/
c_api.h
1044 lines (968 loc) · 49.4 KB
/
c_api.h
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
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
/*!
* \file c_api.h
* \copyright Copyright (c) 2016 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See LICENSE file in the project root for license information.
* \note
* To avoid type conversion on large data, the most of our exposed interface supports both float32 and float64,
* except the following:
* 1. gradient and Hessian;
* 2. current score for training and validation data.
* .
* The reason is that they are called frequently, and the type conversion on them may be time-cost.
*/
#ifndef LIGHTGBM_C_API_H_
#define LIGHTGBM_C_API_H_
#include <LightGBM/export.h>
#include <cstdint>
#include <cstring>
typedef void* DatasetHandle; /*!< \brief Handle of dataset. */
typedef void* BoosterHandle; /*!< \brief Handle of booster. */
#define C_API_DTYPE_FLOAT32 (0) /*!< \brief float32 (single precision float). */
#define C_API_DTYPE_FLOAT64 (1) /*!< \brief float64 (double precision float). */
#define C_API_DTYPE_INT32 (2) /*!< \brief int32. */
#define C_API_DTYPE_INT64 (3) /*!< \brief int64. */
#define C_API_DTYPE_INT8 (4) /*!< \brief int8. */
#define C_API_PREDICT_NORMAL (0) /*!< \brief Normal prediction, with transform (if needed). */
#define C_API_PREDICT_RAW_SCORE (1) /*!< \brief Predict raw score. */
#define C_API_PREDICT_LEAF_INDEX (2) /*!< \brief Predict leaf index. */
#define C_API_PREDICT_CONTRIB (3) /*!< \brief Predict feature contributions (SHAP values). */
/*!
* \brief Get string message of the last error.
* \return Error information
*/
LIGHTGBM_C_EXPORT const char* LGBM_GetLastError();
// --- start Dataset interface
/*!
* \brief Load dataset from file (like LightGBM CLI version does).
* \param filename The name of the file
* \param parameters Additional parameters
* \param reference Used to align bin mapper with other dataset, nullptr means isn't used
* \param[out] out A loaded dataset
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromFile(const char* filename,
const char* parameters,
const DatasetHandle reference,
DatasetHandle* out);
/*!
* \brief Allocate the space for dataset and bucket feature bins according to sampled data.
* \param sample_data Sampled data, grouped by the column
* \param sample_indices Indices of sampled data
* \param ncol Number of columns
* \param num_per_col Size of each sampling column
* \param num_sample_row Number of sampled rows
* \param num_total_row Number of total rows
* \param parameters Additional parameters
* \param[out] out Created dataset
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromSampledColumn(double** sample_data,
int** sample_indices,
int32_t ncol,
const int* num_per_col,
int32_t num_sample_row,
int32_t num_total_row,
const char* parameters,
DatasetHandle* out);
/*!
* \brief Allocate the space for dataset and bucket feature bins according to reference dataset.
* \param reference Used to align bin mapper with other dataset
* \param num_total_row Number of total rows
* \param[out] out Created dataset
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateByReference(const DatasetHandle reference,
int64_t num_total_row,
DatasetHandle* out);
/*!
* \brief Push data to existing dataset, if ``nrow + start_row == num_total_row``, will call ``dataset->FinishLoad``.
* \param dataset Handle of dataset
* \param data Pointer to the data space
* \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \param nrow Number of rows
* \param ncol Number of columns
* \param start_row Row start index
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetPushRows(DatasetHandle dataset,
const void* data,
int data_type,
int32_t nrow,
int32_t ncol,
int32_t start_row);
/*!
* \brief Push data to existing dataset, if ``nrow + start_row == num_total_row``, will call ``dataset->FinishLoad``.
