udacity-project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "gather": {
     "logged": 1598275788035
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workspace name: quick-starts-ws-136280\n",
      "Azure region: southcentralus\n",
      "Subscription id: 1b944a9b-fdae-4f97-aeb1-b7eea0beac53\n",
      "Resource group: aml-quickstarts-136280\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import Workspace, Experiment\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "exp = Experiment(workspace=ws, name=\"udacity-project\")\n",
    "\n",
    "print('Workspace name: ' + ws.name, \n",
    "      'Azure region: ' + ws.location, \n",
    "      'Subscription id: ' + ws.subscription_id, \n",
    "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
    "\n",
    "run = exp.start_logging()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "gather": {
     "logged": 1598275788675
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found new cluster\n"
     ]
    }
   ],
   "source": [
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "from azureml.core.compute_target import ComputeTargetException\n",
    "\n",
    "# TODO: Create compute cluster\n",
    "# Use vm_size = \"Standard_D2_V2\" in your provisioning configuration.\n",
    "# max_nodes should be no greater than 4.\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "cluster_name = \"udacity-project\"\n",
    "try:\n",
    "    compute_cluster = ComputeTarget(ws, cluster_name)\n",
    "    print(\"Found new cluster\")\n",
    "except ComputeTargetException:\n",
    "    print(\"Create new cluster\")\n",
    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', max_nodes=4)\n",
    "\n",
    "    compute_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n",
    "    print(\"Computer cluster created\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "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,randint\n",
    "from azureml.core.script_run_config import ScriptRunConfig\n",
    "import os\n",
    "\n",
    "# Specify parameter sampler\n",
    "ps = RandomParameterSampling({'C':uniform(0,1),\"max_iter\":randint(200)})\n",
    "\n",
    "# Specify a Policy\n",
    "policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)\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='./', entry_script='train.py', compute_target=compute_cluster, vm_size = 'STANDARD_D2_V2', vm_priority = 'lowpriority')\n",
    "\n",
    "# Create a HyperDriveConfig using the estimator, hyperparameter sampler, and policy.\n",
    "hyperdrive_config = HyperDriveConfig(estimator=est,\n",
    "                                     hyperparameter_sampling=ps,\n",
    "                                     policy=policy,\n",
    "                                     primary_metric_name='Accuracy',\n",
    "                                     primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
    "                                     max_total_runs=10,\n",
    "                                     max_concurrent_runs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "263a1ebb8ee14250bdbfe032742ce23d",
       "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_959e49c3-e77d-42d5-862c-495559c33b33?wsid=/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourcegroups/aml-quickstarts-136280/workspaces/quick-starts-ws-136280\", \"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33\", \"run_properties\": {\"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33\", \"created_utc\": \"2021-01-27T16:56:20.370676Z\", \"properties\": {\"primary_metric_config\": \"{\\\"name\\\": \\\"Accuracy\\\", \\\"goal\\\": \\\"maximize\\\"}\", \"resume_from\": \"null\", \"runTemplate\": \"HyperDrive\", \"azureml.runsource\": \"hyperdrive\", \"platform\": \"AML\", \"ContentSnapshotId\": \"7b7eb425-fb01-4298-9bf0-4ce0b63cfcaf\", \"score\": \"0.9121901871522509\", \"best_child_run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_7\", \"best_metric_status\": \"Succeeded\"}, \"tags\": {\"_aml_system_max_concurrent_jobs\": \"4\", \"max_concurrent_jobs\": \"4\", \"_aml_system_max_total_jobs\": \"10\", \"max_total_jobs\": \"10\", \"_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, 1]], \\\"max_iter\\\": [\\\"randint\\\", [200]]}}\", \"generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"C\\\": [\\\"uniform\\\", [0, 1]], \\\"max_iter\\\": [\\\"randint\\\", [200]]}}\", \"_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/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/udacity-project\\\", \\\"SubscriptionId\\\": \\\"1b944a9b-fdae-4f97-aeb1-b7eea0beac53\\\", \\\"ResourceGroupName\\\": \\\"aml-quickstarts-136280\\\", \\\"WorkspaceName\\\": \\\"quick-starts-ws-136280\\\", \\\"ExperimentName\\\": \\\"udacity-project\\\", \\\"Definition\\\": {\\\"Overrides\\\": {\\\"script\\\": \\\"train.py\\\", \\\"arguments\\\": [], \\\"target\\\": \\\"udacity-project\\\", \\\"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\\\": \\\"STANDARD_D2_V2\\\", \\\"vmPriority\\\": \\\"lowpriority\\\", \\\"retainCluster\\\": false, \\\"name\\\": null, \\\"clusterMaxNodeCount\\\": 