udacity-project_v2.ipynb

{
 "cells": [
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   "metadata": {
    "gather": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing interactive authentication. Please follow the instructions on the terminal.\n",
      "To sign in, use a web browser to open the page https://microsoft.com/devicelogin and enter the code ARDSHADE5 to authenticate.\n",
      "You have logged in. Now let us find all the subscriptions to which you have access...\n",
      "Interactive authentication successfully completed.\n",
      "Workspace name: quick-starts-ws-139501\n",
      "Azure region: southcentralus\n",
      "Subscription id: 6971f5ac-8af1-446e-8034-05acea24681f\n",
      "Resource group: aml-quickstarts-139501\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import Workspace, Experiment\n",
    "\n",
    "#ws = Workspace.get(name=\"aml-quickstarts-139038\")\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": 1,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-learn==0.22.1\n",
      "  Downloading scikit_learn-0.22.1-cp36-cp36m-manylinux1_x86_64.whl (7.0 MB)\n",
      "\u001b[K     |████████████████████████████████| 7.0 MB 6.3 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: scipy>=0.17.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from scikit-learn==0.22.1) (1.5.2)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.11.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from scikit-learn==0.22.1) (1.18.5)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=0.11 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from scikit-learn==0.22.1) (0.14.1)\n",
      "Installing collected packages: scikit-learn\n",
      "  Attempting uninstall: scikit-learn\n",
      "    Found existing installation: scikit-learn 0.22.2.post1\n",
      "    Uninstalling scikit-learn-0.22.2.post1:\n",
      "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
      "Successfully installed scikit-learn-0.22.1\n"
     ]
    }
   ],
   "source": [
    "!pip install -U scikit-learn==0.22.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    },
    "nteract": {
     "transient": {
      "deleting": false
     }
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   },
   "outputs": [],
   "source": [
    "from azureml.core import Environment\n",
    "\n",
    "sklearn_env = Environment.from_conda_specification(name = 'sklearn-env', file_path = 'conda_dependencies.yml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "gather": {
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    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import argparse\n",
    "import os\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import joblib\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import pandas as pd\n",
    "from azureml.core.run import Run\n",
    "from azureml.data.dataset_factory import TabularDatasetFactory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1608833822375
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\n",
       "    \"databricks\": {\n",
       "        \"eggLibraries\": [],\n",
       "        \"jarLibraries\": [],\n",
       "        \"mavenLibraries\": [],\n",
       "        \"pypiLibraries\": [],\n",
       "        \"rcranLibraries\": []\n",
       "    },\n",
       "    \"docker\": {\n",
       "        \"arguments\": [],\n",
       "        \"baseDockerfile\": null,\n",
       "        \"baseImage\": \"mcr.microsoft.com/azureml/intelmpi2018.3-ubuntu16.04:20200821.v1\",\n",
       "        \"baseImageRegistry\": {\n",
       "            \"address\": null,\n",
       "            \"password\": null,\n",
       "            \"registryIdentity\": null,\n",
       "            \"username\": null\n",
       "        },\n",
       "        \"enabled\": false,\n",
       "        \"platform\": {\n",
       "            \"architecture\": \"amd64\",\n",
       "            \"os\": \"Linux\"\n",
       "        },\n",
       "        \"sharedVolumes\": true,\n",
       "        \"shmSize\": null\n",
       "    },\n",
       "    \"environmentVariables\": {\n",
       "        \"EXAMPLE_ENV_VAR\": \"EXAMPLE_VALUE\"\n",
       "    },\n",
       "    \"inferencingStackVersion\": null,\n",
       "    \"name\": \"tutorial-env\",\n",
       "    \"python\": {\n",
       "        \"baseCondaEnvironment\": null,\n",
       "        \"condaDependencies\": {\n",
       "            \"channels\": [\n",
       "                \"anaconda\",\n",
       "                \"conda-forge\"\n",
       "            ],\n",
       "            \"dependencies\": [\n",
       "                \"python=3.6.2\",\n",
       "                {\n",
       "                    \"pip\": [\n",
       "                        \"azureml-dataprep[pandas,fuse]>=1.1.14\",\n",
       "                        \"azureml-defaults~=1.19.0\"\n",
       "                    ]\n",
       "                },\n",
       "                \"scikit-learn==0.22.1\"\n",
       "            ],\n",
       "            \"name\": \"azureml_d1e3d4347b435236e91d2844891ac5e2\"\n",
       "        },\n",
       "        \"condaDependenciesFile\": null,\n",
       "        \"interpreterPath\": \"python\",\n",
       "        \"userManagedDependencies\": false\n",
       "    },\n",
       "    \"r\": null,\n",
       "    \"spark\": {\n",
       "        \"packages\": [],\n",
       "        \"precachePackages\": true,\n",
       "        \"repositories\": []\n",
       "    },\n",
       "    \"version\": \"1\"\n",
       "}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from azureml.core.environment import Environment\n",
    "#from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "# to install required packages\n",
    "#env = Environment('tutorial-env')\n",
    "#cd = CondaDependencies.create(pip_packages=['azureml-dataprep[pandas,fuse]>=1.1.14', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n",
    "\n",
    "#env.python.conda_dependencies = cd\n",
    "\n",
    "# Register environment to re-use later\n",
    "#env.register(workspace = ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "gather": {
