1 /*!
2 * Copyright 2017-2019 XGBoost contributors
3 */
4 #include <gtest/gtest.h>
5 #include <xgboost/objective.h>
6 #include <xgboost/generic_parameters.h>
7 #include <xgboost/json.h>
8 #include "../helpers.h"
9 namespace xgboost {
10
TEST(Objective,DeclareUnifiedTest (LinearRegressionGPair))11 TEST(Objective, DeclareUnifiedTest(LinearRegressionGPair)) {
12 GenericParameter tparam = CreateEmptyGenericParam(GPUIDX);
13 std::vector<std::pair<std::string, std::string>> args;
14
15 std::unique_ptr<ObjFunction> obj {
16 ObjFunction::Create("reg:squarederror", &tparam)
17 };
18
19 obj->Configure(args);
20 CheckObjFunction(obj,
21 {0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
22 {0, 0, 0, 0, 1, 1, 1, 1},
23 {1, 1, 1, 1, 1, 1, 1, 1},
24 {0, 0.1f, 0.9f, 1.0f, -1.0f, -0.9f, -0.1f, 0},
25 {1, 1, 1, 1, 1, 1, 1, 1});
26 CheckObjFunction(obj,
27 {0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
28 {0, 0, 0, 0, 1, 1, 1, 1},
29 {}, // empty weight
30 {0, 0.1f, 0.9f, 1.0f, -1.0f, -0.9f, -0.1f, 0},
31 {1, 1, 1, 1, 1, 1, 1, 1});
32 ASSERT_NO_THROW(obj->DefaultEvalMetric());
33 }
34
TEST(Objective,DeclareUnifiedTest (SquaredLog))35 TEST(Objective, DeclareUnifiedTest(SquaredLog)) {
36 GenericParameter tparam = CreateEmptyGenericParam(GPUIDX);
37 std::vector<std::pair<std::string, std::string>> args;
38
39 std::unique_ptr<ObjFunction> obj { ObjFunction::Create("reg:squaredlogerror", &tparam) };
40 obj->Configure(args);
41 CheckConfigReload(obj, "reg:squaredlogerror");
42
43 CheckObjFunction(obj,
44 {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
45 {1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
46 {1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
47 {-0.5435f, -0.4257f, -0.25475f, -0.05855f, 0.1009f},
48 { 1.3205f, 1.0492f, 0.69215f, 0.34115f, 0.1091f});
49 CheckObjFunction(obj,
50 {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
51 {1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
52 {}, // empty weights
53 {-0.5435f, -0.4257f, -0.25475f, -0.05855f, 0.1009f},
54 { 1.3205f, 1.0492f, 0.69215f, 0.34115f, 0.1091f});
55 ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"rmsle"});
56 }
57
TEST(Objective,DeclareUnifiedTest (PseudoHuber))58 TEST(Objective, DeclareUnifiedTest(PseudoHuber)) {
59 GenericParameter tparam = CreateEmptyGenericParam(GPUIDX);
60 std::vector<std::pair<std::string, std::string>> args;
61
62 std::unique_ptr<ObjFunction> obj { ObjFunction::Create("reg:pseudohubererror", &tparam) };
63 obj->Configure(args);
64 CheckConfigReload(obj, "reg:pseudohubererror");
65
66 CheckObjFunction(obj,
67 {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
68 {1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
69 {1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
70 {-0.668965f, -0.624695f, -0.514496f, -0.196116f, 0.514496f}, // out_grad
71 { 0.410660f, 0.476140f, 0.630510f, 0.9428660f, 0.630510f}); // out_hess
72 CheckObjFunction(obj,
73 {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
74 {1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
75 {}, // empty weights
76 {-0.668965f, -0.624695f, -0.514496f, -0.196116f, 0.514496f}, // out_grad
77 { 0.410660f, 0.476140f, 0.630510f, 0.9428660f, 0.630510f}); // out_hess
78 ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"mphe"});
79 }
80
TEST(Objective,DeclareUnifiedTest (LogisticRegressionGPair))81 TEST(Objective, DeclareUnifiedTest(LogisticRegressionGPair)) {
82 GenericParameter tparam = CreateEmptyGenericParam(GPUIDX);
83 std::vector<std::pair<std::string, std::string>> args;
84 std::unique_ptr<ObjFunction> obj { ObjFunction::Create("reg:logistic", &tparam) };
85
86 obj->Configure(args);
87 CheckConfigReload(obj, "reg:logistic");
88
