选择用于训练的交互数据

按事件值或事件类型筛选训练数据集中使用的记录

1. 创建交互模式并包含EVENT_TYPE和EVENT_VALUE字段使用"name"和"type"如图所示，键值对。

   import boto3
   import json
    
    
   personalize = boto3.client('personalize')
    
   # Create a name for your schema
   schema_name = 'YourSchemaName'
    
   # Define the schema for your dataset
   schema = {
       "type": "record",
       "name": "Interactions",
       "namespace": "com.amazonaws.personalize.schema",
       "fields": [
           {
               "name": "USER_ID",
               "type": "string"
           },
           {
               "name": "ITEM_ID",
               "type": "string"
           },
           {
               "name": "EVENT_VALUE",
               "type": "float"
           },
           {
               "name": "EVENT_TYPE",
               "type": "string"
           },
           { 
               "name": "TIMESTAMP",
               "type": "long"
           }
       ],
       "version": "1.0"
   }
    
   # Create the schema for Amazon Personalize
   create_schema_response = personalize.create_schema(
       name = schema_name,
       schema = json.dumps(schema)
   )
    
   #To get the schema ARN, use the following lines
   schema_arn = create_schema_response['schemaArn']
   print('Schema ARN:' + schema_arn )

2. 格式化您的输入数据以匹配您的架构。有关代码示例，请参阅格式化输入数据。

3. 将数据上传到 Amazon Simple Storage Service (Amazon S3) 存储桶 有关代码示例，请参阅上载到 Amazon S3 存储桶。

4. Amazon PersonalizeCreateDatasetImportJobAPI。请务必记录数据集组 Amazon 资源名称 (ARN)，因为在创建解决方案时将需要它。有关代码示例，请参阅导入批量记录 (AmazonSDK)。

5. 获取当您创建解决方案时要使用的配方的 ARN。在创建解决方案时，您将需要它。

   import boto3
    
   personalize = boto3.client('personalize')
   
   # Display the ARNs of the recipes
   recipe_list = personalize.list_recipes()
   for recipe in recipe_list['recipes']:
       print(recipe['recipeArn'])
       
   # Store the ARN of the recipe that you want to use
    recipe_arn = "arn:aws:personalize:::recipe/aws-recipe-name"

6. 调用 CreateSolution API。如果您要指定事件类型（例如）“purchase”，将其设置在eventType参数。如果您要指定事件值（例如 10），请在 eventValueThreshold 参数中进行设置。您还可以同时指定事件类型和事件值。

   import boto3
    
   personalize = boto3.client('personalize')
   
   # Create the solution
   create_solution_response = personalize.create_solution(
       name = "your-solution-name",
       datasetGroupArn = dataset_group_arn,
       recipeArn = recipe_arn,
       eventType = 'watched',
       solutionConfig = {
           "eventValueThreshold": "0.5"
       }
   )
   
   # Store the solution ARN
   solution_arn = create_solution_response['solutionArn']
   
   # Use the solution ARN to get the solution status
   solution_description = personalize.describe_solution(solutionArn = solution_arn)['solution']
   print('Solution status: ' + solution_description['status'])

7. 获得解决方案后，通过在 CreateSolutionVersion 请求中指定其解决方案 ARN 使用它来训练模型。

   import boto3
    
   personalize = boto3.client('personalize')
   
   # Create a solution version
   create_solution_version_response = personalize.create_solution_version(solutionArn = solution_arn)
   
   
   # Store the solution version ARN
   solution_version_arn = create_solution_version_response['solutionVersionArn']
       
   # Use the solution version ARN to get the solution version status.
   solution_version_description = personalize.describe_solution_version(
       solutionVersionArn = solution_version_arn)['solutionVersion']
   print('Solution version status: ' + solution_version_description['status'])