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Unverified Commit 80662618 authored by Nikita Titov's avatar Nikita Titov Committed by GitHub
Browse files

[python] remove `verbose` argument of `model_from_string()` method of Booster class (#4877)

parent 0e25841d
......@@ -2619,7 +2619,7 @@ class Booster:
self.__num_class = out_num_class.value
self.pandas_categorical = _load_pandas_categorical(file_name=model_file)
elif model_str is not None:
self.model_from_string(model_str, verbose="_silent_false")
self.model_from_string(model_str)
else:
raise TypeError('Need at least one training dataset or model file or model string '
'to create Booster instance')
......@@ -3300,15 +3300,13 @@ class Booster:
ctypes.c_int(end_iteration)))
return self
def model_from_string(self, model_str, verbose='warn'):
def model_from_string(self, model_str):
"""Load Booster from a string.
Parameters
----------
model_str : str
Model will be loaded from this string.
verbose : bool, optional (default=True)
Whether to print messages while loading model.
Returns
-------
......
......@@ -303,7 +303,7 @@ def train(
for dataset_name, eval_name, score, _ in evaluation_result_list:
booster.best_score[dataset_name][eval_name] = score
if not keep_training_booster:
booster.model_from_string(booster.model_to_string(), verbose='_silent_false').free_dataset()
booster.model_from_string(booster.model_to_string()).free_dataset()
return booster
......
......@@ -1023,7 +1023,7 @@ def test_pandas_categorical():
gbm4 = lgb.Booster(model_file='categorical.model')
pred4 = gbm4.predict(X_test)
model_str = gbm4.model_to_string()
gbm4.model_from_string(model_str, False)
gbm4.model_from_string(model_str)
pred5 = gbm4.predict(X_test)
gbm5 = lgb.Booster(model_str=model_str)
pred6 = gbm5.predict(X_test)
......@@ -2146,7 +2146,7 @@ def test_model_size():
num_end_spaces = 2**31 - one_tree_size * total_trees
new_model_str = f"{before_tree_sizes}\n\n{trees}{more_trees}{after_trees}{'':{num_end_spaces}}"
assert len(new_model_str) > 2**31
bst.model_from_string(new_model_str, verbose=False)
bst.model_from_string(new_model_str)
assert bst.num_trees() == total_trees
y_pred_new = bst.predict(X, num_iteration=2)
np.testing.assert_allclose(y_pred, y_pred_new)
......
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