def test_ranking_integrated(self):
global_database = Database("../multichain.db", 100)
personal_database = Database("temp.db", None)
public_key = random.choice(global_database.get_identities())
personal_database.add_blocks(global_database.get_blocks(public_key))
global_ranking = get_ranking(global_database, public_key)
personal_ranking = get_ranking(personal_database, public_key)
ranking_similarity = calculate_ranking_similarity(
personal_ranking, global_ranking)
assert ranking_similarity <= 1
assert ranking_similarity >= 0
def test_ranking_edge_case(self):
superset = {}
superset['A'] = (200, 0)
superset['B'] = (150, 1)
superset['C'] = (0, 2)
subset = {}
subset['C'] = (0, 1)
subset['B'] = (1, 0)
ranking_similarity = calculate_ranking_similarity(subset, superset)
assert ranking_similarity < 1
assert ranking_similarity > 0
def test_ranking_different(self):
superset = {}
superset['A'] = (200, 0)
superset['B'] = (150, 1)
superset['C'] = (100, 2)
superset['D'] = (50, 3)
superset['E'] = (20, 4)
subset = {}
subset['A'] = (250, 0)
subset['D'] = (200, 1)
subset['C'] = (100, 2)
ranking_similarity = calculate_ranking_similarity(subset, superset)
assert ranking_similarity < 1
assert ranking_similarity > 0
def test_ranking_identical(self):
superset = {}
superset['A'] = (200, 0)
superset['B'] = (150, 1)
superset['C'] = (100, 2)
superset['D'] = (50, 3)
superset['E'] = (20, 4)
subset = {}
subset['A'] = (200, 0)
subset['B'] = (150, 1)
subset['C'] = (100, 2)
subset['D'] = (50, 3)
subset['E'] = (20, 4)
ranking_similarity = calculate_ranking_similarity(subset, superset)
self.assertEqual(ranking_similarity, 1)
def log_ranking(self, datafile):
with open(datafile, 'a') as f:
similarity = calculate_ranking_similarity(
self.ranking, self.simulation.rankings[str(self.public_key)])
f.write(str(similarity) + " ")