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statstics.py
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from timeit import default_timer as timer
import random
import os
import time
import graph_generator as gg
import dynamic_program as dp
import integer_program as ip
import weighted_subgraph_problem as wsp
import heuristics as heu
def create_statistics_directory():
directory = 'statistics'
if not os.path.exists(directory):
os.makedirs(directory)
def create_statistics_file(name):
create_statistics_directory()
time_str = time.strftime("%Y%m%d-%H%M%S")
file_name = 'times_' + name + '_' + time_str + '.csv'
file_path = os.path.join('statistics', file_name)
return file_path
def make_statistics(graph_class, iterations, sizes, stat_name=None, deliminator='&', mode='max', rooted=False,
full=False, flowrooted=False, flow=False, dyn=False, sep=False, sep_iter=False, span_heu=False,
set_heu=False):
# weights
weights = (-10, 10, -10, 10)
if not stat_name:
stat_name = graph_class
file_path = create_statistics_file(stat_name)
# Open file
f = open(file_path, 'w')
# Heading
f.write(mode + '-WSP'+ deliminator + 'on' + deliminator + 'random' + deliminator + graph_class.capitalize()
+ 's' + '\n')
f.write('Average' + deliminator + 'times' + deliminator + 'for' + deliminator + str(iterations) + deliminator
+ 'iterations:' + '\n')
f.write('\n')
table_columns = 'graph size'
if dyn:
table_columns += deliminator + 'dyn prog'
if rooted:
table_columns += deliminator + 'IP (rooted)'
if full:
table_columns += deliminator + 'IP'
if flowrooted:
table_columns += deliminator + 'IP (flow rooted)'
if flow:
table_columns += deliminator + 'IP (flow)'
if set_heu:
table_columns += deliminator + 'Node set'
if span_heu:
table_columns += deliminator + 'Spanning tree'
if sep:
table_columns += deliminator + 'IP(sep)'
if sep_iter:
table_columns += deliminator + 'iterations (sep)'
table_columns += '\n'
f.write(table_columns)
# Fill table rows
for n in sizes:
print('Starting size ' + str(n) + ' at ' + time.strftime("%Y%m%d-%H%M%S"))
times = dict()
num_iter = []
for k in range(iterations):
if graph_class == 'path':
G = gg.random_weighted_path(n, *weights)
elif graph_class == 'tree':
G = gg.random_weighted_tree(n, *weights)
elif graph_class == 'SPG':
size = 0
while size != n:
G, D = gg.random_weighted_spg(1000, *weights)
size = G.number_of_nodes()
print(size)
G = G.to_undirected()
else:
G = gg.random_connected_graph(n, 2*n, *weights, multigraph=True)
if (k + 1) % (iterations/10) == 0:
print('Iteration ' + str(k + 1))
if rooted:
start = timer()
(H, weight) = ip.solve_full_ip__rooted(G, mode)
end = timer()
alg = 'IP (rooted)'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
if full:
start = timer()
(H, weight) = ip.solve_full_ip(G, mode)
end = timer()
alg = 'IP'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
if flowrooted:
start = timer()
(H, weight) = ip.solve_flow_ip__rooted(G, mode)
end = timer()
alg = 'IP (flow rooted)'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
if flow:
start = timer()
(H, weight) = ip.solve_flow_ip(G, mode)
end = timer()
alg = 'IP (flow)'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
if set_heu:
start = timer()
H, weight_set = heu.node_set_heuristic(G, mode)
end = timer()
alg = 'Node set'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
if span_heu:
start = timer()
H, weight_tree = heu.spanning_tree_heuristic(G, mode)
end = timer()
alg = 'Spanning tree'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
if sep:
start = timer()
(H, weight, i) = ip.solve_separation_ip(G, mode)
end = timer()
alg = 'IP (separation)'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
num_iter.append(i)
if dyn:
if graph_class == 'path':
start = timer()
(H, weight) = dp.solve_dynamic_prog_on_path(G, mode)
end = timer()
elif graph_class == 'tree':
start = timer()
(H, weight) = dp.solve_dynamic_prog_on_tree(G, mode)
end = timer()
elif graph_class == 'SPG':
start = timer()
(H, weight) = dp.solve_dynamic_prog_on_spg(G, D, mode)
end = timer()
alg = 'Dynamic program'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
av_time = dict()
for alg in times:
av_time[alg] = sum(times[alg]) / float(iterations)
av_iter = sum(num_iter) / float(iterations)
table_row = str(n)
if dyn:
table_row += deliminator + "{0:.6f}".format(av_time['Dynamic program'])
if rooted:
table_row += deliminator + "{0:.6f}".format(av_time['IP (rooted)'])
if full:
table_row += deliminator + "{0:.6f}".format(av_time['IP'])
if flowrooted:
table_row += deliminator + "{0:.6f}".format(av_time['IP (flow rooted)'])
if flow:
table_row += deliminator + "{0:.6f}".format(av_time['IP (flow)'])
if span_heu:
table_row += deliminator + "{0:.6f}".format(av_time['Spanning tree'])
if set_heu:
table_row += deliminator + "{0:.6f}".format(av_time['Node set'])
if sep:
table_row += deliminator + "{0:.6f}".format(av_time['IP (separation)'])
if sep_iter:
table_row += deliminator + "{0:.6f}".format(av_iter)
table_row += '\\\\ \hline'
table_row += '\n'
f.write(table_row)
print(graph_class + ' with size ' + str(n)
+ ' done' + ' at ' + time.strftime("%Y%m%d-%H%M%S"))
f.close()
print(graph_class + ' done' + ' at ' + time.strftime("%Y%m%d-%H%M%S"))
def make_weight_statistics(graph_class, iterations, weights, stat_name=None, deliminator='&', mode='max', rooted=False,
full=False, flowrooted=False, flow=False, dyn=False, sep=False):
size = 100
if not stat_name:
stat_name = graph_class
file_path = create_statistics_file(stat_name)
# Open file
f = open(file_path, 'w')
# Heading
f.write(mode + '-WSP'+ deliminator + 'on' + deliminator + 'random' + deliminator + graph_class.capitalize()
+ 's' + deliminator + 'of' + deliminator + 'size' + deliminator + str(size) + '\n')
f.write('Average' + deliminator + 'times' + deliminator + 'for' + deliminator + str(iterations) + deliminator
+ 'iterations:' + '\n')
f.write('\n')
table_columns = 'Weights'
if dyn:
table_columns += deliminator + 'dyn prog'
if rooted:
table_columns += deliminator + 'IP (rooted)'
if full:
table_columns += deliminator + 'IP'
if flowrooted:
table_columns += deliminator + 'IP (flow rooted)'
if flow:
table_columns += deliminator + 'IP (flow)'
if sep:
table_columns += deliminator + 'IP(sep)'
table_columns += '\n'
f.write(table_columns)
# Fill table rows
for weight in weights:
print('Starting weight ' + str(weight) + ' at ' + time.strftime("%Y%m%d-%H%M%S"))
times = dict()
for k in range(iterations):
if graph_class == 'path':
G = gg.random_weighted_path(size, *weight)
elif graph_class == 'tree':
G = gg.random_weighted_tree(size, *weight)
elif graph_class == 'SPG':
G, D = gg.random_weighted_spg(size, *weight)
else:
G = gg.random_connected_graph(size, 2*size, *weight)
if (k + 1) % (iterations/10) == 0:
print('Iteration ' + str(k + 1))
if rooted:
start = timer()
(H, w) = ip.solve_full_ip__rooted(G, mode)
end = timer()
alg = 'IP (rooted)'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
if full:
start = timer()
(H, w) = ip.solve_full_ip(G, mode)
end = timer()
alg = 'IP'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
if flowrooted:
start = timer()
(H, w) = ip.solve_flow_ip__rooted(G, mode)
end = timer()
alg = 'IP (flow rooted)'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
if flow:
start = timer()
(H, w) = ip.solve_flow_ip(G, mode)
end = timer()
alg = 'IP (flow)'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
if sep:
start = timer()
