File: test_learner.cc

package info (click to toggle)
xgboost 3.0.0-1
  • links: PTS, VCS
  • area: main
  • in suites: trixie
  • size: 13,796 kB
  • sloc: cpp: 67,502; python: 35,503; java: 4,676; ansic: 1,426; sh: 1,320; xml: 1,197; makefile: 204; javascript: 19
file content (837 lines) | stat: -rw-r--r-- 30,327 bytes parent folder | download | duplicates (2)
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
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
/**
 * Copyright 2017-2024, XGBoost contributors
 */
#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include <xgboost/learner.h>         // for Learner
#include <xgboost/logging.h>         // for LogCheck_NE, CHECK_NE, LogCheck_EQ
#include <xgboost/objective.h>       // for ObjFunction
#include <xgboost/version_config.h>  // for XGBOOST_VER_MAJOR, XGBOOST_VER_MINOR

#include <algorithm>                                // for equal, transform
#include <cstddef>                                  // for size_t
#include <iosfwd>                                   // for ofstream
#include <limits>                                   // for numeric_limits
#include <map>                                      // for map
#include <memory>                                   // for unique_ptr, shared_ptr, __shared_ptr_...
#include <random>                                   // for uniform_real_distribution
#include <string>                                   // for allocator, basic_string, string, oper...
#include <thread>                                   // for thread
#include <type_traits>                              // for is_integral
#include <utility>                                  // for pair
#include <vector>                                   // for vector

#include "../../src/collective/communicator-inl.h"  // for GetRank, GetWorldSize
#include "../../src/common/api_entry.h"             // for XGBAPIThreadLocalEntry
#include "../../src/common/io.h"                    // for LoadSequentialFile
#include "../../src/common/linalg_op.h"             // for ElementWiseTransformHost, begin, end
#include "../../src/common/random.h"                // for GlobalRandom
#include "./collective/test_worker.h"               // for TestDistributedGlobal
#include "dmlc/io.h"                                // for Stream
#include "dmlc/omp.h"                               // for omp_get_max_threads
#include "filesystem.h"                             // for TemporaryDirectory
#include "helpers.h"                                // for GetBaseScore, RandomDataGenerator
#include "objective_helpers.h"                      // for MakeObjNamesForTest, ObjTestNameGenerator
#include "xgboost/base.h"                           // for bst_float, Args, bst_feature_t, bst_int
#include "xgboost/context.h"                        // for Context, DeviceOrd
#include "xgboost/data.h"                           // for DMatrix, MetaInfo, DataType
#include "xgboost/host_device_vector.h"             // for HostDeviceVector
#include "xgboost/json.h"                           // for Json, Object, get, String, IsA, opera...
#include "xgboost/linalg.h"                         // for Tensor, TensorView
#include "xgboost/logging.h"                        // for ConsoleLogger
#include "xgboost/predictor.h"                      // for PredictionCacheEntry
#include "xgboost/string_view.h"                    // for StringView

namespace xgboost {
TEST(Learner, Basic) {
  using Arg = std::pair<std::string, std::string>;
  auto args = {Arg("tree_method", "exact")};
  auto mat_ptr = RandomDataGenerator{10, 10, 0.0f}.GenerateDMatrix();
  auto learner = std::unique_ptr<Learner>(Learner::Create({mat_ptr}));
  learner->SetParams(args);


  auto major = XGBOOST_VER_MAJOR;
  auto minor = XGBOOST_VER_MINOR;
  auto patch = XGBOOST_VER_PATCH;

  static_assert(std::is_integral_v<decltype(major)>, "Wrong major version type");
  static_assert(std::is_integral_v<decltype(minor)>, "Wrong minor version type");
  static_assert(std::is_integral_v<decltype(patch)>, "Wrong patch version type");
}

TEST(Learner, ParameterValidation) {
  ConsoleLogger::Configure({{"verbosity", "2"}});
  size_t constexpr kRows = 1;
  size_t constexpr kCols = 1;
  auto p_mat = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix();

  auto learner = std::unique_ptr<Learner>(Learner::Create({p_mat}));
  learner->SetParam("validate_parameters", "1");
  learner->SetParam("Knock-Knock", "Who's-there?");
  learner->SetParam("Silence", "....");
  learner->SetParam("tree_method", "exact");

  testing::internal::CaptureStderr();
  learner->Configure();
  std::string output = testing::internal::GetCapturedStderr();

