examples/WatsonOpenScaleAndAzureMLengineExampleOutput.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "<img src=\"https://github.com/pmservice/ai-openscale-tutorials/raw/master/notebooks/images/banner.png\" align=\"left\" alt=\"banner\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Azure Machine Learning Studio engine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows how to monitor the model deployed on Azure Machine Learning Studio using Watson OpenScale python sdk."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contents\n",
    "\n",
    "1. [Setup](#setup)\n",
    "2. [Binding machine learning engine](#binding)\n",
    "3. [Subscriptions](#subsciption)\n",
    "4. [Performance monitor, scoring and payload logging](#scoring)\n",
    "5. [Quality monitor and feedback logging](#feedback)\n",
    "6. [Fairness, Drift monitor and explainability](#monitors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"setup\"></a>\n",
    "# 1. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample model creation using [Azure Machine Learning Studio](https://studio.azureml.net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Copy [credit risk experiment](https://gallery.cortanaintelligence.com/Experiment/German-credit-risk-created-on-1-9-2019) from Azure ML Studio Gallery \n",
    "- Run experiment to train a model\n",
    "- Create (deploy) web service (new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** Classic web services are not supported."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation and authentication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "Successfully installed PyJWT-2.1.0 ibm-cloud-sdk-core-3.5.2 ibm-watson-openscale-3.0.9\n",
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "Successfully installed py4j-0.10.7 pyspark-2.4.0\n",
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "Successfully installed lime-0.2.0.1\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade --upgrade ibm-watson-openscale --no-cache | tail -n 1\n",
    "!pip install --upgrade pyspark==2.4 --no-cache | tail -n 1\n",
    "!pip install --upgrade lime --no-cache | tail -n 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Action:** Restart the kernel (Kernel->Restart)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ACTION: Get Watson OpenScale `apikey`\n",
    "\n",
    "How to install IBM Cloud (bluemix) console: [instruction](https://console.bluemix.net/docs/cli/reference/ibmcloud/download_cli.html#install_use)\n",
    "\n",
    "How to get api key using bluemix console:\n",
    "```\n",
    "bx login --sso\n",
    "bx iam api-key-create 'my_key'\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "CLOUD_API_KEY = '******'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "DB_CREDENTIALS=None\n",
    "#DB_CREDENTIALS= {\"hostname\":\"\",\"username\":\"\",\"password\":\"\",\"database\":\"\",\"port\":\"\",\"ssl\":True,\"sslmode\":\"\",\"certificate_base64\":\"\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "SCHEMA_NAME = 'azure_dm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.0.1'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ibm_cloud_sdk_core.authenticators import IAMAuthenticator,BearerTokenAuthenticator\n",
    "\n",
    "from ibm_watson_openscale import *\n",
    "from ibm_watson_openscale.supporting_classes.enums import *\n",
    "from ibm_watson_openscale.supporting_classes import *\n",
    "\n",
    "\n",
    "authenticator = IAMAuthenticator(apikey=CLOUD_API_KEY)\n",
    "#authenticator = BearerTokenAuthenticator(bearer_token=IAM_TOKEN) ## uncomment if using IAM token\n",
    "wos_client = APIClient(authenticator=authenticator)\n",
    "wos_client.version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Download and preview training data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-07-29 12:36:07--  https://raw.githubusercontent.com/IBM/watson-openscale-samples/main/IBM%20Cloud/WML/assets/data/credit_risk/credit_risk_training.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 694222 (678K) [text/plain]\n",
      "Saving to: ‘credit_risk_training.csv’\n",
      "\n",
      "credit_risk_trainin 100%[===================>] 677.95K  --.-KB/s    in 0.005s  \n",
      "\n",
      "2021-07-29 12:36:08 (128 MB/s) - ‘credit_risk_training.csv’ saved [694222/694222]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!rm -rf credit_risk_training.csv\n",
    "!wget \"https://raw.githubusercontent.com/IBM/watson-openscale-samples/main/IBM%20Cloud/WML/assets/data/credit_risk/credit_risk_training.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "training_data_pd = pd.read_csv(\n",
    "        \"credit_risk_training.csv\",\n",
    "        dtype={'LoanDuration': int, 'LoanAmount': int, 'InstallmentPercent': int, 'CurrentResidenceDuration': int, 'Age': int, 'ExistingCreditsCount': int, 'Dependents': int})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "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>CheckingStatus</th>\n",
       "      <th>LoanDuration</th>\n",
       "      <th>CreditHistory</th>\n",
       "      <th>LoanPurpose</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>ExistingSavings</th>\n",
       "      <th>EmploymentDuration</th>\n",
       "      <th>InstallmentPercent</th>\n",
       "      <th>Sex</th>\n",
       "      <th>OthersOnLoan</th>\n",
       "      <th>...</th>\n",
       "      <th>OwnsProperty</th>\n",
       "      <th>Age</th>\n",
       "      <th>InstallmentPlans</th>\n",
       "      <th>Housing</th>\n",
       "      <th>ExistingCreditsCount</th>\n",
       "      <th>Job</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>ForeignWorker</th>\n",
       "      <th>Risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_to_200</td>\n",
       "      <td>31</td>\n",
       "      <td>credits_paid_to_date</td>\n",
       "      <td>other</td>\n",
       "      <td>1889</td>\n",
       "      <td>100_to_500</td>\n",
       "      <td>less_1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>savings_insurance</td>\n",
       "      <td>32</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>No Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>less_0</td>\n",
       "      <td>18</td>\n",
       "      <td>credits_paid_to_date</td>\n",
       "      <td>car_new</td>\n",
       "      <td>462</td>\n",
       "      <td>less_100</td>\n",
       "      <td>1_to_4</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>savings_insurance</td>\n",
       "      <td>37</td>\n",
       "      <td>stores</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>No Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>less_0</td>\n",
       "      <td>15</td>\n",
       "      <td>prior_payments_delayed</td>\n",
       "      <td>furniture</td>\n",
       "      <td>250</td>\n",
       "      <td>less_100</td>\n",
       "      <td>1_to_4</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real_estate</td>\n",
       "      <td>28</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>No Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0_to_200</td>\n",
       "      <td>28</td>\n",
       "      <td>credits_paid_to_date</td>\n",
       "      <td>retraining</td>\n",
       "      <td>3693</td>\n",
       "      <td>less_100</td>\n",
       "      <td>greater_7</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>savings_insurance</td>\n",
       "      <td>32</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>No Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>no_checking</td>\n",
       "      <td>28</td>\n",
       "      <td>prior_payments_delayed</td>\n",
       "      <td>education</td>\n",
       "      <td>6235</td>\n",
       "      <td>500_to_1000</td>\n",
       "      <td>greater_7</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>unknown</td>\n",
       "      <td>57</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>Risk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  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  ...       OwnsProperty Age  InstallmentPlans Housing  \\\n",
       "0         none  ...  savings_insurance  32              none     own   \n",
       "1         none  ...  savings_insurance  37            stores     own   \n",
       "2         none  ...        real_estate  28              none     own   \n",
       "3         none  ...  savings_insurance  32              none     own   \n",
       "4         none  ...            unknown  57              none     own   \n",
       "\n",
       "  ExistingCreditsCount      Job Dependents  Telephone ForeignWorker     Risk  \n",
       "0                    1  skilled          1       none           yes  No Risk  \n",
       "1                    2  skilled          1       none           yes  No Risk  \n",
       "2                    2  skilled          1        yes            no  No Risk  \n",
       "3                    1  skilled          1       none           yes  No Risk  \n",
       "4                    2  skilled          1       none           yes     Risk  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data_pd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save training data to IBM Cloud Object Storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In next cells, you will need to paste some credentials to Cloud Object Storage. If you haven't worked with COS yet please visit getting started with COS tutorial. You can find COS_API_KEY_ID and COS_RESOURCE_CRN variables in Service Credentials in menu of your COS instance. Used COS Service Credentials must be created with Role parameter set as Writer. Later training data file will be loaded to the bucket of your instance and used as training refecence in subsription. COS_ENDPOINT variable can be found in Endpoint field of the menu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "IAM_URL=\"https://iam.ng.bluemix.net/oidc/token\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "COS_API_KEY_ID = \"*******\n",
    "COS_RESOURCE_CRN = \"*******\" # eg \"crn:v1:bluemix:public:cloud-object-storage:global:a/3bf0d9003abfb5d29761c3e97696b71c:d6f04d83-6c4f-4a62-a165-696756d63903::\"\n",
    "COS_ENDPOINT = \"https://******\" # Current list avaiable at https://control.cloud-object-storage.cloud.ibm.com/v2/endpoints\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a globally unique BUCKET_NAME in COS and use it below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "BUCKET_NAME = \"*****\" #example: \"<your_name>-<date>-credit-risk-training-data\"\n",
    "training_data_file_name=\"credit_risk_training.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ibm_boto3\n",
    "from ibm_botocore.client import Config, ClientError\n",
    "\n",
    "cos_client = ibm_boto3.resource(\"s3\",\n",
    "    ibm_api_key_id=COS_API_KEY_ID,\n",
    "    ibm_service_instance_id=COS_RESOURCE_CRN,\n",
    "    ibm_auth_endpoint=\"https://iam.bluemix.net/oidc/token\",\n",
    "    config=Config(signature_version=\"oauth\"),\n",
    "    endpoint_url=COS_ENDPOINT\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "s3.Object(bucket_name='scottda-openscale-7-29-2021', key='credit_risk_training.csv')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cos_client.Object(BUCKET_NAME, training_data_file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'credit_risk_training.csv'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data_file_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<_io.BufferedReader name='credit_risk_training.csv'>\n"
     ]
    }
   ],
   "source": [
    "with open(training_data_file_name, \"rb\") as file_data:\n",
    "    print(file_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(training_data_file_name, \"rb\") as file_data:\n",
    "    cos_client.Object(BUCKET_NAME, training_data_file_name).upload_fileobj(\n",
    "        Fileobj=file_data\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataMart setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<HTML>\n",
       "        <body>\n",
       "            <h3>Data Marts</h3>\n",
       "            <table style='border: 1px solid #dddddd; font-family: Courier'>\n",
       "                <th style='border: 1px solid #dddddd'>name</th><th style='border: 1px solid #dddddd'>description</th><th style='border: 1px solid #dddddd'>internal_database</th><th style='border: 1px solid #dddddd'>status</th><th style='border: 1px solid #dddddd'>created_at</th><th style='border: 1px solid #dddddd'>id</th>\n",
       "                <tr><td style='border: 1px solid #dddddd'>WOS Data Mart</td><td style='border: 1px solid #dddddd'>Data Mart created by WOS tutorial notebook</td><td style='border: 1px solid #dddddd'>True</td><td style='border: 1px solid #dddddd'>active</td><td style='border: 1px solid #dddddd'>2021-07-27 18:41:18.400000+00:00</td><td style='border: 1px solid #dddddd'>a08b0e60-6844-4c14-81cb-a3a6c9d199f9</td></tr>\n",
       "            </table>\n",
       "        </body>\n",
       "        </HTML>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wos_client.data_marts.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using existing datamart a08b0e60-6844-4c14-81cb-a3a6c9d199f9\n"
     ]
    }
   ],
   "source": [
    "data_marts = wos_client.data_marts.list().result.data_marts\n",
    "if len(data_marts) == 0:\n",
    "    if DB_CREDENTIALS is not None:\n",
    "        if SCHEMA_NAME is None: \n",
    "            print(\"Please specify the SCHEMA_NAME and rerun the cell\")\n",
    "\n",
    "        print('Setting up external datamart')\n",
    "        added_data_mart_result = wos_client.data_marts.add(\n",
    "                background_mode=False,\n",
    "                name=\"WOS Data Mart\",\n",
    "                description=\"Data Mart created by WOS tutorial notebook\",\n",
    "                database_configuration=DatabaseConfigurationRequest(\n",
    "                  database_type=DatabaseType.POSTGRESQL,\n",
    "                    credentials=PrimaryStorageCredentialsLong(\n",
    "                        hostname=DB_CREDENTIALS['hostname'],\n",
    "                        username=DB_CREDENTIALS['username'],\n",
    "                        password=DB_CREDENTIALS['password'],\n",
    "                        db=DB_CREDENTIALS['database'],\n",
    "                        port=DB_CREDENTIALS['port'],\n",
    "                        ssl=True,\n",
    "                        sslmode=DB_CREDENTIALS['sslmode'],\n",
    "                        certificate_base64=DB_CREDENTIALS['certificate_base64']\n",
    "                    ),\n",
    "                    location=LocationSchemaName(\n",
    "                        schema_name= SCHEMA_NAME\n",
    "                    )\n",
    "                )\n",
    "             ).result\n",
    "    else:\n",
    "        print('Setting up internal datamart')\n",
    "        added_data_mart_result = wos_client.data_marts.add(\n",
    "                background_mode=False,\n",
    "                name=\"WOS Data Mart\",\n",
    "                description=\"Data Mart created by WOS tutorial notebook\", \n",
    "                internal_database = True).result\n",
    "        \n",
    "    data_mart_id = added_data_mart_result.metadata.id\n",
    "    \n",
    "else:\n",
    "    data_mart_id=data_marts[0].metadata.id\n",
    "    print('Using existing datamart {}'.format(data_mart_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"binding\"></a>\n",
    "# 2. Bind machine learning engines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bind  `Azure` machine learning studio\n",
    "\n",
    "Provide credentials using following fields:\n",
    "- `client_id`\n",
    "- `client_secret`\n",
    "- `subscription_id`\n",
    "- `tenant`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "AZURE_ENGINE_CREDENTIALS =  {\n",
    "    \"client_id\": \"******\",\n",
    "    \"client_secret\": \"******\",\n",
    "    \"tenant\": \"******\",\n",
    "    \"subscription_id\": \"******\"\n",
    "  }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "SERVICE_PROVIDER_NAME = \"Azure Machine Learning\"\n",
    "SERVICE_PROVIDER_DESCRIPTION = \"Added by Azure tutorial WOS notebook.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted existing service_provider for WML instance: 8fcc9236-fd3e-40f8-be8f-f18d30e7178e\n"
     ]
    }
   ],
   "source": [
    "service_providers = wos_client.service_providers.list().result.service_providers\n",
    "for service_provider in service_providers:\n",
    "    service_instance_name = service_provider.entity.name\n",
    "    if service_instance_name == SERVICE_PROVIDER_NAME:\n",
    "        service_provider_id = service_provider.metadata.id\n",
    "        wos_client.service_providers.delete(service_provider_id)\n",
    "        print(\"Deleted existing service_provider for WML instance: {}\".format(service_provider_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=================================================================================\n",
      "\n",
      " Waiting for end of adding service provider ba1b62a0-2a80-414e-9177-bcf71d5a8909 \n",
      "\n",
      "=================================================================================\n",
      "\n",
      "\n",
      "\n",
      "active\n",
      "\n",
      "-----------------------------------------------\n",
      " Successfully finished adding service provider \n",
      "-----------------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "added_service_provider_result = wos_client.service_providers.add(\n",
    "        name=SERVICE_PROVIDER_NAME,\n",
    "        description=SERVICE_PROVIDER_DESCRIPTION,\n",
    "        service_type=ServiceTypes.AZURE_MACHINE_LEARNING,\n",
    "        credentials=AzureCredentials(\n",
    "            subscription_id= AZURE_ENGINE_CREDENTIALS['subscription_id'], \n",
    "            client_id = AZURE_ENGINE_CREDENTIALS['client_id'], \n",
    "            client_secret= AZURE_ENGINE_CREDENTIALS['client_secret'],\n",
    "            tenant = AZURE_ENGINE_CREDENTIALS['tenant']\n",
    "        ),\n",
    "        background_mode=False\n",
    "    ).result\n",
    "service_provider_id = added_service_provider_result.metadata.id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<HTML>\n",
       "        <body>\n",
       "            <h3>Service Providers</h3>\n",
       "            <table style='border: 1px solid #dddddd; font-family: Courier'>\n",
       "                <th style='border: 1px solid #dddddd'>instance_id</th><th style='border: 1px solid #dddddd'>status</th><th style='border: 1px solid #dddddd'>name</th><th style='border: 1px solid #dddddd'>service_type</th><th style='border: 1px solid #dddddd'>created_at</th><th style='border: 1px solid #dddddd'>id</th>\n",
       "                <tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>active</td><td style='border: 1px solid #dddddd'>Azure Machine Learning</td><td style='border: 1px solid #dddddd'>azure_machine_learning</td><td style='border: 1px solid #dddddd'>2021-07-29 12:50:37.092000+00:00</td><td style='border: 1px solid #dddddd'>ba1b62a0-2a80-414e-9177-bcf71d5a8909</td></tr>\n",
       "            </table>\n",
       "        </body>\n",
       "        </HTML>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wos_client.service_providers.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'resources': [{'metadata': {'guid': 'f33b3eddae0d51f790fe883f221bdaf2',\n",
       "    'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/acd8abca89e54be0a61a1da62a87224a/execute?api-version=2.0&format=swagger',\n",
       "    'created_at': '2019-05-06T11:33:11.2195519Z',\n",
       "    'modified_at': '2019-05-06T11:33:30.3979827Z'},\n",
       "   'entity': {'name': 'adult.census.binary',\n",
       "    'deployment_rn': '/subscriptions/744bca72-2299-451c-b682-ed6fb75fb671/resourceGroups/bias_explainability/providers/Microsoft.MachineLearning/webServices/adult.census.binary',\n",
       "    'type': 'online',\n",
       "    'description': '',\n",
       "    'scoring_endpoint': {'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/acd8abca89e54be0a61a1da62a87224a/execute?api-version=2.0&format=swagger',\n",
       "     'credentials': {'token': '3glK6bPnKySmatyPOeh9gPvM4MaB8J8oOH/1Ea17Qm4A/KMLsUQ4angBmzH6GUGTLfN9dQMY/wUVNJ7UpgJYzA=='},\n",
       "     'request_headers': {'Content-Type': 'application/json'}},\n",
       "    'asset': {'asset_id': 'c08abd85de8de5a1cf691a3535d28a2e',\n",
       "     'asset_rn': 'adult.census.binary',\n",
       "     'url': 'https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/acd8abca89e54be0a61a1da62a87224a/swagger.json',\n",
       "     'name': 'adult.census.binary',\n",
       "     'asset_type': 'model',\n",
       "     'created_at': '2019-05-06T11:33:11.2195519Z'},\n",
       "    'asset_properties': {'problem_type': 'binary',\n",
       "     'asset_revision': '1561122207000',\n",
       "     'input_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'age', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'workclass', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'fnlwgt', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'education', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'education-num', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'marital-status', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'occupation', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'relationship', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'race', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'sex', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'capital-gain', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'capital-loss', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'hours-per-week', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'native-country', 'type': 'string', 'nullable': False}]},\n",