     "logged": 1614335978177
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "\n",
    "# TODO: Create compute cluster\n",
    "# Use vm_size = \"Standard_D2_V2\" in your provisioning configuration.\n",
    "# max_nodes should be no greater than 4.\n",
    "\n",
    "##compute_target_ws = ws.compute_targets[\"compute-cluster\"]\n",
    "\n",
    "compute_cluster = \"compute-cluster\"\n",
    "\n",
    "try:\n",
    "    compute_target_ws = ComputeTarget(workspace=ws, name=compute_cluster)\n",
    "except ComputeTargetException:\n",
    "    compute_config = AmlCompute.provisioning_configuration(vm_size='Standard_D2_V2', max_nodes=4)\n",
    "    compute_target_ws = ComputeTarget.create (ws, compute_cluster, compute_config)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "gather": {
     "logged": 1614335994548
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "from azureml.train.sklearn import SKLearn\n",
    "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n",
    "from azureml.train.hyperdrive.policy import BanditPolicy\n",
    "from azureml.train.hyperdrive.sampling import RandomParameterSampling\n",
    "from azureml.train.hyperdrive.runconfig import HyperDriveConfig\n",
    "from azureml.train.hyperdrive.parameter_expressions import uniform, choice\n",
    "from azureml.core import ScriptRunConfig \n",
    "from azureml.core import Environment\n",
    "import os\n",
    "\n",
    "# Specify parameter sampler\n",
    "#https://www.kaggle.com/joparga3/2-tuning-parameters-for-logistic-regression\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
    "ps = RandomParameterSampling( {\n",
    "    \"--C\": choice(0.001,0.01,0.1,1,10,100),\n",
    "    \"--max_iter\": choice(50, 100, 150, 200)    \n",
    "   })\n",
    "\n",
    "# Specify a Policy\n",
    "policy = BanditPolicy(slack_factor = 0.1, evaluation_interval=1, delay_evaluation=5)\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 = ### YOUR CODE HERE ###\n",
    "\n",
    "# create or retrieve an environment\n",
    "#env = Environment.get(ws, name='MyEnvironment')\n",
    "\n",
    "src = ScriptRunConfig(source_directory='.',\n",
    "                            script='train.py',\n",
    "                            compute_target= compute_target_ws,\n",
    "                            environment=sklearn_env)\n",
    "\n",
    "\n",
    "# Create a HyperDriveConfig using the estimator, hyperparameter sampler, and policy.\n",
    "hyperdrive_config = HyperDriveConfig(run_config=src,\n",
    "                                hyperparameter_sampling=ps,\n",
    "                                policy=policy,\n",
    "                                primary_metric_name='Accuracy',\n",
    "                                primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
    "                                max_total_runs=12,\n",
    "                                max_concurrent_runs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "gather": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>udacity-project</td><td>HD_445a73e1-f8e1-4684-8f6f-449122f46a70</td><td>hyperdrive</td><td>Running</td><td><a href=\"https://ml.azure.com/experiments/udacity-project/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70?wsid=/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourcegroups/aml-quickstarts-139501/workspaces/quick-starts-ws-139501\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
      ],
      "text/plain": [
       "Run(Experiment: udacity-project,\n",
       "Id: HD_445a73e1-f8e1-4684-8f6f-449122f46a70,\n",
       "Type: hyperdrive,\n",
       "Status: Running)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Submit your hyperdrive run to the experiment and show run details with the widget.\n",
    "\n",
    "run = exp.submit(hyperdrive_config)\n",
    "run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1614336152919
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     "outputs_hidden": false,
     "source_hidden": false
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    "nteract": {
     "transient": {
      "deleting": false
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   },
   "outputs": [
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     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23c4c17353fb48c385d0db5e9a502b6a",
       "version_major": 2,
       "version_minor": 0
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      "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\": \"Running\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/udacity-project/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70?wsid=/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourcegroups/aml-quickstarts-139501/workspaces/quick-starts-ws-139501\", \"run_id\": \"HD_445a73e1-f8e1-4684-8f6f-449122f46a70\", \"run_properties\": {\"run_id\": \"HD_445a73e1-f8e1-4684-8f6f-449122f46a70\", \"created_utc\": \"2021-02-26T10:42:17.733146Z\", \"properties\": {\"primary_metric_config\": \"{\\\"name\\\": \\\"Accuracy\\\", \\\"goal\\\": \\\"maximize\\\"}\", \"resume_from\": \"null\", \"runTemplate\": \"HyperDrive\", \"azureml.runsource\": \"hyperdrive\", \"platform\": \"AML\", \"ContentSnapshotId\": \"84d052a9-ced8-482c-8d47-691f828d15c3\"}, \"tags\": {\"_aml_system_max_concurrent_jobs\": \"4\", \"max_concurrent_jobs\": \"4\", \"_aml_system_max_total_jobs\": \"12\", \"max_total_jobs\": \"12\", \"_aml_system_max_duration_minutes\": \"10080\", \"max_duration_minutes\": \"10080\", \"_aml_system_policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 1, \\\"delay_evaluation\\\": 5, \\\"slack_factor\\\": 0.1}}\", \"policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 1, \\\"delay_evaluation\\\": 5, \\\"slack_factor\\\": 0.1}}\", \"_aml_system_generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"--C\\\": [\\\"choice\\\", [[0.001, 0.01, 0.1, 1, 10, 100]]], \\\"--max_iter\\\": [\\\"choice\\\", [[50, 100, 150, 200]]]}}\", \"generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"--C\\\": [\\\"choice\\\", [[0.001, 0.01, 0.1, 1, 10, 100]]], \\\"--max_iter\\\": [\\\"choice\\\", [[50, 100, 150, 