sdk/python/responsible-ai/responsibleaidashboard-diabetes-decision-making/responsibleaidashboard-diabetes-decision-making.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ea706a5",
   "metadata": {},
   "source": [
    "# Plan real-world action using counterfactual example analysis and causal analysis\n",
    "\n",
    "This notebooks is an adaptation of the [corresponding notebook in the `responsible-ai-toolbox` repository](https://github.com/microsoft/responsible-ai-toolbox/blob/main/notebooks/responsibleaidashboard/responsibleaidashboard-diabetes-decision-making.ipynb), modified to work with the Responsible AI components in AzureML.\n",
    "\n",
    "We will use the Responsible AI components to assess a regression model trained on diabetes progression data. Next, we will walk through the API calls necessary to create a widget with model analysis insights, then undertake a visual analysis of the model.\n",
    "\n",
    "First, we need to specify the version of the RAI components which are available in the workspace. This was specified when the components were uploaded, and will have defaulted to '1':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39e98eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "version_string = \"1\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d2f23a",
   "metadata": {},
   "source": [
    "We also need to give the name of the compute cluster we want to use in AzureML. Later in this notebook, we will create it if it does not already exist:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "669b2f85",
   "metadata": {},
   "outputs": [],
   "source": [
    "compute_name = \"rai-cluster\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce21b3c2",
   "metadata": {},
   "source": [
    "Finally, we need to specify a version for the data and components we will create while running this notebook. This should be unique for the workspace, but the specific value doesn't matter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "586f2d00",
   "metadata": {},
   "outputs": [],
   "source": [
    "rai_diabetes_decision_making_example_version_string = \"1\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e45a6cc",
   "metadata": {},
   "source": [
    "## Configure workspace details and get a handle to the workspace\n",
    "\n",
    "To connect to a workspace, we need identifier parameters - a subscription, resource group and workspace name. We will use these details in the MLClient from `azure.ai.ml` to get a handle to the required Azure Machine Learning workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "445774ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enter details of your AML workspace\n",
    "subscription_id = \"<SUBSCRIPTION_ID>\"\n",
    "resource_group = \"<RESOURCE_GROUP>\"\n",
    "workspace = \"<AML_WORKSPACE_NAME>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da052977",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle to the workspace\n",
    "from azure.ai.ml import MLClient\n",
    "from azure.identity import DefaultAzureCredential\n",
    "\n",
    "credential = DefaultAzureCredential()\n",
    "ml_client = MLClient(\n",
    "    credential=credential,\n",
    "    subscription_id=subscription_id,\n",
    "    resource_group_name=resource_group,\n",
    "    workspace_name=workspace,\n",
    ")\n",
    "print(ml_client)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b959d49e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get handle to azureml registry for the RAI built in components\n",
    "registry_name = \"azureml\"\n",
    "ml_client_registry = MLClient(\n",
    "    credential=credential,\n",
    "    subscription_id=subscription_id,\n",
    "    resource_group_name=resource_group,\n",
    "    registry_name=registry_name,\n",
    ")\n",
    "print(ml_client_registry)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0427fb99",
   "metadata": {},
   "source": [
    "## Accessing the data\n",
    "\n",
    "First, we need to obtain the dataset and upload it to our AzureML workspace:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41e4e2c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_path = \"data-diabetes-regression/train/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c33d0935",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_path = \"data-diabetes-regression/test/\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc42427d",
   "metadata": {},
   "source": [
    "Load some data for a quick view:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5552d0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import mltable\n",
    "\n",
    "tbl = mltable.load(train_data_path)\n",
    "train_df: pd.DataFrame = tbl.to_pandas_dataframe()\n",
    "\n",
    "# test dataset should have less than 5000 rows\n",
    "test_df = mltable.load(test_data_path).to_pandas_dataframe()\n",
    "assert len(test_df.index) <= 5000\n",
    "\n",
    "display(train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17a601b4",
   "metadata": {},
   "source": [
