TrustyAI KServe Explainer

The TrustyAI KServe integration provides explanations for predictions made by AI/ML models using the built-in KServe explainer support. It supports LIME and SHAP explanation methods, configurable directly within KServe InferenceServices.

Features

• Explainability: Integrated support for LIME and SHAP explanation methods to interpret model predictions via the :explain endpoint.

Deployment on KServe

The TrustyAI explainer can be added to KServe InferenceServices. Here are YAML configurations to deploy explainers with LIME and SHAP:

LIME and SHAP Explainer InferenceService

By default, the TrustyAI KServe explainer will use the both the LIME and SHAP explainer. You can deploy the explainers using the following YAML configuration:

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "explainer-test-all"
  annotations:
    sidecar.istio.io/inject: "true"
    sidecar.istio.io/rewriteAppHTTPProbers: "true"
    serving.knative.openshift.io/enablePassthrough: "true"
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      protocolVersion: v2
      runtime: kserve-sklearnserver
      storageUri: https://github.com/trustyai-explainability/model-collection/raw/main/credit-score/model.joblib
  explainer:
    containers:
      - name: explainer
        image: quay.io/trustyai/trustyai-kserve-explainer:latest

Example: Using the both the LIME and SHAP Explainer

You can interact with the LIME and SHAP explainer using the following curl command:

payload='{"data": {"ndarray": [[1.0, 2.0]]}}'  # Adjust payload as per your input requirements
curl -s -H "Host: ${HOST}" \
     -H "Content-Type: application/json" \
     "http://${GATEWAY}/v1/models/explainer-test-all:explain" -d $payload

This command sends a JSON payload to the :explain endpoint and retrieves an explanation for the prediction. The response structure includes the explainer type and saliencies of each feature contributing to the prediction, as shown below:

{
    "timestamp": "2024-05-06T21:42:45.307+00:00",
    "LIME": {
      "saliencies": {
          "outputs-0": [
              {
                  "name": "inputs-12",
                  "score": 0.8496797810357467,
                  "confidence": 0
              },
              {
                  "name": "inputs-5",
                  "score": 0.6830766647546147,
                  "confidence": 0
              },
              {
                  "name": "inputs-7",
                  "score": 0.6768475400887952,
                  "confidence": 0
              },
              // Additional features
            ]
        }
    }
    "SHAP": {
      "saliencies": {
        // Additional features
      }
    }
}

LIME Explainer InferenceService

To use the LIME explainer only, you can deploy the explainer by specifying it as an environment variable and using the following YAML configuration (initial part will be identical to the previous InferenceService):

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "explainer-test-lime"
  annotations:
    sidecar.istio.io/inject: "true"
    sidecar.istio.io/rewriteAppHTTPProbers: "true"
    serving.knative.openshift.io/enablePassthrough: "true"
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      protocolVersion: v2
      runtime: kserve-sklearnserver
      storageUri: https://github.com/trustyai-explainability/model-collection/raw/main/credit-score/model.joblib
  explainer:
    containers:
      - name: explainer
        image: quay.io/trustyai/trustyai-kserve-explainer:latest
        env:
          - name: EXPLAINER_TYPE # <- specify LIME here
            value: "LIME"

SHAP Explainer InferenceService

To use the SHAP explainer only:

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "explainer-test-lime"
  annotations:
    sidecar.istio.io/inject: "true"
    sidecar.istio.io/rewriteAppHTTPProbers: "true"
    serving.knative.openshift.io/enablePassthrough: "true"
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      protocolVersion: v2
      runtime: kserve-sklearnserver
      storageUri: https://github.com/trustyai-explainability/model-collection/raw/main/credit-score/model.joblib
  explainer:
    containers:
      - name: explainer
        image: quay.io/trustyai/trustyai-kserve-explainer:latest
        env:
          - name: EXPLAINER_TYPE # <- specify SHAP here
            value: "SHAP"

The explanation request for either LIME or SHAP will be identical to both LIME and SHAP.

Configuration

The following environment variables can be used in the InferenceService to customize the explainer:

Name	Description	Default
EXPLAINER_TYPE	ALL, LIME or SHAP, the explainer to use.	ALL
LIME_SAMPLES	The number of samples to use in LIME	200
LIME_RETRIES	Number of LIME retries	2
LIME_WLR	Use LIME Weighted Linear Regression, true or false	true
LIME_NORMALIZE_WEIGHTS	Whether LIME should normalize the weights, true or false	true
EXPLAINER_SHAP_BACKGROUND_QUEUE	The number of observations to keep in memory for SHAP's background	10
EXPLAINER_SHAP_BACKGROUND_DIVERSITY	The number of synthetic samples to generate for diversity	10

Contributing

To get started with contributing to this project:

Prerequisites

• JDK 11+
• Maven 3.8.1+
• Docker (optional, for containerization)

Clone the repository

git clone https://github.com/trustyai/trustyai-kserve-explainer.git
cd trustyai-kserve

Build the project

mvn clean package

Run locally

mvn quarkus:dev

Docker Integration

Build and run the container:

docker build -f src/main/docker/Dockerfile.jvm -t trustyai-kserve .
docker run -i --rm -p 8080:8080 trustyai-kserve

License

This project is licensed under the Apache License Version 2.0 - see the LICENSE file for details.