cpd4.8/notebooks/python_sdk/experiments/autoai/Use AutoAI and Lale to predict credit risk.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "# Use AutoAI and Lale to predict credit risk with `ibm-watson-machine-learning`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "This notebook contains the steps and code to demonstrate support of AutoAI experiments in Watson Machine Learning service. It introduces commands for data retrieval, training experiments, persisting pipelines, testing pipelines, refining pipelines, and scoring.\n",
        "\n",
        "Some familiarity with Python is helpful. This notebook uses Python 3.10.\n",
        "\n",
        "\n",
        "## Learning goals\n",
        "\n",
        "The learning goals of this notebook are:\n",
        "\n",
        "-  Work with Watson Machine Learning experiments to train AutoAI models.\n",
        "-  Compare trained models quality and select the best one for further refinement.\n",
        "-  Refine the best model and test new variations.\n",
        "-  Perform online deployment and score the trained model.\n",
        "\n",
        "\n",
        "## Contents\n",
        "\n",
        "This notebook contains the following parts:\n",
        "\n",
        "1. [Setup](#setup)\n",
        "2. [Optimizer definition](#definition)\n",
        "3. [Experiment Run](#run)\n",
        "4. [Pipelines comparison and testing](#comparison)\n",
        "5. [Historical runs](#runs)\n",
        "6. [Pipeline refinement and testing](#refinery)\n",
        "7. [Deploy and Score](#scoring)\n",
        "8. [Clean up](#cleanup)\n",
        "8. [Summary and next steps](#summary)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "<a id=\"setup\"></a>\n",
        "## 1. Set up the environment\n",
        "\n",
        "Before you use the sample code in this notebook, contact with your Cloud Pack for Data administrator and ask for your account credentials."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Connection to WML\n",
        "\n",
        "Authenticate the Watson Machine Learning service on IBM Cloud Pack for Data. You need to provide platform `url`, your `username` and `api_key`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "username = 'PASTE YOUR USERNAME HERE'\n",
        "api_key = 'PASTE YOUR API_KEY HERE'\n",
        "url = 'PASTE THE PLATFORM URL HERE'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "wml_credentials = {\n",
        "    \"username\": username,\n",
        "    \"apikey\": api_key,\n",
        "    \"url\": url,\n",
        "    \"instance_id\": 'openshift',\n",
        "    \"version\": '4.8'\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "Alternatively you can use `username` and `password` to authenticate WML services.\n",
        "\n",
        "```\n",
        "wml_credentials = {\n",
        "    \"username\": ***,\n",
        "    \"password\": ***,\n",
        "    \"url\": ***,\n",
        "    \"instance_id\": 'openshift',\n",
        "    \"version\": '4.8'\n",
        "}\n",
        "\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Install and import the `ibm-watson-machine-learning` package\n",
        "**Note:** `ibm-watson-machine-learning` documentation can be found <a href=\"http://ibm-wml-api-pyclient.mybluemix.net/\" target=\"_blank\" rel=\"noopener no referrer\">here</a>."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "!pip install ibm-watson-machine-learning | tail -n 1\n",
        "!pip install -U autoai-libs | tail -n 1\n",
        "!pip install -U lale | tail -n 1\n",
        "!pip install -U scikit-learn==1.1.1 | tail -n 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "from ibm_watson_machine_learning import APIClient\n",
        "\n",
        "client = APIClient(wml_credentials)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Working with spaces\n",
        "\n",
        "First of all, you need to create a space that will be used for your work. If you do not have space already created, you can use `{PLATFORM_URL}/ml-runtime/spaces?context=icp4data` to create one.\n",
        "\n",
        "- Click New Deployment Space\n",
        "- Create an empty space\n",
        "- Go to space `Settings` tab\n",
        "- Copy `space_id` and paste it below\n",
        "\n",
        "**Tip**: You can also use SDK to prepare the space for your work. More information can be found [here](https://github.com/IBM/watson-machine-learning-samples/blob/master/cpd4.7/notebooks/python_sdk/instance-management/Space%20management.ipynb).\n",
        "\n",
        "**Action**: Assign space ID below"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "space_id = 'PASTE YOUR SPACE ID HERE'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "You can use the `list` method to print all existing spaces."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "client.spaces.list(limit=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "To be able to interact with all resources available in Watson Machine Learning, you need to set the **space** which you will be using."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'SUCCESS'"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.set.default_space(space_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "<a id=\"definition\"></a>\n",
        "## 2. Optimizer definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Training data connection\n",
        "\n",
        "Define connection information to an external database.\n",
        "\n",
        "This example uses the German Credit Risk dataset. \n",
        "The dataset can be downloaded from [here](https://github.com/IBM/watson-machine-learning-samples/blob/master/data/credit_risk/german_credit_data_biased_training.csv)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Connection configuration\n",
        "\n",
        "Credentials for database should be passed as a python dictionary.  \n",
        "For the vast number of supported datasets the credentials should follow the bellow pattern.  \n",
        "**Warning**: Database name should be slected from the list of all the supported databases, in order to look it up use `client.connections.list_datasource_types()`  \n",
        "**Warning**: Input table should be uploaded in database under the location `/schema_name/table_name`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "db_name = 'PUT YOUR DATABASE NAME HERE'\n",
        "schema_name = 'PUT YOUR SCHEMA NAME HERE'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "table_name = 'CREDIT_RISK'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "db_credentials = {\n",
        "    \"database\": \"***\",\n",
        "    \"password\": \"***\",\n",
        "    \"port\": \"***\",\n",
        "    \"host\": \"***\",\n",
        "    \"ssl\": \"***\",\n",
        "    \"username\": \"***\"\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "#### Create connection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "conn_meta_props= {\n",
        "    client.connections.ConfigurationMetaNames.NAME: f\"Connection to Database - {db_name} \",\n",
        "    client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: client.connections.get_datasource_type_uid_by_name(db_name),\n",
        "    client.connections.ConfigurationMetaNames.DESCRIPTION: \"Connection to external Database\",\n",
        "    client.connections.ConfigurationMetaNames.PROPERTIES: db_credentials\n",
        "}\n",
        "\n",
        "conn_details = client.connections.create(meta_props=conn_meta_props)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "connection_id = client.connections.get_uid(conn_details)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "#### Download training data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "!wget -q 'https://raw.githubusercontent.com/IBM/watson-machine-learning-samples/master/cpd4.8/data/credit_risk/german_credit_data_biased_training.csv' -O \\\n",
        "            'credit_risk.csv'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "**Hint**: To install `pandas` execute `!pip install pandas`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from ibm_watson_machine_learning.helpers import DataConnection, DatabaseLocation\n",
        "\n",
        "\n",
        "credit_risk_conn = DataConnection(\n",
        "    connection_asset_id=connection_id,\n",
        "    location=DatabaseLocation(\n",
        "        schema_name=schema_name,\n",
        "        table_name=table_name\n",
        "    )\n",
        ")\n",
        "\n",
        "credit_risk_conn._wml_client = client\n",
        "credit_risk_conn.write(pd.read_csv(\"credit_risk.csv\"))\n",
        "training_data_reference=[credit_risk_conn]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Optimizer configuration\n",
        "\n",
        "Provide the input information for AutoAI optimizer:\n",
        "- `name` - experiment name\n",
        "- `prediction_type` - type of the problem\n",
        "- `prediction_column` - target column name\n",
        "- `scoring` - optimization metric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "from ibm_watson_machine_learning.experiment import AutoAI\n",
        "\n",
        "experiment = AutoAI(wml_credentials, space_id=space_id)\n",
        "\n",
        "pipeline_optimizer = experiment.optimizer(\n",
        "    name='Credit Risk Prediction - AutoAI',\n",
        "    desc='Sample notebook',\n",
        "    prediction_type=AutoAI.PredictionType.BINARY,\n",
        "    prediction_column='Risk',\n",
        "    scoring=AutoAI.Metrics.ROC_AUC_SCORE,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "Configuration parameters can be retrieved via `get_params()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'name': 'Credit Risk Prediction - AutoAI',\n",
              " 'desc': 'Sample notebook',\n",
              " 'prediction_type': 'binary',\n",
              " 'prediction_column': 'Risk',\n",
              " 'scoring': 'roc_auc',\n",
              " 'test_size': 0.1,\n",
              " 'max_num_daub_ensembles': 2,\n",
              " 't_shirt_size': 'm',\n",
              " 'train_sample_rows_test_size': None,\n",
              " 'daub_include_only_estimators': ['ExtraTreesClassifierEstimator',\n",
              "  'GradientBoostingClassifierEstimator',\n",
              "  'LGBMClassifierEstimator',\n",
              "  'LogisticRegressionEstimator',\n",
              "  'RandomForestClassifierEstimator',\n",
              "  'XGBClassifierEstimator',\n",
              "  'DecisionTreeClassifierEstimator'],\n",
