rmontanana/BayesNet
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Begin Test Folding

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Ricardo Montañana Gómez
2023-10-06 17:08:54 +02:00
parent b9e0028e9d
commit 17e079edd5
10 changed files with 250 additions and 55 deletions
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22
tests/TestBayesMetrics.cc
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@@ -22,19 +22,13 @@ TEST_CASE("Metrics Test", "[BayesNet]")
{"diabetes", 0.0345470614}
};
map<string, vector<pair<int, int>>> resultsMST = {
{"glass", {{0,6}, {0,5}, {0,3}, {5,1}, {5,8}, {6,2}, {6,7}, {7,4}}},
{"glass", {{0,6}, {0,5}, {0,3}, {6,2}, {6,7}, {5,1}, {5,8}, {5,4}}},
{"iris", {{0,1},{0,2},{1,3}}},
{"ecoli", {{0,1}, {0,2}, {1,5}, {1,3}, {5,6}, {5,4}}},
{"diabetes", {{0,7}, {0,2}, {0,6}, {2,3}, {3,4}, {3,5}, {4,1}}}
};
auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadDataset(file_name, true, true);
int classNumStates = statesDisc.at(classNameDisc).size();
auto yresized = torch::transpose(yDisc.view({ yDisc.size(0), 1 }), 0, 1);
torch::Tensor dataset = torch::cat({ XDisc, yresized }, 0);
int nSamples = dataset.size(1);
double epsilon = 1e-5;
torch::Tensor weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
bayesnet::Metrics metrics(dataset, featuresDisc, classNameDisc, classNumStates);
auto raw = RawDatasets(file_name, true);
bayesnet::Metrics metrics(raw.dataset, raw.featurest, raw.classNamet, raw.classNumStates);
SECTION("Test Constructor")
{
@@ -43,21 +37,21 @@ TEST_CASE("Metrics Test", "[BayesNet]")
SECTION("Test SelectKBestWeighted")
{
vector<int> kBest = metrics.SelectKBestWeighted(weights, true, resultsKBest.at(file_name).first);
vector<int> kBest = metrics.SelectKBestWeighted(raw.weights, true, resultsKBest.at(file_name).first);
REQUIRE(kBest.size() == resultsKBest.at(file_name).first);
REQUIRE(kBest == resultsKBest.at(file_name).second);
}
SECTION("Test Mutual Information")
{
auto result = metrics.mutualInformation(dataset.index({ 1, "..." }), dataset.index({ 2, "..." }), weights);
REQUIRE(result == Catch::Approx(resultsMI.at(file_name)).epsilon(epsilon));
auto result = metrics.mutualInformation(raw.dataset.index({ 1, "..." }), raw.dataset.index({ 2, "..." }), raw.weights);
REQUIRE(result == Catch::Approx(resultsMI.at(file_name)).epsilon(raw.epsilon));
}
SECTION("Test Maximum Spanning Tree")
{
auto weights_matrix = metrics.conditionalEdge(weights);
auto result = metrics.maximumSpanningTree(featuresDisc, weights_matrix, 0);
auto weights_matrix = metrics.conditionalEdge(raw.weights);
auto result = metrics.maximumSpanningTree(raw.featurest, weights_matrix, 0);
REQUIRE(result == resultsMST.at(file_name));
}
}