1}, \\\"command\\\": \\\"\\\"}, \\\"TargetDetails\\\": null, \\\"SnapshotId\\\": \\\"7b7eb425-fb01-4298-9bf0-4ce0b63cfcaf\\\", \\\"TelemetryValues\\\": {\\\"amlClientType\\\": \\\"azureml-sdk-train\\\", \\\"amlClientModule\\\": \\\"[Scrubbed]\\\", \\\"amlClientFunction\\\": \\\"[Scrubbed]\\\", \\\"tenantId\\\": \\\"660b3398-b80e-49d2-bc5b-ac1dc93b5254\\\", \\\"amlClientRequestId\\\": \\\"bfd57872-8904-4e33-a6a6-69313d945506\\\", \\\"amlClientSessionId\\\": \\\"094a1fff-6c6c-4c3f-816f-e15c0487582f\\\", \\\"subscriptionId\\\": \\\"1b944a9b-fdae-4f97-aeb1-b7eea0beac53\\\", \\\"estimator\\\": \\\"SKLearn\\\", \\\"samplingMethod\\\": \\\"RANDOM\\\", \\\"terminationPolicy\\\": \\\"Bandit\\\", \\\"primaryMetricGoal\\\": \\\"maximize\\\", \\\"maxTotalRuns\\\": 10, \\\"maxConcurrentRuns\\\": 4, \\\"maxDurationMinutes\\\": 10080, \\\"vmSize\\\": \\\"STANDARD_D2_V2\\\"}}}\", \"platform_config\": \"{\\\"ServiceAddress\\\": \\\"https://southcentralus.experiments.azureml.net\\\", \\\"ServiceArmScope\\\": \\\"subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/udacity-project\\\", \\\"SubscriptionId\\\": \\\"1b944a9b-fdae-4f97-aeb1-b7eea0beac53\\\", \\\"ResourceGroupName\\\": \\\"aml-quickstarts-136280\\\", \\\"WorkspaceName\\\": \\\"quick-starts-ws-136280\\\", \\\"ExperimentName\\\": \\\"udacity-project\\\", \\\"Definition\\\": {\\\"Overrides\\\": {\\\"script\\\": \\\"train.py\\\", \\\"arguments\\\": [], \\\"target\\\": \\\"udacity-project\\\", \\\"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\\\": \\\"STANDARD_D2_V2\\\", \\\"vmPriority\\\": \\\"lowpriority\\\", \\\"retainCluster\\\": false, \\\"name\\\": null, \\\"clusterMaxNodeCount\\\": 1}, \\\"command\\\": \\\"\\\"}, \\\"TargetDetails\\\": null, \\\"SnapshotId\\\": \\\"7b7eb425-fb01-4298-9bf0-4ce0b63cfcaf\\\", \\\"TelemetryValues\\\": {\\\"amlClientType\\\": \\\"azureml-sdk-train\\\", \\\"amlClientModule\\\": \\\"[Scrubbed]\\\", \\\"amlClientFunction\\\": \\\"[Scrubbed]\\\", \\\"tenantId\\\": \\\"660b3398-b80e-49d2-bc5b-ac1dc93b5254\\\", \\\"amlClientRequestId\\\": \\\"bfd57872-8904-4e33-a6a6-69313d945506\\\", \\\"amlClientSessionId\\\": \\\"094a1fff-6c6c-4c3f-816f-e15c0487582f\\\", \\\"subscriptionId\\\": \\\"1b944a9b-fdae-4f97-aeb1-b7eea0beac53\\\", \\\"estimator\\\": \\\"SKLearn\\\", \\\"samplingMethod\\\": \\\"RANDOM\\\", \\\"terminationPolicy\\\": \\\"Bandit\\\", \\\"primaryMetricGoal\\\": \\\"maximize\\\", \\\"maxTotalRuns\\\": 10, \\\"maxConcurrentRuns\\\": 4, \\\"maxDurationMinutes\\\": 10080, \\\"vmSize\\\": \\\"STANDARD_D2_V2\\\"}}}\", \"_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\": \"\\\"2021-01-27T16:56:21.567040\\\"\", \"progress_metadata_evaluation_timestamp\": \"\\\"2021-01-27T16:56:21.567040\\\"\", \"_aml_system_progress_metadata_digest\": \"\\\"dd8baa1f7c8e6d8645ff58cbf183c0f3e7e7ae52d33ba4f5a73cc2e6dca678d5\\\"\", \"progress_metadata_digest\": \"\\\"dd8baa1f7c8e6d8645ff58cbf183c0f3e7e7ae52d33ba4f5a73cc2e6dca678d5\\\"\", \"_aml_system_progress_metadata_active_timestamp\": \"\\\"2021-01-27T16:56:21.567040\\\"\", \"progress_metadata_active_timestamp\": \"\\\"2021-01-27T16:56:21.567040\\\"\", \"_aml_system_HD_959e49c3-e77d-42d5-862c-495559c33b33_0\": \"{\\\"C\\\": 0.436824662740651, \\\"max_iter\\\": 155}\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_0\": \"{\\\"C\\\": 0.436824662740651, \\\"max_iter\\\": 155}\", \"_aml_system_HD_959e49c3-e77d-42d5-862c-495559c33b33_1\": \"{\\\"C\\\": 0.5572233668914675, \\\"max_iter\\\": 83}\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_1\": \"{\\\"C\\\": 0.5572233668914675, \\\"max_iter\\\": 83}\", \"_aml_system_HD_959e49c3-e77d-42d5-862c-495559c33b33_2\": \"{\\\"C\\\": 0.6071330893646767, \\\"max_iter\\\": 16}\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_2\": \"{\\\"C\\\": 0.6071330893646767, \\\"max_iter\\\": 16}\", \"_aml_system_HD_959e49c3-e77d-42d5-862c-495559c33b33_3\": \"{\\\"C\\\": 0.07078110236450386, \\\"max_iter\\\": 87}\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_3\": \"{\\\"C\\\": 0.07078110236450386, \\\"max_iter\\\": 87}\", \"_aml_system_environment_preparation_status\": \"PREPARED\", \"environment_preparation_status\": \"PREPARED\", \"_aml_system_prepare_run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_preparation\", \"prepare_run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_preparation\", \"_aml_system_HD_959e49c3-e77d-42d5-862c-495559c33b33_4\": \"{\\\"C\\\": 0.0714140641038693, \\\"max_iter\\\": 