stacking_regressor_scalecast.py

from sklearn.linear_model import LinearRegression
from sklearn.linear_model import ElasticNet
from sklearn.neural_network import MLPRegressor
from sklearn.neighbors import KNeighborsRegressor
from xgboost import XGBRegressor

results = mvf.export_model_summaries()

estimators = [
    ("mlr", LinearRegression()),
    (
        "elasticnet",
        ElasticNet(
            **results.loc[
                results["ModelNickname"] == "elasticnet", "HyperParams"
            ].values[0]
        ),
    ),
    (
        "mlp",
        MLPRegressor(
            **results.loc[results["ModelNickname"] == "mlp", "HyperParams"].values[
                0
            ]
        ),
    ),
]

final_estimator = KNeighborsRegressor(
    **results.loc[results["ModelNickname"] == "knn", "HyperParams"].values[0]
)