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TPOT now supports sparse matrices with a new built-in TPOT configurations, "TPOT sparse". We are using a custom OneHotEncoder implementation that supports missing values and continuous features.
We have added an "early stopping" option for stopping the optimization process if no improvement is made within a set number of generations. Look up the
early_stopparameter to access this functionality.
TPOT now reduces the number of duplicated pipelines between generations, which saves you time during the optimization process.
TPOT now supports custom scoring functions via the command-line mode.
We have added a new optional argument,
periodic_checkpoint_folder, that allows TPOT to periodically save the best pipeline so far to a local folder during optimization process.
TPOT no longer uses
n_jobs=1to avoid the potential freezing issue that scikit-learn suffers from.
We have added
pandasas a dependency to read input datasets instead of
recfromcsvfunction is unable to parse datasets with complex data types.
Fixed a bug that
DEFAULTin the parameter(s) of nested estimator raises
KeyErrorwhen exporting pipelines.
Fixed a bug related to setting
random_statein nested estimators. The issue would happen with pipeline with
ExtraTreesClassifieras nested estimator) or
StackingEstimatorif nested estimator has
Fixed a bug in the missing value imputation function in TPOT to impute along columns instead rows.
Refined input checking for sparse matrices in TPOT.