This commit is contained in:
Nicola Bertoldi 2014-11-18 15:16:16 +01:00
commit 46596ed49a
331 changed files with 14558 additions and 6617 deletions

1
.gitignore vendored
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@ -1,3 +1,4 @@
tools
*.d
*.pyc
*.lo

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@ -1,3 +1,4 @@
Please see the Moses website on how to compile and run Moses
http://www.statmt.org/moses/?n=Development.GetStarted

42
Jamroot
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@ -114,7 +114,7 @@ requirements += [ option.get "with-mm" : : <define>PT_UG ] ;
requirements += [ option.get "with-mm" : : <define>MAX_NUM_FACTORS=4 ] ;
requirements += [ option.get "unlabelled-source" : : <define>UNLABELLED_SOURCE ] ;
if [ option.get "with-lbllm" ] {
if [ option.get "with-oxlm" ] {
external-lib boost_serialization ;
external-lib gomp ;
requirements += <library>boost_serialization ;
@ -125,6 +125,19 @@ if [ option.get "with-cmph" : : "yes" ] {
requirements += <define>HAVE_CMPH ;
}
if [ option.get "with-icu" : : "yes" ]
{
external-lib icuuc ;
external-lib icuio ;
external-lib icui18n ;
requirements += <library>icuuc/<link>shared ;
requirements += <library>icuio/<link>shared ;
requirements += <library>icui18n/<link>shared ;
requirements += <cxxflags>-fPIC ;
requirements += <address-model>64 ;
requirements += <runtime-link>shared ;
}
if [ option.get "with-probing-pt" : : "yes" ]
{
external-lib boost_serialization ;
@ -154,14 +167,13 @@ project : requirements
;
#Add directories here if you want their incidental targets too (i.e. tests).
build-projects lm util phrase-extract search moses moses/LM mert moses-cmd moses-chart-cmd mira scripts regression-testing ;
build-projects lm util phrase-extract search moses moses/LM mert moses-cmd mira scripts regression-testing ;
if [ option.get "with-mm" : : "yes" ]
{
alias mm :
moses/TranslationModel/UG//spe-check-coverage2
moses/TranslationModel/UG//count-ptable-features
moses/TranslationModel/UG//ptable-lookup
moses/TranslationModel/UG//sim-pe
moses/TranslationModel/UG//spe-check-coverage
moses/TranslationModel/UG/mm//mtt-build
moses/TranslationModel/UG/mm//mtt-dump
@ -180,9 +192,19 @@ else
alias mm ;
}
if [ option.get "with-rephraser" : : "yes" ]
{
alias rephraser :
contrib/rephraser//paraphrase
;
}
else
{
alias rephraser ;
}
alias programs :
lm//programs
moses-chart-cmd//moses_chart
moses-cmd//programs
OnDiskPt//CreateOnDiskPt
OnDiskPt//queryOnDiskPt
@ -199,11 +221,12 @@ biconcor
mira//mira
contrib/server//mosesserver
mm
rephraser
;
install-bin-libs programs ;
install-headers headers-base : [ path.glob-tree biconcor contrib lm mert misc moses-chart-cmd moses-cmd OnDiskPt phrase-extract symal util : *.hh *.h ] : . ;
install-headers headers-base : [ path.glob-tree biconcor contrib lm mert misc moses-cmd OnDiskPt phrase-extract symal util : *.hh *.h ] : . ;
install-headers headers-moses : moses//headers-to-install : moses ;
alias install : prefix-bin prefix-lib headers-base headers-moses ;
@ -217,3 +240,10 @@ if [ path.exists $(TOP)/dist ] && $(prefix) != dist {
echo "To disable this message, delete $(TOP)/dist ." ;
echo ;
}
local temp = [ _shell "mkdir bin" ] ;
local temp = [ _shell "rm bin/moses_chart" ] ;
local temp = [ _shell "cd bin && ln -s moses moses_chart" ] ;

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@ -1,2 +1,2 @@
exe biconcor : Vocabulary.cpp SuffixArray.cpp TargetCorpus.cpp Alignment.cpp Mismatch.cpp PhrasePair.cpp PhrasePairCollection.cpp biconcor.cpp base64.cpp ;
exe phrase-lookup : Vocabulary.cpp SuffixArray.cpp phrase-lookup.cpp ;

132
biconcor/phrase-lookup.cpp Normal file
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@ -0,0 +1,132 @@
#include "SuffixArray.h"
#include <getopt.h>
using namespace std;
size_t lookup( string );
vector<string> tokenize( const char input[] );
SuffixArray suffixArray;
int main(int argc, char* argv[]) {
// handle parameters
string query;
string fileNameSuffix;
string fileNameSource;
int loadFlag = false;
int saveFlag = false;
int createFlag = false;
int queryFlag = false;
int stdioFlag = false; // receive requests from STDIN, respond to STDOUT
string info = "usage: biconcor\n\t[--load model-file]\n\t[--save model-file]\n\t[--create corpus]\n\t[--query string]\n\t[--stdio]\n";
while(1) {
static struct option long_options[] = {
{"load", required_argument, 0, 'l'},
{"save", required_argument, 0, 's'},
{"create", required_argument, 0, 'c'},
{"query", required_argument, 0, 'q'},
{"stdio", no_argument, 0, 'i'},
{0, 0, 0, 0}
};
int option_index = 0;
int c = getopt_long (argc, argv, "l:s:c:q:i", long_options, &option_index);
if (c == -1) break;
switch (c) {
case 'l':
fileNameSuffix = string(optarg);
loadFlag = true;
break;
case 's':
fileNameSuffix = string(optarg);
saveFlag = true;
break;
case 'c':
fileNameSource = string(optarg);
createFlag = true;
break;
case 'q':
query = string(optarg);
queryFlag = true;
break;
case 'i':
stdioFlag = true;
break;
default:
cerr << info;
exit(1);
}
}
if (stdioFlag) {
queryFlag = true;
}
// check if parameter settings are legal
if (saveFlag && !createFlag) {
cerr << "error: cannot save without creating\n" << info;
exit(1);
}
if (saveFlag && loadFlag) {
cerr << "error: cannot load and save at the same time\n" << info;
exit(1);
}
if (!loadFlag && !createFlag) {
cerr << "error: neither load or create - i have no info!\n" << info;
exit(1);
}
// do your thing
if (createFlag) {
cerr << "will create\n";
cerr << "corpus is in " << fileNameSource << endl;
suffixArray.Create( fileNameSource );
if (saveFlag) {
suffixArray.Save( fileNameSuffix );
cerr << "will save in " << fileNameSuffix << endl;
}
}
if (loadFlag) {
cerr << "will load from " << fileNameSuffix << endl;
suffixArray.Load( fileNameSuffix );
}
if (stdioFlag) {
while(true) {
string query;
if (getline(cin, query, '\n').eof()) {
return 0;
}
cout << lookup( query ) << endl;
}
}
else if (queryFlag) {
cout << lookup( query ) << endl;
}
return 0;
}
size_t lookup( string query ) {
cerr << "query is " << query << endl;
vector< string > queryString = tokenize( query.c_str() );
return suffixArray.Count( queryString );
}
vector<string> tokenize( const char input[] )
{
vector< string > token;
bool betweenWords = true;
int start=0;
int i=0;
for(; input[i] != '\0'; i++) {
bool isSpace = (input[i] == ' ' || input[i] == '\t');
if (!isSpace && betweenWords) {
start = i;
betweenWords = false;
} else if (isSpace && !betweenWords) {
token.push_back( string( input+start, i-start ) );
betweenWords = true;
}
}
if (!betweenWords)
token.push_back( string( input+start, i-start ) );
return token;
}

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@ -1,18 +1,16 @@
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@ -44,15 +42,14 @@
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<listOptionValue builtIn="false" value="OnDiskPt"/>
<listOptionValue builtIn="false" value="moses"/>
<listOptionValue builtIn="false" value="irstlm"/>
<listOptionValue builtIn="false" value="search"/>
<listOptionValue builtIn="false" value="lm"/>
<listOptionValue builtIn="false" value="util"/>
<listOptionValue builtIn="false" value="boost_iostreams-mt"/>
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<listOptionValue builtIn="false" value="bz2"/>
@ -60,13 +57,7 @@
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<listOptionValue builtIn="false" value="&quot;${workspace_loc:}/../../randlm/lib&quot;"/>
<listOptionValue builtIn="false" value="&quot;${workspace_loc:}/OnDiskPt/Debug&quot;"/>
<listOptionValue builtIn="false" value="&quot;${workspace_loc:}/util/Debug&quot;"/>
<listOptionValue builtIn="false" value="&quot;${workspace_loc:}/moses/Debug&quot;"/>
@ -90,12 +81,12 @@
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@ -151,5 +142,12 @@
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@ -18,7 +18,7 @@
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@ -37,14 +37,26 @@
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@ -3,7 +3,10 @@
<name>consolidate</name>
<comment></comment>
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<project>moses</project>
<project>OnDiskPt</project>
<project>search</project>
<project>util</project>
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<buildSpec>

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@ -18,7 +18,7 @@
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View File

@ -3,6 +3,7 @@
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@ -18,7 +18,7 @@
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<inputType id="cdt.managedbuild.tool.gnu.c.compiler.input.1583852187" superClass="cdt.managedbuild.tool.gnu.c.compiler.input"/>
<tool id="cdt.managedbuild.tool.gnu.c.compiler.exe.release.1635883096" name="GCC C Compiler" superClass="cdt.managedbuild.tool.gnu.c.compiler.exe.release">
<option defaultValue="gnu.c.optimization.level.most" id="gnu.c.compiler.exe.release.option.optimization.level.74859509" name="Optimization Level" superClass="gnu.c.compiler.exe.release.option.optimization.level" valueType="enumerated"/>
<option id="gnu.c.compiler.exe.release.option.debugging.level.1604502606" name="Debug Level" superClass="gnu.c.compiler.exe.release.option.debugging.level" value="gnu.c.debugging.level.none" valueType="enumerated"/>
<inputType id="cdt.managedbuild.tool.gnu.c.compiler.input.624155660" superClass="cdt.managedbuild.tool.gnu.c.compiler.input"/>
</tool>
<tool id="cdt.managedbuild.tool.gnu.c.linker.exe.release.1007421110" name="GCC C Linker" superClass="cdt.managedbuild.tool.gnu.c.linker.exe.release"/>
<tool id="cdt.managedbuild.tool.gnu.cpp.linker.exe.release.195880914" name="GCC C++ Linker" superClass="cdt.managedbuild.tool.gnu.cpp.linker.exe.release">
<inputType id="cdt.managedbuild.tool.gnu.cpp.linker.input.518921609" superClass="cdt.managedbuild.tool.gnu.cpp.linker.input">
<tool id="cdt.managedbuild.tool.gnu.c.linker.exe.release.727800742" name="GCC C Linker" superClass="cdt.managedbuild.tool.gnu.c.linker.exe.release"/>
<tool id="cdt.managedbuild.tool.gnu.cpp.linker.exe.release.1586891175" name="GCC C++ Linker" superClass="cdt.managedbuild.tool.gnu.cpp.linker.exe.release">
<inputType id="cdt.managedbuild.tool.gnu.cpp.linker.input.1588265513" superClass="cdt.managedbuild.tool.gnu.cpp.linker.input">
<additionalInput kind="additionalinputdependency" paths="$(USER_OBJS)"/>
<additionalInput kind="additionalinput" paths="$(LIBS)"/>
</inputType>
</tool>
<tool id="cdt.managedbuild.tool.gnu.assembler.exe.release.330494310" name="GCC Assembler" superClass="cdt.managedbuild.tool.gnu.assembler.exe.release">
<inputType id="cdt.managedbuild.tool.gnu.assembler.input.1407747418" superClass="cdt.managedbuild.tool.gnu.assembler.input"/>
<tool id="cdt.managedbuild.tool.gnu.assembler.exe.release.727000276" name="GCC Assembler" superClass="cdt.managedbuild.tool.gnu.assembler.exe.release">
<inputType id="cdt.managedbuild.tool.gnu.assembler.input.665044877" superClass="cdt.managedbuild.tool.gnu.assembler.input"/>
</tool>
</toolChain>
</folderInfo>
@ -147,32 +138,31 @@
</cconfiguration>
</storageModule>
<storageModule moduleId="cdtBuildSystem" version="4.0.0">
<project id="moses-chart-cmd.cdt.managedbuild.target.gnu.exe.532411209" name="Executable" projectType="cdt.managedbuild.target.gnu.exe"/>
<project id="server.cdt.managedbuild.target.gnu.exe.580879474" name="Executable" projectType="cdt.managedbuild.target.gnu.exe"/>
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<storageModule moduleId="scannerConfiguration">
<autodiscovery enabled="true" problemReportingEnabled="true" selectedProfileId=""/>
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<autodiscovery enabled="true" problemReportingEnabled="true" selectedProfileId="org.eclipse.cdt.managedbuilder.core.GCCManagedMakePerProjectProfileC"/>
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<storageModule moduleId="org.eclipse.cdt.core.LanguageSettingsProviders"/>
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<configuration configurationName="Release">
<resource resourceType="PROJECT" workspacePath="/moses-chart-cmd"/>
<resource resourceType="PROJECT" workspacePath="/server"/>
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<configuration configurationName="Debug">
<resource resourceType="PROJECT" workspacePath="/moses-chart-cmd"/>
<resource resourceType="PROJECT" workspacePath="/server"/>
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<storageModule moduleId="org.eclipse.cdt.internal.ui.text.commentOwnerProjectMappings"/>
</cproject>

View File

@ -0,0 +1,39 @@
<?xml version="1.0" encoding="UTF-8"?>
<projectDescription>
<name>server</name>
<comment></comment>
<projects>
<project>lm</project>
<project>moses</project>
<project>OnDiskPt</project>
<project>search</project>
<project>util</project>
</projects>
<buildSpec>
<buildCommand>
<name>org.eclipse.cdt.managedbuilder.core.genmakebuilder</name>
<triggers>clean,full,incremental,</triggers>
<arguments>
</arguments>
</buildCommand>
<buildCommand>
<name>org.eclipse.cdt.managedbuilder.core.ScannerConfigBuilder</name>
<triggers>full,incremental,</triggers>
<arguments>
</arguments>
</buildCommand>
</buildSpec>
<natures>
<nature>org.eclipse.cdt.core.cnature</nature>
<nature>org.eclipse.cdt.core.ccnature</nature>
<nature>org.eclipse.cdt.managedbuilder.core.managedBuildNature</nature>
<nature>org.eclipse.cdt.managedbuilder.core.ScannerConfigNature</nature>
</natures>
<linkedResources>
<link>
<name>mosesserver.cpp</name>
<type>1</type>
<locationURI>PARENT-2-PROJECT_LOC/server/mosesserver.cpp</locationURI>
</link>
</linkedResources>
</projectDescription>

View File

@ -0,0 +1 @@
exe paraphrase : paraphrase.cpp ../../moses//moses ../..//boost_program_options ;

View File

@ -0,0 +1,148 @@
// $Id$
// vim:tabstop=2
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2014- 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
***********************************************************************/
/**
* Compute paraphrases from the phrase table
**/
#include <cmath>
#include <iostream>
#include <map>
#include <boost/program_options.hpp>
#include "util/double-conversion/double-conversion.h"
#include "util/exception.hh"
#include "util/file_piece.hh"
#include "util/string_piece.hh"
#include "util/tokenize_piece.hh"
//using namespace Moses;
using namespace std;
namespace po = boost::program_options;
typedef multimap<float,string> Probs;
static float threshold = 1e-04;
static size_t maxE = 10000; //histogram pruning
static void add(const string& e, const vector<float> scores,
Probs& p_e_given_f, Probs& p_f_given_e) {
if (scores[0] > threshold) {
p_f_given_e.insert(pair<float,string>(scores[0],e));
}
while(p_f_given_e.size() > maxE) p_f_given_e.erase(p_f_given_e.begin());
if (scores[2] > threshold) {
p_e_given_f.insert(pair<float,string>(scores[2],e));
}
while(p_e_given_f.size() > maxE) p_e_given_f.erase(p_e_given_f.begin());
}
static void finalise(Probs& p_e_given_f, Probs& p_f_given_e) {
//cerr << "Sizes: p(e|f): " << p_e_given_f.size() << " p(f|e): " << p_f_given_e.size() << endl;
for (Probs::const_iterator e1_iter = p_f_given_e.begin() ;
e1_iter != p_f_given_e.end(); ++e1_iter) {
for (Probs::const_iterator e2_iter = p_e_given_f.begin() ;
e2_iter != p_e_given_f.end(); ++e2_iter) {
if (e1_iter->second == e2_iter->second) continue;
cout << e1_iter->second << " ||| " << e2_iter->second << " ||| " <<
e1_iter->first * e2_iter->first << " ||| " << endl;
}
}
p_e_given_f.clear();
p_f_given_e.clear();
}
int main(int argc, char** argv) {
string input_file;
po::options_description desc("Allowed options");
desc.add_options()
("help,h", "Print help message and exit")
("threshold,t", po::value<float>(&threshold), "Threshold for p(e|f) and p(f|e)")
("max-target,m", po::value<size_t>(&maxE), "Maximum number of target phrases")
("input-file", po::value<string>(&input_file)->required(), "Input phrase table")
;
po::positional_options_description pos;
pos.add("input-file",1);
po::variables_map vm;
po::store(po::command_line_parser(argc,argv).options(desc).positional(pos).run(), vm);
if (vm.count("help")) {
cerr << "Usage: " << string(argv[0]) + " [options] input-file" << endl;
cerr << desc << endl;
return 0;
}
po::notify(vm);
cerr << "Reading from " << input_file << endl;
util::FilePiece in(input_file.c_str(), &std::cerr);
vector<float> scoreVector;
StringPiece line;
double_conversion::StringToDoubleConverter converter(double_conversion::StringToDoubleConverter::NO_FLAGS, NAN, NAN, "inf", "nan");
string previousSourcePhrase;
Probs p_f_given_e_table;
Probs p_e_given_f_table;
size_t count = 0;
while(true) {
try {
line = in.ReadLine();
} catch (const util::EndOfFileException &e) {
break;
}
++count;
util::TokenIter<util::MultiCharacter> pipes(line, " ||| ");
StringPiece sourcePhrase(*pipes);
StringPiece targetPhrase(*++pipes);
StringPiece scoreString(*++pipes);
scoreVector.clear();
for (util::TokenIter<util::AnyCharacter, true> s(scoreString, " \t"); s; ++s) {
int processed;
float score = converter.StringToFloat(s->data(), s->length(), &processed);
UTIL_THROW_IF2(isnan(score), "Bad score " << *s << " on line " << count);
scoreVector.push_back(score);
}
if (sourcePhrase.size() && sourcePhrase != previousSourcePhrase) {
finalise(p_e_given_f_table, p_f_given_e_table);
}
add(targetPhrase.as_string(),scoreVector, p_e_given_f_table, p_f_given_e_table);
previousSourcePhrase = sourcePhrase.as_string();
}
finalise(p_e_given_f_table, p_f_given_e_table);
return 0;
}

0
contrib/rt/Empty.c Normal file
View File

9
contrib/rt/README Normal file
View File

@ -0,0 +1,9 @@
FOR OSX ONLY
------------
This creates an empty library file
librt.a
It should be used when you are compile with Eclipse on OSX.
The Eclipse projects are set up to link to librt but OSX doesn't have it so this just creates a dummy library.

