udacity-project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "gather": {
     "logged": 1611400203256
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workspace name: quick-starts-ws-136232\n",
      "Azure region: southcentralus\n",
      "Subscription id: 5a4ab2ba-6c51-4805-8155-58759ad589d8\n",
      "Resource group: aml-quickstarts-136232\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": 2,
   "metadata": {
    "gather": {
     "logged": 1611400203677
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found existing cluster, use it.\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",
    "cpu_cluster_name = \"cpu-cluster\"\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": "code",
   "execution_count": 3,
   "metadata": {
    "gather": {
     "logged": 1611400207412
    },
    "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 choice\n",
    "import os\n",
    "\n",
    "# Specify parameter sampler\n",
    "ps = RandomParameterSampling({\"--C\":choice(0.01,0.1,1,10,100), \"--max_iter\":choice(50,100,150,200)})\n",
    "\n",
    "# Specify a Policy\n",
    "policy = BanditPolicy(evaluation_interval = 1, 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 = cpu_cluster_name, 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 = 20,\n",
    "max_concurrent_runs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "gather": {
     "logged": 1611398774796
    },
    "scrolled": true
   },
   "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": "6205182ba2bf4eef8ed1a5c294ddb7a8",
       "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_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4?wsid=/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourcegroups/aml-quickstarts-136232/workspaces/quick-starts-ws-136232\", \"run_id\": \"HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4\", \"run_properties\": {\"run_id\": \"HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4\", \"created_utc\": \"2021-01-27T09:20:15.377156Z\", \"properties\": {\"primary_metric_config\": \"{\\\"name\\\": \\\"Accuracy\\\", \\\"goal\\\": \\\"maximize\\\"}\", \"resume_from\": \"null\", \"runTemplate\": \"HyperDrive\", \"azureml.runsource\": \"hyperdrive\", \"platform\": \"AML\", \"ContentSnapshotId\": \"2a5f1821-aadb-465d-a0e7-8cfe1c85e4e0\", \"score\": \"0.91442097596504\", \"best_child_run_id\": \"HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_0\", \"best_metric_status\": \"Succeeded\"}, \"tags\": {\"_aml_system_max_concurrent_jobs\": \"4\", \"max_concurrent_jobs\": \"4\", \"_aml_system_max_total_jobs\": \"20\", \"max_total_jobs\": \"20\", \"_aml_system_max_duration_minutes\": \"10080\", \"max_duration_minutes\": \"10080\", \"_aml_system_policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 1, \\\"delay_evaluation\\\": 0, \\\"slack_factor\\\": 0.1}}\", \"policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 1, \\\"delay_evaluation\\\": 0, \\\"slack_factor\\\": 0.1}}\", \"_aml_system_generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"--C\\\": [\\\"choice\\\", [[0.01, 0.1, 1, 10, 100]]], \\\"--max_iter\\\": [\\\"choice\\\", [[50, 100, 150, 200]]]}}\", \"generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"--C\\\": [\\\"choice\\\", [[0.01, 0.1, 1, 10, 100]]], \\\"--max_iter\\\": [\\\"choice\\\", [[50, 100, 150, 200]]]}}\", 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\\\"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\\\": \\\"2a5f1821-aadb-465d-a0e7-8cfe1c85e4e0\\\", \\\"TelemetryValues\\\": {\\\"amlClientType\\\": \\\"azureml-sdk-train\\\", \\\"amlClientModule\\\": \\\"[Scrubbed]\\\", \\\"amlClientFunction\\\": \\\"[Scrubbed]\\\", \\\"tenantId\\\": \\\"660b3398-b80e-49d2-bc5b-ac1dc93b5254\\\", \\\"amlClientRequestId\\\": \\\"3c6d8905-e5d7-4c5a-af09-8c2e1d691fcb\\\", \\\"amlClientSessionId\\\": \\\"fb6ba9b3-5942-4186-a04d-f79ce0593ab7\\\", \\\"subscriptionId\\\": \\\"5a4ab2ba-6c51-4805-8155-58759ad589d8\\\", \\\"estimator\\\": \\\"SKLearn\\\", \\\"samplingMethod\\\": \\\"RANDOM\\\", 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\"HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4\", \"categories\": [0], \"series\": [{\"data\": [{\"metric_name\": [\"Accuracy\", \"Accuracy\"], \"timestamp\": [\"2021-01-27 09:32:42.663250+00:00\", \"2021-01-27 09:32:42.663250+00:00\"], \"run_id\": [\"HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_0\", \"HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_0\"], \"metric_value\": [0.91442097596504, 0.91442097596504], \"final\": [false, true]}]}]}], \"run_logs\": \"[2021-01-27T09:20:15.663642][API][INFO]Experiment created\\r\\n[2021-01-27T09:20:16.8467138Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.