https://southcentralus.experiments.azureml.net\\\", \\\"ServiceArmScope\\\": \\\"subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/quick-starts-ws-135824\\\", \\\"SubscriptionId\\\": \\\"5a4ab2ba-6c51-4805-8155-58759ad589d8\\\", \\\"ResourceGroupName\\\": \\\"aml-quickstarts-135824\\\", \\\"WorkspaceName\\\": \\\"quick-starts-ws-135824\\\", \\\"ExperimentName\\\": \\\"quick-starts-ws-135824\\\", \\\"Definition\\\": {\\\"Overrides\\\": {\\\"script\\\": \\\"train.py\\\", \\\"arguments\\\": [], \\\"target\\\": \\\"hyperdriveml\\\", \\\"framework\\\": \\\"Python\\\", \\\"communicator\\\": \\\"None\\\", \\\"maxRunDurationSeconds\\\": 2592000, \\\"nodeCount\\\": 1, \\\"environment\\\": {\\\"name\\\": \\\"sklearn-env\\\", \\\"version\\\": null, 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null, \\\"clusterMaxNodeCount\\\": null}, \\\"command\\\": \\\"\\\"}, \\\"TargetDetails\\\": null, \\\"SnapshotId\\\": \\\"2272f6f3-2b5a-4023-995c-e0dea799876f\\\", \\\"TelemetryValues\\\": {\\\"amlClientType\\\": \\\"azureml-sdk-train\\\", \\\"amlClientModule\\\": \\\"[Scrubbed]\\\", \\\"amlClientFunction\\\": \\\"[Scrubbed]\\\", \\\"tenantId\\\": \\\"660b3398-b80e-49d2-bc5b-ac1dc93b5254\\\", \\\"amlClientRequestId\\\": \\\"e6612243-1452-4cd1-ba26-729082c5c475\\\", \\\"amlClientSessionId\\\": \\\"9c4181d2-0c60-48c3-a8ad-9a6a80f0be3a\\\", \\\"subscriptionId\\\": \\\"5a4ab2ba-6c51-4805-8155-58759ad589d8\\\", \\\"estimator\\\": \\\"NoneType\\\", \\\"samplingMethod\\\": \\\"RANDOM\\\", \\\"terminationPolicy\\\": \\\"Bandit\\\", \\\"primaryMetricGoal\\\": \\\"maximize\\\", \\\"maxTotalRuns\\\": 20, \\\"maxConcurrentRuns\\\": 4, \\\"maxDurationMinutes\\\": 10080, \\\"vmSize\\\": null}}}\", \"_aml_system_resume_child_runs\": \"null\", \"resume_child_runs\": \"null\", 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Please be patient as it can take a few minutes.\\r\\n[2021-01-23T20:49:36.130582][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-01-23T20:55:13.8699534Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_2'\\r\\n[2021-01-23T20:55:13.8428235Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_0'\\r\\n[2021-01-23T20:55:13.8556511Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_3'\\r\\n[2021-01-23T20:55:13.8555572Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_1'\\r\\n[2021-01-23T20:55:13.8422224Z][SCHEDULER][INFO]The execution environment was successfully prepared.\\r\\n[2021-01-23T20:55:14.9713287Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_3'\\r\\n[2021-01-23T20:55:14.9688363Z][SCHEDULER][INFO]Successfully scheduled a job. 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applied.\\r\\n[2021-01-23T20:56:45.835873][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_0, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_1, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_2, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T20:57:15.944973][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_0, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_1, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_2, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T20:57:48.265809][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_0, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_1, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_2, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T20:58:13.608855][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-01-23T20:58:13.777175][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-23T20:58:19.0847835Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_4'\\r\\n[2021-01-23T20:58:19.0862375Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_5'\\r\\n[2021-01-23T20:58:19.1072141Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_6'\\r\\n[2021-01-23T20:58:20.1306576Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_5'\\r\\n[2021-01-23T20:58:20.9242128Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_4'\\r\\n[2021-01-23T20:58:21.3165571Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_6'\\r\\n[2021-01-23T20:58:21.435049][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_5] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T20:58:44.090523][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-23T20:58:44.427484][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-23T20:58:51.823359][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_4, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_5, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_6] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T20:58:51.8603642Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_7'\\r\\n[2021-01-23T20:58:53.2667914Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_7'\\r\\n[2021-01-23T20:59:22.047703][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_7] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T20:59:45.162757][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-23T20:59:44.998288][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-01-23T20:59:51.941334][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_7] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T20:59:54.5609099Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_10'\\r\\n[2021-01-23T20:59:54.6345115Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