def evaluate(bits):
"""
Calculates an average of the F-Measure for every query that has recorded interactions
"""
candidate = CandidateSolution(bits)
f_measures = []
#for query in SolrRepository.interactive_queries():
for query in ["red lobster"]:
results = SolrRepository.search(query, candidate.name_boost.value, candidate.description_boost.value)
precision = CandidateSolution.calculate_precision(query, results)
recall = CandidateSolution.calculate_recall(query, results)
f_measures.append(CandidateSolution.f_measure(precision, recall))
return sum(f_measures) / len(f_measures),
def test_total_matches(self):
expected = 2
actual = SolrRepository.total_matches("seafood")
self.assertEqual(actual, expected)
def test_interactive_queries(self):
expected = ["red lobster", "crabs", "seafood", "lobster"]
results = SolrRepository.interactive_queries()
self.assertItemsEqual(results, expected)
def test_search_zero_boost_description(self):
results = SolrRepository.search("red", 2, 0)
self.assertEqual(len(results), 1)
result = list(results)[0]
self.assertEqual(result["name"], "Red Lobster")
def test_search_zero_boost_name(self):
results = SolrRepository.search("red", 0, 2)
self.assertEqual(len(results), 1)
result = list(results)[0]
self.assertEqual(result["name"], "Joe's Crab Shack")
def test_search(self):
results = SolrRepository.search("crab shack", 10, 2)
self.assertEqual(len(results), 1, "Expected one result")
result = list(results)[0]
self.assertEqual(result["name"], "Joe's Crab Shack")
def calculate_recall(query, results):
correct_matches = CandidateSolution.discounted_correct_matches(query, results)
total_matches = float(SolrRepository.total_matches(query))
missed_matches = total_matches - correct_matches
return correct_matches / (correct_matches + missed_matches)
def test_discounted_correct_matches(self):
query = "red lobster"
results = SolrRepository.search(query, 1, 1)
actual = CandidateSolution.discounted_correct_matches(query, results)
expected = 1. / math.log(3)
self.assertEqual(actual, expected)
