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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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12
tests/cpp/gbm/test_gblinear.cc
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@@ -19,15 +19,11 @@ namespace gbm {
TEST(GBLinear, JsonIO) {
size_t constexpr kRows = 16, kCols = 16;
LearnerModelParam param;
param.num_feature = kCols;
param.num_output_group = 1;
Context ctx;
LearnerModelParam mparam{MakeMP(kCols, .5, 1)};
GenericParameter gparam;
gparam.Init(Args{});
std::unique_ptr<GradientBooster> gbm {
CreateTrainedGBM("gblinear", Args{}, kRows, kCols, &param, &gparam) };
std::unique_ptr<GradientBooster> gbm{
CreateTrainedGBM("gblinear", Args{}, kRows, kCols, &mparam, &ctx)};
Json model { Object() };
gbm->SaveModel(&model);
ASSERT_TRUE(IsA<Object>(model));

46
tests/cpp/gbm/test_gbtree.cc
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@@ -18,15 +18,11 @@ namespace xgboost {
TEST(GBTree, SelectTreeMethod) {
size_t constexpr kCols = 10;
GenericParameter generic_param;
generic_param.UpdateAllowUnknown(Args{});
LearnerModelParam mparam;
mparam.base_score = 0.5;
mparam.num_feature = kCols;
mparam.num_output_group = 1;
Context ctx;
LearnerModelParam mparam{MakeMP(kCols, .5, 1)};
std::unique_ptr<GradientBooster> p_gbm {
GradientBooster::Create("gbtree", &generic_param, &mparam)};
GradientBooster::Create("gbtree", &ctx, &mparam)};
auto& gbtree = dynamic_cast<gbm::GBTree&> (*p_gbm);
// Test if `tree_method` can be set
@@ -45,7 +41,7 @@ TEST(GBTree, SelectTreeMethod) {
ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
#ifdef XGBOOST_USE_CUDA
generic_param.UpdateAllowUnknown(Args{{"gpu_id", "0"}});
ctx.UpdateAllowUnknown(Args{{"gpu_id", "0"}});
gbtree.Configure({{"tree_method", "gpu_hist"}});
ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
gbtree.Configure({{"booster", "dart"}, {"tree_method", "gpu_hist"}});
@@ -55,15 +51,11 @@ TEST(GBTree, SelectTreeMethod) {
TEST(GBTree, PredictionCache) {
size_t constexpr kRows = 100, kCols = 10;
GenericParameter generic_param;
generic_param.UpdateAllowUnknown(Args{});
LearnerModelParam mparam;
mparam.base_score = 0.5;
mparam.num_feature = kCols;
mparam.num_output_group = 1;
Context ctx;
LearnerModelParam mparam{MakeMP(kCols, .5, 1)};
std::unique_ptr<GradientBooster> p_gbm {
GradientBooster::Create("gbtree", &generic_param, &mparam)};
GradientBooster::Create("gbtree", &ctx, &mparam)};
auto& gbtree = dynamic_cast<gbm::GBTree&> (*p_gbm);
gbtree.Configure({{"tree_method", "hist"}});
@@ -176,16 +168,11 @@ TEST(GBTree, ChoosePredictor) {
TEST(GBTree, JsonIO) {
size_t constexpr kRows = 16, kCols = 16;
LearnerModelParam mparam;
mparam.num_feature = kCols;
mparam.num_output_group = 1;
mparam.base_score = 0.5;
GenericParameter gparam;
gparam.Init(Args{});
Context ctx;
LearnerModelParam mparam{MakeMP(kCols, .5, 1)};
std::unique_ptr<GradientBooster> gbm {
CreateTrainedGBM("gbtree", Args{}, kRows, kCols, &mparam, &gparam) };
CreateTrainedGBM("gbtree", Args{}, kRows, kCols, &mparam, &ctx) };
Json model {Object()};
model["model"] = Object();
@@ -215,16 +202,11 @@ TEST(GBTree, JsonIO) {
TEST(Dart, JsonIO) {
size_t constexpr kRows = 16, kCols = 16;
LearnerModelParam mparam;
mparam.num_feature = kCols;
mparam.base_score = 0.5;
mparam.num_output_group = 1;
Context ctx;
LearnerModelParam mparam{MakeMP(kCols, .5, 1)};
GenericParameter gparam;
gparam.Init(Args{});
std::unique_ptr<GradientBooster> gbm {
CreateTrainedGBM("dart", Args{}, kRows, kCols, &mparam, &gparam) };
std::unique_ptr<GradientBooster> gbm{
CreateTrainedGBM("dart", Args{}, kRows, kCols, &mparam, &ctx)};
Json model {Object()};
model["model"] = Object();