rmontanana/BayesNet
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Complete Conditional Mutual Information and test

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This commit is contained in:
Ricardo Montañana Gómez 2024-05-15 11:09:23 +02:00
parent 521bfd2a8e
commit 0e24135d46
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
2 changed files with 12 additions and 24 deletions
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2
bayesnet/utils/BayesMetrics.cc
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@ -204,7 +204,7 @@ namespace bayesnet {
// I(X;Y|C) = H(Y|C) - H(Y|X,C) // I(X;Y|C) = H(Y|C) - H(Y|X,C)
double Metrics::conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights) double Metrics::conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights)
{ {
return conditionalEntropy(firstFeature, labels, weights) - conditionalEntropy(firstFeature, secondFeature, labels, weights); return std::max(conditionalEntropy(firstFeature, labels, weights) - conditionalEntropy(firstFeature, secondFeature, labels, weights), 0.0);
} }
/* /*
Compute the maximum spanning tree considering the weights as distances Compute the maximum spanning tree considering the weights as distances

34
tests/TestBayesMetrics.cc
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@ -101,32 +101,20 @@ TEST_CASE("Entropy Test", "[Metrics]")
} }
TEST_CASE("Conditional Entropy", "[Metrics]") TEST_CASE("Conditional Entropy", "[Metrics]")
{ {
auto raw = RawDatasets("mfeat-factors", true); auto raw = RawDatasets("iris", true);
bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates); bayesnet::Metrics metrics(raw.dataset, raw.features, raw.className, raw.classNumStates);
bayesnet::Metrics metrics2(raw.dataset, raw.features, raw.className, raw.classNumStates); auto expected = std::map<std::pair<int, int>, double>{
auto feature0 = raw.dataset.index({ 0, "..." }); { { 0, 1 }, 0.0 },
auto feature1 = raw.dataset.index({ 1, "..." }); { { 0, 2 }, 0.287696 },
auto feature2 = raw.dataset.index({ 2, "..." }); { { 0, 3 }, 0.403749 },
auto feature3 = raw.dataset.index({ 3, "..." }); { { 1, 2 }, 1.17112 },
platform::Timer timer; { { 1, 3 }, 1.31852 },
double result, greatest = 0; { { 2, 3 }, 0.210068 },
int best_i, best_j; };
timer.start();
for (int i = 0; i < raw.features.size() - 1; ++i) { for (int i = 0; i < raw.features.size() - 1; ++i) {
if (i % 50 == 0) {
std::cout << "i=" << i << " Time=" << timer.getDurationString(true) << std::endl;
}
for (int j = i + 1; j < raw.features.size(); ++j) { for (int j = i + 1; j < raw.features.size(); ++j) {
result = metrics.conditionalMutualInformation(raw.dataset.index({ i, "..." }), raw.dataset.index({ j, "..." }), raw.yt, raw.weights); double result = metrics.conditionalMutualInformation(raw.dataset.index({ i, "..." }), raw.dataset.index({ j, "..." }), raw.yt, raw.weights);
if (result > greatest) { REQUIRE(result == Catch::Approx(expected.at({ i, j })).epsilon(raw.epsilon));
greatest = result;
best_i = i;
best_j = j;
}
} }
} }
timer.stop();
std::cout << "CMI(" << best_i << "," << best_j << ")=" << greatest << "\n";
std::cout << "Time=" << timer.getDurationString() << std::endl;
// Se pueden precalcular estos valores y utilizarlos en el algoritmo como entrada
} }