  responsible_ai/sm-clarify_online_explainability_tabular/sm-clarify_online_explainability_tabular.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a46bd98",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Tabular Online Explainability with SageMaker Clarify"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a4b915e",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
    "\n",
    "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/responsible_ai|sm-clarify_online_explainability_tabular|sm-clarify_online_explainability_tabular.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2789005",
   "metadata": {},
   "source": [
    "1. [Introduction](#Introduction)\n",
    "1. [General Setup](#General-Setup)\n",
    "    1. [Install dependencies](#Install-dependencies)\n",
    "    1. [Import libraries](#Import-libraries)\n",
    "    1. [Set configurations](#Set-configurations)\n",
    "    1. [Create serializer and deserializer](#Create-serializer-and-deserializer)\n",
    "    1. [For visualization](#For-visualization)\n",
    "1. [Prepare data](#Prepare-data)\n",
    "    1. [Download data](#Download-data)\n",
    "    1. [Loading the data: Adult Dataset](#Loading-the-data:-Adult-Dataset)\n",
    "    1. [Encode and Upload the Dataset](#Encode-and-Upload-the-Dataset)\n",
    "1. [Train XGBoost Model](#Train-XGBoost-Model)\n",
    "1. [Create endpoint](#Create-endpoint)\n",
    "    1. [Create model](#Create-model)\n",
    "    1. [Create endpoint config](#Create-endpoint-config)\n",
    "    1. [Create endpoint](#Create-endpoint)\n",
    "1. [Invoke endpoint](#Invoke-endpoint)\n",
    "    1. [Single record request](#Single-record-request)\n",
    "    1. [Single record request, no explanation](#Single-record-request,-no-explanation)\n",
    "    1. [Batch request, explain both](#Batch-request,-explain-both)\n",
    "    1. [Batch request with more records, explain some of the records](#Batch-request-with-more-records,-explain-some-of-the-records)\n",
    "1. [Cleanup](#Cleanup)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2116c025",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Amazon SageMaker Clarify helps improve your machine learning models by detecting potential bias and helping explain how these models make predictions. The fairness and explainability functionality provided by SageMaker Clarify takes a step towards enabling AWS customers to build trustworthy and understandable machine learning models. \n",
    "\n",
    "SageMaker Clarify currently supports explainability for SageMaker models as an offline processing job. This example notebook showcases a new feature for explainability on a [SageMaker real-time inference](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html) endpoint, a.k.a. [Online Explainability](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability.html).\n",
    "\n",
    "This example notebook walks you through:  \n",
    "1. Key terms and concepts needed to understand SageMaker Clarify\n",
    "1. Training the model on a training dataset.\n",
    "1. Create a model from trained model artifacts, create an endpoint configuration with the new SageMaker Clarify explainer configuration, and create an endpoint using the same explainer configuration.\n",
    "1. Invoke the endpoint with single and batch request with different `EnableExplanations` query.\n",
    "1. Explaining the importance of the various input features on the model's decision.\n",
    "\n",
    "\n",
    "In doing so, the notebook will first train a [SageMaker XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) model using training dataset, then use SageMaker Clarify to analyze a testing dataset in CSV format."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf64fc0a",
   "metadata": {},
   "source": [
    "## General Setup\n",
    "\n",
    "We recommend you use `Python 3 (Data Science)` kernel on SageMaker Studio or `conda_python3` kernel on SageMaker Notebook Instance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48047964",
   "metadata": {},
   "source": [
    "### Install dependencies\n",
    "\n",
    "Install required dependencies. We use `shap` and `matplotlib` to visualize the feature attributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec349708",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install --upgrade -r requirements.txt --quiet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ec24d29",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "85c9245b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import io\n",
    "import os\n",
    "import pprint\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sagemaker import get_execution_role, Session\n",
    "from sagemaker.serializers import CSVSerializer\n",
    "from sagemaker.deserializers import JSONDeserializer\n",
    "from sagemaker.utils import unique_name_from_base"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b662efac",
   "metadata": {},
   "source": [
    "### Set configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0787380b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Region: us-west-2\n",
      "Role: arn:aws:iam::000000000000:role/service-role/SMClarifySageMaker-ExecutionRole\n",
      "Demo S3 key: s3://sagemaker-us-west-2-000000000000/sagemaker/DEMO-Tabular-Adult-1686294366-1e35\n",
      "Demo model name: DEMO-Tabular-Adult-1686294366-1e35-model\n",
      "Demo endpoint config name: DEMO-Tabular-Adult-1686294366-1e35-endpoint-config\n",
      "Demo endpoint name: DEMO-Tabular-Adult-1686294366-1e35-endpoint\n"
     ]
    }
   ],
   "source": [
    "boto3_session = boto3.session.Session()\n",
    "sagemaker_client = boto3.client(\"sagemaker\")\n",
    "sagemaker_runtime_client = boto3.client(\"sagemaker-runtime\")\n",
    "\n",
    "# Initialize sagemaker session\n",
    "sagemaker_session = Session(\n",
    "    boto_session=boto3_session,\n",
    "    sagemaker_client=sagemaker_client,\n",
    "    sagemaker_runtime_client=sagemaker_runtime_client,\n",
    ")\n",
    "\n",
    "region = sagemaker_session.boto_region_name\n",
    "print(f\"Region: {region}\")\n",
    "\n",
    "role = get_execution_role()\n",
    "print(f\"Role: {role}\")\n",
    "\n",
    "s3_client = boto3.client(\"s3\")\n",
    "\n",
    "prefix = unique_name_from_base(\"DEMO-Tabular-Adult\")\n",
    "\n",
    "s3_bucket = sagemaker_session.default_bucket()\n",
    "s3_prefix = f\"sagemaker/{prefix}\"\n",
    "s3_key = f\"s3://{s3_bucket}/{s3_prefix}\"\n",
    "print(f\"Demo S3 key: {s3_key}\")\n",
    "\n",
    "model_name = f\"{prefix}-model\"\n",
    "print(f\"Demo model name: {model_name}\")\n",
    "endpoint_config_name = f\"{prefix}-endpoint-config\"\n",
    "print(f\"Demo endpoint config name: {endpoint_config_name}\")\n",
    "endpoint_name = f\"{prefix}-endpoint\"\n",
    "print(f\"Demo endpoint name: {endpoint_name}\")\n",
    "\n",
    "# Instance type for training and hosting\n",
    "instance_type = \"ml.m5.xlarge\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c44c759",
   "metadata": {},
   "source": [
    "### Create serializer and deserializer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0a7aba",
   "metadata": {},
   "source": [
    "CSV serializer to serialize test data to string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79569f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_serializer = CSVSerializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e64de94",
   "metadata": {},
   "source": [
    "JSON deserializer to deserialize invoke endpoint response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "201230ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "json_deserializer = JSONDeserializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57480844",
   "metadata": {},
   "source": [
    "### For visualization\n",
    "\n",
    "SHAP plots are useful visualization tools to interpret the explanations. For example, [SHAP additive force layout](https://shap.readthedocs.io/en/latest/generated/shap.plots.force.html) shows how each feature contributes to pushing the base value (also called the expected value which is the mean predictions of the training dataset) to the corresponding prediction. Features that push the prediction higher are in red color, while those push the prediction lower are in blue. We have some methods implemented for visualization in `visualization_utils.py` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f780c60",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run visualization_utils.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aefb875e",
   "metadata": {},
   "source": [
    "## Prepare data\n",
    "\n",
    "### Download data\n",
    "Data Source: [https://archive.ics.uci.edu/ml/machine-learning-databases/adult/](https://archive.ics.uci.edu/ml/machine-learning-databases/adult/)\n",
    "\n",
    "Let's __download__ the data and save it in the local folder with the name adult.data and adult.test from UCI repository$^{[2]}$.\n",
    "\n",
    "$^{[2]}$Dua Dheeru, and Efi Karra Taniskidou. \"[UCI Machine Learning Repository](http://archive.ics.uci.edu/ml)\". Irvine, CA: University of California, School of Information and Computer Science (2017)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b94940af",
   "metadata": {},
   "outputs": [],
   "source": [
    "adult_columns = [\n",
    "    \"Age\",\n",
    "    \"Workclass\",\n",
    "    \"fnlwgt\",\n",
    "    \"Education\",\n",
    "    \"Education-Num\",\n",
    "    \"Marital Status\",\n",
    "    \"Occupation\",\n",
    "    \"Relationship\",\n",
    "    \"Ethnic group\",\n",
    "    \"Sex\",\n",
    "    \"Capital Gain\",\n",
    "    \"Capital Loss\",\n",
    "    \"Hours per week\",\n",
    "    \"Country\",\n",
    "    \"Target\",\n",
    "]\n",
    "if not os.path.isfile(\"adult.data\"):\n",
    "    s3_client.download_file(\n",
    "        f\"sagemaker-example-files-prod-{region}\",\n",
    "        \"datasets/tabular/uci_adult/adult.data\",\n",
    "        \"adult.data\",\n",
    "    )\n",
    "    print(f\"adult.data saved!\")\n",
    "else:\n",