  ASSERT_TRUE(output.find(R"(Parameters: { "Knock-Knock", "Silence" })") != std::string::npos);

  // whitespace
  learner->SetParam("tree method", "exact");
  ASSERT_THAT([&] { learner->Configure(); }, GMockThrow(R"("tree method" contains whitespace)"));
}

TEST(Learner, CheckGroup) {
  using Arg = std::pair<std::string, std::string>;
  size_t constexpr kNumGroups = 4;
  size_t constexpr kNumRows = 17;
  bst_feature_t constexpr kNumCols = 15;

  std::shared_ptr<DMatrix> p_mat{RandomDataGenerator{kNumRows, kNumCols, 0.0f}.GenerateDMatrix()};
  std::vector<bst_float> weight(kNumGroups, 1);
  std::vector<bst_group_t> group(kNumGroups);
  group[0] = 2;
  group[1] = 3;
  group[2] = 7;
  group[3] = 5;
  std::vector<bst_float> labels (kNumRows);
  for (size_t i = 0; i < kNumRows; ++i) {
    labels[i] = i % 2;
  }

  p_mat->SetInfo("weight", Make1dInterfaceTest(weight.data(), kNumGroups));
  p_mat->SetInfo("group", Make1dInterfaceTest(group.data(), kNumGroups));
  p_mat->SetInfo("label", Make1dInterfaceTest(labels.data(), kNumRows));

  std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {p_mat};
  auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
  learner->SetParams({Arg{"objective", "rank:pairwise"}});
  EXPECT_NO_THROW(learner->UpdateOneIter(0, p_mat));

  group.resize(kNumGroups+1);
  group[3] = 4;
  group[4] = 1;
  p_mat->SetInfo("group", Make1dInterfaceTest(group.data(), kNumGroups+1));
  EXPECT_ANY_THROW(learner->UpdateOneIter(0, p_mat));
}

TEST(Learner, CheckMultiBatch) {
  auto p_fmat =
      RandomDataGenerator{512, 128, 0.8}.Batches(4).GenerateSparsePageDMatrix("temp", true);
  ASSERT_FALSE(p_fmat->SingleColBlock());

  std::vector<std::shared_ptr<DMatrix>> mat{p_fmat};
  auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
  learner->SetParams(Args{{"objective", "binary:logistic"}});
  learner->UpdateOneIter(0, p_fmat);
}

TEST(Learner, Configuration) {
  std::string const emetric = "eval_metric";
  {
    std::unique_ptr<Learner> learner { Learner::Create({nullptr}) };
    learner->SetParam(emetric, "auc");
    learner->SetParam(emetric, "rmsle");
    learner->SetParam("foo", "bar");

    // eval_metric is not part of configuration
    auto attr_names = learner->GetConfigurationArguments();
    ASSERT_EQ(attr_names.size(), 1ul);
    ASSERT_EQ(attr_names.find(emetric), attr_names.cend());
    ASSERT_EQ(attr_names.at("foo"), "bar");
  }

  {
    std::unique_ptr<Learner> learner { Learner::Create({nullptr}) };
    learner->SetParams({{"foo", "bar"}, {emetric, "auc"}, {emetric, "entropy"}, {emetric, "KL"}});
    auto attr_names = learner->GetConfigurationArguments();
    ASSERT_EQ(attr_names.size(), 1ul);
    ASSERT_EQ(attr_names.at("foo"), "bar");
  }
}

TEST(Learner, JsonModelIO) {
  // Test of comparing JSON object directly.
  size_t constexpr kRows = 8;
  int32_t constexpr kIters = 4;

  std::shared_ptr<DMatrix> p_dmat{RandomDataGenerator{kRows, 10, 0}.GenerateDMatrix()};
  p_dmat->Info().labels.Reshape(kRows);
  CHECK_NE(p_dmat->Info().num_col_, 0);

  {
    std::unique_ptr<Learner> learner { Learner::Create({p_dmat}) };
    learner->Configure();
    Json out { Object() };
    learner->SaveModel(&out);

    dmlc::TemporaryDirectory tmpdir;

    std::ofstream fout (tmpdir.path + "/model.json");
    fout << out;
    fout.close();

    auto loaded_str = common::LoadSequentialFile(tmpdir.path + "/model.json");
    Json loaded = Json::Load(StringView{loaded_str.data(), loaded_str.size()});

    learner->LoadModel(loaded);
    learner->Configure();

