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[backport] Fix inference with categorical feature. (#8591) (#8602) (#8638)

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* Fix inference with categorical feature. (#8591)

* Fix windows build on buildkite. (#8602)

* workaround.
This commit is contained in:
Jiaming Yuan
2023-01-06 01:17:49 +08:00
committed by GitHub
parent 1a834b2b85
commit 067b704e58
7 changed files with 79 additions and 31 deletions
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64
tests/cpp/common/test_categorical.cc
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@@ -1,11 +1,14 @@
/*!
* Copyright 2021 by XGBoost Contributors
* Copyright 2021-2022 by XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <xgboost/json.h>
#include <xgboost/learner.h>
#include <limits>
#include "../../../src/common/categorical.h"
#include "../helpers.h"
namespace xgboost {
namespace common {
@@ -15,29 +18,76 @@ TEST(Categorical, Decision) {
ASSERT_TRUE(common::InvalidCat(a));
std::vector<uint32_t> cats(256, 0);
ASSERT_TRUE(Decision(cats, a, true));
ASSERT_TRUE(Decision(cats, a));
// larger than size
a = 256;
ASSERT_TRUE(Decision(cats, a, true));
ASSERT_TRUE(Decision(cats, a));
// negative
a = -1;
ASSERT_TRUE(Decision(cats, a, true));
ASSERT_TRUE(Decision(cats, a));
CatBitField bits{cats};
bits.Set(0);
a = -0.5;
ASSERT_TRUE(Decision(cats, a, true));
ASSERT_TRUE(Decision(cats, a));
// round toward 0
a = 0.5;
ASSERT_FALSE(Decision(cats, a, true));
ASSERT_FALSE(Decision(cats, a));
// valid
a = 13;
bits.Set(a);
ASSERT_FALSE(Decision(bits.Bits(), a, true));
ASSERT_FALSE(Decision(bits.Bits(), a));
}
/**
* Test for running inference with input category greater than the one stored in tree.
*/
TEST(Categorical, MinimalSet) {
std::size_t constexpr kRows = 256, kCols = 1, kCat = 3;
std::vector<FeatureType> types{FeatureType::kCategorical};
auto Xy =
RandomDataGenerator{kRows, kCols, 0.0}.Type(types).MaxCategory(kCat).GenerateDMatrix(true);
std::unique_ptr<Learner> learner{Learner::Create({Xy})};
learner->SetParam("max_depth", "1");
learner->SetParam("tree_method", "hist");
learner->Configure();
learner->UpdateOneIter(0, Xy);
Json model{Object{}};
learner->SaveModel(&model);
auto tree = model["learner"]["gradient_booster"]["model"]["trees"][0];
ASSERT_GE(get<I32Array const>(tree["categories"]).size(), 1);
auto v = get<I32Array const>(tree["categories"])[0];
HostDeviceVector<float> predt;
{
std::vector<float> data{static_cast<float>(kCat),
static_cast<float>(kCat + 1), 32.0f, 33.0f, 34.0f};
auto test = GetDMatrixFromData(data, data.size(), kCols);
learner->Predict(test, false, &predt, 0, 0, false, /*pred_leaf=*/true);
ASSERT_EQ(predt.Size(), data.size());
auto const& h_predt = predt.ConstHostSpan();
for (auto v : h_predt) {
ASSERT_EQ(v, 1); // left child of root node
}
}
{
std::unique_ptr<Learner> learner{Learner::Create({Xy})};
learner->LoadModel(model);
std::vector<float> data = {static_cast<float>(v)};
auto test = GetDMatrixFromData(data, data.size(), kCols);
learner->Predict(test, false, &predt, 0, 0, false, /*pred_leaf=*/true);
auto const& h_predt = predt.ConstHostSpan();
for (auto v : h_predt) {
ASSERT_EQ(v, 2); // right child of root node
}
}
}
} // namespace common
} // namespace xgboost