このページは Cloud Translation API によって翻訳されました。

表形式分類の予測

predict メソッドを使用して、表形式分類の予測を取得します。

このコードサンプルを含む詳細なドキュメントについては、以下をご覧ください。

コードサンプル

Java

このサンプルを試す前に、Vertex AI クイックスタート: クライアントライブラリの使用にある Java の設定手順を完了してください。詳細については、Vertex AI Java API のリファレンスドキュメントをご覧ください。

Vertex AI に対する認証を行うには、アプリケーションのデフォルト認証情報を設定します。詳細については、ローカル開発環境の認証を設定するをご覧ください。


import com.google.cloud.aiplatform.util.ValueConverter;
import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.PredictResponse;
import com.google.cloud.aiplatform.v1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1.PredictionServiceSettings;
import com.google.cloud.aiplatform.v1.schema.predict.prediction.TabularClassificationPredictionResult;
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 PredictTabularClassificationSample {

  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";
    predictTabularClassification(instance, project, endpointId);
  }

  static void predictTabularClassification(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 Classification Response");
      System.out.format("\tDeployed Model Id: %s\n", predictResponse.getDeployedModelId());

      System.out.println("Predictions");
      for (Value prediction : predictResponse.getPredictionsList()) {
        TabularClassificationPredictionResult.Builder resultBuilder =
            TabularClassificationPredictionResult.newBuilder();
        TabularClassificationPredictionResult result =
            (TabularClassificationPredictionResult)
                ValueConverter.fromValue(resultBuilder, prediction);

        for (int i = 0; i < result.getClassesCount(); i++) {
          System.out.printf("\tClass: %s", result.getClasses(i));
          System.out.printf("\tScore: %f", result.getScores(i));
        }
      }
    }
  }
}

Node.js

このサンプルを試す前に、Vertex AI クイックスタート: クライアントライブラリの使用にある Node.js の設定手順を完了してください。詳細については、Vertex AI Node.js API のリファレンスドキュメントをご覧ください。

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const endpointId = 'YOUR_ENDPOINT_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const aiplatform = require('@google-cloud/aiplatform');
const {prediction} =
  aiplatform.protos.google.cloud.aiplatform.v1.schema.predict;

// Imports the Google Cloud Prediction service client
const {PredictionServiceClient} = aiplatform.v1;

// Import the helper module for converting arbitrary protobuf.Value objects.
const {helpers} = aiplatform;

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const predictionServiceClient = new PredictionServiceClient(clientOptions);

async function predictTablesClassification() {
  // Configure the endpoint resource
  const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;
  const parameters = helpers.toValue({});

  const instance = helpers.toValue({
    petal_length: '1.4',
    petal_width: '1.3',
    sepal_length: '5.1',
    sepal_width: '2.8',
  });

  const instances = [instance];
  const request = {
    endpoint,
    instances,
    parameters,
  };

  // Predict request
  const [response] = await predictionServiceClient.predict(request);

  console.log('Predict tabular classification response');
  console.log(`\tDeployed model id : ${response.deployedModelId}\n`);
  const predictions = response.predictions;
  console.log('Predictions :');
  for (const predictionResultVal of predictions) {
    const predictionResultObj =
      prediction.TabularClassificationPredictionResult.fromValue(
        predictionResultVal
      );
    for (const [i, class_] of predictionResultObj.classes.entries()) {
      console.log(`\tClass: ${class_}`);
      console.log(`\tScore: ${predictionResultObj.scores[i]}\n\n`);
    }
  }
}
predictTablesClassification();

Python

このサンプルを試す前に、Vertex AI クイックスタート: クライアントライブラリの使用にある Python の設定手順を完了してください。詳細については、Vertex AI Python API のリファレンスドキュメントをご覧ください。

from typing import Dict

from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value


def predict_tabular_classification_sample(
    project: str,
    endpoint_id: str,
    instance_dict: Dict,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)
    # for more info on the instance schema, please use get_model_sample.py
    # and look at the yaml found in instance_schema_uri
    instance = json_format.ParseDict(instance_dict, Value())
    instances = [instance]
    parameters_dict = {}
    parameters = json_format.ParseDict(parameters_dict, Value())
    endpoint = client.endpoint_path(
        project=project, location=location, endpoint=endpoint_id
    )
    response = client.predict(
        endpoint=endpoint, instances=instances, parameters=parameters
    )
    print("response")
    print(" deployed_model_id:", response.deployed_model_id)
    # See gs://google-cloud-aiplatform/schema/predict/prediction/tabular_classification_1.0.0.yaml for the format of the predictions.
    predictions = response.predictions
    for prediction in predictions:
        print(" prediction:", dict(prediction))

次のステップ

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