Obtenir un modèle

Montre comment récupérer des modèles.

En savoir plus

Pour obtenir une documentation détaillée incluant cet exemple de code, consultez les articles suivants :

• Explication des prédictions
• Gérer les modèles

Exemple de code

Java

Pour vous authentifier auprès d'AutoML Tables, configurez les Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local.

import com.google.cloud.automl.v1beta1.AutoMlClient;
import com.google.cloud.automl.v1beta1.Model;
import com.google.cloud.automl.v1beta1.ModelName;
import com.google.cloud.automl.v1beta1.TablesModelColumnInfo;
import io.grpc.StatusRuntimeException;
import java.io.IOException;
import java.text.DateFormat;
import java.text.SimpleDateFormat;

public class TablesGetModel {

  public static void main(String[] args) throws IOException, StatusRuntimeException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String region = "YOUR_REGION";
    String modelId = "YOUR_MODEL_ID";
    getModel(projectId, region, modelId);
  }

  // Demonstrates using the AutoML client to get model details.
  public static void getModel(String projectId, String computeRegion, String modelId)
      throws IOException, StatusRuntimeException {
    // 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, computeRegion, modelId);

      // Get complete detail of the model.
      Model model = client.getModel(modelFullId);

      // Display the model information.
      System.out.format("Model name: %s%n", model.getName());
      System.out.format(
          "Model Id: %s\n", model.getName().split("/")[model.getName().split("/").length - 1]);
      System.out.format("Model display name: %s%n", model.getDisplayName());
      System.out.format("Dataset Id: %s%n", model.getDatasetId());
      System.out.println("Tables Model Metadata: ");
      System.out.format(
          "\tTraining budget: %s%n", model.getTablesModelMetadata().getTrainBudgetMilliNodeHours());
      System.out.format(
          "\tTraining cost: %s%n", model.getTablesModelMetadata().getTrainBudgetMilliNodeHours());

      DateFormat dateFormat = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSZ");
      String createTime =
          dateFormat.format(new java.util.Date(model.getCreateTime().getSeconds() * 1000));
      System.out.format("Model create time: %s%n", createTime);

      System.out.format("Model deployment state: %s%n", model.getDeploymentState());

      // Get features of top importance
      for (TablesModelColumnInfo info :
          model.getTablesModelMetadata().getTablesModelColumnInfoList()) {
        System.out.format(
            "Column: %s - Importance: %.2f%n",
            info.getColumnDisplayName(), info.getFeatureImportance());
      }
    }
  }
}

Node.js

Pour vous authentifier auprès d'AutoML Tables, configurez les Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local.

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

/**
 * Demonstrates using the AutoML client to get model details.
 * 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";

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

// Get complete detail of the model.
client
  .getModel({name: modelFullId})
  .then(responses => {
    const model = responses[0];

    // Display the model information.
    console.log(`Model name: ${model.name}`);
    console.log(`Model Id: ${model.name.split('/').pop(-1)}`);
    console.log(`Model display name: ${model.displayName}`);
    console.log(`Dataset Id: ${model.datasetId}`);
    console.log('Tables model metadata: ');
    console.log(
      `\tTraining budget: ${model.tablesModelMetadata.trainBudgetMilliNodeHours}`
    );
    console.log(
      `\tTraining cost: ${model.tablesModelMetadata.trainCostMilliNodeHours}`
    );
    console.log(`Model deployment state: ${model.deploymentState}`);
  })
  .catch(err => {
    console.error(err);
  });

Python

Pour vous authentifier auprès d'AutoML Tables, configurez les Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local.

# 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'

from google.cloud import automl_v1beta1 as automl

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

# Get complete detail of the model.
model = client.get_model(model_display_name=model_display_name)

# Retrieve deployment state.
if model.deployment_state == automl.Model.DeploymentState.DEPLOYED:
    deployment_state = "deployed"
else:
    deployment_state = "undeployed"

# get features of top importance
feat_list = [
    (column.feature_importance, column.column_display_name)
    for column in model.tables_model_metadata.tables_model_column_info
]
feat_list.sort(reverse=True)
if len(feat_list) < 10:
    feat_to_show = len(feat_list)
else:
    feat_to_show = 10

# Display the model information.
print(f"Model name: {model.name}")
print("Model id: {}".format(model.name.split("/")[-1]))
print(f"Model display name: {model.display_name}")
print("Features of top importance:")
for feat in feat_list[:feat_to_show]:
    print(feat)
print(f"Model create time: {model.create_time}")
print(f"Model deployment state: {deployment_state}")

Étapes suivantes

Pour rechercher et filtrer des exemples de code pour d'autres produits Google Cloud, consultez l'exemple de navigateur Google Cloud.