Update demo

docs/modules/ROOT/examples/inference-service-explainer-lime-k8s.yaml → docs/modules/ROOT/examples/inference-service-explainer-k8s.yaml

@@ -1,7 +1,7 @@
 apiVersion: "serving.kserve.io/v1beta1"
 kind: "InferenceService"
 metadata:
-  name: "explainer-test-lime"
+  name: "explainer-test"
 spec:
   predictor: <1>
     model:

docs/modules/ROOT/examples/inference-service-explainer.yaml

@@ -0,0 +1,20 @@
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: "explainer-test"
+  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/bank-churn/model.joblib
+  explainer:
+    containers:
+        - name: explainer
+          image: quay.io/trustyai/trustyai-kserve-explainer:latest

docs/modules/ROOT/examples/kserve-explainer-saliencies.json

@@ -0,0 +1,74 @@
+{
+    "timestamp": "2024-05-06T21:42:45.307+00:00",
+    "type": "explanation",
+    "saliencies": {
+        "LIME": {
+            "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
+                },
+                {
+                    "name": "inputs-9",
+                    "score": 0.018349706373627164,
+                    "confidence": 0
+                },
+                {
+                    "name": "inputs-3",
+                    "score": 0.10709513039521452,
+                    "confidence": 0
+                },
+                {
+                    "name": "inputs-11",
+                    "score": 0,
+                    "confidence": 0
+                }
+            ]
+        },
+        "SHAP": {
+            "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
+                },
+                {
+                    "name": "inputs-9",
+                    "score": 0.018349706373627164,
+                    "confidence": 0
+                },
+                {
+                    "name": "inputs-3",
+                    "score": 0.10709513039521452,
+                    "confidence": 0
+                },
+                {
+                    "name": "inputs-11",
+                    "score": 0,
+                    "confidence": 0
+                }
+            ]
+        }
+    }
+}

docs/modules/ROOT/pages/saliency-explanations-with-kserve.adoc

@@ -39,18 +39,18 @@ The `InferenceService` to deploy will have a new key (`explainer`), and will be
 
 [,yaml,highlight=2..5]
 ----
-include::example$inference-service-explainer-lime-k8s.yaml[]
+include::example$inference-service-explainer-k8s.yaml[]
 ----
 <1> The `predictor` field is the same as you would use for a regular `InferenceService`.
 <2> In this case we are using an `sklearn` model, by specifying the URI location, but you can use any other model supported by KServe.
-<3> The `explainer` field is a new key that specifies the explainer to use. In this case, the `lime` explainer is used by default.
+<3> The `explainer` field is a new key that specifies the explainer to use. In this case, the `lime` and `shap` explainers are used by default.
 <4> The image of the KServe TrustyAI explainer must be specified in the `explainer.image` field.
 
 We can deploy it with
 
 [source,shell]
 ----
-kubectl apply -f inference-service-explainer-lime.yaml -n $NAMESPACE
+kubectl apply -f inference-service-explainer.yaml -n $NAMESPACE
 ----
 
 And wait for the `InferenceService` to be ready.
@@ -63,8 +63,8 @@ kubectl get pods -n $NAMESPACE
 [source,text]
 ----
 NAME                                                              READY   STATUS    RESTARTS   AGE
-explainer-test-lime-explainer-00001-deployment-c6fff8b4-5x4qg     2/2     Running   0          41m
-explainer-test-lime-predictor-00001-deployment-dfd47bb47-lwl5b    2/2     Running   0          41m
+explainer-test-explainer-00001-deployment-c6fff8b4-5x4qg     2/2     Running   0          41m
+explainer-test-predictor-00001-deployment-dfd47bb47-lwl5b    2/2     Running   0          41m
 ----
 
 You will see that in addition to the `predictor` pod, there is also an `explainer` pod running. These two pods are responsible for the prediction and explanation, respectively.
@@ -85,9 +85,9 @@ To request a prediction, we can use the following command:
 
 [source,shell]
 ----
-curl -s -H "Host: explainer-test-lime.${NAMESPACE}.example.com" \
+curl -s -H "Host: explainer-test.${NAMESPACE}.example.com" \
     -H "Content-Type: application/json" \
-    "http://localhost:8080/v1/models/explainer-test-lime:predict" \
+    "http://localhost:8080/v1/models/explainer-test:predict" \
     -d @payload.json
 ----
 
@@ -104,9 +104,9 @@ To request an explanation, we can use the a very similar command and payload, si
 
 [source,shell]
 ----
-curl -s -H "Host: explainer-test-lime.${NAMESPACE}.example.com" \
+curl -s -H "Host: explainer-test.${NAMESPACE}.example.com" \
     -H "Content-Type: application/json" \
-    "http://localhost:8080/v1/models/explainer-test-lime:explain" \ <1>
+    "http://localhost:8080/v1/models/explainer-test:explain" \ <1>
     -d @payload.json
 ----
 <1> The verb `predict` is replaced with `explain`.
@@ -115,7 +115,16 @@ This produce a saliency map, similar to:
 
 [source,json]
 ----
-include::example$kserve-explainer-lime-saliencies.json[]
+include::example$kserve-explainer-saliencies.json[]
 ----
 
 From the above saliency map, we can see that the most important feature is `inputs-12`.
+
+== Additional configuration
+
+To get only `LIME` or `SHAP` explanations we simply add the following lines to the `InferenceService` spec:
+
+[source,json]
+----
+include::example$inference-service-explainer-lime.yaml[]
+----