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[WIP] Add chosen metric argument to clarify early stopping behaviour #6424

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[WIP] Add chosen metric argument to clarify early stopping behaviour #6424

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4ce23bd
feat: add chosen_metric attribute in early stopping callback class
sami-ka Apr 20, 2024
dbdc17c
feat: add chosen_metric_early_stopping parameter to create early stop…
sami-ka Apr 20, 2024
53698f7
feat: add chosen_metric_early_stopping to the list of parameters
sami-ka Apr 20, 2024
44fcae2
refactor: replace argument name and add chekcs +warning
sami-ka Apr 26, 2024
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feat: add chosen_metric_early_stopping parameter to create early stop… 
…ping callback through parameters
  • Loading branch information
@sami-ka
sami-ka committed Apr 20, 2024
commit dbdc17c43de32431acb5e91b18d347d51b6d2694
16 changes: 15 additions & 1 deletion python-package/lightgbm/engine.py
View file
Original file line number Diff line number Diff line change
@@ -191,7 +191,13 @@ def train(
if params["early_stopping_round"] is None:
params.pop("early_stopping_round")
first_metric_only = params.get("first_metric_only", False)

chosen_metric_early_stopping = params.get("chosen_metric_early_stopping", None)
# Test if both parameters are used
if (first_metric_only + (chosen_metric_early_stopping is not None)) == 2:
error_message = """
Only one of first_metric_only and chosen_metric_early_stopping parameters should be used"""
raise ValueError(error_message)

predictor: Optional[_InnerPredictor] = None
if isinstance(init_model, (str, Path)):
predictor = _InnerPredictor.from_model_file(model_file=init_model, pred_parameter=params)
@@ -241,6 +247,7 @@ def train(
callback.early_stopping(
stopping_rounds=params["early_stopping_round"], # type: ignore[arg-type]
first_metric_only=first_metric_only,
chosen_metric=chosen_metric_early_stopping,
verbose=_choose_param_value(
main_param_name="verbosity",
params=params,
@@ -716,6 +723,12 @@ def cv(
if params["early_stopping_round"] is None:
params.pop("early_stopping_round")
first_metric_only = params.get("first_metric_only", False)
chosen_metric_early_stopping = params.get("chosen_metric_early_stopping", None)
# Test if both parameters are used
if (first_metric_only + (chosen_metric_early_stopping is not None)) == 2:
error_message = """
Only one of first_metric_only and chosen_metric_early_stopping parameters should be used"""
raise ValueError(error_message)

if isinstance(init_model, (str, Path)):
predictor = _InnerPredictor.from_model_file(
@@ -765,6 +778,7 @@ def cv(
callback.early_stopping(
stopping_rounds=params["early_stopping_round"], # type: ignore[arg-type]
first_metric_only=first_metric_only,
chosen_metric=chosen_metric_early_stopping,
verbose=_choose_param_value(
main_param_name="verbosity",
params=params,