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experiments_synthetic_birth_death.py
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experiments_synthetic_birth_death.py
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from __future__ import division
from muturank import Muturank_new
from synthetic import SyntheticDataConverter
from metrics import evaluate
from dblp import dblp_loader
import networkx as nx
from itertools import combinations_with_replacement
import random
from tensor import TensorFact
import pickle
from collections import OrderedDict
import time
import json
from tabulate import tabulate
import pprint
import datetime
class Data(object):
def __init__(self, comms, graphs, timeFrames, number_of_dynamic_communities, dynamic_truth=[]):
self.comms = comms
self.graphs = graphs
self.timeFrames = timeFrames
self.number_of_dynamic_communities = number_of_dynamic_communities
self.dynamic_truth = dynamic_truth
def object_decoder(obj, num):
if 'type' in obj[num] and obj[num]['type'] == 'hand':
edges = {int(tf): [(edge[0], edge[1]) for edge in edges] for tf, edges in obj[num]['edges'].iteritems()}
graphs = {}
for i, edges in edges.items():
graphs[i] = nx.Graph(edges)
comms = {int(tf): {int(id): com for id, com in coms.iteritems()} for tf, coms in obj[num]['comms'].iteritems() }
dynamic_coms = {int(id): [str(node) for node in com] for id, com in obj[num]['dynamic_truth'].iteritems()}
return Data(comms, graphs, len(graphs), len(dynamic_coms), dynamic_coms)
return obj
# def evaluate(results, ground_truth, method, name):
# nmi = NMI(ground_truth, method.dynamic_coms, evaluation_type="sets").results
# omega = Omega(ground_truth, method.dynamic_coms)
# bcubed = Bcubed(ground_truth, method.dynamic_coms)
# results["Method"].append(name)
# results['NMI'].append(nmi['NMI<Max>'])
# results['Omega'].append(omega.omega_score)
# results['Bcubed-Precision'].append(bcubed.precision)
# results['Bcubed-Recall'].append(bcubed.recall)
# results['Bcubed-F1'].append(bcubed.fscore)
# return results
def run_experiments(data, ground_truth, network_num):
all_res = []
# Timerank with one connection - default q
# mutu4 = Muturank_new(data.graphs, threshold=1e-6, alpha=0.85, beta=0.85, connection='one',
# clusters=len(ground_truth), default_q=True)
# all_res.append(
# evaluate.get_results(ground_truth, mutu4.dynamic_coms, "Timerank with one connection - default q", mutu4.tfs,
# eval="dynamic", duration=mutu4.duration))
# all_res.append(
# evaluate.get_results(ground_truth, mutu4.dynamic_coms, "Timerank with one connection - default q", mutu4.tfs,
# eval="sets", duration=mutu4.duration))
# all_res.append(
# evaluate.get_results(ground_truth, mutu4.dynamic_coms, "Timerank with one connection - default q", mutu4.tfs,
# eval="per_tf", duration=mutu4.duration))
# f = open(results_file, 'a')
# f.write(tabulate(all_res, headers="keys", tablefmt="fancy_grid").encode('utf8') + "\n")
# f.close()
# Timerank with all connections - default q
mutu5 = Muturank_new(data.graphs, threshold=1e-6, alpha=0.85, beta=0.85, connection='all',
clusters=len(ground_truth), default_q=True)
all_res.append(evaluate.get_results(ground_truth, mutu5.dynamic_coms, "Timerank with all connections - default q"
, mutu5.tfs,
eval="dynamic", duration=mutu5.duration))
all_res.append(evaluate.get_results(ground_truth, mutu5.dynamic_coms, "Timerank with all connections - default "
