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Remove unoptimized implementation of conditionalEntropy

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This commit is contained in:
Ricardo Montañana Gómez
2024-05-15 01:24:27 +02:00
parent e2e0fb0c40
commit 521bfd2a8e
4 changed files with 67 additions and 62 deletions
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57
bayesnet/utils/BayesMetrics.cc
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@@ -177,6 +177,8 @@ namespace bayesnet {
// Total weight sum
double totalWeight = torch::sum(weights).item<double>();
if (totalWeight == 0)
return 0;
// Compute the conditional entropy
double conditionalEntropy = 0.0;
@@ -192,63 +194,8 @@ namespace bayesnet {
conditionalEntropy -= (jointFreq / totalWeight) * std::log(p_y_given_xc);
}
}
return conditionalEntropy;
}
double Metrics::conditionalEntropy2(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights)
{
int numSamples = firstFeature.size(0);
// Get unique values for each variable
auto [uniqueX, countsX] = at::_unique(firstFeature);
auto [uniqueC, countsC] = at::_unique(labels);
// Compute p(x,c) for each unique value of X and C
std::map<int, std::map<std::pair<int, int>, double>> jointCounts;
double totalWeight = 0;
for (auto i = 0; i < numSamples; i++) {
int x = firstFeature[i].item<int>();
int y = secondFeature[i].item<int>();
int c = labels[i].item<int>();
const auto key = std::make_pair(x, c);
jointCounts[y][key] += weights[i].item<double>();
totalWeight += weights[i].item<float>();
}
if (totalWeight == 0)
return 0;
double entropyValue = 0;
// Iterate over unique values of X and C
for (int i = 0; i < uniqueX.size(0); i++) {
int x_val = uniqueX[i].item<int>();
for (int j = 0; j < uniqueC.size(0); j++) {
int c_val = uniqueC[j].item<int>();
double p_xc = 0; // Probability of (X=x, C=c)
double entropy_f = 0;
// Find joint counts for this specific (X,C) combination
for (auto& [y, jointCount] : jointCounts) {
auto joint_count_xc = jointCount.find({ x_val, c_val });
if (joint_count_xc != jointCount.end()) {
p_xc += joint_count_xc->second;
}
}
// Only calculate conditional entropy if p(X=x, C=c) > 0
if (p_xc > 0) {
p_xc /= totalWeight;
for (auto& [y, jointCount] : jointCounts) {
auto key = std::make_pair(x_val, c_val);
double p_y_xc = jointCount[key] / p_xc;
if (p_y_xc > 0) {
entropy_f -= p_y_xc * log(p_y_xc);
}
}
}
entropyValue += p_xc * entropy_f;
}
}
return entropyValue;
return 0;
}
// I(X;Y) = H(Y) - H(Y|X)
double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& weights)
{

1
bayesnet/utils/BayesMetrics.h
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@@ -25,7 +25,6 @@ namespace bayesnet {
// Elements of Information Theory, 2nd Edition, Thomas M. Cover, Joy A. Thomas p. 14
double entropy(const torch::Tensor& feature, const torch::Tensor& weights);
double conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
double conditionalEntropy2(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature, const torch::Tensor& labels, const torch::Tensor& weights);
protected:
torch::Tensor samples; // n+1xm torch::Tensor used to fit the model where samples[-1] is the y std::vector
std::string className;