7 #ifndef TREELITE_TREE_IMPL_H_ 8 #define TREELITE_TREE_IMPL_H_ 17 #include <unordered_map> 28 inline std::string GetString(T x) {
29 return std::to_string(x);
33 inline std::string GetString<float>(
float x) {
34 std::ostringstream oss;
35 oss << std::setprecision(std::numeric_limits<float>::max_digits10) << x;
40 inline std::string GetString<double>(
double x) {
41 std::ostringstream oss;
42 oss << std::setprecision(std::numeric_limits<double>::max_digits10) << x;
51 ContiguousArray<T>::ContiguousArray()
52 : buffer_(nullptr), size_(0), capacity_(0), owned_buffer_(true) {}
55 ContiguousArray<T>::~ContiguousArray() {
56 if (buffer_ && owned_buffer_) {
62 ContiguousArray<T>::ContiguousArray(ContiguousArray&& other) noexcept
63 : buffer_(other.buffer_), size_(other.size_), capacity_(other.capacity_),
64 owned_buffer_(other.owned_buffer_) {
65 other.buffer_ =
nullptr;
66 other.size_ = other.capacity_ = 0;
71 ContiguousArray<T>::operator=(ContiguousArray&& other) noexcept {
72 if (buffer_ && owned_buffer_) {
75 buffer_ = other.buffer_;
77 capacity_ = other.capacity_;
78 owned_buffer_ = other.owned_buffer_;
79 other.buffer_ =
nullptr;
80 other.size_ = other.capacity_ = 0;
85 inline ContiguousArray<T>
86 ContiguousArray<T>::Clone()
const {
87 ContiguousArray clone;
88 clone.buffer_ =
static_cast<T*
>(std::malloc(
sizeof(T) * capacity_));
90 throw std::runtime_error(
"Could not allocate memory for the clone");
92 std::memcpy(clone.buffer_, buffer_,
sizeof(T) * size_);
94 clone.capacity_ = capacity_;
95 clone.owned_buffer_ =
true;
101 ContiguousArray<T>::UseForeignBuffer(
void* prealloc_buf, std::size_t size) {
102 if (buffer_ && owned_buffer_) {
105 buffer_ =
static_cast<T*
>(prealloc_buf);
108 owned_buffer_ =
false;
111 template <
typename T>
113 ContiguousArray<T>::Data() {
117 template <
typename T>
119 ContiguousArray<T>::Data()
const {
123 template <
typename T>
125 ContiguousArray<T>::End() {
126 return &buffer_[Size()];
129 template <
typename T>
131 ContiguousArray<T>::End()
const {
132 return &buffer_[Size()];
135 template <
typename T>
137 ContiguousArray<T>::Back() {
138 return buffer_[Size() - 1];
141 template <
typename T>
143 ContiguousArray<T>::Back()
const {
144 return buffer_[Size() - 1];
147 template <
typename T>
149 ContiguousArray<T>::Size()
const {
153 template <
typename T>
155 ContiguousArray<T>::Reserve(std::size_t newsize) {
156 if (!owned_buffer_) {
157 throw std::runtime_error(
"Cannot resize when using a foreign buffer; clone first");
159 T* newbuf =
static_cast<T*
>(std::realloc(static_cast<void*>(buffer_),
sizeof(T) * newsize));
161 throw std::runtime_error(
"Could not expand buffer");
167 template <
typename T>
169 ContiguousArray<T>::Resize(std::size_t newsize) {
170 if (!owned_buffer_) {
171 throw std::runtime_error(
"Cannot resize when using a foreign buffer; clone first");
173 if (newsize > capacity_) {
174 std::size_t newcapacity = capacity_;
175 if (newcapacity == 0) {
178 while (newcapacity <= newsize) {
181 Reserve(newcapacity);
186 template <
typename T>
188 ContiguousArray<T>::Resize(std::size_t newsize, T t) {
189 if (!owned_buffer_) {
190 throw std::runtime_error(
"Cannot resize when using a foreign buffer; clone first");
192 std::size_t oldsize = Size();
194 for (std::size_t i = oldsize; i < newsize; ++i) {