    Json new_in { Object() };
    learner->SaveModel(&new_in);
    ASSERT_EQ(new_in, out);
  }

  {
    std::unique_ptr<Learner> learner { Learner::Create({p_dmat}) };
    for (int32_t iter = 0; iter < kIters; ++iter) {
      learner->UpdateOneIter(iter, p_dmat);
    }
    learner->SetAttr("best_score", "15.2");

    Json out { Object() };
    learner->SaveModel(&out);

    learner->LoadModel(out);
    Json new_in { Object() };
    learner->Configure();
    learner->SaveModel(&new_in);

    ASSERT_TRUE(IsA<Object>(out["learner"]["attributes"]));
    ASSERT_EQ(get<Object>(out["learner"]["attributes"]).size(), 1ul);
    ASSERT_EQ(out, new_in);
  }
}

TEST(Learner, ConfigIO) {
  bst_idx_t n_samples = 128;
  bst_feature_t n_features = 12;
  std::shared_ptr<DMatrix> p_fmat{
      RandomDataGenerator{n_samples, n_features, 0}.Classes(2).GenerateDMatrix(true)};

  auto serialised_model_tmp = std::string{};
  std::string eval_res_0;
  std::string eval_res_1;
  {
    std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
    learner->SetParams(Args{{"eval_metric", "ndcg"}, {"eval_metric", "map"}});
    learner->Configure();
    learner->UpdateOneIter(0, p_fmat);
    eval_res_0 = learner->EvalOneIter(0, {p_fmat}, {"Train"});
    common::MemoryBufferStream fo(&serialised_model_tmp);
    learner->Save(&fo);
  }

  {
    common::MemoryBufferStream fi(&serialised_model_tmp);
    std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
    learner->Load(&fi);
    eval_res_1 = learner->EvalOneIter(0, {p_fmat}, {"Train"});
  }
  ASSERT_EQ(eval_res_0, eval_res_1);
}

// Crashes the test runner if there are race condiditions.
//
// Build with additional cmake flags to enable thread sanitizer
// which definitely catches problems. Note that OpenMP needs to be
// disabled, otherwise thread sanitizer will also report false
// positives.
//
// ```
// -DUSE_SANITIZER=ON -DENABLED_SANITIZERS=thread -DUSE_OPENMP=OFF
// ```
TEST(Learner, MultiThreadedPredict) {
  size_t constexpr kRows = 1000;
  size_t constexpr kCols = 100;

  std::shared_ptr<DMatrix> p_dmat{RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix()};
  p_dmat->Info().labels.Reshape(kRows);
  CHECK_NE(p_dmat->Info().num_col_, 0);

  std::shared_ptr<DMatrix> p_data{RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix()};
  CHECK_NE(p_data->Info().num_col_, 0);

  std::shared_ptr<Learner> learner{Learner::Create({p_dmat})};
  learner->Configure();

  std::vector<std::thread> threads;

#if defined(__linux__)
  auto n_threads = std::thread::hardware_concurrency() * 4u;
#else
  auto n_threads = std::thread::hardware_concurrency();
#endif

  for (decltype(n_threads) thread_id = 0; thread_id < n_threads; ++thread_id) {
    threads.emplace_back([learner, p_data] {
      size_t constexpr kIters = 10;
      auto &entry = learner->GetThreadLocal().prediction_entry;
      HostDeviceVector<float> predictions;
      for (size_t iter = 0; iter < kIters; ++iter) {
        learner->Predict(p_data, false, &entry.predictions, 0, 0);

        learner->Predict(p_data, false, &predictions, 0, 0, false, true);  // leaf
        learner->Predict(p_data, false, &predictions, 0, 0, false, false, true);  // contribs
      }
    });
  }
  for (auto &thread : threads) {
    thread.join();
  }
}