    "    print(f\"adult.data already on disk.\")\n",
    "\n",
    "if not os.path.isfile(\"adult.test\"):\n",
    "    s3_client.download_file(\n",
    "        f\"sagemaker-example-files-prod-{region}\",\n",
    "        \"datasets/tabular/uci_adult/adult.test\",\n",
    "        \"adult.test\",\n",
    "    )\n",
    "    print(f\"adult.test saved!\")\n",
    "else:\n",
    "    print(f\"adult.test already on disk.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b39109e",
   "metadata": {},
   "source": [
    "### Loading the data: Adult Dataset\n",
    "From the UCI repository of machine learning datasets, this database contains 14 features concerning demographic characteristics of 45,222 rows (32,561 for training and 12,661 for testing). The task is to predict whether a person has a yearly income that is more or less than $50,000.\n",
    "\n",
    "Here are the features and their possible values:\n",
    "1. **Age**: continuous.\n",
    "1. **Workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n",
    "1. **Fnlwgt**: continuous (the number of people the census takers believe that observation represents).\n",
    "1. **Education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n",
    "1. **Education-num**: continuous.\n",
    "1. **Marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n",
    "1. **Occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n",
    "1. **Relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n",
    "1. **Ethnic group**: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n",
    "1. **Sex**: Female, Male.\n",
    "    * **Note**: this data is extracted from the 1994 Census and enforces a binary option on Sex\n",
    "1. **Capital-gain**: continuous.\n",
    "1. **Capital-loss**: continuous.\n",
    "1. **Hours-per-week**: continuous.\n",
    "1. **Native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.\n",
    "\n",
    "Next, we specify our binary prediction task:  \n",
    "15. **Target**: <=50,000, >$50,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f6d7062",
   "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>Age</th>\n",
       "      <th>Workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>Education</th>\n",
       "      <th>Education-Num</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Ethnic group</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Capital Gain</th>\n",
       "      <th>Capital Loss</th>\n",
       "      <th>Hours per week</th>\n",
       "      <th>Country</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age         Workclass  fnlwgt  Education  Education-Num   \n",
       "0   39         State-gov   77516  Bachelors             13  \\\n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       Marital Status         Occupation   Relationship Ethnic group     Sex   \n",
       "0       Never-married       Adm-clerical  Not-in-family        White    Male  \\\n",
       "1  Married-civ-spouse    Exec-managerial        Husband        White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family        White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband        Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife        Black  Female   \n",
       "\n",
       "   Capital Gain  Capital Loss  Hours per week        Country Target  \n",
       "0          2174             0              40  United-States  <=50K  \n",
       "1             0             0              13  United-States  <=50K  \n",
       "2             0             0              40  United-States  <=50K  \n",
       "3             0             0              40  United-States  <=50K  \n",
       "4             0             0              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data = pd.read_csv(\n",
    "    \"adult.data\", names=adult_columns, sep=r\"\\s*,\\s*\", engine=\"python\", na_values=\"?\"\n",
    ").dropna()\n",
    "\n",
    "testing_data = pd.read_csv(\n",
    "    \"adult.test\", names=adult_columns, sep=r\"\\s*,\\s*\", engine=\"python\", na_values=\"?\", skiprows=1\n",
    ").dropna()\n",
    "\n",
    "training_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c52d24f",
   "metadata": {},
   "source": [
    "### Encode and Upload the Dataset\n",
    "Here we encode the training and test data. Encoding input data is not necessary for SageMaker Clarify, but is necessary for the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e258c0de",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "\n",
    "def number_encode_features(df):\n",
    "    result = df.copy()\n",
    "    encoders = {}\n",
    "    for column in result.columns:\n",
    "        if result.dtypes[column] == np.object:\n",
    "            encoders[column] = preprocessing.LabelEncoder()\n",
    "            #  print('Column:', column, result[column])\n",
    "            result[column] = encoders[column].fit_transform(result[column].fillna(\"None\"))\n",
    "    return result, encoders\n",
    "\n",
    "\n",
    "training_data = pd.concat([training_data[\"Target\"], training_data.drop([\"Target\"], axis=1)], axis=1)\n",
    "training_data, _ = number_encode_features(training_data)\n",
    "training_data.to_csv(\"train_data.csv\", index=False, header=False)\n",
    "\n",
    "testing_data, _ = number_encode_features(testing_data)\n",
    "test_features = testing_data.drop([\"Target\"], axis=1)\n",
    "test_target = testing_data[\"Target\"]\n",
    "test_features.to_csv(\"test_features.csv\", index=False, header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64e24d8",
   "metadata": {},
   "source": [
    "A quick note about our encoding: the \"Female\" Sex value has been encoded as 0 and \"Male\" as 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "90c3e935",
   "metadata": {},
   "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>Target</th>\n",
       "      <th>Age</th>\n",
       "      <th>Workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>Education</th>\n",
       "      <th>Education-Num</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Ethnic group</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Capital Gain</th>\n",
       "      <th>Capital Loss</th>\n",
       "      <th>Hours per week</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>77516</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>83311</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>215646</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>234721</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>338409</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Target  Age  Workclass  fnlwgt  Education  Education-Num  Marital Status   \n",
       "0       0   39          5   77516          9             13               4  \\\n",
       "1       0   50          4   83311          9             13               2   \n",
       "2       0   38          2  215646         11              9               0   \n",
       "3       0   53          2  234721          1              7               2   \n",
       "4       0   28          2  338409          9             13               2   \n",
       "\n",
       "   Occupation  Relationship  Ethnic group  Sex  Capital Gain  Capital Loss   \n",
       "0           0             1             4    1          2174             0  \\\n",
       "1           3             0             4    1             0             0   \n",
       "2           5             1             4    1             0             0   \n",
       "3           5             0             2    1             0             0   \n",
       "4           9             5             2    0             0             0   \n",
       "\n",
       "   Hours per week  Country  \n",
       "0              40       38  \n",
       "1              13       38  \n",
       "2              40       38  \n",
       "3              40       38  \n",
       "4              40        4  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01c8a06b",
   "metadata": {},
   "source": [
    "Get the feature names and the label names from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "efe526bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature names: ['Age', 'Workclass', 'fnlwgt', 'Education', 'Education-Num', 'Marital Status', 'Occupation', 'Relationship', 'Ethnic group', 'Sex', 'Capital Gain', 'Capital Loss', 'Hours per week', 'Country']\n",
      "Label name: Target\n"
     ]
    }
   ],
   "source": [
    "feature_headers = testing_data.columns.to_list()\n",
    "label_header = feature_headers.pop()\n",
    "print(f\"Feature names: {feature_headers}\")\n",
    "print(f\"Label name: {label_header}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ec8ebd",
   "metadata": {},
   "source": [
    "Lastly, let's upload the data to S3 so that they can be used by the training job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "637902e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.s3 import S3Uploader\n",
    "from sagemaker.inputs import TrainingInput\n",
    "\n",
    "train_uri = S3Uploader.upload(\"train_data.csv\", \"s3://{}/{}\".format(s3_bucket, prefix))\n",
    "train_input = TrainingInput(train_uri, content_type=\"csv\")\n",
    "test_uri = S3Uploader.upload(\"test_features.csv\", \"s3://{}/{}\".format(s3_bucket, prefix))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a953f7d8",
   "metadata": {},
   "source": [
    "## Train XGBoost Model\n",
    "\n",
    "Since our focus is on understanding how to use SageMaker Clarify, we keep it simple by using a standard XGBoost model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dc99c39e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: sagemaker-xgboost-2023-06-09-07-06-10-867\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2023-06-09 07:06:11 Starting - Starting the training job..\n",
      "2023-06-09 07:06:27 Starting - Preparing the instances for training.........\n",
      "2023-06-09 07:07:21 Downloading - Downloading input data.....\n",
      "2023-06-09 07:07:47 Training - Downloading the training image...\n",
      "2023-06-09 07:08:07 Training - Training image download completed. Training in progress.....\n",
