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: 1.26.0\n"
          ]
        }
      ],
      "execution_count": 1,
      "metadata": {
        "gather": {
          "logged": 1618616631206
        }
      }
    },
    {
      "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": [
            "Performing interactive authentication. Please follow the instructions on the terminal.\n",
            "To sign in, use a web browser to open the page https://microsoft.com/devicelogin and enter the code F4G3CFL7F to authenticate.\n",
            "You have logged in. Now let us find all the subscriptions to which you have access...\n",
            "Interactive authentication successfully completed.\n",
            "quick-starts-ws-142884\n",
            "aml-quickstarts-142884\n",
            "southcentralus\n",
            "9e65f93e-bdd8-437b-b1e8-0647cd6098f7\n"
          ]
        }
      ],
      "execution_count": 2,
      "metadata": {
        "gather": {
          "logged": 1618616669728
        }
      }
    },
    {
      "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 = 'ml-experiment-1'\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: ml-experiment-1,\nWorkspace: quick-starts-ws-142884)",
            "text/html": "<table style=\"width:100%\"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>ml-experiment-1</td><td>quick-starts-ws-142884</td><td><a href=\"https://ml.azure.com/experiments/id/57342159-8615-4491-8c5f-af5b8807d853?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-142884/workspaces/quick-starts-ws-142884&amp;tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\" 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": 1618616698365
        }
      }
    },
    {
      "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 = \"ml-compute\"\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": [
            "Found existing cluster, use it.\n",
            "Succeeded\n",
            "AmlCompute wait for completion finished\n",
            "\n",
            "Minimum number of nodes requested have been provisioned\n"
          ]
        }
      ],
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1618616833257
        }
      }
    },
    {
      "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 = \"Bank-marketing\"\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": 1618616926767
        }
      }
    },
    {
      "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": 1618616946320
        }
      }
    },
    {
      "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": 1618616983938
        }
      }
    },
    {
      "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": 1618617011515
        }
      }
    },
    {
      "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": 1618617042701
        }
      }
    },
    {
      "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": 1618617050550
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_run = experiment.submit(pipeline)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Created step automl_module [b314bd36][776230b8-7aee-477c-98da-15a9383134b9], (This step will run and generate new outputs)\n",
            "Submitted PipelineRun 65467d06-e84a-441b-8f23-9a07a5159489\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/runs/65467d06-e84a-441b-8f23-9a07a5159489?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-142884/workspaces/quick-starts-ws-142884&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n"
          ]
        }
      ],
      "execution_count": 11,
      "metadata": {
        "gather": {
          "logged": 1618617061673
        }
      }
    },
    {
      "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": "3fbac690694f49478c35ed772a2ada85"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/runs/65467d06-e84a-441b-8f23-9a07a5159489?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-142884/workspaces/quick-starts-ws-142884&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\", \"run_id\": \"65467d06-e84a-441b-8f23-9a07a5159489\", \"run_properties\": {\"run_id\": \"65467d06-e84a-441b-8f23-9a07a5159489\", \"created_utc\": \"2021-04-16T23:51:00.716161Z\", \"properties\": {\"azureml.runsource\": \"azureml.PipelineRun\", \"runSource\": \"SDK\", \"runType\": \"SDK\", \"azureml.parameters\": \"{}\"}, \"tags\": {\"azureml.pipelineComponent\": \"pipelinerun\"}, \"end_time_utc\": \"2021-04-17T00:21:36.252235Z\", \"status\": \"Completed\", \"log_files\": {\"logs/azureml/executionlogs.txt\": \"https://mlstrg142884.blob.core.windows.net/azureml/ExperimentRun/dcid.65467d06-e84a-441b-8f23-9a07a5159489/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=BY9skFOIBfbLfjx%2B7srI39SCPbPWBTlFZ4xZoyGIFsg%3D&st=2021-04-17T00%3A41%3A50Z&se=2021-04-17T08%3A51%3A50Z&sp=r\", \"logs/azureml/stderrlogs.txt\": \"https://mlstrg142884.blob.core.windows.net/azureml/ExperimentRun/dcid.65467d06-e84a-441b-8f23-9a07a5159489/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=RaZ%2F8Cxzzhg7E3XyjTG0IeX01cThyDq3TRY66ISiON8%3D&st=2021-04-17T00%3A41%3A50Z&se=2021-04-17T08%3A51%3A50Z&sp=r\", \"logs/azureml/stdoutlogs.txt\": \"https://mlstrg142884.blob.core.windows.net/azureml/ExperimentRun/dcid.65467d06-e84a-441b-8f23-9a07a5159489/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=XSyrI9H03sVZoy%2BGFT6Oh6q0CDUG2QL3JSMp38q9m88%3D&st=2021-04-17T00%3A41%3A51Z&se=2021-04-17T08%3A51%3A51Z&sp=r\"}, \"log_groups\": [[\"logs/azureml/executionlogs.txt\", \"logs/azureml/stderrlogs.txt\", \"logs/azureml/stdoutlogs.txt\"]], \"run_duration\": \"0:30:35\", \"run_number\": \"69\", \"run_queued_details\": {\"status\": \"Finished\", \"details\": null}}, \"child_runs\": [{\"run_id\": \"fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b\", \"name\": \"automl_module\", \"status\": \"Finished\", \"start_time\": \"2021-04-16T23:51:22.242156Z\", \"created_time\": \"2021-04-16T23:51:04.054728Z\", \"end_time\": \"2021-04-17T00:19:52.306136Z\", \"duration\": \"0:28:48\", \"run_number\": 70, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-04-16T23:51:04.054728Z\", \"is_reused\": \"\"}], \"children_metrics\": {\"categories\": null, \"series\": null, \"metricName\": null}, \"run_metrics\": [], \"run_logs\": \"[2021-04-16 23:51:03Z] Submitting 1 runs, first five are: b314bd36:fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b\\n[2021-04-17 00:21:35Z] Completing processing run id fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b.\\n\\nRun is completed.\", \"graph\": {\"datasource_nodes\": {\"9a950a79\": {\"node_id\": \"9a950a79\", \"name\": \"Bank-marketing\"}}, \"module_nodes\": {\"b314bd36\": {\"node_id\": \"b314bd36\", \"name\": \"automl_module\", \"status\": \"Finished\", \"_is_reused\": false, \"run_id\": \"fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b\"}}, \"edges\": [{\"source_node_id\": \"9a950a79\", \"source_node_name\": \"Bank-marketing\", \"source_name\": \"data\", \"target_name\": \"training_data\", \"dst_node_id\": \"b314bd36\", \"dst_node_name\": \"automl_module\"}], \"child_runs\": [{\"run_id\": \"fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b\", \"name\": \"automl_module\", \"status\": \"Finished\", \"start_time\": \"2021-04-16T23:51:22.242156Z\", \"created_time\": \"2021-04-16T23:51:04.054728Z\", \"end_time\": \"2021-04-17T00:19:52.306136Z\", \"duration\": \"0:28:48\", \"run_number\": 70, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-04-16T23:51:04.054728Z\", \"is_reused\": \"\"}]}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.26.0\"}, \"loading\": false}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "gather": {
          "logged": 1618617072744
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_run.wait_for_completion()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PipelineRunId: 65467d06-e84a-441b-8f23-9a07a5159489\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/runs/65467d06-e84a-441b-8f23-9a07a5159489?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-142884/workspaces/quick-starts-ws-142884&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n",
            "PipelineRun Status: Running\n",
            "\n",
            "\n",
            "StepRunId: fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/runs/fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-142884/workspaces/quick-starts-ws-142884&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n",
            "StepRun( automl_module ) Status: Running\n",
            "\n",
            "StepRun(automl_module) Execution Summary\n",
            "=========================================\n",
            "StepRun( automl_module ) Status: Finished\n",
