void crossValidate(GArgReader& args)
{
// Parse options
unsigned int seed = getpid() * (unsigned int)time(NULL);
size_t folds = 2;
while(args.next_is_flag())
{
if(args.if_pop("-seed"))
seed = args.pop_uint();
else if(args.if_pop("-folds"))
folds = args.pop_uint();
else
ThrowError("Invalid crossvalidate option: ", args.peek());
}
if(folds < 2)
ThrowError("There must be at least 2 folds.");
// Load the data
if(args.size() < 1)
ThrowError("No dataset specified.");
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
// Instantiate the recommender
GRand prng(seed);
GCollaborativeFilter* pModel = InstantiateAlgorithm(prng, args);
Holder<GCollaborativeFilter> hModel(pModel);
if(args.size() > 0)
ThrowError("Superfluous argument: ", args.peek());
// Do cross-validation
double mae;
double mse = pModel->crossValidate(*pData, folds, &mae);
cout << "RMSE=" << sqrt(mse) << ", MSE=" << mse << ", MAE=" << mae << "\n";
}
void enumerateValues(GArgReader& args)
{
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
size_t col = args.pop_uint();
if(pData->relation()->valueCount(col) > 0)
((GArffRelation*)pData->relation().get())->setAttrValueCount(col, 0);
else
{
size_t n = 0;
map<double,size_t> themap;
for(size_t i = 0; i < pData->rows(); i++)
{
double* pRow = pData->row(i);
map<double,size_t>::iterator it = themap.find(pRow[col]);
if(it == themap.end())
{
themap[pRow[col]] = n;
pRow[col] = (double)n;
n++;
}
else
pRow[col] = (double)it->second;
}
}
pData->print(cout);
}
void wilcoxon(GArgReader& args)
{
size_t n = args.pop_uint();
double w = args.pop_double();
double p = GMath::wilcoxonPValue(n, w);
cout << p << "\n";
}
void transacc(GArgReader& args)
{
// Parse options
unsigned int seed = getpid() * (unsigned int)time(NULL);
while(args.next_is_flag())
{
if(args.if_pop("-seed"))
seed = args.pop_uint();
else
ThrowError("Invalid crossvalidate option: ", args.peek());
}
// Load the data
if(args.size() < 1)
ThrowError("No training set specified.");
GMatrix* pTrain = loadData(args.pop_string());
Holder<GMatrix> hTrain(pTrain);
if(args.size() < 1)
ThrowError("No test set specified.");
GMatrix* pTest = loadData(args.pop_string());
Holder<GMatrix> hTest(pTest);
// Instantiate the recommender
GRand prng(seed);
GCollaborativeFilter* pModel = InstantiateAlgorithm(prng, args);
Holder<GCollaborativeFilter> hModel(pModel);
if(args.size() > 0)
ThrowError("Superfluous argument: ", args.peek());
// Do cross-validation
double mae;
double mse = pModel->trainAndTest(*pTrain, *pTest, &mae);
cout << "MSE=" << mse << ", MAE=" << mae << "\n";
}
void fuzzykmeans(GArgReader& args)
{
// Load the file and params
GMatrix data;
loadData(data, args.pop_string());
int clusters = args.pop_uint();
// Parse Options
unsigned int nSeed = getpid() * (unsigned int)time(NULL);
double fuzzifier = 1.3;
size_t reps = 1;
while(args.size() > 0)
{
if(args.if_pop("-seed"))
nSeed = args.pop_uint();
else if(args.if_pop("-fuzzifier"))
fuzzifier = args.pop_double();
else if(args.if_pop("-reps"))
reps = args.pop_uint();
else
throw Ex("Invalid option: ", args.peek());
}
// Do the clustering
GRand prng(nSeed);
GFuzzyKMeans clusterer(clusters, &prng);
clusterer.setFuzzifier(fuzzifier);
clusterer.setReps(reps);
GMatrix* pOut = clusterer.reduce(data);
std::unique_ptr<GMatrix> hOut(pOut);
pOut->print(cout);
}
void splitClass(GArgReader& args)
{
const char* filename = args.pop_string();
GMatrix* pData = loadData(filename);
Holder<GMatrix> hData(pData);
size_t classAttr = args.pop_uint();
bool dropClass = false;
while(args.size() > 0)
{
if(args.if_pop("-dropclass"))
dropClass = true;
else
ThrowError("Invalid option: ", args.peek());
}
for(size_t i = 0; i < pData->relation()->valueCount(classAttr); i++)
{
GMatrix tmp(pData->relation(), pData->heap());
pData->splitByNominalValue(&tmp, classAttr, i);
