def get_optimal_n_clusters(dataset):
algorothms = ['Bigclam']
datasets = [dataset]
table = {}
variants = []
if dataset == 'amazon.txt':
optimal_n_clusters = 0 #?????
elif dataset == 'cliques.txt':
optimal_n_clusters = 2
elif dataset == 'cycles.txt':
optimal_n_clusters = 4
elif dataset == 'facebook.txt':
variants = []
elif dataset == 'football.txt':
optimal_n_clusters = 12
elif dataset == 'karate.txt':
optimal_n_clusters = 2
elif dataset == 'nested.txt':
optimal_n_clusters = 6
elif dataset == 'polbooks.txt':
optimal_n_clusters = 3
elif dataset == 'protein_new.txt':
optimal_n_clusters = 13
elif dataset == 'scientists_new.txt':
variants = [250, 275, 300, 325, 350]
elif dataset == 'stars.txt':
optimal_n_clusters = 5
for i in variants:
result = make_experiment(algorothms, datasets, n_clusters=i)
print result
table[i, 'My modularity'] = result['Bigclam', dataset, 'My modularity']
table[i, 'Time'] = result['Bigclam', dataset, 'Time']
write_choice(table, variants, dataset, 'n_clusters')
def get_optimal_n_steps(dataset):
algorothms = ['Walktrap']
datasets = [dataset]
table = {}
variants = [45, 55]
for i in variants:
result = make_experiment(algorothms, datasets, n_steps=i)
for measure in all_measures:
table[i, measure] = result['Walktrap', dataset, measure]
write_choice(table, variants, dataset, 'n_steps')
def get_optimal_n_clique_size(dataset):
algorothms = ['CFinder']
datasets = [dataset]
table = {}
variants = [3]
for i in variants:
result = make_experiment(algorothms, datasets, clique_size=i)
for measure in all_measures:
if ('CFinder', dataset, measure) in result.keys():
table[i, measure] = result['CFinder', dataset, measure]
write_choice(table, variants, dataset, 'clique_size')
def get_optimal_thresholds(dataset):
algorothms = ['SCAN']
datasets = [dataset]
table = {}
#variants = [[1, 0.3], [1, 0.4], [1, 0.5], [1, 0.6], [1, 0.7], [1, 0.8], [2, 0.3], [2, 0.4], [2, 0.5], [2, 0.6], [2, 0.7], [2, 0.8],
# [3, 0.3], [3, 0.4], [3, 0.5], [3, 0.6], [3, 0.7], [3, 0.8]]
#str_variants = ['1, 0.3', '1, 0.4','1, 0.5', '1, 0.6', '1, 0.7', '1, 0.8', '2, 0.3', '2, 0.4','2, 0.5', '2, 0.6', '2, 0.7', '2, 0.8',
# '3, 0.3', '3, 0.4','3, 0.5', '3, 0.6', '3, 0.7', '3, 0.8']
variants = [[2, 0.5]]
str_variants = ['2, 0.5']
for i in xrange(len(variants)):
result = make_experiment(algorothms, datasets, neighbours_threshold=variants[i][0], similarity_threshold=variants[i][1])
for measure in all_measures:
table[str_variants[i], measure] = result['SCAN', dataset, measure]
write_choice(table, str_variants, dataset, 'thresholds')