def __init__(self):
super(TestRanking, self).__init__()
# Rank-based metrics: KendallTau, SpearmanRho, MeanReciprocalRank, ReciprocalRank
self.kendall = KendallTau()
self.kendall.load(self.GT_RANKING, self.TEST_RANKING)
self.spearman = SpearmanRho()
self.spearman.load(self.GT_RANKING, self.TEST_RANKING)
self.mrr = MeanReciprocalRank()
for elem in self.TEST_DECISION:
self.mrr.load(self.GT_DECISION, elem)
def __init__(self):
super(TestRanking, self).__init__()
# Rank-based metrics: KendallTau, SpearmanRho, MeanReciprocalRank, ReciprocalRank
self.kendall = KendallTau()
self.kendall.load(self.GT_RANKING, self.TEST_RANKING)
self.spearman = SpearmanRho()
self.spearman.load(self.GT_RANKING, self.TEST_RANKING)
self.mrr = MeanReciprocalRank()
for elem in self.TEST_DECISION:
self.mrr.load(self.GT_DECISION, elem)
def test_RANK_MeanReciprocalRank_add_entry(self):
mrr = MeanReciprocalRank()
QUERY = 'invented'
mrr.load(self.GT_DECISION, QUERY)
assert_equal(mrr.compute(), 0.0)
def test_RANK_MeanReciprocalRank_compute_one(self):
mrr = MeanReciprocalRank()
QUERY = 'instrumental'
assert_equal(mrr.compute(self.GT_DECISION, QUERY), 0.5)
class TestRanking(Test):
def __init__(self):
super(TestRanking, self).__init__()
# Rank-based metrics: KendallTau, SpearmanRho, MeanReciprocalRank, ReciprocalRank
self.kendall = KendallTau()
self.kendall.load(self.GT_RANKING, self.TEST_RANKING)
self.spearman = SpearmanRho()
self.spearman.load(self.GT_RANKING, self.TEST_RANKING)
self.mrr = MeanReciprocalRank()
for elem in self.TEST_DECISION:
self.mrr.load(self.GT_DECISION, elem)
# TEST_CORR Spearman
def test_RANK_Spearman_compute_all(self):
assert_equal(self.spearman.compute(), 0.5) #0.55 ?
#def test_RANK_Spearman_compute_tied_ranks():
# assert_equal(spearman.compute(tied_ranks=True), 0.5) #In fact, it uses Pearsonr corr. of the ranks
def test_RANK_Spearman_compute_floats(self):
spearman = SpearmanRho(self.DATA_PRED)
assert_equal(spearman.compute(), 0.947368) #0.95 ?
#def test_RANK_Spearman_compute_floats_tied_ranks():
# spearman = SpearmanRho(self.DATA_PRED)
# assert_equal(spearman.compute(tied_ranks=True), 0.930024) #In fact, it uses Pearsonr corr. of the ranks
def test_RANK_Spearman_load_test(self):
spearman = SpearmanRho()
spearman.load_test(self.TEST_DATA)
assert_equal(len(spearman.get_test()), len(self.TEST_DATA))
def test_RANK_Spearman_load_ground_truth(self):
spearman = SpearmanRho()
spearman.load_ground_truth(self.GT_DATA)
assert_equal(len(spearman.get_ground_truth()), len(self.TEST_DATA))
def test_RANK_Spearman_add_entry(self):
self.spearman.add(('guitar', 4), ('guitar', 4)) #add tag 'guitar' at rank-4
assert_equal(len(self.spearman.get_test()), len(self.TEST_RANKING)+1)
assert_equal(len(self.spearman.get_ground_truth()), len(self.GT_RANKING)+1)
assert_equal(self.spearman.compute(), 0.763158) #0.775 ?
