def test_prob_item(self):
self.__init_test(test.SMALL_DEL_FILE)
smooth_func = 'JM'
lamb = 0.5
p = SmoothEstimator(smooth_func, lamb, self.annots, 1)
#Item probabilities
self.assertAlmostEquals(p.prob_item(0), 5 / 10)
self.assertAlmostEquals(p.prob_item(1), 1 / 10)
self.assertAlmostEquals(p.prob_item(2), 2 / 10)
self.assertAlmostEquals(p.prob_item(3), 1 / 10)
self.assertAlmostEquals(p.prob_item(4), 1 / 10)
def test_prob_item(self):
self.__init_test(test.SMALL_DEL_FILE)
smooth_func = 'JM'
lamb = 0.5
p = SmoothEstimator(smooth_func, lamb, self.annots, 1)
#Item probabilities
self.assertAlmostEquals(p.prob_item(0), 5 / 10)
self.assertAlmostEquals(p.prob_item(1), 1 / 10)
self.assertAlmostEquals(p.prob_item(2), 2 / 10)
self.assertAlmostEquals(p.prob_item(3), 1 / 10)
self.assertAlmostEquals(p.prob_item(4), 1 / 10)
def test_prob_item_given_tag(self):
self.__init_test(test.SMALL_DEL_FILE)
lambda_ = 0.3
smooth_func = 'Bayes'
p = SmoothEstimator(smooth_func, lambda_, self.annots, 1)
for tag in [0, 1, 2, 3, 4, 5]:
pis = []
pits = []
for item in [0, 1, 2, 3, 4]:
pi = p.prob_item(item)
pti = p.prob_tag_given_item(item, tag)
pis.append(pi)
pits.append(pti * pi)
#Assert
pis = np.array(pis)
pis /= pis.sum()
pits = np.array(pits)
pits /= pits.sum()
gamma_items = np.array([0, 1, 2, 3, 4])
assert_array_almost_equal(pis, p.prob_items(gamma_items))
assert_array_almost_equal(pits, p.prob_items_given_tag(tag,
gamma_items))
self.assertAlmostEqual(1, sum(p.prob_items(gamma_items)))
self.assertAlmostEqual(1,
sum(p.prob_items_given_tag(tag, gamma_items)))
def test_prob_item_given_tag(self):
self.__init_test(test.SMALL_DEL_FILE)
lambda_ = 0.3
smooth_func = 'Bayes'
p = SmoothEstimator(smooth_func, lambda_, self.annots, 1)
for tag in [0, 1, 2, 3, 4, 5]:
pis = []
pits = []
for item in [0, 1, 2, 3, 4]:
pi = p.prob_item(item)
pti = p.prob_tag_given_item(item, tag)
pis.append(pi)
pits.append(pti * pi)
#Assert
pis = np.array(pis)
pis /= pis.sum()
pits = np.array(pits)
pits /= pits.sum()
gamma_items = np.array([0, 1, 2, 3, 4])
assert_array_almost_equal(pis, p.prob_items(gamma_items))
assert_array_almost_equal(pits,
p.prob_items_given_tag(tag, gamma_items))
self.assertAlmostEqual(1, sum(p.prob_items(gamma_items)))
self.assertAlmostEqual(
1, sum(p.prob_items_given_tag(tag, gamma_items)))
def test_gamma_items_prob_items(self):
self.__init_test(test.SMALL_DEL_FILE)
lambda_ = 0.3
smooth_func = 'Bayes'
p = SmoothEstimator(smooth_func, lambda_, self.annots, 1)
gamma_items = np.array([1, 2])
pi_1 = p.prob_item(1)
pi_2 = p.prob_item(2)
gamma_pi = p.prob_items(gamma_items)
self.assertEqual(gamma_pi[0], pi_1 / (pi_1 + pi_2))
self.assertEqual(gamma_pi[1], pi_2 / (pi_1 + pi_2))
def test_gamma_items_prob_items(self):
self.__init_test(test.SMALL_DEL_FILE)
lambda_ = 0.3
smooth_func = 'Bayes'
p = SmoothEstimator(smooth_func, lambda_, self.annots, 1)
gamma_items = np.array([1, 2])
pi_1 = p.prob_item(1)
pi_2 = p.prob_item(2)
gamma_pi = p.prob_items(gamma_items)
self.assertEqual(gamma_pi[0], pi_1 / (pi_1 + pi_2))
self.assertEqual(gamma_pi[1], pi_2 / (pi_1 + pi_2))
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)))