              " 'cognito_transform_names': None,\n",
              " 'data_join_graph': False,\n",
              " 'csv_separator': ',',\n",
              " 'excel_sheet': 0,\n",
              " 'encoding': 'utf-8',\n",
              " 'positive_label': None,\n",
              " 'run_id': None}"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipeline_optimizer.get_params()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "<a id=\"run\"></a>\n",
        "## 3. Experiment run\n",
        "\n",
        "Call the `fit()` method to trigger the AutoAI experiment. You can either use interactive mode (synchronous job) or background mode (asychronous job) by specifying `background_model=True`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training job 91e1541a-fee1-4bb9-8211-68eed6abfdc4 completed: 100%|████████| [10:01<00:00,  6.01s/it]\n"
          ]
        }
      ],
      "source": [
        "run_details = pipeline_optimizer.fit(\n",
        "            training_data_reference=training_data_reference,\n",
        "            background_mode=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "You can use the `get_run_status()` method to monitor AutoAI jobs in background mode."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'completed'"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipeline_optimizer.get_run_status()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "<a id=\"comparison\"></a>\n",
        "## 4. Pipelines comparison and testing\n",
        "\n",
        "You can list trained pipelines and evaluation metrics information in\n",
        "the form of a Pandas DataFrame by calling the `summary()` method. You can\n",
        "use the DataFrame to compare all discovered pipelines and select the one\n",
        "you like for further testing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>Number of enhancements</th>\n",
              "      <th>Estimator</th>\n",
              "      <th>training_roc_auc_(optimized)</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline Name</th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pipeline_4</th>\n",
              "      <td>4</td>\n",
              "      <td>XGBClassifierEstimator</td>\n",
              "      <td>0.853971</td>\n",
              "      <td>0.702532</td>\n",
              "      <td>0.786750</td>\n",
              "      <td>0.762646</td>\n",
              "      <td>-0.432601</td>\n",
              "      <td>0.664671</td>\n",
              "      <td>0.686865</td>\n",
              "      <td>0.794</td>\n",
              "      <td>0.761765</td>\n",
              "      <td>0.649368</td>\n",
              "      <td>0.683077</td>\n",
              "      <td>-0.434525</td>\n",
              "      <td>0.783778</td>\n",
              "      <td>0.858985</td>\n",
              "      <td>0.750276</td>\n",
              "      <td>0.667448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_7</th>\n",
              "      <td>3</td>\n",
              "      <td>GradientBoostingClassifierEstimator</td>\n",
              "      <td>0.853637</td>\n",
              "      <td>0.744186</td>\n",
              "      <td>0.781843</td>\n",
              "      <td>0.752330</td>\n",
              "      <td>-0.430732</td>\n",
              "      <td>0.574850</td>\n",
              "      <td>0.754036</td>\n",
              "      <td>0.792</td>\n",
              "      <td>0.737876</td>\n",
              "      <td>0.581504</td>\n",
              "      <td>0.648649</td>\n",
              "      <td>-0.435807</td>\n",
              "      <td>0.796667</td>\n",
              "      <td>0.859524</td>\n",
              "      <td>0.743037</td>\n",
              "      <td>0.656293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_8</th>\n",
              "      <td>4</td>\n",
              "      <td>GradientBoostingClassifierEstimator</td>\n",
              "      <td>0.853637</td>\n",
              "      <td>0.744186</td>\n",
              "      <td>0.781843</td>\n",
              "      <td>0.752330</td>\n",
              "      <td>-0.430732</td>\n",
              "      <td>0.574850</td>\n",
              "      <td>0.754036</td>\n",
              "      <td>0.792</td>\n",
              "      <td>0.737876</td>\n",
              "      <td>0.581504</td>\n",
              "      <td>0.648649</td>\n",
              "      <td>-0.435807</td>\n",
              "      <td>0.796667</td>\n",
              "      <td>0.859524</td>\n",
              "      <td>0.743037</td>\n",
              "      <td>0.656293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_2</th>\n",
              "      <td>2</td>\n",
              "      <td>XGBClassifierEstimator</td>\n",
              "      <td>0.853223</td>\n",
              "      <td>0.674847</td>\n",
              "      <td>0.783110</td>\n",
              "      <td>0.759399</td>\n",
              "      <td>-0.437251</td>\n",
              "      <td>0.658683</td>\n",
              "      <td>0.674910</td>\n",
              "      <td>0.780</td>\n",
              "      <td>0.749762</td>\n",
              "      <td>0.657352</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>-0.439138</td>\n",
              "      <td>0.779556</td>\n",
              "      <td>0.856000</td>\n",
              "      <td>0.749096</td>\n",
              "      <td>0.665831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_3</th>\n",
              "      <td>3</td>\n",
              "      <td>XGBClassifierEstimator</td>\n",
              "      <td>0.853223</td>\n",
              "      <td>0.674847</td>\n",
              "      <td>0.783110</td>\n",
              "      <td>0.759399</td>\n",
              "      <td>-0.437251</td>\n",
              "      <td>0.658683</td>\n",
              "      <td>0.674910</td>\n",
              "      <td>0.780</td>\n",
              "      <td>0.749762</td>\n",
              "      <td>0.657352</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>-0.439138</td>\n",
              "      <td>0.779556</td>\n",
              "      <td>0.856000</td>\n",
              "      <td>0.749096</td>\n",
              "      <td>0.665831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_6</th>\n",
              "      <td>2</td>\n",
              "      <td>GradientBoostingClassifierEstimator</td>\n",
              "      <td>0.850494</td>\n",
              "      <td>0.755906</td>\n",
              "      <td>0.778339</td>\n",
              "      <td>0.765539</td>\n",
              "      <td>-0.432066</td>\n",
              "      <td>0.574850</td>\n",
              "      <td>0.759112</td>\n",
              "      <td>0.796</td>\n",
              "      <td>0.740879</td>\n",
              "      <td>0.592149</td>\n",
              "      <td>0.653061</td>\n",
              "      <td>-0.430092</td>\n",
              "      <td>0.800667</td>\n",
              "      <td>0.856503</td>\n",
              "      <td>0.748694</td>\n",
              "      <td>0.665064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_1</th>\n",
              "      <td>1</td>\n",
              "      <td>XGBClassifierEstimator</td>\n",
              "      <td>0.849883</td>\n",
              "      <td>0.692810</td>\n",
              "      <td>0.782921</td>\n",
              "      <td>0.760481</td>\n",
              "      <td>-0.437680</td>\n",
              "      <td>0.634731</td>\n",
              "      <td>0.692303</td>\n",
              "      <td>0.784</td>\n",
              "      <td>0.746795</td>\n",
              "      <td>0.638057</td>\n",
              "      <td>0.662500</td>\n",
              "      <td>-0.438008</td>\n",
              "      <td>0.783778</td>\n",
              "      <td>0.854903</td>\n",
              "      <td>0.747457</td>\n",
              "      <td>0.663639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_5</th>\n",
              "      <td>1</td>\n",
              "      <td>GradientBoostingClassifierEstimator</td>\n",
              "      <td>0.848791</td>\n",
              "      <td>0.766129</td>\n",
              "      <td>0.780104</td>\n",
              "      <td>0.751335</td>\n",
              "      <td>-0.432413</td>\n",
              "      <td>0.568862</td>\n",
              "      <td>0.768005</td>\n",
              "      <td>0.798</td>\n",
              "      <td>0.740888</td>\n",
              "      <td>0.573520</td>\n",
              "      <td>0.652921</td>\n",
              "      <td>-0.442652</td>\n",
              "      <td>0.799333</td>\n",
              "      <td>0.848789</td>\n",
              "      <td>0.743049</td>\n",
              "      <td>0.656371</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Number of enhancements                            Estimator  \\\n",
              "Pipeline Name                                                                \n",
              "Pipeline_4                          4               XGBClassifierEstimator   \n",
              "Pipeline_7                          3  GradientBoostingClassifierEstimator   \n",
              "Pipeline_8                          4  GradientBoostingClassifierEstimator   \n",
              "Pipeline_2                          2               XGBClassifierEstimator   \n",
              "Pipeline_3                          3               XGBClassifierEstimator   \n",
              "Pipeline_6                          2  GradientBoostingClassifierEstimator   \n",
              "Pipeline_1                          1               XGBClassifierEstimator   \n",
              "Pipeline_5                          1  GradientBoostingClassifierEstimator   \n",
              "\n",
              "               training_roc_auc_(optimized)  holdout_precision  \\\n",
              "Pipeline Name                                                    \n",
              "Pipeline_4                         0.853971           0.702532   \n",
              "Pipeline_7                         0.853637           0.744186   \n",
              "Pipeline_8                         0.853637           0.744186   \n",
              "Pipeline_2                         0.853223           0.674847   \n",
              "Pipeline_3                         0.853223           0.674847   \n",
              "Pipeline_6                         0.850494           0.755906   \n",
              "Pipeline_1                         0.849883           0.692810   \n",
