udacity-project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "gather": {
     "logged": 1598275788035
    }
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'azureml'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-d349e2e408ab>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mazureml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mWorkspace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExperiment\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mws\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mWorkspace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"quick-starts-ws-134375\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mexp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mExperiment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mworkspace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mws\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"udacity-project\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'azureml'"
     ]
    }
   ],
   "source": [
    "from azureml.core import Workspace, Experiment\n",
    "\n",
    "ws = Workspace.get(name=\"quick-starts-ws-127327\")\n",
    "exp = Experiment(workspace=ws, name=\"udacity-project\")\n",
    "\n",
    "print('Workspace name: ' + ws.name, \n",
    "      'Azure region: ' + ws.location, \n",
    "      'Subscription id: ' + ws.subscription_id, \n",
    "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
    "\n",
    "run = exp.start_logging()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "gather": {
     "logged": 1598275788675
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating\n",
      "Succeeded\n",
      "AmlCompute wait for completion finished\n",
      "\n",
      "Minimum number of nodes requested have been provisioned\n"
     ]
    }
   ],
   "source": [
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "from azureml.core.compute_target import ComputeTargetException\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",
    "### YOUR CODE HERE ###\n",
    "\n",
    "# Choose a name for your CPU cluster\n",
    "cpu_cluster_name = \"new-compute\"\n",
    "\n",
    "# Verify that cluster does not exist already\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Hyper Drive Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "gather": {
     "logged": 1598275789986
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'SKLearn' estimator is deprecated. Please use 'ScriptRunConfig' from 'azureml.core.script_run_config' with your own defined environment or the AzureML-Tutorial curated environment.\n"
     ]
    }
   ],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "from azureml.train.sklearn import SKLearn\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( {\n",
    "    '--C' : uniform(0.01,100),\n",
    "    '--max_iter': choice(16,32,64,128,256)\n",
    "    }\n",
    ")\n",
    "\n",
    "# Specify a Policy\n",
    "policy = BanditPolicy(slack_factor=0.1, evaluation_interval = 2)\n",
    "\n",
    "if \"training\" not in os.listdir():\n",
    "    os.mkdir(\"./training\")\n",
    "\n",
    "# Create a SKLearn estimator for use with train.py\n",
    "est = SKLearn(source_directory =\".\",\n",
    "             entry_script = \"train.py\",\n",
    "             compute_target = cpu_cluster)\n",
    "\n",
    "# Create a HyperDriveConfig using the estimator, hyperparameter sampler, and policy.\n",
    "hyperdrive_config = HyperDriveConfig(hyperparameter_sampling = ps,\n",
    "                                     primary_metric_name = \"Accuracy\", \n",
    "                                     primary_metric_goal = PrimaryMetricGoal.MAXIMIZE, \n",
    "                                     max_total_runs = 25, \n",
    "                                     max_concurrent_runs=4, \n",
    "                                     policy=policy, \n",
    "                                     estimator=est, \n",
    "                                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:If 'script' has been provided here and a script file name has been specified in 'run_config', 'script' provided in ScriptRunConfig initialization will take precedence.\n"
     ]
    }
   ],
   "source": [
    "# Submit your hyperdrive run to the experiment and show run details with the widget.\n",
    "\n",
    "hyperdrive_run  = exp.submit(config = hyperdrive_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d645f9c1f8448aab13b1d917b1ec809",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/udacity-project/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c?wsid=/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourcegroups/aml-quickstarts-134375/workspaces/quick-starts-ws-134375\", \"run_id\": \"HD_0b541e24-d624-472f-8945-19fcbf95859c\", \"run_properties\": {\"run_id\": \"HD_0b541e24-d624-472f-8945-19fcbf95859c\", \"created_utc\": \"2021-01-11T18:54:34.096807Z\", \"properties\": {\"primary_metric_config\": \"{\\\"name\\\": \\\"Accuracy\\\", \\\"goal\\\": \\\"maximize\\\"}\", \"resume_from\": \"null\", \"runTemplate\": \"HyperDrive\", \"azureml.runsource\": \"hyperdrive\", \"platform\": \"AML\", \"ContentSnapshotId\": \"2f5f82e6-0e08-40d3-adb2-4c2d589ee6c1\", \"score\": \"0.9094081942336874\", \"best_child_run_id\": \"HD_0b541e24-d624-472f-8945-19fcbf95859c_23\", \"best_metric_status\": \"Succeeded\"}, \"tags\": {\"_aml_system_max_concurrent_jobs\": \"4\", \"max_concurrent_jobs\": \"4\", \"_aml_system_max_total_jobs\": \"25\", \"max_total_jobs\": \"25\", \"_aml_system_max_duration_minutes\": \"10080\", \"max_duration_minutes\": \"10080\", \"_aml_system_policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 2, \\\"delay_evaluation\\\": 0, \\\"slack_factor\\\": 0.1}}\", \"policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 2, \\\"delay_evaluation\\\": 0, \\\"slack_factor\\\": 0.1}}\", \"_aml_system_generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"--C\\\": [\\\"uniform\\\", [0.01, 100]], \\\"--max_iter\\\": [\\\"choice\\\", [[16, 32, 64, 128, 256]]]}}\", \"generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"--C\\\": [\\\"uniform\\\", [0.01, 100]], \\\"--max_iter\\\": [\\\"choice\\\", [[16, 32, 64, 128, 256]]]}}\", \"_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/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/udacity-project\\\", \\\"SubscriptionId\\\": \\\"81cefad3-d2c9-4f77-a466-99a7f541c7bb\\\", \\\"ResourceGroupName\\\": \\\"aml-quickstarts-134375\\\", \\\"WorkspaceName\\\": \\\"quick-starts-ws-134375\\\", \\\"ExperimentName\\\": \\\"udacity-project\\\", \\\"Definition\\\": {\\\"Overrides\\\": {\\\"script\\\": \\\"train.py\\\", \\\"arguments\\\": [], \\\"target\\\": \\\"new-compute\\\", \\\"framework\\\": \\\"Python\\\", \\\"communicator\\\": \\\"None\\\", \\\"maxRunDurationSeconds\\\": null, \\\"nodeCount\\\": 