def train_result(param, dmat: xgb.DMatrix, num_rounds: int) -> dict:
result: xgb.callback.TrainingCallback.EvalsLog = {}
booster = xgb.train(
param,
dmat,
num_rounds,
[(dmat, "train")],
verbose_eval=False,
evals_result=result,
)
assert booster.num_features() == dmat.num_col()
assert booster.num_boosted_rounds() == num_rounds
return result
def dmatrix_to_numpy(dmatrix: xgb.DMatrix) -> np.ndarray:
"""Convert DMatrix to 2d numpy array
Parameters
----------
dmatrix : xgb.DMatrix
DMatrix to convert
Returns
-------
np.ndarray
2d numpy array with the corresponding DMatrix feature values
Raises
------
InvalidInput
Input is not a valid DMatrix
"""
if not isinstance(dmatrix, xgb.DMatrix):
raise InvalidInput("Type error: input parameter is not DMatrix")
stream_parser = DMatrixStreamParserV0_80 if version.parse(xgb.__version__) < version.parse('1.0.0') \
else DMatrixStreamParserV1_0_0
# We set delete=False to avoid permissions error. This way, file can be accessed
# by XGBoost without being deleted while handle is closed
try:
with tempfile.NamedTemporaryFile(delete=False) as fp:
dmatrix.save_binary(fp.name)
result = stream_parser(fp, dmatrix.num_row(),
dmatrix.num_col()).parse()
finally:
# We can safely remove the temp file now, parsing process finished
with suppress(OSError):
os.remove(fp.name)
return result
