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Eliminate redundant memory and enhance memory usage

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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
This commit is contained in:
Ricardo Montañana Gómez
2025-06-27 18:20:06 +02:00
parent acfc14c5c3
commit c408352daa
2 changed files with 71 additions and 34 deletions
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79
ArffFiles.hpp
View File

@@ -66,7 +66,9 @@ public:
return s; return s;
} }
std::vector<std::vector<float>>& getX() { return X; } std::vector<std::vector<float>>& getX() { return X; }
const std::vector<std::vector<float>>& getX() const { return X; }
std::vector<int>& getY() { return y; } std::vector<int>& getY() { return y; }
const std::vector<int>& getY() const { return y; }
std::map<std::string, bool> getNumericAttributes() const { return numeric_features; } std::map<std::string, bool> getNumericAttributes() const { return numeric_features; }
std::vector<std::pair<std::string, std::string>> getAttributes() const { return attributes; }; std::vector<std::pair<std::string, std::string>> getAttributes() const { return attributes; };
std::vector<std::string> split(const std::string& text, char delimiter) std::vector<std::string> split(const std::string& text, char delimiter)
@@ -86,8 +88,7 @@ protected:
std::vector<std::pair<std::string, std::string>> attributes; std::vector<std::pair<std::string, std::string>> attributes;
std::string className; std::string className;
std::string classType; std::string classType;
std::vector<std::vector<float>> X; std::vector<std::vector<float>> X; // X[feature][sample] - feature-major layout
std::vector<std::vector<std::string>> Xs;
std::vector<int> y; std::vector<int> y;
std::map<std::string, std::vector<std::string>> states; std::map<std::string, std::vector<std::string>> states;
private: private:
@@ -128,34 +129,64 @@ private:
} }
void generateDataset(int labelIndex) void generateDataset(int labelIndex)
{ {
X = std::vector<std::vector<float>>(attributes.size(), std::vector<float>(lines.size())); const size_t numSamples = lines.size();
Xs = std::vector<std::vector<std::string>>(attributes.size(), std::vector<std::string>(lines.size())); const size_t numFeatures = attributes.size();
auto yy = std::vector<std::string>(lines.size(), "");
for (size_t i = 0; i < lines.size(); i++) { // Pre-allocate with feature-major layout: X[feature][sample]
std::stringstream ss(lines[i]); X.assign(numFeatures, std::vector<float>(numSamples));
std::string value;
// Temporary storage for categorical data per feature (only for non-numeric features)
std::vector<std::vector<std::string>> categoricalData(numFeatures);
for (size_t i = 0; i < numFeatures; ++i) {
if (!numeric_features[attributes[i].first]) {
categoricalData[i].reserve(numSamples);
}
}
std::vector<std::string> yy;
yy.reserve(numSamples);
// Parse each sample
for (size_t sampleIdx = 0; sampleIdx < numSamples; ++sampleIdx) {
const auto tokens = split(lines[sampleIdx], ',');
int pos = 0; int pos = 0;
int xIndex = 0; int featureIdx = 0;
auto tokens = split(lines[i], ',');
for (const auto& token : tokens) { for (const auto& token : tokens) {
if (pos++ == labelIndex) { if (pos++ == labelIndex) {
yy[i] = token; yy.push_back(token);
} else { } else {
if (numeric_features[attributes[xIndex].first]) { const auto& featureName = attributes[featureIdx].first;
X[xIndex][i] = stof(token); if (numeric_features.at(featureName)) {
// Parse numeric value with exception handling
try {
X[featureIdx][sampleIdx] = std::stof(token);
} catch (const std::exception& e) {
throw std::invalid_argument("Invalid numeric value '" + token + "' at sample " + std::to_string(sampleIdx) + ", feature " + featureName);
}
} else { } else {
Xs[xIndex][i] = token; // Store categorical value temporarily
categoricalData[featureIdx].push_back(token);
} }
xIndex++; featureIdx++;
} }
} }
} }
for (size_t i = 0; i < attributes.size(); i++) {
if (!numeric_features[attributes[i].first]) { // Convert categorical features to numeric
auto data = factorize(attributes[i].first, Xs[i]); for (size_t featureIdx = 0; featureIdx < numFeatures; ++featureIdx) {
std::transform(data.begin(), data.end(), X[i].begin(), [](int x) { return float(x);}); if (!numeric_features[attributes[featureIdx].first]) {
const auto& featureName = attributes[featureIdx].first;
auto encodedValues = factorize(featureName, categoricalData[featureIdx]);
// Copy encoded values to X[feature][sample]
for (size_t sampleIdx = 0; sampleIdx < numSamples; ++sampleIdx) {
X[featureIdx][sampleIdx] = static_cast<float>(encodedValues[sampleIdx]);
}
} }
} }
y = factorize(className, yy); y = factorize(className, yy);
} }
void loadCommon(std::string fileName) void loadCommon(std::string fileName)
@@ -176,9 +207,13 @@ private:
if (line.find("@attribute") != std::string::npos || line.find("@ATTRIBUTE") != std::string::npos) { if (line.find("@attribute") != std::string::npos || line.find("@ATTRIBUTE") != std::string::npos) {
std::stringstream ss(line); std::stringstream ss(line);
ss >> keyword >> attribute; ss >> keyword >> attribute;
type = ""; // Efficiently build type string
while (ss >> type_w) std::ostringstream typeStream;
type += type_w + " "; while (ss >> type_w) {
if (typeStream.tellp() > 0) typeStream << " ";
typeStream << type_w;
}
type = typeStream.str();
attributes.emplace_back(trim(attribute), trim(type)); attributes.emplace_back(trim(attribute), trim(type));
continue; continue;
} }

