f0064c07ab
* Refactor configuration [Part II].
* General changes:
** Remove `Init` methods to avoid ambiguity.
** Remove `Configure(std::map<>)` to avoid redundant copying and prepare for
parameter validation. (`std::vector` is returned from `InitAllowUnknown`).
** Add name to tree updaters for easier debugging.
* Learner changes:
** Make `LearnerImpl` the only source of configuration.
All configurations are stored and carried out by `LearnerImpl::Configure()`.
** Remove booster in C API.
Originally kept for "compatibility reason", but did not state why. So here
we just remove it.
** Add a `metric_names_` field in `LearnerImpl`.
** Remove `LazyInit`. Configuration will always be lazy.
** Run `Configure` before every iteration.
* Predictor changes:
** Allocate both cpu and gpu predictor.
** Remove cpu_predictor from gpu_predictor.
`GBTree` is now used to dispatch the predictor.
** Remove some GPU Predictor tests.
* IO
No IO changes. The binary model format stability is tested by comparing
hashing value of save models between two commits
47 lines
1.3 KiB
C++
47 lines
1.3 KiB
C++
/*!
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* Copyright 2015 by Contributors
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* \file objective.cc
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* \brief Registry of all objective functions.
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*/
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#include <xgboost/objective.h>
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#include <dmlc/registry.h>
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#include "../common/host_device_vector.h"
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namespace dmlc {
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DMLC_REGISTRY_ENABLE(::xgboost::ObjFunctionReg);
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} // namespace dmlc
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namespace xgboost {
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// implement factory functions
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ObjFunction* ObjFunction::Create(const std::string& name, GenericParameter const* tparam) {
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auto *e = ::dmlc::Registry< ::xgboost::ObjFunctionReg>::Get()->Find(name);
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if (e == nullptr) {
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for (const auto& entry : ::dmlc::Registry< ::xgboost::ObjFunctionReg>::List()) {
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LOG(INFO) << "Objective candidate: " << entry->name;
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}
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LOG(FATAL) << "Unknown objective function " << name;
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}
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auto pobj = (e->body)();
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pobj->tparam_ = tparam;
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return pobj;
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}
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} // namespace xgboost
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namespace xgboost {
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namespace obj {
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// List of files that will be force linked in static links.
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#ifdef XGBOOST_USE_CUDA
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DMLC_REGISTRY_LINK_TAG(regression_obj_gpu);
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DMLC_REGISTRY_LINK_TAG(hinge_obj_gpu);
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DMLC_REGISTRY_LINK_TAG(multiclass_obj_gpu);
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#else
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DMLC_REGISTRY_LINK_TAG(regression_obj);
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DMLC_REGISTRY_LINK_TAG(hinge_obj);
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DMLC_REGISTRY_LINK_TAG(multiclass_obj);
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#endif // XGBOOST_USE_CUDA
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DMLC_REGISTRY_LINK_TAG(rank_obj);
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} // namespace obj
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} // namespace xgboost
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