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 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 GRecommenderLib::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
throw Ex("Invalid crossvalidate option: ", args.peek());
}
if(folds < 2)
throw Ex("There must be at least 2 folds.");
// 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);
// Do cross-validation
double mae;
double mse;
mse = pModel->crossValidate(data, folds, &mae);
cout << "RMSE=" << sqrt(mse) << ", MSE=" << mse << ", MAE=" << mae << "\n";
}
void GRecommenderLib::fillMissingValues(GArgReader& args)
{
unsigned int seed = getpid() * (unsigned int)time(NULL);
bool normalize = true;
while(args.next_is_flag())
{
if(args.if_pop("-seed"))
seed = args.pop_uint();
else if(args.if_pop("-nonormalize"))
normalize = false;
else
throw Ex("Invalid option: ", args.peek());
}
// Load the data and the filter
GMatrix dataOrig;
dataOrig.loadArff(args.pop_string());
// Parse params
vector<size_t> ignore;
while(args.next_is_flag())
{
if(args.if_pop("-ignore"))
parseAttributeList(ignore, args, dataOrig.cols());
else
throw Ex("Invalid option: ", args.peek());
}
// Throw out the ignored attributes
std::sort(ignore.begin(), ignore.end());
for(size_t i = ignore.size() - 1; i < ignore.size(); i--)
dataOrig.deleteColumns(ignore[i], 1);
GRelation* pOrigRel = dataOrig.relation().clone();
std::unique_ptr<GRelation> hOrigRel(pOrigRel);
GCollaborativeFilter* pModel = InstantiateAlgorithm(args);
std::unique_ptr<GCollaborativeFilter> hModel(pModel);
if(args.size() > 0)
throw Ex("Superfluous argument: ", args.peek());
pModel->rand().setSeed(seed);
// Convert to all normalized real values
GNominalToCat* pNtc = new GNominalToCat();
GIncrementalTransform* pFilter = pNtc;
std::unique_ptr<GIncrementalTransformChainer> hChainer;
if(normalize)
{
GIncrementalTransformChainer* pChainer = new GIncrementalTransformChainer(new GNormalize(), pNtc);
hChainer.reset(pChainer);
pFilter = pChainer;
}
pNtc->preserveUnknowns();
pFilter->train(dataOrig);
GMatrix* pData = pFilter->transformBatch(dataOrig);
std::unique_ptr<GMatrix> hData(pData);
// Convert to 3-column form
GMatrix* pMatrix = new GMatrix(0, 3);
std::unique_ptr<GMatrix> hMatrix(pMatrix);
size_t dims = pData->cols();
for(size_t i = 0; i < pData->rows(); i++)
{
GVec& row = pData->row(i);
for(size_t j = 0; j < dims; j++)
{
if(row[j] != UNKNOWN_REAL_VALUE)
{
GVec& vec = pMatrix->newRow();
vec[0] = (double)i;
vec[1] = (double)j;
vec[2] = row[j];
}
}
}
// Train the collaborative filter
pModel->train(*pMatrix);
hMatrix.release();
pMatrix = NULL;
// Predict values for missing elements
for(size_t i = 0; i < pData->rows(); i++)
{
GVec& row = pData->row(i);
for(size_t j = 0; j < dims; j++)
{
if(row[j] == UNKNOWN_REAL_VALUE)
row[j] = pModel->predict(i, j);
GAssert(row[j] != UNKNOWN_REAL_VALUE);
}
}
// Convert the data back to its original form
GMatrix* pOut = pFilter->untransformBatch(*pData);
pOut->setRelation(hOrigRel.release());
pOut->print(cout);
}