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:alt: Logo of FEDOT framework
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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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.. |coverage| image:: https://codecov.io/gh/aimclub/FEDOT/branch/master/graph/badge.svg
:alt: Coverage Status
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.. |pypi| image:: https://badge.fury.io/py/fedot.svg
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:target: https://badge.fury.io/py/fedot
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:target: https://img.shields.io/pypi/pyversions/fedot
.. |license| image:: https://img.shields.io/github/license/aimclub/FEDOT
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.. |downloads_stats| image:: https://static.pepy.tech/personalized-badge/fedot?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=Downloads
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:target: https://itmo.ru
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:alt: Acknowledgement to NCCR
:target: https://actcognitive.org/
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"description": ".. |eng| image:: https://img.shields.io/badge/lang-en-red.svg\n :target: /README_en.rst\n\n.. |rus| image:: https://img.shields.io/badge/lang-ru-yellow.svg\n :target: /README.rst\n\n.. image:: /docs/fedot_logo.png\n :alt: Logo of FEDOT framework\n\n.. start-badges\n.. list-table::\n :stub-columns: 1\n\n * - package\n - | |pypi| |python|\n * - tests\n - | |build| |integration| |coverage|\n * - docs\n - |docs|\n * - license\n - | |license|\n * - stats\n - | |downloads_stats|\n * - support\n - | |tg|\n * - languages\n - | |eng| |rus|\n * - mirror\n - | |gitlab|\n * - funding\n - | |ITMO| |NCCR|\n.. end-badges\n\n**FEDOT** is an open-source framework for automated modeling and machine learning (AutoML) problems. This framework is distributed under the 3-Clause BSD license.\n\nIt 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.\n\n.. image:: /docs/fedot-workflow.png\n :alt: The structure of the AutoML workflow in FEDOT\n\nThe 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.\n\nThe 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/>`__.\n\nMore details about FEDOT are available in the next video:\n\n\n.. image:: https://res.cloudinary.com/marcomontalbano/image/upload/v1606396758/video_to_markdown/images/youtube--RjbuV6i6de4-c05b58ac6eb4c4700831b2b3070cd403.jpg\n :target: http://www.youtube.com/watch?v=RjbuV6i6de4\n :alt: Introducing Fedot\n\nFEDOT concepts\n==============\n\n- **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>`_;\n- **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;\n- **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>`_;\n- **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;\n- **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;\n- **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;\n- **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.\n\nInstallation\n============\n\n- Package installer for Python **pip**\n\nThe simplest way to install FEDOT is using ``pip``:\n\n.. code-block::\n\n $ pip install fedot\n\nInstallation with optional dependencies for image and text processing, and for DNNs:\n\n.. code-block::\n\n $ pip install fedot[extra]\n\n- **Docker** container\n\nAvailable docker images can be found here `here <https://github.com/aimclub/FEDOT/tree/master/docker/README_en.rst>`_.\n\nHow to Use\n==========\n\nFEDOT 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.\n\nTo use the API, follow these steps:\n\n1. Import ``Fedot`` class\n\n.. code-block:: python\n\n from fedot.api.main import Fedot\n\n2. Initialize the Fedot object and define the type of modeling problem. It provides a fit/predict interface:\n\n- ``Fedot.fit()`` begins the optimization and returns the resulting composite pipeline;\n- ``Fedot.predict()`` predicts target values for the given input data using an already fitted pipeline;\n- ``Fedot.get_metrics()`` estimates the quality of predictions using selected metrics.