    "We are going to create two Datasets in AzureML, one for the train and one for the test datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a865d832",
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.ai.ml.entities import Data\n",
    "from azure.ai.ml.constants import AssetTypes\n",
    "\n",
    "input_train_data = \"diabetes_decision_train_mltable\"\n",
    "input_test_data = \"diabetes_decision_test_mltable\"\n",
    "\n",
    "try:\n",
    "    # Try getting data already registered in workspace\n",
    "    train_data = ml_client.data.get(\n",
    "        name=input_train_data,\n",
    "        version=rai_diabetes_decision_making_example_version_string,\n",
    "    )\n",
    "    test_data = ml_client.data.get(\n",
    "        name=input_test_data,\n",
    "        version=rai_diabetes_decision_making_example_version_string,\n",
    "    )\n",
    "except Exception as e:\n",
    "    train_data = Data(\n",
    "        path=train_data_path,\n",
    "        type=AssetTypes.MLTABLE,\n",
    "        description=\"RAI diabetes decision example training data\",\n",
    "        name=input_train_data,\n",
    "        version=rai_diabetes_decision_making_example_version_string,\n",
    "    )\n",
    "    ml_client.data.create_or_update(train_data)\n",
    "\n",
    "    test_data = Data(\n",
    "        path=test_data_path,\n",
    "        type=AssetTypes.MLTABLE,\n",
    "        description=\"RAI diabetes decision example test data\",\n",
    "        name=input_test_data,\n",
    "        version=rai_diabetes_decision_making_example_version_string,\n",
    "    )\n",
    "    ml_client.data.create_or_update(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08755d7d",
   "metadata": {},
   "source": [
    "## A model training pipeline\n",
    "\n",
    "To simplify the model creation process, we're going to use a pipeline. This will have two stages:\n",
    "\n",
    "1. The actual training component\n",
    "1. A model registration component\n",
    "\n",
    "We have to register the model in AzureML in order for our RAI insights components to use it.\n",
    "\n",
    "### The Training Component\n",
    "\n",
    "The training component is for this particular model. In this case, we are going to train a `RandomForestRegressor` on the input data and save it using MLFlow. We need command line arguments to specify the location of the input data, the location where MLFlow should write the output model, and the name of the target column in the dataset.\n",
    "\n",
    "We start by creating a directory to hold the component source:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c0f9bde",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.makedirs(\"component_src\", exist_ok=True)\n",
    "os.makedirs(\"register_model_src\", exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b95f7d",
   "metadata": {},
   "source": [
    "Next, put our training script into the directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdb4e1af",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile component_src/diabetes_decision_training_script.py\n",
    "\n",
    "import argparse\n",
    "import os\n",
    "import shutil\n",
    "import tempfile\n",
    "\n",
    "\n",
    "from azureml.core import Run\n",
    "\n",
    "import mlflow\n",
    "import mlflow.sklearn\n",
    "\n",
    "import mltable\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "def parse_args():\n",
    "    # setup arg parser\n",
    "    parser = argparse.ArgumentParser()\n",
    "\n",
    "    # add arguments\n",
    "    parser.add_argument(\"--training_data\", type=str, help=\"Path to training data\")\n",
    "    parser.add_argument(\"--target_column_name\", type=str, help=\"Name of target column\")\n",
    "    parser.add_argument(\"--model_output\", type=str, help=\"Path of output model\")\n",
    "\n",
    "    # parse args\n",
    "    args = parser.parse_args()\n",
    "\n",
    "    # return args\n",
    "    return args\n",
    "\n",
    "\n",
    "def main(args):\n",
    "    current_experiment = Run.get_context().experiment\n",
    "    tracking_uri = current_experiment.workspace.get_mlflow_tracking_uri()\n",
    "    print(\"tracking_uri: {0}\".format(tracking_uri))\n",
    "    mlflow.set_tracking_uri(tracking_uri)\n",
    "    mlflow.set_experiment(current_experiment.name)\n",
    "\n",
    "    # Read in data\n",
    "    print(\"Reading data\")\n",
    "    tbl = mltable.load(args.training_data)\n",
    "    all_data = tbl.to_pandas_dataframe()\n",
    "\n",
    "    print(\"Extracting X_train, y_train\")\n",
    "    print(\"all_data cols: {0}\".format(all_data.columns))\n",
    "    y_train = all_data[args.target_column_name]\n",
    "    X_train = all_data.drop(labels=args.target_column_name, axis=\"columns\")\n",
    "    print(\"X_train cols: {0}\".format(X_train.columns))\n",
    "\n",
    "    print(\"Training model\")\n",
    "    # The estimator can be changed to suit\n",
    "    model = RandomForestRegressor()\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # Saving model with mlflow - leave this section unchanged\n",