\\r\\n[2021-01-27T09:20:19.550265][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-01-27T09:20:19.890561][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T09:25:55.2964135Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_0'\\r\\n[2021-01-27T09:25:53.9289412Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_3'\\r\\n[2021-01-27T09:25:55.2635395Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_0'\\r\\n[2021-01-27T09:25:54.3342140Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_2'\\r\\n[2021-01-27T09:25:55.2795161Z][SCHEDULER][INFO]The execution environment was successfully prepared.\\r\\n[2021-01-27T09:25:54.8290722Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_1'\\r\\n[2021-01-27T09:25:56.3041741Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_2'\\r\\n[2021-01-27T09:25:56.4606777Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_1'\\r\\n[2021-01-27T09:25:56.6135581Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_3'\\r\\n[2021-01-27T09:30:25.180692][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_0, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T09:30:56.053324][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_0, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_1, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_2, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T09:31:25.828381][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_0, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_1, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_2, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T09:31:58.564291][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_0, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_1, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_2, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T09:32:37.992547][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T09:32:37.816191][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-01-27T09:33:06.2250308Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_4'\\r\\n[2021-01-27T09:33:06.2822350Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_6'\\r\\n[2021-01-27T09:33:06.3537472Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_5'\\r\\n[2021-01-27T09:33:07.6224894Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_4'\\r\\n[2021-01-27T09:33:07.6403539Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_5'\\r\\n[2021-01-27T09:33:07.8661753Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_6'\\r\\n[2021-01-27T09:33:08.062220][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-27T09:33:08.377257][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T09:33:38.4025842Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_7'\\r\\n[2021-01-27T09:33:39.6007504Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_7'\\r\\n[2021-01-27T09:34:41.042209][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2021-01-27T09:34:41.227629][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T09:35:11.029669][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T09:35:10.849680][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-01-27T09:35:11.9571038Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_8'\\r\\n[2021-01-27T09:35:12.1616686Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_11'\\r\\n[2021-01-27T09:35:12.0100301Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_9'\\r\\n[2021-01-27T09:35:12.0434906Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_10'\\r\\n[2021-01-27T09:35:13.0816626Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_11'\\r\\n[2021-01-27T09:35:13.1843779Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_8'\\r\\n[2021-01-27T09:35:13.3579317Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_9'\\r\\n[2021-01-27T09:35:13.4472102Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_10'\\r\\n[2021-01-27T09:35:29.099338][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_8, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_9, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_10, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_11] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T09:36:00.914435][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_9] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T09:36:33.787364][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_9] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T09:36:45.382635][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-01-27T09:36:45.755553][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T09:36:46.6228879Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_12'\\r\\n[2021-01-27T09:36:46.6257575Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_14'\\r\\n[2021-01-27T09:36:46.6241359Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_13'\\r\\n[2021-01-27T09:36:46.6303999Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_15'\\r\\n[2021-01-27T09:36:47.8277997Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_13'\\r\\n[2021-01-27T09:36:47.9821499Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_12'\\r\\n[2021-01-27T09:36:48.4485063Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_15'\\r\\n[2021-01-27T09:36:49.0429168Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_14'\\