\n\nNumPy 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()``:\n\n.. code-block:: python\n\n model = Fedot(problem='classification', timeout=5, preset='best_quality', n_jobs=-1)\n model.fit(features=x_train, target=y_train)\n prediction = model.predict(features=x_test)\n metrics = model.get_metrics(target=y_test)\n\nMore 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.\n\nExamples & Tutorials\n====================\n\nJupyter notebooks with tutorials are located in `examples repository <https://github.com/ITMO-NSS-team/fedot-examples>`__. There you can find the following guides:\n\n* `Intro to AutoML <https://github.com/ITMO-NSS-team/fedot-examples/blob/main/notebooks/latest/1_intro_to_automl.ipynb>`__\n* `Intro to FEDOT functionality <https://github.com/ITMO-NSS-team/fedot-examples/blob/main/notebooks/latest/2_intro_to_fedot.ipynb>`__\n* `Intro to time series forecasting with FEDOT <https://github.com/ITMO-NSS-team/fedot-examples/blob/main/notebooks/latest/3_intro_ts_forecasting.ipynb>`__\n* `Advanced time series forecasting <https://github.com/ITMO-NSS-team/fedot-examples/blob/main/notebooks/latest/4_auto_ts_forecasting.ipynb>`__\n* `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>`__\n* `Hybrid modelling with custom models <https://github.com/ITMO-NSS-team/fedot-examples/blob/main/notebooks/latest/6_hybrid_modelling.ipynb>`__\n\nNotebooks are issued with the corresponding release versions (the default version is 'latest').\n\nAlso, external examples are available:\n\n* `Kaggle: baseline for Microsoft Stock - Time Series Analysis task <https://www.kaggle.com/dreamlone/microsoft-stocks-price-prediction-automl>`__\n\nExtended examples:\n\n- Credit scoring problem, i.e. `binary classification task <https://github.com/aimclub/FEDOT/blob/master/cases/credit_scoring/credit_scoring_problem.py>`__\n- Time series forecasting, i.e. `random process regression <https://github.com/aimclub/FEDOT/blob/master/cases/metocean_forecasting_problem.py>`__\n- Spam detection, i.e. `natural language preprocessing <https://github.com/aimclub/FEDOT/blob/master/cases/spam_detection.py>`__\n- Wine variety prediction with `multi-modal data <https://github.com/aimclub/FEDOT/blob/master/examples/advanced/multimodal_text_num_example.py>`__\n\n\nAlso, several video tutorials are available `available <https://www.youtube.com/playlist?list=PLlbcHj5ytaFUjAxpZf7FbEaanmqpDYhnc>`__ (in Russian).\n\nPublications About FEDOT\n========================\n\nWe also published several posts devoted to different aspects of the framework:\n\nIn English:\n\n- How AutoML helps to create composite AI? - `towardsdatascience.com <https://towardsdatascience.com/how-automl-helps-to-create-composite-ai-f09e05287563>`__\n- AutoML for time series: definitely a good idea - `towardsdatascience.com <https://towardsdatascience.com/automl-for-time-series-definitely-a-good-idea-c51d39b2b3f>`__\n- AutoML for time series: advanced approaches with FEDOT framework - `towardsdatascience.com <https://towardsdatascience.com/automl-for-time-series-advanced-approaches-with-fedot-framework-4f9d8ea3382c>`__\n- Winning a flood-forecasting hackathon with hydrology and AutoML - `towardsdatascience.com <https://towardsdatascience.com/winning-a-flood-forecasting-hackathon-with-hydrology-and-automl-156a8a7a4ede>`__\n- Clean AutoML for \u201cDirty\u201d Data - `towardsdatascience.com <https://towardsdatascience.com/clean-automl-for-dirty-data-how-and-why-to-automate-preprocessing-of-tables-in-machine-learning-d79ac87780d3>`__\n- FEDOT as a factory of human-competitive results - `youtube.com <https://www.youtube.com/watch?v=9Rhqcsrolb8&ab_channel=NSS-Lab>`__\n- Hyperparameters Tuning for Machine Learning Model Ensembles - `towardsdatascience.com <https://towardsdatascience.com/hyperparameters-tuning-for-machine-learning-model-ensembles-8051782b538b>`__\n\nIn Russian:\n\n- \u041a\u0430\u043a AutoML \u043f\u043e\u043c\u043e\u0433\u0430\u0435\u0442 \u0441\u043e\u0437\u0434\u0430\u0432\u0430\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0438 \u043a\u043e\u043c\u043f\u043e\u0437\u0438\u0442\u043d\u043e\u0433\u043e \u0418\u0418 \u2014 \u0433\u043e\u0432\u043e\u0440\u0438\u043c \u043e \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u043d\u043e\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438 \u0438 \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0435 FEDOT - `habr.com <https://habr.com/ru/company/spbifmo/blog/558450>`__\n- \u041f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0440\u044f\u0434\u043e\u0432 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e AutoML - `habr.com <https://habr.com/ru/post/559796/>`__\n- \u041a\u0430\u043a \u043c\u044b \u201c\u043f\u043e\u0432\u0435\u0440\u043d\u0443\u043b\u0438 \u0440\u0435\u043a\u0438 \u0432\u0441\u043f\u044f\u0442\u044c\u201d \u043d\u0430 Emergency DataHack 2021, \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u0432 \u0433\u0438\u0434\u0440\u043e\u043b\u043e\u0433\u0438\u044e \u0438 AutoML - `habr.com <https://habr.com/ru/post/577886/>`__\n- \u0427\u0438\u0441\u0442\u044b\u0439 AutoML \u0434\u043b\u044f \u201c\u0433\u0440\u044f\u0437\u043d\u044b\u0445\u201d \u0434\u0430\u043d\u043d\u044b\u0445: \u043a\u0430\u043a \u0438 \u0437\u0430\u0447\u0435\u043c \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043f\u0440\u0435\u0434\u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0443 \u0442\u0430\u0431\u043b\u0438\u0446 \u0432 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438 - `ODS blog <https://habr.com/ru/company/ods/blog/657525/>`__\n- \u0424\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f FEDOT (\u041a\u043e\u043d\u0444\u0435\u0440\u0435\u043d\u0446\u0438\u044f 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>`__\n- \u041f\u0440\u043e \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0443 \u0433\u0438\u043f\u0435\u0440\u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u043e\u0432 \u0430\u043d\u0441\u0430\u043c\u0431\u043b\u0435\u0439 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f - `habr.com <https://habr.com/ru/post/672486/>`__\n\nIn Chinese:\n\n- \u751f\u6210\u5f0f\u81ea\u52a8\u673a\u5668\u5b66\u4e60\u7cfb\u7edf (presentation at the \"Open Innovations 2.0\" conference) - `youtube.com <https://www.youtube.com/watch?v=PEET0EbCSCY>`__\n\n\nProject Structure\n=================\n\nThe latest stable release of FEDOT is in the `master branch <https://github.com/aimclub/FEDOT/tree/master>`__.\n\nThe repository includes the following directories:\n\n* 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\n* 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\n* All *unit and integration tests* can be observed in the `test <https://github.com/aimclub/FEDOT/tree/master/test>`__ directory\n* The sources of the documentation are in the `docs <https://github.com/aimclub/FEDOT/tree/master/docs>`__ directory\n\nCurrent R&D and future plans\n============================\n\nCurrently, we are working on new features and trying to improve the performance and the user experience of FEDOT.\nThe major ongoing tasks and plans:\n\n* Implementation of meta-learning based at GNN and RL (see `MetaFEDOT <https://github.com/ITMO-NSS-team/MetaFEDOT>`__)\n* Improvement of the optimisation-related algorithms implemented in `GOLEM <https://github.com/aimclub/GOLEM/>`__.\n* Support for more complicated pipeline design patters, especially for time series forecasting.\n\nAny contribution is welcome. Our R&D team is open for cooperation with other scientific teams as well as with industrial partners.\n\nDocumentation\n=============\n\nAlso, a detailed FEDOT API description is available in `Read the Docs <https://fedot.readthedocs.io/en/latest/>`__.\n\nContribution Guide\n==================\n\n- The contribution guide is available in this `repository <https://github.com/aimclub/FEDOT/blob/master/docs/source/contribution.rst>`__.\n\nAcknowledgments\n===============\n\nWe acknowledge the contributors for their important impact and the participants of numerous scientific conferences and workshops for their valuable advice and suggestions.