Component Details

The Alien project is designed for active learning, focusing on efficient sample selection and robust machine learning model training and evaluation. Its core functionality revolves around iteratively selecting the most informative data points, training predictive models, and assessing their performance. The architecture is modular, with distinct components handling data, models, statistical computations, and experimental workflows, all underpinned by a flexible numerical backend and a set of foundational utilities.

Core Utilities & Foundation

This foundational component provides a comprehensive suite of general-purpose utility functions, core architectural elements, and reusable decorators. It includes functionalities for type checking, data manipulation (dictionaries, arrays/tensors), file system operations, and managing reproducibility. Furthermore, it defines fundamental classes and decorators for object-oriented programming, such as final for preventing subclassing and abstract_group for defining abstract method groups, contributing to the architectural structure and enforcing design patterns.

Data Management & Generation

This component is responsible for the entire data lifecycle, from generating new samples to managing and transforming various types of datasets. It provides functionalities for data loading, manipulation (reshaping, flattening), and ensuring data is in the correct format for model training and evaluation. It acts as the primary source and manager of data for the entire system.

Machine Learning Core

This central component encompasses a wide array of machine learning models, including general Model, Regressor, and Classifier abstractions, as well as specific implementations for various frameworks. It provides functionalities for model initialization, training, prediction (including sample-based and ensemble predictions), and uncertainty estimation. It is the core predictive engine of the project.

Statistical & Numerical Operations

This component provides the mathematical and numerical backbone of the project. It offers a unified interface for array and tensor operations, abstracting over different backend libraries (NumPy, PyTorch). It also includes a comprehensive suite of statistical functions for analyzing data distributions, uncertainty, and relationships, along with specialized matrix operations for lazy evaluation and ensemble handling.

Active Learning & Experimentation

This component drives the active learning process and facilitates the evaluation of experiments. It provides various algorithms for efficiently selecting the most informative samples for model training, often used in active learning scenarios. Additionally, it offers a structured framework for setting up, executing, and evaluating experiments, including managing data splits, logging results, and computing performance metrics.