evalml.automl.automl_algorithm.IterativeAlgorithm.__init__¶
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IterativeAlgorithm.__init__(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, _frozen_pipeline_parameters=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.
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.
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.
pipeline_params (dict or None) – Pipeline-level parameters that should be passed to the proposed pipelines.
_frozen_pipeline_parameters (dict or None) – Pipeline-level parameters are frozen and used in the proposed pipelines.
_estimator_family_order (list(ModelFamily) or None) – specify the sort order for the first batch. Defaults to _ESTIMATOR_FAMILY_ORDER.