       "     'output_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'age', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'workclass', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'fnlwgt', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'education', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'education-num', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'marital-status', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'occupation', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'relationship', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'race', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'sex', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'capital-gain', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'capital-loss', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'hours-per-week', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'native-country', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Scored Labels',\n",
       "        'type': 'string',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'prediction'}},\n",
       "       {'name': 'Scored Probabilities',\n",
       "        'type': 'double',\n",
       "        'nullable': True,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'ProbabilityVector',\n",
       "        'type': {'type': 'array',\n",
       "         'elementType': 'double',\n",
       "         'containsNull': True},\n",
       "        'nullable': True,\n",
       "        'metadata': {'modeling_role': 'probability'}}]},\n",
       "     'prediction_field': 'Scored Labels',\n",
       "     'probability_fields': ['Scored Probabilities']}}},\n",
       "  {'metadata': {'guid': '627490e93cfb9cd6a4f50888126b9c08',\n",
       "    'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/012a1de54537430ea745fb37bb96c156/execute?api-version=2.0&format=swagger',\n",
       "    'created_at': '2020-07-16T04:04:47.0459261Z',\n",
       "    'modified_at': '2020-07-16T04:05:03.7328445Z'},\n",
       "   'entity': {'name': 'AdultCensusIB',\n",
       "    'deployment_rn': '/subscriptions/744bca72-2299-451c-b682-ed6fb75fb671/resourceGroups/bias_explainability/providers/Microsoft.MachineLearning/webServices/AdultCensusIB',\n",
       "    'type': 'online',\n",
       "    'description': '',\n",
       "    'scoring_endpoint': {'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/012a1de54537430ea745fb37bb96c156/execute?api-version=2.0&format=swagger',\n",
       "     'credentials': {'token': 'O2PKHlhX40hF/i7qDGQXTuxm63ywXHAEVjyk0VIxzGDf/bhel5eJDWj0xpVfVQT1SCjdl2cSAo1WpynYi/YxIw=='},\n",
       "     'request_headers': {'Content-Type': 'application/json'}},\n",
       "    'asset': {'asset_id': 'c667be1d57dcfc12eab8f58bf328f0fc',\n",
       "     'asset_rn': 'AdultCensusIB',\n",
       "     'url': 'https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/012a1de54537430ea745fb37bb96c156/swagger.json',\n",
       "     'name': 'AdultCensusIB',\n",
       "     'asset_type': 'model',\n",
       "     'created_at': '2020-07-16T04:04:47.0459261Z'},\n",
       "    'asset_properties': {'problem_type': 'binary',\n",
       "     'asset_revision': '1602200745000',\n",
       "     'input_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'age', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'workclass', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'fnlwgt', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'education', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'education-num', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Marital', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'occupation', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'relationship', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'race', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'sex', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'capitalgain', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'loss', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'hoursper', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'citizen_status', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'label', 'type': 'string', 'nullable': False}]},\n",
       "     'output_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'workclass', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'fnlwgt', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'education', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'education-num', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Marital', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'occupation', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'relationship', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'capitalgain', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'loss', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'hoursper', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'citizen_status', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'label', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Scored Labels',\n",
       "        'type': 'string',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'prediction'}},\n",
       "       {'name': 'Scored Probabilities',\n",
       "        'type': 'double',\n",
       "        'nullable': True,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'ProbabilityVector',\n",
       "        'type': {'type': 'array',\n",
       "         'elementType': 'double',\n",
       "         'containsNull': True},\n",
       "        'nullable': True,\n",
       "        'metadata': {'modeling_role': 'probability'}}]},\n",
       "     'prediction_field': 'Scored Labels',\n",
       "     'probability_fields': ['Scored Probabilities']}}},\n",
       "  {'metadata': {'guid': 'e726bb31ef973e0a80cd3e9d9cefa86f',\n",
       "    'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/b0ea1e59b97a40c7aa3fff9a26b4ed75/execute?api-version=2.0&format=swagger',\n",
       "    'created_at': '2019-05-08T08:09:09.3483819Z',\n",
       "    'modified_at': '2019-05-08T08:09:26.0200883Z'},\n",
       "   'entity': {'name': 'adultcensusincome.logistic.regression',\n",
       "    'deployment_rn': '/subscriptions/744bca72-2299-451c-b682-ed6fb75fb671/resourceGroups/bias_explainability/providers/Microsoft.MachineLearning/webServices/adultcensusincome.logistic.regression',\n",
       "    'type': 'online',\n",
       "    'description': '',\n",
       "    'scoring_endpoint': {'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/b0ea1e59b97a40c7aa3fff9a26b4ed75/execute?api-version=2.0&format=swagger',\n",
       "     'credentials': {'token': 'n7nDhTrnTgdLwPRvo4Oyw67pe80sdsyVz5K2SVl8sHFQ6yMLx8qbxOMgnoufJRKxfV2Vcfa8nZB/BtuKkQr72A=='},\n",
       "     'request_headers': {'Content-Type': 'application/json'}},\n",
       "    'asset': {'asset_id': '417532dc6dae852809393ae097703de5',\n",
       "     'asset_rn': 'adultcensusincome.logistic.regression',\n",
       "     'url': 'https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/b0ea1e59b97a40c7aa3fff9a26b4ed75/swagger.json',\n",
       "     'name': 'adultcensusincome.logistic.regression',\n",
       "     'asset_type': 'model',\n",
       "     'created_at': '2019-05-08T08:09:09.3483819Z'},\n",
       "    'asset_properties': {'problem_type': 'binary',\n",
       "     'asset_revision': '1557503823000',\n",
       "     'input_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'age', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'workclass', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'education', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'marital-status', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'occupation', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'relationship', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'race', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'sex', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'capital-gain', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'capital-loss', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'hours-per-week', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'native-country', 'type': 'string', 'nullable': False}]},\n",
       "     'output_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'age', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'workclass', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'education', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'marital-status', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'occupation', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'relationship', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'race', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'sex', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'capital-gain', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'capital-loss', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'hours-per-week', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'native-country', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Scored Labels',\n",
       "        'type': 'string',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'prediction'}},\n",
       "       {'name': 'Scored Probabilities',\n",
       "        'type': 'double',\n",
       "        'nullable': True,\n",
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       "     'prediction_field': 'Scored Labels'}}},\n",
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       "       {'name': 'OthersOnLoan', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'CurrentResidenceDuration',\n",
       "        'type': 'integer',\n",
       "        'nullable': False},\n",
       "       {'name': 'OwnsProperty', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Age', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'InstallmentPlans', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Housing', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'ExistingCreditsCount', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Job', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Dependents', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Telephone', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'ForeignWorker', 'type': 'string', 'nullable': False}]},\n",
       "     'output_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'CheckingStatus',\n",
       "        'type': 'string',\n",
       "        'nullable': False},\n",
       "       {'name': 'LoanDuration', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'CreditHistory', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'LoanPurpose', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'LoanAmount', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'ExistingSavings', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'EmploymentDuration', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'InstallmentPercent', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Sex', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'OthersOnLoan', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'CurrentResidenceDuration',\n",
       "        'type': 'integer',\n",
       "        'nullable': False},\n",
       "       {'name': 'OwnsProperty', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Age', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'InstallmentPlans', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Housing', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'ExistingCreditsCount', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Job', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Dependents', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Telephone', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'ForeignWorker', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Scored Labels',\n",
       "        'type': 'string',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'prediction'}},\n",
       "       {'name': 'Scored Probabilities',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'ProbabilityVector',\n",
       "        'type': {'type': 'array',\n",
       "         'elementType': 'double',\n",
       "         'containsNull': False},\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'probability'}}]},\n",
       "     'prediction_field': 'Scored Labels',\n",
       "     'probability_fields': ['Scored Probabilities']}}},\n",
       "  {'metadata': {'guid': '1d51f63dd7a231c4e3b40e9f0c22474a',\n",
       "    'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/3c75e67cd7724193a8434a876cbf3748/execute?api-version=2.0&format=swagger',\n",
       "    'created_at': '2018-12-21T09:22:14.1837647Z',\n",
       "    'modified_at': '2018-12-21T09:35:36.1081856Z'},\n",
       "   'entity': {'name': 'DPJC-Titanic-Retraining.2018.12.21.9.21.43.439',\n",
       "    'deployment_rn': '/subscriptions/744bca72-2299-451c-b682-ed6fb75fb671/resourceGroups/ai-ops-squad/providers/Microsoft.MachineLearning/webServices/DPJC-Titanic-Retraining.2018.12.21.9.21.43.439',\n",
       "    'type': 'online',\n",
       "    'description': '',\n",
       "    'scoring_endpoint': {'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/3c75e67cd7724193a8434a876cbf3748/execute?api-version=2.0&format=swagger',\n",
       "     'credentials': {'token': 'P7VtKZGDtApnoCIh7epkfKiEk4fKOKvK04bizZxOnEE0mBbVpHBaf8NRmP2qrkA1AZQN5GOQ2iuT3hkjfeULRQ=='},\n",
       "     'request_headers': {'Content-Type': 'application/json'}},\n",
       "    'asset': {'asset_id': 'bc108cd66382ae734b60ae853c5242a1',\n",
       "     'asset_rn': 'DPJC-Titanic-Retraining.2018.12.21.9.21.43.439',\n",
       "     'url': 'https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/3c75e67cd7724193a8434a876cbf3748/swagger.json',\n",
       "     'name': 'DPJC-Titanic-Retraining.2018.12.21.9.21.43.439',\n",
       "     'asset_type': 'model',\n",
       "     'created_at': '2018-12-21T09:22:14.1837647Z'},\n",
       "    'asset_properties': {'problem_type': 'regression',\n",
       "     'asset_revision': '1546466756000',\n",
       "     'input_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'PassengerId', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Survived', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Pclass', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Name', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Sex', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Age', 'type': 'double', 'nullable': True},\n",
       "       {'name': 'SibSp', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Parch', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Ticket', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Fare', 'type': 'double', 'nullable': False},\n",
       "       {'name': 'Cabin', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Embarked', 'type': 'string', 'nullable': False}]},\n",
       "     'output_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'Accuracy', 'type': 'double', 'nullable': False},\n",
       "       {'name': 'Precision', 'type': 'double', 'nullable': False},\n",
       "       {'name': 'Recall', 'type': 'double', 'nullable': False},\n",
       "       {'name': 'F-Score', 'type': 'double', 'nullable': False},\n",
       "       {'name': 'AUC', 'type': 'double', 'nullable': False},\n",
       "       {'name': 'Average Log Loss', 'type': 'double', 'nullable': False},\n",
       "       {'name': 'Training Log Loss', 'type': 'double', 'nullable': False}]}}}},\n",
       "  {'metadata': {'guid': 'c749bd77f4c4951d4fdcf386fc0fcd5e',\n",
       "    'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/72e846162959473b884480ed0d184179/execute?api-version=2.0&format=swagger',\n",
       "    'created_at': '2018-12-19T10:24:03.9383824Z',\n",
       "    'modified_at': '2018-12-21T09:20:34.3251679Z'},\n",
       "   'entity': {'name': 'DPJC-Titanic.2018.12.19.10.23.28.594',\n",
       "    'deployment_rn': '/subscriptions/744bca72-2299-451c-b682-ed6fb75fb671/resourceGroups/ai-ops-squad/providers/Microsoft.MachineLearning/webServices/DPJC-Titanic.2018.12.19.10.23.28.594',\n",
       "    'type': 'online',\n",
       "    'description': '',\n",
       "    'scoring_endpoint': {'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/72e846162959473b884480ed0d184179/execute?api-version=2.0&format=swagger',\n",
       "     'credentials': {'token': 'Iu7rOjtkH9t82KPMMe5I48ZvX5PXpHT/j9Iie4HdHsZYhVSg8RvrZgfBbJHN5pugUuZ+qRwk770AmmhuZ5WnXA=='},\n",
       "     'request_headers': {'Content-Type': 'application/json'}},\n",
       "    'asset': {'asset_id': '12df12e7f50e1114978bcd54471381d0',\n",
       "     'asset_rn': 'DPJC-Titanic.2018.12.19.10.23.28.594',\n",
       "     'url': 'https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/72e846162959473b884480ed0d184179/swagger.json',\n",
       "     'name': 'DPJC-Titanic.2018.12.19.10.23.28.594',\n",
       "     'asset_type': 'model',\n",
       "     'created_at': '2018-12-19T10:24:03.9383824Z'},\n",
       "    'asset_properties': {'problem_type': 'binary',\n",
       "     'asset_revision': '1548635679000',\n",
       "     'input_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'PassengerId', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Pclass', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Name', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Sex', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Age', 'type': 'double', 'nullable': True},\n",
       "       {'name': 'SibSp', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Parch', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Ticket', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Fare', 'type': 'double', 'nullable': False},\n",
       "       {'name': 'Cabin', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Embarked', 'type': 'string', 'nullable': False}]},\n",
       "     'output_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'PassengerId', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Pclass', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Name', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Sex', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Age', 'type': 'double', 'nullable': True},\n",
       "       {'name': 'SibSp', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Parch', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Ticket', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Fare', 'type': 'double', 'nullable': False},\n",
       "       {'name': 'Cabin', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Embarked', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Scored Labels',\n",
       "        'type': 'integer',\n",
       "        'nullable': True,\n",
       "        'metadata': {'modeling_role': 'prediction'}},\n",
       "       {'name': 'Scored Probabilities',\n",
       "        'type': 'double',\n",
       "        'nullable': True,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'ProbabilityVector',\n",
       "        'type': {'type': 'array',\n",
       "         'elementType': 'double',\n",
       "         'containsNull': True},\n",
       "        'nullable': True,\n",
       "        'metadata': {'modeling_role': 'probability'}}]},\n",
       "     'prediction_field': 'Scored Labels',\n",
       "     'probability_fields': ['Scored Probabilities']}}},\n",
       "  {'metadata': {'guid': 'b246d2d772ffcde97019ac6d0c0af994',\n",
       "    'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/96b59ce7c0734dc88f1147097e12f552/execute?api-version=2.0&format=swagger',\n",
       "    'created_at': '2019-02-13T08:06:27.6046787Z',\n",
       "    'modified_at': '2019-02-13T08:07:21.6247869Z'},\n",
       "   'entity': {'name': 'ExecutePythonScr.2019.2.13.8.6.20.493',\n",
       "    'deployment_rn': '/subscriptions/744bca72-2299-451c-b682-ed6fb75fb671/resourceGroups/ai-ops-squad/providers/Microsoft.MachineLearning/webServices/ExecutePythonScr.2019.2.13.8.6.20.493',\n",
       "    'type': 'online',\n",
       "    'description': 'This experiment demonstrates how to use execute python script module to perform a simple nature language processing task - tokenize on the amazon book review dataset.',\n",
       "    'scoring_endpoint': {'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/96b59ce7c0734dc88f1147097e12f552/execute?api-version=2.0&format=swagger',\n",
       "     'credentials': {'token': 'AENya7Rh3yUcQ533+jhjAkqzZGwCjgNBXG4cSdtjeh6UJbaONjdMgAG9HmXqjDRiCQtUfLqj+WmkaOALSkwIaQ=='},\n",
       "     'request_headers': {'Content-Type': 'application/json'}},\n",
       "    'asset': {'asset_id': '7d56bf771bfcd6d9b7d9a58bf3fdf24d',\n",
       "     'asset_rn': 'ExecutePythonScr.2019.2.13.8.6.20.493',\n",
       "     'url': 'https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/96b59ce7c0734dc88f1147097e12f552/swagger.json',\n",
       "     'name': 'ExecutePythonScr.2019.2.13.8.6.20.493',\n",
       "     'asset_type': 'model',\n",
       "     'created_at': '2019-02-13T08:06:27.6046787Z'},\n",
       "    'asset_properties': {'problem_type': 'regression',\n",
       "     'asset_revision': '1556293089000',\n",
       "     'input_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'Col1', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Col2', 'type': 'string', 'nullable': False}]},\n",
       "     'output_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'Standard Output',\n",
       "        'type': 'string',\n",
       "        'nullable': False},\n",
       "       {'name': 'Graphics', 'type': 'string', 'nullable': False}]}}}},\n",
       "  {'metadata': {'guid': '6145fc17a53dc0223d90aaf45b7cd2b7',\n",
       "    'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/76b0363585584fcbb4ec7a16aa62dd86/execute?api-version=2.0&format=swagger',\n",
       "    'created_at': '2019-02-01T17:17:55.575148Z',\n",
       "    'modified_at': '2019-02-01T17:18:15.5669353Z'},\n",
       "   'entity': {'name': 'FastpathGermanCreditRisk.2019.2.1.17.17.21.412',\n",
       "    'deployment_rn': '/subscriptions/744bca72-2299-451c-b682-ed6fb75fb671/resourceGroups/ai-ops-squad/providers/Microsoft.MachineLearning/webServices/FastpathGermanCreditRisk.2019.2.1.17.17.21.412',\n",
