udacity-project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "gather": {
     "logged": 1610894575776
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workspace name: quick-starts-ws-135073\n",
      "Azure region: southcentralus\n",
      "Subscription id: cdbe0b43-92a0-4715-838a-f2648cc7ad21\n",
      "Resource group: aml-quickstarts-135073\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import Workspace, Experiment\n",
    "\n",
    "ws = Workspace.get(name=\"quick-starts-ws-135073\")\n",
    "exp = Experiment(workspace=ws, name=\"quick-starts-ws-135073\")\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": 27,
   "metadata": {
    "gather": {
     "logged": 1610894599858
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CreatingAmlCompute is getting created. Consider calling wait_for_completion() first\n",
      "\n",
      "AmlCompute is getting created. Consider calling wait_for_completion() first\n",
      "\n",
      "\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",
    "\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",
    "from azureml.core.compute_target import ComputeTargetException\n",
    "\n",
    "# Choose a name for your CPU cluster\n",
    "cpu_cluster_name = \"cpucluster\"\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": 28,
   "metadata": {
    "gather": {
     "logged": 1610894622003
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:azureml.train.sklearn:'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\n",
    "from azureml.core.script_run_config import ScriptRunConfig\n",
    "from azureml.train.hyperdrive import choice\n",
    "import os\n",
    "\n",
    "# Specify parameter sampler\n",
    "ps = RandomParameterSampling({\n",
    "       \"--C\": uniform(0.0001, 1),\n",
    "       \"--max_iter\": choice(80, 100, 120, 140, 180)\n",
    "   })\n",
    "\n",
    "# Specify a Policy\n",
    "policy = BanditPolicy(evaluation_interval=100, slack_factor=0.2, slack_amount=None, delay_evaluation=200)\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=\".\", compute_target=\"cpucluster\", \n",
    "                vm_size=\"STANDARD_DS2_V2\", vm_priority='lowpriority', entry_script='train.py')\n",
    "\n",
    "# Create a HyperDriveConfig using the estimator, hyperparameter sampler, and policy.\n",
    "hyperdrive_config = HyperDriveConfig(ps, primary_metric_name='Accuracy', primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, max_total_runs=4, max_concurrent_runs=4, policy=policy, estimator=est)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting conda_dependencies.yml\n"
     ]
    }
   ],
   "source": [
    "%%writefile conda_dependencies.yml\n",
    "dependencies:\n",
    "- python=3.6.2\n",
    "- scikit-learn\n",
    "- pip:\n",
    "  - azureml-defaults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "gather": {
     "logged": 1610894630764
    }
   },
   "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": "a52bcadf45f7426195ee2d9db86a8509",
       "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/quick-starts-ws-135073/runs/HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f?wsid=/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourcegroups/aml-quickstarts-135073/workspaces/quick-starts-ws-135073\", \"run_id\": \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f\", \"run_properties\": {\"run_id\": \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f\", \"created_utc\": \"2021-01-17T16:08:47.85998Z\", \"properties\": {\"primary_metric_config\": \"{\\\"name\\\": \\\"Accuracy\\\", \\\"goal\\\": \\\"maximize\\\"}\", \"resume_from\": \"null\", \"runTemplate\": \"HyperDrive\", \"azureml.runsource\": \"hyperdrive\", \"platform\": \"AML\", \"ContentSnapshotId\": \"3cc760e5-4d2b-49e3-8e99-d7e2d246dde5\", \"score\": \"0.9102446201949604\", \"best_child_run_id\": \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_3\", \"best_metric_status\": \"Succeeded\"}, \"tags\": {\"_aml_system_max_concurrent_jobs\": \"4\", \"max_concurrent_jobs\": \"4\", \"_aml_system_max_total_jobs\": \"4\", \"max_total_jobs\": \"4\", \"_aml_system_max_duration_minutes\": \"10080\", \"max_duration_minutes\": \"10080\", \"_aml_system_policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 100, \\\"delay_evaluation\\\": 200, \\\"slack_factor\\\": 0.2}}\", \"policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 100, \\\"delay_evaluation\\\": 200, \\\"slack_factor\\\": 0.2}}\", \"_aml_system_generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"--C\\\": [\\\"uniform\\\", [0.0001, 1]], \\\"--max_iter\\\": [\\\"choice\\\", [[80, 100, 120, 140, 180]]]}}\", \"generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"--C\\\": [\\\"uniform\\\", [0.0001, 1]], \\\"--max_iter\\\": [\\\"choice\\\", [[80, 100, 120, 140, 180]]]}}\", \"_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/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourceGroups/aml-quickstarts-135073/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135073/experiments/quick-starts-ws-135073\\\", \\\"SubscriptionId\\\": \\\"cdbe0b43-92a0-4715-838a-f2648cc7ad21\\\", \\\"ResourceGroupName\\\": \\\"aml-quickstarts-135073\\\", \\\"WorkspaceName\\\": \\\"quick-starts-ws-135073\\\", \\\"ExperimentName\\\": \\\"quick-starts-ws-135073\\\", \\\"Definition\\\": {\\\"Overrides\\\": {\\\"script\\\": \\\"train.py\\\", \\\"arguments\\\": [], \\\"target\\\": \\\"cpucluster\\\", \\\"framework\\\": \\\"Python\\\", \\\"communicator\\\": \\\"None\\\", \\\"maxRunDurationSeconds\\\": null, \\\"nodeCount\\\": 1, \\\"environment\\\": {\\\"name\\\": null, \\\"version\\\": null, \\\"environmentVariables\\\": {\\\"EXAMPLE_ENV_VAR\\\": \\\"EXAMPLE_VALUE\\\"}, \\\"python\\\": {\\\"userManagedDependencies\\\": false, \\\"interpreterPath\\\": \\\"python\\\", \\\"condaDependenciesFile\\\": null, \\\"baseCondaEnvironment\\\": null, \\\"condaDependencies\\\": {\\\"name\\\": \\\"project_environment\\\", \\\"dependencies\\\": [\\\"python=3.6.2\\\", {\\\"pip\\\": [\\\"azureml-defaults\\\", \\\"scikit-learn==0.20.3\\\", \\\"scipy==1.2.1\\\", \\\"joblib==0.13.2\\\"]}], \\\"channels\\\": [\\\"anaconda\\\", \\\"conda-forge\\\"]}}, \\\"docker\\\": {\\\"enabled\\\": true, \\\"baseImage\\\": \\\"mcr.microsoft.com/azureml/intelmpi2018.3-ubuntu16.04:20200423.v1\\\", \\\"baseDockerfile\\\": null, \\\"sharedVolumes\\\": true, \\\"shmSize\\\": \\\"2g\\\", \\\"arguments\\\": [], \\\"baseImageRegistry\\\": {\\\"address\\\": null, \\\"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_DS2_V2\\\", \\\"vmPriority\\\": \\\"lowpriority\\\", \\\"retainCluster\\\": false, \\\"name\\\": null, \\\"clusterMaxNodeCount\\\": 