Source code for evalml.pipelines.components.estimators.regressors.et_regressor

"""Extra Trees Regressor."""
from typing import Dict, List, Optional, Union

import pandas as pd
from sklearn.ensemble import ExtraTreesRegressor as SKExtraTreesRegressor
from import Integer

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

[docs]class ExtraTreesRegressor(Estimator): """Extra Trees Regressor. 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 Regressor" 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.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION, ] """[ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION, ]""" def __init__( self, n_estimators: int = 100, max_features: str = "auto", max_depth: int = 6, min_samples_split: int = 2, min_weight_fraction_leaf: float = 0.0, n_jobs: int = -1, random_seed: Union[int, float] = 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_regressor = SKExtraTreesRegressor(random_state=random_seed, **parameters) super().__init__( parameters=parameters, component_obj=et_regressor, random_seed=random_seed, )
[docs] def get_prediction_intervals( self, X: pd.DataFrame, y: Optional[pd.Series] = None, coverage: List[float] = None, predictions: pd.Series = None, ) -> Dict[str, pd.Series]: """Find the prediction intervals using the fitted ExtraTreesRegressor. Args: X (pd.DataFrame): Data of shape [n_samples, n_features]. y (pd.Series): Target data. Optional. coverage (list[float]): A list of floats between the values 0 and 1 that the upper and lower bounds of the prediction interval should be calculated for. predictions (pd.Series): Optional list of predictions to use. If None, will generate predictions using `X`. Returns: dict: Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper. """ if coverage is None: coverage = [0.95] X, _ = self._manage_woodwork(X, y) X ="Datetime") if predictions is None: predictions = self._component_obj.predict(X) estimators = self._component_obj.estimators_ return get_prediction_intevals_for_tree_regressors( X, predictions, coverage, estimators, )