def METHOD_NAME(self):
    fd = FeatureDivergence(self._load_task_params())
    date_from = datetime.date(2020, 10, 7)
    date_to = datetime.date(2020, 10, 8) # divergence should be lesser than day before
    res = fd.score_divergence_daily(date_from, date_to, divergence_model_name='test_density_model.pkl', top_n=2)
    self.assertIsInstance(res, dict)
    self.assertIn('divergence', res)
    self.assertIn('importance', res)
    importance = res['importance']
    self.assertIsInstance(importance, dict)
    self.assertIn('petal_length', importance)
    self.assertIn('petal_width', importance)
    self.assertNotIn('sepal_length', importance)
    self.assertNotIn('sepal_width', importance)
    self.assertNotIn('species', importance)
    divergence = res['divergence']
    self.assertIsInstance(divergence, dict)
    self.assertIn(str(date_from), divergence)
    self.assertIn(str(date_to), divergence)
    self.assertIsInstance(divergence[str(date_to)], dict)
    self.assertIn('petal_length', divergence[str(date_to)])
    self.assertIn('petal_width', divergence[str(date_to)])
    self.assertNotIn('sepal_length', divergence[str(date_to)])
    self.assertNotIn('sepal_width', divergence[str(date_to)])
    self.assertNotIn('species', divergence[str(date_to)])
    self.assertIsInstance(divergence[str(date_to)]['petal_width'], float)
    self.assertTrue(divergence[str(date_to)]['petal_width'] < divergence[str(date_from)]['petal_width'])