1}, \\\"command\\\": \\\"\\\"}, \\\"TargetDetails\\\": null, \\\"SnapshotId\\\": \\\"3cc760e5-4d2b-49e3-8e99-d7e2d246dde5\\\", \\\"TelemetryValues\\\": {\\\"amlClientType\\\": \\\"azureml-sdk-train\\\", \\\"amlClientModule\\\": \\\"[Scrubbed]\\\", \\\"amlClientFunction\\\": \\\"[Scrubbed]\\\", \\\"tenantId\\\": \\\"660b3398-b80e-49d2-bc5b-ac1dc93b5254\\\", \\\"amlClientRequestId\\\": \\\"b2db5c06-ea54-4541-bf2d-51266111230a\\\", \\\"amlClientSessionId\\\": \\\"914f0012-9863-49f7-93aa-367b7507580b\\\", \\\"subscriptionId\\\": \\\"cdbe0b43-92a0-4715-838a-f2648cc7ad21\\\", \\\"estimator\\\": \\\"SKLearn\\\", \\\"samplingMethod\\\": \\\"RANDOM\\\", \\\"terminationPolicy\\\": \\\"Bandit\\\", \\\"primaryMetricGoal\\\": \\\"maximize\\\", \\\"maxTotalRuns\\\": 4, \\\"maxConcurrentRuns\\\": 4, \\\"maxDurationMinutes\\\": 10080, \\\"vmSize\\\": \\\"STANDARD_DS2_V2\\\"}}}\", \"platform_config\": \"{\\\"ServiceAddress\\\": \\\"https://southcentralus.experiments.azureml.net\\\", \\\"ServiceArmScope\\\": \\\"subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourceGroups/aml-quickstarts-135073/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135073/experiments/quick-starts-ws-135073\\\", \\\"SubscriptionId\\\": \\\"cdbe0b43-92a0-4715-838a-f2648cc7ad21\\\", \\\"ResourceGroupName\\\": \\\"aml-quickstarts-135073\\\", \\\"WorkspaceName\\\": \\\"quick-starts-ws-135073\\\", \\\"ExperimentName\\\": \\\"quick-starts-ws-135073\\\", \\\"Definition\\\": {\\\"Overrides\\\": {\\\"script\\\": \\\"train.py\\\", \\\"arguments\\\": [], \\\"target\\\": \\\"cpucluster\\\", \\\"framework\\\": \\\"Python\\\", \\\"communicator\\\": \\\"None\\\", \\\"maxRunDurationSeconds\\\": null, \\\"nodeCount\\\": 1, \\\"environment\\\": {\\\"name\\\": null, \\\"version\\\": null, \\\"environmentVariables\\\": {\\\"EXAMPLE_ENV_VAR\\\": \\\"EXAMPLE_VALUE\\\"}, \\\"python\\\": {\\\"userManagedDependencies\\\": false, \\\"interpreterPath\\\": \\\"python\\\", \\\"condaDependenciesFile\\\": null, \\\"baseCondaEnvironment\\\": null, \\\"condaDependencies\\\": {\\\"name\\\": \\\"project_environment\\\", \\\"dependencies\\\": [\\\"python=3.6.2\\\", {\\\"pip\\\": [\\\"azureml-defaults\\\", \\\"scikit-learn==0.20.3\\\", \\\"scipy==1.2.1\\\", \\\"joblib==0.13.2\\\"]}], \\\"channels\\\": [\\\"anaconda\\\", \\\"conda-forge\\\"]}}, \\\"docker\\\": {\\\"enabled\\\": true, \\\"baseImage\\\": \\\"mcr.microsoft.com/azureml/intelmpi2018.3-ubuntu16.04:20200423.v1\\\", \\\"baseDockerfile\\\": null, \\\"sharedVolumes\\\": true, \\\"shmSize\\\": \\\"2g\\\", \\\"arguments\\\": [], \\\"baseImageRegistry\\\": {\\\"address\\\": null, \\\"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_DS2_V2\\\", \\\"vmPriority\\\": \\\"lowpriority\\\", \\\"retainCluster\\\": false, \\\"name\\\": null, \\\"clusterMaxNodeCount\\\": 1}, \\\"command\\\": \\\"\\\"}, \\\"TargetDetails\\\": null, \\\"SnapshotId\\\": \\\"3cc760e5-4d2b-49e3-8e99-d7e2d246dde5\\\", \\\"TelemetryValues\\\": {\\\"amlClientType\\\": \\\"azureml-sdk-train\\\", \\\"amlClientModule\\\": \\\"[Scrubbed]\\\", \\\"amlClientFunction\\\": \\\"[Scrubbed]\\\", \\\"tenantId\\\": \\\"660b3398-b80e-49d2-bc5b-ac1dc93b5254\\\", \\\"amlClientRequestId\\\": \\\"b2db5c06-ea54-4541-bf2d-51266111230a\\\", \\\"amlClientSessionId\\\": \\\"914f0012-9863-49f7-93aa-367b7507580b\\\", \\\"subscriptionId\\\": \\\"cdbe0b43-92a0-4715-838a-f2648cc7ad21\\\", \\\"estimator\\\": \\\"SKLearn\\\", \\\"samplingMethod\\\": \\\"RANDOM\\\", \\\"terminationPolicy\\\": \\\"Bandit\\\", \\\"primaryMetricGoal\\\": \\\"maximize\\\", \\\"maxTotalRuns\\\": 4, \\\"maxConcurrentRuns\\\": 4, \\\"maxDurationMinutes\\\": 10080, \\\"vmSize\\\": \\\"STANDARD_DS2_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\": \"\\\"2021-01-17T16:08:48.571614\\\"\", \"progress_metadata_evaluation_timestamp\": \"\\\"2021-01-17T16:08:48.571614\\\"\", \"_aml_system_progress_metadata_digest\": \"\\\"547522ab36db6d59356f5b5d6cad9ed600b207c9ed73ca1128cd92e6d84ec629\\\"\", \"progress_metadata_digest\": \"\\\"547522ab36db6d59356f5b5d6cad9ed600b207c9ed73ca1128cd92e6d84ec629\\\"\", \"_aml_system_progress_metadata_active_timestamp\": \"\\\"2021-01-17T16:08:48.571614\\\"\", \"progress_metadata_active_timestamp\": \"\\\"2021-01-17T16:08:48.571614\\\"\", \"_aml_system_HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_0\": \"{\\\"--C\\\": 0.6271060155109139, \\\"--max_iter\\\": 100}\", \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_0\": \"{\\\"--C\\\": 0.6271060155109139, \\\"--max_iter\\\": 100}\", \"_aml_system_HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_1\": \"{\\\"--C\\\": 0.4718147652167749, \\\"--max_iter\\\": 80}\", \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_1\": \"{\\\"--C\\\": 0.4718147652167749, \\\"--max_iter\\\": 80}\", \"_aml_system_HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_2\": \"{\\\"--C\\\": 0.7708409901282677, \\\"--max_iter\\\": 80}\", \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_2\": \"{\\\"--C\\\": 0.7708409901282677, \\\"--max_iter\\\": 80}\", \"_aml_system_HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_3\": \"{\\\"--C\\\": 0.08097124199230814, \\\"--max_iter\\\": 80}\", \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_3\": \"{\\\"--C\\\": 0.08097124199230814, \\\"--max_iter\\\": 80}\", \"_aml_system_environment_preparation_status\": \"PREPARED\", \"environment_preparation_status\": \"PREPARED\", \"_aml_system_prepare_run_id\": \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_preparation\", \"prepare_run_id\": \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_preparation\", \"_aml_system_final_best_metric_update_retry_count\": \"1\", \"final_best_metric_update_retry_count\": \"1\"}, \"end_time_utc\": \"2021-01-17T16:16:02.904243Z\", \"status\": \"Completed\", \"log_files\": {\"azureml-logs/hyperdrive.txt\": \"https://mlstrg135073.blob.core.windows.net/azureml/ExperimentRun/dcid.HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f/azureml-logs/hyperdrive.txt?sv=2019-02-02&sr=b&sig=eE0tlSQ3Nzq5SUwSwODw3LqFOLEMBZnRkd4OUjawgqk%3D&st=2021-01-17T16%3A06%3A27Z&se=2021-01-18T00%3A16%3A27Z&sp=r\"}, \"log_groups\": [[\"azureml-logs/hyperdrive.txt\"]], \"run_duration\": \"0:07:15\", \"hyper_parameters\": {\"--C\": [\"uniform\", [0.0001, 