feat: adds ValueConverter utility and demo samples (#108)

* feat: adds value converter utility class and demo samples

* feat: samples updated for EJCL

* fix: removed local file references

* feat: adds ValueConverter tests

Co-authored-by: yoshi-code-bot <[email protected]>

Author: telpirion

aiplatform/snippets/pom.xml

@@ -27,7 +27,7 @@
     <dependency>
       <groupId>com.google.cloud</groupId>
       <artifactId>google-cloud-aiplatform</artifactId>
-      <version>0.1.0</version>
+      <version>0.1.1-SNAPSHOT</version>
     </dependency>
     <!-- [END aiplatform_install_with_bom] -->
     <dependency>

aiplatform/snippets/src/main/java/aiplatform/CreateTrainingPipelineImageClassificationSample.java

@@ -17,7 +17,7 @@
 package aiplatform;
 
 // [START aiplatform_create_training_pipeline_image_classification_sample]
-
+import com.google.cloud.aiplatform.util.ValueConverter;
 import com.google.cloud.aiplatform.v1beta1.DeployedModelRef;
 import com.google.cloud.aiplatform.v1beta1.EnvVar;
 import com.google.cloud.aiplatform.v1beta1.ExplanationMetadata;
@@ -38,8 +38,8 @@
 import com.google.cloud.aiplatform.v1beta1.SampledShapleyAttribution;
 import com.google.cloud.aiplatform.v1beta1.TimestampSplit;
 import com.google.cloud.aiplatform.v1beta1.TrainingPipeline;
-import com.google.protobuf.Value;
-import com.google.protobuf.util.JsonFormat;
+import com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlImageClassificationInputs;
+import com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType;
 import com.google.rpc.Status;
 import java.io.IOException;
 
@@ -74,11 +74,13 @@ static void createTrainingPipelineImageClassificationSample(
               + "automl_image_classification_1.0.0.yaml";
       LocationName locationName = LocationName.of(project, location);
 
-      String jsonString =
-          "{\"multiLabel\": false, \"modelType\": \"CLOUD\", \"budgetMilliNodeHours\": 8000,"
-              + " \"disableEarlyStopping\": false}";
-      Value.Builder trainingTaskInputs = Value.newBuilder();
-      JsonFormat.parser().merge(jsonString, trainingTaskInputs);
+      AutoMlImageClassificationInputs autoMlImageClassificationInputs =
+          AutoMlImageClassificationInputs.newBuilder()
+              .setModelType(ModelType.CLOUD)
+              .setMultiLabel(false)
+              .setBudgetMilliNodeHours(8000)
+              .setDisableEarlyStopping(false)
+              .build();
 
       InputDataConfig trainingInputDataConfig =
           InputDataConfig.newBuilder().setDatasetId(datasetId).build();
@@ -87,7 +89,7 @@ static void createTrainingPipelineImageClassificationSample(
           TrainingPipeline.newBuilder()
               .setDisplayName(trainingPipelineDisplayName)
               .setTrainingTaskDefinition(trainingTaskDefinition)
-              .setTrainingTaskInputs(trainingTaskInputs)
+              .setTrainingTaskInputs(ValueConverter.toValue(autoMlImageClassificationInputs))
               .setInputDataConfig(trainingInputDataConfig)
               .setModelToUpload(model)
               .build();

aiplatform/snippets/src/main/java/aiplatform/PredictImageClassificationSample.java

@@ -19,12 +19,15 @@
 // [START aiplatform_predict_image_classification_sample]
 
 import com.google.api.client.util.Base64;
+import com.google.cloud.aiplatform.util.ValueConverter;
 import com.google.cloud.aiplatform.v1beta1.EndpointName;
 import com.google.cloud.aiplatform.v1beta1.PredictResponse;
 import com.google.cloud.aiplatform.v1beta1.PredictionServiceClient;
 import com.google.cloud.aiplatform.v1beta1.PredictionServiceSettings;
+import com.google.cloud.aiplatform.v1beta1.schema.predict.instance.ImageClassificationPredictionInstance;
+import com.google.cloud.aiplatform.v1beta1.schema.predict.params.ImageClassificationPredictionParams;
+import com.google.cloud.aiplatform.v1beta1.schema.predict.prediction.ClassificationPredictionResult;
 import com.google.protobuf.Value;
-import com.google.protobuf.util.JsonFormat;
 import java.io.IOException;
 import java.nio.charset.StandardCharsets;
 import java.nio.file.Files;
@@ -60,23 +63,42 @@ static void predictImageClassification(String project, String fileName, String e
       byte[] contents = Base64.encodeBase64(Files.readAllBytes(Paths.get(fileName)));
       String content = new String(contents, StandardCharsets.UTF_8);
 
-      Value parameter = Value.newBuilder().setNumberValue(0).setNumberValue(5).build();
-
-      String contentDict = "{\"content\": \"" + content + "\"}";
-      Value.Builder instance = Value.newBuilder();
-      JsonFormat.parser().merge(contentDict, instance);
+      ImageClassificationPredictionInstance predictionInstance =
+          ImageClassificationPredictionInstance.newBuilder()
+          .setContent(content)
+          .build();
 
