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Anchors Tabular Explanation for Income Prediction

This example uses a US income dataset to show an example of explanation on tabular data. You can also try out the Jupyter notebook for a visual walkthrough.

Prerequisites​

Create the InferenceService with Alibi Explainer​

We can create an InferenceService with a trained sklearn predictor for this dataset and an associated model explainer. The black box explainer algorithm we will use is the Tabular version of Anchors from the Alibi open source library. More details on this algorithm and configuration settings can be found in the Seldon Alibi documentation.

The InferenceService is shown below:

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "income"
spec:
  predictor:
    minReplicas: 1
    model:
      modelFormat:
        name: sklearn
      storageUri: "gs://kfserving-examples/models/sklearn/1.3/income/model"
      resources:
        requests:
          cpu: 0.1
          memory: 1Gi
        limits:
          cpu: 1
          memory: 1Gi   
  explainer:
    minReplicas: 1
    containers:
      - name: kserve-container
        image: kserve/alibi-explainer:v0.12.1
        args:
        - --model_name=income
        - --http_port=8080
        - --predictor_host=income-predictor.default
        - --storage_uri=/mnt/models
        - AnchorTabular
        env:
          - name: STORAGE_URI
            value: "gs://kfserving-examples/models/sklearn/1.3/income/explainer"
        resources:
          requests:
            cpu: 0.1
            memory: 1Gi
          limits:
            cpu: 1
            memory: 4Gi

Create the InferenceService with the above yaml:

kubectl create -f income.yaml

Run the inference​

The first step is to determine the ingress IP and ports and set INGRESS_HOST and INGRESS_PORT:

MODEL_NAME=income
SERVICE_HOSTNAME=$(kubectl get inferenceservice income -o jsonpath='{.status.url}' | cut -d "/" -f 3)

Test the predictor:

curl -H "Host: $SERVICE_HOSTNAME" -H "Content-Type: application/json" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d '{"instances":[[39, 7, 1, 1, 1, 1, 4, 1, 2174, 0, 40, 9]]}'

You should receive the response showing the prediction is for low salary:

Expected Output
{"predictions": [0]}

Run the explanation​

Now let's get an explanation for this:

curl -H "Host: $SERVICE_HOSTNAME" -H "Content-Type: application/json" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:explain -d '{"instances":[[39, 7, 1, 1, 1, 1, 4, 1, 2174, 0, 40, 9]]}'

The returned explanation will be like:

note

The output is truncated for brevity.

Expected Output
{
  "names": [
    "Marital Status = Never-Married",
    "Workclass = State-gov"
  ],
  "precision": 0.9724770642201835,
  "coverage": 0.0147,
  "raw": {
    "feature": [
      3,
      1
    ],
    "mean": [
      0.9129746835443038,
      0.9724770642201835
    ],
    "precision": [
      0.9129746835443038,
      0.9724770642201835
    ],
    "coverage": [
      0.3327,
      0.0147
    ],
    "examples": [
      {
        "covered": [
          [
            30,
            "Self-emp-not-inc",
            "Bachelors",
            "Never-Married",
            "Sales",
            "Unmarried",
            "White",
            "Male",
            "Capital Gain <= 0.00",
            "Capital Loss <= 0.00",
            40,
            "United-States"
          ],
         ...
        ]
      },
    ]
    "all_precision": 0,
    "num_preds": 1000101,
    "names": [
      "Marital Status = Never-Married",
      "Workclass = State-gov"
    ],
    "instance": [
      [
        39
      ],
      [
        7
      ],
      [
        "28.00 < Age <= 37.00"
      ],
      [
        "28.00 < Age <= 37.00"
      ],
      [
        "28.00 < Age <= 37.00"
      ],
      [
        "28.00 < Age <= 37.00"
      ],
      [
        4
      ],
      [
        "28.00 < Age <= 37.00"
      ],
      [
        2174
      ],
      [
        "Age <= 28.00"
      ],
      [
        40
      ],
      [
        9
      ]
    ],
    "prediction": 0
  }
}