зеркало из https://github.com/microsoft/LightGBM.git
Родитель
23403a7c2c
Коммит
02d212b4c0
|
@ -76,13 +76,16 @@ if [[ $TASK == "lint" ]]; then
|
|||
conda install -q -y -n $CONDA_ENV \
|
||||
cmakelint \
|
||||
cpplint \
|
||||
flake8 \
|
||||
isort \
|
||||
mypy \
|
||||
pycodestyle \
|
||||
pydocstyle \
|
||||
"r-lintr>=3.0"
|
||||
echo "Linting Python code"
|
||||
pycodestyle --ignore=E501,W503 --exclude=./.nuget,./external_libs . || exit -1
|
||||
flake8 \
|
||||
--ignore=E501,W503 \
|
||||
--exclude=./.nuget,./external_libs,./python-package/build \
|
||||
. || exit -1
|
||||
pydocstyle --convention=numpy --add-ignore=D105 --match-dir="^(?!^external_libs|test|example).*" --match="(?!^test_|setup).*\.py" . || exit -1
|
||||
isort . --check-only || exit -1
|
||||
mypy --ignore-missing-imports python-package/ || true
|
||||
|
|
|
@ -105,7 +105,7 @@ autodoc_mock_imports = [
|
|||
'scipy.sparse',
|
||||
]
|
||||
try:
|
||||
import sklearn
|
||||
import sklearn # noqa: F401
|
||||
except ImportError:
|
||||
autodoc_mock_imports.append('sklearn')
|
||||
# hide type hints in API docs
|
||||
|
|
|
@ -36,14 +36,14 @@ except ImportError:
|
|||
|
||||
"""matplotlib"""
|
||||
try:
|
||||
import matplotlib
|
||||
import matplotlib # noqa: F401
|
||||
MATPLOTLIB_INSTALLED = True
|
||||
except ImportError:
|
||||
MATPLOTLIB_INSTALLED = False
|
||||
|
||||
"""graphviz"""
|
||||
try:
|
||||
import graphviz
|
||||
import graphviz # noqa: F401
|
||||
GRAPHVIZ_INSTALLED = True
|
||||
except ImportError:
|
||||
GRAPHVIZ_INSTALLED = False
|
||||
|
|
|
@ -96,7 +96,7 @@ def silent_call(cmd: List[str], raise_error: bool = False, error_msg: str = '')
|
|||
with open(LOG_PATH, "ab") as log:
|
||||
subprocess.check_call(cmd, stderr=log, stdout=log)
|
||||
return 0
|
||||
except Exception as err:
|
||||
except Exception:
|
||||
if raise_error:
|
||||
raise Exception("\n".join((error_msg, LOG_NOTICE)))
|
||||
return 1
|
||||
|
|
|
@ -3,7 +3,7 @@ import pytest
|
|||
|
||||
import lightgbm as lgb
|
||||
|
||||
from .utils import SERIALIZERS, pickle_and_unpickle_object, pickle_obj, unpickle_obj
|
||||
from .utils import SERIALIZERS, pickle_and_unpickle_object
|
||||
|
||||
|
||||
def reset_feature_fraction(boosting_round):
|
||||
|
|
|
@ -1723,7 +1723,7 @@ def test_dask_methods_and_sklearn_equivalents_have_similar_signatures(methods):
|
|||
|
||||
@pytest.mark.parametrize('task', tasks)
|
||||
def test_training_succeeds_when_data_is_dataframe_and_label_is_column_array(task, cluster):
|
||||
with Client(cluster) as client:
|
||||
with Client(cluster):
|
||||
_, _, _, _, dX, dy, dw, dg = _create_data(
|
||||
objective=task,
|
||||
output='dataframe',
|
||||
|
@ -1802,7 +1802,7 @@ def _tested_estimators():
|
|||
@pytest.mark.parametrize("estimator", _tested_estimators())
|
||||
@pytest.mark.parametrize("check", sklearn_checks_to_run())
|
||||
def test_sklearn_integration(estimator, check, cluster):
|
||||
with Client(cluster) as client:
|
||||
with Client(cluster):
|
||||
estimator.set_params(local_listen_port=18000, time_out=5)
|
||||
name = type(estimator).__name__
|
||||
check(name, estimator)
|
||||
|
|
|
@ -2903,7 +2903,7 @@ def test_forced_split_feature_indices(tmp_path):
|
|||
"forcedsplits_filename": tmp_split_file
|
||||
}
|
||||
with pytest.raises(lgb.basic.LightGBMError, match="Forced splits file includes feature index"):
|
||||
bst = lgb.train(params, lgb_train)
|
||||
lgb.train(params, lgb_train)
|
||||
|
||||
|
||||
def test_forced_bins():
|
||||
|
|
|
@ -445,9 +445,19 @@ def test_clone_and_property():
|
|||
gbm.fit(X, y)
|
||||
|
||||
gbm_clone = clone(gbm)
|
||||
|
||||
# original estimator is unaffected
|
||||
assert gbm.n_estimators == 10
|
||||
assert gbm.verbose == -1
|
||||
assert isinstance(gbm.booster_, lgb.Booster)
|
||||
assert isinstance(gbm.feature_importances_, np.ndarray)
|
||||
|
||||
# new estimator is unfitted, but has the same parameters
|
||||
assert gbm_clone.__sklearn_is_fitted__() is False
|
||||
assert gbm_clone.n_estimators == 10
|
||||
assert gbm_clone.verbose == -1
|
||||
assert gbm_clone.get_params() == gbm.get_params()
|
||||
|
||||
X, y = load_digits(n_class=2, return_X_y=True)
|
||||
clf = lgb.LGBMClassifier(n_estimators=10, verbose=-1)
|
||||
clf.fit(X, y)
|
||||
|
|
Загрузка…
Ссылка в новой задаче