1]], \"--max_iter\": [\"choice\", [[80, 100, 120, 140, 180]]]}}, \"child_runs\": [{\"run_id\": \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_3\", \"run_number\": 73, \"metric\": 0.91024462, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-17T16:12:54.287891Z\", \"end_time\": \"2021-01-17T16:15:09.983808Z\", \"created_time\": \"2021-01-17T16:09:21.766614Z\", \"created_time_dt\": \"2021-01-17T16:09:21.766614Z\", \"duration\": \"0:05:48\", \"hyperdrive_id\": \"423875c8-e7c5-4c26-ba3e-c7a8f99af11f\", \"arguments\": null, \"param_--C\": 0.08097124199230814, \"param_--max_iter\": 80, \"best_metric\": 0.91024462}, {\"run_id\": \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_1\", \"run_number\": 74, \"metric\": 0.9100607, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-17T16:12:54.494852Z\", \"end_time\": \"2021-01-17T16:15:21.172013Z\", \"created_time\": \"2021-01-17T16:09:22.23671Z\", \"created_time_dt\": \"2021-01-17T16:09:22.23671Z\", \"duration\": \"0:05:58\", \"hyperdrive_id\": \"423875c8-e7c5-4c26-ba3e-c7a8f99af11f\", \"arguments\": null, \"param_--C\": 0.4718147652167749, \"param_--max_iter\": 80, \"best_metric\": 0.91024462}, {\"run_id\": \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_0\", \"run_number\": 75, \"metric\": 0.9100607, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-17T16:12:59.558549Z\", \"end_time\": \"2021-01-17T16:15:13.680722Z\", \"created_time\": \"2021-01-17T16:09:22.505037Z\", \"created_time_dt\": \"2021-01-17T16:09:22.505037Z\", \"duration\": \"0:05:51\", \"hyperdrive_id\": \"423875c8-e7c5-4c26-ba3e-c7a8f99af11f\", \"arguments\": null, \"param_--C\": 0.6271060155109139, \"param_--max_iter\": 100, \"best_metric\": 0.91024462}, {\"run_id\": \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_2\", \"run_number\": 76, \"metric\": 0.9100607, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2021-01-17T16:12:59.373176Z\", \"end_time\": \"2021-01-17T16:15:12.435265Z\", \"created_time\": \"2021-01-17T16:09:22.528758Z\", \"created_time_dt\": \"2021-01-17T16:09:22.528758Z\", \"duration\": \"0:05:49\", \"hyperdrive_id\": \"423875c8-e7c5-4c26-ba3e-c7a8f99af11f\", \"arguments\": null, \"param_--C\": 0.7708409901282677, \"param_--max_iter\": 80, \"best_metric\": 0.91024462}], \"children_metrics\": {\"categories\": [0], \"series\": {\"Regularization Strength:\": [{\"categories\": [73, 74, 75, 76], \"mode\": \"markers\", \"name\": \"Regularization Strength:\", \"stepped\": false, \"type\": \"scatter\", \"data\": [0.08097124199230814, 0.4718147652167749, 0.6271060155109139, 0.7708409901282677]}, {\"categories\": [73, 74, 75, 76], \"mode\": \"lines\", \"name\": \"Regularization Strength:_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [0.08097124199230814, 0.4718147652167749, 0.6271060155109139, 0.7708409901282677]}], \"Max iterations:\": [{\"categories\": [73, 74, 75, 76], \"mode\": \"markers\", \"name\": \"Max iterations:\", \"stepped\": false, \"type\": \"scatter\", \"data\": [80, 80, 100, 80]}, {\"categories\": [73, 74, 75, 76], \"mode\": \"lines\", \"name\": \"Max iterations:_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [80, 80, 100, 100]}], \"Accuracy\": [{\"categories\": [73, 74, 75, 76], \"mode\": \"markers\", \"name\": \"Accuracy\", \"stepped\": false, \"type\": \"scatter\", \"data\": [0.9102446201949604, 0.9100606952363436, 0.9100606952363436, 0.9100606952363436]}, {\"categories\": [73, 74, 75, 76], \"mode\": \"lines\", \"name\": \"Accuracy_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [0.9102446201949604, 0.9102446201949604, 0.9102446201949604, 0.9102446201949604]}]}, \"metricName\": null, \"primaryMetricName\": \"Accuracy\", \"showLegend\": false}, \"run_metrics\": [{\"name\": \"best_child_by_primary_metric\", \"run_id\": \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f\", \"categories\": [0], \"series\": [{\"data\": [{\"metric_name\": [\"Accuracy\", \"Accuracy\"], \"timestamp\": [\"2021-01-17 16:15:26.844950+00:00\", \"2021-01-17 16:15:26.844950+00:00\"], \"run_id\": [\"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_3\", \"HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_3\"], \"metric_value\": [0.9102446201949604, 0.9102446201949604], \"final\": [false, true]}]}]}], \"run_logs\": \"[2021-01-17T16:08:48.191282][API][INFO]Experiment created\\r\\n[2021-01-17T16:08:48.679285][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-01-17T16:08:48.855780][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-17T16:08:49.9499756Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.\\r\\n[2021-01-17T16:09:21.142898][GENERATOR][INFO]Max number of jobs '4' reached for experiment.\\r\\n[2021-01-17T16:09:21.1728076Z][SCHEDULER][INFO]Scheduling job, id='HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_3'\\r\\n[2021-01-17T16:09:21.1666036Z][SCHEDULER][INFO]Scheduling job, id='HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_2'\\r\\n[2021-01-17T16:09:21.1361771Z][SCHEDULER][INFO]Scheduling job, id='HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_1'\\r\\n[2021-01-17T16:09:21.1344080Z][SCHEDULER][INFO]The execution environment was successfully prepared.\\r\\n[2021-01-17T16:09:21.642744][GENERATOR][INFO]All jobs generated.