_9'\\r\\n[2021-01-23T20:59:54.6562410Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_8'\\r\\n[2021-01-23T20:59:55.4671329Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_10'\\r\\n[2021-01-23T20:59:55.6774136Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_9'\\r\\n[2021-01-23T20:59:55.7371277Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_8'\\r\\n[2021-01-23T21:00:22.456004][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_8, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_9, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_10] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T21:00:49.189477][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-23T21:00:49.467594][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-23T21:00:53.379662][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_8, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_9, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_10] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T21:00:56.9319981Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_11'\\r\\n[2021-01-23T21:00:58.0644414Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_11'\\r\\n[2021-01-23T21:01:20.588473][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-23T21:01:20.857071][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-23T21:01:27.080846][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_11] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T21:01:28.7202982Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_12'\\r\\n[2021-01-23T21:01:29.6653738Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_12'\\r\\n[2021-01-23T21:01:51.831710][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-23T21:01:51.525880][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-01-23T21:01:59.400061][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_11, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_12] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T21:02:00.2890793Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_13'\\r\\n[2021-01-23T21:02:00.2718645Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_14'\\r\\n[2021-01-23T21:02:01.0417897Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_13'\\r\\n[2021-01-23T21:02:01.2084968Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_14'\\r\\n[2021-01-23T21:02:24.752546][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-23T21:02:25.039899][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-23T21:02:30.123832][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_12, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_13, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_14] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T21:02:31.9420789Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_15'\\r\\n[2021-01-23T21:02:33.0671429Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_15'\\r\\n[2021-01-23T21:02:57.743034][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-23T21:02:58.028338][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-23T21:03:01.091256][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_13, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_14, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_15] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T21:03:04.0159248Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_16'\\r\\n[2021-01-23T21:03:05.0377062Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_16'\\r\\n[2021-01-23T21:03:31.240402][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_15, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_16] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T21:03:58.094431][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-01-23T21:03:58.268994][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-23T21:04:02.456985][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_16] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T21:04:06.7510306Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_17'\\r\\n[2021-01-23T21:04:06.7870129Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_19'\\r\\n[2021-01-23T21:04:06.7522594Z][SCHEDULER][INFO]Scheduling job, id='HD_9f812997-0631-461f-978b-844a8a428b1f_18'\\r\\n[2021-01-23T21:04:07.8815565Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_17'\\r\\n[2021-01-23T21:04:08.0077852Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_19'\\r\\n[2021-01-23T21:04:08.3076470Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_9f812997-0631-461f-978b-844a8a428b1f_18'\\r\\n[2021-01-23T21:04:32.238184][GENERATOR][INFO]All jobs generated.\\r\\n[2021-01-23T21:04:31.949311][GENERATOR][INFO]Max number of jobs '20' reached for experiment.\\r\\n[2021-01-23T21:04:33.710792][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_17, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_18, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_19] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T21:05:04.721585][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_17, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_18, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-135824/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135824/experiments/**SCRUBBED**/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_19] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-23T21:06:23.737240][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\": \"1.19.0\"}, \"loading\": false}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "gather": {