r\\n[2021-01-27T09:37:04.098123][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_12, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_13, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_14, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_15] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T09:37:34.790282][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_12, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_13, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_14, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_15] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T09:38:16.189034][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-01-27T09:38:16.377293][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-27T09:38:22.9250399Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_18'\\r\\n[2021-01-27T09:38:22.8782691Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_16'\\r\\n[2021-01-27T09:38:22.8792901Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_17'\\r\\n[2021-01-27T09:38:22.9381365Z][SCHEDULER][INFO]Scheduling job, id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_19'\\r\\n[2021-01-27T09:38:24.1232533Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_18'\\r\\n[2021-01-27T09:38:24.2959525Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_19'\\r\\n[2021-01-27T09:38:24.9718887Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_16'\\r\\n[2021-01-27T09:38:25.2217096Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_17'\\r\\n[2021-01-27T09:38:36.273717][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_16, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_17, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_18, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_19] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T09:38:47.290803][GENERATOR][INFO]Max number of jobs '20' reached for experiment.\\r\\n[2021-01-27T09:38:47.867715][GENERATOR][INFO]All jobs generated.\\r\\n[2021-01-27T09:39:07.257944][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_16, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_17, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_18, https://southcentralus.experiments.azureml.net/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experiments/**SCRUBBED**/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_19] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-27T09:40:42.625525][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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RunId: HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4\n",
      "Web View: https://ml.azure.com/experiments/udacity-project/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4?wsid=/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourcegroups/aml-quickstarts-136232/workspaces/quick-starts-ws-136232\n",
      "\n",
      "Streaming azureml-logs/hyperdrive.txt\n",
      "=====================================\n",
      "\n",
      "\"<START>[2021-01-27T09:20:15.663642][API][INFO]Experiment created<END>\\n\"<START>[2021-01-27T09:20:16.8467138Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.<END>\"<START>[2021-01-27T09:20:19.550265][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space<END>\\n\"\"<START>[2021-01-27T09:20:19.890561][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.<END>\\n\"\n",
      "\n",
      "Execution Summary\n",
      "=================\n",
      "RunId: HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4\n",
      "Web View: https://ml.azure.com/experiments/udacity-project/runs/HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4?wsid=/subscriptions/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourcegroups/aml-quickstarts-136232/workspaces/quick-starts-ws-136232\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'runId': 'HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4',\n",
       " 'target': 'cpu-cluster',\n",
       " 'status': 'Completed',\n",
       " 'startTimeUtc': '2021-01-27T09:20:15.429353Z',\n",
       " 'endTimeUtc': '2021-01-27T09:40:42.378241Z',\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': '2a5f1821-aadb-465d-a0e7-8cfe1c85e4e0',\n",
       "  'score': '0.91442097596504',\n",
       "  'best_child_run_id': 'HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4_0',\n",
       "  'best_metric_status': 'Succeeded'},\n",
       " 'inputDatasets': [],\n",
       " 'outputDatasets': [],\n",
       " 'logFiles': {'azureml-logs/hyperdrive.txt': 'https://mlstrg136232.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_dcefd7e9-54a6-465c-a7b9-9da3bdd514e4/azureml-logs/hyperdrive.txt?sv=2019-02-02&sr=b&sig=EQ6bmemF6aca%2B1Va%2FZZYYgejeVncF1ui7oON2fa4cUs%3D&st=2021-01-27T09%3A30%3A55Z&se=2021-01-27T17%3A40%3A55Z&sp=r'},\n",
       " 'submittedBy': 'ODL_User 136232'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Submit your hyperdrive run to the experiment and show run details with the widget.\n",
    "\n",