1, \\\"environment\\\": {\\\"name\\\": null, \\\"version\\\": null, \\\"environmentVariables\\\": {\\\"EXAMPLE_ENV_VAR\\\": \\\"EXAMPLE_VALUE\\\"}, \\\"python\\\": {\\\"userManagedDependencies\\\": true, \\\"interpreterPath\\\": \\\"python\\\", \\\"condaDependenciesFile\\\": null, \\\"baseCondaEnvironment\\\": null, \\\"condaDependencies\\\": {\\\"name\\\": \\\"project_environment\\\", \\\"dependencies\\\": [\\\"python=3.6.2\\\", {\\\"pip\\\": [\\\"azureml-defaults\\\"]}], \\\"channels\\\": [\\\"anaconda\\\", \\\"conda-forge\\\"]}}, \\\"docker\\\": {\\\"enabled\\\": true, \\\"baseImage\\\": \\\"sklearn:0.20.3-cpu\\\", \\\"baseDockerfile\\\": null, \\\"sharedVolumes\\\": true, \\\"shmSize\\\": \\\"2g\\\", \\\"arguments\\\": [], \\\"baseImageRegistry\\\": {\\\"address\\\": \\\"viennaprivate.azurecr.io\\\", \\\"username\\\": null, \\\"password\\\": null, \\\"registryIdentity\\\": null}, \\\"platform\\\": {\\\"os\\\": \\\"Linux\\\", \\\"architecture\\\": \\\"amd64\\\"}}, \\\"spark\\\": {\\\"repositories\\\": [], \\\"packages\\\": [], \\\"precachePackages\\\": false}, \\\"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\\\": {}, \\\"sourceDirectoryDataStore\\\": null, \\\"amlcompute\\\": {\\\"vmSize\\\": null, \\\"vmPriority\\\": null, \\\"retainCluster\\\": false, \\\"name\\\": null, \\\"clusterMaxNodeCount\\\": 1}, \\\"command\\\": \\\"\\\"}, \\\"TargetDetails\\\": null, \\\"SnapshotId\\\": \\\"2f5f82e6-0e08-40d3-adb2-4c2d589ee6c1\\\", \\\"TelemetryValues\\\": {\\\"amlClientType\\\": \\\"azureml-sdk-train\\\", \\\"amlClientModule\\\": \\\"[Scrubbed]\\\", \\\"amlClientFunction\\\": \\\"[Scrubbed]\\\", \\\"tenantId\\\": \\\"660b3398-b80e-49d2-bc5b-ac1dc93b5254\\\", \\\"amlClientRequestId\\\": \\\"6b979051-be33-4160-bb19-fd65b316312e\\\", \\\"amlClientSessionId\\\": \\\"6004d1cb-c5f4-493e-bd58-3c8572d788ce\\\", \\\"subscriptionId\\\": \\\"81cefad3-d2c9-4f77-a466-99a7f541c7bb\\\", \\\"estimator\\\": \\\"SKLearn\\\", \\\"samplingMethod\\\": \\\"RANDOM\\\", \\\"terminationPolicy\\\": \\\"Bandit\\\", \\\"primaryMetricGoal\\\": \\\"maximize\\\", \\\"maxTotalRuns\\\": 25, \\\"maxConcurrentRuns\\\": 4, \\\"maxDurationMinutes\\\": 10080, \\\"vmSize\\\": null}}}\", \"platform_config\": \"{\\\"ServiceAddress\\\": \\\"https://southcentralus.experiments.azureml.net\\\", \\\"ServiceArmScope\\\": \\\"subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/udacity-project\\\", \\\"SubscriptionId\\\": \\\"81cefad3-d2c9-4f77-a466-99a7f541c7bb\\\", \\\"ResourceGroupName\\\": \\\"aml-quickstarts-134375\\\", \\\"WorkspaceName\\\": \\\"quick-starts-ws-134375\\\", \\\"ExperimentName\\\": \\\"udacity-project\\\", \\\"Definition\\\": {\\\"Overrides\\\": {\\\"script\\\": \\\"train.py\\\", \\\"arguments\\\": [], \\\"target\\\": \\\"new-compute\\\", \\\"framework\\\": \\\"Python\\\", \\\"communicator\\\": \\\"None\\\", \\\"maxRunDurationSeconds\\\": null, \\\"nodeCount\\\": 1, \\\"environment\\\": {\\\"name\\\": null, \\\"version\\\": null, \\\"environmentVariables\\\": {\\\"EXAMPLE_ENV_VAR\\\": \\\"EXAMPLE_VALUE\\\"}, \\\"python\\\": {\\\"userManagedDependencies\\\": true, \\\"interpreterPath\\\": \\\"python\\\", \\\"condaDependenciesFile\\\": null, \\\"baseCondaEnvironment\\\": null, \\\"condaDependencies\\\": {\\\"name\\\": \\\"project_environment\\\", \\\"dependencies\\\": [\\\"python=3.6.2\\\", {\\\"pip\\\": [\\\"azureml-defaults\\\"]}], \\\"channels\\\": [\\\"anaconda\\\", \\\"conda-forge\\\"]}}, \\\"docker\\\": {\\\"enabled\\\": true, \\\"baseImage\\\": \\\"sklearn:0.20.3-cpu\\\", \\\"baseDockerfile\\\": null, \\\"sharedVolumes\\\": true, \\\"shmSize\\\": \\\"2g\\\", \\\"arguments\\\": [], \\\"baseImageRegistry\\\": {\\\"address\\\": \\\"viennaprivate.azurecr.io\\\", \\\"username\\\": null, \\\"password\\\": null, \\\"registryIdentity\\\": null}, \\\"platform\\\": {\\\"os\\\": \\\"Linux\\\", \\\"architecture\\\": \\\"amd64\\\"}}, \\\"spark\\\": {\\\"repositories\\\": [], \\\"packages\\\": [], \\\"precachePackages\\\": false}, \\\"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\\\": {}, \\\"sourceDirectoryDataStore\\\": null, \\\"amlcompute\\\": {\\\"vmSize\\\": null, \\\"vmPriority\\\": null, \\\"retainCluster\\\": false, \\\"name\\\": null, \\\"clusterMaxNodeCount\\\": 1}, \\\"command\\\": \\\"\\\"}, \\\"TargetDetails\\\": null, \\\"SnapshotId\\\": \\\"2f5f82e6-0e08-40d3-adb2-4c2d589ee6c1\\\", \\\"TelemetryValues\\\": {\\\"amlClientType\\\": \\\"azureml-sdk-train\\\", \\\"amlClientModule\\\": \\\"[Scrubbed]\\\", \\\"amlClientFunction\\\": \\\"[Scrubbed]\\\", \\\"tenantId\\\": \\\"660b3398-b80e-49d2-bc5b-ac1dc93b5254\\\", \\\"amlClientRequestId\\\": \\\"6b979051-be33-4160-bb19-fd65b316312e\\\", \\\"amlClientSessionId\\\": \\\"6004d1cb-c5f4-493e-bd58-3c8572d788ce\\\", \\\"subscriptionId\\\": \\\"81cefad3-d2c9-4f77-a466-99a7f541c7bb\\\", \\\"estimator\\\": \\\"SKLearn\\\", \\\"samplingMethod\\\": \\\"RANDOM\\\", \\\"terminationPolicy\\\": \\\"Bandit\\\", \\\"primaryMetricGoal\\\": \\\"maximize\\\", \\\"maxTotalRuns\\\": 25, \\\"maxConcurrentRuns\\\": 4, \\\"maxDurationMinutes\\\": 10080, \\\"vmSize\\\": null}}}\", \"_aml_system_resume_child_runs\": \"null\", \"resume_child_runs\": \"null\", \"_aml_system_all_jobs_generated\": \"true\", \"all_jobs_generated\": \"true\", \"_aml_system_cancellation_requested\": \"false\", \"cancellation_requested\": \"false\", \"_aml_system_progress_metadata_evaluation_timestamp\": \"\\\"2021-01-11T18:54:34.862244\\\"\", \"progress_metadata_evaluation_timestamp\": \"\\\"2021-01-11T18:54:34.862244\\\"\", \"_aml_system_progress_metadata_digest\": \"\\\"bdd4dfea3fd7ea443381c0d6b33eff80c0535de65a4032e9d7b38970b8039c0b\\\"\", \"progress_metadata_digest\": \"\\\"bdd4dfea3fd7ea443381c0d6b33eff80c0535de65a4032e9d7b38970b8039c0b\\\"\", \"_aml_system_progress_metadata_active_timestamp\": \"\\\"2021-01-11T18:54:34.862244\\\"\", \"progress_metadata_active_timestamp\": \"\\\"2021-01-11T18:54:34.862244\\\"\", \"_aml_system_HD_0b541e24-d624-472f-8945-19fcbf95859c_0\": \"{\\\"--C\\\": 80.24640794766708, \\\"--max_iter\\\": 128}\", \"HD_0b541e24-d624-472f-8945-19fcbf95859c_0\": \"{\\\"--C\\\": 80.24640794766708, \\\"--max_iter\\\": 128}\", 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\"[2021-01-11T18:54:34.327998][API][INFO]Experiment created\\r\\n[2021-01-11T18:54:35.041289][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-01-11T18:54:35.292583][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-11T18:54:35.9007899Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.\\r\\n[2021-01-11T18:55:06.4574703Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_1'\\r\\n[2021-01-11T18:55:06.4562811Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_0'\\r\\n[2021-01-11T18:55:06.4555625Z][SCHEDULER][INFO]The execution environment was successfully prepared.