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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.

.. image:: /docs/fedot-workflow.png :alt: The structure of the AutoML workflow in FEDOT

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 <https://actcognitive.org/>__.

More details about FEDOT are available in the next video:

.. image:: https://res.cloudinary.com/marcomontalbano/image/upload/v1606396758/video_to_markdown/images/youtube--RjbuV6i6de4-c05b58ac6eb4c4700831b2b3070cd403.jpg :target: http://www.youtube.com/watch?v=RjbuV6i6de4 :alt: Introducing Fedot

FEDOT concepts

• Flexibility. FEDOT can be used to automate the construction of solutions for various problems <https://fedot.readthedocs.io/en/master/introduction/fedot_features/main_features.html#involved-tasks>_, data types <https://fedot.readthedocs.io/en/master/introduction/fedot_features/automation_features.html#data-nature>_ (texts, images, tables), and models <https://fedot.readthedocs.io/en/master/advanced/automated_pipelines_design.html>_;
• Extensibility. Pipeline optimization algorithms are data- and task-independent, yet you can use special strategies <https://fedot.readthedocs.io/en/master/api/strategies.html>_ 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 <https://fedot.readthedocs.io/en/master/api/strategies.html#module-fedot.core.operations.evaluation.custom>_;
• Tuningability. Various hyper-parameters tuning methods <https://fedot.readthedocs.io/en/master/advanced/hyperparameters_tuning.html>_ are supported including models' custom evaluation metrics and search spaces;
• Versatility. FEDOT is not limited to specific modeling tasks <https://fedot.readthedocs.io/en/master/advanced/architecture.html>_, for example, it can be used in ODE or PDE;
• Reproducibility. Resulting pipelines can be exported separately as JSON <https://fedot.readthedocs.io/en/master/advanced/pipeline_import_export.html>_ or together with your input data as ZIP archive <https://fedot.readthedocs.io/en/master/advanced/project_import_export.html>_ for experiments reproducibility;
• Customizability. FEDOT allows managing models complexity <https://fedot.readthedocs.io/en/master/introduction/fedot_features/automation_features.html#models-used>_ and thereby achieving desired quality.

Installation

• Package installer for Python pip

The simplest way to install FEDOT is using pip:

.. code-block::

$ pip install fedot

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

.. code-block::

$ pip install fedot[extra]

• Docker container

Available docker images can be found here here <https://github.com/aimclub/FEDOT/tree/master/docker/README_en.rst>_.

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:

1. Import Fedot class

.. code-block:: python

from fedot.api.main import Fedot

2. 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():

.. code-block:: python

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 <https://fedot.readthedocs.io/en/latest/api/api.html> section and advanced approaches are in the advanced <https://github.com/aimclub/FEDOT/tree/master/examples/advanced> section.

Examples & Tutorials

Jupyter notebooks with tutorials are located in examples repository <https://github.com/ITMO-NSS-team/fedot-examples>__. There you can find the following guides:

• Intro to AutoML <https://github.com/ITMO-NSS-team/fedot-examples/blob/main/notebooks/latest/1_intro_to_automl.ipynb>__
• Intro to FEDOT functionality <https://github.com/ITMO-NSS-team/fedot-examples/blob/main/notebooks/latest/2_intro_to_fedot.ipynb>__
• Intro to time series forecasting with FEDOT <https://github.com/ITMO-NSS-team/fedot-examples/blob/main/notebooks/latest/3_intro_ts_forecasting.ipynb>__
• Advanced time series forecasting <https://github.com/ITMO-NSS-team/fedot-examples/blob/main/notebooks/latest/4_auto_ts_forecasting.ipynb>__
• Gap-filling in time series and out-of-sample forecasting <https://github.com/ITMO-NSS-team/fedot-examples/blob/main/notebooks/latest/5_ts_specific_cases.ipynb>__
• Hybrid modelling with custom models <https://github.com/ITMO-NSS-team/fedot-examples/blob/main/notebooks/latest/6_hybrid_modelling.ipynb>__

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 <https://www.kaggle.com/dreamlone/microsoft-stocks-price-prediction-automl>__

Extended examples:

• Credit scoring problem, i.e. binary classification task <https://github.com/aimclub/FEDOT/blob/master/cases/credit_scoring/credit_scoring_problem.py>__
• Time series forecasting, i.e. random process regression <https://github.com/aimclub/FEDOT/blob/master/cases/metocean_forecasting_problem.py>__
• Spam detection, i.e. natural language preprocessing <https://github.com/aimclub/FEDOT/blob/master/cases/spam_detection.py>__
• Wine variety prediction with multi-modal data <https://github.com/aimclub/FEDOT/blob/master/examples/advanced/multimodal_text_num_example.py>__