1}\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_4\": \"{\\\"C\\\": 0.0714140641038693, \\\"max_iter\\\": 1}\", \"_aml_system_HD_959e49c3-e77d-42d5-862c-495559c33b33_5\": \"{\\\"C\\\": 0.028778059113898413, \\\"max_iter\\\": 134}\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_5\": \"{\\\"C\\\": 0.028778059113898413, \\\"max_iter\\\": 134}\", \"_aml_system_HD_959e49c3-e77d-42d5-862c-495559c33b33_6\": \"{\\\"C\\\": 0.4916468812639915, \\\"max_iter\\\": 176}\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_6\": \"{\\\"C\\\": 0.4916468812639915, \\\"max_iter\\\": 176}\", \"_aml_system_HD_959e49c3-e77d-42d5-862c-495559c33b33_7\": \"{\\\"C\\\": 0.30839742327167596, \\\"max_iter\\\": 184}\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_7\": \"{\\\"C\\\": 0.30839742327167596, \\\"max_iter\\\": 184}\", \"_aml_system_HD_959e49c3-e77d-42d5-862c-495559c33b33_8\": \"{\\\"C\\\": 0.7649735857530743, \\\"max_iter\\\": 72}\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_8\": \"{\\\"C\\\": 0.7649735857530743, \\\"max_iter\\\": 72}\", \"_aml_system_HD_959e49c3-e77d-42d5-862c-495559c33b33_9\": \"{\\\"C\\\": 0.3967972131805668, \\\"max_iter\\\": 166}\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_9\": \"{\\\"C\\\": 0.3967972131805668, \\\"max_iter\\\": 166}\"}, \"end_time_utc\": \"2021-01-27T17:05:44.755266Z\", \"status\": \"Completed\", \"log_files\": {\"azureml-logs/hyperdrive.txt\": \"https://mlstrg136280.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_959e49c3-e77d-42d5-862c-495559c33b33/azureml-logs/hyperdrive.txt?sv=2019-02-02&sr=b&sig=e%2B6d%2Bw3yXcvEBjP4XZ0gnntrpdSgU53hIhvSxudrZmU%3D&st=2021-01-27T17%3A56%3A00Z&se=2021-01-28T02%3A06%3A00Z&sp=r\"}, \"log_groups\": [[\"azureml-logs/hyperdrive.txt\"]], \"run_duration\": \"0:09:24\", \"hyper_parameters\": {\"C\": [\"uniform\", [0, 1]], \"max_iter\": [\"randint\", [200]]}}, \"child_runs\": [{\"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_1\", \"run_number\": 34, \"metric\": 0.91036925, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-27T16:57:25.692526Z\", \"end_time\": \"2021-01-27T16:58:17.626581Z\", \"created_time\": \"2021-01-27T16:56:54.7009Z\", \"created_time_dt\": \"2021-01-27T16:56:54.7009Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"959e49c3-e77d-42d5-862c-495559c33b33\", \"arguments\": null, \"param_C\": 0.5572233668914675, \"param_max_iter\": 83, \"best_metric\": 0.91036925}, {\"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_3\", \"run_number\": 35, \"metric\": 0.90692969, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-27T16:57:54.902312Z\", \"end_time\": \"2021-01-27T16:58:53.4413Z\", \"created_time\": \"2021-01-27T16:56:54.739517Z\", \"created_time_dt\": \"2021-01-27T16:56:54.739517Z\", \"duration\": \"0:01:58\", \"hyperdrive_id\": \"959e49c3-e77d-42d5-862c-495559c33b33\", \"arguments\": null, \"param_C\": 0.07078110236450386, \"param_max_iter\": 87, \"best_metric\": 0.91036925}, {\"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_2\", \"run_number\": 36, \"metric\": 0.90784016, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-27T16:58:22.005751Z\", \"end_time\": \"2021-01-27T16:59:11.020484Z\", \"created_time\": \"2021-01-27T16:56:54.885318Z\", \"created_time_dt\": \"2021-01-27T16:56:54.885318Z\", \"duration\": \"0:02:16\", \"hyperdrive_id\": \"959e49c3-e77d-42d5-862c-495559c33b33\", \"arguments\": null, \"param_C\": 0.6071330893646767, \"param_max_iter\": 16, \"best_metric\": 0.91036925}, {\"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_0\", \"run_number\": 37, \"metric\": 0.90996459, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-27T16:58:54.239482Z\", \"end_time\": \"2021-01-27T16:59:44.812194Z\", \"created_time\": \"2021-01-27T16:56:57.196606Z\", \"created_time_dt\": \"2021-01-27T16:56:57.196606Z\", \"duration\": \"0:02:47\", \"hyperdrive_id\": \"959e49c3-e77d-42d5-862c-495559c33b33\", \"arguments\": null, \"param_C\": 0.436824662740651, \"param_max_iter\": 155, \"best_metric\": 0.91036925}, {\"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_4\", \"run_number\": 38, \"metric\": 0.88548306, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-27T16:59:48.58536Z\", \"end_time\": \"2021-01-27T17:00:32.112587Z\", \"created_time\": \"2021-01-27T16:58:31.316217Z\", \"created_time_dt\": \"2021-01-27T16:58:31.316217Z\", \"duration\": \"0:02:00\", \"hyperdrive_id\": \"959e49c3-e77d-42d5-862c-495559c33b33\", \"arguments\": null, \"param_C\": 0.0714140641038693, \"param_max_iter\": 1, \"best_metric\": 0.91036925}, {\"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_5\", \"run_number\": 39, \"metric\": 