Recupero di una valutazione di modelli per la regressione tabulare

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Riceve una valutazione del modello per la regressione tabulare utilizzando il metodo get_model_Evaluation.

Per saperne di più

Per la documentazione dettagliata che include questo esempio di codice, vedi quanto segue:

Esempio di codice

Java

Per scoprire come installare e utilizzare la libreria client per Vertex AI, consulta la pagina relativa alle librerie client di Vertex AI. Per ulteriori informazioni, consulta la documentazione di riferimento per l'API Vertex AI Java.


import com.google.cloud.aiplatform.v1.ModelEvaluation;
import com.google.cloud.aiplatform.v1.ModelEvaluationName;
import com.google.cloud.aiplatform.v1.ModelServiceClient;
import com.google.cloud.aiplatform.v1.ModelServiceSettings;
import java.io.IOException;

public class GetModelEvaluationTabularRegressionSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    // To obtain evaluationId run the code block below after setting modelServiceSettings.
    //
    // try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings))
    // {
    //   String location = "us-central1";
    //   ModelName modelFullId = ModelName.of(project, location, modelId);
    //   ListModelEvaluationsRequest modelEvaluationsrequest =
    //   ListModelEvaluationsRequest.newBuilder().setParent(modelFullId.toString()).build();
    //   for (ModelEvaluation modelEvaluation :
    //     modelServiceClient.listModelEvaluations(modelEvaluationsrequest).iterateAll()) {
    //       System.out.format("Model Evaluation Name: %s%n", modelEvaluation.getName());
    //   }
    // }
    String project = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    String evaluationId = "YOUR_EVALUATION_ID";
    getModelEvaluationTabularRegression(project, modelId, evaluationId);
  }

  static void getModelEvaluationTabularRegression(
      String project, String modelId, String evaluationId) throws IOException {
    ModelServiceSettings modelServiceSettings =
        ModelServiceSettings.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 (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {
      String location = "us-central1";
      ModelEvaluationName modelEvaluationName =
          ModelEvaluationName.of(project, location, modelId, evaluationId);
      ModelEvaluation modelEvaluation = modelServiceClient.getModelEvaluation(modelEvaluationName);

      System.out.println("Get Model Evaluation Tabular Regression Response");
      System.out.format("\tName: %s\n", modelEvaluation.getName());
      System.out.format("\tMetrics Schema Uri: %s\n", modelEvaluation.getMetricsSchemaUri());
      System.out.format("\tMetrics: %s\n", modelEvaluation.getMetrics());
      System.out.format("\tCreate Time: %s\n", modelEvaluation.getCreateTime());
      System.out.format("\tSlice Dimensions: %s\n", modelEvaluation.getSliceDimensionsList());
    }
  }
}

Node.js

Per scoprire come installare e utilizzare la libreria client per Vertex AI, consulta la pagina relativa alle librerie client di Vertex AI. Per ulteriori informazioni, consulta la documentazione di riferimento per l'API Vertex AI Node.js.

/**
 * TODO(developer): Uncomment these variables before running the sample
 * (not necessary if passing values as arguments). To obtain evaluationId,
 * instantiate the client and run the following the commands.
 */
// const parentName = `projects/${project}/locations/${location}/models/${modelId}`;
// const evalRequest = {
//   parent: parentName
// };
// const [evalResponse] = await modelServiceClient.listModelEvaluations(evalRequest);
// console.log(evalResponse);

// const modelId = 'YOUR_MODEL_ID';
// const evaluationId = 'YOUR_EVALUATION_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

// Imports the Google Cloud Model Service Client library
const {ModelServiceClient} = require('@google-cloud/aiplatform');

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

// Instantiates a client
const modelServiceClient = new ModelServiceClient(clientOptions);

async function getModelEvaluationTabularRegression() {
  // Configure the parent resources
  const name = `projects/${project}/locations/${location}/models/${modelId}/evaluations/${evaluationId}`;
  const request = {
    name,
  };

  // Get model evaluation request
  const [response] = await modelServiceClient.getModelEvaluation(request);

  console.log('Get model evaluation tabular regression response');
  console.log(`\tName : ${response.name}`);
  console.log(`\tMetrics schema uri : ${response.metricsSchemaUri}`);
  console.log(`\tMetrics : ${JSON.stringify(response.metrics)}`);
  console.log(`\tCreate time : ${JSON.stringify(response.createTime)}`);
  console.log(`\tSlice dimensions : ${response.sliceDimensions}`);

  const modelExplanation = response.modelExplanation;
  console.log('\tModel explanation');
  if (!modelExplanation) {
    console.log('\t\t{}');
  } else {
    const meanAttributions = modelExplanation.meanAttributions;
    if (!meanAttributions) {
      console.log('\t\t\t []');
    } else {
      for (const meanAttribution of meanAttributions) {
        console.log('\t\tMean attribution');
        console.log(
          `\t\t\tBaseline output value : \
            ${meanAttribution.baselineOutputValue}`
        );
        console.log(
          `\t\t\tInstance output value : \
            ${meanAttribution.instanceOutputValue}`
        );
        console.log(
          `\t\t\tFeature attributions : \
            ${JSON.stringify(meanAttribution.featureAttributions)}`
        );
        console.log(`\t\t\tOutput index : ${meanAttribution.outputIndex}`);
        console.log(
          `\t\t\tOutput display name : \
            ${meanAttribution.outputDisplayName}`
        );
        console.log(
          `\t\t\tApproximation error : \
            ${meanAttribution.approximationError}`
        );
      }
    }
  }
}
getModelEvaluationTabularRegression();

Python

Per scoprire come installare e utilizzare la libreria client per Vertex AI, consulta la pagina relativa alle librerie client di Vertex AI. Per ulteriori informazioni, consulta la documentazione di riferimento per l'API Vertex AI Python.

from google.cloud import aiplatform

def get_model_evaluation_tabular_regression_sample(
    project: str,
    model_id: str,
    evaluation_id: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    """
    To obtain evaluation_id run the following commands where LOCATION
    is the region where the model is stored, PROJECT is the project ID,
    and MODEL_ID is the ID of your model.

    model_client = aiplatform.gapic.ModelServiceClient(
        client_options={
            'api_endpoint':'LOCATION-aiplatform.googleapis.com'
            }
        )
    evaluations = model_client.list_model_evaluations(parent='projects/PROJECT/locations/LOCATION/models/MODEL_ID')
    print("evaluations:", evaluations)
    """
    # 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.ModelServiceClient(client_options=client_options)
    name = client.model_evaluation_path(
        project=project, location=location, model=model_id, evaluation=evaluation_id
    )
    response = client.get_model_evaluation(name=name)
    print("response:", response)

Passaggi successivi

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