Eliminate redundant memory and enhance memory usage
1. Eliminated Redundant Memory Usage - Before: Maintained both X (float) and Xs (string) vectors simultaneously → 2x memory usage - After: Use temporary categoricalData only during processing, deallocated automatically → ~50% memory reduction 2. Implemented Memory Pre-allocation - Before: Vectors grew dynamically causing memory fragmentation - After: X.assign(numFeatures, std::vector<float>(numSamples)) pre-allocates all memory upfront - Benefit: Eliminates reallocation overhead and memory fragmentation 3. Added Robust Exception Handling - Before: stof(token) could crash on malformed data - After: Wrapped in try-catch with descriptive error messages - Improvement: Prevents crashes and provides debugging information 4. Optimized String Processing - Before: type += type_w + " " caused O(n²) string concatenation - After: Used std::ostringstream for efficient string building - Benefit: Better performance on files with complex attribute types
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@@ -66,7 +66,9 @@ public:
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return s;
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}
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std::vector<std::vector<float>>& getX() { return X; }
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const std::vector<std::vector<float>>& getX() const { return X; }
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std::vector<int>& getY() { return y; }
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const std::vector<int>& getY() const { return y; }
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std::map<std::string, bool> getNumericAttributes() const { return numeric_features; }
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std::vector<std::pair<std::string, std::string>> getAttributes() const { return attributes; };
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std::vector<std::string> split(const std::string& text, char delimiter)
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@@ -86,8 +88,7 @@ protected:
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std::vector<std::pair<std::string, std::string>> attributes;
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std::string className;
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std::string classType;
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std::vector<std::vector<float>> X;
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std::vector<std::vector<std::string>> Xs;
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std::vector<std::vector<float>> X; // X[feature][sample] - feature-major layout
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std::vector<int> y;
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std::map<std::string, std::vector<std::string>> states;
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private:
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@@ -128,34 +129,64 @@ private:
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}
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void generateDataset(int labelIndex)
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{
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X = std::vector<std::vector<float>>(attributes.size(), std::vector<float>(lines.size()));
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Xs = std::vector<std::vector<std::string>>(attributes.size(), std::vector<std::string>(lines.size()));
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auto yy = std::vector<std::string>(lines.size(), "");
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for (size_t i = 0; i < lines.size(); i++) {
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std::stringstream ss(lines[i]);
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std::string value;
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const size_t numSamples = lines.size();
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const size_t numFeatures = attributes.size();
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// Pre-allocate with feature-major layout: X[feature][sample]
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X.assign(numFeatures, std::vector<float>(numSamples));
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// Temporary storage for categorical data per feature (only for non-numeric features)
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std::vector<std::vector<std::string>> categoricalData(numFeatures);
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for (size_t i = 0; i < numFeatures; ++i) {
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if (!numeric_features[attributes[i].first]) {
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categoricalData[i].reserve(numSamples);
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}
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}
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std::vector<std::string> yy;
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yy.reserve(numSamples);
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// Parse each sample
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for (size_t sampleIdx = 0; sampleIdx < numSamples; ++sampleIdx) {
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const auto tokens = split(lines[sampleIdx], ',');
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int pos = 0;
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int xIndex = 0;
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auto tokens = split(lines[i], ',');
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int featureIdx = 0;
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for (const auto& token : tokens) {
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if (pos++ == labelIndex) {
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yy[i] = token;
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yy.push_back(token);
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} else {
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if (numeric_features[attributes[xIndex].first]) {
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X[xIndex][i] = stof(token);
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const auto& featureName = attributes[featureIdx].first;
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if (numeric_features.at(featureName)) {
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// Parse numeric value with exception handling
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try {
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X[featureIdx][sampleIdx] = std::stof(token);
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} catch (const std::exception& e) {
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throw std::invalid_argument("Invalid numeric value '" + token + "' at sample " + std::to_string(sampleIdx) + ", feature " + featureName);
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}
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} else {
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Xs[xIndex][i] = token;
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// Store categorical value temporarily
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categoricalData[featureIdx].push_back(token);
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}
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xIndex++;
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featureIdx++;
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}
