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Categorical data support in CPU sketching. (#7221)

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This commit is contained in:
Jiaming Yuan
2021-09-17 04:37:09 +08:00
committed by GitHub
parent 9f63d6fead
commit 31c1e13f90
7 changed files with 129 additions and 57 deletions
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14
tests/cpp/common/test_hist_util.cc
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@@ -1,3 +1,6 @@
/*!
* Copyright 2019-2021 by XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <vector>
#include <string>
@@ -388,5 +391,16 @@ TEST(HistUtil, SketchFromWeights) {
TestSketchFromWeights(true);
TestSketchFromWeights(false);
}
TEST(HistUtil, SketchCategoricalFeatures) {
TestCategoricalSketch(1000, 256, 32, false,
[](DMatrix *p_fmat, int32_t num_bins) {
return SketchOnDMatrix(p_fmat, num_bins);
});
TestCategoricalSketch(1000, 256, 32, true,
[](DMatrix *p_fmat, int32_t num_bins) {
return SketchOnDMatrix(p_fmat, num_bins);
});
}
} // namespace common
} // namespace xgboost

47
tests/cpp/common/test_hist_util.cu
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@@ -1,3 +1,6 @@
/*!
* Copyright 2019-2021 by XGBoost Contributors
*/
#include <dmlc/filesystem.h>
#include <gtest/gtest.h>
@@ -126,43 +129,15 @@ TEST(HistUtil, DeviceSketchCategoricalAsNumeric) {
}
}
void TestCategoricalSketch(size_t n, size_t num_categories, int32_t num_bins, bool weighted) {
auto x = GenerateRandomCategoricalSingleColumn(n, num_categories);
auto dmat = GetDMatrixFromData(x, n, 1);
dmat->Info().feature_types.HostVector().push_back(FeatureType::kCategorical);
if (weighted) {
std::vector<float> weights(n, 0);
SimpleLCG lcg;
SimpleRealUniformDistribution<float> dist(0, 1);
for (auto& v : weights) {
v = dist(&lcg);
}
dmat->Info().weights_.HostVector() = weights;
}
ASSERT_EQ(dmat->Info().feature_types.Size(), 1);
auto cuts = DeviceSketch(0, dmat.get(), num_bins);
std::sort(x.begin(), x.end());
auto n_uniques = std::unique(x.begin(), x.end()) - x.begin();
ASSERT_NE(n_uniques, x.size());
ASSERT_EQ(cuts.TotalBins(), n_uniques);
ASSERT_EQ(n_uniques, num_categories);
auto& values = cuts.cut_values_.HostVector();
ASSERT_TRUE(std::is_sorted(values.cbegin(), values.cend()));
auto is_unique = (std::unique(values.begin(), values.end()) - values.begin()) == n_uniques;
ASSERT_TRUE(is_unique);
x.resize(n_uniques);
for (size_t i = 0; i < n_uniques; ++i) {
ASSERT_EQ(x[i], values[i]);
}
}
TEST(HistUtil, DeviceSketchCategoricalFeatures) {
TestCategoricalSketch(1000, 256, 32, false);
TestCategoricalSketch(1000, 256, 32, true);
TestCategoricalSketch(1000, 256, 32, false,
[](DMatrix *p_fmat, int32_t num_bins) {
return DeviceSketch(0, p_fmat, num_bins);
});
TestCategoricalSketch(1000, 256, 32, true,
[](DMatrix *p_fmat, int32_t num_bins) {
return DeviceSketch(0, p_fmat, num_bins);
});
}
void TestMixedSketch() {

45
tests/cpp/common/test_hist_util.h
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@@ -1,3 +1,6 @@
/*!
* Copyright 2019-2021 by XGBoost Contributors
*/
#pragma once
#include <gtest/gtest.h>
#include <dmlc/filesystem.h>
@@ -5,6 +8,8 @@
#include <vector>
#include <string>
#include <fstream>
#include "../helpers.h"
#include "../../../src/common/hist_util.h"
#include "../../../src/data/simple_dmatrix.h"
#include "../../../src/data/adapter.h"
@@ -206,5 +211,45 @@ inline void ValidateCuts(const HistogramCuts& cuts, DMatrix* dmat,
}
}
/**
* \brief Test for sketching on categorical data.
*
* \param sketch Sketch function, can be on device or on host.
*/
template <typename Fn>
void TestCategoricalSketch(size_t n, size_t num_categories, int32_t num_bins,
bool weighted, Fn sketch) {
auto x = GenerateRandomCategoricalSingleColumn(n, num_categories);
auto dmat = GetDMatrixFromData(x, n, 1);
dmat->Info().feature_types.HostVector().push_back(FeatureType::kCategorical);
if (weighted) {
std::vector<float> weights(n, 0);
SimpleLCG lcg;
SimpleRealUniformDistribution<float> dist(0, 1);
for (auto& v : weights) {
v = dist(&lcg);
}
dmat->Info().weights_.HostVector() = weights;
}
ASSERT_EQ(dmat->Info().feature_types.Size(), 1);
auto cuts = sketch(dmat.get(), num_bins);
std::sort(x.begin(), x.end());
auto n_uniques = std::unique(x.begin(), x.end()) - x.begin();
ASSERT_NE(n_uniques, x.size());
ASSERT_EQ(cuts.TotalBins(), n_uniques);
ASSERT_EQ(n_uniques, num_categories);
auto& values = cuts.cut_values_.HostVector();
ASSERT_TRUE(std::is_sorted(values.cbegin(), values.cend()));
auto is_unique = (std::unique(values.begin(), values.end()) - values.begin()) == n_uniques;
ASSERT_TRUE(is_unique);
x.resize(n_uniques);
for (size_t i = 0; i < n_uniques; ++i) {
ASSERT_EQ(x[i], values[i]);
}
}
} // namespace common
} // namespace xgboost

8
tests/cpp/common/test_quantile.cc
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@@ -43,12 +43,14 @@ void TestDistributedQuantile(size_t rows, size_t cols) {
// Generate cuts for distributed environment.
auto sparsity = 0.5f;
auto rank = rabit::GetRank();
HostSketchContainer sketch_distributed(column_size, n_bins, false, OmpGetNumThreads(0));
auto m = RandomDataGenerator{rows, cols, sparsity}
.Seed(rank)
.Lower(.0f)
.Upper(1.0f)
.GenerateDMatrix();
HostSketchContainer sketch_distributed(
column_size, n_bins, m->Info().feature_types.ConstHostSpan(), false,
OmpGetNumThreads(0));
for (auto const &page : m->GetBatches<SparsePage>()) {
sketch_distributed.PushRowPage(page, m->Info());
}
@@ -59,7 +61,9 @@ void TestDistributedQuantile(size_t rows, size_t cols) {
rabit::Finalize();
CHECK_EQ(rabit::GetWorldSize(), 1);
std::for_each(column_size.begin(), column_size.end(), [=](auto& size) { size *= world; });
HostSketchContainer sketch_on_single_node(column_size, n_bins, false, OmpGetNumThreads(0));
HostSketchContainer sketch_on_single_node(
column_size, n_bins, m->Info().feature_types.ConstHostSpan(), false,
OmpGetNumThreads(0));
for (auto rank = 0; rank < world; ++rank) {
auto m = RandomDataGenerator{rows, cols, sparsity}
.Seed(rank)