def test_preferences(self):
d = PreferenceDict()
a, b = (0, 1, 2), (3, 4, 5)
d[a, b] = 1
e, f = (9, 9), (1, 1)
d[e, f] = -1
self.assertEqual(sorted(d.preferences()), [(0, 1, 2), (1, 1),
(3, 4, 5), (9, 9)])
def test_fit_simple_logit(self):
with self.test_session():
data = PreferenceDict()
a = (0,)
b = (1,)
c = (2,)
d = (3,)
data[a, b] = 1
data[a, c] = 1
data[c, b] = -1
data[c, d] = 1
m = BinaryPreferenceModel(
n_iter=500,
n_samples=20,
link='logit'
)
X, y = preprocess_data(data)
m.fit(X, y)
mean = m.mean([
(0,),
(1,),
(2,),
(3,),
])
self.assertTrue(
all(x > y for x, y in zip(mean, mean[1:]))
)
def test_basic(self):
d = PreferenceDict()
a, b = (0, 1, 2), (3, 4, 5)
d[a, b] = 1
self.assertEqual(d[a, b], 1)
self.assertEqual(d[b, a], -1)
e, f = (9, 9), (1, 1)
d[e, f] = -1
self.assertEqual(d[e, f], -1)
self.assertEqual(d[f, e], 1)
self.assertEqual(len(d), 2)