def test_multinomial_proportion_choice_model_default(self):
choice_features = dict(min_normalizer=0)
mnp_model = choice_model.MultinomialProportionalChoiceModel(
choice_features=choice_features)
mnp_model.score_documents(self._user_state,
np.array([[0.8, 0.6], [0.6, 0.8]]))
# The scores are the dot product between user features and doc features.
self.assertAlmostEqual(mnp_model._scores[0], 1, delta=0.001)
self.assertAlmostEqual(mnp_model._scores[1], 0.96, delta=0.001)
self.assertEqual(mnp_model._score_no_click, 0)
def test_multinomial_proportion_min_normalizer(self):
choice_features = dict(min_normalizer=0.5, no_click_mass=0.5)
mnp_model = choice_model.MultinomialProportionalChoiceModel(
choice_features=choice_features)
mnp_model.score_documents(self._user_state,
np.array([[0.8, 0.6], [0.6, 0.8]]))
# The scores the dot product user features and doc features minus the
# min_normalizer.
self.assertAlmostEqual(mnp_model._scores[0], 0.5, delta=0.001)
self.assertAlmostEqual(mnp_model._scores[1], 0.46, delta=0.001)
self.assertAlmostEqual(mnp_model._score_no_click, 0, delta=0.001)