2
contrib/rt/compile.sh Executable file
View File

@ -0,0 +1,2 @@
gcc -c Empty.c -o Empty.o
ar rcs librt.a Empty.o

View File

@ -5,6 +5,10 @@ import path ;
with-xmlrpc-c = [ option.get "with-xmlrpc-c" ] ;
if $(with-xmlrpc-c) {
echo "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" ;
echo "!!! You are linking the XMLRPC-C library; Do NOT use v.1.25.29 !!!" ;
echo "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" ;
build-moses-server = true ;
xmlrpc-command = $(with-xmlrpc-c)/bin/xmlrpc-c-config ;
if ! [ path.exists $(xmlrpc-command) ] {
@ -35,7 +39,7 @@ if $(build-moses-server) = true
xmlrpc-linkflags = [ shell_or_die "$(xmlrpc-command) c++2 abyss-server --libs" ] ;
xmlrpc-cxxflags = [ shell_or_die "$(xmlrpc-command) c++2 abyss-server --cflags" ] ;
exe mosesserver : mosesserver.cpp ../../moses//moses ../../OnDiskPt//OnDiskPt ../../moses-cmd/IOWrapper.cpp ../..//boost_filesystem : <linkflags>$(xmlrpc-linkflags) <cxxflags>$(xmlrpc-cxxflags) ;
exe mosesserver : mosesserver.cpp ../../moses//moses ../../OnDiskPt//OnDiskPt ../..//boost_filesystem : <linkflags>$(xmlrpc-linkflags) <cxxflags>$(xmlrpc-cxxflags) ;
} else {
alias mosesserver ;
}

View File

@ -9,6 +9,7 @@
#include "moses/Hypothesis.h"
#include "moses/Manager.h"
#include "moses/StaticData.h"
#include "moses/ThreadPool.h"
#include "moses/TranslationModel/PhraseDictionaryDynSuffixArray.h"
#include "moses/TranslationModel/PhraseDictionaryMultiModelCounts.h"
#if PT_UG
@ -16,7 +17,7 @@
#endif
#include "moses/TreeInput.h"
#include "moses/LM/ORLM.h"
#include "moses-cmd/IOWrapper.h"
#include "moses/IOWrapper.h"
#ifdef WITH_THREADS
#include <boost/thread.hpp>
@ -27,7 +28,6 @@
#include <xmlrpc-c/server_abyss.hpp>
using namespace Moses;
using namespace MosesCmd;
using namespace std;
typedef std::map<std::string, xmlrpc_c::value> params_t;
@ -199,24 +199,29 @@ public:
}
};
class Translator : public xmlrpc_c::method
{
/**
* Required so that translations can be sent to a thread pool.
**/
class TranslationTask : public virtual Moses::Task {
public:
Translator() {
// signature and help strings are documentation -- the client
// can query this information with a system.methodSignature and
// system.methodHelp RPC.
this->_signature = "S:S";
this->_help = "Does translation";
}
TranslationTask(xmlrpc_c::paramList const& paramList,
boost::condition_variable& cond, boost::mutex& mut)
: m_paramList(paramList),
m_cond(cond),
m_mut(mut),
m_done(false)
{}
void
execute(xmlrpc_c::paramList const& paramList,
xmlrpc_c::value * const retvalP) {
virtual bool DeleteAfterExecution() {return false;}
const params_t params = paramList.getStruct(0);
paramList.verifyEnd(1);
bool IsDone() const {return m_done;}
const map<string, xmlrpc_c::value>& GetRetData() { return m_retData;}
virtual void Run() {
const params_t params = m_paramList.getStruct(0);
m_paramList.verifyEnd(1);
params_t::const_iterator si = params.find("text");
if (si == params.end()) {
throw xmlrpc_c::fault(
@ -268,43 +273,44 @@ public:
}
stringstream out, graphInfo, transCollOpts;
map<string, xmlrpc_c::value> retData;
if (staticData.IsChart()) {
TreeInput tinput;
const vector<FactorType>&
inputFactorOrder = staticData.GetInputFactorOrder();
inputFactorOrder = staticData.GetInputFactorOrder();
stringstream in(source + "\n");
tinput.Read(in,inputFactorOrder);
ChartManager manager(0,tinput);
ChartManager manager(tinput);
manager.ProcessSentence();
const ChartHypothesis *hypo = manager.GetBestHypothesis();
outputChartHypo(out,hypo);
if (addGraphInfo) {
const size_t translationId = tinput.GetTranslationId();
// const size_t translationId = tinput.GetTranslationId();
std::ostringstream sgstream;
manager.OutputSearchGraphMoses(sgstream);
retData.insert(pair<string, xmlrpc_c::value>("sg", xmlrpc_c::value_string(sgstream.str())));
m_retData.insert(pair<string, xmlrpc_c::value>("sg", xmlrpc_c::value_string(sgstream.str())));
}
} else {
size_t lineNumber = 0; // TODO: Include sentence request number here?
Sentence sentence;
sentence.SetTranslationId(lineNumber);
const vector<FactorType> &
inputFactorOrder = staticData.GetInputFactorOrder();
inputFactorOrder = staticData.GetInputFactorOrder();
stringstream in(source + "\n");
sentence.Read(in,inputFactorOrder);
size_t lineNumber = 0; // TODO: Include sentence request number here?
Manager manager(lineNumber, sentence, staticData.GetSearchAlgorithm());
manager.ProcessSentence();
Manager manager(sentence, staticData.GetSearchAlgorithm());
manager.ProcessSentence();
const Hypothesis* hypo = manager.GetBestHypothesis();
vector<xmlrpc_c::value> alignInfo;
outputHypo(out,hypo,addAlignInfo,alignInfo,reportAllFactors);
if (addAlignInfo) {
retData.insert(pair<string, xmlrpc_c::value>("align", xmlrpc_c::value_array(alignInfo)));
m_retData.insert(pair<string, xmlrpc_c::value>("align", xmlrpc_c::value_array(alignInfo)));
}
if (addWordAlignInfo) {
stringstream wordAlignment;
OutputAlignment(wordAlignment, hypo);
IOWrapper::OutputAlignment(wordAlignment, hypo);
vector<xmlrpc_c::value> alignments;
string alignmentPair;
while (wordAlignment >> alignmentPair) {
@ -314,26 +320,31 @@ public:
wordAlignInfo["target-word"] = xmlrpc_c::value_int(atoi(alignmentPair.substr(pos + 1).c_str()));
alignments.push_back(xmlrpc_c::value_struct(wordAlignInfo));
}
retData.insert(pair<string, xmlrpc_c::value_array>("word-align", alignments));
m_retData.insert(pair<string, xmlrpc_c::value_array>("word-align", alignments));
}
if (addGraphInfo) {
insertGraphInfo(manager,retData);
insertGraphInfo(manager,m_retData);
(const_cast<StaticData&>(staticData)).SetOutputSearchGraph(false);
}
if (addTopts) {
insertTranslationOptions(manager,retData);
insertTranslationOptions(manager,m_retData);
}
if (nbest_size>0) {
outputNBest(manager, retData, nbest_size, nbest_distinct,
outputNBest(manager, m_retData, nbest_size, nbest_distinct,
reportAllFactors, addAlignInfo, addScoreBreakdown);
}
}
pair<string, xmlrpc_c::value>
text("text", xmlrpc_c::value_string(out.str()));
retData.insert(text);
m_retData.insert(text);
XVERBOSE(1,"Output: " << out.str() << endl);
*retvalP = xmlrpc_c::value_struct(retData);
{
boost::lock_guard<boost::mutex> lock(m_mut);
m_done = true;
}
m_cond.notify_one();
}
void outputHypo(ostream& out, const Hypothesis* hypo, bool addAlignmentInfo, vector<xmlrpc_c::value>& alignInfo, bool reportAllFactors = false) {
@ -461,7 +472,7 @@ public:
if ((int)edges.size() > 0) {
stringstream wordAlignment;
OutputAlignment(wordAlignment, edges[0]);
IOWrapper::OutputAlignment(wordAlignment, edges[0]);
vector<xmlrpc_c::value> alignments;
string alignmentPair;
while (wordAlignment >> alignmentPair) {
@ -479,7 +490,7 @@ public:
{
// should the score breakdown be reported in a more structured manner?
ostringstream buf;
MosesCmd::OutputAllFeatureScores(path.GetScoreBreakdown(),buf);
IOWrapper::OutputAllFeatureScores(path.GetScoreBreakdown(),buf);
nBestXMLItem["fvals"] = xmlrpc_c::value_string(buf.str());
}
@ -520,7 +531,43 @@ public:
}
}
retData.insert(pair<string, xmlrpc_c::value>("topt", xmlrpc_c::value_array(toptsXml)));
}
private:
xmlrpc_c::paramList const& m_paramList;
map<string, xmlrpc_c::value> m_retData;
boost::condition_variable& m_cond;
boost::mutex& m_mut;
bool m_done;
};
class Translator : public xmlrpc_c::method
{
public:
Translator(size_t numThreads = 10) : m_threadPool(numThreads) {
// signature and help strings are documentation -- the client
// can query this information with a system.methodSignature and
// system.methodHelp RPC.
this->_signature = "S:S";
this->_help = "Does translation";
}
void
execute(xmlrpc_c::paramList const& paramList,
xmlrpc_c::value * const retvalP) {
boost::condition_variable cond;
boost::mutex mut;
TranslationTask task(paramList,cond,mut);
m_threadPool.Submit(&task);
boost::unique_lock<boost::mutex> lock(mut);
while (!task.IsDone()) {
cond.wait(lock);
}
*retvalP = xmlrpc_c::value_struct(task.GetRetData());
}
private:
Moses::ThreadPool m_threadPool;
};
static
@ -580,6 +627,7 @@ int main(int argc, char** argv)
int port = 8080;
const char* logfile = "/dev/null";
bool isSerial = false;
size_t numThreads = 10; //for translation tasks
for (int i = 0; i < argc; ++i) {
if (!strcmp(argv[i],"--server-port")) {
@ -598,6 +646,14 @@ int main(int argc, char** argv)
} else {
logfile = argv[i];
}
} else if (!strcmp(argv[i], "--threads")) {
++i;
if (i>=argc) {
cerr << "Error: Missing argument to --threads" << endl;
exit(1);
} else {
numThreads = atoi(argv[i]);
}
} else if (!strcmp(argv[i], "--serial")) {
cerr << "Running single-threaded server" << endl;
isSerial = true;
@ -627,7 +683,7 @@ int main(int argc, char** argv)
xmlrpc_c::registry myRegistry;
xmlrpc_c::methodPtr const translator(new Translator);
xmlrpc_c::methodPtr const translator(new Translator(numThreads));
xmlrpc_c::methodPtr const updater(new Updater);
xmlrpc_c::methodPtr const optimizer(new Optimizer);

View File

@ -7,4 +7,4 @@ all: filter-pt
filter-pt: filter-pt.cpp
./check-install $(SALMDIR)
$(CXX) -O6 $(INC) $(OBJS) -o filter-pt filter-pt.cpp
$(CXX) -O6 $(INC) $(OBJS) -o filter-pt filter-pt.cpp -lboost_thread -lboost_system

