EpistasisLab/tpot 0.4

Major upgrade

on GitHub

In TPOT 0.4, we've made some major changes to the internals of TPOT and added some convenience functions. We've summarized the changes below.

• Added new sklearn models and preprocessors
  • AdaBoostClassifier
  • BernoulliNB
  • ExtraTreesClassifier
  • GaussianNB
  • MultinomialNB
  • LinearSVC
  • PassiveAggressiveClassifier
  • GradientBoostingClassifier
  • RBFSampler
  • FastICA
  • FeatureAgglomeration
  • Nystroem
• Added operator that inserts virtual features for the count of features with values of zero
• Reworked parameterization of TPOT operators
  • Reduced parameter search space with information from a scikit-learn benchmark
  • TPOT no longer generates arbitrary parameter values, but uses a fixed parameter set instead
• Removed XGBoost as a dependency
  • Too many users were having install issues with XGBoost
  • Replaced with scikit-learn's GradientBoostingClassifier
• Improved descriptiveness of TPOT command line parameter documentation
• Removed min/max/avg details during fit() when verbosity > 1
  • Replaced with tqdm progress bar
  • Added tqdm as a dependency
• Added fit_predict() convenience function
• Added get_params() function so TPOT can operate in scikit-learn's cross_val_score & related functions