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: 1.20.0\n"
          ]
        }
      ],
      "execution_count": 1,
      "metadata": {
        "gather": {
          "logged": 1612022013844
        }
      }
    },
    {
      "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 ES47FUE52 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-136602\n",
            "aml-quickstarts-136602\n",
            "southcentralus\n",
            "aa7cf8e8-d23f-4bce-a7b9-1f0b4e0ac8ee\n"
          ]
        }
      ],
      "execution_count": 2,
      "metadata": {
        "gather": {
          "logged": 1612022058550
        }
      }
    },
    {
      "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 = 'project-pipeline-deployment'\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: project-pipeline-deployment,\nWorkspace: quick-starts-ws-136602)",
            "text/html": "<table style=\"width:100%\"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>project-pipeline-deployment</td><td>quick-starts-ws-136602</td><td><a href=\"https://ml.azure.com/experiments/project-pipeline-deployment?wsid=/subscriptions/aa7cf8e8-d23f-4bce-a7b9-1f0b4e0ac8ee/resourcegroups/aml-quickstarts-136602/workspaces/quick-starts-ws-136602\" 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": 1612022085634
        }
      }
    },
    {
      "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 = \"project-twoo\"\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": 24,
      "metadata": {
        "gather": {
          "logged": 1612023396906
        }
      }
    },
    {
      "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": 25,
          "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": 25,
      "metadata": {
        "gather": {
          "logged": 1612023401319
        }
      }
    },
    {
      "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": 26,
          "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": 26,
      "metadata": {
        "gather": {
          "logged": 1612023402206
        }
      }
    },
    {
      "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\": 60,\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": 27,
      "metadata": {
        "gather": {
          "logged": 1612023403789
        }
      }
    },
    {
      "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": 28,
      "metadata": {
        "gather": {
          "logged": 1612023405762
        }
      }
    },
    {
      "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": 29,
      "metadata": {
        "tags": [
          "automlstep-remarks-sample1"
        ],
        "gather": {
          "logged": 1612023407652
        }
      }
    },
    {
      "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": 30,
      "metadata": {
        "tags": [
          "automlstep-remarks-sample2"
        ],
        "gather": {
          "logged": 1612023408613
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_run = experiment.submit(pipeline)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Created step automl_module [b30976e7][000605a4-6a9a-4359-bf61-198f85964fbd], (This step will run and generate new outputs)\n",
            "Submitted PipelineRun 776d44dd-fa87-4d6c-9524-7913fb908d9a\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/experiments/project-pipeline-deployment/runs/776d44dd-fa87-4d6c-9524-7913fb908d9a?wsid=/subscriptions/aa7cf8e8-d23f-4bce-a7b9-1f0b4e0ac8ee/resourcegroups/aml-quickstarts-136602/workspaces/quick-starts-ws-136602\n"
          ]
        }
      ],
      "execution_count": 31,
      "metadata": {
        "gather": {
          "logged": 1612023410721
        }
      }
    },
    {
      "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": "15f663917e6f46c08563066a1754f563"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/project-pipeline-deployment/runs/776d44dd-fa87-4d6c-9524-7913fb908d9a?wsid=/subscriptions/aa7cf8e8-d23f-4bce-a7b9-1f0b4e0ac8ee/resourcegroups/aml-quickstarts-136602/workspaces/quick-starts-ws-136602\", \"run_id\": \"776d44dd-fa87-4d6c-9524-7913fb908d9a\", \"run_properties\": {\"run_id\": \"776d44dd-fa87-4d6c-9524-7913fb908d9a\", \"created_utc\": \"2021-01-30T16:16:50.145392Z\", \"properties\": {\"azureml.runsource\": \"azureml.PipelineRun\", \"runSource\": \"SDK\", \"runType\": \"SDK\", \"azureml.parameters\": \"{}\"}, \"tags\": {\"azureml.pipelineComponent\": \"pipelinerun\"}, \"end_time_utc\": \"2021-01-30T16:39:15.818137Z\", \"status\": \"Completed\", \"log_files\": {\"logs/azureml/executionlogs.txt\": \"https://mlstrg136602.blob.core.windows.net/azureml/ExperimentRun/dcid.776d44dd-fa87-4d6c-9524-7913fb908d9a/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=YA4%2FJf18Yhz%2BA4tXNgOCoKdiJLj%2FLTGyYrBlIHVfHsw%3D&st=2021-01-30T16%3A07%3A05Z&se=2021-01-31T00%3A17%3A05Z&sp=r\", \"logs/azureml/stderrlogs.txt\": \"https://mlstrg136602.blob.core.windows.net/azureml/ExperimentRun/dcid.776d44dd-fa87-4d6c-9524-7913fb908d9a/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=eB6SPH7hFznaDYt8MnFnf3di6iIPF0PqX0Q5xITaYUo%3D&st=2021-01-30T16%3A07%3A05Z&se=2021-01-31T00%3A17%3A05Z&sp=r\", \"logs/azureml/stdoutlogs.txt\": \"https://mlstrg136602.blob.core.windows.net/azureml/ExperimentRun/dcid.776d44dd-fa87-4d6c-9524-7913fb908d9a/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=1VScQX9CGj5%2BsoahSlaP90dpMc1ReaR%2Bk4sfeZXKLDE%3D&st=2021-01-30T16%3A07%3A05Z&se=2021-01-31T00%3A17%3A05Z&sp=r\"}, \"log_groups\": [[\"logs/azureml/executionlogs.txt\", \"logs/azureml/stderrlogs.txt\", \"logs/azureml/stdoutlogs.txt\"]], \"run_duration\": \"0:22:25\"}, \"child_runs\": [{\"run_id\": \"444300dc-3d03-4c1d-b1c7-439b4cc56015\", \"name\": \"automl_module\", \"status\": \"Finished\", \"start_time\": \"2021-01-30T16:17:21.693937Z\", \"created_time\": \"2021-01-30T16:17:00.026148Z\", \"end_time\": \"2021-01-30T16:36:56.822055Z\", \"duration\": \"0:19:56\", \"run_number\": 8, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-01-30T16:17:00.026148Z\", \"is_reused\": \"\"}], \"children_metrics\": {\"categories\": null, \"series\": null, \"metricName\": null}, \"run_metrics\": [], \"run_logs\": \"[2021-01-30 16:16:59Z] Submitting 1 runs, first five are: b30976e7:444300dc-3d03-4c1d-b1c7-439b4cc56015\\n[2021-01-30 16:39:15Z] Completing processing run id 444300dc-3d03-4c1d-b1c7-439b4cc56015.\\n\\nRun is completed.\", \"graph\": {\"datasource_nodes\": {\"9e7b7e3a\": {\"node_id\": \"9e7b7e3a\", \"name\": \"Bank-marketing\"}}, \"module_nodes\": {\"b30976e7\": {\"node_id\": \"b30976e7\", \"name\": \"automl_module\", \"status\": \"Finished\", \"_is_reused\": false, \"run_id\": \"444300dc-3d03-4c1d-b1c7-439b4cc56015\"}}, \"edges\": [{\"source_node_id\": \"9e7b7e3a\", \"source_node_name\": \"Bank-marketing\", \"source_name\": \"data\", \"target_name\": \"training_data\", \"dst_node_id\": \"b30976e7\", \"dst_node_name\": \"automl_module\"}], \"child_runs\": [{\"run_id\": \"444300dc-3d03-4c1d-b1c7-439b4cc56015\", \"name\": \"automl_module\", \"status\": \"Finished\", \"start_time\": \"2021-01-30T16:17:21.693937Z\", \"created_time\": \"2021-01-30T16:17:00.026148Z\", \"end_time\": \"2021-01-30T16:36:56.822055Z\", \"duration\": \"0:19:56\", \"run_number\": 8, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-01-30T16:17:00.026148Z\", \"is_reused\": \"\"}]}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.20.0\"}, \"loading\": false}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 38,
      "metadata": {
        "gather": {
          "logged": 1612024759011
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_run.wait_for_completion()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PipelineRunId: 776d44dd-fa87-4d6c-9524-7913fb908d9a\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/experiments/project-pipeline-deployment/runs/776d44dd-fa87-4d6c-9524-7913fb908d9a?wsid=/subscriptions/aa7cf8e8-d23f-4bce-a7b9-1f0b4e0ac8ee/resourcegroups/aml-quickstarts-136602/workspaces/quick-starts-ws-136602\n",
            "PipelineRun Status: Running\n",
            "\n",
            "\n",
            "StepRunId: 444300dc-3d03-4c1d-b1c7-439b4cc56015\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/experiments/project-pipeline-deployment/runs/444300dc-3d03-4c1d-b1c7-439b4cc56015?wsid=/subscriptions/aa7cf8e8-d23f-4bce-a7b9-1f0b4e0ac8ee/resourcegroups/aml-quickstarts-136602/workspaces/quick-starts-ws-136602\n",
            "StepRun( automl_module ) Status: Running\n",
            "\n",
            "StepRun(automl_module) Execution Summary\n",
            "=========================================\n",
            "StepRun( automl_module ) Status: Finished\n",