      "2023-06-09 07:08:33 Uploading - Uploading generated training model.\n",
      "2023-06-09 07:08:44 Completed - Training job completed\n"
     ]
    }
   ],
   "source": [
    "from sagemaker.image_uris import retrieve\n",
    "from sagemaker.estimator import Estimator\n",
    "\n",
    "container = retrieve(\"xgboost\", region, version=\"1.3-1\")\n",
    "xgb = Estimator(\n",
    "    container,\n",
    "    role,\n",
    "    instance_count=1,\n",
    "    instance_type=instance_type,\n",
    "    disable_profiler=True,\n",
    "    debugger_hook_config=False,\n",
    ")\n",
    "\n",
    "xgb.set_hyperparameters(\n",
    "    max_depth=5,\n",
    "    eta=0.2,\n",
    "    gamma=4,\n",
    "    min_child_weight=6,\n",
    "    subsample=0.8,\n",
    "    objective=\"binary:logistic\",\n",
    "    num_round=800,\n",
    ")\n",
    "\n",
    "xgb.fit({\"train\": train_input}, logs=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "796a8b78",
   "metadata": {},
   "source": [
    "Create a new model object which will be used to create the SageMaker model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b4f2e34b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Image': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.3-1',\n",
       " 'Environment': {},\n",
       " 'ModelDataUrl': 's3://sagemaker-us-west-2-000000000000/sagemaker-xgboost-2023-06-09-07-06-10-867/output/model.tar.gz'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = xgb.create_model(name=model_name)\n",
    "container_def = model.prepare_container_def()\n",
    "container_def"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2519d5f9",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Create endpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c22d4a",
   "metadata": {},
   "source": [
    "### Create model\n",
    "\n",
    "The following parameters are required to create a SageMaker model:\n",
    "\n",
    "* `ExecutionRoleArn`: The ARN of the IAM role that Amazon SageMaker can assume to access the model artifacts/ docker images for deployment\n",
    "\n",
    "* `ModelName`: name of the SageMaker model.\n",
    "\n",
    "* `PrimaryContainer`: The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14991a1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model created: DEMO-Tabular-Adult-1686294366-1e35-model\n"
     ]
    }
   ],
   "source": [
    "sagemaker_client.create_model(\n",
    "    ExecutionRoleArn=role,\n",
    "    ModelName=model_name,\n",
    "    PrimaryContainer=container_def,\n",
    ")\n",
    "print(f\"Model created: {model_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c8ce1b6",
   "metadata": {},
   "source": [
    "### Create endpoint config\n",
    "\n",
    "Create an endpoint configuration by calling the `create_endpoint_config` API. Here, supply the same `model_name` used in the `create_model` API call. The `create_endpoint_config` now supports the additional parameter `ClarifyExplainerConfig` to enable the Clarify explainer. The SHAP baseline is mandatory, it can be provided either as inline baseline data (the `ShapBaseline` parameter) or by a S3 baseline file (the `ShapBaselineUri` parameter). Baseline dataset type shall be the same as input dataset type, and baseline samples shall only include features. For more details on baseline selection please [refer this documentation](https://docs.aws.amazon.com/en_us/sagemaker/latest/dg/clarify-feature-attribute-shap-baselines.html).\n",
    "\n",
    "Please see [the API documentation](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html) for details on other config parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e64aa1c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use the mean of the test data as the SHAP baseline: [38.76832669322709, 2.2148738379814077, 189616.37025232404, 10.272377158034528, 10.112749003984064, 2.5952855245683932, 5.989043824701195, 1.401394422310757, 3.6836653386454183, 0.6737715803452855, 1120.301593625498, 89.04189907038513, 40.951593625498006, 35.4675962815405]\n"
     ]
    }
   ],
   "source": [
    "baseline = test_features.mean().to_list()  # Inline baseline data\n",
    "print(f\"Use the mean of the test data as the SHAP baseline: {baseline}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d196e917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'EndpointConfigArn': 'arn:aws:sagemaker:us-west-2:000000000000:endpoint-config/demo-tabular-adult-1686294366-1e35-endpoint-config',\n",
       " 'ResponseMetadata': {'RequestId': 'f413c1f7-561f-4577-84c2-54ab7aaa83dc',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amzn-requestid': 'f413c1f7-561f-4577-84c2-54ab7aaa83dc',\n",
       "   'content-type': 'application/x-amz-json-1.1',\n",
       "   'content-length': '131',\n",
       "   'date': 'Fri, 09 Jun 2023 07:08:47 GMT'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sagemaker_client.create_endpoint_config(\n",
    "    EndpointConfigName=endpoint_config_name,\n",
    "    ProductionVariants=[\n",
    "        {\n",
    "            \"VariantName\": \"TestVariant\",\n",
    "            \"ModelName\": model_name,\n",
    "            \"InitialInstanceCount\": 1,\n",
    "            \"InstanceType\": instance_type,\n",
    "        }\n",
    "    ],\n",
    "    ExplainerConfig={\n",
    "        \"ClarifyExplainerConfig\": {\n",
    "            # \"EnableExplanations\": \"`false`\",  # By default explanations are enabled, but you can change the condition by this parameter.\n",
    "            \"InferenceConfig\": {\n",
    "                \"FeatureHeaders\": feature_headers,\n",
    "            },\n",
    "            \"ShapConfig\": {\n",
    "                \"ShapBaselineConfig\": {\n",
    "                    \"ShapBaseline\": csv_serializer.serialize(baseline),  # inline baseline data\n",
    "                }\n",
    "            },\n",
    "        }\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a7c19b",
   "metadata": {},
   "source": [
    "### Create endpoint\n",
    "\n",
    "Once you have your model and endpoint configuration ready, use the `create_endpoint` API to create your endpoint. The `endpoint_name` must be unique within an AWS Region in your AWS account. The `create_endpoint` API is synchronous in nature and returns an immediate response with the endpoint status being `Creating` state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "70975b6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'EndpointArn': 'arn:aws:sagemaker:us-west-2:000000000000:endpoint/demo-tabular-adult-1686294366-1e35-endpoint',\n",
       " 'ResponseMetadata': {'RequestId': '9bd0678a-8792-4482-ba11-851fb84851ae',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amzn-requestid': '9bd0678a-8792-4482-ba11-851fb84851ae',\n",
       "   'content-type': 'application/x-amz-json-1.1',\n",
       "   'content-length': '111',\n",
       "   'date': 'Fri, 09 Jun 2023 07:08:48 GMT'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sagemaker_client.create_endpoint(\n",
    "    EndpointName=endpoint_name,\n",
    "    EndpointConfigName=endpoint_config_name,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53fa01f8",
   "metadata": {},
   "source": [
    "Wait for the endpoint to be in \"InService\" state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6270f18a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----!"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'EndpointName': 'DEMO-Tabular-Adult-1686294366-1e35-endpoint',\n",
       " 'EndpointArn': 'arn:aws:sagemaker:us-west-2:000000000000:endpoint/demo-tabular-adult-1686294366-1e35-endpoint',\n",
       " 'EndpointConfigName': 'DEMO-Tabular-Adult-1686294366-1e35-endpoint-config',\n",
       " 'ProductionVariants': [{'VariantName': 'TestVariant',\n",
       "   'DeployedImages': [{'SpecifiedImage': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.3-1',\n",
       "     'ResolvedImage': '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost@sha256:d9deabb1be8135a1adee48079d2b6c8cc449c91270c2ead09b1ae80faef1914f',\n",
       "     'ResolutionTime': datetime.datetime(2023, 6, 9, 7, 8, 49, 655000, tzinfo=tzlocal())}],\n",
       "   'CurrentWeight': 1.0,\n",
       "   'DesiredWeight': 1.0,\n",
       "   'CurrentInstanceCount': 1,\n",
       "   'DesiredInstanceCount': 1}],\n",
       " 'EndpointStatus': 'InService',\n",
       " 'CreationTime': datetime.datetime(2023, 6, 9, 7, 8, 49, 147000, tzinfo=tzlocal()),\n",
       " 'LastModifiedTime': datetime.datetime(2023, 6, 9, 7, 10, 58, 162000, tzinfo=tzlocal()),\n",
       " 'ExplainerConfig': {'ClarifyExplainerConfig': {'InferenceConfig': {'FeatureHeaders': ['Age',\n",
       "     'Workclass',\n",
       "     'fnlwgt',\n",
       "     'Education',\n",
       "     'Education-Num',\n",
       "     'Marital Status',\n",
       "     'Occupation',\n",
       "     'Relationship',\n",
       "     'Ethnic group',\n",
       "     'Sex',\n",
       "     'Capital Gain',\n",
       "     'Capital Loss',\n",
       "     'Hours per week',\n",
       "     'Country']},\n",
       "   'ShapConfig': {'ShapBaselineConfig': {'ShapBaseline': '38.76832669322709,2.2148738379814077,189616.37025232404,10.272377158034528,10.112749003984064,2.5952855245683932,5.989043824701195,1.401394422310757,3.6836653386454183,0.6737715803452855,1120.301593625498,89.04189907038513,40.951593625498006,35.4675962815405'}}}},\n",
       " 'ResponseMetadata': {'RequestId': 'dcbdc2f7-d140-4fbc-9fa2-dd9a4f24da6a',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amzn-requestid': 'dcbdc2f7-d140-4fbc-9fa2-dd9a4f24da6a',\n",
       "   'content-type': 'application/x-amz-json-1.1',\n",
       "   'content-length': '1360',\n",
       "   'date': 'Fri, 09 Jun 2023 07:11:19 GMT'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sagemaker_session.wait_for_endpoint(endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3742fae",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Invoke endpoint\n",
    "\n",