    "hdr = exp.submit(config=hyperdrive_config, show_output=True)\n",
    "RunDetails(hdr).show()\n",
    "\n",
    "hdr.get_status()\n",
    "hdr.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Regularization Strength:': 10.0, 'Max iterations:': 50, 'Accuracy': 0.91442097596504}\n"
     ]
    }
   ],
   "source": [
    "best_run = hdr.get_best_run_by_primary_metric()\n",
    "best_run_metrics = best_run.get_metrics()\n",
    "parameter_values = best_run.get_details()\n",
    "\n",
    "print(best_run_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "gather": {
     "logged": 1598276310862
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "# Save the model from the best run\n",
    "\n",
    "\n",
    "hyperdrive_model = best_run.register_model(model_name='hyperdrive_model',model_path='outputs/model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.data.dataset_factory import TabularDatasetFactory\n",
    "\n",
    "url = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
    "\n",
    "ds = TabularDatasetFactory.from_delimited_files(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "gather": {
     "logged": 1598275726969
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading an estimated of 1 files\n",
      "Uploading ./training/train_data.csv\n",
      "Uploaded ./training/train_data.csv, 1 files out of an estimated total of 1\n",
      "Uploaded 1 files\n"
     ]
    }
   ],
   "source": [
    "from train import clean_data\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "from azureml.core import Dataset\n",
    "\n",
    "# Use the clean_data function from train.py to clean the data.\n",
    "x, y = clean_data(ds)\n",
    "\n",
    "y_dataframe = pd.DataFrame(y,columns=['y'])\n",
    "all_data = pd.concat([x,y_dataframe], axis = 1)\n",
    "\n",
    "x_train, x_test = train_test_split(all_data, test_size=0.2, random_state=1)\n",
    "\n",
    "x_train.to_csv(\"training/train_data.csv\", index = False)\n",
    "\n",
    "data_store = ws.get_default_datastore()\n",
    "data_store.upload(src_dir = \"./training\", target_path = 'udacity-project', overwrite = True,  show_progress = True)\n",
    "train_data = Dataset.Tabular.from_delimited_files(path=data_store.path(\"udacity-project/train_data.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
    "\n",
    "automl_config = AutoMLConfig(\n",
    "    experiment_timeout_minutes=30,\n",
    "    task=\"classification\",\n",
    "    primary_metric=\"accuracy\",\n",
    "    training_data= train_data,\n",
    "    label_column_name= \"y\",\n",
    "    n_cross_validations=4,\n",
    "    compute_target=cpu_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 cpu-cluster with default configuration\n",
      "Running on remote compute: cpu-cluster\n",
      "Parent Run ID: AutoML_40fd204c-1e33-451b-82e4-edad8740e10d\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: 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",
      "|2961                             |1                                |26360                                 |\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                          0:00:51       0.9152    0.9152\n",
      "         1   MaxAbsScaler XGBoostClassifier                 0:00:49       0.9159    0.9159\n",
      "         2   MaxAbsScaler RandomForest                      0:00:52       0.8923    0.9159\n",
      "         3   MaxAbsScaler RandomForest                      0:00:53       0.8877    0.9159\n",
      "         4   MaxAbsScaler RandomForest                      0:00:45       0.8198    0.9159\n",
      "         5   MaxAbsScaler RandomForest                      0:00:49       0.7446    0.9159\n",
      "         6   SparseNormalizer XGBoostClassifier             0:01:00       0.9132    0.9159\n",
      "         7   MaxAbsScaler GradientBoosting                  0:00:58       0.9022    0.9159\n",
      "         8   StandardScalerWrapper RandomForest             0:00:51       0.8998    0.9159\n",
      "         9   MaxAbsScaler LogisticRegression                0:00:48       0.9085    0.9159\n",
      "        10   MaxAbsScaler ExtremeRandomTrees                0:01:38       0.8877    0.9159\n",
      "        11   SparseNormalizer XGBoostClassifier             0:01:01       0.9129    0.9159\n",
      "        12   MaxAbsScaler LightGBM                          0:00:49       0.8906    0.9159\n",
      "        13   MaxAbsScaler LightGBM                          0:00:46       0.9046    0.9159\n",
      "        14   SparseNormalizer XGBoostClassifier             0:01:35       0.9131    0.9159\n",
      "        15   StandardScalerWrapper LightGBM                 0:00:49       0.8936    0.9159\n",
      "        16   StandardScalerWrapper RandomForest             0:00:58       0.8877    0.9159\n",
      "        17   StandardScalerWrapper LightGBM                 0:00:50       0.8877    0.9159\n",
      "        18   SparseNormalizer XGBoostClassifier             0:00:53       0.9123    0.9159\n",
