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

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Azure Machine Learning Pipeline with AutoMLStep (Udacity Course 2)\n",
    "This notebook demonstrates the use of AutoMLStep in Azure Machine Learning Pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Azure Machine Learning and Pipeline SDK-specific imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "gather": {
     "logged": 1612385590615
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SDK version: 1.20.0\n"
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Workspace\n",
    "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "gather": {
     "logged": 1612385640933
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing interactive authentication. Please follow the instructions on the terminal.\n",
      "To sign in, use a web browser to open the page https://microsoft.com/devicelogin and enter the code E5GQ4WRMU 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-137116\n",
      "aml-quickstarts-137116\n",
      "southcentralus\n",
      "9a7511b8-150f-4a58-8528-3e7d50216c31\n"
     ]
    }
   ],
   "source": [
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "gather": {
     "logged": 1612385726893
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>bank-marketing-experiment</td><td>quick-starts-ws-137116</td><td><a href=\"https://ml.azure.com/experiments/bank-marketing-experiment?wsid=/subscriptions/9a7511b8-150f-4a58-8528-3e7d50216c31/resourcegroups/aml-quickstarts-137116/workspaces/quick-starts-ws-137116\" 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>"
      ],
      "text/plain": [
       "Experiment(Name: bank-marketing-experiment,\n",
       "Workspace: quick-starts-ws-137116)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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 = 'bank-marketing-experiment'\n",
    "project_folder = './pipeline-project'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "gather": {
     "logged": 1612385798734
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "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 = \"aml-cluster\"\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()."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "gather": {
     "logged": 1612385982558
    }
   },
   "outputs": [
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       "    <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",
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       "                age      duration      campaign         pdays      previous  \\\n",
       "count  32950.000000  32950.000000  32950.000000  32950.000000  32950.000000   \n",
       "mean      40.040212    257.335205      2.561730    962.174780      0.174780   \n",
       "std       10.432313    257.331700      2.763646    187.646785      0.496503   \n",
       "min       17.000000      0.000000      1.000000      0.000000      0.000000   \n",
       "25%       32.000000    102.000000      1.000000    999.000000      0.000000   \n",
       "50%       38.000000    179.000000      2.000000    999.000000      0.000000   \n",
       "75%       47.000000    318.000000      3.000000    999.000000      0.000000   \n",
       "max       98.000000   4918.000000     56.000000    999.000000      7.000000   \n",
       "\n",
       "       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m   nr.employed  \n",
       "count  32950.000000    32950.000000   32950.000000  32950.000000  32950.000000  \n",
       "mean       0.076228       93.574243     -40.518680      3.615654   5166.859608  \n",
       "std        1.572242        0.578636       4.623004      1.735748     72.208448  \n",
       "min       -3.400000       92.201000     -50.800000      0.634000   4963.600000  \n",
       "25%       -1.800000       93.075000     -42.700000      1.344000   5099.100000  \n",
       "50%        1.100000       93.749000     -41.800000      4.857000   5191.000000  \n",
       "75%        1.400000       93.994000     -36.400000      4.961000   5228.100000  \n",
       "max        1.400000       94.767000     -26.900000      5.045000   5228.100000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "gather": {
     "logged": 1612385995026
    }
   },
   "outputs": [
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       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
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       "      <th>0</th>\n",
       "      <td>57</td>\n",
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       "      <td>no</td>\n",
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       "      <td>thu</td>\n",
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       "      <td>999</td>\n",
       "      <td>0</td>\n",
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       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
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       "      <td>no</td>\n",
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       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>blue-collar</td>\n",
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       "      <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>"
      ],
      "text/plain": [
       "   age          job  marital    education  default housing loan    contact  \\\n",
       "0   57   technician  married  high.school       no      no  yes   cellular   \n",
       "1   55      unknown  married      unknown  unknown     yes   no  telephone   \n",
       "2   33  blue-collar  married     basic.9y       no      no   no   cellular   \n",
       "3   36       admin.  married  high.school       no      no   no  telephone   \n",
       "4   27    housemaid  married  high.school       no     yes   no   cellular   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         1      failure         -1.8   \n",
       "1   may         thu  ...         2    999         0  nonexistent          1.1   \n",
       "2   may         fri  ...         1    999         1      failure         -1.8   \n",
       "3   jun         fri  ...         4    999         0  nonexistent          1.4   \n",
       "4   jul         fri  ...         2    999         0  nonexistent          1.4   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          92.893          -46.2      1.299       5099.1  no  \n",
       "1          93.994          -36.4      4.860       5191.0  no  \n",
       "2          92.893          -46.2      1.313       5099.1  no  \n",
       "3          94.465          -41.8      4.967       5228.1  no  \n",
       "4          93.918          -42.7      4.963       5228.1  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.take(5).to_pandas_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "gather": {
     "logged": 1612386287453
    }
   },
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"experiment_timeout_minutes\": 20,\n",
    "    \"max_concurrent_iterations\": 5,\n",
    "    \"primary_metric\" : 'AUC_weighted'\n",
    "}\n",
    "automl_config = AutoMLConfig(compute_target=compute_target,\n",
    "                             task = \"classification\",\n",
    "                             training_data=dataset,\n",
    "                             label_column_name=\"y\",   \n",
    "                             path = project_folder,\n",
    "                             enable_early_stopping= True,\n",
    "                             featurization= 'auto',\n",
    "                             debug_log = \"automl_errors.log\",\n",
    "                             **automl_settings\n",
    "                            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Pipeline and AutoMLStep\n",
    "\n",
    "You can define outputs for the AutoMLStep using TrainingOutput."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "gather": {
     "logged": 1612386298806
    }
   },
   "outputs": [],
   "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'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create an AutoMLStep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "gather": {
     "logged": 1612386307283
    },
    "tags": [
     "automlstep-remarks-sample1"
    ]
   },
   "outputs": [],
   "source": [
    "automl_step = AutoMLStep(\n",
    "    name='automl_module',\n",
    "    automl_config=automl_config,\n",
    "    outputs=[metrics_data, model_data],\n",
    "    allow_reuse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "gather": {
     "logged": 1612386312780
    },
    "tags": [
     "automlstep-remarks-sample2"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.pipeline.core import Pipeline\n",
    "pipeline = Pipeline(\n",
    "    description=\"pipeline_with_automlstep\",\n",
    "    workspace=ws,    \n",
    "    steps=[automl_step])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "gather": {
     "logged": 1612386323232
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created step automl_module [814bbfc6][64e7f6f8-925f-4d68-a13b-2af05e6e7c57], (This step will run and generate new outputs)\n",
      "Submitted PipelineRun bf36e1de-0cbc-4753-9220-177347bfbf1b\n",
      "Link to Azure Machine Learning Portal: https://ml.azure.com/experiments/bank-marketing-experiment/runs/bf36e1de-0cbc-4753-9220-177347bfbf1b?wsid=/subscriptions/9a7511b8-150f-4a58-8528-3e7d50216c31/resourcegroups/aml-quickstarts-137116/workspaces/quick-starts-ws-137116\n"
     ]
    }
   ],
   "source": [
    "pipeline_run = experiment.submit(pipeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "gather": {
     "logged": 1612386334809
