// Renormalize the training set using the test set's mean and std dev.
int Renormalize(MData& trainSet, MData& testSet)
{
int result = 0;
float *trainMean = trainSet.GetMeans(), *testMean = testSet.GetMeans(),
*trainStdDev = trainSet.GetStdDevs(), *testStdDev = testSet.GetStdDevs(),
**trainFeatures = trainSet.GetData();
bool norm = true;
// Check if re-normalization is actually needed
for(int i = 0; i < trainSet.GetDims(); i++) {
if( trainMean[i] != testMean[i] ||
trainStdDev[i] != testStdDev[i] ) {
norm = false;
break;
}
}
if( norm ) {
for(int obj = 0; obj < trainSet.GetNumObjs(); obj++) {
for(int dim = 0; dim < trainSet.GetDims(); dim++) {
trainFeatures[obj][dim] = (trainFeatures[obj][dim] * trainStdDev[dim]) + trainMean[dim];
trainFeatures[obj][dim] = (trainFeatures[obj][dim] - testMean[dim]) / testStdDev[dim];
}
}
}
return result;
}
int GenerateMap(MData& trainSet, MData& testSet, Classifier *classifier,
string slide, string outFileName)
{
int result = 0, offset, slideObjs;
float **train = trainSet.GetData(), **test = testSet.GetData(),
*scores = (float*)malloc(testSet.GetNumObjs() * sizeof(float));
result = TrainClassifier(classifier, trainSet);
classifier->ScoreBatch(test, testSet.GetNumObjs(), testSet.GetDims(), scores);
offset = testSet.GetSlideOffset(slide, slideObjs);
ofstream outFile(outFileName.c_str());
if( outFile.is_open() ) {
outFile << "score,X,Y" << endl;
for(int i = offset; i < offset + slideObjs; i++) {
outFile << scores[i] << "," << testSet.GetXCentroid(i) << "," << testSet.GetYCentroid(i) << endl;
}
outFile.close();
} else {
cerr << "Unable to create " << outFileName << endl;
result = -10;
}
if( scores )
free(scores);
return result;
}
int TrainClassifier(Classifier *classifier, MData& trainSet)
{
int result = 0;
int *labels = trainSet.GetLabels(), numObjs = trainSet.GetNumObjs(),
numDims = trainSet.GetDims();
float **data = trainSet.GetData();
if( !classifier->Train(data[0], labels, numObjs, numDims) ) {
cerr << "Classifier traiing FAILED" << endl;
result = -20;
}
return result;
}
int TrainClassifier(Classifier *classifier, MData& trainSet, int iteration)
{
int result = 0;
int *labels = trainSet.GetLabels(), dims = trainSet.GetDims(), count,
*iterationList = trainSet.GetIterationList();;
float **data = trainSet.GetData();
count = 0;
while( iterationList[count] <= iteration && count < trainSet.GetNumObjs() )
count++;
cout << "Train set size: " << count << endl;
if( !classifier->Train(data[0], labels, count, dims) ) {
cerr << "Classifier traiing FAILED" << endl;
result = -10;
}
return result;
}
int ApplyClassifier(MData& trainSet, MData& testSet, Classifier *classifier,
string testFileName, string outFileName)
{
int result = 0, dims = trainSet.GetDims(), *trainLabel = trainSet.GetLabels(),
numTestObjs = testSet.GetNumObjs();
float **test = testSet.GetData(), **train = trainSet.GetData(),
*predScore = NULL;
if( dims != testSet.GetDims() ) {
cerr << "Training and test set dimensions do not match" << endl;
result = -30;
}
if( result == 0 ) {
cout << "Allocating prediction buffer" << endl;
predScore = (float*)malloc(numTestObjs * sizeof(float));
if( predScore == NULL ) {
cerr << "Unable to allocae prediction buffer" << endl;
result = -31;
}
}
if( result == 0 ) {
cout << "Training classifier..." << endl;
