iterative_algorithm
============================================================
.. py:module:: evalml.automl.automl_algorithm.iterative_algorithm
.. autoapi-nested-parse::
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
~~~~~~~~~~~~~~~
.. autoapisummary::
evalml.automl.automl_algorithm.iterative_algorithm.IterativeAlgorithm
Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: IterativeAlgorithm(X, y, problem_type, sampler_name=None, allowed_model_families=None, excluded_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, search_parameters=None, _estimator_family_order=None, allow_long_running_models=False, features=None, verbose=False, exclude_featurizers=None)
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.
:param X: Training data.
:type X: pd.DataFrame
:param y: Target data.
:type y: pd.Series
:param problem_type: Problem type associated with training data.
:type problem_type: ProblemType
:param sampler_name: Sampler to use for preprocessing. Defaults to None.
:type sampler_name: BaseSampler
:param allowed_model_families: 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.
:type allowed_model_families: list(str, ModelFamily)
:param excluded_model_families: A list of model families to exclude from the estimators used when building pipelines.
:type excluded_model_families: list(str, ModelFamily)
:param allowed_component_graphs: 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"] }
:type allowed_component_graphs: dict
:param max_batches: The maximum number of batches to be evaluated. Used to determine ensembling. Defaults to None.
:type max_batches: int
:param max_iterations: The maximum number of iterations to be evaluated. Used to determine ensembling. Defaults to None.
:type max_iterations: int
:param tuner_class: A subclass of Tuner, to be used to find parameters for each pipeline. The default of None indicates the SKOptTuner will be used.
:type tuner_class: class
:param random_seed: Seed for the random number generator. Defaults to 0.
:type random_seed: int
:param pipelines_per_batch: The number of pipelines to be evaluated in each batch, after the first batch. Defaults to 5.
:type pipelines_per_batch: int
:param n_jobs: Non-negative integer describing level of parallelism used for pipelines. Defaults to None.
:type n_jobs: int or None
:param number_features: The number of columns in the input features. Defaults to None.
:type number_features: int
:param ensembling: If True, runs ensembling in a separate batch after every allowed pipeline class has been iterated over. Defaults to False.
:type ensembling: boolean
:param text_in_ensembling: 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.
:type text_in_ensembling: boolean
:param search_parameters: Pipeline-level parameters and custom hyperparameter ranges specified for pipelines to iterate over. Hyperparameter ranges
must be passed in as skopt.space objects. Defaults to None.
:type search_parameters: dict or None
:param _estimator_family_order: specify the sort order for the first batch. Defaults to None, which uses _ESTIMATOR_FAMILY_ORDER.
:type _estimator_family_order: list(ModelFamily) or None
:param allow_long_running_models: Whether or not to allow longer-running models for large multiclass problems. If False and no pipelines, component graphs, or model families are provided,
AutoMLSearch will not use Elastic Net or XGBoost when there are more than 75 multiclass targets and will not use CatBoost when there are more than 150 multiclass targets. Defaults to False.
:type allow_long_running_models: bool
:param features: List of features to run DFS on in AutoML pipelines. Defaults to None. Features will only be computed if the columns used by the feature exist in the input and if the feature itself is not in input.
:type features: list
:param verbose: Whether or not to display logging information regarding pipeline building. Defaults to False.
:type verbose: boolean
:param exclude_featurizers: A list of featurizer components to exclude from the pipelines built by IterativeAlgorithm.
Valid options are "DatetimeFeaturizer", "EmailFeaturizer", "URLFeaturizer", "NaturalLanguageFeaturizer", "TimeSeriesFeaturizer"
:type exclude_featurizers: list[str]
**Methods**
.. autoapisummary::
:nosignatures:
evalml.automl.automl_algorithm.iterative_algorithm.IterativeAlgorithm.add_result
evalml.automl.automl_algorithm.iterative_algorithm.IterativeAlgorithm.batch_number
evalml.automl.automl_algorithm.iterative_algorithm.IterativeAlgorithm.default_max_batches
evalml.automl.automl_algorithm.iterative_algorithm.IterativeAlgorithm.next_batch
evalml.automl.automl_algorithm.iterative_algorithm.IterativeAlgorithm.num_pipelines_per_batch
evalml.automl.automl_algorithm.iterative_algorithm.IterativeAlgorithm.pipeline_number
.. py:method:: add_result(self, score_to_minimize, pipeline, trained_pipeline_results, cached_data=None)
Register results from evaluating a pipeline.
:param score_to_minimize: The score obtained by this pipeline on the primary objective, converted so that lower values indicate better pipelines.
:type score_to_minimize: float
:param pipeline: The trained pipeline object which was used to compute the score.
:type pipeline: PipelineBase
:param trained_pipeline_results: Results from training a pipeline.
:type trained_pipeline_results: dict
:param cached_data: A dictionary of cached data, where the keys are the model family. Expected to be of format
{model_family: {hash1: trained_component_graph, hash2: trained_component_graph...}...}.
Defaults to None.
:type cached_data: dict
:raises ValueError: If default parameters are not in the acceptable hyperparameter ranges.
.. py:method:: batch_number(self)
:property:
Returns the number of batches which have been recommended so far.
.. py:method:: default_max_batches(self)
:property:
Returns the number of max batches AutoMLSearch should run by default.
.. py:method:: next_batch(self)
Get the next batch of pipelines to evaluate.
:returns: A list of instances of PipelineBase subclasses, ready to be trained and evaluated.
:rtype: list[PipelineBase]
:raises AutoMLAlgorithmException: If no results were reported from the first batch.
.. py:method:: num_pipelines_per_batch(self, batch_number)
Return the number of pipelines in the nth batch.
:param batch_number: which batch to calculate the number of pipelines for.
:type batch_number: int
:returns: number of pipelines in the given batch.
:rtype: int
.. py:method:: pipeline_number(self)
:property:
Returns the number of pipelines which have been recommended so far.