int main(int argc, char **argv) {
srand ( time(NULL) );
try {
CMDLine cmdline(argc, argv);
cout << "----------------------------------------------------------------------------" << endl;
cout << "libFM" << endl;
cout << " Version: 1.40" << endl;
cout << " Author: Steffen Rendle, [email protected]" << endl;
cout << " WWW: http://www.libfm.org/" << endl;
cout << " License: Free for academic use. See license.txt." << endl;
cout << "----------------------------------------------------------------------------" << endl;
const string param_task = cmdline.registerParameter("task", "r=regression, c=binary classification [MANDATORY]");
const string param_meta_file = cmdline.registerParameter("meta", "filename for meta information about data set");
const string param_train_file = cmdline.registerParameter("train", "filename for training data [MANDATORY]");
const string param_test_file = cmdline.registerParameter("test", "filename for test data [MANDATORY]");
const string param_val_file = cmdline.registerParameter("validation", "filename for validation data (only for SGDA)");
const string param_out = cmdline.registerParameter("out", "filename for output");
const string param_dim = cmdline.registerParameter("dim", "'k0,k1,k2': k0=use bias, k1=use 1-way interactions, k2=dim of 2-way interactions; default=1,1,8");
const string param_regular = cmdline.registerParameter("regular", "'r0,r1,r2' for SGD and ALS: r0=bias regularization, r1=1-way regularization, r2=2-way regularization");
const string param_init_stdev = cmdline.registerParameter("init_stdev", "stdev for initialization of 2-way factors; default=0.1");
const string param_num_iter = cmdline.registerParameter("iter", "number of iterations; default=100");
const string param_learn_rate = cmdline.registerParameter("learn_rate", "learn_rate for SGD; default=0.1");
const string param_method = cmdline.registerParameter("method", "learning method (SGD, SGDA, ALS, MCMC); default=MCMC");
const string param_verbosity = cmdline.registerParameter("verbosity", "how much infos to print; default=0");
const string param_r_log = cmdline.registerParameter("rlog", "write measurements within iterations to a file; default=''");
const string param_help = cmdline.registerParameter("help", "this screen");
const string param_relation = cmdline.registerParameter("relation", "BS: filenames for the relations, default=''");
const string param_cache_size = cmdline.registerParameter("cache_size", "cache size for data storage (only applicable if data is in binary format), default=infty");
const string param_do_sampling = "do_sampling";
const string param_do_multilevel = "do_multilevel";
const string param_num_eval_cases = "num_eval_cases";
if (cmdline.hasParameter(param_help) || (argc == 1)) {
cmdline.print_help();
return 0;
}
cmdline.checkParameters();
if (! cmdline.hasParameter(param_method)) { cmdline.setValue(param_method, "mcmc"); }
if (! cmdline.hasParameter(param_init_stdev)) { cmdline.setValue(param_init_stdev, "0.1"); }
if (! cmdline.hasParameter(param_dim)) { cmdline.setValue(param_dim, "1,1,8"); }
if (! cmdline.getValue(param_method).compare("als")) { // als is an mcmc without sampling and hyperparameter inference
cmdline.setValue(param_method, "mcmc");
if (! cmdline.hasParameter(param_do_sampling)) { cmdline.setValue(param_do_sampling, "0"); }
if (! cmdline.hasParameter(param_do_multilevel)) { cmdline.setValue(param_do_multilevel, "0"); }
}
// (1) Load the data
cout << "Loading train...\t" << endl;
Data train(
cmdline.getValue(param_cache_size, 0),
! (!cmdline.getValue(param_method).compare("mcmc")), // no original data for mcmc
! (!cmdline.getValue(param_method).compare("sgd") || !cmdline.getValue(param_method).compare("sgda")) // no transpose data for sgd, sgda
);
train.load(cmdline.getValue(param_train_file));
if (cmdline.getValue(param_verbosity, 0) > 0) { train.debug(); }
cout << "Loading test... \t" << endl;
Data test(
cmdline.getValue(param_cache_size, 0),
! (!cmdline.getValue(param_method).compare("mcmc")), // no original data for mcmc
! (!cmdline.getValue(param_method).compare("sgd") || !cmdline.getValue(param_method).compare("sgda")) // no transpose data for sgd, sgda
);
test.load(cmdline.getValue(param_test_file));
if (cmdline.getValue(param_verbosity, 0) > 0) { test.debug(); }
Data* validation = NULL;
if (cmdline.hasParameter(param_val_file)) {
if (cmdline.getValue(param_method).compare("sgda")) {
cout << "WARNING: Validation data is only used for SGDA. The data is ignored." << endl;
} else {
cout << "Loading validation set...\t" << endl;
validation = new Data(
cmdline.getValue(param_cache_size, 0),
! (!cmdline.getValue(param_method).compare("mcmc")), // no original data for mcmc
! (!cmdline.getValue(param_method).compare("sgd") || !cmdline.getValue(param_method).compare("sgda")) // no transpose data for sgd, sgda
);
validation->load(cmdline.getValue(param_val_file));
if (cmdline.getValue(param_verbosity, 0) > 0) { validation->debug(); }
}
}
DVector<RelationData*> relation;
// (1.2) Load relational data
{
vector<string> rel = cmdline.getStrValues(param_relation);
cout << "#relations: " << rel.size() << endl;
relation.setSize(rel.size());
train.relation.setSize(rel.size());
test.relation.setSize(rel.size());
for (uint i = 0; i < rel.size(); i++) {
relation(i) = new RelationData(
cmdline.getValue(param_cache_size, 0),
! (!cmdline.getValue(param_method).compare("mcmc")), // no original data for mcmc
! (!cmdline.getValue(param_method).compare("sgd") || !cmdline.getValue(param_method).compare("sgda")) // no transpose data for sgd, sgda
);
relation(i)->load(rel[i]);
train.relation(i).data = relation(i);
test.relation(i).data = relation(i);
train.relation(i).load(rel[i] + ".train", train.num_cases);
test.relation(i).load(rel[i] + ".test", test.num_cases);
}
}
// (1.3) Load meta data
cout << "Loading meta data...\t" << endl;
// (main table)
uint num_all_attribute = max(train.num_feature, test.num_feature);
if (validation != NULL) {
num_all_attribute = max(num_all_attribute, (uint) validation->num_feature);
}
DataMetaInfo meta_main(num_all_attribute);
if (cmdline.hasParameter(param_meta_file)) {
meta_main.loadGroupsFromFile(cmdline.getValue(param_meta_file));
}
// build the joined meta table
for (uint r = 0; r < train.relation.dim; r++) {
train.relation(r).data->attr_offset = num_all_attribute;
num_all_attribute += train.relation(r).data->num_feature;
}
