Error in mlxtend regression algorithm when using random forest to select a function

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) 

In the above code, I want to use regression to vote mlxtend , and also use a random forest to select the appropriate functions. However, this code does not work, and I get an 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()`. 

How to fix it?