            "{'runId': '444300dc-3d03-4c1d-b1c7-439b4cc56015', 'target': 'project-twoo', 'status': 'Completed', 'startTimeUtc': '2021-01-30T16:17:21.693937Z', 'endTimeUtc': '2021-01-30T16:36:56.822055Z', 'properties': {'ContentSnapshotId': '55861082-906c-4c0f-b603-b24fd57dcda6', 'StepType': 'AutoMLStep', 'azureml.moduleid': '000605a4-6a9a-4359-bf61-198f85964fbd', 'azureml.runsource': 'azureml.StepRun', 'azureml.nodeid': 'b30976e7', 'azureml.pipelinerunid': '776d44dd-fa87-4d6c-9524-7913fb908d9a', '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': 'project-twoo', 'RawAMLSettingsString': None, 'AMLSettingsJsonString': '{\"path\": null, \"name\": \"placeholder\", \"subscription_id\": \"aa7cf8e8-d23f-4bce-a7b9-1f0b4e0ac8ee\", \"resource_group\": \"aml-quickstarts-136602\", \"workspace_name\": \"quick-starts-ws-136602\", \"region\": \"southcentralus\", \"compute_target\": \"project-twoo\", \"spark_service\": null, \"azure_service\": null, \"many_models\": false, \"pipeline_fetch_max_batch_size\": 1, \"iterations\": 1000, \"primary_metric\": \"AUC_weighted\", \"task_type\": \"classification\", \"data_script\": null, \"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\": 60, \"experiment_exit_score\": null, \"whitelist_models\": null, \"blacklist_algos\": null, \"supported_models\": [\"BernoulliNaiveBayes\", \"LogisticRegression\", \"LightGBM\", \"ExtremeRandomTrees\", \"AveragedPerceptronClassifier\", \"KNN\", \"XGBoostClassifier\", \"MultinomialNaiveBayes\", \"SGD\", \"DecisionTree\", \"TensorFlowLinearClassifier\", \"SVM\", \"LinearSVM\", \"TensorFlowDNN\", \"GradientBoosting\", \"RandomForest\"], \"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, \"force_text_dnn\": false, \"enable_feature_sweeping\": true, \"enable_early_stopping\": true, \"early_stopping_n_iters\": 10, \"metrics\": null, \"enable_ensembling\": true, \"enable_stack_ensembling\": true, \"ensemble_iterations\": 15, \"enable_tf\": false, \"enable_subsampling\": null, \"subsample_seed\": null, \"enable_nimbusml\": false, \"enable_streaming\": false, \"force_streaming\": false, \"track_child_runs\": true, \"allowed_private_models\": [], \"label_column_name\": \"y\", \"weight_column_name\": null, \"cv_split_column_names\": null, \"enable_local_managed\": false, \"_local_managed_run_id\": null, \"cost_mode\": 1, \"lag_length\": 0, \"metric_operation\": \"maximize\", \"preprocess\": true}', 'DataPrepJsonString': '{\\\\\"training_data\\\\\": {\\\\\"datasetId\\\\\": \\\\\"c3094fb0-f93d-4312-8a3c-f23e9c71d21a\\\\\"}, \\\\\"datasets\\\\\": 0}', 'EnableSubsampling': 'False', 'runTemplate': 'AutoML', 'Orchestrator': 'automl', 'ClientType': 'Others', '_aml_system_scenario_identification': 'Remote.Parent', 'root_attribution': 'azureml.StepRun', 'snapshotId': '55861082-906c-4c0f-b603-b24fd57dcda6', 'SetupRunId': '444300dc-3d03-4c1d-b1c7-439b4cc56015_setup', 'SetupRunContainerId': 'dcid.444300dc-3d03-4c1d-b1c7-439b4cc56015_setup', 'ClientSdkVersion': '1.20.0', 'FeaturizationRunJsonPath': 'featurizer_container.json', 'FeaturizationRunId': '444300dc-3d03-4c1d-b1c7-439b4cc56015_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': '444300dc-3d03-4c1d-b1c7-439b4cc56015_ModelExplain'}, 'inputDatasets': [], 'outputDatasets': [], 'logFiles': {'logs/azureml/executionlogs.txt': 'https://mlstrg136602.blob.core.windows.net/azureml/ExperimentRun/dcid.444300dc-3d03-4c1d-b1c7-439b4cc56015/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=KVpsysPwxoZM0c70MBn4pIVuu3ncV9%2FJjd2CUYFPav4%3D&st=2021-01-30T16%3A19%3A01Z&se=2021-01-31T00%3A29%3A01Z&sp=r', 'logs/azureml/stderrlogs.txt': 'https://mlstrg136602.blob.core.windows.net/azureml/ExperimentRun/dcid.444300dc-3d03-4c1d-b1c7-439b4cc56015/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=2ounvPRYg%2Bx2tTPapEO3faTanBD%2FMqSduS5t9zx2A%2Fk%3D&st=2021-01-30T16%3A19%3A01Z&se=2021-01-31T00%3A29%3A01Z&sp=r', 'logs/azureml/stdoutlogs.txt': 'https://mlstrg136602.blob.core.windows.net/azureml/ExperimentRun/dcid.444300dc-3d03-4c1d-b1c7-439b4cc56015/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=PJXw%2FI%2BrnjHfrvHkSQqnEg%2BXEpUHi%2FzabI%2B5hlyIdRI%3D&st=2021-01-30T16%3A19%3A01Z&se=2021-01-31T00%3A29%3A01Z&sp=r'}, 'submittedBy': 'ODL_User 136602'}\n",
            "\n",
            "\n",
            "\n",
            "PipelineRun Execution Summary\n",
            "==============================\n",
            "PipelineRun Status: Finished\n",
            "{'runId': '776d44dd-fa87-4d6c-9524-7913fb908d9a', 'status': 'Completed', 'startTimeUtc': '2021-01-30T16:16:54.25122Z', 'endTimeUtc': '2021-01-30T16:39:15.818137Z', 'properties': {'azureml.runsource': 'azureml.PipelineRun', 'runSource': 'SDK', 'runType': 'SDK', 'azureml.parameters': '{}'}, 'inputDatasets': [], 'outputDatasets': [], 'logFiles': {'logs/azureml/executionlogs.txt': 'https://mlstrg136602.blob.core.windows.net/azureml/ExperimentRun/dcid.776d44dd-fa87-4d6c-9524-7913fb908d9a/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=YA4%2FJf18Yhz%2BA4tXNgOCoKdiJLj%2FLTGyYrBlIHVfHsw%3D&st=2021-01-30T16%3A07%3A05Z&se=2021-01-31T00%3A17%3A05Z&sp=r', 'logs/azureml/stderrlogs.txt': 'https://mlstrg136602.blob.core.windows.net/azureml/ExperimentRun/dcid.776d44dd-fa87-4d6c-9524-7913fb908d9a/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=eB6SPH7hFznaDYt8MnFnf3di6iIPF0PqX0Q5xITaYUo%3D&st=2021-01-30T16%3A07%3A05Z&se=2021-01-31T00%3A17%3A05Z&sp=r', 'logs/azureml/stdoutlogs.txt': 'https://mlstrg136602.blob.core.windows.net/azureml/ExperimentRun/dcid.776d44dd-fa87-4d6c-9524-7913fb908d9a/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=1VScQX9CGj5%2BsoahSlaP90dpMc1ReaR%2Bk4sfeZXKLDE%3D&st=2021-01-30T16%3A07%3A05Z&se=2021-01-31T00%3A17%3A05Z&sp=r'}, 'submittedBy': 'ODL_User 136602'}\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 37,
          "data": {
            "text/plain": "'Finished'"
          },
          "metadata": {}
        }
      ],
      "execution_count": 37,
      "metadata": {
        "gather": {
          "logged": 1612024758842
        }
      }
    },
    {
      "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/444300dc-3d03-4c1d-b1c7-439b4cc56015/metrics_data\n",
            "Downloaded azureml/444300dc-3d03-4c1d-b1c7-439b4cc56015/metrics_data, 1 files out of an estimated total of 1\n"
          ]
        }
      ],
      "execution_count": 39,
      "metadata": {
        "gather": {
          "logged": 1612024889922
        }
      }
    },
    {
      "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": 40,
          "data": {
            "text/plain": "                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_6  \\\nlog_loss                                          [0.25033977785435796]   \naccuracy                                           [0.8986342943854325]   \nprecision_score_macro                              [0.7879589751076721]   \nAUC_weighted                                       [0.8931072137105515]   \nrecall_score_weighted                              [0.8986342943854325]   \nprecision_score_micro                              [0.8986342943854325]   \nbalanced_accuracy                                  [0.5876859554651596]   \nf1_score_micro                                     [0.8986342943854325]   \nnorm_macro_recall                                 [0.17537191093031912]   \nprecision_score_weighted                            [0.879573345905093]   \nAUC_micro                                          [0.9665675449766397]   \nf1_score_macro                                     [0.6188899385530886]   \nrecall_score_macro                                 [0.5876859554651596]   \nrecall_score_micro                                 [0.8986342943854325]   \nweighted_accuracy                                  [0.9758343559290874]   \naverage_precision_score_macro                      [0.7441345544245972]   \nf1_score_weighted                                  [0.8722751025145846]   \naverage_precision_score_weighted                   [0.9314544711072148]   \nmatthews_correlation                              [0.31780470649179715]   \nAUC_macro                                          [0.8931072137105514]   \naverage_precision_score_micro                      [0.9680512746390288]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_5  \\\nlog_loss                                          [0.24661122213565168]   \naccuracy                                           [0.9001517450682853]   \nprecision_score_macro                              [0.7963108229032296]   \nAUC_weighted                                       [0.8981581818552293]   \nrecall_score_weighted                              [0.9001517450682853]   \nprecision_score_micro                              [0.9001517450682853]   \nbalanced_accuracy                                   [0.594461023215837]   \nf1_score_micro                                     [0.9001517450682853]   \nnorm_macro_recall                                  [0.1889220464316741]   \nprecision_score_weighted                           [0.8825470158152294]   \nAUC_micro                                          [0.9675622926169922]   \nf1_score_macro                                     [0.6282264041200302]   \nrecall_score_macro                                  [0.594461023215837]   \nrecall_score_micro                                 [0.9001517450682853]   \nweighted_accuracy                                  [0.9760464825101347]   \naverage_precision_score_macro                      [0.7306481851982262]   \nf1_score_weighted                                  [0.8749663249165839]   \naverage_precision_score_weighted                   [0.9287772711070046]   \nmatthews_correlation                              [0.33460318899475994]   \nAUC_macro                                          [0.8981581818552293]   \naverage_precision_score_micro                      [0.9684179858920094]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_23  \\\nlog_loss                                           [0.18115151864375498]   \naccuracy                                            [0.9128983308042489]   \nprecision_score_macro                               [0.7860811293290488]   \nAUC_weighted                                        [0.9448491887516278]   \nrecall_score_weighted                               [0.9128983308042489]   \nprecision_score_micro                               [0.9128983308042489]   \nbalanced_accuracy                                   [0.7508386635472644]   \nf1_score_micro                                      [0.9128983308042489]   \nnorm_macro_recall                                   [0.5016773270945287]   \nprecision_score_weighted                            [0.9080335867085474]   \nAUC_micro                                           [0.9793693944704005]   \nf1_score_macro                                      [0.7668111420906795]   \nrecall_score_macro                                  [0.7508386635472644]   \nrecall_score_micro                                  [0.9128983308042489]   \nweighted_accuracy                                   [0.9531333625443325]   \naverage_precision_score_macro                       [0.8085204474402641]   \nf1_score_weighted                                   [0.9100415644605403]   \naverage_precision_score_weighted                    [0.9516860732237478]   \nmatthews_correlation                                [0.5357619178216784]   \nAUC_macro                                           [0.9448491887516277]   \naverage_precision_score_micro                        [0.980352027134298]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_36  \\\nlog_loss                                           [0.24604619760082583]   \naccuracy                                            [0.8880121396054628]   \nprecision_score_macro                               [0.4440060698027314]   \nAUC_weighted                                         [0.934308239186288]   \nrecall_score_weighted                               [0.8880121396054628]   \nprecision_score_micro                               [0.8880121396054628]   \nbalanced_accuracy                                                  [0.5]   \nf1_score_micro                                      [0.8880121396054628]   \nnorm_macro_recall                                                  [0.0]   \nprecision_score_weighted                             [0.788565560086672]   \nAUC_micro                                           [0.9743930772932732]   \nf1_score_macro                                      [0.4703423886834914]   \nrecall_score_macro                                                 [0.5]   \nrecall_score_micro                                  [0.8880121396054628]   \nweighted_accuracy                                   [0.9843450583187134]   \naverage_precision_score_macro                       [0.7951181599264726]   \nf1_score_weighted                                   [0.8353395018439429]   \naverage_precision_score_weighted                    [0.9474863785724514]   \nmatthews_correlation                                               [0.0]   \nAUC_macro                                            [0.934308239186288]   \naverage_precision_score_micro                       [0.9756340409323144]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_42  \\\nlog_loss                                           [0.17858324434463335]   \naccuracy                                            [0.9116843702579667]   \nprecision_score_macro                               [0.7852393780805413]   \nAUC_weighted                                        [0.9451400118922584]   \nrecall_score_weighted                               [0.9116843702579667]   \nprecision_score_micro                               [0.9116843702579667]   \nbalanced_accuracy                                   [0.7359455549442713]   \nf1_score_micro                                      [0.9116843702579667]   \nnorm_macro_recall                                  [0.47189110988854255]   \nprecision_score_weighted                            [0.9050318363975759]   \nAUC_micro                                           [0.9797055823303346]   \nf1_score_macro                                      [0.7572938163705586]   \nrecall_score_macro                                  [0.7359455549442713]   \nrecall_score_micro                                  [0.9116843702579667]   \nweighted_accuracy                                   [0.9553155644458374]   \naverage_precision_score_macro                       [0.8171616074642105]   \nf1_score_weighted                                   [0.9075132403774469]   \naverage_precision_score_weighted                    [0.9536272740055581]   \nmatthews_correlation                                [0.5188485842832073]   \nAUC_macro                                           [0.9451400118922584]   \naverage_precision_score_micro                        [0.980657776389614]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_13  \\\nlog_loss                                           [0.33655623030329523]   \naccuracy                                            [0.9062215477996965]   \nprecision_score_macro                                [0.822098675416211]   \nAUC_weighted                                        [0.9308878256246675]   \nrecall_score_weighted                               [0.9062215477996965]   \nprecision_score_micro                               [0.9062215477996965]   \nbalanced_accuracy                                   [0.6227454260188535]   \nf1_score_micro                                      [0.9062215477996965]   \nnorm_macro_recall                                  [0.24549085203770704]   \nprecision_score_weighted                            [0.8929725418691179]   \nAUC_micro                                           [0.9758990146932517]   \nf1_score_macro                                      [0.6650154684677185]   \nrecall_score_macro                                  [0.6227454260188535]   \nrecall_score_micro                                  [0.9062215477996965]   \nweighted_accuracy                                   [0.9766010009385309]   \naverage_precision_score_macro                       [0.7985126174047921]   \nf1_score_weighted                                    [0.885603431576398]   \naverage_precision_score_weighted                     [0.947605275820125]   \nmatthews_correlation                                [0.3976739324324451]   \nAUC_macro                                           [0.9308878256246677]   \naverage_precision_score_micro                       [0.9766643355999638]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_0  \\\nlog_loss                                          [0.17775706110025447]   \naccuracy                                           [0.9116843702579667]   \nprecision_score_macro                              [0.7819118765348991]   \nAUC_weighted                                       [0.9450464668693167]   \nrecall_score_weighted                              [0.9116843702579667]   \nprecision_score_micro                              [0.9116843702579667]   \nbalanced_accuracy                                  [0.7513392683482543]   \nf1_score_micro                                     [0.9116843702579667]   \nnorm_macro_recall                                  [0.5026785366965085]   \nprecision_score_weighted                           [0.9072720074188747]   \nAUC_micro                                           [0.979695082216353]   \nf1_score_macro                                     [0.7653697272147331]   \nrecall_score_macro                                 [0.7513392683482543]   \nrecall_score_micro                                 [0.9116843702579667]   \nweighted_accuracy                                  [0.9514937218005303]   \naverage_precision_score_macro                      [0.8151093723721079]   \nf1_score_weighted                                  [0.9091539479147899]   \naverage_precision_score_weighted                   [0.9531771295804466]   \nmatthews_correlation                               [0.5323740218566827]   \nAUC_macro                                          [0.9450464668693166]   \naverage_precision_score_micro                      [0.9806603102489483]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_3  \\\nlog_loss                                             [0.51444454543788]   \naccuracy                                           [0.7569044006069803]   \nprecision_score_macro                              [0.6251483540508465]   \nAUC_weighted                                       [0.8786285743923741]   \nrecall_score_weighted                              [0.7569044006069803]   \nprecision_score_micro                              [0.7569044006069803]   \nbalanced_accuracy                                  [0.7672090425620592]   \nf1_score_micro                                     [0.7569044006069803]   \nnorm_macro_recall                                  [0.5344180851241185]   \nprecision_score_weighted                            [0.888557432271358]   \nAUC_micro                                          [0.8616835643281653]   \nf1_score_macro                                       [0.63232316393531]   \nrecall_score_macro                                 [0.7672090425620592]   \nrecall_score_micro                                 [0.7569044006069803]   \nweighted_accuracy                                  [0.7543460367453827]   \naverage_precision_score_macro                      [0.7487830567680671]   \nf1_score_weighted                                  [0.7984098282572395]   \naverage_precision_score_weighted                   [0.9304659489430059]   \nmatthews_correlation                              [0.36573636332278625]   \nAUC_macro                                          [0.8786285743923741]   \naverage_precision_score_micro                      [0.8546273138424763]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_1  \\\nlog_loss                                           [0.1874363495858499]   \naccuracy                                           [0.9071320182094081]   \nprecision_score_macro                              [0.7723958081530135]   \nAUC_weighted                                       [0.9392346349984347]   \nrecall_score_weighted                              [0.9071320182094081]   \nprecision_score_micro                              [0.9071320182094081]   \nbalanced_accuracy                                  [0.7191727470931578]   \nf1_score_micro                                     [0.9071320182094081]   \nnorm_macro_recall                                 [0.43834549418631563]   \nprecision_score_weighted                           [0.8991976076061607]   \nAUC_micro                                          [0.9781770788959222]   \nf1_score_macro                                     [0.7416848907681176]   \nrecall_score_macro                                 [0.7191727470931578]   \nrecall_score_micro                                 [0.9071320182094081]   \nweighted_accuracy                                  [0.9537972210153172]   \naverage_precision_score_macro                      [0.8065229883244922]   \nf1_score_weighted                                  [0.9021127651963996]   \naverage_precision_score_weighted                   [0.9505970434373063]   \nmatthews_correlation                                [0.488678780261868]   \nAUC_macro                                          [0.9392346349984347]   \naverage_precision_score_micro                      [0.9791945367231853]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_31  ...  \\\nlog_loss                                           [0.17753033221737446]  ...   \naccuracy                                            [0.9144157814871017]  ...   \nprecision_score_macro                                [0.791450436626639]  ...   \nAUC_weighted                                        [0.9459430171882032]  ...   \nrecall_score_weighted                               [0.9144157814871017]  ...   \nprecision_score_micro                               [0.9144157814871017]  ...   \nbalanced_accuracy                                   [0.7505089404961036]  ...   \nf1_score_micro                                      [0.9144157814871017]  ...   \nnorm_macro_recall                                   [0.5010178809922072]  ...   \nprecision_score_weighted                            [0.9090794627426896]  ...   \nAUC_micro                                           [0.9799541771341596]  ...   \nf1_score_macro                                      [0.7687891908032249]  ...   \nrecall_score_macro                                  [0.7505089404961036]  ...   \nrecall_score_micro                                  [0.9144157814871017]  ...   \nweighted_accuracy                                   [0.9551094165001367]  ...   \naverage_precision_score_macro                       [0.8198941458185378]  ...   \nf1_score_weighted                                   [0.9111858226879949]  ...   \naverage_precision_score_weighted                    [0.9543200270191108]  ...   \nmatthews_correlation                                 [0.540410733003948]  ...   \nAUC_macro                                           [0.9459430171882033]  ...   \naverage_precision_score_micro                       [0.9808900541538087]  ...   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_41  \\\nlog_loss                                           [0.18212320322735842]   \naccuracy                                            [0.9150227617602428]   \nprecision_score_macro                               [0.7920274421073921]   \nAUC_weighted                                         [0.943697010449257]   \nrecall_score_weighted                               [0.9150227617602428]   \nprecision_score_micro                               [0.9150227617602428]   \nbalanced_accuracy                                   [0.7567713629972936]   \nf1_score_micro                                      [0.9150227617602428]   \nnorm_macro_recall                                   [0.5135427259945873]   \nprecision_score_weighted                            [0.9103936567676629]   \nAUC_micro                                           [0.9792477220969833]   \nf1_score_macro                                      [0.7727908552875257]   \nrecall_score_macro                                  [0.7567713629972936]   \nrecall_score_micro                                  [0.9150227617602428]   \nweighted_accuracy                                   [0.9543123034451771]   \naverage_precision_score_macro                       [0.8146358499907052]   \nf1_score_weighted                                    [0.912294877094483]   \naverage_precision_score_weighted                    [0.9529012007897981]   \nmatthews_correlation                                [0.5476651690313282]   \nAUC_macro                                           [0.9436970104492569]   \naverage_precision_score_micro                       [0.9802204735535665]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_25  \\\nlog_loss                                           [0.18377453013344744]   \naccuracy                                            [0.9165402124430956]   \nprecision_score_macro                               [0.7978332522829199]   \nAUC_weighted                                        [0.9430264500867838]   \nrecall_score_weighted                               [0.9165402124430956]   \nprecision_score_micro                               [0.9165402124430956]   \nbalanced_accuracy                                   [0.7552575081458265]   \nf1_score_micro                                      [0.9165402124430956]   \nnorm_macro_recall                                    [0.510515016291653]   \nprecision_score_weighted                            [0.9113037932659365]   \nAUC_micro                                           [0.9789654164009017]   \nf1_score_macro                                      [0.7742346026690958]   \nrecall_score_macro                                  [0.7552575081458265]   \nrecall_score_micro                                  [0.9165402124430956]   \nweighted_accuracy                                   [0.9565823452967744]   \naverage_precision_score_macro                        [0.812573046671845]   \nf1_score_weighted                                   [0.9133307204795383]   \naverage_precision_score_weighted                     [0.952383837559749]   \nmatthews_correlation                                [0.5514496307758505]   \nAUC_macro                                           [0.9430264500867838]   \naverage_precision_score_micro                       [0.9799669312828488]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_37  \\\nlog_loss                                           [0.18734695010191202]   \naccuracy                                            [0.9101669195751139]   \nprecision_score_macro                               [0.7821726267699607]   \nAUC_weighted                                        [0.9396787423103214]   \nrecall_score_weighted                               [0.9101669195751139]   \nprecision_score_micro                               [0.9101669195751139]   \nbalanced_accuracy                                   [0.7256180917926747]   \nf1_score_micro                                      [0.9101669195751139]   \nnorm_macro_recall                                  [0.45123618358534934]   \nprecision_score_weighted                            [0.9024524528275779]   \nAUC_micro                                            [0.978246987549536]   \nf1_score_macro                                      [0.7494492260392775]   \nrecall_score_macro                                  [0.7256180917926747]   \nrecall_score_micro                                  [0.9101669195751139]   \nweighted_accuracy                                   [0.9559854015521687]   \naverage_precision_score_macro                       [0.8078015388629372]   \nf1_score_weighted                                   [0.9051730389877936]   \naverage_precision_score_weighted                    [0.9509656788257426]   \nmatthews_correlation                                [0.5046315472023726]   \nAUC_macro                                           [0.9396787423103213]   \naverage_precision_score_micro                       [0.9792878371980246]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_21  \\\nlog_loss                                           [0.22923370640917518]   \naccuracy                                            [0.8998482549317147]   \nprecision_score_macro                               [0.8509287925696595]   \nAUC_weighted                                        [0.9403502288611404]   \nrecall_score_weighted                               [0.8998482549317147]   \nprecision_score_micro                               [0.8998482549317147]   \nbalanced_accuracy                                   [0.5682392418592676]   \nf1_score_micro                                      [0.8998482549317147]   \nnorm_macro_recall                                   [0.1364784837185351]   \nprecision_score_weighted                            [0.8904507721146121]   \nAUC_micro                                           [0.9761176749616031]   \nf1_score_macro                                       [0.593012549294383]   \nrecall_score_macro                                  [0.5682392418592676]   \nrecall_score_micro                                  [0.8998482549317147]   \nweighted_accuracy                                   [0.9821778030055749]   \naverage_precision_score_macro                       [0.8172913885914492]   \nf1_score_weighted                                   [0.8672449503131379]   \naverage_precision_score_weighted                    [0.9528629085612494]   \nmatthews_correlation                                [0.3094971066200246]   \nAUC_macro                                           [0.9403502288611405]   \naverage_precision_score_micro                       [0.9770786013335764]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_32  \\\nlog_loss                                           [0.18603556814655484]   \naccuracy                                             [0.910773899848255]   \nprecision_score_macro                               [0.7837744349823996]   \nAUC_weighted                                        [0.9397834015934143]   \nrecall_score_weighted                                [0.910773899848255]   \nprecision_score_micro                                [0.910773899848255]   \nbalanced_accuracy                                   [0.7283281188929456]   \nf1_score_micro                                      [0.9107738998482551]   \nnorm_macro_recall                                  [0.45665623778589115]   \nprecision_score_weighted                              [0.90329509440897]   \nAUC_micro                                           [0.9781233809445958]   \nf1_score_macro                                      [0.7518148950517209]   \nrecall_score_macro                                  [0.7283281188929456]   \nrecall_score_micro                                   [0.910773899848255]   \nweighted_accuracy                                   [0.9560702521845876]   \naverage_precision_score_macro                       [0.8038374183722943]   \nf1_score_weighted                                   [0.9059514802867371]   \naverage_precision_score_weighted                    [0.9500867934766738]   \nmatthews_correlation                                [0.5090920660526534]   \nAUC_macro                                           [0.9397834015934143]   \naverage_precision_score_micro                       [0.9791660115712217]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_4  \\\nlog_loss                                          [0.24897509343357427]   \naccuracy                                           [0.8880121396054628]   \nprecision_score_macro                              [0.4440060698027314]   \nAUC_weighted                                       [0.9023774328652376]   \nrecall_score_weighted                              [0.8880121396054628]   \nprecision_score_micro                              [0.8880121396054628]   \nbalanced_accuracy                                                 [0.5]   \nf1_score_micro                                     [0.8880121396054628]   \nnorm_macro_recall                                                 [0.0]   \nprecision_score_weighted                            [0.788565560086672]   \nAUC_micro                                          [0.9680422583534624]   \nf1_score_macro                                     [0.4703423886834914]   \nrecall_score_macro                                                [0.5]   \nrecall_score_micro                                 [0.8880121396054628]   \nweighted_accuracy                                  [0.9843450583187134]   \naverage_precision_score_macro                      [0.7345187412902255]   \nf1_score_weighted                                  [0.8353395018439429]   \naverage_precision_score_weighted                   [0.9289088651828795]   \nmatthews_correlation                                              [0.0]   \nAUC_macro                                          [0.9023774328652376]   \naverage_precision_score_micro                      [0.9679761489926731]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_20  \\\nlog_loss                                           [0.22589233807484954]   \naccuracy                                            [0.9004552352048558]   \nprecision_score_macro                               [0.8202786854702324]   \nAUC_weighted                                        [0.9304904908242522]   \nrecall_score_weighted                               [0.9004552352048558]   \nprecision_score_micro                               [0.9004552352048558]   \nbalanced_accuracy                                   [0.5827905869626023]   \nf1_score_micro                                      [0.9004552352048558]   \nnorm_macro_recall                                  [0.16558117392520466]   \nprecision_score_weighted                            [0.8859664258327548]   \nAUC_micro                                           [0.9744804861368561]   \nf1_score_macro                                      [0.6136146528695394]   \nrecall_score_macro                                  [0.5827905869626023]   \nrecall_score_micro                                  [0.9004552352048558]   \nweighted_accuracy                                   [0.9793227746800656]   \naverage_precision_score_macro                        [0.787108791806625]   \nf1_score_weighted                                   [0.8719631449552753]   \naverage_precision_score_weighted                     [0.945222197717833]   \nmatthews_correlation                                [0.3256750549961802]   \nAUC_macro                                           [0.9304904908242521]   \naverage_precision_score_micro                       [0.9756497344937594]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_26  \\\nlog_loss                                            [0.3304940796991938]   \naccuracy                                            [0.9074355083459787]   \nprecision_score_macro                               [0.7992420189049403]   \nAUC_weighted                                        [0.9350098268583507]   \nrecall_score_weighted                               [0.9074355083459787]   \nprecision_score_micro                               [0.9074355083459787]   \nbalanced_accuracy                                   [0.6554005116264423]   \nf1_score_micro                                      [0.9074355083459787]   \nnorm_macro_recall                                   [0.3108010232528846]   \nprecision_score_weighted                            [0.8935012190755486]   \nAUC_micro                                           [0.9771930155820769]   \nf1_score_macro                                      [0.6969780573488569]   \nrecall_score_macro                                  [0.6554005116264423]   \nrecall_score_micro                                  [0.9074355083459787]   \nweighted_accuracy                                   [0.9700089806001332]   \naverage_precision_score_macro                       [0.8063549152507894]   \nf1_score_weighted                                   [0.8929502722795345]   \naverage_precision_score_weighted                    [0.9499966225005592]   \nmatthews_correlation                               [0.43128813031641494]   \nAUC_macro                                           [0.9350098268583507]   \naverage_precision_score_micro                       [0.9781626875518643]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_45  \\\nlog_loss                                           [0.23244422431209238]   \naccuracy                                            [0.8995447647951441]   \nprecision_score_macro                               [0.7475201765396933]   \nAUC_weighted                                        [0.9173126830379719]   \nrecall_score_weighted                               [0.8995447647951441]   \nprecision_score_micro                               [0.8995447647951441]   \nbalanced_accuracy                                   [0.7279261531508001]   \nf1_score_micro                                      [0.8995447647951441]   \nnorm_macro_recall                                  [0.45585230630160023]   \nprecision_score_weighted                            [0.8957195178165474]   \nAUC_micro                                           [0.9724459508935459]   \nf1_score_macro                                      [0.7371258820055797]   \nrecall_score_macro                                  [0.7279261531508001]   \nrecall_score_micro                                  [0.8995447647951441]   \nweighted_accuracy                                   [0.9421530238615385]   \naverage_precision_score_macro                       [0.7673410208172029]   \nf1_score_weighted                                   [0.8974753350144277]   \naverage_precision_score_weighted                    [0.9390797760292473]   \nmatthews_correlation                               [0.47504240512147633]   \nAUC_macro                                            [0.917312683037972]   \naverage_precision_score_micro                       [0.9732858342558575]   \n\n                                 444300dc-3d03-4c1d-b1c7-439b4cc56015_2  \nlog_loss                                          [0.23484993846731433]  \naccuracy                                           [0.8925644916540213]  \nprecision_score_macro                              [0.8936443673285779]  \nAUC_weighted                                        [0.899077423788592]  \nrecall_score_weighted                              [0.8925644916540213]  \nprecision_score_micro                              [0.8925644916540213]  \nbalanced_accuracy                                  [0.5226934668526453]  \nf1_score_micro                                     [0.8925644916540213]  \nnorm_macro_recall                                [0.045386933705290566]  \nprecision_score_weighted                           [0.8927965803774446]  \nAUC_micro                                          [0.9674504756137156]  \nf1_score_macro                                     [0.5152752520803067]  \nrecall_score_macro                                 [0.5226934668526453]  \nrecall_score_micro                                 [0.8925644916540213]  \nweighted_accuracy                                  [0.9843934622702696]  \naverage_precision_score_macro                       [0.754172708722851]  \nf1_score_weighted                                  [0.8471392306114343]  \naverage_precision_score_weighted                   [0.9326229771706379]  \nmatthews_correlation                              [0.18903074249128482]  \nAUC_macro                                           [0.899077423788592]  \naverage_precision_score_micro                      [0.9671349594718173]  \n\n[21 rows x 49 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>444300dc-3d03-4c1d-b1c7-439b4cc56015_6</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_5</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_23</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_36</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_42</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_13</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_0</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_3</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_1</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_31</th>\n      <th>...</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_41</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_25</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_37</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_21</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_32</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_4</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_20</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_26</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_45</th>\n      <th>444300dc-3d03-4c1d-b1c7-439b4cc56015_2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>log_loss</th>\n      <td>[0.25033977785435796]</td>\n      <td>[0.24661122213565168]</td>\n      <td>[0.18115151864375498]</td>\n      <td>[0.24604619760082583]</td>\n      <td>[0.17858324434463335]</td>\n      <td>[0.33655623030329523]</td>\n      <td>[0.17775706110025447]</td>\n      <td>[0.51444454543788]</td>\n      <td>[0.1874363495858499]</td>\n      <td>[0.17753033221737446]</td>\n      <td>...