int MetalabelFeature::ExtractFeature(const vector<TokenCitation*>& tokenVector, CitationSet& citationSet, UniGramFeature& uniGrams, BiGramFeature& biGrams, JournalSet& journalSet, FeatureSet& allFeatures, int printLog)
{
int rtn = 0;
allFeatures.mFeatures.clear();
allFeatures.mMaxIndex = uniGrams.mDictionary.rbegin()->first + 1;
allFeatures.mFeatures.resize(tokenVector.size());
FeatureSet biFeatures;
biFeatures.mMaxIndex = biGrams.mDictionary.rbegin()->first + 1;
biFeatures.mFeatures.resize(tokenVector.size());
FeatureSet jourFeatures;
jourFeatures.mMaxIndex = journalSet.mJournals.rbegin()->first + 1;
jourFeatures.mFeatures.resize(tokenVector.size());
int numThreads = omp_get_num_procs();
if (printLog != SILENT)
clog << "CPU number: " << numThreads << endl;
omp_set_num_threads(numThreads);
if (printLog != SILENT)
clog << "Start Parallel Extract Features" << endl;
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < tokenVector.size(); i++)
{
uniGrams.Extract(*tokenVector[i], allFeatures.mFeatures[i]);
}
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < tokenVector.size(); i++)
{
biGrams.Extract(*tokenVector[i], biFeatures.mFeatures[i]);
}
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < tokenVector.size(); ++i)
{
Journal* ptrJournal = NULL;
ptrJournal = journalSet.SearchJournalTitle(citationSet[tokenVector[i]->mPmid]->mJournalTitle);
if (ptrJournal != NULL)
{
jourFeatures.mFeatures[i][ptrJournal->mJournalId] = 1.0;
}
else
{
cerr << "Error: can't find \"" << citationSet[tokenVector[i]->mPmid]->mJournalTitle << " in pmid " << tokenVector[i]->mPmid << endl;
}
}
rtn = allFeatures.Merge(biFeatures);
CHECK_RTN(rtn);
rtn = allFeatures.Merge(jourFeatures);
CHECK_RTN(rtn);
rtn = allFeatures.Normalize();
CHECK_RTN(rtn);
return 0;
}
int MetalabelFeature::ExtractFeature(const vector<TokenCitation*>& tokenVector, UniGramFeature& uniGrams, BiGramFeature& biGrams, feature_node** &featureSpace, int printLog)
{
int numThreads = omp_get_num_procs();
if (printLog != SILENT)
clog << "CPU number: " << numThreads << endl;
omp_set_num_threads(numThreads);
if (printLog != SILENT)
clog << "Extract unigram & bigram" << endl;
if (printLog != SILENT)
clog << "Make Feature table" << endl;
int featureNum = (int)tokenVector.size();
featureSpace = NULL;
featureSpace = Malloc(feature_node*, featureNum);
memset(featureSpace, 0, sizeof(feature_node*)* featureNum);
int uniMaxIndex = uniGrams.mDictionary.rbegin()->first + 1;
int biMaxIndex = biGrams.mDictionary.rbegin()->first + 1;
if (printLog != SILENT)
clog << "Extract features parallel" << endl;
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < (int)tokenVector.size(); i++)
{
FeatureSet tabAllFeatures;
tabAllFeatures.mMaxIndex = uniMaxIndex;
tabAllFeatures.mFeatures.resize(1);
FeatureSet tabBiFeatures;
tabBiFeatures.mMaxIndex = biMaxIndex;
tabBiFeatures.mFeatures.resize(1);
uniGrams.Extract(*tokenVector[i], tabAllFeatures.mFeatures[0]);
biGrams.Extract(*tokenVector[i], tabBiFeatures.mFeatures[0]);
tabAllFeatures.Merge(tabBiFeatures);
tabAllFeatures.Normalize();
featureSpace[i] = NULL;
LinearMachine::TransFeatures(featureSpace[i], tabAllFeatures.mFeatures[0]);
}
return 0;
