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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