This documentation is for AutoML Natural Language, which is different from Vertex AI. If you are using Vertex AI, see the Vertex AI documentation.

List a model evaluation for sentiment analysis

List a model evaluation for text sentiment analysis.

Code sample

Go

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"google.golang.org/api/iterator"
	automlpb "google.golang.org/genproto/googleapis/cloud/automl/v1"
)

// listModelEvaluation lists existing model evaluations.
func listModelEvaluations(w io.Writer, projectID string, location string, modelID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// modelID := "TRL123456789..."

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %v", err)
	}
	defer client.Close()

	req := &automlpb.ListModelEvaluationsRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s/models/%s", projectID, location, modelID),
	}

	it := client.ListModelEvaluations(ctx, req)

	// Iterate over all results
	for {
		evaluation, err := it.Next()
		if err == iterator.Done {
			break
		}
		if err != nil {
			return fmt.Errorf("ListModelEvaluations.Next: %v", err)
		}

		fmt.Fprintf(w, "Model evaluation name: %v\n", evaluation.GetName())
		fmt.Fprintf(w, "Model annotation spec id: %v\n", evaluation.GetAnnotationSpecId())
		fmt.Fprintf(w, "Create Time:\n")
		fmt.Fprintf(w, "\tseconds: %v\n", evaluation.GetCreateTime().GetSeconds())
		fmt.Fprintf(w, "\tnanos: %v\n", evaluation.GetCreateTime().GetNanos())
		fmt.Fprintf(w, "Evaluation example count: %v\n", evaluation.GetEvaluatedExampleCount())
		fmt.Fprintf(w, "Sentiment analysis model evaluation metrics: %v\n", evaluation.GetTextSentimentEvaluationMetrics())
	}

	return nil
}

Java


import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.ListModelEvaluationsRequest;
import com.google.cloud.automl.v1.ModelEvaluation;
import com.google.cloud.automl.v1.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(
            "Sentiment Analysis Model Evaluation Metrics: %s\n",
            modelEvaluation.getTextSentimentEvaluationMetrics());
      }
    }
  }
}

Node.js

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const modelId = 'YOUR_MODEL_ID';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function listModelEvaluations() {
  // Construct request
  const request = {
    parent: client.modelPath(projectId, location, modelId),
    filter: '',
  };

  const [response] = await client.listModelEvaluations(request);

  console.log('List of model evaluations:');
  for (const evaluation of response) {
    console.log(`Model evaluation name: ${evaluation.name}`);
    console.log(`Model annotation spec id: ${evaluation.annotationSpecId}`);
    console.log(`Model display name: ${evaluation.displayName}`);
    console.log('Model create time');
    console.log(`\tseconds ${evaluation.createTime.seconds}`);
    console.log(`\tnanos ${evaluation.createTime.nanos / 1e9}`);
    console.log(
      `Evaluation example count: ${evaluation.evaluatedExampleCount}`
    );
    console.log(
      `Sentiment analysis model evaluation metrics: ${evaluation.textSentimentEvaluationMetrics}`
    );
  }
}

listModelEvaluations();

Python

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"

client = automl.AutoMlClient()
# Get the full path of the model.
model_full_id = client.model_path(project_id, "us-central1", model_id)

print("List of model evaluations:")
for evaluation in client.list_model_evaluations(parent=model_full_id, filter=""):
    print("Model evaluation name: {}".format(evaluation.name))
    print("Model annotation spec id: {}".format(evaluation.annotation_spec_id))
    print("Create Time: {}".format(evaluation.create_time))
    print("Evaluation example count: {}".format(evaluation.evaluated_example_count))
    print(
        "Sentiment analysis model evaluation metrics: {}".format(
            evaluation.text_sentiment_evaluation_metrics
        )
    )

What's next

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