def test_list_to_1d_numpy(collection, dtype):
collection2y = {
'1d_np': np.random.rand(10),
'2d_np': np.random.rand(10, 1),
'pd_float': np.random.rand(10),
'pd_str': ['a', 'b'],
'1d_list': [1] * 10,
'2d_list': [[1], [2]],
}
y = collection2y[collection]
if collection.startswith('pd'):
if not PANDAS_INSTALLED:
pytest.skip('pandas is not installed')
else:
y = pd_Series(y)
if isinstance(y, np.ndarray) and len(y.shape) == 2:
with pytest.warns(UserWarning, match='column-vector'):
lgb.basic.list_to_1d_numpy(y)
return
elif isinstance(y, list) and isinstance(y[0], list):
with pytest.raises(TypeError):
lgb.basic.list_to_1d_numpy(y)
return
elif isinstance(y, pd_Series) and y.dtype == object:
with pytest.raises(ValueError):
lgb.basic.list_to_1d_numpy(y)
return
result = lgb.basic.list_to_1d_numpy(y, dtype=dtype)
assert result.size == 10
assert result.dtype == dtype
# all changes should be made on copies and not modify the original
expected_params = {
"local_listen_port": 1234,
"port": 2222,
"metric": "auc",
"num_trees": 81
}
assert original_params == expected_params
@pytest.mark.skipif(not PANDAS_INSTALLED, reason='pandas is not installed')
@pytest.mark.parametrize('y', [
np.random.rand(10),
np.random.rand(10, 1),
pd_Series(np.random.rand(10)),
pd_Series(['a', 'b']), [1] * 10, [[1], [2]]
])
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
def test_list_to_1d_numpy(y, dtype):
if isinstance(y, np.ndarray) and len(y.shape) == 2:
with pytest.warns(UserWarning, match='column-vector'):
lgb.basic.list_to_1d_numpy(y)
return
elif isinstance(y, list) and isinstance(y[0], list):
with pytest.raises(TypeError):
lgb.basic.list_to_1d_numpy(y)
return
elif isinstance(y, pd_Series) and y.dtype == object:
with pytest.raises(ValueError):
lgb.basic.list_to_1d_numpy(y)
expected_params = {
"local_listen_port": 1234,
"port": 2222,
"metric": "auc",
"num_trees": 81
}
assert original_params == expected_params
@pytest.mark.skipif(not PANDAS_INSTALLED, reason='pandas is not installed')
@pytest.mark.parametrize(
'y',
[
np.random.rand(10),
np.random.rand(10, 1),
pd_Series(np.random.rand(10)),
pd_Series(['a', 'b']),
[1] * 10,
[[1], [2]]
])
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
def test_list_to_1d_numpy(y, dtype):
if isinstance(y, np.ndarray) and len(y.shape) == 2:
with pytest.warns(UserWarning, match='column-vector'):
lgb.basic.list_to_1d_numpy(y)
return
elif isinstance(y, list) and isinstance(y[0], list):
with pytest.raises(TypeError):
lgb.basic.list_to_1d_numpy(y)
return
elif isinstance(y, pd_Series) and y.dtype == object: