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[backport] Always use partition based categorical splits. (#7857) (#7865)

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This commit is contained in:
Jiaming Yuan
2022-05-06 19:14:19 +08:00
committed by GitHub
parent f4eb6b984e
commit b1b6246e35
13 changed files with 77 additions and 103 deletions
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27
tests/cpp/tree/gpu_hist/test_evaluate_splits.cu
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@@ -57,8 +57,7 @@ void TestEvaluateSingleSplit(bool is_categorical) {
GPUHistEvaluator<GradientPair> evaluator{
tparam, static_cast<bst_feature_t>(feature_min_values.size()), 0};
dh::device_vector<common::CatBitField::value_type> out_cats;
DeviceSplitCandidate result =
evaluator.EvaluateSingleSplit(input, 0, ObjInfo{ObjInfo::kRegression}).split;
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, 0).split;
EXPECT_EQ(result.findex, 1);
EXPECT_EQ(result.fvalue, 11.0);
@@ -101,8 +100,7 @@ TEST(GpuHist, EvaluateSingleSplitMissing) {
dh::ToSpan(feature_histogram)};
GPUHistEvaluator<GradientPair> evaluator(tparam, feature_set.size(), 0);
DeviceSplitCandidate result =
evaluator.EvaluateSingleSplit(input, 0, ObjInfo{ObjInfo::kRegression}).split;
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, 0).split;
EXPECT_EQ(result.findex, 0);
EXPECT_EQ(result.fvalue, 1.0);
@@ -114,10 +112,8 @@ TEST(GpuHist, EvaluateSingleSplitMissing) {
TEST(GpuHist, EvaluateSingleSplitEmpty) {
TrainParam tparam = ZeroParam();
GPUHistEvaluator<GradientPair> evaluator(tparam, 1, 0);
DeviceSplitCandidate result = evaluator
.EvaluateSingleSplit(EvaluateSplitInputs<GradientPair>{}, 0,
ObjInfo{ObjInfo::kRegression})
.split;
DeviceSplitCandidate result =
evaluator.EvaluateSingleSplit(EvaluateSplitInputs<GradientPair>{}, 0).split;
EXPECT_EQ(result.findex, -1);
EXPECT_LT(result.loss_chg, 0.0f);
}
@@ -152,8 +148,7 @@ TEST(GpuHist, EvaluateSingleSplitFeatureSampling) {
dh::ToSpan(feature_histogram)};
GPUHistEvaluator<GradientPair> evaluator(tparam, feature_min_values.size(), 0);
DeviceSplitCandidate result =
evaluator.EvaluateSingleSplit(input, 0, ObjInfo{ObjInfo::kRegression}).split;
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, 0).split;
EXPECT_EQ(result.findex, 1);
EXPECT_EQ(result.fvalue, 11.0);
@@ -191,8 +186,7 @@ TEST(GpuHist, EvaluateSingleSplitBreakTies) {
dh::ToSpan(feature_histogram)};
GPUHistEvaluator<GradientPair> evaluator(tparam, feature_min_values.size(), 0);
DeviceSplitCandidate result =
evaluator.EvaluateSingleSplit(input, 0, ObjInfo{ObjInfo::kRegression}).split;
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, 0).split;
EXPECT_EQ(result.findex, 0);
EXPECT_EQ(result.fvalue, 1.0);
@@ -243,8 +237,8 @@ TEST(GpuHist, EvaluateSplits) {
GPUHistEvaluator<GradientPair> evaluator{
tparam, static_cast<bst_feature_t>(feature_min_values.size()), 0};
evaluator.EvaluateSplits(input_left, input_right, ObjInfo{ObjInfo::kRegression},
evaluator.GetEvaluator(), dh::ToSpan(out_splits));
evaluator.EvaluateSplits(input_left, input_right, evaluator.GetEvaluator(),
dh::ToSpan(out_splits));
DeviceSplitCandidate result_left = out_splits[0];
EXPECT_EQ(result_left.findex, 1);
@@ -264,8 +258,7 @@ TEST_F(TestPartitionBasedSplit, GpuHist) {
cuts_.cut_values_.SetDevice(0);
cuts_.min_vals_.SetDevice(0);
ObjInfo task{ObjInfo::kRegression};
evaluator.Reset(cuts_, dh::ToSpan(ft), task, info_.num_col_, param_, 0);
evaluator.Reset(cuts_, dh::ToSpan(ft), info_.num_col_, param_, 0);
dh::device_vector<GradientPairPrecise> d_hist(hist_[0].size());
auto node_hist = hist_[0];
@@ -282,7 +275,7 @@ TEST_F(TestPartitionBasedSplit, GpuHist) {
cuts_.cut_values_.ConstDeviceSpan(),
cuts_.min_vals_.ConstDeviceSpan(),
dh::ToSpan(d_hist)};
auto split = evaluator.EvaluateSingleSplit(input, 0, ObjInfo{ObjInfo::kRegression}).split;
auto split = evaluator.EvaluateSingleSplit(input, 0).split;
ASSERT_NEAR(split.loss_chg, best_score_, 1e-16);
}
} // namespace tree