TEST(Learner, BinaryModelIO) {
  size_t constexpr kRows = 8;
  int32_t constexpr kIters = 4;
  auto p_dmat = RandomDataGenerator{kRows, 10, 0}.GenerateDMatrix();
  p_dmat->Info().labels.Reshape(kRows);

  std::unique_ptr<Learner> learner{Learner::Create({p_dmat})};
  learner->SetParam("eval_metric", "rmsle");
  learner->Configure();
  for (int32_t iter = 0; iter < kIters; ++iter) {
    learner->UpdateOneIter(iter, p_dmat);
  }
  dmlc::TemporaryDirectory tempdir;
  std::string const fname = tempdir.path + "binary_model_io.bin";
  {
    // Make sure the write is complete before loading.
    std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(fname.c_str(), "w"));
    learner->SaveModel(fo.get());
  }

  learner.reset(Learner::Create({p_dmat}));
  std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r"));
  learner->LoadModel(fi.get());
  learner->Configure();
  Json config { Object() };
  learner->SaveConfig(&config);
  std::string config_str;
  Json::Dump(config, &config_str);
  ASSERT_NE(config_str.find("rmsle"), std::string::npos);
  ASSERT_EQ(config_str.find("WARNING"), std::string::npos);
}

#if defined(XGBOOST_USE_CUDA)
// Tests for automatic GPU configuration.
TEST(Learner, GPUConfiguration) {
  using Arg = std::pair<std::string, std::string>;
  size_t constexpr kRows = 10;
  auto p_dmat = RandomDataGenerator(kRows, 10, 0).GenerateDMatrix();
  std::vector<std::shared_ptr<DMatrix>> mat {p_dmat};
  std::vector<bst_float> labels(kRows);
  for (size_t i = 0; i < labels.size(); ++i) {
    labels[i] = i;
  }
  p_dmat->Info().labels.Data()->HostVector() = labels;
  p_dmat->Info().labels.Reshape(kRows);
  {
    std::unique_ptr<Learner> learner {Learner::Create(mat)};
    learner->SetParams({Arg{"booster", "gblinear"},
                        Arg{"updater", "gpu_coord_descent"}});
    learner->UpdateOneIter(0, p_dmat);
    ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
  }
  {
    std::unique_ptr<Learner> learner{Learner::Create(mat)};
    learner->SetParams({Arg{"tree_method", "gpu_hist"}});
    learner->Configure();
    ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
    learner->UpdateOneIter(0, p_dmat);
    ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
  }
  {
    std::unique_ptr<Learner> learner {Learner::Create(mat)};
    learner->SetParams({Arg{"tree_method", "gpu_hist"},
                        Arg{"gpu_id", "-1"}});
    learner->UpdateOneIter(0, p_dmat);
    ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
  }
  {
    // with CPU algorithm
    std::unique_ptr<Learner> learner {Learner::Create(mat)};
    learner->SetParams({Arg{"tree_method", "hist"}});
    learner->UpdateOneIter(0, p_dmat);
    ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CPU());
  }
  {
    // with CPU algorithm, but `gpu_id` takes priority
    std::unique_ptr<Learner> learner {Learner::Create(mat)};
    learner->SetParams({Arg{"tree_method", "hist"}, Arg{"gpu_id", "0"}});
    learner->UpdateOneIter(0, p_dmat);
    ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
  }
}
#endif  // defined(XGBOOST_USE_CUDA)

TEST(Learner, Seed) {
  auto m = RandomDataGenerator{10, 10, 0}.GenerateDMatrix();
  std::unique_ptr<Learner> learner {
    Learner::Create({m})
  };
  auto seed = std::numeric_limits<int64_t>::max();
  learner->SetParam("seed", std::to_string(seed));
  learner->Configure();
  Json config { Object() };
  learner->SaveConfig(&config);
  ASSERT_EQ(std::to_string(seed),
            get<String>(config["learner"]["generic_param"]["seed"]));

  seed = std::numeric_limits<int64_t>::min();
  learner->SetParam("seed", std::to_string(seed));
  learner->Configure();
  learner->SaveConfig(&config);
  ASSERT_EQ(std::to_string(seed),
            get<String>(config["learner"]["generic_param"]["seed"]));
}