\n\nSide Projects\n=============\n- The optimisation core implemented in `GOLEM <https://github.com/aimclub/GOLEM/>`__ repository.\n- The prototype of the web-GUI for FEDOT is available in the `FEDOT.WEB <https://github.com/aimclub/FEDOT.Web>`__ repository.\n- The prototype of FEDOT-based meta-AutoML in the `MetaFEDOT <https://github.com/ITMO-NSS-team/MetaFEDOT>`__ repository.\n\nContacts\n========\n- `Telegram channel for solving problems and answering questions about FEDOT <https://t.me/FEDOT_helpdesk>`_\n- `Natural System Simulation Team <https://itmo-nss-team.github.io/>`_\n- `Anna Kalyuzhnaya <https://scholar.google.com/citations?user=bjiILqcAAAAJ&hl=ru>`_, Team leader (anna.kalyuzhnaya@itmo.ru)\n- `Newsfeed <https://t.me/NSS_group>`_\n- `Youtube channel <https://www.youtube.com/channel/UC4K9QWaEUpT_p3R4FeDp5jA>`_\n\nSupported by\n============\n\n- `National Center for Cognitive Research of ITMO University <https://actcognitive.org/>`_\n\nCitation\n========\n\n@article{nikitin2021automated,\n title = {Automated evolutionary approach for the design of composite machine learning pipelines},\n 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},\n journal = {Future Generation Computer Systems},\n year = {2021},\n issn = {0167-739X},\n doi = {https://doi.org/10.1016/j.future.2021.08.022}}\n\n@inproceedings{polonskaia2021multi,\n title={Multi-Objective Evolutionary Design of Composite Data-Driven Models},\n author={Polonskaia, Iana S. and Nikitin, Nikolay O. and Revin, Ilia and Vychuzhanin, Pavel and Kalyuzhnaya, Anna V.},\n booktitle={2021 IEEE Congress on Evolutionary Computation (CEC)},\n year={2021},\n pages={926-933},\n doi={10.1109/CEC45853.2021.9504773}}\n\n\nOther papers - in `ResearchGate <https://www.researchgate.net/project/Evolutionary-multi-modal-AutoML-with-FEDOT-framework>`_.\n\n.. |docs| image:: https://readthedocs.org/projects/ebonite/badge/?style=flat\n :target: https://fedot.readthedocs.io/en/latest/\n :alt: Documentation Status\n\n.. |build| image:: https://github.com/aimclub/FEDOT/actions/workflows/unit-build.yml/badge.svg\n :alt: Build Status\n :target: https://github.com/aimclub/FEDOT/actions/workflows/unit-build.yml\n\n.. |integration| image:: https://github.com/aimclub/FEDOT/actions/workflows/integration-build.yml/badge.svg\n :alt: Integration Build Status\n :target: https://github.com/aimclub/FEDOT/actions/workflows/integration-build.yml\n\n.. |coverage| image:: https://codecov.io/gh/aimclub/FEDOT/branch/master/graph/badge.svg\n :alt: Coverage Status\n :target: https://codecov.io/gh/aimclub/FEDOT\n\n.. |pypi| image:: https://badge.fury.io/py/fedot.svg\n :alt: Supported Python Versions\n :target: https://badge.fury.io/py/fedot\n\n.. |python| image:: https://img.shields.io/pypi/pyversions/fedot.svg\n :alt: Supported Python Versions\n :target: https://img.shields.io/pypi/pyversions/fedot\n\n.. |license| image:: https://img.shields.io/github/license/aimclub/FEDOT\n :alt: Supported Python Versions\n :target: https://github.com/aimclub/FEDOT/blob/master/LICENSE.md\n\n.. |downloads_stats| image:: https://static.pepy.tech/personalized-badge/fedot?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=Downloads\n :target: https://pepy.tech/project/fedot\n\n.. |tg| image:: https://img.shields.io/badge/Telegram-Group-blue.svg\n :target: https://t.me/FEDOT_helpdesk\n :alt: Telegram Chat\n\n.. |ITMO| image:: https://raw.githubusercontent.com/aimclub/open-source-ops/43bb283758b43d75ec1df0a6bb4ae3eb20066323/badges/ITMO_badge_rus.svg\n :alt: Acknowledgement to ITMO\n :target: https://itmo.ru\n\n.. |NCCR| image:: https://raw.githubusercontent.com/aimclub/open-source-ops/43bb283758b43d75ec1df0a6bb4ae3eb20066323/badges/NCCR_badge.svg\n :alt: Acknowledgement to NCCR\n :target: https://actcognitive.org/\n\n.. |gitlab| image:: https://img.shields.io/badge/mirror-GitLab-orange\n :alt: GitLab mirror for this repository\n :target: https://gitlab.actcognitive.org/itmo-nss-team/FEDOT\n",
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