Obtener una predicción en línea

Recupera una predicción en línea para una sola instancia de datos de predicción.

Explora más

Para obtener documentación en la que se incluye esta muestra de código, consulta lo siguiente:

Muestra de código

Java

Para autenticarte en AutoML Tables, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local. 

import com.google.cloud.automl.v1beta1.AnnotationPayload;
import com.google.cloud.automl.v1beta1.ExamplePayload;
import com.google.cloud.automl.v1beta1.ModelName;
import com.google.cloud.automl.v1beta1.PredictRequest;
import com.google.cloud.automl.v1beta1.PredictResponse;
import com.google.cloud.automl.v1beta1.PredictionServiceClient;
import com.google.cloud.automl.v1beta1.Row;
import com.google.cloud.automl.v1beta1.TablesAnnotation;
import com.google.protobuf.Value;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

class TablesPredict {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    // Values should match the input expected by your model.
    List<Value> values = new ArrayList<>();
    // values.add(Value.newBuilder().setBoolValue(true).build());
    // values.add(Value.newBuilder().setNumberValue(10).build());
    // values.add(Value.newBuilder().setStringValue("YOUR_STRING").build());
    predict(projectId, modelId, values);
  }

  static void predict(String projectId, String modelId, List<Value> values) throws IOException {
    // 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 client = PredictionServiceClient.create()) {
      // Get the full path of the model.
      ModelName name = ModelName.of(projectId, "us-central1", modelId);
      Row row = Row.newBuilder().addAllValues(values).build();
      ExamplePayload payload = ExamplePayload.newBuilder().setRow(row).build();

      // Feature importance gives you visibility into how the features in a specific prediction
      // request informed the resulting prediction. For more info, see:
      // https://cloud.google.com/automl-tables/docs/features#local
      PredictRequest request =
          PredictRequest.newBuilder()
              .setName(name.toString())
              .setPayload(payload)
              .putParams("feature_importance", "true")
              .build();

      PredictResponse response = client.predict(request);

      System.out.println("Prediction results:");
      for (AnnotationPayload annotationPayload : response.getPayloadList()) {
        TablesAnnotation tablesAnnotation = annotationPayload.getTables();
        System.out.format(
            "Classification label: %s%n", tablesAnnotation.getValue().getStringValue());
        System.out.format("Classification score: %.3f%n", tablesAnnotation.getScore());
        // Get features of top importance
        tablesAnnotation
            .getTablesModelColumnInfoList()
            .forEach(
                info ->
                    System.out.format(
                        "\tColumn: %s - Importance: %.2f%n",
                        info.getColumnDisplayName(), info.getFeatureImportance()));
      }
    }
  }
}

Node.js

Para autenticarte en AutoML Tables, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local. 


/**
 * Demonstrates using the AutoML client to request prediction from
 * automl tables using csv.
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
// const modelId = '[MODEL_ID]' e.g., "TBL000000000000";
// const inputs = [{ numberValue: 1 }, { stringValue: 'value' }, { stringValue: 'value2' } ...]

const automl = require('@google-cloud/automl');

// Create client for prediction service.
const automlClient = new automl.v1beta1.PredictionServiceClient();

// Get the full path of the model.
const modelFullId = automlClient.modelPath(projectId, computeRegion, modelId);

async function predict() {
  // Set the payload by giving the row values.
  const payload = {
    row: {
      values: inputs,
    },
  };

  // Params is additional domain-specific parameters.
  // Currently there is no additional parameters supported.
  const [response] = await automlClient.predict({
    name: modelFullId,
    payload: payload,
    params: {feature_importance: true},
  });
  console.log('Prediction results:');

  for (const result of response.payload) {
    console.log(`Predicted class name: ${result.displayName}`);
    console.log(`Predicted class score: ${result.tables.score}`);

    // Get features of top importance
    const featureList = result.tables.tablesModelColumnInfo.map(
      columnInfo => {
        return {
          importance: columnInfo.featureImportance,
          displayName: columnInfo.columnDisplayName,
        };
      }
    );
    // Sort features by their importance, highest importance first
    featureList.sort((a, b) => {
      return b.importance - a.importance;
    });

    // Print top 10 important features
    console.log('Features of top importance');
    console.log(featureList.slice(0, 10));
  }
}
predict();

Python

Para autenticarte en AutoML Tables, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local. 

# TODO(developer): Uncomment and set the following variables
# project_id = 'PROJECT_ID_HERE'
# compute_region = 'COMPUTE_REGION_HERE'
# model_display_name = 'MODEL_DISPLAY_NAME_HERE'
# inputs = {'value': 3, ...}

from google.cloud import automl_v1beta1 as automl

client = automl.TablesClient(project=project_id, region=compute_region)

if feature_importance:
    response = client.predict(
        model_display_name=model_display_name,
        inputs=inputs,
        feature_importance=True,
    )
else:
    response = client.predict(model_display_name=model_display_name, inputs=inputs)

print("Prediction results:")
for result in response.payload:
    print(f"Predicted class name: {result.tables.value}")
    print(f"Predicted class score: {result.tables.score}")

    if feature_importance:
        # get features of top importance
        feat_list = [
            (column.feature_importance, column.column_display_name)
            for column in result.tables.tables_model_column_info
        ]
        feat_list.sort(reverse=True)
        if len(feat_list) < 10:
            feat_to_show = len(feat_list)
        else:
            feat_to_show = 10

        print("Features of top importance:")
        for feat in feat_list[:feat_to_show]:
            print(feat)

¿Qué sigue?

Para buscar y filtrar muestras de código para otros productos de Google Cloud, consulta el navegador de muestra de Google Cloud.