EpistasisLab/tpot v0.8

More built-in configurations, missing data support, and detailed API documentation

on GitHub

TPOT now detects whether there are missing values in your dataset and replaces them with the median value of the column.

TPOT now allows you to set a group parameter in the fit function so you can use the GroupKFold cross-validation strategy.

TPOT now allows you to set a subsample ratio of the training instance with the subsample parameter. For example, setting subsample=0.5 tells TPOT to create a fixed subsample of half of the training data for the pipeline optimization process. This parameter can be useful for speeding up the pipeline optimization process, but may give less accurate performance estimates from cross-validation.

TPOT now has more built-in configurations, including TPOT MDR and TPOT light, for both classification and regression problems.

TPOTClassifier and TPOTRegressor now expose three useful internal attributes, fitted_pipeline_, pareto_front_fitted_pipelines_, and evaluated_individuals_. These attributes are described in the API documentation.

Oh, TPOT now has thorough API documentation. Check it out!

Fixed a reproducibility issue where setting random_seed didn't necessarily result in the same results every time. This bug was present since TPOT v0.7.

Refined input checking in TPOT.

Removed Python 2 uncompliant code.