\\r\\n[2021-01-11T18:55:06.4610058Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_3'\\r\\n[2021-01-11T18:55:06.4592156Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_2'\\r\\n[2021-01-11T18:55:07.4962797Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_2'\\r\\n[2021-01-11T18:55:07.5774611Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_1'\\r\\n[2021-01-11T18:55:07.8247675Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_3'\\r\\n[2021-01-11T18:55:08.4641723Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_0'\\r\\n[2021-01-11T18:58:40.019745][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_0, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_1, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_2, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T18:59:10.295001][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_0, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_1, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_2, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T18:59:40.177309][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_0, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_1, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_2, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:00:36.107642][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-01-11T19:00:36.413131][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-11T19:00:43.7303536Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_7'\\r\\n[2021-01-11T19:00:43.7355094Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_5'\\r\\n[2021-01-11T19:00:43.8137587Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_6'\\r\\n[2021-01-11T19:00:44.6619005Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_5'\\r\\n[2021-01-11T19:00:44.8638583Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_7'\\r\\n[2021-01-11T19:00:44.9962143Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_4'\\r\\n[2021-01-11T19:00:45.5799470Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_6'\\r\\n[2021-01-11T19:00:43.6677534Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_4'\\r\\n[2021-01-11T19:01:40.852852][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_5, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_6, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_7] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:02:07.159597][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-01-11T19:02:07.480705][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-11T19:02:17.3635100Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_9'\\r\\n[2021-01-11T19:02:17.3643135Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_8'\\r\\n[2021-01-11T19:02:17.3616830Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_11'\\r\\n[2021-01-11T19:02:17.3627767Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_10'\\r\\n[2021-01-11T19:02:18.4476115Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_10'\\r\\n[2021-01-11T19:02:18.6297157Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_11'\\r\\n[2021-01-11T19:02:19.4664020Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_8'\\r\\n[2021-01-11T19:02:18.9666937Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_9'\\r\\n[2021-01-11T19:02:41.032112][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_8, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_9, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_10, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_11] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:03:10.967393][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_8, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_9, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_10, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_11] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:03:39.070374][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-01-11T19:03:39.278143][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-11T19:03:51.9704961Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_12'\\r\\n[2021-01-11T19:03:51.9716330Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_13'\\r\\n[2021-01-11T19:03:51.9875459Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_15'\\r\\n[2021-01-11T19:03:51.9840519Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_14'\\r\\n[2021-01-11T19:03:52.9817716Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_13'\\r\\n[2021-01-11T19:03:53.1402704Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_15'\\r\\n[2021-01-11T19:03:53.3092605Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_12'\\r\\n[2021-01-11T19:03:54.2161421Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_14'\\r\\n[2021-01-11T19:04:11.068273][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_12, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_13, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_14, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_15] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:04:41.315913][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_12, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_13, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_14, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_15] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:05:12.471159][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-01-11T19:05:12.815966][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-11T19:05:27.2433035Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_16'\\r\\n[2021-01-11T19:05:27.2490305Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_18'\\r\\n[2021-01-11T19:05:27.2475719Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_17'\\r\\n[2021-01-11T19:05:27.2501750Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_19'\\r\\n[2021-01-11T19:05:28.1199779Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_17'\\r\\n[2021-01-11T19:05:28.1545148Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_18'\\r\\n[2021-01-11T19:05:28.8677325Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_16'\\r\\n[2021-01-11T19:05:43.252083][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_16, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_17, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_18] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:06:07.2537032Z][SCHEDULER][WARNING]Failed to schedule job due to transient issue, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_19', will retry later.\\r\\n[2021-01-11T19:06:14.145871][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_16, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_17, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_18] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:06:38.1648602Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_19'\\r\\n[2021-01-11T19:06:39.0560363Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_19'\\r\\n[2021-01-11T19:06:45.233382][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-01-11T19:06:45.392204][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-11T19:07:10.0295246Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_21'\\r\\n[2021-01-11T19:07:10.0284253Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_20'\\r\\n[2021-01-11T19:07:10.0311139Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_22'\\r\\n[2021-01-11T19:07:10.7698736Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_22'\\r\\n[2021-01-11T19:07:16.760834][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_19] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:07:25.7618605Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_20'\\r\\n[2021-01-11T19:07:31.3117125Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_21'\\r\\n[2021-01-11T19:07:47.374345][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_20, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_21, https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_22] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:07:47.734255][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-11T19:07:48.024216][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-11T19:08:03.0212182Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_23'\\r\\n[2021-01-11T19:08:04.0661188Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_23'\\r\\n[2021-01-11T19:08:18.585614][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_23] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:08:18.727866][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-11T19:08:19.003933][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-11T19:08:35.3533204Z][SCHEDULER][INFO]Scheduling job, id='HD_0b541e24-d624-472f-8945-19fcbf95859c_24'\\r\\n[2021-01-11T19:08:49.646663][GENERATOR][INFO]Max number of jobs '25' reached for experiment.