* \param dataset Handle of dataset
* \param indptr Pointer to row headers
* \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
* \param indices Pointer to column indices
* \param data Pointer to the data space
* \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \param nindptr Number of rows in the matrix + 1
* \param nelem Number of nonzero elements in the matrix
* \param num_col Number of columns
* \param start_row Row start index
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetPushRowsByCSR(DatasetHandle dataset,
const void* indptr,
int indptr_type,
const int32_t* indices,
const void* data,
int data_type,
int64_t nindptr,
int64_t nelem,
int64_t num_col,
int64_t start_row);
/*!
* \brief Create a dataset from CSR format.
* \param indptr Pointer to row headers
* \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
* \param indices Pointer to column indices
* \param data Pointer to the data space
* \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \param nindptr Number of rows in the matrix + 1
* \param nelem Number of nonzero elements in the matrix
* \param num_col Number of columns
* \param parameters Additional parameters
* \param reference Used to align bin mapper with other dataset, nullptr means isn't used
* \param[out] out Created dataset
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSR(const void* indptr,
int indptr_type,
const int32_t* indices,
const void* data,
int data_type,
int64_t nindptr,
int64_t nelem,
int64_t num_col,
const char* parameters,
const DatasetHandle reference,
DatasetHandle* out);
/*!
* \brief Create a dataset from CSR format through callbacks.
* \param get_row_funptr Pointer to ``std::function<void(int idx, std::vector<std::pair<int, double>>& ret)>``
* (called for every row and expected to clear and fill ``ret``)
* \param num_rows Number of rows
* \param num_col Number of columns
* \param parameters Additional parameters
* \param reference Used to align bin mapper with other dataset, nullptr means isn't used
* \param[out] out Created dataset
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSRFunc(void* get_row_funptr,
int num_rows,
int64_t num_col,
const char* parameters,
const DatasetHandle reference,
DatasetHandle* out);
/*!
* \brief Create a dataset from CSC format.
* \param col_ptr Pointer to column headers
* \param col_ptr_type Type of ``col_ptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
* \param indices Pointer to row indices
* \param data Pointer to the data space
* \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \param ncol_ptr Number of columns in the matrix + 1
* \param nelem Number of nonzero elements in the matrix
* \param num_row Number of rows
* \param parameters Additional parameters
* \param reference Used to align bin mapper with other dataset, nullptr means isn't used
* \param[out] out Created dataset
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSC(const void* col_ptr,
int col_ptr_type,
const int32_t* indices,
const void* data,
int data_type,
int64_t ncol_ptr,
int64_t nelem,
int64_t num_row,
const char* parameters,
const DatasetHandle reference,
DatasetHandle* out);
/*!
* \brief Create dataset from dense matrix.
* \param data Pointer to the data space
* \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \param nrow Number of rows
* \param ncol Number of columns
* \param is_row_major 1 for row-major, 0 for column-major
* \param parameters Additional parameters
* \param reference Used to align bin mapper with other dataset, nullptr means isn't used
* \param[out] out Created dataset
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromMat(const void* data,
int data_type,
int32_t nrow,
int32_t ncol,
int is_row_major,
const char* parameters,
const DatasetHandle reference,
DatasetHandle* out);
/*!
* \brief Create dataset from array of dense matrices.
* \param nmat Number of dense matrices
* \param data Pointer to the data space
* \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \param nrow Number of rows
* \param ncol Number of columns
* \param is_row_major 1 for row-major, 0 for column-major
* \param parameters Additional parameters
* \param reference Used to align bin mapper with other dataset, nullptr means isn't used
* \param[out] out Created dataset
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromMats(int32_t nmat,
const void** data,
int data_type,
int32_t* nrow,
int32_t ncol,
int is_row_major,
const char* parameters,
const DatasetHandle reference,
DatasetHandle* out);
/*!
* \brief Create subset of a data.
* \param handle Handle of full dataset
* \param used_row_indices Indices used in subset
* \param num_used_row_indices Length of ``used_row_indices``
* \param parameters Additional parameters
* \param[out] out Subset of data
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetGetSubset(const DatasetHandle handle,
const int32_t* used_row_indices,
int32_t num_used_row_indices,
const char* parameters,
DatasetHandle* out);
/*!
