def run_experiment(opt_name,result_file, dataset, extra_cmd):
#clear the file if it already exists
open(result_file,'w').close()
#evaluate the result
cmd_prefix = run_util.exe_name + extra_cmd + ' -opt %s' %opt_name
cmd_postfix = ' >> %s' %result_file
if 'synthetic' in dataset:
bs_list = l1_def.get_lambda_list(dataset, opt_name)
else:
temp_list = extra_cmd.split()
train_file = ''
for k in range(0,len(temp_list)):
if temp_list[k] == '-i':
train_file = temp_list[k+1]
break;
if train_file == '':
print 'no input file is specified!'
sys.exit()
data_valid_dim = run_util.get_valid_dim(train_file)
lambda_list = l1_def.get_lambda_list(dataset, opt_name)
bs_list = []
b_num = len(lambda_list)
for i in range(0,b_num):
dim = data_valid_dim * (1 - lambda_list[i])
if dim > 0:
bs_list.append(dim)
for bs in bs_list:
cmd = cmd_prefix + ' -k %d' %bs + cmd_postfix
print cmd
os.system(cmd)
def run_experiment(opt_name, result_file, dataset, extra_cmd):
#clear the file if it already exists
open(result_file, 'w').close()
#evaluate the result
cmd_prefix = run_util.exe_name + extra_cmd + ' -opt %s' % opt_name
cmd_postfix = ' >> %s' % result_file
if 'synthetic' in dataset:
bs_list = l1_def.get_lambda_list(dataset, opt_name)
else:
temp_list = extra_cmd.split()
train_file = ''
for k in range(0, len(temp_list)):
if temp_list[k] == '-i':
train_file = temp_list[k + 1]
break
if train_file == '':
print 'no input file is specified!'
sys.exit()
data_valid_dim = run_util.get_valid_dim(train_file)
lambda_list = l1_def.get_lambda_list(dataset, opt_name)
bs_list = []
b_num = len(lambda_list)
for i in range(0, b_num):
dim = data_valid_dim * (1 - lambda_list[i])
if dim > 0:
bs_list.append(dim)
for bs in bs_list:
cmd = cmd_prefix + ' -k %d' % bs + cmd_postfix
print cmd
os.system(cmd)
def run_experiment(opt_name,result_file, dataset, extra_cmd):
#clear the file if it already exists
open(result_file,'w').close()
#evaluate the result
cmd_prefix = run_util.exe_name + extra_cmd + ' -opt %s' %opt_name
cmd_postfix = ' >> %s' %result_file
lambda_list = l1_def.get_lambda_list(dataset, opt_name)
if opt_name == 'AROW-FS' or opt_name == 'SGD-FS' or opt_name == 'OFSGD':
temp_list = extra_cmd.split()
train_file = ''
for k in range(0,len(temp_list)):
if temp_list[k] == '-i':
train_file = temp_list[k+1]
break;
if train_file == '':
print 'no input file is specified!'
sys.exit()
data_valid_dim = run_util.get_valid_dim(train_file)
for l1 in lambda_list:
if opt_name == 'AROW-FS' or opt_name == 'SGD-FS' or opt_name == 'OFSGD':
l1 = (int)(l1 * data_valid_dim)
if l1 < 1:
continue
cmd = cmd_prefix + ' -k %d' %l1 + cmd_postfix
else:
cmd = cmd_prefix + ' -l1 %e' %l1 + cmd_postfix
print cmd
os.system(cmd)
def run_fgm(train_file, test_file,ds, ori_result_file):
result_all = []
train_exe_name = exe_path.fgm_train
test_exe_name = exe_path.fgm_test
#make the result dir
dst_folder = './%s' %ds
run_util.create_dir(dst_folder)
if 'synthetic' in ds:
bs_list = l1_def.get_lambda_list(ds,'fgm')
else:
data_valid_dim = run_util.get_valid_dim(train_file)
lambda_list = l1_def.get_lambda_list(ds,'fgm')
bs_list = []
b_num = len(lambda_list)
for i in range(0,b_num):
dim = (int)(data_valid_dim * (1 - lambda_list[i]))
if dim > 0:
bs_list.append(dim)
for bs in bs_list:
result_once = [0,0,0,0]
model_file = dst_folder + '/fgm_model%g' %bs
predict_file = dst_folder + '/fgm_predict%g' %bs
result_file = dst_folder + '/' + ori_result_file + '_%d' %bs
#clear the file if it already exists
open(result_file,'w').close()
