Merge pull request #11 from christinaexyou/update-readme

Update README and other minor edits
trustyai-explainability · Aug 13, 2024 · a83698f · a83698f
2 parents 403bac9 + e37522b
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diff --git a/README.md b/README.md
@@ -12,15 +12,15 @@ The TrustyAI KServe integration provides explanations for predictions made by AI
 
 The TrustyAI explainer can be added to KServe `InferenceServices`. Here are YAML configurations to deploy explainers with LIME and SHAP:
 
-### LIME Explainer `InferenceService`
+### LIME and SHAP Explainer `InferenceService`
 
-By default, the TrustyAI KServe explainer will use the LIME explainer. You can deploy the explainer using the following YAML configuration:
+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-lime"
+  name: "explainer-test-all"
   annotations:
     sidecar.istio.io/inject: "true"
     sidecar.istio.io/rewriteAppHTTPProbers: "true"
@@ -39,41 +39,85 @@ spec:
         image: quay.io/trustyai/trustyai-kserve-explainer:latest
 ```
 
-### Example: Using the LIME Explainer
+### Example: Using the both the LIME and SHAP Explainer
 
-You can interact with the LIME explainer using the following `curl` command:
+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-lime:explain" -d $payload
+     "http://${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 saliencies of each feature contributing to the prediction, as shown below:
+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
 {
-  "saliencies": {
-    "value": {
-      "output": {"value": {"underlyingObject": 1}, "type": "NUMBER", "score": 1.0, "name": "value"},
-      "perFeatureImportance": [
-        {
-          "feature": {"name": "f", "type": "NUMBER", "value": {"underlyingObject": 0.9}},
-          "score": 0.7474712680313286
+    "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
+            ]
         }
-        // Additional features...
-      ]
     }
-  },
-  "availableCFs": [],
-  "sourceExplainer": "LIME"
+    "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, 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`):
+To use the **SHAP explainer only**:
 
 
 ```yaml
@@ -102,15 +146,15 @@ spec:
             value: "SHAP"
 ```
 
-The explanation request will be identical to the LIME explainer case.
+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`                                                         | `LIME` or `SHAP`, the explainer to use.                            | `LIME`         |
+| `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`        |

diff --git a/src/main/java/org/kie/trustyai/ConfigService.java b/src/main/java/org/kie/trustyai/ConfigService.java
@@ -8,7 +8,7 @@
 @ApplicationScoped
 public class ConfigService {
 
-    @ConfigProperty(name = "explainer.type")
+    @ConfigProperty(name = "explainer.type", defaultValue = "ALL")
     ExplainerType explainerType;
     @ConfigProperty(name = "lime.samples", defaultValue = "200")
     int limeSamples;

diff --git a/src/main/java/org/kie/trustyai/payloads/SaliencyExplanationResponse.java b/src/main/java/org/kie/trustyai/payloads/SaliencyExplanationResponse.java
@@ -37,12 +37,13 @@ public void setSaliencies(Map<ExplainerType, Map<String, List<FeatureSaliency>>>
     @Override
     public String toString() {
         return "SaliencyExplanationResponse{" +
-                "timestamp=" + timestamp +
-                ", type='" + type + '\'' +
-                ", saliencies=" + saliencies +
-                '}';
+        "timestamp=" + timestamp + '\'' +
+        ", LIME=" + saliencies.get(ExplainerType.LIME) +
+        ", SHAP=" + saliencies.get(ExplainerType.SHAP) +
+        '}';
     }
 
+
     public static class FeatureSaliency {
 
         private String name;
@@ -146,5 +147,4 @@ public static SaliencyExplanationResponse fromSaliencyResults(@Nonnull SaliencyR
 
         return new SaliencyExplanationResponse(combinedMap);
     }
-
 }