              "Pipeline_5                         0.848791           0.766129   \n",
              "\n",
              "               training_average_precision  holdout_average_precision  \\\n",
              "Pipeline Name                                                          \n",
              "Pipeline_4                       0.786750                   0.762646   \n",
              "Pipeline_7                       0.781843                   0.752330   \n",
              "Pipeline_8                       0.781843                   0.752330   \n",
              "Pipeline_2                       0.783110                   0.759399   \n",
              "Pipeline_3                       0.783110                   0.759399   \n",
              "Pipeline_6                       0.778339                   0.765539   \n",
              "Pipeline_1                       0.782921                   0.760481   \n",
              "Pipeline_5                       0.780104                   0.751335   \n",
              "\n",
              "               training_neg_log_loss  holdout_recall  training_precision  \\\n",
              "Pipeline Name                                                              \n",
              "Pipeline_4                 -0.432601        0.664671            0.686865   \n",
              "Pipeline_7                 -0.430732        0.574850            0.754036   \n",
              "Pipeline_8                 -0.430732        0.574850            0.754036   \n",
              "Pipeline_2                 -0.437251        0.658683            0.674910   \n",
              "Pipeline_3                 -0.437251        0.658683            0.674910   \n",
              "Pipeline_6                 -0.432066        0.574850            0.759112   \n",
              "Pipeline_1                 -0.437680        0.634731            0.692303   \n",
              "Pipeline_5                 -0.432413        0.568862            0.768005   \n",
              "\n",
              "               holdout_accuracy  holdout_balanced_accuracy  training_recall  \\\n",
              "Pipeline Name                                                                 \n",
              "Pipeline_4                0.794                   0.761765         0.649368   \n",
              "Pipeline_7                0.792                   0.737876         0.581504   \n",
              "Pipeline_8                0.792                   0.737876         0.581504   \n",
              "Pipeline_2                0.780                   0.749762         0.657352   \n",
              "Pipeline_3                0.780                   0.749762         0.657352   \n",
              "Pipeline_6                0.796                   0.740879         0.592149   \n",
              "Pipeline_1                0.784                   0.746795         0.638057   \n",
              "Pipeline_5                0.798                   0.740888         0.573520   \n",
              "\n",
              "               holdout_f1  holdout_neg_log_loss  training_accuracy  \\\n",
              "Pipeline Name                                                        \n",
              "Pipeline_4       0.683077             -0.434525           0.783778   \n",
              "Pipeline_7       0.648649             -0.435807           0.796667   \n",
              "Pipeline_8       0.648649             -0.435807           0.796667   \n",
              "Pipeline_2       0.666667             -0.439138           0.779556   \n",
              "Pipeline_3       0.666667             -0.439138           0.779556   \n",
              "Pipeline_6       0.653061             -0.430092           0.800667   \n",
              "Pipeline_1       0.662500             -0.438008           0.783778   \n",
              "Pipeline_5       0.652921             -0.442652           0.799333   \n",
              "\n",
              "               holdout_roc_auc  training_balanced_accuracy  training_f1  \n",
              "Pipeline Name                                                            \n",
              "Pipeline_4            0.858985                    0.750276     0.667448  \n",
              "Pipeline_7            0.859524                    0.743037     0.656293  \n",
              "Pipeline_8            0.859524                    0.743037     0.656293  \n",
              "Pipeline_2            0.856000                    0.749096     0.665831  \n",
              "Pipeline_3            0.856000                    0.749096     0.665831  \n",
              "Pipeline_6            0.856503                    0.748694     0.665064  \n",
              "Pipeline_1            0.854903                    0.747457     0.663639  \n",
              "Pipeline_5            0.848789                    0.743049     0.656371  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "summary = pipeline_optimizer.summary()\n",
        "summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "You can visualize the scoring metric calculated on a holdout data set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "pd.options.plotting.backend = \"plotly\"\n",
        "\n",
        "summary.holdout_roc_auc.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Get selected pipeline model\n",
        "\n",
        "Download and reconstruct a scikit-learn pipeline model object from the\n",
        "AutoAI training job."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        },
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "best_pipeline = pipeline_optimizer.get_pipeline()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "Check confusion matrix for selected pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fn</th>\n",
              "      <th>fp</th>\n",
              "      <th>tn</th>\n",
              "      <th>tp</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>true_class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>No Risk</th>\n",
              "      <td>47</td>\n",
              "      <td>56</td>\n",
              "      <td>111</td>\n",
              "      <td>286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Risk</th>\n",
              "      <td>56</td>\n",
              "      <td>47</td>\n",
              "      <td>286</td>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            fn  fp   tn   tp\n",
              "true_class                  \n",
              "No Risk     47  56  111  286\n",
              "Risk        56  47  286  111"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipeline_optimizer.get_pipeline_details()['confusion_matrix']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "Check features importance for selected pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>features_importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>NewFeature_14_round(sum(LoanDuration__Age))</th>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_1_sum(LoanDuration__Age)</th>\n",
              "      <td>0.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_6_pca_15</th>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExistingCreditsCount</th>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OwnsProperty</th>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_3_pca_0</th>\n",
              "      <td>0.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EmploymentDuration</th>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_11_round(LoanAmount)</th>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CurrentResidenceDuration</th>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OthersOnLoan</th>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_5_pca_13</th>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Telephone</th>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_8_pca_21</th>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CheckingStatus</th>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Housing</th>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_19_round(pca_15)</th>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_7_pca_20</th>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CreditHistory</th>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_0_sum(LoanDuration__LoanAmount)</th>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InstallmentPlans</th>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LoanAmount</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_2_sum(LoanAmount__Age)</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_15_round(sum(LoanAmount__Age))</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_12_round(Age)</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_9_pca_22</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_18_round(pca_9)</th>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Job</th>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_4_pca_1</th>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_10_round(LoanDuration)</th>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_13_round(sum(LoanDuration__LoanAmount))</th>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_17_round(pca_1)</th>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependents</th>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NewFeature_16_round(pca_0)</th>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LoanDuration</th>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExistingSavings</th>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LoanPurpose</th>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InstallmentPercent</th>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ForeignWorker</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    features_importance\n",
              "NewFeature_14_round(sum(LoanDuration__Age))                        1.00\n",
              "NewFeature_1_sum(LoanDuration__Age)                                0.89\n",
              "NewFeature_6_pca_15                                                0.38\n",
              "ExistingCreditsCount                                               0.32\n",
              "OwnsProperty                                                       0.31\n",
              "NewFeature_3_pca_0                                                 0.27\n",
              "EmploymentDuration                                                 0.25\n",
              "NewFeature_11_round(LoanAmount)                                    0.23\n",
              "CurrentResidenceDuration                                           0.18\n",
