Source code for evalml.pipelines.components.estimators.classifiers.et_classifier

"""Extra Trees Classifier."""
from sklearn.ensemble import ExtraTreesClassifier as SKExtraTreesClassifier
from import Integer

from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes

[docs]class ExtraTreesClassifier(Estimator): """Extra Trees Classifier. Args: n_estimators (float): The number of trees in the forest. Defaults to 100. max_features (int, float or {"auto", "sqrt", "log2"}): The number of features to consider when looking for the best split: - If int, then consider max_features features at each split. - If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split. - If "auto", then max_features=sqrt(n_features). - If "sqrt", then max_features=sqrt(n_features). - If "log2", then max_features=log2(n_features). - If None, then max_features = n_features. The search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features. Defaults to "auto". max_depth (int): The maximum depth of the tree. Defaults to 6. min_samples_split (int or float): The minimum number of samples required to split an internal node: - If int, then consider min_samples_split as the minimum number. - If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split. Defaults to 2. min_weight_fraction_leaf (float): The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Defaults to 0.0. n_jobs (int or None): Number of jobs to run in parallel. -1 uses all processes. Defaults to -1. random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "Extra Trees Classifier" hyperparameter_ranges = { "n_estimators": Integer(10, 1000), "max_features": ["auto", "sqrt", "log2"], "max_depth": Integer(4, 10), } """{ "n_estimators": Integer(10, 1000), "max_features": ["auto", "sqrt", "log2"], "max_depth": Integer(4, 10), } """ model_family = ModelFamily.EXTRA_TREES """ModelFamily.EXTRA_TREES""" 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, ]""" def __init__( self, n_estimators=100, max_features="auto", max_depth=6, min_samples_split=2, min_weight_fraction_leaf=0.0, n_jobs=-1, random_seed=0, **kwargs, ): parameters = { "n_estimators": n_estimators, "max_features": max_features, "max_depth": max_depth, "min_samples_split": min_samples_split, "min_weight_fraction_leaf": min_weight_fraction_leaf, "n_jobs": n_jobs, } parameters.update(kwargs) et_classifier = SKExtraTreesClassifier(random_state=random_seed, **parameters) super().__init__( parameters=parameters, component_obj=et_classifier, random_seed=random_seed, )