void Misc::limpiarLabeled(const std::string& input, const std::string& output) {
std::ifstream labeled(input.c_str());
std::ofstream cleanLabeled(output.c_str());
std::string header, line;
std::vector<std::string> lineVec;
int bytesLeidos = 0;
int bytesTotales = 33556378; // Hardcodeadisimo
std::cout << "LIMPIANDO LABELED...\n";
// Header:
labeled >> header >> header >> header;
labeled.ignore(1,'\n');
while (labeled.peek() != EOF) {
std::getline(labeled, line);
lineVec = split(line, '\t');
processText(lineVec[2]);
removeStopwords(lineVec[2]);
cleanLabeled << lineVec[0] + '\t' << lineVec[1] << '\t' << lineVec[2];
labeled.ignore(1, '\n');
bytesLeidos += line.size() + 1;
std::cout << "\r" << (int)(bytesLeidos*100.0/bytesTotales) << "%";
if (labeled.peek() != EOF)
cleanLabeled << '\n';
}
std::cout << "\r100%\n";
}
// virtual
void GNeighborTransducer::transduce(GData* pDataLabeled, GData* pDataUnlabeled, int labelDims)
{
if(labelDims != 1)
ThrowError("Only 1 nominal label is supported");
if(!pDataLabeled->relation()->areNominal(pDataLabeled->relation()->size() - 1, 1))
ThrowError("Only nominal labels are supported");
if(!pDataLabeled->relation()->areContinuous(0, pDataLabeled->relation()->size() - 1))
ThrowError("Only continuous features are supported");
if(pDataLabeled->cols() != pDataUnlabeled->cols())
ThrowError("relations don't match");
// Make a dataset containing all rows
GData dataAll(pDataLabeled->relation());
dataAll.reserve(pDataLabeled->rows() + pDataUnlabeled->rows());
GReleaseDataHolder hDataAll(&dataAll);
for(size_t i = 0; i < pDataUnlabeled->rows(); i++)
dataAll.takeRow(pDataUnlabeled->row(i));
for(size_t i = 0; i < pDataLabeled->rows(); i++)
dataAll.takeRow(pDataLabeled->row(i));
int featureDims = pDataLabeled->cols() - labelDims;
sp_relation pRelInputs = new GUniformRelation(featureDims, 0);
dataAll.setRelation(pRelInputs);
// Find friends
GNeighborFinder* pNF;
if(m_intrinsicDims == 0)
pNF = new GNeighborFinderCacheWrapper(new GKdTree(&dataAll, 0, m_friendCount, NULL, true), true);
else
pNF = new GManifoldNeighborFinder(
&dataAll,
m_friendCount, // littleK
m_friendCount * 4, // bigK
m_intrinsicDims, // intrinsicDims
m_alpha, // alpha
m_beta, // beta
false, // prune?
m_pRand);
Holder<GNeighborFinder> hNF(pNF);
GTEMPBUF(size_t, neighbors, m_friendCount);
int labelValues = pDataLabeled->relation()->valueCount(featureDims);
GTEMPBUF(double, tallys, labelValues);
// Label the unlabeled patterns
GBitTable labeled(pDataUnlabeled->rows());
GData labelList(3); // pattern index, most likely label, confidence
labelList.newRows(pDataUnlabeled->rows());
for(size_t i = 0; i < pDataUnlabeled->rows(); i++)
labelList.row(i)[0] = i;
while(labelList.rows() > 0)
{
// Compute the most likely label and the confidence for each pattern
for(size_t i = 0; i < labelList.rows(); i++)
{
// Find the most common label
double* pRow = labelList.row(i);
size_t index = (size_t)pRow[0];
pNF->neighbors(neighbors, index);
GVec::setAll(tallys, 0.0, labelValues);
for(int j = 0; j < m_friendCount; j++)
{
if(neighbors[j] >= dataAll.rows())
continue;
double* pFriend = dataAll.row(neighbors[j]);
if(neighbors[j] >= pDataUnlabeled->rows())
{
if((int)pFriend[featureDims] >= 0 && (int)pFriend[featureDims] < labelValues)
tallys[(int)pFriend[featureDims]] += 1.0;
}
else if(labeled.bit(neighbors[j]))
{
if((int)pFriend[featureDims] >= 0 && (int)pFriend[featureDims] < labelValues)
tallys[(int)pFriend[featureDims]] += 0.6;
}
}
int label = GVec::indexOfMax(tallys, labelValues, m_pRand);
double conf = tallys[label];
// Penalize for dissenting votes
for(int j = 0; j < m_friendCount; j++)
{
if(neighbors[j] >= dataAll.rows())
continue;
double* pFriend = dataAll.row(neighbors[j]);
if(neighbors[j] >= pDataUnlabeled->rows())
{
if((int)pFriend[featureDims] != label)
conf *= 0.5;
}
else if(labeled.bit(neighbors[j]))
{
if((int)pFriend[featureDims] != label)
conf *= 0.8;
}
}
pRow[1] = label;
pRow[2] = conf;
}
labelList.sort(2);
// Assign the labels to the patterns we are most confident about
size_t maxCount = MAX((size_t)5, pDataLabeled->rows() / 5);
size_t count = 0;
for(size_t i = labelList.rows() - 1; i < labelList.rows(); i--)
{
double* pRow = labelList.row(i);
size_t index = (size_t)pRow[0];
int label = (int)pRow[1];
pDataUnlabeled->row(index)[featureDims] = label;
labeled.set(index);
labelList.deleteRow(i);
if(count >= maxCount)
break;
count++;
}
}
}