View File

@ -4,6 +4,8 @@
#include <cstdio>
#include <cstdlib>
#include <algorithm>
#include <fstream>
#include <sstream>
#include "_SuffixArraySearchApplicationBase.h"
@ -11,18 +13,16 @@
#include <iostream>
#include <set>
#include <boost/thread/tss.hpp>
#include <boost/thread.hpp>
#include <boost/unordered_map.hpp>
#ifdef WIN32
#include "WIN32_functions.h"
#else
#include <unistd.h>
#endif
typedef std::vector<TextLenType> SentIdSet;
typedef std::pair<SentIdSet, clock_t> ClockedSentIdSet;
typedef std::map<std::string, ClockedSentIdSet> PhraseSetMap;
#undef min
// constants
const size_t MINIMUM_SIZE_TO_KEEP = 10000; // increase this to improve memory usage,
// reduce for speed
@ -39,12 +39,9 @@ double sig_filter_limit = 0; // keep phrase pairs with -log(sig) > si
// higher = filter-more
bool pef_filter_only = false; // only filter based on pef
bool hierarchical = false;
int max_cache = 0;
// globals
PhraseSetMap esets;
PhraseSetMap fsets;
double p_111 = 0.0; // alpha
size_t pt_lines = 0;
size_t nremoved_sigfilter = 0;
size_t nremoved_pfefilter = 0;
@ -52,6 +49,69 @@ C_SuffixArraySearchApplicationBase e_sa;
C_SuffixArraySearchApplicationBase f_sa;
int num_lines;
boost::mutex in_mutex;
boost::mutex out_mutex;
boost::mutex err_mutex;
typedef boost::shared_ptr<std::vector<TextLenType> > SentIdSet;
class Cache {
typedef std::pair<SentIdSet, clock_t> ClockedSet;
typedef boost::unordered_map<std::string, ClockedSet> ClockedMap;
public:
SentIdSet get(const std::string& phrase) {
boost::shared_lock<boost::shared_mutex> lock(m_mutex);
if(m_cont.count(phrase)) {
ClockedSet& set = m_cont[phrase];
set.second = clock();
return set.first;
}
return SentIdSet( new SentIdSet::element_type() );
}
void put(const std::string& phrase, const SentIdSet set) {
boost::unique_lock<boost::shared_mutex> lock(m_mutex);
m_cont[phrase] = std::make_pair(set, clock());
}
static void set_max_cache(size_t max_cache) {
s_max_cache = max_cache;
}
void prune() {
if(s_max_cache > 0) {
boost::upgrade_lock<boost::shared_mutex> lock(m_mutex);
if(m_cont.size() > s_max_cache) {
std::vector<clock_t> clocks;
for(ClockedMap::iterator it = m_cont.begin(); it != m_cont.end(); it++)
clocks.push_back(it->second.second);
std::sort(clocks.begin(), clocks.end());
clock_t out = clocks[m_cont.size() - s_max_cache];
boost::upgrade_to_unique_lock<boost::shared_mutex> uniq_lock(lock);
for(ClockedMap::iterator it = m_cont.begin(); it != m_cont.end(); it++)
if(it->second.second < out)
m_cont.erase(it);
}
}
}
private:
ClockedMap m_cont;
boost::shared_mutex m_mutex;
static size_t s_max_cache;
};
size_t Cache::s_max_cache = 0;
Cache f_cache;
Cache e_cache;
#undef min
void usage()
{
std::cerr << "\nFilter phrase table using significance testing as described\n"
@ -59,12 +119,13 @@ void usage()
<< "by Discarding Most of the Phrasetable. EMNLP 2007.\n"
<< "\nUsage:\n"
<< "\n filter-pt -e english.suf-arr -f french.suf-arr\n"
<< " [-c] [-p] [-l threshold] [-n num] < PHRASE-TABLE > FILTERED-PHRASE-TABLE\n\n"
<< " [-c] [-p] [-l threshold] [-n num] [-t num] < PHRASE-TABLE > FILTERED-PHRASE-TABLE\n\n"
<< " [-l threshold] >0.0, a+e, or a-e: keep values that have a -log significance > this\n"
<< " [-n num ] 0, 1...: 0=no filtering, >0 sort by P(e|f) and keep the top num elements\n"
<< " [-c ] add the cooccurence counts to the phrase table\n"
<< " [-p ] add -log(significance) to the phrasetable\n"
<< " [-h ] filter hierarchical rule table\n"
<< " [-t num ] use num threads\n"
<< " [-m num ] limit cache to num most recent phrases\n";
exit(1);
}
@ -133,9 +194,6 @@ PTEntry::PTEntry(const std::string& str, int index) :
*fp++=0;
this->pfe = atof(f);
// std::cerr << "L: " << f_phrase << " ::: " << e_phrase << " ::: " << scores << " ::: " << pfe << std::endl;
// std::cerr << "X: " << extra << "\n";
}
struct PfeComparer {
@ -168,7 +226,8 @@ std::ostream& operator << (std::ostream& os, const PTEntry& pp)
void print(int a, int b, int c, int d, float p)
{
std::cerr << a << "\t" << b << "\t P=" << p << "\n"
<< c << "\t" << d << "\t xf=" << (double)(b)*(double)(c)/(double)(a+1)/(double)(d+1) << "\n\n";
<< c << "\t" << d << "\t xf="
<< (double)(b)*(double)(c)/(double)(a+1)/(double)(d+1) << "\n\n";
}
// 2x2 (one-sided) Fisher's exact test
@ -184,13 +243,13 @@ double fisher_exact(int cfe, int ce, int cf)
int d = (num_lines - ce - cf + cfe);
int n = a + b + c + d;
double cp = exp(lgamma(1+a+c) + lgamma(1+b+d) + lgamma(1+a+b) + lgamma(1+c+d) - lgamma(1+n) - lgamma(1+a) - lgamma(1+b) - lgamma(1+c) - lgamma(1+d));
double cp = exp(lgamma(1+a+c) + lgamma(1+b+d) + lgamma(1+a+b) + lgamma(1+c+d)
- lgamma(1+n) - lgamma(1+a) - lgamma(1+b) - lgamma(1+c)
- lgamma(1+d));
double total_p = 0.0;
int tc = std::min(b,c);
for (int i=0; i<=tc; i++) {
total_p += cp;
// double lg = lgamma(1+a+c) + lgamma(1+b+d) + lgamma(1+a+b) + lgamma(1+c+d) - lgamma(1+n) - lgamma(1+a) - lgamma(1+b) - lgamma(1+c) - lgamma(1+d); double cp = exp(lg);
// print(a,b,c,d,cp);
double coef = (double)(b)*(double)(c)/(double)(a+1)/(double)(d+1);
cp *= coef;
++a;
@ -202,86 +261,73 @@ double fisher_exact(int cfe, int ce, int cf)
}
template <class setType>
setType ordered_set_intersect(setType & set_1, setType & set_2)
void ordered_set_intersect(setType& out, const setType set_1, const setType set_2)
{
setType set_out;
std::set_intersection(set_1.begin(), set_1.end(), set_2.begin(), set_2.end(), inserter(set_out,set_out.begin()) );
return set_out;
std::set_intersection(set_1->begin(), set_1->end(), set_2->begin(),
set_2->end(), inserter(*out, out->begin()) );
}
SentIdSet lookup_phrase(const std::string & phrase, C_SuffixArraySearchApplicationBase & my_sa)
void lookup_phrase(SentIdSet& ids, const std::string& phrase,
C_SuffixArraySearchApplicationBase & my_sa, Cache& cache)
{
SentIdSet occur_set;
vector<S_SimplePhraseLocationElement> locations;
locations = my_sa.locateExactPhraseInCorpus(phrase.c_str());
if(locations.size()==0) {
cerr<<"No occurrences found!!\n";
ids = cache.get(phrase);
if(ids->empty()) {
vector<S_SimplePhraseLocationElement> locations;
locations = my_sa.locateExactPhraseInCorpus(phrase.c_str());
if(locations.size()==0) {
cerr<<"No occurrences found!!\n";
}
for (vector<S_SimplePhraseLocationElement>::iterator i=locations.begin();
i != locations.end(); ++i) {
ids->push_back(i->sentIdInCorpus);
}
std::sort(ids->begin(), ids->end());
SentIdSet::element_type::iterator it =
std::unique(ids->begin(), ids->end());
ids->resize(it - ids->begin());
if(ids->size() >= MINIMUM_SIZE_TO_KEEP)
cache.put(phrase, ids);
}
for (vector<S_SimplePhraseLocationElement>::iterator i=locations.begin(); i != locations.end(); ++i) {
occur_set.push_back(i->sentIdInCorpus);
}
std::sort(occur_set.begin(), occur_set.end());
SentIdSet::iterator it = std::unique(occur_set.begin(), occur_set.end());
occur_set.resize(it - occur_set.begin());
return occur_set;
}
// slight simplicifaction: we consider all sentences in which "a" and "b" occur to be instances of the rule "a [X][X] b".
SentIdSet lookup_multiple_phrases(vector<std::string> & phrases, C_SuffixArraySearchApplicationBase & my_sa, const std::string & rule, PhraseSetMap & cache)
{
void lookup_multiple_phrases(SentIdSet& ids, vector<std::string> & phrases,
C_SuffixArraySearchApplicationBase & my_sa,
const std::string & rule, Cache& cache)
{
if (phrases.size() == 1) {
return lookup_phrase(phrases.front(), my_sa);
lookup_phrase(ids, phrases.front(), my_sa, cache);
}
else {
SentIdSet main_set;
ClockedSentIdSet & clocked_first_set = cache[phrases.front()];
SentIdSet & first_set = clocked_first_set.first;
clocked_first_set.second = clock();
SentIdSet main_set( new SentIdSet::element_type() );
bool first = true;
if (first_set.empty()) {
first_set = lookup_phrase(phrases.front(), my_sa);
}
for (vector<std::string>::iterator phrase=phrases.begin()+1; phrase != phrases.end(); ++phrase) {
ClockedSentIdSet & clocked_temp_set = cache[*phrase];
SentIdSet & temp_set = clocked_temp_set.first;
clocked_temp_set.second = clock();
if (temp_set.empty()) {
temp_set = lookup_phrase(*phrase, my_sa);
}
SentIdSet first_set( new SentIdSet::element_type() );
lookup_phrase(first_set, phrases.front(), my_sa, cache);
for (vector<std::string>::iterator phrase=phrases.begin()+1;
phrase != phrases.end(); ++phrase) {
SentIdSet temp_set( new SentIdSet::element_type() );
lookup_phrase(temp_set, *phrase, my_sa, cache);
if (first) {
main_set = ordered_set_intersect(first_set,temp_set);
ordered_set_intersect(main_set, first_set, temp_set);
first = false;
}
else {
main_set = ordered_set_intersect(main_set,temp_set);
}
if (temp_set.size() < MINIMUM_SIZE_TO_KEEP) {
cache.erase(*phrase);
SentIdSet new_set( new SentIdSet::element_type() );
ordered_set_intersect(new_set, main_set, temp_set);
main_set->swap(*new_set);
}
}
if (first_set.size() < MINIMUM_SIZE_TO_KEEP) {
cache.erase(phrases.front());
}
return main_set;
ids->swap(*main_set);
}
}
SentIdSet find_occurrences(const std::string& rule, C_SuffixArraySearchApplicationBase & my_sa, PhraseSetMap & cache)
void find_occurrences(SentIdSet& ids, const std::string& rule,
C_SuffixArraySearchApplicationBase& my_sa, Cache& cache)
{
SentIdSet sa_set;
// we search for hierarchical rules by stripping away NT and looking for terminals sequences
// if a rule contains multiple sequences of terminals, we intersect their occurrences.
if (hierarchical) {
@ -305,76 +351,142 @@ SentIdSet find_occurrences(const std::string& rule, C_SuffixArraySearchApplicati
phrases.push_back(rule.substr(pos,NTStartPos-pos));
}
sa_set = lookup_multiple_phrases(phrases, my_sa, rule, cache);
lookup_multiple_phrases(ids, phrases, my_sa, rule, cache);
}
else {
sa_set = lookup_phrase(rule, my_sa);
lookup_phrase(ids, rule, my_sa, cache);
}
return sa_set;
}
// input: unordered list of translation options for a single source phrase
void compute_cooc_stats_and_filter(std::vector<PTEntry*>& options)
void compute_cooc_stats_and_filter(std::vector<PTEntry*>& options,
Cache& f_cache, Cache& e_cache)
{
if (pfe_filter_limit>0 && options.size() > pfe_filter_limit) {
if (pfe_filter_limit > 0 && options.size() > pfe_filter_limit) {
nremoved_pfefilter += (options.size() - pfe_filter_limit);
std::nth_element(options.begin(), options.begin()+pfe_filter_limit, options.end(), PfeComparer());
for (std::vector<PTEntry*>::iterator i=options.begin()+pfe_filter_limit; i != options.end(); ++i)
std::nth_element(options.begin(), options.begin() + pfe_filter_limit,
options.end(), PfeComparer());
for (std::vector<PTEntry*>::iterator i = options.begin() + pfe_filter_limit;
i != options.end(); ++i)
delete *i;
options.erase(options.begin()+pfe_filter_limit,options.end());
options.erase(options.begin() + pfe_filter_limit,options.end());
}
if (pef_filter_only) return;
// std::cerr << "f phrase: " << options.front()->f_phrase << "\n";
SentIdSet fset;
fset = find_occurrences(options.front()->f_phrase, f_sa, fsets);
size_t cf = fset.size();
for (std::vector<PTEntry*>::iterator i=options.begin(); i != options.end(); ++i) {
if (pef_filter_only)
return;
if (options.empty())
return;
SentIdSet fset( new SentIdSet::element_type() );
find_occurrences(fset, options.front()->f_phrase, f_sa, f_cache);
size_t cf = fset->size();
for (std::vector<PTEntry*>::iterator i = options.begin();
i != options.end(); ++i) {
const std::string& e_phrase = (*i)->e_phrase;
size_t cef=0;
ClockedSentIdSet& clocked_eset = esets[e_phrase];
SentIdSet & eset = clocked_eset.first;
clocked_eset.second = clock();
if (eset.empty()) {
eset = find_occurrences(e_phrase, e_sa, esets);
//std::cerr << "Looking up e-phrase: " << e_phrase << "\n";
}
size_t ce=eset.size();
if (ce < cf) {
for (SentIdSet::iterator i=eset.begin(); i != eset.end(); ++i) {
if (std::binary_search(fset.begin(), fset.end(), *i)) cef++;
}
} else {
for (SentIdSet::iterator i=fset.begin(); i != fset.end(); ++i) {
if (std::binary_search(eset.begin(), eset.end(), *i)) cef++;
}
}
SentIdSet eset( new SentIdSet::element_type() );
find_occurrences(eset, e_phrase, e_sa, e_cache);
size_t ce = eset->size();
SentIdSet efset( new SentIdSet::element_type() );
ordered_set_intersect(efset, fset, eset);
size_t cef = efset->size();
double nlp = -log(fisher_exact(cef, cf, ce));
(*i)->set_cooc_stats(cef, cf, ce, nlp);
if (ce < MINIMUM_SIZE_TO_KEEP) {
esets.erase(e_phrase);
}
}
std::vector<PTEntry*>::iterator new_end =
std::remove_if(options.begin(), options.end(), NlogSigThresholder(sig_filter_limit));
std::remove_if(options.begin(), options.end(),
NlogSigThresholder(sig_filter_limit));
nremoved_sigfilter += (options.end() - new_end);
options.erase(new_end,options.end());
}
void prune_cache(PhraseSetMap & psm) {
if(max_cache && psm.size() > max_cache) {
std::vector<clock_t> clocks;
for(PhraseSetMap::iterator it = psm.begin(); it != psm.end(); it++)
clocks.push_back(it->second.second);
void filter(std::istream* in, std::ostream* out, int pfe_index) {
std::vector<std::string> lines;
std::string prev = "";
std::vector<PTEntry*> options;
while(true) {
{
boost::mutex::scoped_lock lock(in_mutex);
if(in->eof())
break;
lines.clear();
std::string line;
while(getline(*in, line) && lines.size() < 500000)
lines.push_back(line);
}
std::sort(clocks.begin(), clocks.end());
clock_t out = clocks[psm.size()-max_cache];
for(PhraseSetMap::iterator it = psm.begin(); it != psm.end(); it++)
if(it->second.second < out)
psm.erase(it);
std::stringstream out_temp;
for(std::vector<std::string>::iterator it = lines.begin(); it != lines.end(); it++) {
size_t tmp_lines = ++pt_lines;
if(tmp_lines % 10000 == 0) {
boost::mutex::scoped_lock lock(err_mutex);
std::cerr << ".";
if(tmp_lines % 500000 == 0)
std::cerr << "[n:" << tmp_lines << "]\n";
if(tmp_lines % 10000000 == 0) {
float pfefper = (100.0*(float)nremoved_pfefilter)/(float)pt_lines;
float sigfper = (100.0*(float)nremoved_sigfilter)/(float)pt_lines;
std::cerr << "------------------------------------------------------\n"
<< " unfiltered phrases pairs: " << pt_lines << "\n"
<< "\n"
<< " P(f|e) filter [first]: " << nremoved_pfefilter << " (" << pfefper << "%)\n"
<< " significance filter: " << nremoved_sigfilter << " (" << sigfper << "%)\n"
<< " TOTAL FILTERED: " << (nremoved_pfefilter + nremoved_sigfilter) << " (" << (sigfper + pfefper) << "%)\n"
<< "\n"
<< " FILTERED phrase pairs: " << (pt_lines - nremoved_pfefilter - nremoved_sigfilter) << " (" << (100.0-sigfper - pfefper) << "%)\n"
<< "------------------------------------------------------\n";
}
}
if(pt_lines % 10000 == 0) {
f_cache.prune();
e_cache.prune();
}
if(it->length() > 0) {
PTEntry* pp = new PTEntry(it->c_str(), pfe_index);
if (prev != pp->f_phrase) {
prev = pp->f_phrase;
if (!options.empty()) { // always true after first line
compute_cooc_stats_and_filter(options, f_cache, e_cache);
}
for (std::vector<PTEntry*>::iterator i = options.begin();
i != options.end(); ++i) {
out_temp << **i << '\n';
delete *i;
}
options.clear();
options.push_back(pp);
} else {
options.push_back(pp);
}
}
}
boost::mutex::scoped_lock lock(out_mutex);
*out << out_temp.str() << std::flush;
}
compute_cooc_stats_and_filter(options, f_cache, e_cache);
boost::mutex::scoped_lock lock(out_mutex);
for (std::vector<PTEntry*>::iterator i = options.begin();
i != options.end(); ++i) {
*out << **i << '\n';
delete *i;
}
*out << std::flush;
}
int main(int argc, char * argv[])
@ -383,7 +495,9 @@ int main(int argc, char * argv[])
const char* efile=0;
const char* ffile=0;
int pfe_index = 2;
while ((c = getopt(argc, argv, "cpf:e:i:n:l:m:h")) != -1) {
int threads = 1;
size_t max_cache = 0;
while ((c = getopt(argc, argv, "cpf:e:i:n:t:l:m:h")) != -1) {
switch (c) {
case 'e':
efile = optarg;
@ -398,6 +512,14 @@ int main(int argc, char * argv[])
pfe_filter_limit = atoi(optarg);
std::cerr << "P(f|e) filter limit: " << pfe_filter_limit << std::endl;
break;
case 't':
threads = atoi(optarg);
std::cerr << "Using threads: " << threads << std::endl;
break;
case 'm':
max_cache = atoi(optarg);
std::cerr << "Using max phrases in caches: " << max_cache << std::endl;
break;
case 'c':
print_cooc_counts = true;
break;
@ -407,9 +529,6 @@ int main(int argc, char * argv[])
case 'h':
hierarchical = true;
break;
case 'm':
max_cache = atoi(optarg);
break;
case 'l':
std::cerr << "-l = " << optarg << "\n";
if (strcmp(optarg,"a+e") == 0) {
@ -429,12 +548,13 @@ int main(int argc, char * argv[])
usage();
}
}
if (sig_filter_limit == 0.0) pef_filter_only = true;
//-----------------------------------------------------------------------------
if (optind != argc || ((!efile || !ffile) && !pef_filter_only)) {
usage();
}
//load the indexed corpus with vocabulary(noVoc=false) and with offset(noOffset=false)
if (!pef_filter_only) {
e_sa.loadData_forSearch(efile, false, false);
@ -460,52 +580,17 @@ int main(int argc, char * argv[])
std::cerr << "Filtering using P(e|f) only. n=" << pfe_filter_limit << std::endl;
}
char tmpString[10000];
std::string prev = "";
std::vector<PTEntry*> options;
size_t pt_lines = 0;
while(!cin.eof()) {
cin.getline(tmpString,10000,'\n');
if(++pt_lines%10000==0) {
std::cerr << ".";
prune_cache(esets);
prune_cache(fsets);
if(pt_lines%500000==0)
std::cerr << "[n:"<<pt_lines<<"]\n";
}
Cache::set_max_cache(max_cache);
std::ios_base::sync_with_stdio(false);
boost::thread_group threadGroup;
for(int i = 0; i < threads; i++)
threadGroup.add_thread(new boost::thread(filter, &std::cin, &std::cout, pfe_index));
threadGroup.join_all();
if(strlen(tmpString)>0) {
PTEntry* pp = new PTEntry(tmpString, pfe_index);
if (prev != pp->f_phrase) {
prev = pp->f_phrase;
if (!options.empty()) { // always true after first line
compute_cooc_stats_and_filter(options);
}
for (std::vector<PTEntry*>::iterator i=options.begin(); i != options.end(); ++i) {
std::cout << **i << std::endl;
delete *i;
}
options.clear();
options.push_back(pp);
} else {
options.push_back(pp);
}
// for(int i=0;i<locations.size(); i++){
// cout<<"SentId="<<locations[i].sentIdInCorpus<<" Pos="<<(int)locations[i].posInSentInCorpus<<endl;
// }
}
}
compute_cooc_stats_and_filter(options);
for (std::vector<PTEntry*>::iterator i=options.begin(); i != options.end(); ++i) {
std::cout << **i << std::endl;
delete *i;
}
float pfefper = (100.0*(float)nremoved_pfefilter)/(float)pt_lines;
float sigfper = (100.0*(float)nremoved_sigfilter)/(float)pt_lines;
std::cerr << "\n\n------------------------------------------------------\n"
<< " unfiltered phrases pairs: " << pt_lines << "\n"
<< "\n"
@ -514,7 +599,5 @@ int main(int argc, char * argv[])
<< " TOTAL FILTERED: " << (nremoved_pfefilter + nremoved_sigfilter) << " (" << (sigfper + pfefper) << "%)\n"
<< "\n"
<< " FILTERED phrase pairs: " << (pt_lines - nremoved_pfefilter - nremoved_sigfilter) << " (" << (100.0-sigfper - pfefper) << "%)\n"
<< "------------------------------------------------------\n";
return 0;
<< "------------------------------------------------------\n";
}

View File

@ -0,0 +1,8 @@
ad af 500 1000
bd bf 5 10
der le 20285 102586
der NULL 12926 704917
gipfel sommet 3485 7322
pass col 419 2911
pass passeport 7 28
sitzung séance 14 59

View File

@ -0,0 +1,8 @@
af ad 500 1000
bf bd 5 10
col pass 419 615
le der 20285 113635
passeport pass 7 615
retrouvé NULL 34 1016136
séance sitzung 14 33
sommet gipfel 3485 5700

View File

@ -0,0 +1,8 @@
ad af 0.5
bd bf 0.5
der le 0.1977365
der NULL 0.0183369
gipfel sommet 0.4759629
pass col 0.1439368
pass passeport 0.2500000
sitzung séance 0.2372881

View File

@ -0,0 +1,8 @@
af ad 0.5
bf bd 0.5
col pass 0.6813008
le der 0.1785101
passeport pass 0.0113821
retrouvé NULL 0.0000335
séance sitzung 0.4242424
sommet gipfel 0.6114035

View File

@ -0,0 +1,8 @@
ad [X][X] [X] ||| af [X][X] [X] ||| 0.5 0.5 0.5 0.5 2.718 ||| 0-0 1-1 ||| 1000 1000
bd [X] ||| bf [X] ||| 0.5 0.5 0.5 0.5 2.718 ||| 0-0 ||| 10 10
der gipfel [X] ||| sommet [X] ||| 0.00327135 0.00872768 0.0366795 0.611403 2.718 ||| 1-0 ||| 5808 518
der [X][X] pass [X] ||| le [X][X] col [X] ||| 0.0173565 0.0284616 0.288889 0.121619 2.718 ||| 0-0 1-1 2-2 ||| 749 45
pass [X] ||| col [X] ||| 0.1952 0.143937 0.628866 0.681301 2.718 ||| 0-0 ||| 1875 582
pass [X] ||| passeport retrouvé [X] ||| 0.5 0.25 0.00171821 3.813e-07 2.718 ||| 0-0 ||| 2 582
pass [X] ||| passeport [X] ||| 0.266667 0.25 0.00687285 0.0113821 2.718 ||| 0-0 ||| 15 582
[X][X] sitzung [X] ||| [X][X] séance [X] ||| 0.272727 0.237288 0.352941 0.424242 2.718 ||| 0-0 1-1 ||| 22 17