       "    'type': 'online',\n",
       "    'description': '',\n",
       "    'scoring_endpoint': {'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/76b0363585584fcbb4ec7a16aa62dd86/execute?api-version=2.0&format=swagger',\n",
       "     'credentials': {'token': 'hHISV39Lh7k4z56QHu8O49gYxbvGOyJrRqsT6MXifczS6KwIoLi7K695TM4GLP+x+2eqYCJ61OmG8GdUFIGXRA=='},\n",
       "     'request_headers': {'Content-Type': 'application/json'}},\n",
       "    'asset': {'asset_id': '4fff12e2b55fe1038b2e04d98be759c5',\n",
       "     'asset_rn': 'FastpathGermanCreditRisk.2019.2.1.17.17.21.412',\n",
       "     'url': 'https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/76b0363585584fcbb4ec7a16aa62dd86/swagger.json',\n",
       "     'name': 'FastpathGermanCreditRisk.2019.2.1.17.17.21.412',\n",
       "     'asset_type': 'model',\n",
       "     'created_at': '2019-02-01T17:17:55.575148Z'},\n",
       "    'asset_properties': {'problem_type': 'binary',\n",
       "     'asset_revision': '1554710833000',\n",
       "     'input_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'CheckingStatus',\n",
       "        'type': 'string',\n",
       "        'nullable': False},\n",
       "       {'name': 'LoanDuration', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'CreditHistory', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'LoanPurpose', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'LoanAmount', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'ExistingSavings', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'EmploymentDuration', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'InstallmentPercent', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Sex', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'OthersOnLoan', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'CurrentResidenceDuration',\n",
       "        'type': 'integer',\n",
       "        'nullable': False},\n",
       "       {'name': 'OwnsProperty', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Age', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'InstallmentPlans', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Housing', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'ExistingCreditsCount', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Job', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Dependents', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Telephone', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'ForeignWorker', 'type': 'string', 'nullable': False}]},\n",
       "     'output_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'CheckingStatus',\n",
       "        'type': 'string',\n",
       "        'nullable': False},\n",
       "       {'name': 'LoanDuration', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'CreditHistory', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'LoanPurpose', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'LoanAmount', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'ExistingSavings', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'EmploymentDuration', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'InstallmentPercent', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Sex', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'OthersOnLoan', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'CurrentResidenceDuration',\n",
       "        'type': 'integer',\n",
       "        'nullable': False},\n",
       "       {'name': 'OwnsProperty', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Age', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'InstallmentPlans', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Housing', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'ExistingCreditsCount', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Job', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Dependents', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Telephone', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'ForeignWorker', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Scored Labels',\n",
       "        'type': 'string',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'prediction'}},\n",
       "       {'name': 'Scored Probabilities',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'ProbabilityVector',\n",
       "        'type': {'type': 'array',\n",
       "         'elementType': 'double',\n",
       "         'containsNull': False},\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'probability'}}]},\n",
       "     'prediction_field': 'Scored Labels',\n",
       "     'probability_fields': ['Scored Probabilities']}}},\n",
       "  {'metadata': {'guid': '29c5a6bbdb6bba0277cc4bb6f48ea725',\n",
       "    'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/676ff47f4bdd40a385dd1404de121856/execute?api-version=2.0&format=swagger',\n",
       "    'created_at': '2019-05-15T09:52:01.6785081Z',\n",
       "    'modified_at': '2019-05-15T09:52:12.167899Z'},\n",
       "   'entity': {'name': 'forestcover.multiclass',\n",
       "    'deployment_rn': '/subscriptions/744bca72-2299-451c-b682-ed6fb75fb671/resourceGroups/bias_explainability/providers/Microsoft.MachineLearning/webServices/forestcover.multiclass',\n",
       "    'type': 'online',\n",
       "    'description': '',\n",
       "    'scoring_endpoint': {'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/676ff47f4bdd40a385dd1404de121856/execute?api-version=2.0&format=swagger',\n",
       "     'credentials': {'token': 'fkxm6kqL0CxIaOGhE74qRG0k9wcDYMMlzvTiqdEfiak0bxSwxkzkzLEiI8p2tBzibAzqjkGiI1XjZh51j/e1lQ=='},\n",
       "     'request_headers': {'Content-Type': 'application/json'}},\n",
       "    'asset': {'asset_id': '8b3fa6de5b35bb77fdc640877f5ee16b',\n",
       "     'asset_rn': 'forestcover.multiclass',\n",
       "     'url': 'https://ussouthcentral.services.azureml.net/subscriptions/744bca722299451cb682ed6fb75fb671/services/676ff47f4bdd40a385dd1404de121856/swagger.json',\n",
       "     'name': 'forestcover.multiclass',\n",
       "     'asset_type': 'model',\n",
       "     'created_at': '2019-05-15T09:52:01.6785081Z'},\n",
       "    'asset_properties': {'problem_type': 'multiclass',\n",
       "     'asset_revision': '1558081819000',\n",
       "     'input_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'Elevation', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Aspect', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'Slope', 'type': 'integer', 'nullable': False},\n",
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       "     'output_data_schema': {'type': 'struct',\n",
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       "       {'name': 'f434', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f435', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f436', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f437', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f438', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f439', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f440', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f441', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f442', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f443', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f444', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f445', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f446', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f447', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f448', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f449', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f450', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f451', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f452', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f453', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f454', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f455', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f456', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f457', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f458', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f459', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f460', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f461', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f462', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f463', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f464', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f465', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f466', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f467', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f468', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f469', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f470', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f471', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f472', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f473', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f474', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f475', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f476', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f477', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f478', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f479', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f480', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f482', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f483', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f484', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f485', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f486', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f487', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f488', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f489', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f490', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f491', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f492', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f493', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f494', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f495', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f496', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f497', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f498', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f499', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f500', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f501', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f502', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f503', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f504', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f505', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f506', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f507', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f508', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f509', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f510', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f511', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f512', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f513', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f514', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f515', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f516', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f517', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f519', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f524', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f525', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f531', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f533', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f540', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f542', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f555', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f556', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f678', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f683', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f685', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f686', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f687', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f688', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f689', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f690', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f692', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f693', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f694', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f695', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f696', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f697', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'f699', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f700', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f701', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f702', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f703', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f704', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f705', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f706', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f707', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f708', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f709', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f710', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f711', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f712', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f713', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f714', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f715', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f716', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f717', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f718', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f719', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f720', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f721', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f722', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f723', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f724', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f725', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f726', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f727', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f728', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f729', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f730', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f731', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f732', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f733', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f734', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f735', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f736', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f737', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f738', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f739', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f740', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'f741', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'Scored Labels',\n",
       "        'type': 'string',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'prediction'}},\n",
       "       {'name': 'Scored Probabilities',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'ProbabilityVector',\n",
       "        'type': {'type': 'array',\n",
       "         'elementType': 'double',\n",
       "         'containsNull': False},\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'probability'}}]},\n",
       "     'prediction_field': 'Scored Labels',\n",
       "     'probability_fields': ['Scored Probabilities']}}},\n",
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       "    'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/1e37d868a2b04ffda2fb769493b181c8/execute?api-version=2.0&format=swagger',\n",
       "    'created_at': '2018-11-29T13:44:14.5020986Z',\n",
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       "    'type': 'online',\n",
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       "    'scoring_endpoint': {'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/1e37d868a2b04ffda2fb769493b181c8/execute?api-version=2.0&format=swagger',\n",
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       "       {'name': 'f689', 'type': 'integer', 'nullable': False},\n",
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       "       {'name': 'Scored Probabilities for Class \"0\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"1\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"2\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"3\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"4\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"5\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"6\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"7\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"8\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"9\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Labels',\n",
       "        'type': 'integer',\n",
       "        'nullable': False,\n",
       "        'metadata': {'values': ['0',\n",
       "          '1',\n",
       "          '2',\n",
       "          '3',\n",
       "          '4',\n",
       "          '5',\n",
       "          '6',\n",
       "          '7',\n",
       "          '8',\n",
       "          '9'],\n",
       "         'modeling_role': 'prediction'}},\n",
       "       {'name': 'ProbabilityVector',\n",
       "        'type': {'type': 'array',\n",
       "         'elementType': 'double',\n",
       "         'containsNull': False},\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'probability'}}]},\n",
       "     'prediction_field': 'Scored Labels',\n",
       "     'probability_fields': ['Scored Probabilities for Class \"0\"',\n",
       "      'Scored Probabilities for Class \"1\"',\n",
       "      'Scored Probabilities for Class \"2\"',\n",
       "      'Scored Probabilities for Class \"3\"',\n",
       "      'Scored Probabilities for Class \"4\"',\n",
       "      'Scored Probabilities for Class \"5\"',\n",
       "      'Scored Probabilities for Class \"6\"',\n",
       "      'Scored Probabilities for Class \"7\"',\n",
       "      'Scored Probabilities for Class \"8\"',\n",
       "      'Scored Probabilities for Class \"9\"']}}},\n",
       "  {'metadata': {'guid': 'e02ccf6dc31219ad5044ab4baa803310',\n",
       "    'url': 'https://ussouthcentral.services.azureml.net:443/subscriptions/744bca722299451cb682ed6fb75fb671/services/3998cb2d73c34734945a198ff67242dc/execute?api-version=2.0&format=swagger',\n",
       "    'created_at': '2019-01-05T05:55:41.6061263Z',\n",
       "    'modified_at': '2019-01-05T05:55:54.0145301Z'},\n",
       "   'entity': {'name': 'MultiClass-ProductLineTestAutomation',\n",
       "    'deployment_rn': '/subscriptions/744bca72-2299-451c-b682-ed6fb75fb671/resourceGroups/bias_explainability/providers/Microsoft.MachineLearning/webServices/MultiClass-ProductLineTestAutomation',\n",
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       "     'asset_revision': '1550100651000',\n",
       "     'input_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'GENDER', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'AGE', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'MARITAL_STATUS', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'PROFESSION', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'PRODUCT_LINE', 'type': 'string', 'nullable': False}]},\n",
       "     'output_data_schema': {'type': 'struct',\n",
       "      'fields': [{'name': 'GENDER', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'AGE', 'type': 'integer', 'nullable': False},\n",
       "       {'name': 'MARITAL_STATUS', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'PROFESSION', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'PRODUCT_LINE', 'type': 'string', 'nullable': False},\n",
       "       {'name': 'Scored Probabilities for Class \"Camping Equipment\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"Golf Equipment\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"Mountaineering Equipment\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"Outdoor Protection\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Probabilities for Class \"Personal Accessories\"',\n",
       "        'type': 'double',\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'class_probability'}},\n",
       "       {'name': 'Scored Labels',\n",
       "        'type': 'string',\n",
       "        'nullable': False,\n",
       "        'metadata': {'values': ['Camping Equipment',\n",
       "          'Golf Equipment',\n",
       "          'Mountaineering Equipment',\n",
       "          'Outdoor Protection',\n",
       "          'Personal Accessories'],\n",
       "         'modeling_role': 'prediction'}},\n",
       "       {'name': 'ProbabilityVector',\n",
       "        'type': {'type': 'array',\n",
       "         'elementType': 'double',\n",
       "         'containsNull': False},\n",
       "        'nullable': False,\n",
       "        'metadata': {'modeling_role': 'probability'}}]},\n",
       "     'prediction_field': 'Scored Labels',\n",
       "     'probability_fields': ['Scored Probabilities for Class \"Camping Equipment\"',\n",
       "      'Scored Probabilities for Class \"Golf Equipment\"',\n",
       "      'Scored Probabilities for Class \"Mountaineering Equipment\"',\n",
       "      'Scored Probabilities for Class \"Outdoor Protection\"',\n",
       "      'Scored Probabilities for Class \"Personal Accessories\"']}}}],\n",