\\r\\n[2021-01-11T19:08:49.866000][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_23] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:08:49.907826][GENERATOR][INFO]All jobs generated.\\r\\n[2021-01-11T19:09:07.6385775Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_0b541e24-d624-472f-8945-19fcbf95859c_24'\\r\\n[2021-01-11T19:09:20.611322][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_24] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:09:51.372685][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experiments/**SCRUBBED**/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_24] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-11T19:10:34.978664][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": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RunId: HD_0b541e24-d624-472f-8945-19fcbf95859c\n",
      "Web View: https://ml.azure.com/experiments/udacity-project/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c?wsid=/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourcegroups/aml-quickstarts-134375/workspaces/quick-starts-ws-134375\n",
      "\n",
      "Streaming azureml-logs/hyperdrive.txt\n",
      "=====================================\n",
      "\n",
      "\"<START>[2021-01-11T18:54:34.327998][API][INFO]Experiment created<END>\\n\"\"<START>[2021-01-11T18:54:35.041289][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space<END>\\n\"\"<START>[2021-01-11T18:54:35.292583][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.<END>\\n\"<START>[2021-01-11T18:54:35.9007899Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.<END>\n",
      "\n",
      "Execution Summary\n",
      "=================\n",
      "RunId: HD_0b541e24-d624-472f-8945-19fcbf95859c\n",
      "Web View: https://ml.azure.com/experiments/udacity-project/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c?wsid=/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourcegroups/aml-quickstarts-134375/workspaces/quick-starts-ws-134375\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'runId': 'HD_0b541e24-d624-472f-8945-19fcbf95859c',\n",
       " 'target': 'new-compute',\n",
       " 'status': 'Completed',\n",
       " 'startTimeUtc': '2021-01-11T18:54:34.149118Z',\n",
       " 'endTimeUtc': '2021-01-11T19:10:34.741241Z',\n",
       " 'properties': {'primary_metric_config': '{\"name\": \"Accuracy\", \"goal\": \"maximize\"}',\n",
       "  'resume_from': 'null',\n",
       "  'runTemplate': 'HyperDrive',\n",
       "  'azureml.runsource': 'hyperdrive',\n",
       "  'platform': 'AML',\n",
       "  'ContentSnapshotId': '2f5f82e6-0e08-40d3-adb2-4c2d589ee6c1',\n",
       "  'score': '0.9094081942336874',\n",
       "  'best_child_run_id': 'HD_0b541e24-d624-472f-8945-19fcbf95859c_23',\n",
       "  'best_metric_status': 'Succeeded'},\n",
       " 'inputDatasets': [],\n",
       " 'outputDatasets': [],\n",
       " 'logFiles': {'azureml-logs/hyperdrive.txt': 'https://mlstrg134375.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_0b541e24-d624-472f-8945-19fcbf95859c/azureml-logs/hyperdrive.txt?sv=2019-02-02&sr=b&sig=5e2UU7jwvekO4FS%2B0U0RJwSfV8NnAsrC0KX51XdDGhU%3D&st=2021-01-11T19%3A01%3A03Z&se=2021-01-12T03%3A11%3A03Z&sp=r'}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RunDetails(hyperdrive_run).show()\n",
    "hyperdrive_run.wait_for_completion(show_output =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "gather": {
     "logged": 1598276310862
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Run Id:  HD_0b541e24-d624-472f-8945-19fcbf95859c_23\n",
      "\n",
      "Accuracy:  0.9094081942336874\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "# Get your best run and save the model from that run.\n",
    "\n",
    "best_run = hyperdrive_run.get_best_run_by_primary_metric()\n",
    "best_run_metrics = best_run.get_metrics()\n",
    "\n",
    "print('Best Run Id: ', best_run.id)\n",
    "print('\\nAccuracy: ', best_run_metrics['Accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['azureml-logs/55_azureml-execution-tvmps_869307f306aee470c5b0c6f75e0c9449c45f97c768bd4e6320b9197a33900e1d_d.txt',\n",
       " 'azureml-logs/65_job_prep-tvmps_869307f306aee470c5b0c6f75e0c9449c45f97c768bd4e6320b9197a33900e1d_d.txt',\n",
       " 'azureml-logs/70_driver_log.txt',\n",
       " 'azureml-logs/75_job_post-tvmps_869307f306aee470c5b0c6f75e0c9449c45f97c768bd4e6320b9197a33900e1d_d.txt',\n",
       " 'azureml-logs/process_info.json',\n",
       " 'azureml-logs/process_status.json',\n",
       " 'logs/azureml/104_azureml.log',\n",
       " 'logs/azureml/job_prep_azureml.log',\n",
       " 'logs/azureml/job_release_azureml.log',\n",
       " 'outputs/model.joblib']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_run.get_file_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = best_run.register_model(model_name = 'model_hd', model_path = 'outputs/model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run.download_file('outputs/model.joblib', 'outputs/project_model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>udacity-project</td><td>HD_0b541e24-d624-472f-8945-19fcbf95859c_23</td><td>azureml.scriptrun</td><td>Completed</td><td><a href=\"https://ml.azure.com/experiments/udacity-project/runs/HD_0b541e24-d624-472f-8945-19fcbf95859c_23?wsid=/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourcegroups/aml-quickstarts-134375/workspaces/quick-starts-ws-134375\" 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>"
      ],
      "text/plain": [
       "Run(Experiment: udacity-project,\n",
       "Id: HD_0b541e24-d624-472f-8945-19fcbf95859c_23,\n",
       "Type: azureml.scriptrun,\n",
       "Status: Completed)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auto ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.data.dataset_factory import TabularDatasetFactory\n",
    "\n",
    "# Create TabularDataset using TabularDatasetFactory\n",
    "# Data is available at: \n",
    "# \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
    "\n",
    "ds = TabularDatasetFactory.from_delimited_files(path=\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.299</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>unknown</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.860</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.313</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.967</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jul</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital    education  default housing loan    contact  \\\n",
