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Initial support for multioutput regression. (#7514)

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* Add num target model parameter, which is configured from input labels.
* Change elementwise metric and indexing for weights.
* Add demo.
* Add tests.
This commit is contained in:
Jiaming Yuan
2021-12-18 09:28:38 +08:00
committed by GitHub
parent 9ab73f737e
commit 58a6723eb1
22 changed files with 306 additions and 67 deletions
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42
src/metric/elementwise_metric.cu
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@@ -37,20 +37,26 @@ class ElementWiseMetricsReduction {
PackedReduceResult
CpuReduceMetrics(const HostDeviceVector<bst_float> &weights,
const HostDeviceVector<bst_float> &labels,
linalg::TensorView<float const, 2> labels,
const HostDeviceVector<bst_float> &preds,
int32_t n_threads) const {
size_t ndata = labels.Size();
auto n_targets = std::max(labels.Shape(1), static_cast<size_t>(1));
auto h_labels = labels.Values();
const auto& h_labels = labels.HostVector();
const auto& h_weights = weights.HostVector();
const auto& h_preds = preds.HostVector();
std::vector<double> score_tloc(n_threads, 0.0);
std::vector<double> weight_tloc(n_threads, 0.0);
// We sum over losses over all samples and targets instead of performing this for each
// target since the first one approach more accurate while the second approach is used
// for approximation in distributed setting. For rmse:
// - sqrt(1/w(sum_t0 + sum_t1 + ... + sum_tm)) // multi-target
// - sqrt(avg_t0) + sqrt(avg_t1) + ... sqrt(avg_tm) // distributed
common::ParallelFor(ndata, n_threads, [&](size_t i) {
float wt = h_weights.size() > 0 ? h_weights[i] : 1.0f;
float wt = h_weights.size() > 0 ? h_weights[i / n_targets] : 1.0f;
auto t_idx = omp_get_thread_num();
score_tloc[t_idx] += policy_.EvalRow(h_labels[i], h_preds[i]) * wt;
weight_tloc[t_idx] += wt;
@@ -66,14 +72,15 @@ class ElementWiseMetricsReduction {
PackedReduceResult DeviceReduceMetrics(
const HostDeviceVector<bst_float>& weights,
const HostDeviceVector<bst_float>& labels,
linalg::TensorView<float const, 2> labels,
const HostDeviceVector<bst_float>& preds) {
size_t n_data = preds.Size();
auto n_targets = std::max(labels.Shape(1), static_cast<size_t>(1));
thrust::counting_iterator<size_t> begin(0);
thrust::counting_iterator<size_t> end = begin + n_data;
auto s_label = labels.DeviceSpan();
auto s_label = labels.Values();
auto s_preds = preds.DeviceSpan();
auto s_weights = weights.DeviceSpan();
@@ -86,7 +93,7 @@ class ElementWiseMetricsReduction {
thrust::cuda::par(alloc),
begin, end,
[=] XGBOOST_DEVICE(size_t idx) {
float weight = is_null_weight ? 1.0f : s_weights[idx];
float weight = is_null_weight ? 1.0f : s_weights[idx / n_targets];
float residue = d_policy.EvalRow(s_label[idx], s_preds[idx]);
residue *= weight;
@@ -100,26 +107,22 @@ class ElementWiseMetricsReduction {
#endif // XGBOOST_USE_CUDA
PackedReduceResult Reduce(
const GenericParameter &ctx,
const HostDeviceVector<bst_float>& weights,
const HostDeviceVector<bst_float>& labels,
const HostDeviceVector<bst_float>& preds) {
PackedReduceResult Reduce(const GenericParameter& ctx, const HostDeviceVector<bst_float>& weights,
linalg::Tensor<float, 2> const& labels,
const HostDeviceVector<bst_float>& preds) {
PackedReduceResult result;
if (ctx.gpu_id < 0) {
auto n_threads = ctx.Threads();
result = CpuReduceMetrics(weights, labels, preds, n_threads);
result = CpuReduceMetrics(weights, labels.HostView(), preds, n_threads);
}
#if defined(XGBOOST_USE_CUDA)
else { // NOLINT
device_ = ctx.gpu_id;
preds.SetDevice(device_);
labels.SetDevice(device_);
weights.SetDevice(device_);
preds.SetDevice(ctx.gpu_id);
weights.SetDevice(ctx.gpu_id);
dh::safe_cuda(cudaSetDevice(device_));
result = DeviceReduceMetrics(weights, labels, preds);
dh::safe_cuda(cudaSetDevice(ctx.gpu_id));
result = DeviceReduceMetrics(weights, labels.View(ctx.gpu_id), preds);
}
#endif // defined(XGBOOST_USE_CUDA)
return result;
@@ -128,7 +131,6 @@ class ElementWiseMetricsReduction {
private:
EvalRow policy_;
#if defined(XGBOOST_USE_CUDA)
int device_{-1};
#endif // defined(XGBOOST_USE_CUDA)
};
@@ -364,7 +366,7 @@ struct EvalEWiseBase : public Metric {
CHECK_EQ(preds.Size(), info.labels.Size())
<< "label and prediction size not match, "
<< "hint: use merror or mlogloss for multi-class classification";
auto result = reducer_.Reduce(*tparam_, info.weights_, *info.labels.Data(), preds);
auto result = reducer_.Reduce(*tparam_, info.weights_, info.labels, preds);
double dat[2] { result.Residue(), result.Weights() };