</td>\n      <td>[0.18212320322735842]</td>\n      <td>[0.18377453013344744]</td>\n      <td>[0.18734695010191202]</td>\n      <td>[0.22923370640917518]</td>\n      <td>[0.18603556814655484]</td>\n      <td>[0.24897509343357427]</td>\n      <td>[0.22589233807484954]</td>\n      <td>[0.3304940796991938]</td>\n      <td>[0.23244422431209238]</td>\n      <td>[0.23484993846731433]</td>\n    </tr>\n    <tr>\n      <th>accuracy</th>\n      <td>[0.8986342943854325]</td>\n      <td>[0.9001517450682853]</td>\n      <td>[0.9128983308042489]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.7569044006069803]</td>\n      <td>[0.9071320182094081]</td>\n      <td>[0.9144157814871017]</td>\n      <td>...</td>\n      <td>[0.9150227617602428]</td>\n      <td>[0.9165402124430956]</td>\n      <td>[0.9101669195751139]</td>\n      <td>[0.8998482549317147]</td>\n      <td>[0.910773899848255]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9004552352048558]</td>\n      <td>[0.9074355083459787]</td>\n      <td>[0.8995447647951441]</td>\n      <td>[0.8925644916540213]</td>\n    </tr>\n    <tr>\n      <th>precision_score_macro</th>\n      <td>[0.7879589751076721]</td>\n      <td>[0.7963108229032296]</td>\n      <td>[0.7860811293290488]</td>\n      <td>[0.4440060698027314]</td>\n      <td>[0.7852393780805413]</td>\n      <td>[0.822098675416211]</td>\n      <td>[0.7819118765348991]</td>\n      <td>[0.6251483540508465]</td>\n      <td>[0.7723958081530135]</td>\n      <td>[0.791450436626639]</td>\n      <td>...</td>\n      <td>[0.7920274421073921]</td>\n      <td>[0.7978332522829199]</td>\n      <td>[0.7821726267699607]</td>\n      <td>[0.8509287925696595]</td>\n      <td>[0.7837744349823996]</td>\n      <td>[0.4440060698027314]</td>\n      <td>[0.8202786854702324]</td>\n      <td>[0.7992420189049403]</td>\n      <td>[0.7475201765396933]</td>\n      <td>[0.8936443673285779]</td>\n    </tr>\n    <tr>\n      <th>AUC_weighted</th>\n      <td>[0.8931072137105515]</td>\n      <td>[0.8981581818552293]</td>\n      <td>[0.9448491887516278]</td>\n      <td>[0.934308239186288]</td>\n      <td>[0.9451400118922584]</td>\n      <td>[0.9308878256246675]</td>\n      <td>[0.9450464668693167]</td>\n      <td>[0.8786285743923741]</td>\n      <td>[0.9392346349984347]</td>\n      <td>[0.9459430171882032]</td>\n      <td>...</td>\n      <td>[0.943697010449257]</td>\n      <td>[0.9430264500867838]</td>\n      <td>[0.9396787423103214]</td>\n      <td>[0.9403502288611404]</td>\n      <td>[0.9397834015934143]</td>\n      <td>[0.9023774328652376]</td>\n      <td>[0.9304904908242522]</td>\n      <td>[0.9350098268583507]</td>\n      <td>[0.9173126830379719]</td>\n      <td>[0.899077423788592]</td>\n    </tr>\n    <tr>\n      <th>recall_score_weighted</th>\n      <td>[0.8986342943854325]</td>\n      <td>[0.9001517450682853]</td>\n      <td>[0.9128983308042489]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.7569044006069803]</td>\n      <td>[0.9071320182094081]</td>\n      <td>[0.9144157814871017]</td>\n      <td>...</td>\n      <td>[0.9150227617602428]</td>\n      <td>[0.9165402124430956]</td>\n      <td>[0.9101669195751139]</td>\n      <td>[0.8998482549317147]</td>\n      <td>[0.910773899848255]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9004552352048558]</td>\n      <td>[0.9074355083459787]</td>\n      <td>[0.8995447647951441]</td>\n      <td>[0.8925644916540213]</td>\n    </tr>\n    <tr>\n      <th>precision_score_micro</th>\n      <td>[0.8986342943854325]</td>\n      <td>[0.9001517450682853]</td>\n      <td>[0.9128983308042489]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.7569044006069803]</td>\n      <td>[0.9071320182094081]</td>\n      <td>[0.9144157814871017]</td>\n      <td>...</td>\n      <td>[0.9150227617602428]</td>\n      <td>[0.9165402124430956]</td>\n      <td>[0.9101669195751139]</td>\n      <td>[0.8998482549317147]</td>\n      <td>[0.910773899848255]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9004552352048558]</td>\n      <td>[0.9074355083459787]</td>\n      <td>[0.8995447647951441]</td>\n      <td>[0.8925644916540213]</td>\n    </tr>\n    <tr>\n      <th>balanced_accuracy</th>\n      <td>[0.5876859554651596]</td>\n      <td>[0.594461023215837]</td>\n      <td>[0.7508386635472644]</td>\n      <td>[0.5]</td>\n      <td>[0.7359455549442713]</td>\n      <td>[0.6227454260188535]</td>\n      <td>[0.7513392683482543]</td>\n      <td>[0.7672090425620592]</td>\n      <td>[0.7191727470931578]</td>\n      <td>[0.7505089404961036]</td>\n      <td>...</td>\n      <td>[0.7567713629972936]</td>\n      <td>[0.7552575081458265]</td>\n      <td>[0.7256180917926747]</td>\n      <td>[0.5682392418592676]</td>\n      <td>[0.7283281188929456]</td>\n      <td>[0.5]</td>\n      <td>[0.5827905869626023]</td>\n      <td>[0.6554005116264423]</td>\n      <td>[0.7279261531508001]</td>\n      <td>[0.5226934668526453]</td>\n    </tr>\n    <tr>\n      <th>f1_score_micro</th>\n      <td>[0.8986342943854325]</td>\n      <td>[0.9001517450682853]</td>\n      <td>[0.9128983308042489]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.7569044006069803]</td>\n      <td>[0.9071320182094081]</td>\n      <td>[0.9144157814871017]</td>\n      <td>...</td>\n      <td>[0.9150227617602428]</td>\n      <td>[0.9165402124430956]</td>\n      <td>[0.9101669195751139]</td>\n      <td>[0.8998482549317147]</td>\n      <td>[0.9107738998482551]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9004552352048558]</td>\n      <td>[0.9074355083459787]</td>\n      <td>[0.8995447647951441]</td>\n      <td>[0.8925644916540213]</td>\n    </tr>\n    <tr>\n      <th>norm_macro_recall</th>\n      <td>[0.17537191093031912]</td>\n      <td>[0.1889220464316741]</td>\n      <td>[0.5016773270945287]</td>\n      <td>[0.0]</td>\n      <td>[0.47189110988854255]</td>\n      <td>[0.24549085203770704]</td>\n      <td>[0.5026785366965085]</td>\n      <td>[0.5344180851241185]</td>\n      <td>[0.43834549418631563]</td>\n      <td>[0.5010178809922072]</td>\n      <td>...</td>\n      <td>[0.5135427259945873]</td>\n      <td>[0.510515016291653]</td>\n      <td>[0.45123618358534934]</td>\n      <td>[0.1364784837185351]</td>\n      <td>[0.45665623778589115]</td>\n      <td>[0.0]</td>\n      <td>[0.16558117392520466]</td>\n      <td>[0.3108010232528846]</td>\n      <td>[0.45585230630160023]</td>\n      <td>[0.045386933705290566]</td>\n    </tr>\n    <tr>\n      <th>precision_score_weighted</th>\n      <td>[0.879573345905093]</td>\n      <td>[0.8825470158152294]</td>\n      <td>[0.9080335867085474]</td>\n      <td>[0.788565560086672]</td>\n      <td>[0.9050318363975759]</td>\n      <td>[0.8929725418691179]</td>\n      <td>[0.9072720074188747]</td>\n      <td>[0.888557432271358]</td>\n      <td>[0.8991976076061607]</td>\n      <td>[0.9090794627426896]</td>\n      <td>...</td>\n      <td>[0.9103936567676629]</td>\n      <td>[0.9113037932659365]</td>\n      <td>[0.9024524528275779]</td>\n      <td>[0.8904507721146121]</td>\n      <td>[0.90329509440897]</td>\n      <td>[0.788565560086672]</td>\n      <td>[0.8859664258327548]</td>\n      <td>[0.8935012190755486]</td>\n      <td>[0.8957195178165474]</td>\n      <td>[0.8927965803774446]</td>\n    </tr>\n    <tr>\n      <th>AUC_micro</th>\n      <td>[0.9665675449766397]</td>\n      <td>[0.9675622926169922]</td>\n      <td>[0.9793693944704005]</td>\n      <td>[0.9743930772932732]</td>\n      <td>[0.9797055823303346]</td>\n      <td>[0.9758990146932517]</td>\n      <td>[0.979695082216353]</td>\n      <td>[0.8616835643281653]</td>\n      <td>[0.9781770788959222]</td>\n      <td>[0.9799541771341596]</td>\n      <td>...</td>\n      <td>[0.9792477220969833]</td>\n      <td>[0.9789654164009017]</td>\n      <td>[0.978246987549536]</td>\n      <td>[0.9761176749616031]</td>\n      <td>[0.9781233809445958]</td>\n      <td>[0.9680422583534624]</td>\n      <td>[0.9744804861368561]</td>\n      <td>[0.9771930155820769]</td>\n      <td>[0.9724459508935459]</td>\n      <td>[0.9674504756137156]</td>\n    </tr>\n    <tr>\n      <th>f1_score_macro</th>\n      <td>[0.6188899385530886]</td>\n      <td>[0.6282264041200302]</td>\n      <td>[0.7668111420906795]</td>\n      <td>[0.4703423886834914]</td>\n      <td>[0.7572938163705586]</td>\n      <td>[0.6650154684677185]</td>\n      <td>[0.7653697272147331]</td>\n      <td>[0.63232316393531]</td>\n      <td>[0.7416848907681176]</td>\n      <td>[0.7687891908032249]</td>\n      <td>...</td>\n      <td>[0.7727908552875257]</td>\n      <td>[0.7742346026690958]</td>\n      <td>[0.7494492260392775]</td>\n      <td>[0.593012549294383]</td>\n      <td>[0.7518148950517209]</td>\n      <td>[0.4703423886834914]</td>\n      <td>[0.6136146528695394]</td>\n      <td>[0.6969780573488569]</td>\n      <td>[0.7371258820055797]</td>\n      <td>[0.5152752520803067]</td>\n    </tr>\n    <tr>\n      <th>recall_score_macro</th>\n      <td>[0.5876859554651596]</td>\n      <td>[0.594461023215837]</td>\n      <td>[0.7508386635472644]</td>\n      <td>[0.5]</td>\n      <td>[0.7359455549442713]</td>\n      <td>[0.6227454260188535]</td>\n      <td>[0.7513392683482543]</td>\n      <td>[0.7672090425620592]</td>\n      <td>[0.7191727470931578]</td>\n      <td>[0.7505089404961036]</td>\n      <td>...</td>\n      <td>[0.7567713629972936]</td>\n      <td>[0.7552575081458265]</td>\n      <td>[0.7256180917926747]</td>\n      <td>[0.5682392418592676]</td>\n      <td>[0.7283281188929456]</td>\n      <td>[0.5]</td>\n      <td>[0.5827905869626023]</td>\n      <td>[0.6554005116264423]</td>\n      <td>[0.7279261531508001]</td>\n      <td>[0.5226934668526453]</td>\n    </tr>\n    <tr>\n      <th>recall_score_micro</th>\n      <td>[0.8986342943854325]</td>\n      <td>[0.9001517450682853]</td>\n      <td>[0.9128983308042489]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.7569044006069803]</td>\n      <td>[0.9071320182094081]</td>\n      <td>[0.9144157814871017]</td>\n      <td>...