       " 'count': 67}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asset_deployment_details = wos_client.service_providers.list_assets(data_mart_id=data_mart_id, service_provider_id=service_provider_id).result\n",
    "asset_deployment_details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Add deployment ID from `asset_deployment_details` above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "## select your model deployment GUID and get model asset details\n",
    "deployment_id='******'\n",
    "for model_asset_details in asset_deployment_details['resources']:\n",
    "    if model_asset_details['metadata']['guid']==deployment_id:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sample scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"Results\": {\n",
      "    \"output1\": [\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"19\",\n",
      "        \"CreditHistory\": \"credits_paid_to_date\",\n",
      "        \"LoanPurpose\": \"business\",\n",
      "        \"LoanAmount\": \"1867\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"1_to_4\",\n",
      "        \"InstallmentPercent\": \"4\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"3\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"42\",\n",
      "        \"InstallmentPlans\": \"stores\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"unskilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.000162622847710736\"\n",
      "      }\n",
      "    ]\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import time\n",
    "import json\n",
    "\n",
    "scoring_url = model_asset_details['entity']['scoring_endpoint']['url']\n",
    "\n",
    "data = {\n",
    "            \"Inputs\": {\n",
    "                \"input1\":\n",
    "                    [{'CheckingStatus': 'no_checking',\n",
    "                      'LoanDuration': 19,\n",
    "                      'CreditHistory': 'credits_paid_to_date',\n",
    "                      'LoanPurpose': 'business',\n",
    "                      'LoanAmount': 1867,\n",
    "                      'ExistingSavings': '100_to_500',\n",
    "                      'EmploymentDuration': '1_to_4',\n",
    "                      'InstallmentPercent': 4,\n",
    "                      'Sex': 'male',\n",
    "                      'OthersOnLoan': 'none',\n",
    "                      'CurrentResidenceDuration': 3,\n",
    "                      'OwnsProperty': 'car_other',\n",
    "                      'Age': 42,\n",
    "                      'InstallmentPlans': 'stores',\n",
    "                      'Housing': 'own',\n",
    "                      'ExistingCreditsCount': 2,\n",
    "                      'Job': 'unskilled',\n",
    "                      'Dependents': 1,\n",
    "                      'Telephone': 'none',\n",
    "                      'ForeignWorker': 'yes'}]\n",
    "            },\n",
    "            \"GlobalParameters\": {\n",
    "            }\n",
    "        }\n",
    "\n",
    "body = str.encode(json.dumps(data))\n",
    "\n",
    "\n",
    "token = model_asset_details['entity']['scoring_endpoint']['credentials']['token']\n",
    "headers = model_asset_details['entity']['scoring_endpoint']['request_headers']\n",
    "headers['Authorization'] = ('Bearer ' + token)\n",
    "\n",
    "start_time = time.time()\n",
    "response = requests.post(url=scoring_url, data=body, headers=headers)\n",
    "response_time = int(time.time() - start_time)*1000\n",
    "result = response.json()\n",
    "\n",
    "print(json.dumps(result, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"subsciption\"></a>\n",
    "# 3. Subscriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add subscriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List available deployments.\n",
    "\n",
    "**Note:** Depending on number of assets it may take some time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<HTML>\n",
       "        <body>\n",
       "            <h3>Subscriptions</h3>\n",
       "            <table style='border: 1px solid #dddddd; font-family: Courier'>\n",
       "                <th style='border: 1px solid #dddddd'>asset_id</th><th style='border: 1px solid #dddddd'>asset_name</th><th style='border: 1px solid #dddddd'>data_mart_id</th><th style='border: 1px solid #dddddd'>deployment_id</th><th style='border: 1px solid #dddddd'>deployment_name</th><th style='border: 1px solid #dddddd'>service_provider_id</th><th style='border: 1px solid #dddddd'>status</th><th style='border: 1px solid #dddddd'>created_at</th><th style='border: 1px solid #dddddd'>id</th>\n",
       "                \n",
       "            </table>\n",
       "        </body>\n",
       "        </HTML>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wos_client.subscriptions.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "feature_columns = ['CheckingStatus', 'LoanDuration', 'CreditHistory', 'LoanPurpose', 'LoanAmount', 'ExistingSavings', 'EmploymentDuration', 'InstallmentPercent', 'Sex', 'OthersOnLoan', 'CurrentResidenceDuration', 'OwnsProperty', 'Age', 'InstallmentPlans', 'Housing', 'ExistingCreditsCount', 'Job', 'Dependents', 'Telephone', 'ForeignWorker']\n",
    "categorical_columns = ['CheckingStatus', 'CreditHistory', 'LoanPurpose', 'ExistingSavings', 'EmploymentDuration', 'Sex', 'OthersOnLoan', 'OwnsProperty', 'InstallmentPlans', 'Housing', 'Job', 'Telephone', 'ForeignWorker']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ibm_watson_openscale.base_classes.watson_open_scale_v2 import ScoringEndpointCredentialsAzureScoringEndpointCredentials,ScoringEndpointRequest\n",
    "def get_output_schema_fields_for_azure(azure_ouput_schema, target_field, target_role = \"decoded-target\", field_type = str, is_nullable = True):\n",
    "        fields = []\n",
    "        found = False\n",
    "        for field in azure_ouput_schema['fields']:\n",
    "            if field['name']==target_field:\n",
    "                metadata = {}\n",
    "                if \"metadata\" in field:\n",
    "                    metadata = field[\"metadata\"]\n",
    "                metadata[\"modeling_role\"]=target_role    \n",
    "                field[\"metadata\"] = metadata\n",
    "                found = True\n",
    "    \n",
    "            spark_field = SparkStructFieldObject(\n",
    "                name=field['name'],\n",
    "                type=field['type'],\n",
    "                nullable=field['nullable'],\n",
    "            )\n",
    "            if \"metadata\" in field:\n",
    "                spark_field.metadata = field[\"metadata\"]\n",
    "                \n",
    "            fields.append(spark_field)   \n",
    "        \n",
    "        if found is False:\n",
    "            metadata = {}\n",
    "            metadata[\"modeling_role\"] = target_role\n",
    "            fields.append(SparkStructFieldObject(\n",
    "                name=target_field,\n",
    "                type=field_type,\n",
    "                nullable=nullable,\n",
    "                metadata = metadata\n",
    "            )) \n",
    "        return fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "azure_asset = Asset(\n",
    "            asset_id=model_asset_details[\"entity\"][\"asset\"][\"asset_id\"],\n",
    "            name=model_asset_details[\"entity\"][\"asset\"][\"name\"],\n",
    "            url=model_asset_details[\"entity\"][\"asset\"][\"url\"],\n",
    "            asset_type=AssetTypes.MODEL,\n",
    "            input_data_type=InputDataType.STRUCTURED,\n",
    "            problem_type=ProblemType.BINARY_CLASSIFICATION,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "deployment_scoring_endpoint = model_asset_details['entity']['scoring_endpoint']\n",
    "scoring_endpoint = ScoringEndpointRequest(url = model_asset_details['entity']['scoring_endpoint']['url']\n",
    "                                          ,request_headers = model_asset_details['entity']['scoring_endpoint']['request_headers'],\n",
    "                                          credentials= ScoringEndpointCredentialsAzureScoringEndpointCredentials(model_asset_details['entity']['scoring_endpoint']['credentials']['token']) )\n",
    "\n",
    "deployment = AssetDeploymentRequest(\n",
    "    deployment_id=model_asset_details['metadata']['guid'],\n",
    "    url=model_asset_details['metadata']['url'],\n",
    "    name=model_asset_details['entity']['name'],\n",
    "    description=model_asset_details['entity']['description'],\n",
    "    deployment_type=model_asset_details['entity']['type'],\n",
    "    scoring_endpoint = scoring_endpoint\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'struct',\n",
       " 'fields': [{'name': 'CheckingStatus', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'LoanDuration', 'type': 'integer', 'nullable': False},\n",
       "  {'name': 'CreditHistory', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'LoanPurpose', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'LoanAmount', 'type': 'integer', 'nullable': False},\n",
       "  {'name': 'ExistingSavings', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'EmploymentDuration', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'InstallmentPercent', 'type': 'integer', 'nullable': False},\n",
       "  {'name': 'Sex', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'OthersOnLoan', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'CurrentResidenceDuration', 'type': 'integer', 'nullable': False},\n",
       "  {'name': 'OwnsProperty', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'Age', 'type': 'integer', 'nullable': False},\n",
       "  {'name': 'InstallmentPlans', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'Housing', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'ExistingCreditsCount', 'type': 'integer', 'nullable': False},\n",
       "  {'name': 'Job', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'Dependents', 'type': 'integer', 'nullable': False},\n",
       "  {'name': 'Telephone', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'ForeignWorker', 'type': 'string', 'nullable': False},\n",
       "  {'name': 'Scored Labels',\n",
       "   'type': 'string',\n",
       "   'nullable': False,\n",
       "   'metadata': {'modeling_role': 'decoded-target'}},\n",
       "  {'name': 'Scored Probabilities',\n",
       "   'type': 'double',\n",
       "   'nullable': False,\n",
       "   'metadata': {'modeling_role': 'class_probability'}},\n",
       "  {'name': 'ProbabilityVector',\n",
       "   'type': {'type': 'array', 'elementType': 'double', 'containsNull': False},\n",
       "   'nullable': False,\n",
       "   'metadata': {'modeling_role': 'probability'}}]}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data_reference = TrainingDataReference(type='cos',\n",
    "                                              location=COSTrainingDataReferenceLocation(bucket = BUCKET_NAME,\n",
    "                                                                                        file_name = training_data_file_name),\n",
    "                                              connection=COSTrainingDataReferenceConnection(\n",
    "                                                                        resource_instance_id= COS_RESOURCE_CRN,\n",
    "                                                                        url= COS_ENDPOINT,\n",
    "                                                                        api_key= COS_API_KEY_ID,\n",
    "                                                                        iam_url=IAM_URL)\n",
    "                                               )\n",
    "os = model_asset_details['entity']['asset_properties']['output_data_schema']\n",
    "input_s= model_asset_details['entity']['asset_properties']['input_data_schema']\n",
    "input_schema = SparkStruct(\n",
    "        type=model_asset_details['entity']['asset_properties']['input_data_schema']['type'],\n",
    "        fields=model_asset_details['entity']['asset_properties']['input_data_schema']['fields']\n",
    "    )\n",
    "output_schema = SparkStruct(\n",
    "        type=model_asset_details['entity']['asset_properties']['output_data_schema']['type'],\n",
    "        fields=get_output_schema_fields_for_azure(model_asset_details['entity']['asset_properties']['output_data_schema'], target_field = \"Scored Labels\", target_role = \"decoded-target\")\n",
    "    )\n",
    "output_schema.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "asset_properties = AssetPropertiesRequest(\n",
    "        label_column=\"Risk\",\n",
    "        prediction_field='Scored Labels',\n",
    "        probability_fields=['Scored Probabilities'],\n",
    "        training_data_reference=training_data_reference,\n",
    "        training_data_schema=None,\n",
    "        input_data_schema=input_schema,\n",
    "        output_data_schema=output_schema,\n",
    "        feature_fields=feature_columns,\n",
    "        categorical_fields=categorical_columns\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "=============================================================================\n",
      "\n",
      " Waiting for end of adding subscription 62d30e9f-8fdc-4006-88f5-f76a7edb72e7 \n",
      "\n",
      "=============================================================================\n",
      "\n",
      "\n",
      "\n",
      "active\n",
      "\n",
      "-------------------------------------------\n",
      " Successfully finished adding subscription \n",
      "-------------------------------------------\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'62d30e9f-8fdc-4006-88f5-f76a7edb72e7'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subscription_details = wos_client.subscriptions.add(\n",
    "        data_mart_id=data_mart_id,\n",
    "        service_provider_id=service_provider_id,\n",
    "        asset=azure_asset,\n",
    "        deployment=deployment,\n",
    "        asset_properties=asset_properties,\n",
    "        background_mode=False\n",
    ").result\n",
    "subscription_id = subscription_details.metadata.id\n",
    "subscription_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"scoring\"></a>\n",
    "# 4. Scoring and payload logging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Score the credit risk model and measure response time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Payload data set id:  eedaadcd-2c2d-4326-9278-25569d957852\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "time.sleep(5)\n",
    "payload_data_set_id = None\n",
    "payload_data_set_id = wos_client.data_sets.list(type=DataSetTypes.PAYLOAD_LOGGING, \n",
    "                                                target_target_id=subscription_id, \n",
    "                                                target_target_type=TargetTypes.SUBSCRIPTION).result.data_sets[0].metadata.id\n",
    "if payload_data_set_id is None:\n",
    "    print(\"Payload data set not found. Please check subscription status.\")\n",
    "else:\n",
    "    print(\"Payload data set id: \", payload_data_set_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"Results\": {\n",
      "    \"output1\": [\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"29\",\n",
      "        \"CreditHistory\": \"outstanding_credit\",\n",
      "        \"LoanPurpose\": \"appliances\",\n",
      "        \"LoanAmount\": \"6599\",\n",
      "        \"ExistingSavings\": \"500_to_1000\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"42\",\n",
      "        \"InstallmentPlans\": \"stores\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"unskilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.0259256958961487\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"4\",\n",
      "        \"CreditHistory\": \"all_credits_paid_back\",\n",
      "        \"LoanPurpose\": \"car_new\",\n",
      "        \"LoanAmount\": \"779\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"less_1\",\n",
      "        \"InstallmentPercent\": \"2\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"28\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"management_self-employed\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"6.17629630141892E-05\"\n",
      "      }\n",
      "    ]\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import time\n",
    "import json\n",
    "\n",
    "subscription_details=wos_client.subscriptions.get(subscription_id).result.to_dict()\n",
    "scoring_url = subscription_details['entity']['deployment']['scoring_endpoint']['url']\n",
    "\n",
    "data = {\n",
    "            \"Inputs\": {\n",
    "                \"input1\":\n",
    "                    training_data_pd.sample(2).drop(['Risk'],axis=1).to_dict('records'),\n",
    "            },\n",
    "            \"GlobalParameters\": {\n",
    "            }\n",
    "        }\n",
    "\n",
    "body = str.encode(json.dumps(data))\n",
    "\n",
    "token = model_asset_details['entity']['scoring_endpoint']['credentials']['token']\n",
    "headers = subscription_details['entity']['deployment']['scoring_endpoint']['request_headers']\n",
    "headers['Authorization'] = ('Bearer ' + token)\n",
    "\n",
    "start_time = time.time()\n",
    "response = requests.post(url=scoring_url, data=body, headers=headers)\n",
    "response_time = int(time.time() - start_time)*1000\n",
    "result = response.json()\n",
    "\n",
    "print(json.dumps(result, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions to adjust payload data format for OpenScale "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_user_input_2_openscale(input_data):\n",
    "    users_records = input_data['Inputs']['input1']\n",
    "    openscale_fields = list(users_records[0])\n",
    "    openscale_values = [[rec[k] for k in openscale_fields] for rec in users_records] \n",
    "\n",
    "    return {'fields':openscale_fields, 'values':openscale_values}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def convert_user_output_2_openscale(output_data):\n",
    "    users_records = output_data['Results']['output1']\n",
    "    openscale_fields = [\"Scored Labels\",\"Scored Probabilities\"]\n",
    "    openscale_values = [[np.double(rec[k]) if k=='Scored Probabilities' else rec[k] for k in openscale_fields ] for rec in users_records] \n",
    "\n",
    "    return {'fields':openscale_fields, 'values':openscale_values}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "openscale_input=convert_user_input_2_openscale(data)\n",
    "openscale_output=convert_user_output_2_openscale(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store the request and response in payload logging table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing explicit payload logging.....\n",
      "Number of records in the payload logging table: 2\n"
     ]
    }
   ],
   "source": [
    "import uuid\n",
    "from ibm_watson_openscale.supporting_classes.payload_record import PayloadRecord\n",
    "\n",
    "print(\"Performing explicit payload logging.....\")\n",
    "wos_client.data_sets.store_records(data_set_id=payload_data_set_id, request_body=[PayloadRecord(\n",
    "           scoring_id=str(uuid.uuid4()),\n",
    "           request=openscale_input,\n",
    "           response=openscale_output,\n",
    "           response_time=460\n",
    ")])\n",
    "time.sleep(5)\n",
    "pl_records_count = wos_client.data_sets.get_records_count(payload_data_set_id)\n",
    "print(\"Number of records in the payload logging table: {}\".format(pl_records_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<HTML>\n",
       "        <body>\n",
       "            <h3>Data Set eedaadcd-2c2d-4326-9278-25569d957852 Records</h3>\n",
       "            <table style='border: 1px solid #dddddd; font-family: Courier'>\n",
       "                <th style='border: 1px solid #dddddd'>asset_revision</th><th style='border: 1px solid #dddddd'>LoanAmount</th><th style='border: 1px solid #dddddd'>scoring_id</th><th style='border: 1px solid #dddddd'>InstallmentPercent</th><th style='border: 1px solid #dddddd'>ExistingSavings</th><th style='border: 1px solid #dddddd'>scoring_timestamp</th><th style='border: 1px solid #dddddd'>prediction_probability</th><th style='border: 1px solid #dddddd'>ProbabilityVector</th><th style='border: 1px solid #dddddd'>CheckingStatus</th><th style='border: 1px solid #dddddd'>LoanPurpose</th><th style='border: 1px solid #dddddd'>Scored Probabilities</th><th style='border: 1px solid #dddddd'>Job</th><th style='border: 1px solid #dddddd'>InstallmentPlans</th><th style='border: 1px solid #dddddd'>OthersOnLoan</th><th style='border: 1px solid #dddddd'>LoanDuration</th><th style='border: 1px solid #dddddd'>Age</th><th style='border: 1px solid #dddddd'>CreditHistory</th><th style='border: 1px solid #dddddd'>CurrentResidenceDuration</th><th style='border: 1px solid #dddddd'>ForeignWorker</th><th style='border: 1px solid #dddddd'>Housing</th><th style='border: 1px solid #dddddd'>ExistingCreditsCount</th><th style='border: 1px solid #dddddd'>deployment_id</th><th style='border: 1px solid #dddddd'>Telephone</th><th style='border: 1px solid #dddddd'>Scored Labels</th><th style='border: 1px solid #dddddd'>Sex</th><th style='border: 1px solid #dddddd'>Dependents</th><th style='border: 1px solid #dddddd'>OwnsProperty</th><th style='border: 1px solid #dddddd'>EmploymentDuration</th>\n",