200]]]}}\", \"_aml_system_primary_metric_config\": \"{\\\"name\\\": \\\"Accuracy\\\", \\\"goal\\\": \\\"maximize\\\"}\", \"primary_metric_config\": \"{\\\"name\\\": \\\"Accuracy\\\", \\\"goal\\\": \\\"maximize\\\"}\", \"_aml_system_platform_config\": \"{\\\"ServiceAddress\\\": \\\"https://southcentralus.experiments.azureml.net\\\", \\\"ServiceArmScope\\\": \\\"subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/udacity-project\\\", \\\"SubscriptionId\\\": \\\"6971f5ac-8af1-446e-8034-05acea24681f\\\", \\\"ResourceGroupName\\\": \\\"aml-quickstarts-139501\\\", \\\"WorkspaceName\\\": \\\"quick-starts-ws-139501\\\", \\\"ExperimentName\\\": \\\"udacity-project\\\", \\\"Definition\\\": {\\\"Overrides\\\": {\\\"script\\\": \\\"train.py\\\", \\\"arguments\\\": [], \\\"target\\\": \\\"compute-cluster\\\", \\\"framework\\\": \\\"Python\\\", \\\"communicator\\\": \\\"None\\\", \\\"maxRunDurationSeconds\\\": 2592000, \\\"nodeCount\\\": 1, \\\"environment\\\": {\\\"name\\\": \\\"sklearn-env\\\", \\\"version\\\": null, \\\"environmentVariables\\\": {\\\"EXAMPLE_ENV_VAR\\\": \\\"EXAMPLE_VALUE\\\"}, \\\"python\\\": {\\\"userManagedDependencies\\\": false, \\\"interpreterPath\\\": \\\"python\\\", \\\"condaDependenciesFile\\\": null, \\\"baseCondaEnvironment\\\": null, \\\"condaDependencies\\\": {\\\"dependencies\\\": [\\\"python=3.6.2\\\", \\\"scikit-learn\\\", {\\\"pip\\\": [\\\"azureml-defaults\\\"]}]}}, \\\"docker\\\": {\\\"enabled\\\": false, \\\"baseImage\\\": \\\"mcr.microsoft.com/azureml/intelmpi2018.3-ubuntu16.04:20210104.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\\\": true}, \\\"databricks\\\": {\\\"mavenLibraries\\\": [], \\\"pypiLibraries\\\": [], \\\"rcranLibraries\\\": [], \\\"jarLibraries\\\": [], \\\"eggLibraries\\\": []}, \\\"r\\\": null, \\\"inferencingStackVersion\\\": null}, \\\"history\\\": {\\\"outputCollection\\\": true, \\\"snapshotProject\\\": true, \\\"directoriesToWatch\\\": [\\\"logs\\\"]}, \\\"spark\\\": {\\\"configuration\\\": {\\\"spark.app.name\\\": \\\"Azure ML Experiment\\\", \\\"spark.yarn.maxAppAttempts\\\": 1}}, \\\"hdi\\\": {\\\"yarnDeployMode\\\": \\\"cluster\\\"}, \\\"tensorflow\\\": {\\\"workerCount\\\": 1, \\\"parameterServerCount\\\": 1}, \\\"mpi\\\": {\\\"processCountPerNode\\\": 1, \\\"nodeCount\\\": 1}, \\\"paralleltask\\\": {\\\"maxRetriesPerWorker\\\": 0, \\\"workerCountPerNode\\\": 1, \\\"terminalExitCodes\\\": null}, \\\"dataReferences\\\": {}, \\\"data\\\": {}, \\\"outputData\\\": {}, \\\"sourceDirectoryDataStore\\\": null, \\\"amlcompute\\\": {\\\"vmSize\\\": null, \\\"vmPriority\\\": null, \\\"retainCluster\\\": false, \\\"name\\\": null, \\\"clusterMaxNodeCount\\\": null}, \\\"command\\\": \\\"\\\"}, \\\"TargetDetails\\\": null, \\\"SnapshotId\\\": \\\"84d052a9-ced8-482c-8d47-691f828d15c3\\\", \\\"TelemetryValues\\\": {\\\"amlClientType\\\": \\\"azureml-sdk-train\\\", \\\"amlClientModule\\\": \\\"[Scrubbed]\\\", \\\"amlClientFunction\\\": \\\"[Scrubbed]\\\", \\\"tenantId\\\": \\\"660b3398-b80e-49d2-bc5b-ac1dc93b5254\\\", \\\"amlClientRequestId\\\": \\\"9d4f8f25-63f4-4879-ae76-cc7eb15886d0\\\", \\\"amlClientSessionId\\\": \\\"77cebde9-5dcc-4821-9081-f6bb66c8ef46\\\", \\\"subscriptionId\\\": \\\"6971f5ac-8af1-446e-8034-05acea24681f\\\", \\\"estimator\\\": \\\"NoneType\\\", \\\"samplingMethod\\\": \\\"RANDOM\\\", \\\"terminationPolicy\\\": \\\"Bandit\\\", \\\"primaryMetricGoal\\\": \\\"maximize\\\", \\\"maxTotalRuns\\\": 12, \\\"maxConcurrentRuns\\\": 4, \\\"maxDurationMinutes\\\": 10080, \\\"vmSize\\\": null}}}\", \"platform_config\": \"{\\\"ServiceAddress\\\": \\\"https://southcentralus.experiments.azureml.net\\\", 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Please be patient as it can take a few minutes.\\r\\n[2021-02-26T10:47:53.4135566Z][SCHEDULER][INFO]The execution environment was successfully prepared.\\r\\n[2021-02-26T10:47:53.4454661Z][SCHEDULER][INFO]Scheduling job, id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_2'\\r\\n[2021-02-26T10:47:53.4284251Z][SCHEDULER][INFO]Scheduling job, id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_3'\\r\\n[2021-02-26T10:47:53.4138726Z][SCHEDULER][INFO]Scheduling job, id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_0'\\r\\n[2021-02-26T10:47:53.4994932Z][SCHEDULER][INFO]Scheduling job, id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_1'\\r\\n[2021-02-26T10:47:54.0291318Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_1'\\r\\n[2021-02-26T10:47:54.5680402Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_3'\\r\\n[2021-02-26T10:47:54.6317349Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_2'\\r\\n[2021-02-26T10:47:55.0254534Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_0'\\r\\n[2021-02-26T10:48:20.870992][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_1] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-02-26T10:48:50.785797][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_1] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-02-26T10:49:21.072024][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_1] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-02-26T10:49:48.958642][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-26T10:49:49.234927][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-26T10:49:51.163959][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_3] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-02-26T10:49:55.7799477Z][SCHEDULER][INFO]Scheduling job, id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_4'\\r\\n[2021-02-26T10:49:56.8895233Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_4'\\r\\n[2021-02-26T10:50:22.997304][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_0, https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_2, https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_3, https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_4] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-02-26T10:50:52.853963][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_0, https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_2, https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_4] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-02-26T10:50:54.700034][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2021-02-26T10:50:54.983618][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-26T10:50:57.4297511Z][SCHEDULER][INFO]Scheduling