DataMetaInfo meta(num_all_attribute);
{
meta.num_attr_groups = meta_main.num_attr_groups;
for (uint r = 0; r < relation.dim; r++)
meta.num_attr_groups += relation(r)->meta->num_attr_groups;
meta.num_attr_per_group.setSize(meta.num_attr_groups);
meta.num_attr_per_group.init(0);
for (uint i = 0; i < meta_main.attr_group.dim; i++) {
meta.attr_group(i) = meta_main.attr_group(i);
meta.num_attr_per_group(meta.attr_group(i))++;
}
uint attr_cntr = meta_main.attr_group.dim;
uint attr_group_cntr = meta_main.num_attr_groups;
for (uint r = 0; r < relation.dim; r++) {
for (uint i = 0; i < relation(r)->meta->attr_group.dim; i++) {
meta.attr_group(i+attr_cntr) = attr_group_cntr + relation(r)->meta->attr_group(i);
meta.num_attr_per_group(attr_group_cntr + relation(r)->meta->attr_group(i))++;
}
attr_cntr += relation(r)->meta->attr_group.dim;
attr_group_cntr += relation(r)->meta->num_attr_groups;
}
if (cmdline.getValue(param_verbosity, 0) > 0)
{ meta.debug(); }
}
meta.num_relations = train.relation.dim;
// (2) Setup the factorization machine
fm_model fm;
{
fm.num_attribute = num_all_attribute;
fm.init_stdev = cmdline.getValue(param_init_stdev, 0.1);
// set the number of dimensions in the factorization
{
vector<int> dim = cmdline.getIntValues(param_dim);
assert(dim.size() == 3);
fm.k0 = dim[0] != 0;
fm.k1 = dim[1] != 0;
fm.num_factor = dim[2];
}
fm.init();
}
// (3) Setup the learning method:
fm_learn* fml;
if (! cmdline.getValue(param_method).compare("sgd")) {
fml = new fm_learn_sgd_element();
((fm_learn_sgd*)fml)->num_iter = cmdline.getValue(param_num_iter, 100);
} else if (! cmdline.getValue(param_method).compare("sgda")) {
assert(validation != NULL);
fml = new fm_learn_sgd_element_adapt_reg();
((fm_learn_sgd*)fml)->num_iter = cmdline.getValue(param_num_iter, 100);
((fm_learn_sgd_element_adapt_reg*)fml)->validation = validation;
} else if (! cmdline.getValue(param_method).compare("mcmc")) {
fm.w.init_normal(fm.init_mean, fm.init_stdev);
fml = new fm_learn_mcmc_simultaneous();
fml->validation = validation;
((fm_learn_mcmc*)fml)->num_iter = cmdline.getValue(param_num_iter, 100);
((fm_learn_mcmc*)fml)->num_eval_cases = cmdline.getValue(param_num_eval_cases, test.num_cases);
((fm_learn_mcmc*)fml)->do_sample = cmdline.getValue(param_do_sampling, true);
((fm_learn_mcmc*)fml)->do_multilevel = cmdline.getValue(param_do_multilevel, true);
} else {
throw "unknown method";
}
fml->fm = &fm;
fml->max_target = train.max_target;
fml->min_target = train.min_target;
fml->meta = &meta;
if (! cmdline.getValue("task").compare("r") ) {
fml->task = 0;
} else if (! cmdline.getValue("task").compare("c") ) {
fml->task = 1;
for (uint i = 0; i < train.target.dim; i++)
{ if (train.target(i) <= 0.0) { train.target(i) = -1.0; } else {train.target(i) = 1.0; } }
for (uint i = 0; i < test.target.dim; i++)
{ if (test.target(i) <= 0.0) { test.target(i) = -1.0; } else {test.target(i) = 1.0; } }
if (validation != NULL) {
for (uint i = 0; i < validation->target.dim; i++)
{ if (validation->target(i) <= 0.0) { validation->target(i) = -1.0; } else {validation->target(i) = 1.0; } }
}
} else {
throw "unknown task";
}
// (4) init the logging
RLog* rlog = NULL;
if (cmdline.hasParameter(param_r_log)) {
ofstream* out_rlog = NULL;
string r_log_str = cmdline.getValue(param_r_log);
out_rlog = new ofstream(r_log_str.c_str());
if (! out_rlog->is_open()) {
throw "Unable to open file " + r_log_str;
}
cout << "logging to " << r_log_str.c_str() << endl;
rlog = new RLog(out_rlog);
}
fml->log = rlog;
fml->init();
if (! cmdline.getValue(param_method).compare("mcmc")) {
// set the regularization; for als and mcmc this can be individual per group
{
vector<double> reg = cmdline.getDblValues(param_regular);
assert((reg.size() == 0) || (reg.size() == 1) || (reg.size() == 3) || (reg.size() == (1+meta.num_attr_groups*2)));
if (reg.size() == 0) {
fm.reg0 = 0.0;
fm.regw = 0.0;
fm.regv = 0.0;
((fm_learn_mcmc*)fml)->w_lambda.init(fm.regw);
((fm_learn_mcmc*)fml)->v_lambda.init(fm.regv);
} else if (reg.size() == 1) {
fm.reg0 = reg[0];
fm.regw = reg[0];
fm.regv = reg[0];
((fm_learn_mcmc*)fml)->w_lambda.init(fm.regw);
((fm_learn_mcmc*)fml)->v_lambda.init(fm.regv);
} else if (reg.size() == 3) {
fm.reg0 = reg[0];
fm.regw = reg[1];
fm.regv = reg[2];
((fm_learn_mcmc*)fml)->w_lambda.init(fm.regw);
((fm_learn_mcmc*)fml)->v_lambda.init(fm.regv);
} else {
fm.reg0 = reg[0];
fm.regw = 0.0;
fm.regv = 0.0;
int j = 1;
for (uint g = 0; g < meta.num_attr_groups; g++) {
((fm_learn_mcmc*)fml)->w_lambda(g) = reg[j];
j++;
}
for (uint g = 0; g < meta.num_attr_groups; g++) {
for (int f = 0; f < fm.num_factor; f++) {
((fm_learn_mcmc*)fml)->v_lambda(g,f) = reg[j];
}
j++;
}
}
}
} else {
// set the regularization; for standard SGD, groups are not supported
{
vector<double> reg = cmdline.getDblValues(param_regular);
assert((reg.size() == 0) || (reg.size() == 1) || (reg.size() == 3));
if (reg.size() == 0) {
fm.reg0 = 0.0;
fm.regw = 0.0;
fm.regv = 0.0;
} else if (reg.size() == 1) {
fm.reg0 = reg[0];
fm.regw = reg[0];
fm.regv = reg[0];
} else {
fm.reg0 = reg[0];
fm.regw = reg[1];
fm.regv = reg[2];
}
}
}
{
fm_learn_sgd* fmlsgd= dynamic_cast<fm_learn_sgd*>(fml);
if (fmlsgd) {
// set the learning rates (individual per layer)
{
vector<double> lr = cmdline.getDblValues(param_learn_rate);
assert((lr.size() == 1) || (lr.size() == 3));
if (lr.size() == 1) {
fmlsgd->learn_rate = lr[0];
fmlsgd->learn_rates.init(lr[0]);
} else {
fmlsgd->learn_rate = 0;
fmlsgd->learn_rates(0) = lr[0];
fmlsgd->learn_rates(1) = lr[1];
fmlsgd->learn_rates(2) = lr[2];
}
}
}
}
if (rlog != NULL) {
rlog->init();
}
if (cmdline.getValue(param_verbosity, 0) > 0) {
fm.debug();
fml->debug();
}
// () learn
fml->learn(train, test);
// () Prediction at the end (not for mcmc and als)
if (cmdline.getValue(param_method).compare("mcmc")) {
cout << "Final\t" << "Train=" << fml->evaluate(train) << "\tTest=" << fml->evaluate(test) << endl;
}
// () Save prediction
if (cmdline.hasParameter(param_out)) {
DVector<double> pred;
pred.setSize(test.num_cases);
fml->predict(test, pred);
pred.save(cmdline.getValue(param_out));
}
} catch (string &e) {
cerr << endl << "ERROR: " << e << endl;
} catch (char const* &e) {
cerr << endl << "ERROR: " << e << endl;
}
}
int main(int argc, char **argv) {
srand ( time(NULL) );
try {
CMDLine cmdline(argc, argv);
std::cout << "libFM" << std::endl;
std::cout << " Version: 1.10" << std::endl;
std::cout << " Author: Steffen Rendle, [email protected], http://www.libfm.org/" << std::endl;