      "        19   StandardScalerWrapper LightGBM                 0:00:48       0.9044    0.9159\n",
      "        20   StandardScalerWrapper ExtremeRandomTrees       0:01:08       0.8877    0.9159\n",
      "        21   MaxAbsScaler LightGBM                          0:00:47       0.8879    0.9159\n",
      "        22   SparseNormalizer LightGBM                      0:00:47       0.9060    0.9159\n",
      "        23   SparseNormalizer LightGBM                      0:00:47       0.9119    0.9159\n",
      "        24   StandardScalerWrapper LightGBM                 0:00:55       0.9089    0.9159\n",
      "        25   StandardScalerWrapper XGBoostClassifier        0:01:21       0.8877    0.9159\n",
      "        26    VotingEnsemble                                0:01:29       0.9175    0.9175\n",
      "        27    StackEnsemble                                 0:01:40       0.9163    0.9175\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18246c646f3e4a83b5095e5db2cdd734",
       "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"
    },
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{\\\\\\\\\\\\\"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}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_management\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_retired\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_self-employed\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_services\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"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}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"contact_cellular\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"contact_telephone\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"education_basic.4y\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"education_basic.6y\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"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\\\\\\\\\\\\\": \\\\\\\\\\\\\"y\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}]}, \\\\\\\\\\\\\"localData\\\\\\\\\\\\\": {}, \\\\\\\\\\\\\"isEnabled\\\\\\\\\\\\\": true, \\\\\\\\\\\\\"name\\\\\\\\\\\\\": null, \\\\\\\\\\\\\"annotation\\\\\\\\\\\\\": null}], \\\\\\\\\\\\\"inspectors\\\\\\\\\\\\\": [], \\\\\\\\\\\\\"meta\\\\\\\\\\\\\": {\\\\\\\\\\\\\"savedDatasetId\\\\\\\\\\\\\": \\\\\\\\\\\\\"ea8cba91-d330-4219-a53c-7e54909e09b3\\\\\\\\\\\\\", \\\\\\\\\\\\\"datasetType\\\\\\\\\\\\\": \\\\\\\\\\\\\"tabular\\\\\\\\\\\\\", \\\\\\\\\\\\\"subscriptionId\\\\\\\\\\\\\": \\\\\\\\\\\\\"5a4ab2ba-6c51-4805-8155-58759ad589d8\\\\\\\\\\\\\", \\\\\\\\\\\\\"workspaceId\\\\\\\\\\\\\": \\\\\\\\\\\\\"fbed2c4a-a9af-4648-8cd3-b607f00f7105\\\\\\\\\\\\\", \\\\\\\\\\\\\"workspaceLocation\\\\\\\\\\\\\": \\\\\\\\\\\\\"southcentralus\\\\\\\\\\\\\"}}\\\\\", \\\\\"activities\\\\\": 0}',\n",
       "  'EnableSubsampling': None,\n",
       "  'runTemplate': 'AutoML',\n",
       "  'azureml.runsource': 'automl',\n",
       "  'display_task_type': 'classification',\n",
       "  'dependencies_versions': '{\"azureml-widgets\": \"1.20.0\", \"azureml-train\": \"1.20.0\", \"azureml-train-restclients-hyperdrive\": \"1.20.0\", \"azureml-train-core\": \"1.20.0\", \"azureml-train-automl\": \"1.20.0\", \"azureml-train-automl-runtime\": \"1.20.0\", \"azureml-train-automl-client\": \"1.20.0\", \"azureml-tensorboard\": \"1.20.0\", \"azureml-telemetry\": \"1.20.0\", \"azureml-sdk\": \"1.20.0\", \"azureml-samples\": \"0+unknown\", \"azureml-pipeline\": \"1.20.0\", \"azureml-pipeline-steps\": \"1.20.0\", \"azureml-pipeline-core\": \"1.20.0\", \"azureml-opendatasets\": \"1.20.0\", \"azureml-model-management-sdk\": \"1.0.1b6.post1\", \"azureml-mlflow\": \"1.20.0.post1\", \"azureml-interpret\": \"1.20.0\", \"azureml-explain-model\": \"1.20.0\", \"azureml-defaults\": \"1.20.0\", \"azureml-dataset-runtime\": \"1.20.0\", \"azureml-dataprep\": \"2.7.3\", \"azureml-dataprep-rslex\": \"1.5.0\", \"azureml-dataprep-native\": \"27.0.0\", \"azureml-datadrift\": \"1.20.0\", \"azureml-core\": \"1.20.0\", \"azureml-contrib-services\": \"1.20.0\", \"azureml-contrib-server\": \"1.20.0\", \"azureml-contrib-reinforcementlearning\": \"1.20.0\", \"azureml-contrib-pipeline-steps\": \"1.20.0\", \"azureml-contrib-notebook\": \"1.20.0\", \"azureml-contrib-interpret\": \"1.20.0\", \"azureml-contrib-gbdt\": \"1.20.0\", \"azureml-contrib-fairness\": \"1.20.0\", \"azureml-contrib-dataset\": \"1.20.0\", \"azureml-cli-common\": \"1.20.0\", \"azureml-automl-runtime\": \"1.20.0\", \"azureml-automl-core\": \"1.20.0\", \"azureml-accel-models\": \"1.20.0\"}',\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/5a4ab2ba-6c51-4805-8155-58759ad589d8/resourceGroups/aml-quickstarts-136232/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136232/experimentids/a8deb6fc-d8ae-4407-9d25-ef301f73f0ff/cancel/AutoML_40fd204c-1e33-451b-82e4-edad8740e10d',\n",
       "  'ClientSdkVersion': '1.20.0.post1',\n",
       "  'snapshotId': '00000000-0000-0000-0000-000000000000',\n",
