Remove FeatureSel, add SelectKBest to BayesMetrics
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@@ -1 +0,0 @@
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add_library(FeatureSelect FeatureSelect.cpp)
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@@ -1,119 +0,0 @@
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#include "FeatureSelect.h"
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namespace features {
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SelectKBestWeighted::SelectKBestWeighted(samples_t& samples, labels_t& labels, weights_t& weights, int k, bool nat)
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: samples(samples), labels(labels), weights(weights), k(k), nat(nat)
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{
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if (samples.size() == 0 || samples[0].size() == 0)
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throw invalid_argument("features must be a non-empty matrix");
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if (samples.size() != labels.size())
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throw invalid_argument("number of samples (" + to_string(samples.size()) + ") and labels (" + to_string(labels.size()) + ") must be equal");
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if (samples.size() != weights.size())
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throw invalid_argument("number of samples and weights must be equal");
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if (k < 1 || k > static_cast<int>(samples[0].size()))
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throw invalid_argument("k must be between 1 and number of features");
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numFeatures = 0;
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numClasses = 0;
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numSamples = 0;
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fitted = false;
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}
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SelectKBestWeighted& SelectKBestWeighted::fit()
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{
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auto labelsCopy = labels;
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numFeatures = samples[0].size();
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numSamples = samples.size();
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// compute number of classes
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sort(labelsCopy.begin(), labelsCopy.end());
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auto last = unique(labelsCopy.begin(), labelsCopy.end());
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labelsCopy.erase(last, labelsCopy.end());
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numClasses = labelsCopy.size();
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// compute scores
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scores.reserve(numFeatures);
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for (int i = 0; i < numFeatures; ++i) {
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scores.push_back(MutualInformation(i));
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features.push_back(i);
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}
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// sort & reduce scores and features
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sort(features.begin(), features.end(), [&](int i, int j)
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{ return scores[i] > scores[j]; });
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sort(scores.begin(), scores.end(), greater<precision_t>());
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features.resize(k);
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scores.resize(k);
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fitted = true;
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return *this;
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}
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precision_t SelectKBestWeighted::entropyLabel()
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{
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return entropy(labels);
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}
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precision_t SelectKBestWeighted::entropy(const sample_t& data)
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{
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precision_t ventropy = 0, totalWeight = 0;
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score_t counts(numClasses + 1, 0);
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for (auto i = 0; i < static_cast<int>(data.size()); ++i) {
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counts[data[i]] += weights[i];
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totalWeight += weights[i];
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}
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for (auto count : counts) {
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precision_t p = count / totalWeight;
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if (p > 0) {
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if (nat) {
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ventropy -= p * log(p);
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} else {
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ventropy -= p * log2(p);
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}
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}
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}
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return ventropy;
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}
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// H(Y|X) = sum_{x in X} p(x) H(Y|X=x)
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precision_t SelectKBestWeighted::conditionalEntropy(const int feature)
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{
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unordered_map<value_t, precision_t> featureCounts;
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unordered_map<value_t, unordered_map<value_t, precision_t>> jointCounts;
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featureCounts.clear();
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jointCounts.clear();
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precision_t totalWeight = 0;
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for (auto i = 0; i < numSamples; i++) {
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featureCounts[samples[i][feature]] += weights[i];
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jointCounts[samples[i][feature]][labels[i]] += weights[i];
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totalWeight += weights[i];
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}
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if (totalWeight == 0)
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throw invalid_argument("Total weight should not be zero");
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precision_t entropy = 0;
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for (auto& [feat, count] : featureCounts) {
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auto p_f = count / totalWeight;
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precision_t entropy_f = 0;
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for (auto& [label, jointCount] : jointCounts[feat]) {
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auto p_l_f = jointCount / count;
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if (p_l_f > 0) {
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if (nat) {
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entropy_f -= p_l_f * log(p_l_f);
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} else {
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entropy_f -= p_l_f * log2(p_l_f);
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}
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}
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}
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entropy += p_f * entropy_f;
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}
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return entropy;
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}
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// I(X;Y) = H(Y) - H(Y|X)
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precision_t SelectKBestWeighted::MutualInformation(const int i)
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{
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return entropyLabel() - conditionalEntropy(i);
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}
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score_t SelectKBestWeighted::getScores() const
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{
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if (!fitted)
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throw logic_error("score not fitted");
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return scores;
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}
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//Return the indices of the selected features
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labels_t SelectKBestWeighted::getFeatures() const
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{
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if (!fitted)
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throw logic_error("score not fitted");
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return features;
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}
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}
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@@ -1,38 +0,0 @@
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#ifndef SELECT_K_BEST_WEIGHTED_H
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#define SELECT_K_BEST_WEIGHTED_H
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#include <map>
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#include <vector>
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#include <string>
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using namespace std;
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namespace features {
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typedef float precision_t;
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typedef int value_t;
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typedef vector<value_t> sample_t;
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typedef vector<sample_t> samples_t;
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typedef vector<value_t> labels_t;
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typedef vector<precision_t> score_t, weights_t;
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class SelectKBestWeighted {
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private:
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const samples_t samples;
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const labels_t labels;
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const weights_t weights;
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const int k;
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bool nat; // use natural log or log2
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int numFeatures, numClasses, numSamples;
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bool fitted;
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score_t scores; // scores of the features
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labels_t features; // indices of the selected features
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precision_t entropyLabel();
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precision_t entropy(const sample_t&);
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precision_t conditionalEntropy(const int);
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precision_t MutualInformation(const int);
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public:
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SelectKBestWeighted(samples_t&, labels_t&, weights_t&, int, bool);
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SelectKBestWeighted& fit();
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score_t getScores() const;
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labels_t getFeatures() const; //Return the indices of the selected features
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static inline string version() { return "0.1.0"; };
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};
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}
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#endif
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