aml-pipelines-with-automated-machine-learning-step.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
        "Licensed under the MIT License."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/NotebookVM/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.png)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Azure Machine Learning Pipeline with AutoMLStep (Udacity Course 2)\n",
        "This notebook demonstrates the use of AutoMLStep in Azure Machine Learning Pipeline."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introduction\n",
        "In this example we showcase how you can use AzureML Dataset to load data for AutoML via AML Pipeline. \n",
        "\n",
        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Create or Attach existing AmlCompute to a workspace.\n",
        "3. Define data loading in a `TabularDataset`.\n",
        "4. Configure AutoML using `AutoMLConfig`.\n",
        "5. Use AutoMLStep\n",
        "6. Train the model using AmlCompute\n",
        "7. Explore the results.\n",
        "8. Test the best fitted model."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Azure Machine Learning and Pipeline SDK-specific imports"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import logging\n",
        "import os\n",
        "import csv\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "import pkg_resources\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.core.dataset import Dataset\n",
        "\n",
        "from azureml.pipeline.steps import AutoMLStep\n",
        "\n",
        "# Check core SDK version number\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SDK version: 1.19.0\n"
          ]
        }
      ],
      "execution_count": 1,
      "metadata": {
        "gather": {
          "logged": 1608751186701
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initialize Workspace\n",
        "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "quick-starts-ws-131509\n",
            "aml-quickstarts-131509\n",
            "southcentralus\n",
            "9e65f93e-bdd8-437b-b1e8-0647cd6098f7\n"
          ]
        }
      ],
      "execution_count": 2,
      "metadata": {
        "gather": {
          "logged": 1608751194937
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create an Azure ML experiment\n",
        "Let's create an experiment named \"automlstep-classification\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n",
        "\n",
        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step.\n",
        "\n",
        "*Udacity Note:* There is no need to create an Azure ML experiment, this needs to re-use the experiment that was already created\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Choose a name for the run history container in the workspace.\n",
        "# NOTE: update these to match your existing experiment name\n",
        "experiment_name = 'BankMarketing-Experiment'\n",
        "project_folder = './pipeline-project'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "experiment"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "Experiment(Name: BankMarketing-Experiment,\nWorkspace: quick-starts-ws-131509)",
            "text/html": "<table style=\"width:100%\"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>BankMarketing-Experiment</td><td>quick-starts-ws-131509</td><td><a href=\"https://ml.azure.com/experiments/BankMarketing-Experiment?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-131509/workspaces/quick-starts-ws-131509\" 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.experiment.Experiment?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "gather": {
          "logged": 1608751207246
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create or Attach an AmlCompute cluster\n",
        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
        "\n",
        "**Udacity Note** There is no need to create a new compute target, it can re-use the previous cluster"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# NOTE: update the cluster name to match the existing cluster\n",
        "# Choose a name for your CPU cluster\n",
        "amlcompute_cluster_name = \"auto-ml\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
        "    print('Found existing cluster, use it.')\n",
        "except ComputeTargetException:\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',# for GPU, use \"STANDARD_NC6\"\n",
        "                                                           #vm_priority = 'lowpriority', # optional\n",
        "                                                           max_nodes=4)\n",
        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
        "\n",
        "compute_target.wait_for_completion(show_output=True, min_node_count = 1, timeout_in_minutes = 10)\n",
        "# For a more detailed view of current AmlCompute status, use get_status()."
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating\n",
            "Succeeded.................................................................................................................\n",
            "AmlCompute wait for completion finished\n",
            "\n",
            "Wait timeout has been reached\n",
            "Current provisioning state of AmlCompute is \"Succeeded\" and current node count is \"0\"\n"
          ]
        }
      ],
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1608751824950
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data\n",
        "\n",
        "**Udacity note:** Make sure the `key` is the same name as the dataset that is uploaded, and that the description matches. If it is hard to find or unknown, loop over the `ws.datasets.keys()` and `print()` them.\n",
        "If it *isn't* found because it was deleted, it can be recreated with the link that has the CSV "
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Try to load the dataset from the Workspace. Otherwise, create it from the file\n",
        "# NOTE: update the key to match the dataset name\n",
        "found = False\n",
        "key = \"BankMarketing Dataset\"\n",
        "description_text = \"Bank Marketing DataSet for Udacity Course 2\"\n",
        "\n",
        "if key in ws.datasets.keys(): \n",
        "        found = True\n",
        "        dataset = ws.datasets[key] \n",
        "\n",
        "if not found:\n",
        "        # Create AML Dataset and register it into Workspace\n",
        "        example_data = 'https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv'\n",
        "        dataset = Dataset.Tabular.from_delimited_files(example_data)        \n",
        "        #Register Dataset in Workspace\n",
        "        dataset = dataset.register(workspace=ws,\n",
        "                                   name=key,\n",
        "                                   description=description_text)\n",
        "\n",
        "\n",
        "df = dataset.to_pandas_dataframe()\n",
        "df.describe()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "                age      duration      campaign         pdays      previous  \\\ncount  32950.000000  32950.000000  32950.000000  32950.000000  32950.000000   \nmean      40.040212    257.335205      2.561730    962.174780      0.174780   \nstd       10.432313    257.331700      2.763646    187.646785      0.496503   \nmin       17.000000      0.000000      1.000000      0.000000      0.000000   \n25%       32.000000    102.000000      1.000000    999.000000      0.000000   \n50%       38.000000    179.000000      2.000000    999.000000      0.000000   \n75%       47.000000    318.000000      3.000000    999.000000      0.000000   \nmax       98.000000   4918.000000     56.000000    999.000000      7.000000   \n\n       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m   nr.employed  \ncount  32950.000000    32950.000000   32950.000000  32950.000000  32950.000000  \nmean       0.076228       93.574243     -40.518680      3.615654   5166.859608  \nstd        1.572242        0.578636       4.623004      1.735748     72.208448  \nmin       -3.400000       92.201000     -50.800000      0.634000   4963.600000  \n25%       -1.800000       93.075000     -42.700000      1.344000   5099.100000  \n50%        1.100000       93.749000     -41.800000      4.857000   5191.000000  \n75%        1.400000       93.994000     -36.400000      4.961000   5228.100000  \nmax        1.400000       94.767000     -26.900000      5.045000   5228.100000  ",
            "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>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>emp.var.rate</th>\n      <th>cons.price.idx</th>\n      <th>cons.conf.idx</th>\n      <th>euribor3m</th>\n      <th>nr.employed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>40.040212</td>\n      <td>257.335205</td>\n      <td>2.561730</td>\n      <td>962.174780</td>\n      <td>0.174780</td>\n      <td>0.076228</td>\n      <td>93.574243</td>\n      <td>-40.518680</td>\n      <td>3.615654</td>\n      <td>5166.859608</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>10.432313</td>\n      <td>257.331700</td>\n      <td>2.763646</td>\n      <td>187.646785</td>\n      <td>0.496503</td>\n      <td>1.572242</td>\n      <td>0.578636</td>\n      <td>4.623004</td>\n      <td>1.735748</td>\n      <td>72.208448</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>17.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-3.400000</td>\n      <td>92.201000</td>\n      <td>-50.800000</td>\n      <td>0.634000</td>\n      <td>4963.600000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>32.000000</td>\n      <td>102.000000</td>\n      <td>1.000000</td>\n      <td>999.000000</td>\n      <td>0.000000</td>\n      <td>-1.800000</td>\n      <td>93.075000</td>\n      <td>-42.700000</td>\n      <td>1.344000</td>\n      <td>5099.100000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>38.000000</td>\n      <td>179.000000</td>\n      <td>2.000000</td>\n      <td>999.000000</td>\n      <td>0.000000</td>\n      <td>1.100000</td>\n      <td>93.749000</td>\n      <td>-41.800000</td>\n      <td>4.857000</td>\n      <td>5191.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>47.000000</td>\n      <td>318.000000</td>\n      <td>3.000000</td>\n      <td>999.000000</td>\n      <td>0.000000</td>\n      <td>1.400000</td>\n      <td>93.994000</td>\n      <td>-36.400000</td>\n      <td>4.961000</td>\n      <td>5228.100000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>98.000000</td>\n      <td>4918.000000</td>\n      <td>56.000000</td>\n      <td>999.000000</td>\n      <td>7.000000</td>\n      <td>1.400000</td>\n      <td>94.767000</td>\n      <td>-26.900000</td>\n      <td>5.045000</td>\n      <td>5228.100000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "gather": {
          "logged": 1608751913531
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Review the Dataset Result\n",
        "\n",
        "You can peek the result of a TabularDataset at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records for all the steps in the TabularDataset, which makes it fast even against large datasets.\n",
        "\n",