       "0   57   technician  married  high.school       no      no  yes   cellular   \n",
       "1   55      unknown  married      unknown  unknown     yes   no  telephone   \n",
       "2   33  blue-collar  married     basic.9y       no      no   no   cellular   \n",
       "3   36       admin.  married  high.school       no      no   no  telephone   \n",
       "4   27    housemaid  married  high.school       no     yes   no   cellular   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         1      failure         -1.8   \n",
       "1   may         thu  ...         2    999         0  nonexistent          1.1   \n",
       "2   may         fri  ...         1    999         1      failure         -1.8   \n",
       "3   jun         fri  ...         4    999         0  nonexistent          1.4   \n",
       "4   jul         fri  ...         2    999         0  nonexistent          1.4   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          92.893          -46.2      1.299       5099.1  no  \n",
       "1          93.994          -36.4      4.860       5191.0  no  \n",
       "2          92.893          -46.2      1.313       5099.1  no  \n",
       "3          94.465          -41.8      4.967       5228.1  no  \n",
       "4          93.918          -42.7      4.963       5228.1  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ds.to_pandas_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "gather": {
     "logged": 1598275726969
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "from train import clean_data\n",
    "\n",
    "# Use the clean_data function to clean your data.\n",
    "x, y = clean_data(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>...</td>\n",
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       "      <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>"
      ],
      "text/plain": [
       "   age  marital  default  housing  loan  month  day_of_week  duration  \\\n",
       "0   57        1        0        0     1      5            1       371   \n",
       "1   55        1        0        1     0      5            4       285   \n",
       "2   33        1        0        0     0      5            5        52   \n",
       "3   36        1        0        0     0      6            5       355   \n",
       "4   27        1        0        1     0      7            5       189   \n",
       "\n",
       "   campaign  pdays  ...  contact_telephone  education_basic.4y  \\\n",
       "0         1    999  ...                  0                   0   \n",
       "1         2    999  ...                  1                   0   \n",
       "2         1    999  ...                  0                   0   \n",
       "3         4    999  ...                  1                   0   \n",
       "4         2    999  ...                  0                   0   \n",
       "\n",
       "   education_basic.6y  education_basic.9y  education_high.school  \\\n",
       "0                   0                   0                      1   \n",
       "1                   0                   0                      0   \n",
       "2                   0                   1                      0   \n",
       "3                   0                   0                      1   \n",
       "4                   0                   0                      1   \n",
       "\n",
       "   education_illiterate  education_professional.course  \\\n",
       "0                     0                              0   \n",
       "1                     0                              0   \n",
       "2                     0                              0   \n",
       "3                     0                              0   \n",
       "4                     0                              0   \n",
       "\n",
       "   education_university.degree  education_unknown  result  \n",
       "0                            0                  0       0  \n",
       "1                            0                  1       0  \n",
       "2                            0                  0       0  \n",
       "3                            0                  0       0  \n",
       "4                            0                  0       0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['result']=y\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "if not os.path.isdir('data'):\n",
    "    os.mkdir('data')\n",
    "    \n",
    "# Save the train data to a csv to be uploaded to the datastore\n",
    "pd.DataFrame(x).to_csv(\"data/train_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading an estimated of 1 files\n",
      "Uploading ./data/train_data.csv\n",
      "Uploaded ./data/train_data.csv, 1 files out of an estimated total of 1\n",
      "Uploaded 1 files\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import Workspace, Dataset\n",
    "from azureml.data.datapath import DataPath\n",
    "\n",
    "datastore = ws.get_default_datastore()\n",
    "datastore.upload(src_dir='./data', target_path='data', overwrite=True, show_progress=True)\n",
    "\n",
    "datastore_path =[\n",
    "    DataPath(datastore, 'data/train_data.csv')\n",
    "]\n",
    "\n",
    "# Upload the training data as a tabular dataset for access during training on remote compute\n",
    "train_data = Dataset.Tabular.from_delimited_files(path=datastore_path)\n",
    "label = \"result\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "gather": {
     "logged": 1598275665403
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "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=train_data,\n",
    "    label_column_name=label,\n",
    "    compute_target=cpu_cluster_name,\n",
    "    n_cross_validations=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on remote.\n",
      "No run_configuration provided, running on new-compute with default configuration\n",
      "Running on remote compute: new-compute\n",
      "Parent Run ID: AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\n",
      "\n",
      "Current status: FeaturesGeneration. Generating features for the dataset.\n",
      "Current status: DatasetBalancing. Performing class balancing sweeping\n",
      "Current status: DatasetCrossValidationSplit. Beginning model selection.\n",
      "Current status: ModelSelection. Beginning model selection.\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",
      "\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                          0:00:58       0.9161    0.9161\n",
      "         1   MaxAbsScaler XGBoostClassifier                 0:01:09       0.9155    0.9161\n",
      "         2   MinMaxScaler RandomForest                      0:00:52       0.8969    0.9161\n",
      "         3   MinMaxScaler RandomForest                      0:00:57       0.8880    0.9161\n",
      "         4   MinMaxScaler RandomForest                      0:00:54       0.8144    0.9161\n",
      "         5   SparseNormalizer XGBoostClassifier             0:01:07       0.9124    0.9161\n",
      "         6   MaxAbsScaler GradientBoosting                  0:01:11       0.9033    0.9161\n",