199 template <
typename T>
201 ContiguousArray<T>::Clear() {
202 if (!owned_buffer_) {
203 throw std::runtime_error(
"Cannot clear when using a foreign buffer; clone first");
208 template <
typename T>
210 ContiguousArray<T>::PushBack(T t) {
211 if (!owned_buffer_) {
212 throw std::runtime_error(
"Cannot add element when using a foreign buffer; clone first");
214 if (size_ == capacity_) {
215 Reserve(capacity_ * 2);
217 buffer_[size_++] = t;
220 template <
typename T>
222 ContiguousArray<T>::Extend(
const std::vector<T>& other) {
223 if (!owned_buffer_) {
224 throw std::runtime_error(
"Cannot add elements when using a foreign buffer; clone first");
226 std::size_t newsize = size_ + other.size();
227 if (newsize > capacity_) {
228 std::size_t newcapacity = capacity_;
229 if (newcapacity == 0) {
232 while (newcapacity <= newsize) {
235 Reserve(newcapacity);
237 std::memcpy(&buffer_[size_], static_cast<const void*>(other.data()),
sizeof(T) * other.size());
241 template <
typename T>
243 ContiguousArray<T>::operator[](std::size_t idx) {
247 template <
typename T>
249 ContiguousArray<T>::operator[](std::size_t idx)
const {
253 template <
typename T>
255 ContiguousArray<T>::at(std::size_t idx) {
257 throw std::runtime_error(
"nid out of range");
262 template <
typename T>
264 ContiguousArray<T>::at(std::size_t idx)
const {
266 throw std::runtime_error(
"nid out of range");
271 template <
typename T>
273 ContiguousArray<T>::at(
int idx) {
274 if (idx < 0 || static_cast<std::size_t>(idx) >= Size()) {
275 throw std::runtime_error(
"nid out of range");
277 return buffer_[
static_cast<std::size_t
>(idx)];
280 template <
typename T>
282 ContiguousArray<T>::at(
int idx)
const {
283 if (idx < 0 || static_cast<std::size_t>(idx) >= Size()) {
284 throw std::runtime_error(
"nid out of range");
286 return buffer_[
static_cast<std::size_t
>(idx)];
289 template<
typename Container>
290 inline std::vector<std::pair<std::string, std::string> >
291 ModelParam::InitAllowUnknown(
const Container& kwargs) {
292 std::vector<std::pair<std::string, std::string>> unknowns;
293 for (
const auto& e : kwargs) {
294 if (e.first ==
"pred_transform") {
295 std::strncpy(this->pred_transform, e.second.c_str(),
296 TREELITE_MAX_PRED_TRANSFORM_LENGTH - 1);
297 this->pred_transform[TREELITE_MAX_PRED_TRANSFORM_LENGTH - 1] =
'\0';
298 }
else if (e.first ==
"sigmoid_alpha") {
299 this->sigmoid_alpha = dmlc::stof(e.second,
nullptr);
300 }
else if (e.first ==
"global_bias") {
301 this->global_bias = dmlc::stof(e.second,
nullptr);
307 inline std::map<std::string, std::string>
308 ModelParam::__DICT__()
const {
309 std::map<std::string, std::string> ret;
310 ret.emplace(
"pred_transform", std::string(this->pred_transform));
311 ret.emplace(
"sigmoid_alpha", GetString(this->sigmoid_alpha));
312 ret.emplace(
"global_bias", GetString(this->global_bias));
316 inline PyBufferFrame GetPyBufferFromArray(
void* data,
const char* format,
317 std::size_t itemsize, std::size_t nitem) {
318 return PyBufferFrame{data,
const_cast<char*
>(format), itemsize, nitem};
322 template <
typename T>
323 inline const char* InferFormatString() {
326 return (std::is_unsigned<T>::value ?
"=B" :
"=b");
328 return (std::is_unsigned<T>::value ?
"=H" :
"=h");
330 if (std::is_integral<T>::value) {
331 return (std::is_unsigned<T>::value ?
