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Calculate base_score based on input labels for mae. (#8107)

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Fit an intercept as base score for abs loss.
This commit is contained in:
Jiaming Yuan
2022-09-20 20:53:54 +08:00
committed by GitHub
parent 4f42aa5f12
commit fffb1fca52
42 changed files with 999 additions and 343 deletions
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69
tests/cpp/predictor/test_gpu_predictor.cu
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@@ -1,5 +1,5 @@
/*!
* Copyright 2017-2020 XGBoost contributors
* Copyright 2017-2022 XGBoost contributors
*/
#include <gtest/gtest.h>
#include <xgboost/c_api.h>
@@ -34,14 +34,10 @@ TEST(GPUPredictor, Basic) {
int n_row = i, n_col = i;
auto dmat = RandomDataGenerator(n_row, n_col, 0).GenerateDMatrix();
LearnerModelParam param;
param.num_feature = n_col;
param.num_output_group = 1;
param.base_score = 0.5;
GenericParameter ctx;
ctx.UpdateAllowUnknown(Args{});
gbm::GBTreeModel model = CreateTestModel(&param, &ctx);
Context ctx;
ctx.gpu_id = 0;
LearnerModelParam mparam{MakeMP(n_col, .5, 1, ctx.gpu_id)};
gbm::GBTreeModel model = CreateTestModel(&mparam, &ctx);
// Test predict batch
PredictionCacheEntry gpu_out_predictions;
@@ -93,15 +89,12 @@ TEST(GPUPredictor, ExternalMemoryTest) {
std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &lparam));
gpu_predictor->Configure({});
LearnerModelParam param;
param.num_feature = 5;
const int n_classes = 3;
param.num_output_group = n_classes;
param.base_score = 0.5;
Context ctx;
ctx.gpu_id = 0;
LearnerModelParam mparam{MakeMP(5, .5, n_classes, ctx.gpu_id)};
GenericParameter ctx;
ctx.UpdateAllowUnknown(Args{});
gbm::GBTreeModel model = CreateTestModel(&param, &ctx, n_classes);
gbm::GBTreeModel model = CreateTestModel(&mparam, &ctx, n_classes);
std::vector<std::unique_ptr<DMatrix>> dmats;
dmats.push_back(CreateSparsePageDMatrix(400));
@@ -171,15 +164,10 @@ TEST(GpuPredictor, LesserFeatures) {
TEST(GPUPredictor, ShapStump) {
cudaSetDevice(0);
LearnerModelParam param;
param.num_feature = 1;
param.num_output_group = 1;
param.base_score = 0.5;
GenericParameter ctx;
ctx.UpdateAllowUnknown(Args{});
gbm::GBTreeModel model(&param, &ctx);
Context ctx;
ctx.gpu_id = 0;
LearnerModelParam mparam{MakeMP(1, .5, 1, ctx.gpu_id)};
gbm::GBTreeModel model(&mparam, &ctx);
std::vector<std::unique_ptr<RegTree>> trees;
trees.push_back(std::unique_ptr<RegTree>(new RegTree));
@@ -193,24 +181,20 @@ TEST(GPUPredictor, ShapStump) {
auto dmat = RandomDataGenerator(3, 1, 0).GenerateDMatrix();
gpu_predictor->PredictContribution(dmat.get(), &predictions, model);
auto& phis = predictions.HostVector();
auto base_score = mparam.BaseScore(Context::kCpuId)(0);
EXPECT_EQ(phis[0], 0.0);
EXPECT_EQ(phis[1], param.base_score);
EXPECT_EQ(phis[1], base_score);
EXPECT_EQ(phis[2], 0.0);
EXPECT_EQ(phis[3], param.base_score);
EXPECT_EQ(phis[3], base_score);
EXPECT_EQ(phis[4], 0.0);
EXPECT_EQ(phis[5], param.base_score);
EXPECT_EQ(phis[5], base_score);
}
TEST(GPUPredictor, Shap) {
LearnerModelParam param;
param.num_feature = 1;
param.num_output_group = 1;
param.base_score = 0.5;
GenericParameter ctx;
ctx.UpdateAllowUnknown(Args{});
gbm::GBTreeModel model(&param, &ctx);
Context ctx;
ctx.gpu_id = 0;
LearnerModelParam mparam{MakeMP(1, .5, 1, ctx.gpu_id)};
gbm::GBTreeModel model(&mparam, &ctx);
std::vector<std::unique_ptr<RegTree>> trees;
trees.push_back(std::unique_ptr<RegTree>(new RegTree));
@@ -258,14 +242,9 @@ TEST(GPUPredictor, PredictLeafBasic) {
std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &lparam));
gpu_predictor->Configure({});
LearnerModelParam param;
param.num_feature = kCols;
param.base_score = 0.0;
param.num_output_group = 1;
GenericParameter ctx;
ctx.UpdateAllowUnknown(Args{});
gbm::GBTreeModel model = CreateTestModel(&param, &ctx);
LearnerModelParam mparam{MakeMP(kCols, .0, 1)};
Context ctx;
gbm::GBTreeModel model = CreateTestModel(&mparam, &ctx);
HostDeviceVector<float> leaf_out_predictions;
gpu_predictor->PredictLeaf(dmat.get(), &leaf_out_predictions, model);