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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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22
tests/cpp/helpers.cc
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@@ -144,15 +144,26 @@ void CheckRankingObjFunction(std::unique_ptr<xgboost::ObjFunction> const& obj,
CheckObjFunctionImpl(obj, preds, labels, weights, info, out_grad, out_hess);
}
xgboost::bst_float GetMetricEval(xgboost::Metric * metric,
xgboost::bst_float GetMetricEval(xgboost::Metric* metric,
xgboost::HostDeviceVector<xgboost::bst_float> const& preds,
std::vector<xgboost::bst_float> labels,
std::vector<xgboost::bst_float> weights,
std::vector<xgboost::bst_uint> groups) {
return GetMultiMetricEval(
metric, preds,
xgboost::linalg::Tensor<float, 2>{labels.begin(), labels.end(), {labels.size()}, -1}, weights,
groups);
}
double GetMultiMetricEval(xgboost::Metric* metric,
xgboost::HostDeviceVector<xgboost::bst_float> const& preds,
xgboost::linalg::Tensor<float, 2> const& labels,
std::vector<xgboost::bst_float> weights,
std::vector<xgboost::bst_uint> groups) {
xgboost::MetaInfo info;
info.num_row_ = labels.size();
info.labels =
xgboost::linalg::Tensor<float, 2>{labels.begin(), labels.end(), {labels.size()}, -1};
info.num_row_ = labels.Shape(0);
info.labels.Reshape(labels.Shape()[0], labels.Shape()[1]);
info.labels.Data()->Copy(*labels.Data());
info.weights_.HostVector() = weights;
info.group_ptr_ = groups;
@@ -344,13 +355,14 @@ RandomDataGenerator::GenerateDMatrix(bool with_label, bool float_label,
RandomDataGenerator gen(rows_, 1, 0);
if (!float_label) {
gen.Lower(0).Upper(classes).GenerateDense(out->Info().labels.Data());
out->Info().labels.Reshape(out->Info().labels.Size());
out->Info().labels.Reshape(this->rows_);
auto& h_labels = out->Info().labels.Data()->HostVector();
for (auto& v : h_labels) {
v = static_cast<float>(static_cast<uint32_t>(v));
}
} else {
gen.GenerateDense(out->Info().labels.Data());
out->Info().labels.Reshape(this->rows_);
}
}
if (device_ >= 0) {