@Override
protected void buildModel() throws Exception {
// Pre-calculating similarity:
int completeModelCount = 0;
LLORMAUpdater[] learners = new LLORMAUpdater[multiThreadCount];
int[] anchorUser = new int[modelMax];
int[] anchorItem = new int[modelMax];
int modelCount = 0;
int[] runningThreadList = new int[multiThreadCount];
int runningThreadCount = 0;
int waitingThreadPointer = 0;
int nextRunningSlot = 0;
cumPrediction = new SparseMatrix(testIndexMatrix);
cumWeight = new SparseMatrix(testIndexMatrix);
// Parallel training:
while (completeModelCount < modelMax) {
int u_t = (int) Math.floor(Math.random() * numUsers);
List<Integer> itemList = trainMatrix.getColumns(u_t);
if (itemList != null) {
if (runningThreadCount < multiThreadCount && modelCount < modelMax) {
// Selecting a new anchor point:
int idx = (int) Math.floor(Math.random() * itemList.size());
int i_t = itemList.get(idx);
anchorUser[modelCount] = u_t;
anchorItem[modelCount] = i_t;
// Preparing weight vectors:
DenseVector w =
kernelSmoothing(numUsers, u_t, KernelSmoothing.EPANECHNIKOV_KERNEL, 0.8, false);
DenseVector v =
kernelSmoothing(numItems, i_t, KernelSmoothing.EPANECHNIKOV_KERNEL, 0.8, true);
// Starting a new local model learning:
learners[nextRunningSlot] =
new LLORMAUpdater(
modelCount,
localNumFactors,
numUsers,
numItems,
u_t,
i_t,
localLRate,
localRegU,
localRegI,
localNumIters,
w,
v,
trainMatrix);
learners[nextRunningSlot].start();
runningThreadList[runningThreadCount] = modelCount;
runningThreadCount++;
modelCount++;
nextRunningSlot++;
} else if (runningThreadCount > 0) {
// Joining a local model which was done with learning:
try {
learners[waitingThreadPointer].join();
} catch (InterruptedException ie) {
System.out.println("Join failed: " + ie);
}
int mp = waitingThreadPointer;
int mc = completeModelCount;
completeModelCount++;
// Predicting with the new local model and all previous
// models:
predictMatrix = new SparseMatrix(testIndexMatrix);
for (MatrixEntry me : testMatrix) {
int u = me.row();
int i = me.column();
double weight =
KernelSmoothing.kernelize(
getUserSimilarity(anchorUser[mc], u),
0.8,
KernelSmoothing.EPANECHNIKOV_KERNEL)
* KernelSmoothing.kernelize(
getItemSimilarity(anchorItem[mc], i),
0.8,
KernelSmoothing.EPANECHNIKOV_KERNEL);
double newPrediction =
(learners[mp].getUserFeatures().row(u).inner(learners[mp].getItemFeatures().row(i)))
* weight;
cumWeight.set(u, i, cumWeight.get(u, i) + weight);
cumPrediction.set(u, i, cumPrediction.get(u, i) + newPrediction);
double prediction = cumPrediction.get(u, i) / cumWeight.get(u, i);
if (Double.isNaN(prediction) || prediction == 0.0) {
prediction = globalMean;
}
if (prediction < minRate) {
prediction = minRate;
} else if (prediction > maxRate) {
prediction = maxRate;
}
predictMatrix.set(u, i, prediction);
}
if (completeModelCount % 5 == 0) {
Logs.debug(
"{}{} iter {}:[MAE,RMSE,NMAE,rMAE,rRMSE,MPE] {}",
algoName,
foldInfo,
completeModelCount,
"[" + Recommender.getEvalInfo(evalRatings()) + "]");
}
nextRunningSlot = waitingThreadPointer;
waitingThreadPointer = (waitingThreadPointer + 1) % multiThreadCount;
runningThreadCount--;
}
}
}
}