Also, several video tutorials are available available <https://www.youtube.com/playlist?list=PLlbcHj5ytaFUjAxpZf7FbEaanmqpDYhnc>__ (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 <https://towardsdatascience.com/how-automl-helps-to-create-composite-ai-f09e05287563>__
• AutoML for time series: definitely a good idea - towardsdatascience.com <https://towardsdatascience.com/automl-for-time-series-definitely-a-good-idea-c51d39b2b3f>__
• AutoML for time series: advanced approaches with FEDOT framework - towardsdatascience.com <https://towardsdatascience.com/automl-for-time-series-advanced-approaches-with-fedot-framework-4f9d8ea3382c>__
• Winning a flood-forecasting hackathon with hydrology and AutoML - towardsdatascience.com <https://towardsdatascience.com/winning-a-flood-forecasting-hackathon-with-hydrology-and-automl-156a8a7a4ede>__
• Clean AutoML for “Dirty” Data - towardsdatascience.com <https://towardsdatascience.com/clean-automl-for-dirty-data-how-and-why-to-automate-preprocessing-of-tables-in-machine-learning-d79ac87780d3>__
• FEDOT as a factory of human-competitive results - youtube.com <https://www.youtube.com/watch?v=9Rhqcsrolb8&ab_channel=NSS-Lab>__
• Hyperparameters Tuning for Machine Learning Model Ensembles - towardsdatascience.com <https://towardsdatascience.com/hyperparameters-tuning-for-machine-learning-model-ensembles-8051782b538b>__

In Russian:

• Как AutoML помогает создавать модели композитного ИИ — говорим о структурном обучении и фреймворке FEDOT - habr.com <https://habr.com/ru/company/spbifmo/blog/558450>__
• Прогнозирование временных рядов с помощью AutoML - habr.com <https://habr.com/ru/post/559796/>__
• Как мы “повернули реки вспять” на Emergency DataHack 2021, объединив гидрологию и AutoML - habr.com <https://habr.com/ru/post/577886/>__
• Чистый AutoML для “грязных” данных: как и зачем автоматизировать предобработку таблиц в машинном обучении - ODS blog <https://habr.com/ru/company/ods/blog/657525/>__
• Фреймворк автоматического машинного обучения FEDOT (Конференция Highload++ 2022) - presentation <https://docs.yandex.ru/docs/view?url=ya-disk-public%3A%2F%2Fi27LScu3s3IIHDzIXt9O5EiEAMl6ThY6QLu3X1oYH%2FFiAl%2BLcNp4O4yTSYd2gRZnW5aDQ4kMZEXE%2BwNjbq78ug%3D%3D%3A%2F%D0%94%D0%B5%D0%BD%D1%8C%201%2F4.%D0%A1%D0%B8%D0%BD%D0%BD%D0%B0%D0%BA%D1%81%2F9.Open%20source-%D1%82%D1%80%D0%B8%D0%B1%D1%83%D0%BD%D0%B0_HL_FEDOT.pptx&name=9.Open%20source-%D1%82%D1%80%D0%B8%D0%B1%D1%83%D0%BD%D0%B0_HL_FEDOT.pptx>__
• Про настройку гиперпараметров ансамблей моделей машинного обучения - habr.com <https://habr.com/ru/post/672486/>__

In Chinese:

• 生成式自动机器学习系统 (presentation at the "Open Innovations 2.0" conference) - youtube.com <https://www.youtube.com/watch?v=PEET0EbCSCY>__

Project Structure

The latest stable release of FEDOT is in the master branch <https://github.com/aimclub/FEDOT/tree/master>__.

The repository includes the following directories:

• Package core <https://github.com/aimclub/FEDOT/tree/master/fedot/core>__ contains the main classes and scripts. It is the core of the FEDOT framework
• Package examples <https://github.com/aimclub/FEDOT/tree/master/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 <https://github.com/aimclub/FEDOT/tree/master/test>__ directory
• The sources of the documentation are in the docs <https://github.com/aimclub/FEDOT/tree/master/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:

• Implementation of meta-learning based at GNN and RL (see MetaFEDOT <https://github.com/ITMO-NSS-team/MetaFEDOT>__)
• Improvement of the optimisation-related algorithms implemented in GOLEM <https://github.com/aimclub/GOLEM/>__.
• Support for more complicated pipeline design patters, especially for time series forecasting.

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 <https://fedot.readthedocs.io/en/latest/>__.

Contribution Guide

• The contribution guide is available in this repository <https://github.com/aimclub/FEDOT/blob/master/docs/source/contribution.rst>__.

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 optimisation core implemented in GOLEM <https://github.com/aimclub/GOLEM/>__ repository.
• The prototype of the web-GUI for FEDOT is available in the FEDOT.WEB <https://github.com/aimclub/FEDOT.Web>__ repository.
• The prototype of FEDOT-based meta-AutoML in the MetaFEDOT <https://github.com/ITMO-NSS-team/MetaFEDOT>__ repository.

Contacts

• Telegram channel for solving problems and answering questions about FEDOT <https://t.me/FEDOT_helpdesk>_
• Natural System Simulation Team <https://itmo-nss-team.github.io/>_
• Anna Kalyuzhnaya <https://scholar.google.com/citations?user=bjiILqcAAAAJ&hl=ru>_, Team leader (anna.kalyuzhnaya@itmo.ru)
• Newsfeed <https://t.me/NSS_group>_
• Youtube channel <https://www.youtube.com/channel/UC4K9QWaEUpT_p3R4FeDp5jA>_

Supported by

• National Center for Cognitive Research of ITMO University <https://actcognitive.org/>_

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 <https://www.researchgate.net/project/Evolutionary-multi-modal-AutoML-with-FEDOT-framework>_.

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