def run_exp(param):
"""Run an experiment according to given parameters."""
first_gen = read_first_gen_files(param[0])
e = experiment(condition=param[1],
comm_self_connected=param[2],
run_num=param[3],
first_gen=first_gen,
today=today,
pop=pop,
gen=gen,
trial_num=trial_num,
include_top=top)
e.run()
e_filename = 'Data/{}_comm_cs_conn_Run{}.exp'.format(today, param[3])
pickle.dump(e, open(e_filename, 'wb'))
result = 'done'
print('ok:', param)
return result
from Experiment import experiment
from Generate_First_Gen import read_first_gen_files
import sys
import pickle
pop = 10
gen = 3
trial_num = 3
top = 2
today = '2019_04_32'
run_num = int(sys.argv[1]) # number designating the current run
first_gen_file = 'FirstGen/Run{}Pop{}65.txt'.format(run_num, pop)
first_gen = read_first_gen_files(first_gen_file)
e = experiment(condition='comm',
comm_self_connected=False,
run_num=run_num,
first_gen=first_gen,
today=today,
pop=pop,
gen=gen,
trial_num=trial_num,
include_top=top)
if __name__ == '__main__':
e.run()
e_filename = 'Data/{}_comm_cs_disconn_Run{}.exp'.format(today, run_num)
pickle.dump(e, open(e_filename, 'wb'))
, 'rank_score', 'rank_score_upper', 'correct_score', 'correct_score_upper', 'correct_score_hungarian'\
, 'pairs_computed'])
for a in adaptiveLSH:
for n in noise:
for b in bandNumber:
for l in noise_level:
df = experiment(df,
filename='metadata/email.edges',
nodeAttributeFile=None,
multipleGraph=False,
is_perm=False,
has_noise=n,
noise_level=l,
plotAttribute=False,
plotBucket=False,
plotCorrectness=False,
GraphType='Undirected',
bandNumber=b,
adaptiveLSH=a,
LSHType='Cosine',
compute_sim=True,
compute_hungarian=False,
loop_num=1)
df = experiment(df,
filename='metadata/email.edges',
nodeAttributeFile=None,
multipleGraph=False,
is_perm=False,
has_noise=n,
noise_level=l,
n = noise[0]
l = noise_level[0]
for gfile in gfiles:
for b in bandNumber:
for thr in thresholds:
if thr > b:
continue
df = experiment(df,
filename=gfile[0],
nodeAttributeFile=None,
multipleGraph=False,
is_perm=False,
has_noise=n,
noise_level=l,
plotAttribute=False,
plotBucket=False,
plotCorrectness=False,
GraphType=gfile[1],
bandNumber=b,
adaptiveLSH=a,
LSHType='Cosine',
compute_sim=True,
compute_hungarian=False,
loop_num=3,
threshold=thr)
df = experiment(df,
filename=gfile[0],
nodeAttributeFile=None,
multipleGraph=False,
is_perm=False,
has_noise=n,
noise_level=l,
elif cond == '3':
genome_size = 69
elif cond == '4':
genome_size = 65
else:
print('Error: please enter a valid condition.')
run_num = int(sys.argv[2])
# The corresponding first gen files should be generated before running this
# via the generate_first_gen function
# There should be 2 different files for each run
# The pop argument is there mostly to avoid naming conflict
first_gen_file = 'FirstGen/{}Run{}Pop{}{}.txt'.format(
prefix, run_num, pop, genome_size)
first_gen = read_first_gen_files(first_gen_file)
e = experiment(condition=cond,
run_num=run_num,
first_gen=first_gen,
today=prefix,
pop=pop,
gen=gen,
trial_num=trial_num,
include_top=top)
e.run()
e_filename = 'Data/Cond{}Run{}/{}_{}_{}_Run{}.exp'.format(
cond, run_num, prefix, e.condition, e.csc, run_num)
pickle.dump(e, open(e_filename, 'wb'))