              "OthersOnLoan                                                       0.18\n",
              "NewFeature_5_pca_13                                                0.17\n",
              "Telephone                                                          0.11\n",
              "NewFeature_8_pca_21                                                0.08\n",
              "CheckingStatus                                                     0.06\n",
              "Housing                                                            0.06\n",
              "NewFeature_19_round(pca_15)                                        0.05\n",
              "NewFeature_7_pca_20                                                0.05\n",
              "CreditHistory                                                      0.04\n",
              "NewFeature_0_sum(LoanDuration__LoanAmount)                         0.04\n",
              "InstallmentPlans                                                   0.04\n",
              "LoanAmount                                                         0.03\n",
              "NewFeature_2_sum(LoanAmount__Age)                                  0.03\n",
              "Age                                                                0.03\n",
              "NewFeature_15_round(sum(LoanAmount__Age))                          0.03\n",
              "NewFeature_12_round(Age)                                           0.03\n",
              "NewFeature_9_pca_22                                                0.03\n",
              "NewFeature_18_round(pca_9)                                         0.02\n",
              "Job                                                                0.02\n",
              "NewFeature_4_pca_1                                                 0.02\n",
              "Sex                                                                0.02\n",
              "NewFeature_10_round(LoanDuration)                                  0.02\n",
              "NewFeature_13_round(sum(LoanDuration__LoanAmount))                 0.02\n",
              "NewFeature_17_round(pca_1)                                         0.01\n",
              "Dependents                                                         0.01\n",
              "NewFeature_16_round(pca_0)                                         0.01\n",
              "LoanDuration                                                       0.01\n",
              "ExistingSavings                                                    0.01\n",
              "LoanPurpose                                                        0.01\n",
              "InstallmentPercent                                                 0.01\n",
              "ForeignWorker                                                      0.00"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipeline_optimizer.get_pipeline_details()['features_importance']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Convert the pipeline model to a Python script and download it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<a download=\"pipeline_script.py\" href=\"data:binary;base64,from autoai_libs.transformers.exportable import NumpyColumnSelector
from autoai_libs.transformers.exportable import CompressStrings
from autoai_libs.transformers.exportable import NumpyReplaceMissingValues
from autoai_libs.transformers.exportable import NumpyReplaceUnknownValues
from autoai_libs.transformers.exportable import boolean2float
from autoai_libs.transformers.exportable import CatImputer
from autoai_libs.transformers.exportable import CatEncoder
import numpy as np
from autoai_libs.transformers.exportable import float32_transform
from autoai_libs.transformers.exportable import FloatStr2Float
from autoai_libs.transformers.exportable import NumImputer
from autoai_libs.transformers.exportable import OptStandardScaler
from lale.lib.lale import ConcatFeatures
from autoai_libs.transformers.exportable import NumpyPermuteArray
from autoai_libs.cognito.transforms.transform_utils import TNoOp
from xgboost import XGBClassifier
import lale

lale.wrap_imported_operators()
numpy_column_selector_0 = NumpyColumnSelector(
    columns=[
        0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
    ]
)
compress_strings = CompressStrings(
    compress_type="hash",
    dtypes_list=[
        "char_str", "int_num", "char_str", "char_str", "char_str", "char_str",
        "int_num", "char_str", "char_str", "int_num", "char_str", "int_num",
        "char_str", "char_str", "int_num", "char_str", "int_num", "char_str",
        "char_str",
    ],
    missing_values_reference_list=["", "-", "?", float("nan")],
    misslist_list=[
        [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [],
        [], [],
    ],
)
numpy_replace_missing_values_0 = NumpyReplaceMissingValues(
    missing_values=[], filling_values=float("nan")
)
numpy_replace_unknown_values = NumpyReplaceUnknownValues(
    filling_values=float("nan"),
    filling_values_list=[
        float("nan"), float("nan"), float("nan"), float("nan"), float("nan"),
        float("nan"), float("nan"), float("nan"), float("nan"), float("nan"),
        float("nan"), float("nan"), float("nan"), float("nan"), float("nan"),
        float("nan"), float("nan"), float("nan"), float("nan"),
    ],
    known_values_list=[
        [
            227259264688753646810077375790908286508,
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        ],
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            22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
            38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
            54, 56, 57, 59, 60, 61, 62, 63, 64,
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            208131593747174447223154626949015695834,
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        ],
        [
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            246789352403109329930648176329125946981,
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        ],
        [
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            179222502770383138876302755836424893586,
            283364312271660996400883763491949419861,
        ],
        [1, 2, 3, 4, 5, 6],
        [
            52149379001264932068757487059177351405,
            10381015089147753033583386570985939629,
        ],
        [
            90380513159839424205657141466603309780,
            72020144360318906788270974425375628494,
            68186749286663113704472210246844540664,
        ],
        [1, 2, 3, 4, 5, 6],
        [
            184210183797580735197442541762508088723,
            112784645159752486323086095897586579102,
            194064631024616485005022213750224679171,
            230715114430321724850934713783197932106,
        ],
        [
            19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
            35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
            67, 70, 71, 73, 74,
        ],
        [
            251696305476606261219998849299484831626,
            68186749286663113704472210246844540664,
            129843969358953857049997737920307864573,
        ],
        [
            226204649993248704235747196731638161812,
            240703879997496844699455009880498205665,
            243184888600665221836895697954701357177,
        ],
        [1, 2, 3, 4],
        [
            328286527295663582663365503319902632676,
            119641707607939038914465000864290288880,
            283364312271660996400883763491949419861,
            27741019508977055807423991753468819528,
        ],
        [1, 2],
        [
            68186749286663113704472210246844540664,
            220736790854050750400968561922076059550,
        ],
        [
            169662019754859674907370307324476606919,
            220736790854050750400968561922076059550,
        ],
    ],
    missing_values_reference_list=["", "-", "?", float("nan")],
)
cat_imputer = CatImputer(
    strategy="most_frequent",
    missing_values=float("nan"),
    sklearn_version_family="23",
)
cat_encoder = CatEncoder(
    encoding="ordinal",
    categories="auto",
    dtype=np.float64,
    handle_unknown="error",
    sklearn_version_family="23",
)
numpy_column_selector_1 = NumpyColumnSelector(columns=[4])
float_str2_float = FloatStr2Float(
    dtypes_list=["int_num"], missing_values_reference_list=[]
)
numpy_replace_missing_values_1 = NumpyReplaceMissingValues(
    missing_values=[], filling_values=float("nan")
)
num_imputer = NumImputer(strategy="median", missing_values=float("nan"))
opt_standard_scaler = OptStandardScaler(
    num_scaler_copy=None,
    num_scaler_with_mean=None,
    num_scaler_with_std=None,
    use_scaler_flag=False,
)
numpy_permute_array = NumpyPermuteArray(
    axis=0,
    permutation_indices=[
        0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 4,
    ],
)
t_no_op = TNoOp(
    fun=None, name="no_action", datatypes="x", feat_constraints=[]
)
xgb_classifier = XGBClassifier(
    gamma=0.44579800091381966,
    learning_rate=0.021330806558379982,
    min_child_weight=5,
    missing=float("nan"),
    n_estimators=574,
    n_jobs=2,
    random_state=33,
    reg_alpha=0.724822538978647,
    reg_lambda=0.2506742141014951,
    silent=True,
    subsample=0.41147527714871196,
    verbosity=0,
    tree_method="hist",
)
pipeline = (
    (
        (
            numpy_column_selector_0
            >> compress_strings
            >> numpy_replace_missing_values_0
            >> numpy_replace_unknown_values
            >> boolean2float()
            >> cat_imputer
            >> cat_encoder
            >> float32_transform()
        )
        & (
            numpy_column_selector_1
            >> float_str2_float
            >> numpy_replace_missing_values_1
            >> num_imputer
            >> opt_standard_scaler
            >> float32_transform()
        )
    )
    >> ConcatFeatures()
    >> numpy_permute_array
    >> t_no_op
    >> xgb_classifier
)\" target=\"_blank\">Download file.</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from ibm_watson_machine_learning.helpers import pipeline_to_script\n",
        "pipeline_to_script(best_pipeline)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Visualize pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/svg+xml": [
              "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
              "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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              "<title>cluster:(root)</title>\n",
