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[python][sklearn] do not replace empty dict with None for evals_result_ #4884

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Dec 18, 2021
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[python][sklearn] do not replace empty dict with None for evals_result_ #4884

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3c2d019
Update sklearn.py
StrikerRUS Dec 12, 2021
9fb638e
Update sklearn.py
StrikerRUS Dec 12, 2021
9ca2a9c
Update test_sklearn.py
StrikerRUS Dec 12, 2021
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8 changes: 2 additions & 6 deletions python-package/lightgbm/sklearn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -779,11 +779,7 @@ def _get_meta_data(collection, name, i):
callbacks=callbacks
)

if evals_result:
self._evals_result = evals_result
else: # reset after previous call to fit()
self._evals_result = None

self._evals_result = evals_result
self._best_iteration = self._Booster.best_iteration
self._best_score = self._Booster.best_score

Expand Down Expand Up @@ -910,7 +906,7 @@ def booster_(self):

@property
def evals_result_(self):
""":obj:`dict` or :obj:`None`: The evaluation results if validation sets have been specified."""
""":obj:`dict`: The evaluation results if validation sets have been specified."""
if not self.__sklearn_is_fitted__():
raise LGBMNotFittedError('No results found. Need to call fit with eval_set beforehand.')
return self._evals_result
Expand Down
6 changes: 3 additions & 3 deletions tests/python_package_test/test_sklearn.py
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Expand Up @@ -784,7 +784,7 @@ def test_metrics():

# no metric
gbm = lgb.LGBMRegressor(metric='None', **params).fit(**params_fit)
assert gbm.evals_result_ is None
assert gbm.evals_result_ == {}

# non-default metric in eval_metric
gbm = lgb.LGBMRegressor(**params).fit(eval_metric='mape', **params_fit)
Expand Down Expand Up @@ -833,7 +833,7 @@ def test_metrics():
# no metric
gbm = lgb.LGBMRegressor(objective='regression_l1', metric='None',
**params).fit(**params_fit)
assert gbm.evals_result_ is None
assert gbm.evals_result_ == {}

# non-default metric in eval_metric for non-default objective
gbm = lgb.LGBMRegressor(objective='regression_l1',
Expand Down Expand Up @@ -878,7 +878,7 @@ def test_metrics():
# no metric
gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric='None',
**params).fit(**params_fit)
assert gbm.evals_result_ is None
assert gbm.evals_result_ == {}

# default regression metric with non-default metric in eval_metric for custom objective
gbm = lgb.LGBMRegressor(objective=custom_dummy_obj,
Expand Down