"=L" :
"=l");
333 if (!std::is_floating_point<T>::value) {
334 throw std::runtime_error(
"Could not infer format string");
339 if (std::is_integral<T>::value) {
340 return (std::is_unsigned<T>::value ?
"=Q" :
"=q");
342 if (!std::is_floating_point<T>::value) {
343 throw std::runtime_error(
"Could not infer format string");
348 throw std::runtime_error(
"Unrecognized type");
353 template <
typename T>
354 inline PyBufferFrame GetPyBufferFromArray(ContiguousArray<T>* vec,
const char* format) {
355 return GetPyBufferFromArray(static_cast<void*>(vec->Data()), format,
sizeof(T), vec->Size());
358 template <
typename T>
359 inline PyBufferFrame GetPyBufferFromArray(ContiguousArray<T>* vec) {
360 static_assert(std::is_arithmetic<T>::value,
361 "Use GetPyBufferFromArray(vec, format) for composite types; specify format string manually");
362 return GetPyBufferFromArray(vec, InferFormatString<T>());
365 inline PyBufferFrame GetPyBufferFromScalar(
void* data,
const char* format, std::size_t itemsize) {
366 return GetPyBufferFromArray(data, format, itemsize, 1);
369 template <
typename T>
370 inline PyBufferFrame GetPyBufferFromScalar(T* scalar,
const char* format) {
371 static_assert(std::is_standard_layout<T>::value,
"T must be in the standard layout");
372 return GetPyBufferFromScalar(static_cast<void*>(scalar), format,
sizeof(T));
375 inline PyBufferFrame GetPyBufferFromScalar(TypeInfo* scalar) {
376 using T = std::underlying_type<TypeInfo>::type;
377 return GetPyBufferFromScalar(reinterpret_cast<T*>(scalar), InferFormatString<T>());
380 inline PyBufferFrame GetPyBufferFromScalar(TaskType* scalar) {
381 using T = std::underlying_type<TaskType>::type;
382 return GetPyBufferFromScalar(reinterpret_cast<T*>(scalar), InferFormatString<T>());
385 template <
typename T>
386 inline PyBufferFrame GetPyBufferFromScalar(T* scalar) {
387 static_assert(std::is_arithmetic<T>::value,
388 "Use GetPyBufferFromScalar(scalar, format) for composite types; " 389 "specify format string manually");
390 return GetPyBufferFromScalar(scalar, InferFormatString<T>());
393 template <
typename T>
394 inline void InitArrayFromPyBuffer(ContiguousArray<T>* vec, PyBufferFrame frame) {
395 if (
sizeof(T) != frame.itemsize) {
396 throw std::runtime_error(
"Incorrect itemsize");
398 vec->UseForeignBuffer(frame.buf, frame.nitem);
401 inline void InitScalarFromPyBuffer(TypeInfo* scalar, PyBufferFrame buffer) {
402 using T = std::underlying_type<TypeInfo>::type;
403 if (
sizeof(T) != buffer.itemsize) {
404 throw std::runtime_error(
"Incorrect itemsize");
406 if (buffer.nitem != 1) {
407 throw std::runtime_error(
"nitem must be 1 for a scalar");
409 T* t =
static_cast<T*
>(buffer.buf);
410 *scalar =
static_cast<TypeInfo>(*t);
413 inline void InitScalarFromPyBuffer(TaskType* scalar, PyBufferFrame buffer) {
414 using T = std::underlying_type<TaskType>::type;
415 if (
sizeof(T) != buffer.itemsize) {
416 throw std::runtime_error(
"Incorrect itemsize");
418 if (buffer.nitem != 1) {
419 throw std::runtime_error(
"nitem must be 1 for a scalar");
421 T* t =
static_cast<T*
>(buffer.buf);
422 *scalar =
static_cast<TaskType>(*t);
425 template <
typename T>
426 inline void InitScalarFromPyBuffer(T* scalar, PyBufferFrame buffer) {
427 static_assert(std::is_standard_layout<T>::value,
"T must be in the standard layout");
428 if (
sizeof(T) != buffer.itemsize) {
429 throw std::runtime_error(
"Incorrect itemsize");
431 if (buffer.nitem != 1) {
432 throw std::runtime_error(
