Complete Folding Test
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@@ -66,27 +66,28 @@ TEST_CASE("StratifiedKFold Test", "[Platform][StratifiedKFold]")
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auto [train_indicesv, test_indicesv] = stratified_kfoldv.getFold(fold);
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REQUIRE(train_indicest == train_indicesv);
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REQUIRE(test_indicest == test_indicesv);
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bool result = train_indicest.size() == number || train_indicest.size() == number + 1;
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REQUIRE(result);
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REQUIRE(train_indicest.size() + test_indicest.size() == raw.nSamples);
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// In the worst case scenario, the number of samples in the training set is number + raw.classNumStates
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// because in that fold can come one remainder sample from each class.
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REQUIRE(train_indicest.size() <= number + raw.classNumStates);
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// If the number of samples in any class is less than the number of folds, then the fold is faulty.
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// and the number of samples in the training set + test set will be less than nSamples
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if (!stratified_kfoldt.isFaulty()) {
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REQUIRE(train_indicest.size() + test_indicest.size() == raw.nSamples);
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} else {
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REQUIRE(train_indicest.size() + test_indicest.size() <= raw.nSamples);
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}
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auto train_t = torch::tensor(train_indicest);
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auto ytrain = raw.yt.index({ train_t });
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cout << "dataset=" << file_name << endl;
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cout << "nSamples=" << raw.nSamples << endl;;
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cout << "number=" << number << endl;
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cout << "train_indices.size()=" << train_indicest.size() << endl;
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cout << "test_indices.size()=" << test_indicest.size() << endl;
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cout << "Class Name = " << raw.classNamet << endl;
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// Check that the class labels have been equally assign to each fold
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for (const auto& idx : train_indicest) {
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counts[fold][ytrain[idx].item<int>()]++;
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counts[fold][raw.yt[idx].item<int>()]++;
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}
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}
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// Test the fold counting of every class
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for (int fold = 0; fold < nFolds; ++fold) {
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for (int j = 1; j < nFolds - 1; ++j) {
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for (int k = 0; k < raw.classNumStates; ++k) {
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REQUIRE(abs(counts.at(fold).at(k) - counts.at(fold).at(j)) <= 1);
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REQUIRE(abs(counts.at(fold).at(k) - counts.at(j).at(k)) <= 1);
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}
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}
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}
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