Multiprocessing support and custom operator configurations

TPOT 0.7 is now out, featuring multiprocessing support for Linux and macOS, customizable operator configurations, and more.

• TPOT now has multiprocessing support (Linux and macOS only). TPOT allows you to use multiple processes for accelerating pipeline optimization in TPOT with the n_jobs parameter in both TPOTClassifier and TPOTRegressor.

• TPOT now allows you to customize the operators and parameters explored during the optimization process. TPOT allows you to customize the list of operators and parameters in optimization process of TPOT with the config_dict parameter. The format of this customized dictionary can be found in the online documentation.

• TPOT now allows you to specify a time limit for evaluating a single pipeline (default limit is 5 minutes) in optimization process with the max_eval_time_mins parameter, so TPOT won't spend hours evaluating overly-complex pipelines.

• We tweaked TPOT's underlying evolutionary optimization algorithm to work even better, including using the mu+lambda algorithm. This algorithm gives you more control of how many pipelines are generated every iteration with the offspring_size parameter.

• Fixed a reproducibility issue where setting random_seed didn't necessarily result in the same results every time. This bug was present since version 0.6.

• Refined the default operators and parameters in TPOT, so TPOT 0.7 should work even better than 0.6.

• TPOT now supports sample weights in the fitness function if some if your samples are more important to classify correctly than others. The sample weights option works the same as in scikit-learn, e.g., tpot.fit(x_train, y_train, sample_weights=sample_weights).

• The default scoring metric in TPOT has been changed from balanced accuracy to accuracy, the same default metric for classification algorithms in scikit-learn. Balanced accuracy can still be used by setting scoring='balanced_accuracy' when creating a TPOT instance.