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}
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}
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for (size_t i = 0; i < attributes.size(); i++) {
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if (!numeric_features[attributes[i].first]) {
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auto data = factorize(attributes[i].first, Xs[i]);
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std::transform(data.begin(), data.end(), X[i].begin(), [](int x) { return float(x);});
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// Convert categorical features to numeric
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for (size_t featureIdx = 0; featureIdx < numFeatures; ++featureIdx) {
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if (!numeric_features[attributes[featureIdx].first]) {
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const auto& featureName = attributes[featureIdx].first;
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auto encodedValues = factorize(featureName, categoricalData[featureIdx]);
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// Copy encoded values to X[feature][sample]
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for (size_t sampleIdx = 0; sampleIdx < numSamples; ++sampleIdx) {
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X[featureIdx][sampleIdx] = static_cast<float>(encodedValues[sampleIdx]);
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}
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}
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}
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y = factorize(className, yy);
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}
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void loadCommon(std::string fileName)
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@@ -176,9 +207,13 @@ private:
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if (line.find("@attribute") != std::string::npos || line.find("@ATTRIBUTE") != std::string::npos) {
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std::stringstream ss(line);
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ss >> keyword >> attribute;
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type = "";
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while (ss >> type_w)
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type += type_w + " ";
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// Efficiently build type string
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std::ostringstream typeStream;
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while (ss >> type_w) {
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if (typeStream.tellp() > 0) typeStream << " ";
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typeStream << type_w;
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}
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type = typeStream.str();
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attributes.emplace_back(trim(attribute), trim(type));
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continue;
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}
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@@ -34,15 +34,16 @@ TEST_CASE("Load Test", "[ArffFiles]")
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REQUIRE(arff.getLines().size() == 150);
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REQUIRE(arff.getLines()[0] == "5.1,3.5,1.4,0.2,Iris-setosa");
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REQUIRE(arff.getLines()[149] == "5.9,3.0,5.1,1.8,Iris-virginica");
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REQUIRE(arff.getX().size() == 4);
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REQUIRE(arff.getX().size() == 4); // 4 features
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for (int i = 0; i < 4; ++i) {
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REQUIRE(arff.getX()[i].size() == 150);
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REQUIRE(arff.getX()[i].size() == 150); // 150 samples per feature
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}
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// Test first 4 samples: X[feature][sample]
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auto expected = std::vector<std::vector<float>>{
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{5.1, 4.9, 4.7, 4.6},
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{3.5, 3.0, 3.2, 3.1},
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{1.4, 1.4, 1.3, 1.5},
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{0.2, 0.2, 0.2, 0.2}
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{5.1, 4.9, 4.7, 4.6}, // Feature 0 (sepallength)
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{3.5, 3.0, 3.2, 3.1}, // Feature 1 (sepalwidth)
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{1.4, 1.4, 1.3, 1.5}, // Feature 2 (petallength)
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{0.2, 0.2, 0.2, 0.2} // Feature 3 (petalwidth)
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};
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for (int i = 0; i < 4; ++i) {
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for (int j = 0; j < 4; ++j)
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@@ -79,15 +80,16 @@ TEST_CASE("Load with class name", "[ArffFiles]")
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REQUIRE(arff.getLines().size() == 214);
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REQUIRE(arff.getLines()[0] == "1.51793,12.79,3.5,1.12,73.03,0.64,8.77,0,0,'build wind float'");
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REQUIRE(arff.getLines()[149] == "1.51813,13.43,3.98,1.18,72.49,0.58,8.15,0,0,'build wind non-float'");
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REQUIRE(arff.getX().size() == 9);
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REQUIRE(arff.getX().size() == 9); // 9 features
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for (int i = 0; i < 9; ++i) {
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REQUIRE(arff.getX()[i].size() == 214);
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REQUIRE(arff.getX()[i].size() == 214); // 214 samples per feature
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}
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// Test first 4 samples: X[feature][sample]
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std::vector<std::vector<float>> expected = {
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{1.51793, 1.51643, 1.51793, 1.51299},
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{12.79, 12.16, 13.21, 14.4 },
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{3.5, 3.52, 3.48, 1.74},
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{1.12, 1.35, 1.41, 1.54}
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{1.51793, 1.51643, 1.51793, 1.51299}, // Feature 0
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{12.79, 12.16, 13.21, 14.4}, // Feature 1
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{3.5, 3.52, 3.48, 1.74}, // Feature 2
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{1.12, 1.35, 1.41, 1.54} // Feature 3
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};
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for (int i = 0; i < 4; ++i) {
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for (int j = 0; j < 4; ++j)
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