int main(int argc, const char ** argv) {
//* GraphChi initialization will read the command line arguments and the configuration file. */
graphchi_init(argc, argv);
/* Metrics object for keeping track of performance counters
and other information. Currently required. */
metrics m("climf-inmemory-factors");
/* Basic arguments for application. NOTE: File will be automatically 'sharded'. */
sgd_lambda = get_option_float("sgd_lambda", 1e-3);
sgd_gamma = get_option_float("sgd_gamma", 1e-4);
sgd_step_dec = get_option_float("sgd_step_dec", 1.0);
binary_relevance_thresh = get_option_float("binary_relevance_thresh", 0);
halt_on_mrr_decrease = get_option_int("halt_on_mrr_decrease", 0);
num_ratings = get_option_int("num_ratings", 10000); //number of top predictions over which we compute actual MRR
verbose = get_option_int("verbose", 0);
debug = get_option_int("debug", 0);
parse_command_line_args();
parse_implicit_command_line();
/* Preprocess data if needed, or discover preprocess files */
bool allow_square = false;
int nshards = convert_matrixmarket<EdgeDataType>(training, 0, 0, 3, TRAINING, allow_square);
init_feature_vectors<std::vector<vertex_data> >(M+N, latent_factors_inmem, !load_factors_from_file, 0.01);
if (validation != ""){
int vshards = convert_matrixmarket<EdgeDataType>(validation, 0, 0, 3, VALIDATION);
init_mrr_engine<VertexDataType, EdgeDataType>(pvalidation_engine, vshards);
}
if (load_factors_from_file)
{
load_matrix_market_matrix(training + "_U.mm", 0, D);
load_matrix_market_matrix(training + "_V.mm", M, D);
}
print_config();
/* Run */
SGDVerticesInMemProgram program;
graphchi_engine<VertexDataType, EdgeDataType> engine(training, nshards, false, m);
set_engine_flags(engine);
pengine = &engine;
engine.run(program, niters);
/* Output latent factor matrices in matrix-market format */
output_sgd_result(training);
test_predictions(&climf_predict);
/* Report execution metrics */
if (!quiet)
metrics_report(m);
return 0;
}
int main(int argc, const char ** argv) {
print_copyright();
//* GraphChi initialization will read the command line arguments and the configuration file. */
graphchi_init(argc, argv);
/* Metrics object for keeping track of performance counters
and other information. Currently required. */
metrics m("sgd-inmemory-factors");
/* Basic arguments for application. NOTE: File will be automatically 'sharded'. */
sgd_lambda = get_option_float("sgd_lambda", 1e-3);
sgd_gamma = get_option_float("sgd_gamma", 1e-3);
sgd_step_dec = get_option_float("sgd_step_dec", 0.9);
parse_command_line_args();
parse_implicit_command_line();
/* Preprocess data if needed, or discover preprocess files */
int nshards = convert_matrixmarket<EdgeDataType>(training, NULL, 0, 0, 3, TRAINING, false);
init_feature_vectors<std::vector<vertex_data> >(M+N, latent_factors_inmem, !load_factors_from_file);
if (validation != ""){
int vshards = convert_matrixmarket<EdgeDataType>(validation, NULL, 0, 0, 3, VALIDATION, false);
init_validation_rmse_engine<VertexDataType, EdgeDataType>(pvalidation_engine, vshards, &sgd_predict);
}
/* load initial state from disk (optional) */
if (load_factors_from_file){
load_matrix_market_matrix(training + "_U.mm", 0, D);
load_matrix_market_matrix(training + "_V.mm", M, D);
}
print_config();
/* Run */
SGDVerticesInMemProgram program;
graphchi_engine<VertexDataType, EdgeDataType> engine(training, nshards, false, m);
set_engine_flags(engine);
pengine = &engine;
engine.run(program, niters);
/* Output latent factor matrices in matrix-market format */
output_sgd_result(training);
test_predictions(&sgd_predict);
/* Report execution metrics */
if (!quiet)
metrics_report(m);
return 0;
}
int main(int argc, const char ** argv) {
// print_copyright();
write_copyright();
//* GraphChi initialization will read the command line arguments and the configuration file. */
graphchi_init(argc, argv);
/* Metrics object for keeping track of performance counters
and other information. Currently required. */
metrics m("sgd-inmemory-factors");
/* Basic arguments for application. NOTE: File will be automatically 'sharded'. */
sgd_lambda = get_option_float("sgd_lambda", 1e-3);
sgd_gamma = get_option_float("sgd_gamma", 1e-3);
sgd_step_dec = get_option_float("sgd_step_dec", 0.9);
int file_format = get_option_int("ff", 3);
parse_command_line_args();
parse_implicit_command_line();
/* Preprocess data if needed, or discover preprocess files */
int nshards = convert_matrixmarket<EdgeDataType>(training, 0, 0, file_format, TRAINING, false);
init_feature_vectors<std::vector<vertex_data> >(M+N, latent_factors_inmem, !load_factors_from_file);
if (validation != ""){
int vshards = convert_matrixmarket<EdgeDataType>(validation, 0, 0, 3, VALIDATION, false);
init_validation_rmse_engine<VertexDataType, EdgeDataType>(pvalidation_engine, vshards, &sgd_predict);
}
/* load initial state from disk (optional) */
if (load_factors_from_file){
load_matrix_market_matrix(training + "_U.mm", 0, D);
load_matrix_market_matrix(training + "_V.mm", M, D);
}
print_config();
/* Run */
SGDVerticesInMemProgram program;
graphchi_engine<VertexDataType, EdgeDataType> engine(training, nshards, false, m);
set_engine_flags(engine);
pengine = &engine;
timer train_timer;
engine.run(program, niters);
// std::cout << "Trn Time for file test: " << std::setw(10) << train_timer.current_time() / niters << std::endl;
std::ofstream ofs(result.c_str(), std::ofstream::out | std::ofstream::app);
ofs << D << " " << train_timer.current_time() << " ";
/* Run TopN program */
n_top = get_option_int("n_int", 10);
/*timer test_timer1;
ofs << test_timer1.current_time() << " ";*/
//run_general_topn_program(pengine, &latent_factors_inmem, &sgd_predict);
timer index_timer;
kd_Node* mroot = init_kdtree(&latent_factors_inmem);
ofs << index_timer.current_time() << " ";
timer test_timer;
/* construct kd tree index */
// ofs << "constructing index: " << test_timer.current_time() << " ";
run_kd_topn_program(pengine, &latent_factors_inmem, mroot);
// std::coua << "Tst Time: " << std::setw(10) << test_timer.current_time() << std::endl;
ofs << test_timer.current_time() << std::endl;
ofs.close();
/* Output latent factor matrices in matrix-market format */
output_sgd_result(training);
test_predictions(&sgd_predict);
/* Report execution metrics */
if (!quiet)
metrics_report(m);
return 0;
}