}
int MetalabelFeature::ExtractFeature(const std::vector<TokenCitation*>& tokenVector, UniGramFeature& uniGrams, FeatureSet& allFeatures, int printLog)
{
int rtn = 0;
allFeatures.mFeatures.clear();
allFeatures.mMaxIndex = uniGrams.mDictionary.rbegin()->first + 1;
allFeatures.mFeatures.resize(tokenVector.size());
int numThreads = omp_get_num_procs();
if (printLog != SILENT)
clog << "CPU number: " << numThreads << endl;
omp_set_num_threads(numThreads);
if (printLog != SILENT)
clog << "Start Parallel Extract Features" << endl;
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < tokenVector.size(); i++)
{
uniGrams.Extract(*tokenVector[i], allFeatures.mFeatures[i]);
}
rtn = allFeatures.Normalize();
CHECK_RTN(rtn);
return 0;
}
int MetalabelFeature::ExtractFeature(const vector<TokenCitation*>& tokenVector, CitationSet& citationSet, UniGramFeature& uniGrams, BiGramFeature& biGrams, JournalSet& journalSet, feature_node** &featureSpace, int printLog)
{
int numThreads = omp_get_num_procs();
if (printLog != SILENT)
clog << "CPU number: " << numThreads << endl;
omp_set_num_threads(numThreads);
if (printLog != SILENT)
clog << "Extract unigram & bigram" << endl;
if (printLog != SILENT)
clog << "Make Feature table" << endl;
int featureNum = (int)tokenVector.size();
featureSpace = NULL;
featureSpace = Malloc(feature_node*, featureNum);
memset(featureSpace, 0, sizeof(feature_node*)* featureNum);
int uniMaxIndex = uniGrams.mDictionary.rbegin()->first + 1;
int biMaxIndex = biGrams.mDictionary.rbegin()->first + 1;
int jourMaxIndex = journalSet.mJournals.rbegin()->first + 1;
if (printLog != SILENT)
clog << "Extract features parallel" << endl;
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < (int)tokenVector.size(); i++)
{
FeatureSet tabAllFeatures;
tabAllFeatures.mMaxIndex = uniMaxIndex;
tabAllFeatures.mFeatures.resize(1);
FeatureSet tabBiFeatures;
tabBiFeatures.mMaxIndex = biMaxIndex;
tabBiFeatures.mFeatures.resize(1);
uniGrams.Extract(*tokenVector[i], tabAllFeatures.mFeatures[0]);
biGrams.Extract(*tokenVector[i], tabBiFeatures.mFeatures[0]);
FeatureSet tabJourFeatures;
tabJourFeatures.mMaxIndex = jourMaxIndex;
tabJourFeatures.mFeatures.resize(1);
if (citationSet[tokenVector[i]->mPmid]->mJournalTitle != NULL)
{
Journal* ptrJournal = journalSet.SearchJournalTitle(citationSet[tokenVector[i]->mPmid]->mJournalTitle);
if (ptrJournal != NULL)
{
tabJourFeatures.mFeatures[0][ptrJournal->mJournalId] = 1.0;
}
else
cerr << "Error: \"" << citationSet[tokenVector[i]->mPmid]->mJournalTitle << "\" can't find journal in journal set in pmid " << tokenVector[i]->mPmid << endl;
}
else
cerr << "Error: " << tokenVector[i]->mPmid << " can't find journal title in citation" << endl;
tabAllFeatures.Merge(tabBiFeatures);
tabAllFeatures.Merge(tabJourFeatures);
tabAllFeatures.Normalize();
featureSpace[i] = NULL;
LinearMachine::TransFeatures(featureSpace[i], tabAllFeatures.mFeatures[0]);
}
return 0;
}
int SaveNeighbor()
{
int rtn = 0;
LhtcDocumentSet lshtcTrainSet, lshtcTestSet;
UniGramFeature uniGrams;
string trainsetFile = "../data/loc_train.bin";
string testsetFile = "../data/loc_test.bin";
vector<Feature> lshtcTrainFeatureSet, lshtcTestFeatureSet;