"nitem must be 1 for a scalar");
434 T* t =
static_cast<T*
>(buffer.buf);
438 template <
typename T>
439 inline void ReadScalarFromFile(T* scalar, FILE* fp) {
440 static_assert(std::is_standard_layout<T>::value,
"T must be in the standard layout");
441 if (std::fread(scalar,
sizeof(T), 1, fp) < 1) {
442 throw std::runtime_error(
"Could not read a scalar");
446 template <
typename T>
447 inline void WriteScalarToFile(T* scalar, FILE* fp) {
448 static_assert(std::is_standard_layout<T>::value,
"T must be in the standard layout");
449 if (std::fwrite(scalar,
sizeof(T), 1, fp) < 1) {
450 throw std::runtime_error(
"Could not write a scalar");
454 template <
typename T>
455 inline void ReadArrayFromFile(ContiguousArray<T>* vec, FILE* fp) {
457 if (std::fread(&nelem,
sizeof(nelem), 1, fp) < 1) {
458 throw std::runtime_error(
"Could not read the number of elements");
462 const auto nelem_size_t =
static_cast<std::size_t
>(nelem);
463 if (std::fread(vec->Data(),
sizeof(T), nelem_size_t, fp) < nelem_size_t) {
464 throw std::runtime_error(
"Could not read an array");
468 template <
typename T>
469 inline void WriteArrayToFile(ContiguousArray<T>* vec, FILE* fp) {
470 static_assert(
sizeof(uint64_t) >=
sizeof(
size_t),
"size_t too large");
471 const auto nelem =
static_cast<uint64_t
>(vec->Size());
472 if (std::fwrite(&nelem,
sizeof(nelem), 1, fp) < 1) {
473 throw std::runtime_error(
"Could not write the number of elements");
475 const auto nelem_size_t = vec->Size();
476 if (std::fwrite(vec->Data(),
sizeof(T), nelem_size_t, fp) < nelem_size_t) {
477 throw std::runtime_error(
"Could not write an array");
481 template <
typename ThresholdType,
typename LeafOutputType>
482 inline Tree<ThresholdType, LeafOutputType>
483 Tree<ThresholdType, LeafOutputType>::Clone()
const {
484 Tree<ThresholdType, LeafOutputType> tree;
485 tree.num_nodes = num_nodes;
486 tree.nodes_ = nodes_.Clone();
487 tree.leaf_vector_ = leaf_vector_.Clone();
488 tree.leaf_vector_offset_ = leaf_vector_offset_.Clone();
489 tree.matching_categories_ = matching_categories_.Clone();
490 tree.matching_categories_offset_ = matching_categories_offset_.Clone();
494 template <
typename ThresholdType,
typename LeafOutputType>
496 Tree<ThresholdType, LeafOutputType>::GetFormatStringForNode() {
497 if (std::is_same<ThresholdType, float>::value) {
498 return "T{=l=l=L=f=Q=d=d=b=b=?=?=?=?xx}";
500 return "T{=l=l=Lxxxx=d=Q=d=d=b=b=?=?=?=?xx}";
504 constexpr std::size_t kNumFramePerTree = 6;
506 template <
typename ThresholdType,
typename LeafOutputType>
507 template <
typename ScalarHandler,
typename PrimitiveArrayHandler,
typename CompositeArrayHandler>
509 Tree<ThresholdType, LeafOutputType>::SerializeTemplate(
510 ScalarHandler scalar_handler, PrimitiveArrayHandler primitive_array_handler,
511 CompositeArrayHandler composite_array_handler) {
512 scalar_handler(&num_nodes);
513 composite_array_handler(&nodes_, GetFormatStringForNode());
514 primitive_array_handler(&leaf_vector_);
515 primitive_array_handler(&leaf_vector_offset_);
516 primitive_array_handler(&matching_categories_);
517 primitive_array_handler(&matching_categories_offset_);
520 template <
typename ThresholdType,
typename LeafOutputType>
521 template <
typename ScalarHandler,
typename ArrayHandler>
523 Tree<ThresholdType, LeafOutputType>::DeserializeTemplate(
524 ScalarHandler scalar_handler, ArrayHandler array_handler) {
525 scalar_handler(&num_nodes);
526 array_handler(&nodes_);
