def make_optimal_experiment(algorithms=None, datasets=None):
if algorithms == None:
raise TypeError("Algorithms are not given\n")
if datasets == None:
raise TypeError("Datasets are not given\n")
for algorithm in algorithms:
if algorithm not in ['Spectral', 'SCAN', 'GreedyNewman', 'Walktrap', 'LPA', 'CFinder', 'Clauset-Newman', 'Bigclam']:
print 'Algorithm '+algorithm+' is unavailable!\n'
for dataset in datasets:
if dataset not in ['football.txt', 'polbooks.txt', 'protein_new.txt', 'amazon.txt', 'scientists_new.txt', 'karate.txt',
'facebook.txt', 'cliques.txt', 'nested.txt', 'stars.txt', 'cycles.txt']:
print 'Dataset '+dataset+' is unavailable!\n'
result = {}
for dataset in datasets:
if dataset not in ['football.txt', 'polbooks.txt', 'protein_new.txt', 'amazon.txt', 'scientists_new.txt', 'karate.txt',
'facebook.txt', 'cliques.txt', 'nested.txt', 'stars.txt', 'cycles.txt']:
continue
from get_parameters import get_optimal_parameters
parameters = get_optimal_parameters(dataset, recompute=False)
n_clusters = parameters['n_clusters']
n_steps = parameters['n_steps']
clique_size = parameters['clique_size']
neighbours_threshold = parameters['neighbours_threshold']
similarity_threshold = parameters['similarity_threshold']
for algorithm in algorithms:
if algorithm not in ['Spectral', 'SCAN', 'GreedyNewman', 'Walktrap', 'LPA', 'CFinder', 'Clauset-Newman', 'Bigclam']:
continue
from load_data import download_graph
n_vertex, edge_list = download_graph('data\\'+dataset)
from model_builder import clustering
#try:
lbls, clrs, exectime = clustering(algorithm, n_vertex, edge_list, n_clusters, neighbours_threshold, similarity_threshold, n_steps, clique_size)
#except:
# continue
from load_data import write_labels, write_clusters
if lbls != None:
write_labels(algorithm, dataset, lbls)
if clrs != None:
write_clusters(algorithm, dataset, clrs)
result[algorithm, dataset, 'Time'] = exectime
from cluster_metrics import compute_my_modularity, compute_overlapping_modularity, compute_modularity, compute_ratio_cut, compute_normalized_cut
if algorithm in ['LPA', 'Walktrap', 'GreedyNewman', 'Clauset-Newman', 'Spectral']:
result[algorithm, dataset, 'My modularity'] = compute_overlapping_modularity(clrs, n_vertex, edge_list)
result[algorithm, dataset, 'Modularity'] = compute_modularity(lbls, edge_list)
result[algorithm, dataset, 'RatioCut'] = compute_ratio_cut(lbls, clrs, edge_list)
result[algorithm, dataset, 'NormCut'] = compute_normalized_cut(lbls, clrs, edge_list)
elif algorithm in ['Bigclam', 'CFinder']:
result[algorithm, dataset, 'My modularity'] = compute_overlapping_modularity(clrs, n_vertex, edge_list)
elif algorithm in ['SCAN']:
result[algorithm, dataset, 'My modularity'] = compute_overlapping_modularity(clrs, n_vertex, edge_list)
result[algorithm, dataset, 'RatioCut'] = compute_ratio_cut(lbls, clrs, edge_list)
result[algorithm, dataset, 'NormCut'] = compute_normalized_cut(lbls, clrs, edge_list)
lbls_true = None
clrs_true = None
import os
if os.path.isfile('data\\'+dataset[:-4]+'_labels.txt'):
from load_data import download_labels
lbls_true = download_labels('data\\'+dataset[:-4]+'_labels.txt')
from transform_functions import compute_clusters_from_labels
clrs_true = compute_clusters_from_labels(lbls_true)
elif os.path.isfile('data\\'+dataset[:-4]+'_clusters.txt'):
from load_data import download_clusters
clrs_true = download_clusters('data\\'+dataset[:-4]+'_clusters.txt')
if clrs_true == None:
result[algorithm, dataset, 'Precision'] = None