* \brief Save feature names to dataset.
* \param handle Handle of dataset
* \param feature_names Feature names
* \param num_feature_names Number of feature names
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetSetFeatureNames(DatasetHandle handle,
const char** feature_names,
int num_feature_names);
/*!
* \brief Get feature names of dataset.
* \param handle Handle of dataset
* \param[out] feature_names Feature names, should pre-allocate memory
* \param[out] num_feature_names Number of feature names
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetGetFeatureNames(DatasetHandle handle,
char** feature_names,
int* num_feature_names);
/*!
* \brief Free space for dataset.
* \param handle Handle of dataset to be freed
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetFree(DatasetHandle handle);
/*!
* \brief Save dataset to binary file.
* \param handle Handle of dataset
* \param filename The name of the file
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetSaveBinary(DatasetHandle handle,
const char* filename);
/*!
* \brief Save dataset to text file, intended for debugging use only.
* \param handle Handle of dataset
* \param filename The name of the file
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetDumpText(DatasetHandle handle,
const char* filename);
/*!
* \brief Set vector to a content in info.
* \note
* - \a group only works for ``C_API_DTYPE_INT32``;
* - \a label and \a weight only work for ``C_API_DTYPE_FLOAT32``;
* - \a init_score only works for ``C_API_DTYPE_FLOAT64``.
* \param handle Handle of dataset
* \param field_name Field name, can be \a label, \a weight, \a init_score, \a group
* \param field_data Pointer to data vector
* \param num_element Number of elements in ``field_data``
* \param type Type of ``field_data`` pointer, can be ``C_API_DTYPE_INT32``, ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetSetField(DatasetHandle handle,
const char* field_name,
const void* field_data,
int num_element,
int type);
/*!
* \brief Get info vector from dataset.
* \param handle Handle of dataset
* \param field_name Field name
* \param[out] out_len Used to set result length
* \param[out] out_ptr Pointer to the result
* \param[out] out_type Type of result pointer, can be ``C_API_DTYPE_INT8``, ``C_API_DTYPE_INT32``, ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetGetField(DatasetHandle handle,
const char* field_name,
int* out_len,
const void** out_ptr,
int* out_type);
/*!
* \brief Update parameters for a dataset.
* \param handle Handle of dataset
* \param parameters Parameters
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetUpdateParam(DatasetHandle handle,
const char* parameters);
/*!
* \brief Get number of data points.
* \param handle Handle of dataset
* \param[out] out The address to hold number of data points
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetGetNumData(DatasetHandle handle,
int* out);
/*!
* \brief Get number of features.
* \param handle Handle of dataset
* \param[out] out The address to hold number of features
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetGetNumFeature(DatasetHandle handle,
int* out);
/*!
* \brief Add features from ``source`` to ``target``.
* \param target The handle of the dataset to add features to
* \param source The handle of the dataset to take features from
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetAddFeaturesFrom(DatasetHandle target,
DatasetHandle source);
// --- start Booster interfaces
/*!
* \brief Create a new boosting learner.
* \param train_data Training dataset
* \param parameters Parameters in format 'key1=value1 key2=value2'
* \param[out] out Handle of created booster
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterCreate(const DatasetHandle train_data,
const char* parameters,
BoosterHandle* out);
/*!
* \brief Load an existing booster from model file.
* \param filename Filename of model
* \param[out] out_num_iterations Number of iterations of this booster
* \param[out] out Handle of created booster
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterCreateFromModelfile(const char* filename,
int* out_num_iterations,
BoosterHandle* out);
/*!
* \brief Load an existing booster from string.
* \param model_str Model string
* \param[out] out_num_iterations Number of iterations of this booster
* \param[out] out Handle of created booster
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterLoadModelFromString(const char* model_str,
int* out_num_iterations,
BoosterHandle* out);
/*!
* \brief Free space for booster.