       "                <tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>6599</td><td style='border: 1px solid #dddddd'>7cede571-3a79-4d88-8b50-1ef7ab7c60ee-1</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>500_to_1000</td><td style='border: 1px solid #dddddd'>2021-07-29T13:04:17.267Z</td><td style='border: 1px solid #dddddd'>0.9740743041038513</td><td style='border: 1px solid #dddddd'>[0.9740743041038513, 0.0259256958961487]</td><td style='border: 1px solid #dddddd'>no_checking</td><td style='border: 1px solid #dddddd'>appliances</td><td style='border: 1px solid #dddddd'>0.0259256958961487</td><td style='border: 1px solid #dddddd'>unskilled</td><td style='border: 1px solid #dddddd'>stores</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>29</td><td style='border: 1px solid #dddddd'>42</td><td style='border: 1px solid #dddddd'>outstanding_credit</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>car_other</td><td style='border: 1px solid #dddddd'>4_to_7</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>779</td><td style='border: 1px solid #dddddd'>7cede571-3a79-4d88-8b50-1ef7ab7c60ee-2</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>less_100</td><td style='border: 1px solid #dddddd'>2021-07-29T13:04:17.267Z</td><td style='border: 1px solid #dddddd'>0.9999382370369858</td><td style='border: 1px solid #dddddd'>[0.9999382370369858, 6.17629630141892e-05]</td><td style='border: 1px solid #dddddd'>less_0</td><td style='border: 1px solid #dddddd'>car_new</td><td style='border: 1px solid #dddddd'>6.17629630141892e-05</td><td style='border: 1px solid #dddddd'>management_self-employed</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>4</td><td style='border: 1px solid #dddddd'>28</td><td style='border: 1px solid #dddddd'>all_credits_paid_back</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>female</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>savings_insurance</td><td style='border: 1px solid #dddddd'>less_1</td></tr>\n",
       "            </table>\n",
       "        </body>\n",
       "        </HTML>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wos_client.data_sets.show_records(data_set_id=payload_data_set_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"feedback\"></a>\n",
    "# 5. Feedback logging & quality (accuracy) monitoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enable quality monitoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "===================================================================================\n",
      "\n",
      " Waiting for end of monitor instance creation 4a675973-a9ce-4f7a-891e-0d458f5a08b3 \n",
      "\n",
      "===================================================================================\n",
      "\n",
      "\n",
      "\n",
      "active\n",
      "\n",
      "---------------------------------------\n",
      " Monitor instance successfully created \n",
      "---------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "time.sleep(10)\n",
    "target = Target(\n",
    "        target_type=TargetTypes.SUBSCRIPTION,\n",
    "        target_id=subscription_id\n",
    ")\n",
    "parameters = {\n",
    "    \"min_feedback_data_size\": 10\n",
    "}\n",
    "thresholds = [\n",
    "                {\n",
    "                    \"metric_id\": \"area_under_roc\",\n",
    "                    \"type\": \"lower_limit\",\n",
    "                    \"value\": .80\n",
    "                }\n",
    "            ]\n",
    "quality_monitor_details = wos_client.monitor_instances.create(\n",
    "    data_mart_id=data_mart_id,\n",
    "    background_mode=False,\n",
    "    monitor_definition_id=wos_client.monitor_definitions.MONITORS.QUALITY.ID,\n",
    "    target=target,\n",
    "    parameters=parameters,\n",
    "    thresholds=thresholds\n",
    ").result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4a675973-a9ce-4f7a-891e-0d458f5a08b3'"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quality_monitor_instance_id = quality_monitor_details.metadata.id\n",
    "quality_monitor_instance_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feedback records logging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feedback records are used to evaluate your model. The predicted values are compared to real values (feedback records)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rm: cannot remove 'additional_feedback_data_v2.json': No such file or directory\n",
      "--2021-07-29 13:04:41--  https://raw.githubusercontent.com/IBM/watson-openscale-samples/main/IBM%20Cloud/WML/assets/data/credit_risk/additional_feedback_data_v2.json\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.111.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 50890 (50K) [text/plain]\n",
      "Saving to: ‘additional_feedback_data_v2.json’\n",
      "\n",
      "additional_feedback 100%[===================>]  49.70K  --.-KB/s    in 0.001s  \n",
      "\n",
      "2021-07-29 13:04:41 (46.6 MB/s) - ‘additional_feedback_data_v2.json’ saved [50890/50890]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!rm additional_feedback_data_v2.json\n",
    "!wget https://raw.githubusercontent.com/IBM/watson-openscale-samples/main/IBM%20Cloud/WML/assets/data/credit_risk/additional_feedback_data_v2.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get feedback logging dataset ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "feedback_dataset_id = None\n",
    "feedback_dataset = wos_client.data_sets.list(type=DataSetTypes.FEEDBACK, \n",
    "                                                target_target_id=subscription_id, \n",
    "                                                target_target_type=TargetTypes.SUBSCRIPTION).result\n",
    "#print(feedback_dataset)\n",
    "feedback_dataset_id = feedback_dataset.data_sets[0].metadata.id\n",
    "if feedback_dataset_id is None:\n",
    "    print(\"Feedback data set not found. Please check quality monitor status.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "==========================================================================================\n",
      "\n",
      " Waiting for end of storing records with request id: 0009ac78-7630-47b2-98dc-179e08092c69 \n",
      "\n",
      "==========================================================================================\n",
      "\n",
      "\n",
      "\n",
      "active\n",
      "\n",
      "---------------------------------------\n",
      " Successfully finished storing records \n",
      "---------------------------------------\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<ibm_cloud_sdk_core.detailed_response.DetailedResponse at 0x7f815ce81e90>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('additional_feedback_data_v2.json') as feedback_file:\n",
    "    additional_feedback_data = json.load(feedback_file)\n",
    "wos_client.data_sets.store_records(feedback_dataset_id, request_body=additional_feedback_data, background_mode=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# manually records scoring and store as feedback data\n",
    "\n",
    "# records = [\n",
    "#     [\"no_checking\",\"28\",\"outstanding_credit\",\"appliances\",\"5990\",\"500_to_1000\",\"greater_7\",\"5\",\"male\",\"co-applicant\",\"3\",\"car_other\",\"55\",\"none\",\"free\",\"2\",\"skilled\",\"2\",\"yes\",\"yes\",\"Risk\"],\n",
    "#     [\"greater_200\",\"22\",\"all_credits_paid_back\",\"car_used\",\"3376\",\"less_100\",\"less_1\",\"3\",\"female\",\"none\",\"2\",\"car_other\",\"32\",\"none\",\"own\",\"1\",\"skilled\",\"1\",\"none\",\"yes\",\"No Risk\"],\n",
    "#     [\"no_checking\",\"39\",\"credits_paid_to_date\",\"vacation\",\"6434\",\"unknown\",\"greater_7\",\"5\",\"male\",\"none\",\"4\",\"car_other\",\"39\",\"none\",\"own\",\"2\",\"skilled\",\"2\",\"yes\",\"yes\",\"Risk\"],\n",
    "#     [\"0_to_200\",\"20\",\"credits_paid_to_date\",\"furniture\",\"2442\",\"less_100\",\"unemployed\",\"3\",\"female\",\"none\",\"1\",\"real_estate\",\"42\",\"none\",\"own\",\"1\",\"skilled\",\"1\",\"none\",\"yes\",\"No Risk\"],\n",
    "#     [\"greater_200\",\"4\",\"all_credits_paid_back\",\"education\",\"4206\",\"less_100\",\"unemployed\",\"1\",\"female\",\"none\",\"3\",\"savings_insurance\",\"27\",\"none\",\"own\",\"1\",\"management_self-employed\",\"1\",\"none\",\"yes\",\"No Risk\"],\n",
    "#     [\"greater_200\",\"23\",\"credits_paid_to_date\",\"car_used\",\"2963\",\"greater_1000\",\"greater_7\",\"4\",\"male\",\"none\",\"4\",\"car_other\",\"46\",\"none\",\"own\",\"2\",\"skilled\",\"1\",\"none\",\"yes\",\"Risk\"],\n",
    "#     [\"no_checking\",\"31\",\"prior_payments_delayed\",\"vacation\",\"2673\",\"500_to_1000\",\"1_to_4\",\"3\",\"male\",\"none\",\"2\",\"real_estate\",\"35\",\"stores\",\"rent\",\"1\",\"skilled\",\"2\",\"none\",\"yes\",\"Risk\"],\n",
    "#     [\"no_checking\",\"37\",\"prior_payments_delayed\",\"other\",\"6971\",\"500_to_1000\",\"1_to_4\",\"3\",\"male\",\"none\",\"3\",\"savings_insurance\",\"54\",\"none\",\"own\",\"2\",\"skilled\",\"1\",\"yes\",\"yes\",\"Risk\"],\n",
    "#     [\"0_to_200\",\"39\",\"prior_payments_delayed\",\"appliances\",\"5685\",\"100_to_500\",\"1_to_4\",\"4\",\"female\",\"none\",\"2\",\"unknown\",\"37\",\"none\",\"own\",\"2\",\"skilled\",\"1\",\"yes\",\"yes\",\"Risk\"],\n",
    "#     [\"no_checking\",\"38\",\"prior_payments_delayed\",\"appliances\",\"4990\",\"500_to_1000\",\"greater_7\",\"4\",\"male\",\"none\",\"4\",\"car_other\",\"50\",\"bank\",\"own\",\"2\",\"unemployed\",\"2\",\"yes\",\"yes\",\"Risk\"]]\n",
    "\n",
    "# fields = feature_columns.copy()\n",
    "# fields.append('Risk')\n",
    "\n",
    "# payload_scoring =  [{\"fields\": fields, \"values\": records}]\n",
    "#wos_client.data_sets.store_records(feedback_dataset_id, request_body=payload_scoring, background_mode=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_client.data_sets.get_records_count(data_set_id=feedback_dataset_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "========================================================================\n",
      "\n",
      " Waiting for end of monitoring run 172cdf6a-c5fd-482f-abbb-608a0b3a58c2 \n",
      "\n",
      "========================================================================\n",
      "\n",
      "\n",
      "\n",
      "running\n",
      "finished\n",
      "\n",
      "---------------------------\n",
      " Successfully finished run \n",
      "---------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "run_details = wos_client.monitor_instances.run(monitor_instance_id=quality_monitor_instance_id, background_mode=False).result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<HTML>\n",
       "        <body>\n",
       "            <h3>4a675973-a9ce-4f7a-891e-0d458f5a08b3 Monitor Runs Metrics from: 2021-07-22 13:05:04.421054  till: 2021-07-29 13:05:04.421076</h3>\n",
       "            <table style='border: 1px solid #dddddd; font-family: Courier'>\n",
       "                <th style='border: 1px solid #dddddd'>ts</th><th style='border: 1px solid #dddddd'>id</th><th style='border: 1px solid #dddddd'>measurement_id</th><th style='border: 1px solid #dddddd'>value</th><th style='border: 1px solid #dddddd'>lower_limit</th><th style='border: 1px solid #dddddd'>upper_limit</th><th style='border: 1px solid #dddddd'>tags</th><th style='border: 1px solid #dddddd'>monitor_definition_id</th><th style='border: 1px solid #dddddd'>monitor_instance_id</th><th style='border: 1px solid #dddddd'>run_id</th><th style='border: 1px solid #dddddd'>target_type</th><th style='border: 1px solid #dddddd'>target_id</th>\n",
       "                <tr><td style='border: 1px solid #dddddd'>2021-07-29 13:04:48.243000+00:00</td><td style='border: 1px solid #dddddd'>true_positive_rate</td><td style='border: 1px solid #dddddd'>c4e1809a-1ba3-45f8-86ba-6099a99f290f</td><td style='border: 1px solid #dddddd'>0.6060606060606061</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['model_type:original']</td><td style='border: 1px solid #dddddd'>quality</td><td style='border: 1px solid #dddddd'>4a675973-a9ce-4f7a-891e-0d458f5a08b3</td><td style='border: 1px solid #dddddd'>172cdf6a-c5fd-482f-abbb-608a0b3a58c2</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:04:48.243000+00:00</td><td style='border: 1px solid #dddddd'>area_under_roc</td><td style='border: 1px solid #dddddd'>c4e1809a-1ba3-45f8-86ba-6099a99f290f</td><td style='border: 1px solid #dddddd'>0.7414918414918414</td><td style='border: 1px solid #dddddd'>0.8</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['model_type:original']</td><td style='border: 1px solid #dddddd'>quality</td><td style='border: 1px solid #dddddd'>4a675973-a9ce-4f7a-891e-0d458f5a08b3</td><td style='border: 1px solid #dddddd'>172cdf6a-c5fd-482f-abbb-608a0b3a58c2</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:04:48.243000+00:00</td><td style='border: 1px solid #dddddd'>precision</td><td style='border: 1px solid #dddddd'>c4e1809a-1ba3-45f8-86ba-6099a99f290f</td><td style='border: 1px solid #dddddd'>0.7142857142857143</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['model_type:original']</td><td style='border: 1px solid #dddddd'>quality</td><td style='border: 1px solid #dddddd'>4a675973-a9ce-4f7a-891e-0d458f5a08b3</td><td style='border: 1px solid #dddddd'>172cdf6a-c5fd-482f-abbb-608a0b3a58c2</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:04:48.243000+00:00</td><td style='border: 1px solid #dddddd'>f1_measure</td><td style='border: 1px solid #dddddd'>c4e1809a-1ba3-45f8-86ba-6099a99f290f</td><td style='border: 1px solid #dddddd'>0.6557377049180327</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['model_type:original']</td><td style='border: 1px solid #dddddd'>quality</td><td style='border: 1px solid #dddddd'>4a675973-a9ce-4f7a-891e-0d458f5a08b3</td><td style='border: 1px solid #dddddd'>172cdf6a-c5fd-482f-abbb-608a0b3a58c2</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:04:48.243000+00:00</td><td style='border: 1px solid #dddddd'>accuracy</td><td style='border: 1px solid #dddddd'>c4e1809a-1ba3-45f8-86ba-6099a99f290f</td><td style='border: 1px solid #dddddd'>0.7857142857142857</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['model_type:original']</td><td style='border: 1px solid #dddddd'>quality</td><td style='border: 1px solid #dddddd'>4a675973-a9ce-4f7a-891e-0d458f5a08b3</td><td style='border: 1px solid #dddddd'>172cdf6a-c5fd-482f-abbb-608a0b3a58c2</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:04:48.243000+00:00</td><td style='border: 1px solid #dddddd'>log_loss</td><td style='border: 1px solid #dddddd'>c4e1809a-1ba3-45f8-86ba-6099a99f290f</td><td style='border: 1px solid #dddddd'>0.754676424586092</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['model_type:original']</td><td style='border: 1px solid #dddddd'>quality</td><td style='border: 1px solid #dddddd'>4a675973-a9ce-4f7a-891e-0d458f5a08b3</td><td style='border: 1px solid #dddddd'>172cdf6a-c5fd-482f-abbb-608a0b3a58c2</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:04:48.243000+00:00</td><td style='border: 1px solid #dddddd'>false_positive_rate</td><td style='border: 1px solid #dddddd'>c4e1809a-1ba3-45f8-86ba-6099a99f290f</td><td style='border: 1px solid #dddddd'>0.12307692307692308</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['model_type:original']</td><td style='border: 1px solid #dddddd'>quality</td><td style='border: 1px solid #dddddd'>4a675973-a9ce-4f7a-891e-0d458f5a08b3</td><td style='border: 1px solid #dddddd'>172cdf6a-c5fd-482f-abbb-608a0b3a58c2</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:04:48.243000+00:00</td><td style='border: 1px solid #dddddd'>area_under_pr</td><td style='border: 1px solid #dddddd'>c4e1809a-1ba3-45f8-86ba-6099a99f290f</td><td style='border: 1px solid #dddddd'>0.6399196042053185</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['model_type:original']</td><td style='border: 1px solid #dddddd'>quality</td><td style='border: 1px solid #dddddd'>4a675973-a9ce-4f7a-891e-0d458f5a08b3</td><td style='border: 1px solid #dddddd'>172cdf6a-c5fd-482f-abbb-608a0b3a58c2</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:04:48.243000+00:00</td><td style='border: 1px solid #dddddd'>recall</td><td style='border: 1px solid #dddddd'>c4e1809a-1ba3-45f8-86ba-6099a99f290f</td><td style='border: 1px solid #dddddd'>0.6060606060606061</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['model_type:original']</td><td style='border: 1px solid #dddddd'>quality</td><td style='border: 1px solid #dddddd'>4a675973-a9ce-4f7a-891e-0d458f5a08b3</td><td style='border: 1px solid #dddddd'>172cdf6a-c5fd-482f-abbb-608a0b3a58c2</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr>\n",
       "            </table>\n",
       "        </body>\n",
       "        </HTML>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time.sleep(5)\n",
    "wos_client.monitor_instances.show_metrics(monitor_instance_id=quality_monitor_instance_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Fairness, Drift monitoring and explanations <a id=\"monitors\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fairness monitor configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<HTML>\n",
       "        <body>\n",
       "            <h3>Monitor instances</h3>\n",
       "            <table style='border: 1px solid #dddddd; font-family: Courier'>\n",
       "                <th style='border: 1px solid #dddddd'>data_mart_id</th><th style='border: 1px solid #dddddd'>status</th><th style='border: 1px solid #dddddd'>target_id</th><th style='border: 1px solid #dddddd'>target_type</th><th style='border: 1px solid #dddddd'>monitor_definition_id</th><th style='border: 1px solid #dddddd'>created_at</th><th style='border: 1px solid #dddddd'>id</th>\n",
       "                <tr><td style='border: 1px solid #dddddd'>a08b0e60-6844-4c14-81cb-a3a6c9d199f9</td><td style='border: 1px solid #dddddd'>active</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>quality</td><td style='border: 1px solid #dddddd'>2021-07-29 13:04:34.663000+00:00</td><td style='border: 1px solid #dddddd'>4a675973-a9ce-4f7a-891e-0d458f5a08b3</td></tr>\n",
       "            </table>\n",
       "        </body>\n",
       "        </HTML>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wos_client.monitor_instances.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "===================================================================================\n",
      "\n",
      " Waiting for end of monitor instance creation 3a68d116-dc8f-4fd2-aba1-9e10dabb3797 \n",
      "\n",
      "===================================================================================\n",
      "\n",
      "\n",
      "\n",
      "active\n",
      "\n",
      "---------------------------------------\n",
      " Monitor instance successfully created \n",
      "---------------------------------------\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'3a68d116-dc8f-4fd2-aba1-9e10dabb3797'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = Target(\n",
    "    target_type=TargetTypes.SUBSCRIPTION,\n",
    "    target_id=subscription_id\n",
    "\n",
    ")\n",
    "parameters = {\n",
    "    \"features\": [\n",
    "        {\"feature\": \"Sex\",\n",
    "         \"majority\": ['male'],\n",
    "         \"minority\": ['female'],\n",
    "         \"threshold\": 0.95\n",
    "         },\n",
    "        {\"feature\": \"Age\",\n",
    "         \"majority\": [[26, 75]],\n",
    "         \"minority\": [[18, 25]],\n",
    "         \"threshold\": 0.95\n",
    "         }\n",
    "    ],\n",
    "    \"favourable_class\": [\"No Risk\"],\n",
    "    \"unfavourable_class\": [\"Risk\"],\n",
    "    \"min_records\": 40\n",
    "}\n",
    "\n",
    "fairness_monitor_details = wos_client.monitor_instances.create(\n",
    "    data_mart_id=data_mart_id,\n",
    "    background_mode=False,\n",
    "    monitor_definition_id=wos_client.monitor_definitions.MONITORS.FAIRNESS.ID,\n",
    "    target=target,\n",
    "    parameters=parameters).result\n",
    "fairness_monitor_instance_id =fairness_monitor_details.metadata.id\n",
    "fairness_monitor_instance_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Payload logging and Run fairness monitor on demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"Results\": {\n",
      "    \"output1\": [\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"7\",\n",
      "        \"CreditHistory\": \"no_credits\",\n",
      "        \"LoanPurpose\": \"repairs\",\n",
      "        \"LoanAmount\": \"250\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"unemployed\",\n",
      "        \"InstallmentPercent\": \"1\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"real_estate\",\n",
      "        \"Age\": \"19\",\n",
      "        \"InstallmentPlans\": \"stores\",\n",
      "        \"Housing\": \"rent\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"unskilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"2.08330470741203E-06\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"36\",\n",
      "        \"CreditHistory\": \"prior_payments_delayed\",\n",
      "        \"LoanPurpose\": \"furniture\",\n",
      "        \"LoanAmount\": \"2224\",\n",
      "        \"ExistingSavings\": \"500_to_1000\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"41\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.160731717944145\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"0_to_200\",\n",
      "        \"LoanDuration\": \"14\",\n",
      "        \"CreditHistory\": \"credits_paid_to_date\",\n",
      "        \"LoanPurpose\": \"furniture\",\n",
      "        \"LoanAmount\": \"1326\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"1_to_4\",\n",
      "        \"InstallmentPercent\": \"2\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"real_estate\",\n",
      "        \"Age\": \"30\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.000551531033124775\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"30\",\n",
      "        \"CreditHistory\": \"prior_payments_delayed\",\n",
      "        \"LoanPurpose\": \"furniture\",\n",
      "        \"LoanAmount\": \"5373\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"guarantor\",\n",
      "        \"CurrentResidenceDuration\": \"3\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"51\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"free\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"no\",\n",
      "        \"Scored Labels\": \"Risk\",\n",
      "        \"Scored Probabilities\": \"0.997457802295685\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"18\",\n",
      "        \"CreditHistory\": \"credits_paid_to_date\",\n",
      "        \"LoanPurpose\": \"radio_tv\",\n",
      "        \"LoanAmount\": \"2043\",\n",
      "        \"ExistingSavings\": \"greater_1000\",\n",
