"vscode:/vscode.git/clone" did not exist on "ef76db45c73338156a1f722914f910f82e60dcec"
dataset.cpp 55.9 KB
Newer Older
1
2
3
4
/*!
 * Copyright (c) 2016 Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License. See LICENSE file in the project root for license information.
 */
Guolin Ke's avatar
Guolin Ke committed
5
#include <LightGBM/dataset.h>
6

Guolin Ke's avatar
Guolin Ke committed
7
#include <LightGBM/feature_group.h>
8
#include <LightGBM/utils/array_args.h>
9
#include <LightGBM/utils/openmp_wrapper.h>
Guolin Ke's avatar
Guolin Ke committed
10
#include <LightGBM/utils/threading.h>
Guolin Ke's avatar
Guolin Ke committed
11

12
#include <limits>
zhangyafeikimi's avatar
zhangyafeikimi committed
13
#include <chrono>
Guolin Ke's avatar
Guolin Ke committed
14
#include <cstdio>
Guolin Ke's avatar
Guolin Ke committed
15
#include <sstream>
16
#include <unordered_map>
Guolin Ke's avatar
Guolin Ke committed
17

18

Guolin Ke's avatar
Guolin Ke committed
19
20
namespace LightGBM {

21
const char* Dataset::binary_file_token = "______LightGBM_Binary_File_Token______\n";
Guolin Ke's avatar
Guolin Ke committed
22

Guolin Ke's avatar
Guolin Ke committed
23
Dataset::Dataset() {
24
  data_filename_ = "noname";
Guolin Ke's avatar
Guolin Ke committed
25
  num_data_ = 0;
Guolin Ke's avatar
Guolin Ke committed
26
  is_finish_load_ = false;
Guolin Ke's avatar
Guolin Ke committed
27
28
}

29
Dataset::Dataset(data_size_t num_data) {
Guolin Ke's avatar
Guolin Ke committed
30
  CHECK(num_data > 0);
Guolin Ke's avatar
Guolin Ke committed
31
  data_filename_ = "noname";
Guolin Ke's avatar
Guolin Ke committed
32
  num_data_ = num_data;
Guolin Ke's avatar
Guolin Ke committed
33
  metadata_.Init(num_data_, NO_SPECIFIC, NO_SPECIFIC);
Guolin Ke's avatar
Guolin Ke committed
34
  is_finish_load_ = false;
Guolin Ke's avatar
Guolin Ke committed
35
  group_bin_boundaries_.push_back(0);
Guolin Ke's avatar
Guolin Ke committed
36
37
}

Guolin Ke's avatar
Guolin Ke committed
38
Dataset::~Dataset() {
Guolin Ke's avatar
Guolin Ke committed
39
}
Guolin Ke's avatar
Guolin Ke committed
40

Guolin Ke's avatar
Guolin Ke committed
41
42
43
44
45
46
47
48
49
50
std::vector<std::vector<int>> NoGroup(
  const std::vector<int>& used_features) {
  std::vector<std::vector<int>> features_in_group;
  features_in_group.resize(used_features.size());
  for (size_t i = 0; i < used_features.size(); ++i) {
    features_in_group[i].emplace_back(used_features[i]);
  }
  return features_in_group;
}

51
int GetConfilctCount(const std::vector<bool>& mark, const int* indices, int num_indices, data_size_t max_cnt) {
Guolin Ke's avatar
Guolin Ke committed
52
53
54
55
  int ret = 0;
  for (int i = 0; i < num_indices; ++i) {
    if (mark[indices[i]]) {
      ++ret;
56
57
58
    }
    if (ret > max_cnt) {
      return -1;
Guolin Ke's avatar
Guolin Ke committed
59
60
61
62
    }
  }
  return ret;
}
63
64

void MarkUsed(std::vector<bool>* mark, const int* indices, data_size_t num_indices) {
Guolin Ke's avatar
Guolin Ke committed
65
  auto& ref_mark = *mark;
Guolin Ke's avatar
Guolin Ke committed
66
  for (int i = 0; i < num_indices; ++i) {
Guolin Ke's avatar
Guolin Ke committed
67
    ref_mark[indices[i]] = true;
Guolin Ke's avatar
Guolin Ke committed
68
69
70
  }
}

Guolin Ke's avatar
Guolin Ke committed
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
std::vector<int> FixSampleIndices(const BinMapper* bin_mapper, int num_total_samples, int num_indices, const int* sample_indices, const double* sample_values) {
  std::vector<int> ret;
  if (bin_mapper->GetDefaultBin() == bin_mapper->GetMostFreqBin()) {
    return ret;
  }
  int i = 0, j = 0;
  while (i < num_total_samples) {
    if (j < num_indices && sample_indices[j] < i) {
      ++j;
    } else if (j < num_indices && sample_indices[j] == i) {
      if (bin_mapper->ValueToBin(sample_values[j]) != bin_mapper->GetMostFreqBin()) {
        ret.push_back(i);
      }
      ++i;
    } else {
      ret.push_back(i++);
    }
  }
  return ret;
}

Guolin Ke's avatar
Guolin Ke committed
92
93
94
95
std::vector<std::vector<int>> FindGroups(const std::vector<std::unique_ptr<BinMapper>>& bin_mappers,
                                         const std::vector<int>& find_order,
                                         int** sample_indices,
                                         const int* num_per_col,
96
                                         int num_sample_col,
97
                                         data_size_t total_sample_cnt,
Guolin Ke's avatar
Guolin Ke committed
98
                                         data_size_t num_data,
99
                                         bool is_use_gpu,
Guolin Ke's avatar
Guolin Ke committed
100
                                         bool is_sparse,
101
                                         std::vector<int8_t>* multi_val_group) {
Guolin Ke's avatar
Guolin Ke committed
102
  const int max_search_group = 100;
103
104
105
106
  const int max_bin_per_group = 256;
  const data_size_t single_val_max_conflict_cnt = static_cast<data_size_t>(total_sample_cnt / 10000);
  multi_val_group->clear();

Guolin Ke's avatar
Guolin Ke committed
107
108
109
  Random rand(num_data);
  std::vector<std::vector<int>> features_in_group;
  std::vector<std::vector<bool>> conflict_marks;
110
111
  std::vector<data_size_t> group_used_row_cnt;
  std::vector<data_size_t> group_total_data_cnt;
Guolin Ke's avatar
Guolin Ke committed
112
113
  std::vector<int> group_num_bin;

114
  // first round: fill the single val group
Guolin Ke's avatar
Guolin Ke committed
115
  for (auto fidx : find_order) {
116
    bool is_filtered_feature = fidx >= num_sample_col;
117
    const data_size_t cur_non_zero_cnt = is_filtered_feature ? 0 : num_per_col[fidx];
Guolin Ke's avatar
Guolin Ke committed
118
119
    std::vector<int> available_groups;
    for (int gid = 0; gid < static_cast<int>(features_in_group.size()); ++gid) {
120
121
122
      auto cur_num_bin = group_num_bin[gid] + bin_mappers[fidx]->num_bin() + (bin_mappers[fidx]->GetDefaultBin() == 0 ? -1 : 0);
      if (group_total_data_cnt[gid] + cur_non_zero_cnt <= total_sample_cnt + single_val_max_conflict_cnt) {
        if (!is_use_gpu || cur_num_bin <= max_bin_per_group) {
Guolin Ke's avatar
Guolin Ke committed
123
124
          available_groups.push_back(gid);
        }
Guolin Ke's avatar
Guolin Ke committed
125
126
127
128
129
130
      }
    }
    std::vector<int> search_groups;
    if (!available_groups.empty()) {
      int last = static_cast<int>(available_groups.size()) - 1;
      auto indices = rand.Sample(last, std::min(last, max_search_group - 1));
131
      // always push the last group
Guolin Ke's avatar
Guolin Ke committed
132
133
134
135
136
      search_groups.push_back(available_groups.back());
      for (auto idx : indices) {
        search_groups.push_back(available_groups[idx]);
      }
    }
137
138
    int best_gid = -1;
    int best_conflict_cnt = -1;
Guolin Ke's avatar
Guolin Ke committed
139
    for (auto gid : search_groups) {
140
141
142
143
144
      const data_size_t rest_max_cnt = single_val_max_conflict_cnt - group_total_data_cnt[gid] + group_used_row_cnt[gid];
      const data_size_t cnt = is_filtered_feature ? 0 : GetConfilctCount(conflict_marks[gid], sample_indices[fidx], num_per_col[fidx], rest_max_cnt);
      if (cnt >= 0 && cnt <= rest_max_cnt && cnt <= cur_non_zero_cnt / 2) {
        best_gid = gid;
        best_conflict_cnt = cnt;
Guolin Ke's avatar
Guolin Ke committed
145
146
147
        break;
      }
    }
148
149
150
151
152
153
154
155
156
    if (best_gid >= 0) {
      features_in_group[best_gid].push_back(fidx);
      group_total_data_cnt[best_gid] += cur_non_zero_cnt;
      group_used_row_cnt[best_gid] += cur_non_zero_cnt - best_conflict_cnt;
      if (!is_filtered_feature) {
        MarkUsed(&conflict_marks[best_gid], sample_indices[fidx], num_per_col[fidx]);
      }
      group_num_bin[best_gid] += bin_mappers[fidx]->num_bin() + (bin_mappers[fidx]->GetDefaultBin() == 0 ? -1 : 0);
    } else {
Guolin Ke's avatar
Guolin Ke committed
157
158
159
      features_in_group.emplace_back();
      features_in_group.back().push_back(fidx);
      conflict_marks.emplace_back(total_sample_cnt, false);
160
161
162
      if (!is_filtered_feature) {
        MarkUsed(&(conflict_marks.back()), sample_indices[fidx], num_per_col[fidx]);
      }
163
164
165
166
167
      group_total_data_cnt.emplace_back(cur_non_zero_cnt);
      group_used_row_cnt.emplace_back(cur_non_zero_cnt);
      group_num_bin.push_back(1 + bin_mappers[fidx]->num_bin() + (bin_mappers[fidx]->GetDefaultBin() == 0 ? -1 : 0));
    }
  }
Guolin Ke's avatar
Guolin Ke committed
168
169
170
171
  if (!is_sparse) {
    multi_val_group->resize(features_in_group.size(), false);
    return features_in_group;
  }
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
  std::vector<int> second_round_features;
  std::vector<std::vector<int>> features_in_group2;
  std::vector<std::vector<bool>> conflict_marks2;