    "There are expanding business needs and legislative regulations that require explanations of _why_ a model made the decision it did. SageMaker Clarify uses SHAP to explain the contribution that each input feature makes to the final decision.\n",
    "\n",
    "Below are the several different combination of endpoint invocation, call them one by one and visualize the explanations by running the subsequent cell. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36419959",
   "metadata": {},
   "source": [
    "### Single record request\n",
    "\n",
    "Put only one record in the request body, and then send the request to the endpoint to get its predictions and explanations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "38a0dc1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Body': <botocore.response.StreamingBody object at 0x7fa873edd8d0>,\n",
      " 'ContentType': 'application/json',\n",
      " 'InvokedProductionVariant': 'TestVariant',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '1391',\n",
      "                                      'content-type': 'application/json',\n",
      "                                      'date': 'Fri, 09 Jun 2023 07:11:20 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'TestVariant',\n",
      "                                      'x-amzn-requestid': 'd0c4f3a0-13ad-4344-8557-b3f476e48e42'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': 'd0c4f3a0-13ad-4344-8557-b3f476e48e42',\n",
      "                      'RetryAttempts': 0}}\n"
     ]
    }
   ],
   "source": [
    "request_records = test_features.iloc[:1, :]\n",
    "response = sagemaker_runtime_client.invoke_endpoint(\n",
    "    EndpointName=endpoint_name,\n",
    "    ContentType=\"text/csv\",\n",
    "    Body=csv_serializer.serialize(request_records.to_numpy()),\n",
    ")\n",
    "pprint.pprint(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4202f8e2",
   "metadata": {},
   "source": [
    "Print the response body which is JSON. Please see the developer guide for its schema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a8d231c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'explanations': {'kernel_shap': [[{'attributions': [{'attribution': [-0.002863750863790409]}],\n",
      "                                    'feature_header': 'Age'},\n",
      "                                   {'attributions': [{'attribution': [-7.958259263737853e-05]}],\n",
      "                                    'feature_header': 'Workclass'},\n",
      "                                   {'attributions': [{'attribution': [-0.0005799106931207973]}],\n",
      "                                    'feature_header': 'fnlwgt'},\n",
      "                                   {'attributions': [{'attribution': [7.400679338294426e-05]}],\n",
      "                                    'feature_header': 'Education'},\n",
      "                                   {'attributions': [{'attribution': [-0.0011474692113115962]}],\n",
      "                                    'feature_header': 'Education-Num'},\n",
      "                                   {'attributions': [{'attribution': [-0.0010910584437894126]}],\n",
      "                                    'feature_header': 'Marital Status'},\n",
      "                                   {'attributions': [{'attribution': [4.752810313976612e-05]}],\n",
      "                                    'feature_header': 'Occupation'},\n",
      "                                   {'attributions': [{'attribution': [-0.003110098727907769]}],\n",
      "                                    'feature_header': 'Relationship'},\n",
      "                                   {'attributions': [{'attribution': [-0.0010510744473187237]}],\n",
      "                                    'feature_header': 'Ethnic group'},\n",
      "                                   {'attributions': [{'attribution': [-2.486117335041576e-05]}],\n",
      "                                    'feature_header': 'Sex'},\n",
      "                                   {'attributions': [{'attribution': [0.007341078375308486]}],\n",
      "                                    'feature_header': 'Capital Gain'},\n",
      "                                   {'attributions': [{'attribution': [0.0005919217832840569]}],\n",
      "                                    'feature_header': 'Capital Loss'},\n",
      "                                   {'attributions': [{'attribution': [-0.0003354581308615858]}],\n",
      "                                    'feature_header': 'Hours per week'},\n",
      "                                   {'attributions': [{'attribution': [2.9343273508735876e-05]}],\n",
      "                                    'feature_header': 'Country'}]]},\n",
      " 'predictions': {'content_type': 'text/csv; charset=utf-8',\n",
      "                 'data': '0.0006380207487381995\\n'},\n",
      " 'version': '1.0'}\n"
     ]
    }
   ],
   "source": [
    "result = json_deserializer.deserialize(response[\"Body\"], content_type=response[\"ContentType\"])\n",
    "pprint.pprint(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f6693da",
   "metadata": {},
   "source": [
    "Use SHAP plots to visualize the result. [SHAP additive force layout](https://shap.readthedocs.io/en/latest/generated/shap.plots.force.html) shows how each feature contributes to pushing the base value (also called the expected value which is the mean predictions of the training dataset) to the corresponding prediction. Features that push the prediction higher are in red color, while those push the prediction lower are in blue.\n",
    "\n",
    "The expected value is the average of the model predictions over the baseline. Here we predict the baseline data and then compute the expected value. Only the predictions are needed, so the `EnableExplanations` parameter is used to disable the explanations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2eaa7ced",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected value: 0.0028374067042022\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n"
     ]
    }
   ],
   "source": [
    "response = sagemaker_runtime_client.invoke_endpoint(\n",
    "    EndpointName=endpoint_name,\n",
    "    ContentType=\"text/csv\",\n",
    "    Body=csv_serializer.serialize(baseline),\n",
    "    EnableExplanations=\"`false`\",  # Do not provide explanations\n",
    ")\n",
    "json_object = json_deserializer.deserialize(response[\"Body\"], content_type=response[\"ContentType\"])\n",
    "expected_value = float(\n",
    "    pd.read_csv(io.StringIO(json_object[\"predictions\"][\"data\"]), header=None)\n",
    "    .astype(float)\n",
    "    .mean(axis=1)\n",
    ")\n",
    "print(f\"expected value: {expected_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "da8a8e1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Inference output: \n",
      "Record: 1\tModel Prediction: 0.0006380207487381\n",
      "Visualize the SHAP values for Record number 1 with Model Prediction: 0.0006380207487381\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_result(result, request_records, expected_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3430f125",
   "metadata": {},
   "source": [
    "### Single record request, no explanation\n",
    "\n",
    "Use the `EnableExplanations` parameter to disable the explanations for this request."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f4cd5d44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'explanations': {},\n",
      " 'predictions': {'content_type': 'text/csv; charset=utf-8',\n",
      "                 'data': '0.0006380207487381995\\n'},\n",
      " 'version': '1.0'}\n"
     ]
    }
   ],
   "source": [
    "request_records = test_features.iloc[:1, :]\n",
    "response = sagemaker_runtime_client.invoke_endpoint(\n",
    "    EndpointName=endpoint_name,\n",
    "    ContentType=\"text/csv\",\n",
    "    Body=csv_serializer.serialize(request_records.to_numpy()),\n",
    "    EnableExplanations=\"`false`\",  # Do not provide explanations\n",
    ")\n",
    "result = json_deserializer.deserialize(response[\"Body\"], content_type=response[\"ContentType\"])\n",
    "pprint.pprint(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3709a842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Inference output: \n",
      "Record: 1\tModel Prediction: 0.0006380207487381\n",
      "No Clarify explanations for the record(s)\n"
     ]
    }
   ],
   "source": [
    "visualize_result(result, request_records, expected_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8824447",
   "metadata": {},
   "source": [
    "### Batch request, explain both\n",
    "\n",
    "Put two records in the request body, and then send the request to the endpoint to get their predictions and explanations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "61434d42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'explanations': {'kernel_shap': [[{'attributions': [{'attribution': [-0.002949592623471876]}],\n",
      "                                    'feature_header': 'Age'},\n",
      "                                   {'attributions': [{'attribution': [9.292116176143873e-05]}],\n",
      "                                    'feature_header': 'Workclass'},\n",
      "                                   {'attributions': [{'attribution': [-0.0006494697445634267]}],\n",
      "                                    'feature_header': 'fnlwgt'},\n",
      "                                   {'attributions': [{'attribution': [4.697105974365071e-05]}],\n",
      "                                    'feature_header': 'Education'},\n",
      "                                   {'attributions': [{'attribution': [-0.0010410828803403037]}],\n",
      "                                    'feature_header': 'Education-Num'},\n",
      "                                   {'attributions': [{'attribution': [-0.0010605378944009967]}],\n",
      "                                    'feature_header': 'Marital Status'},\n",
      "                                   {'attributions': [{'attribution': [0.00011443244050032733]}],\n",
      "                                    'feature_header': 'Occupation'},\n",
