def train_model(path_list, dst_folder):
train_file = path_list[0]
test_file = path_list[1]
result_all = {}
#random the file
if rand_num > 1:
rand_file = train_file + '_rand'
else:
rand_file = train_file
rand_file_cache = rand_file + '_cache'
for k in range(0, rand_num):
if rand_num > 1:
print 'shuffle datset...'
sol_shuffle.sol_shuffle(train_file, rand_file)
cmd_data = dataset.get_cmd_data_by_file(rand_file, test_file, is_cache)
dataset.analyze(rand_file)
for opt in opt_list:
for mp_method in mp_list:
cmd_mp = ' -mpt %s ' % mp_method
for m in range(0, run_time):
print '-----------------------------------'
print ' Experiment on %s' % opt + ' Random %d' % k + ' Multi-Pass %s' % mp_method + ' Round %d' % m
print '-----------------------------------'
result_file = dst_folder + '/%s' % opt + '_rand_%d' % k + '_mp_%s' % mp_method + '_round_%d' % m + '.txt'
cmd = cmd_data
cmd += cmd_mp
cmd += extra_cmd
if is_default_param == False:
cmd += dataset.get_model_param(ds, opt)
run_mpol.run_mpol(opt, result_file, ds, cmd)
print '\nparsing result...'
#write the result to file
parse_file = dst_folder + '/%s' % opt + '_%d' % k + '_%s' % mp_method + '_%d' % m + '.txt'
result_once = run_util.parse_result(
result_file, parse_file)
result_all = add_to_dict(opt, mp_method, result_all,
result_once)
if mp_method == 'none':
break
#remove previous file
if rand_num > 1:
os.system('rm -f %s' % rand_file_cache)
os.system('rm -f %s' % rand_file)
#average the result
for opt in opt_list:
for mp in mp_list:
rows = len(result_all[opt][mp])
cols = len(result_all[opt][mp][0])
divid = rand_num
if mp != 'none':
divid *= run_time
for k in range(0, rows):
for m in range(0, cols):
result_all[opt][mp][k][m] /= divid
return result_all
def train_model(path_list,dst_folder):
train_file = path_list[0]
test_file = path_list[1]
result_all = {}
#random the file
if rand_num > 1:
rand_file = train_file + '_rand'
else:
rand_file = train_file
rand_file_cache = rand_file + '_cache'
for k in range(0,rand_num):
if rand_num > 1:
print 'shuffle datset...'
sol_shuffle.sol_shuffle(train_file, rand_file)
cmd_data = dataset.get_cmd_data_by_file(rand_file, test_file, is_cache)
dataset.analyze(rand_file);
for opt in opt_list:
print '-----------------------------------'
print ' Experiment on %s' %opt + ' Random %d' %k
print '-----------------------------------'
if opt == 'vw':
result_file = 'vw_result_%d' %k + '.txt'
result_once = run_vw.run_vw(rand_file, test_file, ds, result_file, is_cache)
elif opt == 'liblinear':
result_file = 'liblinear_result_%d' %k + '.txt'
result_once = run_liblinear.run_liblinear(rand_file, test_file, ds, result_file)
elif opt == 'fgm':
result_file = 'fgm_result_%d' %k + '.txt'
result_once = run_fgm.run_fgm(rand_file, test_file, ds, result_file)
elif opt == 'mRMR':
result_file = dst_folder + '/%s' %opt + '_result_%d' %k + '.txt'
result_once = run_mRMR.run_mRMR(rand_file, test_file, ds, result_file)
print '\nparsing result...'
#write the result to file
parse_file = dst_folder +'/%s' %opt + '_%d' %k + '.txt'
result_once2 = run_util.parse_result(result_file, parse_file);
bs_num = len(result_once)
if bs_num != len(result_once2):
print 'inconsistent parsing result'
for m in range(0,bs_num):
result_once[m][0] = result_once2[m][0]
result_once[m][1] = result_once2[m][1]
if result_once[m][2] == 0:
result_once[m][2] = result_once2[m][2]
if result_once[m][3] == 0:
result_once[m][3] = result_once2[m][3]
else:
result_file = dst_folder + '/%s' %opt + '_result_%d' %k + '.txt'
cmd = cmd_data
cmd += extra_cmd
if is_default_param == False:
cmd += dataset.get_model_param(ds, opt)
run_experiment.run_experiment(opt,result_file,ds, cmd)
print '\nparsing result...'
#write the result to file
parse_file = dst_folder +'/%s' %opt + '_%d' %k + '.txt'
result_once = run_util.parse_result(result_file, parse_file);
result_all = add_to_dict(opt,result_all, result_once)
#remove previous file
if rand_num > 1:
os.system('rm -f %s' %rand_file_cache)
os.system('rm -f %s' %rand_file)
#average the result
for opt in opt_list:
rows = len(result_all[opt])
cols = len(result_all[opt][0])
for k in range(0,rows):
for m in range(0,cols):
result_all[opt][k][m] /= rand_num
return result_all
def train_model(path_list,dst_folder):
train_file = path_list[0]
test_file = path_list[1]
result_all = {}
#random the file
if rand_num > 1:
rand_file = train_file + '_rand'
else:
rand_file = train_file
rand_file_cache = rand_file + '_cache'
for k in range(0,rand_num):
if rand_num > 1:
print 'shuffle datset...'
sol_shuffle.sol_shuffle(train_file, rand_file)
cmd_data = dataset.get_cmd_data_by_file(rand_file, test_file, is_cache)
dataset.analyze(rand_file);
for opt in opt_list:
for mp_method in mp_list:
cmd_mp = ' -mpt %s ' %mp_method
for m in range(0,run_time):
print '-----------------------------------'
print ' Experiment on %s' %opt + ' Random %d' %k + ' Multi-Pass %s' %mp_method + ' Round %d' %m
print '-----------------------------------'
result_file = dst_folder + '/%s' %opt + '_rand_%d' %k + '_mp_%s' %mp_method + '_round_%d' %m + '.txt'
cmd = cmd_data
cmd += cmd_mp
cmd += extra_cmd
if is_default_param == False:
cmd += dataset.get_model_param(ds, opt)
run_mpol.run_mpol(opt,result_file,ds, cmd)
print '\nparsing result...'
#write the result to file
parse_file = dst_folder +'/%s' %opt + '_%d' %k + '_%s' %mp_method + '_%d' %m + '.txt'
result_once = run_util.parse_result(result_file, parse_file);
result_all = add_to_dict(opt,mp_method,result_all, result_once)
if mp_method == 'none':
break
#remove previous file
if rand_num > 1:
os.system('rm -f %s' %rand_file_cache)
os.system('rm -f %s' %rand_file)
#average the result
for opt in opt_list:
for mp in mp_list:
rows = len(result_all[opt][mp])
cols = len(result_all[opt][mp][0])
divid = rand_num
if mp != 'none':
divid *= run_time
for k in range(0,rows):
for m in range(0,cols):
result_all[opt][mp][k][m] /= divid
return result_all