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-rw-r--r--pro-train/mr_pro_reduce.cc128
1 files changed, 96 insertions, 32 deletions
diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc
index 491ceb3a..9b422f33 100644
--- a/pro-train/mr_pro_reduce.cc
+++ b/pro-train/mr_pro_reduce.cc
@@ -16,7 +16,7 @@ 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
+// 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) {
@@ -25,8 +25,11 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
("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")
+ ("sigma_squared,s",po::value<double>()->default_value(0.1), "Sigma squared for Gaussian prior")
+ ("min_reg,r",po::value<double>()->default_value(1e-8), "When tuning (-T) regularization strength, minimum regularization strenght")
+ ("max_reg,R",po::value<double>()->default_value(10.0), "When tuning (-T) regularization strength, maximum regularization strenght")
+ ("testset,t",po::value<string>(), "Optional held-out test set")
+ ("tune_regularizer,T", "Use the held out test set (-t) to tune the regularization strength")
("help,h", "Help");
po::options_description dcmdline_options;
dcmdline_options.add(opts);
@@ -95,8 +98,6 @@ void GradAdd(const SparseVector<double>& v, const double scale, vector<double>*
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
@@ -130,39 +131,23 @@ double TrainingInference(const vector<double>& x,
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;
+// return held-out log likelihood
+double LearnParameters(const vector<pair<bool, SparseVector<double> > >& training,
+ const vector<pair<bool, SparseVector<double> > >& testing,
+ const double sigsq,
+ const unsigned memory_buffers,
+ vector<double>* px) {
+ vector<double>& x = *px;
vector<double> vg(FD::NumFeats(), 0.0);
bool converged = false;
- LBFGSOptimizer opt(FD::NumFeats(), conf["memory_buffers"].as<unsigned>());
+ LBFGSOptimizer opt(FD::NumFeats(), memory_buffers);
+ double tppl = 0.0;
while(!converged) {
+ fill(vg.begin(), vg.end(), 0.0);
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()) {
@@ -173,7 +158,6 @@ int main(int argc, char** argv) {
// 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;
@@ -202,11 +186,91 @@ int main(int argc, char** argv) {
cerr << " BIAS: " << x[0] << endl;
}
}
+ return tppl;
+}
+
+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 bool tune_regularizer = conf.count("tune_regularizer");
+ if (tune_regularizer && !conf.count("testset")) {
+ cerr << "--tune_regularizer requires --testset to be set\n";
+ return 1;
+ }
+ const double min_reg = conf["min_reg"].as<double>();
+ const double max_reg = conf["max_reg"].as<double>();
+ double sigsq = conf["sigma_squared"].as<double>();
+ assert(sigsq > 0.0);
+ assert(min_reg > 0.0);
+ assert(max_reg > 0.0);
+ assert(max_reg > min_reg);
+ 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;
+ double tppl = 0.0;
+ vector<pair<double,double> > sp;
+ vector<double> smoothed;
+ if (tune_regularizer) {
+ sigsq = min_reg;
+ const double steps = 18;
+ double sweep_factor = exp((log(max_reg) - log(min_reg)) / steps);
+ cerr << "SWEEP FACTOR: " << sweep_factor << endl;
+ while(sigsq < max_reg) {
+ tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x);
+ sp.push_back(make_pair(sigsq, tppl));
+ sigsq *= sweep_factor;
+ }
+ smoothed.resize(sp.size(), 0);
+ smoothed[0] = sp[0].second;
+ smoothed.back() = sp.back().second;
+ for (int i = 1; i < sp.size()-1; ++i) {
+ double prev = sp[i-1].second;
+ double next = sp[i+1].second;
+ double cur = sp[i].second;
+ smoothed[i] = (prev*0.2) + cur * 0.6 + (0.2*next);
+ }
+ double best_ppl = 9999999;
+ unsigned best_i = 0;
+ for (unsigned i = 0; i < sp.size(); ++i) {
+ if (smoothed[i] < best_ppl) {
+ best_ppl = smoothed[i];
+ best_i = i;
+ }
+ }
+ sigsq = sp[best_i].first;
+ tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x);
+ }
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);
}
+ cout.precision(15);
+ cout << "# sigma^2=" << sigsq << "\theld out perplexity=";
+ if (tppl) { cout << tppl << endl; } else { cout << "N/A\n"; }
+ if (sp.size()) {
+ cout << "# Parameter sweep:\n";
+ for (int i = 0; i < sp.size(); ++i) {
+ cout << "# " << sp[i].first << "\t" << sp[i].second << "\t" << smoothed[i] << endl;
+ }
+ }
w.InitFromVector(x);
w.WriteToFile("-");
return 0;
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