std::cout << " License: Free for academic use. See license.txt." << std::endl;
std::cout << "----------------------------------------------------------------------------" << std::endl;
const std::string param_task = cmdline.registerParameter("task", "r=regression, c=binary classification [MANDATORY]");
const std::string param_train_file = cmdline.registerParameter("train", "filename for training data [MANDATORY]");
const std::string param_test_file = cmdline.registerParameter("test", "filename for test data [MANDATORY]");
const std::string param_out = cmdline.registerParameter("out", "filename for output");
const std::string param_dim = cmdline.registerParameter("dim", "'k0,k1,k2': k0=use bias, k1=use 1-way interactions, k2=dim of 2-way interactions [MANDATORY]");
const std::string param_regular = cmdline.registerParameter("regular", "'r0,r1,r2': r0=bias regularization, r1=1-way regularization, r2=2-way regularization [MANDATORY]");
const std::string param_init_stdev = cmdline.registerParameter("init_stdev", "stdev for initialization of 2-way factors; default=0.01");
const std::string param_num_iter = cmdline.registerParameter("iter", "number of iterations for SGD; default=100");
const std::string param_learn_rate = cmdline.registerParameter("learn_rate", "learn_rate for SGD; default=0.1");
const std::string param_method = cmdline.registerParameter("method", "learning method (SGD or ALS); default=SGD");
const std::string param_verbosity = cmdline.registerParameter("verbosity", "how much infos to print; default=0");
const std::string param_r_log = cmdline.registerParameter("rlog", "write measurements within iterations to a file; default=''");
const std::string param_help = cmdline.registerParameter("help", "this screen");
if (cmdline.hasParameter(param_help) || (argc == 1)) {
cmdline.print_help();
return 0;
}
cmdline.checkParameters();
// (1) Load the data
std::cout << "Loading train...\t";
Data train;
train.load(cmdline.getValue(param_train_file));
if (cmdline.getValue(param_verbosity, 0) > 0) { train.debug(); }
std::cout << "Loading test... \t";
Data test;
test.load(cmdline.getValue(param_test_file));
if (cmdline.getValue(param_verbosity, 0) > 0) { test.debug(); }
// (2) Setup the factorization machine
fm_model fm;
{
fm.num_attribute = max(train.num_feature, test.num_feature);
fm.init_stdev = cmdline.getValue(param_init_stdev, 0.01);
// set the number of dimensions in the factorization
{
vector<int> dim = cmdline.getIntValues(param_dim);
assert(dim.size() == 3);
fm.k0 = dim[0] != 0;
fm.k1 = dim[1] != 0;
fm.num_factor = dim[2];
}
fm.init();
// set the regularization
{
vector<double> reg = cmdline.getDblValues(param_regular);
assert(reg.size() == 3);
fm.reg0 = reg[0];
fm.regw.init(reg[1]);
fm.regv.init(reg[2]);
}
}
// (3) Setup the learning method:
fm_learn* fml;
if (! cmdline.getValue(param_method, "SGD").compare("sgd")) {
fml = new fm_learn_sgd_element();
((fm_learn_sgd*)fml)->num_iter = cmdline.getValue(param_num_iter, 100);
((fm_learn_sgd*)fml)->learn_rate = cmdline.getValue(param_learn_rate, 0.1);
} else if (! cmdline.getValue(param_method).compare("als")) {
fml = new fm_learn_als_simultaneous();
((fm_learn_als*)fml)->num_iter = cmdline.getValue(param_num_iter, 100);
if (cmdline.getValue("task").compare("r") ) {
throw "ALS can only solve regression tasks.";
}
} else {
throw "unknown method";
}
fml->fm = &fm;
fml->max_target = train.max_target;
fml->min_target = train.min_target;
if (! cmdline.getValue("task").compare("r") ) {
fml->task = 0;
} else if (! cmdline.getValue("task").compare("c") ) {
fml->task = 1;
for (uint i = 0; i < train.target.dim; i++) { if (train.target(i) <= 0.0) { train.target(i) = -1.0; } else {train.target(i) = 1.0; } }
for (uint i = 0; i < test.target.dim; i++) { if (test.target(i) <= 0.0) { test.target(i) = -1.0; } else {test.target(i) = 1.0; } }
} else {
throw "unknown task";
}
// (4) init the logging
RLog* rlog = NULL;
if (cmdline.hasParameter(param_r_log)) {
ofstream* out_rlog = NULL;
std::string r_log_str = cmdline.getValue(param_r_log);
out_rlog = new ofstream(r_log_str.c_str());
if (! out_rlog->is_open()) {
throw "Unable to open file " + r_log_str;
}
std::cout << "logging to " << r_log_str.c_str() << std::endl;
rlog = new RLog(out_rlog);
}
fml->log = rlog;
fml->init();
if (rlog != NULL) {
rlog->init();
}
if (cmdline.getValue(param_verbosity, 0) > 0) {
fm.debug();
fml->debug();
}
// () learn
fml->learn(train, test);
// () Prediction
std::cout << "Final\t" << "Train=" << fml->evaluate(train) << "\tTest=" << fml->evaluate(test) << std::endl;
// () Save prediction
if (cmdline.hasParameter(param_out)) {
DVector<double> pred;
pred.setSize(test.data.dim);
fml->predict(test, pred);
pred.save(cmdline.getValue(param_out));
}
} catch (std::string &e) {
std::cerr << e << std::endl;
} catch (char const* &e) {
std::cerr << e << std::endl;
}
}
int main(int argc, char **argv) {
try {
CMDLine cmdline(argc, argv);
std::cout << "----------------------------------------------------------------------------" << std::endl;
std::cout << "libFM" << std::endl;
std::cout << " Version: 1.4.2" << std::endl;
std::cout << " Author: Steffen Rendle, [email protected]" << std::endl;
std::cout << " WWW: http://www.libfm.org/" << std::endl;
std::cout << "This program comes with ABSOLUTELY NO WARRANTY; for details see license.txt." << std::endl;
std::cout << "This is free software, and you are welcome to redistribute it under certain" << std::endl;
std::cout << "conditions; for details see license.txt." << std::endl;
std::cout << "----------------------------------------------------------------------------" << std::endl;
const std::string param_task = cmdline.registerParameter("task", "r=regression, c=binary classification [MANDATORY]");
const std::string param_meta_file = cmdline.registerParameter("meta", "filename for meta information about data set");
const std::string param_train_file = cmdline.registerParameter("train", "filename for training data [MANDATORY]");
const std::string param_test_file = cmdline.registerParameter("test", "filename for test data [MANDATORY]");
const std::string param_val_file = cmdline.registerParameter("validation", "filename for validation data (only for SGDA)");
const std::string param_out = cmdline.registerParameter("out", "filename for output");
const std::string param_dim = cmdline.registerParameter("dim", "'k0,k1,k2': k0=use bias, k1=use 1-way interactions, k2=dim of 2-way interactions; default=1,1,8");
