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코드 샘플
Java
AutoML Tables에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
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
AutoML Tables에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
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
AutoML Tables에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
# 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}")
다음 단계
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