job, id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_5'\\r\\n[2021-02-26T10:50:58.1488370Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_5'\\r\\n[2021-02-26T10:51:23.185090][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_0, https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_2, https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_4, https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_5] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-02-26T10:51:55.105939][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2021-02-26T10:51:55.422152][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-26T10:51:59.2122559Z][SCHEDULER][INFO]Scheduling job, id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_7'\\r\\n[2021-02-26T10:51:59.2217984Z][SCHEDULER][INFO]Scheduling job, id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_9'\\r\\n[2021-02-26T10:51:59.2216969Z][SCHEDULER][INFO]Scheduling job, id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_8'\\r\\n[2021-02-26T10:51:59.2111410Z][SCHEDULER][INFO]Scheduling job, id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_6'\\r\\n[2021-02-26T10:52:00.0944723Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_6'\\r\\n[2021-02-26T10:52:00.2406202Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_8'\\r\\n[2021-02-26T10:52:00.4868330Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_7'\\r\\n[2021-02-26T10:52:00.6817392Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_9'\\r\\n[2021-02-26T10:52:24.320399][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_6, https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_7, https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_8, https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_9] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-02-26T10:52:55.276572][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experiments/**SCRUBBED**/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_7] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-02-26T10:53:25.991497][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2021-02-26T10:53:26.113698][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2021-02-26T10:53:31.7420678Z][SCHEDULER][INFO]Scheduling job, id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_11'\\r\\n[2021-02-26T10:53:31.8379945Z][SCHEDULER][INFO]Scheduling job, id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_10'\\r\\n[2021-02-26T10:53:32.8330635Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_11'\\r\\n[2021-02-26T10:53:33.7088724Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_445a73e1-f8e1-4684-8f6f-449122f46a70_10'\\n\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.20.0\"}, \"loading\": false}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "\n",
    "RunDetails(run).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1614236171924
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RunId: HD_445a73e1-f8e1-4684-8f6f-449122f46a70\n",
      "Web View: https://ml.azure.com/experiments/udacity-project/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70?wsid=/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourcegroups/aml-quickstarts-139501/workspaces/quick-starts-ws-139501\n",
      "\n",
      "Streaming azureml-logs/hyperdrive.txt\n",
      "=====================================\n",
      "\n",
      "\"<START>[2021-02-26T10:42:18.483522][API][INFO]Experiment created<END>\\n\"\"<START>[2021-02-26T10:42:19.319265][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.<END>\\n\"\"<START>[2021-02-26T10:42:19.186424][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space<END>\\n\"<START>[2021-02-26T10:42:20.1007058Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.<END>\n"
     ]
    }
   ],
   "source": [
    "run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "gather": {
     "logged": 1614337062922
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>udacity-project</td><td>HD_445a73e1-f8e1-4684-8f6f-449122f46a70_0</td><td>azureml.scriptrun</td><td>Completed</td><td><a href=\"https://ml.azure.com/experiments/udacity-project/runs/HD_445a73e1-f8e1-4684-8f6f-449122f46a70_0?wsid=/subscriptions/6971f5ac-8af1-446e-8034-05acea24681f/resourcegroups/aml-quickstarts-139501/workspaces/quick-starts-ws-139501\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run.Run?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
      ],
      "text/plain": [
       "Run(Experiment: udacity-project,\n",
       "Id: HD_445a73e1-f8e1-4684-8f6f-449122f46a70_0,\n",
       "Type: azureml.scriptrun,\n",
       "Status: Completed)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "from azureml.widgets import RunDetails\n",
    "# Get your best run and save the model from that run.\n",
    "# https://github.com/microsoft/MLHyperparameterTuning/blob/f28c893f99ca22e32b0351a5e88873a2124afbd3/05_Train_Best_Model.ipynb\n",
    "\n",
    "best_run = run.get_best_run_by_primary_metric()\n",
    "if best_run is None:\n",
    "    raise Exception(\"No best run was found\")\n",
    "best_run\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1614337187823
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>--C</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>--max_iter</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Value\n",
       "--C            1\n",
       "--max_iter   200"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb\n",
    "parameter_values = best_run.get_details()['runDefinition']['arguments']\n",
    "best_parameters = dict(zip(parameter_values[::2], parameter_values[1::2]))\n",
    "pd.Series(best_parameters, name='Value').to_frame()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1614337266432
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "#register the model\n",
    "model = best_run.register_model(model_name='model', model_path='./outputs/model.pkl',tags=best_run.get_metrics())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1614337271127
