import numpy as np
from sklearn.ensemble import RandomForestClassifier as SKRandomForestClassifier
from sklearn.feature_selection import SelectFromModel as SkSelect
from skopt.space import Real

from .feature_selector import FeatureSelector


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

    name = "RF Classifier 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 = SKRandomForestClassifier(
            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,
        )

8229f74670bf13b908e78908395b0659925cc11f
<mask token>


class RFClassifierSelectFromModel(FeatureSelector):
    <mask token>
    <mask token>
    <mask token>

    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 = SKRandomForestClassifier(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)

<mask token>


class RFClassifierSelectFromModel(FeatureSelector):
    <mask token>
    name = 'RF Classifier 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 = SKRandomForestClassifier(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)

<mask token>


class RFClassifierSelectFromModel(FeatureSelector):
    """Selects top features based on importance weights using a Random Forest classifier."""
    name = 'RF Classifier 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 = SKRandomForestClassifier(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)

import numpy as np
from sklearn.ensemble import RandomForestClassifier as SKRandomForestClassifier
from sklearn.feature_selection import SelectFromModel as SkSelect
from skopt.space import Real
from .feature_selector import FeatureSelector


class RFClassifierSelectFromModel(FeatureSelector):
    """Selects top features based on importance weights using a Random Forest classifier."""
    name = 'RF Classifier 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 = SKRandomForestClassifier(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)

import numpy as np
from sklearn.ensemble import RandomForestClassifier as SKRandomForestClassifier
from sklearn.feature_selection import SelectFromModel as SkSelect
from skopt.space import Real

from .feature_selector import FeatureSelector


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

    name = "RF Classifier 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 = SKRandomForestClassifier(
            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,
        )