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65c2676084f94e23ae5ffc916cc174db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_PipelineWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/aml.mini.widget.v1": "{\"status\": \"Running\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/bank-marketing-experiment/runs/bf36e1de-0cbc-4753-9220-177347bfbf1b?wsid=/subscriptions/9a7511b8-150f-4a58-8528-3e7d50216c31/resourcegroups/aml-quickstarts-137116/workspaces/quick-starts-ws-137116\", \"run_id\": \"bf36e1de-0cbc-4753-9220-177347bfbf1b\", \"run_properties\": {\"run_id\": \"bf36e1de-0cbc-4753-9220-177347bfbf1b\", \"created_utc\": \"2021-02-03T21:05:22.17888Z\", \"properties\": {\"azureml.runsource\": \"azureml.PipelineRun\", \"runSource\": \"SDK\", \"runType\": \"SDK\", \"azureml.parameters\": \"{}\"}, \"tags\": {\"azureml.pipelineComponent\": \"pipelinerun\"}, \"end_time_utc\": null, \"status\": \"Running\", \"log_files\": {\"logs/azureml/executionlogs.txt\": \"https://mlstrg137116.blob.core.windows.net/azureml/ExperimentRun/dcid.bf36e1de-0cbc-4753-9220-177347bfbf1b/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=FXhhWrT1o9OEEWqrw1gtSvI9T%2B3e9KD7lBxrI%2BtnY%2Bg%3D&st=2021-02-03T20%3A55%3A56Z&se=2021-02-04T05%3A05%3A56Z&sp=r\", \"logs/azureml/stderrlogs.txt\": \"https://mlstrg137116.blob.core.windows.net/azureml/ExperimentRun/dcid.bf36e1de-0cbc-4753-9220-177347bfbf1b/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=hufHRUNpJeeHN3wOaf7swpXAoRfpbf0%2B3R5faMrGIow%3D&st=2021-02-03T20%3A55%3A57Z&se=2021-02-04T05%3A05%3A57Z&sp=r\", \"logs/azureml/stdoutlogs.txt\": \"https://mlstrg137116.blob.core.windows.net/azureml/ExperimentRun/dcid.bf36e1de-0cbc-4753-9220-177347bfbf1b/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=Eo9pfSflQwwoDt77VTNK6QALi%2BKN3ExafVf7xqnYT5M%3D&st=2021-02-03T20%3A55%3A57Z&se=2021-02-04T05%3A05%3A57Z&sp=r\"}, \"log_groups\": [[\"logs/azureml/executionlogs.txt\", \"logs/azureml/stderrlogs.txt\", \"logs/azureml/stdoutlogs.txt\"]], \"run_duration\": \"0:01:17\"}, \"child_runs\": [{\"run_id\": \"ef753519-b949-4647-b9a8-147edc7511c3\", \"name\": \"automl_module\", \"status\": \"Running\", \"start_time\": \"2021-02-03T21:06:00.254588Z\", \"created_time\": \"2021-02-03T21:05:37.926163Z\", \"end_time\": \"\", \"duration\": \"0:01:02\", \"run_number\": 94, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-02-03T21:05:37.926163Z\", \"is_reused\": \"\"}], \"children_metrics\": {\"categories\": null, \"series\": null, \"metricName\": null}, \"run_metrics\": [], \"run_logs\": \"[2021-02-03 21:05:37Z] Submitting 1 runs, first five are: 814bbfc6:ef753519-b949-4647-b9a8-147edc7511c3\\n\", \"graph\": {\"datasource_nodes\": {\"70e7e6d4\": {\"node_id\": \"70e7e6d4\", \"name\": \"Bank-marketing\"}}, \"module_nodes\": {\"814bbfc6\": {\"node_id\": \"814bbfc6\", \"name\": \"automl_module\", \"status\": \"Running\", \"_is_reused\": false, \"run_id\": \"ef753519-b949-4647-b9a8-147edc7511c3\"}}, \"edges\": [{\"source_node_id\": \"70e7e6d4\", \"source_node_name\": \"Bank-marketing\", \"source_name\": \"data\", \"target_name\": \"training_data\", \"dst_node_id\": \"814bbfc6\", \"dst_node_name\": \"automl_module\"}], \"child_runs\": [{\"run_id\": \"ef753519-b949-4647-b9a8-147edc7511c3\", \"name\": \"automl_module\", \"status\": \"Running\", \"start_time\": \"2021-02-03T21:06:00.254588Z\", \"created_time\": \"2021-02-03T21:05:37.926163Z\", \"end_time\": \"\", \"duration\": \"0:01:02\", \"run_number\": 94, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-02-03T21:05:37.926163Z\", \"is_reused\": \"\"}]}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.20.0\"}, \"loading\": false}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(pipeline_run).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PipelineRunId: bf36e1de-0cbc-4753-9220-177347bfbf1b\n",
      "Link to Azure Machine Learning Portal: https://ml.azure.com/experiments/bank-marketing-experiment/runs/bf36e1de-0cbc-4753-9220-177347bfbf1b?wsid=/subscriptions/9a7511b8-150f-4a58-8528-3e7d50216c31/resourcegroups/aml-quickstarts-137116/workspaces/quick-starts-ws-137116\n",
      "PipelineRun Status: Running\n",
      "\n",
      "\n",
      "StepRunId: ef753519-b949-4647-b9a8-147edc7511c3\n",
      "Link to Azure Machine Learning Portal: https://ml.azure.com/experiments/bank-marketing-experiment/runs/ef753519-b949-4647-b9a8-147edc7511c3?wsid=/subscriptions/9a7511b8-150f-4a58-8528-3e7d50216c31/resourcegroups/aml-quickstarts-137116/workspaces/quick-starts-ws-137116\n",
      "StepRun( automl_module ) Status: Running\n"
     ]
    }
   ],
   "source": [
    "pipeline_run.wait_for_completion()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "gather": {
     "logged": 1612389591242
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading azureml/ef753519-b949-4647-b9a8-147edc7511c3/metrics_data\n",
      "Downloaded azureml/ef753519-b949-4647-b9a8-147edc7511c3/metrics_data, 1 files out of an estimated total of 1\n"
     ]
    }
   ],
   "source": [
    "metrics_output = pipeline_run.get_pipeline_output(metrics_output_name)\n",
    "num_file_downloaded = metrics_output.download('.', show_progress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "gather": {
     "logged": 1612389602825
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_36</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_43</th>\n",
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       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_3</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_29</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_52</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_2</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_6</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_23</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_26</th>\n",
       "      <th>...</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_15</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_28</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_49</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_35</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_39</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_38</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_4</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_53</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_14</th>\n",
       "      <th>ef753519-b949-4647-b9a8-147edc7511c3_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>recall_score_macro</th>\n",
       "      <td>[0.726802223592981]</td>\n",
       "      <td>[0.7235795512432226]</td>\n",
       "      <td>[0.7191727470931578]</td>\n",
       "      <td>[0.8083938597417417]</td>\n",
       "      <td>[0.6716125124340785]</td>\n",
       "      <td>[0.635417071874068]</td>\n",
       "      <td>[0.5731466508103222]</td>\n",
       "      <td>[0.584987968813386]</td>\n",
       "      <td>[0.6792540293823991]</td>\n",
       "      <td>[0.7411947273949842]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.5104983449014258]</td>\n",
       "      <td>[0.7440635957965869]</td>\n",
       "      <td>[0.7351031866436231]</td>\n",
       "      <td>[0.7411826869464866]</td>\n",
       "      <td>[0.7488121634463099]</td>\n",
       "      <td>[0.7286578419441063]</td>\n",
       "      <td>[0.5]</td>\n",
       "      <td>[0.7413656091448133]</td>\n",
       "      <td>[0.5064333042510193]</td>\n",
       "      <td>[0.7451148195692483]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matthews_correlation</th>\n",
       "      <td>[0.5056898517541892]</td>\n",
       "      <td>[0.4977269796691756]</td>\n",
       "      <td>[0.488678780261868]</td>\n",
       "      <td>[0.43775059949499806]</td>\n",
       "      <td>[0.42648619466677545]</td>\n",
       "      <td>[0.3938665324111809]</td>\n",
       "      <td>[0.31555741112929]</td>\n",
       "      <td>[0.32536093428636176]</td>\n",
       "      <td>[0.4538726815046628]</td>\n",
       "      <td>[0.5264365575296669]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.12037422088722034]</td>\n",
       "      <td>[0.5249976467923607]</td>\n",
       "      <td>[0.5201542992321688]</td>\n",
       "      <td>[0.5195442529553999]</td>\n",
       "      <td>[0.535979307721254]</td>\n",
       "      <td>[0.5043503790518389]</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>[0.5275928480289197]</td>\n",
       "      <td>[0.08812507634163474]</td>\n",
       "      <td>[0.3319462230070767]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision_score_weighted</th>\n",
       "      <td>[0.9026121799418352]</td>\n",
       "      <td>[0.900988490830041]</td>\n",
       "      <td>[0.8991976076061607]</td>\n",
       "      <td>[0.901167922410634]</td>\n",
       "      <td>[0.8889110559014688]</td>\n",
       "      <td>[0.8872411026406429]</td>\n",
       "      <td>[0.8888749630995354]</td>\n",
       "      <td>[0.8843463619650799]</td>\n",
       "      <td>[0.895092517403297]</td>\n",
       "      <td>[0.9064419404986112]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.8800157057300507]</td>\n",
       "      <td>[0.9059653367367347]</td>\n",
       "      <td>[0.905387728519385]</td>\n",
       "      <td>[0.9048930721424753]</td>\n",
       "      <td>[0.9081762042023199]</td>\n",
       "      <td>[0.9021727665937724]</td>\n",
       "      <td>[0.788565560086672]</td>\n",
       "      <td>[0.9066944678995318]</td>\n",
       "      <td>[0.8696955551933787]</td>\n",
       "      <td>[0.881755734573189]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>[0.9101669195751139]</td>\n",
       "      <td>[0.908649468892261]</td>\n",
       "      <td>[0.9071320182094081]</td>\n",
       "      <td>[0.8006069802731411]</td>\n",
       "      <td>[0.9025796661608497]</td>\n",
       "      <td>[0.9034901365705614]</td>\n",
       "      <td>[0.9001517450682853]</td>\n",
       "      <td>[0.9001517450682853]</td>\n",
       "      <td>[0.9077389984825494]</td>\n",
       "      <td>[0.9125948406676783]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.8898330804248862]</td>\n",
       "      <td>[0.9113808801213961]</td>\n",
       "      <td>[0.9122913505311078]</td>\n",
       "      <td>[0.9104704097116844]</td>\n",
       "      <td>[0.91350531107739]</td>\n",
       "      <td>[0.9092564491654022]</td>\n",
       "      <td>[0.8880121396054628]</td>\n",
       "      <td>[0.9128983308042489]</td>\n",
       "      <td>[0.8889226100151745]</td>\n",
       "      <td>[0.7386949924127466]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted_accuracy</th>\n",
       "      <td>[0.955691413656376]</td>\n",
       "      <td>[0.95459732338795]</td>\n",
       "      <td>[0.9537972210153172]</td>\n",
       "      <td>[0.7986737087326132]</td>\n",
       "      <td>[0.959922562875411]</td>\n",
       "      <td>[0.9700454272809962]</td>\n",
       "      <td>[0.9813382646344059]</td>\n",
       "      <td>[0.9783983856764774]</td>\n",
       "      <td>[0.9644656358962787]</td>\n",
       "      <td>[0.955148852498673]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.9840116344243844]</td>\n",
       "      <td>[0.9529212359632853]</td>\n",
       "      <td>[0.9562823787656349]</td>\n",
       "      <td>[0.9524999721188641]</td>\n",
       "      <td>[0.9543941647599228]</td>\n",
       "      <td>[0.9540941982287833]</td>\n",
       "      <td>[0.9843450583187134]</td>\n",
       "      <td>[0.9554852657106753]</td>\n",
       "      <td>[0.9838843584757562]</td>\n",
       "      <td>[0.7371011229831735]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall_score_weighted</th>\n",
       "      <td>[0.9101669195751139]</td>\n",
       "      <td>[0.908649468892261]</td>\n",
       "      <td>[0.9071320182094081]</td>\n",
       "      <td>[0.8006069802731411]</td>\n",
       "      <td>[0.9025796661608497]</td>\n",
       "      <td>[0.9034901365705614]</td>\n",
       "      <td>[0.9001517450682853]</td>\n",
       "      <td>[0.9001517450682853]</td>\n",
       "      <td>[0.9077389984825494]</td>\n",
       "      <td>[0.9125948406676783]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.8898330804248862]</td>\n",
       "      <td>[0.9113808801213961]</td>\n",
       "      <td>[0.9122913505311078]</td>\n",
       "      <td>[0.9104704097116844]</td>\n",
       "      <td>[0.91350531107739]</td>\n",
       "      <td>[0.9092564491654022]</td>\n",
       "      <td>[0.8880121396054628]</td>\n",
       "      <td>[0.9128983308042489]</td>\n",
       "      <td>[0.8889226100151745]</td>\n",
       "      <td>[0.7386949924127466]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC_micro</th>\n",
       "      <td>[0.9783147777591007]</td>\n",
       "      <td>[0.97803403786949]</td>\n",
       "      <td>[0.9781770788959222]</td>\n",