      "        \"EmploymentDuration\": \"1_to_4\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"co-applicant\",\n",
      "        \"CurrentResidenceDuration\": \"4\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"26\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"2\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.162395462393761\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"28\",\n",
      "        \"CreditHistory\": \"outstanding_credit\",\n",
      "        \"LoanPurpose\": \"retraining\",\n",
      "        \"LoanAmount\": \"3249\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"2\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"1\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"41\",\n",
      "        \"InstallmentPlans\": \"stores\",\n",
      "        \"Housing\": \"rent\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.000436662463471293\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"6\",\n",
      "        \"CreditHistory\": \"all_credits_paid_back\",\n",
      "        \"LoanPurpose\": \"car_new\",\n",
      "        \"LoanAmount\": \"250\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"less_1\",\n",
      "        \"InstallmentPercent\": \"1\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"real_estate\",\n",
      "        \"Age\": \"19\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"rent\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"1.91451417776989E-05\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"25\",\n",
      "        \"CreditHistory\": \"prior_payments_delayed\",\n",
      "        \"LoanPurpose\": \"business\",\n",
      "        \"LoanAmount\": \"6867\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"4\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"4\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"41\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.0102055370807648\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"0_to_200\",\n",
      "        \"LoanDuration\": \"17\",\n",
      "        \"CreditHistory\": \"credits_paid_to_date\",\n",
      "        \"LoanPurpose\": \"car_new\",\n",
      "        \"LoanAmount\": \"6382\",\n",
      "        \"ExistingSavings\": \"500_to_1000\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"32\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"3\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.264815270900726\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"0_to_200\",\n",
      "        \"LoanDuration\": \"4\",\n",
      "        \"CreditHistory\": \"all_credits_paid_back\",\n",
      "        \"LoanPurpose\": \"car_used\",\n",
      "        \"LoanAmount\": \"546\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"less_1\",\n",
      "        \"InstallmentPercent\": \"2\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"1\",\n",
      "        \"OwnsProperty\": \"real_estate\",\n",
      "        \"Age\": \"26\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"management_self-employed\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"6.56168708701443E-07\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"24\",\n",
      "        \"CreditHistory\": \"prior_payments_delayed\",\n",
      "        \"LoanPurpose\": \"education\",\n",
      "        \"LoanAmount\": \"2770\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"4\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"46\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"management_self-employed\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"Risk\",\n",
      "        \"Scored Probabilities\": \"0.924100995063782\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"31\",\n",
      "        \"CreditHistory\": \"prior_payments_delayed\",\n",
      "        \"LoanPurpose\": \"other\",\n",
      "        \"LoanAmount\": \"6396\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"39\",\n",
      "        \"InstallmentPlans\": \"stores\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"unskilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"Risk\",\n",
      "        \"Scored Probabilities\": \"0.689380049705505\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"10\",\n",
      "        \"CreditHistory\": \"all_credits_paid_back\",\n",
      "        \"LoanPurpose\": \"car_used\",\n",
      "        \"LoanAmount\": \"250\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"less_1\",\n",
      "        \"InstallmentPercent\": \"1\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"27\",\n",
      "        \"InstallmentPlans\": \"stores\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"3\",\n",
      "        \"Job\": \"unskilled\",\n",
      "        \"Dependents\": \"2\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.000180221933987923\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"0_to_200\",\n",
      "        \"LoanDuration\": \"22\",\n",
      "        \"CreditHistory\": \"prior_payments_delayed\",\n",
      "        \"LoanPurpose\": \"car_new\",\n",
      "        \"LoanAmount\": \"3412\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"1_to_4\",\n",
      "        \"InstallmentPercent\": \"2\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"1\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"29\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"free\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"Risk\",\n",
      "        \"Scored Probabilities\": \"0.959494352340698\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"16\",\n",
      "        \"CreditHistory\": \"prior_payments_delayed\",\n",
      "        \"LoanPurpose\": \"car_used\",\n",
      "        \"LoanAmount\": \"2122\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"1_to_4\",\n",
      "        \"InstallmentPercent\": \"2\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"3\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"32\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.138155072927475\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"25\",\n",
      "        \"CreditHistory\": \"prior_payments_delayed\",\n",
      "        \"LoanPurpose\": \"radio_tv\",\n",
      "        \"LoanAmount\": \"6690\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"1_to_4\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"4\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"45\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"management_self-employed\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.228036865592003\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"15\",\n",
      "        \"CreditHistory\": \"credits_paid_to_date\",\n",
      "        \"LoanPurpose\": \"furniture\",\n",
      "        \"LoanAmount\": \"286\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"less_1\",\n",
      "        \"InstallmentPercent\": \"2\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"3\",\n",
      "        \"OwnsProperty\": \"real_estate\",\n",
      "        \"Age\": \"28\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"rent\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"6.82524041621946E-05\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"0_to_200\",\n",
      "        \"LoanDuration\": \"39\",\n",
      "        \"CreditHistory\": \"outstanding_credit\",\n",
      "        \"LoanPurpose\": \"radio_tv\",\n",
      "        \"LoanAmount\": \"2606\",\n",
      "        \"ExistingSavings\": \"500_to_1000\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"21\",\n",
      "        \"InstallmentPlans\": \"bank\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"unskilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"Risk\",\n",
      "        \"Scored Probabilities\": \"0.969918012619019\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"27\",\n",
      "        \"CreditHistory\": \"outstanding_credit\",\n",
      "        \"LoanPurpose\": \"radio_tv\",\n",
      "        \"LoanAmount\": \"7621\",\n",
      "        \"ExistingSavings\": \"500_to_1000\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"4\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"co-applicant\",\n",
      "        \"CurrentResidenceDuration\": \"5\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"51\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"management_self-employed\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"Risk\",\n",
      "        \"Scored Probabilities\": \"0.995350420475006\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"17\",\n",
      "        \"CreditHistory\": \"no_credits\",\n",
      "        \"LoanPurpose\": \"vacation\",\n",
      "        \"LoanAmount\": \"1970\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"1_to_4\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"36\",\n",
      "        \"InstallmentPlans\": \"bank\",\n",
      "        \"Housing\": \"rent\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"unemployed\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"5.01026715937769E-06\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"13\",\n",
      "        \"CreditHistory\": \"all_credits_paid_back\",\n",
      "        \"LoanPurpose\": \"car_new\",\n",
      "        \"LoanAmount\": \"1950\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"1\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"32\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.000168191429111175\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"12\",\n",
      "        \"CreditHistory\": \"all_credits_paid_back\",\n",
      "        \"LoanPurpose\": \"car_new\",\n",
      "        \"LoanAmount\": \"250\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"less_1\",\n",
      "        \"InstallmentPercent\": \"1\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"1\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"39\",\n",
      "        \"InstallmentPlans\": \"bank\",\n",
      "        \"Housing\": \"rent\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"unemployed\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"2.14355641219299E-05\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"4\",\n",
      "        \"CreditHistory\": \"all_credits_paid_back\",\n",
      "        \"LoanPurpose\": \"car_new\",\n",
      "        \"LoanAmount\": \"250\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"less_1\",\n",
      "        \"InstallmentPercent\": \"1\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"1\",\n",
      "        \"OwnsProperty\": \"real_estate\",\n",
      "        \"Age\": \"19\",\n",
      "        \"InstallmentPlans\": \"bank\",\n",
      "        \"Housing\": \"rent\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"unskilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"1.25590375432694E-07\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"0_to_200\",\n",
      "        \"LoanDuration\": \"18\",\n",
      "        \"CreditHistory\": \"credits_paid_to_date\",\n",
      "        \"LoanPurpose\": \"car_used\",\n",
      "        \"LoanAmount\": \"1598\",\n",
      "        \"ExistingSavings\": \"500_to_1000\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"co-applicant\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"real_estate\",\n",
      "        \"Age\": \"44\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.00101606699172407\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"greater_200\",\n",
      "        \"LoanDuration\": \"31\",\n",
      "        \"CreditHistory\": \"credits_paid_to_date\",\n",
      "        \"LoanPurpose\": \"car_used\",\n",
      "        \"LoanAmount\": \"4071\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"4\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"guarantor\",\n",
      "        \"CurrentResidenceDuration\": \"4\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"47\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"free\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.189026847481728\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"21\",\n",
      "        \"CreditHistory\": \"credits_paid_to_date\",\n",
      "        \"LoanPurpose\": \"furniture\",\n",
      "        \"LoanAmount\": \"1903\",\n",
      "        \"ExistingSavings\": \"500_to_1000\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"4\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"4\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"53\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.0469831675291061\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"0_to_200\",\n",
      "        \"LoanDuration\": \"35\",\n",
      "        \"CreditHistory\": \"prior_payments_delayed\",\n",
      "        \"LoanPurpose\": \"car_used\",\n",
      "        \"LoanAmount\": \"3346\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"1_to_4\",\n",
      "        \"InstallmentPercent\": \"4\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"4\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"40\",\n",
      "        \"InstallmentPlans\": \"bank\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"unemployed\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.00747711956501007\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"0_to_200\",\n",
      "        \"LoanDuration\": \"4\",\n",
      "        \"CreditHistory\": \"no_credits\",\n",
      "        \"LoanPurpose\": \"repairs\",\n",
      "        \"LoanAmount\": \"250\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"unemployed\",\n",
      "        \"InstallmentPercent\": \"1\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"real_estate\",\n",
      "        \"Age\": \"19\",\n",
      "        \"InstallmentPlans\": \"stores\",\n",
      "        \"Housing\": \"rent\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"7.78564128722792E-07\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"42\",\n",
      "        \"CreditHistory\": \"outstanding_credit\",\n",
      "        \"LoanPurpose\": \"other\",\n",
      "        \"LoanAmount\": \"8123\",\n",
      "        \"ExistingSavings\": \"greater_1000\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"4\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"co-applicant\",\n",
      "        \"CurrentResidenceDuration\": \"5\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"43\",\n",
      "        \"InstallmentPlans\": \"stores\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"unskilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"Risk\",\n",
      "        \"Scored Probabilities\": \"0.984596967697144\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"20\",\n",
      "        \"CreditHistory\": \"prior_payments_delayed\",\n",
      "        \"LoanPurpose\": \"retraining\",\n",
      "        \"LoanAmount\": \"2305\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"greater_7\",\n",
      "        \"InstallmentPercent\": \"4\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"3\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"45\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"no\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.00828534737229347\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"4\",\n",
      "        \"CreditHistory\": \"all_credits_paid_back\",\n",
      "        \"LoanPurpose\": \"car_new\",\n",
      "        \"LoanAmount\": \"250\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"less_1\",\n",
      "        \"InstallmentPercent\": \"2\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"1\",\n",
      "        \"OwnsProperty\": \"real_estate\",\n",
      "        \"Age\": \"24\",\n",
      "        \"InstallmentPlans\": \"stores\",\n",
      "        \"Housing\": \"rent\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"2.0071904600627E-06\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"0_to_200\",\n",
      "        \"LoanDuration\": \"33\",\n",
      "        \"CreditHistory\": \"credits_paid_to_date\",\n",
      "        \"LoanPurpose\": \"repairs\",\n",
      "        \"LoanAmount\": \"3946\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"4_to_7\",\n",
      "        \"InstallmentPercent\": \"2\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"33\",\n",
      "        \"InstallmentPlans\": \"stores\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"unskilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.0293259024620056\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"37\",\n",
      "        \"CreditHistory\": \"outstanding_credit\",\n",
      "        \"LoanPurpose\": \"other\",\n",
      "        \"LoanAmount\": \"7916\",\n",
      "        \"ExistingSavings\": \"unknown\",\n",
      "        \"EmploymentDuration\": \"greater_7\",\n",
      "        \"InstallmentPercent\": \"5\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"5\",\n",
      "        \"OwnsProperty\": \"unknown\",\n",
      "        \"Age\": \"53\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"3\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"2\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"Risk\",\n",
      "        \"Scored Probabilities\": \"0.999997675418854\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"39\",\n",
      "        \"CreditHistory\": \"outstanding_credit\",\n",
      "        \"LoanPurpose\": \"other\",\n",
      "        \"LoanAmount\": \"9583\",\n",
      "        \"ExistingSavings\": \"unknown\",\n",
      "        \"EmploymentDuration\": \"greater_7\",\n",
      "        \"InstallmentPercent\": \"5\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"co-applicant\",\n",
      "        \"CurrentResidenceDuration\": \"5\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"44\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"3\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"2\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"Risk\",\n",
      "        \"Scored Probabilities\": \"0.999999940395355\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"24\",\n",
      "        \"CreditHistory\": \"prior_payments_delayed\",\n",
      "        \"LoanPurpose\": \"business\",\n",
      "        \"LoanAmount\": \"4300\",\n",
      "        \"ExistingSavings\": \"100_to_500\",\n",
      "        \"EmploymentDuration\": \"less_1\",\n",
      "        \"InstallmentPercent\": \"2\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"3\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"39\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"2\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.0379052385687828\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"13\",\n",
      "        \"CreditHistory\": \"all_credits_paid_back\",\n",
      "        \"LoanPurpose\": \"car_new\",\n",
      "        \"LoanAmount\": \"2436\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"less_1\",\n",
      "        \"InstallmentPercent\": \"2\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"1\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"19\",\n",
      "        \"InstallmentPlans\": \"stores\",\n",
      "        \"Housing\": \"own\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"4.79053960589226E-05\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"35\",\n",
      "        \"CreditHistory\": \"credits_paid_to_date\",\n",
      "        \"LoanPurpose\": \"car_used\",\n",
      "        \"LoanAmount\": \"250\",\n",
      "        \"ExistingSavings\": \"greater_1000\",\n",
      "        \"EmploymentDuration\": \"less_1\",\n",
      "        \"InstallmentPercent\": \"1\",\n",
      "        \"Sex\": \"female\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"1\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"19\",\n",
      "        \"InstallmentPlans\": \"stores\",\n",
      "        \"Housing\": \"rent\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"unskilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.000978629337623715\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"less_0\",\n",
      "        \"LoanDuration\": \"17\",\n",
      "        \"CreditHistory\": \"credits_paid_to_date\",\n",
      "        \"LoanPurpose\": \"furniture\",\n",
      "        \"LoanAmount\": \"626\",\n",
      "        \"ExistingSavings\": \"500_to_1000\",\n",
      "        \"EmploymentDuration\": \"less_1\",\n",
      "        \"InstallmentPercent\": \"2\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"2\",\n",
      "        \"OwnsProperty\": \"car_other\",\n",
      "        \"Age\": \"28\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"rent\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"none\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.000879146333318204\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"8\",\n",
      "        \"CreditHistory\": \"credits_paid_to_date\",\n",
      "        \"LoanPurpose\": \"car_used\",\n",
      "        \"LoanAmount\": \"6602\",\n",
      "        \"ExistingSavings\": \"less_100\",\n",
      "        \"EmploymentDuration\": \"unemployed\",\n",
      "        \"InstallmentPercent\": \"3\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"none\",\n",
      "        \"CurrentResidenceDuration\": \"4\",\n",
      "        \"OwnsProperty\": \"savings_insurance\",\n",
      "        \"Age\": \"29\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"free\",\n",
      "        \"ExistingCreditsCount\": \"1\",\n",
      "        \"Job\": \"management_self-employed\",\n",
      "        \"Dependents\": \"1\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"No Risk\",\n",
      "        \"Scored Probabilities\": \"0.0112021844834089\"\n",
      "      },\n",
      "      {\n",
      "        \"CheckingStatus\": \"no_checking\",\n",
      "        \"LoanDuration\": \"36\",\n",
      "        \"CreditHistory\": \"prior_payments_delayed\",\n",
      "        \"LoanPurpose\": \"other\",\n",
      "        \"LoanAmount\": \"5835\",\n",
      "        \"ExistingSavings\": \"500_to_1000\",\n",
      "        \"EmploymentDuration\": \"greater_7\",\n",
      "        \"InstallmentPercent\": \"4\",\n",
      "        \"Sex\": \"male\",\n",
