class TestcaseBase(unittest.TestCase):
def setUp(self):
"""
Setup method that is called at the beginning of each test.
"""
self.documents, self.users = 18, 10
documents_cnt, users_cnt = self.documents, self.users
self.n_iterations = 15
self.k_folds = 3
self.hyperparameters = {'n_factors': 5, '_lambda': 0.01}
self.options = {'n_iterations': self.n_iterations, 'k_folds': self.k_folds}
self.initializer = ModelInitializer(self.hyperparameters.copy(), self.n_iterations)
self.n_recommendations = 1
def mock_get_ratings_matrix(self=None):
return [[int(not bool((article + user) % 3)) for article in range(documents_cnt)]
for user in range(users_cnt)]
self.ratings_matrix = numpy.array(mock_get_ratings_matrix())
setattr(DataParser, "get_ratings_matrix", mock_get_ratings_matrix)
self.evaluator = Evaluator(self.ratings_matrix)
self.cf = CollaborativeFiltering(self.initializer, self.evaluator, self.hyperparameters,
self.options, load_matrices=True)
self.cf.train()
self.cf.evaluator.k_folds = self.k_folds
self.test_data = self.cf.test_data
self.predictions = self.cf.get_predictions()
self.rounded_predictions = self.cf.rounded_predictions()
def runTest(self):
cf = CollaborativeFiltering(self.initializer, self.evaluator, self.hyperparameters,
self.options, load_matrices=False)
self.assertEqual(cf.n_factors, self.n_factors)
self.assertEqual(cf.n_items, self.documents)
cf.train()
self.assertEqual(cf.get_predictions().shape, (self.users, self.documents))
self.assertTrue(isinstance(cf, AbstractRecommender))
shape = (self.users, self.documents)
ratings = cf.get_ratings()
self.assertLessEqual(numpy.amax(ratings), 1 + 1e-6)
self.assertGreaterEqual(numpy.amin(ratings), -1e-6)
self.assertTrue(ratings.shape == shape)
rounded_predictions = cf.rounded_predictions()
self.assertLessEqual(numpy.amax(rounded_predictions), 1 + 1e-6)
self.assertGreaterEqual(numpy.amin(rounded_predictions), -1e-6)
self.assertTrue(rounded_predictions.shape == shape)
recall = cf.evaluator.calculate_recall(ratings, cf.get_predictions())
self.assertTrue(-1e-6 <= recall <= 1 + 1e-6)
random_user = int(numpy.random.random() * self.users)
random_item = int(numpy.random.random() * self.documents)
random_prediction = cf.predict(random_user, random_item)
self.assertTrue(isinstance(random_prediction, numpy.float64))
def run_collaborative(self):
"""
Runs collaborative filtering
"""
ALS = CollaborativeFiltering(self.initializer, self.evaluator,
self.hyperparameters, self.options,
self.verbose, self.load_matrices,
self.dump)
ALS.train()
ALS.get_evaluation_report()
print(
ALS.evaluator.calculate_recall(ALS.ratings,
ALS.rounded_predictions()))
print(
ALS.evaluator.recall_at_x(1, ALS.get_predictions(), ALS.test_data,
ALS.rounded_predictions()))