const std::string param_regular = cmdline.registerParameter("regular", "'r0,r1,r2' for SGD and ALS: r0=bias regularization, r1=1-way regularization, r2=2-way regularization");
const std::string param_init_stdev = cmdline.registerParameter("init_stdev", "stdev for initialization of 2-way factors; default=0.1");
const std::string param_num_iter = cmdline.registerParameter("iter", "number of iterations; default=100");
const std::string param_learn_rate = cmdline.registerParameter("learn_rate", "learn_rate for SGD; default=0.1");
const std::string param_method = cmdline.registerParameter("method", "learning method (SGD, SGDA, ALS, MCMC, BPR, BPRA); default=MCMC");
const std::string param_verbosity = cmdline.registerParameter("verbosity", "how much infos to print; default=0");
const std::string param_r_log = cmdline.registerParameter("rlog", "write measurements within iterations to a file; default=''");
const std::string param_seed = cmdline.registerParameter("seed", "integer value, default=None");
const std::string param_help = cmdline.registerParameter("help", "this screen");
const std::string param_relation = cmdline.registerParameter("relation", "BS: filenames for the relations, default=''");
const std::string param_cache_size = cmdline.registerParameter("cache_size", "cache size for data storage (only applicable if data is in binary format), default=infty");
//FABIO PARAMETERS for BPR
const std::string param_neg_sample = cmdline.registerParameter("neg_sample", "number of the negative pair samples drawn for each training observation, default 1 (only for bpr or bpra)");
const std::string param_out_conv = cmdline.registerParameter("out_conv", "filename for output the convergence info (only for bpr or bpra)");
const std::string param_threads = cmdline.registerParameter("threads", "number of threads (only for bpr or bpra)");
const std::string param_out_vectors = cmdline.registerParameter("out_vectors", "filename for output the latent vectors");
const std::string param_resume_state = cmdline.registerParameter("resume_state", "files with the vectors to resume the state of the FM");
//...output ranked list
const std::string param_top_k = cmdline.registerParameter("top_k", "number of recommendation for bpr or bpra, default 100");
const std::string param_list_id_output = cmdline.registerParameter("list_id_output", "list of target ids (in FIXED block), comma separated without spaces, to compute output ranked list for, default all (only for bpr or bpra)");
const std::string param_out_ranked_list_dir= cmdline.registerParameter("out_ranked_list_dir", "directory where to store the output ranked list, one file for each target id (only for bpr or bpra)");
const std::string param_do_sampling = "do_sampling";
const std::string param_do_multilevel = "do_multilevel";
const std::string param_num_eval_cases = "num_eval_cases";
if (cmdline.hasParameter(param_help) || (argc == 1)) {
cmdline.print_help();
return 0;
}
cmdline.checkParameters();
int NUM_THREADS = cmdline.getValue(param_threads, 1);
// Seed
long int seed = cmdline.getValue(param_seed, time(NULL));
srand ( seed );
if (! cmdline.hasParameter(param_method)) { cmdline.setValue(param_method, "mcmc"); }
if (! cmdline.hasParameter(param_init_stdev)) { cmdline.setValue(param_init_stdev, "0.1"); }
if (! cmdline.hasParameter(param_dim)) { cmdline.setValue(param_dim, "1,1,8"); }
if (! cmdline.hasParameter(param_learn_rate)) { cmdline.setValue(param_learn_rate, "0.1"); }
if (! cmdline.getValue(param_method).compare("als")) { // als is an mcmc without sampling and hyperparameter inference
cmdline.setValue(param_method, "mcmc");
if (! cmdline.hasParameter(param_do_sampling)) { cmdline.setValue(param_do_sampling, "0"); }
if (! cmdline.hasParameter(param_do_multilevel)) { cmdline.setValue(param_do_multilevel, "0"); }
}
// (1) Load the data
std::cout << "Loading train...\t" << std::endl;
Data train(
cmdline.getValue(param_cache_size, 0),
! (!cmdline.getValue(param_method).compare("mcmc")), // no original data for mcmc
! (!cmdline.getValue(param_method).compare("sgd") || !cmdline.getValue(param_method).compare("sgda") || !cmdline.getValue(param_method).compare("bpr") || !cmdline.getValue(param_method).compare("bpra")) // no transpose data for sgd, sgda, bpr, bpra
);
train.load(cmdline.getValue(param_train_file));
if (cmdline.getValue(param_verbosity, 0) > 0) { train.debug(); }
std::cout << "Loading test... \t" << std::endl;
Data test(
cmdline.getValue(param_cache_size, 0),
! (!cmdline.getValue(param_method).compare("mcmc")), // no original data for mcmc
! (!cmdline.getValue(param_method).compare("sgd") || !cmdline.getValue(param_method).compare("sgda") || !cmdline.getValue(param_method).compare("bpr") || !cmdline.getValue(param_method).compare("bpra")) // no transpose data for sgd, sgda, bpr, bpra
);
test.load(cmdline.getValue(param_test_file));
if (cmdline.getValue(param_verbosity, 0) > 0) { test.debug(); }
Data* validation = NULL;
if (cmdline.hasParameter(param_val_file)) {
if (cmdline.getValue(param_method).compare("sgda") && cmdline.getValue(param_method).compare("bpra")) {
std::cout << "WARNING: Validation data is only used for SGDA and BPRA. The data is ignored." << std::endl;
} else {
std::cout << "Loading validation set...\t" << std::endl;
validation = new Data(
cmdline.getValue(param_cache_size, 0),
! (!cmdline.getValue(param_method).compare("mcmc")), // no original data for mcmc
! (!cmdline.getValue(param_method).compare("sgd") || !cmdline.getValue(param_method).compare("sgda") || !cmdline.getValue(param_method).compare("bpr") || !cmdline.getValue(param_method).compare("bpra")) // no transpose data for sgd, sgda, bpr, bpra
);
validation->load(cmdline.getValue(param_val_file));
if (cmdline.getValue(param_verbosity, 0) > 0) { validation->debug(); }
}
}
DVector<RelationData*> relation;
// (1.2) Load relational data
{
vector<std::string> rel = cmdline.getStrValues(param_relation);
std::cout << "#relations: " << rel.size() << std::endl;
relation.setSize(rel.size());
train.relation.setSize(rel.size());
test.relation.setSize(rel.size());
for (uint i = 0; i < rel.size(); i++) {
relation(i) = new RelationData(
cmdline.getValue(param_cache_size, 0),
! (!cmdline.getValue(param_method).compare("mcmc")), // no original data for mcmc