\\r\\n[2021-01-17T16:09:21.1349521Z][SCHEDULER][INFO]Scheduling job, id='HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_0'\\r\\n[2021-01-17T16:09:21.8899798Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_3'\\r\\n[2021-01-17T16:09:22.3748776Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_1'\\r\\n[2021-01-17T16:09:22.6304855Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_2'\\r\\n[2021-01-17T16:09:22.7153382Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_0'\\r\\n[2021-01-17T16:12:56.236210][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourceGroups/aml-quickstarts-135073/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135073/experiments/**SCRUBBED**/runs/HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_1, https://southcentralus.experiments.azureml.net/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourceGroups/aml-quickstarts-135073/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135073/experiments/**SCRUBBED**/runs/HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-17T16:13:27.069536][ENFORCER][INFO]Jobs 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https://southcentralus.experiments.azureml.net/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourceGroups/aml-quickstarts-135073/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135073/experiments/**SCRUBBED**/runs/HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-17T16:13:56.793233][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourceGroups/aml-quickstarts-135073/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135073/experiments/**SCRUBBED**/runs/HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_0, 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https://southcentralus.experiments.azureml.net/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourceGroups/aml-quickstarts-135073/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135073/experiments/**SCRUBBED**/runs/HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-17T16:14:58.147395][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourceGroups/aml-quickstarts-135073/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135073/experiments/**SCRUBBED**/runs/HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_0, https://southcentralus.experiments.azureml.net/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourceGroups/aml-quickstarts-135073/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135073/experiments/**SCRUBBED**/runs/HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_1, https://southcentralus.experiments.azureml.net/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourceGroups/aml-quickstarts-135073/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135073/experiments/**SCRUBBED**/runs/HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_2, https://southcentralus.experiments.azureml.net/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourceGroups/aml-quickstarts-135073/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-135073/experiments/**SCRUBBED**/runs/HD_423875c8-e7c5-4c26-ba3e-c7a8f99af11f_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-17T16:16:03.393205][CONTROLLER][INFO]Experiment was 'ExperimentStatus.RUNNING', is 'ExperimentStatus.FINISHED'.\\n\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.19.0\"}, \"loading\": false}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "\n",
    "# Submit your hyperdrive run to the experiment and show run details with the widget.\n",
    "# run a trial from the train.py code in your current directory\n",
    "hyperdrive_run = exp.submit(hyperdrive_config)\n",
    "\n",
    "RunDetails(hyperdrive_run).show()    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "gather": {
     "logged": 1610895318578
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RunId: HD_8cfd41da-0f2d-496a-8855-0236ce845011\n",
      "Web View: https://ml.azure.com/experiments/quick-starts-ws-135073/runs/HD_8cfd41da-0f2d-496a-8855-0236ce845011?wsid=/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourcegroups/aml-quickstarts-135073/workspaces/quick-starts-ws-135073\n",
      "\n",
      "Execution Summary\n",
      "=================\n",
      "RunId: HD_8cfd41da-0f2d-496a-8855-0236ce845011\n",
      "Web View: https://ml.azure.com/experiments/quick-starts-ws-135073/runs/HD_8cfd41da-0f2d-496a-8855-0236ce845011?wsid=/subscriptions/cdbe0b43-92a0-4715-838a-f2648cc7ad21/resourcegroups/aml-quickstarts-135073/workspaces/quick-starts-ws-135073\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['./outputs/best_run.joblib']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "# Get your best run and save the model from that run.\n",
    "model = hyperdrive_run.get_best_run_by_primary_metric(include_failed=True, include_canceled=True, include_resume_from_runs=True)\n",
    "hyperdrive_run.wait_for_completion(show_output=True)\n",
    "\n",
    "best_run = hyperdrive_run.get_metrics()           \n",
    "joblib.dump(value=best_run, filename=\"./outputs/best_run.joblib\") \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "gather": {
     "logged": 1610895323369
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'HD_8cfd41da-0f2d-496a-8855-0236ce845011_0': {'Regularization Strength:': 0.4840389338857808,\n",
       "  'Max iterations:': 100,\n",
       "  'Accuracy': 0.9100606952363436},\n",
       " 'HD_8cfd41da-0f2d-496a-8855-0236ce845011_1': {'Regularization Strength:': 0.95120350202593,\n",
       "  'Max iterations:': 120,\n",
       "  'Accuracy': 0.910152657715652},\n",
       " 'HD_8cfd41da-0f2d-496a-8855-0236ce845011_2': {'Regularization Strength:': 0.4250739263501935,\n",
       "  'Max iterations:': 120,\n",
       "  'Accuracy': 0.9100606952363436},\n",
       " 'HD_8cfd41da-0f2d-496a-8855-0236ce845011_3': {'Regularization Strength:': 0.4705719798485419,\n",
       "  'Max iterations:': 80,\n",
       "  'Accuracy': 0.9100606952363436}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.load('./outputs/best_run.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  AUTO ML  #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "gather": {
     "logged": 1610895340272
    }
   },
   "outputs": [],
   "source": [
    "from azureml.data.dataset_factory import TabularDatasetFactory\n",
    "# Create TabularDataset using TabularDatasetFactory\n",
    "# Data is available at: \n",
    "# \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
    "csv_file = 'https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv'\n",
    "data = TabularDatasetFactory.from_delimited_files(csv_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "gather": {
     "logged": 1610895347675
    }
   },
   "outputs": [],
   "source": [
    "from train import clean_data\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "x, y = clean_data(data)\n",
    "# Use the clean_data function to clean your data.\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)\n",
    "train_data = pd.concat([x_train, y_train], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "gather": {
     "logged": 1610895354440
    }
   },
   "outputs": [],
   "source": [
    "from azureml.train.automl import AutoMLConfig\n",
    "\n",
