udacity-project.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from azureml.core import Workspace, Experiment\n",
        "\n",
        "ws = Workspace.get(name=\"quick-starts-ws-134468\")\n",
        "exp = Experiment(workspace=ws, name=\"udacity-project-1\")\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()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Workspace name: quick-starts-ws-134468\n",
            "Azure region: southcentralus\n",
            "Subscription id: 1b944a9b-fdae-4f97-aeb1-b7eea0beac53\n",
            "Resource group: aml-quickstarts-134468\n"
          ]
        }
      ],
      "execution_count": 2,
      "metadata": {
        "gather": {
          "logged": 1610469049444
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "# TODO: Create compute cluster\n",
        "# Use vm_size = \"Standard_D2_V2\" in your provisioning configuration.\n",
        "# max_nodes should be no greater than 4.\n",
        "\n",
        "### YOUR CODE HERE ###\n",
        "cpu_cluster_name = \"compute-project1\"\n",
        "vm_size = \"Standard_D2_V2\"\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace = ws, name= cpu_cluster_name)\n",
        "    print(\"Found already existing\")\n",
        "except ComputeTargetException:\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size=vm_size, max_nodes=4)\n",
        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
        "\n",
        "compute_target.wait_for_completion(show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found already existing\n",
            "\n",
            "Running\n"
          ]
        }
      ],
      "execution_count": 3,
      "metadata": {
        "gather": {
          "logged": 1610469113095
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.widgets import RunDetails\n",
        "from azureml.train.sklearn import SKLearn\n",
        "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n",
        "from azureml.train.hyperdrive.policy import BanditPolicy\n",
        "from azureml.train.hyperdrive.sampling import RandomParameterSampling\n",
        "from azureml.train.hyperdrive.runconfig import HyperDriveConfig\n",
        "from azureml.train.hyperdrive.parameter_expressions import uniform, choice\n",
        "import os\n",
        "\n",
        "# Specify parameter sampler\n",
        "ps = RandomParameterSampling({\n",
        "    \"--C\": uniform(0.001, 100),\n",
        "    \"--max_iter\": choice(10, 20, 30, 40,50,60,70,80,90,100)\n",
        "}\n",
        ")\n",
        "# Specify a Policy\n",
        "policy = BanditPolicy(evaluation_interval=5, slack_factor=0.5)\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 = SKLearn(source_directory=\".\", compute_target=compute_target, entry_script=\"train.py\")\n",
        "\n",
        "# Create a HyperDriveConfig using the estimator, hyperparameter sampler, and policy.\n",
        "hyperdrive_config = HyperDriveConfig(hyperparameter_sampling=ps,\n",
        "                                    primary_metric_name=\"Accuracy\", \n",
        "                                    estimator=est, \n",
        "                                    policy=policy,\n",
        "                                    primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
        "                                    max_total_runs=6)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "'SKLearn' estimator is deprecated. Please use 'ScriptRunConfig' from 'azureml.core.script_run_config' with your own defined environment or the AzureML-Tutorial curated environment.\n"
          ]
        }
      ],
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1610469236180
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Submit your hyperdrive run to the experiment and show run details with the widget.\n",
        "\n",
        "hypdrive_run = exp.submit(hyperdrive_config, show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ],
      "execution_count": 5,
      "metadata": {
        "gather": {
          "logged": 1610469245961
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "RunDetails(hypdrive_run).show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO'…",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f662ecf8da024b30a6a70e37ad2f9217"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/udacity-project-1/runs/HD_3854fa53-9812-4498-afcd-043ee533c4f7?wsid=/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourcegroups/aml-quickstarts-134468/workspaces/quick-starts-ws-134468\", \"run_id\": \"HD_3854fa53-9812-4498-afcd-043ee533c4f7\", \"run_properties\": {\"run_id\": \"HD_3854fa53-9812-4498-afcd-043ee533c4f7\", \"created_utc\": \"2021-01-12T16:34:04.83766Z\", \"properties\": {\"primary_metric_config\": \"{\\\"name\\\": \\\"Accuracy\\\", \\\"goal\\\": \\\"maximize\\\"}\", \"resume_from\": \"null\", \"runTemplate\": \"HyperDrive\", \"azureml.runsource\": \"hyperdrive\", \"platform\": \"AML\", \"ContentSnapshotId\": \"c8c6e0ee-ed87-45f5-8f36-b5b3ca7492ed\", \"score\": \"0.9\", \"best_child_run_id\": \"HD_3854fa53-9812-4498-afcd-043ee533c4f7_0\", \"best_metric_status\": \"Succeeded\"}, \"tags\": {\"_aml_system_max_concurrent_jobs\": \"6\", \"max_concurrent_jobs\": \"6\", \"_aml_system_max_total_jobs\": \"6\", \"max_total_jobs\": \"6\", \"_aml_system_max_duration_minutes\": \"10080\", \"max_duration_minutes\": \"10080\", \"_aml_system_policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 5, \\\"delay_evaluation\\\": 0, \\\"slack_factor\\\": 0.5}}\", \"policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 5, \\\"delay_evaluation\\\": 0, \\\"slack_factor\\\": 0.5}}\", \"_aml_system_generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"--C\\\": [\\\"uniform\\\", [0.001, 100]], \\\"--max_iter\\\": [\\\"choice\\\", [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100]]]}}\", \"generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"--C\\\": [\\\"uniform\\\", [0.001, 