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[back port] More general predict proba. (#6817) (#6831)

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* Use `output_margin` for `softmax`.
* Add test for dask binary cls.

Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Jiaming Yuan
2021-04-07 04:46:11 +08:00
committed by GitHub
parent 357a78b3de
commit c6a0bdbb5a
4 changed files with 72 additions and 40 deletions
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5
tests/python-gpu/test_gpu_with_dask.py
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@@ -173,6 +173,7 @@ def run_gpu_hist(
assert tm.non_increasing(history["train"][dataset.metric])
@pytest.mark.skipif(**tm.no_cudf())
def test_boost_from_prediction(local_cuda_cluster: LocalCUDACluster) -> None:
from sklearn.datasets import load_breast_cancer
with Client(local_cuda_cluster) as client:
@@ -201,6 +202,7 @@ class TestDistributedGPU:
@settings(deadline=duration(seconds=120), suppress_health_check=suppress)
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_dask_cuda())
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.parametrize(
"local_cuda_cluster", [{"n_workers": 2}], indirect=["local_cuda_cluster"]
)
@@ -275,7 +277,7 @@ class TestDistributedGPU:
X = dask_cudf.from_dask_dataframe(dd.from_dask_array(X_))
y = dask_cudf.from_dask_dataframe(dd.from_dask_array(y_))
w = dask_cudf.from_dask_dataframe(dd.from_dask_array(w_))
run_dask_classifier(X, y, w, model, client)
run_dask_classifier(X, y, w, model, client, 10)
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_dask_cuda())
@@ -453,6 +455,7 @@ async def run_from_dask_array_asyncio(scheduler_address: str) -> dxgb.TrainRetur
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_dask_cuda())
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.mgpu
def test_with_asyncio(local_cuda_cluster: LocalCUDACluster) -> None:
with Client(local_cuda_cluster) as client: