def get_best_current_recommendation(business, user):
# my.factors <- me %*% m@fit@W
# barplot(my.factors)
# my.prediction <- my.factors %*% t(m@fit@W)
# items$title[order(my.prediction, decreasing=T)[1:10]]
try:
r = Recommendation.objects.get(user=user,business=business)
return r.recommendation
except:
pass
NumFactors = 42
print(business.id)
normalizationFactor = getNormFactors(user.id, business.id)
businessAverage = getBusAverageRating(business)
ufset = UserFactor.objects.filter(user=user)
myFactors = np.zeros(NumFactors)
for uf in ufset:
factor = uf.latentFactor
relation = uf.relation
myFactors[factor] = relation
if ufset.count() == 0:
logger.debug("Getting business average since the user has no factors")
print("Getting business average since the user has no factors")
Recommendation.objects.create(user=user,business=business,recommendation=businessAverage)
return businessAverage
bfset = BusinessFactor.objects.filter(business=business)
busFactors = np.zeros(NumFactors)
for bf in bfset:
factor = bf.latentFactor
relation = bf.relation
busFactors[factor] = relation
if bfset.count() == 0:
logger.debug("Getting business average since the business has no factors")
print("Getting business average since the business has no factors")
#cache the recommendations
Recommendation.objects.create(user=user,business=business,recommendation=businessAverage)
return businessAverage
logger.debug("Getting recommendation from actual predictions")
print("Getting recommendation from actual predictions")
prediction = np.dot(myFactors, busFactors) + normalizationFactor
rec = round(prediction * 2) / 2 # round to half
if rec > 4.0:
rec = 4.0
elif rec < 1.0:
rec = 1.0
return rec
def getAllRatMatrix(N, M, allRatings):
arrID2uid = numpy.zeros(N)
arrID2bid = numpy.zeros(M)
bid2arrID = dict()
uid2arrID = dict()
allRatMatrix = []
i = 0
j = 0
c = 0
for r in allRatings:
if c % 100 == 0:
print(c)
c += 1
NormFactor = getNormFactors(r.user.id, r.business.id)
#Need to keep a mapping from the position in the
# array to the actual business and user ID
#arr2... keeps mapping of array position to actual IDs in database
#uid2 and bid2 keeps mapping of database IDs to the array IDS
#this is used to get the normalization factor and can be used
# later to get back recommendations
bPos = 0
if r.business.id in bid2arrID:
bPos = bid2arrID[r.business.id]
else:
bPos = j
bid2arrID[r.business.id] = bPos
arrID2bid[j] = r.business.id
j += 1
uPos = 0
if r.user.id in uid2arrID:
uPos = uid2arrID[r.user.id]
else:
uPos = i
uid2arrID[r.user.id] = uPos
arrID2uid[i] = r.user.id
i += 1
# fp2.write("Rating is " + str(r.rating) + " after normalization " + str(float(r.rating - NormFactor))+ "\n")
allRatMatrix.append([uPos, bPos, float(r.rating - NormFactor)])
return allRatMatrix, bid2arrID, uid2arrID, arrID2bid, arrID2uid
def get_best_current_recommendation(business, user):
# my.factors <- me %*% m@fit@W
# barplot(my.factors)
# my.prediction <- my.factors %*% t(m@fit@W)
# items$title[order(my.prediction, decreasing=T)[1:10]]
NumFactors = 42
ufset = UserFactor.objects.filter(user=user)
myFactors = np.zeros(NumFactors)
for uf in ufset:
factor = uf.latentFactor
relation = uf.relation
myFactors[factor] = relation
if ufset.count() == 0:
return 0
bfset = BusinessFactor.objects.filter(business=business)
busFactors = np.zeros(NumFactors)
for bf in bfset:
factor = bf.latentFactor
relation = bf.relation
busFactors[factor] = relation
if bfset.count() == 0:
return 0
prediction = np.dot(myFactors, busFactors) + getNormFactors(user.id, business.id)
print(prediction)
print("PREDICTION!!!\n")
rec = round(prediction * 2) / 2 # round to half
if rec > 4.0:
rec = 4.0
elif rec < 1.0:
rec = 1.0
#Recommendation.objects.filter(username=user, business=business)
return rec
def run_nmf_mult_k(K, Steps, Alpha):
N = User.objects.count()
M = Business.objects.count()
allRatings = Rating.objects.all()
user_rating_threshold = 0
bus_rating_threshold =0
resultFile = settings.RESULTS_DIR + "u" + str(user_rating_threshold) + "_b" + str(bus_rating_threshold) + "_s" + str(Steps) + "_k" + str(K[0]) + "-" + str(K[len(K) - 1])
predictionFile = settings.RESULTS_DIR + "predictions_" + "u" + str(user_rating_threshold) + "_b" + str(bus_rating_threshold) + "_s" + str(Steps) + "_k" + str(K[0]) + "-" + str(K[len(K) - 1])
print(resultFile)
fp = open(resultFile, "w")
pred_fp = open(predictionFile, "w")
pred_fp.write("#K, f, Difference, Actual, Predicted")
fp.write("#NumUsers = " + str(N + 1) + '\n')
fp.write("#NumBusinesses = " + str(M + 1) + '\n')
fp.write("#UserThresh = " + str(user_rating_threshold) + '\n')
fp.write("#BusinessThresh = " + str(bus_rating_threshold) + '\n')
fp.write("#NumRatings = " + str(allRatings.count()) + '\n')
fp.write("#Steps = " + str(Steps) + '\n')
fp.write("#Alpha = " + str(Alpha) + '\n')
fp.write("#TimeStart = " + str(time.asctime()) + '\n')
fp.write('#\n')
fp.write('#K, AvgRSSRounded, AvgDistRounded, AvgRSSFloat, AvgDistFloat\n')
fp.flush()
print("Moving data to an array...")