  const double dense_threshold = 0.4;
  for (int gid = 0; gid < static_cast<int>(features_in_group.size()); ++gid) {
    const double dense_rate = static_cast<double>(group_used_row_cnt[gid]) / total_sample_cnt;
    if (dense_rate >= dense_threshold) {
      features_in_group2.push_back(std::move(features_in_group[gid]));
      conflict_marks2.push_back(std::move(conflict_marks[gid]));
    } else {
      for (auto fidx : features_in_group[gid]) {
        second_round_features.push_back(fidx);
      }
    }
  }

  features_in_group = features_in_group2;
  conflict_marks = conflict_marks2;
  multi_val_group->resize(features_in_group.size(), false);
  if (!second_round_features.empty()) {
    features_in_group.emplace_back();
    conflict_marks.emplace_back(total_sample_cnt, false);
    bool is_multi_val = is_use_gpu ? true : false;
    int conflict_cnt = 0;
    for (auto fidx : second_round_features) {
      features_in_group.back().push_back(fidx);
      if (!is_multi_val) {
        const int rest_max_cnt = single_val_max_conflict_cnt - conflict_cnt;
        const auto cnt = GetConfilctCount(conflict_marks.back(), sample_indices[fidx], num_per_col[fidx], rest_max_cnt);
        conflict_cnt += cnt;
        if (cnt < 0 || conflict_cnt > single_val_max_conflict_cnt) {
          is_multi_val = true;
          continue;
        }
        MarkUsed(&(conflict_marks.back()), sample_indices[fidx], num_per_col[fidx]);
Guolin Ke's avatar
Guolin Ke committed
208
      }
Guolin Ke's avatar
Guolin Ke committed
209
    }
210
    multi_val_group->push_back(is_multi_val);
Guolin Ke's avatar
Guolin Ke committed
211
212
213
214
  }
  return features_in_group;
}

Guolin Ke's avatar
Guolin Ke committed
215
std::vector<std::vector<int>> FastFeatureBundling(const std::vector<std::unique_ptr<BinMapper>>& bin_mappers,
Guolin Ke's avatar
Guolin Ke committed
216
                                                  int** sample_indices,
Guolin Ke's avatar
Guolin Ke committed
217
                                                  double** sample_values,
Guolin Ke's avatar
Guolin Ke committed
218
                                                  const int* num_per_col,
219
                                                  int num_sample_col,
220
                                                  data_size_t total_sample_cnt,
Guolin Ke's avatar
Guolin Ke committed
221
222
                                                  const std::vector<int>& used_features,
                                                  data_size_t num_data,
223
                                                  bool is_use_gpu,
Guolin Ke's avatar
Guolin Ke committed
224
                                                  bool is_sparse,
225
226
                                                  std::vector<int8_t>* multi_val_group) {
  Common::FunctionTimer fun_timer("Dataset::FastFeatureBundling", global_timer);
Guolin Ke's avatar
Guolin Ke committed
227
  std::vector<size_t> feature_non_zero_cnt;
228
  feature_non_zero_cnt.reserve(used_features.size());
Guolin Ke's avatar
Guolin Ke committed
229
230
  // put dense feature first
  for (auto fidx : used_features) {
231
232
233
234
235
    if (fidx < num_sample_col) {
      feature_non_zero_cnt.emplace_back(num_per_col[fidx]);
    } else {
      feature_non_zero_cnt.emplace_back(0);
    }
Guolin Ke's avatar
Guolin Ke committed
236
237
238
  }
  // sort by non zero cnt
  std::vector<int> sorted_idx;
239
  sorted_idx.reserve(used_features.size());
240
  for (int i = 0; i < static_cast<int>(used_features.size()); ++i) {
Guolin Ke's avatar
Guolin Ke committed
241
242
243
    sorted_idx.emplace_back(i);
  }
  // sort by non zero cnt, bigger first
244
245
  std::stable_sort(sorted_idx.begin(), sorted_idx.end(),
                   [&feature_non_zero_cnt](int a, int b) {
Guolin Ke's avatar
Guolin Ke committed
246
247
248
249
    return feature_non_zero_cnt[a] > feature_non_zero_cnt[b];
  });

  std::vector<int> feature_order_by_cnt;
250
  feature_order_by_cnt.reserve(sorted_idx.size());
Guolin Ke's avatar
Guolin Ke committed
251
252
253
  for (auto sidx : sorted_idx) {
    feature_order_by_cnt.push_back(used_features[sidx]);
  }
254

Guolin Ke's avatar
Guolin Ke committed
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
  std::vector<std::vector<int>> tmp_indices;
  std::vector<int> tmp_num_per_col(num_sample_col, 0);
  for (auto fidx : used_features) {
    if (fidx >= num_sample_col) {
      continue;
    }
    auto ret = FixSampleIndices(bin_mappers[fidx].get(), static_cast<int>(total_sample_cnt), num_per_col[fidx], sample_indices[fidx], sample_values[fidx]);
    if (!ret.empty()) {
      tmp_indices.push_back(ret);
      tmp_num_per_col[fidx] = static_cast<int>(ret.size());
      sample_indices[fidx] = tmp_indices.back().data();
    } else {
      tmp_num_per_col[fidx] = num_per_col[fidx];
    }
  }
270
  std::vector<int8_t> group_is_multi_val, group_is_multi_val2;
Guolin Ke's avatar
Guolin Ke committed
271
272
  auto features_in_group = FindGroups(bin_mappers, used_features, sample_indices, tmp_num_per_col.data(), num_sample_col, total_sample_cnt, num_data, is_use_gpu, is_sparse, &group_is_multi_val);
  auto group2 = FindGroups(bin_mappers, feature_order_by_cnt, sample_indices, tmp_num_per_col.data(), num_sample_col, total_sample_cnt, num_data, is_use_gpu, is_sparse, &group_is_multi_val2);
273

Guolin Ke's avatar
Guolin Ke committed
274
275
  if (features_in_group.size() > group2.size()) {
    features_in_group = group2;
276
    group_is_multi_val = group_is_multi_val2;
Guolin Ke's avatar
Guolin Ke committed
277
278
  }
  // shuffle groups
279
280
  int num_group = static_cast<int>(features_in_group.size());
  Random tmp_rand(num_data);
Guolin Ke's avatar
Guolin Ke committed
281
282
  for (int i = 0; i < num_group - 1; ++i) {
    int j = tmp_rand.NextShort(i + 1, num_group);
283
    std::swap(features_in_group[i], features_in_group[j]);
284
    // Using std::swap for vector<bool> will cause the wrong result.
285
    std::swap(group_is_multi_val[i], group_is_multi_val[j]);
Guolin Ke's avatar
Guolin Ke committed
286
  }
287
288
  *multi_val_group = group_is_multi_val;
  return features_in_group;
Guolin Ke's avatar
Guolin Ke committed
289
290
}

Guolin Ke's avatar
Guolin Ke committed
291
void Dataset::Construct(
Guolin Ke's avatar
Guolin Ke committed
292
  std::vector<std::unique_ptr<BinMapper>>* bin_mappers,
293
  int num_total_features,
294
  const std::vector<std::vector<double>>& forced_bins,
Guolin Ke's avatar
Guolin Ke committed
295
  int** sample_non_zero_indices,
Guolin Ke's avatar
Guolin Ke committed
296
  double** sample_values,
Guolin Ke's avatar
Guolin Ke committed
297
  const int* num_per_col,
298
  int num_sample_col,
Guolin Ke's avatar
Guolin Ke committed
299
  size_t total_sample_cnt,
Guolin Ke's avatar
Guolin Ke committed
300
  const Config& io_config) {
301
302
  num_total_features_ = num_total_features;
  CHECK(num_total_features_ == static_cast<int>(bin_mappers->size()));
Guolin Ke's avatar
Guolin Ke committed
303
304
  // get num_features
  std::vector<int> used_features;
Guolin Ke's avatar
Guolin Ke committed
305
  auto& ref_bin_mappers = *bin_mappers;
Guolin Ke's avatar
Guolin Ke committed
306
  for (int i = 0; i < static_cast<int>(bin_mappers->size()); ++i) {
Guolin Ke's avatar
Guolin Ke committed
307
    if (ref_bin_mappers[i] != nullptr && !ref_bin_mappers[i]->is_trivial()) {
Guolin Ke's avatar
Guolin Ke committed
308
      used_features.emplace_back(i);
Guolin Ke's avatar
Guolin Ke committed
309
    }
Guolin Ke's avatar
Guolin Ke committed
310
  }
Guolin Ke's avatar
Guolin Ke committed
311
  if (used_features.empty()) {
312
    Log::Warning("There are no meaningful features, as all feature values are constant.");
Guolin Ke's avatar
Guolin Ke committed
313
  }
Guolin Ke's avatar
Guolin Ke committed
314
  auto features_in_group = NoGroup(used_features);
315
  std::vector<int8_t> group_is_multi_val(used_features.size(), 0);
316
  if (io_config.enable_bundle && !used_features.empty()) {
Guolin Ke's avatar
Guolin Ke committed
317
    features_in_group = FastFeatureBundling(*bin_mappers,
318
                                            sample_non_zero_indices, sample_values, num_per_col, num_sample_col, static_cast<data_size_t>(total_sample_cnt),
Guolin Ke's avatar
Guolin Ke committed
319
                                            used_features, num_data_, io_config.device_type == std::string("gpu"), io_config.is_enable_sparse, &group_is_multi_val);
Guolin Ke's avatar
Guolin Ke committed
320
321
  }

