Model Explainers¶

Seldon provides model explanations using its Alibi Open Source library.

We provide an example notebook showing how to deploy an explainer for Tabular, Text and Image models.

Available Methods¶

Seldon Core supports a subset of the methods currently available in Alibi. Presently this the following:

Method	Explainer Key	Example
Anchor Tabular	`AnchorTabular`	Example
Anchor Text	`AnchorText`	Example
Anchor Images	`AnchorImages`	Example
kernel Shap	`KernelShap`	Example
Integrated Gradients	`IntegratedGradients`	Example
Tree Shap	`TreeShap`	Example

Creating your explainer¶

For Alibi explainers that need to be trained you should

Use python 3.6.8 as the Seldon python Alibi explainer wrapper also runs in python 3.6.8 when it loads your explainer.
Follow the Alibi docs for your particular desired explainer. The Seldon Wrapper presently supports: Anchors (Tabular, Text and Image), KernelShap and Integrated Gradients.
Save your explainer to a file called explainer.dill using the dill python package and store on a bucket store or PVC in your cluster. We support gcs, s3 (including Minio) or Azure blob.

Explain API¶

For the Seldon Protocol an endpoint path will be exposed for:

http://<ingress-gateway>/seldon/<namespace>/<deployment name>/<predictor name>/api/v1.0/explain

So for example if you deployed:

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: income
  namespace: seldon
spec:
  name: income
  annotations:
    seldon.io/rest-timeout: "100000"
  predictors:
  - graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: gs://seldon-models/sklearn/income/model-0.23.2
      name: classifier
    explainer:
      type: AnchorTabular
      modelUri: gs://seldon-models/sklearn/income/explainer-py36-0.5.2
    name: default
    replicas: 1

And were port forwarding to Ambassador on localhost:8003 then the API call would be:

http://localhost:8003/seldon/seldon/income-explainer/default/api/v1.0/explain