"q", mutu5.tfs,
eval="sets", duration=mutu5.duration))
all_res.append(evaluate.get_results(ground_truth, mutu5.dynamic_coms, "Muturank with all connections - default "
"q", mutu5.tfs,
eval="per_tf", duration=mutu5.duration))
f = open(results_file, 'a')
f.write(tabulate(all_res, headers="keys", tablefmt="fancy_grid").encode('utf8') + "\n")
f.close()
# Timerank with next connection - default q
mutu6 = Muturank_new(data.graphs, threshold=1e-6, alpha=0.85, beta=0.85, connection='next',
clusters=len(ground_truth), default_q=True)
all_res.append(evaluate.get_results(ground_truth, mutu6.dynamic_coms, "Timerank with next connection - default q"
, mutu6.tfs, eval="dynamic", duration=mutu6.duration))
all_res.append(evaluate.get_results(ground_truth, mutu6.dynamic_coms, "Timerank with next connection - default q",
mutu6.tfs, eval="sets", duration=mutu6.duration))
all_res.append(evaluate.get_results(ground_truth, mutu6.dynamic_coms, "Timerank with next connection - default q",
mutu6.tfs, eval="per_tf", duration=mutu6.duration))
f = open(results_file, 'a')
f.write(tabulate(all_res, headers="keys", tablefmt="fancy_grid").encode('utf8') + "\n")
f.close()
# NNTF
fact = TensorFact(data.graphs, num_of_coms=len(ground_truth), threshold=1e-4, seeds=10, overlap=False)
all_res.append(evaluate.get_results(ground_truth, fact.dynamic_coms, "NNTF", mutu6.tfs, eval="dynamic",
duration=fact.duration))
all_res.append(evaluate.get_results(ground_truth, fact.dynamic_coms, "NNTF", mutu6.tfs, eval="sets",
duration=fact.duration))
all_res.append(evaluate.get_results(ground_truth, fact.dynamic_coms, "NNTF", mutu6.tfs, eval="per_tf",
duration=fact.duration))
with open(results_file, 'a') as f:
f.write("NNTF\n")
f.write("Error: " + str(fact.error) + "Seed: " + str(fact.best_seed)+"\n")
f.write("A\n")
pprint.pprint(fact.A, stream=f, width=150)
f.write("B\n")
pprint.pprint(fact.B, stream=f, width=150)
f.write("C\n")
pprint.pprint(fact.C, stream=f, width=150)
pprint.pprint(fact.dynamic_coms, stream=f, width=150)
f = open(results_file, 'a')
f.write(tabulate(all_res, headers="keys", tablefmt="fancy_grid").encode('utf8') + "\n")
f.close()
# GED
# import sys
# sys.path.insert(0, '../GED/')
# import preprocessing, Tracker
# start_time = time.time()
# from ged import GedWrite, ReadGEDResults
# ged_data = GedWrite(data)
# graphs = preprocessing.getGraphs(ged_data.fileName)
# tracker = Tracker.Tracker(graphs)
# tracker.compare_communities()
# #outfile = 'tmpfiles/ged_results.csv'
# outfile = './results/GED-events-handdrawn-'+str(network_num)+'.csv'
# with open(outfile, 'w')as f:
# for hypergraph in tracker.hypergraphs:
# hypergraph.calculateEvents(f)
# ged_time = str(datetime.timedelta(seconds=int(time.time() - start_time)))
# print "--- %s seconds ---" % (ged_time)
# ged = ReadGEDResults.ReadGEDResults(file_coms=ged_data.fileName, file_output=outfile)
# with open(results_file, 'a') as f:
# f.write("GED\n")
# pprint.pprint(ged.dynamic_coms, stream=f, width=150)
# all_res.append(evaluate.get_results(ground_truth, ged.dynamic_coms, "GED", mutu6.tfs, eval="dynamic", duration=ged_time))
# all_res.append(evaluate.get_results(ground_truth, ged.dynamic_coms, "GED", mutu6.tfs, eval="sets", duration=ged_time))
# f = open(results_file, 'a')
# f.write(tabulate(all_res, headers="keys", tablefmt="fancy_grid").encode('utf8') + "\n")
# f.close()
#all_res.append(evaluate.get_results(ground_truth, ged.dynamic_coms, "GED", mutu6.tfs, eval="per_tf"))
