xgboost_lib-sys 3.0.5

Native bindings to the xgboost library
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247

import sys

import numpy as np
import pytest
from hypothesis import given, settings, strategies

import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.data import check_inf
from xgboost.testing.data_iter import run_mixed_sparsity
from xgboost.testing.quantile_dmatrix import (
    check_categorical_strings,
    check_ref_quantile_cut,
)

sys.path.append("tests/python")
import test_quantile_dmatrix as tqd


class TestQuantileDMatrix:
    cputest = tqd.TestQuantileDMatrix()

    @pytest.mark.skipif(**tm.no_cupy())
    def test_dmatrix_feature_weights(self) -> None:
        import cupy as cp

        rng = cp.random.RandomState(np.uint64(1994))
        data = rng.randn(5, 5)
        m = xgb.DMatrix(data)

        feature_weights = rng.uniform(size=5)
        m.set_info(feature_weights=feature_weights)

        cp.testing.assert_array_equal(
            cp.array(m.get_float_info("feature_weights")),
            feature_weights.astype(np.float32),
        )

    def test_categorical_strings(self) -> None:
        check_categorical_strings("cuda")

    @pytest.mark.skipif(**tm.no_cupy())
    def test_dmatrix_cupy_init(self) -> None:
        import cupy as cp

        data = cp.random.randn(5, 5)
        xgb.QuantileDMatrix(data, cp.ones(5, dtype=np.float64))

    @pytest.mark.parametrize(
        "on_device,tree_method",
        [(True, "hist"), (False, "gpu_hist"), (False, "hist"), (True, "gpu_hist")],
    )
    def test_initialization(self, on_device: bool, tree_method: str) -> None:
        n_samples, n_features, max_bin = 64, 3, 16
        X, y, w = tm.make_batches(
            n_samples,
            n_features=n_features,
            n_batches=1,
            use_cupy=on_device,
        )

        # Init SparsePage
        Xy = xgb.DMatrix(X[0], y[0], weight=w[0])
        # Init GIDX/Ellpack
        xgb.train(
            {"tree_method": tree_method, "max_bin": max_bin},
            Xy,
            num_boost_round=1,
        )
        # query cuts from GIDX/Ellpack
        qXy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0], max_bin=max_bin, ref=Xy)
        tm.predictor_equal(Xy, qXy)
        with pytest.raises(ValueError, match="Inconsistent"):
            # max_bin changed.
            xgb.QuantileDMatrix(X[0], y[0], weight=w[0], max_bin=max_bin - 1, ref=Xy)

        # No error, DMatrix can be modified for different training session.
        xgb.train(
            {"tree_method": tree_method, "max_bin": max_bin - 1},
            Xy,
            num_boost_round=1,
        )

        # Init Ellpack/GIDX
        Xy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0], max_bin=max_bin)
        # Init GIDX/Ellpack
        xgb.train(
            {"tree_method": tree_method, "max_bin": max_bin},
            Xy,
            num_boost_round=1,
        )
        # query cuts from GIDX/Ellpack
        qXy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0], max_bin=max_bin, ref=Xy)
        tm.predictor_equal(Xy, qXy)
        with pytest.raises(ValueError, match="Inconsistent"):
            # max_bin changed.
            xgb.QuantileDMatrix(X[0], y[0], weight=w[0], max_bin=max_bin - 1, ref=Xy)

        Xy = xgb.DMatrix(X[0], y[0], weight=w[0])
        booster0 = xgb.train(
            {"tree_method": "hist", "max_bin": max_bin, "max_depth": 4},
            Xy,
            num_boost_round=1,
        )
        booster1 = xgb.train(
            {"tree_method": "gpu_hist", "max_bin": max_bin, "max_depth": 4},
            Xy,
            num_boost_round=1,
        )
        qXy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0], max_bin=max_bin, ref=Xy)
        predt0 = booster0.predict(qXy)
        predt1 = booster1.predict(qXy)
        np.testing.assert_allclose(predt0, predt1)

    @pytest.mark.skipif(**tm.no_cupy())
    @pytest.mark.parametrize(
        "tree_method,max_bin",
        [("hist", 16), ("gpu_hist", 16), ("hist", 64), ("gpu_hist", 64)],
    )
    def test_interoperability(self, tree_method: str, max_bin: int) -> None:
        import cupy as cp

        n_samples = 64
        n_features = 3
        X, y, w = tm.make_batches(
            n_samples, n_features=n_features, n_batches=1, use_cupy=False
        )
        # from CPU
        Xy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0], max_bin=max_bin)
        booster_0 = xgb.train(
            {"tree_method": tree_method, "max_bin": max_bin}, Xy, num_boost_round=4
        )