TEST(Learner, ConstantSeed) {
  auto m = RandomDataGenerator{10, 10, 0}.GenerateDMatrix(true);
  std::unique_ptr<Learner> learner{Learner::Create({m})};
  // Use exact as it doesn't initialize column sampler at construction, which alters the rng.
  learner->SetParam("tree_method", "exact");
  learner->Configure();  // seed the global random

  std::uniform_real_distribution<float> dist;
  auto& rng = common::GlobalRandom();
  float v_0 = dist(rng);

  learner->SetParam("", "");
  learner->Configure();  // check configure doesn't change the seed.
  float v_1 = dist(rng);
  CHECK_NE(v_0, v_1);

  {
    rng.seed(Context::kDefaultSeed);
    std::uniform_real_distribution<float> dist;
    float v_2 = dist(rng);
    CHECK_EQ(v_0, v_2);
  }
}

TEST(Learner, FeatureInfo) {
  size_t constexpr kCols = 10;
  auto m = RandomDataGenerator{10, kCols, 0}.GenerateDMatrix(true);
  std::vector<std::string> names(kCols);
  for (size_t i = 0; i < kCols; ++i) {
    names[i] = ("f" + std::to_string(i));
  }

  std::vector<std::string> types(kCols);
  for (size_t i = 0; i < kCols; ++i) {
    types[i] = "q";
  }
  types[8] = "f";
  types[0] = "int";
  types[3] = "i";
  types[7] = "i";

  std::vector<char const*> c_names(kCols);
  for (size_t i = 0; i < names.size(); ++i) {
    c_names[i] = names[i].c_str();
  }
  std::vector<char const*> c_types(kCols);
  for (size_t i = 0; i < types.size(); ++i) {
    c_types[i] = names[i].c_str();
  }

  std::vector<std::string> out_names;
  std::vector<std::string> out_types;

  Json model{Object()};
  {
    std::unique_ptr<Learner> learner{Learner::Create({m})};
    learner->Configure();
    learner->SetFeatureNames(names);
    learner->GetFeatureNames(&out_names);

    learner->SetFeatureTypes(types);
    learner->GetFeatureTypes(&out_types);

    ASSERT_TRUE(std::equal(out_names.begin(), out_names.end(), names.begin()));
    ASSERT_TRUE(std::equal(out_types.begin(), out_types.end(), types.begin()));

    learner->SaveModel(&model);
  }

  {
    std::unique_ptr<Learner> learner{Learner::Create({m})};
    learner->LoadModel(model);

    learner->GetFeatureNames(&out_names);
    learner->GetFeatureTypes(&out_types);
    ASSERT_TRUE(std::equal(out_names.begin(), out_names.end(), names.begin()));
    ASSERT_TRUE(std::equal(out_types.begin(), out_types.end(), types.begin()));
  }
}

TEST(Learner, MultiTarget) {
  size_t constexpr kRows{128}, kCols{10}, kTargets{3};
  auto m = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix();
  m->Info().labels.Reshape(kRows, kTargets);
  linalg::ElementWiseTransformHost(m->Info().labels.HostView(), omp_get_max_threads(),
                                   [](auto i, auto) { return i; });

  {
    std::unique_ptr<Learner> learner{Learner::Create({m})};
    learner->Configure();

    Json model{Object()};
    learner->SaveModel(&model);
    ASSERT_EQ(get<String>(model["learner"]["learner_model_param"]["num_target"]),
              std::to_string(kTargets));
  }
  {
    std::unique_ptr<Learner> learner{Learner::Create({m})};
    learner->SetParam("objective", "multi:softprob");
    // unsupported objective.
    EXPECT_THROW({ learner->Configure(); }, dmlc::Error);
  }
}

/**
 * Test the model initialization sequence is correctly performed.
 */
class InitBaseScore : public ::testing::Test {
 protected:
  std::size_t static constexpr Cols() { return 10; }
  std::shared_ptr<DMatrix> Xy_;

  void SetUp() override { Xy_ = RandomDataGenerator{10, Cols(), 0}.GenerateDMatrix(true); }

 public:
  void TestUpdateConfig() {
    std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
    learner->SetParam("objective", "reg:absoluteerror");
    learner->UpdateOneIter(0, Xy_);
    Json config{Object{}};
    learner->SaveConfig(&config);
    auto base_score = GetBaseScore(config);
    ASSERT_NE(base_score, ObjFunction::DefaultBaseScore());

