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pyoperon-0.3.6
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@foolnotion foolnotion released this 31 Mar 20:54
v0.3.6
a37d3b6
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foolnotion Bogdan Burlacu
GPG key ID: 1EE73B8BD2CE51B0
Learn about vigilant mode.

Changelog

This release is based on Operon rev. 88a15c3 and includes the following features:

  • hard-crafted reverse-mode automatic differentiation module for symbolic expression trees, with much better runtime performance
  • the ability to optimize all tree node coefficients via nonlinear least squares (previously, only leaf nodes were possible)
  • slightly faster interpreter performance (+5-10%)
  • a selection of new evaluators
    • AggregateEvaluator: aggregates multiple objectives into a single scalar (min, max, median, mean, harmonic mean, sum)
    • BayesianInformationCriterionEvaluator: computes the value of the Bayesian Information Criterion (BIC) for a symbolic regression model
    • AkaikeInformationCriterionEvaluator: computes the value of the Akaike Information Criterion (AIC) for a symbolic regression model
    • MinimumDescriptionLengthEvaluator: computes the Minimum Description Length (MDL) of a symbolic regression model
  • various other fixes and improvements

The scikit-learn module now defaults to using the minimum description length to select the best model from the Pareto front. This is configurable with choices between: MSE, BIC, AIC, MDL

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