8942c98054
This PR ensures all DMatrix types have a common interface. * Fix logic in avoiding duplicated DMatrix in sklearn. * Check for consistency between DMatrix types. * Add doc for bounds.
59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
# -*- coding: utf-8 -*-
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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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class TestDeviceQuantileDMatrix:
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def test_dmatrix_numpy_init(self):
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data = np.random.randn(5, 5)
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with pytest.raises(TypeError,
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match='is not supported for DeviceQuantileDMatrix'):
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xgb.DeviceQuantileDMatrix(data, np.ones(5, dtype=np.float64))
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@pytest.mark.skipif(**tm.no_cupy())
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def test_dmatrix_feature_weights(self):
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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):
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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_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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