</td>\n      <td>[0.9150227617602428]</td>\n      <td>[0.9165402124430956]</td>\n      <td>[0.9101669195751139]</td>\n      <td>[0.8998482549317147]</td>\n      <td>[0.910773899848255]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9004552352048558]</td>\n      <td>[0.9074355083459787]</td>\n      <td>[0.8995447647951441]</td>\n      <td>[0.8925644916540213]</td>\n    </tr>\n    <tr>\n      <th>weighted_accuracy</th>\n      <td>[0.9758343559290874]</td>\n      <td>[0.9760464825101347]</td>\n      <td>[0.9531333625443325]</td>\n      <td>[0.9843450583187134]</td>\n      <td>[0.9553155644458374]</td>\n      <td>[0.9766010009385309]</td>\n      <td>[0.9514937218005303]</td>\n      <td>[0.7543460367453827]</td>\n      <td>[0.9537972210153172]</td>\n      <td>[0.9551094165001367]</td>\n      <td>...</td>\n      <td>[0.9543123034451771]</td>\n      <td>[0.9565823452967744]</td>\n      <td>[0.9559854015521687]</td>\n      <td>[0.9821778030055749]</td>\n      <td>[0.9560702521845876]</td>\n      <td>[0.9843450583187134]</td>\n      <td>[0.9793227746800656]</td>\n      <td>[0.9700089806001332]</td>\n      <td>[0.9421530238615385]</td>\n      <td>[0.9843934622702696]</td>\n    </tr>\n    <tr>\n      <th>average_precision_score_macro</th>\n      <td>[0.7441345544245972]</td>\n      <td>[0.7306481851982262]</td>\n      <td>[0.8085204474402641]</td>\n      <td>[0.7951181599264726]</td>\n      <td>[0.8171616074642105]</td>\n      <td>[0.7985126174047921]</td>\n      <td>[0.8151093723721079]</td>\n      <td>[0.7487830567680671]</td>\n      <td>[0.8065229883244922]</td>\n      <td>[0.8198941458185378]</td>\n      <td>...</td>\n      <td>[0.8146358499907052]</td>\n      <td>[0.812573046671845]</td>\n      <td>[0.8078015388629372]</td>\n      <td>[0.8172913885914492]</td>\n      <td>[0.8038374183722943]</td>\n      <td>[0.7345187412902255]</td>\n      <td>[0.787108791806625]</td>\n      <td>[0.8063549152507894]</td>\n      <td>[0.7673410208172029]</td>\n      <td>[0.754172708722851]</td>\n    </tr>\n    <tr>\n      <th>f1_score_weighted</th>\n      <td>[0.8722751025145846]</td>\n      <td>[0.8749663249165839]</td>\n      <td>[0.9100415644605403]</td>\n      <td>[0.8353395018439429]</td>\n      <td>[0.9075132403774469]</td>\n      <td>[0.885603431576398]</td>\n      <td>[0.9091539479147899]</td>\n      <td>[0.7984098282572395]</td>\n      <td>[0.9021127651963996]</td>\n      <td>[0.9111858226879949]</td>\n      <td>...</td>\n      <td>[0.912294877094483]</td>\n      <td>[0.9133307204795383]</td>\n      <td>[0.9051730389877936]</td>\n      <td>[0.8672449503131379]</td>\n      <td>[0.9059514802867371]</td>\n      <td>[0.8353395018439429]</td>\n      <td>[0.8719631449552753]</td>\n      <td>[0.8929502722795345]</td>\n      <td>[0.8974753350144277]</td>\n      <td>[0.8471392306114343]</td>\n    </tr>\n    <tr>\n      <th>average_precision_score_weighted</th>\n      <td>[0.9314544711072148]</td>\n      <td>[0.9287772711070046]</td>\n      <td>[0.9516860732237478]</td>\n      <td>[0.9474863785724514]</td>\n      <td>[0.9536272740055581]</td>\n      <td>[0.947605275820125]</td>\n      <td>[0.9531771295804466]</td>\n      <td>[0.9304659489430059]</td>\n      <td>[0.9505970434373063]</td>\n      <td>[0.9543200270191108]</td>\n      <td>...</td>\n      <td>[0.9529012007897981]</td>\n      <td>[0.952383837559749]</td>\n      <td>[0.9509656788257426]</td>\n      <td>[0.9528629085612494]</td>\n      <td>[0.9500867934766738]</td>\n      <td>[0.9289088651828795]</td>\n      <td>[0.945222197717833]</td>\n      <td>[0.9499966225005592]</td>\n      <td>[0.9390797760292473]</td>\n      <td>[0.9326229771706379]</td>\n    </tr>\n    <tr>\n      <th>matthews_correlation</th>\n      <td>[0.31780470649179715]</td>\n      <td>[0.33460318899475994]</td>\n      <td>[0.5357619178216784]</td>\n      <td>[0.0]</td>\n      <td>[0.5188485842832073]</td>\n      <td>[0.3976739324324451]</td>\n      <td>[0.5323740218566827]</td>\n      <td>[0.36573636332278625]</td>\n      <td>[0.488678780261868]</td>\n      <td>[0.540410733003948]</td>\n      <td>...</td>\n      <td>[0.5476651690313282]</td>\n      <td>[0.5514496307758505]</td>\n      <td>[0.5046315472023726]</td>\n      <td>[0.3094971066200246]</td>\n      <td>[0.5090920660526534]</td>\n      <td>[0.0]</td>\n      <td>[0.3256750549961802]</td>\n      <td>[0.43128813031641494]</td>\n      <td>[0.47504240512147633]</td>\n      <td>[0.18903074249128482]</td>\n    </tr>\n    <tr>\n      <th>AUC_macro</th>\n      <td>[0.8931072137105514]</td>\n      <td>[0.8981581818552293]</td>\n      <td>[0.9448491887516277]</td>\n      <td>[0.934308239186288]</td>\n      <td>[0.9451400118922584]</td>\n      <td>[0.9308878256246677]</td>\n      <td>[0.9450464668693166]</td>\n      <td>[0.8786285743923741]</td>\n      <td>[0.9392346349984347]</td>\n      <td>[0.9459430171882033]</td>\n      <td>...</td>\n      <td>[0.9436970104492569]</td>\n      <td>[0.9430264500867838]</td>\n      <td>[0.9396787423103213]</td>\n      <td>[0.9403502288611405]</td>\n      <td>[0.9397834015934143]</td>\n      <td>[0.9023774328652376]</td>\n      <td>[0.9304904908242521]</td>\n      <td>[0.9350098268583507]</td>\n      <td>[0.917312683037972]</td>\n      <td>[0.899077423788592]</td>\n    </tr>\n    <tr>\n      <th>average_precision_score_micro</th>\n      <td>[0.9680512746390288]</td>\n      <td>[0.9684179858920094]</td>\n      <td>[0.980352027134298]</td>\n      <td>[0.9756340409323144]</td>\n      <td>[0.980657776389614]</td>\n      <td>[0.9766643355999638]</td>\n      <td>[0.9806603102489483]</td>\n      <td>[0.8546273138424763]</td>\n      <td>[0.9791945367231853]</td>\n      <td>[0.9808900541538087]</td>\n      <td>...</td>\n      <td>[0.9802204735535665]</td>\n      <td>[0.9799669312828488]</td>\n      <td>[0.9792878371980246]</td>\n      <td>[0.9770786013335764]</td>\n      <td>[0.9791660115712217]</td>\n      <td>[0.9679761489926731]</td>\n      <td>[0.9756497344937594]</td>\n      <td>[0.9781626875518643]</td>\n      <td>[0.9732858342558575]</td>\n      <td>[0.9671349594718173]</td>\n    </tr>\n  </tbody>\n</table>\n<p>21 rows × 49 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 40,
      "metadata": {
        "gather": {
          "logged": 1612024893049
        }
      }
    },
    {
      "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/444300dc-3d03-4c1d-b1c7-439b4cc56015/model_data\n",
            "Downloaded azureml/444300dc-3d03-4c1d-b1c7-439b4cc56015/model_data, 1 files out of an estimated total of 1\n"
          ]
        }
      ],
      "execution_count": 41,
      "metadata": {
        "gather": {
          "logged": 1612024902466
        }
      }
    },
    {
      "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": 42,
          "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                                                                                                                                 reg_lambda=0.10526315789473684,\n                                                                                                                                 silent=True,\n                                                                                                                                 subsample=0.99,\n                                                                                                                                 subsample_for_bin=200000,\n                                                                                                                                 subsample_freq=0,\n                                                                                                                                 verbose=-10))],\n                                                                                                      verbose=False))],\n                                                                                flatten_transform=None,\n                                                                                weights=[0.16666666666666666,\n                                                                                         0.16666666666666666,\n                                                                                         0.16666666666666666,\n                                                                                         0.16666666666666666,\n                                                                                         0.16666666666666666,\n                                                                                         0.16666666666666666]))],\n                                       'verbose': False},\n                             y_transformer={},\n                             y_transformer_name='LabelEncoder')"
          },
          "metadata": {}
        }
      ],
      "execution_count": 42,
      "metadata": {
        "gather": {
          "logged": 1612024925300
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_model.steps"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 43,