          "logged": 1611436003119
        },
        "id": "Rhet9QuvWsfN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "# Get your best run and save the model from that run.\n",
        "### YOUR CODE HERE ###\n",
        "best_run = hd_run.get_best_run_by_primary_metric()\n",
        "best_run"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "Run(Experiment: quick-starts-ws-135824,\nId: HD_9f812997-0631-461f-978b-844a8a428b1f_1,\nType: azureml.scriptrun,\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>quick-starts-ws-135824</td><td>HD_9f812997-0631-461f-978b-844a8a428b1f_1</td><td>azureml.scriptrun</td><td>Completed</td><td><a href=\"https://ml.azure.com/experiments/quick-starts-ws-135824/runs/HD_9f812997-0631-461f-978b-844a8a428b1f_1?wsid=/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourcegroups/aml-quickstarts-135824/workspaces/quick-starts-ws-135824\" 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": 7,
      "metadata": {
        "gather": {
          "logged": 1611436003496
        },
        "id": "WT7VtwXfWsfO",
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Best run details\", best_run.get_details())\n",
        "print('\\t')\n",
        "print(\"Best run file names:\", best_run.get_file_names())\n",
        "model = best_run.register_model(model_name='bankmark_hyperdrive_best_run', model_path='./outputs/model.joblib')\n",
        "print(model.name, model.id,model.version,sep='\\t')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best run details {'runId': 'HD_9f812997-0631-461f-978b-844a8a428b1f_1', 'target': 'hyperdriveml', 'status': 'Completed', 'startTimeUtc': '2021-01-23T20:55:26.092059Z', 'endTimeUtc': '2021-01-23T20:58:12.499814Z', 'properties': {'_azureml.ComputeTargetType': 'amlcompute', 'ContentSnapshotId': '2272f6f3-2b5a-4023-995c-e0dea799876f', 'ProcessInfoFile': 'azureml-logs/process_info.json', 'ProcessStatusFile': 'azureml-logs/process_status.json'}, 'inputDatasets': [], 'outputDatasets': [], 'runDefinition': {'script': 'train.py', 'command': '', 'useAbsolutePath': False, 'arguments': ['--C', '0.01', '--max_iter', '100'], 'sourceDirectoryDataStore': None, 'framework': 'Python', 'communicator': 'None', 'target': 'hyperdriveml', 'dataReferences': {}, 'data': {}, 'outputData': {}, 'jobName': None, 'maxRunDurationSeconds': 2592000, 'nodeCount': 1, 'priority': None, 'credentialPassthrough': False, 'environment': {'name': 'sklearn-env', 'version': 'Autosave_2021-01-23T20:49:35Z_29860885', 'python': {'interpreterPath': 'python', 'userManagedDependencies': False, 'condaDependencies': {'dependencies': ['python=3.6.2', 'scikit-learn', 'numpy', {'pip': ['azureml-defaults']}], 'name': 'azureml_5d7460dd0fd5853f576fa808d076689c'}, 'baseCondaEnvironment': None}, 'environmentVariables': {'EXAMPLE_ENV_VAR': 'EXAMPLE_VALUE'}, 'docker': {'baseImage': 'mcr.microsoft.com/azureml/intelmpi2018.3-ubuntu16.04:20200821.v1', 'platform': {'os': 'Linux', 'architecture': 'amd64'}, 'baseDockerfile': None, 'baseImageRegistry': {'address': None, 'username': None, 'password': None}, 'enabled': False, 'arguments': []}, 'spark': {'repositories': [], 'packages': [], 'precachePackages': True}, 'inferencingStackVersion': None}, 'history': {'outputCollection': True, 'directoriesToWatch': ['logs'], 'enableMLflowTracking': True, 'snapshotProject': True}, 'spark': {'configuration': {'spark.app.name': 'Azure ML Experiment', 'spark.yarn.maxAppAttempts': '1'}}, 'parallelTask': {'maxRetriesPerWorker': 0, 'workerCountPerNode': 1, 'terminalExitCodes': None, 'configuration': {}}, 'amlCompute': {'name': None, 'vmSize': None, 'retainCluster': False, 'clusterMaxNodeCount': None}, 'aiSuperComputer': {'instanceType': None, 'frameworkImage': None, 'imageVersion': None, 'location': None, 'aiSuperComputerStorageData': None}, 'tensorflow': {'workerCount': 1, 'parameterServerCount': 1}, 'mpi': {'processCountPerNode': 1}, 'hdi': {'yarnDeployMode': 'Cluster'}, 'containerInstance': {'region': None, 'cpuCores': 2.0, 'memoryGb': 3.5}, 'exposedPorts': None, 'docker': {'useDocker': False, 'sharedVolumes': True, 'shmSize': '2g', 'arguments': []}, 'cmk8sCompute': {'configuration': {}}, 'commandReturnCodeConfig': {'returnCode': 'Zero', 'successfulReturnCodes': []}}, 'logFiles': {'azureml-logs/55_azureml-execution-tvmps_31d78587a08a343e499d0cc5fcb84248bbf3985b9f684718039ddd6d68a21539_d.txt': 'https://mlstrg135824.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_9f812997-0631-461f-978b-844a8a428b1f_1/azureml-logs/55_azureml-execution-tvmps_31d78587a08a343e499d0cc5fcb84248bbf3985b9f684718039ddd6d68a21539_d.txt?sv=2019-02-02&sr=b&sig=Te0BxgGehafAw7psOymapXFrOQVLpN8sNdV9XalJwxk%3D&st=2021-01-23T20%3A56%3A43Z&se=2021-01-24T05%3A06%3A43Z&sp=r', 'azureml-logs/65_job_prep-tvmps_31d78587a08a343e499d0cc5fcb84248bbf3985b9f684718039ddd6d68a21539_d.txt': 'https://mlstrg135824.