GAMLET (previously known as MetaFEDOT) is an open platform for sharing meta-learning experiences in AutoML and more general Graph Optimization. The project has 3 major long-term goals:

1. Provide codebase and utilities for experiments in meta-learning (work in progress)
2. Accumulate metaknowledge for popular application fields, such as tabular classification, tabular regression, time series forecasting, etc., based on public datasets and benchmarks (work in progress)
3. Provide user API allowing outer target-independent usage of accumulated meta-knowledge (planned)

This framework consists of several key components that automate and enhance the process of meta-learning. It provides functionalities for dataset and model management, meta-features extraction, dataset similarity assessment. The components work together to facilitate the initial approximation fitting process.

Each of the components may include different implementations while staying compatible. This is achieved by specification and maintaining their external interfaces.

Automate dataset management, including retrieval, caching, and loading into memory. Optimize experiments by minimizing calls to the dataset source and conserve memory usage.

Import and consolidate model evaluation data for datasets. Support experiment selection based on predefined criteria, currently compatible with FEDOT AutoML framework results.

Automates the extraction of meta-features from datasets, improving efficiency by caching values. Can load dataset data if it is necessary for meta-features extraction. For example, one of implementations utilize the PyMFE library for meta-feature extraction.

Assesses dataset similarity based on meta-features. For a given dataset, provides list of similar datasets and optionally calculates similarity measures. For example, one of implementations uses the "NearestNeighbors" model from scikit-learn.

Combines results from Models Loader and Datasets Similarity Assessor. Provides recommendations for models based on loaded data and similar datasets. Possible implementations allow for heuristic-based suggestions.

From the repository root run:

python scripts/main.py <--train|--tune> --config <path_to_your_config>.

Follow configs/train_surrogate_model.yml and configs/tune_surrogate_model.yml as reference for training and hyperparameter search accordingly.