56 lines
2.1 KiB
C++
56 lines
2.1 KiB
C++
#include <catch2/catch_test_macros.hpp>
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#include <catch2/catch_approx.hpp>
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#include <catch2/generators/catch_generators.hpp>
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#include "BayesMetrics.h"
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#include "TestUtils.h"
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using namespace std;
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TEST_CASE("Metrics Test", "[Metrics]")
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{
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string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
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map<string, pair<int, vector<int>>> results = {
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{"glass", {7, { 3, 2, 0, 1, 6, 7, 5 }}},
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{"iris", {3, { 1, 0, 2 }} },
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{"ecoli", {6, { 2, 3, 1, 0, 4, 5 }}},
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{"diabetes", {2, { 2, 0 }}}
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};
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auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadDataset(file_name, true, true);
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int classNumStates = statesDisc.at(classNameDisc).size();
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auto yresized = torch::transpose(yDisc.view({ yDisc.size(0), 1 }), 0, 1);
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torch::Tensor dataset = torch::cat({ XDisc, yresized }, 0);
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int nSamples = dataset.size(1);
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SECTION("Test Constructor")
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{
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bayesnet::Metrics metrics(XDisc, featuresDisc, classNameDisc, classNumStates);
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REQUIRE(metrics.getScoresKBest().size() == 0);
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}
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SECTION("Test SelectKBestWeighted")
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{
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bayesnet::Metrics metrics(XDisc, featuresDisc, classNameDisc, classNumStates);
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torch::Tensor weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
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vector<int> kBest = metrics.SelectKBestWeighted(weights, true, results.at(file_name).first);
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REQUIRE(kBest.size() == results.at(file_name).first);
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REQUIRE(kBest == results.at(file_name).second);
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}
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SECTION("Test mutualInformation")
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{
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// torch::Tensor samples = torch::rand({ 10, 5 });
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// vector<string> features = { "feature1", "feature2", "feature3", "feature4", "feature5" };
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// string className = "class1";
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// int classNumStates = 2;
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// bayesnet::Metrics obj(samples, features, className, classNumStates);
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// torch::Tensor firstFeature = samples.select(1, 0);
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// torch::Tensor secondFeature = samples.select(1, 1);
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// torch::Tensor weights = torch::ones({ 10 });
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// double mi = obj.mutualInformation(firstFeature, secondFeature, weights);
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// REQUIRE(mi >= 0);
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}
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} |