Choosing the interactions data used for training - Amazon Personalize

Choosing the interactions data used for training

You can choose the events in an Interactions dataset that Amazon Personalize uses when creating a solution version (training a model). Choosing interactions data before training allows you to use only a relevant subset of your data for training or remove noise to train a more optimized model. For more information about Interactions datasets, see Datasets and schemas and Interactions dataset.

You can choose interactions data as follows:

  • Choose records based on type – When you configure a solution, if your Interactions dataset includes event types in an EVENT_TYPE column, you can optionally specify an event type to use in training. For example, if your Interactions dataset includes purchase, click, and watch event types, and you want Amazon Personalize to train the model with only watch events, when you configure your solution, you would provide watch as the event type that Amazon Personalize uses in training.

    If your Interactions dataset has multiple event types in an EVENT_TYPE column, and you do not provide an event type when you configure your solution, Amazon Personalize uses all interactions data for training with equal weight regardless of type.

  • Choose records based on type and value – When you configure a solution, if your Interactions dataset includes EVENT_TYPE and EVENT_VALUE fields, you can set a specific value as a threshold to exclude records from training. For example, if your EVENT_VALUE data for events with an EVENT_TYPE of watch is the percentage of a video that a user watched, if you set the event value threshold to 0.5, and the event type to watch, Amazon Personalize trains the model using only watch interaction events with an EVENT_VALUE greater than or equal to 0.5.

Filtering records by event value and event type (AWS SDK)

In the following procedure, you use the AWS SDK for Python (Boto3) to create an Interaction schema that filters a training dataset. You can use a Jupyter (iPython) notebook to accomplish the same task. For more information, see Getting started using Amazon Personalize APIs with Jupyter (iPython) notebooks.

Prerequisites: Complete the prerequisites and verify that your Python environment is set up as described in Getting started (AWS SDK for Python).

To filter records used in a training dataset by event value or event type

  1. Create an Interaction schema and include the EVENT_TYPE and EVENT_VALUE fields using "name" and "type" key-value pairs as shown.

    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. Format your input data to match your schema. For a code sample, see Formatting your input data.

  3. Upload your data to an Amazon Simple Storage Service (Amazon S3) bucket. For a code sample, see Uploading to an Amazon S3 bucket.

  4. Import your data into Amazon Personalize with the CreateDatasetImportJob API. Be sure to record your dataset group Amazon Resource Name (ARN) because you will need it when you create the solution. For a code sample, see Importing bulk records (AWS SDKs).

  5. Get the ARN of the recipe that you want to use when you create your solution. You'll need it when you create the solution.

    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. Call the CreateSolution API. If you want to specify the event type, for example “purchase”, set it in the eventType parameter. If you want to specify an event value, for example 10, set it in the eventValueThreshold parameter. You can also specify both an event type and an event value.

    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. When you have the solution, use it to train a model by specifying its solution ARN in a CreateSolutionVersion request.

    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'])

Training is complete when the status is ACTIVE. For more information, see Creating a solution.

After you train a model, you should evaluate its performance. To optimize your model, you might want to adjust the eventValueThreshold or other hyperparameters. For more information, see Step 4: Evaluating a solution version with metrics.