        X[0] = cp.array(X[0])
        y[0] = cp.array(y[0])
        w[0] = cp.array(w[0])

        # from GPU
        Xy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0], max_bin=max_bin)
        booster_1 = xgb.train(
            {"tree_method": tree_method, "max_bin": max_bin}, Xy, num_boost_round=4
        )
        cp.testing.assert_allclose(
            booster_0.inplace_predict(X[0]), booster_1.inplace_predict(X[0])
        )

        with pytest.raises(ValueError, match=r"Only.*hist.*"):
            xgb.train(
                {"tree_method": "approx", "max_bin": max_bin}, Xy, num_boost_round=4
            )

    def test_ref_quantile_cut(self) -> None:
        check_ref_quantile_cut("cuda")

    @pytest.mark.skipif(**tm.no_cupy())
    def test_metainfo(self) -> None:
        import cupy as cp

        rng = cp.random.RandomState(np.uint64(1994))

        rows = 10
        cols = 3
        data = rng.randn(rows, cols)

        labels = rng.randn(rows)

        fw = rng.randn(rows)
        fw -= fw.min()

        m = xgb.QuantileDMatrix(data=data, label=labels, feature_weights=fw)

        got_fw = m.get_float_info("feature_weights")
        got_labels = m.get_label()

        cp.testing.assert_allclose(fw, got_fw)
        cp.testing.assert_allclose(labels, got_labels)

    @pytest.mark.skipif(**tm.no_cupy())
    @pytest.mark.skipif(**tm.no_cudf())
    def test_ref_dmatrix(self) -> None:
        import cupy as cp

        rng = cp.random.RandomState(np.uint64(1994))
        self.cputest.run_ref_dmatrix(rng, "cuda", False)

    @given(
        strategies.integers(1, 1000),
        strategies.integers(1, 100),
        strategies.fractions(0, 0.99),
    )
    @settings(print_blob=True, deadline=None)
    def test_to_csr(self, n_samples, n_features, sparsity) -> None:
        import cupy as cp

        X, y = tm.make_sparse_regression(n_samples, n_features, sparsity, False)
        h_X = X.astype(np.float32)

        csr = h_X
        h_X = X.toarray()
        h_X[h_X == 0] = np.nan

        h_m = xgb.QuantileDMatrix(data=h_X)
        h_ret = h_m.get_data()

        d_X = cp.array(h_X)

        d_m = xgb.QuantileDMatrix(data=d_X, label=y)
        d_ret = d_m.get_data()

        np.testing.assert_equal(csr.indptr, d_ret.indptr)
        np.testing.assert_equal(csr.indices, d_ret.indices)

        np.testing.assert_equal(h_ret.indptr, d_ret.indptr)
        np.testing.assert_equal(h_ret.indices, d_ret.indices)

        booster = xgb.train({"tree_method": "hist", "device": "cuda:0"}, dtrain=d_m)

        np.testing.assert_allclose(
            booster.predict(d_m),
            booster.predict(xgb.DMatrix(d_m.get_data())),
            atol=1e-6,
        )

    def test_ltr(self) -> None:
        import cupy as cp

        X, y, qid, w = tm.make_ltr(100, 3, 3, 5)
        # make sure GPU is used to run sketching.
        cpX = cp.array(X)
        Xy_qdm = xgb.QuantileDMatrix(cpX, y, qid=qid, weight=w)
        Xy = xgb.DMatrix(X, y, qid=qid, weight=w)
        xgb.train({"tree_method": "gpu_hist", "objective": "rank:ndcg"}, Xy)

        from_dm = xgb.QuantileDMatrix(X, weight=w, ref=Xy)
        from_qdm = xgb.QuantileDMatrix(X, weight=w, ref=Xy_qdm)

        assert tm.predictor_equal(from_qdm, from_dm)

    @pytest.mark.skipif(**tm.no_cupy())
    def test_check_inf(self) -> None:
        import cupy as cp

        rng = cp.random.default_rng(1994)
        check_inf(rng)

    def test_mixed_sparsity(self) -> None:
        run_mixed_sparsity("cuda")