527 if (static_cast<std::size_t>(num_nodes) != nodes_.Size()) {
528 throw std::runtime_error(
"Could not load the correct number of nodes");
530 array_handler(&leaf_vector_);
531 array_handler(&leaf_vector_offset_);
532 array_handler(&matching_categories_);
533 array_handler(&matching_categories_offset_);
536 template <
typename ThresholdType,
typename LeafOutputType>
538 Tree<ThresholdType, LeafOutputType>::GetPyBuffer(std::vector<PyBufferFrame>* dest) {
539 auto scalar_handler = [dest](
auto* field) {
540 dest->push_back(GetPyBufferFromScalar(field));
542 auto primitive_array_handler = [dest](
auto* field) {
543 dest->push_back(GetPyBufferFromArray(field));
545 auto composite_array_handler = [dest](
auto* field,
const char* format) {
546 dest->push_back(GetPyBufferFromArray(field, format));
548 SerializeTemplate(scalar_handler, primitive_array_handler, composite_array_handler);
551 template <
typename ThresholdType,
typename LeafOutputType>
553 Tree<ThresholdType, LeafOutputType>::SerializeToFile(FILE* dest_fp) {
554 auto scalar_handler = [dest_fp](
auto* field) {
555 WriteScalarToFile(field, dest_fp);
557 auto primitive_array_handler = [dest_fp](
auto* field) {
558 WriteArrayToFile(field, dest_fp);
560 auto composite_array_handler = [dest_fp](
auto* field,
const char* format) {
561 WriteArrayToFile(field, dest_fp);
563 SerializeTemplate(scalar_handler, primitive_array_handler, composite_array_handler);
566 template <
typename ThresholdType,
typename LeafOutputType>
568 Tree<ThresholdType, LeafOutputType>::InitFromPyBuffer(std::vector<PyBufferFrame>::iterator begin,
569 std::vector<PyBufferFrame>::iterator end) {
570 if (std::distance(begin, end) != kNumFramePerTree) {
571 throw std::runtime_error(
"Wrong number of frames specified");
573 auto scalar_handler = [&begin](
auto* field) {
574 InitScalarFromPyBuffer(field, *begin++);
576 auto array_handler = [&begin](
auto* field) {
577 InitArrayFromPyBuffer(field, *begin++);
579 DeserializeTemplate(scalar_handler, array_handler);
582 template <
typename ThresholdType,
typename LeafOutputType>
584 Tree<ThresholdType, LeafOutputType>::DeserializeFromFile(FILE* src_fp) {
585 auto scalar_handler = [src_fp](
auto* field) {
586 ReadScalarFromFile(field, src_fp);
588 auto array_handler = [src_fp](
auto* field) {
589 ReadArrayFromFile(field, src_fp);
591 DeserializeTemplate(scalar_handler, array_handler);
594 template <
typename ThresholdType,
typename LeafOutputType>
596 std::memset(
this, 0,
sizeof(
Node));
597 cleft_ = cright_ = -1;
599 info_.leaf_value =
static_cast<LeafOutputType
>(0);
600 info_.threshold =
static_cast<ThresholdType
>(0);
602 sum_hess_ = gain_ = 0.0;
603 data_count_present_ = sum_hess_present_ = gain_present_ =
false;
604 categories_list_right_child_ =
false;
605 split_type_ = SplitFeatureType::kNone;
606 cmp_ = Operator::kNone;
609 template <
typename ThresholdType,
typename LeafOutputType>
612 int nd = num_nodes++;
613 if (nodes_.Size() !=
static_cast<std::size_t
>(nd)) {
614 throw std::runtime_error(
"Invariant violated: nodes_ contains incorrect number of nodes");
616 for (
int nid = nd; nid < num_nodes; ++nid) {
617 leaf_vector_offset_.PushBack(leaf_vector_offset_.Back());
618 matching_categories_offset_.PushBack(matching_categories_offset_.Back());
619 nodes_.Resize(nodes_.Size() + 1);
620 nodes_.Back().Init();
625 template <
typename ThresholdType,
typename LeafOutputType>
629 leaf_vector_.Clear();
630 leaf_vector_offset_.Resize(2, 0);