       "      <td>[0.881765953380415]</td>\n",
       "      <td>[0.9699141339363225]</td>\n",
       "      <td>[0.9737232805487691]</td>\n",
       "      <td>[0.9662645153713839]</td>\n",
       "      <td>[0.9644178769045848]</td>\n",
       "      <td>[0.9763877305247063]</td>\n",
       "      <td>[0.9790159827392864]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.968661212440793]</td>\n",
       "      <td>[0.979086536136741]</td>\n",
       "      <td>[0.977890720524269]</td>\n",
       "      <td>[0.9793498679426453]</td>\n",
       "      <td>[0.979336328321985]</td>\n",
       "      <td>[0.9783628572283843]</td>\n",
       "      <td>[0.9641652294251879]</td>\n",
       "      <td>[0.9793660786449326]</td>\n",
       "      <td>[0.9694834450505547]</td>\n",
       "      <td>[0.8606551979018193]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_score_macro</th>\n",
       "      <td>[0.7501265610044537]</td>\n",
       "      <td>[0.7462485247007249]</td>\n",
       "      <td>[0.7416848907681176]</td>\n",
       "      <td>[0.6778486306801517]</td>\n",
       "      <td>[0.7043745341088892]</td>\n",
       "      <td>[0.674881533645934]</td>\n",
       "      <td>[0.6002318095341351]</td>\n",
       "      <td>[0.6163274132542244]</td>\n",
       "      <td>[0.71617170693669]</td>\n",
       "      <td>[0.7613817409356443]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.4918858319714491]</td>\n",
       "      <td>[0.7612114990956147]</td>\n",
       "      <td>[0.7576713259183312]</td>\n",
       "      <td>[0.7584448322812782]</td>\n",
       "      <td>[0.7666331430514044]</td>\n",
       "      <td>[0.7499559024142635]</td>\n",
       "      <td>[0.4703423886834914]</td>\n",
       "      <td>[0.7618961292903779]</td>\n",
       "      <td>[0.4838482424723856]</td>\n",
       "      <td>[0.6129685636333599]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>norm_macro_recall</th>\n",
       "      <td>[0.45360444718596193]</td>\n",
       "      <td>[0.4471591024864452]</td>\n",
       "      <td>[0.43834549418631563]</td>\n",
       "      <td>[0.6167877194834834]</td>\n",
       "      <td>[0.343225024868157]</td>\n",
       "      <td>[0.2708341437481361]</td>\n",
       "      <td>[0.14629330162064447]</td>\n",
       "      <td>[0.169975937626772]</td>\n",
       "      <td>[0.3585080587647982]</td>\n",
       "      <td>[0.4823894547899683]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.02099668980285152]</td>\n",
       "      <td>[0.48812719159317375]</td>\n",
       "      <td>[0.4702063732872461]</td>\n",
       "      <td>[0.4823653738929732]</td>\n",
       "      <td>[0.4976243268926197]</td>\n",
       "      <td>[0.45731568388821264]</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>[0.4827312182896266]</td>\n",
       "      <td>[0.012866608502038579]</td>\n",
       "      <td>[0.4902296391384966]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_precision_score_weighted</th>\n",
       "      <td>[0.9511077193652759]</td>\n",
       "      <td>[0.9502433276389894]</td>\n",
       "      <td>[0.9505970434373063]</td>\n",
       "      <td>[0.9348502058704878]</td>\n",
       "      <td>[0.9385097261678277]</td>\n",
       "      <td>[0.941035720886307]</td>\n",
       "      <td>[0.9322861133142119]</td>\n",
       "      <td>[0.9275516324761114]</td>\n",
       "      <td>[0.9474347924918001]</td>\n",
       "      <td>[0.9524536910133203]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.933464279983398]</td>\n",
       "      <td>[0.9540019929782575]</td>\n",
       "      <td>[0.9518356658778143]</td>\n",
       "      <td>[0.9525965806282052]</td>\n",
       "      <td>[0.9528873324752057]</td>\n",
       "      <td>[0.9507226515406851]</td>\n",
       "      <td>[0.9201040106775291]</td>\n",
       "      <td>[0.9528341231367937]</td>\n",
       "      <td>[0.9370912962179798]</td>\n",
       "      <td>[0.9252620107777773]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <td>[0.726802223592981]</td>\n",
       "      <td>[0.7235795512432226]</td>\n",
       "      <td>[0.7191727470931578]</td>\n",
       "      <td>[0.8083938597417417]</td>\n",
       "      <td>[0.6716125124340785]</td>\n",
       "      <td>[0.635417071874068]</td>\n",
       "      <td>[0.5731466508103222]</td>\n",
       "      <td>[0.584987968813386]</td>\n",
       "      <td>[0.6792540293823991]</td>\n",
       "      <td>[0.7411947273949842]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.5104983449014258]</td>\n",
       "      <td>[0.7440635957965869]</td>\n",
       "      <td>[0.7351031866436231]</td>\n",
       "      <td>[0.7411826869464866]</td>\n",
       "      <td>[0.7488121634463099]</td>\n",
       "      <td>[0.7286578419441063]</td>\n",
       "      <td>[0.5]</td>\n",
       "      <td>[0.7413656091448133]</td>\n",
       "      <td>[0.5064333042510193]</td>\n",
       "      <td>[0.7451148195692483]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC_weighted</th>\n",
       "      <td>[0.9402965099370749]</td>\n",
       "      <td>[0.9387900645923752]</td>\n",
       "      <td>[0.9392346349984347]</td>\n",
       "      <td>[0.8977872434226735]</td>\n",
       "      <td>[0.9216055660214838]</td>\n",
       "      <td>[0.9242836396238192]</td>\n",
       "      <td>[0.8918378725824169]</td>\n",
       "      <td>[0.8820124035143292]</td>\n",
       "      <td>[0.9329981457709314]</td>\n",
       "      <td>[0.9424512871239444]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.9053542022091445]</td>\n",
       "      <td>[0.9420558047002205]</td>\n",
       "      <td>[0.937102549426041]</td>\n",
       "      <td>[0.9442545758335231]</td>\n",
       "      <td>[0.9432649435858679]</td>\n",
       "      <td>[0.9404118203861465]</td>\n",
       "      <td>[0.8828844098420479]</td>\n",
       "      <td>[0.9445157609470832]</td>\n",
       "      <td>[0.9095544663580606]</td>\n",
       "      <td>[0.8716335369095317]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_score_micro</th>\n",
       "      <td>[0.9101669195751139]</td>\n",
       "      <td>[0.908649468892261]</td>\n",
       "      <td>[0.9071320182094081]</td>\n",
       "      <td>[0.8006069802731413]</td>\n",
       "      <td>[0.9025796661608497]</td>\n",
       "      <td>[0.9034901365705614]</td>\n",
       "      <td>[0.9001517450682853]</td>\n",
       "      <td>[0.9001517450682853]</td>\n",
       "      <td>[0.9077389984825494]</td>\n",
       "      <td>[0.9125948406676783]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.8898330804248862]</td>\n",
       "      <td>[0.9113808801213961]</td>\n",
       "      <td>[0.9122913505311078]</td>\n",
       "      <td>[0.9104704097116844]</td>\n",
       "      <td>[0.91350531107739]</td>\n",
       "      <td>[0.909256449165402]</td>\n",
       "      <td>[0.8880121396054628]</td>\n",
       "      <td>[0.9128983308042489]</td>\n",
       "      <td>[0.8889226100151746]</td>\n",
       "      <td>[0.7386949924127465]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_score_weighted</th>\n",
       "      <td>[0.9053116944383475]</td>\n",
       "      <td>[0.903782369274988]</td>\n",
       "      <td>[0.9021127651963996]</td>\n",
       "      <td>[0.832171688649313]</td>\n",
       "      <td>[0.8922212808312255]</td>\n",
       "      <td>[0.8864458469569265]</td>\n",
       "      <td>[0.8689407356591603]</td>\n",
       "      <td>[0.8724102701520908]</td>\n",
       "      <td>[0.8971238918213456]</td>\n",
       "      <td>[0.9087900123862012]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.8408402607421757]</td>\n",
       "      <td>[0.9081625543129599]</td>\n",
       "      <td>[0.9078853847841633]</td>\n",
       "      <td>[0.9071553913821001]</td>\n",
       "      <td>[0.9103026662505035]</td>\n",
       "      <td>[0.9048347990084314]</td>\n",
       "      <td>[0.8353395018439429]</td>\n",
       "      <td>[0.9090427105674237]</td>\n",
       "      <td>[0.8386872824884566]</td>\n",
       "      <td>[0.7841519919554535]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_precision_score_macro</th>\n",
       "      <td>[0.8081871005501498]</td>\n",
       "      <td>[0.805151813822726]</td>\n",
       "      <td>[0.8065229883244922]</td>\n",
       "      <td>[0.7573616413543638]</td>\n",
       "      <td>[0.7630035396931197]</td>\n",
       "      <td>[0.7710658035358414]</td>\n",
       "      <td>[0.7531033922067467]</td>\n",
       "      <td>[0.7353955036613293]</td>\n",
       "      <td>[0.7953500733144905]</td>\n",
       "      <td>[0.8131628841963428]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.7474811453983421]</td>\n",
       "      <td>[0.8205303182128374]</td>\n",
       "      <td>[0.813334477364347]</td>\n",
       "      <td>[0.8129296078280362]</td>\n",
       "      <td>[0.8147826901358025]</td>\n",
       "      <td>[0.8064196859993378]</td>\n",
       "      <td>[0.7008716650657688]</td>\n",
       "      <td>[0.81385049112966]</td>\n",
       "      <td>[0.7625403089560937]</td>\n",
       "      <td>[0.7305769040076238]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall_score_micro</th>\n",
       "      <td>[0.9101669195751139]</td>\n",
       "      <td>[0.908649468892261]</td>\n",
       "      <td>[0.9071320182094081]</td>\n",
       "      <td>[0.8006069802731411]</td>\n",
       "      <td>[0.9025796661608497]</td>\n",
       "      <td>[0.9034901365705614]</td>\n",
       "      <td>[0.9001517450682853]</td>\n",
       "      <td>[0.9001517450682853]</td>\n",
       "      <td>[0.9077389984825494]</td>\n",
       "      <td>[0.9125948406676783]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.8898330804248862]</td>\n",
       "      <td>[0.9113808801213961]</td>\n",
       "      <td>[0.9122913505311078]</td>\n",
       "      <td>[0.9104704097116844]</td>\n",
       "      <td>[0.91350531107739]</td>\n",
       "      <td>[0.9092564491654022]</td>\n",
       "      <td>[0.8880121396054628]</td>\n",
       "      <td>[0.9128983308042489]</td>\n",
       "      <td>[0.8889226100151745]</td>\n",
       "      <td>[0.7386949924127466]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_precision_score_micro</th>\n",
       "      <td>[0.9793455445540149]</td>\n",
       "      <td>[0.9790596697096496]</td>\n",
       "      <td>[0.9791945367231853]</td>\n",
       "      <td>[0.8763684101842625]</td>\n",
       "      <td>[0.9677719315750181]</td>\n",
       "      <td>[0.9749716762937735]</td>\n",
       "      <td>[0.9667883781853929]</td>\n",
       "      <td>[0.9653020622208739]</td>\n",
       "      <td>[0.9775048906893984]</td>\n",
       "      <td>[0.9800107530929907]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.9699478395078456]</td>\n",
       "      <td>[0.9800413423344667]</td>\n",
       "      <td>[0.9788833115760557]</td>\n",
       "      <td>[0.9803278107514134]</td>\n",
       "      <td>[0.980302824682271]</td>\n",
       "      <td>[0.9793877452489067]</td>\n",
       "      <td>[0.9651706625428083]</td>\n",
       "      <td>[0.9803454244592168]</td>\n",
       "      <td>[0.9705798098580782]</td>\n",
       "      <td>[0.8552682028041905]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision_score_micro</th>\n",