    // already initialized
    auto Xy1 = RandomDataGenerator{100, Cols(), 0}.Seed(321).GenerateDMatrix(true);
    learner->UpdateOneIter(1, Xy1);
    learner->SaveConfig(&config);
    auto base_score1 = GetBaseScore(config);
    ASSERT_EQ(base_score, base_score1);

    Json model{Object{}};
    learner->SaveModel(&model);
    learner.reset(Learner::Create({}));
    learner->LoadModel(model);
    learner->Configure();
    learner->UpdateOneIter(2, Xy1);
    learner->SaveConfig(&config);
    auto base_score2 = GetBaseScore(config);
    ASSERT_EQ(base_score, base_score2);
  }

  void TestBoostFromAvgParam() {
    std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
    learner->SetParam("objective", "reg:absoluteerror");
    learner->SetParam("base_score", "1.3");
    Json config(Object{});
    learner->Configure();
    learner->SaveConfig(&config);

    auto base_score = GetBaseScore(config);
    // no change
    ASSERT_FLOAT_EQ(base_score, 1.3);

    HostDeviceVector<float> predt;
    learner->Predict(Xy_, false, &predt, 0, 0);
    auto h_predt = predt.ConstHostSpan();
    for (auto v : h_predt) {
      ASSERT_FLOAT_EQ(v, 1.3);
    }
    learner->UpdateOneIter(0, Xy_);
    learner->SaveConfig(&config);
    base_score = GetBaseScore(config);
    // no change
    ASSERT_FLOAT_EQ(base_score, 1.3);

    auto from_avg = std::stoi(
        get<String const>(config["learner"]["learner_model_param"]["boost_from_average"]));
    // from_avg is disabled when base score is set
    ASSERT_EQ(from_avg, 0);
    // in the future when we can deprecate the binary model, user can set the parameter directly.
    learner->SetParam("boost_from_average", "1");
    learner->Configure();
    learner->SaveConfig(&config);
    from_avg = std::stoi(
        get<String const>(config["learner"]["learner_model_param"]["boost_from_average"]));
    ASSERT_EQ(from_avg, 1);
  }

  void TestInitAfterLoad() {
    std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
    learner->SetParam("objective", "reg:absoluteerror");
    learner->Configure();

    Json model{Object{}};
    learner->SaveModel(&model);
    auto base_score = GetBaseScore(model);
    ASSERT_EQ(base_score, ObjFunction::DefaultBaseScore());

    learner.reset(Learner::Create({Xy_}));
    learner->LoadModel(model);
    Json config(Object{});
    learner->Configure();
    learner->SaveConfig(&config);
    base_score = GetBaseScore(config);
    ASSERT_EQ(base_score, ObjFunction::DefaultBaseScore());

    learner->UpdateOneIter(0, Xy_);
    learner->SaveConfig(&config);
    base_score = GetBaseScore(config);
    ASSERT_NE(base_score, ObjFunction::DefaultBaseScore());
  }

  void TestInitWithPredt() {
    std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
    learner->SetParam("objective", "reg:absoluteerror");
    HostDeviceVector<float> predt;
    learner->Predict(Xy_, false, &predt, 0, 0);

    auto h_predt = predt.ConstHostSpan();
    for (auto v : h_predt) {
      ASSERT_EQ(v, ObjFunction::DefaultBaseScore());
    }

    Json config(Object{});
    learner->SaveConfig(&config);
    auto base_score = GetBaseScore(config);
    ASSERT_EQ(base_score, ObjFunction::DefaultBaseScore());

    // since prediction is not used for trianing, the train procedure still runs estimation
    learner->UpdateOneIter(0, Xy_);
    learner->SaveConfig(&config);
    base_score = GetBaseScore(config);
    ASSERT_NE(base_score, ObjFunction::DefaultBaseScore());
  }

  void TestUpdateProcess() {
    // Check that when training continuation is performed with update, the base score is
    // not re-evaluated.
    std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
    learner->SetParam("objective", "reg:absoluteerror");
    learner->Configure();

    learner->UpdateOneIter(0, Xy_);
    Json model{Object{}};
    learner->SaveModel(&model);
    auto base_score = GetBaseScore(model);

    auto Xy1 = RandomDataGenerator{100, Cols(), 0}.Seed(321).GenerateDMatrix(true);
    learner.reset(Learner::Create({Xy1}));
    learner->LoadModel(model);
    learner->SetParam("process_type", "update");
    learner->SetParam("updater", "refresh");
    learner->UpdateOneIter(1, Xy1);