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              "<g id=\"a_node1\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.autoai_libs.numpy_column_selector.html\" xlink:title=\"numpy_column_selector_0 = NumpyColumnSelector(columns=[0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])\">\n",
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              "<!-- compress_strings -->\n",
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              "<g id=\"a_node2\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.autoai_libs.compress_strings.html\" xlink:title=\"compress_strings = CompressStrings(compress_type=&#39;hash&#39;, dtypes_list=[&#39;char_str&#39;, &#39;int_num&#39;, &#39;char_str&#39;, &#39;char_str&#39;, &#39;char_str&#39;, &#39;char_str&#39;, &#39;int_num&#39;, &#39;char_str&#39;, &#39;char_str&#39;, &#39;int_num&#39;, &#39;char_str&#39;, &#39;int_num&#39;, &#39;char_str&#39;, &#39;char_str&#39;, &#39;int_num&#39;, &#39;char_str&#39;, &#39;int_num&#39;, &#39;char_str&#39;, &#39;char_s...)\">\n",
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              "</g>\n",
              "</g>\n",
              "<!-- numpy_permute_array&#45;&gt;t_no_op -->\n",
              "<g id=\"edge16\" class=\"edge\">\n",
              "<title>numpy_permute_array&#45;&gt;t_no_op</title>\n",
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              "</g>\n",
              "<!-- xgb_classifier -->\n",
              "<g id=\"node18\" class=\"node\">\n",
              "<title>xgb_classifier</title>\n",
              "<g id=\"a_node18\"><a xlink:title=\"xgb_classifier = XGBClassifier(gamma=0.44579800091381966, learning_rate=0.021330806558379982, min_child_weight=5, missing=float(&#39;nan&#39;), n_estimators=574, n_jobs=2, random_state=33, reg_alpha=0.724822538978647, reg_lambda=0.2506742141014951, silent=True, subsample=0.41147527714871196...)\">\n",
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              "<text text-anchor=\"middle\" x=\"1196.95\" y=\"-59.97\" font-family=\"Times,serif\" font-size=\"11.00\">Classifier</text>\n",
              "</a>\n",
              "</g>\n",
              "</g>\n",
              "<!-- t_no_op&#45;&gt;xgb_classifier -->\n",
              "<g id=\"edge17\" class=\"edge\">\n",
              "<title>t_no_op&#45;&gt;xgb_classifier</title>\n",
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              "</g>\n",
              "</svg>\n"
            ],
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7fd8d2f82790>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "best_pipeline.visualize()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "Each node in the visualization is a machine-learning operator\n",
        "(transformer or estimator). Each edge indicates data flow (transformed\n",
        "output from one operator becomes input to the next).  The input to the\n",
        "root nodes is the initial dataset and the output from the sink node\n",
        "is the final prediction.  When you hover the mouse pointer over a\n",
        "node, a tooltip shows you the configuration arguments of the\n",
        "corresponding operator (tuned hyperparameters). When you click on the\n",
        "hyperlink of a node, it brings you to a documentation page for the\n",
        "operator."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Pipeline source code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "best_pipeline.pretty_print(ipython_display=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "In the pretty-printed code, `>>` is the pipe combinator (dataflow\n",
        "edge) and `&` is the and combinator (combining multiple subpipelines).\n",
        "They correspond to the `make_pipeline` and `make_union` functions from\n",
        "scikit-learn, respectively. If you prefer the functions, you can\n",
        "instead pretty-print your pipeline with\n",
        "`best_pipeline.pretty_print(ipython_display=True, combinators=False)`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Reading training data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "train_df = pipeline_optimizer.get_data_connections()[0].read()\n",
        "\n",
        "train_X = train_df.drop(['Risk'], axis=1).values\n",
        "train_y = train_df.Risk.values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Test pipeline model locally"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['No Risk', 'No Risk', 'No Risk', 'No Risk', 'Risk'], dtype=object)"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predicted_y = best_pipeline.predict(train_X)\n",
        "predicted_y[:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "<a id=\"runs\"></a>\n",
        "## 5. Historical runs\n",
        "\n",
        "In this section you learn to work with historical AutoPipelines fit jobs (runs)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "To list historical runs use method `list()`. You can filter runs by providing experiment name."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>run_id</th>\n",
              "      <th>state</th>\n",
              "      <th>auto_pipeline_optimizer name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-12-08T12:50:22.481Z</td>\n",
              "      <td>91e1541a-fee1-4bb9-8211-68eed6abfdc4</td>\n",
              "      <td>completed</td>\n",
              "      <td>Credit Risk Prediction - AutoAI</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  timestamp                                run_id      state  \\\n",
              "0  2020-12-08T12:50:22.481Z  91e1541a-fee1-4bb9-8211-68eed6abfdc4  completed   \n",
              "\n",
              "      auto_pipeline_optimizer name  \n",
              "0  Credit Risk Prediction - AutoAI  "
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "experiment.runs(filter='Credit Risk Prediction - AutoAI').list()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "To work with historical pipelines found during a particular optimizer\n",
        "run, you need to first provide the `run_id` to select the fitted\n",
        "optimizer.\n",
        "\n",
        "**Note**: you can assign selected `run_id` to the run_id variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "run_id = run_details['metadata']['id']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Get executed optimizer's configuration parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'name': 'Credit Risk Prediction - AutoAI',\n",
              " 'desc': 'Sample notebook',\n",
              " 'prediction_type': 'binary',\n",
              " 'prediction_column': 'Risk',\n",
              " 'scoring': 'roc_auc',\n",
              " 'test_size': 0.1,\n",
              " 'max_num_daub_ensembles': 2.0,\n",
              " 't_shirt_size': 'c076e82c-b2a7-4d20-9c0f-1f0c2fdf5a24',\n",
              " 'daub_include_only_estimators': ['ExtraTreesClassifierEstimator',\n",
              "  'GradientBoostingClassifierEstimator',\n",
              "  'LGBMClassifierEstimator',\n",
              "  'LogisticRegressionEstimator',\n",
              "  'RandomForestClassifierEstimator',\n",
              "  'XGBClassifierEstimator',\n",
              "  'DecisionTreeClassifierEstimator'],\n",
              " 'cognito_transform_names': None,\n",
              " 'train_sample_rows_test_size': None,\n",
              " 'csv_separator': ',',\n",
              " 'excel_sheet': 0,\n",
              " 'encoding': 'utf-8'}"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "experiment.runs(filter='Credit Risk Prediction - AutoAI').get_params(run_id=run_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "\n",
        "### Get historical optimizer instance and training details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "historical_opt = experiment.runs.get_optimizer(run_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "run_details = historical_opt.get_run_details()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### List trained pipelines for selected optimizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Number of enhancements</th>\n",
              "      <th>Estimator</th>\n",
              "      <th>training_roc_auc_(optimized)</th>\n",
              "      <th>holdout_precision</th>\n",
              "      <th>training_average_precision</th>\n",
              "      <th>holdout_average_precision</th>\n",
              "      <th>training_neg_log_loss</th>\n",
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              "      <th>training_balanced_accuracy</th>\n",
              "      <th>training_f1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pipeline_4</th>\n",
              "      <td>4</td>\n",
              "      <td>XGBClassifierEstimator</td>\n",
              "      <td>0.853971</td>\n",
              "      <td>0.702532</td>\n",
              "      <td>0.786750</td>\n",
              "      <td>0.762646</td>\n",
              "      <td>-0.432601</td>\n",
              "      <td>0.664671</td>\n",
              "      <td>0.686865</td>\n",
              "      <td>0.794</td>\n",
              "      <td>0.761765</td>\n",
              "      <td>0.649368</td>\n",
              "      <td>0.683077</td>\n",
              "      <td>-0.434525</td>\n",
              "      <td>0.783778</td>\n",
              "      <td>0.858985</td>\n",
              "      <td>0.750276</td>\n",
              "      <td>0.667448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_7</th>\n",
              "      <td>3</td>\n",
              "      <td>GradientBoostingClassifierEstimator</td>\n",
              "      <td>0.853637</td>\n",
              "      <td>0.744186</td>\n",
              "      <td>0.781843</td>\n",
              "      <td>0.752330</td>\n",
              "      <td>-0.430732</td>\n",
              "      <td>0.574850</td>\n",
              "      <td>0.754036</td>\n",
              "      <td>0.792</td>\n",
              "      <td>0.737876</td>\n",
              "      <td>0.581504</td>\n",
              "      <td>0.648649</td>\n",
              "      <td>-0.435807</td>\n",
              "      <td>0.796667</td>\n",
              "      <td>0.859524</td>\n",
              "      <td>0.743037</td>\n",
              "      <td>0.656293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_8</th>\n",
              "      <td>4</td>\n",
              "      <td>GradientBoostingClassifierEstimator</td>\n",
              "      <td>0.853637</td>\n",
              "      <td>0.744186</td>\n",
              "      <td>0.781843</td>\n",
