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Trainer Engine History of Changes (Release notes)

Trainer Engine v. 3.1

New features and improvements:

  • On the automatic model building screen a new slider is added to control the degree of hyperparameter optimisation. With this you can control execution time and the number of models built.
  • Underlying Boruta algorithm implementation was replaced with a more stable one.
  • During descriptor generation, structures can be dropped from the final descriptor set due to various errors. The failure cause is now added to the report.
  • Several performance issues were addressed in this release. Both model building and prediction were improved.

Trainer Engine v. 3.0

New features and improvements:

  • Open-source DeepChem machine learning library is available to create training models. Random Forest, Gradient Tree Boost and Graph

Convolutional Network algorithms can be used.

  • Predictions can be run by a simple file upload. Descriptors are calculated in the background automatically.
  • Machine Learning problem type can be detected automatically.
  • Automatic DB migration tool from v. 2.0 is available.
  • Old plugin configuration got a proper UI element ('Data processing and confidentiality').
  • Automatic Model Building is extended by DeepChem models.
  • Hyper-parameter names are changes to conventional DeepChem terminology.

Trainer Engine v. 2.0

New features and improvements:

  • Open-source descriptors from DeepChem (RDKit and Mordred) are available in Trainer Engine.
  • Descriptor JSON editor improvement: descriptors can be selected from a user-friendly dialogue.
  • Automatic feature selection can be turned off during Automated Model Building.
  • Health and version information box about connected services are added.
  • Descriptors are filtered out based on variance and correlation during automated feature selection.
  • Chemaxon descriptor generation is faster.

Trainer Engine v. 1.3

New features and improvements:

  • Filters are introduced on the Runs page.
  • Single runs on the Analyze page can be managed with single-click operations, e.g. add, remove, archive.

Trainer Engine v. 1.2

New features and improvements:

  • Comprehensive in-app helps are introduced in the GUI.
  • Advanced options for the automatic descriptor selection ('Use advanced configuration') are introduced.
  • Boruta insights are available on the Details page of a descriptor set.

Trainer Engine v. 1.1

New features and improvements:

  • Automated feature selection is introduced.
  • Automated model building is introduced.
  • Training data points and descriptor filtering is introduced in the Analyze page.
  • Observed data distribution for a training model is introduced on the Details page of the model.
  • Runs table paging is introduced.
  • New default descriptor configuration is set.

Trainer Engine v. 1.0

New features and improvements:

  • Trainer Engine GUI is available as an option in the CLI.
  • Configuration files in hJSON format are supported by Trainer Engine.
  • k Nearest Neighbor (kNN) descriptor is available for both regression- and classification-type models.
  • Standardization of the training set before descriptor generation is available using Standardizer.
  • The mtryRatio parameter is available in the Random Forest Classification and Regression models.
  • Gradient Tree Boost Classification and Regression models are available.