! (!cmdline.getValue(param_method).compare("sgd") || !cmdline.getValue(param_method).compare("sgda") || !cmdline.getValue(param_method).compare("bpr") || !cmdline.getValue(param_method).compare("bpra")) // no transpose data for sgd, sgda, bpr, bpra
);
relation(i)->load(rel[i]);
train.relation(i).data = relation(i);
test.relation(i).data = relation(i);
train.relation(i).load(rel[i] + ".train", train.num_cases);
test.relation(i).load(rel[i] + ".test", test.num_cases);
if (cmdline.hasParameter(param_val_file)){
std::cout << "Loading relations validation set...\t" << std::endl;
train.relation(i).loadValidation(rel[i] + ".validation", validation->num_cases);
}
}
}
// (1.3) Load meta data
std::cout << "Loading meta data...\t" << std::endl;
// (main table)
uint num_all_attribute = std::max(train.num_feature, test.num_feature);
if (validation != NULL) {
num_all_attribute = std::max(num_all_attribute, (uint) validation->num_feature);
}
DataMetaInfo meta_main(num_all_attribute);
if (cmdline.hasParameter(param_meta_file)) {
meta_main.loadGroupsFromFile(cmdline.getValue(param_meta_file));
}
// build the joined meta table
for (uint r = 0; r < train.relation.dim; r++) {
train.relation(r).data->attr_offset = num_all_attribute;
num_all_attribute += train.relation(r).data->num_feature;
}
DataMetaInfo meta(num_all_attribute);
{
uint attr_cntr = 0;
meta.num_attr_groups = 0;
int attr_group_cntr = 0;
if (cmdline.hasParameter(param_meta_file)) {
meta.num_attr_groups = meta_main.num_attr_groups;
for (uint i = 0; i < meta_main.attr_group.dim; i++) {
meta.attr_group(i) = meta_main.attr_group(i);
meta.num_attr_per_group(meta.attr_group(i))++;
}
attr_cntr = meta_main.attr_group.dim;
attr_group_cntr = meta_main.num_attr_groups;
}
for (uint r = 0; r < relation.dim; r++) {
meta.num_attr_groups += relation(r)->meta->num_attr_groups;
}
meta.num_attr_per_group.setSize(meta.num_attr_groups);
meta.num_attr_per_group.init(0);
for (uint r = 0; r < relation.dim; r++) {
for (uint i = 0; i < relation(r)->meta->attr_group.dim; i++) {
meta.attr_group(i+attr_cntr) = attr_group_cntr + relation(r)->meta->attr_group(i);
meta.num_attr_per_group(attr_group_cntr + relation(r)->meta->attr_group(i))++;
}
attr_cntr += relation(r)->meta->attr_group.dim;
attr_group_cntr += relation(r)->meta->num_attr_groups;
}
if (cmdline.getValue(param_verbosity, 0) > 0) { meta.debug(); }
}
meta.num_relations = train.relation.dim;
// (2) Setup the factorization machine
fm_model fm;
{
fm.num_attribute = num_all_attribute;
fm.init_stdev = cmdline.getValue(param_init_stdev, 0.1);
// set the number of dimensions in the factorization
{
vector<int> dim = cmdline.getIntValues(param_dim);
assert(dim.size() == 3);
fm.k0 = dim[0] != 0;
fm.k1 = dim[1] != 0;
fm.num_factor = dim[2];
}
fm.init();
}
//(2.1 fabio) resume state from file [IT MUST HAVE BEEN TRAINED WITH THE SAME PARAMETERS]
if (cmdline.hasParameter(param_resume_state)) {
std::cout << "Resuming state from file..." << std::endl;
fm.resumeState(cmdline.getValue(param_resume_state));
}
// (3) Setup the learning method:
fm_learn* fml;
if (! cmdline.getValue(param_method).compare("sgd")) {
fml = new fm_learn_sgd_element();
((fm_learn_sgd*)fml)->num_iter = cmdline.getValue(param_num_iter, 100);
} else if (! cmdline.getValue(param_method).compare("sgda")) {
assert(validation != NULL);
fml = new fm_learn_sgd_element_adapt_reg();
((fm_learn_sgd*)fml)->num_iter = cmdline.getValue(param_num_iter, 100);
((fm_learn_sgd_element_adapt_reg*)fml)->validation = validation;
} else if (! cmdline.getValue(param_method).compare("mcmc")) {
fm.w.init_normal(fm.init_mean, fm.init_stdev);
fml = new fm_learn_mcmc_simultaneous();
fml->validation = validation;
((fm_learn_mcmc*)fml)->num_iter = cmdline.getValue(param_num_iter, 100);
((fm_learn_mcmc*)fml)->num_eval_cases = cmdline.getValue(param_num_eval_cases, test.num_cases);
((fm_learn_mcmc*)fml)->do_sample = cmdline.getValue(param_do_sampling, true);
((fm_learn_mcmc*)fml)->do_multilevel = cmdline.getValue(param_do_multilevel, true);
}
///BAYESIAN PROBABILISTIC LEARNING
else if (! cmdline.getValue(param_method).compare("bpr")) { //FABIO PETRONI modification
if (relation.dim>0){
if (NUM_THREADS>1){
//PARALLEL BLOCK BPR
fml = new fm_learn_sgd_element_BPR_blocks_parallel();
((fm_learn_sgd_element_BPR_blocks_parallel*)fml)->num_neg_samples = cmdline.getValue(param_neg_sample, 1);
((fm_learn_sgd_element_BPR_blocks_parallel*)fml)->NUM_THREADS = NUM_THREADS;
((fm_learn_sgd*)fml)->num_iter = cmdline.getValue(param_num_iter, 100);
if (cmdline.hasParameter(param_out_conv)) {
((fm_learn_sgd_element_BPR_blocks_parallel*)fml)->file_out_conv = cmdline.getValue(param_out_conv);
}
}
else{
//BLOCK BPR
fml = new fm_learn_sgd_element_BPR_blocks();
((fm_learn_sgd_element_BPR_blocks*)fml)->num_neg_samples = cmdline.getValue(param_neg_sample, 1);
((fm_learn_sgd*)fml)->num_iter = cmdline.getValue(param_num_iter, 100);
if (cmdline.hasParameter(param_out_conv)) {
((fm_learn_sgd_element_BPR_blocks*)fml)->file_out_conv = cmdline.getValue(param_out_conv);
}
}
}
else{
throw "bpr error. For BPR --relation is needed";
}
}
else if (! cmdline.getValue(param_method).compare("bpra")) { //FABIO PETRONI modification
if (NUM_THREADS>1){
//PARALLEL BLOCK BPRA (with adaptive regularization)
fml = new fm_learn_sgd_element_BPR_blocks_adapt_reg_parallel();
((fm_learn_sgd_element_BPR_blocks_adapt_reg_parallel*)fml)->num_neg_samples = cmdline.getValue(param_neg_sample, 1);
((fm_learn_sgd_element_BPR_blocks_adapt_reg_parallel*)fml)->NUM_THREADS = NUM_THREADS;
((fm_learn_sgd*)fml)->num_iter = cmdline.getValue(param_num_iter, 100);
if (cmdline.hasParameter(param_out_conv)) {
((fm_learn_sgd_element_BPR_blocks_adapt_reg_parallel*)fml)->file_out_conv = cmdline.getValue(param_out_conv);
}
}
else{
// BLOCK BPRA (with adaptive regularization)
fml = new fm_learn_sgd_element_BPR_blocks_adapt_reg();
((fm_learn_sgd_element_BPR_blocks_adapt_reg*)fml)->num_neg_samples = cmdline.getValue(param_neg_sample, 1);
((fm_learn_sgd*)fml)->num_iter = cmdline.getValue(param_num_iter, 100);
if (cmdline.hasParameter(param_out_conv)) {
((fm_learn_sgd_element_BPR_blocks_adapt_reg*)fml)->file_out_conv = cmdline.getValue(param_out_conv);
}
}
}
else {
throw "unknown method";
}
fml->fm = &fm;
fml->max_target = train.max_target;
fml->min_target = train.min_target;
if (cmdline.getValue(param_method).compare("bpr") || cmdline.getValue(param_method).compare("bpra")){
//there is not a minimum or maximum target. Only the relative ranking is important.
