def test_serendipity(self):
evaluator = reclab.Evaluator(exp, training_set, test_set, user_set,
recommendations)
serendipity = evaluator.serendipity()
self.assertAlmostEqual(1 / 6, serendipity)
def test_novelty(self):
evaluator = reclab.Evaluator(exp, training_set, test_set, user_set,
recommendations)
novelty = evaluator.novelty()
self.assertAlmostEqual(np.log(7) / np.log(2) - 2 / 3, novelty)
def test_diversity(self):
evaluator = reclab.Evaluator(exp, training_set, test_set, user_set,
recommendations)
diversity = evaluator.diversity()
self.assertAlmostEqual((2 - np.sqrt(2)) / 3, diversity)
def test_ndcg(self):
evaluator = reclab.Evaluator(exp, training_set, test_set, user_set,
recommendations)
ndcg = evaluator.ndcg()
self.assertAlmostEqual(np.log(3888) / np.log(11664), ndcg)
def test_recall(self):
evaluator = reclab.Evaluator(exp, training_set, test_set, user_set,
recommendations)
recall = evaluator.recall()
self.assertAlmostEqual(17 / 24, recall)
def test_precision_threshold(self):
local_exp = {'k': 3, 'threshold': 3}
evaluator = reclab.Evaluator(local_exp, training_set, test_set,
user_set, recommendations)
precision = evaluator.precision()
self.assertAlmostEqual(1 / 2, precision)
def test_precision(self):
evaluator = reclab.Evaluator(exp, training_set, test_set, user_set,
recommendations)
precision = evaluator.precision()
self.assertAlmostEqual(5 / 6, precision)
def test_coverage(self):
evaluator = reclab.Evaluator(exp, training_set, test_set, user_set[:1],
recommendations[:1])
coverage = evaluator.coverage()
self.assertAlmostEqual(3 / 4, coverage)