View File

@ -0,0 +1,8 @@
ad af 100 1000
bd bf 1 10
der le 150181 944391
der NULL 54483 3595140
gipfel sommet 3421 9342
pass col 2 70
pass passeport 73 379
sitzung séance 3441 5753

View File

@ -0,0 +1,8 @@
af ad 100 1000
bf bd 1 10
col pass 2 108
le der 150181 1356104
passeport pass 73 108
retrouvé NULL 43 6276240
séance sitzung 3441 6142
sommet gipfel 3421 4908

View File

@ -0,0 +1,8 @@
ad af 0.1
bd bf 0.1
der le 0.1590242
der NULL 0.0151546
gipfel sommet 0.366195
pass col 0.0285714
pass passeport 0.1926121
sitzung séance 0.5981227

View File

@ -0,0 +1,8 @@
af ad 0.1
bf bd 0.1
col pass 0.0185185
le der 0.1107445
passeport pass 0.6759259
retrouvé NULL 0.0000069
séance sitzung 0.5602410
sommet gipfel 0.6970253

View File

@ -0,0 +1,5 @@
ad [X][X] [X] ||| af [X][X] [X] ||| 0.1 0.1 0.1 0.1 2.718 ||| 0-0 1-1 ||| 1000 1000
bd [X] ||| bf [X] ||| 0.1 0.1 0.1 0.1 2.718 ||| 0-0 ||| 10 10
der [X][X] pass [X] ||| le [X][X] passeport [X] ||| 0.16 0.03063 0.4 0.0748551 2.718 ||| 0-0 1-1 2-2 ||| 25 10
pass [X] ||| passeport [X] ||| 0.28022 0.192612 0.607143 0.675926 2.718 ||| 0-0 ||| 182 84
[X][X] sitzung [X] ||| [X][X] séance [X] ||| 0.784521 0.598123 0.516654 0.560241 2.718 ||| 0-0 1-1 ||| 4251 6455

View File

@ -0,0 +1,9 @@
ad [X][X] [X] ||| af [X][X] [X] ||| 0.14 0.136364 0.18 0.3 ||| 0-0 1-1 ||| 10000.0 5000.0
bd [X] ||| bf [X] ||| 0.14 0.136364 0.18 0.3 ||| 0-0 ||| 100.0 50.0
der [X][X] pass [X] ||| le [X][X] passeport [X] ||| 0.16 0.0307772 0.4 0.0128336 ||| 0-0 1-1 2-2 ||| 225.0 40.0
der gipfel [X] ||| sommet [X] ||| 0.00327135 0.00569336 0.0366795 0.651018 ||| 1-0 ||| 5808.0 518.0
der [X][X] pass [X] ||| le [X][X] col [X] ||| 0.0173565 0.0193836 0.288889 0.0675369 ||| 0-0 1-1 2-2 ||| 749.0 45.0
pass [X] ||| col [X] ||| 0.1952 0.121573 0.398693 0.582296 ||| 0-0 ||| 1875.0 918.0
pass [X] ||| passeport [X] ||| 0.280097 0.193033 0.22658 0.11065 ||| 0-0 ||| 1653.0 918.0
pass [X] ||| passeport retrouvé [X] ||| 0.5 0.193033 0.00108932 1.16835e-06 ||| 0-0 ||| 2.0 918.0
[X][X] sitzung [X] ||| [X][X] séance [X] ||| 0.784227 0.597753 0.516546 0.559514 ||| 0-0 1-1 ||| 38281.0 25837.0

View File

@ -1176,6 +1176,9 @@ def compute_lexicalweight(weights,alignment,word_pairs,marginal,mode='counts',ca
mycache[1] = defaultdict(dict)
for x,translations in alignment:
# skip nonterminals
if x.startswith(b'['):
continue
if cache and translations in mycache[1][x]:
lex_step = mycache[1][x][translations]
@ -1870,7 +1873,12 @@ def test():
sys.stderr.write('Regression test 10\n')
Combiner = Combine_TMs([[os.path.join('test','model3'),'primary'],[os.path.join('test','model4'),'primary']],output_file=os.path.join('test','phrase-table_test10'),mode='counts',number_of_features=8,i_e2f=4,i_e2f_lex=5,i_f2e=6,i_f2e_lex=7,reference_file='test/extract')
Combiner.combine_given_tuning_set()
# count-based combination of two hierarchical models, with fixed weights. Same as test 3, but with hierarchical models
# command line: python tmcombine.py combine_given_weights test/model5 test/model6 -w "0.1,0.9;0.1,1;0.2,0.8;0.5,0.5" -o test/phrase-table_test11 -m counts
sys.stderr.write('Regression test 11\n')
Combiner = Combine_TMs([[os.path.join('test','model5'),'primary'],[os.path.join('test','model6'),'primary']],[[0.1,0.9],[0.1,1],[0.2,0.8],[0.5,0.5]],os.path.join('test','phrase-table_test11'),mode='counts')
Combiner.combine_given_weights()
#convert weight vector passed as a command line argument
class to_list(argparse.Action):

View File

@ -58,7 +58,7 @@ if $(FORCE-STATIC) {
rule test_library ( name ) {
if $(FORCE-STATIC) {
return [ test_flags "-l$(name) -static" ] ;
return [ test_flags "-Wl,-Bstatic -l$(name) -Wl,-Bdynamic" ] ;
} else {
return [ test_flags "-l$(name)" ] ;
}
@ -88,7 +88,7 @@ rule auto-shared ( name : additional * ) {
if $(shared-command-line) = "<link>shared" {
return "<link>shared" ;
} else {
if [ test_flags $(additional)" -static -l"$(name) ] {
if [ test_flags $(additional)" -Wl,-Bstatic -l"$(name)" -Wl,-Bdynamic" ] {
return ;
} else {
if $(FORCE-STATIC) {
@ -131,10 +131,7 @@ if $(with-macports) {
#Convenience rule for boost libraries. Defines library boost_$(name).
rule boost-lib ( name macro : deps * ) {
#Link multi-threaded programs against the -mt version if available. Old
#versions of boost do not have -mt tagged versions of all libraries. Sadly,
#boost.jam does not handle this correctly.
flags = $(L-boost-search)" -lboost_"$(name)"-mt$(boost-lib-version)" ;
flags = $(L-boost-search)" -lboost_"$(name)"$(boost-lib-version)" ;
local main ;
if $(name) = "unit_test_framework" {
main = "BOOST_AUTO_TEST_CASE(foo) {}" ;
@ -143,11 +140,11 @@ rule boost-lib ( name macro : deps * ) {
if $(boost-auto-shared) = "<link>shared" {
flags += " -DBOOST_$(macro)" ;
} else {
flags += " -static" ;
flags = " -Wl,-Bstatic $(flags) -Wl,-Bdynamic " ;
}
if [ test_flags $(flags) : $(main) ] {
lib inner_boost_$(name) : : <threading>single $(boost-search) <name>boost_$(name)$(boost-lib-version) : <link>static : <library>$(deps) ;
lib inner_boost_$(name) : : <threading>multi $(boost-search) <name>boost_$(name)-mt$(boost-lib-version) : <link>static : <library>$(deps) ;
lib inner_boost_$(name) : : <threading>multi $(boost-search) <name>boost_$(name)$(boost-lib-version) : <link>static : <library>$(deps) ;
} else {
lib inner_boost_$(name) : : $(boost-search) <name>boost_$(name)$(boost-lib-version) : : <library>$(deps) ;
}

View File

@ -14,7 +14,7 @@ update-if-changed $(ORDER-LOG) $(max-order) ;
max-order += <dependency>$(ORDER-LOG) ;
wrappers = ;
local with-nplm = [ option.get "with-nplm" ] ;
local with-nplm = [ option.get "with-nplm-0.1" ] ;
if $(with-nplm) {
lib neuralLM : : <search>$(with-nplm)/src ;
obj nplm.o : wrappers/nplm.cc : <include>.. <include>$(with-nplm)/src <cxxflags>-fopenmp ;

View File

@ -29,28 +29,44 @@ class StatCollector {
~StatCollector() {}
void CalculateDiscounts() {
void CalculateDiscounts(const DiscountConfig &config) {
counts_.resize(orders_.size());
counts_pruned_.resize(orders_.size());
discounts_.resize(orders_.size());
for (std::size_t i = 0; i < orders_.size(); ++i) {
const OrderStat &s = orders_[i];
counts_[i] = s.count;
counts_pruned_[i] = s.count_pruned;
}
for (unsigned j = 1; j < 4; ++j) {
// TODO: Specialize error message for j == 3, meaning 3+
UTIL_THROW_IF(s.n[j] == 0, BadDiscountException, "Could not calculate Kneser-Ney discounts for "
<< (i+1) << "-grams with adjusted count " << (j+1) << " because we didn't observe any "
<< (i+1) << "-grams with adjusted count " << j << "; Is this small or artificial data?");
}
discounts_ = config.overwrite;
discounts_.resize(orders_.size());
for (std::size_t i = config.overwrite.size(); i < orders_.size(); ++i) {
const OrderStat &s = orders_[i];
try {
for (unsigned j = 1; j < 4; ++j) {
// TODO: Specialize error message for j == 3, meaning 3+
UTIL_THROW_IF(s.n[j] == 0, BadDiscountException, "Could not calculate Kneser-Ney discounts for "
<< (i+1) << "-grams with adjusted count " << (j+1) << " because we didn't observe any "
<< (i+1) << "-grams with adjusted count " << j << "; Is this small or artificial data?");
}
// See equation (26) in Chen and Goodman.
discounts_[i].amount[0] = 0.0;
float y = static_cast<float>(s.n[1]) / static_cast<float>(s.n[1] + 2.0 * s.n[2]);
for (unsigned j = 1; j < 4; ++j) {
discounts_[i].amount[j] = static_cast<float>(j) - static_cast<float>(j + 1) * y * static_cast<float>(s.n[j+1]) / static_cast<float>(s.n[j]);
UTIL_THROW_IF(discounts_[i].amount[j] < 0.0 || discounts_[i].amount[j] > j, BadDiscountException, "ERROR: " << (i+1) << "-gram discount out of range for adjusted count " << j << ": " << discounts_[i].amount[j]);
// See equation (26) in Chen and Goodman.
discounts_[i].amount[0] = 0.0;
float y = static_cast<float>(s.n[1]) / static_cast<float>(s.n[1] + 2.0 * s.n[2]);
for (unsigned j = 1; j < 4; ++j) {
discounts_[i].amount[j] = static_cast<float>(j) - static_cast<float>(j + 1) * y * static_cast<float>(s.n[j+1]) / static_cast<float>(s.n[j]);
UTIL_THROW_IF(discounts_[i].amount[j] < 0.0 || discounts_[i].amount[j] > j, BadDiscountException, "ERROR: " << (i+1) << "-gram discount out of range for adjusted count " << j << ": " << discounts_[i].amount[j]);
}
} catch (const BadDiscountException &e) {
switch (config.bad_action) {
case THROW_UP:
throw;
case COMPLAIN:
std::cerr << e.what() << " Substituting fallback discounts D1=" << config.fallback.amount[1] << " D2=" << config.fallback.amount[2] << " D3+=" << config.fallback.amount[3] << std::endl;
case SILENT:
break;
}
discounts_[i] = config.fallback;
}
}
}
@ -179,7 +195,7 @@ void AdjustCounts::Run(const util::stream::ChainPositions &positions) {
for (NGramStream full(positions[0]); full; ++full)
stats.AddFull(full->Count());
stats.CalculateDiscounts();
stats.CalculateDiscounts(discount_config_);
return;
}
@ -262,7 +278,7 @@ void AdjustCounts::Run(const util::stream::ChainPositions &positions) {
for (NGramStream *s = streams.begin(); s != streams.end(); ++s)
s->Poison();
stats.CalculateDiscounts();
stats.CalculateDiscounts(discount_config_);
// NOTE: See special early-return case for unigrams near the top of this function
}

View File

@ -2,6 +2,7 @@
#define LM_BUILDER_ADJUST_COUNTS_H
#include "lm/builder/discount.hh"
#include "lm/lm_exception.hh"
#include "util/exception.hh"
#include <vector>
@ -19,6 +20,16 @@ class BadDiscountException : public util::Exception {
~BadDiscountException() throw();
};
struct DiscountConfig {
// Overrides discounts for orders [1,discount_override.size()].
std::vector<Discount> overwrite;
// If discounting fails for an order, copy them from here.
Discount fallback;
// What to do when discounts are out of range or would trigger divison by
// zero. It it does something other than THROW_UP, use fallback_discount.
WarningAction bad_action;
};
/* Compute adjusted counts.
* Input: unique suffix sorted N-grams (and just the N-grams) with raw counts.
* Output: [1,N]-grams with adjusted counts.
@ -27,17 +38,28 @@ class BadDiscountException : public util::Exception {
*/
class AdjustCounts {
public:
AdjustCounts(std::vector<uint64_t> &counts, std::vector<uint64_t> &counts_pruned, std::vector<Discount> &discounts, std::vector<uint64_t> &prune_thresholds)
: counts_(counts), counts_pruned_(counts_pruned), discounts_(discounts), prune_thresholds_(prune_thresholds)
// counts: output
// counts_pruned: output
// discounts: mostly output. If the input already has entries, they will be kept.
// prune_thresholds: input. n-grams with normal (not adjusted) count below this will be pruned.
AdjustCounts(
const std::vector<uint64_t> &prune_thresholds,
std::vector<uint64_t> &counts,
std::vector<uint64_t> &counts_pruned,
const DiscountConfig &discount_config,
std::vector<Discount> &discounts)
: prune_thresholds_(prune_thresholds), counts_(counts), counts_pruned_(counts_pruned), discount_config_(discount_config), discounts_(discounts)
{}
void Run(const util::stream::ChainPositions &positions);
private:
const std::vector<uint64_t> &prune_thresholds_;
std::vector<uint64_t> &counts_;
std::vector<uint64_t> &counts_pruned_;
DiscountConfig discount_config_;
std::vector<Discount> &discounts_;
std::vector<uint64_t> &prune_thresholds_;
};
} // namespace builder

View File

@ -75,7 +75,10 @@ BOOST_AUTO_TEST_CASE(Simple) {
chains >> util::stream::kRecycle;
std::vector<uint64_t> counts_pruned(4);
std::vector<uint64_t> prune_thresholds(4);
BOOST_CHECK_THROW(AdjustCounts(counts, counts_pruned, discount, prune_thresholds).Run(for_adjust), BadDiscountException);
DiscountConfig discount_config;
discount_config.fallback = Discount();
discount_config.bad_action = THROW_UP;
BOOST_CHECK_THROW(AdjustCounts(prune_thresholds, counts, counts_pruned, discount_config, discount).Run(for_adjust), BadDiscountException);
}
BOOST_REQUIRE_EQUAL(4UL, counts.size());
BOOST_CHECK_EQUAL(4UL, counts[0]);

View File

@ -69,9 +69,12 @@ class PruneNGramStream {
block_->SetValidSize(dest_.Base() - block_base);
++block_;
StartBlock();
if (block_) {
currentCount_ = current_.CutoffCount();
}
} else {
currentCount_ = current_.CutoffCount();
}
currentCount_ = current_.CutoffCount();
return *this;
}

View File

@ -9,14 +9,66 @@
#include "util/murmur_hash.hh"
#include <assert.h>
#include <math.h>
namespace lm { namespace builder {
namespace {
class Callback {
/* Calculate q, the collapsed probability and backoff, as defined in
* @inproceedings{Heafield-rest,
* author = {Kenneth Heafield and Philipp Koehn and Alon Lavie},
* title = {Language Model Rest Costs and Space-Efficient Storage},
* year = {2012},
* month = {July},
* booktitle = {Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
* address = {Jeju Island, Korea},
* pages = {1169--1178},
* url = {http://kheafield.com/professional/edinburgh/rest\_paper.pdf},
* }
* This is particularly convenient to calculate during interpolation because
* the needed backoff terms are already accessed at the same time.
*/
class OutputQ {
public:
explicit OutputQ(std::size_t order) : q_delta_(order) {}
void Gram(unsigned order_minus_1, float full_backoff, ProbBackoff &out) {
float &q_del = q_delta_[order_minus_1];
if (order_minus_1) {
// Divide by context's backoff (which comes in as out.backoff)
q_del = q_delta_[order_minus_1 - 1] / out.backoff * full_backoff;
} else {
q_del = full_backoff;
}
out.prob = log10f(out.prob * q_del);
// TODO: stop wastefully outputting this!
out.backoff = 0.0;
}
private:
// Product of backoffs in the numerator divided by backoffs in the
// denominator. Does not include
std::vector<float> q_delta_;
};
/* Default: output probability and backoff */
class OutputProbBackoff {
public:
explicit OutputProbBackoff(std::size_t /*order*/) {}
void Gram(unsigned /*order_minus_1*/, float full_backoff, ProbBackoff &out) const {
// Correcting for numerical precision issues. Take that IRST.
out.prob = std::min(0.0f, log10f(out.prob));
out.backoff = log10f(full_backoff);
}
};
template <class Output> class Callback {
public:
Callback(float uniform_prob, const util::stream::ChainPositions &backoffs, const std::vector<uint64_t> &prune_thresholds)
: backoffs_(backoffs.size()), probs_(backoffs.size() + 2), prune_thresholds_(prune_thresholds) {
: backoffs_(backoffs.size()), probs_(backoffs.size() + 2),
prune_thresholds_(prune_thresholds),
output_(backoffs.size() + 1 /* order */) {
probs_[0] = uniform_prob;
for (std::size_t i = 0; i < backoffs.size(); ++i) {
backoffs_.push_back(backoffs[i]);
@ -40,15 +92,9 @@ class Callback {
Payload &pay = gram.Value();
pay.complete.prob = pay.uninterp.prob + pay.uninterp.gamma * probs_[order_minus_1];
probs_[order_minus_1 + 1] = pay.complete.prob;
pay.complete.prob = log10(pay.complete.prob);
if (order_minus_1 < backoffs_.size() && *(gram.end() - 1) != kUNK && *(gram.end() - 1) != kEOS) {
// This skips over ngrams if backoffs have been exhausted.
if(!backoffs_[order_minus_1]) {
pay.complete.backoff = 0.0;
return;
}
float out_backoff;
if (order_minus_1 < backoffs_.size() && *(gram.end() - 1) != kUNK && *(gram.end() - 1) != kEOS) {
if(prune_thresholds_[order_minus_1 + 1] > 0) {
//Compute hash value for current context
uint64_t current_hash = util::MurmurHashNative(gram.begin(), gram.Order() * sizeof(WordIndex));
@ -58,20 +104,22 @@ class Callback {
hashed_backoff = static_cast<const HashGamma*>(backoffs_[order_minus_1].Get());
if(current_hash == hashed_backoff->hash_value) {
pay.complete.backoff = log10(hashed_backoff->gamma);
out_backoff = hashed_backoff->gamma;
++backoffs_[order_minus_1];
} else {
// Has been pruned away so it is not a context anymore
pay.complete.backoff = 0.0;
out_backoff = 1.0;
}
} else {
pay.complete.backoff = log10(*static_cast<const float*>(backoffs_[order_minus_1].Get()));
out_backoff = *static_cast<const float*>(backoffs_[order_minus_1].Get());
++backoffs_[order_minus_1];
}
} else {
// Not a context.
pay.complete.backoff = 0.0;
out_backoff = 1.0;
}
output_.Gram(order_minus_1, out_backoff, pay.complete);
}
void Exit(unsigned, const NGram &) const {}
@ -81,19 +129,29 @@ class Callback {
std::vector<float> probs_;
const std::vector<uint64_t>& prune_thresholds_;
Output output_;
};
} // namespace
Interpolate::Interpolate(uint64_t vocab_size, const util::stream::ChainPositions &backoffs, const std::vector<uint64_t>& prune_thresholds)
Interpolate::Interpolate(uint64_t vocab_size, const util::stream::ChainPositions &backoffs, const std::vector<uint64_t>& prune_thresholds, bool output_q)
: uniform_prob_(1.0 / static_cast<float>(vocab_size)), // Includes <unk> but excludes <s>.
backoffs_(backoffs),
prune_thresholds_(prune_thresholds) {}
prune_thresholds_(prune_thresholds),
output_q_(output_q) {}
// perform order-wise interpolation
void Interpolate::Run(const util::stream::ChainPositions &positions) {
assert(positions.size() == backoffs_.size() + 1);
Callback callback(uniform_prob_, backoffs_, prune_thresholds_);
JointOrder<Callback, SuffixOrder>(positions, callback);
if (output_q_) {
typedef Callback<OutputQ> C;
C callback(uniform_prob_, backoffs_, prune_thresholds_);
JointOrder<C, SuffixOrder>(positions, callback);
} else {
typedef Callback<OutputProbBackoff> C;
C callback(uniform_prob_, backoffs_, prune_thresholds_);
JointOrder<C, SuffixOrder>(positions, callback);
}
}
}} // namespaces