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['azureml-logs/55_azureml-execution-tvmps_811c516192dd5b7a83a03c034db01b3e9ae72638bc95b24780b8d518dcd12a47_p.txt',\n",
       " 'azureml-logs/65_job_prep-tvmps_811c516192dd5b7a83a03c034db01b3e9ae72638bc95b24780b8d518dcd12a47_p.txt',\n",
       " 'azureml-logs/70_driver_log.txt',\n",
       " 'azureml-logs/75_job_post-tvmps_811c516192dd5b7a83a03c034db01b3e9ae72638bc95b24780b8d518dcd12a47_p.txt',\n",
       " 'azureml-logs/process_info.json',\n",
       " 'azureml-logs/process_status.json',\n",
       " 'logs/azureml/107_azureml.log',\n",
       " 'logs/azureml/dataprep/backgroundProcess.log',\n",
       " 'logs/azureml/dataprep/backgroundProcess_Telemetry.log',\n",
       " 'logs/azureml/job_prep_azureml.log',\n",
       " 'logs/azureml/job_release_azureml.log',\n",
       " 'outputs/model.pkl']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Save the best model\n",
    "best_run.get_file_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1614337276075
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "best_run.download_file(best_run.get_file_names()[-1], output_file_path='./outputs/')"
   ]
  },
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    "## AutoML experiment"
   ]
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    "from azureml.data.dataset_factory import TabularDatasetFactory\n",
    "\n",
    "# Create TabularDataset using TabularDatasetFactory\n",
    "# Data is available at: \n",
    "# \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
    "\n",
    "web_path = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
    "ds = TabularDatasetFactory.from_delimited_files(path=web_path)"
   ]
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   "execution_count": 15,
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   "source": [
    "from train import clean_data\n",
    "\n",
    "# Use the clean_data function to clean your data.\n",
    "x, y = clean_data(ds)"
   ]
  },
  {
   "cell_type": "code",
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       "   age  marital  default  housing  loan  month  day_of_week  duration  \\\n",
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       "   age  marital  default  housing  loan  month  day_of_week  duration  \\\n",
       "0   57        1        0        0     1      5            1       371   \n",
       "1   55        1        0        1     0      5            4       285   \n",
       "2   33        1        0        0     0      5            5        52   \n",
       "3   36        1        0        0     0      6            5       355   \n",
       "4   27        1        0        1     0      7            5       189   \n",
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       "   campaign  pdays  ...  contact_telephone  education_basic.4y  \\\n",
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       "4         2    999  ...                  0                   0   \n",
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       "   education_basic.6y  education_basic.9y  education_high.school  \\\n",
       "0                   0                   0                      1   \n",
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       "   education_illiterate  education_professional.course  \\\n",
       "0                     0                              0   \n",
       "1                     0                              0   \n",
       "2                     0                              0   \n",
       "3                     0                              0   \n",
       "4                     0                              0   \n",
       "\n",
       "   education_university.degree  education_unknown  y  \n",
       "0                            0                  0  0  \n",
       "1                            0                  1  0  \n",
       "2                            0                  0  0  \n",
       "3                            0                  0  0  \n",
       "4                            0                  0  0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Full_data = pd.concat([x, y], axis=1)\n",
    "Full_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1614337416704
    },
    "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 ./data/train_data.csv\n",
      "Uploaded ./data/train_data.csv, 1 files out of an estimated total of 1\n",
      "Uploaded 1 files\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isdir('data'):\n",
    "    os.mkdir('data')\n",
    "    \n",
    "# Save the train data to a csv to be uploaded to the datastore\n",
    "pd.DataFrame(Full_data).to_csv(\"data/train_data.csv\", index=False)\n",
    "\n",
    "ds = ws.get_default_datastore()\n",
    "ds.upload(src_dir='./data', target_path='bankmarketing', overwrite=True, show_progress=True)\n",
    "\n",
    " \n",
    "\n",
    "# Upload the training data as a tabular dataset for access during training on remote compute\n",
    "train_data = TabularDatasetFactory.from_delimited_files(path=ds.path('bankmarketing/train_data.csv'))\n",
    "label = \"y\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "gather": {
     "logged": 1614337464654
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "from azureml.train.automl import AutoMLConfig\n",
    "\n",
    "# Set parameters for AutoMLConfig\n",
    "# NOTE: DO NOT CHANGE THE experiment_timeout_minutes PARAMETER OR YOUR INSTANCE WILL TIME OUT.\n",
    "# If you wish to run the experiment longer, you will need to run this notebook in your own\n",
    "# Azure tenant, which will incur personal costs.\n",
    "#https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb\n",
    "\n",
    "#Primary metric: https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/tune-model-hyperparameters\n",
    "\n",
    "'''\n",
    "Metrics used for binary classification\n",
    "Accuracy is the proportion of true results to total cases.\n",
    "Precision is the proportion of true results to positive results.\n",
    "Recall is the fraction of all correct results over all results.\n",