    "# Set parameters for AutoMLConfig\n",
    "# NOTE: DO NOT CHANGE THE experiment_timeout_minutes PARAMETER OR YOUR INSTANCE WILL TIME OUT.\n",
    "# If you wish to run the experiment longer, you will need to run this notebook in your own\n",
    "# Azure tenant, which will incur personal costs.\n",
    "automl_config = AutoMLConfig(\n",
    "    experiment_timeout_minutes=30,\n",
    "    task='classification',\n",
    "    primary_metric='accuracy',\n",
    "    training_data=train_data,\n",
    "    label_column_name='y',\n",
    "    n_cross_validations=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "gather": {
     "logged": 1610897417744
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No run_configuration provided, running on local with default configuration\n",
      "Running on local machine\n",
      "Parent Run ID: AutoML_46aa3c5f-a6bf-4424-a6fe-5198d3605413\n",
      "\n",
      "Current status: DatasetEvaluation. Gathering dataset statistics.\n",
      "Current status: FeaturesGeneration. Generating features for the dataset.\n",
      "Current status: DatasetFeaturization. Beginning to fit featurizers and featurize the dataset.\n",
      "Current status: DatasetFeaturizationCompleted. Completed fit featurizers and featurizing the dataset.\n",
      "Current status: DatasetBalancing. Performing class balancing sweeping\n",
      "Current status: DatasetCrossValidationSplit. Generating individually featurized CV splits.\n",
      "\n",
      "****************************************************************************************************\n",
      "DATA GUARDRAILS: \n",
      "\n",
      "TYPE:         Class balancing detection\n",
      "STATUS:       ALERTED\n",
      "DESCRIPTION:  To decrease model bias, please cancel the current run and fix balancing problem.\n",
      "              Learn more about imbalanced data: https://aka.ms/AutomatedMLImbalancedData\n",
      "DETAILS:      Imbalanced data can lead to a falsely perceived positive effect of a model's accuracy because the input data has bias towards one class.\n",
      "+---------------------------------+---------------------------------+--------------------------------------+\n",
      "|Size of the smallest class       |Name/Label of the smallest class |Number of samples in the training data|\n",
      "+=================================+=================================+======================================+\n",
      "|2473                             |1                                |22076                                 |\n",
      "+---------------------------------+---------------------------------+--------------------------------------+\n",
      "\n",
      "****************************************************************************************************\n",
      "\n",
      "TYPE:         Missing feature values imputation\n",
      "STATUS:       PASSED\n",
      "DESCRIPTION:  No feature missing values were detected in the training data.\n",
      "              Learn more about missing value imputation: https://aka.ms/AutomatedMLFeaturization\n",
      "\n",
      "****************************************************************************************************\n",
      "\n",
      "TYPE:         High cardinality feature detection\n",
      "STATUS:       PASSED\n",
      "DESCRIPTION:  Your inputs were analyzed, and no high cardinality features were detected.\n",
      "              Learn more about high cardinality feature handling: https://aka.ms/AutomatedMLFeaturization\n",
      "\n",
      "****************************************************************************************************\n",
      "Current status: ModelSelection. Beginning model selection.\n",
      "\n",
      "****************************************************************************************************\n",
      "ITERATION: The iteration being evaluated.\n",
      "PIPELINE: A summary description of the pipeline being evaluated.\n",
      "DURATION: Time taken for the current iteration.\n",
      "METRIC: The result of computing score on the fitted pipeline.\n",
      "BEST: The best observed score thus far.\n",
      "****************************************************************************************************\n",
      "\n",
      " ITERATION   PIPELINE                                       DURATION      METRIC      BEST\n",
      "         0   MaxAbsScaler LightGBM                          0:00:38       0.9119    0.9119\n",
      "         1   MaxAbsScaler XGBoostClassifier                 0:00:26       0.9142    0.9142\n",
      "         2   MaxAbsScaler RandomForest                      0:00:28       0.8955    0.9142\n",
      "         3   MaxAbsScaler RandomForest                      0:00:27       0.8880    0.9142\n",
      "         4   MaxAbsScaler RandomForest                      0:00:23       0.8201    0.9142\n",
      "         5   MaxAbsScaler RandomForest                      0:00:28       0.7866    0.9142\n",
      "         6   SparseNormalizer XGBoostClassifier             0:00:35       0.9093    0.9142\n",
      "         7   MaxAbsScaler GradientBoosting                  0:00:27       0.9035    0.9142\n",
      "         8   StandardScalerWrapper RandomForest             0:00:25       0.8995    0.9142\n",
      "         9   MaxAbsScaler LogisticRegression                0:00:25       0.9085    0.9142\n",
      "        10   MaxAbsScaler LightGBM                          0:00:24       0.8911    0.9142\n",
      "        11   SparseNormalizer XGBoostClassifier             0:00:27       0.9102    0.9142\n",
      "        12   MaxAbsScaler ExtremeRandomTrees                0:00:46       0.8880    0.9142\n",
      "        13   StandardScalerWrapper LightGBM                 0:00:24       0.8880    0.9142\n",
      "        14   SparseNormalizer XGBoostClassifier             0:00:39       0.9094    0.9142\n",
      "        15   StandardScalerWrapper ExtremeRandomTrees       0:00:29       0.8880    0.9142\n",
      "        16   StandardScalerWrapper LightGBM                 0:00:24       0.8880    0.9142\n",
      "        17   StandardScalerWrapper LightGBM                 0:00:26       0.9029    0.9142\n",
      "        18   MaxAbsScaler LightGBM                          0:00:27       0.9046    0.9142\n",