      "                                   {'attributions': [{'attribution': [-0.0032267514524764716]}],\n",
      "                                    'feature_header': 'Relationship'},\n",
      "                                   {'attributions': [{'attribution': [-0.0010867091425781194]}],\n",
      "                                    'feature_header': 'Ethnic group'},\n",
      "                                   {'attributions': [{'attribution': [-1.7082021856064373e-06]}],\n",
      "                                    'feature_header': 'Sex'},\n",
      "                                   {'attributions': [{'attribution': [0.007222158474038775]}],\n",
      "                                    'feature_header': 'Capital Gain'},\n",
      "                                   {'attributions': [{'attribution': [0.0005413751447842143]}],\n",
      "                                    'feature_header': 'Capital Loss'},\n",
      "                                   {'attributions': [{'attribution': [-0.000168493045777195]}],\n",
      "                                    'feature_header': 'Hours per week'},\n",
      "                                   {'attributions': [{'attribution': [-3.289925049850505e-05]}],\n",
      "                                    'feature_header': 'Country'}],\n",
      "                                  [{'attributions': [{'attribution': [-0.008404027887723617]}],\n",
      "                                    'feature_header': 'Age'},\n",
      "                                   {'attributions': [{'attribution': [0.0006568933537865196]}],\n",
      "                                    'feature_header': 'Workclass'},\n",
      "                                   {'attributions': [{'attribution': [0.008677237824808965]}],\n",
      "                                    'feature_header': 'fnlwgt'},\n",
      "                                   {'attributions': [{'attribution': [0.0014229503054353967]}],\n",
      "                                    'feature_header': 'Education'},\n",
      "                                   {'attributions': [{'attribution': [-0.01862278130235372]}],\n",
      "                                    'feature_header': 'Education-Num'},\n",
      "                                   {'attributions': [{'attribution': [0.037943425274538894]}],\n",
      "                                    'feature_header': 'Marital Status'},\n",
      "                                   {'attributions': [{'attribution': [-0.025825927504319612]}],\n",
      "                                    'feature_header': 'Occupation'},\n",
      "                                   {'attributions': [{'attribution': [0.054077126686993215]}],\n",
      "                                    'feature_header': 'Relationship'},\n",
      "                                   {'attributions': [{'attribution': [1.0414272365337138e-05]}],\n",
      "                                    'feature_header': 'Ethnic group'},\n",
      "                                   {'attributions': [{'attribution': [0.0009073754613358985]}],\n",
      "                                    'feature_header': 'Sex'},\n",
      "                                   {'attributions': [{'attribution': [0.08438387320628549]}],\n",
      "                                    'feature_header': 'Capital Gain'},\n",
      "                                   {'attributions': [{'attribution': [0.0090709522065053]}],\n",
      "                                    'feature_header': 'Capital Loss'},\n",
      "                                   {'attributions': [{'attribution': [0.014799893299492975]}],\n",
      "                                    'feature_header': 'Hours per week'},\n",
      "                                   {'attributions': [{'attribution': [0.0002624650485291702]}],\n",
      "                                    'feature_header': 'Country'}]]},\n",
      " 'predictions': {'content_type': 'text/csv; charset=utf-8',\n",
      "                 'data': '0.0006380207487381995\\n0.1621972769498825\\n'},\n",
      " 'version': '1.0'}\n"
     ]
    }
   ],
   "source": [
    "request_records = test_features.iloc[:2, :]\n",
    "response = sagemaker_runtime_client.invoke_endpoint(\n",
    "    EndpointName=endpoint_name,\n",
    "    ContentType=\"text/csv\",\n",
    "    Body=csv_serializer.serialize(request_records.to_numpy()),\n",
    ")\n",
    "result = json_deserializer.deserialize(response[\"Body\"], content_type=response[\"ContentType\"])\n",
    "pprint.pprint(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ff81310b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Inference output: \n",
      "Record: 1\tModel Prediction: 0.0006380207487381\n",
      "Record: 2\tModel Prediction: 0.1621972769498825\n",
      "Visualize the SHAP values for Record number 1 with Model Prediction: 0.0006380207487381\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualize the SHAP values for Record number 2 with Model Prediction: 0.1621972769498825\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_result(result, request_records, expected_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6a4732f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'explanations': {},\n",
      " 'predictions': {'content_type': 'text/csv; charset=utf-8',\n",
      "                 'data': '0.0006380207487381995\\n0.1621972769498825\\n'},\n",
      " 'version': '1.0'}\n"
     ]
    }
   ],
   "source": [
    "request_records = test_features.iloc[:2, :]\n",
    "response = sagemaker_runtime_client.invoke_endpoint(\n",
    "    EndpointName=endpoint_name,\n",
    "    ContentType=\"text/csv\",\n",
    "    Body=csv_serializer.serialize(request_records.to_numpy()),\n",
    "    EnableExplanations=\"`false`\",  # Do not provide explanations\n",
    ")\n",
    "result = json_deserializer.deserialize(response[\"Body\"], content_type=response[\"ContentType\"])\n",
    "pprint.pprint(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6caec6c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Inference output: \n",
      "Record: 1\tModel Prediction: 0.0006380207487381\n",
      "Record: 2\tModel Prediction: 0.1621972769498825\n",
      "No Clarify explanations for the record(s)\n"
     ]
    }
   ],
   "source": [
    "visualize_result(result, request_records, expected_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63eabecd",
   "metadata": {},
   "source": [
    "### Batch request with more records, explain some of the records\n",
    "\n",
    "Put a few more records to the request body, and then use the `EnableExplanations` expression to filter the records to be explained according to their predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5922a2c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'explanations': {'kernel_shap': [None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  [{'attributions': [{'attribution': [0.006114497519667782]}],\n",
      "                                    'feature_header': 'Age'},\n",
      "                                   {'attributions': [{'attribution': [0.0003501730095504252]}],\n",
      "                                    'feature_header': 'Workclass'},\n",
      "                                   {'attributions': [{'attribution': [0.0012082672336744121]}],\n",
      "                                    'feature_header': 'fnlwgt'},\n",
      "                                   {'attributions': [{'attribution': [-0.0026339170191323202]}],\n",
      "                                    'feature_header': 'Education'},\n",
      "                                   {'attributions': [{'attribution': [-0.0005213645642889592]}],\n",
      "                                    'feature_header': 'Education-Num'},\n",
      "                                   {'attributions': [{'attribution': [0.011663477401727032]}],\n",
      "                                    'feature_header': 'Marital Status'},\n",
      "                                   {'attributions': [{'attribution': [8.174865259447106e-05]}],\n",
      "                                    'feature_header': 'Occupation'},\n",
      "                                   {'attributions': [{'attribution': [0.014338411399320156]}],\n",
      "                                    'feature_header': 'Relationship'},\n",
      "                                   {'attributions': [{'attribution': [-0.0029147193310081615]}],\n",
      "                                    'feature_header': 'Ethnic group'},\n",
      "                                   {'attributions': [{'attribution': [9.674410290752555e-05]}],\n",
      "                                    'feature_header': 'Sex'},\n",
      "                                   {'attributions': [{'attribution': [0.9645793076903789]}],\n",
      "                                    'feature_header': 'Capital Gain'},\n",
      "                                   {'attributions': [{'attribution': [0.0022727186477716904]}],\n",
      "                                    'feature_header': 'Capital Loss'},\n",
      "                                   {'attributions': [{'attribution': [5.6295284624752195e-06]}],\n",
      "                                    'feature_header': 'Hours per week'},\n",
      "                                   {'attributions': [{'attribution': [-0.00027586537371593334]}],\n",
      "                                    'feature_header': 'Country'}],\n",
      "                                  None,\n",
      "                                  [{'attributions': [{'attribution': [-0.014416842837292257]}],\n",
      "                                    'feature_header': 'Age'},\n",
      "                                   {'attributions': [{'attribution': [0.0017354490398059363]}],\n",
      "                                    'feature_header': 'Workclass'},\n",
      "                                   {'attributions': [{'attribution': [0.012205030643245157]}],\n",
      "                                    'feature_header': 'fnlwgt'},\n",
      "                                   {'attributions': [{'attribution': [-0.0023979374730959888]}],\n",
      "                                    'feature_header': 'Education'},\n",
      "                                   {'attributions': [{'attribution': [0.18028287838935786]}],\n",
      "                                    'feature_header': 'Education-Num'},\n",
      "                                   {'attributions': [{'attribution': [0.02478354177040737]}],\n",
      "                                    'feature_header': 'Marital Status'},\n",
      "                                   {'attributions': [{'attribution': [0.10162519690391414]}],\n",
      "                                    'feature_header': 'Occupation'},\n",
      "                                   {'attributions': [{'attribution': [0.08112310305001733]}],\n",
      "                                    'feature_header': 'Relationship'},\n",
      "                                   {'attributions': [{'attribution': [0.010020417810162857]}],\n",
      "                                    'feature_header': 'Ethnic group'},\n",
      "                                   {'attributions': [{'attribution': [0.0031619555256293466]}],\n",
      "                                    'feature_header': 'Sex'},\n",
      "                                   {'attributions': [{'attribution': [0.5942507276691332]}],\n",
      "                                    'feature_header': 'Capital Gain'},\n",
      "                                   {'attributions': [{'attribution': [0.007065926890266458]}],\n",
      "                                    'feature_header': 'Capital Loss'},\n",
      "                                   {'attributions': [{'attribution': [-0.02629517299171047]}],\n",
      "                                    'feature_header': 'Hours per week'},\n",
      "                                   {'attributions': [{'attribution': [-0.005759148344867238]}],\n",
      "                                    'feature_header': 'Country'}],\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  [{'attributions': [{'attribution': [0.011418133903638427]}],\n",
      "                                    'feature_header': 'Age'},\n",
      "                                   {'attributions': [{'attribution': [-0.019112711834036955]}],\n",
      "                                    'feature_header': 'Workclass'},\n",
      "                                   {'attributions': [{'attribution': [0.017348263799401832]}],\n",
      "                                    'feature_header': 'fnlwgt'},\n",
      "                                   {'attributions': [{'attribution': [0.002910214539856598]}],\n",
      "                                    'feature_header': 'Education'},\n",
      "                                   {'attributions': [{'attribution': [-0.005314536439832644]}],\n",
      "                                    'feature_header': 'Education-Num'},\n",
      "                                   {'attributions': [{'attribution': [0.028713179571532813]}],\n",
      "                                    'feature_header': 'Marital Status'},\n",
      "                                   {'attributions': [{'attribution': [0.0015292863198149292]}],\n",
      "                                    'feature_header': 'Occupation'},\n",
      "                                   {'attributions': [{'attribution': [0.03660920319786565]}],\n",
      "                                    'feature_header': 'Relationship'},\n",
      "                                   {'attributions': [{'attribution': [-0.0013032296085910347]}],\n",
      "                                    'feature_header': 'Ethnic group'},\n",
      "                                   {'attributions': [{'attribution': [9.275861027367022e-05]}],\n",
      "                                    'feature_header': 'Sex'},\n",
      "                                   {'attributions': [{'attribution': [0.8921386858609416]}],\n",
      "                                    'feature_header': 'Capital Gain'},\n",
      "                                   {'attributions': [{'attribution': [0.0008752557697499982]}],\n",
      "                                    'feature_header': 'Capital Loss'},\n",
      "                                   {'attributions': [{'attribution': [0.022843418097394173]}],\n",
      "                                    'feature_header': 'Hours per week'},\n",
      "                                   {'attributions': [{'attribution': [0.0012127614888677307]}],\n",
      "                                    'feature_header': 'Country'}],\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  [{'attributions': [{'attribution': [0.01035479390309033]}],\n",
      "                                    'feature_header': 'Age'},\n",
      "                                   {'attributions': [{'attribution': [-0.005103583972230152]}],\n",
      "                                    'feature_header': 'Workclass'},\n",
      "                                   {'attributions': [{'attribution': [-0.007040638893724631]}],\n",
      "                                    'feature_header': 'fnlwgt'},\n",
      "                                   {'attributions': [{'attribution': [-0.003544816318712271]}],\n",
      "                                    'feature_header': 'Education'},\n",
      "                                   {'attributions': [{'attribution': [-0.0007675659877545565]}],\n",
      "                                    'feature_header': 'Education-Num'},\n",
      "                                   {'attributions': [{'attribution': [0.016652558547761924]}],\n",
      "                                    'feature_header': 'Marital Status'},\n",
      "                                   {'attributions': [{'attribution': [0.008956065853782419]}],\n",
      "                                    'feature_header': 'Occupation'},\n",
      "                                   {'attributions': [{'attribution': [0.017231379844120584]}],\n",
      "                                    'feature_header': 'Relationship'},\n",
      "                                   {'attributions': [{'attribution': [-0.0035400535423557034]}],\n",
      "                                    'feature_header': 'Ethnic group'},\n",
      "                                   {'attributions': [{'attribution': [-0.0005046160853101195]}],\n",
      "                                    'feature_header': 'Sex'},\n",
      "                                   {'attributions': [{'attribution': [0.9571801696561953]}],\n",
      "                                    'feature_header': 'Capital Gain'},\n",
      "                                   {'attributions': [{'attribution': [0.0019992170590623984]}],\n",
      "                                    'feature_header': 'Capital Loss'},\n",
      "                                   {'attributions': [{'attribution': [-0.001246273599893405]}],\n",
      "                                    'feature_header': 'Hours per week'},\n",
      "                                   {'attributions': [{'attribution': [-0.00014971735875568548]}],\n",
      "                                    'feature_header': 'Country'}],\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  [{'attributions': [{'attribution': [-0.027662768090417525]}],\n",
      "                                    'feature_header': 'Age'},\n",
      "                                   {'attributions': [{'attribution': [-0.01821643505293241]}],\n",
      "                                    'feature_header': 'Workclass'},\n",
      "                                   {'attributions': [{'attribution': [0.04339607370894737]}],\n",
      "                                    'feature_header': 'fnlwgt'},\n",
      "                                   {'attributions': [{'attribution': [-0.010147401522498496]}],\n",
      "                                    'feature_header': 'Education'},\n",
      "                                   {'attributions': [{'attribution': [0.06797448630579843]}],\n",
      "                                    'feature_header': 'Education-Num'},\n",
      "                                   {'attributions': [{'attribution': [0.05884761780078573]}],\n",
      "                                    'feature_header': 'Marital Status'},\n",
      "                                   {'attributions': [{'attribution': [0.07402402598913706]}],\n",
      "                                    'feature_header': 'Occupation'},\n",
      "                                   {'attributions': [{'attribution': [0.1003548151764327]}],\n",
      "                                    'feature_header': 'Relationship'},\n",
      "                                   {'attributions': [{'attribution': [0.004394713253695476]}],\n",
      "                                    'feature_header': 'Ethnic group'},\n",
      "                                   {'attributions': [{'attribution': [0.008292519334194545]}],\n",
      "                                    'feature_header': 'Sex'},\n",
      "                                   {'attributions': [{'attribution': [0.6060737888718258]}],\n",
      "                                    'feature_header': 'Capital Gain'},\n",
      "                                   {'attributions': [{'attribution': [0.011160596086372265]}],\n",
      "                                    'feature_header': 'Capital Loss'},\n",
      "                                   {'attributions': [{'attribution': [0.046266414662099326]}],\n",
      "                                    'feature_header': 'Hours per week'},\n",
      "                                   {'attributions': [{'attribution': [-0.00019917668726787685]}],\n",
      "                                    'feature_header': 'Country'}],\n",
      "                                  [{'attributions': [{'attribution': [-0.007034035697966927]}],\n",
      "                                    'feature_header': 'Age'},\n",
      "                                   {'attributions': [{'attribution': [0.001560393579535635]}],\n",