Guolin Ke's avatar
Guolin Ke committed
322
323
324
325
326
327
328
329
330
331
  num_features_ = 0;
  for (const auto& fs : features_in_group) {
    num_features_ += static_cast<int>(fs.size());
  }
  int cur_fidx = 0;
  used_feature_map_ = std::vector<int>(num_total_features_, -1);
  num_groups_ = static_cast<int>(features_in_group.size());
  real_feature_idx_.resize(num_features_);
  feature2group_.resize(num_features_);
  feature2subfeature_.resize(num_features_);
332
  int num_multi_val_group = 0;
Guolin Ke's avatar
Guolin Ke committed
333
  feature_need_push_zeros_.clear();
Guolin Ke's avatar
Guolin Ke committed
334
335
336
  for (int i = 0; i < num_groups_; ++i) {
    auto cur_features = features_in_group[i];
    int cur_cnt_features = static_cast<int>(cur_features.size());
337
338
339
    if (group_is_multi_val[i]) {
      ++num_multi_val_group;
    }
Guolin Ke's avatar
Guolin Ke committed
340
341
342
343
344
345
346
347
    // get bin_mappers
    std::vector<std::unique_ptr<BinMapper>> cur_bin_mappers;
    for (int j = 0; j < cur_cnt_features; ++j) {
      int real_fidx = cur_features[j];
      used_feature_map_[real_fidx] = cur_fidx;
      real_feature_idx_[cur_fidx] = real_fidx;
      feature2group_[cur_fidx] = i;
      feature2subfeature_[cur_fidx] = j;
Guolin Ke's avatar
Guolin Ke committed
348
      cur_bin_mappers.emplace_back(ref_bin_mappers[real_fidx].release());
Guolin Ke's avatar
Guolin Ke committed
349
350
351
      if (cur_bin_mappers.back()->GetDefaultBin() != cur_bin_mappers.back()->GetMostFreqBin()) {
        feature_need_push_zeros_.push_back(cur_fidx);
      }
Guolin Ke's avatar
Guolin Ke committed
352
353
354
      ++cur_fidx;
    }
    feature_groups_.emplace_back(std::unique_ptr<FeatureGroup>(
355
      new FeatureGroup(cur_cnt_features, group_is_multi_val[i], &cur_bin_mappers, num_data_)));
Guolin Ke's avatar
Guolin Ke committed
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
  }
  feature_groups_.shrink_to_fit();
  group_bin_boundaries_.clear();
  uint64_t num_total_bin = 0;
  group_bin_boundaries_.push_back(num_total_bin);
  for (int i = 0; i < num_groups_; ++i) {
    num_total_bin += feature_groups_[i]->num_total_bin_;
    group_bin_boundaries_.push_back(num_total_bin);
  }
  int last_group = 0;
  group_feature_start_.reserve(num_groups_);
  group_feature_cnt_.reserve(num_groups_);
  group_feature_start_.push_back(0);
  group_feature_cnt_.push_back(1);
  for (int i = 1; i < num_features_; ++i) {
    const int group = feature2group_[i];
    if (group == last_group) {
      group_feature_cnt_.back() = group_feature_cnt_.back() + 1;
    } else {
      group_feature_start_.push_back(i);
      group_feature_cnt_.push_back(1);
      last_group = group;
    }
  }
Guolin Ke's avatar
Guolin Ke committed
380
381
382
383
384
385
386
387
388
389
390
391
392
393

  if (!io_config.monotone_constraints.empty()) {
    CHECK(static_cast<size_t>(num_total_features_) == io_config.monotone_constraints.size());
    monotone_types_.resize(num_features_);
    for (int i = 0; i < num_total_features_; ++i) {
      int inner_fidx = InnerFeatureIndex(i);
      if (inner_fidx >= 0) {
        monotone_types_[inner_fidx] = io_config.monotone_constraints[i];
      }
    }
    if (ArrayArgs<int8_t>::CheckAllZero(monotone_types_)) {
      monotone_types_.clear();
    }
  }
Guolin Ke's avatar
Guolin Ke committed
394
395
396
397
398
399
400
401
402
403
404
405
406
  if (!io_config.feature_contri.empty()) {
    CHECK(static_cast<size_t>(num_total_features_) == io_config.feature_contri.size());
    feature_penalty_.resize(num_features_);
    for (int i = 0; i < num_total_features_; ++i) {
      int inner_fidx = InnerFeatureIndex(i);
      if (inner_fidx >= 0) {
        feature_penalty_[inner_fidx] = std::max(0.0, io_config.feature_contri[i]);
      }
    }
    if (ArrayArgs<double>::CheckAll(feature_penalty_, 1.0)) {
      feature_penalty_.clear();
    }
  }
Belinda Trotta's avatar
Belinda Trotta committed
407
408
409
410
411
412
  if (!io_config.max_bin_by_feature.empty()) {
    CHECK(static_cast<size_t>(num_total_features_) == io_config.max_bin_by_feature.size());
    CHECK(*(std::min_element(io_config.max_bin_by_feature.begin(), io_config.max_bin_by_feature.end())) > 1);
    max_bin_by_feature_.resize(num_total_features_);
    max_bin_by_feature_.assign(io_config.max_bin_by_feature.begin(), io_config.max_bin_by_feature.end());
  }
413
  forced_bin_bounds_ = forced_bins;
414
415
416
417
418
419
420
421
422
423
424
425
426
427
  max_bin_ = io_config.max_bin;
  min_data_in_bin_ = io_config.min_data_in_bin;
  bin_construct_sample_cnt_ = io_config.bin_construct_sample_cnt;
  use_missing_ = io_config.use_missing;
  zero_as_missing_ = io_config.zero_as_missing;
}

void Dataset::ResetConfig(const char* parameters) {
  auto param = Config::Str2Map(parameters);
  Config io_config;
  io_config.Set(param);
  if (param.count("max_bin") && io_config.max_bin != max_bin_) {
    Log::Warning("Cannot change max_bin after constructed Dataset handle.");
  }
Belinda Trotta's avatar
Belinda Trotta committed
428
429
430
  if (param.count("max_bin_by_feature") && io_config.max_bin_by_feature != max_bin_by_feature_) {
    Log::Warning("Cannot change max_bin_by_feature after constructed Dataset handle.");
  }
431
432
433
434
435
436
437
438
439
440
441
442
  if (param.count("bin_construct_sample_cnt") && io_config.bin_construct_sample_cnt != bin_construct_sample_cnt_) {
    Log::Warning("Cannot change bin_construct_sample_cnt after constructed Dataset handle.");
  }
  if (param.count("min_data_in_bin") && io_config.min_data_in_bin != min_data_in_bin_) {
    Log::Warning("Cannot change min_data_in_bin after constructed Dataset handle.");
  }
  if (param.count("use_missing") && io_config.use_missing != use_missing_) {
    Log::Warning("Cannot change use_missing after constructed Dataset handle.");
  }
  if (param.count("zero_as_missing") && io_config.zero_as_missing != zero_as_missing_) {
    Log::Warning("Cannot change zero_as_missing after constructed Dataset handle.");
  }
443
444
445
  if (param.count("forcedbins_filename")) {
    Log::Warning("Cannot change forced bins after constructed Dataset handle.");
  }
Guolin Ke's avatar
Guolin Ke committed
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
  if (!io_config.monotone_constraints.empty()) {
    CHECK(static_cast<size_t>(num_total_features_) == io_config.monotone_constraints.size());
    monotone_types_.resize(num_features_);
    for (int i = 0; i < num_total_features_; ++i) {
      int inner_fidx = InnerFeatureIndex(i);
      if (inner_fidx >= 0) {
        monotone_types_[inner_fidx] = io_config.monotone_constraints[i];
      }
    }
    if (ArrayArgs<int8_t>::CheckAllZero(monotone_types_)) {
      monotone_types_.clear();
    }
  }
  if (!io_config.feature_contri.empty()) {
    CHECK(static_cast<size_t>(num_total_features_) == io_config.feature_contri.size());
    feature_penalty_.resize(num_features_);
    for (int i = 0; i < num_total_features_; ++i) {
      int inner_fidx = InnerFeatureIndex(i);
      if (inner_fidx >= 0) {
        feature_penalty_[inner_fidx] = std::max(0.0, io_config.feature_contri[i]);
      }
    }
    if (ArrayArgs<double>::CheckAll(feature_penalty_, 1.0)) {
      feature_penalty_.clear();
    }
  }
Guolin Ke's avatar
Guolin Ke committed
473
474
}

Guolin Ke's avatar
Guolin Ke committed
475
void Dataset::FinishLoad() {
Guolin Ke's avatar
Guolin Ke committed
476
  if (is_finish_load_) { return; }
477
478
  if (num_groups_ > 0) {
    for (int i = 0; i < num_groups_; ++i) {
479
      feature_groups_[i]->FinishLoad();
480
    }
Guolin Ke's avatar
Guolin Ke committed
481
  }
Guolin Ke's avatar
Guolin Ke committed
482
  is_finish_load_ = true;
Guolin Ke's avatar
Guolin Ke committed
483
}
Guolin Ke's avatar
Guolin Ke committed
484

485
486
487
488
489
490
491
492
493
494
void PushDataToMultiValBin(int num_threads, data_size_t num_data, const std::vector<uint32_t> most_freq_bins,
  const std::vector<uint32_t> offsets, std::vector<std::vector<std::unique_ptr<BinIterator>>>& iters, MultiValBin* ret) {
  Common::FunctionTimer fun_time("Dataset::PushDataToMultiValBin", global_timer);
  const data_size_t min_block_size = 4096;
  const int n_block = std::min(num_threads, (num_data + min_block_size - 1) / min_block_size);
  const data_size_t block_size = (num_data + n_block - 1) / n_block;
  if (ret->IsSparse()) {
    #pragma omp parallel for schedule(static)
    for (int tid = 0; tid < n_block; ++tid) {
      std::vector<uint32_t> cur_data;
495
      cur_data.reserve(most_freq_bins.size());
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
      data_size_t start = tid * block_size;
      data_size_t end = std::min(num_data, start + block_size);
      for (size_t j = 0; j < most_freq_bins.size(); ++j) {
        iters[tid][j]->Reset(start);
      }
      for (data_size_t i = start; i < end; ++i) {
        cur_data.clear();
        for (size_t j = 0; j < most_freq_bins.size(); ++j) {
          auto cur_bin = iters[tid][j]->Get(i);
          if (cur_bin == most_freq_bins[j]) {
            continue;
          }
          cur_bin += offsets[j];
          if (most_freq_bins[j] == 0) {
            cur_bin -= 1;
          }
          cur_data.push_back(cur_bin);
        }
        ret->PushOneRow(tid, i, cur_data);
      }
    }
  } else {
    #pragma omp parallel for schedule(static)
    for (int tid = 0; tid < n_block; ++tid) {
520
      std::vector<uint32_t> cur_data(most_freq_bins.size(), 0);
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
      data_size_t start = tid * block_size;
      data_size_t end = std::min(num_data, start + block_size);
      for (size_t j = 0; j < most_freq_bins.size(); ++j) {
        iters[tid][j]->Reset(start);
      }
      for (data_size_t i = start; i < end; ++i) {
        for (size_t j = 0; j < most_freq_bins.size(); ++j) {
          auto cur_bin = iters[tid][j]->Get(i);
          if (cur_bin == most_freq_bins[j]) {
            cur_bin = 0;
          } else {
            cur_bin += offsets[j];
            if (most_freq_bins[j] == 0) {
              cur_bin -= 1;
            }
          }
537
          cur_data[j] = cur_bin;
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
        }
        ret->PushOneRow(tid, i, cur_data);
      }
    }
  }
}

