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Sparse SVM run on RCV1 dataset
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λ mlpack_spike/hogwild ∴ g++ svm_main.cpp -O2 -std=c++11 -Wall -larmadillo -lmlpack -fopenmp | |
λ mlpack_spike/hogwild ∴ ./a.out | |
RMSE : 0.797936 | |
Initial loss : 70176.4 | |
Final loss : 114.332 | |
RMSE : 0.050055 | |
λ mlpack_spike/hogwild ∴ ./a.out | |
RMSE : 0.797936 | |
Initial loss : 70176.4 | |
Final loss : 114.369 | |
RMSE : 0.0496217 | |
# HOGWILD! | |
λ mlpack_spike/hogwild ∴ time OMP_NUM_THREADS=4 ./a.out | |
RMSE : 0.797936 | |
Initial loss : 70176.4 | |
Final loss : 114.276 | |
RMSE : 0.0496217 | |
OMP_NUM_THREADS=4 ./a.out 16.12s user 0.18s system 392% cpu 4.155 total | |
# StandardSGD | |
λ mlpack_spike/hogwild ∴ time ./a.out | |
RMSE : 0.797936 | |
Initial loss : 70176.4 | |
Final loss : 5969.06 | |
RMSE : 0.227869 | |
./a.out 7.53s user 1.44s system 102% cpu 8.763 total |
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#include <mlpack/core.hpp> | |
#include <mlpack/core/optimizers/parallel_sgd/parallel_sgd.hpp> | |
#include <mlpack/core/optimizers/parallel_sgd/decay_policies/exponential_backoff.hpp> | |
#include <mlpack/core/optimizers/parallel_sgd/sparse_svm_function.hpp> | |
using namespace mlpack; | |
using namespace mlpack::optimization; | |
template <typename T> int sgn(T val) { | |
return (T(0) < val) - (val < T(0)); | |
} | |
template <typename FunctionType> | |
float MeanSquaredError(FunctionType& function, arma::mat& iterate) { | |
float MSE = 0.f; | |
for(size_t i = 0; i < function.NumFunctions(); ++i) { | |
MSE += (sgn(arma::dot(iterate, function.Dataset().col(i))) != sgn(function.Labels()(i))); | |
} | |
MSE /= function.NumFunctions(); | |
return MSE; | |
} | |
int main(int argc, char* argv[]) { | |
mlpack::CLI::ParseCommandLine(argc, argv); | |
SparseSVMLossFunction function; | |
// Load the data | |
function.Dataset().load("./RCV1_train_data.arm"); | |
function.Labels().load("./RCV1_train_labels.arm"); | |
ExponentialBackoff decayPolicy(15, 0.2, 0.8); | |
arma::mat iterate(function.Dataset().n_rows, 1, arma::fill::randu); | |
std::cout << "RMSE : " << std::sqrt(MeanSquaredError(function , iterate)) << std::endl; | |
float initialLoss = 0.f; | |
for(size_t i = 0; i < function.NumFunctions(); ++i){ | |
initialLoss += function.Evaluate(iterate, i); | |
} | |
std::cout << "Initial loss : " << initialLoss << std::endl; | |
ParallelSGD<ExponentialBackoff> optimizer(200, function.NumFunctions() / 4, 1e-5, decayPolicy); | |
std::cout << "Final loss : " << optimizer.Optimize(function, iterate) << std::endl; | |
std::cout << "RMSE : " << std::sqrt(MeanSquaredError(function , iterate)) << std::endl; | |
iterate.save("./svm_weights.arm"); | |
} |
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