    "    with tempfile.TemporaryDirectory() as td:\n",
    "        print(\"Saving model with MLFlow to temporary directory\")\n",
    "        tmp_output_dir = os.path.join(td, \"my_model_dir\")\n",
    "        mlflow.sklearn.save_model(sk_model=model, path=tmp_output_dir)\n",
    "\n",
    "        print(\"Copying MLFlow model to output path\")\n",
    "        for file_name in os.listdir(tmp_output_dir):\n",
    "            print(\"  Copying: \", file_name)\n",
    "            # As of Python 3.8, copytree will acquire dirs_exist_ok as\n",
    "            # an option, removing the need for listdir\n",
    "            shutil.copy2(src=os.path.join(tmp_output_dir, file_name), dst=os.path.join(args.model_output, file_name))\n",
    "\n",
    "\n",
    "# run script\n",
    "if __name__ == \"__main__\":\n",
    "    # add space in logs\n",
    "    print(\"*\" * 60)\n",
    "    print(\"\\n\\n\")\n",
    "\n",
    "    # parse args\n",
    "    args = parse_args()\n",
    "\n",
    "    # run main function\n",
    "    main(args)\n",
    "\n",
    "    # add space in logs\n",
    "    print(\"*\" * 60)\n",
    "    print(\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "280531f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile register_model_src/register.py\n",
    "\n",
    "# ---------------------------------------------------------\n",
    "# Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "# ---------------------------------------------------------\n",
    "\n",
    "import argparse\n",
    "import json\n",
    "import os\n",
    "import time\n",
    "\n",
    "\n",
    "from azureml.core import Run\n",
    "\n",
    "import mlflow\n",
    "import mlflow.sklearn\n",
    "\n",
    "# Based on example:\n",
    "# https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-cli\n",
    "# which references\n",
    "# https://github.com/Azure/azureml-examples/tree/main/cli/jobs/train/lightgbm/iris\n",
    "\n",
    "\n",
    "def parse_args():\n",
    "    # setup arg parser\n",
    "    parser = argparse.ArgumentParser()\n",
    "\n",
    "    # add arguments\n",
    "    parser.add_argument(\"--model_input_path\", type=str, help=\"Path to input model\")\n",
    "    parser.add_argument(\n",
    "        \"--model_info_output_path\", type=str, help=\"Path to write model info JSON\"\n",
    "    )\n",
    "    parser.add_argument(\n",
    "        \"--model_base_name\", type=str, help=\"Name of the registered model\"\n",
    "    )\n",
    "    parser.add_argument(\n",
    "        \"--model_name_suffix\", type=int, help=\"Set negative to use epoch_secs\"\n",
    "    )\n",
    "\n",
    "    # parse args\n",
    "    args = parser.parse_args()\n",
    "\n",
    "    # return args\n",
    "    return args\n",
    "\n",
    "\n",
    "def main(args):\n",
    "    current_experiment = Run.get_context().experiment\n",
    "    tracking_uri = current_experiment.workspace.get_mlflow_tracking_uri()\n",
    "    print(\"tracking_uri: {0}\".format(tracking_uri))\n",
    "    mlflow.set_tracking_uri(tracking_uri)\n",
    "    mlflow.set_experiment(current_experiment.name)\n",
    "\n",
    "    print(\"Loading model\")\n",
    "    mlflow_model = mlflow.sklearn.load_model(args.model_input_path)\n",
    "\n",
    "    if args.model_name_suffix < 0:\n",
    "        suffix = int(time.time())\n",
    "    else:\n",
    "        suffix = args.model_name_suffix\n",
    "    registered_name = \"{0}_{1}\".format(args.model_base_name, suffix)\n",
    "    print(f\"Registering model as {registered_name}\")\n",
    "\n",
    "    print(\"Registering via MLFlow\")\n",
    "    mlflow.sklearn.log_model(\n",
    "        sk_model=mlflow_model,\n",
    "        registered_model_name=registered_name,\n",
    "        artifact_path=registered_name,\n",
    "    )\n",
    "\n",
    "    print(\"Writing JSON\")\n",
    "    dict = {\"id\": \"{0}:1\".format(registered_name)}\n",
    "    output_path = os.path.join(args.model_info_output_path, \"model_info.json\")\n",
    "    with open(output_path, \"w\") as of:\n",
    "        json.dump(dict, fp=of)\n",
    "\n",
    "\n",
    "# run script\n",
    "if __name__ == \"__main__\":\n",
    "    # add space in logs\n",
    "    print(\"*\" * 60)\n",
    "    print(\"\\n\\n\")\n",
    "\n",
    "    # parse args\n",
    "    args = parse_args()\n",
    "\n",
    "    # run main function\n",
    "    main(args)\n",
    "\n",
    "    # add space in logs\n",
    "    print(\"*\" * 60)\n",
    "    print(\"\\n\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d412b0ad",
   "metadata": {},
   "source": [
    "Now that the training script is saved on our local drive, we create a YAML file to describe it as a component to AzureML. This involves defining the inputs and outputs, specifing the AzureML environment which can run the script, and telling AzureML how to invoke the training script:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a0ce700",
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.ai.ml import load_component\n",