MultiValBin* Dataset::GetMultiBinFromSparseFeatures() const {
  Common::FunctionTimer fun_time("Dataset::GetMultiBinFromSparseFeatures", global_timer);
  int multi_group_id = -1;
  for (int i = 0; i < num_groups_; ++i) {
    if (feature_groups_[i]->is_multi_val_) {
      if (multi_group_id < 0) {
        multi_group_id = i;
      } else {
        Log::Fatal("Bug. There should be only one multi-val group.");
      }
    }
  }
  if (multi_group_id < 0) {
    return nullptr;
  }
  const auto& offsets = feature_groups_[multi_group_id]->bin_offsets_;
  const int num_feature = feature_groups_[multi_group_id]->num_feature_;
  int num_threads = 1;
  #pragma omp parallel
  #pragma omp master
  {
    num_threads = omp_get_num_threads();
  }

  std::vector<std::vector<std::unique_ptr<BinIterator>>> iters(num_threads);
  std::vector<uint32_t> most_freq_bins;
  double sum_sparse_rate = 0;
  for (int i = 0; i < num_feature; ++i) {
    for (int tid = 0; tid < num_threads; ++tid) {
      iters[tid].emplace_back(feature_groups_[multi_group_id]->SubFeatureIterator(i));
    }
    most_freq_bins.push_back(feature_groups_[multi_group_id]->bin_mappers_[i]->GetMostFreqBin());
    sum_sparse_rate += feature_groups_[multi_group_id]->bin_mappers_[i]->sparse_rate();
  }
  sum_sparse_rate /= num_feature;
580
  Log::Debug("Dataset::GetMultiBinFromSparseFeatures: sparse rate %f", sum_sparse_rate);
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
  std::unique_ptr<MultiValBin> ret;
  ret.reset(MultiValBin::CreateMultiValBin(num_data_, offsets.back(), num_feature, sum_sparse_rate));
  PushDataToMultiValBin(num_threads, num_data_, most_freq_bins, offsets, iters, ret.get());
  ret->FinishLoad();
  return ret.release();
}

MultiValBin* Dataset::GetMultiBinFromAllFeatures() const {
  Common::FunctionTimer fun_time("Dataset::GetMultiBinFromAllFeatures", global_timer);
  int num_threads = 1;
  #pragma omp parallel
  #pragma omp master
  {
    num_threads = omp_get_num_threads();
  }
  double sum_dense_ratio = 0;

  std::unique_ptr<MultiValBin> ret;
  std::vector<std::vector<std::unique_ptr<BinIterator>>> iters(num_threads);
  std::vector<uint32_t> most_freq_bins;
  std::vector<uint32_t> offsets;
  int num_total_bin = 1;
  offsets.push_back(num_total_bin);
  for (int gid = 0; gid < num_groups_; ++gid) {
    if (feature_groups_[gid]->is_multi_val_) {
      for (int fid = 0; fid < feature_groups_[gid]->num_feature_; ++fid) {
        const auto& bin_mapper = feature_groups_[gid]->bin_mappers_[fid];
        sum_dense_ratio += 1.0f - bin_mapper->sparse_rate();
        most_freq_bins.push_back(bin_mapper->GetMostFreqBin());
        num_total_bin += bin_mapper->num_bin();
        if (most_freq_bins.back() == 0) {
          num_total_bin -= 1;
        }
        offsets.push_back(num_total_bin);
        for (int tid = 0; tid < num_threads; ++tid) {
          iters[tid].emplace_back(feature_groups_[gid]->SubFeatureIterator(fid));
        }
      }
    } else {
      most_freq_bins.push_back(0);
      num_total_bin += feature_groups_[gid]->bin_offsets_.back() - 1;
      for (int tid = 0; tid < num_threads; ++tid) {
        iters[tid].emplace_back(feature_groups_[gid]->FeatureGroupIterator());
      }
      offsets.push_back(num_total_bin);
      for (int fid = 0; fid < feature_groups_[gid]->num_feature_; ++fid) {
        const auto& bin_mapper = feature_groups_[gid]->bin_mappers_[fid];
        sum_dense_ratio += 1.0f - bin_mapper->sparse_rate();
      }
    }
  }
  sum_dense_ratio /= static_cast<double>(most_freq_bins.size());
633
  Log::Debug("Dataset::GetMultiBinFromAllFeatures: sparse rate %f", 1.0 - sum_dense_ratio);
634
635
636
637
638
639
640
641
642
  ret.reset(MultiValBin::CreateMultiValBin(num_data_, num_total_bin, static_cast<int>(most_freq_bins.size()), 1.0 - sum_dense_ratio));
  PushDataToMultiValBin(num_threads, num_data_, most_freq_bins, offsets, iters, ret.get());
  ret->FinishLoad();
  return ret.release();
}

MultiValBin* Dataset::TestMultiThreadingMethod(score_t* gradients, score_t* hessians, const std::vector<int8_t>& is_feature_used, bool is_constant_hessian,
  bool force_colwise, bool force_rowwise, bool* is_hist_col_wise) const {
  int num_threads = 1;
643
644
645
646
647
  #pragma omp parallel
  #pragma omp master
  {
    num_threads = omp_get_num_threads();
  }
648
649
  Common::FunctionTimer fun_timer("Dataset::TestMultiThreadingMethod", global_timer);
  if (force_colwise && force_rowwise) {
650
    Log::Fatal("Cannot set both `force_col_wise` and `force_row_wise` to `true` at the same time");
651
652
653
654
655
656
657
658
659
660
  }
  if (num_groups_ <= 0) {
    return nullptr;
  }
  if (force_colwise) {
    *is_hist_col_wise = true;
    return GetMultiBinFromSparseFeatures();
  } else if (force_rowwise) {
    *is_hist_col_wise = false;
    auto ret = GetMultiBinFromAllFeatures();
661
    const int num_bin_aligned = (ret->num_bin() + kAlignedSize - 1) / kAlignedSize * kAlignedSize;
662
663
664
665
666
    hist_buf_.resize(static_cast<size_t>(num_bin_aligned) * 2 * num_threads);
    return ret;
  } else {
    std::unique_ptr<MultiValBin> sparse_bin;
    std::unique_ptr<MultiValBin> all_bin;
667
668
    std::chrono::duration<double, std::milli> col_wise_init_time, row_wise_init_time;
    auto start_time = std::chrono::steady_clock::now();
669
    sparse_bin.reset(GetMultiBinFromSparseFeatures());
670
671
    col_wise_init_time = std::chrono::steady_clock::now() - start_time;
    start_time = std::chrono::steady_clock::now();
672
673
    all_bin.reset(GetMultiBinFromAllFeatures());
    std::vector<hist_t, Common::AlignmentAllocator<hist_t, kAlignedSize>> hist_data(NumTotalBin() * 2);
674
    const int num_bin_aligned = (all_bin->num_bin() + kAlignedSize - 1) / kAlignedSize * kAlignedSize;
675
    hist_buf_.resize(static_cast<size_t>(num_bin_aligned) * 2 * num_threads);
676
    row_wise_init_time = std::chrono::steady_clock::now() - start_time;
677
678
    Log::Debug("init for col-wise cost %f seconds, init for row-wise cost %f seconds",
               col_wise_init_time * 1e-3, row_wise_init_time * 1e-3);
679
    std::chrono::duration<double, std::milli> col_wise_time, row_wise_time;
680
    start_time = std::chrono::steady_clock::now();
681
682
683
684
685
    ConstructHistograms(is_feature_used, nullptr, num_data_, gradients, hessians, gradients, hessians, is_constant_hessian, sparse_bin.get(), true, hist_data.data());
    col_wise_time = std::chrono::steady_clock::now() - start_time;
    start_time = std::chrono::steady_clock::now();
    ConstructHistogramsMultiVal(all_bin.get(), nullptr, num_data_, gradients, hessians, is_constant_hessian, hist_data.data());
    row_wise_time = std::chrono::steady_clock::now() - start_time;
686
    Log::Debug("col-wise cost %f seconds, row-wise cost %f seconds",
687
               col_wise_time * 1e-3, row_wise_time * 1e-3);
688
689
690
    if (col_wise_time < row_wise_time) {
      *is_hist_col_wise = true;
      hist_buf_.clear();
691
692
      auto overhead_cost = row_wise_init_time + row_wise_time + col_wise_time;
      Log::Warning(
693
694
          "Auto-choosing col-wise multi-threading, the overhead of testing was %f "
          "seconds.\nYou can set `force_col_wise=true` to remove the "
695
696
          "overhead.",
          overhead_cost * 1e-3);
697
698
699
      return sparse_bin.release();
    } else {
      *is_hist_col_wise = false;
700
701
      auto overhead_cost = col_wise_init_time + row_wise_time + col_wise_time;
      Log::Warning(
702
703
704
          "Auto-choosing row-wise multi-threading, the overhead of testing was %f "
          "seconds.\nYou can set `force_row_wise=true` to remove the "
          "overhead.\nAnd if memory is not enough, you can set "
705
706
          "`force_col_wise=true`.",
          overhead_cost * 1e-3);
707
      if (all_bin->IsSparse()) {
708
        Log::Debug("Using Sparse Multi-Val Bin");
709
      } else {
710
        Log::Debug("Using Dense Multi-Val Bin");
711
712
713
714
715
716
      }
      return all_bin.release();
    }
  }
}