* \param handle Handle of booster to be freed
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterFree(BoosterHandle handle);
/*!
* \brief Shuffle models.
* \param handle Handle of booster
* \param start_iter The first iteration that will be shuffled
* \param end_iter The last iteration that will be shuffled
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterShuffleModels(BoosterHandle handle,
int start_iter,
int end_iter);
/*!
* \brief Merge model from ``other_handle`` into ``handle``.
* \param handle Handle of booster, will merge another booster into this one
* \param other_handle Other handle of booster
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterMerge(BoosterHandle handle,
BoosterHandle other_handle);
/*!
* \brief Add new validation data to booster.
* \param handle Handle of booster
* \param valid_data Validation dataset
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterAddValidData(BoosterHandle handle,
const DatasetHandle valid_data);
/*!
* \brief Reset training data for booster.
* \param handle Handle of booster
* \param train_data Training dataset
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterResetTrainingData(BoosterHandle handle,
const DatasetHandle train_data);
/*!
* \brief Reset config for booster.
* \param handle Handle of booster
* \param parameters Parameters in format 'key1=value1 key2=value2'
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterResetParameter(BoosterHandle handle,
const char* parameters);
/*!
* \brief Get number of classes.
* \param handle Handle of booster
* \param[out] out_len Number of classes
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumClasses(BoosterHandle handle,
int* out_len);
/*!
* \brief Update the model for one iteration.
* \param handle Handle of booster
* \param[out] is_finished 1 means the update was successfully finished (cannot split any more), 0 indicates failure
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIter(BoosterHandle handle,
int* is_finished);
/*!
* \brief Refit the tree model using the new data (online learning).
* \param handle Handle of booster
* \param leaf_preds Pointer to predicted leaf indices
* \param nrow Number of rows of ``leaf_preds``
* \param ncol Number of columns of ``leaf_preds``
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterRefit(BoosterHandle handle,
const int32_t* leaf_preds,
int32_t nrow,
int32_t ncol);
/*!
* \brief Update the model by specifying gradient and Hessian directly
* (this can be used to support customized loss functions).
* \param handle Handle of booster
* \param grad The first order derivative (gradient) statistics
* \param hess The second order derivative (Hessian) statistics
* \param[out] is_finished 1 means the update was successfully finished (cannot split any more), 0 indicates failure
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIterCustom(BoosterHandle handle,
const float* grad,
const float* hess,
int* is_finished);
/*!
* \brief Rollback one iteration.
* \param handle Handle of booster
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterRollbackOneIter(BoosterHandle handle);
/*!
* \brief Get index of the current boosting iteration.
* \param handle Handle of booster
* \param[out] out_iteration Index of the current boosting iteration
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetCurrentIteration(BoosterHandle handle,
int* out_iteration);
/*!
* \brief Get number of trees per iteration.
* \param handle Handle of booster
* \param[out] out_tree_per_iteration Number of trees per iteration
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterNumModelPerIteration(BoosterHandle handle,
int* out_tree_per_iteration);
/*!
* \brief Get number of weak sub-models.
* \param handle Handle of booster
* \param[out] out_models Number of weak sub-models
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterNumberOfTotalModel(BoosterHandle handle,
int* out_models);
/*!
* \brief Get number of evaluation datasets.
* \param handle Handle of booster
* \param[out] out_len Total number of evaluation datasets
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalCounts(BoosterHandle handle,
int* out_len);
/*!
* \brief Get names of evaluation datasets.
* \param handle Handle of booster
* \param[out] out_len Total number of evaluation datasets
* \param[out] out_strs Names of evaluation datasets, should pre-allocate memory
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalNames(BoosterHandle handle,
int* out_len,
char** out_strs);
/*!
* \brief Get names of features.
* \param handle Handle of booster
* \param[out] out_len Total number of features
* \param[out] out_strs Names of features, should pre-allocate memory
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetFeatureNames(BoosterHandle handle,
int* out_len,
char** out_strs);
/*!
* \brief Get number of features.
