https://github.com/trustyai-explainability/trustyai-kserve-explainer

KServe TrustyAI explainer
https://github.com/trustyai-explainability/trustyai-kserve-explainer

explainable-ai kserve kubernetes model-serving trustyai xai

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KServe TrustyAI explainer

Host: GitHub
URL: https://github.com/trustyai-explainability/trustyai-kserve-explainer
Owner: trustyai-explainability
License: apache-2.0
Created: 2024-04-09T09:00:39.000Z (about 2 years ago)
Default Branch: main
Last Pushed: 2024-08-28T08:50:15.000Z (almost 2 years ago)
Last Synced: 2024-12-06T19:39:27.391Z (over 1 year ago)
Topics: explainable-ai, kserve, kubernetes, model-serving, trustyai, xai
Language: Java
Homepage:
Size: 62.5 KB
Stars: 1
Watchers: 3
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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# TrustyAI KServe Explainer

The TrustyAI KServe integration provides explanations for predictions made by AI/ML models using the built-in [KServe explainer support](https://kserve.github.io/website/0.12/modelserving/explainer/explainer/). 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:

```yaml
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:

```bash
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:

```json
{
"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`):

```yaml
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**:

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

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

### Build the project

```bash
mvn clean package
```

### Run locally

```bash
mvn quarkus:dev
```

### Docker Integration

Build and run the container:

```bash
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](LICENSE) file for details.

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