717
void Dataset::CopyFeatureMapperFrom(const Dataset* dataset) {
Guolin Ke's avatar
Guolin Ke committed
718
  feature_groups_.clear();
Guolin Ke's avatar
Guolin Ke committed
719
  num_features_ = dataset->num_features_;
Guolin Ke's avatar
Guolin Ke committed
720
  num_groups_ = dataset->num_groups_;
Guolin Ke's avatar
Guolin Ke committed
721
  // copy feature bin mapper data
Guolin Ke's avatar
Guolin Ke committed
722
723
724
725
726
727
728
  for (int i = 0; i < num_groups_; ++i) {
    std::vector<std::unique_ptr<BinMapper>> bin_mappers;
    for (int j = 0; j < dataset->feature_groups_[i]->num_feature_; ++j) {
      bin_mappers.emplace_back(new BinMapper(*(dataset->feature_groups_[i]->bin_mappers_[j])));
    }
    feature_groups_.emplace_back(new FeatureGroup(
      dataset->feature_groups_[i]->num_feature_,
729
      dataset->feature_groups_[i]->is_multi_val_,
Guolin Ke's avatar
Guolin Ke committed
730
      &bin_mappers,
731
      num_data_));
Guolin Ke's avatar
Guolin Ke committed
732
  }
Guolin Ke's avatar
Guolin Ke committed
733
  feature_groups_.shrink_to_fit();
Guolin Ke's avatar
Guolin Ke committed
734
735
736
  used_feature_map_ = dataset->used_feature_map_;
  num_total_features_ = dataset->num_total_features_;
  feature_names_ = dataset->feature_names_;
Guolin Ke's avatar
Guolin Ke committed
737
  label_idx_ = dataset->label_idx_;
Guolin Ke's avatar
Guolin Ke committed
738
739
740
741
742
743
  real_feature_idx_ = dataset->real_feature_idx_;
  feature2group_ = dataset->feature2group_;
  feature2subfeature_ = dataset->feature2subfeature_;
  group_bin_boundaries_ = dataset->group_bin_boundaries_;
  group_feature_start_ = dataset->group_feature_start_;
  group_feature_cnt_ = dataset->group_feature_cnt_;
Guolin Ke's avatar
Guolin Ke committed
744
  monotone_types_ = dataset->monotone_types_;
Guolin Ke's avatar
Guolin Ke committed
745
  feature_penalty_ = dataset->feature_penalty_;
746
  forced_bin_bounds_ = dataset->forced_bin_bounds_;
Guolin Ke's avatar
Guolin Ke committed
747
  feature_need_push_zeros_ = dataset->feature_need_push_zeros_;
Guolin Ke's avatar
Guolin Ke committed
748
749
750
751
752
753
754
755
756
}

void Dataset::CreateValid(const Dataset* dataset) {
  feature_groups_.clear();
  num_features_ = dataset->num_features_;
  num_groups_ = num_features_;
  feature2group_.clear();
  feature2subfeature_.clear();
  // copy feature bin mapper data
Guolin Ke's avatar
Guolin Ke committed
757
  feature_need_push_zeros_.clear();
Guolin Ke's avatar
Guolin Ke committed
758
759
760
  for (int i = 0; i < num_features_; ++i) {
    std::vector<std::unique_ptr<BinMapper>> bin_mappers;
    bin_mappers.emplace_back(new BinMapper(*(dataset->FeatureBinMapper(i))));
Guolin Ke's avatar
Guolin Ke committed
761
762
763
    if (bin_mappers.back()->GetDefaultBin() != bin_mappers.back()->GetMostFreqBin()) {
      feature_need_push_zeros_.push_back(i);
    }
764
    feature_groups_.emplace_back(new FeatureGroup(&bin_mappers, num_data_));
Guolin Ke's avatar
Guolin Ke committed
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
    feature2group_.push_back(i);
    feature2subfeature_.push_back(0);
  }

  feature_groups_.shrink_to_fit();
  used_feature_map_ = dataset->used_feature_map_;
  num_total_features_ = dataset->num_total_features_;
  feature_names_ = dataset->feature_names_;
  label_idx_ = dataset->label_idx_;
  real_feature_idx_ = dataset->real_feature_idx_;
  group_bin_boundaries_.clear();
  uint64_t num_total_bin = 0;
  group_bin_boundaries_.push_back(num_total_bin);
  for (int i = 0; i < num_groups_; ++i) {
    num_total_bin += feature_groups_[i]->num_total_bin_;
    group_bin_boundaries_.push_back(num_total_bin);
  }
  int last_group = 0;
  group_feature_start_.reserve(num_groups_);
  group_feature_cnt_.reserve(num_groups_);
  group_feature_start_.push_back(0);
  group_feature_cnt_.push_back(1);
  for (int i = 1; i < num_features_; ++i) {
    const int group = feature2group_[i];
    if (group == last_group) {
      group_feature_cnt_.back() = group_feature_cnt_.back() + 1;
    } else {
      group_feature_start_.push_back(i);
      group_feature_cnt_.push_back(1);
      last_group = group;
    }
  }
Guolin Ke's avatar
Guolin Ke committed
797
  monotone_types_ = dataset->monotone_types_;
Guolin Ke's avatar
Guolin Ke committed
798
  feature_penalty_ = dataset->feature_penalty_;
799
  forced_bin_bounds_ = dataset->forced_bin_bounds_;
Guolin Ke's avatar
Guolin Ke committed
800
801
}

Guolin Ke's avatar
Guolin Ke committed
802
803
804
void Dataset::ReSize(data_size_t num_data) {
  if (num_data_ != num_data) {
    num_data_ = num_data;
805
    OMP_INIT_EX();
Guolin Ke's avatar
Guolin Ke committed
806
    #pragma omp parallel for schedule(static)
Guolin Ke's avatar
Guolin Ke committed
807
    for (int group = 0; group < num_groups_; ++group) {
808
      OMP_LOOP_EX_BEGIN();
Guolin Ke's avatar
Guolin Ke committed
809
      feature_groups_[group]->bin_data_->ReSize(num_data_);
810
      OMP_LOOP_EX_END();
Guolin Ke's avatar
Guolin Ke committed
811
    }
812
    OMP_THROW_EX();
Guolin Ke's avatar
Guolin Ke committed
813
814
815
816
817
  }
}

void Dataset::CopySubset(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data) {
  CHECK(num_used_indices == num_data_);
818
  OMP_INIT_EX();
Guolin Ke's avatar
Guolin Ke committed
819
  #pragma omp parallel for schedule(static)
Guolin Ke's avatar
Guolin Ke committed
820
  for (int group = 0; group < num_groups_; ++group) {
821
    OMP_LOOP_EX_BEGIN();
Guolin Ke's avatar
Guolin Ke committed
822
    feature_groups_[group]->CopySubset(fullset->feature_groups_[group].get(), used_indices, num_used_indices);
823
    OMP_LOOP_EX_END();
Guolin Ke's avatar
Guolin Ke committed
824
  }
825
  OMP_THROW_EX();
Guolin Ke's avatar
Guolin Ke committed
826
  if (need_meta_data) {
Guolin Ke's avatar
Guolin Ke committed
827
    metadata_.Init(fullset->metadata_, used_indices, num_used_indices);
Guolin Ke's avatar
Guolin Ke committed
828
  }
Guolin Ke's avatar
Guolin Ke committed
829
  is_finish_load_ = true;
Guolin Ke's avatar
Guolin Ke committed
830
831
}

832
bool Dataset::SetFloatField(const char* field_name, const float* field_data, data_size_t num_element) {
Guolin Ke's avatar
Guolin Ke committed
833
834
835
  std::string name(field_name);
  name = Common::Trim(name);
  if (name == std::string("label") || name == std::string("target")) {
836
    #ifdef LABEL_T_USE_DOUBLE
837
    Log::Fatal("Don't support LABEL_T_USE_DOUBLE");
838
    #else
839
    metadata_.SetLabel(field_data, num_element);
840
    #endif
Guolin Ke's avatar
Guolin Ke committed
841
  } else if (name == std::string("weight") || name == std::string("weights")) {
842
    #ifdef LABEL_T_USE_DOUBLE
843
    Log::Fatal("Don't support LABEL_T_USE_DOUBLE");
844
    #else
845
    metadata_.SetWeights(field_data, num_element);
846
    #endif
Guolin Ke's avatar
Guolin Ke committed
847
848
849
850
851
852
853
854
855
856
  } else {
    return false;
  }
  return true;
}

bool Dataset::SetDoubleField(const char* field_name, const double* field_data, data_size_t num_element) {
  std::string name(field_name);
  name = Common::Trim(name);
  if (name == std::string("init_score")) {
857
    metadata_.SetInitScore(field_data, num_element);
Guolin Ke's avatar
Guolin Ke committed
858
  } else {
859
    return false;
Guolin Ke's avatar
Guolin Ke committed
860
  }
861
  return true;
Guolin Ke's avatar
Guolin Ke committed
862
863
}

864
865
866
867
bool Dataset::SetIntField(const char* field_name, const int* field_data, data_size_t num_element) {
  std::string name(field_name);
  name = Common::Trim(name);
  if (name == std::string("query") || name == std::string("group")) {
Guolin Ke's avatar
Guolin Ke committed
868
    metadata_.SetQuery(field_data, num_element);
869
870
871
872
873
874
  } else {
    return false;
  }
  return true;
}

