FEDOT Versions Save

Automated modeling and machine learning framework FEDOT

v0.7.3.2

2 weeks ago

Minor fixes and improvements.

What's Changed

Full Changelog: https://github.com/aimclub/FEDOT/compare/v0.7.3.1...v0.7.3.2

v0.7.3.1

2 months ago

Minor fixes and improvements, better installation

v0.7.3

3 months ago

0.7.3 Latest

Hello, AutoML folk! We’re releasing a minor version of FEDOT that includes the following.

PyPi release: https://pypi.org/project/fedot/0.7.3/

Features & Enhancements:

  • Update of GOLEM (core framework) dependency to 0.4.0 version, that has some important features itself
  • Improvement of data preprocessing
  • Improvements for time series forecasting
  • Better API

Full list of changes:

Full Changelog: https://github.com/aimclub/FEDOT/compare/v0.7.2...v0.7.3

v0.7.2

9 months ago

Hello, AutoML folk! We’re releasing a minor version of FEDOT that includes the following.

PyPi release: https://pypi.org/project/fedot/0.7.2/

Features & Enhancements:

  • Update of GOLEM (core framework) dependency to 0.3.2 version, that has some important features itself

Bugfixes:

  • Minor improvements and fixes

v0.7.1

11 months ago

Hello, AutoML folk! We’re releasing a minor version of FEDOT that includes the following.

PyPi release: https://pypi.org/project/fedot/0.7.1/

Features & Enhancements:

  • Update of GOLEM (core framework) dependency to 0.3.1 version, that has some important features itself
  • Added Meta Rules that automatically select best parameters based on dataset: with rules for early_stopping_generations, preset and cross-validation folds were added (#1057)
  • Improved API parameters and made documentation more clear and structured (#1067, #1041)
  • Improve test suite and its performance (#1098)
  • Improved DataMerger for textual data – now multi-column text table can be used with FEDOT (#1052)

Bugfixes:

  • Bug with wrong combinations of operations in pipelines for time series forecasting was fixed.
  • Multiple initial assumptions support was fixed (#1070)
  • Various minor fixes

v0.7.0

1 year ago

Hi, folk!

This release marks major change! Our team separated all the core modules (graph, adapter, optimizer, tuner etc.) into the separate project GOLEM (https://github.com/aimclub/GOLEM).

FEDOT now contains modules related to Data handling, preprocessing, machine learning logic like Pipeline (implementation of ML Graph), ML operations, ML metrics.

There're also few other changes:

  • #1029 -- CLSTM model refactoring
  • #1046 -- Transition of GitHub tests from Python 3.7 to 3.8

v0.6.2

1 year ago

Hi all!

Importantly, release 0.6.2 marks the last self-sufficient version of FEDOT before transition to GOLEM optimization core (https://github.com/aimclub/GOLEM).

This release introduces a number of API enhancements and several bug fixes. Enhancements:

  • #1017 -- Now it's possible to enable memory analytic for debugging the framework performance.
  • #1019 -- It became easier and more effective to work with large datasets.
  • #1021 -- PipelineBuilder now can be used for any Graphs with a help of appropriate Adapter.
  • #990 -- A number of user API improvements.
  • #1025 -- Data preprocessing is now optional and can be disabled through new API parameter.
  • #1031 -- Refactoring of Pipeline Node classes, that now are much simplified.

Bug fixes:

  • #1010 -- Tuning is more benefitial now with correct metric deviation computation.
  • #1012
  • #1022
  • #1023

v0.6.1

1 year ago

Hi, folk! We're making a new minor release with a number of improvements. This is an important release in a sense that this is a last release of self-contained FEDOT. The next major release will mark a separation of the optimizer core into the separate project.

New features, better quality & changes in API

  • More intuitive predict interface for time series forecasting (#930)
  • Pipeline save/load now have more intuitive behavior (#971)
  • Early stopping criteria now can take timeout into considerations, and not only number of iterations (early_stopping_timeout api parameter)
  • Graph nodes now can be accessed by name or uid (#982)
  • Tuner speed is better due to better initial params in the search space (#985)

Enhancements and fixes:

  • Fix inplace modification of data during data definition (resolves #943)
  • Fix regression preprocessing (#955)
  • Less evaluation errors during population selection in corner cases (#956)
  • Fix getting suitable operations for multi ts (#981)
  • Integration tests are fixed & passing now
  • More minor fixes & minor class interface refactorings
  • Important fix for multi-objective optimization (#996)

Documentation is extended

Architectural refactorings are continued:

  • Better PipelineAdapter (#941)
  • Abstracting optimiser core (most tasks in issue #713 are done) Notably, Serializer subsystem is now extendable (#969)

v0.6.0

1 year ago

Hi everyone! We released a new major version of FEDOT - 0.6.0

It includes a lot of major changes:

  • Improvement of API for multi-modal datasets and models;
  • New PipelineBuilder (#597) – that simplifies manual construction of ML Pipelines;
  • Joblib was embedded as a multiprocessing backend (#843). Data exchange between processes minimized (#926);
  • Embedding stratify k fold strategy for cases with imbalance data;
  • New visualization of graphs, pipelines and optimisation history;

Also, this release contains by a lot of architectural refactorings of the framework:

  • New Graph Adapter subsystem (#876);
  • Merging two different implementation of evolutionary optimizer (parameter-free & usual) into one EvoGraphOptimizer (#687)
  • Architectural refactorings of the Graph hierarchy (#750)
  • Introduce notions of Objective & Fitness (#654) – classes that substitutes simple float metric values & abstract single vs. multi-objective metrics
  • Refactored parameter classes – for more intuitive segregation of different parameters controlling optimization process (#852)
  • Refactored DataMerger facility
  • Refactoring of selection operator implementation (#918)

Also, there are various bug-fixes related to ML operations, evolutionary operators & internal Graph operations.

v0.5.1

2 years ago

The most important changes:

  • Cache support for the cross-validation implemented;
  • AutoML can be run without a time limit;
  • Graph operators improved;
  • Multi-task pipelines processing improved;
  • Custom parameters support for external optimizer;
  • Time series processing improved;
  • Multimodal table processing improved;
  • Lightweight docker prepared;
  • Isolation Forest added as new operation
  • Major and minor bugs are fixed.