def knn():
from util import reader
r = reader.stringSepReader("u.data", "\t")
from util import split
train, test1 = split.splitMatrix(r.getMatrix(), 1234567890)
from recommender import knn
k = knn.userKnn(train, 10)
from util import test
print test.hitrate(test1, k.getRec, 10)
import cPickle
output = open("knn.npz", "wb")
cPickle.dump(k, output, -1)
output.close()
def svd():
from util import reader
r = reader.tabSepReader("u.data")
from util import split
train, test1 = split.split(r.getR(), 1234567890)
from recommender import svd
W, H = svd.learnModel(r.getMaxUid(), r.getMaxIid(),
0.0002, train, 1000, 25, r.numberOfTransactions)
import numpy as np
np.savez_compressed(
"SVDModelFile", W=W, H=H)
# modelf = np.load("BPRMFModelFile" + ".npz")
# W = modelf['W']
# H = modelf['H']
# modelf.close
from util import test
t = test.MFtest(W, H, train)
test.hitrate(test1, t.getRec, 10)
def mf():
from util import reader
r = reader.tabSepReader("u.data")
from util import split
train, test1 = split.split(r.getR(), 1234567890)
from recommender import mf
W, H = mf.learnModel(r.getMaxUid(), r.getMaxIid(), 0.01, 0.01, 0.01,
0.1, train, 150, 3, r.numberOfTransactions,
mf.logLoss, mf.dLogLoss)
import numpy as np
# np.savez_compressed(
# "BPRMFModelFile", W=W, H=H)
# modelf = np.load("BPRMFModelFile" + ".npz")
# W = modelf['W']
# H = modelf['H']
# modelf.close
from util import test
t = test.MFtest(W, H, train)
test.hitrate(test1, t.getRec, 10)
def fastBPRMF():
from util import reader
r = reader.fastStringSepReader("u.data", "\t")
from util import split
train, test1 = split.split(r.getR(), 1234567890)
from recommender import fastBPRMF
W, H = fastBPRMF.learnModel(r.getMaxUid(), r.getMaxIid(), 0.01, 0.01, 0.01,
0.1, train, 150, 3)
import numpy as np
# np.savez_compressed(
# "BPRMFModelFile", W=W, H=H)
# modelf = np.load("BPRMFModelFile" + ".npz")
# W = modelf['W']
# H = modelf['H']
# modelf.close
from recommender import mf
from util import test
t = mf.MFrec(W, H, train)
test.hitrate(test1, t.getRec, 10)
