List model evaluations

Demonstrate listing model evaluations.

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

Code sample

Java

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

import com.google.cloud.automl.v1beta1.AutoMlClient;
import com.google.cloud.automl.v1beta1.ListModelEvaluationsRequest;
import com.google.cloud.automl.v1beta1.ModelEvaluation;
import com.google.cloud.automl.v1beta1.ModelName;
import java.io.IOException;

class ListModelEvaluations {

  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";
    listModelEvaluations(projectId, modelId);
  }

  // List model evaluations
  static void listModelEvaluations(String projectId, String modelId) 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 (AutoMlClient client = AutoMlClient.create()) {
      // Get the full path of the model.
      ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
      ListModelEvaluationsRequest modelEvaluationsrequest =
          ListModelEvaluationsRequest.newBuilder().setParent(modelFullId.toString()).build();

      // List all the model evaluations in the model by applying filter.
      System.out.println("List of model evaluations:");
      for (ModelEvaluation modelEvaluation :
          client.listModelEvaluations(modelEvaluationsrequest).iterateAll()) {

        System.out.format("Model Evaluation Name: %s%n", modelEvaluation.getName());
        System.out.format("Model Annotation Spec Id: %s", modelEvaluation.getAnnotationSpecId());
        System.out.println("Create Time:");
        System.out.format("\tseconds: %s%n", modelEvaluation.getCreateTime().getSeconds());
        System.out.format("\tnanos: %s", modelEvaluation.getCreateTime().getNanos() / 1e9);
        System.out.format(
            "Evalution Example Count: %d%n", modelEvaluation.getEvaluatedExampleCount());

        System.out.format(
            "Tables Model Evaluation Metrics: %s%n",
            modelEvaluation.getClassificationEvaluationMetrics());
      }
    }
  }
}

Node.js

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

const automl = require('@google-cloud/automl');
const math = require('mathjs');
const client = new automl.v1beta1.AutoMlClient();

/**
 * Demonstrates using the AutoML client to list model evaluations.
 * 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., "TBL4704590352927948800";
// const filter = '[FILTER_EXPRESSIONS]' e.g., "tablesModelMetadata:*";

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

// List all the model evaluations in the model by applying filter.
client
  .listModelEvaluations({parent: modelFullId, filter: filter})
  .then(responses => {
    const element = responses[0];
    console.log('List of model evaluations:');
    for (let i = 0; i < element.length; i++) {
      const classMetrics = element[i].classificationEvaluationMetrics;
      const regressionMetrics = element[i].regressionEvaluationMetrics;
      const evaluationId = element[i].name.split('/')[7].split('`')[0];

      console.log(`Model evaluation name: ${element[i].name}`);
      console.log(`Model evaluation Id: ${evaluationId}`);
      console.log(
        `Model evaluation annotation spec Id: ${element[i].annotationSpecId}`
      );
      console.log(`Model evaluation display name: ${element[i].displayName}`);
      console.log(
        `Model evaluation example count: ${element[i].evaluatedExampleCount}`
      );

      if (classMetrics) {
        const confidenceMetricsEntries = classMetrics.confidenceMetricsEntry;

        console.log('Table classification evaluation metrics:');
        console.log(`\tModel auPrc: ${math.round(classMetrics.auPrc, 6)}`);
        console.log(`\tModel auRoc: ${math.round(classMetrics.auRoc, 6)}`);
        console.log(
          `\tModel log loss: ${math.round(classMetrics.logLoss, 6)}`
        );

        if (confidenceMetricsEntries.length > 0) {
          console.log('\tConfidence metrics entries:');

          for (const confidenceMetricsEntry of confidenceMetricsEntries) {
            console.log(
              `\t\tModel confidence threshold: ${math.round(
                confidenceMetricsEntry.confidenceThreshold,
                6
              )}`
            );
            console.log(
              `\t\tModel position threshold: ${math.round(
                confidenceMetricsEntry.positionThreshold,
                4
              )}`
            );
            console.log(
              `\t\tModel recall: ${math.round(
                confidenceMetricsEntry.recall * 100,
                2
              )} %`
            );
            console.log(
              `\t\tModel precision: ${math.round(
                confidenceMetricsEntry.precision * 100,
                2
              )} %`
            );
            console.log(
              `\t\tModel false positive rate: ${confidenceMetricsEntry.falsePositiveRate}`
            );
            console.log(
              `\t\tModel f1 score: ${math.round(
                confidenceMetricsEntry.f1Score * 100,
                2
              )} %`
            );
            console.log(
              `\t\tModel recall@1: ${math.round(
                confidenceMetricsEntry.recallAt1 * 100,
                2
              )} %`
            );
            console.log(
              `\t\tModel precision@1: ${math.round(
                confidenceMetricsEntry.precisionAt1 * 100,
                2
              )} %`
            );
            console.log(
              `\t\tModel false positive rate@1: ${confidenceMetricsEntry.falsePositiveRateAt1}`
            );
            console.log(
              `\t\tModel f1 score@1: ${math.round(
                confidenceMetricsEntry.f1ScoreAt1 * 100,
                2
              )} %`
            );
            console.log(
              `\t\tModel true positive count: ${confidenceMetricsEntry.truePositiveCount}`
            );
            console.log(
              `\t\tModel false positive count: ${confidenceMetricsEntry.falsePositiveCount}`
            );
            console.log(
              `\t\tModel false negative count: ${confidenceMetricsEntry.falseNegativeCount}`
            );
            console.log(
              `\t\tModel true negative count: ${confidenceMetricsEntry.trueNegativeCount}`
            );
            console.log('\n');
          }
        }
        console.log(
          `\tModel annotation spec Id: ${classMetrics.annotationSpecId}`
        );
      } else if (regressionMetrics) {
        console.log('Table regression evaluation metrics:');
        console.log(
          `\tModel root mean squared error: ${regressionMetrics.rootMeanSquaredError}`
        );
        console.log(
          `\tModel mean absolute error: ${regressionMetrics.meanAbsoluteError}`
        );
        console.log(
          `\tModel mean absolute percentage error: ${regressionMetrics.meanAbsolutePercentageError}`
        );
        console.log(`\tModel rSquared: ${regressionMetrics.rSquared}`);
      }
      console.log('\n');
    }
  })
  .catch(err => {
    console.error(err);
  });

Python

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

# 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'
# filter = 'filter expression here'

from google.cloud import automl_v1beta1 as automl

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

# List all the model evaluations in the model by applying filter.
response = client.list_model_evaluations(
    model_display_name=model_display_name, filter=filter
)

print("List of model evaluations:")
for evaluation in response:
    print(f"Model evaluation name: {evaluation.name}")
    print("Model evaluation id: {}".format(evaluation.name.split("/")[-1]))
    print(
        "Model evaluation example count: {}".format(
            evaluation.evaluated_example_count
        )
    )
    print(f"Model evaluation time: {evaluation.create_time}")
    print("\n")

What's next

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