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_9f812997-0631-461f-978b-844a8a428b1f_1/azureml-logs/65_job_prep-tvmps_31d78587a08a343e499d0cc5fcb84248bbf3985b9f684718039ddd6d68a21539_d.txt?sv=2019-02-02&sr=b&sig=7XR0zQZmNJFnr0PiGyY2%2BSu8OJHzYn9Hy8ouw%2Fj5vYo%3D&st=2021-01-23T20%3A56%3A43Z&se=2021-01-24T05%3A06%3A43Z&sp=r', 'azureml-logs/70_driver_log.txt': 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'logs/azureml/dataprep/engine_spans_l_674b1545-3f25-43aa-b68b-a49b07c8736c.jsonl': 'https://mlstrg135824.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_9f812997-0631-461f-978b-844a8a428b1f_1/logs/azureml/dataprep/engine_spans_l_674b1545-3f25-43aa-b68b-a49b07c8736c.jsonl?sv=2019-02-02&sr=b&sig=nTI9LeUJbTcB%2F5msKhsdx7WRsgErejMKJ%2BaIrhocMaU%3D&st=2021-01-23T20%3A56%3A43Z&se=2021-01-24T05%3A06%3A43Z&sp=r', 'logs/azureml/dataprep/python_span_l_674b1545-3f25-43aa-b68b-a49b07c8736c.jsonl': 'https://mlstrg135824.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_9f812997-0631-461f-978b-844a8a428b1f_1/logs/azureml/dataprep/python_span_l_674b1545-3f25-43aa-b68b-a49b07c8736c.jsonl?sv=2019-02-02&sr=b&sig=jiYVcQYSmelQlT3l2MY2qsuWDCHP1W4rukJkkyc7CR4%3D&st=2021-01-23T20%3A56%3A43Z&se=2021-01-24T05%3A06%3A43Z&sp=r', 'logs/azureml/job_prep_azureml.log': 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            "\t\n",
            "Best run file names: ['azureml-logs/55_azureml-execution-tvmps_31d78587a08a343e499d0cc5fcb84248bbf3985b9f684718039ddd6d68a21539_d.txt', 'azureml-logs/65_job_prep-tvmps_31d78587a08a343e499d0cc5fcb84248bbf3985b9f684718039ddd6d68a21539_d.txt', 'azureml-logs/70_driver_log.txt', 'azureml-logs/75_job_post-tvmps_31d78587a08a343e499d0cc5fcb84248bbf3985b9f684718039ddd6d68a21539_d.txt', 'logs/azureml/94_azureml.log', 'logs/azureml/dataprep/backgroundProcess.log', 'logs/azureml/dataprep/backgroundProcess_Telemetry.log', 'logs/azureml/dataprep/engine_spans_l_674b1545-3f25-43aa-b68b-a49b07c8736c.jsonl', 'logs/azureml/dataprep/python_span_l_674b1545-3f25-43aa-b68b-a49b07c8736c.jsonl', 'logs/azureml/job_prep_azureml.log', 'logs/azureml/job_release_azureml.log', 'outputs/model.joblib']\n",
            "bankmark_hyperdrive_best_run\tbankmark_hyperdrive_best_run:1\t1\n"
          ]
        }
      ],
      "execution_count": 8,
      "metadata": {
        "gather": {
          "logged": 1611436005576
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hd_run.get_metrics()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "{'HD_9f812997-0631-461f-978b-844a8a428b1f_0': {'Regularization Strength:': 0.05,\n  'Max iterations:': 250,\n  'Accuracy': 0.9072837632776934},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_1': {'Regularization Strength:': 0.01,\n  'Max iterations:': 100,\n  'Accuracy': 0.9088012139605463},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_10': {'Regularization Strength:': 5.0,\n  'Max iterations:': 150,\n  'Accuracy': 0.9074355083459787},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_11': {'Regularization Strength:': 0.05,\n  'Max iterations:': 150,\n  'Accuracy': 0.9080424886191198},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_12': {'Regularization Strength:': 1.0,\n  'Max iterations:': 150,\n  'Accuracy': 0.9077389984825494},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_13': {'Regularization Strength:': 10.0,\n  'Max iterations:': 100,\n  'Accuracy': 0.908649468892261},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_14': {'Regularization Strength:': 0.2,\n  'Max iterations:': 200,\n  'Accuracy': 0.9074355083459787},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_15': {'Regularization Strength:': 1.0,\n  'Max iterations:': 200,\n  'Accuracy': 0.907587253414264},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_16': {'Regularization Strength:': 1.0,\n  'Max iterations:': 250,\n  'Accuracy': 0.9077389984825494},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_17': {'Regularization Strength:': 0.05,\n  'Max iterations:': 100,\n  'Accuracy': 0.9083459787556905},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_18': {'Regularization Strength:': 0.01,\n  'Max iterations:': 150,\n  'Accuracy': 0.907587253414264},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_19': {'Regularization Strength:': 1.0,\n  'Max iterations:': 100,\n  'Accuracy': 0.908649468892261},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_2': {'Regularization Strength:': 0.2,\n  'Max iterations:': 250,\n  'Accuracy': 0.906980273141123},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_3': {'Regularization Strength:': 5.0,\n  'Max iterations:': 200,\n  'Accuracy': 0.9071320182094081},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_4': {'Regularization Strength:': 10.0,\n  'Max iterations:': 200,\n  'Accuracy': 0.9072837632776934},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_5': {'Regularization Strength:': 5.0,\n  'Max iterations:': 250,\n  'Accuracy': 0.908649468892261},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_6': {'Regularization Strength:': 0.05,\n  'Max iterations:': 200,\n  'Accuracy': 0.9074355083459787},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_7': {'Regularization Strength:': 0.01,\n  'Max iterations:': 250,\n  'Accuracy': 0.9074355083459787},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_8': {'Regularization Strength:': 0.2,\n  'Max iterations:': 150,\n  'Accuracy': 0.9077389984825494},\n 'HD_9f812997-0631-461f-978b-844a8a428b1f_9': {'Regularization Strength:': 10.0,\n  'Max iterations:': 250,\n  'Accuracy': 0.9077389984825494}}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "gather": {
