RHOAIENG-7082: Add SHAP support for KServe explainer

README.md

@@ -104,6 +104,20 @@ spec:
 
 The explanation request will be identical to the LIME explainer case.
 
+## 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`         |
+| `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:

pom.xml

@@ -4,7 +4,7 @@
     <modelVersion>4.0.0</modelVersion>
     <groupId>org.kie.trustyai</groupId>
     <artifactId>trustyai-kserve</artifactId>
-    <version>1.0-SNAPSHOT</version>
+    <version>0.2-SNAPSHOT</version>
 
     <properties>
         <compiler-plugin.version>3.11.0</compiler-plugin.version>
@@ -46,6 +46,12 @@
             <artifactId>explainability-connectors</artifactId>
             <version>${trustyai.version}</version>
         </dependency>
+        <dependency>
+            <groupId>org.apache.commons</groupId>
+            <artifactId>commons-collections4</artifactId>
+            <version>4.4</version>
+        </dependency>
+
 
         <dependency>
             <groupId>io.quarkus</groupId>

src/main/java/org/kie/trustyai/CommandLineArgs.java

@@ -14,6 +14,12 @@ public class CommandLineArgs {
 
     @CommandLine.Option(names = "--http_port", description = "The HTTP port of the predictor")
     private int httpPort;
+    @CommandLine.Option(names = "--predictor_protocol", defaultValue = "v1", description = "The predictor protocol version (v1 or v2)")
+    private String predictorProtocol;
+
+    public String getPredictorProtocol() {
+        return predictorProtocol;
+    }
 
     public String getPredictorHost() {
         return predictorHost;
@@ -27,14 +33,13 @@ public int getHttpPort() {
         return httpPort;
     }
 
-    public String getV1HTTPPredictorURI() {
-
-        return "http://" +
-                predictorHost +
-                "/v1/models/" +
-                modelName +
-                ":predict";
+    public String getV1HTTPPredictorURI(String modelName) {
+        return "http://" + predictorHost + "/v1/models/" + modelName + ":predict";
     }
 
+    public String getV2HTTPPredictorURI(String modelName) {
+
+        return "http://" + predictorHost + "/v2/models/" + modelName + "/infer";
+    }
 
 }

src/main/java/org/kie/trustyai/ConfigCommand.java

@@ -1,42 +1,41 @@
 package org.kie.trustyai;
 
+import io.quarkus.logging.Log;
 import io.quarkus.runtime.Quarkus;
 import io.quarkus.runtime.QuarkusApplication;
 import io.quarkus.runtime.annotations.QuarkusMain;
 import jakarta.inject.Inject;
 import picocli.CommandLine;
-import org.jboss.logging.Logger;
+
+import java.util.Arrays;
 
 @QuarkusMain
 public class ConfigCommand implements QuarkusApplication {
 
-    private static final Logger LOGGER = Logger.getLogger(ConfigCommand.class.getName());
-
     @Inject
     CommandLineArgs cmdArgs;
 
     @Override
     public int run(String... args) {
-        LOGGER.debug("Starting application...");
+        Log.info("Starting application...");
         final CommandLine commandLine = new CommandLine(cmdArgs);
-
+        Log.debug("Using command-line arguments: " + Arrays.toString(args));
         try {
             commandLine.parseArgs(args);
             if (commandLine.isUsageHelpRequested()) {
                 commandLine.usage(System.out);
                 return 0;
             }
 
-
-            LOGGER.debug("Configuration loaded successfully.");
+            Log.info("Configuration loaded successfully.");
         } catch (CommandLine.ParameterException e) {
-            LOGGER.error("Error parsing command line: " + e.getMessage());
+            Log.error("Error parsing command line: " + e.getMessage());
             commandLine.usage(System.err);
             return 1;
         }
 
         Quarkus.waitForExit();  // Wait for Quarkus shutdown events
-        LOGGER.debug("Quarkus is waiting for exit...");
+        Log.info("Quarkus is waiting for exit...");
         return 0;
     }
 }

src/main/java/org/kie/trustyai/ConfigService.java

@@ -1,55 +1,56 @@
 package org.kie.trustyai;
 