# Run Timerank - One connection
# mutu1 = Muturank_new(data.graphs, threshold=1e-6, alpha=0.85, beta=0.85, connection='one',
# clusters=len(ground_truth), default_q=False)
# all_res.append(evaluate.get_results(ground_truth, mutu1.dynamic_coms, "Timerank with one connection", mutu1.tfs,
# eval="dynamic", duration=mutu1.duration))
# all_res.append(evaluate.get_results(ground_truth, mutu1.dynamic_coms, "Timerank with one connection", mutu1.tfs,
# eval="sets", duration=mutu1.duration))
# all_res.append(evaluate.get_results(ground_truth, mutu1.dynamic_coms, "Timerank with one connection", mutu1.tfs,
# eval="per_tf", duration=mutu1.duration))
# f = open(results_file, 'a')
# f.write(tabulate(all_res, headers="keys", tablefmt="fancy_grid").encode('utf8') + "\n")
# f.close()
# muturank_res = OrderedDict()
# muturank_res["tf/node"] = ['t' + str(tf) for tf in mutu1.tfs_list]
# for i, node in enumerate(mutu1.node_ids):
# muturank_res[node] = [mutu1.p_new[tf * len(mutu1.node_ids) + i] for tf in range(mutu1.tfs)]
# f = open(results_file, 'a')
# f.write("ONE CONNECTION\n")
# f.write(tabulate(muturank_res, headers="keys", tablefmt="fancy_grid").encode('utf8') + "\n")
# f.write(tabulate(zip(['t' + str(tf) for tf in mutu1.tfs_list], mutu1.q_new), headers="keys",
# tablefmt="fancy_grid").encode('utf8') + "\n")
# f.close()
# # Timerank with all connections
# mutu2 = Muturank_new(data.graphs, threshold=1e-6, alpha=0.85, beta=0.85, connection='all',
# clusters=len(ground_truth), default_q=False)
# all_res.append(evaluate.get_results(ground_truth, mutu2.dynamic_coms, "Timerank with all connections", mutu2.tfs,
# eval="dynamic", duration=mutu2.duration))
# all_res.append(evaluate.get_results(ground_truth, mutu2.dynamic_coms, "Timerank with all connections", mutu2.tfs,
# eval="sets", duration=mutu2.duration))
# all_res.append(evaluate.get_results(ground_truth, mutu2.dynamic_coms, "Timerank with all connections", mutu2.tfs,
# eval="per_tf", duration=mutu2.duration))
# f = open(results_file, 'a')
# f.write(tabulate(all_res, headers="keys", tablefmt="fancy_grid").encode('utf8') + "\n")
# f.close()
# muturank_res = OrderedDict()
# muturank_res["tf/node"] = ['t' + str(tf) for tf in mutu2.tfs_list]
# for i, node in enumerate(mutu2.node_ids):
# muturank_res[node] = [mutu2.p_new[tf * len(mutu2.node_ids) + i] for tf in range(mutu2.tfs)]
# f = open(results_file, 'a')
# f.write("ALL CONNECTIONS\n")
# f.write(tabulate(muturank_res, headers="keys", tablefmt="fancy_grid").encode('utf8') + "\n")
# f.write(tabulate(zip(['t' + str(tf) for tf in mutu2.tfs_list], mutu2.q_new), headers="keys",
# tablefmt="fancy_grid").encode('utf8') + "\n")
# f.close()
# Timerank with next connection
# mutu3 = Muturank_new(data.graphs, threshold=1e-6, alpha=0.85, beta=0.85, connection='next',
# clusters=len(ground_truth), default_q=False)
# all_res.append(evaluate.get_results(ground_truth, mutu3.dynamic_coms, "Timerank with next connection", mutu3.tfs,
# eval="dynamic", duration=mutu3.duration))
# all_res.append(evaluate.get_results(ground_truth, mutu3.dynamic_coms, "Timerank with next connection", mutu3.tfs,
# eval="sets", duration=mutu3.duration))
# all_res.append(evaluate.get_results(ground_truth, mutu3.dynamic_coms, "Timerank