89 CheckObjFunction(obj,
90 { 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1}, // preds
91 { 0, 0, 0, 0, 1, 1, 1, 1}, // labels
92 { 1, 1, 1, 1, 1, 1, 1, 1}, // weights
93 { 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f}, // out_grad
94 {0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f}); // out_hess
95 }
96
TEST(Objective,DeclareUnifiedTest (LogisticRegressionBasic))97 TEST(Objective, DeclareUnifiedTest(LogisticRegressionBasic)) {
98 GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
99 std::vector<std::pair<std::string, std::string>> args;
100 std::unique_ptr<ObjFunction> obj {
101 ObjFunction::Create("reg:logistic", &lparam)
102 };
103
104 obj->Configure(args);
105 CheckConfigReload(obj, "reg:logistic");
106
107 // test label validation
108 EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {10}, {1}, {0}, {0}))
109 << "Expected error when label not in range [0,1f] for LogisticRegression";
110
111 // test ProbToMargin
112 EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.197f, 0.01f);
113 EXPECT_NEAR(obj->ProbToMargin(0.5f), 0, 0.01f);
114 EXPECT_NEAR(obj->ProbToMargin(0.9f), 2.197f, 0.01f);
115 EXPECT_ANY_THROW(obj->ProbToMargin(10))
116 << "Expected error when base_score not in range [0,1f] for LogisticRegression";
117
118 // test PredTransform
119 HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
120 std::vector<bst_float> out_preds = {0.5f, 0.524f, 0.622f, 0.710f, 0.731f};
121 obj->PredTransform(&io_preds);
122 auto& preds = io_preds.HostVector();
123 for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
124 EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
125 }
126 }
127
TEST(Objective,DeclareUnifiedTest (LogisticRawGPair))128 TEST(Objective, DeclareUnifiedTest(LogisticRawGPair)) {
129 GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
130 std::vector<std::pair<std::string, std::string>> args;
131 std::unique_ptr<ObjFunction> obj {
132 ObjFunction::Create("binary:logitraw", &lparam)
133 };
134
135 obj->Configure(args);
136
137 CheckObjFunction(obj,
138 { 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
139 { 0, 0, 0, 0, 1, 1, 1, 1},
140 { 1, 1, 1, 1, 1, 1, 1, 1},
141 { 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
142 {0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
143 }
144
TEST(Objective,DeclareUnifiedTest (PoissonRegressionGPair))145 TEST(Objective, DeclareUnifiedTest(PoissonRegressionGPair)) {
146 GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
147 std::vector<std::pair<std::string, std::string>> args;
148 std::unique_ptr<ObjFunction> obj {
149 ObjFunction::Create("count:poisson", &lparam)
150 };
151
152 args.emplace_back(std::make_pair("max_delta_step", "0.1f"));
153 obj->Configure(args);
154
155 CheckObjFunction(obj,
156 { 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
157 { 0, 0, 0, 0, 1, 1, 1, 1},
158 { 1, 1, 1, 1, 1, 1, 1, 1},
159 { 1, 1.10f, 2.45f, 2.71f, 0, 0.10f, 1.45f, 1.71f},
160 {1.10f, 1.22f, 2.71f, 3.00f, 1.10f, 1.22f, 2.71f, 3.00f});
161 CheckObjFunction(obj,
162 { 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
163 { 0, 0, 0, 0, 1, 1, 1, 1},
164 {}, // Empty weight
165 { 1, 1.10f, 2.45f, 2.71f, 0, 0.10f, 1.45f, 1.71f},
166 {1.10f, 1.22f, 2.71f, 3.00f, 1.10f, 1.22f, 2.71f, 3.00f});
167 }
168
TEST(Objective,DeclareUnifiedTest (PoissonRegressionBasic))169 TEST(Objective, DeclareUnifiedTest(PoissonRegressionBasic)) {
170 GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
171 std::vector<std::pair<std::string, std::string>> args;
172 std::unique_ptr<ObjFunction> obj {
173 ObjFunction::Create("count:poisson", &lparam)
174 };
175
176 obj->Configure(args);
177 CheckConfigReload(obj, "count:poisson");
178
179 // test label validation
