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PredictTabularRegressionSample.java
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PredictTabularRegressionSample.java
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/*
* Copyright 2020 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package aiplatform;
// [START aiplatform_predict_tabular_regression_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.prediction.TabularRegressionPredictionResult;
import com.google.protobuf.ListValue;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;
import java.util.List;
public class PredictTabularRegressionSample {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String project = "YOUR_PROJECT_ID";
String instance = "[{ “feature_column_a”: “value”, “feature_column_b”: “value”}]";
String endpointId = "YOUR_ENDPOINT_ID";
predictTabularRegression(instance, project, endpointId);
}
static void predictTabularRegression(String instance, String project, String endpointId)
throws IOException {
PredictionServiceSettings predictionServiceSettings =
PredictionServiceSettings.newBuilder()
.setEndpoint("us-central1-aiplatform.googleapis.com:443")
.build();
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
try (PredictionServiceClient predictionServiceClient =
PredictionServiceClient.create(predictionServiceSettings)) {
String location = "us-central1";
EndpointName endpointName = EndpointName.of(project, location, endpointId);
ListValue.Builder listValue = ListValue.newBuilder();
JsonFormat.parser().merge(instance, listValue);
List<Value> instanceList = listValue.getValuesList();
Value parameters = Value.newBuilder().setListValue(listValue).build();
PredictResponse predictResponse =
predictionServiceClient.predict(endpointName, instanceList, parameters);
System.out.println("Predict Tabular Regression Response");
System.out.format("\tDisplay Model Id: %s\n", predictResponse.getDeployedModelId());
System.out.println("Predictions");
for (Value prediction : predictResponse.getPredictionsList()) {
TabularRegressionPredictionResult.Builder resultBuilder =
TabularRegressionPredictionResult.newBuilder();
TabularRegressionPredictionResult result =
(TabularRegressionPredictionResult) ValueConverter
.fromValue(resultBuilder, prediction);
System.out.printf("\tUpper bound: %f\n", result.getUpperBound());
System.out.printf("\tLower bound: %f\n", result.getLowerBound());
System.out.printf("\tValue: %f\n", result.getValue());
}
}
}
}
// [END aiplatform_predict_tabular_regression_sample]