100]], \\\"--max_iter\\\": [\\\"choice\\\", [[10, 20, 30, 40, 50, 60, 70, 80, 90, 100]]]}}\", 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40, \"best_metric\": 0.9}], \"children_metrics\": {\"categories\": [0], \"series\": {\"Regularization Strength:\": [{\"categories\": [4, 5, 6, 7, 8, 9], \"mode\": \"markers\", \"name\": \"Regularization Strength:\", \"stepped\": false, \"type\": \"scatter\", \"data\": [49.22067796219082, 72.00660149628659, 99.72822224911005, 39.93810791704049, 69.76476763987131, 21.959326006656646]}, {\"categories\": [4, 5, 6, 7, 8, 9], \"mode\": \"lines\", \"name\": \"Regularization Strength:_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [49.22067796219082, 72.00660149628659, 99.72822224911005, 99.72822224911005, 99.72822224911005, 99.72822224911005]}], \"Max iterations:\": [{\"categories\": [4, 5, 6, 7, 8, 9], \"mode\": \"markers\", \"name\": \"Max iterations:\", \"stepped\": false, \"type\": \"scatter\", \"data\": [80, 60, 90, 50, 20, 40]}, {\"categories\": [4, 5, 6, 7, 8, 9], \"mode\": \"lines\", \"name\": \"Max iterations:_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [80, 80, 90, 90, 90, 90]}], \"Accuracy\": [{\"categories\": [4, 5, 6, 7, 8, 9], \"mode\": \"markers\", \"name\": \"Accuracy\", \"stepped\": false, \"type\": \"scatter\", \"data\": [0.9, 0.9, 0.9, 0.9, 0.9, 0.9]}, {\"categories\": [4, 5, 6, 7, 8, 9], \"mode\": \"lines\", \"name\": \"Accuracy_max\", \"stepped\": true, \"type\": \"scatter\", \"data\": [0.9, 0.9, 0.9, 0.9, 0.9, 0.9]}]}, \"metricName\": null, \"primaryMetricName\": \"Accuracy\", \"showLegend\": false}, \"run_metrics\": [{\"name\": \"best_child_by_primary_metric\", \"run_id\": \"HD_3854fa53-9812-4498-afcd-043ee533c4f7\", \"categories\": [0], \"series\": [{\"data\": [{\"metric_name\": \"Accuracy\", \"timestamp\": \"2021-01-12 16:42:10.367405+00:00\", \"run_id\": \"HD_3854fa53-9812-4498-afcd-043ee533c4f7_0\", \"metric_value\": 0.9, \"final\": true}]}]}], \"run_logs\": \"[2021-01-12T16:34:05.195818][API][INFO]Experiment created\\r\\n[2021-01-12T16:34:05.977188][GENERATOR][INFO]Trying to sample '6' jobs from the hyperparameter space\\r\\n[2021-01-12T16:34:06.244654][GENERATOR][INFO]Successfully sampled '6' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-12T16:34:07.2878599Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.\\r\\n[2021-01-12T16:34:35.621239][GENERATOR][INFO]Max number of jobs '6' reached for experiment.\\r\\n[2021-01-12T16:34:35.761662][GENERATOR][INFO]All jobs generated.\\r\\n[2021-01-12T16:39:41.3414508Z][SCHEDULER][INFO]Scheduling job, id='HD_3854fa53-9812-4498-afcd-043ee533c4f7_2'\\r\\n[2021-01-12T16:39:41.4076927Z][SCHEDULER][INFO]Scheduling job, id='HD_3854fa53-9812-4498-afcd-043ee533c4f7_0'\\r\\n[2021-01-12T16:39:41.2237289Z][SCHEDULER][INFO]Scheduling job, id='HD_3854fa53-9812-4498-afcd-043ee533c4f7_4'\\r\\n[2021-01-12T16:39:41.1974038Z][SCHEDULER][INFO]Scheduling job, id='HD_3854fa53-9812-4498-afcd-043ee533c4f7_5'\\r\\n[2021-01-12T16:39:41.4206711Z][SCHEDULER][INFO]The execution environment was successfully prepared.\\r\\n[2021-01-12T16:39:41.3059614Z][SCHEDULER][INFO]Scheduling job, id='HD_3854fa53-9812-4498-afcd-043ee533c4f7_3'\\r\\n[2021-01-12T16:39:42.1171073Z][SCHEDULER][INFO]Successfully scheduled a job. 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applied.\\r\\n[2021-01-12T16:40:40.872399][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-134468/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134468/experiments/**SCRUBBED**/runs/HD_3854fa53-9812-4498-afcd-043ee533c4f7_0, https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-134468/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134468/experiments/**SCRUBBED**/runs/HD_3854fa53-9812-4498-afcd-043ee533c4f7_1, https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-134468/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134468/experiments/**SCRUBBED**/runs/HD_3854fa53-9812-4498-afcd-043ee533c4f7_2, https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-134468/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134468/experiments/**SCRUBBED**/runs/HD_3854fa53-9812-4498-afcd-043ee533c4f7_3, https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-134468/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134468/experiments/**SCRUBBED**/runs/HD_3854fa53-9812-4498-afcd-043ee533c4f7_4, https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-134468/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134468/experiments/**SCRUBBED**/runs/HD_3854fa53-9812-4498-afcd-043ee533c4f7_5] do not contain any metrics with the primary metric name at this moment, policy cannot be 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applied.\\r\\n[2021-01-12T16:41:41.073101][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-134468/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134468/experiments/**SCRUBBED**/runs/HD_3854fa53-9812-4498-afcd-043ee533c4f7_1, https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-134468/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134468/experiments/**SCRUBBED**/runs/HD_3854fa53-9812-4498-afcd-043ee533c4f7_4, https://southcentralus.experiments.azureml.net/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourceGroups/aml-quickstarts-134468/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134468/experiments/**SCRUBBED**/runs/HD_3854fa53-9812-4498-afcd-043ee533c4f7_5] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-12T16:42:12.570374][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": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610469252407
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "# Get your best run and save the model from that run.\n",
        "\n",
        "hd_best_run = hypdrive_run.get_best_run_by_primary_metric()\n",