180 EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
181 << "Expected error when label < 0 for PoissonRegression";
182
183 // test ProbToMargin
184 EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
185 EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
186 EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
187
188 // test PredTransform
189 HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
190 std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
191 obj->PredTransform(&io_preds);
192 auto& preds = io_preds.HostVector();
193 for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
194 EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
195 }
196 }
197
TEST(Objective,DeclareUnifiedTest (GammaRegressionGPair))198 TEST(Objective, DeclareUnifiedTest(GammaRegressionGPair)) {
199 GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
200 std::vector<std::pair<std::string, std::string>> args;
201 std::unique_ptr<ObjFunction> obj {
202 ObjFunction::Create("reg:gamma", &lparam)
203 };
204
205 obj->Configure(args);
206 CheckObjFunction(obj,
207 {0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
208 {2, 2, 2, 2, 1, 1, 1, 1},
209 {1, 1, 1, 1, 1, 1, 1, 1},
210 {-1, -0.809, 0.187, 0.264, 0, 0.09f, 0.59f, 0.63f},
211 {2, 1.809, 0.813, 0.735, 1, 0.90f, 0.40f, 0.36f});
212 CheckObjFunction(obj,
213 {0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
214 {2, 2, 2, 2, 1, 1, 1, 1},
215 {}, // Empty weight
216 {-1, -0.809, 0.187, 0.264, 0, 0.09f, 0.59f, 0.63f},
217 {2, 1.809, 0.813, 0.735, 1, 0.90f, 0.40f, 0.36f});
218 }
219
TEST(Objective,DeclareUnifiedTest (GammaRegressionBasic))220 TEST(Objective, DeclareUnifiedTest(GammaRegressionBasic)) {
221 GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
222 std::vector<std::pair<std::string, std::string>> args;
223 std::unique_ptr<ObjFunction> obj {
224 ObjFunction::Create("reg:gamma", &lparam)
225 };
226
227 obj->Configure(args);
228 CheckConfigReload(obj, "reg:gamma");
229
230 // test label validation
231 EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {0}, {1}, {0}, {0}))
232 << "Expected error when label = 0 for GammaRegression";
233 EXPECT_ANY_THROW(CheckObjFunction(obj, {-1}, {-1}, {1}, {-1}, {-3}))
234 << "Expected error when label < 0 for GammaRegression";
235
236 // test ProbToMargin
237 EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
238 EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
239 EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
240
241 // test PredTransform
242 HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
243 std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
244 obj->PredTransform(&io_preds);
245 auto& preds = io_preds.HostVector();
246 for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
247 EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
248 }
249 }
250
TEST(Objective,DeclareUnifiedTest (TweedieRegressionGPair))251 TEST(Objective, DeclareUnifiedTest(TweedieRegressionGPair)) {
252 GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
253 std::vector<std::pair<std::string, std::string>> args;
254 std::unique_ptr<ObjFunction> obj {
255 ObjFunction::Create("reg:tweedie", &lparam)
256 };
257
258 args.emplace_back(std::make_pair("tweedie_variance_power", "1.1f"));
259 obj->Configure(args);
260
261 CheckObjFunction(obj,
262 { 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
263 { 0, 0, 0, 0, 1, 1, 1, 1},
264 { 1, 1, 1, 1, 1, 1, 1, 1},
265 { 1, 1.09f, 2.24f, 2.45f, 0, 0.10f, 1.33f, 1.55f},
266 {0.89f, 0.98f, 2.02f, 2.21f, 1, 1.08f, 2.11f, 2.30f});
267 CheckObjFunction(obj,
268 { 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
269 { 0, 0, 0, 0, 1, 1, 1, 1},
270 {}, // Empty weight.