View File

@ -18,7 +18,7 @@ class Interpolate {
public:
// Normally vocab_size is the unigram count-1 (since p(<s>) = 0) but might
// be larger when the user specifies a consistent vocabulary size.
explicit Interpolate(uint64_t vocab_size, const util::stream::ChainPositions &backoffs, const std::vector<uint64_t> &prune_thresholds);
explicit Interpolate(uint64_t vocab_size, const util::stream::ChainPositions &backoffs, const std::vector<uint64_t> &prune_thresholds, bool output_q_);
void Run(const util::stream::ChainPositions &positions);
@ -26,6 +26,7 @@ class Interpolate {
float uniform_prob_;
util::stream::ChainPositions backoffs_;
const std::vector<uint64_t> prune_thresholds_;
bool output_q_;
};
}} // namespaces

View File

@ -33,7 +33,6 @@ std::vector<uint64_t> ParsePruning(const std::vector<std::string> &param, std::s
// convert to vector of integers
std::vector<uint64_t> prune_thresholds;
prune_thresholds.reserve(order);
std::cerr << "Pruning ";
for (std::vector<std::string>::const_iterator it(param.begin()); it != param.end(); ++it) {
try {
prune_thresholds.push_back(boost::lexical_cast<uint64_t>(*it));
@ -66,6 +65,18 @@ std::vector<uint64_t> ParsePruning(const std::vector<std::string> &param, std::s
return prune_thresholds;
}
lm::builder::Discount ParseDiscountFallback(const std::vector<std::string> &param) {
lm::builder::Discount ret;
UTIL_THROW_IF(param.size() > 3, util::Exception, "Specify at most three fallback discounts: 1, 2, and 3+");
UTIL_THROW_IF(param.empty(), util::Exception, "Fallback discounting enabled, but no discount specified");
ret.amount[0] = 0.0;
for (unsigned i = 0; i < 3; ++i) {
float discount = boost::lexical_cast<float>(param[i < param.size() ? i : (param.size() - 1)]);
UTIL_THROW_IF(discount < 0.0 || discount > static_cast<float>(i+1), util::Exception, "The discount for count " << (i+1) << " was parsed as " << discount << " which is not in the range [0, " << (i+1) << "].");
ret.amount[i + 1] = discount;
}
return ret;
}
} // namespace
@ -77,7 +88,11 @@ int main(int argc, char *argv[]) {
std::string text, arpa;
std::vector<std::string> pruning;
std::vector<std::string> discount_fallback;
std::vector<std::string> discount_fallback_default;
discount_fallback_default.push_back("0.5");
discount_fallback_default.push_back("1");
discount_fallback_default.push_back("1.5");
options.add_options()
("help,h", po::bool_switch(), "Show this help message")
@ -86,7 +101,7 @@ int main(int argc, char *argv[]) {
->required()
#endif
, "Order of the model")
("interpolate_unigrams", po::bool_switch(&pipeline.initial_probs.interpolate_unigrams), "Interpolate the unigrams (default: emulate SRILM by not interpolating)")
("interpolate_unigrams", po::value<bool>(&pipeline.initial_probs.interpolate_unigrams)->default_value(true)->implicit_value(true), "Interpolate the unigrams (default) as opposed to giving lots of mass to <unk> like SRI. If you want SRI's behavior with a large <unk> and the old lmplz default, use --interpolate_unigrams 0.")
("skip_symbols", po::bool_switch(), "Treat <s>, </s>, and <unk> as whitespace instead of throwing an exception")
("temp_prefix,T", po::value<std::string>(&pipeline.sort.temp_prefix)->default_value("/tmp/lm"), "Temporary file prefix")
("memory,S", SizeOption(pipeline.sort.total_memory, util::GuessPhysicalMemory() ? "80%" : "1G"), "Sorting memory")
@ -99,7 +114,9 @@ int main(int argc, char *argv[]) {
("verbose_header", po::bool_switch(&pipeline.verbose_header), "Add a verbose header to the ARPA file that includes information such as token count, smoothing type, etc.")
("text", po::value<std::string>(&text), "Read text from a file instead of stdin")
("arpa", po::value<std::string>(&arpa), "Write ARPA to a file instead of stdout")
("prune", po::value<std::vector<std::string> >(&pruning)->multitoken(), "Prune n-grams with count less than or equal to the given threshold. Specify one value for each order i.e. 0 0 1 to prune singleton trigrams and above. The sequence of values must be non-decreasing and the last value applies to any remaining orders. Unigram pruning is not implemented, so the first value must be zero. Default is to not prune, which is equivalent to --prune 0.");
("collapse_values", po::bool_switch(&pipeline.output_q), "Collapse probability and backoff into a single value, q that yields the same sentence-level probabilities. See http://kheafield.com/professional/edinburgh/rest_paper.pdf for more details, including a proof.")
("prune", po::value<std::vector<std::string> >(&pruning)->multitoken(), "Prune n-grams with count less than or equal to the given threshold. Specify one value for each order i.e. 0 0 1 to prune singleton trigrams and above. The sequence of values must be non-decreasing and the last value applies to any remaining orders. Unigram pruning is not implemented, so the first value must be zero. Default is to not prune, which is equivalent to --prune 0.")
("discount_fallback", po::value<std::vector<std::string> >(&discount_fallback)->multitoken()->implicit_value(discount_fallback_default, "0.5 1 1.5"), "The closed-form estimate for Kneser-Ney discounts does not work without singletons or doubletons. It can also fail if these values are out of range. This option falls back to user-specified discounts when the closed-form estimate fails. Note that this option is generally a bad idea: you should deduplicate your corpus instead. However, class-based models need custom discounts because they lack singleton unigrams. Provide up to three discounts (for adjusted counts 1, 2, and 3+), which will be applied to all orders where the closed-form estimates fail.");
po::variables_map vm;
po::store(po::parse_command_line(argc, argv, options), vm);
@ -143,7 +160,7 @@ int main(int argc, char *argv[]) {
#endif
if (pipeline.vocab_size_for_unk && !pipeline.initial_probs.interpolate_unigrams) {
std::cerr << "--vocab_pad requires --interpolate_unigrams" << std::endl;
std::cerr << "--vocab_pad requires --interpolate_unigrams be on" << std::endl;
return 1;
}
@ -153,6 +170,15 @@ int main(int argc, char *argv[]) {
pipeline.disallowed_symbol_action = lm::THROW_UP;
}
if (vm.count("discount_fallback")) {
pipeline.discount.fallback = ParseDiscountFallback(discount_fallback);
pipeline.discount.bad_action = lm::COMPLAIN;
} else {
// Unused, just here to prevent the compiler from complaining about uninitialized.
pipeline.discount.fallback = lm::builder::Discount();
pipeline.discount.bad_action = lm::THROW_UP;
}
// parse pruning thresholds. These depend on order, so it is not done as a notifier.
pipeline.prune_thresholds = ParsePruning(pruning, pipeline.order);

View File

@ -280,7 +280,7 @@ void InterpolateProbabilities(const std::vector<uint64_t> &counts, Master &maste
gamma_chains.push_back(read_backoffs);
gamma_chains.back() >> gammas[i].Source();
}
master >> Interpolate(std::max(master.Config().vocab_size_for_unk, counts[0] - 1 /* <s> is not included */), util::stream::ChainPositions(gamma_chains), config.prune_thresholds);
master >> Interpolate(std::max(master.Config().vocab_size_for_unk, counts[0] - 1 /* <s> is not included */), util::stream::ChainPositions(gamma_chains), config.prune_thresholds, config.output_q);
gamma_chains >> util::stream::kRecycle;
master.BufferFinal(counts);
}
@ -317,7 +317,7 @@ void Pipeline(PipelineConfig config, int text_file, int out_arpa) {
std::vector<uint64_t> counts;
std::vector<uint64_t> counts_pruned;
std::vector<Discount> discounts;
master >> AdjustCounts(counts, counts_pruned, discounts, config.prune_thresholds);
master >> AdjustCounts(config.prune_thresholds, counts, counts_pruned, config.discount, discounts);
{
util::FixedArray<util::stream::FileBuffer> gammas;

View File

@ -1,6 +1,7 @@
#ifndef LM_BUILDER_PIPELINE_H
#define LM_BUILDER_PIPELINE_H
#include "lm/builder/adjust_counts.hh"
#include "lm/builder/initial_probabilities.hh"
#include "lm/builder/header_info.hh"
#include "lm/lm_exception.hh"
@ -19,6 +20,8 @@ struct PipelineConfig {
util::stream::SortConfig sort;
InitialProbabilitiesConfig initial_probs;
util::stream::ChainConfig read_backoffs;
// Include a header in the ARPA with some statistics?
bool verbose_header;
// Estimated vocabulary size. Used for sizing CorpusCount memory and
@ -34,6 +37,12 @@ struct PipelineConfig {
// n-gram count thresholds for pruning. 0 values means no pruning for
// corresponding n-gram order
std::vector<uint64_t> prune_thresholds; //mjd
// What to do with discount failures.
DiscountConfig discount;
// Compute collapsed q values instead of probability and backoff
bool output_q;
/* Computing the perplexity of LMs with different vocabularies is hard. For
* example, the lowest perplexity is attained by a unigram model that

View File

@ -50,7 +50,7 @@ void PrintARPA::Run(const util::stream::ChainPositions &positions) {
out << "\\" << order << "-grams:" << '\n';
for (NGramStream stream(positions[order - 1]); stream; ++stream) {
// Correcting for numerical precision issues. Take that IRST.
out << std::min(0.0f, stream->Value().complete.prob) << '\t' << vocab_.Lookup(*stream->begin());
out << stream->Value().complete.prob << '\t' << vocab_.Lookup(*stream->begin());
for (const WordIndex *i = stream->begin() + 1; i != stream->end(); ++i) {
out << ' ' << vocab_.Lookup(*i);
}

View File

@ -186,7 +186,7 @@ void BleuScorer::prepareStats(size_t sid, const string& text, ScoreStats& entry)
entry.set(stats);
}
statscore_t BleuScorer::calculateScore(const vector<int>& comps) const
statscore_t BleuScorer::calculateScore(const vector<ScoreStatsType>& comps) const
{
UTIL_THROW_IF(comps.size() != kBleuNgramOrder * 2 + 1, util::Exception, "Error");
@ -289,23 +289,6 @@ float sentenceLevelBackgroundBleu(const std::vector<float>& sent, const std::vec
return exp(logbleu) * stats[kBleuNgramOrder*2];
}
float unsmoothedBleu(const std::vector<float>& stats)
{
UTIL_THROW_IF(stats.size() != kBleuNgramOrder * 2 + 1, util::Exception, "Error");
float logbleu = 0.0;
for (int j = 0; j < kBleuNgramOrder; j++) {
logbleu += log(stats[2 * j]) - log(stats[2 * j + 1]);
}
logbleu /= kBleuNgramOrder;
const float brevity = 1.0 - stats[(kBleuNgramOrder * 2)] / stats[1];
if (brevity < 0.0) {
logbleu += brevity;
}
return exp(logbleu);
}
vector<float> BleuScorer::ScoreNbestList(const string& scoreFile, const string& featureFile)
{
vector<string> scoreFiles;

View File

@ -37,7 +37,7 @@ public:
virtual void setReferenceFiles(const std::vector<std::string>& referenceFiles);
virtual void prepareStats(std::size_t sid, const std::string& text, ScoreStats& entry);
virtual statscore_t calculateScore(const std::vector<int>& comps) const;
virtual statscore_t calculateScore(const std::vector<ScoreStatsType>& comps) const;
virtual std::size_t NumberOfScores() const {
return 2 * kBleuNgramOrder + 1;
}
@ -55,6 +55,10 @@ public:
return m_references.get();
}
virtual float getReferenceLength(const std::vector<ScoreStatsType>& totals) const {
return totals[kBleuNgramOrder*2];
}
/**
* Count the ngrams of each type, up to the given length in the input line.
*/
@ -93,11 +97,6 @@ float smoothedSentenceBleu
*/
float sentenceLevelBackgroundBleu(const std::vector<float>& sent, const std::vector<float>& bg);
/**
* Computes plain old BLEU from a vector of stats
*/
float unsmoothedBleu(const std::vector<float>& stats);
}
#endif // MERT_BLEU_SCORER_H_

View File

@ -235,7 +235,7 @@ BOOST_AUTO_TEST_CASE(bleu_clipped_counts)
BOOST_AUTO_TEST_CASE(calculate_actual_score)
{
BOOST_REQUIRE(4 == kBleuNgramOrder);
std::vector<int> stats(2 * kBleuNgramOrder + 1);
std::vector<ScoreStatsType> stats(2 * kBleuNgramOrder + 1);
BleuScorer scorer;
// unigram

View File

@ -52,18 +52,18 @@ void CderScorer::prepareStats(size_t sid, const string& text, ScoreStats& entry)
{
string sentence = this->preprocessSentence(text);
vector<int> stats;
vector<ScoreStatsType> stats;
prepareStatsVector(sid, sentence, stats);
entry.set(stats);
}
void CderScorer::prepareStatsVector(size_t sid, const string& text, vector<int>& stats)
void CderScorer::prepareStatsVector(size_t sid, const string& text, vector<ScoreStatsType>& stats)
{
sent_t cand;
TokenizeAndEncode(text, cand);
float max = -2;
vector<int> tmp;
vector<ScoreStatsType> tmp;
for (size_t rid = 0; rid < m_ref_sentences.size(); ++rid) {
const sent_t& ref = m_ref_sentences[rid][sid];
tmp.clear();
@ -79,7 +79,7 @@ void CderScorer::prepareStatsVector(size_t sid, const string& text, vector<int>&
}
}
float CderScorer::calculateScore(const vector<int>& comps) const
float CderScorer::calculateScore(const vector<ScoreStatsType>& comps) const
{
if (comps.size() != 2) {
throw runtime_error("Size of stat vector for CDER is not 2");
@ -89,7 +89,7 @@ float CderScorer::calculateScore(const vector<int>& comps) const
}
void CderScorer::computeCD(const sent_t& cand, const sent_t& ref,
vector<int>& stats) const
vector<ScoreStatsType>& stats) const
{
int I = cand.size() + 1; // Number of inter-words positions in candidate sentence
int L = ref.size() + 1; // Number of inter-words positions in reference sentence

View File

@ -23,13 +23,13 @@ public:
virtual void prepareStats(std::size_t sid, const std::string& text, ScoreStats& entry);
virtual void prepareStatsVector(std::size_t sid, const std::string& text, std::vector<int>& stats);
virtual void prepareStatsVector(std::size_t sid, const std::string& text, std::vector<ScoreStatsType>& stats);
virtual std::size_t NumberOfScores() const {
return 2;
}
virtual float calculateScore(const std::vector<int>& comps) const;
virtual float calculateScore(const std::vector<ScoreStatsType>& comps) const;
private:
bool m_allowed_long_jumps;
@ -38,7 +38,7 @@ private:
std::vector<std::vector<sent_t> > m_ref_sentences;
void computeCD(const sent_t& cand, const sent_t& ref,
std::vector<int>& stats) const;
std::vector<ScoreStatsType>& stats) const;
// no copying allowed
CderScorer(const CderScorer&);

View File

@ -135,7 +135,7 @@ void Data::load(const std::string &featfile, const std::string &scorefile)
m_score_data->load(scorefile);
}
void Data::loadNBest(const string &file)
void Data::loadNBest(const string &file, bool oneBest)
{
TRACE_ERR("loading nbest from " << file << endl);
util::FilePiece in(file.c_str());
@ -154,6 +154,7 @@ void Data::loadNBest(const string &file)
util::TokenIter<util::MultiCharacter> it(line, util::MultiCharacter("|||"));
sentence_index = ParseInt(*it);
if (oneBest && m_score_data->exists(sentence_index)) continue;
++it;
sentence = it->as_string();
++it;
@ -164,10 +165,9 @@ void Data::loadNBest(const string &file)
++it; // skip model score.
if (it) {
++it;
alignment = it->as_string(); //fifth field (if present) is either phrase or word alignment
++it;
if (it) {
++it;
alignment = it->as_string(); //sixth field (if present) is word alignment
}
}

