获取用于文本分类的模型评估

检索用于文本分类的模型评估。

代码示例

Go

如需向 AutoML Natural Language 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
)

// getModelEvaluation gets a model evaluation.
func getModelEvaluation(w io.Writer, projectID string, location string, modelID string, modelEvaluationID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// modelID := "TRL123456789..."
	// modelEvaluationID := "123456789..."

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

	req := &automlpb.GetModelEvaluationRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/models/%s/modelEvaluations/%s", projectID, location, modelID, modelEvaluationID),
	}

	evaluation, err := client.GetModelEvaluation(ctx, req)
	if err != nil {
		return fmt.Errorf("GetModelEvaluation: %w", 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, "Classification model evaluation metrics: %v\n", evaluation.GetClassificationEvaluationMetrics())

	return nil
}

Java

如需向 AutoML Natural Language 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 


import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.ModelEvaluation;
import com.google.cloud.automl.v1.ModelEvaluationName;
import java.io.IOException;

class GetModelEvaluation {

  static void getModelEvaluation() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    String modelEvaluationId = "YOUR_MODEL_EVALUATION_ID";
    getModelEvaluation(projectId, modelId, modelEvaluationId);
  }

  // Get a model evaluation
  static void getModelEvaluation(String projectId, String modelId, String modelEvaluationId)
      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 evaluation.
      ModelEvaluationName modelEvaluationFullId =
          ModelEvaluationName.of(projectId, "us-central1", modelId, modelEvaluationId);

      // Get complete detail of the model evaluation.
      ModelEvaluation modelEvaluation = client.getModelEvaluation(modelEvaluationFullId);

      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(
          "Classification Model Evaluation Metrics: %s\n",
          modelEvaluation.getClassificationEvaluationMetrics());
    }
  }
}

Node.js

如需向 AutoML Natural Language 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 

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

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

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

async function getModelEvaluation() {
  // Construct request
  const request = {
    name: client.modelEvaluationPath(
      projectId,
      location,
      modelId,
      modelEvaluationId
    ),
  };

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

  console.log(`Model evaluation name: ${response.name}`);
  console.log(`Model annotation spec id: ${response.annotationSpecId}`);
  console.log(`Model display name: ${response.displayName}`);
  console.log('Model create time');
  console.log(`\tseconds ${response.createTime.seconds}`);
  console.log(`\tnanos ${response.createTime.nanos / 1e9}`);
  console.log(`Evaluation example count: ${response.evaluatedExampleCount}`);
  console.log(
    `Classification model evaluation metrics: ${response.classificationEvaluationMetrics}`
  );
}

getModelEvaluation();

Python

如需向 AutoML Natural Language 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 

from google.cloud import automl

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

client = automl.AutoMlClient()
# Get the full path of the model evaluation.
model_path = client.model_path(project_id, "us-central1", model_id)
model_evaluation_full_id = f"{model_path}/modelEvaluations/{model_evaluation_id}"

# Get complete detail of the model evaluation.
response = client.get_model_evaluation(name=model_evaluation_full_id)

print(f"Model evaluation name: {response.name}")
print(f"Model annotation spec id: {response.annotation_spec_id}")
print(f"Create Time: {response.create_time}")
print(f"Evaluation example count: {response.evaluated_example_count}")
print(
    "Classification model evaluation metrics: {}".format(
        response.classification_evaluation_metrics
    )
)

后续步骤

如需搜索和过滤其他 Google Cloud 产品的代码示例，请参阅 Google Cloud 示例浏览器。