(H, w, i) = ip.solve_separation_ip(G, mode)
end = timer()
alg = 'IP (separation)'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
if dyn:
if graph_class == 'path':
start = timer()
(H, weight) = dp.solve_dynamic_prog_on_path(G, mode)
end = timer()
elif graph_class == 'tree':
start = timer()
(H, weight) = dp.solve_dynamic_prog_on_tree(G, mode)
end = timer()
elif graph_class == 'SPG':
start = timer()
(H, weight) = dp.solve_dynamic_prog_on_spg(G, D, mode)
end = timer()
alg = 'Dynamic program'
if alg not in times:
times[alg] = []
times[alg].append(end - start)
av_time = dict()
for alg in times:
av_time[alg] = sum(times[alg]) / float(iterations)
table_row = str(weight)
if dyn:
table_row += deliminator + "{0:.6f}".format(av_time['Dynamic program'])
if rooted:
table_row += deliminator + "{0:.6f}".format(av_time['IP (rooted)'])
if full:
table_row += deliminator + "{0:.6f}".format(av_time['IP'])
if flowrooted:
table_row += deliminator + "{0:.6f}".format(av_time['IP (flow rooted)'])
if flow:
table_row += deliminator + "{0:.6f}".format(av_time['IP (flow)'])
if sep:
table_row += deliminator + "{0:.6f}".format(av_time['IP (separation)'])
table_row += '\\\\ \hline'
table_row += '\n'
f.write(table_row)
print('weight ' + str(weight) + ' done' + ' at ' + time.strftime("%Y%m%d-%H%M%S"))
f.close()
print(graph_class + ' done' + ' at ' + time.strftime("%Y%m%d-%H%M%S"))
def make_relaxation_statistics(iterations, size, stat_name='relaxation', deliminator='&', mode='max', rooted=False,
full=False, flow=False):
# weights
weights = (-10, 10, -10, 10)
file_path = create_statistics_file(stat_name)
# Open file
f = open(file_path, 'w')
# Heading
f.write(mode + '-WSP'+ deliminator + 'on' + deliminator + 'random' + deliminator + 'Graphs'
+ 's' + '\n')
f.write('\n')
table_columns = 'iteration' + deliminator + 'IP'
if full:
table_columns += deliminator + 'LP (full)' + deliminator + 'Gap (full)'
if rooted:
table_columns += deliminator + 'LP (rooted)' + deliminator + 'Gap (rooted)'
if flow:
table_columns += deliminator + 'LP (flow)' + deliminator + 'Gap (flow)'
table_columns += '\n'
f.write(table_columns)
# Fill table rows
for k in range(iterations):
m = random.randint(size - 1, 30)
G = gg.random_connected_graph(size, m, *weights)
if (k + 1) % (iterations/10) == 0:
print('Iteration ' + str(k + 1))
(H, weight_ip) = ip.solve_flow_ip(G, mode)
if rooted:
start = timer()
weight_rooted = ip.solve_full_ip__rooted(G, mode, relaxed=True)
end = timer()
alg = 'LP (rooted)'
if full:
start = timer()
weight_full = ip.solve_full_ip(G, mode, relaxed=True)
end = timer()
alg = 'IP'
if flow:
start = timer()
weight_flow = ip.solve_flow_ip(G, mode, relaxed=True)
end = timer()
alg = 'IP (flow)'
table_row = str(int(weight_ip))
if full:
table_row += deliminator + "{0:.2f}".format(weight_full)
table_row += deliminator + "{0:.2f}".format((1 - weight_ip/weight_full)*100) + '\%'
if rooted:
table_row += deliminator + "{0:.2f}".format(weight_rooted)
table_row += deliminator + "{0:.2f}".format((1 - weight_ip/weight_rooted)*100) + '\%'
if flow:
table_row += deliminator + "{0:.2f}".format(weight_flow)
table_row += deliminator + "{0:.2f}".format((1 - weight_ip/weight_flow)*100) + '\%'
table_row += "\\\\ \hline \n"
f.write(table_row)
f.close()
def make_preprocessing_statistics(iterations, ns, stat_name='preprocess', deliminator='&', mode='max'):
# weights
weights = (-10, 10, -10, 10)
file_path = create_statistics_file(stat_name)
# Open file
f = open(file_path, 'w')
# Heading
f.write(mode + '-WSP'+ deliminator + 'on' + deliminator + 'random' + deliminator + 'Graphs'
+ 's' + '\n')
f.write('Average' + deliminator + 'times' + deliminator + 'for' + deliminator + str(iterations) + deliminator
