graph LR
alphapy_features_create_features["alphapy.features.create_features"]
alphapy_features_apply_transforms["alphapy.features.apply_transforms"]
alphapy_transforms["alphapy.transforms"]
alphapy_features_create_features -- "calls" --> alphapy_features_apply_transforms
alphapy_features_apply_transforms -- "utilizes" --> alphapy_transforms
alphapy_transforms -- "provides services to" --> alphapy_features_apply_transforms
The Feature Engineering subsystem in AlphaPy is responsible for transforming raw or ingested data into a structured and enriched set of features suitable for machine learning models. This involves creating various feature types (numerical, statistical, interaction) and applying necessary data transformations.
This component acts as the primary orchestrator for the entire feature generation process. It is the main entry point for transforming raw input data into a comprehensive set of machine learning features. Its responsibilities include managing the application of different feature types (e.g., numerical, statistical, interaction) and handling data preparation steps like missing value imputation.
Related Classes/Methods:
This component is responsible for managing the sequential application of individual data transformation functions. It functions as a dispatcher, iterating through a list of specified transformations and applying each function from the alphapy.transforms module to the data. This ensures a modular and extensible transformation pipeline.
Related Classes/Methods:
This component serves as a library of atomic, reusable data transformation functions. Each function within this module performs a specific mathematical or statistical operation on data series (e.g., log transformation, standardization, differencing). These functions are the fundamental building blocks for more complex feature engineering operations.
Related Classes/Methods: