iterative_algorithm¶

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.

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(X, y, problem_type, sampler_name=None, allowed_model_families=None, allowed_component_graphs=None, max_batches=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, verbose=False)[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

X (pd.DataFrame) – Training data.
y (pd.Series) – Target data.
problem_type (ProblemType) – Problem type associated with training data.
sampler_name (BaseSampler) – Sampler to use for preprocessing. Defaults to None.
allowed_model_families (list(str, ModelFamily)) – The model families to search. The default of None searches over all model families. Run evalml.pipelines.components.utils.allowed_model_families(“binary”) to see options. Change binary to multiclass or regression depending on the problem type. Note that if allowed_pipelines is provided, this parameter will be ignored.
allowed_component_graphs (dict) –
A dictionary of lists or ComponentGraphs indicating the component graphs allowed in the search. The format should follow { “Name_0”: [list_of_components], “Name_1”: [ComponentGraph(…)] }

The default of None indicates all pipeline component graphs for this problem type are allowed. Setting this field will cause allowed_model_families to be ignored.

e.g. allowed_component_graphs = { “My_Graph”: [“Imputer”, “One Hot Encoder”, “Random Forest Classifier”] }
max_batches (int) – The maximum number of batches to be evaluated. Used to determine ensembling. Defaults to None.
max_iterations (int) – The maximum number of iterations to be evaluated. Used to determine ensembling. Defaults to None.
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 False.
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.
verbose (boolean) – Whether or not to display logging information regarding pipeline building. Defaults to False.

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]¶

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.

Raises

ValueError – If default parameters are not in the acceptable hyperparameter ranges.

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]
Raises: AutoMLAlgorithmException – If no results were reported from the first batch.

property pipeline_number(self)¶: Returns the number of pipelines which have been recommended so far.

default_algorithm

engine