from sklearn.ensemble import StackingClassifier
from sklearn.model_selection import StratifiedKFold
from evalml.model_family import ModelFamily
from evalml.pipelines.components import LogisticRegressionClassifier
from evalml.pipelines.components.ensemble import StackedEnsembleBase
from evalml.problem_types import ProblemTypes
[docs]class StackedEnsembleClassifier(StackedEnsembleBase):
"""Stacked Ensemble Classifier."""
name = "Stacked Ensemble Classifier"
model_family = ModelFamily.ENSEMBLE
supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY,
ProblemTypes.TIME_SERIES_MULTICLASS]
hyperparameter_ranges = {}
_stacking_estimator_class = StackingClassifier
_default_final_estimator = LogisticRegressionClassifier
_default_cv = StratifiedKFold
[docs] def __init__(self, input_pipelines=None, final_estimator=None,
cv=None, n_jobs=-1, random_seed=0, **kwargs):
"""Stacked ensemble classifier.
Arguments:
input_pipelines (list(PipelineBase or subclass obj)): List of pipeline instances to use as the base estimators.
This must not be None or an empty list or else EnsembleMissingPipelinesError will be raised.
final_estimator (Estimator or subclass): The classifier used to combine the base estimators. If None, uses LogisticRegressionClassifier.
cv (int, cross-validation generator or an iterable): Determines the cross-validation splitting strategy used to train final_estimator.
For int/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. Defaults to None.
Possible inputs for cv are:
- None: 3-fold cross validation
- int: the number of folds in a (Stratified) KFold
- An scikit-learn cross-validation generator object
- An iterable yielding (train, test) splits
n_jobs (int or None): Non-negative integer describing level of parallelism used for pipelines.
None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used.
Defaults to None.
- Note: there could be some multi-process errors thrown for values of `n_jobs != 1`. If this is the case, please use `n_jobs = 1`.
random_seed (int): Seed for the random number generator. Defaults to 0.
"""
super().__init__(input_pipelines=input_pipelines, final_estimator=final_estimator,
cv=cv, n_jobs=n_jobs, random_seed=random_seed, **kwargs)