       "  'SetupRunId': 'AutoML_40fd204c-1e33-451b-82e4-edad8740e10d_setup',\n",
       "  'SetupRunContainerId': 'dcid.AutoML_40fd204c-1e33-451b-82e4-edad8740e10d_setup',\n",
       "  'FeaturizationRunJsonPath': 'featurizer_container.json',\n",
       "  'FeaturizationRunId': 'AutoML_40fd204c-1e33-451b-82e4-edad8740e10d_featurize',\n",
       "  'ProblemInfoJsonString': '{\"dataset_num_categorical\": 0, \"is_sparse\": true, \"subsampling\": false, \"dataset_classes\": 2, \"dataset_features\": 122, \"dataset_samples\": 26360, \"single_frequency_class_detected\": false}',\n",
       "  'ModelExplainRunId': 'AutoML_40fd204c-1e33-451b-82e4-edad8740e10d_ModelExplain'},\n",
       " 'inputDatasets': [{'dataset': {'id': 'ea8cba91-d330-4219-a53c-7e54909e09b3'}, 'consumptionDetails': {'type': 'RunInput', 'inputName': 'training_data', 'mechanism': 'Direct'}}],\n",
       " 'outputDatasets': [],\n",
       " 'logFiles': {},\n",
       " 'submittedBy': 'ODL_User 136232'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Submit the automl run\n",
    "\n",
    "automl_run = exp.submit(automl_config,show_output = True)\n",
    "RunDetails(automl_run).show()\n",
    "automl_run.wait_for_completion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run(Experiment: udacity-project,\n",
      "Id: AutoML_40fd204c-1e33-451b-82e4-edad8740e10d_26,\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='l2',\n",
      "                                                                                                random_state=None,\n",
      "                                                                                                solver='saga',\n",
      "                                                                                                tol=0.0001,\n",
      "                                                                                                verbose=0,\n",
      "                                                                                                warm_start=False))],\n",
      "                                                                     verbose=False))],\n",
      "                                               flatten_transform=None,\n",
      "                                               weights=[0.25, 0.25,\n",
      "                                                        0.08333333333333333,\n",
      "                                                        0.08333333333333333,\n",
      "                                                        0.08333333333333333,\n",
      "                                                        0.16666666666666666,\n",
      "                                                        0.08333333333333333]))],\n",
      "         verbose=False)\n"
     ]
    }
   ],
   "source": [
    "# Retrieve and save your best automl model.\n",
    "\n",
    "best_automl_run, fitted_automl_model = automl_run.get_output()\n",
    "\n",
    "print(best_automl_run)\n",
    "print(fitted_automl_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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='l2',\n",
       "                                                                                                random_state=None,\n",
       "                                                                                                solver='saga',\n",
       "                                                                                                tol=0.0001,\n",
       "                                                                                                verbose=0,\n",
       "                                                                                                warm_start=False))],\n",
       "                                                                     verbose=False))],\n",
       "                                               flatten_transform=None,\n",
       "                                               weights=[0.25, 0.25,\n",
       "                                                        0.08333333333333333,\n",
       "                                                        0.08333333333333333,\n",
       "                                                        0.08333333333333333,\n",
       "                                                        0.16666666666666666,\n",
       "                                                        0.08333333333333333]))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_automl_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['outputs/model.joblib']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "joblib.dump(fitted_automl_model, 'outputs/model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpu_cluster.delete()"
   ]
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "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"
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 },
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