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Support categorical data in ellpack. (#6140)

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This commit is contained in:
Jiaming Yuan
2020-09-24 19:28:57 +08:00
committed by GitHub
parent 78d72ef936
commit 14afdb4d92
9 changed files with 189 additions and 43 deletions
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80
tests/cpp/tree/gpu_hist/test_histogram.cu
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@@ -1,6 +1,7 @@
#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"
@@ -30,7 +31,7 @@ void TestDeterministicHistogram(bool is_dense, int shm_size) {
FeatureGroups feature_groups(page->Cuts(), page->is_dense, shm_size,
sizeof(Gradient));
auto rounding = CreateRoundingFactor<Gradient>(gpair.DeviceSpan());
BuildGradientHistogram(page->GetDeviceAccessor(0),
feature_groups.DeviceAccessor(0), gpair.DeviceSpan(),
@@ -67,7 +68,7 @@ void TestDeterministicHistogram(bool is_dense, int shm_size) {
// Use a single feature group to compute the baseline.
FeatureGroups single_group(page->Cuts());
dh::device_vector<Gradient> baseline(num_bins);
BuildGradientHistogram(page->GetDeviceAccessor(0),
single_group.DeviceAccessor(0),
@@ -97,5 +98,80 @@ 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;
size_t constexpr kBins = 256;
auto x = GenerateRandomCategoricalSingleColumn(kRows, num_categories);
auto cat_m = GetDMatrixFromData(x, kRows, 1);
cat_m->Info().feature_types.HostVector().push_back(FeatureType::kCategorical);
BatchParam batch_param{0, static_cast<int32_t>(kBins), 0};
tree::RowPartitioner row_partitioner(0, kRows);
auto ridx = row_partitioner.GetRows(0);
dh::device_vector<GradientPairPrecise> cat_hist(num_categories);
auto gpair = GenerateRandomGradients(kRows, 0, 2);
gpair.SetDevice(0);
auto rounding = CreateRoundingFactor<GradientPairPrecise>(gpair.DeviceSpan());
// Generate hist with cat data.
for (auto const &batch : cat_m->GetBatches<EllpackPage>(batch_param)) {
auto* page = batch.Impl();
FeatureGroups single_group(page->Cuts());
BuildGradientHistogram(page->GetDeviceAccessor(0),
single_group.DeviceAccessor(0),
gpair.DeviceSpan(), ridx, dh::ToSpan(cat_hist),
rounding);
}
// 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);
for (auto const &batch : encode_m->GetBatches<EllpackPage>(batch_param)) {
auto* page = batch.Impl();
FeatureGroups single_group(page->Cuts());
BuildGradientHistogram(page->GetDeviceAccessor(0),
single_group.DeviceAccessor(0),
gpair.DeviceSpan(), ridx, dh::ToSpan(encode_hist),
rounding);
}
std::vector<GradientPairPrecise> h_cat_hist(cat_hist.size());
thrust::copy(cat_hist.begin(), cat_hist.end(), h_cat_hist.begin());
auto cat_sum = std::accumulate(h_cat_hist.begin(), h_cat_hist.end(), GradientPairPrecise{});
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);
}
}
TEST(Histogram, GPUHistCategorical) {
for (size_t num_categories = 2; num_categories < 8; ++num_categories) {
TestGPUHistogramCategorical(num_categories);
}
}
} // namespace tree
} // namespace xgboost