vector<int> lshtcTrainFeatureID, lshtcTestFeatureID;
Feature tempFeature;
lshtcTrainFeatureSet.clear();
lshtcTestFeatureSet.clear();
lshtcTrainFeatureID.clear();
lshtcTestFeatureID.clear();
clog << "Load Unigram Dictionary" << endl;
rtn = uniGrams.Load("lshtc_unigram_dictionary_loctrain.bin");
CHECK_RTN(rtn);
clog << "Total " << uniGrams.mDictionary.size() << " unigrams" << endl;
rtn = lshtcTrainSet.LoadBin(trainsetFile, FULL_LOG);
CHECK_RTN(rtn);
int trainSize = (int)lshtcTrainSet.Size();
for (std::map<int, LhtcDocument>::iterator it = lshtcTrainSet.mLhtcDocuments.begin(); it != lshtcTrainSet.mLhtcDocuments.end(); ++it)
lshtcTrainFeatureID.push_back(it->first);
vector<LhtcDocument*> vecTrainDocument;
vecTrainDocument.reserve(lshtcTrainSet.Size());
for (map<int, LhtcDocument>::iterator it = lshtcTrainSet.mLhtcDocuments.begin(); it != lshtcTrainSet.mLhtcDocuments.end(); ++it)
vecTrainDocument.push_back(&(it->second));
clog << "Prepare for Extract Features" << endl;
FeatureSet allTrainFeatures;
allTrainFeatures.mFeatures.resize(vecTrainDocument.size());
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < (int)vecTrainDocument.size(); i++)
{
uniGrams.ExtractLhtc(*vecTrainDocument[i], allTrainFeatures.mFeatures[i]);
if (allTrainFeatures.mFeatures[i].size() == 0) printf("%d Warning!!\n", i);
}
allTrainFeatures.Normalize();//get traindata feature
rtn = lshtcTestSet.LoadBin(testsetFile, FULL_LOG);
CHECK_RTN(rtn);
int testSize = (int)lshtcTestSet.Size();
for (std::map<int, LhtcDocument>::iterator it = lshtcTestSet.mLhtcDocuments.begin(); it != lshtcTestSet.mLhtcDocuments.end(); ++it)
lshtcTestFeatureID.push_back(it->first);
vector<LhtcDocument*> vecTestDocument;
vecTestDocument.reserve(lshtcTestSet.Size());
for (map<int, LhtcDocument>::iterator it = lshtcTestSet.mLhtcDocuments.begin(); it != lshtcTestSet.mLhtcDocuments.end(); ++it)
vecTestDocument.push_back(&(it->second));
clog << "Prepare for Extract Features" << endl;
FeatureSet allTestFeatures;
allTestFeatures.mFeatures.resize(vecTestDocument.size());
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < (int)vecTestDocument.size(); i++)
{
uniGrams.ExtractLhtc(*vecTestDocument[i], allTestFeatures.mFeatures[i]);
if (allTestFeatures.mFeatures[i].size() == 0) printf("%d Warning!!\n", i);
}
allTestFeatures.Normalize();//get testdata feature
int sigSize = allTestFeatures.Size() / 5;
for (int i = 0; i < 5; ++i)
{
string filename = "../data/lshtc_neighbor" + intToString(i) + ".bin";
if (FileExist(filename))
continue;
clog << i << "th, sigSize = " << sigSize << endl;
FeatureSet locFeatures;
vector<int> locIds;
for (int j = sigSize*i; j < sigSize*(i + 1); ++j)
{
locFeatures.AddInstance(allTestFeatures[j]);
locIds.push_back(lshtcTestFeatureID[j]);
}
FeatureNeighbor featureneighbor;
rtn = featureneighbor.Build(allTrainFeatures.mFeatures, locFeatures.mFeatures, lshtcTrainFeatureID, locIds);
CHECK_RTN(rtn);
rtn = featureneighbor.SaveBin(filename, STATUS_ONLY);
CHECK_RTN(rtn);
clog << "Save bin completed" << endl;
}
return 0;
}
//only unigram feature,predictscore saved by model, scores predicted by one model save in one line