            "{'runId': 'fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b', 'target': 'ml-compute', 'status': 'Completed', 'startTimeUtc': '2021-04-16T23:51:22.242156Z', 'endTimeUtc': '2021-04-17T00:19:52.306136Z', 'properties': {'ContentSnapshotId': '3794c763-1bf5-47a6-ada7-8c78f2fc2580', 'StepType': 'AutoMLStep', 'azureml.moduleid': '776230b8-7aee-477c-98da-15a9383134b9', 'azureml.runsource': 'azureml.StepRun', 'azureml.nodeid': 'b314bd36', 'azureml.pipelinerunid': '65467d06-e84a-441b-8f23-9a07a5159489', 'num_iterations': '1000', 'training_type': 'TrainFull', 'acquisition_function': 'EI', 'metrics': 'accuracy', 'primary_metric': 'AUC_weighted', 'train_split': '0', 'MaxTimeSeconds': None, 'acquisition_parameter': '0', 'num_cross_validation': None, 'target': 'ml-compute', 'RawAMLSettingsString': None, 'AMLSettingsJsonString': '{\"path\": null, \"name\": \"placeholder\", \"subscription_id\": \"9e65f93e-bdd8-437b-b1e8-0647cd6098f7\", \"resource_group\": \"aml-quickstarts-142884\", \"workspace_name\": \"quick-starts-ws-142884\", \"region\": \"southcentralus\", \"compute_target\": \"ml-compute\", \"spark_service\": null, \"azure_service\": null, \"many_models\": false, \"pipeline_fetch_max_batch_size\": 1, \"enable_batch_run\": false, \"enable_run_restructure\": false, \"iterations\": 1000, \"primary_metric\": \"AUC_weighted\", \"task_type\": \"classification\", \"data_script\": null, \"test_size\": 0.0, \"validation_size\": 0.0, \"n_cross_validations\": null, \"y_min\": null, \"y_max\": null, \"num_classes\": null, \"featurization\": \"auto\", \"_ignore_package_version_incompatibilities\": false, \"is_timeseries\": false, \"max_cores_per_iteration\": 1, \"max_concurrent_iterations\": 5, \"iteration_timeout_minutes\": null, \"mem_in_mb\": null, \"enforce_time_on_windows\": false, \"experiment_timeout_minutes\": 20, \"experiment_exit_score\": null, \"whitelist_models\": null, \"blacklist_algos\": null, \"supported_models\": [\"LightGBM\", \"LogisticRegression\", \"GradientBoosting\", \"BernoulliNaiveBayes\", \"DecisionTree\", \"LinearSVM\", \"SGD\", \"RandomForest\", \"ExtremeRandomTrees\", \"SVM\", \"AveragedPerceptronClassifier\", \"TensorFlowDNN\", \"TensorFlowLinearClassifier\", \"XGBoostClassifier\", \"KNN\", \"MultinomialNaiveBayes\"], \"private_models\": [], \"auto_blacklist\": true, \"blacklist_samples_reached\": false, \"exclude_nan_labels\": true, \"verbosity\": 20, \"_debug_log\": \"automl_errors.log\", \"show_warnings\": false, \"model_explainability\": true, \"service_url\": null, \"sdk_url\": null, \"sdk_packages\": null, \"enable_onnx_compatible_models\": false, \"enable_split_onnx_featurizer_estimator_models\": false, \"vm_type\": \"STANDARD_DS12_V2\", \"telemetry_verbosity\": 20, \"send_telemetry\": true, \"enable_dnn\": false, \"scenario\": \"SDK-1.13.0\", \"environment_label\": null, \"save_mlflow\": false, \"force_text_dnn\": false, \"enable_feature_sweeping\": true, \"enable_early_stopping\": true, \"early_stopping_n_iters\": 10, \"metrics\": null, \"enable_metric_confidence\": false, \"enable_ensembling\": true, \"enable_stack_ensembling\": true, \"ensemble_iterations\": 15, \"enable_tf\": false, \"enable_subsampling\": null, \"subsample_seed\": null, \"enable_nimbusml\": false, \"enable_streaming\": false, \"force_streaming\": false, \"track_child_runs\": true, \"allowed_private_models\": [], \"label_column_name\": \"y\", \"weight_column_name\": null, \"cv_split_column_names\": null, \"enable_local_managed\": false, \"_local_managed_run_id\": null, \"cost_mode\": 1, \"lag_length\": 0, \"metric_operation\": \"maximize\", \"preprocess\": true}', 'DataPrepJsonString': '{\\\\\"training_data\\\\\": {\\\\\"datasetId\\\\\": \\\\\"db037a14-61b5-4c79-99bf-2bcd45b0f590\\\\\"}, \\\\\"datasets\\\\\": 0}', 'EnableSubsampling': 'False', 'runTemplate': 'AutoML', 'Orchestrator': 'automl', 'ClientType': 'Others', '_aml_system_scenario_identification': 'Remote.Parent', 'root_attribution': 'azureml.StepRun', 'snapshotId': '3794c763-1bf5-47a6-ada7-8c78f2fc2580', 'SetupRunId': 'fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_setup', 'SetupRunContainerId': 'dcid.fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_setup', 'ClientSdkVersion': '1.26.0', 'FeaturizationRunJsonPath': 'featurizer_container.json', 'FeaturizationRunId': 'fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_featurize', 'ProblemInfoJsonString': '{\"dataset_num_categorical\": 0, \"is_sparse\": true, \"subsampling\": false, \"dataset_classes\": 2, \"dataset_features\": 132, \"dataset_samples\": 29655, \"single_frequency_class_detected\": false}', 'ModelExplainRunId': 'fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_ModelExplain'}, 'inputDatasets': [], 'outputDatasets': [], 'logFiles': {'logs/azureml/executionlogs.txt': 'https://mlstrg142884.blob.core.windows.net/azureml/ExperimentRun/dcid.fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=rIktGC5ubsQ%2BU%2BGe8g5n62HrNrKDvyVst7hWvCMvz8c%3D&st=2021-04-16T23%3A41%3A30Z&se=2021-04-17T07%3A51%3A30Z&sp=r', 'logs/azureml/stderrlogs.txt': 'https://mlstrg142884.blob.core.windows.net/azureml/ExperimentRun/dcid.fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=B9ZeAZySqXaXPsV%2BhsrKAbtdGD7uv%2BSQSHXw%2BmUfBXg%3D&st=2021-04-16T23%3A41%3A30Z&se=2021-04-17T07%3A51%3A30Z&sp=r', 'logs/azureml/stdoutlogs.txt': 'https://mlstrg142884.blob.core.windows.net/azureml/ExperimentRun/dcid.fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=z799iv52ErqAWbTjjmZ%2B9C3H6xip%2BbQKkp%2Frd5KFDKU%3D&st=2021-04-16T23%3A41%3A30Z&se=2021-04-17T07%3A51%3A30Z&sp=r'}, 'submittedBy': 'ODL_User 142884'}\n",
            "\n",
            "\n",
            "\n",
            "PipelineRun Execution Summary\n",
            "==============================\n",
            "PipelineRun Status: Finished\n",
            "{'runId': '65467d06-e84a-441b-8f23-9a07a5159489', 'status': 'Completed', 'startTimeUtc': '2021-04-16T23:51:02.260136Z', 'endTimeUtc': '2021-04-17T00:21:36.252235Z', 'properties': {'azureml.runsource': 'azureml.PipelineRun', 'runSource': 'SDK', 'runType': 'SDK', 'azureml.parameters': '{}'}, 'inputDatasets': [], 'outputDatasets': [], 'logFiles': {'logs/azureml/executionlogs.txt': 'https://mlstrg142884.blob.core.windows.net/azureml/ExperimentRun/dcid.65467d06-e84a-441b-8f23-9a07a5159489/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=bI%2BAxPT8o0D274oaBX4GlthCi%2FLKDdTJVMSKkebJ4wA%3D&st=2021-04-16T23%3A41%3A12Z&se=2021-04-17T07%3A51%3A12Z&sp=r', 'logs/azureml/stderrlogs.txt': 'https://mlstrg142884.blob.core.windows.net/azureml/ExperimentRun/dcid.65467d06-e84a-441b-8f23-9a07a5159489/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=pyKoWrW94For%2F626QVr32ktJkaCPn3%2BU%2FT04GVJZVpA%3D&st=2021-04-16T23%3A41%3A12Z&se=2021-04-17T07%3A51%3A12Z&sp=r', 'logs/azureml/stdoutlogs.txt': 'https://mlstrg142884.blob.core.windows.net/azureml/ExperimentRun/dcid.65467d06-e84a-441b-8f23-9a07a5159489/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=r5JQwKvZB2zDzWIcC2m%2FylxjcfEHnrDarBPZDVTY9TE%3D&st=2021-04-16T23%3A41%3A12Z&se=2021-04-17T07%3A51%3A12Z&sp=r'}, 'submittedBy': 'ODL_User 142884'}\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "'Finished'"
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "gather": {
          "logged": 1618618898126
        }
      }
    },
    {
      "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/fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b/metrics_data\n",
            "Downloaded azureml/fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b/metrics_data, 1 files out of an estimated total of 1\n"
          ]
        }
      ],
      "execution_count": 14,
      "metadata": {
        "gather": {
          "logged": 1618619662611
        }
      }
    },
    {
      "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": "                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_0  \\\nf1_score_micro                                     [0.9116843702579667]   \nmatthews_correlation                               [0.5323740218566827]   \nAUC_weighted                                       [0.9450464668693167]   \nnorm_macro_recall                                  [0.5026785366965085]   \nlog_loss                                          [0.17775706110025447]   \naverage_precision_score_macro                      [0.8151093723721079]   \nf1_score_weighted                                  [0.9091539479147899]   \nf1_score_macro                                     [0.7653697272147331]   \nrecall_score_weighted                              [0.9116843702579667]   \nAUC_micro                                           [0.979695082216353]   \nprecision_score_macro                              [0.7819118765348991]   \nrecall_score_macro                                 [0.7513392683482543]   \nAUC_macro                                          [0.9450464668693166]   \naverage_precision_score_weighted                   [0.9531771295804466]   \nweighted_accuracy                                  [0.9514937218005303]   \naccuracy                                           [0.9116843702579667]   \nrecall_score_micro                                 [0.9116843702579667]   \naverage_precision_score_micro                      [0.9806603102489483]   \nbalanced_accuracy                                  [0.7513392683482543]   \nprecision_score_micro                              [0.9116843702579667]   \nprecision_score_weighted                           [0.9072720074188747]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_8  \\\nf1_score_micro                                     [0.8880121396054628]   \nmatthews_correlation                                              [0.0]   \nAUC_weighted                                       [0.8904143210946804]   \nnorm_macro_recall                                                 [0.0]   \nlog_loss                                           [0.2685166977237752]   \naverage_precision_score_macro                      [0.7343712102413749]   \nf1_score_weighted                                  [0.8353395018439429]   \nf1_score_macro                                     [0.4703423886834914]   \nrecall_score_weighted                              [0.8880121396054628]   \nAUC_micro                                          [0.9656628772614966]   \nprecision_score_macro                              [0.4440060698027314]   \nrecall_score_macro                                                [0.5]   \nAUC_macro                                          [0.8904143210946807]   \naverage_precision_score_weighted                   [0.9264029121952708]   \nweighted_accuracy                                  [0.9843450583187134]   \naccuracy                                           [0.8880121396054628]   \nrecall_score_micro                                 [0.8880121396054628]   \naverage_precision_score_micro                      [0.9644680365284293]   \nbalanced_accuracy                                                 [0.5]   \nprecision_score_micro                              [0.8880121396054628]   \nprecision_score_weighted                            [0.788565560086672]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_10  \\\nf1_score_micro                                      [0.7204855842185129]   \nmatthews_correlation                                [0.3063290622312932]   \nAUC_weighted                                        [0.8369505619184695]   \nnorm_macro_recall                                   [0.4578825111559386]   \nlog_loss                                             [0.559941333720253]   \naverage_precision_score_macro                       [0.7053478447201323]   \nf1_score_weighted                                   [0.7700481135023433]   \nf1_score_macro                                      [0.5962053324615282]   \nrecall_score_weighted                               [0.7204855842185129]   \nAUC_micro                                           [0.8339443816330901]   \nprecision_score_macro                               [0.6024689653800139]   \nrecall_score_macro                                  [0.7289412555779693]   \nAUC_macro                                           [0.8369505619184695]   \naverage_precision_score_weighted                    [0.9143570643371473]   \nweighted_accuracy                                   [0.7183862697420001]   \naccuracy                                            [0.7204855842185129]   \nrecall_score_micro                                  [0.7204855842185129]   \naverage_precision_score_micro                       [0.8147075813032243]   \nbalanced_accuracy                                   [0.7289412555779693]   \nprecision_score_micro                               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   [0.4440060698027314]   \nrecall_score_macro                                                 [0.5]   \nAUC_macro                                           [0.8835938701150512]   \naverage_precision_score_weighted                    [0.9246730409720553]   \nweighted_accuracy                                   [0.9843450583187134]   \naccuracy                                            [0.8880121396054628]   \nrecall_score_micro                                  [0.8880121396054628]   \naverage_precision_score_micro                        [0.965005452828571]   \nbalanced_accuracy                                                  [0.5]   \nprecision_score_micro                               [0.8880121396054628]   \nprecision_score_weighted                             [0.788565560086672]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_13  \\\nf1_score_micro                                      [0.9062215477996965]   \nmatthews_correlation                                [0.3976739324324451]   \nAUC_weighted                                        [0.9308878256246675]   \nnorm_macro_recall                                  [0.24549085203770704]   \nlog_loss                                           [0.33655623030329523]   \naverage_precision_score_macro                       [0.7985126174047921]   \nf1_score_weighted                                    [0.885603431576398]   \nf1_score_macro                                      [0.6650154684677185]   \nrecall_score_weighted                               [0.9062215477996965]   \nAUC_micro                                           [0.9758990146932517]   \nprecision_score_macro                                [0.822098675416211]   \nrecall_score_macro                                  [0.6227454260188535]   \nAUC_macro                                           [0.9308878256246677]   \naverage_precision_score_weighted                     [0.947605275820125]   \nweighted_accuracy                                   [0.9766010009385309]   \naccuracy                                            [0.9062215477996965]   \nrecall_score_micro                                  [0.9062215477996965]   \naverage_precision_score_micro                       [0.9766643355999638]   \nbalanced_accuracy                                   [0.6227454260188535]   \nprecision_score_micro                               [0.9062215477996965]   \nprecision_score_weighted                            [0.8929725418691179]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_12  \\\nf1_score_micro                                      [0.7599393019726859]   \nmatthews_correlation                               [0.36059627632868885]   \nAUC_weighted                                         [0.875266510696549]   \nnorm_macro_recall                                   [0.5236261385170242]   \nlog_loss                                            [0.5710907781970072]   \naverage_precision_score_macro                       [0.7399097985687175]   \nf1_score_weighted                                   [0.8004483444063107]   \nf1_score_macro                                      [0.6325688930241739]   \nrecall_score_weighted                               [0.7599393019726859]   \nAUC_micro                                             [0.85149569057822]   \nprecision_score_macro                               [0.6241627040147162]   \nrecall_score_macro                                  [0.7618130692585121]   \nAUC_macro                                           [0.8752665106965491]   \naverage_precision_score_weighted                    [0.9282339852826944]   \nweighted_accuracy                                   [0.7594740962401628]   \naccuracy                                            [0.7599393019726859]   \nrecall_score_micro                                  [0.7599393019726859]   \naverage_precision_score_micro                       [0.8504426989626127]   \nbalanced_accuracy                                   [0.7618130692585121]   \nprecision_score_micro                               [0.7599393019726859]   \nprecision_score_weighted                            [0.8865820244822358]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_7  \\\nf1_score_micro                                     [0.8880121396054628]   \nmatthews_correlation                                              [0.0]   \nAUC_weighted                                       [0.8399917013524204]   \nnorm_macro_recall                                                 [0.0]   \nlog_loss                                          [0.27910246410437195]   \naverage_precision_score_macro                      [0.7233000422549825]   \nf1_score_weighted                                  [0.8353395018439429]   \nf1_score_macro                                     [0.4703423886834914]   \nrecall_score_weighted                              [0.8880121396054628]   \nAUC_micro                                          [0.9556341631340077]   \nprecision_score_macro                              [0.4440060698027314]   \nrecall_score_macro                                                [0.5]   \nAUC_macro                                          [0.8399917013524203]   \naverage_precision_score_weighted                   [0.9173876420701099]   \nweighted_accuracy                                  [0.9843450583187134]   \naccuracy                                           [0.8880121396054628]   \nrecall_score_micro                                 [0.8880121396054628]   \naverage_precision_score_micro                      [0.9544330030307935]   \nbalanced_accuracy                                                 [0.5]   \nprecision_score_micro                              [0.8880121396054628]   \nprecision_score_weighted                            [0.788565560086672]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_9  \\\nf1_score_micro                                     [0.7308042488619121]   \nmatthews_correlation                              [0.31519940886436215]   \nAUC_weighted                                       [0.8458336343445457]   \nnorm_macro_recall                                 [0.46713420654370585]   \nlog_loss                                           [0.5486286109897168]   \naverage_precision_score_macro                      [0.7199122584683162]   \nf1_score_weighted                                  [0.7779065562045757]   \nf1_score_macro                                     [0.6041105967887157]   \nrecall_score_weighted                               [0.730804248861912]   \nAUC_micro                                          [0.8346064414515025]   \nprecision_score_macro                              [0.6063406040027899]   \nrecall_score_macro                                 [0.7335671032718529]   \nAUC_macro                                          [0.8458336343445458]   \naverage_precision_score_weighted                   [0.9188507630936452]   \nweighted_accuracy                                  [0.7301183068458709]   \naccuracy                                            [0.730804248861912]   \nrecall_score_micro                                  [0.730804248861912]   \naverage_precision_score_micro                      [0.8106954008017577]   \nbalanced_accuracy                                  [0.7335671032718529]   \nprecision_score_micro                               [0.730804248861912]   \nprecision_score_weighted                           [0.8781198865858602]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_31  \\\nf1_score_micro                                      [0.9147192716236722]   \nmatthews_correlation                                [0.5415601539975136]   \nAUC_weighted                                        [0.9443527517981948]   \nnorm_macro_recall                                   [0.5013596444918653]   \nlog_loss                                           [0.17995261094858092]   \naverage_precision_score_macro                       [0.8166277342564503]   \nf1_score_weighted                                   [0.9114397543809099]   \nf1_score_macro                                      [0.7693088121818761]   \nrecall_score_weighted                               [0.9147192716236723]   \nAUC_micro                                           [0.9794775272231574]   \nprecision_score_macro                               [0.7924920300426865]   \nrecall_score_macro                                  [0.7506798222459327]   \nAUC_macro                                           [0.9443527517981947]   \naverage_precision_score_weighted                    [0.9534265068233939]   \nweighted_accuracy                                   [0.9554458297121392]   \naccuracy                                            [0.9147192716236723]   \nrecall_score_micro                                  [0.9147192716236723]   \naverage_precision_score_micro                       [0.9804480170351562]   \nbalanced_accuracy                                   [0.7506798222459327]   \nprecision_score_micro                               [0.9147192716236723]   \nprecision_score_weighted                            [0.9093275652660812]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_33  ...  \\\nf1_score_micro                                      [0.9138088012139606]  ...   \nmatthews_correlation                                [0.5330974582677548]  ...   \nAUC_weighted                                        [0.9335510802134679]  ...   \nnorm_macro_recall                                  [0.48849303598982674]  ...   \nlog_loss                                           [0.32244278655390163]  ...   \naverage_precision_score_macro                       [0.8096894722982819]  ...   \nf1_score_weighted                                   [0.9100568177697264]  ...   \nf1_score_macro                                      [0.7646958834226492]  ...   \nrecall_score_weighted                               [0.9138088012139606]  ...   \nAUC_micro                                           [0.9770057635494068]  ...   \nprecision_score_macro                                [0.790887360794903]  ...   \nrecall_score_macro                                  [0.7442465179949134]  ...   \nAUC_macro                                           [0.9335510802134679]  ...   \naverage_precision_score_weighted                    [0.9502604175969057]  ...   \nweighted_accuracy                                   [0.9559065295550965]  ...   \naccuracy                                            [0.9138088012139606]  ...   \nrecall_score_micro                                  [0.9138088012139606]  ...   \naverage_precision_score_micro                       [0.9776421218704889]  ...   \nbalanced_accuracy                                   [0.7442465179949134]  ...   \nprecision_score_micro                               [0.9138088012139606]  ...   \nprecision_score_weighted                             [0.907774725299005]  ...   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_18  \\\nf1_score_micro                                      [0.7128983308042488]   \nmatthews_correlation                                [0.3018466023038423]   \nAUC_weighted                                        [0.8577569200162267]   \nnorm_macro_recall                                  [0.45407495086570826]   \nlog_loss                                            [0.5593749821530971]   \naverage_precision_score_macro                       [0.7255295764642636]   \nf1_score_weighted                                   [0.7642725876086041]   \nf1_score_macro                                      [0.5909691909743874]   \nrecall_score_weighted                               [0.7128983308042489]   \nAUC_micro                                           [0.8312950370842841]   \nprecision_score_macro                                 [0.60032635707901]   \nrecall_score_macro                                  [0.7270374754328541]   \nAUC_macro                                           [0.8577569200162267]   \naverage_precision_score_weighted                    [0.9226930515329753]   \nweighted_accuracy                                   [0.7093879636503571]   \naccuracy                                            [0.7128983308042489]   \nrecall_score_micro                                  [0.7128983308042489]   \naverage_precision_score_micro                       [0.8369425142740393]   \nbalanced_accuracy                                   [0.7270374754328541]   \nprecision_score_micro                               [0.7128983308042489]   \nprecision_score_weighted                            [0.8768359985923969]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_21  \\\nf1_score_micro                                      [0.9101669195751139]   \nmatthews_correlation                                [0.4867731611986173]   \nAUC_weighted                                        [0.9342679499932388]   \nnorm_macro_recall                                   [0.4109757023749321]   \nlog_loss                                            [0.1921852994995217]   \naverage_precision_score_macro                       [0.7998321444303222]   \nf1_score_weighted                                   [0.9026632441364442]   \nf1_score_macro                                      [0.7372589501995638]   \nrecall_score_weighted                               [0.9101669195751139]   \nAUC_micro                                           [0.9766515228619257]   \nprecision_score_macro                               [0.7882750842617063]   \nrecall_score_macro                                   [0.705487851187466]   \nAUC_macro                                           [0.9342679499932389]   \naverage_precision_score_weighted                    [0.9486146431518476]   \nweighted_accuracy                                   [0.9609831957806473]   \naccuracy                                            [0.9101669195751139]   \nrecall_score_micro                                  [0.9101669195751139]   \naverage_precision_score_micro                       [0.9777871805237555]   \nbalanced_accuracy                                    [0.705487851187466]   \nprecision_score_micro                               [0.9101669195751139]   \nprecision_score_weighted                            [0.9000274768383943]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_32  \\\nf1_score_micro                                      [0.9125948406676783]   \nmatthews_correlation                                 [0.530535514228425]   \nAUC_weighted                                        [0.9423114326837048]   \nnorm_macro_recall                                  [0.49186250919241936]   \nlog_loss                                           [0.18420134724861562]   \naverage_precision_score_macro                       [0.8130064306176901]   \nf1_score_weighted                                   [0.9092961593409308]   \nf1_score_macro                                      [0.7638698118841445]   \nrecall_score_weighted                               [0.9125948406676783]   \nAUC_micro                                           [0.9790132195513965]   \nprecision_score_macro                               [0.7861246045360897]   \nrecall_score_macro                                  [0.7459312545962097]   \nAUC_macro                                           [0.9423114326837048]   \naverage_precision_score_weighted                    [0.9524012051949555]   \nweighted_accuracy                                   [0.9539729009155015]   \naccuracy                                            [0.9125948406676783]   \nrecall_score_micro                                  [0.9125948406676783]   \naverage_precision_score_micro                       [0.9800058297942514]   \nbalanced_accuracy                                   [0.7459312545962097]   \nprecision_score_micro                               [0.9125948406676783]   \nprecision_score_weighted                            [0.9071064203941039]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_34  \\\nf1_score_micro                                      [0.9077389984825494]   \nmatthews_correlation                               [0.48767795941601105]   \nAUC_weighted                                        [0.9389373285393824]   \nnorm_macro_recall                                   [0.4319242303837938]   \nlog_loss                                            [0.1880124255406952]   \naverage_precision_score_macro                       [0.8072509489251203]   \nf1_score_weighted                                   [0.9021770532058848]   \nf1_score_macro                                      [0.7405646205646206]   \nrecall_score_weighted                               [0.9077389984825494]   \nAUC_micro                                           [0.9780497880404623]   \nprecision_score_macro                               [0.7753142511695126]   \nrecall_score_macro                                  [0.7159621151918969]   \nAUC_macro                                           [0.9389373285393825]   \naverage_precision_score_weighted                     [0.950761405989792]   \nweighted_accuracy                                   [0.9553520111267004]   \naccuracy                                            [0.9077389984825494]   \nrecall_score_micro                                  [0.9077389984825494]   \naverage_precision_score_micro                       [0.9790992488308239]   \nbalanced_accuracy                                   [0.7159621151918969]   \nprecision_score_micro                               [0.9077389984825494]   \nprecision_score_weighted                            [0.8992110967163239]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_19  \\\nf1_score_micro                                      [0.9125948406676783]   \nmatthews_correlation                                [0.5254139610791995]   \nAUC_weighted                                         [0.943998021661693]   \nnorm_macro_recall                                   [0.4800211911893555]   \nlog_loss                                           [0.17981385781039308]   \naverage_precision_score_macro                       [0.8126929119384294]   \nf1_score_weighted                                   [0.9086613440609772]   \nf1_score_macro                                      [0.7607503025413473]   \nrecall_score_weighted                               [0.9125948406676783]   \nAUC_micro                                           [0.9792565642982309]   \nprecision_score_macro                                 [0.78754962860383]   \nrecall_score_macro                                  [0.7400105955946777]   \nAUC_macro                                            [0.943998021661693]   \naverage_precision_score_weighted                    [0.9525161907226625]   \nweighted_accuracy                                   [0.9554428403944659]   \naccuracy                                            [0.9125948406676783]   \nrecall_score_micro                                  [0.9125948406676783]   \naverage_precision_score_micro                       [0.9802395848606664]   \nbalanced_accuracy                                   [0.7400105955946777]   \nprecision_score_micro                               [0.9125948406676783]   \nprecision_score_weighted                            [0.9062798949414683]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_23  \\\nf1_score_micro                                      [0.9107738998482551]   \nmatthews_correlation                                [0.5217153406413008]   \nAUC_weighted                                        [0.9411370258610308]   \nnorm_macro_recall                                   [0.4850754009932443]   \nlog_loss                                           [0.19281962981552705]   \naverage_precision_score_macro                       [0.8097851285823288]   \nf1_score_weighted                                   [0.9075335307123638]   \nf1_score_macro                                      [0.7595759614181874]   \nrecall_score_weighted                                [0.910773899848255]   \nAUC_micro                                           [0.9784231868306466]   \nprecision_score_macro                               [0.7805614303499377]   \nrecall_score_macro                                  [0.7425377004966222]   \nAUC_macro                                            [0.941137025861031]   \naverage_precision_score_weighted                    [0.9514170948185521]   \nweighted_accuracy                                   [0.9525423974350735]   \naccuracy                                             [0.910773899848255]   \nrecall_score_micro                                   [0.910773899848255]   \naverage_precision_score_micro                       [0.9793211817234202]   \nbalanced_accuracy                                   [0.7425377004966222]   \nprecision_score_micro                                [0.910773899848255]   \nprecision_score_weighted                            [0.9053097572540955]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_26  \\\nf1_score_micro                                      [0.9089529590288316]   \nmatthews_correlation                                [0.5183718146674563]   \nAUC_weighted                                        [0.9424512871239444]   \nnorm_macro_recall                                  [0.49012961079713335]   \nlog_loss                                           [0.20743540323316234]   \naverage_precision_score_macro                       [0.8160119279976998]   \nf1_score_weighted                                   [0.9064064693565229]   \nf1_score_macro                                      [0.7584206945362264]   \nrecall_score_weighted                               [0.9089529590288316]   \nAUC_micro                                           [0.9790270354908458]   \nprecision_score_macro                               [0.7741206941206942]   \nrecall_score_macro                                  [0.7450648053985667]   \nAUC_macro                                           [0.9424512871239443]   \naverage_precision_score_weighted                    [0.9530727307978201]   \nweighted_accuracy                                   [0.9496419544756812]   \naccuracy                                            [0.9089529590288316]   \nrecall_score_micro                                  [0.9089529590288316]   \naverage_precision_score_micro                       [0.9800141185919266]   \nbalanced_accuracy                                   [0.7450648053985667]   \nprecision_score_micro                               [0.9089529590288316]   \nprecision_score_weighted                            [0.9044670462454985]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_27  \\\nf1_score_micro                                      [0.9138088012139606]   \nmatthews_correlation                                [0.5381268713966005]   \nAUC_weighted                                        [0.9440702643526778]   \nnorm_macro_recall                                   [0.5003343539928906]   \nlog_loss                                           [0.18047415972399278]   \naverage_precision_score_macro                       [0.8167288234994515]   \nf1_score_weighted                                   [0.9106786487153445]   \nf1_score_macro                                      [0.7677536498053239]   \nrecall_score_weighted                               [0.9138088012139606]   \nAUC_micro                                           [0.9794490663878916]   \nprecision_score_macro                               [0.7893870143116419]   \nrecall_score_macro                                  [0.7501671769964453]   \nAUC_macro                                           [0.9440702643526777]   \naverage_precision_score_weighted                    [0.9534157741149873]   \nweighted_accuracy                                   [0.9544365900761322]   \naccuracy                                            [0.9138088012139606]   \nrecall_score_micro                                  [0.9138088012139606]   \naverage_precision_score_micro                       [0.9804154714516231]   \nbalanced_accuracy                                   [0.7501671769964453]   \nprecision_score_micro                               [0.9138088012139606]   \nprecision_score_weighted                            [0.9085876546672915]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_39  \\\nf1_score_micro                                      [0.9056145675265553]   \nmatthews_correlation                               [0.38402553359693004]   \nAUC_weighted                                        [0.9464292660698309]   \nnorm_macro_recall                                  [0.21875642543165008]   \nlog_loss                                            [0.2259016655239658]   \naverage_precision_score_macro                       [0.8228644635984469]   \nf1_score_weighted                                   [0.8820971522827097]   \nf1_score_macro                                      [0.6499194272691463]   \nrecall_score_weighted                               [0.9056145675265553]   \nAUC_micro                                           [0.9781876711161668]   \nprecision_score_macro                               [0.8370772085057799]   \nrecall_score_macro                                   [0.609378212715825]   \nAUC_macro                                           [0.9464292660698308]   \naverage_precision_score_weighted                    [0.9550355922261399]   \nweighted_accuracy                                   [0.9791620413682476]   \naccuracy                                            [0.9056145675265553]   \nrecall_score_micro                                  [0.9056145675265553]   \naverage_precision_score_micro                       [0.9792738418629331]   \nbalanced_accuracy                                    [0.609378212715825]   \nprecision_score_micro                               [0.9056145675265553]   \nprecision_score_weighted                            [0.8940690872102101]   \n\n                                 fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_30  \nf1_score_micro                                      [0.8880121396054628]  \nmatthews_correlation                                               [0.0]  \nAUC_weighted                                        [0.9271858508058763]  \nnorm_macro_recall                                                  [0.0]  \nlog_loss                                           [0.24275331984516882]  \naverage_precision_score_macro                       [0.7841255279063619]  \nf1_score_weighted                                   [0.8353395018439429]  \nf1_score_macro                                      [0.4703423886834914]  \nrecall_score_weighted                               [0.8880121396054628]  \nAUC_micro                                           [0.9729764829684007]  \nprecision_score_macro                               [0.4440060698027314]  \nrecall_score_macro                                                 [0.5]  \nAUC_macro                                           [0.9271858508058763]  \naverage_precision_score_weighted                    [0.9434384837698533]  \nweighted_accuracy                                   [0.9843450583187134]  \naccuracy                                            [0.8880121396054628]  \nrecall_score_micro                                  [0.8880121396054628]  \naverage_precision_score_micro                       [0.9732705157966541]  \nbalanced_accuracy                                                  [0.5]  \nprecision_score_micro                               [0.8880121396054628]  \nprecision_score_weighted                             [0.788565560086672]  \n\n[21 rows x 39 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>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_0</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_8</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_10</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_15</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_13</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_12</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_7</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_9</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_31</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_33</th>\n      <th>...</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_18</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_21</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_32</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_34</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_19</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_23</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_26</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_27</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_39</th>\n      <th>fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b_30</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>f1_score_micro</th>\n      <td>[0.9116843702579667]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.7204855842185129]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.7599393019726859]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.7308042488619121]</td>\n      <td>[0.9147192716236722]</td>\n      <td>[0.9138088012139606]</td>\n      <td>...