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#include <cstdlib>
#include <sstream>
#include <iostream>
#include <fstream>
#include <vector>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "filelib.h"
#include "weights.h"
#include "sparse_vector.h"
#include "optimize.h"
using namespace std;
namespace po = boost::program_options;
// since this is a ranking model, there should be equal numbers of
// positive and negative examples so the bias should be 0
static const double MAX_BIAS = 1e-10;
void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("weights,w", po::value<string>(), "Weights from previous iteration (used as initialization and interpolation")
("interpolation,p",po::value<double>()->default_value(0.9), "Output weights are p*w + (1-p)*w_prev")
("memory_buffers,m",po::value<unsigned>()->default_value(200), "Number of memory buffers (LBFGS)")
("sigma_squared,s",po::value<double>()->default_value(1.0), "Sigma squared for Gaussian prior")
("testset,t",po::value<string>(), "Optional held-out test set to tune regularizer")
("help,h", "Help");
po::options_description dcmdline_options;
dcmdline_options.add(opts);
po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
if (conf->count("help")) {
cerr << dcmdline_options << endl;
exit(1);
}
}
void ParseSparseVector(string& line, size_t cur, SparseVector<double>* out) {
SparseVector<double>& x = *out;
size_t last_start = cur;
size_t last_comma = string::npos;
while(cur <= line.size()) {
if (line[cur] == ' ' || cur == line.size()) {
if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) {
cerr << "[ERROR] " << line << endl << " position = " << cur << endl;
exit(1);
}
const int fid = FD::Convert(line.substr(last_start, last_comma - last_start));
if (cur < line.size()) line[cur] = 0;
const double val = strtod(&line[last_comma + 1], NULL);
x.set_value(fid, val);
last_comma = string::npos;
last_start = cur+1;
} else {
if (line[cur] == '=')
last_comma = cur;
}
++cur;
}
}
void ReadCorpus(istream* pin, vector<pair<bool, SparseVector<double> > >* corpus) {
istream& in = *pin;
corpus->clear();
bool flag = false;
int lc = 0;
string line;
SparseVector<double> x;
while(getline(in, line)) {
++lc;
if (lc % 1000 == 0) { cerr << '.'; flag = true; }
if (lc % 40000 == 0) { cerr << " [" << lc << "]\n"; flag = false; }
if (line.empty()) continue;
const size_t ks = line.find("\t");
assert(string::npos != ks);
assert(ks == 1);
const bool y = line[0] == '1';
x.clear();
ParseSparseVector(line, ks + 1, &x);
corpus->push_back(make_pair(y, x));
}
if (flag) cerr << endl;
}
void GradAdd(const SparseVector<double>& v, const double scale, vector<double>* acc) {
for (SparseVector<double>::const_iterator it = v.begin();
it != v.end(); ++it) {
(*acc)[it->first] += it->second * scale;
}
}
double TrainingInference(const vector<double>& x,
const vector<pair<bool, SparseVector<double> > >& corpus,
vector<double>* g = NULL) {
if (g) fill(g->begin(), g->end(), 0.0);
double cll = 0;
for (int i = 0; i < corpus.size(); ++i) {
const double dotprod = corpus[i].second.dot(x) + x[0]; // x[0] is bias
double lp_false = dotprod;
double lp_true = -dotprod;
if (0 < lp_true) {
lp_true += log1p(exp(-lp_true));
lp_false = log1p(exp(lp_false));
} else {
lp_true = log1p(exp(lp_true));
lp_false += log1p(exp(-lp_false));
}
lp_true*=-1;
lp_false*=-1;
if (corpus[i].first) { // true label
cll -= lp_true;
if (g) {
// g -= corpus[i].second * exp(lp_false);
GradAdd(corpus[i].second, -exp(lp_false), g);
(*g)[0] -= exp(lp_false); // bias
}
} else { // false label
cll -= lp_false;
if (g) {
// g += corpus[i].second * exp(lp_true);
GradAdd(corpus[i].second, exp(lp_true), g);
(*g)[0] += exp(lp_true); // bias
}
}
}
return cll;
}
int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
string line;
vector<pair<bool, SparseVector<double> > > training, testing;
SparseVector<double> old_weights;
const double psi = conf["interpolation"].as<double>();
if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; }
if (conf.count("weights")) {
Weights w;
w.InitFromFile(conf["weights"].as<string>());
w.InitSparseVector(&old_weights);
}
ReadCorpus(&cin, &training);
if (conf.count("testset")) {
ReadFile rf(conf["testset"].as<string>());
ReadCorpus(rf.stream(), &testing);
}
cerr << "Number of features: " << FD::NumFeats() << endl;
vector<double> x(FD::NumFeats(), 0.0); // x[0] is bias
for (SparseVector<double>::const_iterator it = old_weights.begin();
it != old_weights.end(); ++it)
x[it->first] = it->second;
vector<double> vg(FD::NumFeats(), 0.0);
bool converged = false;
LBFGSOptimizer opt(FD::NumFeats(), conf["memory_buffers"].as<unsigned>());
while(!converged) {
double cll = TrainingInference(x, training, &vg);
double ppl = cll / log(2);
ppl /= training.size();
ppl = pow(2.0, ppl);
double tppl = 0.0;
// evaluate optional held-out test set
if (testing.size()) {
tppl = TrainingInference(x, testing) / log(2);
tppl /= testing.size();
tppl = pow(2.0, tppl);
}
// handle regularizer
#if 1
const double sigsq = conf["sigma_squared"].as<double>();
double norm = 0;
for (int i = 1; i < x.size(); ++i) {
const double mean_i = 0.0;
const double param = (x[i] - mean_i);
norm += param * param;
vg[i] += param / sigsq;
}
const double reg = norm / (2.0 * sigsq);
#else
double reg = 0;
#endif
cll += reg;
cerr << cll << " (REG=" << reg << ")\tPPL=" << ppl << "\t TEST_PPL=" << tppl << "\t";
try {
vector<double> old_x = x;
do {
opt.Optimize(cll, vg, &x);
converged = opt.HasConverged();
} while (!converged && x == old_x);
} catch (...) {
cerr << "Exception caught, assuming convergence is close enough...\n";
converged = true;
}
if (fabs(x[0]) > MAX_BIAS) {
cerr << "Biased model learned. Are your training instances wrong?\n";
cerr << " BIAS: " << x[0] << endl;
}
}
Weights w;
if (conf.count("weights")) {
for (int i = 1; i < x.size(); ++i)
x[i] = (x[i] * psi) + old_weights.get(i) * (1.0 - psi);
}
w.InitFromVector(x);
w.WriteToFile("-");
return 0;
}
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