GitHub - leffff/fast-ensemble: Library for high level model ensembling (original) (raw)

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fast-ensemble

Scikit-learn-style library for effecient and convenient high level table model ensembling

Usage Example:

Initialize Stack

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 import StackingTransformer from fast_ensemble import ( CatBoostRegressorWrapper, LGBMRegressorWrapper, XGBRegressorWrapper, )

stack_1 = 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, stratified=True, stratification_bins=7 )

And another one

stack_2 = StackingTransformer( models=[ ("Dummy Regressor", DummyRegressor()), ], main_metric=mean_squared_error, regression=True, n_folds=5, random_state=None, shuffle=False, verbose=True, stratified=True, stratification_bins=7 )

Train your stacks (and get transformed dataframes)

X, y = make_regression(n_targets=1)

X_1_trans = stack_1.fit_transform(X, y) X_2_trans = stack_2.fit_transform(X, y)

Want to merge 2 stacks for convenience? Here you go!

stack_1.merge(stack_2)

stack_1.get_scores(prettified=True)

   catboost       xgboost          lgmb      boosting  Dummy Regressor

0 9852.055535 23389.781003 8872.055479 13130.504063 21344.359900 1 14259.407424 20177.587908 12040.548492 14088.529604 28620.260635 2 16393.254421 24267.409682 9503.011118 15067.349045 33377.287468 3 12694.791124 16349.931831 7188.301326 10675.853608 29510.019041 4 17505.264716 12158.834533 10273.547605 9621.041119 39099.670810