631 matching_categories_.Clear();
632 matching_categories_offset_.Resize(2, 0);
635 SetLeaf(0, static_cast<LeafOutputType>(0));
638 template <
typename ThresholdType,
typename LeafOutputType>
641 const int cleft = this->AllocNode();
642 const int cright = this->AllocNode();
643 nodes_.at(nid).cleft_ = cleft;
644 nodes_.at(nid).cright_ = cright;
647 template <
typename ThresholdType,
typename LeafOutputType>
648 inline std::vector<unsigned>
650 std::unordered_map<unsigned, bool> tmp;
651 for (
int nid = 0; nid < num_nodes; ++nid) {
653 if (type != SplitFeatureType::kNone) {
654 const bool flag = (type == SplitFeatureType::kCategorical);
655 const uint32_t split_index = SplitIndex(nid);
656 if (tmp.count(split_index) == 0) {
657 tmp[split_index] = flag;
659 if (tmp[split_index] != flag) {
660 throw std::runtime_error(
"Feature " + std::to_string(split_index) +
661 " cannot be simultaneously be categorical and numerical.");
666 std::vector<unsigned> result;
667 for (
const auto& kv : tmp) {
669 result.push_back(kv.first);
672 std::sort(result.begin(), result.end());
676 template <
typename ThresholdType,
typename LeafOutputType>
679 int nid,
unsigned split_index, ThresholdType threshold,
bool default_left,
Operator cmp) {
680 Node& node = nodes_.at(nid);
681 if (split_index >= ((1U << 31U) - 1)) {
682 throw std::runtime_error(
"split_index too big");
684 if (default_left) split_index |= (1U << 31U);
685 node.sindex_ = split_index;
686 (node.info_).threshold = threshold;
688 node.split_type_ = SplitFeatureType::kNumerical;
689 node.categories_list_right_child_ =
false;
692 template <
typename ThresholdType,
typename LeafOutputType>
695 int nid,
unsigned split_index,
bool default_left,
696 const std::vector<uint32_t>& categories_list,
bool categories_list_right_child) {
697 if (split_index >= ((1U << 31U) - 1)) {
698 throw std::runtime_error(
"split_index too big");
701 const std::size_t end_oft = matching_categories_offset_.Back();
702 const std::size_t new_end_oft = end_oft + categories_list.size();
703 if (end_oft != matching_categories_.Size()) {
704 throw std::runtime_error(
"Invariant violated");
706 if (!std::all_of(&matching_categories_offset_.at(nid + 1), matching_categories_offset_.End(),
707 [end_oft](std::size_t x) {
return (x == end_oft); })) {
708 throw std::runtime_error(
"Invariant violated");
711 matching_categories_.Extend(categories_list);
712 if (new_end_oft != matching_categories_.Size()) {
713 throw std::runtime_error(
"Invariant violated");
715 std::for_each(&matching_categories_offset_.at(nid + 1), matching_categories_offset_.End(),
716 [new_end_oft](std::size_t& x) { x = new_end_oft; });
717 std::sort(&matching_categories_.at(end_oft), matching_categories_.End());
719 Node& node = nodes_.at(nid);
720 if (default_left) split_index |= (1U << 31U);
721 node.sindex_ = split_index;
722 node.split_type_ = SplitFeatureType::kCategorical;
723 node.categories_list_right_child_ = categories_list_right_child;
726 template <
typename ThresholdType,
typename LeafOutputType>
729 Node& node = nodes_.at(nid);
730 (node.info_).leaf_value = value;
733 node.split_type_ = SplitFeatureType::kNone;
736 template <
typename ThresholdType,
typename LeafOutputType>
739 int nid,
const std::vector<LeafOutputType>& node_leaf_vector) {
740 const std::size_t end_oft = leaf_vector_offset_.Back();
741 const std::size_t new_end_oft = end_oft + node_leaf_vector.size();
742 if (end_oft != leaf_vector_.Size()) {
