Library for high level model ensembling
Project description
fast-ensemble
Library for effecient and convenient high level table model ensembling
Usage Example:
from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from sklearn.datasets import make_regression
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
from fast_ensemble.stacking import StackingTransformer
from fast_ensemble.wrappers import (
CatBoostRegressorWrapper,
LGBMRegressorWrapper,
XGBRegressorWrapper,
)
stack = StackingTransformer(
models=[
(
"catboost",
CatBoostRegressorWrapper(
CatBoostRegressor(verbose=0),
use_best_model=True,
early_stopping_rounds=100,
),
),
(
"xgboost",
XGBRegressorWrapper(
XGBRegressor(), use_best_model=True, early_stopping_rounds=100
),
),
(
"lgmb",
LGBMRegressorWrapper(
LGBMRegressor(), use_best_model=True, early_stopping_rounds=100
),
),
("boosting", GradientBoostingRegressor()),
],
main_metric=mean_squared_error,
regression=True,
n_folds=5,
random_state=None,
shuffle=False,
verbose=True,
)
X, y = make_regression(n_targets=1)
X_trans = stack.fit_transform(X, y)
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