temp merge, disable 1 line, SetValid
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@@ -1,5 +1,5 @@
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/*!
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* Copyright 2019-2022 by Contributors
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/**
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* Copyright 2019-2023, XGBoost Contributors
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* \file aft_obj.cu
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* \brief Definition of AFT loss for survival analysis.
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* \author Avinash Barnwal, Hyunsu Cho and Toby Hocking
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@@ -41,11 +41,9 @@ class AFTObj : public ObjFunction {
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ObjInfo Task() const override { return ObjInfo::kSurvival; }
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template <typename Distribution>
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void GetGradientImpl(const HostDeviceVector<bst_float> &preds,
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const MetaInfo &info,
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HostDeviceVector<GradientPair> *out_gpair,
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size_t ndata, int device, bool is_null_weight,
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float aft_loss_distribution_scale) {
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void GetGradientImpl(const HostDeviceVector<bst_float>& preds, const MetaInfo& info,
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linalg::Matrix<GradientPair>* out_gpair, size_t ndata, int device,
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bool is_null_weight, float aft_loss_distribution_scale) {
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common::Transform<>::Init(
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[=] XGBOOST_DEVICE(size_t _idx,
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common::Span<GradientPair> _out_gpair,
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@@ -66,16 +64,17 @@ class AFTObj : public ObjFunction {
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_out_gpair[_idx] = GradientPair(grad * w, hess * w);
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},
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common::Range{0, static_cast<int64_t>(ndata)}, this->ctx_->Threads(), device).Eval(
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out_gpair, &preds, &info.labels_lower_bound_, &info.labels_upper_bound_,
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out_gpair->Data(), &preds, &info.labels_lower_bound_, &info.labels_upper_bound_,
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&info.weights_);
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}
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void GetGradient(const HostDeviceVector<bst_float>& preds, const MetaInfo& info, int /*iter*/,
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HostDeviceVector<GradientPair>* out_gpair) override {
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linalg::Matrix<GradientPair>* out_gpair) override {
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const size_t ndata = preds.Size();
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CHECK_EQ(info.labels_lower_bound_.Size(), ndata);
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CHECK_EQ(info.labels_upper_bound_.Size(), ndata);
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out_gpair->Resize(ndata);
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out_gpair->SetDevice(ctx_->Device());
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out_gpair->Reshape(ndata, 1);
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const int device = ctx_->gpu_id;
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const float aft_loss_distribution_scale = param_.aft_loss_distribution_scale;
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const bool is_null_weight = info.weights_.Size() == 0;
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