        "`TabularDataset` objects are composed of a list of transformation steps (optional)."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.take(5).to_pandas_dataframe()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "   age          job  marital    education  default housing loan    contact  \\\n0   57   technician  married  high.school       no      no  yes   cellular   \n1   55      unknown  married      unknown  unknown     yes   no  telephone   \n2   33  blue-collar  married     basic.9y       no      no   no   cellular   \n3   36       admin.  married  high.school       no      no   no  telephone   \n4   27    housemaid  married  high.school       no     yes   no   cellular   \n\n  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n0   may         mon  ...         1    999         1      failure         -1.8   \n1   may         thu  ...         2    999         0  nonexistent          1.1   \n2   may         fri  ...         1    999         1      failure         -1.8   \n3   jun         fri  ...         4    999         0  nonexistent          1.4   \n4   jul         fri  ...         2    999         0  nonexistent          1.4   \n\n   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n0          92.893          -46.2      1.299       5099.1  no  \n1          93.994          -36.4      4.860       5191.0  no  \n2          92.893          -46.2      1.313       5099.1  no  \n3          94.465          -41.8      4.967       5228.1  no  \n4          93.918          -42.7      4.963       5228.1  no  \n\n[5 rows x 21 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>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>month</th>\n      <th>day_of_week</th>\n      <th>...</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>poutcome</th>\n      <th>emp.var.rate</th>\n      <th>cons.price.idx</th>\n      <th>cons.conf.idx</th>\n      <th>euribor3m</th>\n      <th>nr.employed</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>57</td>\n      <td>technician</td>\n      <td>married</td>\n      <td>high.school</td>\n      <td>no</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>cellular</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>1</td>\n      <td>999</td>\n      <td>1</td>\n      <td>failure</td>\n      <td>-1.8</td>\n      <td>92.893</td>\n      <td>-46.2</td>\n      <td>1.299</td>\n      <td>5099.1</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>55</td>\n      <td>unknown</td>\n      <td>married</td>\n      <td>unknown</td>\n      <td>unknown</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>thu</td>\n      <td>...</td>\n      <td>2</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.860</td>\n      <td>5191.0</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>blue-collar</td>\n      <td>married</td>\n      <td>basic.9y</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>may</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>1</td>\n      <td>999</td>\n      <td>1</td>\n      <td>failure</td>\n      <td>-1.8</td>\n      <td>92.893</td>\n      <td>-46.2</td>\n      <td>1.313</td>\n      <td>5099.1</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>36</td>\n      <td>admin.</td>\n      <td>married</td>\n      <td>high.school</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>jun</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>4</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.4</td>\n      <td>94.465</td>\n      <td>-41.8</td>\n      <td>4.967</td>\n      <td>5228.1</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>27</td>\n      <td>housemaid</td>\n      <td>married</td>\n      <td>high.school</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>jul</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>2</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.4</td>\n      <td>93.918</td>\n      <td>-42.7</td>\n      <td>4.963</td>\n      <td>5228.1</td>\n      <td>no</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "gather": {
          "logged": 1608751924862
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train\n",
        "This creates a general AutoML settings object.\n",
        "**Udacity notes:** These inputs must match what was used when training in the portal. `label_column_name` has to be `y` for example."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "automl_settings = {\n",
        "    \"experiment_timeout_minutes\": 20,\n",
        "    \"max_concurrent_iterations\": 5,\n",
        "    \"primary_metric\" : 'AUC_weighted'\n",
        "}\n",
        "automl_config = AutoMLConfig(compute_target=compute_target,\n",
        "                             task = \"classification\",\n",
        "                             training_data=dataset,\n",
        "                             label_column_name=\"y\",   \n",
        "                             path = project_folder,\n",
        "                             enable_early_stopping= True,\n",
        "                             featurization= 'auto',\n",
        "                             debug_log = \"automl_errors.log\",\n",
        "                             **automl_settings\n",
        "                            )"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "gather": {
          "logged": 1608751932156
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Create Pipeline and AutoMLStep\n",
        "\n",
        "You can define outputs for the AutoMLStep using TrainingOutput."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.pipeline.core import PipelineData, TrainingOutput\n",
        "\n",
        "ds = ws.get_default_datastore()\n",
        "metrics_output_name = 'metrics_output'\n",
        "best_model_output_name = 'best_model_output'\n",
        "\n",
        "metrics_data = PipelineData(name='metrics_data',\n",
        "                           datastore=ds,\n",
        "                           pipeline_output_name=metrics_output_name,\n",
        "                           training_output=TrainingOutput(type='Metrics'))\n",
        "model_data = PipelineData(name='model_data',\n",
        "                           datastore=ds,\n",
        "                           pipeline_output_name=best_model_output_name,\n",
        "                           training_output=TrainingOutput(type='Model'))"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "gather": {
          "logged": 1608751939667
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create an AutoMLStep."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "automl_step = AutoMLStep(\n",
        "    name='automl_module',\n",
        "    automl_config=automl_config,\n",
        "    outputs=[metrics_data, model_data],\n",
        "    allow_reuse=True)"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "tags": [
          "automlstep-remarks-sample1"
        ],
        "gather": {
          "logged": 1608751953271
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.pipeline.core import Pipeline\n",
        "pipeline = Pipeline(\n",
        "    description=\"pipeline_with_automlstep\",\n",
        "    workspace=ws,    \n",
        "    steps=[automl_step])"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "tags": [
          "automlstep-remarks-sample2"
        ],
        "gather": {
          "logged": 1608751963081
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_run = experiment.submit(pipeline)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Created step automl_module [50d671c5][6d1a9975-03bc-4b3e-b260-00ae53369895], (This step will run and generate new outputs)\n",
            "Submitted PipelineRun 65d282c3-3ba3-4cc2-9a2d-9db4f3374469\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/experiments/BankMarketing-Experiment/runs/65d282c3-3ba3-4cc2-9a2d-9db4f3374469?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-131509/workspaces/quick-starts-ws-131509\n"
          ]
        }
      ],
      "execution_count": 11,
      "metadata": {
        "gather": {
          "logged": 1608751970852
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(pipeline_run).show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "_PipelineWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', …",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e0b2e57b987b400b803f2729f60b2dc7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/BankMarketing-Experiment/runs/65d282c3-3ba3-4cc2-9a2d-9db4f3374469?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-131509/workspaces/quick-starts-ws-131509\", \"run_id\": \"65d282c3-3ba3-4cc2-9a2d-9db4f3374469\", \"run_properties\": {\"run_id\": \"65d282c3-3ba3-4cc2-9a2d-9db4f3374469\", \"created_utc\": \"2020-12-23T19:32:50.165531Z\", \"properties\": {\"azureml.runsource\": \"azureml.PipelineRun\", \"runSource\": \"SDK\", \"runType\": \"SDK\", \"azureml.parameters\": \"{}\"}, \"tags\": {\"azureml.pipelineComponent\": \"pipelinerun\"}, \"end_time_utc\": \"2020-12-23T20:16:28.948386Z\", \"status\": \"Completed\", \"log_files\": {\"logs/azureml/executionlogs.txt\": \"https://mlstrg131509.blob.core.windows.net/azureml/ExperimentRun/dcid.65d282c3-3ba3-4cc2-9a2d-9db4f3374469/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=T4iMF4JO%2Bvq2oLbrA14zRniGBuh4BaXoFszLWdJd27k%3D&st=2020-12-23T19%3A22%3A57Z&se=2020-12-24T03%3A32%3A57Z&sp=r\", \"logs/azureml/stderrlogs.txt\": \"https://mlstrg131509.blob.core.windows.net/azureml/ExperimentRun/dcid.65d282c3-3ba3-4cc2-9a2d-9db4f3374469/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=768Xe3bJfmp4o2AeYbMuCjB6AChcXykTsn59Zt8p5I0%3D&st=2020-12-23T19%3A22%3A57Z&se=2020-12-24T03%3A32%3A57Z&sp=r\", \"logs/azureml/stdoutlogs.txt\": \"https://mlstrg131509.blob.core.windows.net/azureml/ExperimentRun/dcid.65d282c3-3ba3-4cc2-9a2d-9db4f3374469/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=sXXn1HxKD176HTspYD4mroOz1QNyCV4IfuRrOhgCmrA%3D&st=2020-12-23T19%3A22%3A57Z&se=2020-12-24T03%3A32%3A57Z&sp=r\"}, \"log_groups\": [[\"logs/azureml/executionlogs.txt\", \"logs/azureml/stderrlogs.txt\", \"logs/azureml/stdoutlogs.txt\"]], \"run_duration\": \"0:43:38\"}, \"child_runs\": [{\"run_id\": \"\", \"name\": \"automl_module\", \"status\": \"NotStarted\", \"start_time\": \"\", \"created_time\": \"\", \"end_time\": \"\", \"duration\": \"\"}], \"children_metrics\": {\"categories\": null, \"series\": null, \"metricName\": null}, \"run_metrics\": [], \"run_logs\": \"[2020-12-23 19:32:57Z] Submitting 1 runs, first five are: 50d671c5:f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a\\n[2020-12-23 20:16:28Z] Completing processing run id f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a.\\n\\nRun is completed.\", \"graph\": {\"datasource_nodes\": {\"4064b04f\": {\"node_id\": \"4064b04f\", \"name\": \"BankMarketing Dataset\"}}, \"module_nodes\": {\"50d671c5\": {\"node_id\": \"50d671c5\", \"name\": \"automl_module\", \"status\": \"NotStarted\"}}, \"edges\": [{\"source_node_id\": \"4064b04f\", \"source_node_name\": \"BankMarketing Dataset\", \"source_name\": \"data\", \"target_name\": \"training_data\", \"dst_node_id\": \"50d671c5\", \"dst_node_name\": \"automl_module\"}], \"child_runs\": [{\"run_id\": \"\", \"name\": \"automl_module\", \"status\": \"NotStarted\", \"start_time\": \"\", \"created_time\": \"\", \"end_time\": \"\", \"duration\": \"\"}]}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.19.0\"}, \"loading\": false}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "gather": {
          "logged": 1608751977015
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_run.wait_for_completion()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PipelineRunId: 65d282c3-3ba3-4cc2-9a2d-9db4f3374469\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/experiments/BankMarketing-Experiment/runs/65d282c3-3ba3-4cc2-9a2d-9db4f3374469?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-131509/workspaces/quick-starts-ws-131509\n",
            "PipelineRun Status: Running\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "PipelineRun Execution Summary\n",
            "==============================\n",
            "PipelineRun Status: Finished\n",
