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EpistasisLab/tpot: Version 0.11.0

Randy Olson; Weixuan Fu; Nathan; PGijsbers; Grishma Jena; Tom Augspurger; PRONOjit Saha; Sebastian Raschka; sahilshah1194; sohnam; DanKoretsky; kadarakos; Geoffrey Bradway; bartdp1; Jose Ortiz; Michal Ficek; Akshay Varik; Ted; James Myatt; screwed99; Matt Ritter; kamalasaurus; Randy Carnevale; Matthew Rocklin; Hristo; David Cottrell; Bharat Raghunathan; Alexander Whipp

  • Support for Python 3.4 and below has been officially dropped. Also support for scikit-learn 0.20 or below has been dropped.
  • The support of a metric function with the signature score_func(y_true, y_pred) for scoring parameter has been dropped.
  • Refine StackingEstimator for not stacking NaN/Infinity predication probabilities.
  • Fix a bug that population doesn't persist by warm_start=True when max_time_mins is not default value.
  • Now the random_state parameter in TPOT is used for pipeline evaluation instead of using a fixed random seed of 42 before. The set_param_recursive function has been moved to and it can be used in exported codes for setting random_state recursively in scikit-learn Pipeline. It is used to set random_state in fitted_pipeline_ attribute and exported pipelines.
  • TPOT can independently use generations and max_time_mins to limit the optimization process through using one of the parameters or both.
  • .export() function will return string of exported pipeline if output filename is not specified.
  • Add SGDClassifier and SGDRegressor into TPOT default configs.
  • Documentation has been updated.
  • Fix minor bugs.

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