              "      <td>0.752330</td>\n",
              "      <td>-0.430732</td>\n",
              "      <td>0.574850</td>\n",
              "      <td>0.754036</td>\n",
              "      <td>0.792</td>\n",
              "      <td>0.737876</td>\n",
              "      <td>0.581504</td>\n",
              "      <td>0.648649</td>\n",
              "      <td>-0.435807</td>\n",
              "      <td>0.796667</td>\n",
              "      <td>0.859524</td>\n",
              "      <td>0.743037</td>\n",
              "      <td>0.656293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_2</th>\n",
              "      <td>2</td>\n",
              "      <td>XGBClassifierEstimator</td>\n",
              "      <td>0.853223</td>\n",
              "      <td>0.674847</td>\n",
              "      <td>0.783110</td>\n",
              "      <td>0.759399</td>\n",
              "      <td>-0.437251</td>\n",
              "      <td>0.658683</td>\n",
              "      <td>0.674910</td>\n",
              "      <td>0.780</td>\n",
              "      <td>0.749762</td>\n",
              "      <td>0.657352</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>-0.439138</td>\n",
              "      <td>0.779556</td>\n",
              "      <td>0.856000</td>\n",
              "      <td>0.749096</td>\n",
              "      <td>0.665831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_3</th>\n",
              "      <td>3</td>\n",
              "      <td>XGBClassifierEstimator</td>\n",
              "      <td>0.853223</td>\n",
              "      <td>0.674847</td>\n",
              "      <td>0.783110</td>\n",
              "      <td>0.759399</td>\n",
              "      <td>-0.437251</td>\n",
              "      <td>0.658683</td>\n",
              "      <td>0.674910</td>\n",
              "      <td>0.780</td>\n",
              "      <td>0.749762</td>\n",
              "      <td>0.657352</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>-0.439138</td>\n",
              "      <td>0.779556</td>\n",
              "      <td>0.856000</td>\n",
              "      <td>0.749096</td>\n",
              "      <td>0.665831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_6</th>\n",
              "      <td>2</td>\n",
              "      <td>GradientBoostingClassifierEstimator</td>\n",
              "      <td>0.850494</td>\n",
              "      <td>0.755906</td>\n",
              "      <td>0.778339</td>\n",
              "      <td>0.765539</td>\n",
              "      <td>-0.432066</td>\n",
              "      <td>0.574850</td>\n",
              "      <td>0.759112</td>\n",
              "      <td>0.796</td>\n",
              "      <td>0.740879</td>\n",
              "      <td>0.592149</td>\n",
              "      <td>0.653061</td>\n",
              "      <td>-0.430092</td>\n",
              "      <td>0.800667</td>\n",
              "      <td>0.856503</td>\n",
              "      <td>0.748694</td>\n",
              "      <td>0.665064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_1</th>\n",
              "      <td>1</td>\n",
              "      <td>XGBClassifierEstimator</td>\n",
              "      <td>0.849883</td>\n",
              "      <td>0.692810</td>\n",
              "      <td>0.782921</td>\n",
              "      <td>0.760481</td>\n",
              "      <td>-0.437680</td>\n",
              "      <td>0.634731</td>\n",
              "      <td>0.692303</td>\n",
              "      <td>0.784</td>\n",
              "      <td>0.746795</td>\n",
              "      <td>0.638057</td>\n",
              "      <td>0.662500</td>\n",
              "      <td>-0.438008</td>\n",
              "      <td>0.783778</td>\n",
              "      <td>0.854903</td>\n",
              "      <td>0.747457</td>\n",
              "      <td>0.663639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pipeline_5</th>\n",
              "      <td>1</td>\n",
              "      <td>GradientBoostingClassifierEstimator</td>\n",
              "      <td>0.848791</td>\n",
              "      <td>0.766129</td>\n",
              "      <td>0.780104</td>\n",
              "      <td>0.751335</td>\n",
              "      <td>-0.432413</td>\n",
              "      <td>0.568862</td>\n",
              "      <td>0.768005</td>\n",
              "      <td>0.798</td>\n",
              "      <td>0.740888</td>\n",
              "      <td>0.573520</td>\n",
              "      <td>0.652921</td>\n",
              "      <td>-0.442652</td>\n",
              "      <td>0.799333</td>\n",
              "      <td>0.848789</td>\n",
              "      <td>0.743049</td>\n",
              "      <td>0.656371</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Number of enhancements                            Estimator  \\\n",
              "Pipeline Name                                                                \n",
              "Pipeline_4                          4               XGBClassifierEstimator   \n",
              "Pipeline_7                          3  GradientBoostingClassifierEstimator   \n",
              "Pipeline_8                          4  GradientBoostingClassifierEstimator   \n",
              "Pipeline_2                          2               XGBClassifierEstimator   \n",
              "Pipeline_3                          3               XGBClassifierEstimator   \n",
              "Pipeline_6                          2  GradientBoostingClassifierEstimator   \n",
              "Pipeline_1                          1               XGBClassifierEstimator   \n",
              "Pipeline_5                          1  GradientBoostingClassifierEstimator   \n",
              "\n",
              "               training_roc_auc_(optimized)  holdout_precision  \\\n",
              "Pipeline Name                                                    \n",
              "Pipeline_4                         0.853971           0.702532   \n",
              "Pipeline_7                         0.853637           0.744186   \n",
              "Pipeline_8                         0.853637           0.744186   \n",
              "Pipeline_2                         0.853223           0.674847   \n",
              "Pipeline_3                         0.853223           0.674847   \n",
              "Pipeline_6                         0.850494           0.755906   \n",
              "Pipeline_1                         0.849883           0.692810   \n",
              "Pipeline_5                         0.848791           0.766129   \n",
              "\n",
              "               training_average_precision  holdout_average_precision  \\\n",
              "Pipeline Name                                                          \n",
              "Pipeline_4                       0.786750                   0.762646   \n",
              "Pipeline_7                       0.781843                   0.752330   \n",
              "Pipeline_8                       0.781843                   0.752330   \n",
              "Pipeline_2                       0.783110                   0.759399   \n",
              "Pipeline_3                       0.783110                   0.759399   \n",
              "Pipeline_6                       0.778339                   0.765539   \n",
              "Pipeline_1                       0.782921                   0.760481   \n",
              "Pipeline_5                       0.780104                   0.751335   \n",
              "\n",
              "               training_neg_log_loss  holdout_recall  training_precision  \\\n",
              "Pipeline Name                                                              \n",
              "Pipeline_4                 -0.432601        0.664671            0.686865   \n",
              "Pipeline_7                 -0.430732        0.574850            0.754036   \n",
              "Pipeline_8                 -0.430732        0.574850            0.754036   \n",
              "Pipeline_2                 -0.437251        0.658683            0.674910   \n",
              "Pipeline_3                 -0.437251        0.658683            0.674910   \n",
              "Pipeline_6                 -0.432066        0.574850            0.759112   \n",
              "Pipeline_1                 -0.437680        0.634731            0.692303   \n",
              "Pipeline_5                 -0.432413        0.568862            0.768005   \n",
              "\n",
              "               holdout_accuracy  holdout_balanced_accuracy  training_recall  \\\n",
              "Pipeline Name                                                                 \n",
              "Pipeline_4                0.794                   0.761765         0.649368   \n",
              "Pipeline_7                0.792                   0.737876         0.581504   \n",
              "Pipeline_8                0.792                   0.737876         0.581504   \n",
              "Pipeline_2                0.780                   0.749762         0.657352   \n",
              "Pipeline_3                0.780                   0.749762         0.657352   \n",
              "Pipeline_6                0.796                   0.740879         0.592149   \n",
              "Pipeline_1                0.784                   0.746795         0.638057   \n",
              "Pipeline_5                0.798                   0.740888         0.573520   \n",
              "\n",
              "               holdout_f1  holdout_neg_log_loss  training_accuracy  \\\n",
              "Pipeline Name                                                        \n",
              "Pipeline_4       0.683077             -0.434525           0.783778   \n",
              "Pipeline_7       0.648649             -0.435807           0.796667   \n",
              "Pipeline_8       0.648649             -0.435807           0.796667   \n",
              "Pipeline_2       0.666667             -0.439138           0.779556   \n",
              "Pipeline_3       0.666667             -0.439138           0.779556   \n",
              "Pipeline_6       0.653061             -0.430092           0.800667   \n",
              "Pipeline_1       0.662500             -0.438008           0.783778   \n",
              "Pipeline_5       0.652921             -0.442652           0.799333   \n",
              "\n",
              "               holdout_roc_auc  training_balanced_accuracy  training_f1  \n",
              "Pipeline Name                                                            \n",
