getml.hyperopt.tune_feature_learners(pipeline, container, train='train', validation='validation', n_iter=0, score=None, num_threads=0)[source]

A high-level interface for optimizing the feature learners of a Pipeline.

Efficiently optimizes the hyperparameters for the set of feature learners (from feature_learning) of a given pipeline by breaking each feature learner’s hyperparameter space down into carefully curated subspaces and optimizing the hyperparameters for each subspace in a sequential multi-step process. For further details about the actual recipes behind the tuning routines refer to tuning routines.

pipeline (Pipeline):

Base pipeline used to derive all models fitted and scored during the hyperparameter optimization. It defines the data schema and any hyperparameters that are not optimized.

container (Container):

The data container used for the hyperparameter tuning.

train (str, optional):

The name of the subset in ‘container’ used for training.

validation (str, optional):

The name of the subset in ‘container’ used for validation.

n_iter (int, optional):

The number of iterations.

score (str, optional):

The score to optimize. Must be from metrics.

num_threads (int, optional):

The number of parallel threads to use. If set to 0, the number of threads will be inferred.


We assume that you have already set up your Pipeline and Container.

tuned_pipeline = getml.hyperopt.tune_predictors(

A Pipeline containing tuned versions of the feature learners.


Not supported in the getML community edition.