Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Implement a general array view. (#7365)

Browse Source 
* Replace existing matrix and vector view.

This is to prepare for handling higher dimension data and prediction when we support multi-target models.
This commit is contained in:
Jiaming Yuan
2021-11-05 04:16:11 +08:00
committed by GitHub
parent 232144ca09
commit b06040b6d0
11 changed files with 418 additions and 146 deletions
Download Patch File Download Diff File Expand all files Collapse all files

31
src/metric/auc.cc
View File

@@ -13,6 +13,7 @@
#include <vector>
#include "rabit/rabit.h"
#include "xgboost/linalg.h"
#include "xgboost/host_device_vector.h"
#include "xgboost/metric.h"
@@ -83,41 +84,45 @@ double MultiClassOVR(common::Span<float const> predts, MetaInfo const &info,
CHECK_NE(n_classes, 0);
auto const &labels = info.labels_.ConstHostVector();
std::vector<double> results(n_classes * 3, 0);
auto s_results = common::Span<double>(results);
auto local_area = s_results.subspan(0, n_classes);
auto tp = s_results.subspan(n_classes, n_classes);
auto auc = s_results.subspan(2 * n_classes, n_classes);
std::vector<double> results_storage(n_classes * 3, 0);
linalg::TensorView<double> results(results_storage,
{n_classes, static_cast<size_t>(3)},
GenericParameter::kCpuId);
auto local_area = results.Slice(linalg::All(), 0);
auto tp = results.Slice(linalg::All(), 1);
auto auc = results.Slice(linalg::All(), 2);
auto weights = OptionalWeights{info.weights_.ConstHostSpan()};
auto predts_t = linalg::TensorView<float const, 2>(
predts, {static_cast<size_t>(info.num_row_), n_classes},
GenericParameter::kCpuId);
if (!info.labels_.Empty()) {
common::ParallelFor(n_classes, n_threads, [&](auto c) {
std::vector<float> proba(info.labels_.Size());
std::vector<float> response(info.labels_.Size());
for (size_t i = 0; i < proba.size(); ++i) {
proba[i] = predts[i * n_classes + c];
proba[i] = predts_t(i, c);
response[i] = labels[i] == c ? 1.0f : 0.0;
}
double fp;
std::tie(fp, tp[c], auc[c]) = binary_auc(proba, response, weights);
local_area[c] = fp * tp[c];
std::tie(fp, tp(c), auc(c)) = binary_auc(proba, response, weights);
local_area(c) = fp * tp(c);
});
}
// we have 2 averages going in here, first is among workers, second is among
// classes. allreduce sums up fp/tp auc for each class.
rabit::Allreduce<rabit::op::Sum>(results.data(), results.size());
rabit::Allreduce<rabit::op::Sum>(results.Values().data(), results.Values().size());
double auc_sum{0};
double tp_sum{0};
for (size_t c = 0; c < n_classes; ++c) {
if (local_area[c] != 0) {
if (local_area(c) != 0) {
// normalize and weight it by prevalence. After allreduce, `local_area`
// means the total covered area (not area under curve, rather it's the
// accessible area for each worker) for each class.
auc_sum += auc[c] / local_area[c] * tp[c];
tp_sum += tp[c];
auc_sum += auc(c) / local_area(c) * tp(c);
tp_sum += tp(c);
} else {
auc_sum = std::numeric_limits<double>::quiet_NaN();
break;