def test_RANK_Spearman_different_list_sizes(self):
TEST_DATA = ['classical', 'invented', 'baroque']
GT_DATA = ['classical', 'instrumental', 'piano', 'baroque']
spearman = SpearmanRho()
spearman.load_ground_truth(GT_DATA)
spearman.load_test(TEST_DATA)
assert_raises(ValueError, spearman.compute) #Raise: GT & TEST list have different sizes
# TEST_CORR Kendall
def test_RANK_Kendall_compute_all(self):
assert_equal(self.kendall.compute(), 0.4)
def test_RANK_Kendall_compute_floats(self):
kendall = KendallTau(self.DATA_PRED)
assert_equal(kendall.compute(), 0.888889)
def test_RANK_Kendall_load_test(self):
kendall = KendallTau()
kendall.load_test(self.TEST_DATA)
assert_equal(len(kendall.get_test()), len(self.TEST_DATA))
def test_RANK_Kendall_load_ground_truth(self):
kendall = KendallTau()
kendall.load_ground_truth(self.GT_DATA)
assert_equal(len(kendall.get_ground_truth()), len(self.GT_DATA))
def test_RANK_Kendall_add_entry(self):
self.kendall.add(('guitar', 4.0), ('guitar', 4.0)) #add tag 'guitar'
assert_equal(len(self.kendall.get_test()), len(self.TEST_RANKING)+1)
assert_equal(len(self.kendall.get_ground_truth()), len(self.GT_RANKING)+1)
assert_equal(self.kendall.compute(), 0.666667)
def test_RANK_Kendall_diff_elems(self):
TEST_DECISION = ['class', 'invented', 'baro', 'instru']
GT_DECISION = ['classical', 'instrumental', 'piano', 'baroque']
kendall = KendallTau()
kendall.load_ground_truth(self.GT_DECISION)
kendall.load_test(self.TEST_DECISION)
assert_raises(ValueError, kendall.compute) #Different elements
# TEST_RANK ReciprocalRank
def test_RANK_ReciprocalRank_compute(self):
rr = ReciprocalRank()
QUERY = 'instrumental'
assert_equal(rr.compute(self.GT_DECISION, QUERY), 0.5)
def test_RANK_ReciprocalRank_add_entry(self):
rr= ReciprocalRank()
QUERY = 'invented'
rr.load(self.GT_DECISION, QUERY)
assert_equal(rr.compute(), 0.0)
# TEST_RANK MeanReciprocalRank
# Internally, MeanReciprocalRank uses a list of ReciprocalRank results
def test_RANK_MeanReciprocalRank_compute_all(self):
assert_equal(self.mrr.compute(), 0.4375)
def test_RANK_MeanReciprocalRank_compute_one(self):
mrr = MeanReciprocalRank()
QUERY = 'instrumental'
assert_equal(mrr.compute(self.GT_DECISION, QUERY), 0.5)
def test_RANK_MeanReciprocalRank_load(self):
mrr = MeanReciprocalRank()
assert_raises(ValueError, mrr.load, self.GT_DECISION, self.TEST_RANKING)
def test_RANK_MeanReciprocalRank_load_test(self):
mrr = MeanReciprocalRank()
assert_raises(NotImplementedError, mrr.load_test, self.TEST_RANKING)
def test_RANK_MeanReciprocalRank_load_ground_truth(self):
mrr = MeanReciprocalRank()
assert_raises(NotImplementedError, mrr.load_ground_truth, self.GT_RANKING)
def test_RANK_MeanReciprocalRank_add_entry(self):
mrr = MeanReciprocalRank()
QUERY = 'invented'
mrr.load(self.GT_DECISION, QUERY)
assert_equal(mrr.compute(), 0.0)
#mAP tests
def test_RANK_AveragePrecision(self):
GT_DECISION = [1, 2, 4]
