đī¸ Assignment & Expression
The Assignment entity defines the assignment of a value to a variable. Specifically, a variable (with an identifier of type ID) is assigned a value resulting from the evaluation of an arithmetic expression, which follows the standard mathematical order of operations.
đī¸ Beaver Model
The BeaverModel defines the structure and key components that must be included in a Beaver language file.
đī¸ Comments
The language defines two types of comments:
đī¸ Connection
The Connection entity lets you configure the connection with kafka. The user needs to define 2 variables:
đī¸ Data
The Data entity is used to define the characteristics of the data. Specifically, the user specifies the following parameters:
đī¸ DataModel
A subclass of Model used to define classes that process data (e.g., transformers, filters, feature extractors).
đī¸ Features
Features are the elements (fields) of the data. Four categories of features are defined:
đī¸ Model
The parent class of ModelTypes. It functions as the base entity and is used as a reference type in other entities of the language.
đī¸ Model Groups
Entities of type ModelGroups define the broader categories (or "families") of ModelModules. This classification serves to categorize the different functional roles that modules perform within the context of building a machine learning pipeline.
đī¸ Model Modules
Entities of type ModelModules define the modules of the River library that correspond to specific categories of algorithms.
đī¸ Model Names
Entities of type ModelNames define the set of allowed names for machine learning models supported by the Beaver language.
đī¸ Model Types
Each of the seven ModelGroups categories corresponds to a ModelType, which defines the structure of the individual models belonging to each group. ModelTypes are sub-entities of the Model entity and shape the internal representation of machine learning units in the Beaver language.
đī¸ Params
Each parameter entity has 2 parameters :
đī¸ Pipeline
The Pipeline represents the overall system used for data collection and processing, algorithm training, prediction generation, metric usage, and result presentation.