域数据集组入门（SDK适用于 Python (Boto3)）

使用以下代码创建 Amazon Personalize 架构。将 gs-domain-interactions-schema 替换为架构的名称。

import json
schema = {
  "type": "record",
  "name": "Interactions",
  "namespace": "com.amazonaws.personalize.schema",
  "fields": [
      {
          "name": "USER_ID",
          "type": "string"
      },
      {
          "name": "ITEM_ID",
          "type": "string"
      },
      {
          "name": "EVENT_TYPE",
          "type": "string"
      },
      {
          "name": "TIMESTAMP",
          "type": "long"
      }
  ],
  "version": "1.0"
}

create_interactions_schema_response = personalize.create_schema(
    name='gs-domain-interactions-schema',
    schema=json.dumps(schema),
    domain='VIDEO_ON_DEMAND'
)

interactions_schema_arn = create_interactions_schema_response['schemaArn']
print(json.dumps(create_interactions_schema_response, indent=2))

使用以下代码创建数据集组。将 dataset group name 替换为数据集组的名称。

response = personalize.create_dataset_group(
  name = 'dataset group name',
  domain = 'VIDEO_ON_DEMAND'
)
dsg_arn = response['datasetGroupArn']

description = personalize.describe_dataset_group(datasetGroupArn = dsg_arn)['datasetGroup']

print('Name: ' + description['name'])
print('ARN: ' + description['datasetGroupArn'])
print('Status: ' + description['status'])

使用以下代码在新数据集组中创建物品交互数据集。为数据集命名，并提供前面步骤中的schema_arn和dataset_group_arn。

response = personalize.create_dataset(
    name = 'interactions-dataset-name',
    schemaArn = interactions_schema_arn,
    datasetGroupArn = dsg_arn,
    datasetType = 'INTERACTIONS'
)

dataset_arn = response['datasetArn']

使用以下代码通过数据集导入作业导入数据。代码使用 describe_dataset_import_job 方法来跟踪作业的状态。

将以下内容作为参数传递：任务的名称、上一步dataset_arn中的名称、存储训练数据的 Amazon S3 存储桶路径 (s3://bucket name/folder name/ratings.csv) 以及您的IAM服务角色的路径ARN。您在入门先决条件期间创建了此角色。Amazon Personalize 需要权限才能访问存储桶。有关授予访问权限的信息，请参阅向 Amazon Personalize 授予访问 Amazon S3 资源的权限。

import time
response = personalize.create_dataset_import_job(
    jobName = 'JobName',
    datasetArn = 'dataset_arn',
    dataSource = {'dataLocation':'s3://amzn-s3-demo-bucket/filename.csv'},
    roleArn = 'role_arn'
)

dataset_interactions_import_job_arn = response['datasetImportJobArn']

description = personalize.describe_dataset_import_job(
    datasetImportJobArn = dataset_interactions_import_job_arn)['datasetImportJob']

print('Name: ' + description['jobName'])
print('ARN: ' + description['datasetImportJobArn'])
print('Status: ' + description['status'])

max_time = time.time() + 3*60*60 # 3 hours
while time.time() < max_time:
    describe_dataset_import_job_response = personalize.describe_dataset_import_job(
        datasetImportJobArn = dataset_interactions_import_job_arn
    )
    status = describe_dataset_import_job_response["datasetImportJob"]['status']
    print("Interactions DatasetImportJob: {}".format(status))
    
    if status == "ACTIVE" or status == "CREATE FAILED":
        break
        
    time.sleep(60)