      "        \"OthersOnLoan\": \"co-applicant\",\n",
      "        \"CurrentResidenceDuration\": \"4\",\n",
      "        \"OwnsProperty\": \"unknown\",\n",
      "        \"Age\": \"54\",\n",
      "        \"InstallmentPlans\": \"none\",\n",
      "        \"Housing\": \"free\",\n",
      "        \"ExistingCreditsCount\": \"3\",\n",
      "        \"Job\": \"skilled\",\n",
      "        \"Dependents\": \"2\",\n",
      "        \"Telephone\": \"yes\",\n",
      "        \"ForeignWorker\": \"yes\",\n",
      "        \"Scored Labels\": \"Risk\",\n",
      "        \"Scored Probabilities\": \"0.999995648860931\"\n",
      "      }\n",
      "    ]\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import time\n",
    "import json\n",
    "\n",
    "scoring_url = subscription_details['entity']['deployment']['scoring_endpoint']['url']\n",
    "\n",
    "payload_data = {\n",
    "            \"Inputs\": {\n",
    "                \"input1\":\n",
    "                    training_data_pd.sample(40).drop(['Risk'],axis=1).to_dict('records'),\n",
    "            },\n",
    "            \"GlobalParameters\": {\n",
    "            }\n",
    "        }\n",
    "\n",
    "pl_body = str.encode(json.dumps(payload_data))\n",
    "\n",
    "\n",
    "token = model_asset_details['entity']['scoring_endpoint']['credentials']['token']\n",
    "headers = subscription_details['entity']['deployment']['scoring_endpoint']['request_headers']\n",
    "headers['Authorization'] = ('Bearer ' + token)\n",
    "\n",
    "start_time = time.time()\n",
    "response = requests.post(url=scoring_url, data=pl_body, headers=headers)\n",
    "response_time = int(time.time() - start_time)*1000\n",
    "result = response.json()\n",
    "\n",
    "print(json.dumps(result, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store the request and response in payload logging table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "openscale_paylod_input=convert_user_input_2_openscale(payload_data)\n",
    "openscale_payload_output=convert_user_output_2_openscale(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing explicit payload logging.....\n",
      "Number of records in the payload logging table: 42\n"
     ]
    }
   ],
   "source": [
    "import uuid\n",
    "from ibm_watson_openscale.supporting_classes.payload_record import PayloadRecord\n",
    "\n",
    "print(\"Performing explicit payload logging.....\")\n",
    "wos_client.data_sets.store_records(data_set_id=payload_data_set_id, request_body=[PayloadRecord(\n",
    "           scoring_id=str(uuid.uuid4()),\n",
    "           request=openscale_paylod_input,\n",
    "           response=openscale_payload_output,\n",
    "           response_time=460\n",
    ")])\n",
    "time.sleep(5)\n",
    "pl_records_count = wos_client.data_sets.get_records_count(payload_data_set_id)\n",
    "print(\"Number of records in the payload logging table: {}\".format(pl_records_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<HTML>\n",
       "        <body>\n",
       "            <h3>Data Set eedaadcd-2c2d-4326-9278-25569d957852 Records</h3>\n",
       "            <table style='border: 1px solid #dddddd; font-family: Courier'>\n",
       "                <th style='border: 1px solid #dddddd'>asset_revision</th><th style='border: 1px solid #dddddd'>LoanAmount</th><th style='border: 1px solid #dddddd'>scoring_id</th><th style='border: 1px solid #dddddd'>InstallmentPercent</th><th style='border: 1px solid #dddddd'>ExistingSavings</th><th style='border: 1px solid #dddddd'>scoring_timestamp</th><th style='border: 1px solid #dddddd'>prediction_probability</th><th style='border: 1px solid #dddddd'>ProbabilityVector</th><th style='border: 1px solid #dddddd'>CheckingStatus</th><th style='border: 1px solid #dddddd'>LoanPurpose</th><th style='border: 1px solid #dddddd'>Scored Probabilities</th><th style='border: 1px solid #dddddd'>Job</th><th style='border: 1px solid #dddddd'>InstallmentPlans</th><th style='border: 1px solid #dddddd'>OthersOnLoan</th><th style='border: 1px solid #dddddd'>LoanDuration</th><th style='border: 1px solid #dddddd'>Age</th><th style='border: 1px solid #dddddd'>CreditHistory</th><th style='border: 1px solid #dddddd'>CurrentResidenceDuration</th><th style='border: 1px solid #dddddd'>ForeignWorker</th><th style='border: 1px solid #dddddd'>Housing</th><th style='border: 1px solid #dddddd'>ExistingCreditsCount</th><th style='border: 1px solid #dddddd'>deployment_id</th><th style='border: 1px solid #dddddd'>Telephone</th><th style='border: 1px solid #dddddd'>Scored Labels</th><th style='border: 1px solid #dddddd'>Sex</th><th style='border: 1px solid #dddddd'>Dependents</th><th style='border: 1px solid #dddddd'>OwnsProperty</th><th style='border: 1px solid #dddddd'>EmploymentDuration</th>\n",
       "                <tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>250</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-1</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>100_to_500</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.9999979166952926</td><td style='border: 1px solid #dddddd'>[0.9999979166952926, 2.08330470741203e-06]</td><td style='border: 1px solid #dddddd'>less_0</td><td style='border: 1px solid #dddddd'>repairs</td><td style='border: 1px solid #dddddd'>2.08330470741203e-06</td><td style='border: 1px solid #dddddd'>unskilled</td><td style='border: 1px solid #dddddd'>stores</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>7</td><td style='border: 1px solid #dddddd'>19</td><td style='border: 1px solid #dddddd'>no_credits</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>rent</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>female</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>real_estate</td><td style='border: 1px solid #dddddd'>unemployed</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>546</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-10</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>less_100</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.9999993438312913</td><td style='border: 1px solid #dddddd'>[0.9999993438312913, 6.56168708701443e-07]</td><td style='border: 1px solid #dddddd'>0_to_200</td><td style='border: 1px solid #dddddd'>car_used</td><td style='border: 1px solid #dddddd'>6.56168708701443e-07</td><td style='border: 1px solid #dddddd'>management_self-employed</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>4</td><td style='border: 1px solid #dddddd'>26</td><td style='border: 1px solid #dddddd'>all_credits_paid_back</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>female</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>real_estate</td><td style='border: 1px solid #dddddd'>less_1</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>2770</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-11</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>100_to_500</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.924100995063782</td><td style='border: 1px solid #dddddd'>[0.075899004936218, 0.924100995063782]</td><td style='border: 1px solid #dddddd'>no_checking</td><td style='border: 1px solid #dddddd'>education</td><td style='border: 1px solid #dddddd'>0.924100995063782</td><td style='border: 1px solid #dddddd'>management_self-employed</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>24</td><td style='border: 1px solid #dddddd'>46</td><td style='border: 1px solid #dddddd'>prior_payments_delayed</td><td style='border: 1px solid #dddddd'>4</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>car_other</td><td style='border: 1px solid #dddddd'>4_to_7</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>6396</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-12</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>100_to_500</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.689380049705505</td><td style='border: 1px solid #dddddd'>[0.310619950294495, 0.689380049705505]</td><td style='border: 1px solid #dddddd'>no_checking</td><td style='border: 1px solid #dddddd'>other</td><td style='border: 1px solid #dddddd'>0.689380049705505</td><td style='border: 1px solid #dddddd'>unskilled</td><td style='border: 1px solid #dddddd'>stores</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>31</td><td style='border: 1px solid #dddddd'>39</td><td style='border: 1px solid #dddddd'>prior_payments_delayed</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>car_other</td><td style='border: 1px solid #dddddd'>4_to_7</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>250</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-13</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>100_to_500</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.9998197780660121</td><td style='border: 1px solid #dddddd'>[0.9998197780660121, 0.000180221933987923]</td><td style='border: 1px solid #dddddd'>less_0</td><td style='border: 1px solid #dddddd'>car_used</td><td style='border: 1px solid #dddddd'>0.000180221933987923</td><td style='border: 1px solid #dddddd'>unskilled</td><td style='border: 1px solid #dddddd'>stores</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>10</td><td style='border: 1px solid #dddddd'>27</td><td style='border: 1px solid #dddddd'>all_credits_paid_back</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>female</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>savings_insurance</td><td style='border: 1px solid #dddddd'>less_1</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>3412</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-14</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>less_100</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.959494352340698</td><td style='border: 1px solid #dddddd'>[0.040505647659302, 0.959494352340698]</td><td style='border: 1px solid #dddddd'>0_to_200</td><td style='border: 1px solid #dddddd'>car_new</td><td style='border: 1px solid #dddddd'>0.959494352340698</td><td style='border: 1px solid #dddddd'>skilled</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>22</td><td style='border: 1px solid #dddddd'>29</td><td style='border: 1px solid #dddddd'>prior_payments_delayed</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>free</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>savings_insurance</td><td style='border: 1px solid #dddddd'>1_to_4</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>2122</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-15</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>less_100</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.861844927072525</td><td style='border: 1px solid #dddddd'>[0.861844927072525, 0.138155072927475]</td><td style='border: 1px solid #dddddd'>less_0</td><td style='border: 1px solid #dddddd'>car_used</td><td style='border: 1px solid #dddddd'>0.138155072927475</td><td style='border: 1px solid #dddddd'>skilled</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>16</td><td style='border: 1px solid #dddddd'>32</td><td style='border: 1px solid #dddddd'>prior_payments_delayed</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>car_other</td><td style='border: 1px solid #dddddd'>1_to_4</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>6690</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-16</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>100_to_500</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.771963134407997</td><td style='border: 1px solid #dddddd'>[0.771963134407997, 0.228036865592003]</td><td style='border: 1px solid #dddddd'>no_checking</td><td style='border: 1px solid #dddddd'>radio_tv</td><td style='border: 1px solid #dddddd'>0.228036865592003</td><td style='border: 1px solid #dddddd'>management_self-employed</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>25</td><td style='border: 1px solid #dddddd'>45</td><td style='border: 1px solid #dddddd'>prior_payments_delayed</td><td style='border: 1px solid #dddddd'>4</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>car_other</td><td style='border: 1px solid #dddddd'>1_to_4</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>286</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-17</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>100_to_500</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.9999317475958378</td><td style='border: 1px solid #dddddd'>[0.9999317475958378, 6.82524041621946e-05]</td><td style='border: 1px solid #dddddd'>less_0</td><td style='border: 1px solid #dddddd'>furniture</td><td style='border: 1px solid #dddddd'>6.82524041621946e-05</td><td style='border: 1px solid #dddddd'>skilled</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>15</td><td style='border: 1px solid #dddddd'>28</td><td style='border: 1px solid #dddddd'>credits_paid_to_date</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>rent</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>female</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>real_estate</td><td style='border: 1px solid #dddddd'>less_1</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>2606</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-18</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>500_to_1000</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.969918012619019</td><td style='border: 1px solid #dddddd'>[0.030081987380981, 0.969918012619019]</td><td style='border: 1px solid #dddddd'>0_to_200</td><td style='border: 1px solid #dddddd'>radio_tv</td><td style='border: 1px solid #dddddd'>0.969918012619019</td><td style='border: 1px solid #dddddd'>unskilled</td><td style='border: 1px solid #dddddd'>bank</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>39</td><td style='border: 1px solid #dddddd'>21</td><td style='border: 1px solid #dddddd'>outstanding_credit</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>car_other</td><td style='border: 1px solid #dddddd'>4_to_7</td></tr>\n",
       "            </table>\n",
       "        </body>\n",
       "        </HTML>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wos_client.data_sets.show_records(payload_data_set_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "========================================================================\n",
      "\n",
      " Waiting for end of monitoring run c3287e31-f552-431c-89b7-e3499bc48f03 \n",
      "\n",
      "========================================================================\n",
      "\n",
      "\n",
      "\n",
      "running..\n",
      "finished\n",
      "\n",
      "---------------------------\n",
      " Successfully finished run \n",
      "---------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "run_details = wos_client.monitor_instances.run(monitor_instance_id=fairness_monitor_instance_id, background_mode=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<HTML>\n",
       "        <body>\n",
       "            <h3>3a68d116-dc8f-4fd2-aba1-9e10dabb3797 Monitor Runs Metrics from: 2021-07-22 13:05:47.844846  till: 2021-07-29 13:05:47.844870</h3>\n",
       "            <table style='border: 1px solid #dddddd; font-family: Courier'>\n",
       "                <th style='border: 1px solid #dddddd'>ts</th><th style='border: 1px solid #dddddd'>id</th><th style='border: 1px solid #dddddd'>measurement_id</th><th style='border: 1px solid #dddddd'>value</th><th style='border: 1px solid #dddddd'>lower_limit</th><th style='border: 1px solid #dddddd'>upper_limit</th><th style='border: 1px solid #dddddd'>tags</th><th style='border: 1px solid #dddddd'>monitor_definition_id</th><th style='border: 1px solid #dddddd'>monitor_instance_id</th><th style='border: 1px solid #dddddd'>run_id</th><th style='border: 1px solid #dddddd'>target_type</th><th style='border: 1px solid #dddddd'>target_id</th>\n",
       "                <tr><td style='border: 1px solid #dddddd'>2021-07-29 13:05:18.572451+00:00</td><td style='border: 1px solid #dddddd'>fairness_value</td><td style='border: 1px solid #dddddd'>543c8776-ae04-40a6-afa5-2826c1714d27</td><td style='border: 1px solid #dddddd'>103.3</td><td style='border: 1px solid #dddddd'>80.0</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['feature:Sex', 'fairness_metric_type:debiased_fairness', 'feature_value:female']</td><td style='border: 1px solid #dddddd'>fairness</td><td style='border: 1px solid #dddddd'>3a68d116-dc8f-4fd2-aba1-9e10dabb3797</td><td style='border: 1px solid #dddddd'>c3287e31-f552-431c-89b7-e3499bc48f03</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:05:18.572451+00:00</td><td style='border: 1px solid #dddddd'>fairness_value</td><td style='border: 1px solid #dddddd'>543c8776-ae04-40a6-afa5-2826c1714d27</td><td style='border: 1px solid #dddddd'>96.7</td><td style='border: 1px solid #dddddd'>80.0</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['feature:Age', 'fairness_metric_type:debiased_fairness', 'feature_value:18-25']</td><td style='border: 1px solid #dddddd'>fairness</td><td style='border: 1px solid #dddddd'>3a68d116-dc8f-4fd2-aba1-9e10dabb3797</td><td style='border: 1px solid #dddddd'>c3287e31-f552-431c-89b7-e3499bc48f03</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:05:18.572451+00:00</td><td style='border: 1px solid #dddddd'>fairness_value</td><td style='border: 1px solid #dddddd'>69d23bdc-92ac-451f-b1a0-28b052901075</td><td style='border: 1px solid #dddddd'>103.3</td><td style='border: 1px solid #dddddd'>80.0</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['feature:Sex', 'fairness_metric_type:fairness', 'feature_value:female']</td><td style='border: 1px solid #dddddd'>fairness</td><td style='border: 1px solid #dddddd'>3a68d116-dc8f-4fd2-aba1-9e10dabb3797</td><td style='border: 1px solid #dddddd'>c3287e31-f552-431c-89b7-e3499bc48f03</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:05:18.572451+00:00</td><td style='border: 1px solid #dddddd'>fairness_value</td><td style='border: 1px solid #dddddd'>69d23bdc-92ac-451f-b1a0-28b052901075</td><td style='border: 1px solid #dddddd'>96.7</td><td style='border: 1px solid #dddddd'>80.0</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>['feature:Age', 'fairness_metric_type:fairness', 'feature_value:18-25']</td><td style='border: 1px solid #dddddd'>fairness</td><td style='border: 1px solid #dddddd'>3a68d116-dc8f-4fd2-aba1-9e10dabb3797</td><td style='border: 1px solid #dddddd'>c3287e31-f552-431c-89b7-e3499bc48f03</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr>\n",
       "            </table>\n",
       "        </body>\n",
       "        </HTML>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time.sleep(10)\n",
    "\n",
    "wos_client.monitor_instances.show_metrics(monitor_instance_id=fairness_monitor_instance_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explainability configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "===================================================================================\n",
      "\n",
      " Waiting for end of monitor instance creation fb780969-54eb-4b50-9687-bcbb4d2712cd \n",
      "\n",
      "===================================================================================\n",
      "\n",
      "\n",
      "\n",
      "active\n",
      "\n",
      "---------------------------------------\n",
      " Monitor instance successfully created \n",
      "---------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "target = Target(\n",
    "    target_type=TargetTypes.SUBSCRIPTION,\n",
    "    target_id=subscription_id\n",
    ")\n",
    "parameters = {\n",
    "    \"enabled\": True\n",
    "}\n",
    "explainability_details = wos_client.monitor_instances.create(\n",
    "    data_mart_id=data_mart_id,\n",
    "    background_mode=False,\n",
    "    monitor_definition_id=wos_client.monitor_definitions.MONITORS.EXPLAINABILITY.ID,\n",
    "    target=target,\n",
    "    parameters=parameters\n",
    ").result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fb780969-54eb-4b50-9687-bcbb4d2712cd'"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explainability_monitor_id = explainability_details.metadata.id\n",
    "explainability_monitor_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<HTML>\n",
       "        <body>\n",
       "            <h3>Data Set eedaadcd-2c2d-4326-9278-25569d957852 Records</h3>\n",
       "            <table style='border: 1px solid #dddddd; font-family: Courier'>\n",
       "                <th style='border: 1px solid #dddddd'>asset_revision</th><th style='border: 1px solid #dddddd'>debiased_probability</th><th style='border: 1px solid #dddddd'>LoanAmount</th><th style='border: 1px solid #dddddd'>scoring_id</th><th style='border: 1px solid #dddddd'>InstallmentPercent</th><th style='border: 1px solid #dddddd'>ExistingSavings</th><th style='border: 1px solid #dddddd'>scoring_timestamp</th><th style='border: 1px solid #dddddd'>prediction_probability</th><th style='border: 1px solid #dddddd'>ProbabilityVector</th><th style='border: 1px solid #dddddd'>CheckingStatus</th><th style='border: 1px solid #dddddd'>LoanPurpose</th><th style='border: 1px solid #dddddd'>Scored Probabilities</th><th style='border: 1px solid #dddddd'>Job</th><th style='border: 1px solid #dddddd'>InstallmentPlans</th><th style='border: 1px solid #dddddd'>OthersOnLoan</th><th style='border: 1px solid #dddddd'>LoanDuration</th><th style='border: 1px solid #dddddd'>Age</th><th style='border: 1px solid #dddddd'>CreditHistory</th><th style='border: 1px solid #dddddd'>CurrentResidenceDuration</th><th style='border: 1px solid #dddddd'>ForeignWorker</th><th style='border: 1px solid #dddddd'>Housing</th><th style='border: 1px solid #dddddd'>ExistingCreditsCount</th><th style='border: 1px solid #dddddd'>deployment_id</th><th style='border: 1px solid #dddddd'>Telephone</th><th style='border: 1px solid #dddddd'>Scored Labels</th><th style='border: 1px solid #dddddd'>Sex</th><th style='border: 1px solid #dddddd'>Dependents</th><th style='border: 1px solid #dddddd'>OwnsProperty</th><th style='border: 1px solid #dddddd'>EmploymentDuration</th><th style='border: 1px solid #dddddd'>debiased_prediction</th>\n",
