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
|
import sys
import unittest
import pytest
import numpy as np
import xgboost as xgb
from hypothesis import given, strategies, assume, settings, note
from xgboost import testing as tm
rng = np.random.RandomState(1994)
shap_parameter_strategy = strategies.fixed_dictionaries(
{
"max_depth": strategies.integers(1, 11),
"max_leaves": strategies.integers(0, 256),
"num_parallel_tree": strategies.sampled_from([1, 10]),
}
).filter(lambda x: x["max_depth"] > 0 or x["max_leaves"] > 0)
class TestSYCLPredict(unittest.TestCase):
def test_predict(self):
iterations = 10
np.random.seed(1)
test_num_rows = [10, 1000, 5000]
test_num_cols = [10, 50, 500]
for num_rows in test_num_rows:
for num_cols in test_num_cols:
dtrain = xgb.DMatrix(
np.random.randn(num_rows, num_cols),
label=[0, 1] * int(num_rows / 2),
)
dval = xgb.DMatrix(
np.random.randn(num_rows, num_cols),
label=[0, 1] * int(num_rows / 2),
)
dtest = xgb.DMatrix(
np.random.randn(num_rows, num_cols),
label=[0, 1] * int(num_rows / 2),
)
watchlist = [(dtrain, "train"), (dval, "validation")]
res = {}
param = {
"objective": "binary:logistic",
"eval_metric": "logloss",
"tree_method": "hist",
"device": "cpu",
"max_depth": 1,
"verbosity": 0,
}
bst = xgb.train(
param, dtrain, iterations, evals=watchlist, evals_result=res
)
assert tm.non_increasing(res["train"]["logloss"])
cpu_pred_train = bst.predict(dtrain, output_margin=True)
cpu_pred_test = bst.predict(dtest, output_margin=True)
cpu_pred_val = bst.predict(dval, output_margin=True)
bst.set_param({"device": "sycl"})
sycl_pred_train = bst.predict(dtrain, output_margin=True)
sycl_pred_test = bst.predict(dtest, output_margin=True)
sycl_pred_val = bst.predict(dval, output_margin=True)
np.testing.assert_allclose(cpu_pred_train, sycl_pred_train, rtol=1e-6)
np.testing.assert_allclose(cpu_pred_val, sycl_pred_val, rtol=1e-6)
np.testing.assert_allclose(cpu_pred_test, sycl_pred_test, rtol=1e-6)
@pytest.mark.skipif(**tm.no_sklearn())
def test_multi_predict(self):
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
n = 1000
X, y = make_regression(n, random_state=rng)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test)
params = {}
params["tree_method"] = "hist"
params["device"] = "cpu"
bst = xgb.train(params, dtrain)
cpu_predict = bst.predict(dtest)
bst.set_param({"device": "sycl"})
predict0 = bst.predict(dtest)
predict1 = bst.predict(dtest)
assert np.allclose(predict0, predict1)
assert np.allclose(predict0, cpu_predict)
@pytest.mark.skipif(**tm.no_sklearn())
def test_sklearn(self):
m, n = 15000, 14
tr_size = 2500
X = np.random.rand(m, n)
y = 200 * np.matmul(X, np.arange(-3, -3 + n))
X_train, y_train = X[:tr_size, :], y[:tr_size]
X_test, y_test = X[tr_size:, :], y[tr_size:]
# First with cpu_predictor
params = {
"tree_method": "hist",
"device": "cpu",
"n_jobs": -1,
"verbosity": 0,
"seed": 123,
}
m = xgb.XGBRegressor(**params).fit(X_train, y_train)
cpu_train_score = m.score(X_train, y_train)
cpu_test_score = m.score(X_test, y_test)
# Now with sycl_predictor
params["device"] = "sycl"
m.set_params(**params)
sycl_train_score = m.score(X_train, y_train)
sycl_test_score = m.score(X_test, y_test)
assert np.allclose(cpu_train_score, sycl_train_score)
assert np.allclose(cpu_test_score, sycl_test_score)
@given(
strategies.integers(1, 10), tm.make_dataset_strategy(), shap_parameter_strategy
)
@settings(deadline=None)
def test_shap(self, num_rounds, dataset, param):
if dataset.name.endswith("-l1"): # not supported by the exact tree method
return
param.update({"tree_method": "hist", "device": "cpu"})
param = dataset.set_params(param)
dmat = dataset.get_dmat()
bst = xgb.train(param, dmat, num_rounds)
test_dmat = xgb.DMatrix(dataset.X, dataset.y, dataset.w, dataset.margin)
bst.set_param({"device": "sycl"})
shap = bst.predict(test_dmat, pred_contribs=True)
margin = bst.predict(test_dmat, output_margin=True)
assume(len(dataset.y) > 0)
assert np.allclose(np.sum(shap, axis=len(shap.shape) - 1), margin, 1e-3, 1e-3)
@given(
strategies.integers(1, 10), tm.make_dataset_strategy(), shap_parameter_strategy
)
@settings(deadline=None, max_examples=20)
def test_shap_interactions(self, num_rounds, dataset, param):
if dataset.name.endswith("-l1"): # not supported by the exact tree method
return
param.update({"tree_method": "hist", "device": "cpu"})
param = dataset.set_params(param)
dmat = dataset.get_dmat()
bst = xgb.train(param, dmat, num_rounds)
test_dmat = xgb.DMatrix(dataset.X, dataset.y, dataset.w, dataset.margin)
bst.set_param({"device": "sycl"})
shap = bst.predict(test_dmat, pred_interactions=True)
margin = bst.predict(test_dmat, output_margin=True)
assume(len(dataset.y) > 0)
assert np.allclose(
np.sum(shap, axis=(len(shap.shape) - 1, len(shap.shape) - 2)),
margin,
1e-3,
1e-3,
)
|