if( !classifier->Train(train[0], trainLabel, trainSet.GetNumObjs(), dims) ) {
cerr << "Classifier training failed" << endl;
result = -32;
}
}
if( result == 0 ) {
cout << "Applying classifier..." << endl;
if( ! classifier->ScoreBatch(test, numTestObjs, dims, predScore) ) {
cerr << "Applying classifier failed" << endl;
result = -33;
}
}
if( result == 0 ) {
// Copy original test file so we can just append the
// score data
//
string cmd = "cp " + testFileName + " " + outFileName;
result = system(cmd.c_str());
}
if( result == 0 ) {
hid_t fileId;
hsize_t dims[2];
herr_t status;
fileId = H5Fopen(outFileName.c_str(), H5F_ACC_RDWR, H5P_DEFAULT);
if( fileId < 0 ) {
cerr << "Unable to open: " << outFileName << endl;
result = -34;
}
if( result == 0 ) {
dims[0] = numTestObjs;
dims[1] = 1;
status = H5LTmake_dataset(fileId, "/pred_score", 2, dims,
H5T_NATIVE_FLOAT, predScore);
if( status < 0 ) {
cerr << "Unable to write score data" << endl;
result = -35;
}
}
if( fileId >= 0 ) {
H5Fclose(fileId);
}
}
if( predScore )
free(predScore);
return result;
}
int CalcROC(MData& trainSet, MData& testSet, Classifier *classifier, string testFile,
string outFileName)
{
int result = 0, *trainLabels = trainSet.GetLabels(),
*testLabels = testSet.GetLabels(),
*predClass = (int*)malloc(testSet.GetNumObjs() * sizeof(int));
float **train = trainSet.GetData(), **test = testSet.GetData(),
*scores = (float*)malloc(testSet.GetNumObjs() * sizeof(float));
if( testLabels == NULL ) {
cerr << "Test set has no lables" << endl;
result = -10;
}
if( predClass == NULL || scores == NULL ) {
result = -11;
cerr << "Unable to allocate results buffer" << endl;
}
ofstream outFile(outFileName.c_str());
if( !outFile.is_open() ) {
cerr << "Unable to create " << outFileName << endl;
result = -12;
}
if( result == 0 ) {
int TP = 0, FP = 0, TN = 0, FN = 0, P = 0, N = 0;
cout << "Saving to: " << outFileName << endl;
outFile << "labels,";
for(int idx = 0; idx < testSet.GetNumObjs() - 1; idx++)
outFile << testLabels[idx] << ",";
outFile << testLabels[testSet.GetNumObjs() - 1] << endl;
result = TrainClassifier(classifier, trainSet);
classifier->ScoreBatch(test, testSet.GetNumObjs(), testSet.GetDims(), scores);
for(int i = 0; i < testSet.GetNumObjs(); i++) {
if( scores[i] >= 0.0f ) {
predClass[i] = 1;
} else {
predClass[i] = -1;
}
}
// Calculate confusion matrix
TP = FP = TN = FN = P = N = 0;
for(int i = 0; i < testSet.GetNumObjs(); i++) {
if( testLabels[i] == 1 ) {
P++;
if( predClass[i] == 1 ) {
TP++;
} else {
FN++;
}
} else {
N++;
if( predClass[i] == -1 ) {
TN++;
} else {
FP++;
}
}
}
printf("\t%4d\t%4d\n", TP, FP);
printf("\t%4d\t%4d\n\n", FN, TN);
cout << "FP rate: " << (float)FP / (float)N << endl;
cout << "TP rate: " << (float)TP / (float)P << endl;
cout << "Precision: " << (float)TP / (float)(TP + FP) << endl;
cout << "Accuracy: " << (float)(TP + TN) / (float)(N + P) << endl;
outFile << "scores,";
for(int idx = 0; idx < testSet.GetNumObjs() - 1; idx++) {
outFile << ((scores[idx] + 1.0f) / 2.0) << ",";
}
outFile << ((scores[testSet.GetNumObjs() - 1] + 1.0f) / 2.0) << endl;
outFile.close();
}
if( predClass )
free(predClass);
if( scores )
free(scores);
return result;
}
// Apply the classifier to each slide in the test set. Save the counts of positive and negative
// classes for each slide to a csv file.
