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985deba931
We intend to replace heuristics based code completion ranking with a Decision Forest Model. This patch introduces a format for representing the model and an inference runtime that is code-generated at build time. - Forest.json contains all the trees as an array of trees. - Features.json describes the features to be used. - Codegen file takes the above two files and generates CompletionModel containing Feature struct and corresponding Evaluate function. The Evaluate function maps a feature to a real number describing the relevance of this candidate. - The codegen is part of build system and these files are generated at build time. - Proposes a way to test the generated runtime using a test model. - Replicates the model structure in unittests. - unittest tests both the test model (for correct tree traversal) and the real model (for sanity). This reverts commit 549e55b3d5634870aa9d42135f51ad46a6a0e347.
30 lines
815 B
C++
30 lines
815 B
C++
#include "DecisionForestRuntimeTest.h"
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#include "decision_forest_model/CategoricalFeature.h"
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#include "gtest/gtest.h"
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namespace clang {
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namespace clangd {
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TEST(DecisionForestRuntime, Evaluate) {
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using Example = ::ns1::ns2::test::Example;
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using Cat = ::ns1::ns2::TestEnum;
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using ::ns1::ns2::test::Evaluate;
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Example E;
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E.setANumber(200); // True
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E.setAFloat(0); // True: +10.0
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E.setACategorical(Cat::A); // True: +5.0
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EXPECT_EQ(Evaluate(E), 15.0);
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E.setANumber(200); // True
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E.setAFloat(-2.5); // False: -20.0
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E.setACategorical(Cat::B); // True: +5.0
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EXPECT_EQ(Evaluate(E), -15.0);
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E.setANumber(100); // False
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E.setACategorical(Cat::C); // True: +3.0, False: -6.0
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EXPECT_EQ(Evaluate(E), -3.0);
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}
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} // namespace clangd
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} // namespace clang
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