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import sys
from itertools import islice
import autograd.numpy as np
from autograd import grad
def get_ratings(filename):
with open(filename) as fh:
for line in fh:
yield map(int, line.strip().split())
def get_train_test(filename):
l = [(i, j) for (i, j, _) in get_ratings(filename)]
n = max(i for (i, _) in l)
m = max(j for (_, j) in l)
g = get_ratings(filename)
train = islice(g, 100000)
test = islice(g, 100000)
return n, m, list(train), list(test)
def kl(muu, muv, sigmau, sigmav):
sigma = 5.
r = (np.log(sigma / sigmav).sum() + np.log(sigma / sigmau).sum()
+ np.sum(sigmau ** 2 / (2 * sigma ** 2))
+ np.sum(sigmav ** 2 / (2 * sigma ** 2))
+ np.sum(muu ** 2 / (2 * sigma ** 2))
+ np.sum(muv ** 2 / (2 * sigma ** 2))
)
return r
def likelihood(params, r):
su, mu, sv, mv = params
s = len(mu)
ns = 50
u = su * np.random.normal(size=(ns, s)) + mu
v = sv * np.random.normal(size=(ns, s)) + mv
p = np.sum(u * v, axis=1)
m = np.sum((r - p) ** 2 / 2.)
return m / float(ns)
gl = grad(likelihood)
def sgd(ratings, muu, muv, sigmau, sigmav):
lamb = 0.05
sigma = 5.
eps = 1e-5
for e in xrange(10):
print l(ratings, muu, muv, sigmau, sigmav)
# gradient update on the KL part
sigmau = sigmau + lamb * 1. / sigmau
sigmav = sigmav + lamb * 1. / sigmav
sigmau = sigmau - lamb * sigmau / (sigma ** 2)
sigmav = sigmav - lamb * sigmav / (sigma ** 2)
muu = muu - lamb * muu / (sigma ** 2)
muv = muv - lamb * muv / (sigma ** 2)
for k, (i, j, r) in enumerate(ratings):
if k % 1000 == 0:
print k
su, mu, sv, mv = sigmau[i], muu[i], sigmav[j], muv[j]
gsu, gmu, gsv, gmv = gl((su, mu, sv, mv), r)
sigmau[i] = np.maximum(sigmau[i] - lamb * gsu, eps)
muu[i] = muu[i] - lamb * gmu
sigmav[j] = np.maximum(sigmav[j] - lamb * gsv, eps)
muv[j] = muv[j] - lamb * gmv
def l(ratings, muu, muv, sigmau, sigmav):
res = kl(muu, muv, sigmau, sigmav)
for (i, j, r) in ratings:
su, mu, sv, mv = sigmau[i], muu[i], sigmav[j], muv[j]
res += likelihood((su, mu, sv, mv), r)
return res
if __name__ == "__main__":
n, m, train, test = get_train_test(sys.argv[1])
print len(train)
k = 2
muu, sigmau = 0.01 * np.random.normal(size=(n+1, k)), 0.01 * np.ones((n+1, k))
muv, sigmav = 0.01 * np.random.normal(size=(m+1, k)), 0.01 * np.ones((n+1, k))
sgd(train, muu, muv, sigmau, sigmav)
print l(train, muu, muv, sigmau, sigmav)
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