View File

@ -67,7 +67,7 @@ public:
m_feature_data->Features(f);
}
void loadNBest(const std::string &file);
void loadNBest(const std::string &file, bool oneBest=false);
void load(const std::string &featfile, const std::string &scorefile);

View File

@ -12,6 +12,7 @@
#include <vector>
#include <iostream>
#include <stdexcept>
#include <boost/lexical_cast.hpp>
#include "FeatureArray.h"
namespace MosesTuning
@ -103,7 +104,7 @@ public:
inline int getName(std::size_t idx) const {
idx2name::const_iterator i = m_index_to_array_name.find(idx);
if (i != m_index_to_array_name.end())
throw std::runtime_error("there is no entry at index " + idx);
throw std::runtime_error("there is no entry at index " + boost::lexical_cast<std::string>(idx));
return i->second;
}
@ -116,7 +117,7 @@ public:
throw std::runtime_error("Error: you required an too big index");
std::map<std::size_t, std::string>::const_iterator it = m_index_to_feature_name.find(idx);
if (it == m_index_to_feature_name.end()) {
throw std::runtime_error("Error: specified id is unknown: " + idx);
throw std::runtime_error("Error: specified id is unknown: " + boost::lexical_cast<std::string>(idx));
} else {
return it->second;
}

View File

@ -28,7 +28,7 @@ Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#include "util/exception.hh"
#include "util/file_piece.hh"
#include "BleuScorer.h"
#include "Scorer.h"
#include "HopeFearDecoder.h"
using namespace std;
@ -39,7 +39,7 @@ namespace MosesTuning {
static const ValType BLEU_RATIO = 5;
ValType HopeFearDecoder::Evaluate(const AvgWeightVector& wv) {
vector<ValType> stats(kBleuNgramOrder*2+1,0);
vector<ValType> stats(scorer_->NumberOfScores(),0);
for(reset(); !finished(); next()) {
vector<ValType> sent;
MaxModel(wv,&sent);
@ -47,7 +47,7 @@ ValType HopeFearDecoder::Evaluate(const AvgWeightVector& wv) {
stats[i]+=sent[i];
}
}
return unsmoothedBleu(stats);
return scorer_->calculateScore(stats);
}
NbestHopeFearDecoder::NbestHopeFearDecoder(
@ -55,8 +55,10 @@ NbestHopeFearDecoder::NbestHopeFearDecoder(
const vector<string>& scoreFiles,
bool streaming,
bool no_shuffle,
bool safe_hope
bool safe_hope,
Scorer* scorer
) : safe_hope_(safe_hope) {
scorer_ = scorer;
if (streaming) {
train_.reset(new StreamingHypPackEnumerator(featureFiles, scoreFiles));
} else {
@ -93,7 +95,7 @@ void NbestHopeFearDecoder::HopeFear(
for(size_t i=0; i< train_->cur_size(); i++) {
const MiraFeatureVector& vec=train_->featuresAt(i);
ValType score = wv.score(vec);
ValType bleu = sentenceLevelBackgroundBleu(train_->scoresAt(i),backgroundBleu);
ValType bleu = scorer_->calculateSentenceLevelBackgroundScore(train_->scoresAt(i),backgroundBleu);
// Hope
if(i==0 || (hope_scale*score + bleu) > hope_score) {
hope_score = hope_scale*score + bleu;
@ -124,9 +126,9 @@ void NbestHopeFearDecoder::HopeFear(
hopeFear->fearFeatures = train_->featuresAt(fear_index);
hopeFear->hopeStats = train_->scoresAt(hope_index);
hopeFear->hopeBleu = sentenceLevelBackgroundBleu(hopeFear->hopeStats, backgroundBleu);
hopeFear->hopeBleu = scorer_->calculateSentenceLevelBackgroundScore(hopeFear->hopeStats, backgroundBleu);
const vector<float>& fear_stats = train_->scoresAt(fear_index);
hopeFear->fearBleu = sentenceLevelBackgroundBleu(fear_stats, backgroundBleu);
hopeFear->fearBleu = scorer_->calculateSentenceLevelBackgroundScore(fear_stats, backgroundBleu);
hopeFear->modelStats = train_->scoresAt(model_index);
hopeFear->hopeFearEqual = (hope_index == fear_index);
@ -158,7 +160,8 @@ HypergraphHopeFearDecoder::HypergraphHopeFearDecoder
bool no_shuffle,
bool safe_hope,
size_t hg_pruning,
const MiraWeightVector& wv
const MiraWeightVector& wv,
Scorer* scorer
) :
num_dense_(num_dense) {
@ -169,6 +172,7 @@ HypergraphHopeFearDecoder::HypergraphHopeFearDecoder
SparseVector weights;
wv.ToSparse(&weights);
scorer_ = scorer;
static const string kWeights = "weights";
fs::directory_iterator dend;
@ -260,9 +264,9 @@ void HypergraphHopeFearDecoder::HopeFear(
//Only C++11
//hopeFear->modelStats.assign(std::begin(modelHypo.bleuStats), std::end(modelHypo.bleuStats));
vector<ValType> fearStats(kBleuNgramOrder*2+1);
hopeFear->hopeStats.reserve(kBleuNgramOrder*2+1);
hopeFear->modelStats.reserve(kBleuNgramOrder*2+1);
vector<ValType> fearStats(scorer_->NumberOfScores());
hopeFear->hopeStats.reserve(scorer_->NumberOfScores());
hopeFear->modelStats.reserve(scorer_->NumberOfScores());
for (size_t i = 0; i < fearStats.size(); ++i) {
hopeFear->modelStats.push_back(modelHypo.bleuStats[i]);
hopeFear->hopeStats.push_back(hopeHypo.bleuStats[i]);
@ -320,7 +324,7 @@ void HypergraphHopeFearDecoder::MaxModel(const AvgWeightVector& wv, vector<ValTy
size_t sentenceId = *sentenceIdIter_;
SparseVector weights;
wv.ToSparse(&weights);
vector<ValType> bg(kBleuNgramOrder*2+1);
vector<ValType> bg(scorer_->NumberOfScores());
Viterbi(*(graphs_[sentenceId]), weights, 0, references_, sentenceId, bg, &bestHypo);
stats->resize(bestHypo.bleuStats.size());
/*

View File

@ -37,6 +37,8 @@ Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
namespace MosesTuning {
class Scorer;
/** To be filled in by the decoder */
struct HopeFearData {
MiraFeatureVector modelFeatures;
@ -60,6 +62,8 @@ public:
virtual void next() = 0;
virtual bool finished() = 0;
virtual ~HopeFearDecoder() {};
/**
* Calculate hope, fear and model hypotheses
**/
@ -76,6 +80,8 @@ public:
/** Calculate bleu on training set */
ValType Evaluate(const AvgWeightVector& wv);
protected:
Scorer* scorer_;
};
@ -86,7 +92,8 @@ public:
const std::vector<std::string>& scoreFiles,
bool streaming,
bool no_shuffle,
bool safe_hope
bool safe_hope,
Scorer* scorer
);
virtual void reset();
@ -120,7 +127,8 @@ public:
bool no_shuffle,
bool safe_hope,
size_t hg_pruning,
const MiraWeightVector& wv
const MiraWeightVector& wv,
Scorer* scorer_
);
virtual void reset();

165
mert/HwcmScorer.cpp Normal file
View File

@ -0,0 +1,165 @@
#include "HwcmScorer.h"
#include <fstream>
#include "ScoreStats.h"
#include "Util.h"
#include "util/tokenize_piece.hh"
// HWCM score (Liu and Gildea, 2005). Implements F1 instead of precision for better modelling of hypothesis length.
// assumes dependency trees on target side (generated by scripts/training/wrappers/conll2mosesxml.py ; use with option --brackets for reference).
// reads reference trees from separate file {REFERENCE_FILE}.trees to support mix of string-based and tree-based metrics.
using namespace std;
namespace MosesTuning
{
HwcmScorer::HwcmScorer(const string& config)
: StatisticsBasedScorer("HWCM",config) {}
HwcmScorer::~HwcmScorer() {}
void HwcmScorer::setReferenceFiles(const vector<string>& referenceFiles)
{
// For each line in the reference file, create a tree object
if (referenceFiles.size() != 1) {
throw runtime_error("HWCM only supports a single reference");
}
m_ref_trees.clear();
m_ref_hwc.clear();
ifstream in((referenceFiles[0] + ".trees").c_str());
if (!in) {
throw runtime_error("Unable to open " + referenceFiles[0] + ".trees");
}
string line;
while (getline(in,line)) {
line = this->preprocessSentence(line);
TreePointer tree (boost::make_shared<InternalTree>(line));
m_ref_trees.push_back(tree);
vector<map<string, int> > hwc (kHwcmOrder);
vector<string> history(kHwcmOrder);
extractHeadWordChain(tree, history, hwc);
m_ref_hwc.push_back(hwc);
vector<int> totals(kHwcmOrder);
for (size_t i = 0; i < kHwcmOrder; i++) {
for (map<string, int>::const_iterator it = m_ref_hwc.back()[i].begin(); it != m_ref_hwc.back()[i].end(); it++) {
totals[i] += it->second;
}
}
m_ref_lengths.push_back(totals);
}
TRACE_ERR(endl);
}
void HwcmScorer::extractHeadWordChain(TreePointer tree, vector<string> & history, vector<map<string, int> > & hwc) {
if (tree->GetLength() > 0) {
string head = getHead(tree);
if (head.empty()) {
for (std::vector<TreePointer>::const_iterator it = tree->GetChildren().begin(); it != tree->GetChildren().end(); ++it) {
extractHeadWordChain(*it, history, hwc);
}
}
else {
vector<string> new_history(kHwcmOrder);
new_history[0] = head;
hwc[0][head]++;
for (size_t hist_idx = 0; hist_idx < kHwcmOrder-1; hist_idx++) {
if (!history[hist_idx].empty()) {
string chain = history[hist_idx] + " " + head;
hwc[hist_idx+1][chain]++;
if (hist_idx+2 < kHwcmOrder) {
new_history[hist_idx+1] = chain;
}
}
}
for (std::vector<TreePointer>::const_iterator it = tree->GetChildren().begin(); it != tree->GetChildren().end(); ++it) {
extractHeadWordChain(*it, new_history, hwc);
}
}
}
}
string HwcmScorer::getHead(TreePointer tree) {
// assumption (only true for dependency parse: each constituent has a preterminal label, and corresponding terminal is head)
// if constituent has multiple preterminals, first one is picked; if it has no preterminals, empty string is returned
for (std::vector<TreePointer>::const_iterator it = tree->GetChildren().begin(); it != tree->GetChildren().end(); ++it)
{
TreePointer child = *it;
if (child->GetLength() == 1 && child->GetChildren()[0]->IsTerminal()) {
return child->GetChildren()[0]->GetLabel();
}
}
return "";
}
void HwcmScorer::prepareStats(size_t sid, const string& text, ScoreStats& entry)
{
if (sid >= m_ref_trees.size()) {
stringstream msg;
msg << "Sentence id (" << sid << ") not found in reference set";
throw runtime_error(msg.str());
}
string sentence = this->preprocessSentence(text);
// if sentence has '|||', assume that tree is in second position (n-best-list);
// otherwise, assume it is in first position (calling 'evaluate' with tree as reference)
util::TokenIter<util::MultiCharacter> it(sentence, util::MultiCharacter("|||"));
++it;
if (it) {
sentence = it->as_string();
}
TreePointer tree (boost::make_shared<InternalTree>(sentence));
vector<map<string, int> > hwc_test (kHwcmOrder);
vector<string> history(kHwcmOrder);
extractHeadWordChain(tree, history, hwc_test);
ostringstream stats;
for (size_t i = 0; i < kHwcmOrder; i++) {
int correct = 0;
int test_total = 0;
for (map<string, int>::const_iterator it = hwc_test[i].begin(); it != hwc_test[i].end(); it++) {
test_total += it->second;
map<string, int>::const_iterator it2 = m_ref_hwc[sid][i].find(it->first);
if (it2 != m_ref_hwc[sid][i].end()) {
correct += std::min(it->second, it2->second);
}
}
stats << correct << " " << test_total << " " << m_ref_lengths[sid][i] << " " ;
}
string stats_str = stats.str();
entry.set(stats_str);
}
float HwcmScorer::calculateScore(const vector<ScoreStatsType>& comps) const
{
float precision = 0;
float recall = 0;
for (size_t i = 0; i < kHwcmOrder; i++) {
float matches = comps[i*3];
float test_total = comps[1+(i*3)];
float ref_total = comps[2+(i*3)];
if (test_total > 0) {
precision += matches/test_total;
}
if (ref_total > 0) {
recall += matches/ref_total;
}
}
precision /= (float)kHwcmOrder;
recall /= (float)kHwcmOrder;
return (2*precision*recall)/(precision+recall); // f1-score
}
}

64
mert/HwcmScorer.h Normal file
View File

@ -0,0 +1,64 @@
#ifndef MERT_HWCM_SCORER_H_
#define MERT_HWCM_SCORER_H_
#include <string>
#include <vector>
#include "StatisticsBasedScorer.h"
#include "moses/FF/InternalTree.h"
using Moses::TreePointer;
using Moses::InternalTree;
namespace MosesTuning
{
class ScoreStats;
const size_t kHwcmOrder = 4;
/**
* HWCM scoring (Liu and Gildea 2005), but F1 instead of precision.
*/
class HwcmScorer: public StatisticsBasedScorer
{
public:
explicit HwcmScorer(const std::string& config = "");
~HwcmScorer();
virtual void setReferenceFiles(const std::vector<std::string>& referenceFiles);
virtual void prepareStats(std::size_t sid, const std::string& text, ScoreStats& entry);
virtual std::size_t NumberOfScores() const {
return kHwcmOrder*3;
}
virtual float calculateScore(const std::vector<ScoreStatsType>& comps) const;
virtual float getReferenceLength(const std::vector<ScoreStatsType>& totals) const {
return totals[2];
}
//TODO: actually, we use trees which we store in place of alignment. Maybe use something analogous to Phrase Properties to cleanly store trees?
bool useAlignment() const {
return true;
}
private:
// data extracted from reference files
std::vector<TreePointer> m_ref_trees;
std::vector<std::vector<std::map<std::string, int> > > m_ref_hwc;
std::vector<std::vector<int> > m_ref_lengths;
void extractHeadWordChain(TreePointer tree, std::vector<std::string> & history, std::vector<std::map<std::string, int> > & hwc);
std::string getHead(TreePointer tree);
// no copying allowed
HwcmScorer(const HwcmScorer&);
HwcmScorer& operator=(const HwcmScorer&);
};
}
#endif // MERT_HWCM_SCORER_H_

View File

@ -64,7 +64,7 @@ double_conversion::StringToDoubleConverter converter(double_conversion::StringTo
**/
static pair<Edge*,size_t> ReadEdge(util::FilePiece &from, Graph &graph) {
Edge* edge = graph.NewEdge();
StringPiece line = NextLine(from);
StringPiece line = from.ReadLine(); //Don't allow comments within edge lists
util::TokenIter<util::MultiCharacter> pipes(line, util::MultiCharacter(" ||| "));
//Target
for (util::TokenIter<util::SingleCharacter, true> i(*pipes, util::SingleCharacter(' ')); i; ++i) {

View File

@ -153,6 +153,41 @@ void InterpolatedScorer::score(const candidates_t& candidates, const diffs_t& di
}
/** Interpolated scorer gets a vector of sufficient statistics, calls all scorers with corresponding statistics,
and combines them with weights **/
float InterpolatedScorer::calculateScore(const std::vector<ScoreStatsType>& totals) const
{
size_t scorerNum = 0;
size_t last = 0;
float score = 0;
for (ScopedVector<Scorer>::const_iterator itsc = m_scorers.begin();
itsc != m_scorers.end(); ++itsc) {
int numScoresScorer = (*itsc)->NumberOfScores();
std::vector<ScoreStatsType> totals_scorer(totals.begin()+last, totals.begin()+last+numScoresScorer);
score += (*itsc)->calculateScore(totals_scorer) * m_scorer_weights[scorerNum];
last += numScoresScorer;
scorerNum++;
}
return score;
}
float InterpolatedScorer::getReferenceLength(const std::vector<ScoreStatsType>& totals) const
{
size_t scorerNum = 0;
size_t last = 0;
float refLen = 0;
for (ScopedVector<Scorer>::const_iterator itsc = m_scorers.begin();
itsc != m_scorers.end(); ++itsc) {
int numScoresScorer = (*itsc)->NumberOfScores();
std::vector<ScoreStatsType> totals_scorer(totals.begin()+last, totals.begin()+last+numScoresScorer);
refLen += (*itsc)->getReferenceLength(totals_scorer) * m_scorer_weights[scorerNum];
last += numScoresScorer;
scorerNum++;
}
return refLen;
}
void InterpolatedScorer::setReferenceFiles(const vector<string>& referenceFiles)
{
for (ScopedVector<Scorer>::iterator itsc = m_scorers.begin();

View File

@ -39,6 +39,10 @@ public:
virtual void setScoreData(ScoreData* data);
virtual float calculateScore(const std::vector<ScoreStatsType>& totals) const;
virtual float getReferenceLength(const std::vector<ScoreStatsType>& totals) const;
/**
* Set the factors, which should be used for this metric
*/

View File

@ -29,6 +29,8 @@ SemposOverlapping.cpp
InterpolatedScorer.cpp
Point.cpp
PerScorer.cpp
HwcmScorer.cpp
../moses/FF/InternalTree.cpp
Scorer.cpp
ScorerFactory.cpp
Optimizer.cpp

View File

@ -144,7 +144,7 @@ void MeteorScorer::prepareStats(size_t sid, const string& text, ScoreStats& entr
entry.set(stats_str);
}
float MeteorScorer::calculateScore(const vector<int>& comps) const
float MeteorScorer::calculateScore(const vector<ScoreStatsType>& comps) const
{
string score;
stringstream input;
@ -181,7 +181,7 @@ void MeteorScorer::setReferenceFiles(const vector<string>& referenceFiles) {}
void MeteorScorer::prepareStats(size_t sid, const string& text, ScoreStats& entry) {}
float MeteorScorer::calculateScore(const vector<int>& comps) const
float MeteorScorer::calculateScore(const vector<ScoreStatsType>& comps) const
{
// Should never be reached
return 0.0;

View File

@ -54,7 +54,7 @@ public:
return 23;
}
virtual float calculateScore(const std::vector<int>& comps) const;
virtual float calculateScore(const std::vector<ScoreStatsType>& comps) const;
private:
// Meteor and process IO

