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

Feature interaction for GPU Hist. (#4534)

Browse Source 
* GPU hist Interaction Constraints.
* Duplicate related parameters.
* Add tests for CPU interaction constraint.
* Add better error reporting.
* Thorough tests.
This commit is contained in:
Jiaming Yuan
2019-06-19 18:11:02 +08:00
committed by GitHub
parent 570374effe
commit ae05948e32
14 changed files with 1201 additions and 76 deletions
Download Patch File Download Diff File Expand all files Collapse all files

67
tests/cpp/tree/test_gpu_hist.cu
View File

@@ -1,7 +1,6 @@
/*!
* Copyright 2017-2018 XGBoost contributors
* Copyright 2017-2019 XGBoost contributors
*/
#include <thrust/device_vector.h>
#include <xgboost/base.h>
#include <random>
@@ -16,6 +15,7 @@
#include "../../../src/tree/updater_gpu_hist.cu"
#include "../../../src/tree/updater_gpu_common.cuh"
#include "../../../src/common/common.h"
#include "../../../src/tree/constraints.cuh"
namespace xgboost {
namespace tree {
@@ -91,11 +91,13 @@ void BuildGidx(DeviceShard<GradientSumT>* shard, int n_rows, int n_cols,
TEST(GpuHist, BuildGidxDense) {
int constexpr kNRows = 16, kNCols = 8;
TrainParam param;
param.max_depth = 1;
param.max_leaves = 0;
DeviceShard<GradientPairPrecise> shard(0, 0, 0, kNRows, param, kNCols);
tree::TrainParam param;
std::vector<std::pair<std::string, std::string>> args {
{"max_depth", "1"},
{"max_leaves", "0"},
};
param.Init(args);
DeviceShard<GradientPairPrecise> shard(0, 0, 0, kNRows, param, kNCols, kNCols);
BuildGidx(&shard, kNRows, kNCols);
std::vector<common::CompressedByteT> h_gidx_buffer(shard.gidx_buffer.size());
@@ -130,10 +132,14 @@ TEST(GpuHist, BuildGidxDense) {
TEST(GpuHist, BuildGidxSparse) {
int constexpr kNRows = 16, kNCols = 8;
TrainParam param;
param.max_depth = 1;
param.max_leaves = 0;
std::vector<std::pair<std::string, std::string>> args {
{"max_depth", "1"},
{"max_leaves", "0"},
};
param.Init(args);
DeviceShard<GradientPairPrecise> shard(0, 0, 0, kNRows, param, kNCols);
DeviceShard<GradientPairPrecise> shard(0, 0, 0, kNRows, param, kNCols,
kNCols);
BuildGidx(&shard, kNRows, kNCols, 0.9f);
std::vector<common::CompressedByteT> h_gidx_buffer(shard.gidx_buffer.size());
@@ -173,10 +179,13 @@ void TestBuildHist(bool use_shared_memory_histograms) {
int const kNRows = 16, kNCols = 8;
TrainParam param;
param.max_depth = 6;
param.max_leaves = 0;
DeviceShard<GradientSumT> shard(0, 0, 0, kNRows, param, kNCols);
std::vector<std::pair<std::string, std::string>> args {
{"max_depth", "6"},
{"max_leaves", "0"},
};
param.Init(args);
DeviceShard<GradientSumT> shard(0, 0, 0, kNRows, param, kNCols,
kNCols);
BuildGidx(&shard, kNRows, kNCols);
xgboost::SimpleLCG gen;
@@ -263,17 +272,21 @@ TEST(GpuHist, EvaluateSplits) {
constexpr int kNCols = 8;
TrainParam param;
param.max_depth = 1;
param.colsample_bynode = 1;
param.colsample_bylevel = 1;
param.colsample_bytree = 1;
param.min_child_weight = 0.01;
// Disable all parameters.
param.reg_alpha = 0.0;
param.reg_lambda = 0;
param.max_delta_step = 0.0;
std::vector<std::pair<std::string, std::string>> args {
{"max_depth", "1"},
{"max_leaves", "0"},
// Disable all other parameters.
{"colsample_bynode", "1"},
{"colsample_bylevel", "1"},
{"colsample_bytree", "1"},
{"min_child_weight", "0.01"},
{"reg_alpha", "0"},
{"reg_lambda", "0"},
{"max_delta_step", "0"}
};
param.Init(args);
for (size_t i = 0; i < kNCols; ++i) {
param.monotone_constraints.emplace_back(0);
}
@@ -282,7 +295,8 @@ TEST(GpuHist, EvaluateSplits) {
// Initialize DeviceShard
std::unique_ptr<DeviceShard<GradientPairPrecise>> shard{
new DeviceShard<GradientPairPrecise>(0, 0, 0, kNRows, param, kNCols)};
new DeviceShard<GradientPairPrecise>(0, 0, 0, kNRows, param, kNCols,
kNCols)};
// Initialize DeviceShard::node_sum_gradients
shard->node_sum_gradients = {{6.4f, 12.8f}};
@@ -352,14 +366,13 @@ TEST(GpuHist, ApplySplit) {
TrainParam param;
std::vector<std::pair<std::string, std::string>> args = {};
param.InitAllowUnknown(args);
// Initialize shard
for (size_t i = 0; i < kNCols; ++i) {
param.monotone_constraints.emplace_back(0);
}
std::unique_ptr<DeviceShard<GradientPairPrecise>> shard{
new DeviceShard<GradientPairPrecise>(0, 0, 0, kNRows, param, kNCols)};
new DeviceShard<GradientPairPrecise>(0, 0, 0, kNRows, param, kNCols,
kNCols)};
shard->ridx_segments.resize(3); // 3 nodes.
shard->node_sum_gradients.resize(3);