       List<Value> instances = new ArrayList<>();
-      instances.add(instance.build());
+      instances.add(ValueConverter.toValue(predictionInstance));
+
+      ImageClassificationPredictionParams predictionParams =
+          ImageClassificationPredictionParams.newBuilder()
+          .setConfidenceThreshold((float) 0.5)
+          .setMaxPredictions(5)
+          .build();
 
       PredictResponse predictResponse =
-          predictionServiceClient.predict(endpointName, instances, parameter);
+          predictionServiceClient.predict(endpointName, instances,
+              ValueConverter.toValue(predictionParams));
       System.out.println("Predict Image Classification Response");
       System.out.format("\tDeployed Model Id: %s\n", predictResponse.getDeployedModelId());
 
       System.out.println("Predictions");
       for (Value prediction : predictResponse.getPredictionsList()) {
-        System.out.format("\tPrediction: %s\n", prediction);
+
+        ClassificationPredictionResult.Builder resultBuilder =
+            ClassificationPredictionResult.newBuilder();
+        // Display names and confidences values correspond to
+        // IDs in the ID list.
+        ClassificationPredictionResult result =
+            (ClassificationPredictionResult) ValueConverter.fromValue(resultBuilder, prediction);
+        int counter = 0;
+        for (Long id : result.getIdsList()) {
+          System.out.printf("Label ID: %d\n", id);
+          System.out.printf("Label: %s\n", result.getDisplayNames(counter));
+          System.out.printf("Confidence: %.4f\n", result.getConfidences(counter));
+          counter++;
+        }
       }
     }
   }

aiplatform/snippets/src/main/java/aiplatform/PredictTextClassificationSingleLabelSample.java

@@ -17,13 +17,14 @@
 package aiplatform;
 
 // [START aiplatform_predict_text_classification_sample]
-
+import com.google.cloud.aiplatform.util.ValueConverter;
 import com.google.cloud.aiplatform.v1beta1.EndpointName;
 import com.google.cloud.aiplatform.v1beta1.PredictResponse;
 import com.google.cloud.aiplatform.v1beta1.PredictionServiceClient;
 import com.google.cloud.aiplatform.v1beta1.PredictionServiceSettings;
+import com.google.cloud.aiplatform.v1beta1.schema.predict.instance.TextClassificationPredictionInstance;
+import com.google.cloud.aiplatform.v1beta1.schema.predict.prediction.ClassificationPredictionResult;
 import com.google.protobuf.Value;
-import com.google.protobuf.util.JsonFormat;
 import java.io.IOException;
 import java.util.ArrayList;
 import java.util.List;
@@ -52,25 +53,38 @@ static void predictTextClassificationSingleLabel(
     try (PredictionServiceClient predictionServiceClient =
         PredictionServiceClient.create(predictionServiceSettings)) {
       String location = "us-central1";
-      String jsonString = "{\"content\": \"" + content + "\"}";
-
       EndpointName endpointName = EndpointName.of(project, location, endpointId);
 
-      Value parameter = Value.newBuilder().setNumberValue(0).setNumberValue(5).build();
-      Value.Builder instance = Value.newBuilder();
-      JsonFormat.parser().merge(jsonString, instance);
+      TextClassificationPredictionInstance predictionInstance = TextClassificationPredictionInstance
+          .newBuilder()
+          .setContent(content)
+          .build();
 
       List<Value> instances = new ArrayList<>();
-      instances.add(instance.build());
+      instances.add(ValueConverter.toValue(predictionInstance));
 
       PredictResponse predictResponse =
-          predictionServiceClient.predict(endpointName, instances, parameter);
+          predictionServiceClient.predict(endpointName, instances, ValueConverter.EMPTY_VALUE);
       System.out.println("Predict Text Classification Response");
       System.out.format("\tDeployed Model Id: %s\n", predictResponse.getDeployedModelId());
 
-      System.out.println("Predictions");
+      System.out.println("Predictions:\n\n");
       for (Value prediction : predictResponse.getPredictionsList()) {
-        System.out.format("\tPrediction: %s\n", prediction);
+
+        ClassificationPredictionResult.Builder resultBuilder =
+            ClassificationPredictionResult.newBuilder();
+
+        // Display names and confidences values correspond to
+        // IDs in the ID list.
+        ClassificationPredictionResult result =
+            (ClassificationPredictionResult) ValueConverter.fromValue(resultBuilder, prediction);
+        int counter = 0;
+        for (Long id : result.getIdsList()) {
+          System.out.printf("Label ID: %d\n", id);
+          System.out.printf("Label: %s\n", result.getDisplayNames(counter));
+          System.out.printf("Confidence: %.4f\n", result.getConfidences(counter));
+          counter++;
+        }
       }
     }
   }