      "         7   SparseNormalizer XGBoostClassifier             0:00:54       0.9016    0.9161\n",
      "         8   MinMaxScaler LightGBM                          0:00:55       0.8924    0.9161\n",
      "         9   MinMaxScaler LogisticRegression                0:01:00       0.9102    0.9161\n",
      "        10   StandardScalerWrapper LogisticRegression       0:01:12       0.9098    0.9161\n",
      "        11   MinMaxScaler LightGBM                          0:00:53       0.8880    0.9161\n",
      "        12   StandardScalerWrapper LightGBM                 0:00:57       0.8970    0.9161\n",
      "        13   StandardScalerWrapper LightGBM                 0:01:01       0.8997    0.9161\n",
      "        14   SparseNormalizer XGBoostClassifier             0:01:49       0.9141    0.9161\n",
      "        15   RobustScaler ExtremeRandomTrees                0:01:14       0.8983    0.9161\n",
      "        16   StandardScalerWrapper XGBoostClassifier        0:00:58       0.9121    0.9161\n",
      "        17   SparseNormalizer LightGBM                      0:00:55       0.9128    0.9161\n",
      "        18   MaxAbsScaler ExtremeRandomTrees                0:01:10       0.8895    0.9161\n",
      "        19   StandardScalerWrapper LightGBM                 0:00:55       0.9083    0.9161\n",
      "        20   SparseNormalizer XGBoostClassifier             0:01:26       0.9155    0.9161\n",
      "        21   MaxAbsScaler LogisticRegression                0:01:04       0.8554    0.9161\n",
      "        22    VotingEnsemble                                0:01:20       0.9177    0.9177\n",
      "        23    StackEnsemble                                 0:01:37       0.9159    0.9177\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "95a814f469a341f0b02e4fde22cb680f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_AutoMLWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', 's…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/experiment_AutoML/runs/AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0?wsid=/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourcegroups/aml-quickstarts-134375/workspaces/quick-starts-ws-134375\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"run_properties\": {\"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"created_utc\": \"2021-01-11T19:20:32.798138Z\", \"properties\": {\"num_iterations\": \"1000\", \"training_type\": \"TrainFull\", \"acquisition_function\": \"EI\", \"primary_metric\": \"accuracy\", \"train_split\": \"0\", \"acquisition_parameter\": \"0\", \"num_cross_validation\": \"5\", \"target\": \"new-compute\", \"AMLSettingsJsonString\": 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     "text": [
      "\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",
      "\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                          0:00:58       0.9161    0.9161\n",
      "         1   MaxAbsScaler XGBoostClassifier                 0:01:09       0.9155    0.9161\n",
      "         2   MinMaxScaler RandomForest                      0:00:52       0.8969    0.9161\n",
      "         3   MinMaxScaler RandomForest                      0:00:57       0.8880    0.9161\n",
      "         4   MinMaxScaler RandomForest                      0:00:54       0.8144    0.9161\n",
      "         5   SparseNormalizer XGBoostClassifier             0:01:07       0.9124    0.9161\n",
      "         6   MaxAbsScaler GradientBoosting                  0:01:11       0.9033    0.9161\n",
      "         7   SparseNormalizer XGBoostClassifier             0:00:54       0.9016    0.9161\n",
      "         8   MinMaxScaler LightGBM                          0:00:55       0.8924    0.9161\n",
      "         9   MinMaxScaler LogisticRegression                0:01:00       0.9102    0.9161\n",
      "        10   StandardScalerWrapper LogisticRegression       0:01:12       0.9098    0.9161\n",
      "        11   MinMaxScaler LightGBM                          0:00:53       0.8880    0.9161\n",
      "        12   StandardScalerWrapper LightGBM                 0:00:57       0.8970    0.9161\n",
      "        13   StandardScalerWrapper LightGBM                 0:01:01       0.8997    0.9161\n",
      "        14   SparseNormalizer XGBoostClassifier             0:01:49       0.9141    0.9161\n",
      "        15   RobustScaler ExtremeRandomTrees                0:01:14       0.8983    0.9161\n",
      "        16   StandardScalerWrapper XGBoostClassifier        0:00:58       0.9121    0.9161\n",
      "        17   SparseNormalizer LightGBM                      0:00:55       0.9128    0.9161\n",
      "        18   MaxAbsScaler ExtremeRandomTrees                0:01:10       0.8895    0.9161\n",
      "        19   StandardScalerWrapper LightGBM                 0:00:55       0.9083    0.9161\n",
      "        20   SparseNormalizer XGBoostClassifier             0:01:26       0.9155    0.9161\n",
      "        21   MaxAbsScaler LogisticRegression                0:01:04       0.8554    0.9161\n",
      "        22    VotingEnsemble                                0:01:20       0.9177    0.9177\n",
      "        23    StackEnsemble                                 0:01:37       0.9159    0.9177\n"
     ]
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       "{'runId': 'AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0',\n",
       " 'target': 'new-compute',\n",
       " 'status': 'Completed',\n",
       " 'startTimeUtc': '2021-01-11T19:20:47.463907Z',\n",
       " 'endTimeUtc': '2021-01-11T20:03:36.142183Z',\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': 'new-compute',\n",
       "  'AMLSettingsJsonString': '{\"path\":null,\"name\":\"experiment_AutoML\",\"subscription_id\":\"81cefad3-d2c9-4f77-a466-99a7f541c7bb\",\"resource_group\":\"aml-quickstarts-134375\",\"workspace_name\":\"quick-starts-ws-134375\",\"region\":\"southcentralus\",\"compute_target\":\"new-compute\",\"spark_service\":null,\"azure_service\":\"remote\",\"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\":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,\"whitelist_models\":null,\"blacklist_algos\":[\"TensorFlowLinearClassifier\",\"TensorFlowDNN\"],\"supported_models\":[\"GradientBoosting\",\"LinearSVM\",\"RandomForest\",\"TensorFlowLinearClassifier\",\"AveragedPerceptronClassifier\",\"LogisticRegression\",\"BernoulliNaiveBayes\",\"DecisionTree\",\"LightGBM\",\"SVM\",\"XGBoostClassifier\",\"ExtremeRandomTrees\",\"MultinomialNaiveBayes\",\"SGD\",\"TensorFlowDNN\",\"KNN\"],\"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\":\"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\":\"result\",\"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\\\\\": \\\\\"{\\\\\\\\\\\\\"blocks\\\\\\\\\\\\\": [{\\\\\\\\\\\\\"id\\\\\\\\\\\\\": \\\\\\\\\\\\\"ffaa8235-95c1-450a-852a-9a5047ff437b\\\\\\\\\\\\\", \\\\\\\\\\\\\"type\\\\\\\\\\\\\": \\\\\\\\\\\\\"Microsoft.DPrep.GetDatastoreFilesBlock\\\\\\\\\\\\\", \\\\\\\\\\\\\"arguments\\\\\\\\\\\\\": {\\\\\\\\\\\\\"datastores\\\\\\\\\\\\\": [{\\\\\\\\\\\\\"datastoreName\\\\\\\\\\\\\": \\\\\\\\\\\\\"workspaceblobstore\\\\\\\\\\\\\", \\\\\\\\\\\\\"path\\\\\\\\\\\\\": \\\\\\\\\\\\\"data/train_data.csv\\\\\\\\\\\\\", \\\\\\\\\\\\\"resourceGroup\\\\\\\\\\\\\": \\\\\\\\\\\\\"aml-quickstarts-134375\\\\\\\\\\\\\", \\\\\\\\\\\\\"subscription\\\\\\\\\\\\\": \\\\\\\\\\\\\"81cefad3-d2c9-4f77-a466-99a7f541c7bb\\\\\\\\\\\\\", \\\\\\\\\\\\\"workspaceName\\\\\\\\\\\\\": \\\\\\\\\\\\\"quick-starts-ws-134375\\\\\\\\\\\\\"}]}, \\\\\\\\\\\\\"localData\\\\\\\\\\\\\": {}, \\\\\\\\\\\\\"isEnabled\\\\\\\\\\\\\": true, \\\\\\\\\\\\\"name\\\\\\\\\\\\\": null, 