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#include <algorithm>
#include <fstream>
#include <boost/algorithm/string/join.hpp>
#include <tclap/CmdLine.h>
#include <Eigen/Core>
#include <Eigen/Dense>
#include "param.h"
#include "neuralLM.h"
using namespace std;
using namespace boost;
using namespace TCLAP;
using namespace Eigen;
using namespace nplm;
int main (int argc, char *argv[])
{
param myParam;
bool normalization;
bool numberize, ngramize, add_start_stop;
try {
// program options //
CmdLine cmd("Tests a two-layer neural probabilistic language model.", ' ' , "0.1");
ValueArg<int> num_threads("", "num_threads", "Number of threads. Default: maximum.", false, 0, "int", cmd);
ValueArg<int> minibatch_size("", "minibatch_size", "Minibatch size. Default: none.", false, 0, "int", cmd);
ValueArg<bool> arg_ngramize("", "ngramize", "If true, convert lines to ngrams. Default: true.", false, true, "bool", cmd);
ValueArg<bool> arg_numberize("", "numberize", "If true, convert words to numbers. Default: true.", false, true, "bool", cmd);
ValueArg<bool> arg_add_start_stop("", "add_start_stop", "If true, prepend <s> and append </s>. Default: true.", false, true, "bool", cmd);
ValueArg<bool> arg_normalization("", "normalization", "Normalize probabilities. 1 = yes, 0 = no. Default: 0.", false, 0, "bool", cmd);
ValueArg<string> arg_test_file("", "test_file", "Test file (one tokenized sentence per line).", true, "", "string", cmd);
ValueArg<string> arg_model_file("", "model_file", "Language model file.", true, "", "string", cmd);
cmd.parse(argc, argv);
myParam.model_file = arg_model_file.getValue();
myParam.test_file = arg_test_file.getValue();
normalization = arg_normalization.getValue();
numberize = arg_numberize.getValue();
ngramize = arg_ngramize.getValue();
add_start_stop = arg_add_start_stop.getValue();
myParam.minibatch_size = minibatch_size.getValue();
myParam.num_threads = num_threads.getValue();
cerr << "Command line: " << endl;
cerr << boost::algorithm::join(vector<string>(argv, argv+argc), " ") << endl;
const string sep(" Value: ");
cerr << arg_test_file.getDescription() << sep << arg_test_file.getValue() << endl;
cerr << arg_model_file.getDescription() << sep << arg_model_file.getValue() << endl;
cerr << arg_normalization.getDescription() << sep << arg_normalization.getValue() << endl;
cerr << arg_ngramize.getDescription() << sep << arg_ngramize.getValue() << endl;
cerr << arg_add_start_stop.getDescription() << sep << arg_add_start_stop.getValue() << endl;
cerr << minibatch_size.getDescription() << sep << minibatch_size.getValue() << endl;
cerr << num_threads.getDescription() << sep << num_threads.getValue() << endl;
}
catch (TCLAP::ArgException &e)
{
cerr << "error: " << e.error() << " for arg " << e.argId() << endl;
exit(1);
}
myParam.num_threads = setup_threads(myParam.num_threads);
///// Create language model
neuralLM lm(myParam.model_file);
lm.set_normalization(normalization);
lm.set_log_base(10);
lm.set_cache(1048576);
int ngram_size = lm.get_order();
int minibatch_size = myParam.minibatch_size;
if (minibatch_size)
lm.set_width(minibatch_size);
///// Read test data
double log_likelihood = 0.0;
ifstream test_file(myParam.test_file.c_str());
if (!test_file)
{
cerr << "error: could not open " << myParam.test_file << endl;
exit(1);
}
string line;
vector<int> start;
vector<vector<int> > ngrams;
while (getline(test_file, line))
{
vector<string> words;
splitBySpace(line, words);
vector<vector<int> > sent_ngrams;
preprocessWords(words, sent_ngrams, ngram_size, lm.get_vocabulary(), numberize, add_start_stop, ngramize);
start.push_back(ngrams.size());
copy(sent_ngrams.begin(), sent_ngrams.end(), back_inserter(ngrams));
}
start.push_back(ngrams.size());
if (minibatch_size == 0)
{
// Score one n-gram at a time. This is how the LM would be queried from a decoder.
for (int sent_id=0; sent_id<start.size()-1; sent_id++)
{
double sent_log_prob = 0.0;
for (int j=start[sent_id]; j<start[sent_id+1]; j++)
sent_log_prob += lm.lookup_ngram(ngrams[j]);
cout << sent_log_prob << endl;
log_likelihood += sent_log_prob;
}
}
else
{
// Score a whole minibatch at a time.
Matrix<double,1,Dynamic> log_probs(ngrams.size());
Matrix<int,Dynamic,Dynamic> minibatch(ngram_size, minibatch_size);
minibatch.setZero();
for (int test_id = 0; test_id < ngrams.size(); test_id += minibatch_size)
{
int current_minibatch_size = minibatch_size<ngrams.size()-test_id ? minibatch_size : ngrams.size()-test_id;
for (int j=0; j<current_minibatch_size; j++)
minibatch.col(j) = Map< Matrix<int,Dynamic,1> > (ngrams[test_id+j].data(), ngram_size);
lm.lookup_ngram(minibatch.leftCols(current_minibatch_size), log_probs.middleCols(test_id, current_minibatch_size));
}
for (int sent_id=0; sent_id<start.size()-1; sent_id++)
{
double sent_log_prob = 0.0;
for (int j=start[sent_id]; j<start[sent_id+1]; j++)
sent_log_prob += log_probs[j];
cout << sent_log_prob << endl;
log_likelihood += sent_log_prob;
}
}
cerr << "Test log10-likelihood: " << log_likelihood << endl;
#ifdef USE_CHRONO
cerr << "Propagation times:";
for (int i=0; i<timer.size(); i++)
cerr << " " << timer.get(i);
cerr << endl;
#endif
}
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