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#include <sstream>
#include <iostream>
#include <fstream>
#include <vector>
#include <cassert>
#include <cmath>
#include <boost/shared_ptr.hpp>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "optimize.h"
#include "fdict.h"
#include "weights.h"
#include "sparse_vector.h"
using namespace std;
namespace po = boost::program_options;
void SanityCheck(const vector<double>& w) {
for (int i = 0; i < w.size(); ++i) {
assert(!std::isnan(w[i]));
assert(!std::isinf(w[i]));
}
}
struct FComp {
const vector<double>& w_;
FComp(const vector<double>& w) : w_(w) {}
bool operator()(int a, int b) const {
return fabs(w_[a]) > fabs(w_[b]);
}
};
void ShowLargestFeatures(const vector<double>& w) {
vector<int> fnums(w.size());
for (int i = 0; i < w.size(); ++i)
fnums[i] = i;
vector<int>::iterator mid = fnums.begin();
mid += (w.size() > 10 ? 10 : w.size());
partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
cerr << "TOP FEATURES:";
for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
}
cerr << endl;
}
void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("input_weights,i",po::value<string>(),"Input feature weights file")
("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file")
("optimization_method,m", po::value<string>()->default_value("lbfgs"), "Optimization method (sgd, lbfgs, rprop)")
("state,s",po::value<string>(),"Read (and write if output_state is not set) optimizer state from this state file. In the first iteration, the file should not exist.")
("input_format,f",po::value<string>()->default_value("b64"),"Encoding of the input (b64 or text)")
("output_state,S", po::value<string>(), "Output state file (optional override)")
("correction_buffers,M", po::value<int>()->default_value(10), "Number of gradients for LBFGS to maintain in memory")
("eta,e", po::value<double>()->default_value(0.1), "Learning rate for SGD (eta)")
("gaussian_prior,p","Use a Gaussian prior on the weights")
("means,u", po::value<string>(), "File containing the means for Gaussian prior")
("sigma_squared", po::value<double>()->default_value(1.0), "Sigma squared term for spherical Gaussian prior");
po::options_description clo("Command line options");
clo.add_options()
("config", po::value<string>(), "Configuration file")
("help,h", "Print this help message and exit");
po::options_description dconfig_options, dcmdline_options;
dconfig_options.add(opts);
dcmdline_options.add(opts).add(clo);
po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
if (conf->count("config")) {
ifstream config((*conf)["config"].as<string>().c_str());
po::store(po::parse_config_file(config, dconfig_options), *conf);
}
po::notify(*conf);
if (conf->count("help") || !conf->count("input_weights") || !conf->count("state")) {
cerr << dcmdline_options << endl;
exit(1);
}
}
int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
const bool use_b64 = conf["input_format"].as<string>() == "b64";
vector<weight_t> lambdas;
Weights::InitFromFile(conf["input_weights"].as<string>(), &lambdas);
const string s_obj = "**OBJ**";
int num_feats = FD::NumFeats();
cerr << "Number of features: " << num_feats << endl;
const bool gaussian_prior = conf.count("gaussian_prior");
vector<weight_t> means(num_feats, 0);
if (conf.count("means")) {
if (!gaussian_prior) {
cerr << "Don't use --means without --gaussian_prior!\n";
exit(1);
}
Weights::InitFromFile(conf["means"].as<string>(), &means);
}
boost::shared_ptr<BatchOptimizer> o;
const string omethod = conf["optimization_method"].as<string>();
if (omethod == "rprop")
o.reset(new RPropOptimizer(num_feats)); // TODO add configuration
else
o.reset(new LBFGSOptimizer(num_feats, conf["correction_buffers"].as<int>()));
cerr << "Optimizer: " << o->Name() << endl;
string state_file = conf["state"].as<string>();
{
ifstream in(state_file.c_str(), ios::binary);
if (in)
o->Load(&in);
else
cerr << "No state file found, assuming ITERATION 1\n";
}
double objective = 0;
vector<double> gradient(num_feats, 0);
// 0<TAB>**OBJ**=12.2;Feat1=2.3;Feat2=-0.2;
// 0<TAB>**OBJ**=1.1;Feat1=1.0;
int total_lines = 0; // TODO - this should be a count of the
// training instances!!
while(cin) {
string line;
getline(cin, line);
if (line.empty()) continue;
++total_lines;
int feat;
double val;
size_t i = line.find("\t");
assert(i != string::npos);
++i;
if (use_b64) {
SparseVector<double> g;
double obj;
if (!B64::Decode(&obj, &g, &line[i], line.size() - i)) {
cerr << "B64 decoder returned error, skipping gradient!\n";
cerr << " START: " << line.substr(0,line.size() > 200 ? 200 : line.size()) << endl;
if (line.size() > 200)
cerr << " END: " << line.substr(line.size() - 200, 200) << endl;
cout << "-1\tRESTART\n";
exit(99);
}
objective += obj;
const SparseVector<double>& cg = g;
for (SparseVector<double>::const_iterator it = cg.begin(); it != cg.end(); ++it) {
if (it->first >= num_feats) {
cerr << "Unexpected feature in gradient: " << FD::Convert(it->first) << endl;
abort();
}
gradient[it->first] -= it->second;
}
} else { // text encoding - your gradients will not be accurate!
while (i < line.size()) {
size_t start = i;
while (line[i] != '=' && i < line.size()) ++i;
if (i == line.size()) { cerr << "FORMAT ERROR\n"; break; }
string fname = line.substr(start, i - start);
if (fname == s_obj) {
feat = -1;
} else {
feat = FD::Convert(line.substr(start, i - start));
if (feat >= num_feats) {
cerr << "Unexpected feature in gradient: " << line.substr(start, i - start) << endl;
abort();
}
}
++i;
start = i;
while (line[i] != ';' && i < line.size()) ++i;
if (i - start == 0) continue;
val = atof(line.substr(start, i - start).c_str());
++i;
if (feat == -1) {
objective += val;
} else {
gradient[feat] -= val;
}
}
}
}
if (gaussian_prior) {
const double sigsq = conf["sigma_squared"].as<double>();
double norm = 0;
for (int k = 1; k < lambdas.size(); ++k) {
const double& lambda_k = lambdas[k];
if (lambda_k) {
const double param = (lambda_k - means[k]);
norm += param * param;
gradient[k] += param / sigsq;
}
}
const double reg = norm / (2.0 * sigsq);
cerr << "REGULARIZATION TERM: " << reg << endl;
objective += reg;
}
cerr << "EVALUATION #" << o->EvaluationCount() << " OBJECTIVE: " << objective << endl;
double gnorm = 0;
for (int i = 0; i < gradient.size(); ++i)
gnorm += gradient[i] * gradient[i];
cerr << " GNORM=" << sqrt(gnorm) << endl;
vector<double> old = lambdas;
int c = 0;
while (old == lambdas) {
++c;
if (c > 1) { cerr << "Same lambdas, repeating optimization\n"; }
o->Optimize(objective, gradient, &lambdas);
assert(c < 5);
}
old.clear();
SanityCheck(lambdas);
ShowLargestFeatures(lambdas);
Weights::WriteToFile(conf["output_weights"].as<string>(), lambdas, false);
const bool conv = o->HasConverged();
if (conv) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
if (conf.count("output_state"))
state_file = conf["output_state"].as<string>();
ofstream out(state_file.c_str(), ios::binary);
cerr << "Writing state to: " << state_file << endl;
o->Save(&out);
out.close();
cout << o->EvaluationCount() << "\t" << conv << endl;
return 0;
}
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