evalml.automl.automl_algorithm.IterativeAlgorithm.__init__

IterativeAlgorithm.__init__(allowed_pipelines=None, max_iterations=None, tuner_class=None, text_columns=None, random_state=0, pipelines_per_batch=5, n_jobs=- 1, number_features=None, ensembling=False, pipeline_params=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 subclasses 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_state (int, np.random.RandomState) – The random seed/state. Defaults to 0.

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

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

  • number_features (int) – The number of columns in the input features.

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

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

evalml.automl.automl_algorithm.IterativeAlgorithm evalml.automl.automl_algorithm.IterativeAlgorithm.add_result