std::ostringstream oss;
PathData pd;
GFile::parsePath(filename, &pd);
string fn;
fn.assign(filename + pd.fileStart, pd.extStart - pd.fileStart);
oss << fn << "_";
pData->relation()->printAttrValue(oss, classAttr, (double)i);
oss << ".arff";
string s = oss.str();
if(dropClass)
tmp.deleteColumn(classAttr);
tmp.saveArff(s.c_str());
}
}
void nominalToCat(GArgReader& args)
{
// Load the file
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
// Parse Options
int maxValues = 12;
while(args.size() > 0)
{
if(args.if_pop("-maxvalues"))
maxValues = args.pop_uint();
else
ThrowError("Invalid option: ", args.peek());
}
// Transform the data
GNominalToCat transform(maxValues);
transform.train(*pData);
GMatrix* pDataNew = transform.transformBatch(*pData);
Holder<GMatrix> hDataNew(pDataNew);
// Print results
pDataNew->print(cout);
}
void showError(GArgReader& args, const char* szAppName, const char* szMessage)
{
cerr << "_________________________________\n";
cerr << szMessage << "\n\n";
args.set_pos(1);
const char* szCommand = args.peek();
UsageNode* pUsageTree = makeClusterUsageTree();
std::unique_ptr<UsageNode> hUsageTree(pUsageTree);
if(szCommand)
{
UsageNode* pUsageCommand = pUsageTree->choice(szCommand);
if(pUsageCommand)
{
cerr << "Brief Usage Information:\n\n";
cerr << szAppName << " ";
pUsageCommand->print(cerr, 0, 3, 76, 1000, true);
}
else
{
cerr << "Brief Usage Information:\n\n";
pUsageTree->print(cerr, 0, 3, 76, 1, false);
}
}
else
{
pUsageTree->print(cerr, 0, 3, 76, 1, false);
cerr << "\nFor more specific usage information, enter as much of the command as you know.\n";
}
cerr << "\nTo see full usage information, run:\n " << szAppName << " usage\n\n";
cerr << "For a graphical tool that will help you to build a command, run:\n waffles_wizard\n";
cerr.flush();
}
void neighbors(GArgReader& args)
{
// Load the data
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
int neighborCount = args.pop_uint();
// Find the neighbors
GKdTree neighborFinder(pData, neighborCount, NULL, true);
GTEMPBUF(size_t, neighbors, neighborCount);
GTEMPBUF(double, distances, neighborCount);
double sumClosest = 0;
double sumAll = 0;
for(size_t i = 0; i < pData->rows(); i++)
{
neighborFinder.neighbors(neighbors, distances, i);
neighborFinder.sortNeighbors(neighbors, distances);
sumClosest += sqrt(distances[0]);
for(int j = 0; j < neighborCount; j++)
sumAll += sqrt(distances[j]);
}
cout.precision(14);
cout << "average closest neighbor distance = " << (sumClosest / pData->rows()) << "\n";
cout << "average neighbor distance = " << (sumAll / (pData->rows() * neighborCount)) << "\n";
}
void GRecommenderLib::transacc(GArgReader& args)
{
// Parse options
unsigned int seed = getpid() * (unsigned int)time(NULL);
while(args.next_is_flag())
{
if(args.if_pop("-seed"))
seed = args.pop_uint();
else
throw Ex("Invalid crossvalidate option: ", args.peek());
}
// Load the data
if(args.size() < 1)
throw Ex("No training set specified.");
GMatrix train;
loadData(train, args.pop_string());
if(args.size() < 1)
throw Ex("No test set specified.");
GMatrix test;
loadData(test, args.pop_string());
// Instantiate the recommender
GCollaborativeFilter* pModel = InstantiateAlgorithm(args);
std::unique_ptr<GCollaborativeFilter> hModel(pModel);
if(args.size() > 0)
throw Ex("Superfluous argument: ", args.peek());
pModel->rand().setSeed(seed);
// Do cross-validation
double mae;
double mse = pModel->trainAndTest(train, test, &mae);
cout << "MSE=" << mse << ", MAE=" << mae << "\n";
}
void correlation(GArgReader& args)
{
GMatrix* pA = loadData(args.pop_string());
Holder<GMatrix> hA(pA);
int attr1 = args.pop_uint();
int attr2 = args.pop_uint();
// Parse Options
bool aboutorigin = false;
while(args.size() > 0)
{
if(args.if_pop("-aboutorigin"))
aboutorigin = true;
