def nf1(first_partition, second_partition):
"""
Compute the Normalized F1 score of the optimal algorithms matches among the partitions in input.
Works on overlapping/non-overlapping complete/partial coverage partitions.
:param first_partition: NodeClustering object
:param second_partition: NodeClustering object
:return: MatchingResult instance
:Example:
>>> from cdlib import evaluation, algorithms
>>> g = nx.karate_club_graph()
>>> louvain_communities = algorithms.louvain(g)
>>> leiden_communities = algorithms.leiden(g)
>>> evaluation.nf1(louvain_communities,leiden_communities)
:Reference:
1. Rossetti, G., Pappalardo, L., & Rinzivillo, S. (2016). `A novel approach to evaluate algorithms detection internal on ground truth. <https://www.researchgate.net/publication/287204505_A_novel_approach_to_evaluate_community_detection_algorithms_on_ground_truth/>`_
2. Rossetti, G. (2017). : `RDyn: graph benchmark handling algorithms dynamics. Journal of Complex Networks. <https://academic.oup.com/comnet/article-abstract/5/6/893/3925036?redirectedFrom=PDF/>`_ 5(6), 893-912.
"""
nf = NF1(first_partition.communities, second_partition.communities)
results = nf.summary()
return results['scores'].loc["NF1"][0]
def f1(first_partition, second_partition):
"""
Compute the average F1 score of the optimal algorithms matches among the partitions in input.
Works on overlapping/non-overlapping complete/partial coverage partitions.
:param first_partition: NodeClustering object
:param second_partition: NodeClustering object
:return: F1 score (harmonic mean of precision and recall)
:Example:
>>> from cdlib import evaluation, algorithms
>>> g = nx.karate_club_graph()
>>> louvain_communities = algorithms.louvain(g)
>>> leiden_communities = algorithms.leiden(g)
>>> evaluation.f1(louvain_communities,leiden_communities)
:Reference:
1. Rossetti, G., Pappalardo, L., & Rinzivillo, S. (2016). `A novel approach to evaluate algorithms detection internal on ground truth. <https://www.researchgate.net/publication/287204505_A_novel_approach_to_evaluate_community_detection_algorithms_on_ground_truth/>`_ In Complex Networks VII (pp. 133-144). Springer, Cham.
"""
nf = NF1(first_partition.communities, second_partition.communities)
results = nf.summary()
return MatchingResult(results['details']['F1 mean'][0],
results['details']['F1 std'][0])
def NF1scores(filenames):
communities = []
for filename in filenames:
comm = readCommunities(filename)
comms = [tuple(x) for x in comm]
communities.append(comms)
nf = NF1(communities[0], communities[1])
results = nf.summary()
print(results['scores'])
print(results['details'])
# Visualising the Precision-Recall density scatter-plot
nf.plot()
def run_nf1_evaluation(partition_list):
for partition in partition_list:
_partition_list_copy = partition_list.copy()
_partition_list_copy.remove(partition)
index = partition_list.index([partition][0])
for comparable_partition in _partition_list_copy[index:]:
nf = NF1(partition[0], comparable_partition[0])
res = nf.summary()
print(
f"Comparison between {partition[1]} and {comparable_partition[1]}:"
)
print(res["scores"])
print("____________________________________________________")
def test_nf1(self):
coms = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]
gt = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]
nf = NF1(coms, gt)
res = nf.summary()
self.assertEqual(len(res), 2)
def test_plot(self):
coms = [(1, 2, 67), (4, 6, 11), (7, 8, 9)]
gt = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]
nf = NF1(coms, gt)
nf.plot()
# timing = (stop-start).seconds/60
# print "ELAPSED TIME:" + str(timing) + " minutes"
p_demon = apply_demon(g)
# stop = datetime.datetime.now()
# timing = (stop-start).seconds/60
# print "ELAPSED TIME:" + str(timing) + " minutes"
ps = [p_labelprop, p_louvain, p_gn, p_demon]
for i in range(len(ps)):
for j in range(i + 1, len(ps)):
if ps[i] == p_labelprop:
if ps[j] == p_louvain:
comp = NF1(ps[i], ps[j]).summary()
comp['scores'].to_csv(path_or_buf='./comparisons_ita/' +
'label_propagation/label_' +
'propagation_louvain_scores.csv')
comp['details'].to_csv(path_or_buf='./comparisons_ita/' +
'label_propagation/label_' +
'propagation_louvain_details.csv')
elif ps[j] == p_gn:
for iteration in ps[j]:
comp = NF1(ps[i], ps[j][iteration]).summary()
comp['scores'].to_csv(
path_or_buf='./comparisons_ita/' +
'label_propagation/label_' + 'propagation_gn_it_' +
str(iteration) + '_scores.csv')
comp['details'].to_csv(
path_or_buf='./comparisons_ita/' +
def nf1go(x, y):
a = NF1(y, x)
score = a.get_f1()[0]
return score