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Validate out of range categorical value. (#7576)

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* Use float in CPU categorical set to preserve the input value.
* Check out of range values.
This commit is contained in:
Jiaming Yuan
2022-01-18 20:16:19 +08:00
committed by GitHub
parent d6ea5cc1ed
commit deab0e32ba
8 changed files with 86 additions and 38 deletions
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15
tests/python-gpu/test_gpu_updaters.py
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@@ -60,20 +60,9 @@ class TestGPUUpdaters:
rounds = 4
self.cputest.run_categorical_basic(rows, cols, rounds, cats, "gpu_hist")
@pytest.mark.skipif(**tm.no_cupy())
def test_invalid_categorical(self):
import cupy as cp
rng = np.random.default_rng()
X = rng.normal(loc=0, scale=1, size=1000).reshape(100, 10)
y = rng.normal(loc=0, scale=1, size=100)
# Check is performe during sketching.
Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
with pytest.raises(ValueError):
xgb.train({"tree_method": "gpu_hist"}, Xy)
X, y = cp.array(X), cp.array(y)
with pytest.raises(ValueError):
Xy = xgb.DeviceQuantileDMatrix(X, y, feature_types=["c"] * 10)
self.cputest.run_invalid_category("gpu_hist")
@pytest.mark.skipif(**tm.no_cupy())
@given(parameter_strategy, strategies.integers(1, 20),

35
tests/python/test_updaters.py
View File

@@ -133,6 +133,41 @@ class TestTreeMethod:
w = [0, 0, 1, 0]
model.fit(X, y, sample_weight=w)
def run_invalid_category(self, tree_method: str) -> None:
rng = np.random.default_rng()
# too large
X = rng.integers(low=0, high=4, size=1000).reshape(100, 10)
y = rng.normal(loc=0, scale=1, size=100)
X[13, 7] = np.iinfo(np.int32).max + 1
# Check is performed during sketching.
Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
with pytest.raises(ValueError):
xgb.train({"tree_method": tree_method}, Xy)
X[13, 7] = 16777216
Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
with pytest.raises(ValueError):
xgb.train({"tree_method": tree_method}, Xy)
# mixed positive and negative values
X = rng.normal(loc=0, scale=1, size=1000).reshape(100, 10)
y = rng.normal(loc=0, scale=1, size=100)
Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
with pytest.raises(ValueError):
xgb.train({"tree_method": tree_method}, Xy)
if tree_method == "gpu_hist":
import cupy as cp
X, y = cp.array(X), cp.array(y)
with pytest.raises(ValueError):
Xy = xgb.DeviceQuantileDMatrix(X, y, feature_types=["c"] * 10)
def test_invalid_category(self) -> None:
self.run_invalid_category("approx")
def run_categorical_basic(self, rows, cols, rounds, cats, tree_method):
onehot, label = tm.make_categorical(rows, cols, cats, True)
cat, _ = tm.make_categorical(rows, cols, cats, False)