90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
import numpy as np
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import xgboost as xgb
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import pytest
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import sys
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sys.path.append("tests/python")
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import testing as tm
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import test_quantile_dmatrix as tqd
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class TestDeviceQuantileDMatrix:
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cputest = tqd.TestQuantileDMatrix()
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@pytest.mark.skipif(**tm.no_cupy())
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def test_dmatrix_feature_weights(self) -> None:
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import cupy as cp
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rng = cp.random.RandomState(1994)
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data = rng.randn(5, 5)
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m = xgb.DMatrix(data)
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feature_weights = rng.uniform(size=5)
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m.set_info(feature_weights=feature_weights)
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cp.testing.assert_array_equal(
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cp.array(m.get_float_info('feature_weights')),
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feature_weights.astype(np.float32))
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@pytest.mark.skipif(**tm.no_cupy())
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def test_dmatrix_cupy_init(self) -> None:
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import cupy as cp
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data = cp.random.randn(5, 5)
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xgb.DeviceQuantileDMatrix(data, cp.ones(5, dtype=np.float64))
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@pytest.mark.skipif(**tm.no_cupy())
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def test_from_host(self) -> None:
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import cupy as cp
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n_samples = 64
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n_features = 3
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X, y, w = tm.make_batches(
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n_samples, n_features=n_features, n_batches=1, use_cupy=False
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)
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Xy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0])
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booster_0 = xgb.train({"tree_method": "gpu_hist"}, Xy, num_boost_round=4)
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X[0] = cp.array(X[0])
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y[0] = cp.array(y[0])
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w[0] = cp.array(w[0])
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Xy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0])
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booster_1 = xgb.train({"tree_method": "gpu_hist"}, Xy, num_boost_round=4)
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cp.testing.assert_allclose(
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booster_0.inplace_predict(X[0]), booster_1.inplace_predict(X[0])
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)
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with pytest.raises(ValueError, match="not initialized with CPU"):
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# Training on CPU with GPU data is not supported.
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xgb.train({"tree_method": "hist"}, Xy, num_boost_round=4)
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with pytest.raises(ValueError, match=r"Only.*hist.*"):
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xgb.train({"tree_method": "approx"}, Xy, num_boost_round=4)
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@pytest.mark.skipif(**tm.no_cupy())
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def test_metainfo(self) -> None:
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import cupy as cp
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rng = cp.random.RandomState(1994)
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rows = 10
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cols = 3
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data = rng.randn(rows, cols)
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labels = rng.randn(rows)
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fw = rng.randn(rows)
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fw -= fw.min()
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m = xgb.DeviceQuantileDMatrix(data=data, label=labels, feature_weights=fw)
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got_fw = m.get_float_info("feature_weights")
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got_labels = m.get_label()
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cp.testing.assert_allclose(fw, got_fw)
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cp.testing.assert_allclose(labels, got_labels)
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@pytest.mark.skipif(**tm.no_cupy())
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@pytest.mark.skipif(**tm.no_cudf())
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def test_ref_dmatrix(self) -> None:
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import cupy as cp
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rng = cp.random.RandomState(1994)
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self.cputest.run_ref_dmatrix(rng, "gpu_hist", False)
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