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
[docs]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(.01, 1),
"threshold": ['mean', -np.inf]
}
[docs] def __init__(self, number_features=None, n_estimators=10, max_depth=None,
percent_features=0.5, threshold=-np.inf, n_jobs=-1, random_state=0):
max_features = None
if number_features:
max_features = max(1, int(percent_features * number_features))
parameters = {"percent_features": percent_features,
"threshold": threshold}
estimator = SKRandomForestClassifier(random_state=random_state,
n_estimators=n_estimators,
max_depth=max_depth,
n_jobs=n_jobs)
feature_selection = SkSelect(estimator=estimator,
max_features=max_features,
threshold=threshold)
super().__init__(parameters=parameters,
component_obj=feature_selection,
random_state=random_state)