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{\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"marital\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"default\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"housing\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"loan\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"month\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"day_of_week\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"duration\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"campaign\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"pdays\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"previous\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"poutcome\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"emp.var.rate\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 3}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"cons.price.idx\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 3}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"cons.conf.idx\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 3}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"euribor3m\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 3}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"nr.employed\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 3}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_admin.\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_blue-collar\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_entrepreneur\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_housemaid\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, 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{\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_student\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_technician\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_unemployed\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_unknown\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, 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\\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"education_basic.9y\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"education_high.school\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"education_illiterate\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"education_professional.course\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"education_university.degree\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"education_unknown\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"result\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}]}, \\\\\\\\\\\\\"localData\\\\\\\\\\\\\": {}, \\\\\\\\\\\\\"isEnabled\\\\\\\\\\\\\": true, \\\\\\\\\\\\\"name\\\\\\\\\\\\\": null, \\\\\\\\\\\\\"annotation\\\\\\\\\\\\\": null}], \\\\\\\\\\\\\"inspectors\\\\\\\\\\\\\": [], \\\\\\\\\\\\\"meta\\\\\\\\\\\\\": {\\\\\\\\\\\\\"savedDatasetId\\\\\\\\\\\\\": \\\\\\\\\\\\\"79b6ce3e-fc16-4533-8649-344459d6018f\\\\\\\\\\\\\", \\\\\\\\\\\\\"datasetType\\\\\\\\\\\\\": \\\\\\\\\\\\\"tabular\\\\\\\\\\\\\", \\\\\\\\\\\\\"subscriptionId\\\\\\\\\\\\\": \\\\\\\\\\\\\"81cefad3-d2c9-4f77-a466-99a7f541c7bb\\\\\\\\\\\\\", \\\\\\\\\\\\\"workspaceId\\\\\\\\\\\\\": \\\\\\\\\\\\\"270ff02f-263d-494f-b859-49e55f265eb1\\\\\\\\\\\\\", \\\\\\\\\\\\\"workspaceLocation\\\\\\\\\\\\\": \\\\\\\\\\\\\"southcentralus\\\\\\\\\\\\\"}}\\\\\", \\\\\"activities\\\\\": 0}',\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\", \"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': 'Remote.Parent',\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",
       "  'CancelUri': 'https://southcentralus.experiments.azureml.net/jasmine/v1.0/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourceGroups/aml-quickstarts-134375/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134375/experimentids/e707b4ae-e3f3-467f-a0fa-5d90b7170f6e/cancel/AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0',\n",
       "  'ClientSdkVersion': '1.19.0',\n",
       "  'snapshotId': '00000000-0000-0000-0000-000000000000',\n",
       "  'SetupRunId': 'AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0_setup',\n",
       "  'SetupRunContainerId': 'dcid.AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0_setup',\n",
       "  'FeaturizationRunJsonPath': 'featurizer_container.json',\n",
       "  'FeaturizationRunId': 'AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0_featurize',\n",
       "  'ProblemInfoJsonString': '{\"dataset_num_categorical\": 0, \"is_sparse\": false, \"subsampling\": false, \"dataset_classes\": 2, \"dataset_features\": 39, \"dataset_samples\": 32950, \"single_frequency_class_detected\": false}',\n",
       "  'ModelExplainRunId': 'AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0_ModelExplain'},\n",
       " 'inputDatasets': [{'dataset': {'id': '79b6ce3e-fc16-4533-8649-344459d6018f'}, 'consumptionDetails': {'type': 'RunInput', 'inputName': 'training_data', 'mechanism': 'Direct'}}],\n",
       " 'outputDatasets': [],\n",
       " 'logFiles': {}}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Submit your automl run\n",
    "\n",
    "automl_exp = Experiment(workspace=ws, name=\"experiment_AutoML\")  \n",
    "auto_run = automl_exp.submit(automl_config, show_output = True)\n",
    "RunDetails(auto_run).show()\n",
    "auto_run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d64c12c082ac4bb99470a88c8a854e28",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_AutoMLWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', 's…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\"series\": [{\"data\": [0.9487788351335558]}]}, {\"name\": \"recall_score_macro\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.7891580384551654]}]}, {\"name\": \"precision_score_micro\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.9177238239757208]}]}, {\"name\": \"precision_score_macro\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.7940786225707102]}]}, {\"name\": \"matthews_correlation\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.5831985681176741]}]}, {\"name\": \"accuracy\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.9177238239757208]}]}, {\"name\": \"AUC_macro\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.948778835133556]}]}, {\"name\": \"balanced_accuracy\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.7891580384551654]}]}, {\"name\": \"recall_score_weighted\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.9177238239757208]}]}, {\"name\": \"recall_score_micro\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.9177238239757208]}]}, {\"name\": \"weighted_accuracy\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.9496562140913742]}]}, {\"name\": \"norm_macro_recall\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.5783160769103308]}]}, {\"name\": \"f1_score_micro\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.9177238239757207]}]}, {\"name\": \"average_precision_score_micro\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.9817708799137058]}]}, {\"name\": \"AUC_micro\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.9809813876269052]}]}, {\"name\": \"f1_score_weighted\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.9173943201187402]}]}, {\"name\": \"precision_score_weighted\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.9170934327252473]}]}, {\"name\": \"average_precision_score_weighted\", \"run_id\": \"AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0\", \"categories\": [0], \"series\": [{\"data\": [0.9563519213081071]}]}], \"run_logs\": \"\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.19.0\"}, \"loading\": false}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RunDetails(auto_run).