    "F-score is a measure that balances precision and recall.\n",
    "AUC is a value that represents the area under the curve when false positives are plotted on the x-axis and true positives are plotted on the y-axis.\n",
    "Average Log Loss is the difference between two probability distributions: the true one, and the one in the model.\n",
    "\n",
    "Metrics used for regression\n",
    "Mean absolute error averages all the errors in the model, where error means the distance of the predicted value from the true value. It's often abbreviated as MAE.\n",
    "Root of mean squared error measures the average of the squares of the errors, and then takes the root of that value. It's often abbreviated as RMSE.\n",
    "Relative absolute error represents the error as a percentage of the true value.\n",
    "Relative squared error normalizes the total squared error by dividing by the total squared error of the predicted values.\n",
    "Coefficient of determination is a single number that indicates how well data fits a model. A value of one means that the model exactly matches the data. A value of zero means that the data is random or otherwise can't be fit to the model. It's often called r2, R2, or r-squared.\n",
    "'''\n",
    "\n",
    "automl_settings = {\n",
    "    \"experiment_timeout_hours\" : 0.5,\n",
    "    \"enable_early_stopping\" : True,\n",
    "    \"iteration_timeout_minutes\": 5,\n",
    "    \"max_concurrent_iterations\": 4,\n",
    "    \"max_cores_per_iteration\": -1,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'accuracy',\n",
    "    \"featurization\": 'auto',\n",
    "    \"verbosity\": logging.INFO,\n",
    "}\n",
    "\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             compute_target='compute-cluster',\n",
    "                             training_data = train_data,\n",
    "                             label_column_name = label,\n",
    "                             **automl_settings\n",
    "                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "gather": {
     "logged": 1614338895043
    },
    "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 compute-cluster with default configuration\n",
      "Running on remote compute: compute-cluster\n",
      "Parent Run ID: AutoML_42bcdb2d-71ce-4b0f-b106-b526d488c85b\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",
      "|3692                             |1                                |32950                                 |\n",
      "+---------------------------------+---------------------------------+--------------------------------------+\n",
      "\n",
      "****************************************************************************************************\n",
      "\n",
      "TYPE:         Missing feature values imputation\n",
      "STATUS:       PASSED\n",
      "DESCRIPTION:  No feature missing values were detected in the training data.\n",
      "              Learn more about missing value imputation: https://aka.ms/AutomatedMLFeaturization\n",
      "\n",
      "****************************************************************************************************\n",
      "\n",
      "TYPE:         High cardinality feature detection\n",
      "STATUS:       PASSED\n",
      "DESCRIPTION:  Your inputs were analyzed, and no high cardinality features were detected.\n",
      "              Learn more about high cardinality feature handling: https://aka.ms/AutomatedMLFeaturization\n",
      "\n",
      "****************************************************************************************************\n",
      "\n",
      "****************************************************************************************************\n",
      "ITERATION: The iteration being evaluated.\n",
      "PIPELINE: A summary description of the pipeline being evaluated.\n",
      "DURATION: Time taken for the current iteration.\n",
      "METRIC: The result of computing score on the fitted pipeline.\n",
      "BEST: The best observed score thus far.\n",
      "****************************************************************************************************\n",
      "\n",
      " ITERATION   PIPELINE                                       DURATION      METRIC      BEST\n",
      "         3   MaxAbsScaler RandomForest                      0:01:06       0.8880    0.8880\n",
      "         0   MaxAbsScaler LightGBM                          0:01:03       0.9152    0.9152\n",
      "         2   MaxAbsScaler RandomForest                      0:01:08       0.8945    0.9152\n",
      "         5   MaxAbsScaler RandomForest                      0:02:04       0.7769    0.9152\n",
      "         1   MaxAbsScaler XGBoostClassifier                 0:02:09       0.9153    0.9153\n",
      "         4   MaxAbsScaler RandomForest                      0:02:13       0.8202    0.9153\n",
      "         6   SparseNormalizer XGBoostClassifier             0:01:16       0.9139    0.9153\n",
      "         9   StandardScalerWrapper XGBoostClassifier        0:01:00       0.9077    0.9153\n",
      "         7   SparseNormalizer XGBoostClassifier             0:01:10       0.9127    0.9153\n",
      "         8   SparseNormalizer XGBoostClassifier             0:01:17       0.9144    0.9153\n",
      "        10   StandardScalerWrapper RandomForest             0:01:04       0.9004    0.9153\n",
      "        11   MaxAbsScaler LogisticRegression                0:01:13       0.9083    0.9153\n",
      "        12   MaxAbsScaler LightGBM                          0:01:09       0.9105    0.9153\n",
      "        13   MaxAbsScaler LightGBM                          0:01:02       0.9008    0.9153\n",
      "        14   StandardScalerWrapper LogisticRegression       0:01:18       0.9090    0.9153\n",
      "        15   SparseNormalizer LightGBM                      0:01:05       0.9108    0.9153\n",
      "        17   SparseNormalizer LightGBM                      0:00:59       0.9101    0.9153\n",
      "        16   MaxAbsScaler LightGBM                          0:01:14       0.9015    0.9153\n",
