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Context

A Context object defines the scope and circumstances in which features are expected to be served. Examples include:

  • weekly batch predictions for an active customer that has made at least one purchase over the past 12 weeks

  • real time predictions for a credit card transaction that has been recently processed

Creating a Context

Use the create() class method to add a Context to the active catalog: 

context = fb.Context.create(
    name="Active Customer", # Name of the context
    primary_entity=["Customer"] # Primary entity related to the context
)

While basic context creation requires only the entity identification, adding a detailed description is recommended. This description could cover:

  • Contextual Subset Details: Characteristics of the entity subset being targeted.
  • Serving Timing: Insights into when predictions are needed, whether in batch or real-time scenarios.
  • Inference Data Availability: What data is available at the time of inference.
  • Constraints: Any legal, operational, or other constraints.
context.update_description(
    "This context identifies 'Active Customers' as those individuals "
    "whose customerid in the Customer Profile have no recorded "
    "termination date, signifying ongoing engagement or activity. \n"
    "The serving of features related to this context can be invoked at "
    "any point in time. \n"
    "Furthermore, there are no specific operational, legal, or other constraints."
)

Association with Observation Table

Once the Context is defined, you can link an observation table using the add_observation_table() method.

context = catalog.get_context("Active Customer")
context.add_observation_table(<observation_table_name>)

You can also define an observation table to be used as the default preview / eda table for the Context using the update_default_eda_table() and update_default_preview_table() methods.

context.update_default_eda_table(<observation_table_name>)

context.update_default_preview_table(<observation_table_name>)

Finally, you can list observation tables associated with the Context using the list_observation_tables() method.

context.list_observation_tables()