else
ThrowError("Invalid option: ", args.peek());
}
double m1, m2;
if(aboutorigin)
{
m1 = 0;
m2 = 0;
}
else
{
m1 = pA->mean(attr1);
m2 = pA->mean(attr2);
}
double corr = pA->linearCorrelationCoefficient(attr1, m1, attr2, m2);
cout.precision(14);
cout << corr << "\n";
}
void threshold(GArgReader& args){
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
unsigned column=args.pop_uint();
if(column >= hData->cols()){
std::stringstream msg;
if(hData->cols() >= 1){
msg << "The column to threshold is too large. It should be in "
<< "the range [0.." << (hData->cols()-1) << "].";
}else{
msg << "This data has no columns to threshold.";
}
ThrowError(msg.str());
}
if(hData->relation()->valueCount(column) != 0){
ThrowError("Can only use threshold on continuous attributes.");
}
double value = args.pop_double();
//Do the actual thresholding
for(size_t i = 0; i < hData->rows(); ++i){
double& v = hData->row(i)[column];
if(v <= value){ v = 0;
}else { v = 1; }
}
//Print the data
hData->print(cout);
}
void dropRows(GArgReader& args)
{
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
size_t newSize = args.pop_uint();
while(pData->rows() > newSize)
pData->deleteRow(pData->rows() - 1);
pData->print(cout);
}
void addMatrices(GArgReader& args)
{
GMatrix* pA = loadData(args.pop_string());
Holder<GMatrix> hA(pA);
GMatrix* pB = loadData(args.pop_string());
Holder<GMatrix> hB(pB);
pA->add(pB, false);
pA->print(cout);
}
void zeroMean(GArgReader& args)
{
GMatrix* pA = loadData(args.pop_string());
Holder<GMatrix> hA(pA);
if(args.size() > 0)
ThrowError("Superfluous arg: ", args.pop_string());
pA->centerMeanAtOrigin();
pA->print(cout);
}
void mergeVert(GArgReader& args)
{
GMatrix* pData1 = loadData(args.pop_string());
Holder<GMatrix> hData1(pData1);
GMatrix* pData2 = loadData(args.pop_string());
Holder<GMatrix> hData2(pData2);
pData1->mergeVert(pData2);
pData1->print(cout);
}
void multiplyScalar(GArgReader& args)
{
GMatrix* pA = loadData(args.pop_string());
Holder<GMatrix> hA(pA);
double scale = args.pop_double();
if(args.size() > 0)
ThrowError("Superfluous arg: ", args.pop_string());
pA->multiply(scale);
pA->print(cout);
}
void align(GArgReader& args)
{
GMatrix* pA = loadData(args.pop_string());
Holder<GMatrix> hA(pA);
GMatrix* pB = loadData(args.pop_string());
Holder<GMatrix> hB(pB);
GMatrix* pC = GMatrix::align(pA, pB);
Holder<GMatrix> hC(pC);
pC->print(cout);
}
void GRecommenderLib::precisionRecall(GArgReader& args)
{
// Parse options
unsigned int seed = getpid() * (unsigned int)time(NULL);
bool ideal = false;
while(args.next_is_flag())
{
if(args.if_pop("-seed"))
seed = args.pop_uint();
else if(args.if_pop("-ideal"))
ideal = true;
else
throw Ex("Invalid option: ", args.peek());
}
// Load the data
if(args.size() < 1)
throw Ex("No dataset specified.");
GMatrix data;
loadData(data, args.pop_string());
// Instantiate the recommender
GCollaborativeFilter* pModel = InstantiateAlgorithm(args);
std::unique_ptr<GCollaborativeFilter> hModel(pModel);
if(args.size() > 0)
throw Ex("Superfluous argument: ", args.peek());
pModel->rand().setSeed(seed);
// Generate precision-recall data
GMatrix* pResults = pModel->precisionRecall(data, ideal);
std::unique_ptr<GMatrix> hResults(pResults);
pResults->deleteColumns(2, 1); // we don't need the false-positive rate column
pResults->print(cout);
}
void precisionRecall(GArgReader& args)
{
// Parse options
unsigned int seed = getpid() * (unsigned int)time(NULL);
bool ideal = false;
while(args.next_is_flag())
{
if(args.if_pop("-seed"))
seed = args.pop_uint();
else if(args.if_pop("-ideal"))
ideal = true;
else
ThrowError("Invalid option: ", args.peek());
}
// Load the data
if(args.size() < 1)
ThrowError("No dataset specified.");
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
// Instantiate the recommender
