def test_same_set(self):
"""A set should be clustered with itself"""
s = randset()
cluster = Cluster(width=10, bandwidth=2)
cluster.add_item(s)
cluster.add_item(s)
self.assertEqual(1, len(cluster.get_clusters()))
def test_same_set(self):
"""A set should be clustered with itself"""
s = randset()
cluster = Cluster(width=10, bandwidth=2)
cluster.add_item(s)
cluster.add_item(s)
self.assertEqual(len(cluster.get_clusters()), 1)
def test_cluster_threshold(self):
"""Expected error for threshold to similarity should be reasonable"""
n_tests = 50
dim = 15
tot_err = 0
for test in range(n_tests):
# Get some sets and their similarities
sets = (randset(), randset())
jsim = jaccard_similarity(*sets)
# Find the threshold at which they cluster together
for threshold in range(1, 100, 5):
threshold = float(threshold) / 100
bandwidth = get_bandwidth(dim, threshold)
num_bands = int(dim / bandwidth)
cluster = Cluster(width=num_bands * bandwidth,
bandwidth=bandwidth)
cluster.add_item(sets[0])
cluster.add_item(sets[1])
if len(cluster.get_clusters()) == 2:
tot_err += abs(jsim - threshold)
break
avg_error = float(tot_err) / n_tests
self.assertLessEqual(avg_error, 0.30)
def test_cluster_threshold(self):
"""Expected error for threshold to similarity should be reasonable"""
n_tests = 50
dim = 15
expected_error = 0.20
tot_err = 0
for test in range(n_tests):
# Get some sets and their similarities
sets = (randset(), randset())
jsim = jaccard_sim(*sets)
# Find the threshold at which they cluster together
for threshold in range(1, 100, 5):
threshold = float(threshold) / 100
bandwidth = get_bandwidth(dim, threshold)
num_bands = int(dim / bandwidth)
cluster = Cluster(width=num_bands * bandwidth,
bandwidth=bandwidth)
cluster.add_item(sets[0])
cluster.add_item(sets[1])
if len(cluster.get_clusters()) == 2:
tot_err += abs(jsim - threshold)
break
avg_err = float(tot_err) / n_tests
self.assertLessEqual(avg_err, expected_error, "Error too large")
def test_dissimilar_sets(self):
"""Two non-similar sets should not be clustered"""
cluster = Cluster(width=10, bandwidth=2)
cluster.add_item("yu5abcdef967")
cluster.add_item("1234567890z")
print cluster.get_clusters()
num_clusters = len(cluster.get_clusters())
self.assertEqual(2, num_clusters)
def test_dissimilar_sets(self):
"""Two non-similar sets should not be clustered"""
cluster = Cluster(width=10, bandwidth=2)
cluster.add_item("12yu5abcdef")
cluster.add_item("1234567890z")
print cluster.get_clusters()
num_clusters = len(cluster.get_clusters())
self.assertEqual(num_clusters, 2,
"Expected 2 clusters, got {}".format(num_clusters))
def test_names(self):
"""Should return 281 clusters of names.
"""
with open(get_resource_name('data/perrys.csv'), 'r') as fhandle:
data = set(line.rstrip() for line in fhandle)
cluster = Cluster(width=20, bandwidth=5, seed=SEED)
shingler = Shingler(3)
for name in data:
shingles = shingler.get_shingles(name)
cluster.add_item(shingles, name)
clusters = cluster.get_clusters()
self.assertEqual(327, len(clusters))
def test_names(self):
"""Should return 281 clusters of names.
"""
with open(get_resource_name('data/perrys.csv'), 'r') as fhandle:
data = set(line.rstrip() for line in fhandle)
cluster = Cluster(width=20, bandwidth=5, seed=SEED)
shingler = Shingler(3)
for name in data:
shingles = shingler.get_shingles(name)
cluster.add_item(shingles, name)
clusters = cluster.get_clusters()
self.assertEqual(len(clusters), 209)
def test_bills(self):
"""Should return 97 clusters of bills.
"""
with open(get_resource_name('data/bills100.txt'), 'r') as fhandle:
data = [line.rstrip().split('|') for line in fhandle]
cluster = Cluster(width=20, bandwidth=5, seed=SEED)
shingler = Shingler(span=3, tokenizer=RegexTokenizer())
for label, text in data:
shingles = shingler.get_shingles(text)
cluster.add_item(shingles, label)
clusters = cluster.get_clusters()
self.assertEqual(96, len(clusters))
def test_bills(self):
"""Should return 97 clusters of bills.
