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Error en regresor de votación mlxtend al usar bosque aleatorio para la selección de características

Question

 from mlxtend.regressor import StackingRegressor
from sklearn.ensemble.forest import RandomForestRegressor as RFR
from sklearn.ensemble import GradientBoostingRegressor as GBR
import xgboost as xgb
rfr = RFR(n_estimators=500, n_jobs=cc.ncpu, random_state=0)
gbr = GBR(n_estimators=1000, random_state=0)
xgr = xgb.XGBRegressor()
mtr = RFR() # meta regressor
regressors = [rfr, gbr, xgr]
model = StackingRegressor(regressors=regressors, meta_regressor=mtr)
param_grid = {
''fs__threshold'': [''median''],
''fs__estimator__max_features'': [''log2''],
''clf__rfr__max_features'': [''auto'', ''log2''],
''clf__gbr__learning_rate'': [0.05, 0.02, 0.01],
''clf__gbr__max_depth'': [4, 5, 6, 7],
''clf__gbr__max_features'': [''auto'', ''log2''],
''clf__gbr__n_estimators'': [500, 1000, 2000],
''clf__xgr__learning_rate'': [0.001, 0.05, 0.1, 0.2],
''clf__xgr__max_depth'': [2, 4, 6],
''clf__xgr__min_child_weight'': [1, 3, 5],
''clf__xgr__n_estimators'': [500, 1000],
''clf__meta-mtr__n_estimators'': [750, 1500]
}
rf_feature_imp = RFR(250, n_jobs=cc.ncpu)
feat_selection = SelectFromModel(rf_feature_imp)
pipeline = Pipeline([(''fs'', feat_selection), (''clf'', model), ])
gs = GridSearchCV(pipeline, param_grid=param_grid, verbose=1, n_jobs=-1, error_score=np.nan)

En el código anterior, quiero usar el regresor de votación mlxtend y también usar un bosque aleatorio para seleccionar las características relevantes. Sin embargo, este código no funciona y recibo un error

ValueError: Invalid parameter xgr for estimator StackingRegressor(meta_regressor=RandomForestRegressor(bootstrap=True, criterion=''mse'', max_depth=None,
max_features=''auto'', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False),
regressors=[RandomForestRegressor(bootstrap=True, criterion=''mse'', max_depth=None,
max_features=''auto'', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=500, n_jobs=5, oob_sc...eg:linear'', reg_alpha=0, reg_lambda=1,
scale_pos_weight=1, seed=0, silent=True, subsample=1)],
verbose=0). Check the list of available parameters with `estimator.get_params().keys()`.

¿Cómo arreglar esto?