fml->max_target = std::numeric_limits<double>::max();
fml->min_target = std::numeric_limits<double>::min();
}
fml->meta = &meta;
if (! cmdline.getValue("task").compare("r") ) {
fml->task = 0;
} else if (! cmdline.getValue("task").compare("c") ) {
fml->task = 1;
for (uint i = 0; i < train.target.dim; i++) { if (train.target(i) <= 0.0) { train.target(i) = -1.0; } else {train.target(i) = 1.0; } }
for (uint i = 0; i < test.target.dim; i++) { if (test.target(i) <= 0.0) { test.target(i) = -1.0; } else {test.target(i) = 1.0; } }
if (validation != NULL) {
for (uint i = 0; i < validation->target.dim; i++) { if (validation->target(i) <= 0.0) { validation->target(i) = -1.0; } else {validation->target(i) = 1.0; } }
}
} else {
throw "unknown task";
}
// (4) init the logging
RLog* rlog = NULL;
if (cmdline.hasParameter(param_r_log)) {
ofstream* out_rlog = NULL;
std::string r_log_str = cmdline.getValue(param_r_log);
out_rlog = new ofstream(r_log_str.c_str());
if (! out_rlog->is_open()) {
throw "Unable to open file " + r_log_str;
}
std::cout << "logging to " << r_log_str.c_str() << std::endl;
rlog = new RLog(out_rlog);
}
fml->log = rlog;
fml->init();
if (! cmdline.getValue(param_method).compare("mcmc")) {
// set the regularization; for als and mcmc this can be individual per group
{
vector<double> reg = cmdline.getDblValues(param_regular);
assert((reg.size() == 0) || (reg.size() == 1) || (reg.size() == 3) || (reg.size() == (1+meta.num_attr_groups*2)));
if (reg.size() == 0) {
fm.reg0 = 0.0;
fm.regw = 0.0;
fm.regv = 0.0;
((fm_learn_mcmc*)fml)->w_lambda.init(fm.regw);
((fm_learn_mcmc*)fml)->v_lambda.init(fm.regv);
} else if (reg.size() == 1) {
fm.reg0 = reg[0];
fm.regw = reg[0];
fm.regv = reg[0];
((fm_learn_mcmc*)fml)->w_lambda.init(fm.regw);
((fm_learn_mcmc*)fml)->v_lambda.init(fm.regv);
} else if (reg.size() == 3) {
fm.reg0 = reg[0];
fm.regw = reg[1];
fm.regv = reg[2];
((fm_learn_mcmc*)fml)->w_lambda.init(fm.regw);
((fm_learn_mcmc*)fml)->v_lambda.init(fm.regv);
} else {
fm.reg0 = reg[0];
fm.regw = 0.0;
fm.regv = 0.0;
int j = 1;
for (uint g = 0; g < meta.num_attr_groups; g++) {
((fm_learn_mcmc*)fml)->w_lambda(g) = reg[j];
j++;
}
for (uint g = 0; g < meta.num_attr_groups; g++) {
for (int f = 0; f < fm.num_factor; f++) {
((fm_learn_mcmc*)fml)->v_lambda(g,f) = reg[j];
}
j++;
}
}
}
} else {
// set the regularization; for standard SGD, groups are not supported
{
vector<double> reg = cmdline.getDblValues(param_regular);
assert((reg.size() == 0) || (reg.size() == 1) || (reg.size() == 3));
if (reg.size() == 0) {
fm.reg0 = 0.0;
fm.regw = 0.0;
fm.regv = 0.0;
} else if (reg.size() == 1) {
fm.reg0 = reg[0];
fm.regw = reg[0];
fm.regv = reg[0];
} else {
fm.reg0 = reg[0];
fm.regw = reg[1];
fm.regv = reg[2];
}
}
}
{
fm_learn_sgd* fmlsgd= dynamic_cast<fm_learn_sgd*>(fml);
if (fmlsgd) {
// set the learning rates (individual per layer)
{
vector<double> lr = cmdline.getDblValues(param_learn_rate);
assert((lr.size() == 1) || (lr.size() == 3));
if (lr.size() == 1) {
fmlsgd->learn_rate = lr[0];
fmlsgd->learn_rates.init(lr[0]);
} else {
fmlsgd->learn_rate = 0;
fmlsgd->learn_rates(0) = lr[0];
fmlsgd->learn_rates(1) = lr[1];
fmlsgd->learn_rates(2) = lr[2];
}
}
}
}
if (rlog != NULL) {
rlog->init();
}
if (cmdline.getValue(param_verbosity, 0) > 0) {
fm.debug();
fml->debug();
}
// () learn
fml->learn(train, test);
// () Prediction at the end (not for mcmc and als)
if (cmdline.getValue(param_method).compare("mcmc")) {
std::cout << "Final\t" << "Train=" << fml->evaluate(train) << "\tTest=" << fml->evaluate(test) << std::endl;
}
//compute output ranked list for target ids
if (cmdline.hasParameter(param_out_ranked_list_dir)){
std::cout << "Compute and store output ranked list for target ids...\t" << std::endl;
int TOP_K = cmdline.getValue(param_top_k, 100);
string out_ranked_list_dir = cmdline.getValue(param_out_ranked_list_dir);
vector<int> target_ids;
if (cmdline.hasParameter(param_list_id_output)){
target_ids = cmdline.getIntValues(param_list_id_output);
}
else{
int n_fixed_cases = train.relation(FIXED_BLOCK).data->num_cases;
target_ids.resize(n_fixed_cases);
for (int i = 0; i<n_fixed_cases; i++){
target_ids[i] = i;
}
}
Recommendation rec;
rec.fm = &fm;
rec.target_ids = target_ids;
rec.MAX_THREADS = NUM_THREADS;
rec.TOP_K = TOP_K;
rec.OUT_DIR = out_ranked_list_dir;
rec.evaluate(train);
}
// () Save prediction
if (cmdline.hasParameter(param_out)) {
DVector<double> pred;
pred.setSize(test.num_cases);
if (cmdline.getValue(param_method).compare("bpr") && cmdline.hasParameter(param_relation)) { //BLOCK BPR
if (NUM_THREADS>1){
//PARALLEL BLOCK BPR
((fm_learn_sgd_element_BPR_blocks_parallel*)fml)->predict(test, pred);
}
else{
//BLOCK BPR
((fm_learn_sgd_element_BPR_blocks*)fml)->predict(test, pred);
}
}
else if (cmdline.getValue(param_method).compare("bpra")){ //BLOCK BPR with adaptive regularization
if (NUM_THREADS>1){
//PARALLEL BLOCK BPRA
((fm_learn_sgd_element_BPR_blocks_adapt_reg_parallel*)fml)->predict(test, pred);
}
else{
//BLOCK BPRA
((fm_learn_sgd_element_BPR_blocks_adapt_reg*)fml)->predict(test, pred);
}
}
else{
fml->predict(test, pred);
}
pred.save(cmdline.getValue(param_out));
}
// () write down the latent vectors (unary and pairwise interactions)
if (cmdline.hasParameter(param_out_vectors)) {
fm.printOutState(cmdline.getValue(param_out_vectors));
}
} catch (std::string &e) {
std::cerr << std::endl << "ERROR: " << e << std::endl;
} catch (char const* &e) {
std::cerr << std::endl << "ERROR: " << e << std::endl;
}
}
int fm_train_test(Value& config, FMFeature trainData, FMFeature testData, FMTarget& prediction)
{
try
{
// (1) Load the data
std::cout << "Loading train...\t" << std::endl;
bool has_x = (string(config["method"].GetString()) != "mcmc"); // no original data for mcmc
bool has_xt = (string(config["method"].GetString()) != "sgd" // no transpose data for sgd, sgda
