def test_prob_user_given_item(self):
self.__init_test(test.SMALL_DEL_FILE)
smooth_func = 'JM'
lamb = 0.5
p = SmoothEstimator(smooth_func, lamb, self.annots, 1)
prob = p.prob_user_given_item(0, 0)
self.assertTrue(prob > 0)
def test_prob_user_given_item(self):
self.__init_test(test.SMALL_DEL_FILE)
smooth_func = 'JM'
lamb = 0.5
p = SmoothEstimator(smooth_func, lamb, self.annots, 1)
prob = p.prob_user_given_item(0, 0)
self.assertTrue(prob > 0)
def test_prob_user_given_item_profsize(self):
self.__init_test(test.SMALL_DEL_FILE)
smooth_func = 'JM'
lamb = 0.5
p = SmoothEstimator(smooth_func, lamb, self.annots,
user_profile_fract_size = 0)
prob = p.prob_user_given_item(0, 0)
self.assertEquals(prob, 0.0)
def test_prob_user_given_item_profsize(self):
self.__init_test(test.SMALL_DEL_FILE)
smooth_func = 'JM'
lamb = 0.5
p = SmoothEstimator(smooth_func,
lamb,
self.annots,
user_profile_fract_size=0)
prob = p.prob_user_given_item(0, 0)
self.assertEquals(prob, 0.0)
def test_prob_items_given_user_and_tag(self):
self.__init_test(test.SMALL_DEL_FILE)
lambda_ = 0.3
smooth_func = 'Bayes'
p = SmoothEstimator(smooth_func, lambda_, self.annots, 1)
for user in [0, 1, 2]:
for tag in [0, 1, 2, 3, 4, 5]:
pitus = []
pius = []
for item in [0, 1, 2, 3, 4]:
pi = p.prob_item(item)
pti = p.prob_tag_given_item(item, tag)
pui = p.prob_user_given_item(item, user)
piu = pui * pi
pitu = pti * pui * pi
pitus.append(pitu)
pius.append(piu)
sum_pitus = sum(pitus)
sum_pius = sum(pius)
for item in [0, 1, 2, 3, 4]:
pitus[item] = pitus[item] / sum_pitus
pius[item] = pius[item] / sum_pius
#Assert
gamma_items = np.array([0, 1, 2, 3, 4])
assert_array_almost_equal(pius,
p.prob_items_given_user(user, gamma_items))
assert_array_almost_equal(pitus,
p.prob_items_given_user_tag(user, tag, gamma_items))
self.assertAlmostEqual(1, sum(p.prob_items_given_user(user,
gamma_items)))
self.assertAlmostEqual(1,
sum(p.prob_items_given_user_tag(user, tag,
gamma_items)))
def test_prob_items_given_user_and_tag(self):
self.__init_test(test.SMALL_DEL_FILE)
lambda_ = 0.3
smooth_func = 'Bayes'
p = SmoothEstimator(smooth_func, lambda_, self.annots, 1)
for user in [0, 1, 2]:
for tag in [0, 1, 2, 3, 4, 5]:
pitus = []
pius = []
for item in [0, 1, 2, 3, 4]:
pi = p.prob_item(item)
pti = p.prob_tag_given_item(item, tag)
pui = p.prob_user_given_item(item, user)
piu = pui * pi
pitu = pti * pui * pi
pitus.append(pitu)
pius.append(piu)
sum_pitus = sum(pitus)
sum_pius = sum(pius)
for item in [0, 1, 2, 3, 4]:
pitus[item] = pitus[item] / sum_pitus
pius[item] = pius[item] / sum_pius
#Assert
gamma_items = np.array([0, 1, 2, 3, 4])
assert_array_almost_equal(
pius, p.prob_items_given_user(user, gamma_items))
assert_array_almost_equal(
pitus, p.prob_items_given_user_tag(user, tag, gamma_items))
self.assertAlmostEqual(
1, sum(p.prob_items_given_user(user, gamma_items)))
self.assertAlmostEqual(
1, sum(p.prob_items_given_user_tag(user, tag,
gamma_items)))