0.90601922, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-27T17:00:38.738336Z\", \"end_time\": \"2021-01-27T17:01:29.087664Z\", \"created_time\": \"2021-01-27T16:59:03.174903Z\", \"created_time_dt\": \"2021-01-27T16:59:03.174903Z\", \"duration\": \"0:02:25\", \"hyperdrive_id\": \"959e49c3-e77d-42d5-862c-495559c33b33\", \"arguments\": null, \"param_C\": 0.028778059113898413, \"param_max_iter\": 134, \"best_metric\": 0.91036925}, {\"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_6\", \"run_number\": 40, \"metric\": 0.91138088, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-27T17:00:57.845456Z\", \"end_time\": \"2021-01-27T17:01:51.973506Z\", \"created_time\": \"2021-01-27T16:59:57.874549Z\", \"created_time_dt\": \"2021-01-27T16:59:57.874549Z\", \"duration\": \"0:01:54\", \"hyperdrive_id\": \"959e49c3-e77d-42d5-862c-495559c33b33\", \"arguments\": null, \"param_C\": 0.4916468812639915, \"param_max_iter\": 176, \"best_metric\": 0.91138088}, {\"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_7\", \"run_number\": 41, \"metric\": 0.91219019, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-27T17:01:57.451735Z\", \"end_time\": \"2021-01-27T17:02:50.530391Z\", \"created_time\": \"2021-01-27T17:00:30.226367Z\", \"created_time_dt\": \"2021-01-27T17:00:30.226367Z\", \"duration\": \"0:02:20\", \"hyperdrive_id\": \"959e49c3-e77d-42d5-862c-495559c33b33\", \"arguments\": null, \"param_C\": 0.30839742327167596, \"param_max_iter\": 184, \"best_metric\": 0.91219019}, {\"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_8\", \"run_number\": 42, \"metric\": 0.90895296, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-27T17:02:55.733736Z\", \"end_time\": \"2021-01-27T17:03:40.660853Z\", \"created_time\": \"2021-01-27T17:01:02.471621Z\", \"created_time_dt\": \"2021-01-27T17:01:02.471621Z\", \"duration\": \"0:02:38\", \"hyperdrive_id\": \"959e49c3-e77d-42d5-862c-495559c33b33\", \"arguments\": null, \"param_C\": 0.7649735857530743, \"param_max_iter\": 72, \"best_metric\": 0.91219019}, {\"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33_9\", \"run_number\": 43, \"metric\": 0.91087506, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-27T17:03:23.914613Z\", \"end_time\": \"2021-01-27T17:05:33.427568Z\", \"created_time\": \"2021-01-27T17:02:05.166859Z\", \"created_time_dt\": \"2021-01-27T17:02:05.166859Z\", \"duration\": \"0:03:28\", \"hyperdrive_id\": \"959e49c3-e77d-42d5-862c-495559c33b33\", \"arguments\": null, \"param_C\": 0.3967972131805668, \"param_max_iter\": 166, \"best_metric\": 0.91219019}], \"children_metrics\": {\"categories\": [0], \"series\": {\"Regularization Strength:\": [{\"categories\": [34, 35, 36, 37, 38, 39, 40, 41, 42, 43], \"mode\": \"markers\", \"name\": \"Regularization Strength:\", \"stepped\": false, \"type\": \"scatter\", \"data\": [0.5572233668914675, 0.07078110236450386, 0.6071330893646767, 0.436824662740651, 0.0714140641038693, 0.028778059113898413, 0.4916468812639915, 0.30839742327167596, 0.7649735857530743, 0.3967972131805668]}, {\"categories\": [34, 35, 36, 37, 38, 39, 40, 41, 42, 43], \"mode\": \"lines\", \"name\": \"Regularization Strength:_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [0.5572233668914675, 0.5572233668914675, 0.6071330893646767, 0.6071330893646767, 0.6071330893646767, 0.6071330893646767, 0.6071330893646767, 0.6071330893646767, 0.7649735857530743, 0.7649735857530743]}], \"Max iterations:\": [{\"categories\": [34, 35, 36, 37, 38, 39, 40, 41, 42, 43], \"mode\": \"markers\", \"name\": \"Max iterations:\", \"stepped\": false, \"type\": \"scatter\", \"data\": [83, 87, 16, 155, 1, 134, 176, 184, 72, 166]}, {\"categories\": [34, 35, 36, 37, 38, 39, 40, 41, 42, 43], \"mode\": \"lines\", \"name\": \"Max iterations:_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [83, 87, 87, 155, 155, 155, 176, 184, 184, 184]}], \"Accuracy\": [{\"categories\": [34, 35, 36, 37, 38, 39, 40, 41, 42, 43], \"mode\": \"markers\", \"name\": \"Accuracy\", \"stepped\": false, \"type\": \"scatter\", \"data\": [0.9103692463328276, 0.9069296914516944, 0.9078401618614061, 0.9099645928174, 0.8854830551340415, 0.9060192210419828, 0.9113808801213961, 0.9121901871522509, 0.9089529590288316, 0.9108750632271118]}, {\"categories\": [34, 35, 36, 37, 38, 39, 40, 41, 42, 43], \"mode\": \"lines\", \"name\": \"Accuracy_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [0.9103692463328276, 0.9103692463328276, 0.9103692463328276, 0.9103692463328276, 0.9103692463328276, 0.9103692463328276, 0.9113808801213961, 0.9121901871522509, 0.9121901871522509, 0.9121901871522509]}]}, \"metricName\": null, \"primaryMetricName\": \"Accuracy\", \"showLegend\": false}, \"run_metrics\": [{\"name\": \"best_child_by_primary_metric\", \"run_id\": \"HD_959e49c3-e77d-42d5-862c-495559c33b33\", \"categories\": [0], \"series\": [{\"data\": [{\"metric_name\": [\"Accuracy\", \"Accuracy\", \"Accuracy\", \"Accuracy\"], \"timestamp\": [\"2021-01-27 16:58:57.939187+00:00\", \"2021-01-27 17:02:35.314814+00:00\", \"2021-01-27 17:03:06.928373+00:00\", \"2021-01-27 17:03:06.928373+00:00\"], \"run_id\": [\"HD_959e49c3-e77d-42d5-862c-495559c33b33_1\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_6\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_7\", \"HD_959e49c3-e77d-42d5-862c-495559c33b33_7\"], \"metric_value\": [0.9103692463328276, 0.9113808801213961, 0.9121901871522509, 0.9121901871522509], \"final\": [false, false, false, true]}]}]}], \"run_logs\": \"[2021-01-27T16:56:21.622755][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-01-27T16:56:21.038987][API][INFO]Experiment created\\r\\n[2021-01-27T16:56:21.925014][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T16:56:23.0043493Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.\\r\\n[2021-01-27T16:56:53.7932976Z][SCHEDULER][INFO]The execution environment was successfully prepared.\\r\\n[2021-01-27T16:56:53.7994532Z][SCHEDULER][INFO]Scheduling job, id='HD_959e49c3-e77d-42d5-862c-495559c33b33_2'\\r\\n[2021-01-27T16:56:53.7988797Z][SCHEDULER][INFO]Scheduling job, id='HD_959e49c3-e77d-42d5-862c-495559c33b33_1'\\r\\n[2021-01-27T16:56:53.7987503Z][SCHEDULER][INFO]Scheduling job, id='HD_959e49c3-e77d-42d5-862c-495559c33b33_0'\\r\\n[2021-01-27T16:56:53.7998373Z][SCHEDULER][INFO]Scheduling job, id='HD_959e49c3-e77d-42d5-862c-495559c33b33_3'\\r\\n[2021-01-27T16:56:54.8129672Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_959e49c3-e77d-42d5-862c-495559c33b33_1'\\r\\n[2021-01-27T16:56:55.0339766Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_959e49c3-e77d-42d5-862c-495559c33b33_3'\\r\\n[2021-01-27T16:56:55.1201873Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_959e49c3-e77d-42d5-862c-495559c33b33_2'\\r\\n[2021-01-27T16:56:57.4389598Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_959e49c3-e77d-42d5-862c-495559c33b33_0'\\r\\n[2021-01-27T16:57:55.278770][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_1] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T16:58:24.745930][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-27T16:58:25.095378][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T16:58:25.423470][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_2, https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T16:58:29.5916107Z][SCHEDULER][INFO]Scheduling job, id='HD_959e49c3-e77d-42d5-862c-495559c33b33_4'\\r\\n[2021-01-27T16:58:31.4388099Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_959e49c3-e77d-42d5-862c-495559c33b33_4'\\r\\n[2021-01-27T16:58:55.148508][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-27T16:58:55.308417][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T16:58:56.277438][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_0] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T16:59:01.9041331Z][SCHEDULER][INFO]Scheduling job, id='HD_959e49c3-e77d-42d5-862c-495559c33b33_5'\\r\\n[2021-01-27T16:59:03.4284202Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_959e49c3-e77d-42d5-862c-495559c33b33_5'\\r\\n[2021-01-27T16:59:25.765345][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-27T16:59:26.294521][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T16:59:27.316082][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_0] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T16:59:33.9789344Z][SCHEDULER][INFO]Scheduling job, id='HD_959e49c3-e77d-42d5-862c-495559c33b33_6'\\r\\n[2021-01-27T16:59:56.832921][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-27T16:59:56.997945][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T16:59:58.1109558Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_959e49c3-e77d-42d5-862c-495559c33b33_6'\\r\\n[2021-01-27T16:59:58.458012][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_4] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T17:00:29.2093060Z][SCHEDULER][INFO]Scheduling job, id='HD_959e49c3-e77d-42d5-862c-495559c33b33_7'\\r\\n[2021-01-27T17:00:30.4311457Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_959e49c3-e77d-42d5-862c-495559c33b33_7'\\r\\n[2021-01-27T17:00:32.664925][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_7] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T17:00:57.279810][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-27T17:00:57.425463][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T17:01:00.9109766Z][SCHEDULER][INFO]Scheduling