I'm using Treelite 2.1.0 via the rapidsai/rapidsai-core-nightly:21.10-cuda11.2-base-ubuntu20.04-py3.8 docker image. In the below code I'd expect to get predictions of [0, 0, 1], as 0 is less than 1, but I get [1, 0, 1] when creating a DMatrix from a dataframe, as the 0 appears to be treated as a missing value. Using data from plain a plain numpy ndarray works as expected.
I can reproduce the pandas behaviour when using numpy by setting missing=0.0, but this parameter seems to have no effect when using Pandas, setting missing=np.nan doesn't help, which is kind of expected as that is supposed to be the default already.
I'm using Treelite 2.1.0 via the
rapidsai/rapidsai-core-nightly:21.10-cuda11.2-base-ubuntu20.04-py3.8
docker image. In the below code I'd expect to get predictions of [0, 0, 1], as 0 is less than 1, but I get [1, 0, 1] when creating a DMatrix from a dataframe, as the 0 appears to be treated as a missing value. Using data from plain a plain numpy ndarray works as expected.I can reproduce the pandas behaviour when using numpy by setting
missing=0.0
, but this parameter seems to have no effect when using Pandas, settingmissing=np.nan
doesn't help, which is kind of expected as that is supposed to be the default already.