+ 'iterations:' + '\n')
f.write('\n')
table_columns = 'n' + deliminator + 'm'
table_columns += deliminator + 'with'
table_columns += deliminator + 'n' + deliminator + 'm'
table_columns += deliminator + 'without'
table_columns += '\n'
f.write(table_columns)
# Fill table rows
for n in ns:
ms = [2*n, 3*n, 4*n]
print('Starting size ' + str(n) + ' at ' + time.strftime("%Y%m%d-%H%M%S"))
times = dict()
#for k in range(iterations):
for m in ms:
G = gg.random_connected_graph(n, m, *weights, multigraph=True)
R = wsp.preprocessing(G, mode)
nr = R.number_of_nodes()
mr = R.number_of_edges()
# if (k + 1) % (iterations/10) == 0:
# print('Iteration ' + str(k + 1))
#
# start = timer()
# (H, weight) = ip.solve_flow_ip(G, mode)
# end = timer()
# alg = 'No preprocessing'
# if alg not in times:
# times[alg] = []
# times[alg].append(end - start)
#
# start = timer()
# (H, weight) = ip.solve_flow_ip(R, mode)
# end = timer()
# alg = 'Preprocessing'
# if alg not in times:
# times[alg] = []
# times[alg].append(end - start)
#
# av_time = dict()
# for alg in times:
# av_time[alg] = sum(times[alg]) / float(iterations)
table_row = str(n)
table_row += deliminator + str(m)
#table_row += deliminator + "{0:.6f}".format(av_time['Preprocessing'])
table_row += deliminator + str(nr) + deliminator + str(mr)
#table_row += deliminator + "{0:.6f}".format(av_time['No preprocessing'])
table_row += '\\\\ \hline' + '\n'
f.write(table_row)
print('size ' + str(n) + ' done' + ' at ' + time.strftime("%Y%m%d-%H%M%S"))
f.close()
def make_heuristic_statistics(iterations, sizes, stat_name='heuristic', deliminator='&', mode='max', set_heu=False,
span_heu=False):
# weights
weights = (-10, 10, -10, 10)
file_path = create_statistics_file(stat_name)
# Open file
f = open(file_path, 'w')
# Heading
f.write(mode + '-WSP'+ deliminator + 'on' + deliminator + 'random' + deliminator + 'Graphs'
+ 's' + '\n')
f.write('\n')
table_columns = 'n'
if set_heu:
table_columns += deliminator + 'min gap' + deliminator + 'max gap' + deliminator + 'av gap'
if span_heu:
table_columns += deliminator + 'min gap' + deliminator + 'max gap' + deliminator + 'av gap'
table_columns += '\\ \hline' + '\n'
f.write(table_columns)
# Fill table rows
for n in sizes:
min_gap_set = 100.0
max_gap_set = 0.0
min_gap_tree = 100.0
max_gap_tree = 0.0
gaps_set = []
gaps_tree = []
for k in range(iterations):
size = 0
while size != n:
G, D = gg.random_weighted_spg(2 * n - 3, *weights)
size = G.number_of_nodes()
G = G.to_undirected()
if (k + 1) % (iterations/10) == 0:
print('Iteration ' + str(k + 1))
G = G.to_undirected()
(H, weight_ip) = dp.solve_dynamic_prog_on_spg(G, D, mode)
if set_heu:
H, weight_set = heu.node_set_heuristic(G, mode)
gap_set = ((weight_ip - weight_set) / float(weight_ip))*100
alg = 'Node set'
gaps_set.append(gap_set)
if gap_set > max_gap_set:
max_gap_set = gap_set
if gap_set < min_gap_set:
min_gap_set = gap_set
if span_heu:
H, weight_tree = heu.spanning_tree_heuristic(G, mode)
gap_tree = ((weight_ip - weight_tree) / float(weight_ip)) * 100
alg = 'Spanning tree'
gaps_tree.append(gap_tree)
if gap_tree > max_gap_tree:
max_gap_tree = gap_tree
if gap_tree < min_gap_tree:
min_gap_tree = gap_tree
table_row = str(n)
if set_heu:
av_gap_set = sum(gaps_set) / float(iterations)
table_row += deliminator + "{0:.2f}".format(max_gap_set)
table_row += deliminator + "{0:.2f}".format(min_gap_set)
table_row += deliminator + "{0:.2f}".format(av_gap_set)
if span_heu:
av_gap_tree = sum(gaps_tree) / float(iterations)
table_row += deliminator + "{0:.2f}".format(max_gap_tree)
table_row += deliminator + "{0:.2f}".format(min_gap_tree)
table_row += deliminator + "{0:.2f}".format(av_gap_tree)
table_row += "\\\\ \hline \n"
f.write(table_row)
f.close()