    Json config(Object{});
    learner->SaveConfig(&config);
    auto base_score1 = GetBaseScore(config);
    ASSERT_EQ(base_score, base_score1);
  }
};

TEST_F(InitBaseScore, TestUpdateConfig) { this->TestUpdateConfig(); }

TEST_F(InitBaseScore, FromAvgParam) { this->TestBoostFromAvgParam(); }

TEST_F(InitBaseScore, InitAfterLoad) { this->TestInitAfterLoad(); }

TEST_F(InitBaseScore, InitWithPredict) { this->TestInitWithPredt(); }

TEST_F(InitBaseScore, UpdateProcess) { this->TestUpdateProcess(); }

class TestColumnSplit : public ::testing::TestWithParam<std::string> {
  void TestBaseScore(std::string objective, float expected_base_score, Json expected_model) {
    auto const world_size = collective::GetWorldSize();
    auto n_threads = collective::GetWorkerLocalThreads(world_size);
    auto const rank = collective::GetRank();

    auto p_fmat = MakeFmatForObjTest(objective, 10, 10);
    std::shared_ptr<DMatrix> sliced{p_fmat->SliceCol(world_size, rank)};
    std::unique_ptr<Learner> learner{Learner::Create({sliced})};
    learner->SetParams(Args{{"nthread", std::to_string(n_threads)},
                            {"tree_method", "approx"},
                            {"objective", objective}});
    if (objective.find("quantile") != std::string::npos) {
      learner->SetParam("quantile_alpha", "0.5");
    }
    if (objective.find("multi") != std::string::npos) {
      learner->SetParam("num_class", "3");
    }
    learner->UpdateOneIter(0, sliced);
    Json config{Object{}};
    learner->SaveConfig(&config);
    auto base_score = GetBaseScore(config);
    ASSERT_EQ(base_score, expected_base_score);

    Json model{Object{}};
    learner->SaveModel(&model);
    ASSERT_EQ(model, expected_model);
  }

 public:
  void Run(std::string objective) {
    auto p_fmat = MakeFmatForObjTest(objective, 10, 10);
    std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
    learner->SetParam("tree_method", "approx");
    learner->SetParam("objective", objective);
    if (objective.find("quantile") != std::string::npos) {
      learner->SetParam("quantile_alpha", "0.5");
    }
    if (objective.find("multi") != std::string::npos) {
      learner->SetParam("num_class", "3");
    }
    learner->UpdateOneIter(0, p_fmat);

    Json config{Object{}};
    learner->SaveConfig(&config);

    Json model{Object{}};
    learner->SaveModel(&model);

    auto constexpr kWorldSize{3};
    auto call = [this, &objective](auto&... args) {
      this->TestBaseScore(objective, args...);
    };
    auto score = GetBaseScore(config);
    collective::TestDistributedGlobal(kWorldSize, [&] {
      call(score, model);
    });
  }
};

TEST_P(TestColumnSplit, Objective) {
  std::string objective = GetParam();
  this->Run(objective);
}

INSTANTIATE_TEST_SUITE_P(ColumnSplitObjective, TestColumnSplit,
                         ::testing::ValuesIn(MakeObjNamesForTest()),
                         [](const ::testing::TestParamInfo<TestColumnSplit::ParamType>& info) {
                           return ObjTestNameGenerator(info);
                         });

namespace {
Json GetModelWithArgs(std::shared_ptr<DMatrix> dmat, std::string const& tree_method,
                      std::string const& device, Args const& args) {
  std::unique_ptr<Learner> learner{Learner::Create({dmat})};
  auto n_threads = collective::GetWorkerLocalThreads(collective::GetWorldSize());
  learner->SetParam("tree_method", tree_method);
  learner->SetParam("device", device);
  learner->SetParam("nthread", std::to_string(n_threads));
  learner->SetParam("objective", "reg:logistic");
  learner->SetParams(args);
  learner->UpdateOneIter(0, dmat);
  Json model{Object{}};
  learner->SaveModel(&model);
  return model;
}

