開始使SDK用 (針對 Python (肉毒 3))

使用下面的代碼在 Amazon Personalize 化創建模式。getting-started-schema以結構描述的名稱取代。

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

create_interactions_schema_response = personalize.create_schema(
    name='getting-started-schema',
    schema=json.dumps(schema)
)

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

使用下列程式碼建立資料集群組。dataset group name以資料集群組的名稱取代。

response = personalize.create_dataset_group(name = 'dataset group name')
dataset_group_arn = response['datasetGroupArn']

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

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

使用下列程式碼在新資料集群組中建立 Item 互動資料集。為資料集指定名稱，schema_arn並dataset_group_arn提供與先前步驟。

response = personalize.create_dataset(
    name = 'datase_name',
    schemaArn = 'schema_arn',
    datasetGroupArn = 'dataset_group_arn',
    datasetType = 'Interactions'
)

dataset_arn = response['datasetArn']

使用以下代碼導入數據集導入作業導入數據。程式碼會使用描述資料集匯入工作的方法來追蹤工作的狀態。

將下列項目當作參數傳遞：任務的名稱、上dataset_arn一步的名稱、儲存訓練資料的 Amazon S3 儲存貯體路徑 (s3://bucket name/folder name/ratings.csv)，以及您的IAM服務角色ARN。您已建立此角色，做為中的一部分入門先決條件。Amazon Personalize 化需要存取存儲桶的權限。請參閱 讓 Amazon Personalize 化訪問 Amazon S3 資源。

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',
    importMode = 'FULL'
)

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)

使用下面的代碼創建一個新的解決方案。傳遞下列項目做為參數：dataset_group_arn從較早的、解決方案的名稱，以及使用者個人化 V2 方案 ()。ARN arn:aws:personalize:::recipe/aws-user-personalization-v2儲存您ARN的新解決方案，以供日後使用。

create_solution_response = personalize.create_solution(
  name='solution name', 
  recipeArn= 'arn:aws:personalize:::recipe/aws-user-personalization-v2', 
  datasetGroupArn = 'dataset group arn'
)
solution_arn = create_solution_response['solutionArn']
print('solution_arn: ', solution_arn)

使用以下代碼創建解決方案版本。作為參數傳遞solution_arn從上一個步驟。下列程式碼會建立解決方案版本。在訓練期間，程式碼會使用DescribeSolutionVersion作業來擷取解決方案版本的狀態。訓練完成後，此方法會傳回新解決方案版本ARN的。存放它以供日後使用。

import time
import json

create_solution_version_response = personalize.create_solution_version(
    solutionArn = solution_arn
)

solution_version_arn = create_solution_version_response['solutionVersionArn']
print(json.dumps(create_solution_version_response, indent=2))

max_time = time.time() + 3*60*60 # 3 hours
while time.time() < max_time:
    describe_solution_version_response = personalize.describe_solution_version(
        solutionVersionArn = solution_version_arn
    )
    status = describe_solution_version_response["solutionVersion"]["status"]
    print("SolutionVersion: {}".format(status))
    
    if status == "ACTIVE" or status == "CREATE FAILED":
        break
        
    time.sleep(60)