&& string(config["method"].GetString()) != "sgda");
Data train(0, has_x, has_xt);
FMMemory trainMem, testMem;
CreateData(train, trainData, trainMem);
if (has_xt)
CreateDataT(train, trainMem);
std::cout << "Loading test... \t" << std::endl;
Data test(0, has_x, has_xt); // no transpose data for sgd, sgda
CreateData(test, testData, testMem);
if (has_xt)
CreateDataT(test, testMem);
uint num_all_attribute = train.num_feature;
DataMetaInfo meta(num_all_attribute);
//meta.num_attr_per_group.setSize(meta.num_attr_groups);
//meta.num_attr_per_group.init(0);
meta.num_relations = train.relation.dim;
// (2) Setup the factorization machine
fm_model fm;
{
fm.num_attribute = num_all_attribute;
fm.init_stdev = config["init_stdev"].GetDouble();
// set the number of dimensions in the factorization
{
const Value& dimValue = config["dim"];
vector<int> dim;
for (int i = 0; i < dimValue.Size(); i++)
dim.push_back(dimValue[i].GetInt());
assert(dim.size() == 3);
fm.k0 = dim[0] != 0;
fm.k1 = dim[1] != 0;
fm.num_factor = dim[2];
}
fm.init();
}
// (3) Setup the learning method:
fm_learn* fml;
if (string(config["method"].GetString()) == "sgd")
{
fml = new fm_learn_sgd_element();
((fm_learn_sgd*) fml)->num_iter = config["iter"].GetInt();
}
else if (string(config["method"].GetString()) == "mcmc")
{
fm.w.init_normal(fm.init_mean, fm.init_stdev);
fml = new fm_learn_mcmc_simultaneous();
fml->validation = NULL;
((fm_learn_mcmc*) fml)->num_iter = config["iter"].GetInt();
((fm_learn_mcmc*) fml)->num_eval_cases = test.num_cases;
((fm_learn_mcmc*) fml)->do_sample = true;
((fm_learn_mcmc*) fml)->do_multilevel = true;
}
else
throw "unknown method";
fml->fm = &fm;
fml->max_target = train.max_target;
fml->min_target = train.min_target;
fml->meta = &meta;
if (string(config["task"].GetString()) == "regression")
{
fml->task = 0;
}
else if (string(config["task"].GetString()) == "classification")
{
fml->task = 1;
for (uint i = 0; i < train.target.dim; i++)
{
if (train.target(i) <= 0.0)
train.target(i) = -1.0;
else
train.target(i) = 1.0;
}
for (uint i = 0; i < test.target.dim; i++)
{
if (test.target(i) <= 0.0)
test.target(i) = -1.0;
else
test.target(i) = 1.0;
}
}
else
throw "unknown task";
fml->log = NULL;
fml->init();
if (string(config["method"].GetString()) == "mcmc")
{
// set the regularization; for als and mcmc this can be individual per group
{
const Value& regValue = config["regular"];
vector<double> reg;
for (int i = 0; i < regValue.Size(); i++)
reg.push_back(regValue[i].GetDouble());
assert(
(reg.size() == 0) || (reg.size() == 1)
|| (reg.size() == 3)
|| (reg.size() == (1 + meta.num_attr_groups * 2)));
if (reg.size() == 0)
{
fm.reg0 = 0.0;
fm.regw = 0.0;
fm.regv = 0.0;
((fm_learn_mcmc*) fml)->w_lambda.init(fm.regw);
((fm_learn_mcmc*) fml)->v_lambda.init(fm.regv);
}
else if (reg.size() == 1)
{
fm.reg0 = reg[0];
fm.regw = reg[0];
fm.regv = reg[0];
((fm_learn_mcmc*) fml)->w_lambda.init(fm.regw);
((fm_learn_mcmc*) fml)->v_lambda.init(fm.regv);
}
else if (reg.size() == 3)
{
fm.reg0 = reg[0];
fm.regw = reg[1];
fm.regv = reg[2];
((fm_learn_mcmc*) fml)->w_lambda.init(fm.regw);
((fm_learn_mcmc*) fml)->v_lambda.init(fm.regv);
}
else
{
fm.reg0 = reg[0];
fm.regw = 0.0;
fm.regv = 0.0;
int j = 1;
for (uint g = 0; g < meta.num_attr_groups; g++)
{
((fm_learn_mcmc*) fml)->w_lambda(g) = reg[j];
j++;
}
for (uint g = 0; g < meta.num_attr_groups; g++)
{
for (int f = 0; f < fm.num_factor; f++)
{
((fm_learn_mcmc*) fml)->v_lambda(g, f) = reg[j];
}
j++;
}
}
}
}
else
{
// set the regularization; for standard SGD, groups are not supported
{
const Value& regValue = config["regular"];
vector<double> reg;
for (int i = 0; i < regValue.Size(); i++)
reg.push_back(regValue[i].GetDouble());
assert(
(reg.size() == 0) || (reg.size() == 1)
|| (reg.size() == 3));
if (reg.size() == 0)
{
fm.reg0 = 0.0;
fm.regw = 0.0;
fm.regv = 0.0;
}
else if (reg.size() == 1)
{
fm.reg0 = reg[0];
fm.regw = reg[0];
fm.regv = reg[0];
}
else
{
fm.reg0 = reg[0];
fm.regw = reg[1];
fm.regv = reg[2];
}
}
}
{
fm_learn_sgd* fmlsgd = dynamic_cast<fm_learn_sgd*>(fml);
if (fmlsgd)
{
// set the learning rates (individual per layer)
{
const Value& lrValue = config["learn_rate"];
vector<double> lr;
for (int i = 0; i < lrValue.Size(); i++)
lr.push_back(lrValue[i].GetDouble());
assert((lr.size() == 1) || (lr.size() == 3));
if (lr.size() == 1)
{
fmlsgd->learn_rate = lr[0];
fmlsgd->learn_rates.init(lr[0]);
}
else
{
fmlsgd->learn_rate = 0;
fmlsgd->learn_rates(0) = lr[0];
fmlsgd->learn_rates(1) = lr[1];
fmlsgd->learn_rates(2) = lr[2];
}
}
}
}
// () learn
fml->learn(train, test);
// () Prediction at the end (not for mcmc and als)
if (string(config["method"].GetString()) != "mcmc")
{
std::cout << "Final\t" << "Train=" << fml->evaluate(train)
<< "\tTest=" << fml->evaluate(test) << std::endl;
}
// () Save prediction
DVector<double> pred;
pred.setSize(test.num_cases);
fml->predict(test, pred);
for (int i = 0; i < test.num_cases; i++)
prediction.push_back(pred(i));
if (config["pred_output"].GetBool())
pred.save(config["pred"].GetString());
if (string(config["method"].GetString()) == "sgd")
{
fm_learn_sgd_element* fml_sgd = dynamic_cast<fm_learn_sgd_element*>(fml);
delete fml_sgd;
}
else if (string(config["method"].GetString()) == "mcmc")
{
fm_learn_mcmc_simultaneous* fml_mcmc = dynamic_cast<fm_learn_mcmc_simultaneous*>(fml);
delete fml_mcmc;
}
} catch (std::string &e)
{
std::cerr << std::endl << "ERROR: " << e << std::endl;
} catch (char const* &e)
{
std::cerr << std::endl << "ERROR: " << e << std::endl;
}
return 0;
}
int executeFM(string train_filename, string test_filename, int k, int learn_iter, int ix) {
std::ostringstream stringStream;
string stats_filename, results_filename;
string k_string;
string aux_str;
int aux_idx;
// Make k part of the string
stringStream.str("");
stringStream << "k" << k;
/* Make the replacement for stats filepath*/
