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Support categorical data for hist. (#7695)

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* Extract partitioner from hist.
* Implement categorical data support by passing the gradient index directly into the partitioner.
* Organize/update document.
* Remove code for negative hessian.
This commit is contained in:
Jiaming Yuan
2022-02-25 03:47:14 +08:00
committed by GitHub
parent f60d95b0ba
commit 83a66b4994
15 changed files with 402 additions and 498 deletions
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18
tests/cpp/tree/hist/test_evaluate_splits.cc
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@@ -40,7 +40,7 @@ template <typename GradientSumT> void TestEvaluateSplits() {
std::iota(row_indices.begin(), row_indices.end(), 0);
row_set_collection.Init();
auto hist_builder = GHistBuilder<GradientSumT>(gmat.cut.Ptrs().back());
auto hist_builder = common::GHistBuilder<GradientSumT>(gmat.cut.Ptrs().back());
hist.Init(gmat.cut.Ptrs().back());
hist.AddHistRow(0);
hist.AllocateAllData();
@@ -94,7 +94,7 @@ TEST(HistEvaluator, Apply) {
RegTree tree;
int static constexpr kNRows = 8, kNCols = 16;
TrainParam param;
param.UpdateAllowUnknown(Args{{}});
param.UpdateAllowUnknown(Args{{"min_child_weight", "0"}, {"reg_lambda", "0.0"}});
auto dmat = RandomDataGenerator(kNRows, kNCols, 0).Seed(3).GenerateDMatrix();
auto sampler = std::make_shared<common::ColumnSampler>();
auto evaluator_ = HistEvaluator<float, CPUExpandEntry>{param, dmat->Info(), 4, sampler,
@@ -102,12 +102,22 @@ TEST(HistEvaluator, Apply) {
CPUExpandEntry entry{0, 0, 10.0f};
entry.split.left_sum = GradStats{0.4, 0.6f};
entry.split.right_sum = GradStats{0.5, 0.7f};
entry.split.right_sum = GradStats{0.5, 0.5f};
evaluator_.ApplyTreeSplit(entry, &tree);
ASSERT_EQ(tree.NumExtraNodes(), 2);
ASSERT_EQ(tree.Stat(tree[0].LeftChild()).sum_hess, 0.6f);
ASSERT_EQ(tree.Stat(tree[0].RightChild()).sum_hess, 0.7f);
ASSERT_EQ(tree.Stat(tree[0].RightChild()).sum_hess, 0.5f);
{
RegTree tree;
entry.split.is_cat = true;
entry.split.split_value = 1.0;
evaluator_.ApplyTreeSplit(entry, &tree);
auto l = entry.split.left_sum;
ASSERT_NEAR(tree[1].LeafValue(), -l.sum_grad / l.sum_hess * param.learning_rate, kRtEps);
ASSERT_NEAR(tree[2].LeafValue(), -param.learning_rate, kRtEps);
}
}
TEST_F(TestPartitionBasedSplit, CPUHist) {