void VerifyColumnSplitWithArgs(std::string const& tree_method, bool use_gpu, Args const& args,
                               Json const& expected_model) {
  auto const world_size = collective::GetWorldSize();
  auto const rank = collective::GetRank();
  auto p_fmat = MakeFmatForObjTest("", 10, 10);
  std::shared_ptr<DMatrix> sliced{p_fmat->SliceCol(world_size, rank)};
  std::string device = "cpu";
  if (use_gpu) {
    device = MakeCUDACtx(DistGpuIdx()).DeviceName();
  }
  auto model = GetModelWithArgs(sliced, tree_method, device, args);
  ASSERT_EQ(model, expected_model);
}

void TestColumnSplitWithArgs(std::string const& tree_method, bool use_gpu, Args const& args,
                             bool federated) {
  auto p_fmat = MakeFmatForObjTest("", 10, 10);
  std::string device = use_gpu ? "cuda:0" : "cpu";
  auto model = GetModelWithArgs(p_fmat, tree_method, device, args);

  auto world_size{3};
  if (use_gpu) {
    world_size = curt::AllVisibleGPUs();
    // Simulate MPU on a single GPU. Federated doesn't use nccl, can run multiple
    // instances on the same GPU.
    if (world_size == 1 && federated) {
      world_size = 3;
    }
  }
  if (federated) {
#if defined(XGBOOST_USE_FEDERATED)
    collective::TestFederatedGlobal(
        world_size, [&] { VerifyColumnSplitWithArgs(tree_method, use_gpu, args, model); });
#else
    GTEST_SKIP_("Not compiled with federated learning.");
#endif  //  defined(XGBOOST_USE_FEDERATED)
  } else {
#if !defined(XGBOOST_USE_NCCL)
    if (use_gpu) {
      GTEST_SKIP_("Not compiled with NCCL.");
      return;
    }
#endif  //  defined(XGBOOST_USE_NCCL)
    collective::TestDistributedGlobal(
        world_size, [&] { VerifyColumnSplitWithArgs(tree_method, use_gpu, args, model); });
  }
}

class ColumnSplitTrainingTest
    : public ::testing::TestWithParam<std::tuple<std::string, bool, bool>> {
 public:
  static void TestColumnSplitColumnSampler(std::string const& tree_method, bool use_gpu,
                                           bool federated) {
    Args args{
        {"colsample_bytree", "0.5"}, {"colsample_bylevel", "0.6"}, {"colsample_bynode", "0.7"}};
    TestColumnSplitWithArgs(tree_method, use_gpu, args, federated);
  }
  static void TestColumnSplitInteractionConstraints(std::string const& tree_method, bool use_gpu,
                                                    bool federated) {
    Args args{{"interaction_constraints", "[[0, 5, 7], [2, 8, 9], [1, 3, 6]]"}};
    TestColumnSplitWithArgs(tree_method, use_gpu, args, federated);
  }
  static void TestColumnSplitMonotoneConstraints(std::string const& tree_method, bool use_gpu,
                                                 bool federated) {
    Args args{{"monotone_constraints", "(1,-1,0,1,1,-1,-1,0,0,1)"}};
    TestColumnSplitWithArgs(tree_method, use_gpu, args, federated);
  }
};

auto WithFed() {
#if defined(XGBOOST_USE_FEDERATED)
  return ::testing::Bool();
#else
  return ::testing::Values(false);
#endif
}
}  // anonymous namespace

TEST_P(ColumnSplitTrainingTest, ColumnSampler) {
  std::apply(TestColumnSplitColumnSampler, GetParam());
}

TEST_P(ColumnSplitTrainingTest, InteractionConstraints) {
  std::apply(TestColumnSplitInteractionConstraints, GetParam());
}

TEST_P(ColumnSplitTrainingTest, MonotoneConstraints) {
  std::apply(TestColumnSplitMonotoneConstraints, GetParam());
}

INSTANTIATE_TEST_SUITE_P(Cpu, ColumnSplitTrainingTest,
                         ::testing::Combine(::testing::Values("hist", "approx"),
                                            ::testing::Values(false), WithFed()));

INSTANTIATE_TEST_SUITE_P(MGPU, ColumnSplitTrainingTest,
                         ::testing::Combine(::testing::Values("hist", "approx"),
                                            ::testing::Values(true), WithFed()));
}  // namespace xgboost