Guolin Ke's avatar
Guolin Ke committed
875
bool Dataset::GetFloatField(const char* field_name, data_size_t* out_len, const float** out_ptr) {
876
877
878
  std::string name(field_name);
  name = Common::Trim(name);
  if (name == std::string("label") || name == std::string("target")) {
879
    #ifdef LABEL_T_USE_DOUBLE
880
    Log::Fatal("Don't support LABEL_T_USE_DOUBLE");
881
    #else
882
883
    *out_ptr = metadata_.label();
    *out_len = num_data_;
884
    #endif
885
  } else if (name == std::string("weight") || name == std::string("weights")) {
886
    #ifdef LABEL_T_USE_DOUBLE
887
    Log::Fatal("Don't support LABEL_T_USE_DOUBLE");
888
    #else
889
890
    *out_ptr = metadata_.weights();
    *out_len = num_data_;
891
    #endif
Guolin Ke's avatar
Guolin Ke committed
892
893
894
895
896
897
898
899
900
901
  } else {
    return false;
  }
  return true;
}

bool Dataset::GetDoubleField(const char* field_name, data_size_t* out_len, const double** out_ptr) {
  std::string name(field_name);
  name = Common::Trim(name);
  if (name == std::string("init_score")) {
902
    *out_ptr = metadata_.init_score();
Guolin Ke's avatar
Guolin Ke committed
903
    *out_len = static_cast<data_size_t>(metadata_.num_init_score());
904
  } else if (name == std::string("feature_penalty")) {
905
    *out_ptr = feature_penalty_.data();
Guolin Ke's avatar
Guolin Ke committed
906
    *out_len = static_cast<data_size_t>(feature_penalty_.size());
907
  } else {
908
909
    return false;
  }
910
  return true;
911
912
}

Guolin Ke's avatar
Guolin Ke committed
913
bool Dataset::GetIntField(const char* field_name, data_size_t* out_len, const int** out_ptr) {
914
915
916
  std::string name(field_name);
  name = Common::Trim(name);
  if (name == std::string("query") || name == std::string("group")) {
917
    *out_ptr = metadata_.query_boundaries();
Guolin Ke's avatar
Guolin Ke committed
918
    *out_len = metadata_.num_queries() + 1;
Guolin Ke's avatar
Guolin Ke committed
919
920
921
  } else {
    return false;
  }
922
  return true;
923
924
}

925
926
927
928
929
bool Dataset::GetInt8Field(const char* field_name, data_size_t* out_len, const int8_t** out_ptr) {
  std::string name(field_name);
  name = Common::Trim(name);
  if (name == std::string("monotone_constraints")) {
    *out_ptr = monotone_types_.data();
Guolin Ke's avatar
Guolin Ke committed
930
    *out_len = static_cast<data_size_t>(monotone_types_.size());
931
932
933
934
935
936
  } else {
    return false;
  }
  return true;
}

Guolin Ke's avatar
Guolin Ke committed
937
void Dataset::SaveBinaryFile(const char* bin_filename) {
Guolin Ke's avatar
Guolin Ke committed
938
  if (bin_filename != nullptr
Guolin Ke's avatar
Guolin Ke committed
939
      && std::string(bin_filename) == data_filename_) {
940
    Log::Warning("Bianry file %s already exists", bin_filename);
Guolin Ke's avatar
Guolin Ke committed
941
942
    return;
  }
Guolin Ke's avatar
Guolin Ke committed
943
  // if not pass a filename, just append ".bin" of original file
Guolin Ke's avatar
Guolin Ke committed
944
  std::string bin_filename_str(data_filename_);
Guolin Ke's avatar
Guolin Ke committed
945
946
947
948
  if (bin_filename == nullptr || bin_filename[0] == '\0') {
    bin_filename_str.append(".bin");
    bin_filename = bin_filename_str.c_str();
  }
Guolin Ke's avatar
Guolin Ke committed
949
  bool is_file_existed = false;
950
951

  if (VirtualFileWriter::Exists(bin_filename)) {
Guolin Ke's avatar
Guolin Ke committed
952
    is_file_existed = true;
953
    Log::Warning("File %s exists, cannot save binary to it", bin_filename);
Guolin Ke's avatar
Guolin Ke committed
954
  }
Guolin Ke's avatar
Guolin Ke committed
955

Guolin Ke's avatar
Guolin Ke committed
956
  if (!is_file_existed) {
957
958
    auto writer = VirtualFileWriter::Make(bin_filename);
    if (!writer->Init()) {
Guolin Ke's avatar
Guolin Ke committed
959
      Log::Fatal("Cannot write binary data to %s ", bin_filename);
Guolin Ke's avatar
Guolin Ke committed
960
    }
961
    Log::Info("Saving data to binary file %s", bin_filename);
962
    size_t size_of_token = std::strlen(binary_file_token);
963
    writer->Write(binary_file_token, size_of_token);
Guolin Ke's avatar
Guolin Ke committed
964
    // get size of header
Guolin Ke's avatar
Guolin Ke committed
965
    size_t size_of_header = sizeof(num_data_) + sizeof(num_features_) + sizeof(num_total_features_)
966
      + sizeof(int) * num_total_features_ + sizeof(label_idx_) + sizeof(num_groups_)
Guolin Ke's avatar
Guolin Ke committed
967
      + 3 * sizeof(int) * num_features_ + sizeof(uint64_t) * (num_groups_ + 1) + 2 * sizeof(int) * num_groups_ + sizeof(int8_t) * num_features_
Belinda Trotta's avatar
Belinda Trotta committed
968
      + sizeof(double) * num_features_ + sizeof(int32_t) * num_total_features_ + sizeof(int) * 3 + sizeof(bool) * 2;
969
970
971
972
    // size of feature names
    for (int i = 0; i < num_total_features_; ++i) {
      size_of_header += feature_names_[i].size() + sizeof(int);
    }
973
974
975
976
    // size of forced bins
    for (int i = 0; i < num_total_features_; ++i) {
      size_of_header += forced_bin_bounds_[i].size() * sizeof(double) + sizeof(int);
    }
977
    writer->Write(&size_of_header, sizeof(size_of_header));
Guolin Ke's avatar
Guolin Ke committed
978
    // write header
979
980
981
982
    writer->Write(&num_data_, sizeof(num_data_));
    writer->Write(&num_features_, sizeof(num_features_));
    writer->Write(&num_total_features_, sizeof(num_total_features_));
    writer->Write(&label_idx_, sizeof(label_idx_));
983
984
985
986
987
    writer->Write(&max_bin_, sizeof(max_bin_));
    writer->Write(&bin_construct_sample_cnt_, sizeof(bin_construct_sample_cnt_));
    writer->Write(&min_data_in_bin_, sizeof(min_data_in_bin_));
    writer->Write(&use_missing_, sizeof(use_missing_));
    writer->Write(&zero_as_missing_, sizeof(zero_as_missing_));
988
989
990
991
992
993
994
995
    writer->Write(used_feature_map_.data(), sizeof(int) * num_total_features_);
    writer->Write(&num_groups_, sizeof(num_groups_));
    writer->Write(real_feature_idx_.data(), sizeof(int) * num_features_);
    writer->Write(feature2group_.data(), sizeof(int) * num_features_);
    writer->Write(feature2subfeature_.data(), sizeof(int) * num_features_);
    writer->Write(group_bin_boundaries_.data(), sizeof(uint64_t) * (num_groups_ + 1));
    writer->Write(group_feature_start_.data(), sizeof(int) * num_groups_);
    writer->Write(group_feature_cnt_.data(), sizeof(int) * num_groups_);
Guolin Ke's avatar
Guolin Ke committed
996
997
998
999
1000
1001
1002
    if (monotone_types_.empty()) {
      ArrayArgs<int8_t>::Assign(&monotone_types_, 0, num_features_);
    }
    writer->Write(monotone_types_.data(), sizeof(int8_t) * num_features_);
    if (ArrayArgs<int8_t>::CheckAllZero(monotone_types_)) {
      monotone_types_.clear();
    }
Guolin Ke's avatar
Guolin Ke committed
1003
1004
1005
1006
1007
1008
1009
    if (feature_penalty_.empty()) {
      ArrayArgs<double>::Assign(&feature_penalty_, 1.0, num_features_);
    }
    writer->Write(feature_penalty_.data(), sizeof(double) * num_features_);
    if (ArrayArgs<double>::CheckAll(feature_penalty_, 1.0)) {
      feature_penalty_.clear();
    }
Belinda Trotta's avatar
Belinda Trotta committed
1010
1011
1012
1013
1014
1015
1016
    if (max_bin_by_feature_.empty()) {
      ArrayArgs<int32_t>::Assign(&max_bin_by_feature_, -1, num_total_features_);
    }
    writer->Write(max_bin_by_feature_.data(), sizeof(int32_t) * num_total_features_);
    if (ArrayArgs<int32_t>::CheckAll(max_bin_by_feature_, -1)) {
      max_bin_by_feature_.clear();
    }
1017
1018
1019
    // write feature names
    for (int i = 0; i < num_total_features_; ++i) {
      int str_len = static_cast<int>(feature_names_[i].size());
1020
      writer->Write(&str_len, sizeof(int));
1021
      const char* c_str = feature_names_[i].c_str();
1022
      writer->Write(c_str, sizeof(char) * str_len);
1023
    }
1024
1025
1026
1027
    // write forced bins
    for (int i = 0; i < num_total_features_; ++i) {
      int num_bounds = static_cast<int>(forced_bin_bounds_[i].size());
      writer->Write(&num_bounds, sizeof(int));
1028

1029
1030
1031
1032
      for (size_t j = 0; j < forced_bin_bounds_[i].size(); ++j) {
        writer->Write(&forced_bin_bounds_[i][j], sizeof(double));
      }
    }
1033

Guolin Ke's avatar
Guolin Ke committed
1034
1035
    // get size of meta data
    size_t size_of_metadata = metadata_.SizesInByte();
1036
    writer->Write(&size_of_metadata, sizeof(size_of_metadata));
Guolin Ke's avatar
Guolin Ke committed
1037
    // write meta data
1038
    metadata_.SaveBinaryToFile(writer.get());
Guolin Ke's avatar
Guolin Ke committed
1039
1040

    // write feature data
Guolin Ke's avatar
Guolin Ke committed
1041
    for (int i = 0; i < num_groups_; ++i) {
Guolin Ke's avatar
Guolin Ke committed
1042
      // get size of feature
Guolin Ke's avatar
Guolin Ke committed
1043
      size_t size_of_feature = feature_groups_[i]->SizesInByte();
1044
      writer->Write(&size_of_feature, sizeof(size_of_feature));
Guolin Ke's avatar
Guolin Ke committed
1045
      // write feature
1046
      feature_groups_[i]->SaveBinaryToFile(writer.get());
Guolin Ke's avatar
Guolin Ke committed
1047
1048
1049
1050
    }
  }
}

