def generate_file_stats(in_dir_path, out_dir_path, G_name, req):
G_path = os.path.join(os.path.abspath(in_dir_path), G_name)
# remove vertices with empty preference list
subprocess.run(
['sed', '-i', '-E', '/^[[:lower:][:upper:][:digit:]]+ : ;/d', G_path],
check=True)
# generate matchings that are needed
matchings = {}
for mdesc, cppopt, seadesc in (('H', '-h', sea.HRLQ_HHEURISTIC),
('S', '-s', sea.STABLE),
('P', '-p', sea.MAX_CARD_POPULAR),
('M', '-m', sea.POP_AMONG_MAX_CARD)):
if mdesc in req:
mpath = os.path.join(os.path.abspath(out_dir_path),
'{}_{}'.format(mdesc, G_name))
subprocess.run([
os.path.join(CPPCODE_DIR, 'graphmatching'), '-A', cppopt, '-i',
G_path, '-o', mpath
],
check=True)
matchings[seadesc] = sea.read_matching(mpath)
# generate statistics for the files
sea.generate_hr_tex(graph_parser.read_graph(G_path), matchings,
out_dir_path, G_name)
def main():
# dirpath = '/home/amitrawt/Dropbox/projects/matching/tests+data/hand-crafted'
dirpath = '/media/amitrawt/5073c06b-f306-4e3a-9e18-23d2c9921453/ms_project/MIN_BP/couples/Experiment3'
# dirpath = '/home/amitrawt/Dropbox/SEA2017/dataset/HR/mahdian_prob_ml/n1_1000_n2_100_k_5'
# dirpath = '/home/amitrawt/Dropbox/SEA2017/dataset/HR/mrandom/n1_1000_n2_100_k_5'
# dirpath = '/home/amitrawt/Dropbox/projects/matching/n1_100_n2_10_k_5'
# G = graph_parser.read_graph(os.path.join(dirpath, '1000_10_5_100_1.txt'))
files = ['100-10-10-100-3-5-false-5-5-Iteration1.txt', '100-10-10-100-3-5-false-5-5-Iteration2.txt',
'100-10-10-100-3-5-false-5-5-Iteration3.txt']
# files = ['1000_100_5_10_1.txt', '1000_100_5_10_2.txt', '1000_100_5_10_3.txt']
#files = ['100_10_5_10_1.txt', '100_10_5_10_2.txt', '100_10_5_10_3.txt']
for filename in files:
filepath = os.path.join(dirpath, filename)
G = graph_parser.read_graph(filepath)
mat_h = rank_matrix(G, G.B, G.A)
avg_h = avg_rank(G.B, mat_h)
generate_line_plot('H', 'avg rank',
range(0, len(avg_h)), [p for _, p in avg_h],
'H_{}.png'.format(filename))
mat_r = rank_matrix(G, G.A, G.B)
avg_r = avg_rank(G.A, mat_r)
generate_line_plot('R', 'avg rank',
range(0, len(avg_r)), [p for _, p in avg_r],
'R_{}.png'.format(filename))
def main():
# dirpath = '/home/amitrawt/Dropbox/projects/matching/tests+data/hand-crafted'
dirpath = '/media/amitrawt/5073c06b-f306-4e3a-9e18-23d2c9921453/ms_project/MIN_BP/couples/Experiment3'
# dirpath = '/home/amitrawt/Dropbox/SEA2017/dataset/HR/mahdian_prob_ml/n1_1000_n2_100_k_5'
# dirpath = '/home/amitrawt/Dropbox/SEA2017/dataset/HR/mrandom/n1_1000_n2_100_k_5'
# dirpath = '/home/amitrawt/Dropbox/projects/matching/n1_100_n2_10_k_5'
# G = graph_parser.read_graph(os.path.join(dirpath, '1000_10_5_100_1.txt'))
files = [
'100-10-10-100-3-5-false-5-5-Iteration1.txt',
'100-10-10-100-3-5-false-5-5-Iteration2.txt',
'100-10-10-100-3-5-false-5-5-Iteration3.txt'
]
# files = ['1000_100_5_10_1.txt', '1000_100_5_10_2.txt', '1000_100_5_10_3.txt']
#files = ['100_10_5_10_1.txt', '100_10_5_10_2.txt', '100_10_5_10_3.txt']
for filename in files:
filepath = os.path.join(dirpath, filename)
G = graph_parser.read_graph(filepath)
mat_h = rank_matrix(G, G.B, G.A)
avg_h = avg_rank(G.B, mat_h)
generate_line_plot('H', 'avg rank', range(0, len(avg_h)),
[p for _, p in avg_h], 'H_{}.png'.format(filename))
mat_r = rank_matrix(G, G.A, G.B)
avg_r = avg_rank(G.A, mat_r)
generate_line_plot('R', 'avg rank', range(0, len(avg_r)),
[p for _, p in avg_r], 'R_{}.png'.format(filename))
def main():
