本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
發佈指標屬性報告至 Amazon S3
對於所有大量資料，如果您在建立指標屬性時提供 Amazon S3 儲存貯體，您可以選擇在每次為互動資料建立資料集匯入任務時，將指標報告發佈至 Amazon S3 儲存貯體。
若要將指標發佈至 Amazon S3，請在指標屬性中提供 Amazon S3 儲存貯體的路徑。然後，當您建立資料集匯入任務時，將報告發佈至 Amazon S3。當任務完成時，您可以在 Amazon S3 儲存貯體中找到指標。每次發佈指標時，Amazon Personalize 都會在您的 Amazon S3 儲存貯體中建立新的檔案。檔案名稱包含匯入方法和日期，如下所示：
AggregatedAttributionMetrics - ImportMethod -
Timestamp.csv
以下是指標報告 CSV 檔案的前幾列可能如何出現的範例。此範例中的指標會在 15 分鐘內報告來自兩個不同建議者的總點擊次數。在 EVENT_ATTRIBUTION_SOURCE 欄中，透過其 Amazon Resource Name (ARN) 來識別每個建議者。
METRIC_NAME,EVENT_TYPE,VALUE,MATH_FUNCTION,EVENT_ATTRIBUTION_SOURCE,TIMESTAMP
COUNTWATCHES,WATCH,12.0,samplecount,arn:aws:personalize:us-west-2:acctNum:recommender/recommender1Name,1666925124
COUNTWATCHES,WATCH,112.0,samplecount,arn:aws:personalize:us-west-2:acctNum:recommender/recommender2Name,1666924224
COUNTWATCHES,WATCH,10.0,samplecount,arn:aws:personalize:us-west-2:acctNum:recommender/recommender1Name,1666924224
COUNTWATCHES,WATCH,254.0,samplecount,arn:aws:personalize:us-west-2:acctNum:recommender/recommender2Name,1666922424
COUNTWATCHES,WATCH,112.0,samplecount,arn:aws:personalize:us-west-2:acctNum:recommender/recommender1Name,1666922424
COUNTWATCHES,WATCH,100.0,samplecount,arn:aws:personalize:us-west-2:acctNum:recommender/recommender2Name,1666922424
......
.....
將大量資料的指標發佈至 Amazon S3 （主控台）
若要使用 Amazon Personalize 主控台將指標發佈至 Amazon S3 儲存貯體，請建立資料集匯入任務，然後在將事件指標發佈至 S3 中為此匯入任務選擇發佈指標。 S3
如需逐步說明，請參閱 建立資料集匯入任務 （主控台）。
將大量資料的指標發佈至 Amazon S3 (AWS CLI)
若要使用 AWS Command Line Interface (AWS CLI) 將指標發佈至 Amazon S3 儲存貯體，請使用下列程式碼來建立資料集匯入任務並提供 publishAttributionMetricsToS3旗標。如果您不想發佈特定任務的指標，請省略 旗標。如需每個參數的資訊，請參閱 CreateDatasetImportJob。
aws personalize create-dataset-import-job \
--job-name dataset import job name \
--dataset-arn dataset arn \
--data-source dataLocation=s3://amzn-s3-demo-bucket/filename \
--role-arn roleArn \
--import-mode INCREMENTAL \
--publish-attribution-metrics-to-s3
將大量資料的指標發佈至 Amazon S3 (AWS SDKs)
若要使用 SDK 將指標發佈至 Amazon S3 儲存貯體，請建立資料集匯入任務並publishAttributionMetricsToS3設為 true。 AWS SDKs 如需每個參數的資訊，請參閱 CreateDatasetImportJob。
- SDK for Python (Boto3)
-
import boto3
personalize = boto3.client('personalize')
response = personalize.create_dataset_import_job(
jobName = 'YourImportJob',
datasetArn = 'dataset_arn',
dataSource = {'dataLocation':'s3://amzn-s3-demo-bucket/file.csv'},
roleArn = 'role_arn',
importMode = 'INCREMENTAL',
publishAttributionMetricsToS3 = True
)
dsij_arn = response['datasetImportJobArn']
print ('Dataset Import Job arn: ' + dsij_arn)
description = personalize.describe_dataset_import_job(
datasetImportJobArn = dsij_arn)['datasetImportJob']
print('Name: ' + description['jobName'])
print('ARN: ' + description['datasetImportJobArn'])
print('Status: ' + description['status'])
- SDK for Java 2.x
-
public static String createPersonalizeDatasetImportJob(PersonalizeClient personalizeClient,
String jobName,
String datasetArn,
String s3BucketPath,
String roleArn,
ImportMode importMode,
boolean publishToS3) {
long waitInMilliseconds = 60 * 1000;
String status;
String datasetImportJobArn;
try {
DataSource importDataSource = DataSource.builder()
.dataLocation(s3BucketPath)
.build();
CreateDatasetImportJobRequest createDatasetImportJobRequest = CreateDatasetImportJobRequest.builder()
.datasetArn(datasetArn)
.dataSource(importDataSource)
.jobName(jobName)
.roleArn(roleArn)
.importMode(importMode)
.publishAttributionMetricsToS3(publishToS3)
.build();
datasetImportJobArn = personalizeClient.createDatasetImportJob(createDatasetImportJobRequest)
.datasetImportJobArn();
DescribeDatasetImportJobRequest describeDatasetImportJobRequest = DescribeDatasetImportJobRequest.builder()
.datasetImportJobArn(datasetImportJobArn)
.build();
long maxTime = Instant.now().getEpochSecond() + 3 * 60 * 60;
while (Instant.now().getEpochSecond() < maxTime) {
DatasetImportJob datasetImportJob = personalizeClient
.describeDatasetImportJob(describeDatasetImportJobRequest)
.datasetImportJob();
status = datasetImportJob.status();
System.out.println("Dataset import job status: " + status);
if (status.equals("ACTIVE") || status.equals("CREATE FAILED")) {
break;
}
try {
Thread.sleep(waitInMilliseconds);
} catch (InterruptedException e) {
System.out.println(e.getMessage());
}
}
return datasetImportJobArn;
} catch (PersonalizeException e) {
System.out.println(e.awsErrorDetails().errorMessage());
}
return "";
}
- SDK for JavaScript v3
// Get service clients and commands using ES6 syntax.
import { CreateDatasetImportJobCommand, PersonalizeClient } from
"@aws-sdk/client-personalize";
// create personalizeClient
const personalizeClient = new PersonalizeClient({
region: "REGION"
});
// Set the dataset import job parameters.
export const datasetImportJobParam = {
datasetArn: 'DATASET_ARN', /* required */
dataSource: {
dataLocation: 's3://amzn-s3-demo-bucket/<folderName>/<CSVfilename>.csv' /* required */
},
jobName: 'NAME', /* required */
roleArn: 'ROLE_ARN', /* required */
importMode: "FULL", /* optional, default is FULL */
publishAttributionMetricsToS3: true /* set to true to publish metrics to Amazon S3 bucket */
};
export const run = async () => {
try {
const response = await personalizeClient.send(new CreateDatasetImportJobCommand(datasetImportJobParam));
console.log("Success", response);
return response; // For unit tests.
} catch (err) {
console.log("Error", err);
}
};
run();
