將指標歸因報告發佈到 Amazon S3

對於所有大量資料，如果您在建立指標歸因時提供 Amazon S3 儲存貯體，則可以選擇在每次為互動資料建立資料集匯入任務時，將指標報告發佈到 Amazon S3 儲存貯體。

若要將指標發佈到 Amazon S3，您可以在指標歸因中提供指向 Amazon S3 儲存貯體的路徑。然後，您可以在建立資料集匯入任務時將報表發佈到 Amazon S3。任務完成後，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 儲存貯體中建立一個新檔案。檔案名稱包括匯入方法和日期，如下所示：

AggregatedAttributionMetrics - ImportMethod - Timestamp.csv

以下是量度報表CSV檔案前幾列顯示方式的範例。此範例中的量度會報告兩個不同推薦人在 15 分鐘間隔內的總點擊次數。每個推薦人在 EVENT _ _ ATTRIBUTION SOURCE 欄中都以其 Amazon 資源名稱 (ARN) 來識別。

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到 Amazon S3 (主控台)

若要使用 Amazon Personalize 主控台將指標發佈到 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到 S3 中為此匯入任務選擇發佈指標。

如需 step-by-step 指示，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到 Amazon S3 (AWS CLI)

若要使用 AWS Command Line Interface (AWS CLI) 將指標發佈到 Amazon S3 儲存貯體，請使用下列程式碼建立資料集匯入任務並提供publishAttributionMetricsToS3旗標。如果您不想發佈特定工作的量度，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到 Amazon S3 (AWS SDKs)

若要使用將指標發佈到 Amazon S3 儲存貯體 AWS SDKs，請建立資料集匯入任務並設publishAttributionMetricsToS3定為 true。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();