       "                <tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>[0.9999979166952926, 2.08330470741203e-06]</td><td style='border: 1px solid #dddddd'>250</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-1</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>100_to_500</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.9999979166952926</td><td style='border: 1px solid #dddddd'>[0.9999979166952926, 2.08330470741203e-06]</td><td style='border: 1px solid #dddddd'>less_0</td><td style='border: 1px solid #dddddd'>repairs</td><td style='border: 1px solid #dddddd'>2.08330470741203e-06</td><td style='border: 1px solid #dddddd'>unskilled</td><td style='border: 1px solid #dddddd'>stores</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>7</td><td style='border: 1px solid #dddddd'>19</td><td style='border: 1px solid #dddddd'>no_credits</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>rent</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>female</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>real_estate</td><td style='border: 1px solid #dddddd'>unemployed</td><td style='border: 1px solid #dddddd'>No Risk</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>[0.9999993438312913, 6.56168708701443e-07]</td><td style='border: 1px solid #dddddd'>546</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-10</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>less_100</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.9999993438312913</td><td style='border: 1px solid #dddddd'>[0.9999993438312913, 6.56168708701443e-07]</td><td style='border: 1px solid #dddddd'>0_to_200</td><td style='border: 1px solid #dddddd'>car_used</td><td style='border: 1px solid #dddddd'>6.56168708701443e-07</td><td style='border: 1px solid #dddddd'>management_self-employed</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>4</td><td style='border: 1px solid #dddddd'>26</td><td style='border: 1px solid #dddddd'>all_credits_paid_back</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>female</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>real_estate</td><td style='border: 1px solid #dddddd'>less_1</td><td style='border: 1px solid #dddddd'>No Risk</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>[0.075899004936218, 0.924100995063782]</td><td style='border: 1px solid #dddddd'>2770</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-11</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>100_to_500</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.924100995063782</td><td style='border: 1px solid #dddddd'>[0.075899004936218, 0.924100995063782]</td><td style='border: 1px solid #dddddd'>no_checking</td><td style='border: 1px solid #dddddd'>education</td><td style='border: 1px solid #dddddd'>0.924100995063782</td><td style='border: 1px solid #dddddd'>management_self-employed</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>24</td><td style='border: 1px solid #dddddd'>46</td><td style='border: 1px solid #dddddd'>prior_payments_delayed</td><td style='border: 1px solid #dddddd'>4</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>car_other</td><td style='border: 1px solid #dddddd'>4_to_7</td><td style='border: 1px solid #dddddd'>Risk</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>[0.310619950294495, 0.689380049705505]</td><td style='border: 1px solid #dddddd'>6396</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-12</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>100_to_500</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.689380049705505</td><td style='border: 1px solid #dddddd'>[0.310619950294495, 0.689380049705505]</td><td style='border: 1px solid #dddddd'>no_checking</td><td style='border: 1px solid #dddddd'>other</td><td style='border: 1px solid #dddddd'>0.689380049705505</td><td style='border: 1px solid #dddddd'>unskilled</td><td style='border: 1px solid #dddddd'>stores</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>31</td><td style='border: 1px solid #dddddd'>39</td><td style='border: 1px solid #dddddd'>prior_payments_delayed</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>car_other</td><td style='border: 1px solid #dddddd'>4_to_7</td><td style='border: 1px solid #dddddd'>Risk</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>[0.9998197780660121, 0.000180221933987923]</td><td style='border: 1px solid #dddddd'>250</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-13</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>100_to_500</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.9998197780660121</td><td style='border: 1px solid #dddddd'>[0.9998197780660121, 0.000180221933987923]</td><td style='border: 1px solid #dddddd'>less_0</td><td style='border: 1px solid #dddddd'>car_used</td><td style='border: 1px solid #dddddd'>0.000180221933987923</td><td style='border: 1px solid #dddddd'>unskilled</td><td style='border: 1px solid #dddddd'>stores</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>10</td><td style='border: 1px solid #dddddd'>27</td><td style='border: 1px solid #dddddd'>all_credits_paid_back</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>female</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>savings_insurance</td><td style='border: 1px solid #dddddd'>less_1</td><td style='border: 1px solid #dddddd'>No Risk</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>[0.040505647659302, 0.959494352340698]</td><td style='border: 1px solid #dddddd'>3412</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-14</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>less_100</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.959494352340698</td><td style='border: 1px solid #dddddd'>[0.040505647659302, 0.959494352340698]</td><td style='border: 1px solid #dddddd'>0_to_200</td><td style='border: 1px solid #dddddd'>car_new</td><td style='border: 1px solid #dddddd'>0.959494352340698</td><td style='border: 1px solid #dddddd'>skilled</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>22</td><td style='border: 1px solid #dddddd'>29</td><td style='border: 1px solid #dddddd'>prior_payments_delayed</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>free</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>savings_insurance</td><td style='border: 1px solid #dddddd'>1_to_4</td><td style='border: 1px solid #dddddd'>Risk</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>[0.861844927072525, 0.138155072927475]</td><td style='border: 1px solid #dddddd'>2122</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-15</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>less_100</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.861844927072525</td><td style='border: 1px solid #dddddd'>[0.861844927072525, 0.138155072927475]</td><td style='border: 1px solid #dddddd'>less_0</td><td style='border: 1px solid #dddddd'>car_used</td><td style='border: 1px solid #dddddd'>0.138155072927475</td><td style='border: 1px solid #dddddd'>skilled</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>16</td><td style='border: 1px solid #dddddd'>32</td><td style='border: 1px solid #dddddd'>prior_payments_delayed</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>car_other</td><td style='border: 1px solid #dddddd'>1_to_4</td><td style='border: 1px solid #dddddd'>No Risk</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>[0.771963134407997, 0.228036865592003]</td><td style='border: 1px solid #dddddd'>6690</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-16</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>100_to_500</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.771963134407997</td><td style='border: 1px solid #dddddd'>[0.771963134407997, 0.228036865592003]</td><td style='border: 1px solid #dddddd'>no_checking</td><td style='border: 1px solid #dddddd'>radio_tv</td><td style='border: 1px solid #dddddd'>0.228036865592003</td><td style='border: 1px solid #dddddd'>management_self-employed</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>25</td><td style='border: 1px solid #dddddd'>45</td><td style='border: 1px solid #dddddd'>prior_payments_delayed</td><td style='border: 1px solid #dddddd'>4</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>car_other</td><td style='border: 1px solid #dddddd'>1_to_4</td><td style='border: 1px solid #dddddd'>No Risk</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>[0.9999317475958378, 6.82524041621946e-05]</td><td style='border: 1px solid #dddddd'>286</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-17</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>100_to_500</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.9999317475958378</td><td style='border: 1px solid #dddddd'>[0.9999317475958378, 6.82524041621946e-05]</td><td style='border: 1px solid #dddddd'>less_0</td><td style='border: 1px solid #dddddd'>furniture</td><td style='border: 1px solid #dddddd'>6.82524041621946e-05</td><td style='border: 1px solid #dddddd'>skilled</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>15</td><td style='border: 1px solid #dddddd'>28</td><td style='border: 1px solid #dddddd'>credits_paid_to_date</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>rent</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>No Risk</td><td style='border: 1px solid #dddddd'>female</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>real_estate</td><td style='border: 1px solid #dddddd'>less_1</td><td style='border: 1px solid #dddddd'>No Risk</td></tr><tr><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>[0.030081987380981, 0.969918012619019]</td><td style='border: 1px solid #dddddd'>2606</td><td style='border: 1px solid #dddddd'>b7a124c7-7537-4aa7-8db3-314754016d7f-18</td><td style='border: 1px solid #dddddd'>3</td><td style='border: 1px solid #dddddd'>500_to_1000</td><td style='border: 1px solid #dddddd'>2021-07-29T13:05:10.880Z</td><td style='border: 1px solid #dddddd'>0.969918012619019</td><td style='border: 1px solid #dddddd'>[0.030081987380981, 0.969918012619019]</td><td style='border: 1px solid #dddddd'>0_to_200</td><td style='border: 1px solid #dddddd'>radio_tv</td><td style='border: 1px solid #dddddd'>0.969918012619019</td><td style='border: 1px solid #dddddd'>unskilled</td><td style='border: 1px solid #dddddd'>bank</td><td style='border: 1px solid #dddddd'>none</td><td style='border: 1px solid #dddddd'>39</td><td style='border: 1px solid #dddddd'>21</td><td style='border: 1px solid #dddddd'>outstanding_credit</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>own</td><td style='border: 1px solid #dddddd'>2</td><td style='border: 1px solid #dddddd'>9764f091ac3099d6c5182764c7b97f90</td><td style='border: 1px solid #dddddd'>yes</td><td style='border: 1px solid #dddddd'>Risk</td><td style='border: 1px solid #dddddd'>male</td><td style='border: 1px solid #dddddd'>1</td><td style='border: 1px solid #dddddd'>car_other</td><td style='border: 1px solid #dddddd'>4_to_7</td><td style='border: 1px solid #dddddd'>Risk</td></tr>\n",
       "            </table>\n",
       "        </body>\n",
       "        </HTML>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wos_client.data_sets.show_records(data_set_id=payload_data_set_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running explanations on scoring IDs: ['b7a124c7-7537-4aa7-8db3-314754016d7f-1']\n",
      "{\n",
      "  \"metadata\": {\n",
      "    \"explanation_task_ids\": [\n",
      "      \"1ae035d7-b46e-478d-9d8c-296897334b78\"\n",
      "    ],\n",
      "    \"created_by\": \"IBMid-50M71Q0T71\",\n",
      "    \"created_at\": \"2021-07-29T13:05:55.173108Z\"\n",
      "  }\n",
      "}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1ae035d7-b46e-478d-9d8c-296897334b78'"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl_records_resp = wos_client.data_sets.get_list_of_records(data_set_id=payload_data_set_id, limit=1, offset=0).result\n",
    "scoring_ids = [pl_records_resp[\"records\"][0][\"entity\"][\"values\"][\"scoring_id\"]]\n",
    "print(\"Running explanations on scoring IDs: {}\".format(scoring_ids))\n",
    "explanation_types = [\"lime\", \"contrastive\"]\n",
    "result = wos_client.monitor_instances.explanation_tasks(scoring_ids=scoring_ids, explanation_types=explanation_types).result\n",
    "print(result)\n",
    "explanation_task_id=result.to_dict()['metadata']['explanation_task_ids'][0]\n",
    "explanation_task_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'metadata': {'explanation_task_id': '1ae035d7-b46e-478d-9d8c-296897334b78',\n",
       "  'created_by': 'IBMid-50M71Q0T71',\n",
       "  'created_at': '2021-07-29T13:05:55.173108Z'},\n",
       " 'entity': {'status': {'state': 'in_progress'},\n",
       "  'scoring_id': 'b7a124c7-7537-4aa7-8db3-314754016d7f-1'}}"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_client.monitor_instances.get_explanation_tasks(explanation_task_id=explanation_task_id).result.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drift monitor configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drift requires a trained model to be uploaded manually for Azure. You can train, create and download a drift detection model using template given ( check for Drift detection model generation) [here](https://github.com/IBM-Watson/aios-data-distribution/blob/master/training_statistics_notebook.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-07-29 13:05:57--  https://github.com/IBM/watson-openscale-samples/blob/main/IBM%20Cloud/Azure/assets/models/credit_risk/azure_creditrisk_drift_detection_model.tar.gz?raw=true\n",
      "Resolving github.com (github.com)... 140.82.113.4\n",
      "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://github.com/IBM/watson-openscale-samples/raw/main/IBM%20Cloud/Azure/assets/models/credit_risk/azure_creditrisk_drift_detection_model.tar.gz [following]\n",
      "--2021-07-29 13:05:58--  https://github.com/IBM/watson-openscale-samples/raw/main/IBM%20Cloud/Azure/assets/models/credit_risk/azure_creditrisk_drift_detection_model.tar.gz\n",
      "Reusing existing connection to github.com:443.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://raw.githubusercontent.com/IBM/watson-openscale-samples/main/IBM%20Cloud/Azure/assets/models/credit_risk/azure_creditrisk_drift_detection_model.tar.gz [following]\n",
      "--2021-07-29 13:05:58--  https://raw.githubusercontent.com/IBM/watson-openscale-samples/main/IBM%20Cloud/Azure/assets/models/credit_risk/azure_creditrisk_drift_detection_model.tar.gz\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 229416 (224K) [application/octet-stream]\n",
      "Saving to: ‘creditrisk_azure_drift_detection_model.tar.gz’\n",
      "\n",
      "creditrisk_azure_dr 100%[===================>] 224.04K  --.-KB/s    in 0.004s  \n",
      "\n",
      "2021-07-29 13:05:58 (62.4 MB/s) - ‘creditrisk_azure_drift_detection_model.tar.gz’ saved [229416/229416]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!rm -rf creditrisk_azure_drift_detection_model.tar.gz\n",
    "!wget -O creditrisk_azure_drift_detection_model.tar.gz https://github.com/IBM/watson-openscale-samples/blob/main/IBM%20Cloud/Azure/assets/models/credit_risk/azure_creditrisk_drift_detection_model.tar.gz?raw=true "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ibm_cloud_sdk_core.detailed_response.DetailedResponse at 0x7f815cdba410>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_client.monitor_instances.upload_drift_model(\n",
    "        model_path='creditrisk_azure_drift_detection_model.tar.gz',\n",
    "        data_mart_id=data_mart_id,\n",
    "        subscription_id=subscription_id\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "monitor_instances = wos_client.monitor_instances.list().result.monitor_instances\n",
    "for monitor_instance in monitor_instances:\n",
    "    monitor_def_id=monitor_instance.entity.monitor_definition_id\n",
    "    if monitor_def_id == \"drift\" and monitor_instance.entity.target.target_id == subscription_id:\n",
    "        wos_client.monitor_instances.delete(monitor_instance.metadata.id)\n",
    "        print('Deleted existing drift monitor instance with id: ', monitor_instance.metadata.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "===================================================================================\n",
      "\n",
      " Waiting for end of monitor instance creation d7d1cdc6-ae66-44c5-9c87-1464fc00a60e \n",
      "\n",
      "===================================================================================\n",
      "\n",
      "\n",
      "\n",
      "active\n",
      "\n",
      "---------------------------------------\n",
      " Monitor instance successfully created \n",
      "---------------------------------------\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'d7d1cdc6-ae66-44c5-9c87-1464fc00a60e'"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = Target(\n",
    "    target_type=TargetTypes.SUBSCRIPTION,\n",
    "    target_id=subscription_id\n",
    "\n",
    ")\n",
    "parameters = {\n",
    "    \"min_samples\": 40,\n",
    "    \"drift_threshold\": 0.1,\n",
    "    \"train_drift_model\": False,\n",
    "    \"enable_model_drift\": True,\n",
    "    \"enable_data_drift\": True\n",
    "}\n",
    "\n",
    "drift_monitor_details = wos_client.monitor_instances.create(\n",
    "    data_mart_id=data_mart_id,\n",
    "    background_mode=False,\n",
    "    monitor_definition_id=wos_client.monitor_definitions.MONITORS.DRIFT.ID,\n",
    "    target=target,\n",
    "    parameters=parameters\n",
    ").result\n",
    "\n",
    "drift_monitor_instance_id = drift_monitor_details.metadata.id\n",
    "drift_monitor_instance_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Drift monitor on demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "========================================================================\n",
      "\n",
      " Waiting for end of monitoring run 0f07080d-e45b-41f1-a035-31670b580fb7 \n",
      "\n",
      "========================================================================\n",
      "\n",
      "\n",
      "\n",
      "finished\n",
      "\n",
      "---------------------------\n",
      " Successfully finished run \n",
      "---------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "drift_run_details = wos_client.monitor_instances.run(monitor_instance_id=drift_monitor_instance_id, background_mode=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<HTML>\n",
       "        <body>\n",
       "            <h3>d7d1cdc6-ae66-44c5-9c87-1464fc00a60e Monitor Runs Metrics from: 2021-07-22 13:06:16.906658  till: 2021-07-29 13:06:16.906681</h3>\n",
       "            <table style='border: 1px solid #dddddd; font-family: Courier'>\n",
       "                <th style='border: 1px solid #dddddd'>ts</th><th style='border: 1px solid #dddddd'>id</th><th style='border: 1px solid #dddddd'>measurement_id</th><th style='border: 1px solid #dddddd'>value</th><th style='border: 1px solid #dddddd'>lower_limit</th><th style='border: 1px solid #dddddd'>upper_limit</th><th style='border: 1px solid #dddddd'>tags</th><th style='border: 1px solid #dddddd'>monitor_definition_id</th><th style='border: 1px solid #dddddd'>monitor_instance_id</th><th style='border: 1px solid #dddddd'>run_id</th><th style='border: 1px solid #dddddd'>target_type</th><th style='border: 1px solid #dddddd'>target_id</th>\n",
       "                <tr><td style='border: 1px solid #dddddd'>2021-07-29 13:06:07.593919+00:00</td><td style='border: 1px solid #dddddd'>data_drift_magnitude</td><td style='border: 1px solid #dddddd'>62f14643-e883-43d6-9ae1-ba765ed7de04</td><td style='border: 1px solid #dddddd'>0.09523809523809523</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>[]</td><td style='border: 1px solid #dddddd'>drift</td><td style='border: 1px solid #dddddd'>d7d1cdc6-ae66-44c5-9c87-1464fc00a60e</td><td style='border: 1px solid #dddddd'>0f07080d-e45b-41f1-a035-31670b580fb7</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:06:07.593919+00:00</td><td style='border: 1px solid #dddddd'>drift_magnitude</td><td style='border: 1px solid #dddddd'>62f14643-e883-43d6-9ae1-ba765ed7de04</td><td style='border: 1px solid #dddddd'>0.0</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>0.1</td><td style='border: 1px solid #dddddd'>[]</td><td style='border: 1px solid #dddddd'>drift</td><td style='border: 1px solid #dddddd'>d7d1cdc6-ae66-44c5-9c87-1464fc00a60e</td><td style='border: 1px solid #dddddd'>0f07080d-e45b-41f1-a035-31670b580fb7</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr><tr><td style='border: 1px solid #dddddd'>2021-07-29 13:06:07.593919+00:00</td><td style='border: 1px solid #dddddd'>predicted_accuracy</td><td style='border: 1px solid #dddddd'>62f14643-e883-43d6-9ae1-ba765ed7de04</td><td style='border: 1px solid #dddddd'>0.9285714285714286</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>None</td><td style='border: 1px solid #dddddd'>[]</td><td style='border: 1px solid #dddddd'>drift</td><td style='border: 1px solid #dddddd'>d7d1cdc6-ae66-44c5-9c87-1464fc00a60e</td><td style='border: 1px solid #dddddd'>0f07080d-e45b-41f1-a035-31670b580fb7</td><td style='border: 1px solid #dddddd'>subscription</td><td style='border: 1px solid #dddddd'>62d30e9f-8fdc-4006-88f5-f76a7edb72e7</td></tr>\n",
       "            </table>\n",
       "        </body>\n",
       "        </HTML>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time.sleep(5)\n",
    "wos_client.monitor_instances.show_metrics(monitor_instance_id=drift_monitor_instance_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Congratulations!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have finished the tutorial for IBM Watson OpenScale and Azure Machine Learning Studio. You can now view the [OpenScale Dashboard](https://aiopenscale.cloud.ibm.com/). Click on the tile for the German Credit model to see fairness, accuracy, and performance monitors. Click on the timeseries graph to get detailed information on transactions during a specific time window.\n",
    "\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
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