       "      <td>[0.9101669195751139]</td>\n",
       "      <td>[0.908649468892261]</td>\n",
       "      <td>[0.9071320182094081]</td>\n",
       "      <td>[0.8006069802731411]</td>\n",
       "      <td>[0.9025796661608497]</td>\n",
       "      <td>[0.9034901365705614]</td>\n",
       "      <td>[0.9001517450682853]</td>\n",
       "      <td>[0.9001517450682853]</td>\n",
       "      <td>[0.9077389984825494]</td>\n",
       "      <td>[0.9125948406676783]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.8898330804248862]</td>\n",
       "      <td>[0.9113808801213961]</td>\n",
       "      <td>[0.9122913505311078]</td>\n",
       "      <td>[0.9104704097116844]</td>\n",
       "      <td>[0.91350531107739]</td>\n",
       "      <td>[0.9092564491654022]</td>\n",
       "      <td>[0.8880121396054628]</td>\n",
       "      <td>[0.9128983308042489]</td>\n",
       "      <td>[0.8889226100151745]</td>\n",
       "      <td>[0.7386949924127466]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log_loss</th>\n",
       "      <td>[0.18507357161830795]</td>\n",
       "      <td>[0.187723739649247]</td>\n",
       "      <td>[0.1874363495858499]</td>\n",
       "      <td>[0.4918403937289126]</td>\n",
       "      <td>[0.22534582084950508]</td>\n",
       "      <td>[0.2119826616116293]</td>\n",
       "      <td>[0.24775796734834227]</td>\n",
       "      <td>[0.2553851200062387]</td>\n",
       "      <td>[0.19994256312838007]</td>\n",
       "      <td>[0.18386077817913915]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.23524821588116251]</td>\n",
       "      <td>[0.18595543487996247]</td>\n",
       "      <td>[0.21359907992323046]</td>\n",
       "      <td>[0.17953984826011166]</td>\n",
       "      <td>[0.2066227116858242]</td>\n",
       "      <td>[0.18548794958600107]</td>\n",
       "      <td>[0.26946922127977463]</td>\n",
       "      <td>[0.17891034035615155]</td>\n",
       "      <td>[0.2483589623740718]</td>\n",
       "      <td>[0.5187086268993985]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision_score_macro</th>\n",
       "      <td>[0.7818779971775021]</td>\n",
       "      <td>[0.7770067129496816]</td>\n",
       "      <td>[0.7723958081530135]</td>\n",
       "      <td>[0.6553416040114282]</td>\n",
       "      <td>[0.7649726288332503]</td>\n",
       "      <td>[0.7863945498280172]</td>\n",
       "      <td>[0.8403316440859103]</td>\n",
       "      <td>[0.8113962453677938]</td>\n",
       "      <td>[0.7873023436711378]</td>\n",
       "      <td>[0.7872528061629692]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.8450532724505327]</td>\n",
       "      <td>[0.7823265471422786]</td>\n",
       "      <td>[0.7877039852928842]</td>\n",
       "      <td>[0.7797943689453914]</td>\n",
       "      <td>[0.7886452719255267]</td>\n",
       "      <td>[0.7781112848426953]</td>\n",
       "      <td>[0.4440060698027314]</td>\n",
       "      <td>[0.7883118003819065]</td>\n",
       "      <td>[0.8017900590893292]</td>\n",
       "      <td>[0.6123843666024589]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC_macro</th>\n",
       "      <td>[0.9402965099370748]</td>\n",
       "      <td>[0.9387900645923752]</td>\n",
       "      <td>[0.9392346349984347]</td>\n",
       "      <td>[0.8977872434226735]</td>\n",
       "      <td>[0.9216055660214839]</td>\n",
       "      <td>[0.9242836396238193]</td>\n",
       "      <td>[0.8918378725824169]</td>\n",
       "      <td>[0.8820124035143291]</td>\n",
       "      <td>[0.9329981457709313]</td>\n",
       "      <td>[0.9424512871239443]</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.9053542022091445]</td>\n",
       "      <td>[0.9420558047002205]</td>\n",
       "      <td>[0.937102549426041]</td>\n",
       "      <td>[0.9442545758335232]</td>\n",
       "      <td>[0.9432649435858679]</td>\n",
       "      <td>[0.9404118203861465]</td>\n",
       "      <td>[0.8828844098420479]</td>\n",
       "      <td>[0.9445157609470831]</td>\n",
       "      <td>[0.9095544663580607]</td>\n",
       "      <td>[0.8716335369095318]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_36  \\\n",
       "recall_score_macro                                   [0.726802223592981]   \n",
       "matthews_correlation                                [0.5056898517541892]   \n",
       "precision_score_weighted                            [0.9026121799418352]   \n",
       "accuracy                                            [0.9101669195751139]   \n",
       "weighted_accuracy                                    [0.955691413656376]   \n",
       "recall_score_weighted                               [0.9101669195751139]   \n",
       "AUC_micro                                           [0.9783147777591007]   \n",
       "f1_score_macro                                      [0.7501265610044537]   \n",
       "norm_macro_recall                                  [0.45360444718596193]   \n",
       "average_precision_score_weighted                    [0.9511077193652759]   \n",
       "balanced_accuracy                                    [0.726802223592981]   \n",
       "AUC_weighted                                        [0.9402965099370749]   \n",
       "f1_score_micro                                      [0.9101669195751139]   \n",
       "f1_score_weighted                                   [0.9053116944383475]   \n",
       "average_precision_score_macro                       [0.8081871005501498]   \n",
       "recall_score_micro                                  [0.9101669195751139]   \n",
       "average_precision_score_micro                       [0.9793455445540149]   \n",
       "precision_score_micro                               [0.9101669195751139]   \n",
       "log_loss                                           [0.18507357161830795]   \n",
       "precision_score_macro                               [0.7818779971775021]   \n",
       "AUC_macro                                           [0.9402965099370748]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_43  \\\n",
       "recall_score_macro                                  [0.7235795512432226]   \n",
       "matthews_correlation                                [0.4977269796691756]   \n",
       "precision_score_weighted                             [0.900988490830041]   \n",
       "accuracy                                             [0.908649468892261]   \n",
       "weighted_accuracy                                     [0.95459732338795]   \n",
       "recall_score_weighted                                [0.908649468892261]   \n",
       "AUC_micro                                             [0.97803403786949]   \n",
       "f1_score_macro                                      [0.7462485247007249]   \n",
       "norm_macro_recall                                   [0.4471591024864452]   \n",
       "average_precision_score_weighted                    [0.9502433276389894]   \n",
       "balanced_accuracy                                   [0.7235795512432226]   \n",
       "AUC_weighted                                        [0.9387900645923752]   \n",
       "f1_score_micro                                       [0.908649468892261]   \n",
       "f1_score_weighted                                    [0.903782369274988]   \n",
       "average_precision_score_macro                        [0.805151813822726]   \n",
       "recall_score_micro                                   [0.908649468892261]   \n",
       "average_precision_score_micro                       [0.9790596697096496]   \n",
       "precision_score_micro                                [0.908649468892261]   \n",
       "log_loss                                             [0.187723739649247]   \n",
       "precision_score_macro                               [0.7770067129496816]   \n",
       "AUC_macro                                           [0.9387900645923752]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_1  \\\n",
       "recall_score_macro                                 [0.7191727470931578]   \n",
       "matthews_correlation                                [0.488678780261868]   \n",
       "precision_score_weighted                           [0.8991976076061607]   \n",
       "accuracy                                           [0.9071320182094081]   \n",
       "weighted_accuracy                                  [0.9537972210153172]   \n",
       "recall_score_weighted                              [0.9071320182094081]   \n",
       "AUC_micro                                          [0.9781770788959222]   \n",
       "f1_score_macro                                     [0.7416848907681176]   \n",
       "norm_macro_recall                                 [0.43834549418631563]   \n",
       "average_precision_score_weighted                   [0.9505970434373063]   \n",
       "balanced_accuracy                                  [0.7191727470931578]   \n",
       "AUC_weighted                                       [0.9392346349984347]   \n",
       "f1_score_micro                                     [0.9071320182094081]   \n",
       "f1_score_weighted                                  [0.9021127651963996]   \n",
       "average_precision_score_macro                      [0.8065229883244922]   \n",
       "recall_score_micro                                 [0.9071320182094081]   \n",
       "average_precision_score_micro                      [0.9791945367231853]   \n",
       "precision_score_micro                              [0.9071320182094081]   \n",
       "log_loss                                           [0.1874363495858499]   \n",
       "precision_score_macro                              [0.7723958081530135]   \n",
       "AUC_macro                                          [0.9392346349984347]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_3  \\\n",
       "recall_score_macro                                 [0.8083938597417417]   \n",
       "matthews_correlation                              [0.43775059949499806]   \n",
       "precision_score_weighted                            [0.901167922410634]   \n",
       "accuracy                                           [0.8006069802731411]   \n",
       "weighted_accuracy                                  [0.7986737087326132]   \n",
       "recall_score_weighted                              [0.8006069802731411]   \n",
       "AUC_micro                                           [0.881765953380415]   \n",
       "f1_score_macro                                     [0.6778486306801517]   \n",
       "norm_macro_recall                                  [0.6167877194834834]   \n",
       "average_precision_score_weighted                   [0.9348502058704878]   \n",
       "balanced_accuracy                                  [0.8083938597417417]   \n",
       "AUC_weighted                                       [0.8977872434226735]   \n",
       "f1_score_micro                                     [0.8006069802731413]   \n",
       "f1_score_weighted                                   [0.832171688649313]   \n",
       "average_precision_score_macro                      [0.7573616413543638]   \n",
       "recall_score_micro                                 [0.8006069802731411]   \n",
