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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")
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