      "        19   SparseNormalizer LightGBM                      0:00:33       0.9099    0.9142\n",
      "        20   SparseNormalizer XGBoostClassifier             0:00:25       0.9115    0.9142\n",
      "        21   MaxAbsScaler LightGBM                          0:00:26       0.9041    0.9142\n",
      "        22   MaxAbsScaler LightGBM                          0:00:26       0.9084    0.9142\n",
      "        23   StandardScalerWrapper LightGBM                 0:00:29       0.8928    0.9142\n",
      "        24   StandardScalerWrapper LightGBM                 0:00:28       0.9060    0.9142\n",
      "        25   SparseNormalizer XGBoostClassifier             0:00:32       0.8880    0.9142\n",
      "        26   StandardScalerWrapper LightGBM                 0:00:33       0.9113    0.9142\n",
      "        27   SparseNormalizer LightGBM                      0:00:26       0.9102    0.9142\n",
      "        28   StandardScalerWrapper XGBoostClassifier        0:00:28       0.9066    0.9142\n",
      "        29   SparseNormalizer XGBoostClassifier             0:00:50       0.9099    0.9142\n",
      "        30   TruncatedSVDWrapper XGBoostClassifier          0:00:33       0.8880    0.9142\n",
      "        31   SparseNormalizer XGBoostClassifier             0:01:18       0.9092    0.9142\n",
      "        32   SparseNormalizer XGBoostClassifier             0:00:29       0.9125    0.9142\n",
      "        33   MaxAbsScaler GradientBoosting                  0:00:31       0.9027    0.9142\n",
      "        34   StandardScalerWrapper XGBoostClassifier        0:00:28       0.9124    0.9142\n",
      "        35   SparseNormalizer XGBoostClassifier             0:00:31       0.9132    0.9142\n",
      "        36   SparseNormalizer XGBoostClassifier             0:00:35       0.9065    0.9142\n",
      "        37   MaxAbsScaler LogisticRegression                0:00:27       0.9089    0.9142\n",
      "        38   MaxAbsScaler LightGBM                          0:00:24       0.9056    0.9142\n",
      "        39   SparseNormalizer XGBoostClassifier             0:00:32       0.9105    0.9142\n",
      "        40   StandardScalerWrapper XGBoostClassifier        0:00:42       0.9103    0.9142\n",
      "        41   MaxAbsScaler LightGBM                          0:00:25       0.9051    0.9142\n",
      "        42   StandardScalerWrapper XGBoostClassifier        0:00:29       0.9130    0.9142\n",
      "        43   SparseNormalizer XGBoostClassifier             0:00:41       0.9111    0.9142\n",
      "        44   SparseNormalizer LightGBM                      0:00:28       0.9120    0.9142\n",
      "        45   StandardScalerWrapper XGBoostClassifier        0:00:28       0.9134    0.9142\n",
      "        46   SparseNormalizer LightGBM                      0:00:32       0.9113    0.9142\n",
      "        47   SparseNormalizer XGBoostClassifier             0:00:34       0.9110    0.9142\n",
      "        48   MaxAbsScaler LogisticRegression                0:00:24       0.8603    0.9142\n",
      "        49   SparseNormalizer LightGBM                      0:00:30       0.9104    0.9142\n",
      "        50   StandardScalerWrapper XGBoostClassifier        0:00:26       0.8880    0.9142\n",
      "        51   SparseNormalizer XGBoostClassifier             0:00:41       0.9099    0.9142\n",
      "        52   MaxAbsScaler LightGBM                          0:00:29       0.9084    0.9142\n",
      "        53   SparseNormalizer XGBoostClassifier             0:00:32       0.9108    0.9142\n",
      "        54   VotingEnsemble                                 0:00:45       0.9155    0.9155\n",
      "        55   StackEnsemble                                  0:00:50       0.9133    0.9155\n",
      "Stopping criteria reached at iteration 56. Ending experiment.\n",
      "****************************************************************************************************\n",
      "Current status: BestRunExplainModel. Best run model explanations started\n",
      "Current status: ModelExplanationDataSetSetup. Model explanations data setup completed\n",
      "Current status: PickSurrogateModel. Choosing LightGBM as the surrogate model for explanations\n",
      "Current status: EngineeredFeatureExplanations. Computation of engineered features started\n",
      "Current status: EngineeredFeatureExplanations. Computation of engineered features completed\n",
      "Current status: RawFeaturesExplanations. Computation of raw features started\n",
      "Current status: RawFeaturesExplanations. Computation of raw features completed\n",
      "Current status: BestRunExplainModel. Best run model explanations completed\n",
      "****************************************************************************************************\n"
     ]
    }
   ],
   "source": [
    "# Submit your automl run\n",
    "\n",
    "run = exp.submit(automl_config, show_output=True)\n",
    "### YOUR CODE HERE ###    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "gather": {
     "logged": 1610897425649
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model(workspace=Workspace.create(name='quick-starts-ws-135073', subscription_id='cdbe0b43-92a0-4715-838a-f2648cc7ad21', resource_group='aml-quickstarts-135073'), name=best_automl_model.pkl, id=best_automl_model.pkl:1, version=1, tags={}, properties={})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve and save your best automl model.\n",
    "best_run, best_model = run.get_output()\n",
    "best_run.register_model(model_name='best_automl_model.pkl', model_path = './outputs/')\n",
    "### YOUR CODE HERE ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "gather": {
     "logged": 1610897430240
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ensembled_iterations': '[1, 45, 35, 42, 32, 44, 46, 43, 51]',\n",
       " 'ensembled_algorithms': \"['XGBoostClassifier', 'XGBoostClassifier', 'XGBoostClassifier', 'XGBoostClassifier', 'XGBoostClassifier', 'LightGBM', 'LightGBM', 'XGBoostClassifier', 'XGBoostClassifier']\",\n",
       " 'ensemble_weights': '[0.14285714285714285, 0.07142857142857142, 0.07142857142857142, 0.2857142857142857, 0.14285714285714285, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142]',\n",
       " 'best_individual_pipeline_score': '0.9142054720057982',\n",