        "hd_best_model = hd_best_run.register_model(model_name=\"hd_best_model\" , model_path=\"./\")"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "gather": {
          "logged": 1610469876172
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hd_best_run"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "Run(Experiment: udacity-project-1,\nId: HD_3854fa53-9812-4498-afcd-043ee533c4f7_0,\nType: azureml.scriptrun,\nStatus: Completed)",
            "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-1</td><td>HD_3854fa53-9812-4498-afcd-043ee533c4f7_0</td><td>azureml.scriptrun</td><td>Completed</td><td><a href=\"https://ml.azure.com/experiments/udacity-project-1/runs/HD_3854fa53-9812-4498-afcd-043ee533c4f7_0?wsid=/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourcegroups/aml-quickstarts-134468/workspaces/quick-starts-ws-134468\" 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>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610469878650
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_run_metrics = hd_best_run.get_metrics()\n",
        "parameter_values = hd_best_run.get_details()\n",
        "\n",
        "print(\"Best Run Id: \", hd_best_run.id)\n",
        "print(\"Accuracy: \", best_run_metrics[\"Accuracy\"])\n",
        "print(\"Parameters: \", parameter_values[\"runDefinition\"][\"arguments\"])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Run Id:  HD_3854fa53-9812-4498-afcd-043ee533c4f7_0\n",
            "Accuracy:  0.9\n",
            "Parameters:  ['--C', '72.00660149628659', '--max_iter', '60']\n"
          ]
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610469883468
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.data.dataset_factory import TabularDatasetFactory\n",
        "\n",
        "# Create TabularDataset using TabularDatasetFactory\n",
        "# Data is available at: \n",
        "# \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
        "\n",
        "ds = TabularDatasetFactory.from_delimited_files(\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\")"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "gather": {
          "logged": 1610469898475
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from train import clean_data\n",
        "\n",
        "# Use the clean_data function to clean your data.\n",
        "x, y = clean_data(ds)"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610469947145
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(x))\n",
        "print(type(y))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610470093112
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "y_df = pd.DataFrame(y, columns=['y'])\n",
        "y_df"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "       y\n0      0\n1      0\n2      0\n3      0\n4      0\n...   ..\n32945  0\n32946  0\n32947  0\n32948  0\n32949  0\n\n[32950 rows x 1 columns]",
            "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>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>32945</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32946</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32947</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32948</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32949</th>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>32950 rows × 1 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610470095633
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train=pd.concat([x,y_df], axis=1)\n",
        "x_train"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "       age  marital  default  housing  loan  month  day_of_week  duration  \\\n0       57        1        0        0     1      5            1       371   \n1       55        1        0        1     0      5            4       285   \n2       33        1        0        0     0      5            5        52   \n3       36        1        0        0     0      6            5       355   \n4       27        1        0        1     0      7            5       189   \n...    ...      ...      ...      ...   ...    ...          ...       ...   \n32945   56        1        0        0     1      7            1       116   \n32946   37        1        0        0     1      7            5        69   \n32947   26        0        0        0     0      5            2       135   \n32948   31        0        0        0     0      4            1       386   \n32949   39        1        0        0     0      8            4       179   \n\n       campaign  pdays  ...  contact_telephone  education_basic.4y  \\\n0             1    999  ...                  0                   0   \n1             2    999  ...                  1                   0   \n2             1    999  ...                  0                   0   \n3             4    999  ...                  1                   0   \n4             2    999  ...                  0                   0   \n...         ...    ...  ...                ...                 ...   \n32945         1    999  ...                  0                   1   \n32946         7    999  ...                  0                   0   \n32947         4    999  ...                  0                   0   \n32948         1    999  ...                  0                   0   \n32949         1    999  ...                  0                   1   \n\n       education_basic.6y  education_basic.9y  education_high.school  \\\n0                       0                   0                      1   \n1                       0                   0                      0   \n2                       0                   1                      0   \n3                       0                   0                      1   \n4                       0                   0                      1   \n...                   ...                 ...                    ...   \n32945                   0                   0                      0   \n32946                   0                   0                      0   \n32947                   0                   0                      0   \n32948                   0                   1                      0   \n32949                   0                   0                      0   \n\n       education_illiterate  education_professional.course  \\\n0                         0                              0   \n1                         0                              0   \n2                         0                              0   \n3                         0                              0   \n4                         0                              0   \n...                     ...                            ...   \n32945                     0                              0   \n32946                     0                              0   \n32947                     0                              0   \n32948                     0                              0   \n32949                     0                              0   \n\n       education_university.degree  education_unknown  y  \n0                                0                  0  0  \n1                                0                  1  0  \n2                                0                  0  0  \n3                                0                  0  0  \n4                                0                  0  0  \n...                            ...                ... ..  \n32945                            0                  0  0  \n32946                            1                  0  0  \n32947                            1                  0  0  \n32948                            0                  0  0  \n32949                            0                  0  0  \n\n[32950 rows x 40 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>marital</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>month</th>\n      <th>day_of_week</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>...</th>\n      <th>contact_telephone</th>\n      <th>education_basic.4y</th>\n      <th>education_basic.6y</th>\n      <th>education_basic.9y</th>\n      <th>education_high.school</th>\n      <th>education_illiterate</th>\n      <th>education_professional.course</th>\n      <th>education_university.degree</th>\n      <th>education_unknown</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>57</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>371</td>\n      <td>1</td>\n      <td>999</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>55</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>4</td>\n      <td>285</td>\n      <td>2</td>\n      <td>999</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>5</td>\n      <td>52</td>\n      <td>1</td>\n      <td>999</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>36</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>5</td>\n      <td>355</td>\n      <td>4</td>\n      <td>999</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7</td>\n      <td>5</td>\n      <td>189</td>\n      <td>2</td>\n      <td>999</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>32945</th>\n      <td>56</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>7</td>\n      <td>1</td>\n      <td>116</td>\n      <td>1</td>\n      <td>999</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32946</th>\n      <td>37</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>7</td>\n      <td>5</td>\n      <td>69</td>\n      <td>7</td>\n      <td>999</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32947</th>\n      <td>26</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>2</td>\n      <td>135</td>\n      <td>4</td>\n      <td>999</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32948</th>\n      <td>31</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>386</td>\n      <td>1</td>\n      <td>999</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32949</th>\n      <td>39</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>4</td>\n      <td>179</td>\n      <td>1</td>\n      <td>999</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>32950 rows × 40 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610470099612
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.to_csv('training/train.csv')\n"
      ],
      "outputs": [],
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610470107042
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "default_ds = ws.get_default_datastore()\n",
        "default_ds"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": "{\n  \"name\": \"workspaceblobstore\",\n  \"container_name\": \"azureml-blobstore-c518fc2e-a074-445e-81cc-0784b3819938\",\n  \"account_name\": \"mlstrg134468\",\n  \"protocol\": \"https\",\n  \"endpoint\": \"core.windows.net\"\n}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 17,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610470126984
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "default_ds.upload(src_dir='training/', target_path='d/')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Uploading an estimated of 1 files\n",
            "Target already exists. Skipping upload for d/train.csv\n",
            "Uploaded 0 files\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 22,
          "data": {
            "text/plain": "$AZUREML_DATAREFERENCE_55db6bc0e34e41e28b045f65e49b5134"
          },
          "metadata": {}
        }
      ],
      "execution_count": 22,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610470325032
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core import Dataset\n",
        "\n",
        "final_data= Dataset.Tabular.from_delimited_files(path=[(default_ds,('d/train.csv'))])"
      ],
      "outputs": [],
      "execution_count": 25,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610470460030
