"""Stacked Ensemble Classifier."""
from evalml.model_family import ModelFamily
from evalml.pipelines.components import ElasticNetClassifier
from evalml.pipelines.components.ensemble import StackedEnsembleBase
from evalml.problem_types import ProblemTypes
[docs]class StackedEnsembleClassifier(StackedEnsembleBase):
"""Stacked Ensemble Classifier.
Arguments:
final_estimator (Estimator or subclass): The classifier used to combine the base estimators. If None, uses ElasticNetClassifier.
n_jobs (int or None): 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 -1.
- 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.
Example:
>>> from evalml.pipelines.component_graph import ComponentGraph
>>> from evalml.pipelines.components.estimators.classifiers.decision_tree_classifier import DecisionTreeClassifier
>>> from evalml.pipelines.components.estimators.classifiers.elasticnet_classifier import ElasticNetClassifier
...
>>> component_graph = {
... "Decision Tree": [DecisionTreeClassifier(random_seed=3), "X", "y"],
... "Decision Tree B": [DecisionTreeClassifier(random_seed=4), "X", "y"],
... "Stacked Ensemble": [
... StackedEnsembleClassifier(n_jobs=1, final_estimator=DecisionTreeClassifier()),
... "Decision Tree.x",
... "Decision Tree B.x",
... "y",
... ],
... }
...
>>> cg = ComponentGraph(component_graph)
>>> assert cg.default_parameters == {
... 'Decision Tree Classifier': {'criterion': 'gini',
... 'max_features': 'auto',
... 'max_depth': 6,
... 'min_samples_split': 2,
... 'min_weight_fraction_leaf': 0.0},
... 'Stacked Ensemble Classifier': {'final_estimator': ElasticNetClassifier,
... 'n_jobs': -1}}
"""
name = "Stacked Ensemble Classifier"
model_family = ModelFamily.ENSEMBLE
"""ModelFamily.ENSEMBLE"""
supported_problem_types = [
ProblemTypes.BINARY,
ProblemTypes.MULTICLASS,
ProblemTypes.TIME_SERIES_BINARY,
ProblemTypes.TIME_SERIES_MULTICLASS,
]
"""[
ProblemTypes.BINARY,
ProblemTypes.MULTICLASS,
ProblemTypes.TIME_SERIES_BINARY,
ProblemTypes.TIME_SERIES_MULTICLASS,
]"""
hyperparameter_ranges = {}
"""{}"""
_default_final_estimator = ElasticNetClassifier