job, id='HD_959e49c3-e77d-42d5-862c-495559c33b33_8'\\r\\n[2021-01-27T17:01:03.006322][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_5, https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_6] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T17:01:02.6023067Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_959e49c3-e77d-42d5-862c-495559c33b33_8'\\r\\n[2021-01-27T17:01:33.022856][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_6] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T17:01:58.804562][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-27T17:01:58.941078][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T17:02:02.958275][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_7] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T17:02:04.1055918Z][SCHEDULER][INFO]Scheduling job, id='HD_959e49c3-e77d-42d5-862c-495559c33b33_9'\\r\\n[2021-01-27T17:02:05.4027942Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_959e49c3-e77d-42d5-862c-495559c33b33_9'\\r\\n[2021-01-27T17:02:28.888364][GENERATOR][INFO]Max number of jobs '10' reached for experiment.\\r\\n[2021-01-27T17:02:29.033501][GENERATOR][INFO]All jobs generated.\\r\\n[2021-01-27T17:02:32.950718][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_7] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T17:03:02.839063][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_8] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T17:03:32.890381][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_8, https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_9] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T17:04:03.162707][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_9] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T17:04:33.078990][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_9] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T17:05:02.892504][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_9] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T17:05:32.939574][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-136280/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136280/experiments/**SCRUBBED**/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33_9] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T17:05:45.252382][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\": \"1.20.0\"}, \"loading\": false}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Submit your hyperdrive run to the experiment and show run details with the widget.\n",
    "hd_run = exp.submit(hyperdrive_config)\n",
    "RunDetails(hd_run).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RunId: HD_959e49c3-e77d-42d5-862c-495559c33b33\n",
      "Web View: https://ml.azure.com/experiments/udacity-project/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33?wsid=/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourcegroups/aml-quickstarts-136280/workspaces/quick-starts-ws-136280\n",
      "\n",
      "Execution Summary\n",
      "=================\n",
      "RunId: HD_959e49c3-e77d-42d5-862c-495559c33b33\n",
      "Web View: https://ml.azure.com/experiments/udacity-project/runs/HD_959e49c3-e77d-42d5-862c-495559c33b33?wsid=/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourcegroups/aml-quickstarts-136280/workspaces/quick-starts-ws-136280\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'runId': 'HD_959e49c3-e77d-42d5-862c-495559c33b33',\n",
       " 'target': 'udacity-project',\n",
       " 'status': 'Completed',\n",
       " 'startTimeUtc': '2021-01-27T16:56:20.437856Z',\n",
       " 'endTimeUtc': '2021-01-27T17:05:44.755266Z',\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': '7b7eb425-fb01-4298-9bf0-4ce0b63cfcaf',\n",
       "  'score': '0.9121901871522509',\n",
       "  'best_child_run_id': 'HD_959e49c3-e77d-42d5-862c-495559c33b33_7',\n",
       "  'best_metric_status': 'Succeeded'},\n",
       " 'inputDatasets': [],\n",
       " 'outputDatasets': [],\n",
       " 'logFiles': {'azureml-logs/hyperdrive.txt': 'https://mlstrg136280.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_959e49c3-e77d-42d5-862c-495559c33b33/azureml-logs/hyperdrive.txt?sv=2019-02-02&sr=b&sig=kaDufCAv8cGmMrmFaRBBmXNEnkTVkDt%2F9jyDFJPQqA4%3D&st=2021-01-27T16%3A55%3A46Z&se=2021-01-28T01%3A05%3A46Z&sp=r'},\n",
       " 'submittedBy': 'ODL_User 136280'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hd_run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "gather": {
     "logged": 1598276310862
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best Run ID:  HD_959e49c3-e77d-42d5-862c-495559c33b33_7\n",
      "\n",
      " Accuracy: 0.9121901871522509\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "# Get your best run and save the model from that run.\n",
    "best_run = hd_run.get_best_run_by_primary_metric()\n",
    "best_run_metrics = best_run.get_metrics()\n",
    "parameter_values = best_run.get_details()['runDefinition']['arguments']\n",
    "\n",
    "print('The best Run ID: ', best_run.id)\n",
    "print('\\n Accuracy:', best_run_metrics['Accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run.get_file_names()\n",