       "average_precision_score_micro                      [0.8763684101842625]   \n",
       "precision_score_micro                              [0.8006069802731411]   \n",
       "log_loss                                           [0.4918403937289126]   \n",
       "precision_score_macro                              [0.6553416040114282]   \n",
       "AUC_macro                                          [0.8977872434226735]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_29  \\\n",
       "recall_score_macro                                  [0.6716125124340785]   \n",
       "matthews_correlation                               [0.42648619466677545]   \n",
       "precision_score_weighted                            [0.8889110559014688]   \n",
       "accuracy                                            [0.9025796661608497]   \n",
       "weighted_accuracy                                    [0.959922562875411]   \n",
       "recall_score_weighted                               [0.9025796661608497]   \n",
       "AUC_micro                                           [0.9699141339363225]   \n",
       "f1_score_macro                                      [0.7043745341088892]   \n",
       "norm_macro_recall                                    [0.343225024868157]   \n",
       "average_precision_score_weighted                    [0.9385097261678277]   \n",
       "balanced_accuracy                                   [0.6716125124340785]   \n",
       "AUC_weighted                                        [0.9216055660214838]   \n",
       "f1_score_micro                                      [0.9025796661608497]   \n",
       "f1_score_weighted                                   [0.8922212808312255]   \n",
       "average_precision_score_macro                       [0.7630035396931197]   \n",
       "recall_score_micro                                  [0.9025796661608497]   \n",
       "average_precision_score_micro                       [0.9677719315750181]   \n",
       "precision_score_micro                               [0.9025796661608497]   \n",
       "log_loss                                           [0.22534582084950508]   \n",
       "precision_score_macro                               [0.7649726288332503]   \n",
       "AUC_macro                                           [0.9216055660214839]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_52  \\\n",
       "recall_score_macro                                   [0.635417071874068]   \n",
       "matthews_correlation                                [0.3938665324111809]   \n",
       "precision_score_weighted                            [0.8872411026406429]   \n",
       "accuracy                                            [0.9034901365705614]   \n",
       "weighted_accuracy                                   [0.9700454272809962]   \n",
       "recall_score_weighted                               [0.9034901365705614]   \n",
       "AUC_micro                                           [0.9737232805487691]   \n",
       "f1_score_macro                                       [0.674881533645934]   \n",
       "norm_macro_recall                                   [0.2708341437481361]   \n",
       "average_precision_score_weighted                     [0.941035720886307]   \n",
       "balanced_accuracy                                    [0.635417071874068]   \n",
       "AUC_weighted                                        [0.9242836396238192]   \n",
       "f1_score_micro                                      [0.9034901365705614]   \n",
       "f1_score_weighted                                   [0.8864458469569265]   \n",
       "average_precision_score_macro                       [0.7710658035358414]   \n",
       "recall_score_micro                                  [0.9034901365705614]   \n",
       "average_precision_score_micro                       [0.9749716762937735]   \n",
       "precision_score_micro                               [0.9034901365705614]   \n",
       "log_loss                                            [0.2119826616116293]   \n",
       "precision_score_macro                               [0.7863945498280172]   \n",
       "AUC_macro                                           [0.9242836396238193]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_2  \\\n",
       "recall_score_macro                                 [0.5731466508103222]   \n",
       "matthews_correlation                                 [0.31555741112929]   \n",
       "precision_score_weighted                           [0.8888749630995354]   \n",
       "accuracy                                           [0.9001517450682853]   \n",
       "weighted_accuracy                                  [0.9813382646344059]   \n",
       "recall_score_weighted                              [0.9001517450682853]   \n",
       "AUC_micro                                          [0.9662645153713839]   \n",
       "f1_score_macro                                     [0.6002318095341351]   \n",
       "norm_macro_recall                                 [0.14629330162064447]   \n",
       "average_precision_score_weighted                   [0.9322861133142119]   \n",
       "balanced_accuracy                                  [0.5731466508103222]   \n",
       "AUC_weighted                                       [0.8918378725824169]   \n",
       "f1_score_micro                                     [0.9001517450682853]   \n",
       "f1_score_weighted                                  [0.8689407356591603]   \n",
       "average_precision_score_macro                      [0.7531033922067467]   \n",
       "recall_score_micro                                 [0.9001517450682853]   \n",
       "average_precision_score_micro                      [0.9667883781853929]   \n",
       "precision_score_micro                              [0.9001517450682853]   \n",
       "log_loss                                          [0.24775796734834227]   \n",
       "precision_score_macro                              [0.8403316440859103]   \n",
       "AUC_macro                                          [0.8918378725824169]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_6  \\\n",
       "recall_score_macro                                  [0.584987968813386]   \n",
       "matthews_correlation                              [0.32536093428636176]   \n",
       "precision_score_weighted                           [0.8843463619650799]   \n",
       "accuracy                                           [0.9001517450682853]   \n",
       "weighted_accuracy                                  [0.9783983856764774]   \n",
       "recall_score_weighted                              [0.9001517450682853]   \n",
       "AUC_micro                                          [0.9644178769045848]   \n",
       "f1_score_macro                                     [0.6163274132542244]   \n",
       "norm_macro_recall                                   [0.169975937626772]   \n",
       "average_precision_score_weighted                   [0.9275516324761114]   \n",
       "balanced_accuracy                                   [0.584987968813386]   \n",
       "AUC_weighted                                       [0.8820124035143292]   \n",
       "f1_score_micro                                     [0.9001517450682853]   \n",
       "f1_score_weighted                                  [0.8724102701520908]   \n",
       "average_precision_score_macro                      [0.7353955036613293]   \n",
       "recall_score_micro                                 [0.9001517450682853]   \n",
       "average_precision_score_micro                      [0.9653020622208739]   \n",
       "precision_score_micro                              [0.9001517450682853]   \n",
       "log_loss                                           [0.2553851200062387]   \n",
       "precision_score_macro                              [0.8113962453677938]   \n",
       "AUC_macro                                          [0.8820124035143291]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_23  \\\n",
       "recall_score_macro                                  [0.6792540293823991]   \n",
       "matthews_correlation                                [0.4538726815046628]   \n",
       "precision_score_weighted                             [0.895092517403297]   \n",
       "accuracy                                            [0.9077389984825494]   \n",
       "weighted_accuracy                                   [0.9644656358962787]   \n",
       "recall_score_weighted                               [0.9077389984825494]   \n",
       "AUC_micro                                           [0.9763877305247063]   \n",
       "f1_score_macro                                        [0.71617170693669]   \n",
       "norm_macro_recall                                   [0.3585080587647982]   \n",
       "average_precision_score_weighted                    [0.9474347924918001]   \n",
       "balanced_accuracy                                   [0.6792540293823991]   \n",
       "AUC_weighted                                        [0.9329981457709314]   \n",
       "f1_score_micro                                      [0.9077389984825494]   \n",
       "f1_score_weighted                                   [0.8971238918213456]   \n",
       "average_precision_score_macro                       [0.7953500733144905]   \n",
       "recall_score_micro                                  [0.9077389984825494]   \n",
       "average_precision_score_micro                       [0.9775048906893984]   \n",
       "precision_score_micro                               [0.9077389984825494]   \n",
       "log_loss                                           [0.19994256312838007]   \n",
       "precision_score_macro                               [0.7873023436711378]   \n",
       "AUC_macro                                           [0.9329981457709313]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_26  ...  \\\n",
       "recall_score_macro                                  [0.7411947273949842]  ...   \n",
       "matthews_correlation                                [0.5264365575296669]  ...   \n",
       "precision_score_weighted                            [0.9064419404986112]  ...   \n",
       "accuracy                                            [0.9125948406676783]  ...   \n",
       "weighted_accuracy                                    [0.955148852498673]  ...   \n",
       "recall_score_weighted                               [0.9125948406676783]  ...   \n",
       "AUC_micro                                           [0.9790159827392864]  ...   \n",
       "f1_score_macro                                      [0.7613817409356443]  ...   \n",
       "norm_macro_recall                                   [0.4823894547899683]  ...   \n",
       "average_precision_score_weighted                    [0.9524536910133203]  ...   \n",
       "balanced_accuracy                                   [0.7411947273949842]  ...   \n",
       "AUC_weighted                                        [0.9424512871239444]  ...   \n",
       "f1_score_micro                                      [0.9125948406676783]  ...   \n",
       "f1_score_weighted                                   [0.9087900123862012]  ...   \n",
