Source code for evalml.pipelines.components.transformers.feature_selection.rf_regressor_feature_selector

import numpy as np
from sklearn.ensemble import RandomForestRegressor as SKRandomForestRegressor
from sklearn.feature_selection import SelectFromModel as SkSelect
from skopt.space import Real

from .feature_selector import FeatureSelector


class RFRegressorSelectFromModel(FeatureSelector):
    """Selects top features based on importance weights using a Random Forest regressor."""

    name = "RF Regressor Select From Model"
    hyperparameter_ranges = {
        "percent_features": Real(0.01, 1),
        "threshold": ["mean", -np.inf],
    }

    def __init__(
        self,
        number_features=None,
        n_estimators=10,
        max_depth=None,
        percent_features=0.5,
        threshold=-np.inf,
        n_jobs=-1,
        random_seed=0,
        **kwargs
    ):

        parameters = {
            "number_features": number_features,
            "n_estimators": n_estimators,
            "max_depth": max_depth,
            "percent_features": percent_features,
            "threshold": threshold,
            "n_jobs": n_jobs,
        }
        parameters.update(kwargs)

        estimator = SKRandomForestRegressor(
            random_state=random_seed,
            n_estimators=n_estimators,
            max_depth=max_depth,
            n_jobs=n_jobs,
        )
        max_features = (
            max(1, int(percent_features * number_features)) if number_features else None
        )
        feature_selection = SkSelect(
            estimator=estimator,
            max_features=max_features,
            threshold=threshold,
            **kwargs
        )
        super().__init__(
            parameters=parameters,
            component_obj=feature_selection,
            random_seed=random_seed,
        )