       " 'best_individual_iteration': '1',\n",
       " 'model_explanation': 'True'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_run.get_tags() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "gather": {
     "logged": 1610897438722
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recall_score_micro 0.9154738177206015\n",
      "log_loss 0.177909130857201\n",
      "AUC_micro 0.9802210435406047\n",
      "AUC_macro 0.9457605645070959\n",
      "precision_score_weighted 0.9091326361647714\n",
      "average_precision_score_macro 0.8209161596176471\n",
      "average_precision_score_micro 0.9810499346456378\n",
      "balanced_accuracy 0.7448210394288107\n",
      "matthews_correlation 0.5393187751555846\n",
      "AUC_weighted 0.9457605645070959\n",
      "f1_score_macro 0.7673865382099132\n",
      "precision_score_micro 0.9154738177206015\n",
      "precision_score_macro 0.7970210945866707\n",
      "recall_score_weighted 0.9154738177206015\n",
      "norm_macro_recall 0.48964207885762145\n",
      "recall_score_macro 0.7448210394288107\n",
      "weighted_accuracy 0.9578586743593104\n",
      "average_precision_score_weighted 0.9544016375169213\n",
      "accuracy 0.9154738177206015\n",
      "f1_score_micro 0.9154738177206015\n",
      "f1_score_weighted 0.9114011683696854\n",
      "accuracy_table aml://artifactId/ExperimentRun/dcid.AutoML_46aa3c5f-a6bf-4424-a6fe-5198d3605413_54/accuracy_table\n",
      "confusion_matrix aml://artifactId/ExperimentRun/dcid.AutoML_46aa3c5f-a6bf-4424-a6fe-5198d3605413_54/confusion_matrix\n"
     ]
    }
   ],
   "source": [
    "best_run_metrics = best_run.get_metrics() \n",
    "for metric_name in best_run_metrics:\n",
    "     metric = best_run_metrics[metric_name]\n",
    "     print(metric_name, metric)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1610897529860
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datatransformer\n",
      "{'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",
      "\n",
      "prefittedsoftvotingclassifier\n",
      "{'estimators': ['1', '45', '35', '42', '32', '44', '46', '43', '51'],\n",
      " 'weights': [0.14285714285714285,\n",
      "             0.07142857142857142,\n",
      "             0.07142857142857142,\n",
      "             0.2857142857142857,\n",
      "             0.14285714285714285,\n",
      "             0.07142857142857142,\n",
      "             0.07142857142857142,\n",
      "             0.07142857142857142,\n",
      "             0.07142857142857142]}\n",
      "\n",
      "1 - maxabsscaler\n",
      "{'copy': True}\n",
      "\n",
      "1 - xgboostclassifier\n",
      "{'base_score': 0.5,\n",
      " 'booster': 'gbtree',\n",
      " 'colsample_bylevel': 1,\n",
      " 'colsample_bynode': 1,\n",
      " 'colsample_bytree': 1,\n",
      " 'gamma': 0,\n",
      " 'learning_rate': 0.1,\n",
      " 'max_delta_step': 0,\n",
      " 'max_depth': 3,\n",
      " 'min_child_weight': 1,\n",
      " 'missing': nan,\n",
      " 'n_estimators': 100,\n",
      " 'n_jobs': 1,\n",
      " 'nthread': None,\n",
      " 'objective': 'binary:logistic',\n",
      " 'random_state': 0,\n",
      " 'reg_alpha': 0,\n",
      " 'reg_lambda': 1,\n",
      " 'scale_pos_weight': 1,\n",
      " 'seed': None,\n",
      " 'silent': None,\n",
      " 'subsample': 1,\n",
      " 'tree_method': 'auto',\n",
      " 'verbose': -10,\n",
      " 'verbosity': 0}\n",
      "\n",
      "45 - standardscalerwrapper\n",
      "{'class_name': 'StandardScaler',\n",
      " 'copy': True,\n",
      " 'module_name': 'sklearn.preprocessing._data',\n",
      " 'with_mean': False,\n",
      " 'with_std': False}\n",
      "\n",
      "45 - xgboostclassifier\n",
      "{'base_score': 0.5,\n",
      " 'booster': 'gbtree',\n",
      " 'colsample_bylevel': 1,\n",
      " 'colsample_bynode': 1,\n",
      " 'colsample_bytree': 1,\n",
      " 'eta': 0.4,\n",
      " 'gamma': 0,\n",
      " 'grow_policy': 'lossguide',\n",
      " 'learning_rate': 0.1,\n",
      " 'max_bin': 255,\n",
      " 'max_delta_step': 0,\n",
      " 'max_depth': 9,\n",
      " 'max_leaves': 3,\n",
      " 'min_child_weight': 1,\n",
      " 'missing': nan,\n",
      " 'n_estimators': 400,\n",
      " 'n_jobs': 1,\n",
      " 'nthread': None,\n",
      " 'objective': 'reg:logistic',\n",
      " 'random_state': 0,\n",
      " 'reg_alpha': 1.0416666666666667,\n",
      " 'reg_lambda': 1.25,\n",
      " 'scale_pos_weight': 1,\n",
      " 'seed': None,\n",
      " 'silent': None,\n",
      " 'subsample': 0.6,\n",
      " 'tree_method': 'hist',\n",
      " 'verbose': -10,\n",
      " 'verbosity': 0}\n",
      "\n",
      "35 - sparsenormalizer\n",
      "{'copy': True, 'norm': 'l2'}\n",
      "\n",
      "35 - xgboostclassifier\n",
      "{'base_score': 0.5,\n",
      " 'booster': 'gbtree',\n",
      " 'colsample_bylevel': 1,\n",
      " 'colsample_bynode': 1,\n",
      " 'colsample_bytree': 1,\n",
      " 'eta': 0.3,\n",
      " 'gamma': 0,\n",
      " 'learning_rate': 0.1,\n",
      " 'max_delta_step': 0,\n",
      " 'max_depth': 3,\n",
      " 'max_leaves': 0,\n",
      " 'min_child_weight': 1,\n",
      " 'missing': nan,\n",
      " 'n_estimators': 100,\n",
      " 'n_jobs': 1,\n",
      " 'nthread': None,\n",
      " 'objective': 'reg:logistic',\n",
      " 'random_state': 0,\n",
      " 'reg_alpha': 2.291666666666667,\n",
      " 'reg_lambda': 1.0416666666666667,\n",
      " 'scale_pos_weight': 1,\n",
      " 'seed': None,\n",
      " 'silent': None,\n",
      " 'subsample': 1,\n",
      " 'tree_method': 'auto',\n",
      " 'verbose': -10,\n",
      " 'verbosity': 0}\n",
      "\n",
      "42 - standardscalerwrapper\n",
      "{'class_name': 'StandardScaler',\n",
      " 'copy': True,\n",
      " 'module_name': 'sklearn.preprocessing._data',\n",
      " 'with_mean': False,\n",
      " 'with_std': False}\n",
      "\n",
      "42 - xgboostclassifier\n",
      "{'base_score': 0.5,\n",
      " 'booster': 'gbtree',\n",
      " 'colsample_bylevel': 1,\n",
      " 'colsample_bynode': 1,\n",
      " 'colsample_bytree': 1,\n",
      " 'eta': 0.05,\n",
      " 'gamma': 0,\n",
      " 'learning_rate': 0.1,\n",
      " 'max_delta_step': 0,\n",
      " 'max_depth': 5,\n",
      " 'max_leaves': 0,\n",
      " 'min_child_weight': 1,\n",
      " 'missing': nan,\n",