TEST_DECISION = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
avgp = AveragePrecision()
avgp.load(GT_DECISION, TEST_DECISION)
assert_equal(round(avgp.compute(), 4), 0.9167)
GT_DECISION = [1, 4, 8]
avgp = AveragePrecision()
avgp.load(GT_DECISION, TEST_DECISION)
assert_equal(round(avgp.compute(), 4), 0.625)
GT_DECISION = [3, 5, 9, 25, 39, 44, 56, 71, 89, 123]
TEST_DECISION = [123, 84, 56, 6, 8, 9, 511, 129, 187, 25, 38, 48, 250, 113, 3]
avgp = AveragePrecision()
avgp.load(GT_DECISION, TEST_DECISION)
assert_equal(avgp.compute(), 0.58)
#mAP tests
def test_RANK_MeanAveragePrecision(self):
mavgp = MeanAveragePrecision()
GT_DECISION = [1, 2, 4]
TEST_DECISION = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
mavgp.load(GT_DECISION, TEST_DECISION)
GT_DECISION = [1, 4, 8]
mavgp.load(GT_DECISION, TEST_DECISION)
GT_DECISION = [3, 5, 9, 25, 39, 44, 56, 71, 89, 123]
TEST_DECISION = [123, 84, 56, 6, 8, 9, 511, 129, 187, 25, 38, 48, 250, 113, 3]
mavgp.load(GT_DECISION, TEST_DECISION)
assert_equal(mavgp.compute(), 0.707222)
def test_RANK_MeanReciprocalRank_compute_one(self):
mrr = MeanReciprocalRank()
QUERY = 'instrumental'
assert_equal(mrr.compute(self.GT_DECISION, QUERY), 0.5)
class TestRanking(Test):
def __init__(self):
super(TestRanking, self).__init__()
# Rank-based metrics: KendallTau, SpearmanRho, MeanReciprocalRank, ReciprocalRank
self.kendall = KendallTau()
self.kendall.load(self.GT_RANKING, self.TEST_RANKING)
self.spearman = SpearmanRho()
self.spearman.load(self.GT_RANKING, self.TEST_RANKING)
self.mrr = MeanReciprocalRank()
for elem in self.TEST_DECISION:
self.mrr.load(self.GT_DECISION, elem)
# TEST_CORR Spearman
def test_RANK_Spearman_compute_all(self):
assert_equal(self.spearman.compute(), 0.5) #0.55 ?
#def test_RANK_Spearman_compute_tied_ranks():
# assert_equal(spearman.compute(tied_ranks=True), 0.5) #In fact, it uses Pearsonr corr. of the ranks
def test_RANK_Spearman_compute_floats(self):
spearman = SpearmanRho(self.DATA_PRED)
assert_equal(spearman.compute(), 0.947368) #0.95 ?
#def test_RANK_Spearman_compute_floats_tied_ranks():
# spearman = SpearmanRho(self.DATA_PRED)
# assert_equal(spearman.compute(tied_ranks=True), 0.930024) #In fact, it uses Pearsonr corr. of the ranks
def test_RANK_Spearman_load_test(self):
spearman = SpearmanRho()
spearman.load_test(self.TEST_DATA)
assert_equal(len(spearman.get_test()), len(self.TEST_DATA))
def test_RANK_Spearman_load_ground_truth(self):
spearman = SpearmanRho()
spearman.load_ground_truth(self.GT_DATA)
assert_equal(len(spearman.get_ground_truth()), len(self.TEST_DATA))
def test_RANK_Spearman_add_entry(self):
self.spearman.add(('guitar', 4),
('guitar', 4)) #add tag 'guitar' at rank-4
assert_equal(len(self.spearman.get_test()), len(self.TEST_RANKING) + 1)
assert_equal(len(self.spearman.get_ground_truth()),
len(self.GT_RANKING) + 1)
assert_equal(self.spearman.compute(), 0.763158) #0.775 ?
