List model evaluation slices

Lists model evaluation slices using the list_model_evaluation_slices method.

Explore further

For detailed documentation that includes this code sample, see the following:

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• Evaluate AutoML classification and regression models

Code sample

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

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

public class ListModelEvaluationSliceSample {

  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";
    listModelEvaluationSliceSample(project, modelId, evaluationId);
  }

  static void listModelEvaluationSliceSample(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);

      for (ModelEvaluationSlice modelEvaluationSlice :
          modelServiceClient.listModelEvaluationSlices(modelEvaluationName).iterateAll()) {
        System.out.format("Model Evaluation Slice Name: %s\n", modelEvaluationSlice.getName());
        System.out.format("Metrics Schema Uri: %s\n", modelEvaluationSlice.getMetricsSchemaUri());
        System.out.format("Metrics: %s\n", modelEvaluationSlice.getMetrics());
        System.out.format("Create Time: %s\n", modelEvaluationSlice.getCreateTime());

        Slice slice = modelEvaluationSlice.getSlice();
        System.out.format("Slice Dimensions: %s\n", slice.getDimension());
        System.out.format("Slice Value: %s\n\n", slice.getValue());
      }
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Node.js API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

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

  // Get and print out a list of all the evaluation slices for this resource
  const [response] =
    await modelServiceClient.listModelEvaluationSlices(request);
  console.log('List model evaluation response', response);
  console.log(response);
}
listModelEvaluationSlices();

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from google.cloud import aiplatform


def list_model_evaluation_slices_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)
    parent = client.model_evaluation_path(
        project=project, location=location, model=model_id, evaluation=evaluation_id
    )
    response = client.list_model_evaluation_slices(parent=parent)
    for model_evaluation_slice in response:
        print("model_evaluation_slice:", model_evaluation_slice)

What's next

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