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
71 lines
2.1 KiB
C++
71 lines
2.1 KiB
C++
/*
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* Copyright 2018 by Contributors
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*/
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#pragma once
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#include <dmlc/registry.h>
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#include <xgboost/base.h>
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#include <xgboost/data.h>
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#include <xgboost/generic_parameters.h>
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#include <functional>
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#include <string>
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#include <utility>
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#include <vector>
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#include "../../src/gbm/gblinear_model.h"
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#include "../../src/common/host_device_vector.h"
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namespace xgboost {
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/*!
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* \brief interface of linear updater
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*/
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class LinearUpdater {
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protected:
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GenericParameter const* learner_param_;
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public:
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/*! \brief virtual destructor */
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virtual ~LinearUpdater() = default;
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/*!
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* \brief Initialize the updater with given arguments.
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* \param args arguments to the objective function.
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*/
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virtual void Configure(
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const std::vector<std::pair<std::string, std::string> >& args) = 0;
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/**
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* \brief Updates linear model given gradients.
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*
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* \param in_gpair The gradient pair statistics of the data.
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* \param data Input data matrix.
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* \param model Model to be updated.
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* \param sum_instance_weight The sum instance weights, used to normalise l1/l2 penalty.
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*/
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virtual void Update(HostDeviceVector<GradientPair>* in_gpair, DMatrix* data,
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gbm::GBLinearModel* model,
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double sum_instance_weight) = 0;
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/*!
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* \brief Create a linear updater given name
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* \param name Name of the linear updater.
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*/
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static LinearUpdater* Create(const std::string& name, GenericParameter const*);
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};
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/*!
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* \brief Registry entry for linear updater.
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*/
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struct LinearUpdaterReg
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: public dmlc::FunctionRegEntryBase<LinearUpdaterReg,
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std::function<LinearUpdater*()> > {};
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/*!
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* \brief Macro to register linear updater.
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*/
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#define XGBOOST_REGISTER_LINEAR_UPDATER(UniqueId, Name) \
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static DMLC_ATTRIBUTE_UNUSED ::xgboost::LinearUpdaterReg& \
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__make_##LinearUpdaterReg##_##UniqueId##__ = \
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::dmlc::Registry< ::xgboost::LinearUpdaterReg>::Get()->__REGISTER__( \
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Name)
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} // namespace xgboost
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