async function main()

in automl/tables/list-model-evaluations.v1beta1.js [16:166]
131 lines of code
6 McCabe index (conditional complexity)

async function main(
  projectId = 'YOUR_PROJECT_ID',
  computeRegion = 'YOUR_REGION_NAME',
  modelId = 'MODEL_ID',
  filter = 'FILTER_EXPRESSION'
) {
  // [START automl_tables_list_model_evaluations]
  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);
    });
  // [END automl_tables_list_model_evaluations]
}