result[algorithm, dataset, 'Recall'] = None
result[algorithm, dataset, 'Average F1'] = None
else:
from cluster_metrics import compute_recall, compute_precision, compute_avg_f1
result[algorithm, dataset, 'Precision'] = compute_precision(clrs_true, clrs)
result[algorithm, dataset, 'Recall'] = compute_recall(clrs_true, clrs)
result[algorithm, dataset, 'Average F1'] = compute_avg_f1(clrs_true, clrs)
if algorithm != 'SCAN':
if lbls_true == None:
result[algorithm, dataset, 'NMI'] = None
result[algorithm, dataset, 'ARS'] = None
elif lbls != None:
from cluster_metrics import compute_nmi, compute_ars
result[algorithm, dataset, 'NMI'] = compute_nmi(lbls_true, lbls)
result[algorithm, dataset, 'ARS'] = compute_ars(lbls_true, lbls)
return result
def make_experiment(algorithms=None, datasets=None, **kwargs):
if algorithms == None:
raise TypeError("Algorithms are not given\n")
if datasets == None:
raise TypeError("Datasets are not given\n")
recognized = ['n_clusters', 'neighbours_threshold', 'similarity_threshold', 'n_steps', 'clique_size']
n_clusters=None
neighbours_threshold=None
similarity_threshold=None
n_steps=None
clique_size=None
for key, value in kwargs.items():
if key not in recognized:
raise TypeError(("Keyword argument '%s' is not recognized!\nAvailable keywords are:\n'"
+ "', '".join(recognized) + "'") % key)
if key == recognized[0]:
n_clusters = value
elif key == recognized[1]:
neighbours_threshold = value
elif key == recognized[2]:
similarity_threshold = value
elif key == recognized[3]:
n_steps = value
elif key == recognized[4]:
clique_size = value
for algorithm in algorithms:
if algorithm not in ['Spectral', 'SCAN', 'GreedyNewman', 'Walktrap', 'LPA', 'CFinder', 'Clauset-Newman', 'Bigclam']:
print 'Algorithm '+algorithm+' is unavailable!\n'
for dataset in datasets:
if dataset not in ['football.txt', 'polbooks.txt', 'protein_new.txt', 'amazon.txt', 'scientists_new.txt', 'karate.txt',
'facebook.txt', 'cliques.txt', 'nested.txt', 'stars.txt', 'cycles.txt']:
print 'Dataset '+dataset+' is unavailable!\n'
result = {}
for algorithm in algorithms:
if algorithm not in ['Spectral', 'SCAN', 'GreedyNewman', 'Walktrap', 'LPA', 'CFinder', 'Clauset-Newman', 'Bigclam']:
continue
fit, n_clusters, similarity_threshold, neighbours_threshold, n_steps, clique_size = fit_algo_params(algorithm, n_clusters,
similarity_threshold, neighbours_threshold, n_steps, clique_size)
if not fit:
continue
for dataset in datasets:
if dataset not in ['football.txt', 'polbooks.txt', 'protein_new.txt', 'amazon.txt', 'scientists_new.txt', 'karate.txt',
'facebook.txt', 'cliques.txt', 'nested.txt', 'stars.txt', 'cycles.txt']:
continue
from load_data import download_graph
n_vertex, edge_list = download_graph('data\\'+dataset)
from model_builder import clustering
#try:
lbls, clrs, exectime = clustering(algorithm, n_vertex, edge_list, n_clusters, neighbours_threshold, similarity_threshold, n_steps, clique_size)
#except:
# continue
from load_data import write_labels, write_clusters
if lbls != None:
write_labels(algorithm, dataset, lbls)
if clrs != None:
write_clusters(algorithm, dataset, clrs)
result[algorithm, dataset, 'Time'] = exectime
from cluster_metrics import compute_my_modularity, compute_overlapping_modularity, compute_modularity, compute_ratio_cut, compute_normalized_cut
if algorithm in ['LPA', 'Walktrap', 'GreedyNewman', 'Clauset-Newman', 'Spectral']:
result[algorithm, dataset, 'My modularity'] = compute_overlapping_modularity(clrs, n_vertex, edge_list)
