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#include <iostream>
#include <vector>
#include <sstream>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "sampler.h"
#include "filelib.h"
#include "weights.h"
#include "line_optimizer.h"
#include "hg.h"
#include "hg_io.h"
#include "scorer.h"
#include "oracle_bleu.h"
#include "ff_bleu.h"
boost::shared_ptr<FFRegistry> global_ff_registry;
namespace {
struct init_ff {
init_ff() {
global_ff_registry.reset(new FFRegistry);
global_ff_registry->Register(new FFFactory<BLEUModel>);
}
};
init_ff reg;
}
using namespace std;
namespace po = boost::program_options;
typedef SparseVector<double> Dir;
typedef Dir Point;
void compress_similar(vector<Dir> &dirs,double min_dist,ostream *log=&cerr,bool avg=true) {
if (min_dist<=0) return;
double max_s=1.-min_dist;
unsigned N=dirs.size();
for (int i=0;i<N;++i) {
for (int j=i+1;j<N;++j) {
double s=dirs[i].tanimoto_coef(dirs[j]);
if (s>max_s) {
if (log) *log << "Collapsing similar directions (T="<<s<<" > "<<max_s<<"). dirs["<<i<<"]="<<dirs[i]<<" dirs["<<j<<"]";
if (avg) {
dirs[i]+=dirs[j];
dirs[i]/=2.;
if (log) *log<<" averaged="<<dirs[i];
}
if (log) *log<<endl;
swap(dirs[j],dirs[--N]);
}
}
}
dirs.resize(N);
}
struct oracle_directions {
MT19937 rng;
OracleBleu oracle;
vector<Dir> directions;
bool start_random;
bool include_primary;
bool fear_to_hope;
unsigned n_random;
void AddPrimaryAndRandomDirections() {
LineOptimizer::CreateOptimizationDirections(
fids,n_random,&rng,&directions,include_primary);
}
void Print() {
for (int i = 0; i < dev_set_size; ++i)
for (int j = 0; j < directions.size(); ++j)
cout << forest_file(i) <<" " << i << ' ' << origin << ' ' << directions[j] << endl;
}
void AddOptions(po::options_description *opts) {
oracle.AddOptions(opts);
}
void InitCommandLine(int argc, char *argv[], po::variables_map *conf) {
po::options_description opts("Configuration options");
OracleBleu::AddOptions(&opts);
opts.add_options()
("dev_set_size,s",po::value<unsigned>(&dev_set_size),"[REQD] Development set size (# of parallel sentences)")
("forest_repository,r",po::value<string>(),"[REQD] Path to forest repository")
("weights,w",po::value<string>(),"[REQD] Current feature weights file")
("optimize_feature,o",po::value<vector<string> >(), "Feature to optimize (if none specified, all weights listed in the weights file will be optimized)")
("random_directions,d",po::value<unsigned int>()->default_value(20),"Number of random directions to run the line optimizer in")
("no_primary,n","don't use the primary (orthogonal each feature alone) directions")
("oracle_directions,O",po::value<unsigned>()->default_value(0),"read the forests and choose this many directions based on heading toward a hope max (bleu+modelscore) translation.")
("oracle_start_random",po::bool_switch(&start_random),"sample random subsets of dev set for ALL oracle directions, not just those after a sequential run through it")
("oracle_batch,b",po::value<unsigned>()->default_value(10),"to produce each oracle direction, sum the 'gradient' over this many sentences")
("max_similarity,m",po::value<double>()->default_value(0),"remove directions that are too similar (Tanimoto coeff. less than (1-this)). 0 means don't filter, 1 means only 1 direction allowed?")
("fear_to_hope,f","for each of the oracle_directions, also include a direction from fear to hope (as well as origin to hope)")
("help,h", "Help");
po::options_description dcmdline_options;
dcmdline_options.add(opts);
po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
bool flag = false;
if (conf->count("dev_set_size") == 0) {
cerr << "Please specify the size of the development set using -d N\n";
flag = true;
}
if (conf->count("weights") == 0) {
cerr << "Please specify the starting-point weights using -w <weightfile.txt>\n";
flag = true;
}
if (conf->count("forest_repository") == 0) {
cerr << "Please specify the forest repository location using -r <DIR>\n";
flag = true;
}
if (flag || conf->count("help")) {
cerr << dcmdline_options << endl;
exit(1);
}
}
int main(int argc, char *argv[]) {
po::variables_map conf;
InitCommandLine(argc,argv,&conf);
UseConf(conf);
Run();
return 0;
}
void Run() {
AddPrimaryAndRandomDirections();
AddOracleDirections();
compress_similar(directions,max_similarity);
Print();
}
Point origin; // old weights that gave model 1best.
vector<string> optimize_features;
void UseConf(po::variables_map const& conf) {
oracle.UseConf(conf);
include_primary=!conf.count("no_primary");
if (conf.count("optimize_feature") > 0)
optimize_features=conf["optimize_feature"].as<vector<string> >();
fear_to_hope=conf.count("fear_to_hope");
n_random=conf["random_directions"].as<unsigned int>();
forest_repository=conf["forest_repository"].as<string>();
// dev_set_size=conf["dev_set_size"].as<unsigned int>();
n_oracle=conf["oracle_directions"].as<unsigned>();
oracle_batch=conf["oracle_batch"].as<unsigned>();
max_similarity=conf["max_similarity"].as<double>();
weights_file=conf["weights"].as<string>();
Init();
}
string weights_file;
double max_similarity;
unsigned n_oracle, oracle_batch;
string forest_repository;
unsigned dev_set_size;
vector<Dir> dirs; //best_to_hope_dirs
vector<int> fids;
string forest_file(unsigned i) const {
ostringstream o;
o << forest_repository << '/' << i << ".json.gz";
return o.str();
}
oracle_directions() { }
void Init() {
start_random=false;
assert(DirectoryExists(forest_repository));
vector<string> features;
weights.InitFromFile(weights_file, &features);
if (optimize_features.size())
features=optimize_features;
weights.InitSparseVector(&origin);
fids.clear();
AddFeatureIds(features);
}
Weights weights;
void AddFeatureIds(vector<string> const& features) {
int i = fids.size();
fids.resize(fids.size()+features.size());
for (; i < features.size(); ++i)
fids[i] = FD::Convert(features[i]);
}
Dir const& operator[](unsigned i) {
Dir &dir=dirs[i];
if (dir.empty()) {
ReadFile rf(forest_file(i));
FeatureVector fear,hope,best;
//TODO: get hope/oracle from vlad. random for now.
LineOptimizer::RandomUnitVector(fids,&dir,&rng);
}
return dir;
}
// if start_random is true, immediately sample w/ replacement from src sentences; otherwise, consume them sequentially until exhausted, then random. oracle vectors are summed
void AddOracleDirections() {
MT19937::IntRNG rsg=rng.inclusive(0,dev_set_size-1);
unsigned b=0;
for(unsigned i=0;i<n_oracle;++i) {
directions.push_back(Dir());
Dir &d=directions.back();
for (unsigned j=0;j<oracle_batch;++j,++b)
d+=(*this)[(start_random || b>=dev_set_size)?rsg():b];
d/=(double)oracle_batch;
}
}
};
int main(int argc, char** argv) {
oracle_directions od;
return od.main(argc,argv);
}
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