      "        18   MaxAbsScaler LightGBM                          0:01:05       0.8923    0.9153\n",
      "        19   MaxAbsScaler LightGBM                          0:01:10       0.9140    0.9153\n",
      "        20   MaxAbsScaler ExtremeRandomTrees                0:01:17       0.8913    0.9153\n",
      "        21   SparseNormalizer XGBoostClassifier             0:01:10       0.9130    0.9153\n",
      "        22   StandardScalerWrapper LightGBM                 0:01:02       0.8940    0.9153\n",
      "        23   MaxAbsScaler LightGBM                          0:01:03       0.9080    0.9153\n",
      "        24   StandardScalerWrapper LightGBM                 0:01:02       0.9121    0.9153\n",
      "        25   TruncatedSVDWrapper XGBoostClassifier          0:01:02       0.8880    0.9153\n",
      "        26   SparseNormalizer XGBoostClassifier             0:01:53       0.9114    0.9153\n",
      "        27   StandardScalerWrapper ExtremeRandomTrees       0:01:09       0.8880    0.9153\n",
      "        28   MaxAbsScaler ExtremeRandomTrees                0:01:14       0.8880    0.9153\n",
      "        29   SparseNormalizer LightGBM                      0:01:16       0.9135    0.9153\n",
      "        30   SparseNormalizer XGBoostClassifier             0:01:00       0.8880    0.9153\n",
      "        31   StandardScalerWrapper LightGBM                 0:01:07       0.9086    0.9153\n",
      "        32   MaxAbsScaler LightGBM                          0:01:07       0.9079    0.9153\n",
      "        33   SparseNormalizer LightGBM                      0:01:11       0.9143    0.9153\n",
      "        34   MaxAbsScaler GradientBoosting                  0:00:28          nan    0.9153\n",
      "        35                                                  0:00:10          nan    0.9153\n",
      "        36    VotingEnsemble                                0:01:35       0.9168    0.9168\n",
      "        37    StackEnsemble                                 0:01:36       0.9149    0.9168\n"
     ]
    }
   ],
   "source": [
    "# Submit your automl run\n",
    "\n",
    "automl_run = exp.submit(automl_config ,show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1614338898403
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'runId': 'AutoML_42bcdb2d-71ce-4b0f-b106-b526d488c85b',\n",
       " 'target': 'compute-cluster',\n",
       " 'status': 'Completed',\n",
       " 'startTimeUtc': '2021-02-26T11:05:03.374926Z',\n",
       " 'endTimeUtc': '2021-02-26T11:28:07.970352Z',\n",
       " 'properties': {'num_iterations': '1000',\n",
       "  'training_type': 'TrainFull',\n",
       "  'acquisition_function': 'EI',\n",
       "  'primary_metric': 'accuracy',\n",
       "  'train_split': '0',\n",
       "  'acquisition_parameter': '0',\n",
       "  'num_cross_validation': '5',\n",
       "  'target': 'compute-cluster',\n",
       "  'AMLSettingsJsonString': '{\"path\":null,\"name\":\"udacity-project\",\"subscription_id\":\"6971f5ac-8af1-446e-8034-05acea24681f\",\"resource_group\":\"aml-quickstarts-139501\",\"workspace_name\":\"quick-starts-ws-139501\",\"region\":\"southcentralus\",\"compute_target\":\"compute-cluster\",\"spark_service\":null,\"azure_service\":\"remote\",\"many_models\":false,\"pipeline_fetch_max_batch_size\":1,\"iterations\":1000,\"primary_metric\":\"accuracy\",\"task_type\":\"classification\",\"data_script\":null,\"validation_size\":0.0,\"n_cross_validations\":5,\"y_min\":null,\"y_max\":null,\"num_classes\":null,\"featurization\":\"auto\",\"_ignore_package_version_incompatibilities\":false,\"is_timeseries\":false,\"max_cores_per_iteration\":-1,\"max_concurrent_iterations\":4,\"iteration_timeout_minutes\":5,\"mem_in_mb\":null,\"enforce_time_on_windows\":false,\"experiment_timeout_minutes\":30,\"experiment_exit_score\":null,\"whitelist_models\":null,\"blacklist_algos\":[\"TensorFlowLinearClassifier\",\"TensorFlowDNN\"],\"supported_models\":[\"BernoulliNaiveBayes\",\"SVM\",\"AveragedPerceptronClassifier\",\"LogisticRegression\",\"GradientBoosting\",\"TensorFlowLinearClassifier\",\"XGBoostClassifier\",\"SGD\",\"TensorFlowDNN\",\"LinearSVM\",\"LightGBM\",\"ExtremeRandomTrees\",\"RandomForest\",\"MultinomialNaiveBayes\",\"KNN\",\"DecisionTree\"],\"auto_blacklist\":true,\"blacklist_samples_reached\":false,\"exclude_nan_labels\":true,\"verbosity\":20,\"_debug_log\":\"azureml_automl.log\",\"show_warnings\":false,\"model_explainability\":true,\"service_url\":null,\"sdk_url\":null,\"sdk_packages\":null,\"enable_onnx_compatible_models\":false,\"enable_split_onnx_featurizer_estimator_models\":false,\"vm_type\":\"STANDARD_DS3_V2\",\"telemetry_verbosity\":20,\"send_telemetry\":true,\"enable_dnn\":false,\"scenario\":\"SDK-1.13.0\",\"environment_label\":null,\"force_text_dnn\":false,\"enable_feature_sweeping\":true,\"enable_early_stopping\":true,\"early_stopping_n_iters\":10,\"metrics\":null,\"enable_ensembling\":true,\"enable_stack_ensembling\":true,\"ensemble_iterations\":15,\"enable_tf\":false,\"enable_subsampling\":null,\"subsample_seed\":null,\"enable_nimbusml\":false,\"enable_streaming\":false,\"force_streaming\":false,\"track_child_runs\":true,\"allowed_private_models\":[],\"label_column_name\":\"y\",\"weight_column_name\":null,\"cv_split_column_names\":null,\"enable_local_managed\":false,\"_local_managed_run_id\":null,\"cost_mode\":1,\"lag_length\":0,\"metric_operation\":\"maximize\",\"preprocess\":true}',\n",
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{\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_student\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_technician\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_unemployed\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"column\\\\\\\\\\\\\": {\\\\\\\\\\\\\"type\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\"details\\\\\\\\\\\\\": {\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\": \\\\\\\\\\\\\"job_unknown\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\": 2}, {\\\\\\\\\\\\\"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\\\\\\\\\\\\\": \\\\\\\\\\\\\"6f6c664e-423e-44d5-a498-343eeb8bd6c2\\\\\\\\\\\\\", \\\\\\\\\\\\\"datasetType\\\\\\\\\\\\\": \\\\\\\\\\\\\"tabular\\\\\\\\\\\\\", \\\\\\\\\\\\\"subscriptionId\\\\\\\\\\\\\": \\\\\\\\\\\\\"6971f5ac-8af1-446e-8034-05acea24681f\\\\\\\\\\\\\", \\\\\\\\\\\\\"workspaceId\\\\\\\\\\\\\": \\\\\\\\\\\\\"3f930cde-1455-4fbd-9c2d-89e491f4f07c\\\\\\\\\\\\\", \\\\\\\\\\\\\"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/6971f5ac-8af1-446e-8034-05acea24681f/resourceGroups/aml-quickstarts-139501/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-139501/experimentids/33c9751a-4dcb-4878-bd29-1c2aef6d9624/cancel/AutoML_42bcdb2d-71ce-4b0f-b106-b526d488c85b',\n",