            "{'runId': '65d282c3-3ba3-4cc2-9a2d-9db4f3374469', 'status': 'Completed', 'startTimeUtc': '2020-12-23T19:32:51.430666Z', 'endTimeUtc': '2020-12-23T20:16:28.948386Z', 'properties': {'azureml.runsource': 'azureml.PipelineRun', 'runSource': 'SDK', 'runType': 'SDK', 'azureml.parameters': '{}'}, 'inputDatasets': [], 'logFiles': {'logs/azureml/executionlogs.txt': 'https://mlstrg131509.blob.core.windows.net/azureml/ExperimentRun/dcid.65d282c3-3ba3-4cc2-9a2d-9db4f3374469/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=T4iMF4JO%2Bvq2oLbrA14zRniGBuh4BaXoFszLWdJd27k%3D&st=2020-12-23T19%3A22%3A57Z&se=2020-12-24T03%3A32%3A57Z&sp=r', 'logs/azureml/stderrlogs.txt': 'https://mlstrg131509.blob.core.windows.net/azureml/ExperimentRun/dcid.65d282c3-3ba3-4cc2-9a2d-9db4f3374469/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=768Xe3bJfmp4o2AeYbMuCjB6AChcXykTsn59Zt8p5I0%3D&st=2020-12-23T19%3A22%3A57Z&se=2020-12-24T03%3A32%3A57Z&sp=r', 'logs/azureml/stdoutlogs.txt': 'https://mlstrg131509.blob.core.windows.net/azureml/ExperimentRun/dcid.65d282c3-3ba3-4cc2-9a2d-9db4f3374469/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=sXXn1HxKD176HTspYD4mroOz1QNyCV4IfuRrOhgCmrA%3D&st=2020-12-23T19%3A22%3A57Z&se=2020-12-24T03%3A32%3A57Z&sp=r'}}\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:azureml.pipeline.core.run:Expected a StepRun object but received <class 'azureml.core.run.Run'> instead.\n",
            "This usually indicates a package conflict with one of the dependencies of azureml-core or azureml-pipeline-core.\n",
            "Please check for package conflicts in your python environment\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "'Finished'"
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "gather": {
          "logged": 1608754589895
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Examine Results\n",
        "\n",
        "### Retrieve the metrics of all child runs\n",
        "Outputs of above run can be used as inputs of other steps in pipeline. In this tutorial, we will examine the outputs by retrieve output data and running some tests."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_output = pipeline_run.get_pipeline_output(metrics_output_name)\n",
        "num_file_downloaded = metrics_output.download('.', show_progress=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading azureml/f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a/metrics_data\n",
            "Downloaded azureml/f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a/metrics_data, 1 files out of an estimated total of 1\n"
          ]
        }
      ],
      "execution_count": 14,
      "metadata": {
        "gather": {
          "logged": 1608754625982
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "with open(metrics_output._path_on_datastore) as f:\n",
        "    metrics_output_result = f.read()\n",
        "    \n",
        "deserialized_metrics_output = json.loads(metrics_output_result)\n",
        "df = pd.DataFrame(deserialized_metrics_output)\n",
        "df"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_18  \\\nlog_loss                                            [0.5523734950300616]   \nAUC_weighted                                        [0.8689188788675312]   \naverage_precision_score_micro                       [0.8472904029495727]   \nrecall_score_micro                                  [0.7256449165402125]   \nAUC_macro                                            [0.868918878867531]   \nprecision_score_micro                               [0.7256449165402125]   \nAUC_micro                                           [0.8332426240153266]   \naccuracy                                            [0.7256449165402125]   \nrecall_score_macro                                   [0.755528881331192]   \nf1_score_weighted                                   [0.7746511826390383]   \naverage_precision_score_macro                       [0.7386502996990343]   \nrecall_score_weighted                               [0.7256449165402125]   \nnorm_macro_recall                                    [0.511057762662384]   \nbalanced_accuracy                                    [0.755528881331192]   \nf1_score_micro                                      [0.7256449165402123]   \nprecision_score_weighted                            [0.8862391665995327]   \naverage_precision_score_weighted                    [0.9269179009128578]   \nweighted_accuracy                                   [0.7182255364301822]   \nmatthews_correlation                                [0.3401635358365466]   \nprecision_score_macro                               [0.6132075858803293]   \nf1_score_macro                                      [0.6080168443216213]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_38  \\\nlog_loss                                           [0.21159982967441127]   \nAUC_weighted                                        [0.9374267153471261]   \naverage_precision_score_micro                       [0.9786652953490829]   \nrecall_score_micro                                  [0.9098634294385433]   \nAUC_macro                                           [0.9374267153471261]   \nprecision_score_micro                               [0.9098634294385433]   \nAUC_micro                                            [0.977637796726083]   \naccuracy                                            [0.9098634294385433]   \nrecall_score_macro                                   [0.730183737244071]   \nf1_score_weighted                                    [0.905471355536803]   \naverage_precision_score_macro                       [0.8055599434789882]   \nrecall_score_weighted                               [0.9098634294385433]   \nnorm_macro_recall                                   [0.4603674744881421]   \nbalanced_accuracy                                    [0.730183737244071]   \nf1_score_micro                                      [0.9098634294385431]   \nprecision_score_weighted                            [0.9028472067074211]   \naverage_precision_score_weighted                    [0.9501587291452896]   \nweighted_accuracy                                    [0.954473036756995]   \nmatthews_correlation                                [0.5077160448273371]   \nprecision_score_macro                               [0.7799671962726313]   \nf1_score_macro                                      [0.7516284381840679]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_0  \\\nlog_loss                                          [0.17775706110025447]   \nAUC_weighted                                       [0.9450464668693167]   \naverage_precision_score_micro                      [0.9806603102489483]   \nrecall_score_micro                                 [0.9116843702579667]   \nAUC_macro                                          [0.9450464668693166]   \nprecision_score_micro                              [0.9116843702579667]   \nAUC_micro                                           [0.979695082216353]   \naccuracy                                           [0.9116843702579667]   \nrecall_score_macro                                 [0.7513392683482543]   \nf1_score_weighted                                  [0.9091539479147899]   \naverage_precision_score_macro                      [0.8151093723721079]   \nrecall_score_weighted                              [0.9116843702579667]   \nnorm_macro_recall                                  [0.5026785366965085]   \nbalanced_accuracy                                  [0.7513392683482543]   \nf1_score_micro                                     [0.9116843702579667]   \nprecision_score_weighted                           [0.9072720074188747]   \naverage_precision_score_weighted                   [0.9531771295804466]   \nweighted_accuracy                                  [0.9514937218005303]   \nmatthews_correlation                               [0.5323740218566827]   \nprecision_score_macro                              [0.7819118765348991]   \nf1_score_macro                                     [0.7653697272147331]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_16  \\\nlog_loss                                            [0.5141549754293745]   \nAUC_weighted                                        [0.8645565317580722]   \naverage_precision_score_micro                        [0.825801912533905]   \nrecall_score_micro                                  [0.7107738998482549]   \nAUC_macro                                           [0.8645565317580721]   \nprecision_score_micro                               [0.7107738998482549]   \nAUC_micro                                            [0.845443664355567]   \naccuracy                                            [0.7107738998482549]   \nrecall_score_macro                                  [0.7282095667846631]   \nf1_score_weighted                                    [0.762689511364353]   \naverage_precision_score_macro                       [0.7296894396403012]   \nrecall_score_weighted                               [0.7107738998482549]   \nnorm_macro_recall                                   [0.4564191335693262]   \nbalanced_accuracy                                   [0.7282095667846631]   \nf1_score_micro                                      [0.7107738998482549]   \nprecision_score_weighted                            [0.8773957037038526]   \naverage_precision_score_weighted                     [0.923823141866013]   \nweighted_accuracy                                   [0.7064450953747553]   \nmatthews_correlation                                [0.3027038764499243]   \nprecision_score_macro                               [0.6003788295434084]   \nf1_score_macro                                      [0.5900650397822164]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_40  \\\nlog_loss                                           [0.40995149298006334]   \nAUC_weighted                                                       [0.5]   \naverage_precision_score_micro                       [0.8445594902839405]   \nrecall_score_micro                                  [0.8880121396054628]   \nAUC_macro                                                          [0.5]   \nprecision_score_micro                               [0.8880121396054628]   \nAUC_micro                                           [0.8880121396054628]   \naccuracy                                            [0.8880121396054628]   \nrecall_score_macro                                                 [0.5]   \nf1_score_weighted                                   [0.8353395018439429]   \naverage_precision_score_macro                                      [0.5]   \nrecall_score_weighted                               [0.8880121396054628]   \nnorm_macro_recall                                                  [0.0]   \nbalanced_accuracy                                                  [0.5]   \nf1_score_micro                                      [0.8880121396054628]   \nprecision_score_weighted                             [0.788565560086672]   \naverage_precision_score_weighted                    [0.8011068409624182]   \nweighted_accuracy                                   [0.9843450583187134]   \nmatthews_correlation                                               [0.0]   \nprecision_score_macro                               [0.4440060698027314]   \nf1_score_macro                                      [0.4703423886834914]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_3  \\\nlog_loss                                           [0.5106314122190876]   \nAUC_weighted                                       [0.8837138115058527]   \naverage_precision_score_micro                      [0.8663033584053035]   \nrecall_score_micro                                  [0.770257966616085]   \nAUC_macro                                          [0.8837138115058527]   \nprecision_score_micro                               [0.770257966616085]   \nAUC_micro                                          [0.8712103914285911]   \naccuracy                                            [0.770257966616085]   \nrecall_score_macro                                 [0.7676230487527022]   \nf1_score_weighted                                  [0.8082441051248883]   \naverage_precision_score_macro                      [0.7558224126778292]   \nrecall_score_weighted                               [0.770257966616085]   \nnorm_macro_recall                                  [0.5352460975054043]   \nbalanced_accuracy                                  [0.7676230487527022]   \nf1_score_micro                                      [0.770257966616085]   \nprecision_score_weighted                           [0.8882114520560835]   \naverage_precision_score_weighted                   [0.9327719547173615]   \nweighted_accuracy                                  [0.7709121454482407]   \nmatthews_correlation                                [0.372391050302355]   \nprecision_score_macro                              [0.6295433025963266]   \nf1_score_macro                                     [0.6416393775656939]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_12  \\\nlog_loss                                            [0.5882083446624502]   \nAUC_weighted                                        [0.8719391790636978]   \naverage_precision_score_micro                       [0.8470411285238979]   \nrecall_score_micro                                  [0.7462822458270106]   \nAUC_macro                                           [0.8719391790636977]   \nprecision_score_micro                               [0.7462822458270106]   \nAUC_micro                                           [0.8473216189517847]   \naccuracy                                            [0.7462822458270106]   \nrecall_score_macro                                  [0.7718853675207975]   \nf1_score_weighted                                   [0.7906584396770373]   \naverage_precision_score_macro                       [0.7262005246056114]   \nrecall_score_weighted                               [0.7462822458270106]   \nnorm_macro_recall                                    [0.543770735041595]   \nbalanced_accuracy                                   [0.7718853675207975]   \nf1_score_micro                                      [0.7462822458270106]   \nprecision_score_weighted                            [0.8907403887561155]   \naverage_precision_score_weighted                    [0.9247625111215071]   \nweighted_accuracy                                   [0.7399256832631668]   \nmatthews_correlation                                [0.3670007049048537]   \nprecision_score_macro                               [0.6238477070583401]   \nf1_score_macro                                      [0.6266845557543232]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_23  \\\nlog_loss                                           [0.18853344489960672]   \nAUC_weighted                                        [0.9382454658449523]   \naverage_precision_score_micro                       [0.9787471260989811]   \nrecall_score_micro                                  [0.9077389984825494]   \nAUC_macro                                           [0.9382454658449524]   \nprecision_score_micro                               [0.9077389984825494]   \nAUC_micro                                           [0.9776692049617645]   \naccuracy                                            [0.9077389984825494]   \nrecall_score_macro                                  [0.7159621151918969]   \nf1_score_weighted                                   [0.9021770532058848]   \naverage_precision_score_macro                        [0.800921254969678]   \nrecall_score_weighted                               [0.9077389984825494]   \nnorm_macro_recall                                   [0.4319242303837938]   \nbalanced_accuracy                                   [0.7159621151918969]   \nf1_score_micro                                      [0.9077389984825494]   \nprecision_score_weighted                            [0.8992110967163239]   \naverage_precision_score_weighted                    [0.9492807430329407]   \nweighted_accuracy                                   [0.9553520111267004]   \nmatthews_correlation                               [0.48767795941601105]   \nprecision_score_macro                               [0.7753142511695126]   \nf1_score_macro                                      [0.7405646205646206]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_36  \\\nlog_loss                                            [0.4004804145147236]   \nAUC_weighted                                        [0.9241312816409093]   \naverage_precision_score_micro                       [0.8849923342936359]   \nrecall_score_micro                                  [0.8440060698027314]   \nAUC_macro                                           [0.9241312816409093]   \nprecision_score_micro                               [0.8440060698027314]   \nAUC_micro                                            [0.911758700012204]   \naccuracy                                            [0.8440060698027314]   \nrecall_score_macro                                   [0.858880849574046]   \nf1_score_weighted                                   [0.8663736212569377]   \naverage_precision_score_macro                       [0.7558537899058622]   \nrecall_score_weighted                               [0.8440060698027314]   \nnorm_macro_recall                                    [0.717761699148092]   \nbalanced_accuracy                                    [0.858880849574046]   \nf1_score_micro                                      [0.8440060698027314]   \nprecision_score_weighted                             [0.917796141836863]   \naverage_precision_score_weighted                    [0.9370572540955886]   \nweighted_accuracy                                   [0.8403130643414988]   \nmatthews_correlation                                 [0.529480996617704]   \nprecision_score_macro                                 [0.69529471000753]   \nf1_score_macro                                      [0.7314825149635281]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_35  ...  \\\nlog_loss                                            [0.3258244825797297]  ...   \nAUC_weighted                                        [0.9331884774760255]  ...   \naverage_precision_score_micro                       [0.9779317400141541]  ...   \nrecall_score_micro                                  [0.9092564491654022]  ...   \nAUC_macro                                           [0.9331884774760256]  ...   \nprecision_score_micro                               [0.9092564491654022]  ...   \nAUC_micro                                           [0.9769153151991452]  ...   \naccuracy                                            [0.9092564491654022]  ...   \nrecall_score_macro                                  [0.7061593377382851]  ...   \nf1_score_weighted                                   [0.9020687114140309]  ...   \naverage_precision_score_macro                       [0.8073981238166317]  ...   \nrecall_score_weighted                               [0.9092564491654022]  ...   \nnorm_macro_recall                                   [0.4123186754765702]  ...   \nbalanced_accuracy                                   [0.7061593377382851]  ...   \nf1_score_micro                                       [0.909256449165402]  ...   \nprecision_score_weighted                            [0.8992566709887555]  ...   \naverage_precision_score_weighted                    [0.9500605695771992]  ...   \nweighted_accuracy                                   [0.9596799682488474]  ...   \nmatthews_correlation                                [0.4840063964126206]  ...   \nprecision_score_macro                               [0.7840790457739559]  ...   \nf1_score_macro                                      [0.7364917255558667]  ...   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_14  \\\nlog_loss                                            [0.2613839107881043]   \nAUC_weighted                                         [0.894889200088173]   \naverage_precision_score_micro                       [0.9671032535088344]   \nrecall_score_micro                                  [0.8965098634294385]   \nAUC_macro                                           [0.8948892000881731]   \nprecision_score_micro                               [0.8965098634294385]   \nAUC_micro                                           [0.9668010343533335]   \naccuracy                                            [0.8965098634294385]   \nrecall_score_macro                                  [0.5497816974068579]   \nf1_score_weighted                                   [0.8593681059308887]   \naverage_precision_score_macro                       [0.7466234663416346]   \nrecall_score_weighted                               [0.8965098634294385]   \nnorm_macro_recall                                  [0.09956339481371579]   \nbalanced_accuracy                                   [0.5497816974068579]   \nf1_score_micro                                      [0.8965098634294385]   \nprecision_score_weighted                            [0.8861450139804549]   \naverage_precision_score_weighted                    [0.9310423052945684]   \nweighted_accuracy                                   [0.9825930882146496]   \nmatthews_correlation                                 [0.262052481925017]   \nprecision_score_macro                               [0.8448632070629298]   \nf1_score_macro                                      [0.5635068164486716]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_22  \\\nlog_loss                                           [0.18115151864375498]   \nAUC_weighted                                        [0.9448491887516278]   \naverage_precision_score_micro                        [0.980352027134298]   \nrecall_score_micro                                  [0.9128983308042489]   \nAUC_macro                                           [0.9448491887516277]   \nprecision_score_micro                               [0.9128983308042489]   \nAUC_micro                                           [0.9793693944704005]   \naccuracy                                            [0.9128983308042489]   \nrecall_score_macro                                  [0.7508386635472644]   \nf1_score_weighted                                   [0.9100415644605403]   \naverage_precision_score_macro                       [0.8085204474402641]   \nrecall_score_weighted                               [0.9128983308042489]   \nnorm_macro_recall                                   [0.5016773270945287]   \nbalanced_accuracy                                   [0.7508386635472644]   \nf1_score_micro                                      [0.9128983308042489]   \nprecision_score_weighted                            [0.9080335867085474]   \naverage_precision_score_weighted                    [0.9516860732237478]   \nweighted_accuracy                                   [0.9531333625443325]   \nmatthews_correlation                                [0.5357619178216784]   \nprecision_score_macro                               [0.7860811293290488]   \nf1_score_macro                                      [0.7668111420906795]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_46  \\\nlog_loss                                           [0.19876786744256414]   \nAUC_weighted                                        [0.9477815010549285]   \naverage_precision_score_micro                       [0.9803098581835098]   \nrecall_score_micro                                  [0.9083459787556905]   \nAUC_macro                                           [0.9477815010549285]   \nprecision_score_micro                               [0.9083459787556905]   \nAUC_micro                                           [0.9793081438055086]   \naccuracy                                            [0.9083459787556905]   \nrecall_score_macro                                  [0.6488083660740913]   \nf1_score_weighted                                   [0.8923827412750078]   \naverage_precision_score_macro                        [0.820890520511222]   \nrecall_score_weighted                               [0.9083459787556905]   \nnorm_macro_recall                                  [0.29761673214818263]   \nbalanced_accuracy                                   [0.6488083660740913]   \nf1_score_micro                                      [0.9083459787556905]   \nprecision_score_weighted                            [0.8949643819991476]   \naverage_precision_score_weighted                    [0.9547401673639154]   \nweighted_accuracy                                   [0.9727821476108972]   \nmatthews_correlation                                [0.4303219971725012]   \nprecision_score_macro                               [0.8110998160518927]   \nf1_score_macro                                      [0.6922945146176263]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_8  \\\nlog_loss                                            [0.268346640190641]   \nAUC_weighted                                       [0.8950531354254077]   \naverage_precision_score_micro                      [0.9673206765445774]   \nrecall_score_micro                                 [0.8880121396054628]   \nAUC_macro                                          [0.8950531354254077]   \nprecision_score_micro                              [0.8880121396054628]   \nAUC_micro                                          [0.9665855056979237]   \naccuracy                                           [0.8880121396054628]   \nrecall_score_macro                                                [0.5]   \nf1_score_weighted                                  [0.8353395018439429]   \naverage_precision_score_macro                      [0.7374860123065864]   \nrecall_score_weighted                              [0.8880121396054628]   \nnorm_macro_recall                                                 [0.0]   \nbalanced_accuracy                                                 [0.5]   \nf1_score_micro                                     [0.8880121396054628]   \nprecision_score_weighted                            [0.788565560086672]   \naverage_precision_score_weighted                   [0.9294283764683942]   \nweighted_accuracy                                  [0.9843450583187134]   \nmatthews_correlation                                              [0.0]   \nprecision_score_macro                              [0.4440060698027314]   \nf1_score_macro                                     [0.4703423886834914]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_10  \\\nlog_loss                                            [0.5359981502931094]   \nAUC_weighted                                        [0.8624939103116253]   \naverage_precision_score_micro                        [0.845060890254189]   \nrecall_score_micro                                  [0.7216995447647951]   \nAUC_macro                                           [0.8624939103116253]   \nprecision_score_micro                               [0.7216995447647951]   \nAUC_micro                                           [0.8434602480882193]   \naccuracy                                            [0.7216995447647951]   \nrecall_score_macro                                  [0.7390978369797369]   \nf1_score_weighted                                   [0.7712415879960002]   \naverage_precision_score_macro                       [0.7369014482908167]   \nrecall_score_weighted                               [0.7216995447647951]   \nnorm_macro_recall                                   [0.4781956739594737]   \nbalanced_accuracy                                   [0.7390978369797369]   \nf1_score_micro                                      [0.7216995447647951]   \nprecision_score_weighted                            [0.8805978862678736]   \naverage_precision_score_weighted                    [0.9256058692818808]   \nweighted_accuracy                                   [0.7173800194236665]   \nmatthews_correlation                               [0.31905736696626136]   \nprecision_score_macro                               [0.6064392768054095]   \nf1_score_macro                                      [0.6002847667514776]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_24  \\\nlog_loss                                           [0.18047415972399278]   \nAUC_weighted                                        [0.9440702643526778]   \naverage_precision_score_micro                       [0.9804154714516231]   \nrecall_score_micro                                  [0.9138088012139606]   \nAUC_macro                                           [0.9440702643526777]   \nprecision_score_micro                               [0.9138088012139606]   \nAUC_micro                                           [0.9794490663878916]   \naccuracy                                            [0.9138088012139606]   \nrecall_score_macro                                  [0.7501671769964453]   \nf1_score_weighted                                   [0.9106786487153445]   \naverage_precision_score_macro                       [0.8167288234994515]   \nrecall_score_weighted                               [0.9138088012139606]   \nnorm_macro_recall                                   [0.5003343539928906]   \nbalanced_accuracy                                   [0.7501671769964453]   \nf1_score_micro                                      [0.9138088012139606]   \nprecision_score_weighted                            [0.9085876546672915]   \naverage_precision_score_weighted                    [0.9534157741149873]   \nweighted_accuracy                                   [0.9544365900761322]   \nmatthews_correlation                                [0.5381268713966005]   \nprecision_score_macro                               [0.7893870143116419]   \nf1_score_macro                                      [0.7677536498053239]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_25  \\\nlog_loss                                           [0.29313359336803707]   \nAUC_weighted                                         [0.934374461653024]   \naverage_precision_score_micro                       [0.9755997293287618]   \nrecall_score_micro                                  [0.8880121396054628]   \nAUC_macro                                           [0.9343744616530238]   \nprecision_score_micro                               [0.8880121396054628]   \nAUC_micro                                           [0.9744062484888817]   \naccuracy                                            [0.8880121396054628]   \nrecall_score_macro                                                 [0.5]   \nf1_score_weighted                                   [0.8353395018439429]   \naverage_precision_score_macro                       [0.8004617629290862]   \nrecall_score_weighted                               [0.8880121396054628]   \nnorm_macro_recall                                                  [0.0]   \nbalanced_accuracy                                                  [0.5]   \nf1_score_micro                                      [0.8880121396054628]   \nprecision_score_weighted                             [0.788565560086672]   \naverage_precision_score_weighted                     [0.948588659974036]   \nweighted_accuracy                                   [0.9843450583187134]   \nmatthews_correlation                                               [0.0]   \nprecision_score_macro                               [0.4440060698027314]   \nf1_score_macro                                      [0.4703423886834914]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_30  \\\nlog_loss                                           [0.33391109894761095]   \nAUC_weighted                                         [0.937449406961602]   \naverage_precision_score_micro                       [0.9759845387557008]   \nrecall_score_micro                                  [0.8880121396054628]   \nAUC_macro                                           [0.9374494069616021]   \nprecision_score_micro                               [0.8880121396054628]   \nAUC_micro                                           [0.9750178340751725]   \naccuracy                                            [0.8880121396054628]   \nrecall_score_macro                                                 [0.5]   \nf1_score_weighted                                   [0.8353395018439429]   \naverage_precision_score_macro                       [0.8118036528837411]   \nrecall_score_weighted                               [0.8880121396054628]   \nnorm_macro_recall                                                  [0.0]   \nbalanced_accuracy                                                  [0.5]   \nf1_score_micro                                      [0.8880121396054628]   \nprecision_score_weighted                             [0.788565560086672]   \naverage_precision_score_weighted                     [0.951285264915559]   \nweighted_accuracy                                   [0.9843450583187134]   \nmatthews_correlation                                               [0.0]   \nprecision_score_macro                               [0.4440060698027314]   \nf1_score_macro                                      [0.4703423886834914]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_34  \\\nlog_loss                                            [0.1980353042208354]   \nAUC_weighted                                        [0.9414324799433914]   \naverage_precision_score_micro                       [0.9791781316507459]   \nrecall_score_micro                                  [0.9119878603945372]   \nAUC_macro                                           [0.9414324799433913]   \nprecision_score_micro                               [0.9119878603945372]   \nAUC_micro                                           [0.9781441048537698]   \naccuracy                                            [0.9119878603945372]   \nrecall_score_macro                                  [0.7562466772993088]   \nf1_score_weighted                                   [0.9098828695901484]   \naverage_precision_score_macro                       [0.8055550630893802]   \nrecall_score_weighted                               [0.9119878603945372]   \nnorm_macro_recall                                   [0.5124933545986177]   \nbalanced_accuracy                                   [0.7562466772993088]   \nf1_score_micro                                      [0.9119878603945372]   \nprecision_score_weighted                             [0.908235905100146]   \naverage_precision_score_weighted                    [0.9506362037887797]   \nweighted_accuracy                                   [0.9506541834293613]   \nmatthews_correlation                                [0.5376324650295363]   \nprecision_score_macro                               [0.7820023565770107]   \nf1_score_macro                                      [0.7682391462527285]   \n\n                                 f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_32  \nlog_loss                                           [0.22565866780837046]  \nAUC_weighted                                        [0.9236931945532717]  \naverage_precision_score_micro                       [0.9674609995918976]  \nrecall_score_micro                                  [0.9019726858877086]  \nAUC_macro                                           [0.9236931945532716]  \nprecision_score_micro                               [0.9019726858877086]  \nAUC_micro                                             [0.97013021522931]  \naccuracy                                            [0.9019726858877086]  \nrecall_score_macro                                  [0.6736390125350331]  \nf1_score_weighted                                   [0.8921046809719475]  \naverage_precision_score_macro                       [0.7633397806814068]  \nrecall_score_weighted                               [0.9019726858877086]  \nnorm_macro_recall                                   [0.3472780250700662]  \nbalanced_accuracy                                   [0.6736390125350331]  \nf1_score_micro                                      [0.9019726858877086]  \nprecision_score_weighted                            [0.8885793236447783]  \naverage_precision_score_weighted                    [0.9382753446563435]  \nweighted_accuracy                                   [0.9586617606598207]  \nmatthews_correlation                                [0.4263713882110845]  \nprecision_score_macro                               [0.7617392226996931]  \nf1_score_macro                                      [0.7051973582341619]  \n\n[21 rows x 44 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>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_18</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_38</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_0</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_16</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_40</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_3</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_12</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_23</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_36</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_35</th>\n      <th>...</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_14</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_22</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_46</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_8</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_10</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_24</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_25</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_30</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_34</th>\n      <th>f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a_32</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>log_loss</th>\n      <td>[0.5523734950300616]</td>\n      <td>[0.21159982967441127]</td>\n      <td>[0.17775706110025447]</td>\n      <td>[0.5141549754293745]</td>\n      <td>[0.40995149298006334]</td>\n      <td>[0.5106314122190876]</td>\n      <td>[0.5882083446624502]</td>\n      <td>[0.18853344489960672]</td>\n      <td>[0.4004804145147236]</td>\n      <td>[0.3258244825797297]</td>\n      <td>...</td>\n      <td>[0.2613839107881043]</td>\n      <td>[0.18115151864375498]</td>\n      <td>[0.19876786744256414]</td>\n      <td>[0.268346640190641]</td>\n      <td>[0.5359981502931094]</td>\n      <td>[0.18047415972399278]</td>\n      <td>[0.29313359336803707]</td>\n      <td>[0.33391109894761095]</td>\n      <td>[0.1980353042208354]</td>\n      <td>[0.22565866780837046]</td>\n    </tr>\n    <tr>\n      <th>AUC_weighted</th>\n      <td>[0.8689188788675312]</td>\n      <td>[0.9374267153471261]</td>\n      <td>[0.9450464668693167]</td>\n      <td>[0.8645565317580722]</td>\n      <td>[0.5]</td>\n      <td>[0.8837138115058527]</td>\n      <td>[0.8719391790636978]</td>\n      <td>[0.9382454658449523]</td>\n      <td>[0.9241312816409093]</td>\n      <td>[0.9331884774760255]</td>\n      <td>...</td>\n      <td>[0.894889200088173]</td>\n      <td>[0.9448491887516278]</td>\n      <td>[0.9477815010549285]</td>\n      <td>[0.8950531354254077]</td>\n      <td>[0.8624939103116253]</td>\n      <td>[0.9440702643526778]</td>\n      <td>[0.934374461653024]</td>\n      <td>[0.937449406961602]</td>\n      <td>[0.9414324799433914]</td>\n      <td>[0.9236931945532717]</td>\n    </tr>\n    <tr>\n      <th>average_precision_score_micro</th>\n      <td>[0.8472904029495727]</td>\n      <td>[0.9786652953490829]</td>\n      <td>[0.9806603102489483]</td>\n      <td>[0.825801912533905]</td>\n      <td>[0.8445594902839405]</td>\n      <td>[0.8663033584053035]</td>\n      <td>[0.8470411285238979]</td>\n      <td>[0.9787471260989811]</td>\n      <td>[0.8849923342936359]</td>\n      <td>[0.9779317400141541]</td>\n      <td>...</td>\n      <td>[0.9671032535088344]</td>\n      <td>[0.980352027134298]</td>\n      <td>[0.9803098581835098]</td>\n      <td>[0.9673206765445774]</td>\n      <td>[0.845060890254189]</td>\n      <td>[0.9804154714516231]</td>\n      <td>[0.9755997293287618]</td>\n      <td>[0.9759845387557008]</td>\n      <td>[0.9791781316507459]</td>\n      <td>[0.9674609995918976]</td>\n    </tr>\n    <tr>\n      <th>recall_score_micro</th>\n      <td>[0.7256449165402125]</td>\n      <td>[0.9098634294385433]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.7107738998482549]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.770257966616085]</td>\n      <td>[0.7462822458270106]</td>\n      <td>[0.9077389984825494]</td>\n      <td>[0.8440060698027314]</td>\n      <td>[0.9092564491654022]</td>\n      <td>...</td>\n      <td>[0.8965098634294385]</td>\n      <td>[0.9128983308042489]</td>\n      <td>[0.9083459787556905]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.7216995447647951]</td>\n      <td>[0.9138088012139606]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9119878603945372]</td>\n      <td>[0.9019726858877086]</td>\n    </tr>\n    <tr>\n      <th>AUC_macro</th>\n      <td>[0.868918878867531]</td>\n      <td>[0.9374267153471261]</td>\n      <td>[0.9450464668693166]</td>\n      <td>[0.8645565317580721]</td>\n      <td>[0.5]</td>\n      <td>[0.8837138115058527]</td>\n      <td>[0.8719391790636977]</td>\n      <td>[0.9382454658449524]</td>\n      <td>[0.9241312816409093]</td>\n      <td>[0.9331884774760256]</td>\n      <td>...</td>\n      <td>[0.8948892000881731]</td>\n      <td>[0.9448491887516277]</td>\n      <td>[0.9477815010549285]</td>\n      <td>[0.8950531354254077]</td>\n      <td>[0.8624939103116253]</td>\n      <td>[0.9440702643526777]</td>\n      <td>[0.9343744616530238]</td>\n      <td>[0.9374494069616021]</td>\n      <td>[0.9414324799433913]</td>\n      <td>[0.9236931945532716]</td>\n    </tr>\n    <tr>\n      <th>precision_score_micro</th>\n      <td>[0.7256449165402125]</td>\n      <td>[0.9098634294385433]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.7107738998482549]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.770257966616085]</td>\n      <td>[0.7462822458270106]</td>\n      <td>[0.9077389984825494]</td>\n      <td>[0.8440060698027314]</td>\n      <td>[0.9092564491654022]</td>\n      <td>...</td>\n      <td>[0.8965098634294385]</td>\n      <td>[0.9128983308042489]</td>\n      <td>[0.9083459787556905]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.7216995447647951]</td>\n      <td>[0.9138088012139606]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9119878603945372]</td>\n      <td>[0.9019726858877086]</td>\n    </tr>\n    <tr>\n      <th>AUC_micro</th>\n      <td>[0.8332426240153266]</td>\n      <td>[0.977637796726083]</td>\n      <td>[0.979695082216353]</td>\n      <td>[0.845443664355567]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8712103914285911]</td>\n      <td>[0.8473216189517847]</td>\n      <td>[0.9776692049617645]</td>\n      <td>[0.911758700012204]</td>\n      <td>[0.9769153151991452]</td>\n      <td>...</td>\n      <td>[0.9668010343533335]</td>\n      <td>[0.9793693944704005]</td>\n      <td>[0.9793081438055086]</td>\n      <td>[0.9665855056979237]</td>\n      <td>[0.8434602480882193]</td>\n      <td>[0.9794490663878916]</td>\n      <td>[0.9744062484888817]</td>\n      <td>[0.9750178340751725]</td>\n      <td>[0.9781441048537698]</td>\n      <td>[0.97013021522931]</td>\n    </tr>\n    <tr>\n      <th>accuracy</th>\n      <td>[0.7256449165402125]</td>\n      <td>[0.9098634294385433]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.7107738998482549]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.770257966616085]</td>\n      <td>[0.7462822458270106]</td>\n      <td>[0.9077389984825494]</td>\n      <td>[0.8440060698027314]</td>\n      <td>[0.9092564491654022]</td>\n      <td>...</td>\n      <td>[0.8965098634294385]</td>\n      <td>[0.9128983308042489]</td>\n      <td>[0.9083459787556905]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.7216995447647951]</td>\n      <td>[0.9138088012139606]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9119878603945372]</td>\n      <td>[0.9019726858877086]</td>\n    </tr>\n    <tr>\n      <th>recall_score_macro</th>\n      <td>[0.755528881331192]</td>\n      <td>[0.730183737244071]</td>\n      <td>[0.7513392683482543]</td>\n      <td>[0.7282095667846631]</td>\n      <td>[0.5]</td>\n      <td>[0.7676230487527022]</td>\n      <td>[0.7718853675207975]</td>\n      <td>[0.7159621151918969]</td>\n      <td>[0.858880849574046]</td>\n      <td>[0.7061593377382851]</td>\n      <td>...</td>\n      <td>[0.5497816974068579]</td>\n      <td>[0.7508386635472644]</td>\n      <td>[0.6488083660740913]</td>\n      <td>[0.5]</td>\n      <td>[0.7390978369797369]</td>\n      <td>[0.7501671769964453]</td>\n      <td>[0.5]</td>\n      <td>[0.5]</td>\n      <td>[0.7562466772993088]</td>\n      <td>[0.6736390125350331]</td>\n    </tr>\n    <tr>\n      <th>f1_score_weighted</th>\n      <td>[0.7746511826390383]</td>\n      <td>[0.905471355536803]</td>\n      <td>[0.9091539479147899]</td>\n      <td>[0.762689511364353]</td>\n      <td>[0.8353395018439429]</td>\n      <td>[0.8082441051248883]</td>\n      <td>[0.7906584396770373]</td>\n      <td>[0.9021770532058848]</td>\n      <td>[0.8663736212569377]</td>\n      <td>[0.9020687114140309]</td>\n      <td>...</td>\n      <td>[0.8593681059308887]</td>\n      <td>[0.9100415644605403]</td>\n      <td>[0.8923827412750078]</td>\n      <td>[0.8353395018439429]</td>\n      <td>[0.7712415879960002]</td>\n      <td>[0.9106786487153445]</td>\n      <td>[0.8353395018439429]</td>\n      <td>[0.8353395018439429]</td>\n      <td>[0.9098828695901484]</td>\n      <td>[0.8921046809719475]</td>\n    </tr>\n    <tr>\n      <th>average_precision_score_macro</th>\n      <td>[0.7386502996990343]</td>\n      <td>[0.8055599434789882]</td>\n      <td>[0.8151093723721079]</td>\n      <td>[0.7296894396403012]</td>\n      <td>[0.5]</td>\n      <td>[0.7558224126778292]</td>\n      <td>[0.7262005246056114]</td>\n      <td>[0.800921254969678]</td>\n      <td>[0.7558537899058622]</td>\n      <td>[0.8073981238166317]</td>\n      <td>...</td>\n      <td>[0.7466234663416346]</td>\n      <td>[0.8085204474402641]</td>\n      <td>[0.820890520511222]</td>\n      <td>[0.7374860123065864]</td>\n      <td>[0.7369014482908167]</td>\n      <td>[0.8167288234994515]</td>\n      <td>[0.8004617629290862]</td>\n      <td>[0.8118036528837411]</td>\n      <td>[0.8055550630893802]</td>\n      <td>[0.7633397806814068]</td>\n    </tr>\n    <tr>\n      <th>recall_score_weighted</th>\n      <td>[0.7256449165402125]</td>\n      <td>[0.9098634294385433]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.7107738998482549]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.770257966616085]</td>\n      <td>[0.7462822458270106]</td>\n      <td>[0.9077389984825494]</td>\n      <td>[0.8440060698027314]</td>\n      <td>[0.9092564491654022]</td>\n      <td>...