def main():
# learn_data = Parser.MatrixParser.txttocsc(r"../ml-100k/u1.base")
# test_data = Parser.MatrixParser.txttocsc(r"../ml-100k/u1.test")
pairs_lt = Tools.folder2pairs(r"../ml-100k/")
m_score = 0.0
for pair in pairs_lt:
# bias = Bias(Parser.MatrixParser.txttocsc(pair[0]), "item_{}.png".format(pair[2]), case='item')
# score = bias.predict(Parser.MatrixParser.txttocsc(pair[1]))
# export_name = "{}.sim".format(pair[0].split("/")[-1])
#
# if os.path.isfile(export_name):
# bfile = open(export_name, 'rb')
# sim = pickle.load(bfile)
# bfile.close()
# else:
# sim = SimiTriple(pair[0])
# filehandler = open(export_name, "wb")
# pickle.dump(sim, filehandler)
# filehandler.close()
# svd = SVD(pair[0], k=10)
# score = svd.predict(Parser.MatrixParser.txttocsc(pair[1]))
# fgd = FGD(pair[0], epochs=12, nZ=10, l1_weight=0.1, l2_weight=0.1, learning_rate=0.01)
# score = fgd.predict(Parser.MatrixParser.txttocsc(pair[1]))
svdgd = SVDGD(pair[0], nZ=2, learning_rate=0.1, lambda_4=10, epochs=10)
score = svdgd.predict(Parser.MatrixParser.txttocsc(pair[1]))
print pair[2], score
m_score += score
break
print "Mean", m_score/len(pairs_lt)
def evalSVD(config):
pairs_lt = Tools.folder2pairs(r"../ml-100k/")
m_score = 0.0
for pair in pairs_lt:
svd = SVD(pair[0], k=config[0])
try:
score = svd.predict(Parser.MatrixParser.txttocsc(pair[1]))
m_score += score
except FloatingPointError:
score = -100
m_score += score
print "FP ERROR", pair[2], config
break
except:
score = -500
m_score += score
print "PREDICT ERROR", pair[2], config
break
# m_score += score
m_score /= len(pairs_lt)
print "{:2d}, {:2.4f}".format(config[0], m_score)
return m_score
