rmontanana/BayesNet
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Force mutual information methods to be at least 0

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There were cases where a tiny negative number was returned (less than -1e-7)
Fix mst glass test that is affected with this change
This commit is contained in:
Ricardo Montañana Gómez 2024-05-17 11:15:45 +02:00
parent 291ba0fb0e
commit 2584e8294d
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
4 changed files with 8 additions and 18 deletions
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2
bayesnet/ensembles/BoostA2DE.cc
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@ -155,7 +155,7 @@ namespace bayesnet {
}
}
if (pairSelection.size() > 0) {
notes.push_back("Used pairs not used in train: " + std::to_string(pairSelection.size()));
notes.push_back("Pairs not used in train: " + std::to_string(pairSelection.size()));
status = WARNING;
}
notes.push_back("Number of models: " + std::to_string(n_models));

17
bayesnet/utils/BayesMetrics.cc
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@ -198,24 +198,20 @@ namespace bayesnet {
}
return entropyValue;
}
// H(Y|X,C) = sum_{x in X, c in C} p(x,c) H(Y|X=x,C=c)
// H(X|Y,C) = sum_{y in Y, c in C} p(x,c) H(X|Y=y,C=c)
double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights)
{
// Ensure the tensors are of the same length
assert(firstFeature.size(0) == secondFeature.size(0) && firstFeature.size(0) == labels.size(0) && firstFeature.size(0) == weights.size(0));
// Convert tensors to vectors for easier processing
auto firstFeatureData = firstFeature.accessor<int, 1>();
auto secondFeatureData = secondFeature.accessor<int, 1>();
auto labelsData = labels.accessor<int, 1>();
auto weightsData = weights.accessor<double, 1>();
int numSamples = firstFeature.size(0);
// Maps for joint and marginal probabilities
std::map<std::tuple<int, int, int>, double> jointCount;
std::map<std::tuple<int, int>, double> marginalCount;
// Compute joint and marginal counts
for (int i = 0; i < numSamples; ++i) {
auto keyJoint = std::make_tuple(firstFeatureData[i], labelsData[i], secondFeatureData[i]);
@ -224,34 +220,29 @@ namespace bayesnet {
jointCount[keyJoint] += weightsData[i];
marginalCount[keyMarginal] += weightsData[i];
}
// Total weight sum
double totalWeight = torch::sum(weights).item<double>();
if (totalWeight == 0)
return 0;
// Compute the conditional entropy
double conditionalEntropy = 0.0;
for (const auto& [keyJoint, jointFreq] : jointCount) {
auto [x, c, y] = keyJoint;
auto keyMarginal = std::make_tuple(x, c);
//double p_xc = marginalCount[keyMarginal] / totalWeight;
double p_y_given_xc = jointFreq / marginalCount[keyMarginal];
if (p_y_given_xc > 0) {
conditionalEntropy -= (jointFreq / totalWeight) * std::log(p_y_given_xc);
}
}
return conditionalEntropy;
}
// I(X;Y) = H(Y) - H(Y|X)
// I(X;Y) = H(Y) - H(Y|X) ; I(X;Y) >= 0
double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights)
{
return entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights);
return std::max(entropy(firstFeature, weights) - conditionalEntropy(firstFeature, secondFeature, weights), 0.0);
}
// I(X;Y|C) = H(Y|C) - H(Y|X,C)
// I(X;Y|C) = H(X|C) - H(X|Y,C) >= 0
double Metrics::conditionalMutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights)
{
return std::max(conditionalEntropy(firstFeature, labels, weights) - conditionalEntropy(firstFeature, secondFeature, labels, weights), 0.0);

5
tests/TestBayesMetrics.cc
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@ -11,7 +11,6 @@
#include "TestUtils.h"
#include "Timer.h"
TEST_CASE("Metrics Test", "[Metrics]")
{
std::string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
@ -28,8 +27,8 @@ TEST_CASE("Metrics Test", "[Metrics]")
{"diabetes", 0.0345470614}
};
map<pair<std::string, int>, std::vector<pair<int, int>>> resultsMST = {
{ {"glass", 0}, { {0, 6}, {0, 5}, {0, 3}, {5, 1}, {5, 8}, {5, 4}, {6, 2}, {6, 7} } },
{ {"glass", 1}, { {1, 5}, {5, 0}, {5, 8}, {5, 4}, {0, 6}, {0, 3}, {6, 2}, {6, 7} } },
{ {"glass", 0}, { {0, 6}, {0, 5}, {0, 3}, {3, 4}, {5, 1}, {5, 8}, {6, 2}, {6, 7} } },
{ {"glass", 1}, { {1, 5}, {5, 0}, {5, 8}, {0, 6}, {0, 3}, {3, 4}, {6, 2}, {6, 7} } },
{ {"iris", 0}, { {0, 1}, {0, 2}, {1, 3} } },
{ {"iris", 1}, { {1, 0}, {1, 3}, {0, 2} } },
{ {"ecoli", 0}, { {0, 1}, {0, 2}, {1, 5}, {1, 3}, {5, 6}, {5, 4} } },

2
tests/TestBoostA2DE.cc
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@ -22,7 +22,7 @@ TEST_CASE("Build basic model", "[BoostA2DE]")
REQUIRE(clf.getNumberOfEdges() == 684);
REQUIRE(clf.getNotes().size() == 3);
REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes()[1] == "Used pairs not used in train: 20");
REQUIRE(clf.getNotes()[1] == "Pairs not used in train: 20");
REQUIRE(clf.getNotes()[2] == "Number of models: 38");
auto score = clf.score(raw.Xv, raw.yv);
REQUIRE(score == Catch::Approx(0.919271).epsilon(raw.epsilon));