stats_filename = train_filename;
// Replace dir Data/ -> Results/
aux_str = DATA_DIR;
stats_filename = stats_filename.replace(stats_filename.find(aux_str.c_str(), 0), aux_str.length(), RESULTS_DIR);
// Replace base.lib extension for csv
aux_str = "base.libfm";
if ((aux_idx = stats_filename.find(aux_str.c_str(), 0)) < 0) {
aux_str = "base";
stats_filename.find(aux_str.c_str(), 0);
}
stringStream << STATS_EXT;
k_string = stringStream.str();
stats_filename = stats_filename.replace(stats_filename.find(aux_str.c_str(), 0), aux_str.length(), k_string);
std::cout << "Stats file path is " << stats_filename << endl;
/* Make the replacement for results filepath*/
results_filename = stats_filename;
aux_str = STATS_EXT;
results_filename = results_filename.replace(results_filename.find(aux_str.c_str(), 0), aux_str.length(), RESULTS_EXT);
std::cout << "Results file path is " << stats_filename << endl;
srand(time(NULL));
try {
stringStream.str("");
std::cout << "Loading train dataset...\t" << endl;
Data train(0, 0, 1);
train.load(train_filename);
train.debug();
Data test(0, 0, 1);
std::cout << "Loading test dataset... \t" << endl;
test.load(test_filename);
test.debug();
Data* validation = NULL;
DVector<RelationData*> relation;
// (1.2) Load relational data
{
vector<string> rel = {};
// std::cout << "#relations: " << rel.size() << endl;
relation.setSize(rel.size());
train.relation.setSize(rel.size());
test.relation.setSize(rel.size());
for (uint i = 0; i < rel.size(); i++) {
relation(i) = new RelationData(0, true, false);
relation(i)->load(rel[i]);
train.relation(i).data = relation(i);
test.relation(i).data = relation(i);
train.relation(i).load(rel[i] + ".train", train.num_cases);
test.relation(i).load(rel[i] + ".test", test.num_cases);
}
}
// std::cout << "Loading meta data...\t" << endl;
// (main table)
uint num_all_attribute = max(train.num_feature, test.num_feature);
if (validation != NULL) {
num_all_attribute = max(num_all_attribute, (uint)validation->num_feature);
}
DataMetaInfo meta_main(num_all_attribute);
// meta_main.loadGroupsFromFile(cmdline.getValue(param_meta_file));
// build the joined meta table
for (uint r = 0; r < train.relation.dim; r++) {
train.relation(r).data->attr_offset = num_all_attribute;
num_all_attribute += train.relation(r).data->num_feature;
}
DataMetaInfo meta(num_all_attribute);
{
meta.num_attr_groups = meta_main.num_attr_groups;
for (uint r = 0; r < relation.dim; r++) {
meta.num_attr_groups += relation(r)->meta->num_attr_groups;
}
meta.num_attr_per_group.setSize(meta.num_attr_groups);
meta.num_attr_per_group.init(0);
for (uint i = 0; i < meta_main.attr_group.dim; i++) {
meta.attr_group(i) = meta_main.attr_group(i);
meta.num_attr_per_group(meta.attr_group(i))++;
}
uint attr_cntr = meta_main.attr_group.dim;
uint attr_group_cntr = meta_main.num_attr_groups;
for (uint r = 0; r < relation.dim; r++) {
for (uint i = 0; i < relation(r)->meta->attr_group.dim; i++) {
meta.attr_group(i + attr_cntr) = attr_group_cntr + relation(r)->meta->attr_group(i);
meta.num_attr_per_group(attr_group_cntr + relation(r)->meta->attr_group(i))++;
}
attr_cntr += relation(r)->meta->attr_group.dim;
attr_group_cntr += relation(r)->meta->num_attr_groups;
}
meta.debug();
}
meta.num_relations = train.relation.dim;
// (2) Setup the factorization machine
fm_model fm;
{
fm.num_attribute = num_all_attribute;
fm.init_stdev = 0.1;
// set the number of dimensions in the factorization
{
vector<int> dim = { 1, 1, k };
assert(dim.size() == 3);
fm.k0 = dim[0] != 0;
fm.k1 = dim[1] != 0;
fm.num_factor = dim[2];
}
fm.init();
}
// Setup the learning method:
fm_learn* fml;
fm.w.init_normal(fm.init_mean, fm.init_stdev);
fml = new fm_learn_mcmc_simultaneous();
fml->validation = validation;
((fm_learn_mcmc*)fml)->num_iter = learn_iter;
((fm_learn_mcmc*)fml)->num_eval_cases = test.num_cases;
((fm_learn_mcmc*)fml)->do_sample = true;
((fm_learn_mcmc*)fml)->do_multilevel = true;
fml->fm = &fm;
fml->max_target = train.max_target;
fml->min_target = train.min_target;
fml->task = 0;
fml->meta = &meta;
// std::cout << "Opening output file" << endl;
RLog* rlog = NULL;
ofstream* out_rlog = NULL;
out_rlog = new ofstream(stats_filename);
if (!out_rlog->is_open()) {
throw "Unable to open file " + stats_filename;
}
// std::cout << "logging to " << r_log_str.c_str() << endl;
rlog = new RLog(out_rlog);
//
fml->log = rlog;
fml->init();
// set the regularization; for als and mcmc this can be individual per group
vector<double> reg = {};
assert((reg.size() == 0) || (reg.size() == 1) || (reg.size() == 3) || (reg.size() == (1 + meta.num_attr_groups * 2)));
if (reg.size() == 0) {
fm.reg0 = 0.0;
fm.regw = 0.0;
fm.regv = 0.0;
((fm_learn_mcmc*)fml)->w_lambda.init(fm.regw);
((fm_learn_mcmc*)fml)->v_lambda.init(fm.regv);
}
else if (reg.size() == 1) {
fm.reg0 = reg[0];
fm.regw = reg[0];
fm.regv = reg[0];
((fm_learn_mcmc*)fml)->w_lambda.init(fm.regw);
((fm_learn_mcmc*)fml)->v_lambda.init(fm.regv);
}
else if (reg.size() == 3) {
fm.reg0 = reg[0];
fm.regw = reg[1];
fm.regv = reg[2];
((fm_learn_mcmc*)fml)->w_lambda.init(fm.regw);
((fm_learn_mcmc*)fml)->v_lambda.init(fm.regv);
}
else {
fm.reg0 = reg[0];
fm.regw = 0.0;
fm.regv = 0.0;
int j = 1;
for (uint g = 0; g < meta.num_attr_groups; g++) {
((fm_learn_mcmc*)fml)->w_lambda(g) = reg[j];
j++;
}
for (uint g = 0; g < meta.num_attr_groups; g++) {
for (int f = 0; f < fm.num_factor; f++) {
((fm_learn_mcmc*)fml)->v_lambda(g, f) = reg[j];
}
j++;
}
}
if (rlog != NULL) {
rlog->init();
}
fm.debug();
fml->debug();
// () learn
fml->learn(train, test);
std::cout << "Save prediction" << endl;
DVector<double> pred;
pred.setSize(test.num_cases);
fml->predict(test, pred);
pred.save(results_filename);
}
catch (string &e) {
cerr << endl << "ERROR: " << e << endl;
}
catch (char const* &e) {
cerr << endl << "ERROR: " << e << endl;
}
return 0;
}