       "average_precision_score_macro                       [0.8131628841963428]  ...   \n",
       "recall_score_micro                                  [0.9125948406676783]  ...   \n",
       "average_precision_score_micro                       [0.9800107530929907]  ...   \n",
       "precision_score_micro                               [0.9125948406676783]  ...   \n",
       "log_loss                                           [0.18386077817913915]  ...   \n",
       "precision_score_macro                               [0.7872528061629692]  ...   \n",
       "AUC_macro                                           [0.9424512871239443]  ...   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_15  \\\n",
       "recall_score_macro                                  [0.5104983449014258]   \n",
       "matthews_correlation                               [0.12037422088722034]   \n",
       "precision_score_weighted                            [0.8800157057300507]   \n",
       "accuracy                                            [0.8898330804248862]   \n",
       "weighted_accuracy                                   [0.9840116344243844]   \n",
       "recall_score_weighted                               [0.8898330804248862]   \n",
       "AUC_micro                                            [0.968661212440793]   \n",
       "f1_score_macro                                      [0.4918858319714491]   \n",
       "norm_macro_recall                                  [0.02099668980285152]   \n",
       "average_precision_score_weighted                     [0.933464279983398]   \n",
       "balanced_accuracy                                   [0.5104983449014258]   \n",
       "AUC_weighted                                        [0.9053542022091445]   \n",
       "f1_score_micro                                      [0.8898330804248862]   \n",
       "f1_score_weighted                                   [0.8408402607421757]   \n",
       "average_precision_score_macro                       [0.7474811453983421]   \n",
       "recall_score_micro                                  [0.8898330804248862]   \n",
       "average_precision_score_micro                       [0.9699478395078456]   \n",
       "precision_score_micro                               [0.8898330804248862]   \n",
       "log_loss                                           [0.23524821588116251]   \n",
       "precision_score_macro                               [0.8450532724505327]   \n",
       "AUC_macro                                           [0.9053542022091445]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_28  \\\n",
       "recall_score_macro                                  [0.7440635957965869]   \n",
       "matthews_correlation                                [0.5249976467923607]   \n",
       "precision_score_weighted                            [0.9059653367367347]   \n",
       "accuracy                                            [0.9113808801213961]   \n",
       "weighted_accuracy                                   [0.9529212359632853]   \n",
       "recall_score_weighted                               [0.9113808801213961]   \n",
       "AUC_micro                                            [0.979086536136741]   \n",
       "f1_score_macro                                      [0.7612114990956147]   \n",
       "norm_macro_recall                                  [0.48812719159317375]   \n",
       "average_precision_score_weighted                    [0.9540019929782575]   \n",
       "balanced_accuracy                                   [0.7440635957965869]   \n",
       "AUC_weighted                                        [0.9420558047002205]   \n",
       "f1_score_micro                                      [0.9113808801213961]   \n",
       "f1_score_weighted                                   [0.9081625543129599]   \n",
       "average_precision_score_macro                       [0.8205303182128374]   \n",
       "recall_score_micro                                  [0.9113808801213961]   \n",
       "average_precision_score_micro                       [0.9800413423344667]   \n",
       "precision_score_micro                               [0.9113808801213961]   \n",
       "log_loss                                           [0.18595543487996247]   \n",
       "precision_score_macro                               [0.7823265471422786]   \n",
       "AUC_macro                                           [0.9420558047002205]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_49  \\\n",
       "recall_score_macro                                  [0.7351031866436231]   \n",
       "matthews_correlation                                [0.5201542992321688]   \n",
       "precision_score_weighted                             [0.905387728519385]   \n",
       "accuracy                                            [0.9122913505311078]   \n",
       "weighted_accuracy                                   [0.9562823787656349]   \n",
       "recall_score_weighted                               [0.9122913505311078]   \n",
       "AUC_micro                                            [0.977890720524269]   \n",
       "f1_score_macro                                      [0.7576713259183312]   \n",
       "norm_macro_recall                                   [0.4702063732872461]   \n",
       "average_precision_score_weighted                    [0.9518356658778143]   \n",
       "balanced_accuracy                                   [0.7351031866436231]   \n",
       "AUC_weighted                                         [0.937102549426041]   \n",
       "f1_score_micro                                      [0.9122913505311078]   \n",
       "f1_score_weighted                                   [0.9078853847841633]   \n",
       "average_precision_score_macro                        [0.813334477364347]   \n",
       "recall_score_micro                                  [0.9122913505311078]   \n",
       "average_precision_score_micro                       [0.9788833115760557]   \n",
       "precision_score_micro                               [0.9122913505311078]   \n",
       "log_loss                                           [0.21359907992323046]   \n",
       "precision_score_macro                               [0.7877039852928842]   \n",
       "AUC_macro                                            [0.937102549426041]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_35  \\\n",
       "recall_score_macro                                  [0.7411826869464866]   \n",
       "matthews_correlation                                [0.5195442529553999]   \n",
       "precision_score_weighted                            [0.9048930721424753]   \n",
       "accuracy                                            [0.9104704097116844]   \n",
       "weighted_accuracy                                   [0.9524999721188641]   \n",
       "recall_score_weighted                               [0.9104704097116844]   \n",
       "AUC_micro                                           [0.9793498679426453]   \n",
       "f1_score_macro                                      [0.7584448322812782]   \n",
       "norm_macro_recall                                   [0.4823653738929732]   \n",
       "average_precision_score_weighted                    [0.9525965806282052]   \n",
       "balanced_accuracy                                   [0.7411826869464866]   \n",
       "AUC_weighted                                        [0.9442545758335231]   \n",
       "f1_score_micro                                      [0.9104704097116844]   \n",
       "f1_score_weighted                                   [0.9071553913821001]   \n",
       "average_precision_score_macro                       [0.8129296078280362]   \n",
       "recall_score_micro                                  [0.9104704097116844]   \n",
       "average_precision_score_micro                       [0.9803278107514134]   \n",
       "precision_score_micro                               [0.9104704097116844]   \n",
       "log_loss                                           [0.17953984826011166]   \n",
       "precision_score_macro                               [0.7797943689453914]   \n",
       "AUC_macro                                           [0.9442545758335232]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_39  \\\n",
       "recall_score_macro                                  [0.7488121634463099]   \n",
       "matthews_correlation                                 [0.535979307721254]   \n",
       "precision_score_weighted                            [0.9081762042023199]   \n",
       "accuracy                                              [0.91350531107739]   \n",
       "weighted_accuracy                                   [0.9543941647599228]   \n",
       "recall_score_weighted                                 [0.91350531107739]   \n",
       "AUC_micro                                            [0.979336328321985]   \n",
       "f1_score_macro                                      [0.7666331430514044]   \n",
       "norm_macro_recall                                   [0.4976243268926197]   \n",
       "average_precision_score_weighted                    [0.9528873324752057]   \n",
       "balanced_accuracy                                   [0.7488121634463099]   \n",
       "AUC_weighted                                        [0.9432649435858679]   \n",
       "f1_score_micro                                        [0.91350531107739]   \n",
       "f1_score_weighted                                   [0.9103026662505035]   \n",
       "average_precision_score_macro                       [0.8147826901358025]   \n",
       "recall_score_micro                                    [0.91350531107739]   \n",
       "average_precision_score_micro                        [0.980302824682271]   \n",
       "precision_score_micro                                 [0.91350531107739]   \n",
       "log_loss                                            [0.2066227116858242]   \n",
       "precision_score_macro                               [0.7886452719255267]   \n",
       "AUC_macro                                           [0.9432649435858679]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_38  \\\n",
       "recall_score_macro                                  [0.7286578419441063]   \n",
       "matthews_correlation                                [0.5043503790518389]   \n",
       "precision_score_weighted                            [0.9021727665937724]   \n",
       "accuracy                                            [0.9092564491654022]   \n",
       "weighted_accuracy                                   [0.9540941982287833]   \n",
       "recall_score_weighted                               [0.9092564491654022]   \n",
       "AUC_micro                                           [0.9783628572283843]   \n",
       "f1_score_macro                                      [0.7499559024142635]   \n",
       "norm_macro_recall                                  [0.45731568388821264]   \n",
       "average_precision_score_weighted                    [0.9507226515406851]   \n",
       "balanced_accuracy                                   [0.7286578419441063]   \n",
       "AUC_weighted                                        [0.9404118203861465]   \n",