              "Pipeline_4            0.858985                    0.750276     0.667448  \n",
              "Pipeline_7            0.859524                    0.743037     0.656293  \n",
              "Pipeline_8            0.859524                    0.743037     0.656293  \n",
              "Pipeline_2            0.856000                    0.749096     0.665831  \n",
              "Pipeline_3            0.856000                    0.749096     0.665831  \n",
              "Pipeline_6            0.856503                    0.748694     0.665064  \n",
              "Pipeline_1            0.854903                    0.747457     0.663639  \n",
              "Pipeline_5            0.848789                    0.743049     0.656371  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "historical_opt.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Get selected pipeline and test locally"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "hist_pipeline = historical_opt.get_pipeline(pipeline_name='Pipeline_3')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "pycharm": {
          "is_executing": false,
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['No Risk', 'No Risk', 'No Risk', 'No Risk', 'Risk'], dtype=object)"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predicted_y = hist_pipeline.predict(train_X)\n",
        "predicted_y[:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "<a id=\"refinery\"></a>\n",
        "## 6. Pipeline refinement with [Lale](https://github.com/IBM/lale) and testing\n",
        "\n",
        "In this section you learn how to refine and retrain the best\n",
        "pipeline returned by AutoAI. There are many ways to refine a pipeline.\n",
        "For illustration, simply replace the final estimator in the\n",
        "pipeline by an interpretable model. The call to\n",
        "`wrap_imported_operators()` augments scikit-learn operators with\n",
        "schemas for hyperparameter tuning."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression as LR\n",
        "from sklearn.tree import DecisionTreeClassifier as Tree\n",
        "from sklearn.neighbors import KNeighborsClassifier as KNN\n",
        "from lale.lib.lale import Hyperopt\n",
        "from lale import wrap_imported_operators\n",
        "\n",
        "wrap_imported_operators()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Pipeline decomposition and new definition\n",
        "\n",
        "Start by removing the last step of the pipeline, i.e., the final estimator."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
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              "<!-- cat_imputer&#45;&gt;cat_encoder -->\n",
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              "<g id=\"a_node12\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.autoai_libs.num_imputer.html\" xlink:title=\"num_imputer = NumImputer(strategy=&#39;median&#39;, missing_values=float(&#39;nan&#39;))\">\n",
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              "<title>float32_transform_1</title>\n",
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              "<!-- concat_features&#45;&gt;numpy_permute_array -->\n",
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            "text/plain": [
              "<graphviz.dot.Digraph at 0x7fd8d30cb890>"
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      "source": [
        "new_pipeline = prefix >> (LR | Tree | KNN)\n",
        "new_pipeline.visualize()"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### New optimizer `Hyperopt` configuration and training\n",
        "\n",
        "To automatically select the algorithm and tune its hyperparameters, we\n",
        "create an instance of the `Hyperopt` optimizer and `fit` it to the\n",
        "data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(train_X, train_y, test_size=0.15, random_state=33)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "100%|██████████| 20/20 [02:12<00:00,  6.63s/trial, best loss: -0.8336535460343567]\n"
          ]
        }
      ],
      "source": [
        "hyperopt = Hyperopt(estimator=new_pipeline, cv=3, max_evals=20, scoring='roc_auc')\n",
        "hyperopt_pipelines = hyperopt.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "pipeline_model = hyperopt_pipelines.get_pipeline()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Pipeline model tests and visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "roc_auc_score 75.1%\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "predicted_y = pipeline_model.predict(X_test)\n",
        "score = roc_auc_score(predicted_y=='Risk', y_test=='Risk')\n",
        "print(f'roc_auc_score {score:.1%}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pipeline_model.visualize()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "<a id=\"scoring\"></a>\n",
        "## 7. Deploy and Score\n",
        "\n",
        "In this section you will learn how to deploy and score pipeline model as webservice and batch using WML instance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Webservice deployment creation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Preparing an AutoAI Deployment...\n",
            "Published model uid: 014ffcfa-dcf1-4c26-add5-369243b140dd\n",
            "Deploying model 014ffcfa-dcf1-4c26-add5-369243b140dd using V4 client.\n",
            "\n",
            "\n",
            "#######################################################################################\n",
            "\n",
            "Synchronous deployment creation for uid: '014ffcfa-dcf1-4c26-add5-369243b140dd' started\n",
            "\n",
            "#######################################################################################\n",
            "\n",
            "\n",
            "initializing..\n",
            "ready\n",
            "\n",
            "\n",
            "------------------------------------------------------------------------------------------------\n",
            "Successfully finished deployment creation, deployment_uid='e0f705fd-2fcb-437f-bc83-b17d5296b01d'\n",
            "------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from ibm_watson_machine_learning.deployment import WebService\n",
        "\n",
        "service = WebService(wml_credentials, source_space_id=space_id)\n",
        "\n",
        "service.create(\n",
        "    experiment_run_id=run_id,\n",
        "    model='Pipeline_1', \n",
        "    deployment_name=\"Credit Risk Deployment AutoAI\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "Deployment object could be printed to show basic information:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "name: Credit Risk Deployment AutoAI, id: e0f705fd-2fcb-437f-bc83-b17d5296b01d, scoring_url: https://cpd-wmlautoai-may12.apps.ocp46wmlautoaai.cp.fyre.ibm.com/ml/v4/deployments/e0f705fd-2fcb-437f-bc83-b17d5296b01d/predictions, asset_id: 014ffcfa-dcf1-4c26-add5-369243b140dd\n"
          ]
        }
      ],
      "source": [
        "print(service)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "To show all available information about the deployment use the `.get_params()` method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'entity': {'asset': {'id': '014ffcfa-dcf1-4c26-add5-369243b140dd'},\n",
              "  'custom': {},\n",
              "  'deployed_asset_type': 'model',\n",
              "  'hardware_spec': {'id': 'c076e82c-b2a7-4d20-9c0f-1f0c2fdf5a24',\n",
              "   'name': 'M',\n",
              "   'num_nodes': 1},\n",
              "  'hybrid_pipeline_hardware_specs': [{'hardware_spec': {'name': 'S',\n",
              "     'num_nodes': 1},\n",
              "    'node_runtime_id': 'auto_ai.kb'}],\n",
              "  'name': 'Credit Risk Deployment AutoAI',\n",
              "  'online': {},\n",
              "  'space_id': 'fbd2dc3a-634a-46d5-9edd-8faa22d27b8d',\n",
              "  'status': {'online_url': {'url': 'https://cpd-wmlautoai-may12.apps.ocp46wmlautoaai.cp.fyre.ibm.com/ml/v4/deployments/e0f705fd-2fcb-437f-bc83-b17d5296b01d/predictions'},\n",
              "   'state': 'ready'}},\n",
              " 'metadata': {'created_at': '2021-05-20T08:23:53.001Z',\n",
              "  'id': 'e0f705fd-2fcb-437f-bc83-b17d5296b01d',\n",
              "  'modified_at': '2021-05-20T08:23:53.001Z',\n",
              "  'name': 'Credit Risk Deployment AutoAI',\n",
              "  'owner': '1000330999',\n",
              "  'space_id': 'fbd2dc3a-634a-46d5-9edd-8faa22d27b8d'}}"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "service.get_params()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Scoring of webservice\n",
        "You can make scoring request by calling `score()` on deployed pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'predictions': [{'fields': ['prediction', 'probability'],\n",
              "   'values': [['No Risk', [0.6743601502735059, 0.32563984972649407]],\n",
              "    ['No Risk', [0.7892372484049005, 0.21076275159509952]],\n",
              "    ['No Risk', [0.560817617610811, 0.43918238238918905]],\n",
              "    ['No Risk', [0.8068670237279646, 0.1931329762720355]],\n",
              "    ['Risk', [0.3344503240926395, 0.6655496759073605]],\n",
              "    ['Risk', [0.10565678467912125, 0.8943432153208788]],\n",
              "    ['No Risk', [0.7361802572321648, 0.2638197427678351]],\n",
              "    ['No Risk', [0.8315661007516614, 0.1684338992483386]],\n",
              "    ['No Risk', [0.8599408631598607, 0.1400591368401393]],\n",
              "    ['Risk', [0.12661051706745757, 0.8733894829325424]]]}]}"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions = service.score(payload=train_df.drop(['Risk'], axis=1).iloc[:10])\n",
        "predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "If you want to work with the web service in an external Python application, you can retrieve the service object:\n",
        " - Initialize the service using `service = WebService(wml_credentials)`\n",
        " - Get deployment_id using the `service.list()` method\n",
        " - Get webservice object using the `service.get('deployment_id')` method\n",
        "\n",
        "After that you can call `service.score()` method."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Deleting deployment\n",
        "You can delete the existing deployment by calling the `service.delete()` command.\n",
