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Small refactor to categoricals (#7858)

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This commit is contained in:
Rory Mitchell
2022-05-05 17:47:02 +02:00
committed by GitHub
parent 14ef38b834
commit 7ef54e39ec
7 changed files with 110 additions and 150 deletions
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31
tests/cpp/tree/gpu_hist/test_evaluate_splits.cu
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@@ -24,14 +24,16 @@ void TestEvaluateSingleSplit(bool is_categorical) {
TrainParam tparam = ZeroParam();
GPUTrainingParam param{tparam};
common::HistogramCuts cuts;
cuts.cut_values_.HostVector() = std::vector<float>{1.0, 2.0, 11.0, 12.0};
cuts.cut_ptrs_.HostVector() = std::vector<uint32_t>{0, 2, 4};
cuts.min_vals_.HostVector() = std::vector<float>{0.0, 0.0};
cuts.cut_ptrs_.SetDevice(0);
cuts.cut_values_.SetDevice(0);
cuts.min_vals_.SetDevice(0);
thrust::device_vector<bst_feature_t> feature_set =
std::vector<bst_feature_t>{0, 1};
thrust::device_vector<uint32_t> feature_segments =
std::vector<bst_row_t>{0, 2, 4};
thrust::device_vector<float> feature_values =
std::vector<float>{1.0, 2.0, 11.0, 12.0};
thrust::device_vector<float> feature_min_values =
std::vector<float>{0.0, 0.0};
// Setup gradients so that second feature gets higher gain
thrust::device_vector<GradientPair> feature_histogram =
std::vector<GradientPair>{
@@ -42,22 +44,27 @@ void TestEvaluateSingleSplit(bool is_categorical) {
FeatureType::kCategorical);
common::Span<FeatureType> d_feature_types;
if (is_categorical) {
auto max_cat = *std::max_element(cuts.cut_values_.HostVector().begin(),
cuts.cut_values_.HostVector().end());
cuts.SetCategorical(true, max_cat);
d_feature_types = dh::ToSpan(feature_types);
}
EvaluateSplitInputs<GradientPair> input{1,
parent_sum,
param,
dh::ToSpan(feature_set),
d_feature_types,
dh::ToSpan(feature_segments),
dh::ToSpan(feature_values),
dh::ToSpan(feature_min_values),
cuts.cut_ptrs_.ConstDeviceSpan(),
cuts.cut_values_.ConstDeviceSpan(),
cuts.min_vals_.ConstDeviceSpan(),
dh::ToSpan(feature_histogram)};
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).split;
tparam, static_cast<bst_feature_t>(feature_set.size()), 0};
evaluator.Reset(cuts, dh::ToSpan(feature_types), feature_set.size(), tparam, 0);
DeviceSplitCandidate result =
evaluator.EvaluateSingleSplit(input, 0).split;
EXPECT_EQ(result.findex, 1);
EXPECT_EQ(result.fvalue, 11.0);