parser = argparse.ArgumentParser(description='''Generate statistics in latex
format given a bipartite graph and matchings''')
parser.add_argument('-G', dest='G', help='Bipartite graph', required=True, metavar='')
parser.add_argument('-S', dest='S', help='Stable matching in the graph', metavar='')
parser.add_argument('-P', dest='P', help='Max-cardinality popular matching in the graph', metavar='')
parser.add_argument('-M', dest='M', help='Popular among max-cardinality matchings in the graph', metavar='')
parser.add_argument('-H', dest='H', help='Hospital proposing HRLQ heuristic in the graph', metavar='')
parser.add_argument('-R', dest='R', help='Resident proposing HRLQ heuristic in the graph', metavar='')
parser.add_argument('-O', dest='O', help='Directory where the statistics should be stored', metavar='')
args = parser.parse_args()
G, matchings = graph_parser.read_graph(args.G), {}
for mdesc, mfile in ((STABLE, args.S), (MAX_CARD_POPULAR, args.P),
(POP_AMONG_MAX_CARD, args.M), (HRLQ_HHEURISTIC, args.H),
(HRLQ_RHEURISTIC, args.R)):
if mfile is not None:
M = read_matching(mfile)
matchings[mdesc] = M
# if not matching_utils.is_feasible(G, M):
# raise Exception('{} matching is not feasible for the graph'.format(mdesc))
# print(args.H, matchings)
if args.H: # generate heuristic tex file
generate_heuristic_tex(G, matchings, args.O, os.path.basename(args.G))
else: # generate tex for M_s, M_p, and M_m
generate_hr_tex(G, matchings, args.O, os.path.basename(args.G))
def main():
dirpath = sys.argv[1]
for entry in os.scandir(dirpath):
if entry.name.startswith('1000_') and entry.name.endswith('txt'):
G = graph_parser.read_graph(entry.path)
count, lq_sum = lq_count(G)
print(entry.name, '# lq: {}, lq sum: {}'.format(count, lq_sum))
def main():
import sys
import graph_parser
import matching_algos
if len(sys.argv) < 2:
print('usage: {} <graph file>'.format(sys.argv[0]))
else:
G = graph_parser.read_graph(sys.argv[1])
M1 = matching_algos.stable_matching_man_woman(copy_graph(G))
M2 = matching_algos.popular_matching_man_woman(copy_graph(G))
print(to_easy_format(G, M1), to_easy_format(G, M2), sep='\n')
def generate_stats(dirpath):
for entry in os.scandir(dirpath):
if entry.is_file():
if is_graph_file(entry):
mpath, statpath = corr_matching_and_stats(entry)
if os.path.isfile(mpath):
M = sea.read_matching(mpath)
G = graph_parser.read_graph(entry.path)
M_ = envy_free_matching(G, M)
print_matching_stats(G, M_, statpath)
elif entry.is_dir():
generate_stats(entry.path)
def generate_stats(dirpath):
for entry in os.scandir(dirpath):
if entry.is_file():
if is_graph_file(entry):
mpath, statpath = corr_matching_and_stats(entry)
if os.path.isfile(mpath):
M = sea.read_matching(mpath)
G = graph_parser.read_graph(entry.path)
M_ = envy_free_matching(G, M)
print_matching_stats(G, M_, statpath)
elif entry.is_dir():
generate_stats(entry.path)
def main():
if len(sys.argv) < 4:
print('usage: {} <graph-file> <stable-file> <popular-file>'.format(sys.argv[0]))
else:
import graph_parser, matching_stats
gfile, sfile, pfile = sys.argv[1], sys.argv[2], sys.argv[3]
G = graph_parser.read_graph(gfile)
#M_stable = stable_matching_hospital_residents(G)
#print(G, M_stable, sep='\n')
M_stable = stable_matching_hospital_residents(graph.copy_graph(G))
M_popular = popular_matching_hospital_residents(graph.copy_graph(G))
matching_stats.print_matching(G, M_stable, sfile)
matching_stats.print_matching(G, M_popular, pfile)
def main():
parser = argparse.ArgumentParser(