    "\n",
    "yaml_contents = (\n",
    "    f\"\"\"\n",
    "$schema: http://azureml/sdk-2-0/CommandComponent.json\n",
    "name: rai_diabetes_decision_training_component\n",
    "display_name: Diabetes decision training component for RAI example\n",
    "version: {rai_diabetes_decision_making_example_version_string}\n",
    "type: command\n",
    "inputs:\n",
    "  training_data:\n",
    "    type: path\n",
    "  target_column_name:\n",
    "    type: string\n",
    "outputs:\n",
    "  model_output:\n",
    "    type: path\n",
    "code: ./component_src/\n",
    "environment: azureml://registries/azureml/environments/AzureML-responsibleai-0.20-ubuntu20.04-py38-cpu/versions/4\n",
    "\"\"\"\n",
    "    + r\"\"\"\n",
    "command: >-\n",
    "  python diabetes_decision_training_script.py\n",
    "  --training_data ${{{{inputs.training_data}}}}\n",
    "  --target_column_name ${{{{inputs.target_column_name}}}}\n",
    "  --model_output ${{{{outputs.model_output}}}}\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "yaml_filename = \"RAIDiabetesDecisionTrainingComponent.yaml\"\n",
    "\n",
    "with open(yaml_filename, \"w\") as f:\n",
    "    f.write(yaml_contents.format(yaml_contents))\n",
    "\n",
    "train_model_component = load_component(source=yaml_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4f104c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "yaml_contents = f\"\"\"\n",
    "$schema: http://azureml/sdk-2-0/CommandComponent.json\n",
    "name: register_model\n",
    "display_name: Register Model\n",
    "version: {rai_diabetes_decision_making_example_version_string}\n",
    "type: command\n",
    "is_deterministic: False\n",
    "inputs:\n",
    "  model_input_path:\n",
    "    type: path\n",
    "  model_base_name:\n",
    "    type: string\n",
    "  model_name_suffix: # Set negative to use epoch_secs\n",
    "    type: integer\n",
    "    default: -1\n",
    "outputs:\n",
    "  model_info_output_path:\n",
    "    type: path\n",
    "code: ./register_model_src/\n",
    "environment: azureml://registries/azureml/environments/AzureML-responsibleai-0.20-ubuntu20.04-py38-cpu/versions/4\n",
    "command: >-\n",
    "  python register.py\n",
    "  --model_input_path ${{{{inputs.model_input_path}}}}\n",
    "  --model_base_name ${{{{inputs.model_base_name}}}}\n",
    "  --model_name_suffix ${{{{inputs.model_name_suffix}}}}\n",
    "  --model_info_output_path ${{{{outputs.model_info_output_path}}}}\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "yaml_filename = \"register.yaml\"\n",
    "\n",
    "with open(yaml_filename, \"w\") as f:\n",
    "    f.write(yaml_contents)\n",
    "\n",
    "register_component = load_component(source=yaml_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "693f7706",
   "metadata": {},
   "source": [
    "### Running a training pipeline\n",
    "\n",
    "The component to register the model is part of the suite of RAI components, so we do not have to define it here. As such, we are now ready to run the training pipeline itself.\n",
    "\n",
    "We start by ensuring that the compute cluster named above exists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad3b43ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.ai.ml.entities import AmlCompute\n",
    "\n",
    "all_compute_names = [x.name for x in ml_client.compute.list()]\n",
    "\n",
    "if compute_name in all_compute_names:\n",
    "    print(f\"Found existing compute: {compute_name}\")\n",
    "else:\n",
    "    my_compute = AmlCompute(\n",
    "        name=compute_name,\n",
    "        size=\"Standard_D2_v2\",\n",
    "        min_instances=0,\n",
    "        max_instances=4,\n",
    "        idle_time_before_scale_down=3600,\n",
    "    )\n",
    "    ml_client.compute.begin_create_or_update(my_compute).result()\n",
    "    print(\"Initiated compute creation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7dfa1d7",
   "metadata": {},
   "source": [
    "We continue by defining the name under which we want to register the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d12c37f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "model_name_suffix = int(time.time())\n",
    "model_name = \"rai_diabetes_decision_model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15cfc17e",
   "metadata": {},
   "source": [
    "Next, we define the pipeline using objects from the AzureML SDKv2. As mentioned above, there are two component jobs: one to train the model, and one to register it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de827a6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.ai.ml import dsl, Input\n",
    "\n",
    "target_feature = \"y\"\n",
    "\n",
    "diabetes_train_pq = Input(\n",
    "    type=\"mltable\",\n",
    "    path=f\"azureml:{input_train_data}:{rai_diabetes_decision_making_example_version_string}\",\n",
    "    mode=\"download\",\n",
    ")\n",
    "diabetes_test_pq = Input(\n",
    "    type=\"mltable\",\n",
    "    path=f\"azureml:{input_test_data}:{rai_diabetes_decision_making_example_version_string}\",\n",
