def test_sample_empty():
"""Empty clusterings have AUL=0.0
"""
clusters = []
score1 = aul_score_from_labels(*clusters_to_labels(clusters))
score2 = aul_score_from_clusters(clusters)
assert_almost_equal(score1, 0.0, 4)
assert_almost_equal(score2, 0.0, 4)
def test_sample_empty():
"""Empty clusterings have AUL=0.0
"""
clusters = []
score1 = aul_score_from_labels(*clusters_to_labels(clusters))
score2 = aul_score_from_clusters(clusters)
assert_almost_equal(score1, 0.0, 4)
assert_almost_equal(score2, 0.0, 4)
def simulate_labeling(sample_size=2000, **kwargs):
clusters = simulate_clustering(**kwargs)
tuples = zip(*clusters_to_labels(clusters))
random.shuffle(tuples)
tuples = tuples[:sample_size]
ltrue, lpred = zip(*tuples) or ([], [])
return ltrue, lpred
def simulate_labeling(sample_size=2000, **kwargs):
clusters = simulate_clustering(**kwargs)
tuples = zip(*clusters_to_labels(clusters))
random.shuffle(tuples)
tuples = tuples[:sample_size]
ltrue, lpred = zip(*tuples) or ([], [])
return ltrue, lpred
def test_sample_perverse():
"""Perverese cases are 0.0 < AUL < 0.5
"""
clusters = [[1], [0, 0]]
aul1 = aul_score_from_labels(*clusters_to_labels(clusters))
aul2 = aul_score_from_clusters(clusters)
assert_almost_equal(aul1, 0.1111, 4)
assert_almost_equal(aul2, 0.1111, 4)
auc = RocCurve.from_clusters(clusters).auc_score()
assert_almost_equal(auc, 0.0, 4)
def test_sample_bad():
"""Bad clustering should score poorly
"""
clusters = [[1, 1, 0, 0], [0]]
aul1 = aul_score_from_labels(*clusters_to_labels(clusters))
aul2 = aul_score_from_clusters(clusters)
assert_almost_equal(aul1, 0.5, 4)
assert_almost_equal(aul2, 0.5, 4)
auc = RocCurve.from_clusters(clusters).auc_score()
assert_almost_equal(auc, 0.6667, 4)
def test_sample_neg_class1():
"""Similar to ``test_sample_perfect`` but have a negative of class 1
"""
clusters = [[1, 1, 1, 1, 1], [1], [0]]
aul1 = aul_score_from_labels(*clusters_to_labels(clusters))
aul2 = aul_score_from_clusters(clusters)
assert_almost_equal(aul1, 0.8690, 4)
assert_almost_equal(aul2, 0.8690, 4)
auc = RocCurve.from_clusters(clusters).auc_score()
assert_almost_equal(auc, 0.9167, 4)
def test_sample_cluster0_c0():
"""Similar to ``test_sample_perfect`` but have a cluster of class 0
"""
clusters = [[1, 1, 1, 1], [0, 0], [0]]
aul1 = aul_score_from_labels(*clusters_to_labels(clusters))
aul2 = aul_score_from_clusters(clusters)
assert_almost_equal(aul1, 0.6667, 4)
assert_almost_equal(aul2, 0.6667, 4)
auc = RocCurve.from_clusters(clusters).auc_score()
assert_almost_equal(auc, 1.0, 4)
def test_sample_cluster0_nh():
"""Same as in ``test_sample_perfect`` but cluster 0 not homogeneous
"""
clusters = [[1, 1, 1, 1, 0], [0], [0]]
aul1 = aul_score_from_labels(*clusters_to_labels(clusters))
aul2 = aul_score_from_clusters(clusters)
assert_almost_equal(aul1, 0.8, 4)
assert_almost_equal(aul2, 0.8, 4)
auc = RocCurve.from_clusters(clusters).auc_score()
assert_almost_equal(auc, 0.8333, 4)
def test_sample_perfect():
"""Perfect clustering
"""
clusters = [[1, 1, 1, 1, 1], [0], [0]]
aul1 = aul_score_from_labels(*clusters_to_labels(clusters))
aul2 = aul_score_from_clusters(clusters)
assert_almost_equal(aul1, 1.0, 4)
assert_almost_equal(aul2, 1.0, 4)
auc = RocCurve.from_clusters(clusters).auc_score()
assert_almost_equal(auc, 1.0, 4)
def test_sample_perverse():
"""Perverese cases are 0.0 < AUL < 0.5
"""
clusters = [[1], [0, 0]]
aul1 = aul_score_from_labels(*clusters_to_labels(clusters))
aul2 = aul_score_from_clusters(clusters)
assert_almost_equal(aul1, 0.1111, 4)
assert_almost_equal(aul2, 0.1111, 4)
auc = RocCurve.from_clusters(clusters).auc_score()
assert_almost_equal(auc, 0.0, 4)
def test_sample_bad():
"""Bad clustering should score poorly
"""
clusters = [[1, 1, 0, 0], [0]]
aul1 = aul_score_from_labels(*clusters_to_labels(clusters))
aul2 = aul_score_from_clusters(clusters)
assert_almost_equal(aul1, 0.5, 4)
assert_almost_equal(aul2, 0.5, 4)
auc = RocCurve.from_clusters(clusters).auc_score()
assert_almost_equal(auc, 0.6667, 4)
def test_sample_neg_class1():
"""Similar to ``test_sample_perfect`` but have a negative of class 1
"""
clusters = [[1, 1, 1, 1, 1], [1], [0]]
aul1 = aul_score_from_labels(*clusters_to_labels(clusters))
aul2 = aul_score_from_clusters(clusters)
assert_almost_equal(aul1, 0.8690, 4)
assert_almost_equal(aul2, 0.8690, 4)
auc = RocCurve.from_clusters(clusters).auc_score()
assert_almost_equal(auc, 0.9167, 4)
def test_sample_cluster0_c0():
"""Similar to ``test_sample_perfect`` but have a cluster of class 0
"""
clusters = [[1, 1, 1, 1], [0, 0], [0]]
aul1 = aul_score_from_labels(*clusters_to_labels(clusters))
aul2 = aul_score_from_clusters(clusters)
assert_almost_equal(aul1, 0.6667, 4)
assert_almost_equal(aul2, 0.6667, 4)
auc = RocCurve.from_clusters(clusters).auc_score()
assert_almost_equal(auc, 1.0, 4)
def test_sample_cluster0_nh():
"""Same as in ``test_sample_perfect`` but cluster 0 not homogeneous
"""
clusters = [[1, 1, 1, 1, 0], [0], [0]]
aul1 = aul_score_from_labels(*clusters_to_labels(clusters))
aul2 = aul_score_from_clusters(clusters)
assert_almost_equal(aul1, 0.8, 4)
assert_almost_equal(aul2, 0.8, 4)
auc = RocCurve.from_clusters(clusters).auc_score()
assert_almost_equal(auc, 0.8333, 4)
def test_sample_perfect():
"""Perfect clustering
"""
clusters = [[1, 1, 1, 1, 1], [0], [0]]
aul1 = aul_score_from_labels(*clusters_to_labels(clusters))
aul2 = aul_score_from_clusters(clusters)
assert_almost_equal(aul1, 1.0, 4)
assert_almost_equal(aul2, 1.0, 4)
auc = RocCurve.from_clusters(clusters).auc_score()
assert_almost_equal(auc, 1.0, 4)