1051
void Dataset::DumpTextFile(const char* text_filename) {
Guolin Ke's avatar
Guolin Ke committed
1052
1053
1054
1055
1056
1057
  FILE* file = NULL;
#if _MSC_VER
  fopen_s(&file, text_filename, "wt");
#else
  file = fopen(text_filename, "wt");
#endif
1058
1059
1060
1061
1062
  fprintf(file, "num_features: %d\n", num_features_);
  fprintf(file, "num_total_features: %d\n", num_total_features_);
  fprintf(file, "num_groups: %d\n", num_groups_);
  fprintf(file, "num_data: %d\n", num_data_);
  fprintf(file, "feature_names: ");
1063
  for (auto n : feature_names_) {
1064
1065
1066
    fprintf(file, "%s, ", n.c_str());
  }
  fprintf(file, "\nmonotone_constraints: ");
1067
  for (auto i : monotone_types_) {
1068
1069
1070
    fprintf(file, "%d, ", i);
  }
  fprintf(file, "\nfeature_penalty: ");
1071
  for (auto i : feature_penalty_) {
1072
1073
    fprintf(file, "%lf, ", i);
  }
Belinda Trotta's avatar
Belinda Trotta committed
1074
1075
1076
1077
  fprintf(file, "\nmax_bin_by_feature: ");
  for (auto i : max_bin_by_feature_) {
    fprintf(file, "%d, ", i);
  }
1078
  fprintf(file, "\n");
1079
  for (auto n : feature_names_) {
1080
1081
    fprintf(file, "%s, ", n.c_str());
  }
1082
1083
1084
1085
1086
1087
1088
  fprintf(file, "\nforced_bins: ");
  for (int i = 0; i < num_total_features_; ++i) {
    fprintf(file, "\nfeature %d: ", i);
    for (size_t j = 0; j < forced_bin_bounds_[i].size(); ++j) {
      fprintf(file, "%lf, ", forced_bin_bounds_[i][j]);
    }
  }
1089
1090
  std::vector<std::unique_ptr<BinIterator>> iterators;
  iterators.reserve(num_features_);
1091
  for (int j = 0; j < num_features_; ++j) {
1092
1093
1094
1095
    auto group_idx = feature2group_[j];
    auto sub_idx = feature2subfeature_[j];
    iterators.emplace_back(feature_groups_[group_idx]->SubFeatureIterator(sub_idx));
  }
1096
  for (data_size_t i = 0; i < num_data_; ++i) {
1097
    fprintf(file, "\n");
1098
    for (int j = 0; j < num_total_features_; ++j) {
1099
      auto inner_feature_idx = used_feature_map_[j];
1100
1101
      if (inner_feature_idx < 0) {
        fprintf(file, "NA, ");
1102
      } else {
Guolin Ke's avatar
Guolin Ke committed
1103
        fprintf(file, "%d, ", iterators[inner_feature_idx]->Get(i));
1104
1105
1106
1107
1108
1109
      }
    }
  }
  fclose(file);
}

1110
1111
1112
1113
1114
void Dataset::ConstructHistogramsMultiVal(const MultiValBin* multi_val_bin, const data_size_t* data_indices, data_size_t num_data,
                                          const score_t* gradients, const score_t* hessians,
                                          bool is_constant_hessian,
                                          hist_t* hist_data) const {
  Common::FunctionTimer fun_time("Dataset::ConstructHistogramsMultiVal", global_timer);
1115
1116
1117
  if (multi_val_bin == nullptr) {
    return;
  }
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
  int num_threads = 1;
  #pragma omp parallel
  #pragma omp master
  {
    num_threads = omp_get_num_threads();
  }

  global_timer.Start("Dataset::sparse_bin_histogram");
  const int num_bin = multi_val_bin->num_bin();
  const int num_bin_aligned = (num_bin + kAlignedSize - 1) / kAlignedSize * kAlignedSize;
  const int min_data_block_size = 1024;
  const int n_data_block = std::min(num_threads, (num_data + min_data_block_size - 1) / min_data_block_size);
  const int data_block_size = (num_data + n_data_block - 1) / n_data_block;

1132
  const size_t buf_size = static_cast<size_t>(n_data_block - 1) * num_bin_aligned * 2;
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
  if (hist_buf_.size() < buf_size) {
    hist_buf_.resize(buf_size);
  }

  #pragma omp parallel for schedule(static)
  for (int tid = 0; tid < n_data_block; ++tid) {
    data_size_t start = tid * data_block_size;
    data_size_t end = std::min(start + data_block_size, num_data);
    auto data_ptr = hist_data;
    if (tid > 0) {
      data_ptr = hist_buf_.data() + static_cast<size_t>(num_bin_aligned) * 2 * (tid - 1);
    }
1145
    std::memset(reinterpret_cast<void*>(data_ptr), 0, num_bin * kHistEntrySize);
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
    if (data_indices != nullptr && num_data < num_data_) {
      if (!is_constant_hessian) {
        multi_val_bin->ConstructHistogram(data_indices, start, end, gradients, hessians, data_ptr);
      } else {
        multi_val_bin->ConstructHistogram(data_indices, start, end, gradients, data_ptr);
      }
    } else {
      if (!is_constant_hessian) {
        multi_val_bin->ConstructHistogram(start, end, gradients, hessians, data_ptr);
      } else {
        multi_val_bin->ConstructHistogram(start, end, gradients, data_ptr);
      }
    }
  }
  global_timer.Stop("Dataset::sparse_bin_histogram");

  global_timer.Start("Dataset::sparse_bin_histogram_merge");
  const int min_bin_block_size = 512;
  const int n_bin_block = std::min(num_threads, (num_bin + min_bin_block_size - 1) / min_bin_block_size);
  const int bin_block_size = (num_bin + n_bin_block - 1) / n_bin_block;
  if (!is_constant_hessian) {
    #pragma omp parallel for schedule(static)
    for (int t = 0; t < n_bin_block; ++t) {
      const int start = t * bin_block_size;
      const int end = std::min(start + bin_block_size, num_bin);
      for (int tid = 1; tid < n_data_block; ++tid) {
        auto src_ptr = hist_buf_.data() + static_cast<size_t>(num_bin_aligned) * 2 * (tid - 1);
        for (int i = start * 2; i < end * 2; ++i) {
          hist_data[i] += src_ptr[i];
        }
      }
    }
  } else {
    #pragma omp parallel for schedule(static)
    for (int t = 0; t < n_bin_block; ++t) {
      const int start = t * bin_block_size;
      const int end = std::min(start + bin_block_size, num_bin);
      for (int tid = 1; tid < n_data_block; ++tid) {
        auto src_ptr = hist_buf_.data() + static_cast<size_t>(num_bin_aligned) * 2 * (tid - 1);
        for (int i = start * 2; i < end * 2; ++i) {
          hist_data[i] += src_ptr[i];
        }
      }
1189
      for (int i = start; i < end; ++i) {
1190
1191
1192
1193
1194
1195
1196
        GET_HESS(hist_data, i) = GET_HESS(hist_data, i) * hessians[0];
      }
    }
  }
  global_timer.Stop("Dataset::sparse_bin_histogram_merge");
}

1197
1198
void Dataset::ConstructHistograms(const std::vector<int8_t>& is_feature_used,
                                  const data_size_t* data_indices, data_size_t num_data,
1199
1200
                                  const score_t* gradients, const score_t* hessians,
                                  score_t* ordered_gradients, score_t* ordered_hessians,
1201
                                  bool is_constant_hessian,
1202
1203
1204
1205
                                  const MultiValBin* multi_val_bin, bool is_colwise,
                                  hist_t* hist_data) const {
  Common::FunctionTimer fun_timer("Dataset::ConstructHistograms", global_timer);
  if (num_data < 0 || hist_data == nullptr) {
Guolin Ke's avatar
Guolin Ke committed
1206
1207
    return;
  }
1208
1209
1210
1211
1212
1213
1214
  if (!is_colwise) {
    return ConstructHistogramsMultiVal(multi_val_bin, data_indices, num_data, gradients, hessians, is_constant_hessian, hist_data);
  }
  global_timer.Start("Dataset::Get used group");
  std::vector<int> used_dense_group;
  int multi_val_groud_id = -1;
  used_dense_group.reserve(num_groups_);
Guolin Ke's avatar
Guolin Ke committed
1215
1216
  for (int group = 0; group < num_groups_; ++group) {
    const int f_cnt = group_feature_cnt_[group];
1217
    bool is_group_used = false;
Guolin Ke's avatar
Guolin Ke committed
1218
1219
1220
    for (int j = 0; j < f_cnt; ++j) {
      const int fidx = group_feature_start_[group] + j;
      if (is_feature_used[fidx]) {
1221
        is_group_used = true;
Guolin Ke's avatar
Guolin Ke committed
1222
1223
1224
        break;
      }
    }
1225
    if (is_group_used) {
1226
1227
1228
1229
      if (feature_groups_[group]->is_multi_val_) {
        multi_val_groud_id = group;
      } else {
        used_dense_group.push_back(group);
1230
      }
Guolin Ke's avatar
Guolin Ke committed
1231
    }
1232
1233
1234
1235
1236
1237
1238
1239
1240
  }
  int num_used_dense_group = static_cast<int>(used_dense_group.size());
  global_timer.Stop("Dataset::Get used group");
  global_timer.Start("Dataset::dense_bin_histogram");
  if (num_used_dense_group > 0) {
    auto ptr_ordered_grad = gradients;
    auto ptr_ordered_hess = hessians;
    if (data_indices != nullptr && num_data < num_data_) {
      if (!is_constant_hessian) {
1241
        #pragma omp parallel for schedule(static)
1242
1243
1244
1245
1246
        for (data_size_t i = 0; i < num_data; ++i) {
          ordered_gradients[i] = gradients[data_indices[i]];
          ordered_hessians[i] = hessians[data_indices[i]];
        }
      } else {
1247
        #pragma omp parallel for schedule(static)
1248
1249
        for (data_size_t i = 0; i < num_data; ++i) {
          ordered_gradients[i] = gradients[data_indices[i]];
1250
1251
        }
      }
1252
1253
1254
1255
      ptr_ordered_grad = ordered_gradients;
      ptr_ordered_hess = ordered_hessians;
      if (!is_constant_hessian) {
        OMP_INIT_EX();
1256
        #pragma omp parallel for schedule(static)
1257
1258
1259
1260
1261
1262
1263
        for (int gi = 0; gi < num_used_dense_group; ++gi) {
          OMP_LOOP_EX_BEGIN();
          int group = used_dense_group[gi];
          // feature is not used
          auto data_ptr = hist_data + group_bin_boundaries_[group] * 2;
          const int num_bin = feature_groups_[group]->num_total_bin_;
          std::memset(reinterpret_cast<void*>(data_ptr), 0,
1264
                      num_bin * kHistEntrySize);
1265
          // construct histograms for smaller leaf
1266
          feature_groups_[group]->bin_data_->ConstructHistogram(
1267
              data_indices, 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr);
1268
          OMP_LOOP_EX_END();
1269
        }
1270
1271
1272
1273
        OMP_THROW_EX();