    "    mode=\"download\",\n",
    ")\n",
    "\n",
    "\n",
    "@dsl.pipeline(\n",
    "    compute=compute_name,\n",
    "    description=\"Register Model for RAI Diabetes Decision Making example\",\n",
    "    experiment_name=f\"RAI_Diabetes_Decision_Example_Model_Training_{model_name_suffix}\",\n",
    ")\n",
    "def my_training_pipeline(target_column_name, training_data):\n",
    "    trained_model = train_model_component(\n",
    "        target_column_name=target_column_name, training_data=training_data\n",
    "    )\n",
    "    trained_model.set_limits(timeout=120)\n",
    "\n",
    "    _ = register_component(\n",
    "        model_input_path=trained_model.outputs.model_output,\n",
    "        model_base_name=model_name,\n",
    "        model_name_suffix=model_name_suffix,\n",
    "    )\n",
    "\n",
    "    return {}\n",
    "\n",
    "\n",
    "model_registration_pipeline_job = my_training_pipeline(\n",
    "    target_feature, diabetes_train_pq\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8acc2a29",
   "metadata": {},
   "source": [
    "With the pipeline definition created, we can submit it to AzureML. We define a helper function to do the submission, which waits for the submitted job to complete:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5c0f883",
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.ai.ml.entities import PipelineJob\n",
    "from IPython.core.display import HTML\n",
    "from IPython.display import display\n",
    "\n",
    "\n",
    "def submit_and_wait(ml_client, pipeline_job) -> PipelineJob:\n",
    "    created_job = ml_client.jobs.create_or_update(pipeline_job)\n",
    "    assert created_job is not None\n",
    "\n",
    "    print(\"Pipeline job can be accessed in the following URL:\")\n",
    "    display(HTML('<a href=\"{0}\">{0}</a>'.format(created_job.studio_url)))\n",
    "\n",
    "    while created_job.status not in [\n",
    "        \"Completed\",\n",
    "        \"Failed\",\n",
    "        \"Canceled\",\n",
    "        \"NotResponding\",\n",
    "    ]:\n",
    "        time.sleep(30)\n",
    "        created_job = ml_client.jobs.get(created_job.name)\n",
    "        print(\"Latest status : {0}\".format(created_job.status))\n",
    "    assert created_job.status == \"Completed\"\n",
    "    return created_job\n",
    "\n",
    "\n",
    "# This is the actual submission\n",
    "training_job = submit_and_wait(ml_client, model_registration_pipeline_job)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9889100",
   "metadata": {},
   "source": [
    "##  Creating the RAI Insights\n",
    "\n",
    "We have a registered model, and can now run a pipeline to create the RAI insights. First off, compute the name of the model we registered:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8575b364",
   "metadata": {},
   "outputs": [],
   "source": [
    "expected_model_id = f\"{model_name}_{model_name_suffix}:1\"\n",
    "azureml_model_id = f\"azureml:{expected_model_id}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4bad8a5",
   "metadata": {},
   "source": [
    "Now, we create the RAI pipeline itself. There are four 'component stages' in this pipeline:\n",
    "\n",
    "1. Construct an empty `RAIInsights` object\n",
    "1. Run the RAI tool components\n",
    "1. Gather the tool outputs into a single `RAIInsights` object\n",
    "1. (Optional) Generate a score card in pdf format summarizing model performance, and key aspects from the rai tool components\n",
    "\n",
    "We start by loading the RAI component definitions for use in our pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "924cc6ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "label = \"latest\"\n",
    "\n",
    "rai_constructor_component = ml_client_registry.components.get(\n",
    "    name=\"microsoft_azureml_rai_tabular_insight_constructor\", label=label\n",
    ")\n",
    "\n",
    "# We get latest version and use the same version for all components\n",
    "version = rai_constructor_component.version\n",
    "print(\"The current version of RAI built-in components is: \" + version)\n",
    "\n",
    "rai_causal_component = ml_client_registry.components.get(\n",
    "    name=\"microsoft_azureml_rai_tabular_causal\", version=version\n",
    ")\n",
    "\n",
    "rai_counterfactual_component = ml_client_registry.components.get(\n",
    "    name=\"microsoft_azureml_rai_tabular_counterfactual\", version=version\n",
    ")\n",
    "\n",
    "rai_gather_component = ml_client_registry.components.get(\n",
    "    name=\"microsoft_azureml_rai_tabular_insight_gather\", version=version\n",
    ")\n",
    "\n",
    "rai_scorecard_component = ml_client_registry.components.get(\n",
    "    name=\"microsoft_azureml_rai_tabular_score_card\", version=version\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64a22471",
   "metadata": {},
   "source": [
    "## Score card generation config\n",
    "For score card generation, we need some additional configuration in a separate json file. Here we configure the following model performance metrics for reporting:\n",