* \param handle Handle of booster
* \param[out] out_len Total number of features
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumFeature(BoosterHandle handle,
int* out_len);
/*!
* \brief Get evaluation for training data and validation data.
* \note
* 1. You should call ``LGBM_BoosterGetEvalNames`` first to get the names of evaluation datasets.
* 2. You should pre-allocate memory for ``out_results``, you can get its length by ``LGBM_BoosterGetEvalCounts``.
* \param handle Handle of booster
* \param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on
* \param[out] out_len Length of output result
* \param[out] out_results Array with evaluation results
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetEval(BoosterHandle handle,
int data_idx,
int* out_len,
double* out_results);
/*!
* \brief Get number of predictions for training data and validation data
* (this can be used to support customized evaluation functions).
* \param handle Handle of booster
* \param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on
* \param[out] out_len Number of predictions
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumPredict(BoosterHandle handle,
int data_idx,
int64_t* out_len);
/*!
* \brief Get prediction for training data and validation data.
* \note
* You should pre-allocate memory for ``out_result``, its length is equal to ``num_class * num_data``.
* \param handle Handle of booster
* \param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on
* \param[out] out_len Length of output result
* \param[out] out_result Pointer to array with predictions
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetPredict(BoosterHandle handle,
int data_idx,
int64_t* out_len,
double* out_result);
/*!
* \brief Make prediction for file.
* \param handle Handle of booster
* \param data_filename Filename of file with data
* \param data_has_header Whether file has header or not
* \param predict_type What should be predicted
* - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
* - ``C_API_PREDICT_RAW_SCORE``: raw score;
* - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
* - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
* \param num_iteration Number of iterations for prediction, <= 0 means no limit
* \param parameter Other parameters for prediction, e.g. early stopping for prediction
* \param result_filename Filename of result file in which predictions will be written
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForFile(BoosterHandle handle,
const char* data_filename,
int data_has_header,
int predict_type,
int num_iteration,
const char* parameter,
const char* result_filename);
/*!
* \brief Get number of predictions.
* \param handle Handle of booster
* \param num_row Number of rows
* \param predict_type What should be predicted
* - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
* - ``C_API_PREDICT_RAW_SCORE``: raw score;
* - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
* - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
* \param num_iteration Number of iterations for prediction, <= 0 means no limit
* \param[out] out_len Length of prediction
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterCalcNumPredict(BoosterHandle handle,
int num_row,
int predict_type,
int num_iteration,
int64_t* out_len);
/*!
* \brief Make prediction for a new dataset in CSR format.
* \note
* You should pre-allocate memory for ``out_result``:
* - for normal and raw score, its length is equal to ``num_class * num_data``;
* - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
* - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
* \param handle Handle of booster
* \param indptr Pointer to row headers
* \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
* \param indices Pointer to column indices
* \param data Pointer to the data space
* \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \param nindptr Number of rows in the matrix + 1
* \param nelem Number of nonzero elements in the matrix
* \param num_col Number of columns
* \param predict_type What should be predicted
* - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
* - ``C_API_PREDICT_RAW_SCORE``: raw score;
* - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
* - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
* \param num_iteration Number of iterations for prediction, <= 0 means no limit
* \param parameter Other parameters for prediction, e.g. early stopping for prediction
* \param[out] out_len Length of output result
* \param[out] out_result Pointer to array with predictions
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSR(BoosterHandle handle,
const void* indptr,
int indptr_type,
const int32_t* indices,
const void* data,
int data_type,
int64_t nindptr,
int64_t nelem,
int64_t num_col,
int predict_type,
int num_iteration,
const char* parameter,
int64_t* out_len,
double* out_result);
/*!
* \brief Make prediction for a new dataset in CSR format. This method re-uses the internal predictor structure
* from previous calls and is optimized for single row invocation.
