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

CPU evaluation for cat data. (#7393)

Browse Source 
* Implementation for one hot based.
* Implementation for partition based. (LightGBM)
This commit is contained in:
Jiaming Yuan
2021-11-06 14:41:35 +08:00
committed by GitHub
parent 6ede12412c
commit d7d1b6e3a6
15 changed files with 540 additions and 166 deletions
Download Patch File Download Diff File Expand all files Collapse all files

41
tests/cpp/tree/gpu_hist/test_histogram.cu
View File

@@ -1,9 +1,11 @@
#include <gtest/gtest.h>
#include <vector>
#include "../../helpers.h"
#include "../../../../src/common/categorical.h"
#include "../../../../src/tree/gpu_hist/row_partitioner.cuh"
#include "../../../../src/tree/gpu_hist/histogram.cuh"
#include "../../../../src/tree/gpu_hist/row_partitioner.cuh"
#include "../../categorical_helpers.h"
#include "../../helpers.h"
namespace xgboost {
namespace tree {
@@ -99,16 +101,6 @@ TEST(Histogram, GPUDeterministic) {
}
}
std::vector<float> OneHotEncodeFeature(std::vector<float> x, size_t num_cat) {
std::vector<float> ret(x.size() * num_cat, 0);
size_t n_rows = x.size();
for (size_t r = 0; r < n_rows; ++r) {
bst_cat_t cat = common::AsCat(x[r]);
ret.at(num_cat * r + cat) = 1;
}
return ret;
}
// Test 1 vs rest categorical histogram is equivalent to one hot encoded data.
void TestGPUHistogramCategorical(size_t num_categories) {
size_t constexpr kRows = 340;
@@ -123,7 +115,9 @@ void TestGPUHistogramCategorical(size_t num_categories) {
auto gpair = GenerateRandomGradients(kRows, 0, 2);
gpair.SetDevice(0);
auto rounding = CreateRoundingFactor<GradientPairPrecise>(gpair.DeviceSpan());
// Generate hist with cat data.
/**
* Generate hist with cat data.
*/
for (auto const &batch : cat_m->GetBatches<EllpackPage>(batch_param)) {
auto* page = batch.Impl();
FeatureGroups single_group(page->Cuts());
@@ -133,7 +127,9 @@ void TestGPUHistogramCategorical(size_t num_categories) {
rounding);
}
// Generate hist with one hot encoded data.
/**
* Generate hist with one hot encoded data.
*/
auto x_encoded = OneHotEncodeFeature(x, num_categories);
auto encode_m = GetDMatrixFromData(x_encoded, kRows, num_categories);
dh::device_vector<GradientPairPrecise> encode_hist(2 * num_categories);
@@ -152,20 +148,9 @@ void TestGPUHistogramCategorical(size_t num_categories) {
std::vector<GradientPairPrecise> h_encode_hist(encode_hist.size());
thrust::copy(encode_hist.begin(), encode_hist.end(), h_encode_hist.begin());
for (size_t c = 0; c < num_categories; ++c) {
auto zero = h_encode_hist[c * 2];
auto one = h_encode_hist[c * 2 + 1];
auto chosen = h_cat_hist[c];
auto not_chosen = cat_sum - chosen;
ASSERT_LE(RelError(zero.GetGrad(), not_chosen.GetGrad()), kRtEps);
ASSERT_LE(RelError(zero.GetHess(), not_chosen.GetHess()), kRtEps);
ASSERT_LE(RelError(one.GetGrad(), chosen.GetGrad()), kRtEps);
ASSERT_LE(RelError(one.GetHess(), chosen.GetHess()), kRtEps);
}
ValidateCategoricalHistogram(num_categories,
common::Span<GradientPairPrecise>{h_encode_hist},
common::Span<GradientPairPrecise>{h_cat_hist});
}
TEST(Histogram, GPUHistCategorical) {