f15a6d2b19
* [backport] Fix ranking with quantile dmatrix and group weight. (#8762) * backport test utilities.
156 lines
4.7 KiB
Python
156 lines
4.7 KiB
Python
import sys
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import numpy as np
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import pytest
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from hypothesis import given, settings, strategies
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import xgboost as xgb
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sys.path.append("tests/python")
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import test_quantile_dmatrix as tqd
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import testing as tm
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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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@pytest.mark.parametrize(
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"tree_method,max_bin", [
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("hist", 16), ("gpu_hist", 16), ("hist", 64), ("gpu_hist", 64)
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]
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)
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def test_interoperability(self, tree_method: str, max_bin: int) -> 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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# from CPU
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Xy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0], max_bin=max_bin)
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booster_0 = xgb.train(
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{"tree_method": tree_method, "max_bin": max_bin}, Xy, num_boost_round=4
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)
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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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# from GPU
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Xy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0], max_bin=max_bin)
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booster_1 = xgb.train(
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{"tree_method": tree_method, "max_bin": max_bin}, Xy, num_boost_round=4
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)
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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=r"Only.*hist.*"):
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xgb.train(
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{"tree_method": "approx", "max_bin": max_bin}, Xy, num_boost_round=4
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)
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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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@given(
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strategies.integers(1, 1000),
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strategies.integers(1, 100),
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strategies.fractions(0, 0.99),
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)
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@settings(print_blob=True, deadline=None)
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def test_to_csr(self, n_samples, n_features, sparsity) -> None:
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import cupy as cp
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X, y = tm.make_sparse_regression(n_samples, n_features, sparsity, False)
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h_X = X.astype(np.float32)
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csr = h_X
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h_X = X.toarray()
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h_X[h_X == 0] = np.nan
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h_m = xgb.QuantileDMatrix(data=h_X)
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h_ret = h_m.get_data()
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d_X = cp.array(h_X)
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d_m = xgb.QuantileDMatrix(data=d_X, label=y)
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d_ret = d_m.get_data()
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np.testing.assert_equal(csr.indptr, d_ret.indptr)
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np.testing.assert_equal(csr.indices, d_ret.indices)
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np.testing.assert_equal(h_ret.indptr, d_ret.indptr)
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np.testing.assert_equal(h_ret.indices, d_ret.indices)
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booster = xgb.train(
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{"tree_method": "gpu_hist", "predictor": "gpu_predictor"}, dtrain=d_m
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)
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np.testing.assert_allclose(
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booster.predict(d_m),
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booster.predict(xgb.DMatrix(d_m.get_data())),
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atol=1e-6,
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)
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def test_ltr(self) -> None:
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import cupy as cp
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X, y, qid, w = tm.make_ltr(100, 3, 3, 5)
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# make sure GPU is used to run sketching.
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cpX = cp.array(X)
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Xy_qdm = xgb.QuantileDMatrix(cpX, y, qid=qid, weight=w)
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Xy = xgb.DMatrix(X, y, qid=qid, weight=w)
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xgb.train({"tree_method": "gpu_hist", "objective": "rank:ndcg"}, Xy)
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from_dm = xgb.QuantileDMatrix(X, weight=w, ref=Xy)
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from_qdm = xgb.QuantileDMatrix(X, weight=w, ref=Xy_qdm)
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assert tm.predictor_equal(from_qdm, from_dm)
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