</td>\n      <td>[0.8965098634294385]</td>\n      <td>[0.9128983308042489]</td>\n      <td>[0.9083459787556905]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.7216995447647951]</td>\n      <td>[0.9138088012139606]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9119878603945372]</td>\n      <td>[0.9019726858877086]</td>\n    </tr>\n    <tr>\n      <th>norm_macro_recall</th>\n      <td>[0.511057762662384]</td>\n      <td>[0.4603674744881421]</td>\n      <td>[0.5026785366965085]</td>\n      <td>[0.4564191335693262]</td>\n      <td>[0.0]</td>\n      <td>[0.5352460975054043]</td>\n      <td>[0.543770735041595]</td>\n      <td>[0.4319242303837938]</td>\n      <td>[0.717761699148092]</td>\n      <td>[0.4123186754765702]</td>\n      <td>...</td>\n      <td>[0.09956339481371579]</td>\n      <td>[0.5016773270945287]</td>\n      <td>[0.29761673214818263]</td>\n      <td>[0.0]</td>\n      <td>[0.4781956739594737]</td>\n      <td>[0.5003343539928906]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.5124933545986177]</td>\n      <td>[0.3472780250700662]</td>\n    </tr>\n    <tr>\n      <th>balanced_accuracy</th>\n      <td>[0.755528881331192]</td>\n      <td>[0.730183737244071]</td>\n      <td>[0.7513392683482543]</td>\n      <td>[0.7282095667846631]</td>\n      <td>[0.5]</td>\n      <td>[0.7676230487527022]</td>\n      <td>[0.7718853675207975]</td>\n      <td>[0.7159621151918969]</td>\n      <td>[0.858880849574046]</td>\n      <td>[0.7061593377382851]</td>\n      <td>...</td>\n      <td>[0.5497816974068579]</td>\n      <td>[0.7508386635472644]</td>\n      <td>[0.6488083660740913]</td>\n      <td>[0.5]</td>\n      <td>[0.7390978369797369]</td>\n      <td>[0.7501671769964453]</td>\n      <td>[0.5]</td>\n      <td>[0.5]</td>\n      <td>[0.7562466772993088]</td>\n      <td>[0.6736390125350331]</td>\n    </tr>\n    <tr>\n      <th>f1_score_micro</th>\n      <td>[0.7256449165402123]</td>\n      <td>[0.9098634294385431]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.7107738998482549]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.770257966616085]</td>\n      <td>[0.7462822458270106]</td>\n      <td>[0.9077389984825494]</td>\n      <td>[0.8440060698027314]</td>\n      <td>[0.909256449165402]</td>\n      <td>...</td>\n      <td>[0.8965098634294385]</td>\n      <td>[0.9128983308042489]</td>\n      <td>[0.9083459787556905]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.7216995447647951]</td>\n      <td>[0.9138088012139606]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9119878603945372]</td>\n      <td>[0.9019726858877086]</td>\n    </tr>\n    <tr>\n      <th>precision_score_weighted</th>\n      <td>[0.8862391665995327]</td>\n      <td>[0.9028472067074211]</td>\n      <td>[0.9072720074188747]</td>\n      <td>[0.8773957037038526]</td>\n      <td>[0.788565560086672]</td>\n      <td>[0.8882114520560835]</td>\n      <td>[0.8907403887561155]</td>\n      <td>[0.8992110967163239]</td>\n      <td>[0.917796141836863]</td>\n      <td>[0.8992566709887555]</td>\n      <td>...</td>\n      <td>[0.8861450139804549]</td>\n      <td>[0.9080335867085474]</td>\n      <td>[0.8949643819991476]</td>\n      <td>[0.788565560086672]</td>\n      <td>[0.8805978862678736]</td>\n      <td>[0.9085876546672915]</td>\n      <td>[0.788565560086672]</td>\n      <td>[0.788565560086672]</td>\n      <td>[0.908235905100146]</td>\n      <td>[0.8885793236447783]</td>\n    </tr>\n    <tr>\n      <th>average_precision_score_weighted</th>\n      <td>[0.9269179009128578]</td>\n      <td>[0.9501587291452896]</td>\n      <td>[0.9531771295804466]</td>\n      <td>[0.923823141866013]</td>\n      <td>[0.8011068409624182]</td>\n      <td>[0.9327719547173615]</td>\n      <td>[0.9247625111215071]</td>\n      <td>[0.9492807430329407]</td>\n      <td>[0.9370572540955886]</td>\n      <td>[0.9500605695771992]</td>\n      <td>...</td>\n      <td>[0.9310423052945684]</td>\n      <td>[0.9516860732237478]</td>\n      <td>[0.9547401673639154]</td>\n      <td>[0.9294283764683942]</td>\n      <td>[0.9256058692818808]</td>\n      <td>[0.9534157741149873]</td>\n      <td>[0.948588659974036]</td>\n      <td>[0.951285264915559]</td>\n      <td>[0.9506362037887797]</td>\n      <td>[0.9382753446563435]</td>\n    </tr>\n    <tr>\n      <th>weighted_accuracy</th>\n      <td>[0.7182255364301822]</td>\n      <td>[0.954473036756995]</td>\n      <td>[0.9514937218005303]</td>\n      <td>[0.7064450953747553]</td>\n      <td>[0.9843450583187134]</td>\n      <td>[0.7709121454482407]</td>\n      <td>[0.7399256832631668]</td>\n      <td>[0.9553520111267004]</td>\n      <td>[0.8403130643414988]</td>\n      <td>[0.9596799682488474]</td>\n      <td>...</td>\n      <td>[0.9825930882146496]</td>\n      <td>[0.9531333625443325]</td>\n      <td>[0.9727821476108972]</td>\n      <td>[0.9843450583187134]</td>\n      <td>[0.7173800194236665]</td>\n      <td>[0.9544365900761322]</td>\n      <td>[0.9843450583187134]</td>\n      <td>[0.9843450583187134]</td>\n      <td>[0.9506541834293613]</td>\n      <td>[0.9586617606598207]</td>\n    </tr>\n    <tr>\n      <th>matthews_correlation</th>\n      <td>[0.3401635358365466]</td>\n      <td>[0.5077160448273371]</td>\n      <td>[0.5323740218566827]</td>\n      <td>[0.3027038764499243]</td>\n      <td>[0.0]</td>\n      <td>[0.372391050302355]</td>\n      <td>[0.3670007049048537]</td>\n      <td>[0.48767795941601105]</td>\n      <td>[0.529480996617704]</td>\n      <td>[0.4840063964126206]</td>\n      <td>...</td>\n      <td>[0.262052481925017]</td>\n      <td>[0.5357619178216784]</td>\n      <td>[0.4303219971725012]</td>\n      <td>[0.0]</td>\n      <td>[0.31905736696626136]</td>\n      <td>[0.5381268713966005]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.5376324650295363]</td>\n      <td>[0.4263713882110845]</td>\n    </tr>\n    <tr>\n      <th>precision_score_macro</th>\n      <td>[0.6132075858803293]</td>\n      <td>[0.7799671962726313]</td>\n      <td>[0.7819118765348991]</td>\n      <td>[0.6003788295434084]</td>\n      <td>[0.4440060698027314]</td>\n      <td>[0.6295433025963266]</td>\n      <td>[0.6238477070583401]</td>\n      <td>[0.7753142511695126]</td>\n      <td>[0.69529471000753]</td>\n      <td>[0.7840790457739559]</td>\n      <td>...</td>\n      <td>[0.8448632070629298]</td>\n      <td>[0.7860811293290488]</td>\n      <td>[0.8110998160518927]</td>\n      <td>[0.4440060698027314]</td>\n      <td>[0.6064392768054095]</td>\n      <td>[0.7893870143116419]</td>\n      <td>[0.4440060698027314]</td>\n      <td>[0.4440060698027314]</td>\n      <td>[0.7820023565770107]</td>\n      <td>[0.7617392226996931]</td>\n    </tr>\n    <tr>\n      <th>f1_score_macro</th>\n      <td>[0.6080168443216213]</td>\n      <td>[0.7516284381840679]</td>\n      <td>[0.7653697272147331]</td>\n      <td>[0.5900650397822164]</td>\n      <td>[0.4703423886834914]</td>\n      <td>[0.6416393775656939]</td>\n      <td>[0.6266845557543232]</td>\n      <td>[0.7405646205646206]</td>\n      <td>[0.7314825149635281]</td>\n      <td>[0.7364917255558667]</td>\n      <td>...</td>\n      <td>[0.5635068164486716]</td>\n      <td>[0.7668111420906795]</td>\n      <td>[0.6922945146176263]</td>\n      <td>[0.4703423886834914]</td>\n      <td>[0.6002847667514776]</td>\n      <td>[0.7677536498053239]</td>\n      <td>[0.4703423886834914]</td>\n      <td>[0.4703423886834914]</td>\n      <td>[0.7682391462527285]</td>\n      <td>[0.7051973582341619]</td>\n    </tr>\n  </tbody>\n</table>\n<p>21 rows × 44 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "gather": {
          "logged": 1608754695455
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Retrieve the Best Model"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve best model from Pipeline Run\n",
        "best_model_output = pipeline_run.get_pipeline_output(best_model_output_name)\n",
        "num_file_downloaded = best_model_output.download('.', show_progress=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading azureml/f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a/model_data\n",
            "Downloaded azureml/f20ff74e-de3b-484e-b9b5-f7b8bf0b5c7a/model_data, 1 files out of an estimated total of 1\n"
          ]
        }
      ],
      "execution_count": 16,
      "metadata": {
        "gather": {
          "logged": 1608754713820
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open(best_model_output._path_on_datastore, \"rb\" ) as f:\n",
        "    best_model = pickle.load(f)\n",
        "best_model"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": "PipelineWithYTransformations(Pipeline={'memory': None,\n                                       'steps': [('datatransformer',\n                                                  DataTransformer(enable_dnn=None,\n                                                                  enable_feature_sweeping=None,\n                                                                  feature_sweeping_config=None,\n                                                                  feature_sweeping_timeout=None,\n                                                                  featurization_config=None,\n                                                                  force_text_dnn=None,\n                                                                  is_cross_validation=None,\n                                                                  is_onnx_compatible=None,\n                                                                  logger=None,\n                                                                  observer=None,\n                                                                  task=None,\n                                                                  working_dir=None))...\n                                                                                                                                   n_jobs=1,\n                                                                                                                                   oob_score=True,\n                                                                                                                                   random_state=None,\n                                                                                                                                   verbose=0,\n                                                                                                                                   warm_start=False))],\n                                                                                                      verbose=False))],\n                                                                                flatten_transform=None,\n                                                                                weights=[0.3333333333333333,\n                                                                                         0.06666666666666667,\n                                                                                         0.06666666666666667,\n                                                                                         0.06666666666666667,\n                                                                                         0.06666666666666667,\n                                                                                         0.06666666666666667,\n                                                                                         0.26666666666666666,\n                                                                                         0.06666666666666667]))],\n                                       'verbose': False},\n                             y_transformer={},\n                             y_transformer_name='LabelEncoder')"
          },
          "metadata": {}
        }
      ],