int SavePredictScore(string tokenPath, string uniGramFile, string labelFreqFile, string modelPath, const int modelNum, string scoreFilePath)
{
int rtn = 0;
clog << "Loading Tokenization Result" << endl;
LhtcDocumentSet tokenDocuments;
rtn = tokenDocuments.LoadBin(tokenPath.c_str(), STATUS_ONLY);//"pratest_6020.bin"
CHECK_RTN(rtn);
clog << "Load Unigram Dictionary" << endl;
UniGramFeature uniGrams;
rtn = uniGrams.Load(uniGramFile.c_str());
CHECK_RTN(rtn);
clog << "Total " << uniGrams.mDictionary.size() << " unigrams" << endl;
clog << "Load Label Frequence" << endl;
map<int, double> labelFreq;
rtn = LoadLabelFreq(labelFreqFile.c_str(), labelFreq);
CHECK_RTN(rtn);
vector<pair<int, double> > meshSort;
for (map<int, double>::iterator it = labelFreq.begin(); it != labelFreq.end(); ++it)
meshSort.push_back(make_pair(it->first, it->second));
sort(meshSort.begin(), meshSort.end(), CmpScore);
vector<int> modelIds;
modelIds.clear();
for (size_t i = 0; i < (size_t)modelNum && i < meshSort.size(); ++i)
{
string modelFile = modelPath + "/" + intToString(meshSort[i].first) + ".model";
if (FileExist(modelFile))
{
modelIds.push_back(meshSort[i].first);
}
}
clog << modelIds.size() << " Models Available" << endl;
vector<LhtcDocument*> tokenDocVector;
tokenDocVector.reserve(tokenDocuments.Size());
for (map<int, LhtcDocument>::iterator it = tokenDocuments.mLhtcDocuments.begin(); it != tokenDocuments.mLhtcDocuments.end(); it++)
{
tokenDocVector.push_back(&(it->second));
}
clog << "Prepare for Extract Features" << endl;
FeatureSet allFeatures;
allFeatures.mFeatures.resize(tokenDocVector.size());
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < (int)tokenDocVector.size(); i++)
{
uniGrams.ExtractLhtc(*tokenDocVector[i], allFeatures.mFeatures[i]);
}
allFeatures.Normalize();
vector<int> pmids;
for (map<int, LhtcDocument>::iterator it = tokenDocuments.mLhtcDocuments.begin(); it != tokenDocuments.mLhtcDocuments.end(); ++it)
pmids.push_back(it->first);
if (pmids.size() != (size_t)allFeatures.Size())
{
clog << "Error: pmids.size != allFeatures.size" << endl;
return -1;
}
//clog << "Free Memory" << endl;
//tokenCitations.~TokenCitationSet();
FILE * outScoreFile = fopen(scoreFilePath.c_str(), "wb");
if (outScoreFile == NULL)
return -1;
clog << "Start Predict" << endl;
int numThreads = omp_get_num_procs();
omp_set_num_threads(numThreads);
rtn = Write(outScoreFile, modelIds.size());//(size_t)modelIds.size()
CHECK_RTN(rtn);
for (unsigned int k = 0; k < modelIds.size(); k++)
{
if ((k & 255) == 0)
{
clog << "LOG : Working for model " << modelIds[k] << endl;
}
string modelFile = modelPath + "/" + intToString(modelIds[k]) + ".model";
LinearMachine linearMachine;
rtn = linearMachine.Load(modelFile);
CHECK_RTN(rtn);
pair<int, vector<pair<int, double>>> modelScore;
modelScore.first = modelIds[k];
modelScore.second.resize(allFeatures.Size());
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < allFeatures.Size(); i++)
{
double tmpScore;
modelScore.second[i].first = pmids[i];
linearMachine.Predict(allFeatures[i], modelScore.second[i].second);
}
rtn = Write(outScoreFile, modelScore);
CHECK_RTN(rtn);
}
fclose(outScoreFile);
outScoreFile = NULL;
clog << "Save Complete" << endl;
return 0;
}