    "model = best_run.register_model(model_name='hyperdrive_model', model_path='outputs/model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.data.dataset_factory import TabularDatasetFactory\n",
    "\n",
    "new_path = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
    "ds = TabularDatasetFactory.from_delimited_files(path=new_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "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": 17,
   "metadata": {
    "collapsed": false,
    "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",
    "    compute_target = compute_cluster,\n",
    "    task=\"classification\",\n",
    "    primary_metric=\"accuracy\",\n",
    "    training_data= ds,\n",
    "    label_column_name='y',\n",
    "    n_cross_validations=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "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 udacity-project with default configuration\n",
      "Running on remote compute: udacity-project\n",
      "Parent Run ID: AutoML_0219ad73-fedb-48f1-a32f-a4747e29822d\n",
      "\n",
      "Current status: FeaturesGeneration. Generating features for the dataset.\n",
      "Current status: DatasetFeaturization. Beginning to fit featurizers and featurize the dataset.\n",
      "Current status: DatasetBalancing. Performing class balancing sweeping\n",
      "Current status: DatasetCrossValidationSplit. Generating individually featurized CV splits.\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                             |yes                              |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                          0:00:45       0.9154    0.9154\n",
      "         1   MaxAbsScaler XGBoostClassifier                 0:00:58       0.9148    0.9154\n",
      "         2   MaxAbsScaler RandomForest                      0:00:54       0.8934    0.9154\n",
      "         3   MaxAbsScaler RandomForest                      0:00:45       0.8880    0.9154\n",
      "         4   MaxAbsScaler RandomForest                      0:00:49       0.7878    0.9154\n",
      "         5   MaxAbsScaler RandomForest                      0:00:54       0.7870    0.9154\n",
      "         6   SparseNormalizer XGBoostClassifier             0:01:06       0.9127    0.9154\n",
      "         7   MaxAbsScaler GradientBoosting                  0:00:51       0.9050    0.9154\n",
      "         8   StandardScalerWrapper RandomForest             0:00:54       0.8989    0.9154\n",
      "         9   MaxAbsScaler LogisticRegression                0:00:54       0.9089    0.9154\n",
      "        10   MaxAbsScaler LightGBM                          0:00:42       0.8927    0.9154\n",
      "        11   SparseNormalizer XGBoostClassifier             0:01:03       0.9141    0.9154\n",
      "        12   MaxAbsScaler ExtremeRandomTrees                0:02:09       0.8880    0.9154\n",
      "        13   StandardScalerWrapper RandomForest             0:01:02       0.8880    0.9154\n",
      "        14   SparseNormalizer XGBoostClassifier             0:01:55       0.9121    0.9154\n",
      "        15   MaxAbsScaler LightGBM                          0:00:54       0.9049    0.9154\n",
      "        16   SparseNormalizer XGBoostClassifier             0:01:03       0.9155    0.9155\n",
      "        17   StandardScalerWrapper LightGBM                 0:00:53       0.9116    0.9155\n",
      "        18   MaxAbsScaler LightGBM                          0:00:52       0.9084    0.9155\n",
      "        19   SparseNormalizer LightGBM                      0:00:46       0.9065    0.9155\n",
      "        20   StandardScalerWrapper LightGBM                 0:01:01       0.9108    0.9155\n",
      "        21   StandardScalerWrapper XGBoostClassifier        0:00:42       0.9063    0.9155\n",
      "        22   StandardScalerWrapper LogisticRegression       0:01:00       0.9098    0.9155\n",
      "        23   StandardScalerWrapper LightGBM                 0:00:52       0.9045    0.9155\n",
      "        24    VotingEnsemble                                0:01:22       0.9171    0.9171\n",
      "        25    StackEnsemble                                 0:01:32       0.9158    0.9171\n"
     ]
    }
   ],
   "source": [
    "# Submit your automl run\n",
    "\n",
    "experiment = Experiment(ws, \"automl_experiment\")\n",
    "run = experiment.submit(config=automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run(Experiment: automl_experiment,\n",
      "Id: AutoML_0219ad73-fedb-48f1-a32f-a4747e29822d_24,\n",
      "Type: azureml.scriptrun,\n",
      "Status: Completed)\n"
     ]
    }
   ],
   "source": [
    "best_automl_run, best_automl_model = run.get_output()\n",
    "# best_run_metrics = best_automl_run.get_metrics()\n",