          "logged": 1611436008431
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.data.dataset_factory import TabularDatasetFactory\n",
        "#from azureml.core.dataset import Dataset\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_url = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
        "ds= TabularDatasetFactory.from_delimited_files(path=data_url)\n"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "gather": {
          "logged": 1611436018622
        },
        "id": "__KzvJ1YWsfO"
      }
    },
    {
      "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)\n",
        "x['y'] = y\n",
        "label = \"y\""
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "gather": {
          "logged": 1611436026316
        },
        "id": "VD0eauNMWsfP",
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()\r\n",
        "x.to_csv(\"bankmarketing_train.csv\")"
      ],
      "outputs": [],
      "execution_count": 12,
      "metadata": {
        "gather": {
          "logged": 1611436027922
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core.dataset import Dataset\r\n",
        "datastore = ws.get_default_datastore()\r\n",
        "datastore.upload_files(files = ['bankmarketing_train.csv'],\r\n",
        "                        target_path = './',\r\n",
        "                        overwrite=True,\r\n",
        "                        show_progress=True)\r\n",
        "dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, \"./bankmarketing_train.csv\")])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Uploading an estimated of 1 files\n",
            "Uploading bankmarketing_train.csv\n",
            "Uploaded bankmarketing_train.csv, 1 files out of an estimated total of 1\n",
            "Uploaded 1 files\n"
          ]
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1611436029711
        }
      }
    },
    {
      "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=30,\n",
        "    task='classification',\n",
        "    primary_metric='accuracy',\n",
        "    training_data=x,\n",
        "    label_column_name=label,\n",
        "    n_cross_validations=3)"
      ],
      "outputs": [],
      "execution_count": 14,
      "metadata": {
        "gather": {
          "logged": 1611436029863
        },
        "id": "U6g2OYy7WsfP",
        "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",
        "#ws = Workspace.from_config()\n",
        "#experiment = Experiment(ws, \"exp\")\n",
        "run = exp.submit(automl_config, show_output=True)\n",
        "run.wait_for_completion()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No run_configuration provided, running on local with default configuration\n",
            "Running on local machine\n",
            "Parent Run ID: AutoML_375d101e-8fad-47e3-810a-6a97847f543b\n",
            "\n",
            "Current status: DatasetEvaluation. Gathering dataset statistics.\n",
            "Current status: FeaturesGeneration. Generating features for the dataset.\n",
            "Current status: DatasetFeaturization. Beginning to fit featurizers and featurize the dataset.\n",
            "Current status: DatasetFeaturizationCompleted. Completed fit featurizers and featurizing the dataset.\n",
            "Current status: DatasetBalancing. Performing class balancing sweeping\n",
            "Current status: DatasetCrossValidationSplit. Generating individually featurized CV splits.\n",
            "\n",
            "****************************************************************************************************\n",
            "DATA GUARDRAILS: \n",
            "\n",
            "TYPE:         Class balancing detection\n",
            "STATUS:       ALERTED\n",
            "DESCRIPTION:  To decrease model bias, please cancel the current run and fix balancing problem.\n",
            "              Learn more about imbalanced data: https://aka.ms/AutomatedMLImbalancedData\n",
            "DETAILS:      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",
            "\n",
            "TYPE:         Missing feature values imputation\n",
            "STATUS:       PASSED\n",
            "DESCRIPTION:  No feature missing values were detected in the training data.\n",
            "              Learn more about missing value imputation: https://aka.ms/AutomatedMLFeaturization\n",
            "\n",
            "****************************************************************************************************\n",
            "\n",
            "TYPE:         High cardinality feature detection\n",
            "STATUS:       PASSED\n",
            "DESCRIPTION:  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",
            "Current status: ModelSelection. Beginning model selection.\n",
            "\n",
            "****************************************************************************************************\n",
            "ITERATION: The iteration being evaluated.\n",
            "PIPELINE: A summary description of the pipeline being evaluated.\n",
            "DURATION: Time taken for the current iteration.\n",
            "METRIC: The result of computing score on the fitted pipeline.\n",
            "BEST: The best observed score thus far.\n",