GRand prng(seed);
GCollaborativeFilter* pModel = InstantiateAlgorithm(prng, args);
Holder<GCollaborativeFilter> hModel(pModel);
if(args.size() > 0)
ThrowError("Superfluous argument: ", args.peek());
// Generate precision-recall data
GMatrix* pResults = pModel->precisionRecall(*pData, ideal);
Holder<GMatrix> hResults(pResults);
pResults->deleteColumn(2); // we don't need the false-positive rate column
pResults->print(cout);
}
void squaredDistance(GArgReader& args)
{
GMatrix* pA = loadData(args.pop_string());
Holder<GMatrix> hA(pA);
GMatrix* pB = loadData(args.pop_string());
Holder<GMatrix> hB(pB);
double d = pA->sumSquaredDifference(*pB, false);
cout << "Sum squared distance: " << d << "\n";
cout << "Mean squared distance: " << (d / pA->rows()) << "\n";
cout << "Root mean squared distance: " << sqrt(d / pA->rows()) << "\n";
}
void LoadData(GArgReader &args, std::unique_ptr<GMatrix> &hOutput)
{
// Load the dataset by extension
if(args.size() < 1)
throw Ex("Expected the filename of a datset. (Found end of arguments.)");
const char* szFilename = args.pop_string();
PathData pd;
GFile::parsePath(szFilename, &pd);
GMatrix data;
vector<size_t> abortedCols;
vector<size_t> ambiguousCols;
const char *input_type;
if (args.next_is_flag() && args.if_pop("-input_type")) {
input_type = args.pop_string();
} else { /* deduce it from extension (if any) */
input_type = szFilename + pd.extStart;
if (*input_type != '.') /* no extension - assume ARFF */
input_type = "arff";
else
input_type++;
}
// Now load the data
if(_stricmp(input_type, "arff") == 0)
{
data.loadArff(szFilename);
}
else if(_stricmp(input_type, "csv") == 0)
{
GCSVParser parser;
parser.parse(data, szFilename);
cerr << "\nParsing Report:\n";
for(size_t i = 0; i < data.cols(); i++)
cerr << to_str(i) << ") " << parser.report(i) << "\n";
}
else if(_stricmp(input_type, "dat") == 0)
{
GCSVParser parser;
parser.setSeparator('\0');
parser.parse(data, szFilename);
cerr << "\nParsing Report:\n";
for(size_t i = 0; i < data.cols(); i++)
cerr << to_str(i) << ") " << parser.report(i) << "\n";
}
else
{
throw Ex("Unsupported file format: ", szFilename + pd.extStart);
}
// Split data into a feature matrix and a label matrix
GMatrix* pFeatures = data.cloneSub(0, 0, data.rows(), data.cols());
hOutput.reset(pFeatures);
}
void agglomerativeclusterer(GArgReader& args)
{
// Load the file and params
GMatrix data;
loadData(data, args.pop_string());
int clusters = args.pop_uint();
// Do the clustering
GAgglomerativeClusterer clusterer(clusters);
GMatrix* pOut = clusterer.reduce(data);
std::unique_ptr<GMatrix> hOut(pOut);
pOut->print(cout);
}
void split(GArgReader& args)
{
// Load
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
int pats = (int)pData->rows() - args.pop_uint();
if(pats < 0)
ThrowError("out of range. The data only has ", to_str(pData->rows()), " rows.");
const char* szFilename1 = args.pop_string();
const char* szFilename2 = args.pop_string();
unsigned int nSeed = getpid() * (unsigned int)time(NULL);
bool shouldShuffle = false;
while(args.size() > 0){
if(args.if_pop("-shuffle")){
shouldShuffle = true;
}else if(args.if_pop("-seed")){
nSeed = args.pop_uint();
}else
ThrowError("Invalid option: ", args.peek());
}
// Shuffle if necessary
GRand rng(nSeed);
if(shouldShuffle){
pData->shuffle(rng);
}
// Split
GMatrix other(pData->relation());
pData->splitBySize(&other, pats);
pData->saveArff(szFilename1);
other.saveArff(szFilename2);
}
void breadthFirstUnfolding(GArgReader& args)
{
// Load the file and params
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
unsigned int nSeed = getpid() * (unsigned int)time(NULL);
GRand prng(nSeed);
GNeighborFinder* pNF = instantiateNeighborFinder(pData, &prng, args);
Holder<GNeighborFinder> hNF(pNF);
int targetDims = args.pop_uint();