</td>\n      <td>[0.7128983308042488]</td>\n      <td>[0.9101669195751139]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.9077389984825494]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.9107738998482551]</td>\n      <td>[0.9089529590288316]</td>\n      <td>[0.9138088012139606]</td>\n      <td>[0.9056145675265553]</td>\n      <td>[0.8880121396054628]</td>\n    </tr>\n    <tr>\n      <th>matthews_correlation</th>\n      <td>[0.5323740218566827]</td>\n      <td>[0.0]</td>\n      <td>[0.3063290622312932]</td>\n      <td>[0.0]</td>\n      <td>[0.3976739324324451]</td>\n      <td>[0.36059627632868885]</td>\n      <td>[0.0]</td>\n      <td>[0.31519940886436215]</td>\n      <td>[0.5415601539975136]</td>\n      <td>[0.5330974582677548]</td>\n      <td>...</td>\n      <td>[0.3018466023038423]</td>\n      <td>[0.4867731611986173]</td>\n      <td>[0.530535514228425]</td>\n      <td>[0.48767795941601105]</td>\n      <td>[0.5254139610791995]</td>\n      <td>[0.5217153406413008]</td>\n      <td>[0.5183718146674563]</td>\n      <td>[0.5381268713966005]</td>\n      <td>[0.38402553359693004]</td>\n      <td>[0.0]</td>\n    </tr>\n    <tr>\n      <th>AUC_weighted</th>\n      <td>[0.9450464668693167]</td>\n      <td>[0.8904143210946804]</td>\n      <td>[0.8369505619184695]</td>\n      <td>[0.8835938701150513]</td>\n      <td>[0.9308878256246675]</td>\n      <td>[0.875266510696549]</td>\n      <td>[0.8399917013524204]</td>\n      <td>[0.8458336343445457]</td>\n      <td>[0.9443527517981948]</td>\n      <td>[0.9335510802134679]</td>\n      <td>...</td>\n      <td>[0.8577569200162267]</td>\n      <td>[0.9342679499932388]</td>\n      <td>[0.9423114326837048]</td>\n      <td>[0.9389373285393824]</td>\n      <td>[0.943998021661693]</td>\n      <td>[0.9411370258610308]</td>\n      <td>[0.9424512871239444]</td>\n      <td>[0.9440702643526778]</td>\n      <td>[0.9464292660698309]</td>\n      <td>[0.9271858508058763]</td>\n    </tr>\n    <tr>\n      <th>norm_macro_recall</th>\n      <td>[0.5026785366965085]</td>\n      <td>[0.0]</td>\n      <td>[0.4578825111559386]</td>\n      <td>[0.0]</td>\n      <td>[0.24549085203770704]</td>\n      <td>[0.5236261385170242]</td>\n      <td>[0.0]</td>\n      <td>[0.46713420654370585]</td>\n      <td>[0.5013596444918653]</td>\n      <td>[0.48849303598982674]</td>\n      <td>...</td>\n      <td>[0.45407495086570826]</td>\n      <td>[0.4109757023749321]</td>\n      <td>[0.49186250919241936]</td>\n      <td>[0.4319242303837938]</td>\n      <td>[0.4800211911893555]</td>\n      <td>[0.4850754009932443]</td>\n      <td>[0.49012961079713335]</td>\n      <td>[0.5003343539928906]</td>\n      <td>[0.21875642543165008]</td>\n      <td>[0.0]</td>\n    </tr>\n    <tr>\n      <th>log_loss</th>\n      <td>[0.17775706110025447]</td>\n      <td>[0.2685166977237752]</td>\n      <td>[0.559941333720253]</td>\n      <td>[0.26257530891652975]</td>\n      <td>[0.33655623030329523]</td>\n      <td>[0.5710907781970072]</td>\n      <td>[0.27910246410437195]</td>\n      <td>[0.5486286109897168]</td>\n      <td>[0.17995261094858092]</td>\n      <td>[0.32244278655390163]</td>\n      <td>...</td>\n      <td>[0.5593749821530971]</td>\n      <td>[0.1921852994995217]</td>\n      <td>[0.18420134724861562]</td>\n      <td>[0.1880124255406952]</td>\n      <td>[0.17981385781039308]</td>\n      <td>[0.19281962981552705]</td>\n      <td>[0.20743540323316234]</td>\n      <td>[0.18047415972399278]</td>\n      <td>[0.2259016655239658]</td>\n      <td>[0.24275331984516882]</td>\n    </tr>\n    <tr>\n      <th>average_precision_score_macro</th>\n      <td>[0.8151093723721079]</td>\n      <td>[0.7343712102413749]</td>\n      <td>[0.7053478447201323]</td>\n      <td>[0.720651516860938]</td>\n      <td>[0.7985126174047921]</td>\n      <td>[0.7399097985687175]</td>\n      <td>[0.7233000422549825]</td>\n      <td>[0.7199122584683162]</td>\n      <td>[0.8166277342564503]</td>\n      <td>[0.8096894722982819]</td>\n      <td>...</td>\n      <td>[0.7255295764642636]</td>\n      <td>[0.7998321444303222]</td>\n      <td>[0.8130064306176901]</td>\n      <td>[0.8072509489251203]</td>\n      <td>[0.8126929119384294]</td>\n      <td>[0.8097851285823288]</td>\n      <td>[0.8160119279976998]</td>\n      <td>[0.8167288234994515]</td>\n      <td>[0.8228644635984469]</td>\n      <td>[0.7841255279063619]</td>\n    </tr>\n    <tr>\n      <th>f1_score_weighted</th>\n      <td>[0.9091539479147899]</td>\n      <td>[0.8353395018439429]</td>\n      <td>[0.7700481135023433]</td>\n      <td>[0.8353395018439429]</td>\n      <td>[0.885603431576398]</td>\n      <td>[0.8004483444063107]</td>\n      <td>[0.8353395018439429]</td>\n      <td>[0.7779065562045757]</td>\n      <td>[0.9114397543809099]</td>\n      <td>[0.9100568177697264]</td>\n      <td>...</td>\n      <td>[0.7642725876086041]</td>\n      <td>[0.9026632441364442]</td>\n      <td>[0.9092961593409308]</td>\n      <td>[0.9021770532058848]</td>\n      <td>[0.9086613440609772]</td>\n      <td>[0.9075335307123638]</td>\n      <td>[0.9064064693565229]</td>\n      <td>[0.9106786487153445]</td>\n      <td>[0.8820971522827097]</td>\n      <td>[0.8353395018439429]</td>\n    </tr>\n    <tr>\n      <th>f1_score_macro</th>\n      <td>[0.7653697272147331]</td>\n      <td>[0.4703423886834914]</td>\n      <td>[0.5962053324615282]</td>\n      <td>[0.4703423886834914]</td>\n      <td>[0.6650154684677185]</td>\n      <td>[0.6325688930241739]</td>\n      <td>[0.4703423886834914]</td>\n      <td>[0.6041105967887157]</td>\n      <td>[0.7693088121818761]</td>\n      <td>[0.7646958834226492]</td>\n      <td>...</td>\n      <td>[0.5909691909743874]</td>\n      <td>[0.7372589501995638]</td>\n      <td>[0.7638698118841445]</td>\n      <td>[0.7405646205646206]</td>\n      <td>[0.7607503025413473]</td>\n      <td>[0.7595759614181874]</td>\n      <td>[0.7584206945362264]</td>\n      <td>[0.7677536498053239]</td>\n      <td>[0.6499194272691463]</td>\n      <td>[0.4703423886834914]</td>\n    </tr>\n    <tr>\n      <th>recall_score_weighted</th>\n      <td>[0.9116843702579667]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.7204855842185129]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.7599393019726859]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.730804248861912]</td>\n      <td>[0.9147192716236723]</td>\n      <td>[0.9138088012139606]</td>\n      <td>...</td>\n      <td>[0.7128983308042489]</td>\n      <td>[0.9101669195751139]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.9077389984825494]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.910773899848255]</td>\n      <td>[0.9089529590288316]</td>\n      <td>[0.9138088012139606]</td>\n      <td>[0.9056145675265553]</td>\n      <td>[0.8880121396054628]</td>\n    </tr>\n    <tr>\n      <th>AUC_micro</th>\n      <td>[0.979695082216353]</td>\n      <td>[0.9656628772614966]</td>\n      <td>[0.8339443816330901]</td>\n      <td>[0.9643063362200971]</td>\n      <td>[0.9758990146932517]</td>\n      <td>[0.85149569057822]</td>\n      <td>[0.9556341631340077]</td>\n      <td>[0.8346064414515025]</td>\n      <td>[0.9794775272231574]</td>\n      <td>[0.9770057635494068]</td>\n      <td>...</td>\n      <td>[0.8312950370842841]</td>\n      <td>[0.9766515228619257]</td>\n      <td>[0.9790132195513965]</td>\n      <td>[0.9780497880404623]</td>\n      <td>[0.9792565642982309]</td>\n      <td>[0.9784231868306466]</td>\n      <td>[0.9790270354908458]</td>\n      <td>[0.9794490663878916]</td>\n      <td>[0.9781876711161668]</td>\n      <td>[0.9729764829684007]</td>\n    </tr>\n    <tr>\n      <th>precision_score_macro</th>\n      <td>[0.7819118765348991]</td>\n      <td>[0.4440060698027314]</td>\n      <td>[0.6024689653800139]</td>\n      <td>[0.4440060698027314]</td>\n      <td>[0.822098675416211]</td>\n      <td>[0.6241627040147162]</td>\n      <td>[0.4440060698027314]</td>\n      <td>[0.6063406040027899]</td>\n      <td>[0.7924920300426865]</td>\n      <td>[0.790887360794903]</td>\n      <td>...</td>\n      <td>[0.60032635707901]</td>\n      <td>[0.7882750842617063]</td>\n      <td>[0.7861246045360897]</td>\n      <td>[0.7753142511695126]</td>\n      <td>[0.78754962860383]</td>\n      <td>[0.7805614303499377]</td>\n      <td>[0.7741206941206942]</td>\n      <td>[0.7893870143116419]</td>\n      <td>[0.8370772085057799]</td>\n      <td>[0.4440060698027314]</td>\n    </tr>\n    <tr>\n      <th>recall_score_macro</th>\n      <td>[0.7513392683482543]</td>\n      <td>[0.5]</td>\n      <td>[0.7289412555779693]</td>\n      <td>[0.5]</td>\n      <td>[0.6227454260188535]</td>\n      <td>[0.7618130692585121]</td>\n      <td>[0.5]</td>\n      <td>[0.7335671032718529]</td>\n      <td>[0.7506798222459327]</td>\n      <td>[0.7442465179949134]</td>\n      <td>...</td>\n      <td>[0.7270374754328541]</td>\n      <td>[0.705487851187466]</td>\n      <td>[0.7459312545962097]</td>\n      <td>[0.7159621151918969]</td>\n      <td>[0.7400105955946777]</td>\n      <td>[0.7425377004966222]</td>\n      <td>[0.7450648053985667]</td>\n      <td>[0.7501671769964453]</td>\n      <td>[0.609378212715825]</td>\n      <td>[0.5]</td>\n    </tr>\n    <tr>\n      <th>AUC_macro</th>\n      <td>[0.9450464668693166]</td>\n      <td>[0.8904143210946807]</td>\n      <td>[0.8369505619184695]</td>\n      <td>[0.8835938701150512]</td>\n      <td>[0.9308878256246677]</td>\n      <td>[0.8752665106965491]</td>\n      <td>[0.8399917013524203]</td>\n      <td>[0.8458336343445458]</td>\n      <td>[0.9443527517981947]</td>\n      <td>[0.9335510802134679]</td>\n      <td>...</td>\n      <td>[0.8577569200162267]</td>\n      <td>[0.9342679499932389]</td>\n      <td>[0.9423114326837048]</td>\n      <td>[0.9389373285393825]</td>\n      <td>[0.943998021661693]</td>\n      <td>[0.941137025861031]</td>\n      <td>[0.9424512871239443]</td>\n      <td>[0.9440702643526777]</td>\n      <td>[0.9464292660698308]</td>\n      <td>[0.9271858508058763]</td>\n    </tr>\n    <tr>\n      <th>average_precision_score_weighted</th>\n      <td>[0.9531771295804466]</td>\n      <td>[0.9264029121952708]</td>\n      <td>[0.9143570643371473]</td>\n      <td>[0.9246730409720553]</td>\n      <td>[0.947605275820125]</td>\n      <td>[0.9282339852826944]</td>\n      <td>[0.9173876420701099]</td>\n      <td>[0.9188507630936452]</td>\n      <td>[0.9534265068233939]</td>\n      <td>[0.9502604175969057]</td>\n      <td>...