       "f1_score_micro                                       [0.909256449165402]   \n",
       "f1_score_weighted                                   [0.9048347990084314]   \n",
       "average_precision_score_macro                       [0.8064196859993378]   \n",
       "recall_score_micro                                  [0.9092564491654022]   \n",
       "average_precision_score_micro                       [0.9793877452489067]   \n",
       "precision_score_micro                               [0.9092564491654022]   \n",
       "log_loss                                           [0.18548794958600107]   \n",
       "precision_score_macro                               [0.7781112848426953]   \n",
       "AUC_macro                                           [0.9404118203861465]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_4  \\\n",
       "recall_score_macro                                                [0.5]   \n",
       "matthews_correlation                                              [0.0]   \n",
       "precision_score_weighted                            [0.788565560086672]   \n",
       "accuracy                                           [0.8880121396054628]   \n",
       "weighted_accuracy                                  [0.9843450583187134]   \n",
       "recall_score_weighted                              [0.8880121396054628]   \n",
       "AUC_micro                                          [0.9641652294251879]   \n",
       "f1_score_macro                                     [0.4703423886834914]   \n",
       "norm_macro_recall                                                 [0.0]   \n",
       "average_precision_score_weighted                   [0.9201040106775291]   \n",
       "balanced_accuracy                                                 [0.5]   \n",
       "AUC_weighted                                       [0.8828844098420479]   \n",
       "f1_score_micro                                     [0.8880121396054628]   \n",
       "f1_score_weighted                                  [0.8353395018439429]   \n",
       "average_precision_score_macro                      [0.7008716650657688]   \n",
       "recall_score_micro                                 [0.8880121396054628]   \n",
       "average_precision_score_micro                      [0.9651706625428083]   \n",
       "precision_score_micro                              [0.8880121396054628]   \n",
       "log_loss                                          [0.26946922127977463]   \n",
       "precision_score_macro                              [0.4440060698027314]   \n",
       "AUC_macro                                          [0.8828844098420479]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_53  \\\n",
       "recall_score_macro                                  [0.7413656091448133]   \n",
       "matthews_correlation                                [0.5275928480289197]   \n",
       "precision_score_weighted                            [0.9066944678995318]   \n",
       "accuracy                                            [0.9128983308042489]   \n",
       "weighted_accuracy                                   [0.9554852657106753]   \n",
       "recall_score_weighted                               [0.9128983308042489]   \n",
       "AUC_micro                                           [0.9793660786449326]   \n",
       "f1_score_macro                                      [0.7618961292903779]   \n",
       "norm_macro_recall                                   [0.4827312182896266]   \n",
       "average_precision_score_weighted                    [0.9528341231367937]   \n",
       "balanced_accuracy                                   [0.7413656091448133]   \n",
       "AUC_weighted                                        [0.9445157609470832]   \n",
       "f1_score_micro                                      [0.9128983308042489]   \n",
       "f1_score_weighted                                   [0.9090427105674237]   \n",
       "average_precision_score_macro                         [0.81385049112966]   \n",
       "recall_score_micro                                  [0.9128983308042489]   \n",
       "average_precision_score_micro                       [0.9803454244592168]   \n",
       "precision_score_micro                               [0.9128983308042489]   \n",
       "log_loss                                           [0.17891034035615155]   \n",
       "precision_score_macro                               [0.7883118003819065]   \n",
       "AUC_macro                                           [0.9445157609470831]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_14  \\\n",
       "recall_score_macro                                  [0.5064333042510193]   \n",
       "matthews_correlation                               [0.08812507634163474]   \n",
       "precision_score_weighted                            [0.8696955551933787]   \n",
       "accuracy                                            [0.8889226100151745]   \n",
       "weighted_accuracy                                   [0.9838843584757562]   \n",
       "recall_score_weighted                               [0.8889226100151745]   \n",
       "AUC_micro                                           [0.9694834450505547]   \n",
       "f1_score_macro                                      [0.4838482424723856]   \n",
       "norm_macro_recall                                 [0.012866608502038579]   \n",
       "average_precision_score_weighted                    [0.9370912962179798]   \n",
       "balanced_accuracy                                   [0.5064333042510193]   \n",
       "AUC_weighted                                        [0.9095544663580606]   \n",
       "f1_score_micro                                      [0.8889226100151746]   \n",
       "f1_score_weighted                                   [0.8386872824884566]   \n",
       "average_precision_score_macro                       [0.7625403089560937]   \n",
       "recall_score_micro                                  [0.8889226100151745]   \n",
       "average_precision_score_micro                       [0.9705798098580782]   \n",
       "precision_score_micro                               [0.8889226100151745]   \n",
       "log_loss                                            [0.2483589623740718]   \n",
       "precision_score_macro                               [0.8017900590893292]   \n",
       "AUC_macro                                           [0.9095544663580607]   \n",
       "\n",
       "                                 ef753519-b949-4647-b9a8-147edc7511c3_18  \n",
       "recall_score_macro                                  [0.7451148195692483]  \n",
       "matthews_correlation                                [0.3319462230070767]  \n",
       "precision_score_weighted                             [0.881755734573189]  \n",
       "accuracy                                            [0.7386949924127466]  \n",
       "weighted_accuracy                                   [0.7371011229831735]  \n",
       "recall_score_weighted                               [0.7386949924127466]  \n",
       "AUC_micro                                           [0.8606551979018193]  \n",
       "f1_score_macro                                      [0.6129685636333599]  \n",
       "norm_macro_recall                                   [0.4902296391384966]  \n",
       "average_precision_score_weighted                    [0.9252620107777773]  \n",
       "balanced_accuracy                                   [0.7451148195692483]  \n",
       "AUC_weighted                                        [0.8716335369095317]  \n",
       "f1_score_micro                                      [0.7386949924127465]  \n",
       "f1_score_weighted                                   [0.7841519919554535]  \n",
       "average_precision_score_macro                       [0.7305769040076238]  \n",
       "recall_score_micro                                  [0.7386949924127466]  \n",
       "average_precision_score_micro                       [0.8552682028041905]  \n",
       "precision_score_micro                               [0.7386949924127466]  \n",
       "log_loss                                            [0.5187086268993985]  \n",
       "precision_score_macro                               [0.6123843666024589]  \n",
       "AUC_macro                                           [0.8716335369095318]  \n",
       "\n",
       "[21 rows x 58 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "gather": {
     "logged": 1612389610602
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading azureml/ef753519-b949-4647-b9a8-147edc7511c3/model_data\n",
      "Downloaded azureml/ef753519-b949-4647-b9a8-147edc7511c3/model_data, 1 files out of an estimated total of 1\n"
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "gather": {
     "logged": 1612389663712
    }
   },
   "outputs": [
    {
     "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",
       "                                                                                                                                scale_pos_weight=1,\n",
       "                                                                                                                                seed=None,\n",
       "                                                                                                                                silent=None,\n",
       "                                                                                                                                subsample=0.9,\n",
       "                                                                                                                                tree_method='auto',\n",
       "                                                                                                                                verbose=-10,\n",
       "                                                                                                                                verbosity=0))],\n",
       "                                                                                                      verbose=False))],\n",
       "                                                                                flatten_transform=None,\n",
       "                                                                                weights=[0.35714285714285715,\n",
       "                                                                                         0.07142857142857142,\n",
       "                                                                                         0.07142857142857142,\n",
       "                                                                                         0.07142857142857142,\n",
       "                                                                                         0.21428571428571427,\n",
       "                                                                                         0.07142857142857142,\n",
       "                                                                                         0.14285714285714285]))],\n",
       "                                       'verbose': False},\n",
       "                             y_transformer={},\n",