       "  'ClientSdkVersion': '1.22.0',\n",
       "  'snapshotId': '00000000-0000-0000-0000-000000000000',\n",
       "  'SetupRunId': 'AutoML_42bcdb2d-71ce-4b0f-b106-b526d488c85b_setup',\n",
       "  'SetupRunContainerId': 'dcid.AutoML_42bcdb2d-71ce-4b0f-b106-b526d488c85b_setup',\n",
       "  'FeaturizationRunJsonPath': 'featurizer_container.json',\n",
       "  'FeaturizationRunId': 'AutoML_42bcdb2d-71ce-4b0f-b106-b526d488c85b_featurize',\n",
       "  'ProblemInfoJsonString': '{\"dataset_num_categorical\": 0, \"is_sparse\": true, \"subsampling\": false, \"dataset_classes\": 2, \"dataset_features\": 122, \"dataset_samples\": 32950, \"single_frequency_class_detected\": false}',\n",
       "  'ModelExplainRunId': 'AutoML_42bcdb2d-71ce-4b0f-b106-b526d488c85b_ModelExplain'},\n",
       " 'inputDatasets': [{'dataset': {'id': '6f6c664e-423e-44d5-a498-343eeb8bd6c2'}, 'consumptionDetails': {'type': 'RunInput', 'inputName': 'training_data', 'mechanism': 'Direct'}}],\n",
       " 'outputDatasets': [],\n",
       " 'logFiles': {},\n",
       " 'submittedBy': 'ODL_User 139501'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Wait for the remote run to complete\n",
    "automl_run.wait_for_completion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "gather": {
     "logged": 1614339124925
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:The version of the SDK does not match the version the model was trained on.\n",
      "WARNING:root:The consistency in the result may not be guaranteed.\n",
      "WARNING:root:Package:azureml-automl-core, training version:1.22.0, current version:1.20.0\n",
      "Package:azureml-automl-runtime, training version:1.22.0, current version:1.20.0\n",
      "Package:azureml-core, training version:1.22.0, current version:1.20.0\n",
      "Package:azureml-dataprep, training version:2.9.1, current version:2.7.3\n",
      "Package:azureml-dataprep-native, training version:29.0.0, current version:27.0.0\n",
      "Package:azureml-dataprep-rslex, training version:1.7.0, current version:1.5.0\n",
      "Package:azureml-dataset-runtime, training version:1.22.0, current version:1.20.0\n",
      "Package:azureml-defaults, training version:1.22.0, current version:1.20.0\n",
      "Package:azureml-interpret, training version:1.22.0, current version:1.20.0\n",
      "Package:azureml-pipeline-core, training version:1.22.0, current version:1.20.0\n",
      "Package:azureml-telemetry, training version:1.22.0, current version:1.20.0\n",
      "Package:azureml-train-automl-client, training version:1.22.0, current version:1.20.0\n",
      "Package:azureml-train-automl-runtime, training version:1.22.0, current version:1.20.0\n",
      "WARNING:root:Please ensure the version of your local conda dependencies match the version on which your model was trained in order to properly retrieve your model.\n"
     ]
    },
    {
     "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",
       "                                                                                                reg_lambda=0.42105263157894735,\n",
       "                                                                                                silent=True,\n",
       "                                                                                                subsample=0.05,\n",
       "                                                                                                subsample_for_bin=200000,\n",
       "                                                                                                subsample_freq=0,\n",
       "                                                                                                verbose=-10))],\n",
       "                                                                     verbose=False))],\n",
       "                                               flatten_transform=None,\n",
       "                                               weights=[0.07142857142857142,\n",
       "                                                        0.14285714285714285,\n",
       "                                                        0.21428571428571427,\n",
       "                                                        0.07142857142857142,\n",
       "                                                        0.07142857142857142,\n",
       "                                                        0.21428571428571427,\n",
       "                                                        0.07142857142857142,\n",
       "                                                        0.07142857142857142,\n",
       "                                                        0.07142857142857142]))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve and save your best automl model.\n",
    "\n",
    "best_run_auto, best_model_auto = automl_run.get_output()\n",
    "best_model_auto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "gather": {
     "logged": 1614339889038
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current provisioning state of AmlCompute is \"Deleting\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#save model as pickle\n",
    "\n",
    "model_auto = best_run_auto.register_model(model_name='model_auto', model_path='./outputs',tags=best_run_auto.get_metrics())"
   ]
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "microsoft": {
   "host": {
    "AzureML": {
     "notebookHasBeenCompleted": true
    }
   }
  },
  "nteract": {
   "version": "nteract-front-end@1.0.0"
  }
 },
 "nbformat": 4,
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}