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Fixes the use of eval_metric in case of EarlyStoppingShapRFECV with LightGBM #261

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merged 8 commits into from
Jul 4, 2024
Merged

Fixes the use of eval_metric in case of EarlyStoppingShapRFECV with LightGBM #261

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1aea032
fix custom eval_metric for lightgbm
PaulZhutovsky Jun 7, 2024
698eff7
fix runner
Jul 4, 2024
cbf6e42
fix runner
Jul 4, 2024
7c17253
fix runner
Jul 4, 2024
7358e20
fix runner
Jul 4, 2024
7f9643e
fix test error
Jul 4, 2024
cb38f3b
drop support for python 3.8
Jul 4, 2024
68ba399
update github workflows
Jul 4, 2024
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9 changes: 1 addition & 8 deletions probatus/feature_elimination/early_stopping_feature_elimination.py
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Original file line number Diff line number Diff line change
Expand Up @@ -239,6 +239,7 @@ def _get_fit_params_lightGBM(
"X": X_train,
"y": y_train,
"eval_set": [(X_val, y_val)],
"eval_metric": self.eval_metric,
"callbacks": [
early_stopping(self.early_stopping_rounds, first_metric_only=True),
log_evaluation(1 if self.verbose >= 2 else 0),
Expand Down Expand Up @@ -507,14 +508,6 @@ def _get_feature_shap_values_per_fold(

# Due to deprecation issues (compatibility with Sklearn) set some params
# like below, instead of through fit().
try:
from lightgbm import LGBMModel

if isinstance(model, LGBMModel):
model.set_params(eval_metric=self.eval_metric)
except ImportError:
pass

try:
from xgboost.sklearn import XGBModel

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