testOrderedItems = MCEvaluatorCython.recommendAtk(U, V, p, trainX)
r = SparseUtilsCython.computeR(U, V, maxLocalAuc.w, maxLocalAuc.numRecordAucSamples)
trainObjVec = maxLocalAuc.objectiveApprox(trainOmegaPtr, U, V, r, maxLocalAuc.gi, maxLocalAuc.gp, maxLocalAuc.gq, full=True)
testObjVec = maxLocalAuc.objectiveApprox(testOmegaPtr, U, V, r, maxLocalAuc.gi, maxLocalAuc.gp, maxLocalAuc.gq, allArray=allOmegaPtr, full=True)
itemCounts = numpy.array(X.sum(0)+1, numpy.int32)
beta = 0.5
for p in [1, 3, 5, 10]:
trainPrecision = MCEvaluator.precisionAtK(trainOmegaPtr, trainOrderedItems, p)
testPrecision = MCEvaluator.precisionAtK(testOmegaPtr, testOrderedItems, p)
logging.debug("Train/test precision@" + str(p) + "=" + str(trainPrecision) + "/" + str(testPrecision))
for p in [1, 3, 5, 10]:
trainRecall = MCEvaluator.stratifiedRecallAtK(trainOmegaPtr, trainOrderedItems, p, itemCounts, beta)
testRecall = MCEvaluator.stratifiedRecallAtK(testOmegaPtr, testOrderedItems, p, itemCounts, beta)
logging.debug("Train/test stratified recall@" + str(p) + "=" + str(trainRecall) + "/" + str(testRecall))
plt.figure(0)
plt.plot(trainMeasures[:, 0], label="train")
plt.plot(testMeasures[:, 0], label="test")
plt.xlabel("iteration")
plt.ylabel("objective")
plt.legend()
plt.figure(1)
plt.plot(trainMeasures[:, 1], label="train")
plt.plot(testMeasures[:, 1], label="test")
