95 lines
3.4 KiB
C++
95 lines
3.4 KiB
C++
#include "Folding.h"
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#include <algorithm>
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#include <map>
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Fold::Fold(int k, int n, int seed) : k(k), n(n), seed(seed)
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{
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random_device rd;
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random_seed = default_random_engine(seed == -1 ? rd() : seed);
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srand(seed == -1 ? time(0) : seed);
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}
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KFold::KFold(int k, int n, int seed) : Fold(k, n, seed), indices(vector<int>(n))
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{
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iota(begin(indices), end(indices), 0); // fill with 0, 1, ..., n - 1
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shuffle(indices.begin(), indices.end(), random_seed);
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}
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pair<vector<int>, vector<int>> KFold::getFold(int nFold)
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{
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if (nFold >= k || nFold < 0) {
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throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
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}
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int nTest = n / k;
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auto train = vector<int>();
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auto test = vector<int>();
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for (int i = 0; i < n; i++) {
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if (i >= nTest * nFold && i < nTest * (nFold + 1)) {
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test.push_back(indices[i]);
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} else {
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train.push_back(indices[i]);
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}
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}
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return { train, test };
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}
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StratifiedKFold::StratifiedKFold(int k, torch::Tensor& y, int seed) : Fold(k, y.numel(), seed)
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{
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n = y.numel();
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this->y = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + n);
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build();
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}
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StratifiedKFold::StratifiedKFold(int k, const vector<int>& y, int seed)
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: Fold(k, y.size(), seed)
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{
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this->y = y;
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n = y.size();
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build();
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}
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void StratifiedKFold::build()
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{
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stratified_indices = vector<vector<int>>(k);
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int fold_size = n / k;
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// Compute class counts and indices
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auto class_indices = map<int, vector<int>>();
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vector<int> class_counts(*max_element(y.begin(), y.end()) + 1, 0);
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for (auto i = 0; i < n; ++i) {
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class_counts[y[i]]++;
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class_indices[y[i]].push_back(i);
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}
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// Shuffle class indices
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for (auto& [cls, indices] : class_indices) {
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shuffle(indices.begin(), indices.end(), random_seed);
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}
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// Assign indices to folds
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for (auto label = 0; label < class_counts.size(); ++label) {
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auto num_samples_to_take = class_counts[label] / k;
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if (num_samples_to_take == 0)
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continue;
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auto remainder_samples_to_take = class_counts[label] % k;
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for (auto fold = 0; fold < k; ++fold) {
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auto it = next(class_indices[label].begin(), num_samples_to_take);
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move(class_indices[label].begin(), it, back_inserter(stratified_indices[fold])); // ##
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class_indices[label].erase(class_indices[label].begin(), it);
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}
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while (remainder_samples_to_take > 0) {
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int fold = (rand() % static_cast<int>(k));
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if (stratified_indices[fold].size() == fold_size + 1) {
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continue;
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}
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auto it = next(class_indices[label].begin(), 1);
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stratified_indices[fold].push_back(*class_indices[label].begin());
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class_indices[label].erase(class_indices[label].begin(), it);
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remainder_samples_to_take--;
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}
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}
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}
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pair<vector<int>, vector<int>> StratifiedKFold::getFold(int nFold)
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{
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if (nFold >= k || nFold < 0) {
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throw out_of_range("nFold (" + to_string(nFold) + ") must be less than k (" + to_string(k) + ")");
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}
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vector<int> test_indices = stratified_indices[nFold];
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vector<int> train_indices;
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for (int i = 0; i < k; ++i) {
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if (i == nFold) continue;
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train_indices.insert(train_indices.end(), stratified_indices[i].begin(), stratified_indices[i].end());
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}
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return { train_indices, test_indices };
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} |