271 { 1, 1.09f, 2.24f, 2.45f, 0, 0.10f, 1.33f, 1.55f},
272 {0.89f, 0.98f, 2.02f, 2.21f, 1, 1.08f, 2.11f, 2.30f});
273 ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"tweedie-nloglik@1.1"});
274 }
275
276 #if defined(__CUDACC__)
TEST(Objective,CPU_vs_CUDA)277 TEST(Objective, CPU_vs_CUDA) {
278 GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
279
280 ObjFunction * obj =
281 ObjFunction::Create("reg:squarederror", &lparam);
282 HostDeviceVector<GradientPair> cpu_out_preds;
283 HostDeviceVector<GradientPair> cuda_out_preds;
284
285 constexpr size_t kRows = 400;
286 constexpr size_t kCols = 100;
287 auto pdmat = RandomDataGenerator(kRows, kCols, 0).Seed(0).GenerateDMatrix();
288 HostDeviceVector<float> preds;
289 preds.Resize(kRows);
290 auto& h_preds = preds.HostVector();
291 for (size_t i = 0; i < h_preds.size(); ++i) {
292 h_preds[i] = static_cast<float>(i);
293 }
294 auto& info = pdmat->Info();
295
296 info.labels_.Resize(kRows);
297 auto& h_labels = info.labels_.HostVector();
298 for (size_t i = 0; i < h_labels.size(); ++i) {
299 h_labels[i] = 1 / (float)(i+1);
300 }
301
302 {
303 // CPU
304 lparam.gpu_id = -1;
305 obj->GetGradient(preds, info, 0, &cpu_out_preds);
306 }
307 {
308 // CUDA
309 lparam.gpu_id = 0;
310 obj->GetGradient(preds, info, 0, &cuda_out_preds);
311 }
312
313 auto& h_cpu_out = cpu_out_preds.HostVector();
314 auto& h_cuda_out = cuda_out_preds.HostVector();
315
316 float sgrad = 0;
317 float shess = 0;
318 for (size_t i = 0; i < kRows; ++i) {
319 sgrad += std::pow(h_cpu_out[i].GetGrad() - h_cuda_out[i].GetGrad(), 2);
320 shess += std::pow(h_cpu_out[i].GetHess() - h_cuda_out[i].GetHess(), 2);
321 }
322 ASSERT_NEAR(sgrad, 0.0f, kRtEps);
323 ASSERT_NEAR(shess, 0.0f, kRtEps);
324
325 delete obj;
326 }
327 #endif
328
TEST(Objective,DeclareUnifiedTest (TweedieRegressionBasic))329 TEST(Objective, DeclareUnifiedTest(TweedieRegressionBasic)) {
330 GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
331 std::vector<std::pair<std::string, std::string>> args;
332 std::unique_ptr<ObjFunction> obj {
333 ObjFunction::Create("reg:tweedie", &lparam)
334 };
335
336 obj->Configure(args);
337 CheckConfigReload(obj, "reg:tweedie");
338
339 // test label validation
340 EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
341 << "Expected error when label < 0 for TweedieRegression";
342
343 // test ProbToMargin
344 EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
345 EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
346 EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
347
348 // test PredTransform
349 HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
350 std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
351 obj->PredTransform(&io_preds);
352 auto& preds = io_preds.HostVector();
353 for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
354 EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
355 }
356 }
357
358 // CoxRegression not implemented in GPU code, no need for testing.
359 #if !defined(__CUDACC__)
TEST(Objective,CoxRegressionGPair)360 TEST(Objective, CoxRegressionGPair) {
361 GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
362 std::vector<std::pair<std::string, std::string>> args;
363 std::unique_ptr<ObjFunction> obj {
364 ObjFunction::Create("survival:cox", &lparam)
365 };
366
367 obj->Configure(args);
368 CheckObjFunction(obj,
369 { 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
370 { 0, -2, -2, 2, 3, 5, -10, 100},
371 { 1, 1, 1, 1, 1, 1, 1, 1},
372 { 0, 0, 0, -0.799f, -0.788f, -0.590f, 0.910f, 1.006f},
373 { 0, 0, 0, 0.160f, 0.186f, 0.348f, 0.610f, 0.639f});
374 }
375 #endif
376
377 } // namespace xgboost
378