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Support multi-class with base margin. (#7381)

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This is already partially supported but never properly tested. So the only possible way to use it is calling `numpy.ndarray.flatten` with `base_margin` before passing it into XGBoost. This PR adds proper support
for most of the data types along with tests.
This commit is contained in:
Jiaming Yuan
2021-11-02 13:38:00 +08:00
committed by GitHub
parent 6295dc3b67
commit a13321148a
18 changed files with 274 additions and 92 deletions
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19
tests/python-gpu/test_gpu_with_sklearn.py
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@@ -35,8 +35,25 @@ def test_gpu_binary_classification():
assert err < 0.1
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.skipif(**tm.no_cudf())
def test_boost_from_prediction_gpu_hist():
twskl.run_boost_from_prediction('gpu_hist')
from sklearn.datasets import load_breast_cancer, load_digits
import cupy as cp
import cudf
tree_method = "gpu_hist"
X, y = load_breast_cancer(return_X_y=True)
X, y = cp.array(X), cp.array(y)
twskl.run_boost_from_prediction_binary(tree_method, X, y, None)
twskl.run_boost_from_prediction_binary(tree_method, X, y, cudf.DataFrame)
X, y = load_digits(return_X_y=True)
X, y = cp.array(X), cp.array(y)
twskl.run_boost_from_prediction_multi_clasas(tree_method, X, y, None)
twskl.run_boost_from_prediction_multi_clasas(tree_method, X, y, cudf.DataFrame)
def test_num_parallel_tree():