stacking_sklearn_scalecast.py

f.add_sklearn_estimator(StackingRegressor,'stacking')
f.set_estimator('stacking')
results = f.export('model_summaries')
estimators = [
    ('knn',
     KNeighborsRegressor(**results.loc[results['ModelNickname'] == 'knn','HyperParams'].values[0])),
    ('xgboost',
     XGBRegressor(**results.loc[results['ModelNickname'] == 'xgboost','HyperParams'].values[0])),
    ('lightgbm',
     LGBMRegressor(**results.loc[results['ModelNickname'] == 'lightgbm','HyperParams'].values[0])),
    ('sgd',
     SGDRegressor(**results.loc[results['ModelNickname'] == 'sgd','HyperParams'].values[0])),
]

final_estimator = BaggingRegressor(
    base_estimator = MLPRegressor(
        hidden_layer_sizes=(100,100,100),
        solver='lbfgs',
    ),
    max_samples = 0.9,
    max_features = 0.5,
)

f.manual_forecast(estimators=estimators,final_estimator=final_estimator)