</td>\n      <td>[0.9226930515329753]</td>\n      <td>[0.9486146431518476]</td>\n      <td>[0.9524012051949555]</td>\n      <td>[0.950761405989792]</td>\n      <td>[0.9525161907226625]</td>\n      <td>[0.9514170948185521]</td>\n      <td>[0.9530727307978201]</td>\n      <td>[0.9534157741149873]</td>\n      <td>[0.9550355922261399]</td>\n      <td>[0.9434384837698533]</td>\n    </tr>\n    <tr>\n      <th>weighted_accuracy</th>\n      <td>[0.9514937218005303]</td>\n      <td>[0.9843450583187134]</td>\n      <td>[0.7183862697420001]</td>\n      <td>[0.9843450583187134]</td>\n      <td>[0.9766010009385309]</td>\n      <td>[0.7594740962401628]</td>\n      <td>[0.9843450583187134]</td>\n      <td>[0.7301183068458709]</td>\n      <td>[0.9554458297121392]</td>\n      <td>[0.9559065295550965]</td>\n      <td>...</td>\n      <td>[0.7093879636503571]</td>\n      <td>[0.9609831957806473]</td>\n      <td>[0.9539729009155015]</td>\n      <td>[0.9553520111267004]</td>\n      <td>[0.9554428403944659]</td>\n      <td>[0.9525423974350735]</td>\n      <td>[0.9496419544756812]</td>\n      <td>[0.9544365900761322]</td>\n      <td>[0.9791620413682476]</td>\n      <td>[0.9843450583187134]</td>\n    </tr>\n    <tr>\n      <th>accuracy</th>\n      <td>[0.9116843702579667]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.7204855842185129]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.7599393019726859]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.730804248861912]</td>\n      <td>[0.9147192716236723]</td>\n      <td>[0.9138088012139606]</td>\n      <td>...</td>\n      <td>[0.7128983308042489]</td>\n      <td>[0.9101669195751139]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.9077389984825494]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.910773899848255]</td>\n      <td>[0.9089529590288316]</td>\n      <td>[0.9138088012139606]</td>\n      <td>[0.9056145675265553]</td>\n      <td>[0.8880121396054628]</td>\n    </tr>\n    <tr>\n      <th>recall_score_micro</th>\n      <td>[0.9116843702579667]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.7204855842185129]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.7599393019726859]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.730804248861912]</td>\n      <td>[0.9147192716236723]</td>\n      <td>[0.9138088012139606]</td>\n      <td>...</td>\n      <td>[0.7128983308042489]</td>\n      <td>[0.9101669195751139]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.9077389984825494]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.910773899848255]</td>\n      <td>[0.9089529590288316]</td>\n      <td>[0.9138088012139606]</td>\n      <td>[0.9056145675265553]</td>\n      <td>[0.8880121396054628]</td>\n    </tr>\n    <tr>\n      <th>average_precision_score_micro</th>\n      <td>[0.9806603102489483]</td>\n      <td>[0.9644680365284293]</td>\n      <td>[0.8147075813032243]</td>\n      <td>[0.965005452828571]</td>\n      <td>[0.9766643355999638]</td>\n      <td>[0.8504426989626127]</td>\n      <td>[0.9544330030307935]</td>\n      <td>[0.8106954008017577]</td>\n      <td>[0.9804480170351562]</td>\n      <td>[0.9776421218704889]</td>\n      <td>...</td>\n      <td>[0.8369425142740393]</td>\n      <td>[0.9777871805237555]</td>\n      <td>[0.9800058297942514]</td>\n      <td>[0.9790992488308239]</td>\n      <td>[0.9802395848606664]</td>\n      <td>[0.9793211817234202]</td>\n      <td>[0.9800141185919266]</td>\n      <td>[0.9804154714516231]</td>\n      <td>[0.9792738418629331]</td>\n      <td>[0.9732705157966541]</td>\n    </tr>\n    <tr>\n      <th>balanced_accuracy</th>\n      <td>[0.7513392683482543]</td>\n      <td>[0.5]</td>\n      <td>[0.7289412555779693]</td>\n      <td>[0.5]</td>\n      <td>[0.6227454260188535]</td>\n      <td>[0.7618130692585121]</td>\n      <td>[0.5]</td>\n      <td>[0.7335671032718529]</td>\n      <td>[0.7506798222459327]</td>\n      <td>[0.7442465179949134]</td>\n      <td>...</td>\n      <td>[0.7270374754328541]</td>\n      <td>[0.705487851187466]</td>\n      <td>[0.7459312545962097]</td>\n      <td>[0.7159621151918969]</td>\n      <td>[0.7400105955946777]</td>\n      <td>[0.7425377004966222]</td>\n      <td>[0.7450648053985667]</td>\n      <td>[0.7501671769964453]</td>\n      <td>[0.609378212715825]</td>\n      <td>[0.5]</td>\n    </tr>\n    <tr>\n      <th>precision_score_micro</th>\n      <td>[0.9116843702579667]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.7204855842185129]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.7599393019726859]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.730804248861912]</td>\n      <td>[0.9147192716236723]</td>\n      <td>[0.9138088012139606]</td>\n      <td>...</td>\n      <td>[0.7128983308042489]</td>\n      <td>[0.9101669195751139]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.9077389984825494]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.910773899848255]</td>\n      <td>[0.9089529590288316]</td>\n      <td>[0.9138088012139606]</td>\n      <td>[0.9056145675265553]</td>\n      <td>[0.8880121396054628]</td>\n    </tr>\n    <tr>\n      <th>precision_score_weighted</th>\n      <td>[0.9072720074188747]</td>\n      <td>[0.788565560086672]</td>\n      <td>[0.8770535826411984]</td>\n      <td>[0.788565560086672]</td>\n      <td>[0.8929725418691179]</td>\n      <td>[0.8865820244822358]</td>\n      <td>[0.788565560086672]</td>\n      <td>[0.8781198865858602]</td>\n      <td>[0.9093275652660812]</td>\n      <td>[0.907774725299005]</td>\n      <td>...</td>\n      <td>[0.8768359985923969]</td>\n      <td>[0.9000274768383943]</td>\n      <td>[0.9071064203941039]</td>\n      <td>[0.8992110967163239]</td>\n      <td>[0.9062798949414683]</td>\n      <td>[0.9053097572540955]</td>\n      <td>[0.9044670462454985]</td>\n      <td>[0.9085876546672915]</td>\n      <td>[0.8940690872102101]</td>\n      <td>[0.788565560086672]</td>\n    </tr>\n  </tbody>\n</table>\n<p>21 rows × 39 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "gather": {
          "logged": 1618619669973
        }
      }
    },
    {
      "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/fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b/model_data\n",
            "Downloaded azureml/fcd4a425-a7b2-42c9-9ec1-b4ea6c012a5b/model_data, 1 files out of an estimated total of 1\n"
          ]
        }
      ],
      "execution_count": 16,
      "metadata": {
        "gather": {
          "logged": 1618619698626
        }
      }
    },
    {
      "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=False,\n                                                                                                                                   random_state=None,\n                                                                                                                                   verbose=0,\n                                                                                                                                   warm_start=False))],\n                                                                                                      verbose=False))],\n                                                                                flatten_transform=None,\n                                                                                weights=[0.3076923076923077,\n                                                                                         0.07692307692307693,\n                                                                                         0.07692307692307693,\n                                                                                         0.07692307692307693,\n                                                                                         0.07692307692307693,\n                                                                                         0.23076923076923078,\n                                                                                         0.07692307692307693,\n                                                                                         0.07692307692307693]))],\n                                       'verbose': False},\n                             y_transformer={},\n                             y_transformer_name='LabelEncoder')"
          },
          "metadata": {}
        }
      ],
      "execution_count": 17,
      "metadata": {
        "gather": {
          "logged": 1618619722600
        }
      }
    },
    {