            "****************************************************************************************************\n",
            "\n",
            " ITERATION   PIPELINE                                       DURATION      METRIC      BEST\n",
            "         0   MaxAbsScaler LightGBM                          0:00:40       0.9139    0.9139\n",
            "         1   MaxAbsScaler XGBoostClassifier                 0:00:36       0.9142    0.9142\n",
            "         2   MaxAbsScaler RandomForest                      0:00:34       0.8968    0.9142\n",
            "         3   MaxAbsScaler RandomForest                      0:00:36       0.8880    0.9142\n",
            "         4   MaxAbsScaler RandomForest                      0:00:37       0.8071    0.9142\n",
            "         5   MaxAbsScaler RandomForest                      0:00:29       0.7798    0.9142\n",
            "         6   SparseNormalizer XGBoostClassifier             0:00:42       0.9126    0.9142\n",
            "         7   MaxAbsScaler GradientBoosting                  0:00:36       0.9029    0.9142\n",
            "         8   StandardScalerWrapper RandomForest             0:00:30       0.8997    0.9142\n",
            "         9   MaxAbsScaler LogisticRegression                0:00:31       0.9082    0.9142\n",
            "        10   MaxAbsScaler LightGBM                          0:00:32       0.8914    0.9142\n",
            "        11   SparseNormalizer XGBoostClassifier             0:00:36       0.9129    0.9142\n",
            "        12   MaxAbsScaler ExtremeRandomTrees                0:01:22       0.8880    0.9142\n",
            "        13   StandardScalerWrapper LightGBM                 0:00:28       0.8880    0.9142\n",
            "        14   SparseNormalizer XGBoostClassifier             0:01:12       0.9124    0.9142\n",
            "        15   MaxAbsScaler LightGBM                          0:00:31       0.9093    0.9142\n",
            "        16   StandardScalerWrapper LightGBM                 0:00:28       0.8880    0.9142\n",
            "        17   StandardScalerWrapper ExtremeRandomTrees       0:00:46       0.8880    0.9142\n",
            "        18   MaxAbsScaler LightGBM                          0:00:36       0.9059    0.9142\n",
            "        19   StandardScalerWrapper LightGBM                 0:00:37       0.9085    0.9142\n",
            "        20   MaxAbsScaler LightGBM                          0:00:27       0.8962    0.9142\n",
            "        21   SparseNormalizer RandomForest                  0:00:38       0.8880    0.9142\n",
            "        22   SparseNormalizer XGBoostClassifier             0:00:28       0.8960    0.9142\n",
            "        23   StandardScalerWrapper LightGBM                 0:00:27       0.8906    0.9142\n",
            "        24   StandardScalerWrapper XGBoostClassifier        0:00:48       0.9086    0.9142\n",
            "        25   SparseNormalizer XGBoostClassifier             0:00:29       0.8880    0.9142\n",
            "        26   SparseNormalizer LightGBM                      0:00:31       0.9110    0.9142\n",
            "        27   StandardScalerWrapper XGBoostClassifier        0:00:36       0.9077    0.9142\n",
            "        28   SparseNormalizer XGBoostClassifier             0:00:55       0.9141    0.9142\n",
            "        29   StandardScalerWrapper LightGBM                 0:00:46       0.9037    0.9142\n",
            "        30   SparseNormalizer XGBoostClassifier             0:00:29       0.8880    0.9142\n",
            "        31   SparseNormalizer XGBoostClassifier             0:01:03       0.9125    0.9142\n",
            "        32   MaxAbsScaler GradientBoosting                  0:00:43       0.9023    0.9142\n",
            "        33   SparseNormalizer XGBoostClassifier             0:03:01       0.9108    0.9142\n",
            "        34   VotingEnsemble                                 0:00:53       0.9166    0.9166\n",
            "        35   StackEnsemble                                  0:01:00       0.9154    0.9166\n",
            "Stopping criteria reached at iteration 36. Ending experiment.\n",
            "****************************************************************************************************\n",
            "Current status: BestRunExplainModel. Best run model explanations started\n",
            "Current status: ModelExplanationDataSetSetup. Model explanations data setup completed\n",
            "Current status: PickSurrogateModel. Choosing LightGBM as the surrogate model for explanations\n",
            "Current status: EngineeredFeatureExplanations. Computation of engineered features started\n",
            "Current status: EngineeredFeatureExplanations. Computation of engineered features completed\n",
            "Current status: RawFeaturesExplanations. Computation of raw features started\n",
            "Current status: RawFeaturesExplanations. Computation of raw features completed\n",
            "Current status: BestRunExplainModel. Best run model explanations completed\n",
