iterative_algorithm

Module Contents

Classes Summary

IterativeAlgorithm

An automl algorithm which first fits a base round of pipelines with default parameters, then does a round of parameter tuning on each pipeline in order of performance.

Contents

class evalml.automl.automl_algorithm.iterative_algorithm.IterativeAlgorithm(allowed_pipelines=None, max_iterations=None, tuner_class=None, random_seed=0, pipelines_per_batch=5, n_jobs=- 1, number_features=None, ensembling=False, text_in_ensembling=False, pipeline_params=None, custom_hyperparameters=None, _estimator_family_order=None)[source]

An automl algorithm which first fits a base round of pipelines with default parameters, then does a round of parameter tuning on each pipeline in order of performance.

Parameters

  • allowed_pipelines (list(class)) – A list of PipelineBase instances indicating the pipelines allowed in the search. The default of None indicates all pipelines for this problem type are allowed.

  • max_iterations (int) – The maximum number of iterations to be evaluated.

  • tuner_class (class) – A subclass of Tuner, to be used to find parameters for each pipeline. The default of None indicates the SKOptTuner will be used.

  • random_seed (int) – Seed for the random number generator. Defaults to 0.

  • pipelines_per_batch (int) – The number of pipelines to be evaluated in each batch, after the first batch. Defaults to 5.

  • n_jobs (int or None) – Non-negative integer describing level of parallelism used for pipelines. Defaults to None.

  • number_features (int) – The number of columns in the input features. Defaults to None.

  • ensembling (boolean) – If True, runs ensembling in a separate batch after every allowed pipeline class has been iterated over. Defaults to False.

  • text_in_ensembling (boolean) – If True and ensembling is True, then n_jobs will be set to 1 to avoid downstream sklearn stacking issues related to nltk. Defaults to None.

  • pipeline_params (dict or None) – Pipeline-level parameters that should be passed to the proposed pipelines. Defaults to None.

  • custom_hyperparameters (dict or None) – Custom hyperparameter ranges specified for pipelines to iterate over. Defaults to None.

  • _estimator_family_order (list(ModelFamily) or None) – specify the sort order for the first batch. Defaults to None, which uses _ESTIMATOR_FAMILY_ORDER.

Methods

add_result

Register results from evaluating a pipeline

batch_number

Returns the number of batches which have been recommended so far.

next_batch

Get the next batch of pipelines to evaluate

pipeline_number

Returns the number of pipelines which have been recommended so far.
add_result(self, score_to_minimize, pipeline, trained_pipeline_results)[source]

Register results from evaluating a pipeline

Parameters

  • score_to_minimize (float) – The score obtained by this pipeline on the primary objective, converted so that lower values indicate better pipelines.

  • pipeline (PipelineBase) – The trained pipeline object which was used to compute the score.

  • trained_pipeline_results (dict) – Results from training a pipeline.

property batch_number(self)

Returns the number of batches which have been recommended so far.

next_batch(self)[source]

Get the next batch of pipelines to evaluate

Returns

a list of instances of PipelineBase subclasses, ready to be trained and evaluated.

Return type

list(PipelineBase)

property pipeline_number(self)

Returns the number of pipelines which have been recommended so far.

evalml_algorithm engine