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Multi-target support for L1 error. (#8652)

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- Add matrix support to the median function.
- Iterate through each target for quantile computation.
This commit is contained in:
Jiaming Yuan
2023-01-11 05:51:14 +08:00
committed by GitHub
parent badeff1d74
commit cfa994d57f
19 changed files with 430 additions and 215 deletions
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40
src/common/stats.cc
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@@ -1,11 +1,13 @@
/*!
* Copyright 2022 by XGBoost Contributors
/**
* Copyright 2022-2023 by XGBoost Contributors
*/
#include "stats.h"
#include <cstddef> // std::size_t
#include <numeric> // std::accumulate
#include "common.h" // OptionalWeights
#include "linalg_op.h"
#include "threading_utils.h" // ParallelFor, MemStackAllocator
#include "transform_iterator.h" // MakeIndexTransformIter
#include "xgboost/context.h" // Context
@@ -15,32 +17,32 @@
namespace xgboost {
namespace common {
float Median(Context const* ctx, linalg::Tensor<float, 2> const& t,
HostDeviceVector<float> const& weights) {
CHECK_LE(t.Shape(1), 1) << "Matrix is not yet supported.";
void Median(Context const* ctx, linalg::Tensor<float, 2> const& t,
HostDeviceVector<float> const& weights, linalg::Tensor<float, 1>* out) {
if (!ctx->IsCPU()) {
weights.SetDevice(ctx->gpu_id);
auto opt_weights = OptionalWeights(weights.ConstDeviceSpan());
auto t_v = t.View(ctx->gpu_id);
return cuda_impl::Median(ctx, t_v, opt_weights);
cuda_impl::Median(ctx, t_v, opt_weights, out);
}
auto opt_weights = OptionalWeights(weights.ConstHostSpan());
auto t_v = t.HostView();
auto iter = common::MakeIndexTransformIter(
[&](size_t i) { return linalg::detail::Apply(t_v, linalg::UnravelIndex(i, t_v.Shape())); });
float q{0};
if (opt_weights.Empty()) {
q = common::Quantile(0.5, iter, iter + t_v.Size());
} else {
CHECK_NE(t_v.Shape(1), 0);
auto w_it = common::MakeIndexTransformIter([&](size_t i) {
auto sample_idx = i / t_v.Shape(1);
return opt_weights[sample_idx];
});
q = common::WeightedQuantile(0.5, iter, iter + t_v.Size(), w_it);
out->Reshape(t.Shape(1));
auto h_out = out->HostView();
for (std::size_t i{0}; i < t.Shape(1); ++i) {
auto ti_v = t_v.Slice(linalg::All(), i);
auto iter = linalg::cbegin(ti_v);
float q{0};
if (opt_weights.Empty()) {
q = common::Quantile(0.5, iter, iter + ti_v.Size());
} else {
CHECK_NE(t_v.Shape(1), 0);
auto w_it = common::MakeIndexTransformIter([&](std::size_t i) { return opt_weights[i]; });
q = common::WeightedQuantile(0.5, iter, iter + ti_v.Size(), w_it);
}
h_out(i) = q;
}
return q;
}
void Mean(Context const* ctx, linalg::Vector<float> const& v, linalg::Vector<float>* out) {