      "execution_count": 17,
      "metadata": {
        "gather": {
          "logged": 1608754784138
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_model.steps"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 18,
          "data": {
            "text/plain": "[('datatransformer',\n  DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\n                  feature_sweeping_config=None, feature_sweeping_timeout=None,\n                  featurization_config=None, force_text_dnn=None,\n                  is_cross_validation=None, is_onnx_compatible=None, logger=None,\n                  observer=None, task=None, working_dir=None)),\n ('prefittedsoftvotingclassifier',\n  PreFittedSoftVotingClassifier(classification_labels=None,\n                                estimators=[('28',\n                                             Pipeline(memory=None,\n                                                      steps=[('standardscalerwrapper',\n                                                              <azureml.automl.runtime.shared.model_wrappers.StandardScalerWrapper object at 0x7f02b2886358>),\n                                                             ('xgboostclassifier',\n                                                              XGBoostClassifier(base_score=0.5,\n                                                                                booster='gbtree',\n                                                                                colsample_bylevel=1,\n                                                                                colsample_bynode=1,\n                                                                                colsample_bytree=0.7...\n                                                                                   min_samples_split=0.15052631578947367,\n                                                                                   min_weight_fraction_leaf=0.0,\n                                                                                   n_estimators=10,\n                                                                                   n_jobs=1,\n                                                                                   oob_score=True,\n                                                                                   random_state=None,\n                                                                                   verbose=0,\n                                                                                   warm_start=False))],\n                                                      verbose=False))],\n                                flatten_transform=None,\n                                weights=[0.3333333333333333, 0.06666666666666667,\n                                         0.06666666666666667, 0.06666666666666667,\n                                         0.06666666666666667, 0.06666666666666667,\n                                         0.26666666666666666,\n                                         0.06666666666666667]))]"
          },
          "metadata": {}
        }
      ],
      "execution_count": 18,
      "metadata": {
        "gather": {
          "logged": 1608754930805
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Test the Model\n",
        "#### Load Test Data\n",
        "For the test data, it should have the same preparation step as the train data. Otherwise it might get failed at the preprocessing step."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test = Dataset.Tabular.from_delimited_files(path='https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv')\n",
        "df_test = dataset_test.to_pandas_dataframe()\n",
        "df_test = df_test[pd.notnull(df_test['y'])]\n",
        "\n",
        "y_test = df_test['y']\n",
        "X_test = df_test.drop(['y'], axis=1)"
      ],
      "outputs": [],
      "execution_count": 19,
      "metadata": {
        "gather": {
          "logged": 1608754952062
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Testing Our Best Fitted Model\n",
        "\n",
        "We will use confusion matrix to see how our model works."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "ypred = best_model.predict(X_test)\n",
        "cm = confusion_matrix(y_test, ypred)"
      ],
      "outputs": [],
      "execution_count": 20,
      "metadata": {
        "gather": {
          "logged": 1608755063890
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the confusion matrix\n",
        "pd.DataFrame(cm).style.background_gradient(cmap='Blues', low=0, high=0.9)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "<pandas.io.formats.style.Styler at 0x7f02b2886518>",
            "text/html": "<style  type=\"text/css\" >\n    #T_e1fd69d2_455c_11eb_9816_000d3aeeba94row0_col0 {\n            background-color:  #63a8d3;\n            color:  #000000;\n        }    #T_e1fd69d2_455c_11eb_9816_000d3aeeba94row0_col1 {\n            background-color:  #f7fbff;\n            color:  #000000;\n        }    #T_e1fd69d2_455c_11eb_9816_000d3aeeba94row1_col0 {\n            background-color:  #f7fbff;\n            color:  #000000;\n        }    #T_e1fd69d2_455c_11eb_9816_000d3aeeba94row1_col1 {\n            background-color:  #63a8d3;\n            color:  #000000;\n        }</style><table id=\"T_e1fd69d2_455c_11eb_9816_000d3aeeba94\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_e1fd69d2_455c_11eb_9816_000d3aeeba94level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_e1fd69d2_455c_11eb_9816_000d3aeeba94row0_col0\" class=\"data row0 col0\" >29163</td>\n                        <td id=\"T_e1fd69d2_455c_11eb_9816_000d3aeeba94row0_col1\" class=\"data row0 col1\" >95</td>\n            </tr>\n            <tr>\n                        <th id=\"T_e1fd69d2_455c_11eb_9816_000d3aeeba94level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_e1fd69d2_455c_11eb_9816_000d3aeeba94row1_col0\" class=\"data row1 col0\" >1900</td>\n                        <td id=\"T_e1fd69d2_455c_11eb_9816_000d3aeeba94row1_col1\" class=\"data row1 col1\" >1792</td>\n            </tr>\n    </tbody></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 21,
      "metadata": {
        "gather": {
          "logged": 1608755079857
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Publish and run from REST endpoint\n",
        "\n",
        "Run the following code to publish the pipeline to your workspace. In your workspace in the portal, you can see metadata for the pipeline including run history and durations. You can also run the pipeline manually from the portal.\n",
        "\n",
        "Additionally, publishing the pipeline enables a REST endpoint to rerun the pipeline from any HTTP library on any platform.\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "published_pipeline = pipeline_run.publish_pipeline(\n",
        "    name=\"Bankmarketing Train\", description=\"Training bankmarketing pipeline\", version=\"1.0\")\n",
        "\n",
        "published_pipeline\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 22,
          "data": {
            "text/plain": "Pipeline(Name: Bankmarketing Train,\nId: b50ab1a5-c24e-45c3-962b-71eda58bfa4d,\nStatus: Active,\nEndpoint: https://southcentralus.api.azureml.ms/pipelines/v1.0/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourceGroups/aml-quickstarts-131509/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-131509/PipelineRuns/PipelineSubmit/b50ab1a5-c24e-45c3-962b-71eda58bfa4d)",
            "text/html": "<table style=\"width:100%\"><tr><th>Name</th><th>Id</th><th>Status</th><th>Endpoint</th></tr><tr><td>Bankmarketing Train</td><td><a href=\"https://ml.azure.com/pipelines/b50ab1a5-c24e-45c3-962b-71eda58bfa4d?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-131509/workspaces/quick-starts-ws-131509\" target=\"_blank\" rel=\"noopener\">b50ab1a5-c24e-45c3-962b-71eda58bfa4d</a></td><td>Active</td><td><a href=\"https://southcentralus.api.azureml.ms/pipelines/v1.0/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourceGroups/aml-quickstarts-131509/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-131509/PipelineRuns/PipelineSubmit/b50ab1a5-c24e-45c3-962b-71eda58bfa4d\" target=\"_blank\" rel=\"noopener\">REST Endpoint</a></td></tr></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 22,
      "metadata": {
        "gather": {
          "logged": 1608755104702
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Authenticate once again, to retrieve the `auth_header` so that the endpoint can be used"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
        "\n",
        "interactive_auth = InteractiveLoginAuthentication()\n",
        "auth_header = interactive_auth.get_authentication_header()\n",
        "\n"
      ],
      "outputs": [],
      "execution_count": 23,
      "metadata": {
        "gather": {
          "logged": 1608755129339
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get the REST url from the endpoint property of the published pipeline object. You can also find the REST url in your workspace in the portal. Build an HTTP POST request to the endpoint, specifying your authentication header. Additionally, add a JSON payload object with the experiment name and the batch size parameter. As a reminder, the process_count_per_node is passed through to ParallelRunStep because you defined it is defined as a PipelineParameter object in the step configuration.\n",
        "\n",
        "Make the request to trigger the run. Access the Id key from the response dict to get the value of the run id.\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "rest_endpoint = published_pipeline.endpoint\n",
        "response = requests.post(rest_endpoint, \n",
        "                         headers=auth_header, \n",
        "                         json={\"ExperimentName\": \"pipeline-rest-endpoint\"}\n",
        "                        )"
      ],
      "outputs": [],
      "execution_count": 24,
      "metadata": {
        "gather": {
          "logged": 1608755151393
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    response.raise_for_status()\n",
        "except Exception:    \n",
        "    raise Exception(\"Received bad response from the endpoint: {}\\n\"\n",
        "                    \"Response Code: {}\\n\"\n",
        "                    \"Headers: {}\\n\"\n",
        "                    \"Content: {}\".format(rest_endpoint, response.status_code, response.headers, response.content))\n",
        "\n",
        "run_id = response.json().get('Id')\n",
        "print('Submitted pipeline run: ', run_id)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Submitted pipeline run:  a33e1cde-c96f-489d-85af-d8b0b23146df\n"
          ]
        }
      ],
      "execution_count": 25,
      "metadata": {
        "gather": {
          "logged": 1608755183830
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use the run id to monitor the status of the new run. This will take another 10-15 min to run and will look similar to the previous pipeline run, so if you don't need to see another pipeline run, you can skip watching the full output."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.pipeline.core.run import PipelineRun\n",
        "from azureml.widgets import RunDetails\n",
        "\n",
        "published_pipeline_run = PipelineRun(ws.experiments[\"pipeline-rest-endpoint\"], run_id)\n",
        "RunDetails(published_pipeline_run).show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "_PipelineWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', …",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f57eeedf0b77476b930961a46362e508"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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