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run(Experiment: experiment_AutoML,\n",
      "Id: AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0_22,\n",
      "Type: azureml.scriptrun,\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",
      "                                                                                                fit_intercept=True,\n",
      "                                                                                                intercept_scaling=1,\n",
      "                                                                                                l1_ratio=None,\n",
      "                                                                                                max_iter=100,\n",
      "                                                                                                multi_class='ovr',\n",
      "                                                                                                n_jobs=1,\n",
      "                                                                                                penalty='l1',\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=[0.42857142857142855,\n",
      "                                                        0.21428571428571427,\n",
      "                                                        0.07142857142857142,\n",
      "                                                        0.07142857142857142,\n",
      "                                                        0.07142857142857142,\n",
      "                                                        0.14285714285714285]))],\n",
      "         verbose=False)\n"
     ]
    }
   ],
   "source": [
    "# Retrieve and save your best automl model.\n",
    "\n",
    "best_automl_run, best_automl_model = auto_run.get_output()\n",
    "print(best_automl_run)\n",
    "print(best_automl_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'AUC_weighted': 0.9487788351335558, 'f1_score_macro': 0.791563467976161, 'f1_score_micro': 0.9177238239757207, 'average_precision_score_weighted': 0.9563519213081071, 'AUC_macro': 0.948778835133556, 'precision_score_weighted': 0.9170934327252473, 'average_precision_score_macro': 0.828195832911498, 'precision_score_macro': 0.7940786225707102, 'log_loss': 0.1813383407348414, 'norm_macro_recall': 0.5783160769103308, 'precision_score_micro': 0.9177238239757208, 'average_precision_score_micro': 0.9817708799137058, 'f1_score_weighted': 0.9173943201187402, 'recall_score_macro': 0.7891580384551654, 'accuracy': 0.9177238239757208, 'balanced_accuracy': 0.7891580384551654, 'matthews_correlation': 0.5831985681176741, 'recall_score_micro': 0.9177238239757208, 'recall_score_weighted': 0.9177238239757208, 'weighted_accuracy': 0.9496562140913742, 'AUC_micro': 0.9809813876269052, 'accuracy_table': 'aml://artifactId/ExperimentRun/dcid.AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0_22/accuracy_table', 'confusion_matrix': 'aml://artifactId/ExperimentRun/dcid.AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0_22/confusion_matrix'}\n"
     ]
    }
   ],
   "source": [
    "get_best_automl_metrics = best_automl_run.get_metrics()\n",
    "print(get_best_automl_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['accuracy_table',\n",
       " 'automl_driver.py',\n",
       " 'azureml-logs/55_azureml-execution-tvmps_b27438a3696a14b169f6182a4bf2586ad135fd0a67f8489a4905a8a4bcba6bd2_d.txt',\n",
       " 'azureml-logs/65_job_prep-tvmps_b27438a3696a14b169f6182a4bf2586ad135fd0a67f8489a4905a8a4bcba6bd2_d.txt',\n",
       " 'azureml-logs/70_driver_log.txt',\n",
       " 'azureml-logs/75_job_post-tvmps_b27438a3696a14b169f6182a4bf2586ad135fd0a67f8489a4905a8a4bcba6bd2_d.txt',\n",
       " 'azureml-logs/process_info.json',\n",
       " 'azureml-logs/process_status.json',\n",
       " 'confusion_matrix',\n",
       " 'logs/azureml/101_azureml.log',\n",
       " 'logs/azureml/azureml_automl.log',\n",
       " 'logs/azureml/dataprep/python_span_c040c057-5476-4356-98ea-db70cd6bcde0.jsonl',\n",
       " 'logs/azureml/dataprep/python_span_c556d3ca-0242-43ee-aac7-37e920897307.jsonl',\n",
       " 'logs/azureml/job_prep_azureml.log',\n",
       " 'logs/azureml/job_release_azureml.log',\n",
       " 'outputs/conda_env_v_1_0_0.yml',\n",
       " 'outputs/env_dependencies.json',\n",
       " 'outputs/model.pkl',\n",
       " 'outputs/pipeline_graph.json',\n",
       " 'outputs/scoring_file_v_1_0_0.py']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_automl_run.get_file_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model(workspace=Workspace.create(name='quick-starts-ws-134375', subscription_id='81cefad3-d2c9-4f77-a466-99a7f541c7bb', resource_group='aml-quickstarts-134375'), name=best_automl_model, id=best_automl_model:2, version=2, tags={}, properties={})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "best_automl_run.register_model(model_name = 'best_automl_model', model_path = 'outputs/model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_automl_run.download_file('outputs/model.pkl', 'outputs/best_model_automl.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>experiment_AutoML</td><td>AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0_22</td><td>azureml.scriptrun</td><td>Completed</td><td><a href=\"https://ml.azure.com/experiments/experiment_AutoML/runs/AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0_22?wsid=/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourcegroups/aml-quickstarts-134375/workspaces/quick-starts-ws-134375\" 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>"
      ],
      "text/plain": [
       "Run(Experiment: experiment_AutoML,\n",
       "Id: AutoML_052a89db-a5da-464e-b08e-fb8c1379f3a0_22,\n",
       "Type: azureml.scriptrun,\n",
       "Status: Completed)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_automl_run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current provisioning state of AmlCompute is \"Deleting\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cpu_cluster.delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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