      " 'n_estimators': 100,\n",
      " 'n_jobs': 1,\n",
      " 'nthread': None,\n",
      " 'objective': 'reg:logistic',\n",
      " 'random_state': 0,\n",
      " 'reg_alpha': 1.6666666666666667,\n",
      " 'reg_lambda': 0.9375,\n",
      " 'scale_pos_weight': 1,\n",
      " 'seed': None,\n",
      " 'silent': None,\n",
      " 'subsample': 0.6,\n",
      " 'tree_method': 'auto',\n",
      " 'verbose': -10,\n",
      " 'verbosity': 0}\n",
      "\n",
      "32 - sparsenormalizer\n",
      "{'copy': True, 'norm': 'max'}\n",
      "\n",
      "32 - xgboostclassifier\n",
      "{'base_score': 0.5,\n",
      " 'booster': 'gbtree',\n",
      " 'colsample_bylevel': 1,\n",
      " 'colsample_bynode': 1,\n",
      " 'colsample_bytree': 0.9,\n",
      " 'eta': 0.01,\n",
      " 'gamma': 0,\n",
      " 'learning_rate': 0.1,\n",
      " 'max_delta_step': 0,\n",
      " 'max_depth': 4,\n",
      " 'max_leaves': 0,\n",
      " 'min_child_weight': 1,\n",
      " 'missing': nan,\n",
      " 'n_estimators': 100,\n",
      " 'n_jobs': 1,\n",
      " 'nthread': None,\n",
      " 'objective': 'reg:logistic',\n",
      " 'random_state': 0,\n",
      " 'reg_alpha': 1.0416666666666667,\n",
      " 'reg_lambda': 0.7291666666666667,\n",
      " 'scale_pos_weight': 1,\n",
      " 'seed': None,\n",
      " 'silent': None,\n",
      " 'subsample': 1,\n",
      " 'tree_method': 'auto',\n",
      " 'verbose': -10,\n",
      " 'verbosity': 0}\n",
      "\n",
      "44 - sparsenormalizer\n",
      "{'copy': True, 'norm': 'l1'}\n",
      "\n",
      "44 - lightgbmclassifier\n",
      "{'boosting_type': 'gbdt',\n",
      " 'class_weight': None,\n",
      " 'colsample_bytree': 0.5944444444444444,\n",
      " 'importance_type': 'split',\n",
      " 'learning_rate': 0.036848421052631586,\n",
      " 'max_bin': 190,\n",
      " 'max_depth': 6,\n",
      " 'min_child_samples': 1523,\n",
      " 'min_child_weight': 8,\n",
      " 'min_split_gain': 0.47368421052631576,\n",
      " 'n_estimators': 400,\n",
      " 'n_jobs': 1,\n",
      " 'num_leaves': 251,\n",
      " 'objective': None,\n",
      " 'random_state': None,\n",
      " 'reg_alpha': 0.631578947368421,\n",
      " 'reg_lambda': 0.3157894736842105,\n",
      " 'silent': True,\n",
      " 'subsample': 0.99,\n",
      " 'subsample_for_bin': 200000,\n",
      " 'subsample_freq': 0,\n",
      " 'verbose': -10}\n",
      "\n",
      "46 - sparsenormalizer\n",
      "{'copy': True, 'norm': 'l1'}\n",
      "\n",
      "46 - lightgbmclassifier\n",
      "{'boosting_type': 'goss',\n",
      " 'class_weight': None,\n",
      " 'colsample_bytree': 0.99,\n",
      " 'importance_type': 'split',\n",
      " 'learning_rate': 0.026323157894736843,\n",
      " 'max_bin': 240,\n",
      " 'max_depth': 4,\n",
      " 'min_child_samples': 1,\n",
      " 'min_child_weight': 5,\n",
      " 'min_split_gain': 0.9473684210526315,\n",
      " 'n_estimators': 800,\n",
      " 'n_jobs': 1,\n",
      " 'num_leaves': 92,\n",
      " 'objective': None,\n",
      " 'random_state': None,\n",
      " 'reg_alpha': 0.3157894736842105,\n",
      " 'reg_lambda': 0.21052631578947367,\n",
      " 'silent': True,\n",
      " 'subsample': 1,\n",
      " 'subsample_for_bin': 200000,\n",
      " 'subsample_freq': 0,\n",
      " 'verbose': -10}\n",
      "\n",
      "43 - sparsenormalizer\n",
      "{'copy': True, 'norm': 'l2'}\n",
      "\n",
      "43 - xgboostclassifier\n",
      "{'base_score': 0.5,\n",
      " 'booster': 'gbtree',\n",
      " 'colsample_bylevel': 1,\n",
      " 'colsample_bynode': 1,\n",
      " 'colsample_bytree': 0.7,\n",
      " 'eta': 0.1,\n",
      " 'gamma': 0,\n",
      " 'learning_rate': 0.1,\n",
      " 'max_delta_step': 0,\n",
      " 'max_depth': 4,\n",
      " 'max_leaves': 0,\n",
      " 'min_child_weight': 1,\n",
      " 'missing': nan,\n",
      " 'n_estimators': 100,\n",
      " 'n_jobs': 1,\n",
      " 'nthread': None,\n",
      " 'objective': 'reg:logistic',\n",
      " 'random_state': 0,\n",
      " 'reg_alpha': 0.5208333333333334,\n",
      " 'reg_lambda': 0.8333333333333334,\n",
      " 'scale_pos_weight': 1,\n",
      " 'seed': None,\n",
      " 'silent': None,\n",
      " 'subsample': 0.6,\n",
      " 'tree_method': 'auto',\n",
      " 'verbose': -10,\n",
      " 'verbosity': 0}\n",
      "\n",
      "51 - sparsenormalizer\n",
      "{'copy': True, 'norm': 'l2'}\n",
      "\n",
      "51 - xgboostclassifier\n",
      "{'base_score': 0.5,\n",
      " 'booster': 'gbtree',\n",
      " 'colsample_bylevel': 0.7,\n",
      " 'colsample_bynode': 1,\n",
      " 'colsample_bytree': 1,\n",
      " 'eta': 0.1,\n",
      " 'gamma': 0,\n",
      " 'learning_rate': 0.1,\n",
      " 'max_delta_step': 0,\n",
      " 'max_depth': 6,\n",
      " 'max_leaves': 0,\n",
      " 'min_child_weight': 1,\n",
      " 'missing': nan,\n",
      " 'n_estimators': 200,\n",
      " 'n_jobs': 1,\n",
      " 'nthread': None,\n",
      " 'objective': 'reg:logistic',\n",
      " 'random_state': 0,\n",
      " 'reg_alpha': 0.3125,\n",
      " 'reg_lambda': 1.4583333333333335,\n",
      " 'scale_pos_weight': 1,\n",
      " 'seed': None,\n",
      " 'silent': None,\n",
      " 'subsample': 1,\n",
      " 'tree_method': 'auto',\n",
      " 'verbose': -10,\n",
      " 'verbosity': 0}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "def print_model(best_model, prefix=\"\"):\n",
    "    for step in best_model.steps:\n",
    "        print(prefix + step[0])\n",
    "        if hasattr(step[1], 'estimators') and hasattr(step[1], 'weights'):\n",
    "            pprint({'estimators': list(\n",
    "                e[0] for e in step[1].estimators), 'weights': step[1].weights})\n",
    "            print()\n",
    "            for estimator in step[1].estimators:\n",
    "                print_model(estimator[1], estimator[0] + ' - ')\n",
    "        else:\n",
    "            pprint(step[1].get_params())\n",
    "            print()\n",
    "\n",
    "print_model(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "gather": {
     "logged": 1610897538469
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current provisioning state of AmlCompute is \"Deleting\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cpu_cluster.delete() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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