    "- mean absolute error\n",
    "- mean squared error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3719a702",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "score_card_config_dict = {\n",
    "    \"Model\": {\n",
    "        \"ModelName\": \"Diabetes disease progression measure\",\n",
    "        \"ModelType\": \"Regression\",\n",
    "        \"ModelSummary\": \"This model provides a quantitative measure of disease progression one year after baseline\",\n",
    "    },\n",
    "    \"Metrics\": {\"mean_absolute_error\": {\"threshold\": \"<=5\"}, \"mean_squared_error\": {}},\n",
    "}\n",
    "\n",
    "score_card_config_filename = \"rai_diabetes_decision_score_card_config.json\"\n",
    "\n",
    "with open(score_card_config_filename, \"w\") as f:\n",
    "    json.dump(score_card_config_dict, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cecaea36",
   "metadata": {},
   "source": [
    "Now the pipeline itself. This creates an empty `RAIInsights` object, adds the analyses, and then gathers everything into the final `RAIInsights` output. Where complex objects need to be passed (such as a list of treatment feature names), they must be encoded as JSON strings.\n",
    "\n",
    "Note that the timeout for the counterfactual generation is longer, since this is a comparatively slow process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f62552f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.ai.ml import Input\n",
    "from azure.ai.ml.constants import AssetTypes\n",
    "\n",
    "score_card_config_path = Input(\n",
    "    type=\"uri_file\", path=score_card_config_filename, mode=\"download\"\n",
    ")\n",
    "\n",
    "treatment_feature_names = json.dumps([\"bmi\", \"bp\", \"s2\"])\n",
    "desired_range = json.dumps([50, 120])\n",
    "\n",
    "\n",
    "@dsl.pipeline(\n",
    "    compute=compute_name,\n",
    "    description=\"Example RAI computation on diabetes decision making data\",\n",
    "    experiment_name=f\"RAI_Diabetes_Decision_Example_RAIInsights_Computation_{model_name_suffix}\",\n",
    ")\n",
    "def rai_decision_pipeline(\n",
    "    target_column_name, train_data, test_data, score_card_config_path\n",
    "):\n",
    "    # Initiate the RAIInsights\n",
    "    create_rai_job = rai_constructor_component(\n",
    "        title=\"RAI Dashboard Example\",\n",
    "        task_type=\"regression\",\n",
    "        model_info=expected_model_id,\n",
    "        model_input=Input(type=AssetTypes.MLFLOW_MODEL, path=azureml_model_id),\n",
    "        train_dataset=train_data,\n",
    "        test_dataset=test_data,\n",
    "        target_column_name=target_column_name,\n",
    "    )\n",
    "    create_rai_job.set_limits(timeout=120)\n",
    "\n",
    "    # Add causal analysis\n",
    "    causal_job = rai_causal_component(\n",
    "        rai_insights_dashboard=create_rai_job.outputs.rai_insights_dashboard,\n",
    "        treatment_features=treatment_feature_names,\n",
    "    )\n",
    "    causal_job.set_limits(timeout=120)\n",
    "\n",
    "    # Add counterfactual analysis\n",
    "    counterfactual_job = rai_counterfactual_component(\n",
    "        rai_insights_dashboard=create_rai_job.outputs.rai_insights_dashboard,\n",
    "        total_cfs=20,\n",
    "        desired_range=desired_range,\n",
    "    )\n",
    "    counterfactual_job.set_limits(timeout=600)\n",
    "\n",
    "    # Combine everything\n",
    "    rai_gather_job = rai_gather_component(\n",
    "        constructor=create_rai_job.outputs.rai_insights_dashboard,\n",
    "        insight_2=causal_job.outputs.causal,\n",
    "        insight_3=counterfactual_job.outputs.counterfactual,\n",
    "    )\n",
    "    rai_gather_job.set_limits(timeout=120)\n",
    "\n",
    "    rai_gather_job.outputs.dashboard.mode = \"upload\"\n",
    "    rai_gather_job.outputs.ux_json.mode = \"upload\"\n",
    "\n",
    "    # Generate score card in pdf format for a summary report on model performance,\n",
    "    # and observe distrbution of error between prediction vs ground truth.\n",
    "    rai_scorecard_job = rai_scorecard_component(\n",
    "        dashboard=rai_gather_job.outputs.dashboard,\n",
    "        pdf_generation_config=score_card_config_path,\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"dashboard\": rai_gather_job.outputs.dashboard,\n",
    "        \"ux_json\": rai_gather_job.outputs.ux_json,\n",
    "        \"scorecard\": rai_scorecard_job.outputs.scorecard,\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c26b9a6",
   "metadata": {},
   "source": [
    "Next, we define the pipeline object itself, and ensure that the outputs will be available for download:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4da9f5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import uuid\n",
    "from azure.ai.ml import Output\n",
    "\n",
    "# Pipeline to construct the RAI Insights\n",
    "insights_pipeline_job = rai_decision_pipeline(\n",
    "    target_column_name=target_feature,\n",
    "    train_data=diabetes_train_pq,\n",
