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FEDOT is an open-source framework for automated modeling and machine learning (AutoML) problems. This framework is distributed under the 3-Clause BSD license.

It provides automatic generative design of machine learning pipelines for various real-world problems. The core of FEDOT is based on an evolutionary approach and supports classification (binary and multiclass), regression, clustering, and time series prediction problems.

The key feature of the framework is the complex management of interactions between various blocks of pipelines. It is represented as a graph that defines connections between data preprocessing and model blocks.

The project is maintained by the research team of the Natural Systems Simulation Lab, which is a part of the National Center for Cognitive Research of ITMO University.

More details about FEDOT are available in the next video:

FEDOT concepts

Flexibility. FEDOT can be used to automate the construction of solutions for various problems, data types (texts, images, tables), and models;
Extensibility. Pipeline optimization algorithms are data- and task-independent, yet you can use special strategies for specific tasks or data types (time-series forecasting, NLP, tabular data, etc.) to increase the efficiency;
Integrability. FEDOT supports widely used ML libraries (Scikit-learn, CatBoost, XGBoost, etc.) and allows you to integrate custom ones;
Tuningability. Various hyper-parameters tuning methods are supported including models' custom evaluation metrics and search spaces;
Versatility. FEDOT is not limited to specific modeling tasks, for example, it can be used in ODE or PDE;
Reproducibility. Resulting pipelines can be exported separately as JSON or together with your input data as ZIP archive for experiments reproducibility;
Customizability. FEDOT allows managing models complexity and thereby achieving desired quality.

Installation

The simplest way to install FEDOT is using pip:

$ pip install fedot

Installation with optional dependencies for image and text processing, and for DNNs:

$ pip install fedot[extra]

How to Use

FEDOT provides a high-level API that allows you to use its capabilities in a simple way. The API can be used for classification, regression, and time series forecasting problems.

To use the API, follow these steps:

Import Fedot class

from fedot.api.main import Fedot

Initialize the Fedot object and define the type of modeling problem. It provides a fit/predict interface:

Fedot.fit() begins the optimization and returns the resulting composite pipeline;
Fedot.predict() predicts target values for the given input data using an already fitted pipeline;
Fedot.get_metrics() estimates the quality of predictions using selected metrics.

NumPy arrays, Pandas DataFrames, and the file's path can be used as sources of input data. In the case below, x_train, y_train and x_test are numpy.ndarray():

model = Fedot(problem='classification', timeout=5, preset='best_quality', n_jobs=-1)
model.fit(features=x_train, target=y_train)
prediction = model.predict(features=x_test)
metrics = model.get_metrics(target=y_test)

More information about the API is available in the documentation section and advanced approaches are in the advanced section.

Examples & Tutorials

Jupyter notebooks with tutorials are located in examples repository. There you can find the following guides:

Notebooks are issued with the corresponding release versions (the default version is 'latest').

Also, external examples are available:

Kaggle: baseline for Microsoft Stock - Time Series Analysis task

Extended examples:

Credit scoring problem, i.e. binary classification task
Time series forecasting, i.e. random process regression
Spam detection, i.e. natural language preprocessing
Wine variety prediction with multi-modal data

Also, several video tutorials are available available (in Russian).

Publications About FEDOT

We also published several posts devoted to different aspects of the framework:

In English:

How AutoML helps to create composite AI? - towardsdatascience.com
AutoML for time series: definitely a good idea - towardsdatascience.com
AutoML for time series: advanced approaches with FEDOT framework - towardsdatascience.com
Winning a flood-forecasting hackathon with hydrology and AutoML - towardsdatascience.com
Clean AutoML for “Dirty” Data - towardsdatascience.com
FEDOT as a factory of human-competitive results - youtube.com
Hyperparameters Tuning for Machine Learning Model Ensembles - towardsdatascience.com

In Russian:

Как AutoML помогает создавать модели композитного ИИ — говорим о структурном обучении и фреймворке FEDOT - habr.com
Прогнозирование временных рядов с помощью AutoML - habr.com
Как мы “повернули реки вспять” на Emergency DataHack 2021, объединив гидрологию и AutoML - habr.com
Чистый AutoML для “грязных” данных: как и зачем автоматизировать предобработку таблиц в машинном обучении - ODS blog
Фреймворк автоматического машинного обучения FEDOT (Конференция Highload++ 2022) - presentation
Про настройку гиперпараметров ансамблей моделей машинного обучения - habr.com

In Chinese:

生成式自动机器学习系统 (presentation at the "Open Innovations 2.0" conference) - youtube.com

Project Structure

The latest stable release of FEDOT is in the master branch.

The repository includes the following directories:

Package core contains the main classes and scripts. It is the core of the FEDOT framework
Package examples includes several how-to-use-cases where you can start to discover how FEDOT works
All unit and integration tests can be observed in the test directory
The sources of the documentation are in the docs directory

Current R&D and future plans

Currently, we are working on new features and trying to improve the performance and the user experience of FEDOT. The major ongoing tasks and plans:

Effective and ready-to-use pipeline templates for certain tasks and data types;
Integration with GPU via Rapids framework;
Alternative optimization methods of fixed-shaped pipelines;
Integration with MLFlow for import and export of the pipelines;
Improvement of the high-level API.

Also, we are doing several research tasks related to AutoML time-series benchmarking and multi-modal modeling.

Any contribution is welcome. Our R&D team is open for cooperation with other scientific teams as well as with industrial partners.

Documentation

Also, a detailed FEDOT API description is available in Read the Docs.

Contribution Guide

The contribution guide is available in this repository.

Acknowledgments

We acknowledge the contributors for their important impact and the participants of numerous scientific conferences and workshops for their valuable advice and suggestions.

Side Projects

The prototype of the web-GUI for FEDOT is available in the FEDOT.WEB repository.

Contacts

Supported by

National Center for Cognitive Research of ITMO University

Citation

@article{nikitin2021automated,: title = {Automated evolutionary approach for the design of composite machine learning pipelines}, author = {Nikolay O. Nikitin and Pavel Vychuzhanin and Mikhail Sarafanov and Iana S. Polonskaia and Ilia Revin and Irina V. Barabanova and Gleb Maximov and Anna V. Kalyuzhnaya and Alexander Boukhanovsky}, journal = {Future Generation Computer Systems}, year = {2021}, issn = {0167-739X}, doi = {https://doi.org/10.1016/j.future.2021.08.022}}
@inproceedings{polonskaia2021multi,: title={Multi-Objective Evolutionary Design of Composite Data-Driven Models}, author={Polonskaia, Iana S. and Nikitin, Nikolay O. and Revin, Ilia and Vychuzhanin, Pavel and Kalyuzhnaya, Anna V.}, booktitle={2021 IEEE Congress on Evolutionary Computation (CEC)}, year={2021}, pages={926-933}, doi={10.1109/CEC45853.2021.9504773}}

Other papers - in ResearchGate.

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