View File

@ -139,7 +139,7 @@ ostream& operator<<(ostream& o, const MiraWeightVector& e)
for(size_t i=0; i<e.m_weights.size(); i++) {
if(abs(e.m_weights[i])>1e-8) {
if(i>0) o << " ";
cerr << i << ":" << e.m_weights[i];
o << i << ":" << e.m_weights[i];
}
}
return o;

View File

@ -79,10 +79,10 @@ void PerScorer::prepareStats(size_t sid, const string& text, ScoreStats& entry)
entry.set(stats_str);
}
float PerScorer::calculateScore(const vector<int>& comps) const
float PerScorer::calculateScore(const vector<ScoreStatsType>& comps) const
{
float denom = comps[2];
float num = comps[0] - max(0,comps[1]-comps[2]);
float num = comps[0] - max(0.0f,comps[1]-comps[2]);
if (denom == 0) {
// This shouldn't happen!
return 0.0;

View File

@ -30,7 +30,7 @@ public:
virtual std::size_t NumberOfScores() const {
return 3;
}
virtual float calculateScore(const std::vector<int>& comps) const;
virtual float calculateScore(const std::vector<ScoreStatsType>& comps) const;
private:
// no copying allowed

View File

@ -234,7 +234,7 @@ void PermutationScorer::prepareStats(size_t sid, const string& text, ScoreStats&
}
//Will just be final score
statscore_t PermutationScorer::calculateScore(const vector<int>& comps) const
statscore_t PermutationScorer::calculateScore(const vector<ScoreStatsType>& comps) const
{
//cerr << "*******PermutationScorer::calculateScore" ;
//cerr << " " << comps[0]/comps[1] << endl;

View File

@ -49,7 +49,7 @@ public:
protected:
statscore_t calculateScore(const std::vector<int>& scores) const;
statscore_t calculateScore(const std::vector<ScoreStatsType>& scores) const;
PermutationScorer(const PermutationScorer&);
~PermutationScorer() {};
PermutationScorer& operator=(const PermutationScorer&);

View File

@ -13,6 +13,7 @@
#include <vector>
#include <stdexcept>
#include <string>
#include <boost/lexical_cast.hpp>
#include "ScoreArray.h"
#include "ScoreStats.h"
@ -108,7 +109,7 @@ public:
inline int getName(std::size_t idx) const {
idx2name::const_iterator i = m_index_to_array_name.find(idx);
if (i != m_index_to_array_name.end())
throw std::runtime_error("there is no entry at index " + idx);
throw std::runtime_error("there is no entry at index " + boost::lexical_cast<std::string>(idx));
return i->second;
}
};

View File

@ -42,6 +42,19 @@ public:
*/
virtual std::size_t NumberOfScores() const = 0;
/**
* Calculate score based on a vector of sufficient statistics.
*/
virtual float calculateScore(const std::vector<ScoreStatsType>& totals) const = 0;
float calculateSentenceLevelBackgroundScore(const std::vector<ScoreStatsType>& totals, const std::vector<ScoreStatsType>& bg) {
std::vector<ScoreStatsType> stats(totals.size());
for(size_t i=0; i<stats.size(); i++)
stats[i] = totals[i]+bg[i];
// Get score and scale by reference length (as per Chiang et al 08)
return calculateScore(stats) * getReferenceLength(stats);
}
/**
* Set the reference files. This must be called before prepareStats().
*/
@ -97,6 +110,11 @@ public:
return 0;
}
/**
* Based on vector of sufficient statistics, return length of reference.
*/
virtual float getReferenceLength(const std::vector<ScoreStatsType>& totals) const = 0;
/**
* Set the score data, prior to scoring.
*/

View File

@ -11,6 +11,7 @@
#include "SemposScorer.h"
#include "PermutationScorer.h"
#include "MeteorScorer.h"
#include "HwcmScorer.h"
#include "Reference.h"
using namespace std;
@ -32,6 +33,7 @@ vector<string> ScorerFactory::getTypes()
types.push_back(string("SEMPOS"));
types.push_back(string("LRSCORE"));
types.push_back(string("METEOR"));
types.push_back(string("HWCM"));
return types;
}
@ -56,6 +58,8 @@ Scorer* ScorerFactory::getScorer(const string& type, const string& config)
return (PermutationScorer*) new PermutationScorer(type, config);
} else if (type == "METEOR") {
return new MeteorScorer(config);
} else if (type == "HWCM") {
return new HwcmScorer(config);
} else {
if (type.find(',') != string::npos) {
return new InterpolatedScorer(type, config);

View File

@ -33,9 +33,9 @@ void SemposOverlappingFactory::SetOverlapping(SemposOverlapping* ovr)
g_overlapping = ovr;
}
vector<int> CapMicroOverlapping::prepareStats(const sentence_t& cand, const sentence_t& ref)
vector<ScoreStatsType> CapMicroOverlapping::prepareStats(const sentence_t& cand, const sentence_t& ref)
{
vector<int> stats(2);
vector<ScoreStatsType> stats(2);
sentence_t intersection;
set_intersection(cand.begin(), cand.end(), ref.begin(), ref.end(),
@ -53,12 +53,12 @@ vector<int> CapMicroOverlapping::prepareStats(const sentence_t& cand, const sent
refSum += semposScorer->weight(it->first);
}
stats[0] = (int)(multCoeff * interSum);
stats[1] = (int)(multCoeff * refSum);
stats[0] = (ScoreStatsType)(multCoeff * interSum);
stats[1] = (ScoreStatsType)(multCoeff * refSum);
return stats;
}
float CapMicroOverlapping::calculateScore(const vector<int>& stats) const
float CapMicroOverlapping::calculateScore(const vector<ScoreStatsType>& stats) const
{
if (stats.size() != 2) {
throw std::runtime_error("Size of stats vector has to be 2");
@ -67,9 +67,9 @@ float CapMicroOverlapping::calculateScore(const vector<int>& stats) const
return stats[0] / static_cast<float>(stats[1]);
}
vector<int> CapMacroOverlapping::prepareStats(const sentence_t& cand, const sentence_t& ref)
vector<ScoreStatsType> CapMacroOverlapping::prepareStats(const sentence_t& cand, const sentence_t& ref)
{
vector<int> stats(2 * kMaxNOC);
vector<ScoreStatsType> stats(2 * kMaxNOC);
sentence_t intersection;
set_intersection(cand.begin(), cand.end(), ref.begin(), ref.end(),
@ -92,7 +92,7 @@ vector<int> CapMacroOverlapping::prepareStats(const sentence_t& cand, const sent
return stats;
}
float CapMacroOverlapping::calculateScore(const vector<int>& stats) const
float CapMacroOverlapping::calculateScore(const vector<ScoreStatsType>& stats) const
{
if (stats.size() != 2 * kMaxNOC) {
// TODO: Add some comments. The number "38" looks like a magic number.

View File

@ -7,6 +7,8 @@
#include <utility>
#include <vector>
#include "Types.h"
namespace MosesTuning
{
@ -31,8 +33,8 @@ class SemposOverlapping
{
public:
virtual ~SemposOverlapping() {}
virtual std::vector<int> prepareStats(const sentence_t& cand, const sentence_t& ref) = 0;
virtual float calculateScore(const std::vector<int>& stats) const = 0;
virtual std::vector<ScoreStatsType> prepareStats(const sentence_t& cand, const sentence_t& ref) = 0;
virtual float calculateScore(const std::vector<ScoreStatsType>& stats) const = 0;
virtual std::size_t NumberOfScores() const = 0;
};
@ -61,8 +63,8 @@ public:
CapMicroOverlapping(const SemposScorer* sempos) : semposScorer(sempos) {}
~CapMicroOverlapping() {}
virtual std::vector<int> prepareStats(const sentence_t& cand, const sentence_t& ref);
virtual float calculateScore(const std::vector<int>& stats) const;
virtual std::vector<ScoreStatsType> prepareStats(const sentence_t& cand, const sentence_t& ref);
virtual float calculateScore(const std::vector<ScoreStatsType>& stats) const;
virtual std::size_t NumberOfScores() const {
return 2;
}
@ -83,8 +85,8 @@ public:
CapMacroOverlapping(const SemposScorer* sempos) : semposScorer(sempos) {}
~CapMacroOverlapping() {}
virtual std::vector<int> prepareStats(const sentence_t& cand, const sentence_t& ref);
virtual float calculateScore(const std::vector<int>& stats) const;
virtual std::vector<ScoreStatsType> prepareStats(const sentence_t& cand, const sentence_t& ref);
virtual float calculateScore(const std::vector<ScoreStatsType>& stats) const;
virtual std::size_t NumberOfScores() const {
return kMaxNOC * 2;
}

View File

@ -35,7 +35,7 @@ public:
virtual std::size_t NumberOfScores() const {
return m_ovr->NumberOfScores();
}
virtual float calculateScore(const std::vector<int>& comps) const {
virtual float calculateScore(const std::vector<ScoreStatsType>& comps) const {
return m_ovr->calculateScore(comps);
}

View File

@ -67,7 +67,7 @@ void StatisticsBasedScorer::score(const candidates_t& candidates, const diffs_t
throw runtime_error("No candidates supplied");
}
int numCounts = m_score_data->get(0,candidates[0]).size();
vector<int> totals(numCounts);
vector<ScoreStatsType> totals(numCounts);
for (size_t i = 0; i < candidates.size(); ++i) {
ScoreStats stats = m_score_data->get(i,candidates[i]);
if (stats.size() != totals.size()) {

View File

@ -11,6 +11,8 @@
#include "Scorer.h"
#include "util/exception.hh"
namespace MosesTuning
{
@ -21,6 +23,8 @@ namespace MosesTuning
*/
class StatisticsBasedScorer : public Scorer
{
friend class HopeFearDecoder;
public:
StatisticsBasedScorer(const std::string& name, const std::string& config);
virtual ~StatisticsBasedScorer() {}
@ -38,7 +42,12 @@ protected:
/**
* Calculate the actual score.
*/
virtual statscore_t calculateScore(const std::vector<int>& totals) const = 0;
virtual statscore_t calculateScore(const std::vector<ScoreStatsType>& totals) const = 0;
virtual float getReferenceLength(const std::vector<ScoreStatsType>& totals) const {
UTIL_THROW(util::Exception, "getReferenceLength not implemented for this scorer type.");
return 0;
}
// regularisation
RegularisationType m_regularization_type;

View File

@ -101,7 +101,7 @@ void TerScorer::prepareStats ( size_t sid, const string& text, ScoreStats& entry
entry.set ( stats_str );
}
float TerScorer::calculateScore(const vector<int>& comps) const
float TerScorer::calculateScore(const vector<ScoreStatsType>& comps) const
{
float denom = 1.0 * comps[1];
float num = -1.0 * comps[0];

View File

@ -31,7 +31,7 @@ public:
return kLENGTH + 1;
}
virtual float calculateScore(const std::vector<int>& comps) const;
virtual float calculateScore(const std::vector<ScoreStatsType>& comps) const;
private:
const int kLENGTH;

View File

@ -33,7 +33,7 @@ typedef FeatureStatsType* featstats_t;
typedef std::vector<FeatureStats> featarray_t;
typedef std::vector<FeatureArray> featdata_t;
typedef int ScoreStatsType;
typedef float ScoreStatsType;
typedef ScoreStatsType* scorestats_t;
//typedef std::vector<ScoreStatsType> scorestats_t;
typedef std::vector<ScoreStats> scorearray_t;

View File

@ -14,6 +14,7 @@
#include "ScorerFactory.h"
#include "Timer.h"
#include "Util.h"
#include "Data.h"
using namespace std;
using namespace MosesTuning;
@ -30,17 +31,20 @@ const float g_alpha = 0.05;
class EvaluatorUtil
{
public:
static void evaluate(const string& candFile, int bootstrap);
static void evaluate(const string& candFile, int bootstrap, bool nbest_mode);
static float average(const vector<float>& list);
static string int2string(int n);
static vector<ScoreStats> loadNBest(const string& nBestFile);
static vector<ScoreStats> loadCand(const string& candFile);
private:
EvaluatorUtil() {}
~EvaluatorUtil() {}
};
void EvaluatorUtil::evaluate(const string& candFile, int bootstrap)
{
// load hypothesis from candidate output
vector<ScoreStats> EvaluatorUtil::loadCand(const string& candFile) {
ifstream cand(candFile.c_str());
if (!cand.good()) throw runtime_error("Error opening candidate file");
@ -53,6 +57,34 @@ void EvaluatorUtil::evaluate(const string& candFile, int bootstrap)
g_scorer->prepareStats(entries.size(), line, scoreentry);
entries.push_back(scoreentry);
}
return entries;
}
// load 1-best hypothesis from n-best file (useful if relying on alignment/tree information)
vector<ScoreStats> EvaluatorUtil::loadNBest(const string& nBestFile) {
vector<ScoreStats> entries;
Data data(g_scorer);
data.loadNBest(nBestFile, true);
const ScoreDataHandle & score_data = data.getScoreData();
for (size_t i = 0; i != score_data->size(); i++) {
entries.push_back(score_data->get(i, 0));
}
return entries;
}
void EvaluatorUtil::evaluate(const string& candFile, int bootstrap, bool nbest_input)
{
vector<ScoreStats> entries;
if (nbest_input) {
entries = loadNBest(candFile);
}
else {
entries = loadCand(candFile);
}
int n = entries.size();
if (bootstrap) {
@ -131,6 +163,7 @@ void usage()
cerr << "\tThis is of the form NAME1:VAL1,NAME2:VAL2 etc " << endl;
cerr << "[--reference|-R] comma separated list of reference files" << endl;
cerr << "[--candidate|-C] comma separated list of candidate files" << endl;
cerr << "[--nbest|-n] comma separated list of nbest files (only 1-best is evaluated)" << endl;
cerr << "[--factors|-f] list of factors passed to the scorer (e.g. 0|2)" << endl;
cerr << "[--filter|-l] filter command which will be used to preprocess the sentences" << endl;
cerr << "[--bootstrap|-b] number of booststraped samples (default 0 - no bootstraping)" << endl;
@ -162,6 +195,7 @@ static struct option long_options[] = {
{"scconfig", required_argument, 0, 'c'},
{"reference", required_argument, 0, 'R'},
{"candidate", required_argument, 0, 'C'},
{"nbest", required_argument, 0, 'n'},
{"bootstrap", required_argument, 0, 'b'},
{"rseed", required_argument, 0, 'r'},
{"factors", required_argument, 0, 'f'},
@ -176,6 +210,7 @@ struct ProgramOption {
vector<string> scorer_configs;
string reference;
string candidate;
string nbest;
vector<string> scorer_factors;
vector<string> scorer_filter;
int bootstrap;
@ -185,6 +220,7 @@ struct ProgramOption {
ProgramOption()
: reference(""),
candidate(""),
nbest(""),
bootstrap(0),
seed(0),
has_seed(false) { }
@ -195,7 +231,7 @@ void ParseCommandOptions(int argc, char** argv, ProgramOption* opt)
int c;
int option_index;
int last_scorer_index = -1;
while ((c = getopt_long(argc, argv, "s:c:R:C:b:r:f:l:h", long_options, &option_index)) != -1) {
while ((c = getopt_long(argc, argv, "s:c:R:C:n:b:r:f:l:h", long_options, &option_index)) != -1) {
switch(c) {
case 's':
opt->scorer_types.push_back(string(optarg));
@ -205,6 +241,7 @@ void ParseCommandOptions(int argc, char** argv, ProgramOption* opt)
last_scorer_index++;
break;
case 'c':
if (last_scorer_index == -1) throw runtime_error("You need to specify a scorer before its config string.");
opt->scorer_configs[last_scorer_index] = string(optarg);
break;
case 'R':
@ -213,6 +250,9 @@ void ParseCommandOptions(int argc, char** argv, ProgramOption* opt)
case 'C':
opt->candidate = string(optarg);
break;
case 'n':
opt->nbest = string(optarg);
break;
case 'b':
opt->bootstrap = atoi(optarg);
break;
@ -221,9 +261,11 @@ void ParseCommandOptions(int argc, char** argv, ProgramOption* opt)
opt->has_seed = true;
break;
case 'f':
if (last_scorer_index == -1) throw runtime_error("You need to specify a scorer before its list of factors.");
opt->scorer_factors[last_scorer_index] = string(optarg);
break;
case 'l':
if (last_scorer_index == -1) throw runtime_error("You need to specify a scorer before its filter.");
opt->scorer_filter[last_scorer_index] = string(optarg);
break;
default:
@ -271,8 +313,13 @@ int main(int argc, char** argv)
if (option.reference.length() == 0) throw runtime_error("You have to specify at least one reference file.");
split(option.reference, ',', refFiles);
if (option.candidate.length() == 0) throw runtime_error("You have to specify at least one candidate file.");
split(option.candidate, ',', candFiles);
if (option.candidate.length() == 0 && option.nbest.length() == 0) throw runtime_error("You have to specify at least one candidate (or n-best) file.");
if (option.candidate.length() > 0 && option.nbest.length() > 0) throw runtime_error("You can either specify candidate files or n-best files, but not both.");
bool nbest_input = option.nbest.length() > 0;
if (nbest_input)
split(option.nbest, ',', candFiles);
else
split(option.candidate, ',', candFiles);
if (candFiles.size() > 1) g_has_more_files = true;
if (option.scorer_types.size() > 1) g_has_more_scorers = true;
@ -283,7 +330,7 @@ int main(int argc, char** argv)
g_scorer->setFactors(option.scorer_factors[i]);
g_scorer->setFilter(option.scorer_filter[i]);
g_scorer->setReferenceFiles(refFiles);
EvaluatorUtil::evaluate(*fileIt, option.bootstrap);
EvaluatorUtil::evaluate(*fileIt, option.bootstrap, nbest_input);
delete g_scorer;
}
}