+import io.quarkus.logging.Log;
 import jakarta.annotation.PostConstruct;
 import jakarta.enterprise.context.ApplicationScoped;
 import org.eclipse.microprofile.config.inject.ConfigProperty;
-import org.jboss.logging.Logger;
-
 
 @ApplicationScoped
 public class ConfigService {
 
-    private static final Logger LOGGER = Logger.getLogger(ConfigService.class.getName());
-
-
-
     @ConfigProperty(name = "explainer.type", defaultValue = "LIME")
     ExplainerType explainerType;
+    @ConfigProperty(name = "lime.samples", defaultValue = "200")
+    int limeSamples;
+    @ConfigProperty(name = "lime.retries", defaultValue = "2")
+    int limeRetries;
+    @ConfigProperty(name = "lime.wlr", defaultValue = "true")
+    boolean limeWLR;
+    @ConfigProperty(name = "lime.normalize.weights", defaultValue = "true")
+    boolean limeNormalizeWeights;
+    @ConfigProperty(name = "explainer.shap.background.queue", defaultValue = "10")
+    int queueSize;
+    @ConfigProperty(name = "explainer.shap.background.diversity", defaultValue = "10")
+    int diversitySize;
 
     public int getLimeSamples() {
         return limeSamples;
     }
 
-    @ConfigProperty(name = "lime.samples", defaultValue = "200")
-    int limeSamples;
-
     public int getLimeRetries() {
         return limeRetries;
     }
 
-    @ConfigProperty(name = "lime.retries", defaultValue = "2")
-    int limeRetries;
-
     public boolean getLimeWLR() {
         return limeWLR;
     }
 
-    @ConfigProperty(name = "lime.wlr", defaultValue = "true")
-    boolean limeWLR;
-
-
     public boolean getLimeNormalizeWeights() {
         return limeNormalizeWeights;
     }
 
-    @ConfigProperty(name = "lime.normalize.weights", defaultValue = "true")
-    boolean limeNormalizeWeights;
+    public int getQueueSize() {
+        return queueSize;
+    }
 
+    public int getDiversitySize() {
+        return diversitySize;
+    }
 
     @PostConstruct
     private void validateConfig() {
         if (explainerType == null) {
-            LOGGER.error("Unknown explainer type configured. Falling back to LIME.");
+            Log.error("Unknown explainer type configured. Falling back to LIME.");
             explainerType = ExplainerType.LIME;
         }
     }

src/main/java/org/kie/trustyai/ExplainerFactory.java

@@ -1,34 +1,47 @@
 package org.kie.trustyai;
 
+import java.util.List;
+
+import io.quarkus.logging.Log;
 import jakarta.inject.Inject;
 import jakarta.inject.Singleton;
 import org.kie.trustyai.explainability.local.LocalExplainer;
 import org.kie.trustyai.explainability.local.lime.LimeConfig;
 import org.kie.trustyai.explainability.local.lime.LimeExplainer;
 import org.kie.trustyai.explainability.local.shap.ShapConfig;
 import org.kie.trustyai.explainability.local.shap.ShapKernelExplainer;
-import org.kie.trustyai.explainability.model.*;
-
-import java.util.List;
+import org.kie.trustyai.explainability.model.PredictionInput;
+import org.kie.trustyai.explainability.model.SaliencyResults;
 
 @Singleton
 public class ExplainerFactory {
 
     @Inject
     ConfigService configService;
 