fp2 = open("/tmp/debug-ratings.txt", "w")
allRatMatrix, bid2arrID, uid2arrID, arrID2bid, arrID2uid = getAllRatMatrix(N,M,allRatings)
print("Generating Folds...")
folds = get_folds(allRatMatrix)
print("Fold Generation Complete...")
for k in K:
print("Running on K=" + str(k) + " Starting at time= " + time.asctime())
sumDistRounded = 0
sumRSSRounded = 0
sumDistFloat = 0.0
sumRSSFloat = 0.0
ctr = 0
for f in range(0, 5):
outFold = []
for iterF in range(0, 5):
if iterF != f:
for subelement in folds[iterF]:
outFold.append((subelement))
inFold = copy.deepcopy(folds[f])
time_before = time.clock()
nP, nQ = run_nmf_internal(outFold,N,M,k, Steps, Alpha, fp=fp2)
del outFold
elapsed = time.clock() - time_before;
print("\tK="+str(k)+" Fold=" +str(f)+" TimeElapsed="+ str(elapsed/60) + " minutes")
#for keeping track of rss, average distance for floats and rounded
distFloat = 0.0
rssFloat = 0.0
rssRounded = 0
distRounded = 0
inFoldLen = len(inFold)
for r in inFold:
uid = r[0]
bid = r[1]
NormFactor = getNormFactors(arrID2uid[uid], arrID2bid[bid])
r[2] = r[2] + NormFactor
prediction = numpy.dot(nP[uid],nQ[bid]) + NormFactor
roundR = round(r[2])
roundP = round(prediction)
floatR = float(r[2])
floatP = float(prediction)
if r[2] > 4:
floatR = 4.0
roundR = 4;
elif r[2] < 1:
roundR = 1;
floatR = 1.0
if prediction > 4:
roundP = 4
floatP = 4.0
elif prediction < 1:
roundP = 1
floatP = 1.0
#print("Username " + str(r.username))
#print("Business " + str(r.business.name))
#print("Rating " + str(r.rating))
#print("Prediction " + str(prediction))
rssFloat += math.pow(abs(floatP - floatR),2)
distFloat += abs(floatP - floatR)
rssRounded += math.pow(abs(roundP - roundR),2)
distRounded += abs(roundP - roundR)
#pred_fp.write(str(abs(floatP - floatR)) + ", " + str(floatR) + ", " + str(floatP) + ", " +str(k) + ", " + str(f) + '\n');
pred_fp.write(str(abs(floatP-floatR)) + " " + str(floatR) + " " + str(floatP) + " " + '\n');
if ctr % 1000 == 0:
pred_fp.flush()
ctr += 1
sumDistRounded += distRounded / inFoldLen
sumRSSRounded += rssRounded/ inFoldLen
sumDistFloat += distFloat / inFoldLen
sumRSSFloat += rssFloat/ inFoldLen
print("\t\t RSS_float=" + str(rssFloat/inFoldLen) + " Distance_float=" + str(distFloat/inFoldLen))
print("\t\t RSS_rounded="+ str(rssRounded/inFoldLen) + " Distance_rounded=" + str(distRounded/inFoldLen))
result_1 = str(sumRSSRounded/5)+ ", " + str(sumDistRounded/5)
result_2 = str(sumRSSFloat/5)+ ", " + str(sumDistFloat/5)
fp.write(str(k) + ", " + result_1 + ", " + result_2 + '\n')
fp.flush()
print("\n\n\nK="+str(k) + "Rounded: " + result_1 + " Float: " + result_2)
fp.write("#TimeEnd = "+str(time.asctime())+'\n')
pred_fp.flush()
fp.flush()
fp.close()
fp2.close()
pred_fp.close()