result[algorithm, dataset, 'Modularity'] = compute_modularity(lbls, edge_list)
result[algorithm, dataset, 'RatioCut'] = compute_ratio_cut(lbls, clrs, edge_list)
result[algorithm, dataset, 'NormCut'] = compute_normalized_cut(lbls, clrs, edge_list)
elif algorithm in ['Bigclam', 'CFinder']:
result[algorithm, dataset, 'My modularity'] = compute_overlapping_modularity(clrs, n_vertex, edge_list)
elif algorithm in ['SCAN']:
result[algorithm, dataset, 'My modularity'] = compute_overlapping_modularity(clrs, n_vertex, edge_list)
result[algorithm, dataset, 'RatioCut'] = compute_ratio_cut(lbls, clrs, edge_list)
result[algorithm, dataset, 'NormCut'] = compute_normalized_cut(lbls, clrs, edge_list)
lbls_true = None
clrs_true = None
import os
if os.path.isfile('data\\'+dataset[:-4]+'_labels.txt'):
from load_data import download_labels
lbls_true = download_labels('data\\'+dataset[:-4]+'_labels.txt')
from transform_functions import compute_clusters_from_labels
clrs_true = compute_clusters_from_labels(lbls_true)
elif os.path.isfile('data\\'+dataset[:-4]+'_clusters.txt'):
from load_data import download_clusters
clrs_true = download_clusters('data\\'+dataset[:-4]+'_clusters.txt')
if clrs_true == None:
result[algorithm, dataset, 'Precision'] = None
result[algorithm, dataset, 'Recall'] = None
result[algorithm, dataset, 'Average F1'] = None
else:
from cluster_metrics import compute_recall, compute_precision, compute_avg_f1
result[algorithm, dataset, 'Precision'] = compute_precision(clrs_true, clrs)
result[algorithm, dataset, 'Recall'] = compute_recall(clrs_true, clrs)
result[algorithm, dataset, 'Average F1'] = compute_avg_f1(clrs_true, clrs)
if algorithm != 'SCAN':
if lbls_true == None:
result[algorithm, dataset, 'NMI'] = None
result[algorithm, dataset, 'ARS'] = None
elif lbls != None:
from cluster_metrics import compute_nmi, compute_ars
result[algorithm, dataset, 'NMI'] = compute_nmi(lbls_true, lbls)
result[algorithm, dataset, 'ARS'] = compute_ars(lbls_true, lbls)
return result
print clrs_pred
print time
from load_data import write_labels, write_clusters
#write communities (labels) into 'data\\answers\\'+'labels_'+algorithm+'_'+dataset
write_labels(algorithm, dataset, lbls_pred)
#write communities (clusters) into 'data\\answers\\'+'clusters_'+algorithm+'_'+dataset
write_labels(algorithm, dataset, clrs_pred)
"""
You can calculate goodness (non ground-truth) metrics, such as: modularity, overlapping modularity, ratio cut, normalized cut.
"""
from cluster_metrics import compute_modularity, compute_overlapping_modularity, compute_ratio_cut, compute_normalized_cut
print "Modularity = " + str(compute_modularity(lbls_pred, edge_list))
print "Overlapping modularity = " + str(compute_overlapping_modularity(clrs_pred, n_vertex, edge_list))
print "RatioCut = " + str(compute_ratio_cut(lbls_pred, clrs_pred, edge_list))
print "NormalizedCut = " + str(compute_normalized_cut(lbls_pred, clrs_pred, edge_list))
"""
If ground-truth communities are known, you can load them.
After that you can calculate performance (ground-truth) metrics, such as: average F1-score, average recall, average precision,
normalized mutual information (NMI), adjusted_rand_score (ARS)
"""
#if true labels are known
from load_data import download_labels, download_clusters
lbls_true = download_labels('data\\'+dataset[:-4]+'_labels.txt')
from transform_functions import compute_clusters_from_labels
clrs_true = compute_clusters_from_labels(lbls_true)