# tune_feature_learners¶

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.

Args:
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.

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

Example:

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

tuned_pipeline = getml.hyperopt.tune_predictors(
pipeline=base_pipeline,
container=container)

Returns:

A Pipeline containing tuned versions of the feature learners.

Note:

Not supported in the getML community edition.