26
tests/TestArffFiles.cc
View File

@@ -34,15 +34,16 @@ TEST_CASE("Load Test", "[ArffFiles]")
REQUIRE(arff.getLines().size() == 150); REQUIRE(arff.getLines().size() == 150);
REQUIRE(arff.getLines()[0] == "5.1,3.5,1.4,0.2,Iris-setosa"); REQUIRE(arff.getLines()[0] == "5.1,3.5,1.4,0.2,Iris-setosa");
REQUIRE(arff.getLines()[149] == "5.9,3.0,5.1,1.8,Iris-virginica"); REQUIRE(arff.getLines()[149] == "5.9,3.0,5.1,1.8,Iris-virginica");
REQUIRE(arff.getX().size() == 4); REQUIRE(arff.getX().size() == 4); // 4 features
for (int i = 0; i < 4; ++i) { for (int i = 0; i < 4; ++i) {
REQUIRE(arff.getX()[i].size() == 150); REQUIRE(arff.getX()[i].size() == 150); // 150 samples per feature
} }
// Test first 4 samples: X[feature][sample]
auto expected = std::vector<std::vector<float>>{ auto expected = std::vector<std::vector<float>>{
{5.1, 4.9, 4.7, 4.6}, {5.1, 4.9, 4.7, 4.6}, // Feature 0 (sepallength)
{3.5, 3.0, 3.2, 3.1}, {3.5, 3.0, 3.2, 3.1}, // Feature 1 (sepalwidth)
{1.4, 1.4, 1.3, 1.5}, {1.4, 1.4, 1.3, 1.5}, // Feature 2 (petallength)
{0.2, 0.2, 0.2, 0.2} {0.2, 0.2, 0.2, 0.2} // Feature 3 (petalwidth)
}; };
for (int i = 0; i < 4; ++i) { for (int i = 0; i < 4; ++i) {
for (int j = 0; j < 4; ++j) for (int j = 0; j < 4; ++j)
@@ -79,15 +80,16 @@ TEST_CASE("Load with class name", "[ArffFiles]")
REQUIRE(arff.getLines().size() == 214); REQUIRE(arff.getLines().size() == 214);
REQUIRE(arff.getLines()[0] == "1.51793,12.79,3.5,1.12,73.03,0.64,8.77,0,0,'build wind float'"); REQUIRE(arff.getLines()[0] == "1.51793,12.79,3.5,1.12,73.03,0.64,8.77,0,0,'build wind float'");
REQUIRE(arff.getLines()[149] == "1.51813,13.43,3.98,1.18,72.49,0.58,8.15,0,0,'build wind non-float'"); REQUIRE(arff.getLines()[149] == "1.51813,13.43,3.98,1.18,72.49,0.58,8.15,0,0,'build wind non-float'");
REQUIRE(arff.getX().size() == 9); REQUIRE(arff.getX().size() == 9); // 9 features
for (int i = 0; i < 9; ++i) { for (int i = 0; i < 9; ++i) {
REQUIRE(arff.getX()[i].size() == 214); REQUIRE(arff.getX()[i].size() == 214); // 214 samples per feature
} }
// Test first 4 samples: X[feature][sample]
std::vector<std::vector<float>> expected = { std::vector<std::vector<float>> expected = {
{1.51793, 1.51643, 1.51793, 1.51299}, {1.51793, 1.51643, 1.51793, 1.51299}, // Feature 0
{12.79, 12.16, 13.21, 14.4 }, {12.79, 12.16, 13.21, 14.4}, // Feature 1
{3.5, 3.52, 3.48, 1.74}, {3.5, 3.52, 3.48, 1.74}, // Feature 2
{1.12, 1.35, 1.41, 1.54} {1.12, 1.35, 1.41, 1.54} // Feature 3
}; };
for (int i = 0; i < 4; ++i) { for (int i = 0; i < 4; ++i) {
for (int j = 0; j < 4; ++j) for (int j = 0; j < 4; ++j)