* \note
* You should pre-allocate memory for ``out_result``:
* - for normal and raw score, its length is equal to ``num_class * num_data``;
* - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
* - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
* \param handle Handle of booster
* \param indptr Pointer to row headers
* \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
* \param indices Pointer to column indices
* \param data Pointer to the data space
* \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \param nindptr Number of rows in the matrix + 1
* \param nelem Number of nonzero elements in the matrix
* \param num_col Number of columns
* \param predict_type What should be predicted
* - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
* - ``C_API_PREDICT_RAW_SCORE``: raw score;
* - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
* - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
* \param num_iteration Number of iterations for prediction, <= 0 means no limit
* \param parameter Other parameters for prediction, e.g. early stopping for prediction
* \param[out] out_len Length of output result
* \param[out] out_result Pointer to array with predictions
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRow(BoosterHandle handle,
const void* indptr,
int indptr_type,
const int32_t* indices,
const void* data,
int data_type,
int64_t nindptr,
int64_t nelem,
int64_t num_col,
int predict_type,
int num_iteration,
const char* parameter,
int64_t* out_len,
double* out_result);
/*!
* \brief Make prediction for a new dataset in CSC format.
* \note
* You should pre-allocate memory for ``out_result``:
* - for normal and raw score, its length is equal to ``num_class * num_data``;
* - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
* - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
* \param handle Handle of booster
* \param col_ptr Pointer to column headers
* \param col_ptr_type Type of ``col_ptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
* \param indices Pointer to row indices
* \param data Pointer to the data space
* \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \param ncol_ptr Number of columns in the matrix + 1
* \param nelem Number of nonzero elements in the matrix
* \param num_row Number of rows
* \param predict_type What should be predicted
* - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
* - ``C_API_PREDICT_RAW_SCORE``: raw score;
* - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
* - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
* \param num_iteration Number of iteration for prediction, <= 0 means no limit
* \param parameter Other parameters for prediction, e.g. early stopping for prediction
* \param[out] out_len Length of output result
* \param[out] out_result Pointer to array with predictions
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSC(BoosterHandle handle,
const void* col_ptr,
int col_ptr_type,
const int32_t* indices,
const void* data,
int data_type,
int64_t ncol_ptr,
int64_t nelem,
int64_t num_row,
int predict_type,
int num_iteration,
const char* parameter,
int64_t* out_len,
double* out_result);
/*!
* \brief Make prediction for a new dataset.
* \note
* You should pre-allocate memory for ``out_result``:
* - for normal and raw score, its length is equal to ``num_class * num_data``;
* - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
* - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
* \param handle Handle of booster
* \param data Pointer to the data space
* \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \param nrow Number of rows
* \param ncol Number of columns
* \param is_row_major 1 for row-major, 0 for column-major
* \param predict_type What should be predicted
* - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
* - ``C_API_PREDICT_RAW_SCORE``: raw score;
* - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
* - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
* \param num_iteration Number of iteration for prediction, <= 0 means no limit
* \param parameter Other parameters for prediction, e.g. early stopping for prediction
* \param[out] out_len Length of output result
* \param[out] out_result Pointer to array with predictions
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMat(BoosterHandle handle,
const void* data,
int data_type,
int32_t nrow,
int32_t ncol,
int is_row_major,
int predict_type,
int num_iteration,
const char* parameter,
int64_t* out_len,
double* out_result);
/*!
* \brief Make prediction for a new dataset. This method re-uses the internal predictor structure
* from previous calls and is optimized for single row invocation.
* \note
* You should pre-allocate memory for ``out_result``:
* - for normal and raw score, its length is equal to ``num_class * num_data``;
* - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
* - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
* \param handle Handle of booster
* \param data Pointer to the data space
* \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \param ncol Number columns
* \param is_row_major 1 for row-major, 0 for column-major
* \param predict_type What should be predicted
* - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
* - ``C_API_PREDICT_RAW_SCORE``: raw score;
* - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
* - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
* \param num_iteration Number of iteration for prediction, <= 0 means no limit
* \param parameter Other parameters for prediction, e.g. early stopping for prediction
* \param[out] out_len Length of output result
* \param[out] out_result Pointer to array with predictions
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRow(BoosterHandle handle,
const void* data,
int data_type,
int ncol,
int is_row_major,
int predict_type,
int num_iteration,
const char* parameter,
int64_t* out_len,
double* out_result);
/*!
