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[dask] Support all parameters in regressor and classifier. (#6471)

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* Add eval_metric.
* Add callback.
* Add feature weights.
* Add custom objective.
This commit is contained in:
Jiaming Yuan
2020-12-14 07:35:56 +08:00
committed by GitHub
parent c31e3efa7c
commit a30461cf87
5 changed files with 348 additions and 91 deletions
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37
tests/python-gpu/test_gpu_with_dask.py
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@@ -184,6 +184,43 @@ class TestDistributedGPU:
run_with_dask_array(dxgb.DaskDMatrix, client)
run_with_dask_array(dxgb.DaskDeviceQuantileDMatrix, client)
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_dask_cuda())
def test_early_stopping(self, local_cuda_cluster):
from sklearn.datasets import load_breast_cancer
import cupy
with Client(local_cuda_cluster) as client:
X, y = load_breast_cancer(return_X_y=True)
X, y = da.from_array(X), da.from_array(y)
m = dxgb.DaskDMatrix(client, X, y)
valid = dxgb.DaskDMatrix(client, X, y)
early_stopping_rounds = 5
booster = dxgb.train(client, {'objective': 'binary:logistic',
'eval_metric': 'error',
'tree_method': 'gpu_hist'}, m,
evals=[(valid, 'Valid')],
num_boost_round=1000,
early_stopping_rounds=early_stopping_rounds)[
'booster']
assert hasattr(booster, 'best_score')
dump = booster.get_dump(dump_format='json')
assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
valid_X = X
valid_y = y
cls = dxgb.DaskXGBClassifier(objective='binary:logistic',
tree_method='gpu_hist',
n_estimators=100)
cls.client = client
cls.fit(X, y, early_stopping_rounds=early_stopping_rounds,
eval_set=[(valid_X, valid_y)])
booster = cls.get_booster()
dump = booster.get_dump(dump_format='json')
assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_dask_cuda())
@pytest.mark.mgpu