      "                                    'feature_header': 'Workclass'},\n",
      "                                   {'attributions': [{'attribution': [0.0004912999268773394]}],\n",
      "                                    'feature_header': 'fnlwgt'},\n",
      "                                   {'attributions': [{'attribution': [0.0060591310091570524]}],\n",
      "                                    'feature_header': 'Education'},\n",
      "                                   {'attributions': [{'attribution': [-0.006568399668502312]}],\n",
      "                                    'feature_header': 'Education-Num'},\n",
      "                                   {'attributions': [{'attribution': [0.06976409920150162]}],\n",
      "                                    'feature_header': 'Marital Status'},\n",
      "                                   {'attributions': [{'attribution': [0.041145934033722305]}],\n",
      "                                    'feature_header': 'Occupation'},\n",
      "                                   {'attributions': [{'attribution': [0.257985256352123]}],\n",
      "                                    'feature_header': 'Relationship'},\n",
      "                                   {'attributions': [{'attribution': [0.007931601227768453]}],\n",
      "                                    'feature_header': 'Ethnic group'},\n",
      "                                   {'attributions': [{'attribution': [-0.0024794049128609302]}],\n",
      "                                    'feature_header': 'Sex'},\n",
      "                                   {'attributions': [{'attribution': [0.5851870800506597]}],\n",
      "                                    'feature_header': 'Capital Gain'},\n",
      "                                   {'attributions': [{'attribution': [0.004887694118272029]}],\n",
      "                                    'feature_header': 'Capital Loss'},\n",
      "                                   {'attributions': [{'attribution': [0.01510332370388142]}],\n",
      "                                    'feature_header': 'Hours per week'},\n",
      "                                   {'attributions': [{'attribution': [0.001357845053483464]}],\n",
      "                                    'feature_header': 'Country'}],\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  [{'attributions': [{'attribution': [-0.000980851705019549]}],\n",
      "                                    'feature_header': 'Age'},\n",
      "                                   {'attributions': [{'attribution': [0.0004807654973812414]}],\n",
      "                                    'feature_header': 'Workclass'},\n",
      "                                   {'attributions': [{'attribution': [0.0031277213097771206]}],\n",
      "                                    'feature_header': 'fnlwgt'},\n",
      "                                   {'attributions': [{'attribution': [7.400192357098767e-05]}],\n",
      "                                    'feature_header': 'Education'},\n",
      "                                   {'attributions': [{'attribution': [0.00017167342687329779]}],\n",
      "                                    'feature_header': 'Education-Num'},\n",
      "                                   {'attributions': [{'attribution': [0.0074863069833167595]}],\n",
      "                                    'feature_header': 'Marital Status'},\n",
      "                                   {'attributions': [{'attribution': [0.001955076460624164]}],\n",
      "                                    'feature_header': 'Occupation'},\n",
      "                                   {'attributions': [{'attribution': [0.011361153375526928]}],\n",
      "                                    'feature_header': 'Relationship'},\n",
      "                                   {'attributions': [{'attribution': [-9.979087004974296e-05]}],\n",
      "                                    'feature_header': 'Ethnic group'},\n",
      "                                   {'attributions': [{'attribution': [0.00038409954200004093]}],\n",
      "                                    'feature_header': 'Sex'},\n",
      "                                   {'attributions': [{'attribution': [0.9688885635516323]}],\n",
      "                                    'feature_header': 'Capital Gain'},\n",
      "                                   {'attributions': [{'attribution': [0.0001905171484137408]}],\n",
      "                                    'feature_header': 'Capital Loss'},\n",
      "                                   {'attributions': [{'attribution': [0.0017565704112934566]}],\n",
      "                                    'feature_header': 'Hours per week'},\n",
      "                                   {'attributions': [{'attribution': [3.2072304604779944e-05]}],\n",
      "                                    'feature_header': 'Country'}],\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  [{'attributions': [{'attribution': [0.003839272601114162]}],\n",
      "                                    'feature_header': 'Age'},\n",
      "                                   {'attributions': [{'attribution': [-0.0007661550500716596]}],\n",
      "                                    'feature_header': 'Workclass'},\n",
      "                                   {'attributions': [{'attribution': [0.006065825922088097]}],\n",
      "                                    'feature_header': 'fnlwgt'},\n",
      "                                   {'attributions': [{'attribution': [-0.002889202254488632]}],\n",
      "                                    'feature_header': 'Education'},\n",
      "                                   {'attributions': [{'attribution': [-0.00036132482207158756]}],\n",
      "                                    'feature_header': 'Education-Num'},\n",
      "                                   {'attributions': [{'attribution': [0.01437901443666123]}],\n",
      "                                    'feature_header': 'Marital Status'},\n",
      "                                   {'attributions': [{'attribution': [0.014011952525245633]}],\n",
      "                                    'feature_header': 'Occupation'},\n",
      "                                   {'attributions': [{'attribution': [0.01730869765020524]}],\n",
      "                                    'feature_header': 'Relationship'},\n",
      "                                   {'attributions': [{'attribution': [0.0009597568052142624]}],\n",
      "                                    'feature_header': 'Ethnic group'},\n",
      "                                   {'attributions': [{'attribution': [-0.0001265337547701889]}],\n",
      "                                    'feature_header': 'Sex'},\n",
      "                                   {'attributions': [{'attribution': [0.9324545668913921]}],\n",
      "                                    'feature_header': 'Capital Gain'},\n",
      "                                   {'attributions': [{'attribution': [0.004776708548033699]}],\n",
      "                                    'feature_header': 'Capital Loss'},\n",
      "                                   {'attributions': [{'attribution': [0.007352223996360395]}],\n",
      "                                    'feature_header': 'Hours per week'},\n",
      "                                   {'attributions': [{'attribution': [-0.0010749534623596402]}],\n",
      "                                    'feature_header': 'Country'}],\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None,\n",
      "                                  None]},\n",
      " 'predictions': {'content_type': 'text/csv; charset=utf-8',\n",
      "                 'data': '0.0006380207487381995\\n'\n",
      "                         '0.1621972769498825\\n'\n",
      "                         '0.3054092824459076\\n'\n",
      "                         '0.9972025156021118\\n'\n",
      "                         '0.0013476297026500106\\n'\n",
      "                         '0.970222532749176\\n'\n",
      "                         '0.0024346690624952316\\n'\n",
      "                         '0.010292550548911095\\n'\n",
      "                         '0.8399405479431152\\n'\n",
      "                         '0.5122696161270142\\n'\n",
      "                         '0.0018374125938862562\\n'\n",
      "                         '0.9031689763069153\\n'\n",
      "                         '0.9002657532691956\\n'\n",
      "                         '4.049914423376322e-05\\n'\n",
      "                         '0.24104931950569153\\n'\n",
      "                         '0.001413782243616879\\n'\n",
      "                         '0.5082170367240906\\n'\n",
      "                         '0.0010446715168654919\\n'\n",
      "                         '0.045837871730327606\\n'\n",
      "                         '0.1906164437532425\\n'\n",
      "                         '0.9927980899810791\\n'\n",
      "                         '0.0003221344668418169\\n'\n",
      "                         '0.001426001195795834\\n'\n",
      "                         '0.3073173761367798\\n'\n",
      "                         '0.05194048210978508\\n'\n",
      "                         '0.9933143258094788\\n'\n",
      "                         '0.04457666352391243\\n'\n",
      "                         '0.05030836910009384\\n'\n",
      "                         '0.16129137575626373\\n'\n",
      "                         '0.017369559034705162\\n'\n",
      "                         '0.5470560193061829\\n'\n",
      "                         '0.007983737625181675\\n'\n",
      "                         '0.0005699116736650467\\n'\n",
      "                         '0.0008268037927336991\\n'\n",
      "                         '0.5507656335830688\\n'\n",
      "                         '0.6126627922058105\\n'\n",
      "                         '0.053321998566389084\\n'\n",
      "                         '0.018157348036766052\\n'\n",
      "                         '4.950358561472967e-05\\n'\n",
