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)[source]¶ Returns a pipeline containing tuned feature learners.
- 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.
- 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.
- pipeline (
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.