            "****************************************************************************************************\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "{'runId': 'AutoML_375d101e-8fad-47e3-810a-6a97847f543b',\n 'target': 'local',\n 'status': 'Completed',\n 'startTimeUtc': '2021-01-23T21:08:31.396023Z',\n 'endTimeUtc': '2021-01-23T21:41:58.928154Z',\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': '3',\n  'target': 'local',\n  'AMLSettingsJsonString': '{\"path\":null,\"name\":\"quick-starts-ws-135824\",\"subscription_id\":\"5a4ab2ba-6c51-4805-8155-58759ad589d8\",\"resource_group\":\"aml-quickstarts-135824\",\"workspace_name\":\"quick-starts-ws-135824\",\"region\":\"southcentralus\",\"compute_target\":\"local\",\"spark_service\":null,\"azure_service\":\"Microsoft.AzureNotebookVM\",\"many_models\":false,\"pipeline_fetch_max_batch_size\":1,\"iterations\":1000,\"primary_metric\":\"accuracy\",\"task_type\":\"classification\",\"data_script\":null,\"validation_size\":0.0,\"n_cross_validations\":3,\"y_min\":null,\"y_max\":null,\"num_classes\":2,\"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,\"whitelist_models\":null,\"blacklist_algos\":[\"TensorFlowLinearClassifier\",\"TensorFlowDNN\"],\"supported_models\":[\"LightGBM\",\"LinearSVM\",\"KNN\",\"SGD\",\"SVM\",\"MultinomialNaiveBayes\",\"RandomForest\",\"DecisionTree\",\"XGBoostClassifier\",\"BernoulliNaiveBayes\",\"TensorFlowDNN\",\"ExtremeRandomTrees\",\"GradientBoosting\",\"TensorFlowLinearClassifier\",\"AveragedPerceptronClassifier\",\"LogisticRegression\"],\"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\":\"SDK-1.13.0\",\"environment_label\":null,\"force_text_dnn\":false,\"enable_feature_sweeping\":true,\"enable_early_stopping\":false,\"early_stopping_n_iters\":10,\"metrics\":null,\"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,\"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': None,\n  'EnableSubsampling': None,\n  'runTemplate': 'AutoML',\n  'azureml.runsource': 'automl',\n  'display_task_type': 'classification',\n  'dependencies_versions': '{\"azureml-widgets\": \"1.19.0\", \"azureml-train\": \"1.19.0\", \"azureml-train-restclients-hyperdrive\": \"1.19.0\", \"azureml-train-core\": \"1.19.0\", \"azureml-train-automl\": \"1.19.0\", \"azureml-train-automl-runtime\": \"1.19.0\", \"azureml-train-automl-client\": \"1.19.0\", \"azureml-tensorboard\": \"1.19.0\", \"azureml-telemetry\": \"1.19.0\", \"azureml-sdk\": \"1.19.0\", \"azureml-samples\": \"0+unknown\", \"azureml-pipeline\": \"1.19.0\", \"azureml-pipeline-steps\": \"1.19.0\", \"azureml-pipeline-core\": \"1.19.0\", \"azureml-opendatasets\": \"1.19.0\", \"azureml-model-management-sdk\": \"1.0.1b6.post1\", \"azureml-mlflow\": \"1.19.0\", \"azureml-interpret\": \"1.19.0\", \"azureml-explain-model\": \"1.19.0\", \"azureml-defaults\": \"1.19.0\", \"azureml-dataset-runtime\": \"1.19.0.post1\", \"azureml-dataprep\": \"2.6.1\", \"azureml-dataprep-rslex\": \"1.4.0\", \"azureml-dataprep-native\": \"26.0.0\", \"azureml-datadrift\": \"1.19.0\", \"azureml-core\": \"1.19.0\", \"azureml-contrib-services\": \"1.19.0\", \"azureml-contrib-server\": \"1.19.0\", \"azureml-contrib-reinforcementlearning\": \"1.19.0\", \"azureml-contrib-pipeline-steps\": \"1.19.0\", \"azureml-contrib-notebook\": \"1.19.0\", \"azureml-contrib-interpret\": \"1.19.0\", \"azureml-contrib-gbdt\": \"1.19.0\", \"azureml-contrib-fairness\": \"1.19.0\", \"azureml-contrib-dataset\": \"1.19.0\", \"azureml-cli-common\": \"1.19.0\", \"azureml-automl-runtime\": \"1.19.0\", \"azureml-automl-core\": \"1.19.0\", \"azureml-accel-models\": \"1.19.0\"}',\n  '_aml_system_scenario_identification': 'Local.Parent',\n  'ClientSdkVersion': '1.19.0',\n  'ClientType': 'SDK',\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, \"dataset_classes\": 2, \"dataset_features\": 122, \"dataset_samples\": 32950, \"single_frequency_class_detected\": false}',\n  'feature_skus': 'automatedml_sdk_guardrails'},\n 'inputDatasets': [],\n 'outputDatasets': [],\n 'logFiles': {}}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "gather": {
          "logged": 1611438190498
        },
        "id": "7iOcsslmWsfP",
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve and save your best automl model.\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "best_run, fitted_model = run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Run(Experiment: quick-starts-ws-135824,\n",
            "Id: AutoML_375d101e-8fad-47e3-810a-6a97847f543b_34,\n",
            "Type: None,\n",
            "Status: Completed)\n",
            "Pipeline(memory=None,\n",
            "         steps=[('datatransformer',\n",
            "                 DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\n",
            "                                 feature_sweeping_config=None,\n",
            "                                 feature_sweeping_timeout=None,\n",
            "                                 featurization_config=None, force_text_dnn=None,\n",
            "                                 is_cross_validation=None,\n",
            "                                 is_onnx_compatible=None, logger=None,\n",
            "                                 observer=None, task=None, working_dir=None)),\n",
            "                ('prefittedsoftvotingclassifier',...\n",
            "                                                                                                intercept_scaling=1,\n",
