tune_feature_learners

getml.hyperopt.tune_feature_learners(pipeline, population_table_training, population_table_validation, peripheral_tables=None, n_iter=0, score=None, num_threads=0)

Returns a pipeline containing tuned feature learners.

Parameters

• 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.

• population_table_training (DataFrame) – The population table that pipelines will be trained on.

• population_table_validation (DataFrame) – The population table that pipelines will be evaluated on.

• peripheral_tables (DataFrame, list or dict) – The peripheral tables used to provide additional information for the population tables.

• n_iter (int, optional) – The number of iterations.

• score (str, optional) – The score to optimize. Must be from scores.

• num_threads (int, optional) – 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. Moreover, we assume that you have defined a training set and a validation set as well as the peripheral tables.

tuned_pipeline = getml.hyperopt.tune_feature_learners(
    pipeline=base_pipeline,
    population_table_training=training_set,
    population_table_validation=validation_set,
    peripheral_tables=peripheral_tables)

Returns

A Pipeline containing tuned versions of the feature learners.

Raises

TypeError – If any instance variable is of a wrong type.