// Parse Options
size_t reps = 1;
Holder<GMatrix> hControlData(NULL);
while(args.size() > 0)
{
if(args.if_pop("-seed"))
prng.setSeed(args.pop_uint());
else if(args.if_pop("-reps"))
reps = args.pop_uint();
else
throw Ex("Invalid option: ", args.peek());
}
// Transform the data
GBreadthFirstUnfolding transform(reps, pNF->neighborCount(), targetDims, &prng);
transform.setNeighborFinder(pNF);
GMatrix* pDataAfter = transform.doit(*pData);
Holder<GMatrix> hDataAfter(pDataAfter);
pDataAfter->print(cout);
}
void SwapAttributes(GArgReader& args)
{
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
size_t nAttr1 = args.pop_uint();
size_t nAttr2 = args.pop_uint();
size_t attrCount = pData->relation()->size();
if(nAttr1 >= attrCount)
ThrowError("Index out of range");
if(nAttr2 >= attrCount)
ThrowError("Index out of range");
pData->swapColumns(nAttr1, nAttr2);
pData->print(cout);
}
void addNoise(GArgReader& args)
{
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
double dev = args.pop_double();
// Parse the options
unsigned int seed = getpid() * (unsigned int)time(NULL);
int excludeLast = 0;
while(args.next_is_flag())
{
if(args.if_pop("-seed"))
seed = args.pop_uint();
else if(args.if_pop("-excludelast"))
excludeLast = args.pop_uint();
else
ThrowError("Invalid neighbor finder option: ", args.peek());
}
GRand prng(seed);
size_t cols = pData->cols() - excludeLast;
for(size_t r = 0; r < pData->rows(); r++)
{
double* pRow = pData->row(r);
for(size_t c = 0; c < cols; c++)
*(pRow++) += dev * prng.normal();
}
pData->print(cout);
}
///TODO: this command should be documented
void center(GArgReader& args)
{
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
unsigned int r = args.pop_uint();
size_t cols = pData->cols();
double* pRow = pData->row(r);
for(size_t i = 0; i < r; ++i)
GVec::subtract(pData->row(i), pRow, cols);
for(size_t i = r + 1; i < pData->rows(); ++i)
GVec::subtract(pData->row(i), pRow, cols);
GVec::setAll(pRow, 0.0, cols);
pData->print(cout);
}
void ManifoldSculpting(GArgReader& args)
{
// Load the file and params
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
unsigned int nSeed = getpid() * (unsigned int)time(NULL);
GRand prng(nSeed);
GNeighborFinder* pNF = instantiateNeighborFinder(pData, &prng, args);
Holder<GNeighborFinder> hNF(pNF);
size_t targetDims = args.pop_uint();
// Parse Options
const char* szPreprocessedData = NULL;
double scaleRate = 0.999;
while(args.size() > 0)
{
if(args.if_pop("-seed"))
prng.setSeed(args.pop_uint());
else if(args.if_pop("-continue"))
szPreprocessedData = args.pop_string();
else if(args.if_pop("-scalerate"))
scaleRate = args.pop_double();
else
throw Ex("Invalid option: ", args.peek());
}
// Load the hint data
GMatrix* pDataHint = NULL;
Holder<GMatrix> hDataHint(NULL);
if(szPreprocessedData)
{
pDataHint = loadData(szPreprocessedData);
hDataHint.reset(pDataHint);
if(pDataHint->relation()->size() != targetDims)
throw Ex("Wrong number of dims in the hint data");
if(pDataHint->rows() != pData->rows())
throw Ex("Wrong number of patterns in the hint data");
}
// Transform the data
GManifoldSculpting transform(pNF->neighborCount(), targetDims, &prng);
transform.setSquishingRate(scaleRate);
if(pDataHint)
transform.setPreprocessedData(hDataHint.release());
transform.setNeighborFinder(pNF);
GMatrix* pDataAfter = transform.doit(*pData);
Holder<GMatrix> hDataAfter(pDataAfter);
pDataAfter->print(cout);
}
void shiftColumns(GArgReader& args)
{
GMatrix* pA = loadData(args.pop_string());
Holder<GMatrix> hA(pA);
vector<size_t> cols;
parseAttributeList(cols, args, pA->cols());
double offset = args.pop_double();
for(size_t i = 0; i < pA->rows(); i++)
{
double* pRow = pA->row(i);
for(vector<size_t>::iterator it = cols.begin(); it != cols.end(); it++)
pRow[*it] += offset;
}
pA->print(cout);
}