* \brief Make prediction for a new dataset presented in a form of array of pointers to rows.
* \note
* You should pre-allocate memory for ``out_result``:
* - for normal and raw score, its length is equal to ``num_class * num_data``;
* - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
* - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
* \param handle Handle of booster
* \param data Pointer to the data space
* \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
* \param nrow Number of rows
* \param ncol Number columns
* \param predict_type What should be predicted
* - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
* - ``C_API_PREDICT_RAW_SCORE``: raw score;
* - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
* - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
* \param num_iteration Number of iteration for prediction, <= 0 means no limit
* \param parameter Other parameters for prediction, e.g. early stopping for prediction
* \param[out] out_len Length of output result
* \param[out] out_result Pointer to array with predictions
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMats(BoosterHandle handle,
const void** data,
int data_type,
int32_t nrow,
int32_t ncol,
int predict_type,
int num_iteration,
const char* parameter,
int64_t* out_len,
double* out_result);
/*!
* \brief Save model into file.
* \param handle Handle of booster
* \param start_iteration Start index of the iteration that should be saved
* \param num_iteration Index of the iteration that should be saved, <= 0 means save all
* \param filename The name of the file
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterSaveModel(BoosterHandle handle,
int start_iteration,
int num_iteration,
const char* filename);
/*!
* \brief Save model to string.
* \param handle Handle of booster
* \param start_iteration Start index of the iteration that should be saved
* \param num_iteration Index of the iteration that should be saved, <= 0 means save all
* \param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer
* \param[out] out_len Actual output length
* \param[out] out_str String of model, should pre-allocate memory
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterSaveModelToString(BoosterHandle handle,
int start_iteration,
int num_iteration,
int64_t buffer_len,
int64_t* out_len,
char* out_str);
/*!
* \brief Dump model to JSON.
* \param handle Handle of booster
* \param start_iteration Start index of the iteration that should be dumped
* \param num_iteration Index of the iteration that should be dumped, <= 0 means dump all
* \param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer
* \param[out] out_len Actual output length
* \param[out] out_str JSON format string of model, should pre-allocate memory
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterDumpModel(BoosterHandle handle,
int start_iteration,
int num_iteration,
int64_t buffer_len,
int64_t* out_len,
char* out_str);
/*!
* \brief Get leaf value.
* \param handle Handle of booster
* \param tree_idx Index of tree
* \param leaf_idx Index of leaf
* \param[out] out_val Output result from the specified leaf
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetLeafValue(BoosterHandle handle,
int tree_idx,
int leaf_idx,
double* out_val);
/*!
* \brief Set leaf value.
* \param handle Handle of booster
* \param tree_idx Index of tree
* \param leaf_idx Index of leaf
* \param val Leaf value
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterSetLeafValue(BoosterHandle handle,
int tree_idx,
int leaf_idx,
double val);
/*!
* \brief Get model feature importance.
* \param handle Handle of booster
* \param num_iteration Number of iterations for which feature importance is calculated, <= 0 means use all
* \param importance_type Method of importance calculation:
* - 0 for split, result contains numbers of times the feature is used in a model;
* - 1 for gain, result contains total gains of splits which use the feature
* \param[out] out_results Result array with feature importance
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterFeatureImportance(BoosterHandle handle,
int num_iteration,
int importance_type,
double* out_results);
/*!
* \brief Initialize the network.
* \param machines List of machines in format 'ip1:port1,ip2:port2'
* \param local_listen_port TCP listen port for local machines
* \param listen_time_out Socket time-out in minutes
* \param num_machines Total number of machines
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_NetworkInit(const char* machines,
int local_listen_port,