def test_RANK_Spearman_different_list_sizes(self):
TEST_DATA = ['classical', 'invented', 'baroque']
GT_DATA = ['classical', 'instrumental', 'piano', 'baroque']
spearman = SpearmanRho()
spearman.load_ground_truth(GT_DATA)
spearman.load_test(TEST_DATA)
assert_raises(
ValueError,
spearman.compute) #Raise: GT & TEST list have different sizes
# TEST_CORR Kendall
def test_RANK_Kendall_compute_all(self):
assert_equal(self.kendall.compute(), 0.4)
def test_RANK_Kendall_compute_floats(self):
kendall = KendallTau(self.DATA_PRED)
assert_equal(kendall.compute(), 0.888889)
def test_RANK_Kendall_load_test(self):
kendall = KendallTau()
kendall.load_test(self.TEST_DATA)
assert_equal(len(kendall.get_test()), len(self.TEST_DATA))
def test_RANK_Kendall_load_ground_truth(self):
kendall = KendallTau()
kendall.load_ground_truth(self.GT_DATA)
assert_equal(len(kendall.get_ground_truth()), len(self.GT_DATA))
def test_RANK_Kendall_add_entry(self):
self.kendall.add(('guitar', 4.0), ('guitar', 4.0)) #add tag 'guitar'
assert_equal(len(self.kendall.get_test()), len(self.TEST_RANKING) + 1)
assert_equal(len(self.kendall.get_ground_truth()),
len(self.GT_RANKING) + 1)
assert_equal(self.kendall.compute(), 0.666667)
def test_RANK_Kendall_diff_elems(self):
TEST_DECISION = ['class', 'invented', 'baro', 'instru']
GT_DECISION = ['classical', 'instrumental', 'piano', 'baroque']
kendall = KendallTau()
kendall.load_ground_truth(self.GT_DECISION)
kendall.load_test(self.TEST_DECISION)
assert_raises(ValueError, kendall.compute) #Different elements
# TEST_RANK ReciprocalRank
def test_RANK_ReciprocalRank_compute(self):
rr = ReciprocalRank()
QUERY = 'instrumental'
assert_equal(rr.compute(self.GT_DECISION, QUERY), 0.5)
def test_RANK_ReciprocalRank_add_entry(self):
rr = ReciprocalRank()
QUERY = 'invented'
rr.load(self.GT_DECISION, QUERY)
assert_equal(rr.compute(), 0.0)
# TEST_RANK MeanReciprocalRank
# Internally, MeanReciprocalRank uses a list of ReciprocalRank results
def test_RANK_MeanReciprocalRank_compute_all(self):
assert_equal(self.mrr.compute(), 0.4375)
def test_RANK_MeanReciprocalRank_compute_one(self):
mrr = MeanReciprocalRank()
QUERY = 'instrumental'
assert_equal(mrr.compute(self.GT_DECISION, QUERY), 0.5)
def test_RANK_MeanReciprocalRank_load(self):
mrr = MeanReciprocalRank()
assert_raises(ValueError, mrr.load, self.GT_DECISION,
self.TEST_RANKING)
def test_RANK_MeanReciprocalRank_load_test(self):
mrr = MeanReciprocalRank()
assert_raises(NotImplementedError, mrr.load_test, self.TEST_RANKING)
def test_RANK_MeanReciprocalRank_load_ground_truth(self):
mrr = MeanReciprocalRank()
assert_raises(NotImplementedError, mrr.load_ground_truth,
self.GT_RANKING)
def test_RANK_MeanReciprocalRank_add_entry(self):
mrr = MeanReciprocalRank()
QUERY = 'invented'
mrr.load(self.GT_DECISION, QUERY)
assert_equal(mrr.compute(), 0.0)
#mAP tests
def test_RANK_AveragePrecision(self):
GT_DECISION = [1, 2, 4]
TEST_DECISION = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
avgp = AveragePrecision()
avgp.load(GT_DECISION, TEST_DECISION)
assert_equal(round(avgp.compute(), 4), 0.9167)
GT_DECISION = [1, 4, 8]
avgp = AveragePrecision()
avgp.load(GT_DECISION, TEST_DECISION)
assert_equal(round(avgp.compute(), 4), 0.625)
GT_DECISION = [3, 5, 9, 25, 39, 44, 56, 71, 89, 123]
TEST_DECISION = [
123, 84, 56, 6, 8, 9, 511, 129, 187, 25, 38, 48, 250, 113, 3
]
avgp = AveragePrecision()
avgp.load(GT_DECISION, TEST_DECISION)
assert_equal(avgp.compute(), 0.58)
#mAP tests
def test_RANK_MeanAveragePrecision(self):
mavgp = MeanAveragePrecision()
GT_DECISION = [1, 2, 4]
TEST_DECISION = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
mavgp.load(GT_DECISION, TEST_DECISION)
GT_DECISION = [1, 4, 8]
mavgp.load(GT_DECISION, TEST_DECISION)
GT_DECISION = [3, 5, 9, 25, 39, 44, 56, 71, 89, 123]
TEST_DECISION = [
123, 84, 56, 6, 8, 9, 511, 129, 187, 25, 38, 48, 250, 113, 3
]
mavgp.load(GT_DECISION, TEST_DECISION)
assert_equal(mavgp.compute(), 0.707222)