743 throw std::runtime_error(
"Invariant violated");
745 if (!std::all_of(&leaf_vector_offset_.at(nid + 1), leaf_vector_offset_.End(),
746 [end_oft](std::size_t x) {
return (x == end_oft); })) {
747 throw std::runtime_error(
"Invariant violated");
750 leaf_vector_.Extend(node_leaf_vector);
751 if (new_end_oft != leaf_vector_.Size()) {
752 throw std::runtime_error(
"Invariant violated");
754 std::for_each(&leaf_vector_offset_.at(nid + 1), leaf_vector_offset_.End(),
755 [new_end_oft](std::size_t& x) { x = new_end_oft; });
757 Node& node = nodes_.at(nid);
760 node.split_type_ = SplitFeatureType::kNone;
763 template <
typename ThresholdType,
typename LeafOutputType>
764 inline std::unique_ptr<Model>
766 std::unique_ptr<Model> model = std::make_unique<ModelImpl<ThresholdType, LeafOutputType>>();
767 model->threshold_type_ = TypeToInfo<ThresholdType>();
768 model->leaf_output_type_ = TypeToInfo<LeafOutputType>();
772 template <
typename ThresholdType,
typename LeafOutputType>
775 inline static std::unique_ptr<Model> Dispatch() {
776 return Model::Create<ThresholdType, LeafOutputType>();
780 inline std::unique_ptr<Model>
782 return DispatchWithModelTypes<ModelCreateImpl>(threshold_type, leaf_output_type);
785 template <
typename ThresholdType,
typename LeafOutputType>
788 template <
typename Func>
789 inline static auto Dispatch(
Model* model, Func func) {
793 template <
typename Func>
794 inline static auto Dispatch(
const Model* model, Func func) {
799 template <
typename Func>
801 Model::Dispatch(Func func) {
802 return DispatchWithModelTypes<ModelDispatchImpl>(threshold_type_, leaf_output_type_,
this, func);
805 template <
typename Func>
807 Model::Dispatch(Func func)
const {
808 return DispatchWithModelTypes<ModelDispatchImpl>(threshold_type_, leaf_output_type_,
this, func);
811 template <
typename HeaderPrimitiveFieldHandlerFunc>
813 Model::SerializeTemplate(HeaderPrimitiveFieldHandlerFunc header_primitive_field_handler) {
814 header_primitive_field_handler(&major_ver_);
815 header_primitive_field_handler(&minor_ver_);
816 header_primitive_field_handler(&patch_ver_);
817 header_primitive_field_handler(&threshold_type_);
818 header_primitive_field_handler(&leaf_output_type_);
821 template <
typename HeaderPrimitiveFieldHandlerFunc>
823 Model::DeserializeTemplate(HeaderPrimitiveFieldHandlerFunc header_primitive_field_handler,
825 int major_ver, minor_ver, patch_ver;
826 header_primitive_field_handler(&major_ver);
827 header_primitive_field_handler(&minor_ver);
828 header_primitive_field_handler(&patch_ver);
829 if (major_ver != TREELITE_VER_MAJOR || minor_ver != TREELITE_VER_MINOR) {
830 throw std::runtime_error(
"Cannot deserialize model from a different version of Treelite");
832 header_primitive_field_handler(&threshold_type);
833 header_primitive_field_handler(&leaf_output_type);
836 template <
typename ThresholdType,
typename LeafOutputType>
837 template <
typename HeaderPrimitiveFieldHandlerFunc,
typename HeaderCompositeFieldHandlerFunc,
838 typename TreeHandlerFunc>
841 HeaderPrimitiveFieldHandlerFunc header_primitive_field_handler,
842 HeaderCompositeFieldHandlerFunc header_composite_field_handler,
843 TreeHandlerFunc tree_handler) {
845 header_primitive_field_handler(&num_feature);
846 header_primitive_field_handler(&task_type);
847 header_primitive_field_handler(&average_tree_output);
848 header_composite_field_handler(&task_param,
"T{=B=?xx=I=I}");
849 header_composite_field_handler(
850 ¶m,