          "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=[('31',\n                                             Pipeline(memory=None,\n                                                      steps=[('standardscalerwrapper',\n                                                              <azureml.automl.runtime.shared.model_wrappers.StandardScalerWrapper object at 0x7f8d91244978>),\n                                                             ('xgboostclassifier',\n                                                              XGBoostClassifier(base_score=0.5,\n                                                                                booster='gbtree',\n                                                                                colsample_bylevel=1,\n                                                                                colsample_bynode=1,\n                                                                                colsample_bytree=0.9...\n                                                                                 num_leaves=155,\n                                                                                 objective=None,\n                                                                                 random_state=None,\n                                                                                 reg_alpha=0.42105263157894735,\n                                                                                 reg_lambda=0.10526315789473684,\n                                                                                 silent=True,\n                                                                                 subsample=0.99,\n                                                                                 subsample_for_bin=200000,\n                                                                                 subsample_freq=0,\n                                                                                 verbose=-10))],\n                                                      verbose=False))],\n                                flatten_transform=None,\n                                weights=[0.16666666666666666, 0.16666666666666666,\n                                         0.16666666666666666, 0.16666666666666666,\n                                         0.16666666666666666,\n                                         0.16666666666666666]))]"
          },
          "metadata": {}
        }
      ],
      "execution_count": 43,
      "metadata": {
        "gather": {
          "logged": 1612025015811
        }
      }
    },
    {
      "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": 44,
      "metadata": {
        "gather": {
          "logged": 1612025025552
        }
      }
    },
    {
      "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": 45,
      "metadata": {
        "gather": {
          "logged": 1612025034136
        }
      }
    },
    {
      "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": 46,
          "data": {
            "text/plain": "<pandas.io.formats.style.Styler at 0x7f8d88c792e8>",
            "text/html": "<style  type=\"text/css\" >\n    #T_5842b194_631a_11eb_aa6f_d33815bcdbfbrow0_col0 {\n            background-color:  #63a8d3;\n            color:  #000000;\n        }    #T_5842b194_631a_11eb_aa6f_d33815bcdbfbrow0_col1 {\n            background-color:  #f7fbff;\n            color:  #000000;\n        }    #T_5842b194_631a_11eb_aa6f_d33815bcdbfbrow1_col0 {\n            background-color:  #f7fbff;\n            color:  #000000;\n        }    #T_5842b194_631a_11eb_aa6f_d33815bcdbfbrow1_col1 {\n            background-color:  #63a8d3;\n            color:  #000000;\n        }</style><table id=\"T_5842b194_631a_11eb_aa6f_d33815bcdbfb\" ><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_5842b194_631a_11eb_aa6f_d33815bcdbfblevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_5842b194_631a_11eb_aa6f_d33815bcdbfbrow0_col0\" class=\"data row0 col0\" >28764</td>\n                        <td id=\"T_5842b194_631a_11eb_aa6f_d33815bcdbfbrow0_col1\" class=\"data row0 col1\" >494</td>\n            </tr>\n            <tr>\n                        <th id=\"T_5842b194_631a_11eb_aa6f_d33815bcdbfblevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_5842b194_631a_11eb_aa6f_d33815bcdbfbrow1_col0\" class=\"data row1 col0\" >1390</td>\n                        <td id=\"T_5842b194_631a_11eb_aa6f_d33815bcdbfbrow1_col1\" class=\"data row1 col1\" >2302</td>\n            </tr>\n    </tbody></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 46,
      "metadata": {
        "gather": {
          "logged": 1612025037080
        }
      }
    },
    {
      "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": 47,
          "data": {
            "text/plain": "Pipeline(Name: Bankmarketing Train,\nId: a9e49489-a6aa-4ed5-bfb7-9549f820242d,\nStatus: Active,\nEndpoint: https://southcentralus.api.azureml.ms/pipelines/v1.0/subscriptions/aa7cf8e8-d23f-4bce-a7b9-1f0b4e0ac8ee/resourceGroups/aml-quickstarts-136602/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136602/PipelineRuns/PipelineSubmit/a9e49489-a6aa-4ed5-bfb7-9549f820242d)",
            "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/a9e49489-a6aa-4ed5-bfb7-9549f820242d?wsid=/subscriptions/aa7cf8e8-d23f-4bce-a7b9-1f0b4e0ac8ee/resourcegroups/aml-quickstarts-136602/workspaces/quick-starts-ws-136602\" target=\"_blank\" rel=\"noopener\">a9e49489-a6aa-4ed5-bfb7-9549f820242d</a></td><td>Active</td><td><a href=\"https://southcentralus.api.azureml.ms/pipelines/v1.0/subscriptions/aa7cf8e8-d23f-4bce-a7b9-1f0b4e0ac8ee/resourceGroups/aml-quickstarts-136602/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-136602/PipelineRuns/PipelineSubmit/a9e49489-a6aa-4ed5-bfb7-9549f820242d\" target=\"_blank\" rel=\"noopener\">REST Endpoint</a></td></tr></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 47,
      "metadata": {
        "gather": {
          "logged": 1612025050474
        }
      }
    },
    {
      "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": 48,
      "metadata": {
        "gather": {
          "logged": 1612025061463
        }
      }
    },
    {
      "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": 49,
      "metadata": {
        "gather": {
          "logged": 1612025067715
        }
      }
    },
    {
      "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:  0570b98b-3eab-453e-87a1-06cb706e374b\n"
          ]
        }
      ],
      "execution_count": 50,
      "metadata": {
        "gather": {
          "logged": 1612025071757
        }
      }
    },
    {
      "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": "41d12e69bdc149878db3f11811812d30"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Running\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/pipeline-rest-endpoint/runs/0570b98b-3eab-453e-87a1-06cb706e374b?wsid=/subscriptions/aa7cf8e8-d23f-4bce-a7b9-1f0b4e0ac8ee/resourcegroups/aml-quickstarts-136602/workspaces/quick-starts-ws-136602\", \"run_id\": \"0570b98b-3eab-453e-87a1-06cb706e374b\", \"run_properties\": {\"run_id\": \"0570b98b-3eab-453e-87a1-06cb706e374b\", \"created_utc\": \"2021-01-30T16:44:27.340885Z\", \"properties\": {\"azureml.runsource\": \"azureml.PipelineRun\", \"runSource\": \"Unavailable\", \"runType\": \"HTTP\", \"azureml.parameters\": \"{}\", \"azureml.pipelineid\": \"a9e49489-a6aa-4ed5-bfb7-9549f820242d\"}, \"tags\": {\"azureml.pipelineid\": \"a9e49489-a6aa-4ed5-bfb7-9549f820242d\", \"azureml.pipelineComponent\": \"pipelinerun\"}, \"end_time_utc\": null, \"status\": \"Running\", \"log_files\": {\"logs/azureml/executionlogs.txt\": \"https://mlstrg136602.blob.core.windows.net/azureml/ExperimentRun/dcid.0570b98b-3eab-453e-87a1-06cb706e374b/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=8RSwVC54gEi2%2Fgr4NAXOgiEV9b6XrweA1KtcSZBnxPk%3D&st=2021-01-30T16%3A34%3A39Z&se=2021-01-31T00%3A44%3A39Z&sp=r\", \"logs/azureml/stderrlogs.txt\": \"https://mlstrg136602.blob.core.windows.net/azureml/ExperimentRun/dcid.0570b98b-3eab-453e-87a1-06cb706e374b/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=MM36pB2OY%2FjlurXt5PawRTbRM4bbHnWrJXb5SjXffSs%3D&st=2021-01-30T16%3A34%3A39Z&se=2021-01-31T00%3A44%3A39Z&sp=r\", \"logs/azureml/stdoutlogs.txt\": \"https://mlstrg136602.blob.core.windows.net/azureml/ExperimentRun/dcid.0570b98b-3eab-453e-87a1-06cb706e374b/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=o%2Fr8lUN%2BqX7m7AGUakine76v00Zo6k%2F52DrNOfutX9c%3D&st=2021-01-30T16%3A34%3A39Z&se=2021-01-31T00%3A44%3A39Z&sp=r\"}, \"log_groups\": [[\"logs/azureml/executionlogs.txt\", \"logs/azureml/stderrlogs.txt\", \"logs/azureml/stdoutlogs.txt\"]], \"run_duration\": \"0:02:04\"}, \"child_runs\": [{\"run_id\": \"e7af5244-9be6-46d4-878f-bbf67d9bef11\", \"name\": \"automl_module\", \"status\": \"Running\", \"start_time\": \"2021-01-30T16:44:52.43697Z\", \"created_time\": \"2021-01-30T16:44:34.508982Z\", \"end_time\": \"\", \"duration\": \"0:01:57\", \"run_number\": 2, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-01-30T16:44:34.508982Z\", \"is_reused\": \"\"}], \"children_metrics\": {\"categories\": null, \"series\": null, \"metricName\": null}, \"run_metrics\": [], \"run_logs\": \"[2021-01-30 16:44:34Z] Submitting 1 runs, first five are: b30976e7:e7af5244-9be6-46d4-878f-bbf67d9bef11\\n\", \"graph\": {\"datasource_nodes\": {\"9e7b7e3a\": {\"node_id\": \"9e7b7e3a\", \"name\": \"Bank-marketing\"}}, \"module_nodes\": {\"b30976e7\": {\"node_id\": \"b30976e7\", \"name\": \"automl_module\", \"status\": \"Running\", \"_is_reused\": false, \"run_id\": \"e7af5244-9be6-46d4-878f-bbf67d9bef11\"}}, \"edges\": [{\"source_node_id\": \"9e7b7e3a\", \"source_node_name\": \"Bank-marketing\", \"source_name\": \"data\", \"target_name\": \"training_data\", \"dst_node_id\": \"b30976e7\", \"dst_node_name\": \"automl_module\"}], \"child_runs\": [{\"run_id\": \"e7af5244-9be6-46d4-878f-bbf67d9bef11\", \"name\": \"automl_module\", \"status\": \"Running\", \"start_time\": \"2021-01-30T16:44:52.43697Z\", \"created_time\": \"2021-01-30T16:44:34.508982Z\", \"end_time\": \"\", \"duration\": \"0:01:57\", \"run_number\": 2, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-01-30T16:44:34.508982Z\", \"is_reused\": \"\"}]}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.20.0\"}, \"loading\": false}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 51,
      "metadata": {
        "gather": {
          "logged": 1612025078376
        }
      }
    },
    {
      "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"
    ],
    "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
}