def bagging_metaestimator(X, Y, vrbl_names, n_estimators, p_smpl, p_feat, n_jobs, base_estim):
from sklearn.model_selection import KFold, RepeatedKFold
from sklearn.ensemble import BaggingRegressor
cv = KFold(n_splits=5, shuffle=True)
try: X = X.values
except: pass
try: Y = Y.values
except: pass
max_feats = np.max([int(X.shape[1]*p_feat), 1])
max_n_estim = n_estimators*5
fitted_ensemble = BaggingRegressor(
base_estimator=base_estim,
n_estimators=max_n_estim, # Number of fittings
max_samples=p_smpl, # Select e.g. 50% of training data per random sample
max_features=max_feats, # Select e.g. N/3 variables randomly
bootstrap=False, #
bootstrap_features=False,
oob_score=False,
n_jobs=n_jobs, #8,
random_state=70,
verbose=1).fit(X, Y)
final_ensemble_idx = np.zeros(max_n_estim,dtype=bool)
for i, estimator in enumerate(fitted_ensemble.estimators_):
true_indices = np.abs(estimator.coef_)>0
# Definition of success in fitting: at least one predictor
# needs to be found
if(true_indices.sum() > 0):
final_ensemble_idx[i] = True
fitted_ensemble.estimators_features_ = list(np.array(fitted_ensemble.estimators_features_)[final_ensemble_idx])
fitted_ensemble.estimators_features_ = fitted_ensemble.estimators_features_[0:n_estimators]
fitted_ensemble.estimators_ = list(np.array(fitted_ensemble.estimators_)[final_ensemble_idx])
fitted_ensemble.estimators_ = fitted_ensemble.estimators_[0:n_estimators]
return fitted_ensemble
