titanic04.cc

"Hello world" classification task (team-based classification).

"Hello world" classification task (team-based classification).

 
/* CHANGES IN THIS FILE MUST BE APPLIED TO THE LINKED WIKI PAGE */
 
#include <cstdlib>
 
#include "kernel/vita.h"
 
int main()
{
  using namespace vita;
 
  src_problem titanic("titanic_train.csv", src_problem::default_symbols);
 
  src_search s(titanic, metric_flags::accuracy);
  const auto summary(s.run());
 
  // Now, hopefully, we have a good classifier (`summary.best.solution`).
  // How can we exploit it?
 
  const auto model(s.lambdify(summary.best.solution));
  const auto example(random::element(titanic.data()));
  const auto result(model->tag(example));
 
  std::cout << "Correct class: " << label(example)
            << "   Prediction: " << result.label
            << "   Sureness: " << result.sureness << '\n';
 
  // We can make the model persistent...
  std::stringstream ss;
  serialize::save(ss, model);
 
  // ... and reload it when needed.
  const auto model2(serialize::lambda::load(ss, titanic.sset));
  const auto result2(model2->tag(example));
  std::cout << "   Prediction: " << result2.label
            << "   Sureness: " << result2.sureness << '\n';
  assert(result2.label == result.label);
 
  // If need be, individuals can be printed/exported in alternative languages:
  std::cout << "\nC LANGUAGE\n" << std::string(40, '-') << '\n'
            << out::c_language << summary.best.solution
            << "\n\nPYTHON LANGUAGE\n" << std::string(40, '-') << '\n'
            << out::python_language << summary.best.solution
            << "\n\nGRAPHVIZ FORMAT\n" << std::string(40, '-') << '\n'
            << out::graphviz << summary.best.solution
            << "\n\nLIST (DEBUG) FORMAT\n" << std::string(40, '-') << '\n'
            << out::list << summary.best.solution
            << '\n';
}