Merge pull request 'Fix some issues in FeatureSelect' (#37) from FixSelectFeatures into fix_vcpkg
Reviewed-on: #37
This commit is contained in:
@@ -1,84 +1,141 @@
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// ***************************************************************
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// **
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// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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// **
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#include <limits>
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#include "bayesnet/utils/bayesnetUtils.h"
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#include "FeatureSelect.h"
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namespace bayesnet {
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FeatureSelect::FeatureSelect(const torch::Tensor& samples, const std::vector<std::string>& features, const std::string& className, const int maxFeatures, const int classNumStates, const torch::Tensor& weights) :
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Metrics(samples, features, className, classNumStates), maxFeatures(maxFeatures == 0 ? samples.size(0) - 1 : maxFeatures), weights(weights)
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namespace bayesnet {
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using namespace torch::indexing; // for Ellipsis constant
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//---------------------------------------------------------------------
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// ctor
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//---------------------------------------------------------------------
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FeatureSelect::FeatureSelect(const torch::Tensor& samples,
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const std::vector<std::string>& features,
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const std::string& className,
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int maxFeatures,
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int classNumStates,
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const torch::Tensor& weights)
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: Metrics(samples, features, className, classNumStates),
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maxFeatures(maxFeatures == 0 ? samples.size(0) - 1 : maxFeatures),
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weights(weights)
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{
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}
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//---------------------------------------------------------------------
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// public helpers
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//---------------------------------------------------------------------
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void FeatureSelect::initialize()
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{
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selectedFeatures.clear();
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selectedScores.clear();
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suLabels.clear();
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suFeatures.clear();
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fitted = false;
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}
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//---------------------------------------------------------------------
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// Symmetrical Uncertainty (SU)
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//---------------------------------------------------------------------
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double FeatureSelect::symmetricalUncertainty(int a, int b)
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{
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/*
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Compute symmetrical uncertainty. Normalize* information gain (mutual
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information) with the entropies of the features in order to compensate
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the bias due to high cardinality features. *Range [0, 1]
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(https://www.sciencedirect.com/science/article/pii/S0020025519303603)
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*/
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auto x = samples.index({ a, "..." });
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auto y = samples.index({ b, "..." });
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auto mu = mutualInformation(x, y, weights);
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auto hx = entropy(x, weights);
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auto hy = entropy(y, weights);
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return 2.0 * mu / (hx + hy);
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* Compute symmetrical uncertainty. Normalises the information gain
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* (mutual information) with the entropies of the variables to compensate
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* the bias due to high‑cardinality features. Range: [0, 1]
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* See: https://www.sciencedirect.com/science/article/pii/S0020025519303603
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*/
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auto x = samples.index({ a, Ellipsis }); // row a => feature a
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auto y = (b >= 0) ? samples.index({ b, Ellipsis }) // row b (>=0) => feature b
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: samples.index({ -1, Ellipsis }); // ‑1 treated as last row = labels
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double mu = mutualInformation(x, y, weights);
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double hx = entropy(x, weights);
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double hy = entropy(y, weights);
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const double denom = hx + hy;
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if (denom == 0.0) return 0.0; // perfectly pure variables
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return 2.0 * mu / denom;
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}
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//---------------------------------------------------------------------
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// SU feature–class
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//---------------------------------------------------------------------
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void FeatureSelect::computeSuLabels()
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{
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// Compute Simmetrical Uncertainty between features and labels
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// Compute Symmetrical Uncertainty between each feature and the class labels
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// https://en.wikipedia.org/wiki/Symmetric_uncertainty
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for (int i = 0; i < features.size(); ++i) {
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suLabels.push_back(symmetricalUncertainty(i, -1));
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const int classIdx = static_cast<int>(samples.size(0)) - 1; // labels in last row
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suLabels.reserve(features.size());
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for (int i = 0; i < static_cast<int>(features.size()); ++i) {
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suLabels.emplace_back(symmetricalUncertainty(i, classIdx));
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}
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}
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double FeatureSelect::computeSuFeatures(const int firstFeature, const int secondFeature)
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//---------------------------------------------------------------------
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// SU feature–feature with cache
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//---------------------------------------------------------------------
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double FeatureSelect::computeSuFeatures(int firstFeature, int secondFeature)
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{
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// Compute Simmetrical Uncertainty between features
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// https://en.wikipedia.org/wiki/Symmetric_uncertainty
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try {
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return suFeatures.at({ firstFeature, secondFeature });
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}
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catch (const std::out_of_range& e) {
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double result = symmetricalUncertainty(firstFeature, secondFeature);
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suFeatures[{firstFeature, secondFeature}] = result;
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return result;
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}
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// Order the pair to exploit symmetry => only one entry in the map
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auto ordered = std::minmax(firstFeature, secondFeature);
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const std::pair<int, int> key{ ordered.first, ordered.second };
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auto it = suFeatures.find(key);
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if (it != suFeatures.end()) return it->second;
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double result = symmetricalUncertainty(key.first, key.second);
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suFeatures[key] = result; // store once (symmetry handled by ordering)
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return result;
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}
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//---------------------------------------------------------------------
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// Correlation‑based Feature Selection (CFS) merit
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//---------------------------------------------------------------------
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double FeatureSelect::computeMeritCFS()
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{
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double rcf = 0;
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for (auto feature : selectedFeatures) {
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rcf += suLabels[feature];
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}
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double rff = 0;
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int n = selectedFeatures.size();
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for (const auto& item : doCombinations(selectedFeatures)) {
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rff += computeSuFeatures(item.first, item.second);
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}
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return rcf / sqrt(n + (n * n - n) * rff);
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const int n = static_cast<int>(selectedFeatures.size());
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if (n == 0) return 0.0;
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// average r_cf (feature–class)
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double rcf_sum = 0.0;
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for (int f : selectedFeatures) rcf_sum += suLabels[f];
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const double rcf_avg = rcf_sum / n;
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// average r_ff (feature–feature)
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double rff_sum = 0.0;
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const auto& pairs = doCombinations(selectedFeatures); // generates each unordered pair once
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for (const auto& p : pairs) rff_sum += computeSuFeatures(p.first, p.second);
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const double numPairs = n * (n - 1) * 0.5;
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const double rff_avg = (numPairs > 0) ? rff_sum / numPairs : 0.0;
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// Merit_S = k * r_cf / sqrt( k + k*(k‑1) * r_ff ) (Hall, 1999)
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const double k = static_cast<double>(n);
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return (k * rcf_avg) / std::sqrt(k + k * (k - 1) * rff_avg);
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}
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//---------------------------------------------------------------------
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// getters
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//---------------------------------------------------------------------
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std::vector<int> FeatureSelect::getFeatures() const
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{
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if (!fitted) {
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throw std::runtime_error("FeatureSelect not fitted");
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}
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if (!fitted) throw std::runtime_error("FeatureSelect not fitted");
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return selectedFeatures;
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}
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std::vector<double> FeatureSelect::getScores() const
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{
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if (!fitted) {
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throw std::runtime_error("FeatureSelect not fitted");
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}
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if (!fitted) throw std::runtime_error("FeatureSelect not fitted");
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return selectedScores;
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}
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}
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} // namespace bayesnet
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