-    public LocalExplainer<SaliencyResults> getExplainer(ExplainerType type, List<PredictionInput> background) throws IllegalArgumentException {
+    @Inject
+    StreamingGeneratorManager streamingGeneratorManager;
+
+    public LocalExplainer<SaliencyResults> getExplainer(ExplainerType type) throws IllegalArgumentException {
         return switch (type) {
             case LIME -> {
                 final LimeConfig limeConfig = new LimeConfig()
                         .withNormalizeWeights(configService.getLimeNormalizeWeights())
                         .withSamples(configService.getLimeSamples())
                         .withRetries(configService.getLimeRetries())
                         .withUseWLRLinearModel(configService.getLimeWLR());
+                Log.info("Instating LIME explainer");
                 yield new LimeExplainer(limeConfig);
             }
             case SHAP -> {
-                ShapConfig shapConfig = ShapConfig.builder().withLink(ShapConfig.LinkType.IDENTITY).withBackground(background).build();
+                final int backgroundSize = configService.getQueueSize() + configService.getDiversitySize();
+                Log.debug("Requesting " + backgroundSize + " background samples from SHAP's streaming generator");
+                final List<PredictionInput> background = streamingGeneratorManager.getStreamingGenerator().generate(backgroundSize);
+                Log.debug("The background has a size of " + background.size());
+                final ShapConfig shapConfig = ShapConfig.builder().withRegularizer(5)
+                        .withLink(ShapConfig.LinkType.IDENTITY)
+                        .withBackground(background).build();
+                Log.info("Instantiating SHAP explainer");
                 yield new ShapKernelExplainer(shapConfig);
             }
             default -> throw new IllegalArgumentException("Unsupported explainer type: " + type);

src/main/java/org/kie/trustyai/ExplainerEndpoint.java → src/main/java/org/kie/trustyai/ExplainerV1Endpoint.java

@@ -1,29 +1,34 @@
 package org.kie.trustyai;
 
+import java.util.List;
+import java.util.Objects;
+import java.util.concurrent.ExecutionException;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+import io.quarkus.logging.Log;
 import jakarta.enterprise.inject.Default;
+import jakarta.inject.Inject;
 import jakarta.ws.rs.Consumes;
 import jakarta.ws.rs.POST;
 import jakarta.ws.rs.Path;
 import jakarta.ws.rs.PathParam;
 import jakarta.ws.rs.core.MediaType;
 import jakarta.ws.rs.core.Response;
-import org.jboss.logging.Logger;
+import org.apache.commons.math3.linear.ArrayRealVector;
+import org.apache.commons.math3.linear.RealVector;
 import org.kie.trustyai.connectors.kserve.v1.KServeV1HTTPPredictionProvider;
 import org.kie.trustyai.connectors.kserve.v1.KServeV1RequestPayload;
 import org.kie.trustyai.explainability.local.LocalExplainer;
-import org.kie.trustyai.explainability.model.*;
-
-import jakarta.inject.Inject;
-import com.fasterxml.jackson.databind.ObjectMapper;
+import org.kie.trustyai.explainability.model.Prediction;
+import org.kie.trustyai.explainability.model.PredictionInput;
+import org.kie.trustyai.explainability.model.PredictionOutput;
+import org.kie.trustyai.explainability.model.PredictionProvider;
+import org.kie.trustyai.explainability.model.SaliencyResults;
+import org.kie.trustyai.explainability.model.SimplePrediction;
 import org.kie.trustyai.payloads.SaliencyExplanationResponse;
 
-import java.util.List;
-import java.util.concurrent.ExecutionException;
-
 @Path("/v1/models/{modelName}:explain")
-public class ExplainerEndpoint {
-
-    private static final Logger LOGGER = Logger.getLogger(ExplainerEndpoint.class.getName());
+public class ExplainerV1Endpoint {
 
     @Inject
     ObjectMapper objectMapper;
@@ -38,37 +43,54 @@ public class ExplainerEndpoint {
     @Inject
     ExplainerFactory explainerFactory;
 
+    @Inject
+    StreamingGeneratorManager streamingGeneratorManager;
+
     @POST
     @Consumes(MediaType.APPLICATION_JSON)
-    public Response explainIncome(@PathParam("modelName") String modelName, KServeV1RequestPayload data)
+    public Response explain(@PathParam("modelName") String modelName, KServeV1RequestPayload data)
             throws ExecutionException, InterruptedException {
-        final String predictorURI = cmdArgs.getV1HTTPPredictorURI();
 