View File

@ -46,6 +46,9 @@ de recherches du Canada
#include "MiraFeatureVector.h"
#include "MiraWeightVector.h"
#include "Scorer.h"
#include "ScorerFactory.h"
using namespace std;
using namespace MosesTuning;
@ -57,6 +60,8 @@ int main(int argc, char** argv)
string denseInitFile;
string sparseInitFile;
string type = "nbest";
string sctype = "BLEU";
string scconfig = "";
vector<string> scoreFiles;
vector<string> featureFiles;
vector<string> referenceFiles; //for hg mira
@ -67,6 +72,7 @@ int main(int argc, char** argv)
float decay = 0.999; // Pseudo-corpus decay \gamma
int n_iters = 60; // Max epochs J
bool streaming = false; // Stream all k-best lists?
bool streaming_out = false; // Stream output after each sentence?
bool no_shuffle = false; // Don't shuffle, even for in memory version
bool model_bg = false; // Use model for background corpus
bool verbose = false; // Verbose updates
@ -78,6 +84,8 @@ int main(int argc, char** argv)
desc.add_options()
("help,h", po::value(&help)->zero_tokens()->default_value(false), "Print this help message and exit")
("type,t", po::value<string>(&type), "Either nbest or hypergraph")
("sctype", po::value<string>(&sctype), "the scorer type (default BLEU)")
("scconfig,c", po::value<string>(&scconfig), "configuration string passed to scorer")
("scfile,S", po::value<vector<string> >(&scoreFiles), "Scorer data files")
("ffile,F", po::value<vector<string> > (&featureFiles), "Feature data files")
("hgdir,H", po::value<string> (&hgDir), "Directory containing hypergraphs")
@ -90,6 +98,7 @@ int main(int argc, char** argv)
("dense-init,d", po::value<string>(&denseInitFile), "Weight file for dense features. This should have 'name= value' on each line, or (legacy) should be the Moses mert 'init.opt' format.")
("sparse-init,s", po::value<string>(&sparseInitFile), "Weight file for sparse features")
("streaming", po::value(&streaming)->zero_tokens()->default_value(false), "Stream n-best lists to save memory, implies --no-shuffle")
("streaming-out", po::value(&streaming_out)->zero_tokens()->default_value(false), "Stream weights to stdout after each sentence")
("no-shuffle", po::value(&no_shuffle)->zero_tokens()->default_value(false), "Don't shuffle hypotheses before each epoch")
("model-bg", po::value(&model_bg)->zero_tokens()->default_value(false), "Use model instead of hope for BLEU background")
("verbose", po::value(&verbose)->zero_tokens()->default_value(false), "Verbose updates")
@ -209,25 +218,27 @@ int main(int argc, char** argv)
MiraWeightVector wv(initParams);
// Initialize background corpus
vector<ValType> bg;
for(int j=0; j<kBleuNgramOrder; j++) {
bg.push_back(kBleuNgramOrder-j);
bg.push_back(kBleuNgramOrder-j);
// Initialize scorer
if(sctype != "BLEU" && type == "hypergraph") {
UTIL_THROW(util::Exception, "hypergraph mira only supports BLEU");
}
bg.push_back(kBleuNgramOrder);
boost::scoped_ptr<Scorer> scorer(ScorerFactory::getScorer(sctype, scconfig));
// Initialize background corpus
vector<ValType> bg(scorer->NumberOfScores(), 1);
boost::scoped_ptr<HopeFearDecoder> decoder;
if (type == "nbest") {
decoder.reset(new NbestHopeFearDecoder(featureFiles, scoreFiles, streaming, no_shuffle, safe_hope));
decoder.reset(new NbestHopeFearDecoder(featureFiles, scoreFiles, streaming, no_shuffle, safe_hope, scorer.get()));
} else if (type == "hypergraph") {
decoder.reset(new HypergraphHopeFearDecoder(hgDir, referenceFiles, initDenseSize, streaming, no_shuffle, safe_hope, hgPruning, wv));
decoder.reset(new HypergraphHopeFearDecoder(hgDir, referenceFiles, initDenseSize, streaming, no_shuffle, safe_hope, hgPruning, wv, scorer.get()));
} else {
UTIL_THROW(util::Exception, "Unknown batch mira type: '" << type << "'");
}
// Training loop
cerr << "Initial BLEU = " << decoder->Evaluate(wv.avg()) << endl;
if (!streaming_out)
cerr << "Initial BLEU = " << decoder->Evaluate(wv.avg()) << endl;
ValType bestBleu = 0;
for(int j=0; j<n_iters; j++) {
// MIRA train for one epoch
@ -275,6 +286,8 @@ int main(int argc, char** argv)
}
iNumExamples++;
++sentenceIndex;
if (streaming_out)
cout << wv << endl;
}
// Training Epoch summary
cerr << iNumUpdates << "/" << iNumExamples << " updates"

View File

@ -143,7 +143,7 @@ vector< vector<const Word*> > MosesDecoder::runDecoder(const std::string& source
string filename)
{
// run the decoder
m_manager = new Moses::Manager(0,*m_sentence, search);
m_manager = new Moses::Manager(*m_sentence, search);
m_manager->ProcessSentence();
TrellisPathList nBestList;
m_manager->CalcNBest(nBestSize, nBestList, distinct);
@ -220,7 +220,7 @@ vector< vector<const Word*> > MosesDecoder::runChartDecoder(const std::string& s
size_t epoch)
{
// run the decoder
m_chartManager = new ChartManager(0,*m_sentence);
m_chartManager = new ChartManager(*m_sentence);
m_chartManager->ProcessSentence();
ChartKBestExtractor::KBestVec nBestList;
m_chartManager->CalcNBest(nBestSize, nBestList, distinct);

View File

@ -12,6 +12,8 @@ exe TMining : TransliterationMining.cpp ../moses//moses ;
exe 1-1-Extraction : 1-1-Extraction.cpp ../moses//moses ;
exe prunePhraseTable : prunePhraseTable.cpp ../moses//moses ..//boost_program_options ;
local with-cmph = [ option.get "with-cmph" ] ;
if $(with-cmph) {
exe processPhraseTableMin : processPhraseTableMin.cpp ../moses//moses ;
@ -44,4 +46,4 @@ $(TOP)//boost_iostreams
$(TOP)//boost_program_options
;
alias programs : 1-1-Extraction TMining generateSequences processPhraseTable processLexicalTable queryPhraseTable queryLexicalTable programsMin programsProbing merge-sorted ;
alias programs : 1-1-Extraction TMining generateSequences processPhraseTable processLexicalTable queryPhraseTable queryLexicalTable programsMin programsProbing merge-sorted prunePhraseTable ;

227
misc/prunePhraseTable.cpp Normal file
View File

@ -0,0 +1,227 @@
// $Id$
// vim:tabstop=2
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2014- 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
***********************************************************************/
/**
Prune the phrase table using the same translation pruning that Moses uses during decoding.
**/
#include <cstring>
#include <iostream>
#include <fstream>
#include <map>
#include <string>
#include <vector>
#include <boost/program_options.hpp>
#include <boost/scoped_ptr.hpp>
#include "moses/InputPath.h"
#include "moses/Parameter.h"
#include "moses/TranslationModel/PhraseDictionary.h"
#include "moses/StaticData.h"
#include "util/file_piece.hh"
#include "util/string_piece.hh"
#include "util/tokenize_piece.hh"
#include "util/double-conversion/double-conversion.h"
#include "util/exception.hh"
using namespace Moses;
using namespace std;
namespace po = boost::program_options;
typedef multimap<float,string> Lines;
static void usage(const po::options_description& desc, char** argv) {
cerr << "Usage: " + string(argv[0]) + " [options] input-file output-file" << endl;
cerr << desc << endl;
}
//Find top n translations of source, and send them to output
static void outputTopN(Lines lines, size_t maxPhrases, ostream& out) {
size_t count = 0;
for (Lines::const_reverse_iterator i = lines.rbegin(); i != lines.rend(); ++i) {
out << i->second << endl;
++count;
if (count >= maxPhrases) break;
}
}
/*
static void outputTopN(const Phrase& sourcePhrase, const multimap<float,const TargetPhrase*>& targetPhrases,
size_t maxPhrases, const PhraseDictionary* phraseTable,
const vector<FactorType> & input, const vector<FactorType> & output, ostream& out) {
size_t count = 0;
for (multimap<float,const TargetPhrase*>::const_reverse_iterator i
= targetPhrases.rbegin(); i != targetPhrases.rend() && count < maxPhrases; ++i, ++count) {
const TargetPhrase* targetPhrase = i->second;
out << sourcePhrase.GetStringRep(input);
out << " ||| ";
out << targetPhrase->GetStringRep(output);
out << " ||| ";
const ScoreComponentCollection scores = targetPhrase->GetScoreBreakdown();
vector<float> phraseScores = scores.GetScoresForProducer(phraseTable);
for (size_t j = 0; j < phraseScores.size(); ++j) {
out << exp(phraseScores[j]) << " ";
}
out << "||| ";
const AlignmentInfo& align = targetPhrase->GetAlignTerm();
for (AlignmentInfo::const_iterator j = align.begin(); j != align.end(); ++j) {
out << j->first << "-" << j->second << " ";
}
out << endl;
}
}*/
int main(int argc, char** argv)
{
bool help;
string input_file;
string config_file;
size_t maxPhrases = 100;
po::options_description desc("Allowed options");
desc.add_options()
("help,h", po::value(&help)->zero_tokens()->default_value(false), "Print this help message and exit")
("input-file,i", po::value<string>(&input_file), "Input file")
("config-file,f", po::value<string>(&config_file), "Config file")
("max-phrases,n", po::value<size_t>(&maxPhrases), "Maximum target phrases per source phrase")
;
po::options_description cmdline_options;
cmdline_options.add(desc);
po::variables_map vm;
po::parsed_options parsed = po::command_line_parser(argc,argv).
options(cmdline_options).run();
po::store(parsed, vm);
po::notify(vm);
if (help) {
usage(desc, argv);
exit(0);
}
if (input_file.empty()) {
cerr << "ERROR: Please specify an input file" << endl << endl;
usage(desc, argv);
exit(1);
}
if (config_file.empty()) {
cerr << "ERROR: Please specify a config file" << endl << endl;
usage(desc, argv);
exit(1);
}
vector<string> mosesargs;
mosesargs.push_back(argv[0]);
mosesargs.push_back("-f");
mosesargs.push_back(config_file);
boost::scoped_ptr<Parameter> params(new Parameter());
char** mosesargv = new char*[mosesargs.size()];
for (size_t i = 0; i < mosesargs.size(); ++i) {
mosesargv[i] = new char[mosesargs[i].length() + 1];
strcpy(mosesargv[i], mosesargs[i].c_str());
}
if (!params->LoadParam(mosesargs.size(), mosesargv)) {
params->Explain();
exit(1);
}
if (!StaticData::LoadDataStatic(params.get(),argv[0])) {
exit(1);
}
const StaticData &staticData = StaticData::Instance();
//Find the phrase table to manage the target phrases
PhraseDictionary* phraseTable = NULL;
const vector<FeatureFunction*>& ffs = FeatureFunction::GetFeatureFunctions();
for (size_t i = 0; i < ffs.size(); ++i) {
PhraseDictionary* maybePhraseTable = dynamic_cast< PhraseDictionary*>(ffs[i]);
if (maybePhraseTable) {
UTIL_THROW_IF(phraseTable,util::Exception,"Can only score translations with one phrase table");
phraseTable = maybePhraseTable;
}
}
UTIL_THROW_IF(!phraseTable,util::Exception,"Unable to find scoring phrase table");
//
//Load and prune the phrase table. This is taken (with mods) from moses/TranslationModel/RuleTable/LoaderStandard.cpp
//
std::ostream *progress = NULL;
IFVERBOSE(1) progress = &std::cerr;
util::FilePiece in(input_file.c_str(), progress);
// reused variables
vector<float> scoreVector;
StringPiece line;
double_conversion::StringToDoubleConverter converter(double_conversion::StringToDoubleConverter::NO_FLAGS, NAN, NAN, "inf", "nan");
string previous;
Lines lines;
while(true) {
try {
line = in.ReadLine();
} catch (const util::EndOfFileException &e) {
break;
}
util::TokenIter<util::MultiCharacter> pipes(line, "|||");
StringPiece sourcePhraseString(*pipes);
StringPiece targetPhraseString(*++pipes);
StringPiece scoreString(*++pipes);
scoreVector.clear();
for (util::TokenIter<util::AnyCharacter, true> s(scoreString, " \t"); s; ++s) {
int processed;
float score = converter.StringToFloat(s->data(), s->length(), &processed);
UTIL_THROW_IF2(isnan(score), "Bad score " << *s);
scoreVector.push_back(FloorScore(TransformScore(score)));
}
if (sourcePhraseString != previous) {
outputTopN(lines, maxPhrases, cout);
previous = sourcePhraseString.as_string();
lines.clear();
}
ScoreComponentCollection scores;
scores.Assign(phraseTable,scoreVector);
float score = scores.InnerProduct(staticData.GetAllWeights());
lines.insert(pair<float,string>(score,line.as_string()));
}
if (!lines.empty()) {
outputTopN(lines, maxPhrases, cout);
}
return 0;
}

View File

@ -1,150 +0,0 @@
// $Id$
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (c) 2006 University of Edinburgh
All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the University of Edinburgh nor the names of its contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS
BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
***********************************************************************/
// example file on how to use moses library
#pragma once
#include <fstream>
#include <vector>
#include <set>
#include "moses/TypeDef.h"
#include "moses/Sentence.h"
#include "moses/FactorTypeSet.h"
#include "moses/ChartKBestExtractor.h"
#include "moses/OutputCollector.h"
#include "moses/ChartHypothesis.h"
#include "search/applied.hh"
#include "moses/ChartManager.h"
namespace Moses
{
class FactorCollection;
class ScoreComponentCollection;
}
namespace MosesChartCmd
{
/** Helper class that holds misc variables to write data out to command line.
*/
class IOWrapper
{
protected:
typedef std::vector<std::pair<Moses::Word, Moses::WordsRange> > ApplicationContext;
long m_translationId;
const std::vector<Moses::FactorType> &m_inputFactorOrder;
const std::vector<Moses::FactorType> &m_outputFactorOrder;
const Moses::FactorMask &m_inputFactorUsed;
std::ostream *m_outputSearchGraphStream;
std::ostream *m_detailedTranslationReportingStream;
std::ostream *m_detailedTreeFragmentsTranslationReportingStream;
//DIMw
std::ostream *m_detailedAllTranslationReportingStream;
std::ostream *m_alignmentInfoStream;
std::ostream *m_unknownsStream;
std::string m_inputFilePath;
std::istream *m_inputStream;
Moses::OutputCollector *m_detailOutputCollector;
Moses::OutputCollector *m_detailTreeFragmentsOutputCollector;
//DIMw
Moses::OutputCollector *m_detailAllOutputCollector;
Moses::OutputCollector *m_nBestOutputCollector;
Moses::OutputCollector *m_searchGraphOutputCollector;
Moses::OutputCollector *m_singleBestOutputCollector;
Moses::OutputCollector *m_alignmentInfoCollector;
Moses::OutputCollector *m_unknownsCollector;
typedef std::set< std::pair<size_t, size_t> > Alignments;
std::size_t OutputAlignmentNBest(Alignments &retAlign, const Moses::ChartKBestExtractor::Derivation &derivation, std::size_t startTarget);
size_t OutputAlignment(Alignments &retAlign, const Moses::ChartHypothesis *hypo, size_t startTarget);
void OutputAlignment(std::vector< std::set<size_t> > &retAlignmentsS2T, const Moses::AlignmentInfo &ai);
void OutputTranslationOption(std::ostream &out, ApplicationContext &applicationContext, const Moses::ChartHypothesis *hypo, const Moses::Sentence &sentence, long translationId);
void OutputTranslationOption(std::ostream &out, ApplicationContext &applicationContext, const search::Applied *applied, const Moses::Sentence &sentence, long translationId);
void OutputTranslationOptions(std::ostream &out, ApplicationContext &applicationContext, const Moses::ChartHypothesis *hypo, const Moses::Sentence &sentence, long translationId);
void OutputTranslationOptions(std::ostream &out, ApplicationContext &applicationContext, const search::Applied *applied, const Moses::Sentence &sentence, long translationId);
void OutputTreeFragmentsTranslationOptions(std::ostream &out, ApplicationContext &applicationContext, const Moses::ChartHypothesis *hypo, const Moses::Sentence &sentence, long translationId);
void OutputTreeFragmentsTranslationOptions(std::ostream &out, ApplicationContext &applicationContext, const search::Applied *applied, const Moses::Sentence &sentence, long translationId);
void ReconstructApplicationContext(const Moses::ChartHypothesis &hypo,
const Moses::Sentence &sentence,
ApplicationContext &context);
void ReconstructApplicationContext(const search::Applied *applied,
const Moses::Sentence &sentence,
ApplicationContext &context);
void WriteApplicationContext(std::ostream &out,
const ApplicationContext &context);
void OutputAllFeatureScores(const Moses::ScoreComponentCollection &features
, std::ostream &out);
void OutputFeatureScores( std::ostream& out
, const Moses::ScoreComponentCollection &features
, const Moses::FeatureFunction *ff
, std::string &lastName );
public:
IOWrapper(const std::vector<Moses::FactorType> &inputFactorOrder
, const std::vector<Moses::FactorType> &outputFactorOrder
, const Moses::FactorMask &inputFactorUsed
, size_t nBestSize
, const std::string &nBestFilePath
, const std::string &inputFilePath="");
~IOWrapper();
Moses::InputType* GetInput(Moses::InputType *inputType);
void OutputBestHypo(const Moses::ChartHypothesis *hypo, long translationId);
void OutputBestHypo(search::Applied applied, long translationId);
void OutputBestHypo(const std::vector<const Moses::Factor*>& mbrBestHypo, long translationId);
void OutputBestNone(long translationId);
void OutputNBestList(const std::vector<boost::shared_ptr<Moses::ChartKBestExtractor::Derivation> > &nBestList, long translationId);
void OutputNBestList(const std::vector<search::Applied> &nbest, long translationId);
void OutputDetailedTranslationReport(const Moses::ChartHypothesis *hypo, const Moses::Sentence &sentence, long translationId);
void OutputDetailedTranslationReport(const search::Applied *applied, const Moses::Sentence &sentence, long translationId);
void OutputDetailedTreeFragmentsTranslationReport(const Moses::ChartHypothesis *hypo, const Moses::Sentence &sentence, long translationId);
void OutputDetailedTreeFragmentsTranslationReport(const search::Applied *applied, const Moses::Sentence &sentence, long translationId);
void OutputDetailedAllTranslationReport(const std::vector<boost::shared_ptr<Moses::ChartKBestExtractor::Derivation> > &nBestList, const Moses::ChartManager &manager, const Moses::Sentence &sentence, long translationId);
void Backtrack(const Moses::ChartHypothesis *hypo);
void ResetTranslationId();
Moses::OutputCollector *GetSearchGraphOutputCollector() {
return m_searchGraphOutputCollector;
}
void OutputAlignment(size_t translationId , const Moses::ChartHypothesis *hypo);
void OutputUnknowns(const std::vector<Moses::Phrase*> &, long);
static void FixPrecision(std::ostream &, size_t size=3);
};
}

View File

@ -1,2 +0,0 @@
exe moses_chart : Main.cpp mbr.cpp IOWrapper.cpp TranslationAnalysis.cpp ../moses//moses $(TOP)//boost_iostreams ..//boost_filesystem ..//z ;

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