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_data"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "{\n  \"source\": [\n    \"('workspaceblobstore', 'd/train.csv')\"\n  ],\n  \"definition\": [\n    \"GetDatastoreFiles\",\n    \"ParseDelimited\",\n    \"DropColumns\",\n    \"SetColumnTypes\"\n  ]\n}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 26,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610470470593
        }
      }
    },
    {
      "cell_type": "code",
      "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= final_data,\n",
        "    label_column_name= 'y',\n",
        "    n_cross_validations= 5,\n",
        "    compute_target = compute_target\n",
        "    )"
      ],
      "outputs": [],
      "execution_count": 27,
      "metadata": {
        "gather": {
          "logged": 1610470573520
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Submit your automl run\n",
        "automl_run = exp.submit(automl_config, show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running on remote.\n",
            "No run_configuration provided, running on compute-project1 with default configuration\n",
            "Running on remote compute: compute-project1\n",
            "Parent Run ID: AutoML_5091c0ee-191c-499c-be16-66e9377a7891\n",
            "\n",
            "Current status: FeaturesGeneration. Generating features for 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",
            "         0   MaxAbsScaler LightGBM                          0:00:56       0.9160    0.9160\n",
            "         1   MaxAbsScaler XGBoostClassifier                 0:01:14       0.9159    0.9160\n",
            "         2   MinMaxScaler RandomForest                      0:00:54       0.8970    0.9160\n",
            "         3   MinMaxScaler RandomForest                      0:00:50       0.8880    0.9160\n",
            "         4   MinMaxScaler RandomForest                      0:00:55       0.8094    0.9160\n",
            "         5   SparseNormalizer XGBoostClassifier             0:01:04       0.9079    0.9160\n",
            "         6   MaxAbsScaler GradientBoosting                  0:00:59       0.9036    0.9160\n",
            "         7   SparseNormalizer XGBoostClassifier             0:00:47       0.8999    0.9160\n",
            "         8   MinMaxScaler LightGBM                          0:00:49       0.8912    0.9160\n",
            "         9   MinMaxScaler LogisticRegression                0:00:55       0.9103    0.9160\n",
            "        10   StandardScalerWrapper LogisticRegression       0:01:10       0.9100    0.9160\n",
            "        11   MinMaxScaler LightGBM                          0:01:01       0.8880    0.9160\n",
            "        12   StandardScalerWrapper LightGBM                 0:00:48       0.8954    0.9160\n",
            "        13   StandardScalerWrapper LightGBM                 0:00:47       0.9009    0.9160\n",
            "        14   SparseNormalizer XGBoostClassifier             0:01:41       0.9093    0.9160\n",
            "        15   RobustScaler ExtremeRandomTrees                0:00:58       0.8954    0.9160\n",
            "        16   StandardScalerWrapper XGBoostClassifier        0:00:56       0.9118    0.9160\n",
            "        17   SparseNormalizer LightGBM                      0:00:52       0.9079    0.9160\n",
            "        18   MaxAbsScaler ExtremeRandomTrees                0:01:13       0.8909    0.9160\n",
            "        19   StandardScalerWrapper LightGBM                 0:00:49       0.9087    0.9160\n",
            "        20   SparseNormalizer XGBoostClassifier             0:01:24       0.9102    0.9160\n",
            "        21   MaxAbsScaler LogisticRegression                0:00:56       0.8553    0.9160\n",
            "        22   RobustScaler ExtremeRandomTrees                0:00:35          nan    0.9160\n",
            "        23    VotingEnsemble                                0:01:17       0.9174    0.9174\n",
            "        24    StackEnsemble                                 0:01:28       0.9161    0.9174\n"
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[\"Gathering dataset statistics.\", \"Generating features for the dataset.\", \"Beginning to fit featurizers and featurize the dataset.\", \"Completed fit featurizers and featurizing the dataset.\", \"Performing class balancing sweeping\", \"Generating individually featurized CV splits.\", \"Beginning model selection.\"]}]}, {\"name\": \"AUC_weighted\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.9476226593802393]}]}, {\"name\": \"balanced_accuracy\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.7817474258433911]}]}, {\"name\": \"average_precision_score_macro\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.825689484090504]}]}, {\"name\": \"norm_macro_recall\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.5634948516867821]}]}, {\"name\": \"weighted_accuracy\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.9510793668025513]}]}, {\"name\": \"log_loss\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.18495129832007065]}]}, {\"name\": \"precision_score_macro\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.7945420244571328]}]}, {\"name\": \"recall_score_weighted\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.9173899848254932]}]}, {\"name\": \"average_precision_score_micro\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.9814508601585477]}]}, {\"name\": \"recall_score_macro\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.7817474258433911]}]}, {\"name\": \"AUC_macro\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.9476226593802393]}]}, {\"name\": \"AUC_micro\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.980663551939873]}]}, {\"name\": \"precision_score_weighted\