"""
with open(get_resource_name('data/bills100.txt'), 'r') as fhandle:
data = [line.rstrip().split('|') for line in fhandle]
cluster = Cluster(width=20, bandwidth=5, seed=SEED)
shingler = Shingler(span=3, tokenizer=RegexTokenizer())
for label, text in data:
shingles = shingler.get_shingles(text)
cluster.add_item(shingles, label)
clusters = cluster.get_clusters()
self.assertEqual(len(clusters), 97)
def test_names_kmin_scheme(self):
"""Should return 145 clusters of names.
"""
with open(get_resource_name('data/perrys.csv'), 'r') as fhandle:
data = set(line.rstrip() for line in fhandle)
cluster = Cluster(width=20, bandwidth=5, kmin=2, lsh_scheme="a1",
seed=SEED)
shingler = Shingler(3)
for name in data:
shingles = shingler.get_shingles(name)
cluster.add_item(shingles, name)
clusters = cluster.get_clusters()
# for cluster in clusters:
# print cluster
self.assertEqual(len(clusters), 176)
def get_clusters(args, data):
cluster = Cluster(width=args.width,
bandwidth=args.bandwidth,
lsh_scheme=args.lsh_scheme,
kmin=args.kmin,
hashfun=args.hashfun)
shingler = Shingler(
span=args.shingle_span,
skip=args.shingle_skip,
kmin=args.shingle_kmin,
unique=bool(args.shingle_uniq)
)
content_dict = dict()
for label, text in data:
content_dict[label] = text
shingles = shingler.get_shingles(text)
cluster.add_item(shingles, label)
return cluster.get_clusters()
def run_simulated_manually(filepath, lines_to_read=sys.maxint,
cluster_args=None):
with open(get_resource_name(filepath), 'r') as fhandle:
data = [line.rstrip().split(' ')
for line in islice(fhandle, lines_to_read)]
if cluster_args is None:
cluster_args = dict()
cluster = Cluster(**cluster_args)
shingler = Shingler(span=3)
content_dict = dict()
for pair in data:
if len(pair) > 1:
label, text = pair
else:
label, text = pair[0], ''
content_dict[label] = text
shingles = shingler.get_shingles(text)
cluster.add_item(shingles, label)
clusters = cluster.get_clusters()
is_label_positive = lambda lbl: ':' in lbl
return dict(stats=describe_clusters(clusters, is_label_positive))
def run_simulated_manually(filepath, lines_to_read=sys.maxint,
cluster_args=None):
with open(get_resource_name(filepath), 'r') as fhandle:
data = [line.rstrip().split(' ')
for line in islice(fhandle, lines_to_read)]
if cluster_args is None:
cluster_args = dict()
cluster = Cluster(**cluster_args)
shingler = Shingler(span=3)
content_dict = dict()
for pair in data:
if len(pair) > 1:
label, text = pair
else:
label, text = pair[0], ''
content_dict[label] = text
shingles = shingler.get_shingles(text)
cluster.add_item(shingles, label)
clusters = cluster.get_clusters()
is_label_positive = lambda lbl: ':' in lbl
return clusters, is_label_positive
def test_empty(self):
"""Should place the two empty sets into a separate cluster"""
cluster = Cluster(width=10, bandwidth=2)
cluster.add_item("abcdefg")
cluster.add_item("abcdefghi")
cluster.add_item("")
cluster.add_item("")
self.assertEqual(2, len(cluster.get_clusters()))
def test_empty(self):
"""Should place the two empty sets into a separate cluster"""
cluster = Cluster(width=10, bandwidth=2)
cluster.add_item("abcdefg")
cluster.add_item("abcdefghi")
cluster.add_item("")
cluster.add_item("")
self.assertEqual(len(cluster.get_clusters()), 2)
def test_simulated(opts, data):
cluster = Cluster(width=opts.width,
bandwidth=opts.bandwidth,
lsh_scheme=opts.lsh_scheme)
shingler = Shingler(span=opts.shingle_span)
s = FeatureClusterSummarizer()
content_dict = dict()
for label, text in data:
content_dict[label] = text
shingles = shingler.get_shingles(text)
s.add_features(label, shingles)
cluster.add_item(shingles, label)
clusters = cluster.get_clusters()
c = describe_clusters(clusters, lambda x: len(x.split(':')) > 1)
ti = s.summarize_clusters(clusters)
print json.dumps(dict(
stats=c.dict(),
ratios=dict(
precision=c.get_precision(),
recall=c.get_recall()
),
ti=ti
))
def test_similar_sets(self):
"""Two similar sets should be clustered"""
cluster = Cluster(width=10, bandwidth=2)
cluster.add_item("abcdefg")
cluster.add_item("abcdefghi")
self.assertEqual(1, len(cluster.get_clusters()))
def test_similar_sets(self):
"""Two similar sets should be clustered"""
cluster = Cluster(width=10, bandwidth=2)
cluster.add_item("abcdefg")
cluster.add_item("abcdefghi")
self.assertEqual(len(cluster.get_clusters()), 1)