       "                             y_transformer_name='LabelEncoder')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "gather": {
     "logged": 1612389670405
    }
   },
   "outputs": [
    {
     "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=[('37',\n",
       "                                             Pipeline(memory=None,\n",
       "                                                      steps=[('sparsenormalizer',\n",
       "                                                              <azureml.automl.runtime.shared.model_wrappers.SparseNormalizer object at 0x7f2370f84a20>),\n",
       "                                                             ('xgboostclassifier',\n",
       "                                                              XGBoostClassifier(base_score=0.5,\n",
       "                                                                                booster='gbtree',\n",
       "                                                                                colsample_bylevel=1,\n",
       "                                                                                colsample_bynode=1,\n",
       "                                                                                colsample_bytree=0.5,\n",
       "                                                                                eta=0.4,\n",
       "                                                                                g...\n",
       "                                                                                random_state=0,\n",
       "                                                                                reg_alpha=1.9791666666666667,\n",
       "                                                                                reg_lambda=1.25,\n",
       "                                                                                scale_pos_weight=1,\n",
       "                                                                                seed=None,\n",
       "                                                                                silent=None,\n",
       "                                                                                subsample=0.9,\n",
       "                                                                                tree_method='auto',\n",
       "                                                                                verbose=-10,\n",
       "                                                                                verbosity=0))],\n",
       "                                                      verbose=False))],\n",
       "                                flatten_transform=None,\n",
       "                                weights=[0.35714285714285715, 0.07142857142857142,\n",
       "                                         0.07142857142857142, 0.07142857142857142,\n",
       "                                         0.21428571428571427, 0.07142857142857142,\n",
       "                                         0.14285714285714285]))]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "gather": {
     "logged": 1612389710387
    }
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Our Best Fitted Model\n",
    "\n",
    "We will use confusion matrix to see how our model works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "gather": {
     "logged": 1612389719958
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "ypred = best_model.predict(X_test)\n",
    "cm = confusion_matrix(y_test, ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "gather": {
     "logged": 1612389756968
    }
   },
   "outputs": [
    {
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       "                        <td id=\"T_86646dd4_666b_11eb_84de_c5d4a7843a69row0_col0\" class=\"data row0 col0\" >28914</td>\n",
       "                        <td id=\"T_86646dd4_666b_11eb_84de_c5d4a7843a69row0_col1\" class=\"data row0 col1\" >344</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_86646dd4_666b_11eb_84de_c5d4a7843a69level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_86646dd4_666b_11eb_84de_c5d4a7843a69row1_col0\" class=\"data row1 col0\" >991</td>\n",
       "                        <td id=\"T_86646dd4_666b_11eb_84de_c5d4a7843a69row1_col1\" class=\"data row1 col1\" >2701</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f236d83d5f8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the confusion matrix\n",
    "pd.DataFrame(cm).style.background_gradient(cmap='Blues', low=0, high=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "gather": {
     "logged": 1612389776252
    }
   },
   "outputs": [
    {
     "data": {
      "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/e3a0d4c4-fcaa-46f7-b65e-55a7eb2da259?wsid=/subscriptions/9a7511b8-150f-4a58-8528-3e7d50216c31/resourcegroups/aml-quickstarts-137116/workspaces/quick-starts-ws-137116\" target=\"_blank\" rel=\"noopener\">e3a0d4c4-fcaa-46f7-b65e-55a7eb2da259</a></td><td>Active</td><td><a href=\"https://southcentralus.api.azureml.ms/pipelines/v1.0/subscriptions/9a7511b8-150f-4a58-8528-3e7d50216c31/resourceGroups/aml-quickstarts-137116/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-137116/PipelineRuns/PipelineSubmit/e3a0d4c4-fcaa-46f7-b65e-55a7eb2da259\" target=\"_blank\" rel=\"noopener\">REST Endpoint</a></td></tr></table>"
      ],
      "text/plain": [
       "Pipeline(Name: Bankmarketing Train,\n",
       "Id: e3a0d4c4-fcaa-46f7-b65e-55a7eb2da259,\n",
       "Status: Active,\n",
       "Endpoint: https://southcentralus.api.azureml.ms/pipelines/v1.0/subscriptions/9a7511b8-150f-4a58-8528-3e7d50216c31/resourceGroups/aml-quickstarts-137116/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-137116/PipelineRuns/PipelineSubmit/e3a0d4c4-fcaa-46f7-b65e-55a7eb2da259)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "published_pipeline = pipeline_run.publish_pipeline(\n",
    "    name=\"Bankmarketing Train\", description=\"Training bankmarketing pipeline\", version=\"1.0\")\n",
    "\n",
    "published_pipeline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Authenticate once again, to retrieve the `auth_header` so that the endpoint can be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "gather": {
     "logged": 1612389783095
    }
   },
   "outputs": [],
   "source": [
    "from azureml.core.authentication import InteractiveLoginAuthentication\n",
    "\n",
    "interactive_auth = InteractiveLoginAuthentication()\n",
    "auth_header = interactive_auth.get_authentication_header()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "gather": {
     "logged": 1612389796872
    }
   },
   "outputs": [],
   "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",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "gather": {
     "logged": 1612389806638
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitted pipeline run:  faa2ad90-2cd2-4194-b3f2-fb40a1199c9e\n"
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "gather": {
     "logged": 1612389816140
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22928216ebff4811850bc237ec9ccc04",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_PipelineWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/aml.mini.widget.v1": "{\"status\": \"Running\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/pipeline-rest-endpoint/runs/faa2ad90-2cd2-4194-b3f2-fb40a1199c9e?wsid=/subscriptions/9a7511b8-150f-4a58-8528-3e7d50216c31/resourcegroups/aml-quickstarts-137116/workspaces/quick-starts-ws-137116\", \"run_id\": \"faa2ad90-2cd2-4194-b3f2-fb40a1199c9e\", \"run_properties\": {\"run_id\": \"faa2ad90-2cd2-4194-b3f2-fb40a1199c9e\", \"created_utc\": \"2021-02-03T22:03:16.269667Z\", \"properties\": {\"azureml.runsource\": \"azureml.PipelineRun\", \"runSource\": \"Unavailable\", \"runType\": \"HTTP\", \"azureml.parameters\": \"{}\", \"azureml.pipelineid\": \"e3a0d4c4-fcaa-46f7-b65e-55a7eb2da259\"}, \"tags\": {\"azureml.pipelineid\": \"e3a0d4c4-fcaa-46f7-b65e-55a7eb2da259\", \"azureml.pipelineComponent\": \"pipelinerun\"}, \"end_time_utc\": null, \"status\": \"Running\", \"log_files\": {\"logs/azureml/executionlogs.txt\": \"https://mlstrg137116.blob.core.windows.net/azureml/ExperimentRun/dcid.faa2ad90-2cd2-4194-b3f2-fb40a1199c9e/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=TomEJlioQTWak2slPf3w3bmUpNSQKWcMqMy59U%2Br9Xo%3D&st=2021-02-03T21%3A53%3A36Z&se=2021-02-04T06%3A03%3A36Z&sp=r\", \"logs/azureml/stderrlogs.txt\": \"https://mlstrg137116.blob.core.windows.net/azureml/ExperimentRun/dcid.faa2ad90-2cd2-4194-b3f2-fb40a1199c9e/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=4YzAc9P00OQCW7zQOxcCLIaY5CKJeKvwUuebkVm%2FzQw%3D&st=2021-02-03T21%3A53%3A36Z&se=2021-02-04T06%3A03%3A36Z&sp=r\", \"logs/azureml/stdoutlogs.txt\": \"https://mlstrg137116.blob.core.windows.net/azureml/ExperimentRun/dcid.faa2ad90-2cd2-4194-b3f2-fb40a1199c9e/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=mdMn%2BGzbPEGDlCBBzBsHCqa%2BzTaY9BRmGhstLmhTMmU%3D&st=2021-02-03T21%3A53%3A36Z&se=2021-02-04T06%3A03%3A36Z&sp=r\"}, \"log_groups\": [[\"logs/azureml/executionlogs.txt\", \"logs/azureml/stderrlogs.txt\", \"logs/azureml/stdoutlogs.txt\"]], \"run_duration\": \"0:09:48\"}, \"child_runs\": [{\"run_id\": \"7a6fc9fe-1d54-4bfe-a027-70fad34a2b22\", \"name\": \"automl_module\", \"status\": \"Running\", \"start_time\": \"2021-02-03T22:03:48.471554Z\", \"created_time\": \"2021-02-03T22:03:23.2529Z\", \"end_time\": \"\", \"duration\": \"0:09:42\", \"run_number\": 2, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-02-03T22:03:23.2529Z\", \"is_reused\": \"\"}], \"children_metrics\": {\"categories\": null, \"series\": null, \"metricName\": null}, \"run_metrics\": [], \"run_logs\": \"[2021-02-03 22:03:23Z] Submitting 1 runs, first five are: 814bbfc6:7a6fc9fe-1d54-4bfe-a027-70fad34a2b22\\n\", \"graph\": {\"datasource_nodes\": {\"70e7e6d4\": {\"node_id\": \"70e7e6d4\", \"name\": \"Bank-marketing\"}}, \"module_nodes\": {\"814bbfc6\": {\"node_id\": \"814bbfc6\", \"name\": \"automl_module\", \"status\": \"Running\", \"_is_reused\": false, \"run_id\": \"7a6fc9fe-1d54-4bfe-a027-70fad34a2b22\"}}, \"edges\": [{\"source_node_id\": \"70e7e6d4\", \"source_node_name\": \"Bank-marketing\", \"source_name\": \"data\", \"target_name\": \"training_data\", \"dst_node_id\": \"814bbfc6\", \"dst_node_name\": \"automl_module\"}], \"child_runs\": [{\"run_id\": \"7a6fc9fe-1d54-4bfe-a027-70fad34a2b22\", \"name\": \"automl_module\", \"status\": \"Running\", \"start_time\": \"2021-02-03T22:03:48.471554Z\", \"created_time\": \"2021-02-03T22:03:23.2529Z\", \"end_time\": \"\", \"duration\": \"0:09:42\", \"run_number\": 2, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-02-03T22:03:23.2529Z\", \"is_reused\": \"\"}]}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.20.0\"}, \"loading\": false}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from azureml.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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "sanpil"
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   "how-to-use-azureml",
   "machine-learning-pipelines",
   "intro-to-pipelines"
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  "datasets": [
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