//
int ClassifySlides(MData& trainSet, MData& testSet, Classifier *classifier, string testFile,
string outFileName)
{
int result = 0;
int *pred = NULL, *posSlideCnt = NULL, *negSlideCnt = NULL;
char **slideNames = testSet.GetSlideNames();
ofstream outFile(outFileName.c_str());
if( classifier == NULL ) {
result = -10;
}
if( result == 0 ) {
if( outFile.is_open() ) {
outFile << "slides,";
for(int i = 0; i < testSet.GetNumSlides() - 1; i++) {
outFile << slideNames[i] << ",";
}
outFile << slideNames[testSet.GetNumSlides() - 1] << endl;
pred = (int*)malloc(testSet.GetNumObjs() * sizeof(int));
if( pred == NULL ) {
cerr << "Unable to allocate results buffer" << endl;
result = -11;
}
} else {
cerr << "Unable to open " << outFileName << endl;
result = -12;
}
}
if( result == 0 ) {
result = TrainClassifier(classifier, trainSet);
}
if( result == 0 ) {
if( !classifier->ClassifyBatch(testSet.GetData(), testSet.GetNumObjs(), testSet.GetDims(), pred) ) {
cerr << "Classification failed" << endl;
result = -13;
}
}
if( result == 0 ) {
result = CountResults(testSet, pred, posSlideCnt, negSlideCnt);
}
if( result == 0 ) {
outFile << "positive,";
for( int i = 0; i < testSet.GetNumSlides() - 1; i++ ) {
outFile << posSlideCnt[i] << ",";
}
outFile << posSlideCnt[testSet.GetNumSlides() - 1] << endl;
outFile << "negative,";
for( int i = 0; i < testSet.GetNumSlides() - 1; i++ ) {
outFile << negSlideCnt[i] << ",";
}
outFile << negSlideCnt[testSet.GetNumSlides() - 1] << endl;
}
if( outFile.is_open() ) {
outFile.close();
}
if( posSlideCnt )
free(posSlideCnt);
if( negSlideCnt )
free(negSlideCnt);
return result;
}
bool Picker::RestoreSessionData(MData& testSet)
{
bool result = true;
int numObjs = testSet.GetNumObjs(),
numDims = testSet.GetDims();
result = UpdateBuffers(numObjs, true);
if( result ) {
float *floatData = NULL;
int *intData = NULL;
intData = testSet.GetLabels();
memcpy(m_labels, intData, numObjs * sizeof(int));
intData = testSet.GetIdList();
memcpy(m_ids, intData, numObjs * sizeof(int));
floatData = testSet.GetXCentroidList();
memcpy(m_xCentroid, floatData, numObjs * sizeof(float));
floatData = testSet.GetXClickList();
// Test sets created with earlier versions of HistomicsML don't have clicks
if( floatData == NULL ) {
// Use centroids for click location if not present.
floatData = testSet.GetXCentroidList();
}
memcpy(m_xClick, floatData, numObjs * sizeof(float));
floatData = testSet.GetYCentroidList();
memcpy(m_yCentroid, floatData, numObjs * sizeof(float));
floatData = testSet.GetYClickList();
if( floatData == NULL ) {
floatData = testSet.GetYCentroidList();
}
memcpy(m_yClick, floatData, numObjs * sizeof(float));
floatData = testSet.GetData()[0];
memcpy(m_trainSet[0], floatData, numObjs * numDims * sizeof(float));
char **classNames = testSet.GetClassNames();
// Older versions of al_server did not save class names, set
// defaults if they can't be loaded.
if( classNames ) {
for(int i = 0; i < testSet.GetNumClasses(); i++) {
m_classNames.push_back(string(classNames[i]));
}
} else {
m_classNames.push_back(string("Negative"));
m_classNames.push_back(string("Positive"));
}
// Get slide indices from the dataset, NOT from the training set.
intData = testSet.GetSlideIndices();
char **slideNames = testSet.GetSlideNames();
int idx;
for(int i = 0; i < numObjs; i++) {
m_slideIdx[i] = m_dataset->GetSlideIdx(slideNames[intData[i]]);
// Keep track fo selected items
idx = m_dataset->FindItem(m_xCentroid[i], m_yCentroid[i], slideNames[intData[i]]);
if( idx == -1 ) {
gLogger->LogMsg(EvtLogger::Evt_ERROR, "Unable to find item in dataset:, %f, %f in %s",
m_xCentroid[i], m_yCentroid[i], slideNames[intData[i]]);
result = false;
break;
} else {
m_samples.push_back(idx);
}
}
}
return result;
}