    "# parameter_values = best_automl_run.get_details()['runDefinition']['arguments']\n",
    "print(best_automl_run)\n",
    "# print('The best run Id: ', best_run.id)\n",
    "# print('\\n Accuracy:', best_run_metrics['accuracy'])\n",
    "# print('Best run details:', best_automl_run.get_details())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PipelineWithYTransformations(Pipeline={'memory': None,\n",
       "                                       'steps': [('datatransformer',\n",
       "                                                  DataTransformer(enable_dnn=None,\n",
       "                                                                  enable_feature_sweeping=None,\n",
       "                                                                  feature_sweeping_config=None,\n",
       "                                                                  feature_sweeping_timeout=None,\n",
       "                                                                  featurization_config=None,\n",
       "                                                                  force_text_dnn=None,\n",
       "                                                                  is_cross_validation=None,\n",
       "                                                                  is_onnx_compatible=None,\n",
       "                                                                  logger=None,\n",
       "                                                                  observer=None,\n",
       "                                                                  task=None,\n",
       "                                                                  working_dir=None))...\n",
       "                                                                                                                                 reg_lambda=0.6842105263157894,\n",
       "                                                                                                                                 silent=True,\n",
       "                                                                                                                                 subsample=1,\n",
       "                                                                                                                                 subsample_for_bin=200000,\n",
       "                                                                                                                                 subsample_freq=0,\n",
       "                                                                                                                                 verbose=-10))],\n",
       "                                                                                                      verbose=False))],\n",
       "                                                                                flatten_transform=None,\n",
       "                                                                                weights=[0.07142857142857142,\n",
       "                                                                                         0.21428571428571427,\n",
       "                                                                                         0.21428571428571427,\n",
       "                                                                                         0.07142857142857142,\n",
       "                                                                                         0.2857142857142857,\n",
       "                                                                                         0.07142857142857142,\n",
       "                                                                                         0.07142857142857142]))],\n",
       "                                       'verbose': False},\n",
       "                             y_transformer={},\n",
       "                             y_transformer_name='LabelEncoder')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_automl_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9171470855677206"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_automl_run.get_metrics()['accuracy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model(workspace=Workspace.create(name='quick-starts-ws-136280', subscription_id='1b944a9b-fdae-4f97-aeb1-b7eea0beac53', resource_group='aml-quickstarts-136280'), name=automl_model, id=automl_model:1, version=1, tags={}, properties={})"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_automl_run.register_model(model_name=\"automl_model\", model_path=\"./outputs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "nteract": {
   "version": "nteract-front-end@1.0.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}