"--nseeds",
action="store",
type="string",
dest="nseeds",
help="number of seeds")
parser.add_option("-f",
"--filename",
action="store",
type="string",
dest="filename",
help="filename to save csv output")
(options, args) = parser.parse_args()
while not options.csvfile:
options.csvfile = raw_input("CSV file containing data ? ")
data = load_mcda_input_data(options.csvfile)
if data is None:
exit(1)
options.pclearning = read_multiple_integer(options.pclearning,
"Percentage of data to " \
"use in the learning set")
options.nseeds = read_single_integer(options.nseeds, "Number of seeds")
dt = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
default_filename = "%s/test_mip_mrsort-%s-%s.csv" \
% (DATADIR, data.name, dt)
options.filename = read_csv_filename(options.filename, default_filename)
directory = options.filename + "-data"
if not os.path.exists(directory):
"%s file.csv meta_mrsort|meta_mrsortc|mip_mrsort|lp_utadis|lp_utadis_compat"
% sys.argv[0])
sys.exit(1)
if len(sys.argv) != 3:
usage()
algo = sys.argv[2]
nseg = 4
nmodels = 20
nloop = 7
nmeta = 40
data = load_mcda_input_data(sys.argv[1])
print(data.c)
worst = data.pt.get_worst(data.c)
best = data.pt.get_best(data.c)
t1 = time.time()
if algo == 'meta_mrsort':
heur_init_profiles = HeurMRSortInitProfiles
lp_weights = LpMRSortWeights
heur_profiles = MetaMRSortProfiles4
elif algo == 'meta_mrsortc':
heur_init_profiles = HeurMRSortInitProfiles
lp_weights = LpMRSortMobius
heur_profiles = MetaMRSortProfilesChoquet
dest = "csvfile",
help = "csv file with data")
parser.add_option("-p", "--pclearning", action = "store", type="string",
dest = "pclearning",
help = "Percentage of data to use in learning set")
parser.add_option("-s", "--nseeds", action = "store", type="string",
dest = "nseeds",
help = "number of seeds")
parser.add_option("-f", "--filename", action = "store", type="string",
dest = "filename",
help = "filename to save csv output")
(options, args) = parser.parse_args()
while not options.csvfile:
options.csvfile = raw_input("CSV file containing data ? ")
data = load_mcda_input_data(options.csvfile)
if data is None:
exit(1)
options.pclearning = read_multiple_integer(options.pclearning,
"Percentage of data to " \
"use in the learning set")
options.nseeds = read_single_integer(options.nseeds, "Number of seeds")
dt = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
default_filename = "%s/test_mip_mrsort-%s-%s.csv" \
% (DATADIR, data.name, dt)
options.filename = read_csv_filename(options.filename, default_filename)
directory = options.filename + "-data"
if not os.path.exists(directory):
def usage():
print("%s file.csv meta_mrsort|meta_mrsortc|mip_mrsort|lp_utadis|lp_utadis_compat" % sys.argv[0])
sys.exit(1)
if len(sys.argv) != 3:
usage()
algo = sys.argv[2]
nseg = 4
nmodels = 20
nloop = 7
nmeta = 40
data = load_mcda_input_data(sys.argv[1])
print(data.c)
worst = data.pt.get_worst(data.c)
best = data.pt.get_best(data.c)
t1 = time.time()
if algo == 'meta_mrsort':
heur_init_profiles = HeurMRSortInitProfiles
lp_weights = LpMRSortWeights
heur_profiles = MetaMRSortProfiles4
elif algo == 'meta_mrsortc':
heur_init_profiles = HeurMRSortInitProfiles
lp_weights = LpMRSortMobius
heur_profiles = MetaMRSortProfilesChoquet