        "To list the existing web services you can use `service.list()`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Batch deployment creation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "A batch deployment processes input data from a inline data and return predictions in scoring details or processes from data asset and writes the output to a file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
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              "  CheckingStatus  LoanDuration           CreditHistory LoanPurpose  \\\n",
              "0       0_to_200            31    credits_paid_to_date       other   \n",
              "1         less_0            18    credits_paid_to_date     car_new   \n",
              "2         less_0            15  prior_payments_delayed   furniture   \n",
              "3       0_to_200            28    credits_paid_to_date  retraining   \n",
              "4    no_checking            28  prior_payments_delayed   education   \n",
              "\n",
              "   LoanAmount ExistingSavings EmploymentDuration  InstallmentPercent     Sex  \\\n",
              "0        1889      100_to_500             less_1                   3  female   \n",
              "1         462        less_100             1_to_4                   2  female   \n",
              "2         250        less_100             1_to_4                   2    male   \n",
              "3        3693        less_100          greater_7                   3    male   \n",
              "4        6235     500_to_1000          greater_7                   3    male   \n",
              "\n",
              "  OthersOnLoan  CurrentResidenceDuration       OwnsProperty  Age  \\\n",
              "0         none                         3  savings_insurance   32   \n",
              "1         none                         2  savings_insurance   37   \n",
              "2         none                         3        real_estate   28   \n",
              "3         none                         2  savings_insurance   32   \n",
              "4         none                         3            unknown   57   \n",
              "\n",
              "  InstallmentPlans Housing  ExistingCreditsCount      Job  Dependents  \\\n",
              "0             none     own                     1  skilled           1   \n",
              "1           stores     own                     2  skilled           1   \n",
              "2             none     own                     2  skilled           1   \n",
              "3             none     own                     1  skilled           1   \n",
              "4             none     own                     2  skilled           1   \n",
              "\n",
              "  Telephone ForeignWorker  \n",
              "0      none           yes  \n",
              "1      none           yes  \n",
              "2       yes            no  \n",
              "3      none           yes  \n",
              "4      none           yes  "
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "batch_payload_df = train_df.drop(['Risk'], axis=1)[:5]\n",
        "batch_payload_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "Create batch deployment for `Pipeline_2` created in AutoAI experiment with the `run_id`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Preparing an AutoAI Deployment...\n",
            "Published model uid: 35a98cac-c3b7-448e-9a02-245400c93a82\n",
            "Deploying model 35a98cac-c3b7-448e-9a02-245400c93a82 using V4 client.\n",
            "\n",
            "\n",
            "#######################################################################################\n",
            "\n",
            "Synchronous deployment creation for uid: '35a98cac-c3b7-448e-9a02-245400c93a82' started\n",
            "\n",
            "#######################################################################################\n",
            "\n",
            "\n",
            "ready.\n",
            "\n",
            "\n",
            "------------------------------------------------------------------------------------------------\n",
            "Successfully finished deployment creation, deployment_uid='756558ad-78f1-4d74-9e6d-555aa9cfff21'\n",
            "------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from ibm_watson_machine_learning.deployment import Batch\n",
        "\n",
        "service_batch = Batch(wml_credentials,source_space_id=space_id)\n",
        "service_batch.create(\n",
        "            experiment_run_id=run_id,\n",
        "            model=\"Pipeline_2\",\n",
        "            deployment_name=\"Credit Risk Batch Deployment AutoAI\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Score batch deployment with inline payload as pandas DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "##########################################################################\n",
            "\n",
            "Synchronous scoring for id: '478b6b5b-a80f-43f5-98f9-63e9da889720' started\n",
            "\n",
            "##########################################################################\n",
            "\n",
            "\n",
            "queued..\n",
            "running\n",
            "completed\n",
            "Scoring job  '478b6b5b-a80f-43f5-98f9-63e9da889720' finished successfully.\n"
          ]
        }
      ],
      "source": [
        "scoring_params = service_batch.run_job(\n",
        "            payload=batch_payload_df,\n",
        "            background_mode=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'fields': ['prediction', 'probability'],\n",
              "  'values': [['No Risk', [0.7743273377418518, 0.225672647356987]],\n",
              "   ['No Risk', [0.8309863209724426, 0.16901369392871857]],\n",
              "   ['No Risk', [0.8892747759819031, 0.11072523891925812]],\n",
              "   ['No Risk', [0.7456686496734619, 0.2543313801288605]],\n",
              "   ['Risk', [0.22297507524490356, 0.7770249247550964]]]}]"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scoring_params['entity']['scoring'].get('predictions')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Score batch deployment with payload as connected asset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "Simmilary to training use created connection in order to locate tabe in used database."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Creating data asset...\n",
            "SUCCESS\n"
          ]
        }
      ],
      "source": [
        "from ibm_watson_machine_learning.helpers.connections import AssetLocation, DeploymentOutputAssetLocation\n",
        "\n",
        "\n",
        "batch_payload_filename = \"credit_risk_batch_payload.csv\"\n",
        "batch_payload_df.to_csv(batch_payload_filename, index=False)\n",
        "\n",
        "asset_details = client.data_assets.create(\n",
        "            name=batch_payload_filename,\n",
        "            file_path=batch_payload_filename)\n",
        "asset_id = client.data_assets.get_id(asset_details)\n",
        "\n",
        "payload_reference = DataConnection(data_asset_id=asset_id)\n",
        "results_reference = DataConnection(\n",
        "            location=DeploymentOutputAssetLocation(name=\"batch_output_credit_risk.csv\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "Run scoring job for batch deployment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "##########################################################################\n",
            "\n",
            "Synchronous scoring for id: '80664cc6-3bf8-4fae-9309-f8622972a119' started\n",
            "\n",
            "##########################################################################\n",
            "\n",
            "\n",
            "queued.........\n",
            "running.\n",
            "completed\n",
            "Scoring job  '80664cc6-3bf8-4fae-9309-f8622972a119' finished successfully.\n"
          ]
        }
      ],
      "source": [
        "scoring_params = service_batch.run_job(\n",
        "            payload=[payload_reference],\n",
        "            output_data_reference=results_reference,\n",
        "            background_mode=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Deleting deployment\n",
        "You can delete the existing deployment by calling the `service_batch.delete()` command.\n",
        "To list the existing: \n",
        "- batch services you can use `service_batch.list()`, \n",
        "- scoring jobs you can use `service_batch.list_jobs()`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "<a id=\"cleanup\"></a>\n",
        "## 8. Clean up "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "If you want to clean up all created assets:\n",
        "- experiments\n",
        "- trainings\n",
        "- pipelines\n",
        "- model definitions\n",
        "- models\n",
        "- functions\n",
        "- deployments\n",
        "\n",
        "please follow up this sample [notebook](https://github.com/IBM/watson-machine-learning-samples/blob/master/cpd4.7/notebooks/python_sdk/instance-management/Machine%20Learning%20artifacts%20management.ipynb)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "<a id=\"summary\"></a>\n",
        "## 9. Summary and next steps\n",
        "\n",
        " You successfully completed this notebook!.\n",
        " \n",
        " You learned how to use `ibm-watson-machine-learning` to run AutoAI experiments. \n",
        " \n",
        " Check out our _[Online Documentation](https://www.ibm.com/cloud/watson-studio/autoai)_ for more samples, tutorials, documentation, how-tos, and blog posts. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Authors\n",
        "\n",
        "**Lukasz Cmielowski**, PhD, is an Automation Architect and Data Scientist at IBM with a track record of developing enterprise-level applications that substantially increases clients' ability to turn data into actionable knowledge.\n",
        "\n",
        "**Amadeusz Masny**, Python Software Developer in Watson Machine Learning at IBM\n",
        "\n",
        "**Kiran Kate**, Senior Software Engineer at IBM Research AI\n",
        "\n",
        "**Martin Hirzel**, Research Staff Member and Manager at IBM Research AI\n",
        "\n",
        "**Jan Sołtysik**, Intern in Watson Machine Learning "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "Copyright © 2020-2025 IBM. This notebook and its source code are released under the terms of the MIT License."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
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