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.915729386316712]}]}, {\"name\": \"average_precision_score_weighted\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.9556508445380455]}]}, {\"name\": \"f1_score_micro\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.9173899848254932]}]}, {\"name\": \"matthews_correlation\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.5761138652806735]}]}, {\"name\": \"recall_score_micro\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.9173899848254932]}]}, {\"name\": \"f1_score_weighted\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.9164946786464832]}]}, {\"name\": \"f1_score_macro\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.7878938865304885]}]}, {\"name\": \"accuracy\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.9173899848254932]}]}, {\"name\": \"precision_score_micro\", \"run_id\": \"AutoML_5091c0ee-191c-499c-be16-66e9377a7891\", \"categories\": [0], \"series\": [{\"data\": [0.9173899848254932]}]}], \"run_logs\": \"\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.19.0\"}, \"loading\": false}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 31,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610473418855
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "automl_best_run, model= automl_run.get_output()\n",
        "automl_best_run"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 32,
          "data": {
            "text/plain": "Run(Experiment: udacity-project-1,\nId: AutoML_5091c0ee-191c-499c-be16-66e9377a7891_23,\nType: azureml.scriptrun,\nStatus: Completed)",
            "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-1</td><td>AutoML_5091c0ee-191c-499c-be16-66e9377a7891_23</td><td>azureml.scriptrun</td><td>Completed</td><td><a href=\"https://ml.azure.com/experiments/udacity-project-1/runs/AutoML_5091c0ee-191c-499c-be16-66e9377a7891_23?wsid=/subscriptions/1b944a9b-fdae-4f97-aeb1-b7eea0beac53/resourcegroups/aml-quickstarts-134468/workspaces/quick-starts-ws-134468\" 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>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 32,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610473479576
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve and save your best automl model.\n",
        "\n",
        "automl_model= automl_best_run.register_model(model_name = 'automl_best_model', model_path='./')"
      ],
      "outputs": [],
      "execution_count": 33,
      "metadata": {
        "gather": {
          "logged": 1610473607325
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "metrics = automl_best_run.get_metrics()\n",
        "parameters = automl_best_run.get_details()\n",
        "\n",
        "print(\"Best run ID: \",automl_best_run.id)\n",
        "print(\"Accuracy: \",metrics[\"accuracy\"])\n",
        "print(\"Optimized Parameters: \", parameters)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best run ID:  AutoML_5091c0ee-191c-499c-be16-66e9377a7891_23\n",
            "Accuracy:  0.9173899848254932\n",
            "Optimized Parameters:  {'runId': 'AutoML_5091c0ee-191c-499c-be16-66e9377a7891_23', 'target': 'compute-project1', 'status': 'Completed', 'startTimeUtc': '2021-01-12T17:34:20.468865Z', 'endTimeUtc': '2021-01-12T17:35:37.361079Z', 'properties': {'runTemplate': 'automl_child', 'pipeline_id': '__AutoML_Ensemble__', 'pipeline_spec': '{\"pipeline_id\":\"__AutoML_Ensemble__\",\"objects\":[{\"module\":\"azureml.train.automl.ensemble\",\"class_name\":\"Ensemble\",\"spec_class\":\"sklearn\",\"param_args\":[],\"param_kwargs\":{\"automl_settings\":\"{\\'task_type\\':\\'classification\\',\\'primary_metric\\':\\'accuracy\\',\\'verbosity\\':20,\\'ensemble_iterations\\':15,\\'is_timeseries\\':False,\\'name\\':\\'udacity-project-1\\',\\'compute_target\\':\\'compute-project1\\',\\'subscription_id\\':\\'1b944a9b-fdae-4f97-aeb1-b7eea0beac53\\',\\'region\\':\\'southcentralus\\',\\'spark_service\\':None}\",\"ensemble_run_id\":\"AutoML_5091c0ee-191c-499c-be16-66e9377a7891_23\",\"experiment_name\":\"udacity-project-1\",\"workspace_name\":\"quick-starts-ws-134468\",\"subscription_id\":\"1b944a9b-fdae-4f97-aeb1-b7eea0beac53\",\"resource_group_name\":\"aml-quickstarts-134468\"}}]}', 'training_percent': '100', 'predicted_cost': None, 'iteration': '23', '_aml_system_scenario_identification': 'Remote.Child', '_azureml.ComputeTargetType': 'amlcompute', 'ContentSnapshotId': 'd574cdf6-f748-47a1-9727-e27c95ca0bb0', 'ProcessInfoFile': 'azureml-logs/process_info.json', 'ProcessStatusFile': 'azureml-logs/process_status.json', 'run_template': 'automl_child', 'run_preprocessor': '', 'run_algorithm': 'VotingEnsemble', 'conda_env_data_location': 'aml://artifact/ExperimentRun/dcid.AutoML_5091c0ee-191c-499c-be16-66e9377a7891_23/outputs/conda_env_v_1_0_0.yml', 'model_data_location': 'aml://artifact/ExperimentRun/dcid.AutoML_5091c0ee-191c-499c-be16-66e9377a7891_23/outputs/model.pkl', 'model_size_on_disk': '598355', 'scoring_data_location': 'aml://artifact/ExperimentRun/dcid.AutoML_5091c0ee-191c-499c-be16-66e9377a7891_23/outputs/scoring_file_v_1_0_0.py', 'model_exp_support': 'True', 'pipeline_graph_version': '1.0.0', 'model_name': 'AutoML5091c0ee123', 'staticProperties': '{}', 'score': '0.9173899848254932', 'run_properties': \"classification_labels=None,\\n                              estimators=[('0',\\n                                           Pipeline(memory=None,\\n                                                    steps=[('maxabsscaler',\\n                                                            MaxAbsScaler(copy=True\", 