with next connection", mutu3.tfs,
# eval="per_tf", duration=mutu3.duration))
# f = open(results_file, 'a')
# f.write(tabulate(all_res, headers="keys", tablefmt="fancy_grid").encode('utf8') + "\n")
# f.close()
# muturank_res = OrderedDict()
# muturank_res["tf/node"] = ['t' + str(tf) for tf in mutu3.tfs_list]
# for i, node in enumerate(mutu3.node_ids):
# muturank_res[node] = [mutu3.p_new[tf * len(mutu3.node_ids) + i] for tf in range(mutu3.tfs)]
# f = open(results_file, 'a')
# f.write("NEXT CONNECTION\n")
# f.write(tabulate(muturank_res, headers="keys", tablefmt="fancy_grid").encode('utf8') + "\n")
# f.write(tabulate(zip(['t' + str(tf) for tf in mutu3.tfs_list], mutu3.q_new), headers="keys",
# tablefmt="fancy_grid").encode('utf8') + "\n")
# f.write("GROUND TRUTH\n")
# pprint.pprint(ground_truth, stream=f, width=150)
# f.write("ONE CONNECTION\n")
# pprint.pprint(mutu1.dynamic_coms, stream=f, width=150)
# f.write("ALL CONNECTIONS\n")
# pprint.pprint(mutu2.dynamic_coms, stream=f, width=150)
# f.write("NEXT CONNECTION\n")
# pprint.pprint(mutu3.dynamic_coms, stream=f, width=150)
# f.close()
return all_res
if __name__=="__main__":
import sys
if len(sys.argv) > 1:
folder = sys.argv[1]
else:
folder = "birth_death_data"
from os.path import expanduser
home = expanduser("~")
path_full = home+"/Dropbox/Msc/thesis/data/synthetic_generator/data/"+folder
results_file = "results_synthetic_"+path_full.split("/")[-1]+".txt"
sd = SyntheticDataConverter(path_full, remove_redundant_nodes=True)
# nodes = sd.graphs[0].nodes()
# # edges_1 = random.sample(list(combinations_with_replacement(nodes, 2)), 50)
# # edges_2 = random.sample(list(combinations_with_replacement(nodes, 2)), 207)
# ---------------------------------
#Dynamic Network Generator data (50 nodes/3 tfs)
number_of_dynamic_communities = len(sd.graphs[0])
data = Data(comms=sd.comms, graphs=sd.graphs, timeFrames=len(sd.graphs), number_of_dynamic_communities=len(
sd.dynamic_truth), dynamic_truth=sd.dynamic_truth)
# ---------------------------------
# Dynamic Network Generator data (50 nodes/3 tfs) - Same network everywhere except one tf
# dict = {
# 0: sd.graphs[0],
# 1: sd.graphs[0],
# 2: sd.graphs[2],
# 3: sd.graphs[0],
# 4: sd.graphs[0]
# }
# comms = {0: sd.comms[0], 1: sd.comms[0],2: sd.comms[2], 3: sd.comms[0],4: sd.comms[0]}
# number_of_dynamic_communities = len(sd.comms[0])
# data = Data(comms, dict, len(dict), len(sd.comms[0]))
# ---------------------------------
# dblp = dblp_loader("data/dblp/my_dblp_data.json", start_year=2000, end_year=2004, coms='comp')
# number_of_dynamic_communities = len(dblp.dynamic_coms)
# data = Data(dblp.communities, dblp.graphs, len(dblp.graphs), len(dblp.dynamic_coms))
# ground_truth = dblp.dynamic_coms
# ---------------------------------
# from plot import PlotGraphs
# PlotGraphs(data.graphs, len(data.graphs), 'expand-contract', 100)
all_res = run_experiments(data, data.dynamic_truth, 'birth')
results = OrderedDict()
results["Method"] = []
results['Eval'] = []
results['NMI'] = []
results['Omega'] = []
results['Bcubed-Precision'] = []
results['Bcubed-Recall'] = []
results['Bcubed-F1'] = []
results['Duration'] = []
for res in all_res:
for k, v in res.iteritems():
results[k].extend(v)
f = open(results_file, 'a')
f.write(tabulate(results, headers="keys", tablefmt="fancy_grid").encode('utf8')+"\n")
f.close()