      "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=[('0',\n                                             Pipeline(memory=None,\n                                                      steps=[('maxabsscaler',\n                                                              MaxAbsScaler(copy=True)),\n                                                             ('lightgbmclassifier',\n                                                              LightGBMClassifier(boosting_type='gbdt',\n                                                                                 class_weight=None,\n                                                                                 colsample_bytree=1.0,\n                                                                                 importance_type='split',\n                                                                                 learning_rate=0.1,\n                                                                                 max_depth=-1,\n                                                                                 min_child_samples=20,\n                                                                                 min_child_weight=0.001,\n                                                                                 min_spl...\n                                                                                   min_samples_split=0.15052631578947367,\n                                                                                   min_weight_fraction_leaf=0.0,\n                                                                                   n_estimators=10,\n                                                                                   n_jobs=1,\n                                                                                   oob_score=False,\n                                                                                   random_state=None,\n                                                                                   verbose=0,\n                                                                                   warm_start=False))],\n                                                      verbose=False))],\n                                flatten_transform=None,\n                                weights=[0.3076923076923077, 0.07692307692307693,\n                                         0.07692307692307693, 0.07692307692307693,\n                                         0.07692307692307693, 0.23076923076923078,\n                                         0.07692307692307693,\n                                         0.07692307692307693]))]"
          },
          "metadata": {}
        }
      ],
      "execution_count": 18,
      "metadata": {
        "gather": {
          "logged": 1618619723130
        }
      }
    },
    {
      "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": 1618619743113
        }
      }
    },
    {
      "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": 1618619747969
        }
      }
    },
    {
      "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 0x7fac99a5fef0>",
            "text/html": "<style  type=\"text/css\" >\n    #T_df122eb2_9f14_11eb_8c58_3fa5987f1a48row0_col0 {\n            background-color:  #63a8d3;\n            color:  #000000;\n        }    #T_df122eb2_9f14_11eb_8c58_3fa5987f1a48row0_col1 {\n            background-color:  #f7fbff;\n            color:  #000000;\n        }    #T_df122eb2_9f14_11eb_8c58_3fa5987f1a48row1_col0 {\n            background-color:  #f7fbff;\n            color:  #000000;\n        }    #T_df122eb2_9f14_11eb_8c58_3fa5987f1a48row1_col1 {\n            background-color:  #63a8d3;\n            color:  #000000;\n        }</style><table id=\"T_df122eb2_9f14_11eb_8c58_3fa5987f1a48\" ><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_df122eb2_9f14_11eb_8c58_3fa5987f1a48level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_df122eb2_9f14_11eb_8c58_3fa5987f1a48row0_col0\" class=\"data row0 col0\" >28912</td>\n                        <td id=\"T_df122eb2_9f14_11eb_8c58_3fa5987f1a48row0_col1\" class=\"data row0 col1\" >346</td>\n            </tr>\n            <tr>\n                        <th id=\"T_df122eb2_9f14_11eb_8c58_3fa5987f1a48level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_df122eb2_9f14_11eb_8c58_3fa5987f1a48row1_col0\" class=\"data row1 col0\" >1141</td>\n                        <td id=\"T_df122eb2_9f14_11eb_8c58_3fa5987f1a48row1_col1\" class=\"data row1 col1\" >2551</td>\n            </tr>\n    </tbody></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 21,
      "metadata": {
        "gather": {
          "logged": 1618619755825
        }
      }
    },
    {
      "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: d7b4bdcf-fcf2-41e5-aa44-5b29fca6de98,\nStatus: Active,\nEndpoint: https://southcentralus.api.azureml.ms/pipelines/v1.0/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourceGroups/aml-quickstarts-142884/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-142884/PipelineRuns/PipelineSubmit/d7b4bdcf-fcf2-41e5-aa44-5b29fca6de98)",
            "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/d7b4bdcf-fcf2-41e5-aa44-5b29fca6de98?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-142884/workspaces/quick-starts-ws-142884\" target=\"_blank\" rel=\"noopener\">d7b4bdcf-fcf2-41e5-aa44-5b29fca6de98</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-142884/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-142884/PipelineRuns/PipelineSubmit/d7b4bdcf-fcf2-41e5-aa44-5b29fca6de98\" target=\"_blank\" rel=\"noopener\">REST Endpoint</a></td></tr></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 22,
      "metadata": {
        "gather": {
          "logged": 1618619772983
        }
      }
    },
    {
      "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": 1618619791028
        }
      }
    },
    {
      "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": 1618619819997
        }
      }
    },
    {
      "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:  0d387fe7-7ef1-4aa4-82c3-d9d7d6dc8561\n"
          ]
        }
      ],
      "execution_count": 25,
      "metadata": {
        "gather": {
          "logged": 1618619824971
        }
      }
    },
    {
      "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": "e8069837a0dd48a68a08a0086eca2927"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/runs/0d387fe7-7ef1-4aa4-82c3-d9d7d6dc8561?wsid=/subscriptions/9e65f93e-bdd8-437b-b1e8-0647cd6098f7/resourcegroups/aml-quickstarts-142884/workspaces/quick-starts-ws-142884&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\", \"run_id\": \"0d387fe7-7ef1-4aa4-82c3-d9d7d6dc8561\", \"run_properties\": {\"run_id\": \"0d387fe7-7ef1-4aa4-82c3-d9d7d6dc8561\", \"created_utc\": \"2021-04-17T00:36:58.397357Z\", \"properties\": {\"azureml.runsource\": \"azureml.PipelineRun\", \"runSource\": \"Unavailable\", \"runType\": \"HTTP\", \"azureml.parameters\": \"{}\", \"azureml.pipelineid\": \"d7b4bdcf-fcf2-41e5-aa44-5b29fca6de98\"}, \"tags\": {\"azureml.pipelineid\": \"d7b4bdcf-fcf2-41e5-aa44-5b29fca6de98\", \"azureml.pipelineComponent\": \"pipelinerun\"}, \"end_time_utc\": \"2021-04-17T00:37:02.066194Z\", \"status\": \"Completed\", \"log_files\": {\"logs/azureml/executionlogs.txt\": \"https://mlstrg142884.blob.core.windows.net/azureml/ExperimentRun/dcid.0d387fe7-7ef1-4aa4-82c3-d9d7d6dc8561/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=aaS%2FT%2FQCaWaFgf3tdxSYpGvYmDgIC3MOhZHREgh7I6E%3D&st=2021-04-17T00%3A27%3A25Z&se=2021-04-17T08%3A37%3A25Z&sp=r\", \"logs/azureml/stderrlogs.txt\": \"https://mlstrg142884.blob.core.windows.net/azureml/ExperimentRun/dcid.0d387fe7-7ef1-4aa4-82c3-d9d7d6dc8561/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=QJqz0jiAYDU3X%2BMMG6n%2B14AOrRTsDh1qX0N7KYwM%2BSQ%3D&st=2021-04-17T00%3A27%3A25Z&se=2021-04-17T08%3A37%3A25Z&sp=r\", \"logs/azureml/stdoutlogs.txt\": \"https://mlstrg142884.blob.core.windows.net/azureml/ExperimentRun/dcid.0d387fe7-7ef1-4aa4-82c3-d9d7d6dc8561/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=yvOx4shKgwBQW6ocbZHC%2F3t1GM%2B%2FBA6nUvGGCvLOb4o%3D&st=2021-04-17T00%3A27%3A25Z&se=2021-04-17T08%3A37%3A25Z&sp=r\"}, \"log_groups\": [[\"logs/azureml/executionlogs.txt\", \"logs/azureml/stderrlogs.txt\", \"logs/azureml/stdoutlogs.txt\"]], \"run_duration\": \"0:00:03\", \"run_number\": \"1\", \"run_queued_details\": {\"status\": \"Finished\", \"details\": null}}, \"child_runs\": [{\"run_id\": \"826af5b9-12d9-4c1f-ab59-24dcc8b16b51\", \"name\": \"automl_module\", \"status\": \"Finished\", \"start_time\": \"2021-04-17T00:37:01.639784Z\", \"created_time\": \"2021-04-17T00:37:01.639784Z\", \"end_time\": \"2021-04-17T00:37:01.755429Z\", \"duration\": \"0:00:00\", \"run_number\": 2, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-04-17T00:37:01.639784Z\", \"is_reused\": \"Yes\"}], \"children_metrics\": {\"categories\": null, \"series\": null, \"metricName\": null}, \"run_metrics\": [], \"run_logs\": \"[2021-04-17 00:37:01Z] Completing processing run id 826af5b9-12d9-4c1f-ab59-24dcc8b16b51.\\n[2021-04-17 00:37:01Z] Finishing experiment: no runs left and nothing to schedule.\\n\\nRun is completed.\", \"graph\": {\"datasource_nodes\": {\"9a950a79\": {\"node_id\": \"9a950a79\", \"name\": \"Bank-marketing\"}}, \"module_nodes\": {\"b314bd36\": {\"node_id\": \"b314bd36\", \"name\": \"automl_module\", \"status\": \"Finished\", \"_is_reused\": true, \"run_id\": \"826af5b9-12d9-4c1f-ab59-24dcc8b16b51\"}}, \"edges\": [{\"source_node_id\": \"9a950a79\", \"source_node_name\": \"Bank-marketing\", \"source_name\": \"data\", \"target_name\": \"training_data\", \"dst_node_id\": \"b314bd36\", \"dst_node_name\": \"automl_module\"}], \"child_runs\": [{\"run_id\": \"826af5b9-12d9-4c1f-ab59-24dcc8b16b51\", \"name\": \"automl_module\", \"status\": \"Finished\", \"start_time\": \"2021-04-17T00:37:01.639784Z\", \"created_time\": \"2021-04-17T00:37:01.639784Z\", \"end_time\": \"2021-04-17T00:37:01.755429Z\", \"duration\": \"0:00:00\", \"run_number\": 2, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-04-17T00:37:01.639784Z\", \"is_reused\": \"Yes\"}]}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.26.0\"}, \"loading\": false}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 26,
      "metadata": {
        "gather": {
          "logged": 1618619845898
        }
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {}
    }
  ],
  "metadata": {
    "order_index": 11,
    "exclude_from_index": false,
    "task": "Demonstrates the use of AutoMLStep",
    "deployment": [
      "None"
    ],
    "authors": [
      {
        "name": "sanpil"
      }
    ],
    "star_tag": [
      "featured"
    ],
    "microsoft": {
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
      }
    },
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.9",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "compute": [
      "AML Compute"
    ],
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "tags": [
      "None"
    ],
    "datasets": [
      "Custom"
    ],
    "categories": [
      "how-to-use-azureml",
      "machine-learning-pipelines",
      "intro-to-pipelines"
    ],
    "category": "tutorial",
    "framework": [
      "Automated Machine Learning"
    ],
    "friendly_name": "How to use AutoMLStep with AML Pipelines",
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}