'pipeline_script': '{\"pipeline_id\":\"__AutoML_Ensemble__\",\"objects\":[{\"module\":\"azureml.train.automl.ensemble\",\"class_name\":\"Ensemble\",\"spec_class\":\"sklearn\",\"param_args\":[],\"param_kwargs\":{\"automl_settings\":\"{\\'task_type\\':\\'classification\\',\\'primary_metric\\':\\'accuracy\\',\\'verbosity\\':20,\\'ensemble_iterations\\':15,\\'is_timeseries\\':False,\\'name\\':\\'udacity-project-1\\',\\'compute_target\\':\\'compute-project1\\',\\'subscription_id\\':\\'1b944a9b-fdae-4f97-aeb1-b7eea0beac53\\',\\'region\\':\\'southcentralus\\',\\'spark_service\\':None}\",\"ensemble_run_id\":\"AutoML_5091c0ee-191c-499c-be16-66e9377a7891_23\",\"experiment_name\":\"udacity-project-1\",\"workspace_name\":\"quick-starts-ws-134468\",\"subscription_id\":\"1b944a9b-fdae-4f97-aeb1-b7eea0beac53\",\"resource_group_name\":\"aml-quickstarts-134468\"}}]}', 'training_type': 'MeanCrossValidation', 'num_classes': '', 'framework': 'sklearn', 'fit_time': '27', 'goal': 'accuracy_max', 'class_labels': '', 'primary_metric': 'accuracy', 'errors': '{}', 'fitted_pipeline': \"Pipeline(memory=None,\\n         steps=[('datatransformer',\\n                 DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\\n                                 feature_sweeping_config=None,\\n                                 feature_sweeping_timeout=None,\\n                                 featurization_config=None, force_text_dnn=None,\\n                                 is_cross_validation=None,\\n                                 is_onnx_compatible=None, logger=None,\\n                                 observer=None, task=None, working_dir=None)),\\n                ('prefittedsoftvotingclassifier',...\\n                                                                                                fit_intercept=True,\\n                                                                                                intercept_scaling=1,\\n                                                                                                l1_ratio=None,\\n                                                                                                max_iter=100,\\n                                                                                                multi_class='ovr',\\n                                                                                                n_jobs=1,\\n                                                                                                penalty='l1',\\n                                                                                                random_state=None,\\n                                                                                                solver='saga',\\n                                                                                                tol=0.0001,\\n                                                                                                verbose=0,\\n                                                                                                warm_start=False))],\\n                                                                     verbose=False))],\\n                                               flatten_transform=None,\\n                                               weights=[0.5384615384615384,\\n                                                        0.07692307692307693,\\n                                                        0.07692307692307693,\\n                                                        0.07692307692307693,\\n                                                        0.07692307692307693,\\n                                                        0.15384615384615385]))],\\n         verbose=False)\", 'friendly_errors': '{}', 'onnx_model_resource': '{}', 'error_code': '', 'failure_reason': '', 'feature_skus': 'automatedml_sdk_guardrails', 'dependencies_versions': '{\"azureml-train-automl-runtime\": \"1.19.0\", \"azureml-train-automl-client\": \"1.19.0\", \"azureml-telemetry\": \"1.19.0\", \"azureml-pipeline-core\": \"1.19.0\", \"azureml-model-management-sdk\": \"1.0.1b6.post1\", \"azureml-interpret\": \"1.19.0\", \"azureml-defaults\": \"1.19.0\", \"azureml-dataset-runtime\": \"1.19.0\", \"azureml-dataprep\": \"2.6.1\", \"azureml-dataprep-rslex\": \"1.4.0\", \"azureml-dataprep-native\": \"26.0.0\", \"azureml-core\": \"1.19.0\", \"azureml-automl-runtime\": \"1.19.0\", \"azureml-automl-core\": \"1.19.0\"}', 'num_cores': '4', 'num_logical_cores': '4', 'peak_memory_usage': '581792', 'vm_configuration': 'Intel(R) Xeon(R) CPU E5-2673 v4 @ 2.30GHz', 'core_hours': '0.014571521388888889'}, 'inputDatasets': [], 'outputDatasets': [], 'runDefinition': {'script': 'automl_driver.py', 'command': '', 'useAbsolutePath': False, 'arguments': [], 'sourceDirectoryDataStore': None, 'framework': 'Python', 'communicator': 'None', 'target': 'compute-project1', 'dataReferences': {}, 'data': {}, 'outputData': {}, 'jobName': None, 'maxRunDurationSeconds': None, 'nodeCount': 1, 'priority': None, 'credentialPassthrough': False, 'environment': {'name': 'AutoML-AzureML-AutoML', 'version': 'Autosave_2021-01-12T16:59:02Z_7e6e4894', 'python': {'interpreterPath': 'python', 'userManagedDependencies': False, 'condaDependencies': {'channels': ['anaconda', 'conda-forge', 'pytorch'], 'dependencies': ['python=3.6.2', 'pip=20.2.4', {'pip': ['azureml-core==1.19.0', 'azureml-pipeline-core==1.19.0', 'azureml-telemetry==1.19.0', 'azureml-defaults==1.19.0', 'azureml-interpret==1.19.0', 'azureml-automl-core==1.19.0', 'azureml-automl-runtime==1.19.0', 'azureml-train-automl-client==1.19.0', 'azureml-train-automl-runtime==1.19.0', 'azureml-dataset-runtime==1.19.0', 'inference-schema', 'py-cpuinfo==5.0.0', 'boto3==1.15.18', 'botocore==1.18.18']}, 'numpy~=1.18.0', 'scikit-learn==0.22.1', 'pandas~=0.25.0', 'py-xgboost<=0.90', 'fbprophet==0.5', 'holidays==0.9.11', 'setuptools-git', 'psutil>5.0.0,<6.0.0'], 'name': 'azureml_8eff28b157f42edcd2424a5aae6c8074'}, 'baseCondaEnvironment': None}, 'environmentVariables': {'EXAMPLE_ENV_VAR': 'EXAMPLE_VALUE'}, 'docker': {'baseImage': 'mcr.microsoft.com/azureml/intelmpi2018.3-ubuntu16.04:20201113.v1', 'platform': {'os': 'Linux', 'architecture': 