1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
|
#include <sstream>
#include <iostream>
#include <vector>
#include <cassert>
#include <cmath>
#include "config.h"
#include <boost/shared_ptr.hpp>
#include <boost/algorithm/string.hpp>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "sentence_metadata.h"
#include "scorer.h"
#include "verbose.h"
#include "viterbi.h"
#include "hg.h"
#include "prob.h"
#include "kbest.h"
#include "ff_register.h"
#include "decoder.h"
#include "filelib.h"
#include "fdict.h"
#include "weights.h"
#include "sparse_vector.h"
#include "sampler.h"
using namespace std;
namespace po = boost::program_options;
struct ScorePair
{
ScorePair(double modelscore, double score) : modelscore_(modelscore), score_(score) {}
double modelscore_, score_;
double GetModelScore() { return modelscore_; }
double GetScore() { return score_; }
};
typedef vector<ScorePair> Scores;
/*
* KBestGetter
*
*/
struct KBestList {
vector<SparseVector<double> > feats;
vector<vector<WordID> > sents;
vector<double> scores;
};
struct KBestGetter : public DecoderObserver
{
KBestGetter( const size_t k ) : k_(k) {}
const size_t k_;
KBestList kb;
virtual void
NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
{
GetKBest(smeta.GetSentenceID(), *hg);
}
KBestList* GetKBest() { return &kb; }
void
GetKBest(int sent_id, const Hypergraph& forest)
{
kb.scores.clear();
kb.sents.clear();
kb.feats.clear();
KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest( forest, k_ );
for ( size_t i = 0; i < k_; ++i ) {
const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
kbest.LazyKthBest( forest.nodes_.size() - 1, i );
if (!d) break;
kb.sents.push_back( d->yield);
kb.feats.push_back( d->feature_values );
kb.scores.push_back( d->score );
}
}
};
/*
* NgramCounts
*
*/
struct NgramCounts
{
NgramCounts( const size_t N ) : N_( N ) {
reset();
}
size_t N_;
map<size_t, size_t> clipped;
map<size_t, size_t> sum;
void
operator+=( const NgramCounts& rhs )
{
assert( N_ == rhs.N_ );
for ( size_t i = 0; i < N_; i++ ) {
this->clipped[i] += rhs.clipped.find(i)->second;
this->sum[i] += rhs.sum.find(i)->second;
}
}
void
add( size_t count, size_t ref_count, size_t i )
{
assert( i < N_ );
if ( count > ref_count ) {
clipped[i] += ref_count;
sum[i] += count;
} else {
clipped[i] += count;
sum[i] += count;
}
}
void
reset()
{
size_t i;
for ( i = 0; i < N_; i++ ) {
clipped[i] = 0;
sum[i] = 0;
}
}
void
print()
{
for ( size_t i = 0; i < N_; i++ ) {
cout << i+1 << "grams (clipped):\t" << clipped[i] << endl;
cout << i+1 << "grams:\t\t\t" << sum[i] << endl;
}
}
};
/*class Learnerx
{
public:
virtual void Init(const vector<SparseVector<double> >& kbest, const Scores& scores) {};
virtual void Update(SparseVector<double>& lambdas);
};*/
class SofiaLearner //: public Learnerx FIXME
{
// TODO bool invert_score
public:
void
Init( const size_t sid, const vector<SparseVector<double> >& kbest, /*const*/ Scores& scores )
{
assert( kbest.size() == scores.size() );
ofstream o;
unlink( "/tmo/sofia_ml_training" );
o.open( "/tmp/sofia_ml_training", ios::trunc ); // TODO randomize, filename exists
int fid = 0;
map<int,int>::iterator ff;
for ( size_t k = 0; k < kbest.size(); ++k ) {
SparseVector<double>::const_iterator it = kbest[k].begin();
o << scores[k].GetScore();
for ( ; it != kbest[k].end(); ++it) {
ff = fmap.find( it->first );
if ( ff == fmap.end() ) {
fmap.insert( pair<int,int>(it->first, fid) );
fmap1.insert( pair<int,int>(fid, it->first) );
fid++;
}
o << " "<< fmap[it->first] << ":" << it->second;
}
o << endl;
}
o.close();
}
void
Update(SparseVector<double>& lambdas)
{
string call = "./sofia-ml --training_file /tmp/sofia_ml_training --model_out /tmp/sofia_ml_model --loop_type stochastic --lambda 100 --dimensionality ";
std::stringstream out;
out << fmap.size();
call += out.str();
call += " &>/dev/null";
system ( call.c_str() );
ifstream i;
unlink( "/tmo/sofia_ml_model" );
i.open( "/tmp/sofia_ml_model", ios::in );
string model;
getline( i, model );
//cout << model << endl;
vector<string> strs;
boost::split( strs, model, boost::is_any_of(" ") );
int j = 0;
for ( vector<string>::iterator it = strs.begin(); it != strs.end(); ++it ) {
lambdas.set_value(fmap1[j], atof( it->c_str() ) );
j++;
}
}
private:
map<int,int> fmap;
map<int,int> fmap1;
};
typedef map<vector<WordID>, size_t> Ngrams;
Ngrams make_ngrams( vector<WordID>& s, size_t N );
NgramCounts make_ngram_counts( vector<WordID> hyp, vector<WordID> ref, size_t N );
double brevity_penaly( const size_t hyp_len, const size_t ref_len );
double bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, size_t N, vector<float> weights = vector<float>() );
double stupid_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, size_t N, vector<float> weights = vector<float>() );
double smooth_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, const size_t N, vector<float> weights = vector<float>() );
double approx_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, const size_t N, vector<float> weights = vector<float>() );
void register_and_convert(const vector<string>& strs, vector<WordID>& ids);
void print_FD();
void run_tests();
void test_SetWeights();
#include <boost/assign/std/vector.hpp>
#include <iomanip>
void test_metrics();
double approx_equal( double x, double y );
void test_ngrams();
|