"T{" _TREELITE_STR(TREELITE_MAX_PRED_TRANSFORM_LENGTH)
"s=f=f}");
858 template <
typename ThresholdType,
typename LeafOutputType>
859 template <
typename HeaderFieldHandlerFunc,
typename TreeHandlerFunc>
862 std::size_t num_tree,
863 HeaderFieldHandlerFunc header_field_handler,
864 TreeHandlerFunc tree_handler) {
866 header_field_handler(&num_feature);
867 header_field_handler(&task_type);
868 header_field_handler(&average_tree_output);
869 header_field_handler(&task_param);
870 header_field_handler(¶m);
873 for (std::size_t i = 0; i < num_tree; ++i) {
874 trees.emplace_back();
875 tree_handler(trees.back());
879 template <
typename ThresholdType,
typename LeafOutputType>
882 auto header_primitive_field_handler = [dest](
auto* field) {
883 dest->push_back(GetPyBufferFromScalar(field));
885 auto header_composite_field_handler = [dest](
auto* field,
const char* format) {
886 dest->push_back(GetPyBufferFromScalar(field, format));
889 tree.GetPyBuffer(dest);
891 SerializeTemplate(header_primitive_field_handler, header_composite_field_handler, tree_handler);
894 template <
typename ThresholdType,
typename LeafOutputType>
897 const auto num_tree =
static_cast<uint64_t
>(this->trees.size());
898 WriteScalarToFile(&num_tree, dest_fp);
899 auto header_primitive_field_handler = [dest_fp](
auto* field) {
900 WriteScalarToFile(field, dest_fp);
902 auto header_composite_field_handler = [dest_fp](
auto* field,
const char* format) {
903 WriteScalarToFile(field, dest_fp);
906 tree.SerializeToFile(dest_fp);
908 SerializeTemplate(header_primitive_field_handler, header_composite_field_handler, tree_handler);
911 template <
typename ThresholdType,
typename LeafOutputType>
914 std::vector<PyBufferFrame>::iterator begin, std::vector<PyBufferFrame>::iterator end) {
915 const std::size_t num_frame = std::distance(begin, end);
916 constexpr std::size_t kNumFrameInHeader = 5;
917 if (num_frame < kNumFrameInHeader || (num_frame - kNumFrameInHeader) % kNumFramePerTree != 0) {
918 throw std::runtime_error(
"Wrong number of frames");
920 const std::size_t num_tree = (num_frame - kNumFrameInHeader) / kNumFramePerTree;
922 auto header_field_handler = [&begin](
auto* field) {
923 InitScalarFromPyBuffer(field, *begin++);
928 tree.InitFromPyBuffer(begin, begin + kNumFramePerTree);
929 begin += kNumFramePerTree;
933 DeserializeTemplate(num_tree, header_field_handler, tree_handler);
936 template <
typename ThresholdType,
typename LeafOutputType>
940 ReadScalarFromFile(&num_tree, src_fp);
942 auto header_field_handler = [src_fp](
auto* field) {
943 ReadScalarFromFile(field, src_fp);
947 tree.DeserializeFromFile(src_fp);
950 DeserializeTemplate(num_tree, header_field_handler, tree_handler);
953 inline void InitParamAndCheck(
ModelParam* param,
954 const std::vector<std::pair<std::string, std::string>>& cfg) {
955 auto unknown = param->InitAllowUnknown(cfg);
956 if (!unknown.empty()) {
957 std::ostringstream oss;
958 for (
const auto& kv : unknown) {
959 oss << kv.first <<
", ";
961 std::cerr <<
"\033[1;31mWarning: Unknown parameters found; " 962 <<
"they have been ignored\u001B[0m: " << oss.str() << std::endl;
967 #endif // TREELITE_TREE_IMPL_H_ SplitFeatureType
feature split type
void Init()
initialize the model with a single root node
TaskType
Enum type representing the task type.
in-memory representation of a decision tree
TypeInfo
Types used by thresholds and leaf outputs.
thin wrapper for tree ensemble model
Operator
comparison operators