'amd64'}, 'baseDockerfile': None, 'baseImageRegistry': {'address': None, 'username': None, 'password': None}, 'enabled': True, 'arguments': []}, 'spark': {'repositories': [], 'packages': [], 'precachePackages': True}, 'inferencingStackVersion': None}, 'history': {'outputCollection': True, 'directoriesToWatch': ['logs'], 'enableMLflowTracking': True}, 'spark': {'configuration': {'spark.app.name': 'Azure ML Experiment', 'spark.yarn.maxAppAttempts': '1'}}, 'parallelTask': {'maxRetriesPerWorker': 0, 'workerCountPerNode': 1, 'terminalExitCodes': None, 'configuration': {}}, 'amlCompute': {'name': None, 'vmSize': None, 'retainCluster': False, 'clusterMaxNodeCount': None}, 'aiSuperComputer': {'instanceType': None, 'frameworkImage': None, 'imageVersion': None, 'location': None, 'aiSuperComputerStorageData': None}, 'tensorflow': {'workerCount': 1, 'parameterServerCount': 1}, 'mpi': {'processCountPerNode': 1}, 'hdi': {'yarnDeployMode': 'Cluster'}, 'containerInstance': {'region': None, 'cpuCores': 2.0, 'memoryGb': 3.5}, 'exposedPorts': None, 'docker': {'useDocker': True, 'sharedVolumes': True, 'shmSize': '2g', 'arguments': []}, 'cmk8sCompute': {'configuration': {}}, 'commandReturnCodeConfig': {'returnCode': 'Zero', 'successfulReturnCodes': []}}, 'logFiles': {'azureml-logs/55_azureml-execution-tvmps_c41fc6fb3cbc8cda927eb2a73949c322414763211ba99395118a104cff5205de_d.txt': 'https://mlstrg134468.blob.core.windows.net/azureml/ExperimentRun/dcid.AutoML_5091c0ee-191c-499c-be16-66e9377a7891_23/azureml-logs/55_azureml-execution-tvmps_c41fc6fb3cbc8cda927eb2a73949c322414763211ba99395118a104cff5205de_d.txt?sv=2019-02-02&sr=b&sig=OsvGFWfazLI7H8CWBd5rA0tajbh6jbJjmljXqIgEAGk%3D&st=2021-01-12T17%3A37%3A48Z&se=2021-01-13T01%3A47%3A48Z&sp=r', 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          ]
        }
      ],
      "execution_count": 36,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
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        "gather": {
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        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model._final_estimator"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 37,
          "data": {
            "text/plain": "PreFittedSoftVotingClassifier(classification_labels=None,\n                              estimators=[('0',\n                                           Pipeline(memory=None,\n                                                    steps=[('maxabsscaler',\n                                                            MaxAbsScaler(copy=True)),\n                                                           ('lightgbmclassifier',\n                                                            LightGBMClassifier(boosting_type='gbdt',\n                                                                               class_weight=None,\n                                                                               colsample_bytree=1.0,\n                                                                               importance_type='split',\n                                                                               learning_rate=0.1,\n                                                                               max_depth=-1,\n                                                                               min_child_samples=20,\n                                                                               min_child_weight=0.001,\n                                                                               min_spl...\n                                                                               dual=False,\n                                                                               fit_intercept=True,\n                                                                               intercept_scaling=1,\n                                                                               l1_ratio=None,\n                                                                               max_iter=100,\n                                                                               multi_class='ovr',\n                                                                               n_jobs=1,\n                                                                               penalty='l1',\n                                                                               random_state=None,\n                                                                               solver='saga',\n                                                                               tol=0.0001,\n                                                                               verbose=0,\n                                                                               warm_start=False))],\n                                                    verbose=False))],\n                              flatten_transform=None,\n                              weights=[0.5384615384615384, 0.07692307692307693,\n                                       0.07692307692307693, 0.07692307692307693,\n                                       0.07692307692307693,\n                                       0.15384615384615385])"
          },
          "metadata": {}
        }
      ],
      "execution_count": 37,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1610473703113
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "compute_target.delete()"
      ],
      "outputs": [],
      "execution_count": 38,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
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            "deleting": false
          }
        },
        "gather": {
          "logged": 1610473707985
        }
      }
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  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.9",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}