            "                                                                                                l1_ratio=None,\n",
            "                                                                                                max_iter=100,\n",
            "                                                                                                multi_class='ovr',\n",
            "                                                                                                n_jobs=1,\n",
            "                                                                                                penalty='l2',\n",
            "                                                                                                random_state=None,\n",
            "                                                                                                solver='saga',\n",
            "                                                                                                tol=0.0001,\n",
            "                                                                                                verbose=0,\n",
            "                                                                                                warm_start=False))],\n",
            "                                                                     verbose=False))],\n",
            "                                               flatten_transform=None,\n",
            "                                               weights=[0.08333333333333333,\n",
            "                                                        0.08333333333333333,\n",
            "                                                        0.16666666666666666,\n",
            "                                                        0.16666666666666666,\n",
            "                                                        0.25,\n",
            "                                                        0.08333333333333333,\n",
            "                                                        0.16666666666666666]))],\n",
            "         verbose=False)\n"
          ]
        }
      ],
      "execution_count": 16,
      "metadata": {
        "gather": {
          "logged": 1611438191330
        },
        "id": "pSRFDla0WsfQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.interpret import ExplanationClient\n",
        "\n",
        "# Get model explanation data\n",
        "client = ExplanationClient.from_run(best_run)\n",
        "global_explanation = client.download_model_explanation()\n",
        "local_importance_values = global_explanation.local_importance_values\n",
        "expected_values = global_explanation.expected_values"
      ],
      "outputs": [],
      "execution_count": 17,
      "metadata": {
        "gather": {
          "logged": 1611438193188
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client = ExplanationClient.from_run_id(ws, 'quick-starts-ws-135744', best_run.id)\n",
        "global_explanation = client.download_model_explanation()\n",
        "local_importance_values = global_explanation.local_importance_values\n",
        "expected_values = global_explanation.expected_values"
      ],
      "outputs": [],
      "execution_count": 18,
      "metadata": {
        "gather": {
          "logged": 1611438195175
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "global_explanation_topk = client.download_model_explanation(top_k=4)\n",
        "global_importance_values = global_explanation_topk.get_ranked_global_values()\n",
        "global_importance_names = global_explanation_topk.get_ranked_global_names()"
      ],
      "outputs": [],
      "execution_count": 19,
      "metadata": {
        "gather": {
          "logged": 1611438198393
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('global importance values: {}'.format(global_importance_values))\n",
        "print('global importance names: {}'.format(global_importance_names))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "global importance values: [0.5383595320446376, 0.3912522512445099, 0.16865529451041963, 0.08177701960531822]\n",
            "global importance names: ['duration', 'nr.employed', 'cons.conf.idx', 'emp.var.rate']\n"
          ]
        }
      ],
      "execution_count": 20,
      "metadata": {
        "gather": {
          "logged": 1611438198537
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = best_run.properties['model_name']\n",
        "\n",
        "script_file_name = 'score.py'\n",
        "\n",
        "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'score.py')"
      ],
      "outputs": [],
      "execution_count": 21,
      "metadata": {
        "gather": {
          "logged": 1611438198709
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cpu_cluster.delete()"
      ],
      "outputs": [],
      "execution_count": 22,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1611438199189
        }
      }
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "udacity-project V 1.0.ipynb",
      "provenance": []
    },
    "kernel_info": {
      "name": "python3"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.9",
      "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"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 1
}