-        LOGGER.debug("Using explainer type [" + configService.getExplainerType() + "]");
-        LOGGER.debug("Using predictor URI [" + predictorURI + "]");
+        Log.info("Using explainer type [" + configService.getExplainerType() + "]");
+        Log.info("Using V1 HTTP protocol");
+        final String predictorURI = cmdArgs.getV1HTTPPredictorURI(modelName);
+        final PredictionProvider provider = new KServeV1HTTPPredictionProvider(null, null, predictorURI, 1);
+        Log.info("Using predictor URI [" + predictorURI + "]");
 
-        final PredictionProvider provider = new KServeV1HTTPPredictionProvider(null, null, predictorURI);
         final List<PredictionInput> input = data.toPredictionInputs();
         final PredictionOutput output = provider.predictAsync(input).get().get(0);
         final Prediction prediction = new SimplePrediction(input.get(0), output);
+        final int dimensions = input.get(0).getFeatures().size();
+
+        if (configService.getExplainerType() == ExplainerType.SHAP) {
+            if (Objects.isNull(streamingGeneratorManager.getStreamingGenerator())) {
+                Log.info("Initializing SHAP's Streaming Background Generator with dimension " + dimensions);
+                streamingGeneratorManager.initialize(dimensions);
+            }
+            final double[] numericData = new double[dimensions];
+            for (int i = 0; i < dimensions; i++) {
+                numericData[i] = input.get(0).getFeatures().get(i).getValue().asNumber();
+            }
+            final RealVector vectorData = new ArrayRealVector(numericData);
+            streamingGeneratorManager.getStreamingGenerator().update(vectorData);
+        }
 
         final ExplainerType explainerType = configService.getExplainerType();
 
         try {
-            final LocalExplainer<SaliencyResults> explainer = explainerFactory.getExplainer(explainerType, input);
-
+            final LocalExplainer<SaliencyResults> explainer = explainerFactory.getExplainer(explainerType);
+            Log.info("Sending explaining request to " + predictorURI);
             final SaliencyResults results = explainer.explainAsync(prediction, provider).get();
             final SaliencyExplanationResponse response = SaliencyExplanationResponse.fromSaliencyResults(results);
 
             try {
-                String resultsJson = objectMapper.writeValueAsString(results);
                 return Response.ok(response, MediaType.APPLICATION_JSON).build();
             } catch (Exception e) {
                 return Response.serverError().entity("Error serializing SaliencyResults to JSON: " + e.getMessage())
                         .build();
             }
         } catch (IllegalArgumentException e) {
-            return Response.serverError().entity("Explainer type not supported: " + explainerType).build();
+            return Response.serverError().entity("Error: " + e.getMessage()).build();
         }
 
     }

src/main/java/org/kie/trustyai/StreamingGeneratorManager.java

@@ -0,0 +1,29 @@
+package org.kie.trustyai;
+
+import jakarta.inject.Inject;
+import jakarta.inject.Singleton;
+import org.kie.trustyai.explainability.local.shap.background.StreamingGenerator;
+import org.kie.trustyai.statistics.MultivariateOnlineEstimator;
+import org.kie.trustyai.statistics.distributions.gaussian.MultivariateGaussianParameters;
+import org.kie.trustyai.statistics.estimators.WelfordOnlineEstimator;
+
+@Singleton
+public class StreamingGeneratorManager {
+
+    @Inject
+    ConfigService configService;
+
+    private StreamingGenerator streamingGenerator = null;
+
+    public synchronized void initialize(int dimensions) {
+        if (streamingGenerator == null && configService.getExplainerType() == ExplainerType.SHAP) {
+            final MultivariateOnlineEstimator<MultivariateGaussianParameters> estimator = new WelfordOnlineEstimator(dimensions);
+            streamingGenerator = new StreamingGenerator(dimensions, configService.getQueueSize(), configService.getDiversitySize(), estimator);
+        }
+    }
+
+    public StreamingGenerator getStreamingGenerator() {
+        return streamingGenerator;
+    }
+
+}