    "    test_data=diabetes_test_pq,\n",
    "    score_card_config_path=score_card_config_path,\n",
    ")\n",
    "\n",
    "# Workaround to enable the download\n",
    "rand_path = str(uuid.uuid4())\n",
    "insights_pipeline_job.outputs.dashboard = Output(\n",
    "    path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/dashboard/\",\n",
    "    mode=\"upload\",\n",
    "    type=\"uri_folder\",\n",
    ")\n",
    "insights_pipeline_job.outputs.ux_json = Output(\n",
    "    path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/ux_json/\",\n",
    "    mode=\"upload\",\n",
    "    type=\"uri_folder\",\n",
    ")\n",
    "insights_pipeline_job.outputs.scorecard = Output(\n",
    "    path=f\"azureml://datastores/workspaceblobstore/paths/{rand_path}/scorecard/\",\n",
    "    mode=\"upload\",\n",
    "    type=\"uri_folder\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "204c6fe5",
   "metadata": {},
   "source": [
    "And submit the pipeline to AzureML for execution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc76bf2e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "insights_job = submit_and_wait(ml_client, insights_pipeline_job)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65437c8f",
   "metadata": {},
   "source": [
    "The dashboard should appear in the AzureML portal in the registered model view. The following cell computes the expected URI:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "026e3fd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_id = ml_client._operation_scope.subscription_id\n",
    "rg_name = ml_client._operation_scope.resource_group_name\n",
    "ws_name = ml_client.workspace_name\n",
    "\n",
    "expected_uri = f\"https://ml.azure.com/model/{expected_model_id}/model_analysis?wsid=/subscriptions/{sub_id}/resourcegroups/{rg_name}/workspaces/{ws_name}\"\n",
    "\n",
    "print(f\"Please visit {expected_uri} to see your analysis\")"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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nT3nA9vNKKs/oBSA5PkR9OoTqnX8Xab+zosHtNESb2BD1vzRMizYV6qu9Lr9tHq/0xsZChVuDdFUn/zeYTbvL9aXD/3w4siuU+Y9CpV0cquR4whAAAAAXmj7Jw2rd9vT6YTV+4Cj1FOjlTaPrdBXIqdoG0LwY+wAAAMCFbXzaG9VV4Rp7abzCskrtPOhW17YWxdrqPn38+e4yPbbUqfsGxmjbH9vrN/2jlXFlhDLHttLqKW3kyPFoxjKnKk6a3tu4q0wP3Bir2b9I0B1pkVo5uY36XhKmPyzP1Y79Ln30UBvdkRapB2+O1cujW+lgboXW7Kh5cfe6b8sUajbpP4+302/6R+uOtEi9/0CSpt8Wr1VfIailDAAADsVJREFUlmjt16e+IHzR54V6e3OR1k1rq3ljWmpYrwhNvzVOq6e00YFcj178OF+SdEWKVQO7hSsqLEiJMcG66XKbhvS06eKjFR827irTjGVOLRrfWu9OTFTGlRH63Q0xWju1reIigvXkyly5PFXzpJt2l+mVTwt038AYrXs0WXekReqeflH652PJurZruJzFlXU+/zg9ZlIvIP0vDdPLn+Tr3r8e1t0/i5Ikzfs0X6HmxllqYWA3mwb3tGnyomx9vd+lwT1tKnFVJZnaxoX4lcFJjg9R17YWzXrfqYpKny5Jsmj7XpeWbS0OWHqndUywYm1Bmr0yT0XllTqU79Won0bVq3+hZpN+f0ucBs85qCun79OUm2KVmmiRt9Knf39XrsOFXs0d1fKUCbGGCgmWZtwWr20/uNR/1gGNuy5a16SGqbCsUvM+ydeXe116577EGom2pJgQ/eovh3XXNVHq0d6qA7kVevw9p4JM0tTBcY32fwcAAIDzR592w7Rw2wMBtx0riTl94Hq1j+0hSVqw5XenvQLkxLYBnJsY+wAAAICxNefSeB6vT0XllYqPCJa1jnNPFV7pb/8sVNu4ED1wY6zfxc+S1L9LuIb3idC6b8t0MK9C7ROOz/9d2SFU6V2OXyAcawvSgK7h2vi/Mt13fYxfIOPKjqFqn2DWzix3jT4kxgRr0sAYv/myIJM06qdRytxYqA+3l2hYr5oV26WqAMjfPi/U8D4R6tMh1G9b1zYW9b80XF86XCou9ykitPZzcuw8XNUpVIO62/yOlRwfopsvt+m9rcXKLalUi8jgWs+ZOVgad2203v13Ua3HQv0RhriADO4ZoRdGtdQTK5xa/VWJ2sWH6A8ZCUqIDNaH20vPuH2b1aS3JiTq6Q/yNP+zfL2+oVAdW5n18OA49Whv1ccnpK+iwoL06j2tdN/CI3r03RxJ0m29I7RoQmtNeSu7Rtvdk616akQLPbg4WxnPl2p03yjd07d+YQhJ6pli1bpHk/X7d3P02FKnytw+RYUF6ScdQzV1SJzCLU0XLmgbF6JPH2mr2Stz9cbGQj21Kk9hFpNuvtyml0a3VI921hr36XtJmCYMiNHEhUc0Y5lTVnPV/k/+PEEdW5kDHAUAAABG18KWor4dRmnjnoUBt584Kfrt4fV+JTRPpW+HUWphS2nMrgJoRIx9AAAAwNjOhaXxvJU++XwKGB44WWFZpXbsc6lHO6taRQXX2B4SLPW6KFSLNhVpb47HLwzROdGsqDD/ChRRYUGKiwhSp5Pmv0LNJllqmdHu1MoScEn5hMhgXZJk1sG8CpW6ay5HL0mHC7za/aNH674t07xPA1fU65xoVomrUhGhNR/fMcfOw9YfXIr89XcB94mPCNKRwgpZQ0ynPGextuAa5wVnhjCEwdisJq2e0ibgNnOwNGFAtCYMiK6xrfJvF/v9PWlQjCYNqv+aoeFWkx67NU6P3Rrnd/tXe13ySTKHHH+x6djKrA8frNnXQP03maQxfaM05qQARKvoYO18KvCPNoO6h9d4XJKU2tqsv09MPOXjOFW7ZyIuIkhPjUjQUyMSTrnfyf+P6x9t2+h9AQAAwPnr9h4ztGX/cpV5CgNuL/UUaOrqnnVuL8wcpdt7zGis7gFoIox9AAAAwLj6JA/TziMbA257ev0wv0pwUuMujRcRGqQOLc3adcijwrJKRYeffkK+LtUkOrQwy+Xxqczt87s9OMgUMHBhUt2CGMf7bVJIcMMudC51V6rMXalfp0dpyBURAfcJt5hOey6OnYdB3cM1YUDguVVzsEntE6rORX0rcODMEIbAaXm8Un6JV5W+wNuDTFKMLbhG+ZsTbfvBpRKXT13bWJqmk2dJYVlljRfsE4VZTIoKC9LOLLciQ4NkbuALMAAAAHAqLWwpmpCWqTkbbmuU9iakZXJlOHAeYOwDAAAAxtWcS+PZrCZ1S7Zq/mcF2pnl1lWdQk+5v1Q1PxgSbJKz2CuXxxdwcv/HggpFhJoUYzu71Q5cHp+yC71qHROi2mbqQoJMCg4yKdQcpCE9bQ0+1rHzYA426bpLwxUeoArFMYcLvJKqghgV3qrqGWhahCFwWt8edGnAkwfkLK4MuD0+IkifPNI24DIPknQgt0Ivfpyv7slWXXqehyGmvp2j+Z/VnrAbe220Hh8er//sKddVHUP91jQCAAAAGlOf5GEad/XremXzPWfUzrirXz/tFSIAzh2MfQAAAMCYmntpvBFXR+qv6ws06/1cvTm+9WmXa4gKC1KXJIu273PpcKFX7eL9p50rvNI/dpUpMSYk4FIWjeF/WR7lFHlls/q3vyfbo92HPRp6RUSt4YTEmBC1iw/R9n0uFZSevhrGsdDDyY6dh20/uJRd5FV7a+2PNTI0SBe3tuibA27llXjV4qSlMr496JYjp+KU/UD9EIbAafVoZ1X2vI6n3S+70KvxmUd0SaJFvTtYFRxk0hcOl175NF8VXp/+PjFJCZHnd8Rp3piWmjemZY3b/3vArXf+XaTuyVbdPveQfsyv0LjrYupVygcAAACor/SOo2WzxOjlTaNrLZtfmzBzlCakZTIZCpyHGPsAAACAMTXn0njdk6167NZ43f9mtu6c96NeGdNSSbH+U8lF5ZV6YU2+7u0frRZRwbr7Z1HKeOGQnvkwT38