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Added options to save and load model weights/parameters #3

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64 changes: 62 additions & 2 deletions src/libfm/libfm.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -42,6 +42,7 @@
#include <cstdlib>
#include <cstdio>
#include <iostream>
#include <fstream>
#include <string>
#include <iterator>
#include <algorithm>
Expand Down Expand Up @@ -96,6 +97,14 @@ int main(int argc, char **argv) {
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");


const std::string param_save_model_w0 = cmdline.registerParameter("save_model_w0", "write model w weights to text file; default = ''");
const std::string param_load_model_w0 = cmdline.registerParameter("load_model_w0", "load model w weights from text file; default = ''");
const std::string param_save_model_w = cmdline.registerParameter("save_model_w", "write model w weights to binary file; default = ''");
const std::string param_load_model_w = cmdline.registerParameter("load_model_w", "load model w weights from binary file; default = ''");
const std::string param_save_model_v = cmdline.registerParameter("save_model_v", "write model v weights to binary file; default = ''");
const std::string param_load_model_v = cmdline.registerParameter("load_model_v", "load model v weights from binary file; default = ''");


const std::string param_do_sampling = "do_sampling";
Expand All @@ -111,7 +120,7 @@ int main(int argc, char **argv) {
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"); }
Expand Down Expand Up @@ -238,7 +247,9 @@ int main(int argc, char **argv) {
fm.init();

}




// (3) Setup the learning method:
fm_learn* fml;
if (! cmdline.getValue(param_method).compare("sgd")) {
Expand Down Expand Up @@ -383,9 +394,58 @@ int main(int argc, char **argv) {
fm.debug();
fml->debug();
}


// () Load in the model to the factorization machine
if (cmdline.hasParameter(param_load_model_w0)) {
std::string filename = cmdline.getValue(param_load_model_w0);
std::cout << "reading " << filename << std::endl; std::cout.flush();
std::ifstream in(filename.c_str(), std::ios_base::in | std::ios_base::binary);
if (in.is_open()) {
in.read(reinterpret_cast<char*>(&fm.w0), sizeof(double));
in.close();
} else {
throw "could not open " + filename;
}
}

if (cmdline.hasParameter(param_load_model_w)) {
std::string filename = cmdline.getValue(param_load_model_w);
fm.w.loadFromBinaryFile(filename);
}

if (cmdline.hasParameter(param_load_model_v)) {
std::string filename = cmdline.getValue(param_load_model_v);
fm.v.loadFromBinaryFile(filename);
}

// () learn
fml->learn(train, test);


// () Save the learned model
if (cmdline.hasParameter(param_save_model_w0)) {
std::string filename = cmdline.getValue(param_save_model_w0);
std::cout << "writing " << filename << std::endl; std::cout.flush();
std::ofstream out (filename.c_str(), std::ios_base::binary);
if (out.is_open()) {
out.write(reinterpret_cast<char*>(&fm.w0), sizeof(double));
out.close();
} else {
throw "could not open " + filename;
}
}

if (cmdline.hasParameter(param_save_model_w)) {
std::string filename = cmdline.getValue(param_save_model_w);
fm.w.saveToBinaryFile(filename);
}

if (cmdline.hasParameter(param_save_model_v)) {
std::string filename = cmdline.getValue(param_save_model_v);
fm.v.saveToBinaryFile(filename);
}


// () Prediction at the end (not for mcmc and als)
if (cmdline.getValue(param_method).compare("mcmc")) {
Expand Down