      "                         '0.2998623251914978\\n'\n",
      "                         '0.017528310418128967\\n'\n",
      "                         '0.0281106885522604\\n'\n",
      "                         '0.001090642879717052\\n'\n",
      "                         '0.15454837679862976\\n'\n",
      "                         '0.00018330365128349513\\n'\n",
      "                         '0.005943750962615013\\n'\n",
      "                         '2.2748718038201332e-05\\n'\n",
      "                         '0.9408358335494995\\n'\n",
      "                         '0.17077189683914185\\n'\n",
      "                         '0.00017498192028142512\\n'\n",
      "                         '0.16936400532722473\\n'\n",
      "                         '0.9673966765403748\\n'\n",
      "                         '0.9782292246818542\\n'\n",
      "                         '0.1119702160358429\\n'\n",
      "                         '0.2185535877943039\\n'\n",
      "                         '0.023345354944467545\\n'\n",
      "                         '0.035531185567379\\n'\n",
      "                         '0.00127658701967448\\n'\n",
      "                         '0.026648659259080887\\n'\n",
      "                         '0.997665286064148\\n'\n",
      "                         '0.00016714308003429323\\n'\n",
      "                         '0.03608269244432449\\n'\n",
      "                         '0.21766014397144318\\n'\n",
      "                         '0.9987672567367554\\n'\n",
      "                         '0.5855525732040405\\n'\n",
      "                         '0.08639360219240189\\n'\n",
      "                         '0.0007715810206718743\\n'\n",
      "                         '0.5504859089851379\\n'\n",
      "                         '0.12247973680496216\\n'\n",
      "                         '0.1473180651664734\\n'},\n",
      " 'version': '1.0'}\n"
     ]
    }
   ],
   "source": [
    "request_records = test_features.iloc[:70, :]\n",
    "response = sagemaker_runtime_client.invoke_endpoint(\n",
    "    EndpointName=endpoint_name,\n",
    "    ContentType=\"text/csv\",\n",
    "    Body=csv_serializer.serialize(request_records.to_numpy()),\n",
    "    EnableExplanations=\"[0]>`0.95`\",  # Explain a record only when its prediction is greater than the threshold\n",
    ")\n",
    "result = json_deserializer.deserialize(response[\"Body\"], content_type=response[\"ContentType\"])\n",
    "pprint.pprint(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d73b9455",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Inference output: \n",
      "Record: 1\tModel Prediction: 0.0006380207487381\n",
      "Record: 2\tModel Prediction: 0.1621972769498825\n",
      "Record: 3\tModel Prediction: 0.3054092824459076\n",
      "Record: 4\tModel Prediction: 0.9972025156021118\n",
      "Record: 5\tModel Prediction: 0.00134762970265\n",
      "Record: 6\tModel Prediction: 0.970222532749176\n",
      "Record: 7\tModel Prediction: 0.0024346690624952\n",
      "Record: 8\tModel Prediction: 0.010292550548911\n",
      "Record: 9\tModel Prediction: 0.8399405479431152\n",
      "Record: 10\tModel Prediction: 0.5122696161270142\n",
      "Record: 11\tModel Prediction: 0.0018374125938862\n",
      "Record: 12\tModel Prediction: 0.9031689763069152\n",
      "Record: 13\tModel Prediction: 0.9002657532691956\n",
      "Record: 14\tModel Prediction: 4.049914423376322e-05\n",
      "Record: 15\tModel Prediction: 0.2410493195056915\n",
      "Record: 16\tModel Prediction: 0.0014137822436168\n",
      "Record: 17\tModel Prediction: 0.5082170367240906\n",
      "Record: 18\tModel Prediction: 0.0010446715168654\n",
      "Record: 19\tModel Prediction: 0.0458378717303276\n",
      "Record: 20\tModel Prediction: 0.1906164437532425\n",
      "Record: 21\tModel Prediction: 0.9927980899810792\n",
      "Record: 22\tModel Prediction: 0.0003221344668418\n",
      "Record: 23\tModel Prediction: 0.0014260011957958\n",
      "Record: 24\tModel Prediction: 0.3073173761367798\n",
      "Record: 25\tModel Prediction: 0.051940482109785\n",
      "Record: 26\tModel Prediction: 0.9933143258094788\n",
      "Record: 27\tModel Prediction: 0.0445766635239124\n",
      "Record: 28\tModel Prediction: 0.0503083691000938\n",
      "Record: 29\tModel Prediction: 0.1612913757562637\n",
      "Record: 30\tModel Prediction: 0.0173695590347051\n",
      "Record: 31\tModel Prediction: 0.5470560193061829\n",
      "Record: 32\tModel Prediction: 0.0079837376251816\n",
      "Record: 33\tModel Prediction: 0.000569911673665\n",
      "Record: 34\tModel Prediction: 0.0008268037927336\n",
      "Record: 35\tModel Prediction: 0.5507656335830688\n",
      "Record: 36\tModel Prediction: 0.6126627922058105\n",
      "Record: 37\tModel Prediction: 0.053321998566389\n",
      "Record: 38\tModel Prediction: 0.018157348036766\n",
      "Record: 39\tModel Prediction: 4.950358561472967e-05\n",
      "Record: 40\tModel Prediction: 0.2998623251914978\n",
      "Record: 41\tModel Prediction: 0.0175283104181289\n",
      "Record: 42\tModel Prediction: 0.0281106885522604\n",
      "Record: 43\tModel Prediction: 0.001090642879717\n",
      "Record: 44\tModel Prediction: 0.1545483767986297\n",
      "Record: 45\tModel Prediction: 0.0001833036512834\n",
      "Record: 46\tModel Prediction: 0.005943750962615\n",
      "Record: 47\tModel Prediction: 2.274871803820133e-05\n",
      "Record: 48\tModel Prediction: 0.9408358335494996\n",
      "Record: 49\tModel Prediction: 0.1707718968391418\n",
      "Record: 50\tModel Prediction: 0.0001749819202814\n",
      "Record: 51\tModel Prediction: 0.1693640053272247\n",
      "Record: 52\tModel Prediction: 0.9673966765403748\n",
      "Record: 53\tModel Prediction: 0.9782292246818542\n",
      "Record: 54\tModel Prediction: 0.1119702160358429\n",
      "Record: 55\tModel Prediction: 0.2185535877943039\n",
      "Record: 56\tModel Prediction: 0.0233453549444675\n",
      "Record: 57\tModel Prediction: 0.035531185567379\n",
      "Record: 58\tModel Prediction: 0.0012765870196744\n",
      "Record: 59\tModel Prediction: 0.0266486592590808\n",
      "Record: 60\tModel Prediction: 0.997665286064148\n",
      "Record: 61\tModel Prediction: 0.0001671430800342\n",
      "Record: 62\tModel Prediction: 0.0360826924443244\n",
      "Record: 63\tModel Prediction: 0.2176601439714431\n",
      "Record: 64\tModel Prediction: 0.9987672567367554\n",
      "Record: 65\tModel Prediction: 0.5855525732040405\n",
      "Record: 66\tModel Prediction: 0.0863936021924018\n",
      "Record: 67\tModel Prediction: 0.0007715810206718\n",
      "Record: 68\tModel Prediction: 0.5504859089851379\n",
      "Record: 69\tModel Prediction: 0.1224797368049621\n",
      "Record: 70\tModel Prediction: 0.1473180651664734\n",
      "Visualize the SHAP values for Record number 4 with Model Prediction: 0.9972025156021118\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualize the SHAP values for Record number 6 with Model Prediction: 0.970222532749176\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualize the SHAP values for Record number 21 with Model Prediction: 0.9927980899810792\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualize the SHAP values for Record number 26 with Model Prediction: 0.9933143258094788\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualize the SHAP values for Record number 52 with Model Prediction: 0.9673966765403748\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualize the SHAP values for Record number 53 with Model Prediction: 0.9782292246818542\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualize the SHAP values for Record number 60 with Model Prediction: 0.997665286064148\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualize the SHAP values for Record number 64 with Model Prediction: 0.9987672567367554\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_result(result, request_records, expected_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50ee663",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Cleanup\n",
    "\n",
    "Finally, don’t forget to clean up the resources we set up and used for this demo!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea70e043",
   "metadata": {},
   "outputs": [],
   "source": [
    "sagemaker_client.delete_endpoint(EndpointName=endpoint_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f7c3f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "sagemaker_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "780e3045",
   "metadata": {},
   "outputs": [],
   "source": [
    "sagemaker_client.delete_model(ModelName=model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22453c49",
   "metadata": {},
   "source": [
    "## Notebook CI Test Results\n",
    "\n",
    "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
    "\n",
    "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/responsible_ai|sm-clarify_online_explainability_tabular|sm-clarify_online_explainability_tabular.ipynb)\n",
    "\n",
    "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/responsible_ai|sm-clarify_online_explainability_tabular|sm-clarify_online_explainability_tabular.ipynb)\n",
    "\n",
    "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/responsible_ai|sm-clarify_online_explainability_tabular|sm-clarify_online_explainability_tabular.ipynb)\n",
    "\n",
    "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/responsible_ai|sm-clarify_online_explainability_tabular|sm-clarify_online_explainability_tabular.ipynb)\n",
    "\n",
    "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/responsible_ai|sm-clarify_online_explainability_tabular|sm-clarify_online_explainability_tabular.ipynb)\n",
    "\n",
    "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/responsible_ai|sm-clarify_online_explainability_tabular|sm-clarify_online_explainability_tabular.ipynb)\n",
    "\n",
    "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/responsible_ai|sm-clarify_online_explainability_tabular|sm-clarify_online_explainability_tabular.ipynb)\n",
    "\n",
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