e0cKvivy6naVauqVY46+LVnJc00xJ/5Dt0WvrCjT9tvjqYxeWVerx95wKCZKGXFF7xYdYW5AGXBaup1blaeE/C3Xf9cfn9Xw+af3OMrWJC1Fqa7MkKdwapHbxISou96nC65OO1pywmk26qYdNy7YU69mPap6HrT+45PP51KdDqMKtJt1yRYTGvn5Yb/+ryO+YhWWVevajPLk8tVeoR/0RhkCjCbcG6ZJEi975d5Fmr8xVpU9qERWsjD4Ruv/GWHVqZW7uLjaZULNJy7YUa9aKXPW6KFTvTkpSt+TzuwoGAAAAzg99kofpqcFf6d3tM2q9euRkfTuM0u09ZlAeHziPMfYBAAAA42nOpfFMJmncdTHKLanUEyucSrn/B13dKUw9U6oqw39/2KPPvi1V92SrJgyIkSQN7GbThAHRev6jfH3231JNHBijWFuwPtheonf+VaTruobr4SFxTXbxcJ8OofrHrjLd+lyWRlwdqVJXpeauzde3B9169s4W6p4cuKr9scc7YUCMPv1vme5/M1trvi7ViKsj5fL49PqGAn132KM1U9tU7x9uMalrG6ue+TBPj76boys7hircEqSMKyN0W58IrfqqRC+sydfm3eUae220rGaT/v6vIn24o0QrHzjezu0/idCKL4r9jpld6NX8z/LV+6JQXdzauPOpzcHk8/mIlwAAAACAAWSXOLRl33Jt2b9cpZ587c3bIUlqH9td4eYY9Ukepj7thjERChgMYx8AAAAwlvXfZzbK0njpHUc36L72Hz3608pcrfqqRNmFXgWZpM6JFo27Llqj+0YpMvT4khKVPmnlFyWa9X6uvtxbrgqv1LGVWVMHx+nOayIVaj6ehDhc4FX6E/uV3iW8RiX2F9bk6w/Lnfrkkbbq0c56yvuUuHz6+dwsSdLcUS31xxW5eudfRXJ5fOrezqrHh8fr5sttfiGMF9bk63dvZuvDB9toUPfw6tuLyiv15Pu5+uuGQmUXehVmMema1DDNvC1eV3UK9WvjSKFXU9/O0aJNhTIHm/TXX7fSL6+OlCSVe3z6y/oCPfNhnhzZFQoJlnq2D9W0oXEacoVNQSe0c+yYL39SoMKySnVsZdbDg+M0qHu4rp99IOD5QcMQhgAAAAAAAAAAAACAc8iW/ctZGq8WJ4Yh/j4xSTYr69YjsKDT7wIAAAAAAAAAAAAAOFuOLY3Xt8OoOt+nb4dRemrwV4YOQgD1QWUIAAAAAAAAAAAAADhHsTSePypDoK4IQwAAAAAAAAAAAAAAzguEIVBXLJMBAAAAAAAAAAAAAAAMhcoQAAAAAAAAAAAAAADAUKgMAQAAAAAAAAAAAAAADIUwBAAAAAAAAAAAAAAAMBTCEAAAAAAAAAAAAAAAwFAIQwAAAAAAAAAAAAAAAEMhDAEAAAAAAAAAAAAAAAyFMAQAAAAAAAAAAAAAADAUwhAAAAAAAAAAAAAAAMBQCEMAAAAAAAAAAAAAAABDIQwBAAAAAAAAAAAAAAAMhTAEAAAAAAAAAAAAAAAwFMIQAAAAAAAAAAAAAADAUAhDAAAAAAAAAAAAAAAAQyEMAQAAAAAAAAAAAAAADIUwBAAAAAAAAAAAAAAAMBTCEAAAAAAAAAAAAAAAwFAIQwAAAAAAAAAAAAAAAEMhDAEAAAAAAAAAAAAAAAwl5GwdqLio8GwdCgAAAAAAAAAAAAAAnKMiIqOa/Bgmn8/na/KjAAAAAAAAAAAAAAAAnCUskwEAAAAAAAAAAAAAAAyFMAQAAAAAAAAAAAAAADAUwhAAAAAAAAAAAAAAAMBQCEMAAAAAAAAAAAAAAABDIQwBAAAAAAAAAAAAAAAMhTAEAAAAAAAAAAAAAAAwFMIQAAAAAAAAAAAAAADAUAhDAAAAAAAAAAAAAAAAQyEMAQAAAAAAAAAAAAAADIUwBAAAAAAAAAAAAAAAMBTCEAAAAAAAAAAAAAAAwFAIQwAAAAAAAAA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    }
   },
   "cell_type": "markdown",
   "id": "69cc27ef",
   "metadata": {},
   "source": [
    "Following the link should bring you to a page which looks similar to:\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b84ed01",
   "metadata": {},
   "source": [
    "This page lists all of the RAI dashboards computed for this model. To view one, click on the appropriate entry in the 'Name' column."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69721140",
   "metadata": {},
   "source": [
    "## Downloading the Scorecard PDF\n",
    "\n",
    "We can download the scorecard PDF from our pipeline as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76e9af80",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_directory = \".\"\n",
    "\n",
    "ml_client.jobs.download(\n",
    "    insights_job.name, download_path=target_directory, output_name=\"scorecard\"\n",
    ")"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "f2da80f8",
   "metadata": {},
   "source": [
    "## Take Real-World Action\n",
    "\n",
    "### What-If Counterfactuals Analysis\n",
    "\n",
    "Let's imagine that the diabetes progression scores predicted by the model are used to determine medical insurance rates (note that how to perform an analysis will change based on the real-world scenario under consideration). Suppose that if the score is greater than 120, there is a higher rate. Patient 43's model score of 271.1 (note that this can vary slightly due to random number generation) results in this increased rate, and they want to know how they should change their health to get a lower rate prediction from the model (leading to lower insurance price).\n",
    "\n",
    "The What-If counterfactuals component shows how slightly different feature values affect model predictions. This can be used to solve Patient 43's problem.\n",
    "\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "id": "710729fc",
   "metadata": {},
   "source": [
    "What can Patient 43 do to create the desired change? The top ranked features bar plot shows that `bmi` and `s5` are the best to perturb to bring the model score within 120:\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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    "### Causal Analysis\n",
    "\n",
    "Now suppose that a doctor wishes to know how to reduce the progression of diabetes in her patients. This can be explored in the Causal Inference component of the Responsible AI Toolbox.\n",
    "\n",
    "In the \"Aggregate causal effects\" tab, it is possible to see how perturbing features causes lower disease progression. It appears that increasing s2 (LDL) by one unit, would increase diabetes progression by around 684 units (again, exact values can vary due to random number effects):\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
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   "execution_count": null,
   "id": "65b2a0db-3efc-444c-aec1-8a2a00d70bdb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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