fdf533f2b9
Support adaptive tree, a feature supported by both sklearn and lightgbm. The tree leaf is recomputed based on residue of labels and predictions after construction. For l1 error, the optimal value is the median (50 percentile). This is marked as experimental support for the following reasons: - The value is not well defined for distributed training, where we might have empty leaves for local workers. Right now I just use the original leaf value for computing the average with other workers, which might cause significant errors. - Some follow-ups are required, for exact, pruner, and optimization for quantile function. Also, we need to calculate the initial estimation.
98 lines
3.1 KiB
C++
98 lines
3.1 KiB
C++
/*!
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* Copyright 2020 by Contributors
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* \file iterative_device_dmatrix.h
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*/
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#ifndef XGBOOST_DATA_ITERATIVE_DEVICE_DMATRIX_H_
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#define XGBOOST_DATA_ITERATIVE_DEVICE_DMATRIX_H_
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#include <vector>
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#include <string>
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#include <utility>
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#include <memory>
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#include "xgboost/base.h"
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#include "xgboost/data.h"
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#include "xgboost/c_api.h"
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#include "proxy_dmatrix.h"
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#include "simple_batch_iterator.h"
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namespace xgboost {
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namespace data {
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class IterativeDeviceDMatrix : public DMatrix {
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MetaInfo info_;
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Context ctx_;
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BatchParam batch_param_;
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std::shared_ptr<EllpackPage> page_;
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DMatrixHandle proxy_;
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DataIterResetCallback *reset_;
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XGDMatrixCallbackNext *next_;
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public:
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void Initialize(DataIterHandle iter, float missing, int nthread);
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public:
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explicit IterativeDeviceDMatrix(DataIterHandle iter, DMatrixHandle proxy,
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DataIterResetCallback *reset,
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XGDMatrixCallbackNext *next, float missing,
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int nthread, int max_bin)
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: proxy_{proxy}, reset_{reset}, next_{next} {
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batch_param_ = BatchParam{0, max_bin};
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this->Initialize(iter, missing, nthread);
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}
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~IterativeDeviceDMatrix() override = default;
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bool EllpackExists() const override { return true; }
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bool SparsePageExists() const override { return false; }
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DMatrix *Slice(common::Span<int32_t const> ridxs) override {
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LOG(FATAL) << "Slicing DMatrix is not supported for Device DMatrix.";
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return nullptr;
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}
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BatchSet<SparsePage> GetRowBatches() override {
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LOG(FATAL) << "Not implemented.";
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return BatchSet<SparsePage>(BatchIterator<SparsePage>(nullptr));
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}
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BatchSet<CSCPage> GetColumnBatches() override {
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LOG(FATAL) << "Not implemented.";
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return BatchSet<CSCPage>(BatchIterator<CSCPage>(nullptr));
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}
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BatchSet<SortedCSCPage> GetSortedColumnBatches() override {
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LOG(FATAL) << "Not implemented.";
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return BatchSet<SortedCSCPage>(BatchIterator<SortedCSCPage>(nullptr));
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}
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BatchSet<GHistIndexMatrix> GetGradientIndex(const BatchParam&) override {
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LOG(FATAL) << "Not implemented.";
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return BatchSet<GHistIndexMatrix>(BatchIterator<GHistIndexMatrix>(nullptr));
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}
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BatchSet<EllpackPage> GetEllpackBatches(const BatchParam& param) override;
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bool SingleColBlock() const override { return true; }
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MetaInfo &Info() override { return info_; }
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MetaInfo const &Info() const override { return info_; }
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Context const *Ctx() const override { return &ctx_; }
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};
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#if !defined(XGBOOST_USE_CUDA)
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inline void IterativeDeviceDMatrix::Initialize(DataIterHandle iter, float missing, int nthread) {
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// silent the warning about unused variables.
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(void)(proxy_);
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(void)(reset_);
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(void)(next_);
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common::AssertGPUSupport();
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}
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inline BatchSet<EllpackPage> IterativeDeviceDMatrix::GetEllpackBatches(const BatchParam& param) {
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common::AssertGPUSupport();
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auto begin_iter =
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BatchIterator<EllpackPage>(new SimpleBatchIteratorImpl<EllpackPage>(page_));
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return BatchSet<EllpackPage>(BatchIterator<EllpackPage>(begin_iter));
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}
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#endif // !defined(XGBOOST_USE_CUDA)
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} // namespace data
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} // namespace xgboost
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#endif // XGBOOST_DATA_ITERATIVE_DEVICE_DMATRIX_H_
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