Esta página se ha traducido con Cloud Translation API.

Predecir la clasificación en tablas

Obtiene la predicción de la clasificación tabular mediante el método predict.

Investigar más

Para obtener documentación detallada que incluya este código de muestra, consulta lo siguiente:

Código de ejemplo

Java

Antes de probar este ejemplo, sigue las Java instrucciones de configuración de la guía de inicio rápido de Vertex AI con bibliotecas de cliente. Para obtener más información, consulta la documentación de referencia de la API Java de Vertex AI.

Para autenticarte en Vertex AI, configura las credenciales predeterminadas de la aplicación. Para obtener más información, consulta el artículo Configurar la autenticación en un entorno de desarrollo local.


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 {
    PredictionServicPredictionServiceSettingsceSettings =
        PredictionServicPredictionServiceSettings          .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 (PredictionServicPredictionServiceClientceClient =
        PredictionServicPredictionServiceClientonServiceSettings)) {
      String location = "us-central1";
      EndpointName endEndpointNameEndpointName.of(EndpointNameation, endpointId);

      ListValue.BuildeListValueue = ListValue.newBuiListValue     JsonFormat.parseJsonFormatinstance, listValue);
      List<Value> instanListValuelistValue.getValuesList();

      Value parametersValuelue.newBuilderValuetListValue(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.predictResponse.getDeployedModelId().out.println("Predictions");
      for (Value predictionValueedictResponse.predictResponse.getPredictionsList()larClassificTabularClassificationPredictionResultuilder =
            TabularClassificTabularClassificationPredictionResult       TabularClassificTabularClassificationPredictionResult      (TabularClassificTabularClassificationPredictionResult  ValueConverter.fValueConvertertBuilder, prediction);

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

Node.js

Antes de probar este ejemplo, sigue las Node.js instrucciones de configuración de la guía de inicio rápido de Vertex AI con bibliotecas de cliente. Para obtener más información, consulta la documentación de referencia de la API Node.js de Vertex AI.

/**
 * 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

Antes de probar este ejemplo, sigue las Python instrucciones de configuración de la guía de inicio rápido de Vertex AI con bibliotecas de cliente. Para obtener más información, consulta la documentación de referencia de la API Python de Vertex AI.

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))

Siguientes pasos

Para buscar y filtrar ejemplos de código de otros Google Cloud productos, consulta el Google Cloud navegador de ejemplos.

Predecir la clasificación en tablas Organízate con las colecciones Guarda y clasifica el contenido según tus preferencias.