class WA4Test(unittest.TestCase):
def setUp(self):
self.data = DataIO(verbose=False)
self.data.load('testdata/ratings-ma4.csv')
self.model = UserModel(normalize=False, verbose=False)
self.model.build(self.data)
def test_pearson(self):
# test correlation
S = pearson(self.model.R(), self.model.R()).todense()
# 1. check we don't have numbers more than 1
# user string comparison to avoid float nuances
self.assertTrue('%.2f' % S.max() == '1.00')
# 2. check there are only '1' on the diagonal
self.assertTrue(
sum([S[i, i] for i in range(S.shape[0])]) == S.shape[0])
# 3. check a couple of correlation coefficients
corr_test = [(1648, 5136, 0.40298), (918, 2824, -0.31706)]
for (u1, u2, c) in corr_test:
# check what's in the full matrix
u1 = self.data.new_user_idx(u1)
u2 = self.data.new_user_idx(u2)
# check precomputed
self.assertTrue('%.5f' % S[u1, u2] == '%.5f' % c)
# compute here
self.assertTrue(
'%.5f' % pearson(self.model.R()[u1, :],
self.model.R()[u2, :]).todense() == '%.5f' %
c)
def test_5nn(self):
u = 3712
nns = [(2824, 0.46291), (3867, 0.400275), (5062, 0.247693),
(442, 0.22713), (3853, 0.19366)]
S = pearson(self.model.R(), self.model.R())
leave_top_n(S, 6)
top_neighbours = [
(self.data.old_user_idx(i), S[i, self.data.new_user_idx(u)])
for i in S[:, self.data.new_user_idx(u)].nonzero()[0]
]
top_neighbours.sort(key=lambda a: a[1], reverse=True)
# skip the first element (corr = 1)
self.assertTrue(','.join(['%d,%.6f' % a for a in top_neighbours[1:]])
== ','.join(['%d,%.6f' % a for a in nns]))
# consider moving this test to test_recsys.py
def test_unnormalized(self):
u = 3712
expected = [(641, 5.000), (603, 4.856), (105, 4.739)]
R = user_based_knn(self.model,
5, [self.data.new_user_idx(u)],
range(self.data.num_items()),
pearson,
promote_users=False)
recs = top_ns([R], 3, keep_order=True)
self.assertTrue(','.join(
['%d,%.3f' % (self.data.old_item_idx(a), b) for (
a,
b) in recs[0]]) == ','.join(['%d,%.3f' % a for a in expected]))
# consider moving this test to test_recsys.py
def test_normalized(self):
u = 3712
expected = [(641, 5.900), (603, 5.546), (105, 5.501)]
R = user_based_knn(self.model,
5, [self.data.new_user_idx(u)],
range(self.data.num_items()),
pearson,
promote_users=False,
normalize='normalize')
recs = top_ns([R], 3, keep_order=True)
self.assertTrue(','.join(
['%d,%.3f' % (self.data.old_item_idx(a), b) for (
a,
b) in recs[0]]) == ','.join(['%d,%.3f' % a for a in expected]))
