Calculate base_score based on input labels for mae. (#8107)
Fit an intercept as base score for abs loss.
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47
src/common/stats.cu
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47
src/common/stats.cu
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/*!
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* Copyright 2022 by XGBoost Contributors
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*/
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#include <thrust/iterator/counting_iterator.h> // thrust::make_counting_iterator
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#include "common.h" // common::OptionalWeights
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#include "device_helpers.cuh" // dh::MakeTransformIterator, tcbegin, tcend
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#include "stats.cuh" // common::SegmentedQuantile, common::SegmentedWeightedQuantile
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#include "xgboost/generic_parameters.h" // Context
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#include "xgboost/host_device_vector.h" // HostDeviceVector
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#include "xgboost/linalg.h" // linalg::TensorView, UnravelIndex, Apply
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namespace xgboost {
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namespace common {
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namespace cuda {
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float Median(Context const* ctx, linalg::TensorView<float const, 2> t,
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common::OptionalWeights weights) {
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HostDeviceVector<size_t> segments{0, t.Size()};
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segments.SetDevice(ctx->gpu_id);
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auto d_segments = segments.ConstDeviceSpan();
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auto val_it = dh::MakeTransformIterator<float>(
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thrust::make_counting_iterator(0ul), [=] XGBOOST_DEVICE(size_t i) {
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return linalg::detail::Apply(t, linalg::UnravelIndex(i, t.Shape()));
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});
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HostDeviceVector<float> quantile{0};
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quantile.SetDevice(ctx->gpu_id);
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if (weights.Empty()) {
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common::SegmentedQuantile(ctx, 0.5, dh::tcbegin(d_segments), dh::tcend(d_segments), val_it,
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val_it + t.Size(), &quantile);
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} else {
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CHECK_NE(t.Shape(1), 0);
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auto w_it = dh::MakeTransformIterator<float>(thrust::make_counting_iterator(0ul),
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[=] XGBOOST_DEVICE(size_t i) {
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auto sample_idx = i / t.Shape(1);
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return weights[sample_idx];
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});
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common::SegmentedWeightedQuantile(ctx, 0.5, dh::tcbegin(d_segments), dh::tcend(d_segments),
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val_it, val_it + t.Size(), w_it, w_it + t.Size(), &quantile);
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}
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CHECK_EQ(quantile.Size(), 1);
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return quantile.HostVector().front();
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}
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} // namespace cuda
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} // namespace common
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} // namespace xgboost
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