      } else {
        OMP_INIT_EX();
1274
        #pragma omp parallel for schedule(static)
1275
1276
1277
1278
1279
1280
1281
        for (int gi = 0; gi < num_used_dense_group; ++gi) {
          OMP_LOOP_EX_BEGIN();
          int group = used_dense_group[gi];
          // feature is not used
          auto data_ptr = hist_data + group_bin_boundaries_[group] * 2;
          const int num_bin = feature_groups_[group]->num_total_bin_;
          std::memset(reinterpret_cast<void*>(data_ptr), 0,
1282
                      num_bin * kHistEntrySize);
1283
          // construct histograms for smaller leaf
1284
          feature_groups_[group]->bin_data_->ConstructHistogram(
1285
1286
1287
1288
1289
1290
              data_indices, 0, num_data, ptr_ordered_grad, data_ptr);
          // fixed hessian.
          for (int i = 0; i < num_bin; ++i) {
            GET_HESS(data_ptr, i) = GET_HESS(data_ptr, i) * hessians[0];
          }
          OMP_LOOP_EX_END();
1291
        }
1292
        OMP_THROW_EX();
1293
      }
1294
    } else {
1295
1296
      if (!is_constant_hessian) {
        OMP_INIT_EX();
1297
        #pragma omp parallel for schedule(static)
1298
1299
1300
1301
1302
1303
1304
        for (int gi = 0; gi < num_used_dense_group; ++gi) {
          OMP_LOOP_EX_BEGIN();
          int group = used_dense_group[gi];
          // feature is not used
          auto data_ptr = hist_data + group_bin_boundaries_[group] * 2;
          const int num_bin = feature_groups_[group]->num_total_bin_;
          std::memset(reinterpret_cast<void*>(data_ptr), 0,
1305
                      num_bin * kHistEntrySize);
1306
          // construct histograms for smaller leaf
1307
          feature_groups_[group]->bin_data_->ConstructHistogram(
1308
1309
              0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr);
          OMP_LOOP_EX_END();
1310
        }
1311
1312
1313
        OMP_THROW_EX();
      } else {
        OMP_INIT_EX();
1314
        #pragma omp parallel for schedule(static)
1315
1316
1317
1318
1319
1320
1321
        for (int gi = 0; gi < num_used_dense_group; ++gi) {
          OMP_LOOP_EX_BEGIN();
          int group = used_dense_group[gi];
          // feature is not used
          auto data_ptr = hist_data + group_bin_boundaries_[group] * 2;
          const int num_bin = feature_groups_[group]->num_total_bin_;
          std::memset(reinterpret_cast<void*>(data_ptr), 0,
1322
                      num_bin * kHistEntrySize);
1323
1324
1325
1326
1327
1328
1329
1330
          // construct histograms for smaller leaf
          feature_groups_[group]->bin_data_->ConstructHistogram(
              0, num_data, ptr_ordered_grad, data_ptr);
          // fixed hessian.
          for (int i = 0; i < num_bin; ++i) {
            GET_HESS(data_ptr, i) = GET_HESS(data_ptr, i) * hessians[0];
          }
          OMP_LOOP_EX_END();
1331
        }
1332
        OMP_THROW_EX();
1333
      }
Guolin Ke's avatar
Guolin Ke committed
1334
1335
    }
  }
1336
1337
1338
1339
1340
  global_timer.Stop("Dataset::dense_bin_histogram");
  if (multi_val_groud_id >= 0) {
    ConstructHistogramsMultiVal(multi_val_bin, data_indices, num_data, gradients, hessians, is_constant_hessian,
                                hist_data + group_bin_boundaries_[multi_val_groud_id] * 2);
  }
Guolin Ke's avatar
Guolin Ke committed
1341
1342
}

1343
void Dataset::FixHistogram(int feature_idx, double sum_gradient, double sum_hessian, hist_t* data) const {
Guolin Ke's avatar
Guolin Ke committed
1344
1345
1346
  const int group = feature2group_[feature_idx];
  const int sub_feature = feature2subfeature_[feature_idx];
  const BinMapper* bin_mapper = feature_groups_[group]->bin_mappers_[sub_feature].get();
Guolin Ke's avatar
Guolin Ke committed
1347
1348
  const int most_freq_bin = bin_mapper->GetMostFreqBin();
  if (most_freq_bin > 0) {
Guolin Ke's avatar
Guolin Ke committed
1349
    const int num_bin = bin_mapper->num_bin();
1350
1351
    GET_GRAD(data, most_freq_bin) = sum_gradient;
    GET_HESS(data, most_freq_bin) = sum_hessian;
Guolin Ke's avatar
Guolin Ke committed
1352
    for (int i = 0; i < num_bin; ++i) {
Guolin Ke's avatar
Guolin Ke committed
1353
      if (i != most_freq_bin) {
1354
1355
        GET_GRAD(data, most_freq_bin) -= GET_GRAD(data, i);
        GET_HESS(data, most_freq_bin) -= GET_HESS(data, i);
Guolin Ke's avatar
Guolin Ke committed
1356
1357
1358
1359
1360
      }
    }
  }
}

1361
template<typename T>
Guolin Ke's avatar
Guolin Ke committed
1362
1363
void PushVector(std::vector<T>* dest, const std::vector<T>& src) {
  dest->reserve(dest->size() + src.size());
1364
  for (auto i : src) {
Guolin Ke's avatar
Guolin Ke committed
1365
    dest->push_back(i);
1366
1367
1368
1369
  }
}

template<typename T>
Guolin Ke's avatar
Guolin Ke committed
1370
1371
void PushOffset(std::vector<T>* dest, const std::vector<T>& src, const T& offset) {
  dest->reserve(dest->size() + src.size());
1372
  for (auto i : src) {
Guolin Ke's avatar
Guolin Ke committed
1373
    dest->push_back(i + offset);
1374
1375
1376
1377
  }
}

template<typename T>
Guolin Ke's avatar
Guolin Ke committed
1378
1379
void PushClearIfEmpty(std::vector<T>* dest, const size_t dest_len, const std::vector<T>& src, const size_t src_len, const T& deflt) {
  if (!dest->empty() && !src.empty()) {
1380
    PushVector(dest, src);
Guolin Ke's avatar
Guolin Ke committed
1381
  } else if (!dest->empty() && src.empty()) {
1382
    for (size_t i = 0; i < src_len; ++i) {
Guolin Ke's avatar
Guolin Ke committed
1383
      dest->push_back(deflt);
1384
    }
Guolin Ke's avatar
Guolin Ke committed
1385
  } else if (dest->empty() && !src.empty()) {
1386
    for (size_t i = 0; i < dest_len; ++i) {
Guolin Ke's avatar
Guolin Ke committed
1387
      dest->push_back(deflt);
1388
1389
1390
1391
1392
    }
    PushVector(dest, src);
  }
}

1393
void Dataset::AddFeaturesFrom(Dataset* other) {
1394
  if (other->num_data_ != num_data_) {
1395
1396
    throw std::runtime_error("Cannot add features from other Dataset with a different number of rows");
  }
Guolin Ke's avatar
Guolin Ke committed
1397
1398
1399
  PushVector(&feature_names_, other->feature_names_);
  PushVector(&feature2subfeature_, other->feature2subfeature_);
  PushVector(&group_feature_cnt_, other->group_feature_cnt_);
1400
  PushVector(&forced_bin_bounds_, other->forced_bin_bounds_);
1401
  feature_groups_.reserve(other->feature_groups_.size());
1402
  for (auto& fg : other->feature_groups_) {
1403
1404
    feature_groups_.emplace_back(new FeatureGroup(*fg));
  }
1405
1406
  for (auto feature_idx : other->used_feature_map_) {
    if (feature_idx >= 0) {
1407
1408
1409
1410
1411
      used_feature_map_.push_back(feature_idx + num_features_);
    } else {
      used_feature_map_.push_back(-1);  // Unused feature.
    }
  }
Guolin Ke's avatar
Guolin Ke committed
1412
1413
  PushOffset(&real_feature_idx_, other->real_feature_idx_, num_total_features_);
  PushOffset(&feature2group_, other->feature2group_, num_groups_);
1414
1415
  auto bin_offset = group_bin_boundaries_.back();
  // Skip the leading 0 when copying group_bin_boundaries.
1416
  for (auto i = other->group_bin_boundaries_.begin()+1; i < other->group_bin_boundaries_.end(); ++i) {
1417
1418
    group_bin_boundaries_.push_back(*i + bin_offset);
  }
Guolin Ke's avatar
Guolin Ke committed
1419
  PushOffset(&group_feature_start_, other->group_feature_start_, num_features_);
1420

Guolin Ke's avatar
Guolin Ke committed
1421
1422
  PushClearIfEmpty(&monotone_types_, num_total_features_, other->monotone_types_, other->num_total_features_, (int8_t)0);
  PushClearIfEmpty(&feature_penalty_, num_total_features_, other->feature_penalty_, other->num_total_features_, 1.0);
1423
  PushClearIfEmpty(&max_bin_by_feature_, num_total_features_, other->max_bin_by_feature_, other->num_total_features_, -1);
1424

1425
1426
1427
1428
1429
  num_features_ += other->num_features_;
  num_total_features_ += other->num_total_features_;
  num_groups_ += other->num_groups_;
}

Guolin Ke's avatar
Guolin Ke committed
1430
}  // namespace LightGBM