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
|
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2012- University of Edinburgh
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
***********************************************************************/
#include <boost/algorithm/string/predicate.hpp>
#include "ScoreFeature.h"
#include "DomainFeature.h"
#include "InternalStructFeature.h"
using namespace std;
using namespace boost::algorithm;
namespace MosesTraining
{
const string& ScoreFeatureManager::usage() const
{
const static string& usage = "[--[Sparse]Domain[Indicator|Ratio|Subset|Bin] domain-file [bins]]" ;
return usage;
}
void ScoreFeatureManager::configure(const std::vector<std::string> args)
{
bool domainAdded = false;
bool sparseDomainAdded = false;
for (size_t i = 0; i < args.size(); ++i) {
if (args[i] == "--IgnoreSentenceId") {
m_includeSentenceId = true;
} else if (starts_with(args[i], "--Domain")) {
string type = args[i].substr(8);
++i;
UTIL_THROW_IF(i == args.size(), ScoreFeatureArgumentException, "Missing domain file");
string domainFile = args[i];
UTIL_THROW_IF(domainAdded, ScoreFeatureArgumentException,
"Only allowed one domain feature");
if (type == "Subset") {
m_features.push_back(ScoreFeaturePtr(new SubsetDomainFeature(domainFile)));
} else if (type == "Ratio") {
m_features.push_back(ScoreFeaturePtr(new RatioDomainFeature(domainFile)));
} else if (type == "Indicator") {
m_features.push_back(ScoreFeaturePtr(new IndicatorDomainFeature(domainFile)));
} else {
UTIL_THROW(ScoreFeatureArgumentException, "Unknown domain feature type " << type);
}
domainAdded = true;
m_includeSentenceId = true;
} else if (starts_with(args[i], "--SparseDomain")) {
string type = args[i].substr(14);
++i;
UTIL_THROW_IF(i == args.size(), ScoreFeatureArgumentException, "Missing domain file");
string domainFile = args[i];
UTIL_THROW_IF(sparseDomainAdded, ScoreFeatureArgumentException,
"Only allowed one sparse domain feature");
if (type == "Subset") {
m_features.push_back(ScoreFeaturePtr(new SparseSubsetDomainFeature(domainFile)));
} else if (type == "Ratio") {
m_features.push_back(ScoreFeaturePtr(new SparseRatioDomainFeature(domainFile)));
} else if (type == "Indicator") {
m_features.push_back(ScoreFeaturePtr(new SparseIndicatorDomainFeature(domainFile)));
} else {
UTIL_THROW(ScoreFeatureArgumentException, "Unknown domain feature type " << type);
}
sparseDomainAdded = true;
m_includeSentenceId = true;
} else if(args[i] == "--TreeFeatureSparse") {
//MARIA
m_features.push_back(ScoreFeaturePtr(new InternalStructFeatureSparse()));
} else if(args[i] == "--TreeFeatureDense") {
//MARIA
m_features.push_back(ScoreFeaturePtr(new InternalStructFeatureDense()));
} else {
UTIL_THROW(ScoreFeatureArgumentException,"Unknown score argument " << args[i]);
}
}
}
void ScoreFeatureManager::addPropertiesToPhrasePair(ExtractionPhrasePair &phrasePair,
float count,
int sentenceId) const
{
for (size_t i = 0; i < m_features.size(); ++i) {
m_features[i]->addPropertiesToPhrasePair(phrasePair, count, sentenceId);
}
}
void ScoreFeatureManager::addFeatures(const ScoreFeatureContext& context,
std::vector<float>& denseValues,
std::map<std::string,float>& sparseValues) const
{
for (size_t i = 0; i < m_features.size(); ++i) {
m_features[i]->add(context, denseValues, sparseValues);
}
}
}
|