description='''Generate statistics in latex
format given a bipartite graph and matchings'''
)
parser.add_argument('-G',
dest='G',
help='Bipartite graph',
required=True,
metavar='')
parser.add_argument('-S',
dest='S',
help='Stable matching in the graph',
metavar='')
parser.add_argument('-P',
dest='P',
help='Max-cardinality popular matching in the graph',
metavar='')
parser.add_argument(
'-M',
dest='M',
help='Popular among max-cardinality matchings in the graph',
metavar='')
parser.add_argument('-H',
dest='H',
help='Hospital proposing HRLQ heuristic in the graph',
metavar='')
parser.add_argument('-R',
dest='R',
help='Resident proposing HRLQ heuristic in the graph',
metavar='')
parser.add_argument('-O',
dest='O',
help='Directory where the statistics should be stored',
metavar='')
args = parser.parse_args()
G, matchings = graph_parser.read_graph(args.G), {}
for mdesc, mfile in ((STABLE, args.S), (MAX_CARD_POPULAR, args.P),
(POP_AMONG_MAX_CARD, args.M),
(HRLQ_HHEURISTIC, args.H), (HRLQ_RHEURISTIC, args.R)):
if mfile is not None:
M = read_matching(mfile)
matchings[mdesc] = M
# if not matching_utils.is_feasible(G, M):
# raise Exception('{} matching is not feasible for the graph'.format(mdesc))
# print(args.H, matchings)
if args.H: # generate heuristic tex file
generate_heuristic_tex(G, matchings, args.O, os.path.basename(args.G))
else: # generate tex for M_s, M_p, and M_m
generate_hr_tex(G, matchings, args.O, os.path.basename(args.G))
def main():
if len(sys.argv) < 4:
print('usage: {} <graph-file> <stable-file> <popular-file>'.format(
sys.argv[0]))
else:
import graph_parser, matching_stats
gfile, sfile, pfile = sys.argv[1], sys.argv[2], sys.argv[3]
G = graph_parser.read_graph(gfile)
#M_stable = stable_matching_hospital_residents(G)
#print(G, M_stable, sep='\n')
M_stable = stable_matching_hospital_residents(graph.copy_graph(G))
M_popular = popular_matching_hospital_residents(graph.copy_graph(G))
matching_stats.print_matching(G, M_stable, sfile)
matching_stats.print_matching(G, M_popular, pfile)
def generate_file_stats(in_dir_path, out_dir_path, G_name, req):
G_path = os.path.join(os.path.abspath(in_dir_path), G_name)
# remove vertices with empty preference list
subprocess.run(['sed', '-i', '-E', '/^[[:lower:][:upper:][:digit:]]+ : ;/d', G_path], check=True)
# generate matchings that are needed
matchings = {}
for mdesc, cppopt, seadesc in (('H', '-h', sea.HRLQ_HHEURISTIC), ('S', '-s', sea.STABLE),
('P', '-p', sea.MAX_CARD_POPULAR), ('M', '-m', sea.POP_AMONG_MAX_CARD)):
if mdesc in req:
mpath = os.path.join(os.path.abspath(out_dir_path), '{}_{}'.format(mdesc, G_name))
subprocess.run([os.path.join(CPPCODE_DIR, 'graphmatching'), '-A', cppopt,
'-i', G_path, '-o', mpath], check=True)
matchings[seadesc] = sea.read_matching(mpath)
# generate statistics for the files
sea.generate_hr_tex(graph_parser.read_graph(G_path), matchings, out_dir_path, G_name)
def main():
G = graph_parser.read_graph(sys.argv[1])
G1 = hreduction(graph.copy_graph(G))
print(graph.graph_to_UTF8_string(G1))
def main():
G = graph_parser.read_graph(sys.argv[1])
G1 = hreduction(graph.copy_graph(G))
print(graph.graph_to_UTF8_string(G1))
def main():
import sys
if len(sys.argv) < 2:
print('usage: {} <graph file>'.format(sys.argv[0]))
else:
G = graph_parser.read_graph(sys.argv[1])
def main():
import sys
if len(sys.argv) < 2:
print('usage: {} <graph file>'.format(sys.argv[0]))
else:
G = graph_parser.read_graph(sys.argv[1])