#evaluate the result
train_cmd = train_exe_name + ' -s 12 -c 10 -B %d' %bs + ' %s' %train_file + ' %s' %model_file
test_cmd = test_exe_name + ' %s' %test_file + ' %s' %model_file + ' %s' %predict_file + '>> %s' %result_file
print train_cmd
start_time =time.time()
os.system(train_cmd)
end_time = time.time()
#parse learning time
train_time = (float)(end_time - start_time)
result_once[3] = train_time
#predict
print test_cmd
os.system(test_cmd)
result_once[1] = liblinear_util.parse_error_rate(result_file)
result_once[2] = bs
result_all.append(result_once)
return result_all
def run_fgm(train_file, test_file, ds, ori_result_file):
result_all = []
train_exe_name = exe_path.fgm_train
test_exe_name = exe_path.fgm_test
#make the result dir
dst_folder = './%s' % ds
run_util.create_dir(dst_folder)
if 'synthetic' in ds:
bs_list = l1_def.get_lambda_list(ds, 'fgm')
else:
data_valid_dim = run_util.get_valid_dim(train_file)
lambda_list = l1_def.get_lambda_list(ds, 'fgm')
bs_list = []
b_num = len(lambda_list)
for i in range(0, b_num):
dim = (int)(data_valid_dim * (1 - lambda_list[i]))
if dim > 0:
bs_list.append(dim)
for bs in bs_list:
result_once = [0, 0, 0, 0]
model_file = dst_folder + '/fgm_model%g' % bs
predict_file = dst_folder + '/fgm_predict%g' % bs
result_file = dst_folder + '/' + ori_result_file + '_%d' % bs
#clear the file if it already exists
open(result_file, 'w').close()
#evaluate the result
train_cmd = train_exe_name + ' -s 12 -c 10 -B %d' % bs + ' %s' % train_file + ' %s' % model_file
test_cmd = test_exe_name + ' %s' % test_file + ' %s' % model_file + ' %s' % predict_file + '>> %s' % result_file
print train_cmd
start_time = time.time()
os.system(train_cmd)
end_time = time.time()
#parse learning time
train_time = (float)(end_time - start_time)
result_once[3] = train_time
#predict
print test_cmd
os.system(test_cmd)
result_once[1] = liblinear_util.parse_error_rate(result_file)
result_once[2] = bs
result_all.append(result_once)
return result_all
def run_liblinear(train_file, test_file,ds, result_file):
result_once = [0,0,0,0]
os.system('mkdir ./tmp')
dst_folder = './%s' %ds
tmp_folder = dst_folder + '/liblinear'
#os.system('mkdir -p %s' %tmp_folder)
run_util.create_dir(tmp_folder)
model_file = tmp_folder + '/model'
tmp_file = tmp_folder + '/tmp.txt'
train_exe_name = exe_path.liblinar_train_exe_name
test_exe_name = exe_path.liblinar_test_exe_name
#make the result dir
#cmd = 'mkdir -p %s' %dst_folder
#os.system(cmd)
run_util.create_dir(dst_folder)
result_file = './%s' %tmp_folder + '/' + result_file
#clear the file if it already exists
open(result_file,'w').close()
#evaluate the result
train_cmd = train_exe_name + ' %s' %train_file + ' %s' %model_file
test_cmd = test_exe_name + ' %s' %test_file + ' %s' %model_file + ' %s' %tmp_file + '>> %s' %result_file
print train_cmd
start_time =time.time()
os.system(train_cmd)
end_time = time.time()
#parse learning time
train_time = (float)(end_time - start_time)
result_once[3] = train_time
#predict
print test_cmd
os.system(test_cmd)
result_once[1] = liblinear_util.parse_error_rate(result_file)
valid_dim = run_util.get_valid_dim(train_file)
model_size = liblinear_util.get_model_size(model_file)
result_once[2] = 100 - (model_size * 100.0 / valid_dim)
return [result_once]
def run_liblinear(train_file, test_file, ds, result_file):
result_once = [0, 0, 0, 0]
os.system('mkdir ./tmp')
dst_folder = './%s' % ds
tmp_folder = dst_folder + '/liblinear'
#os.system('mkdir -p %s' %tmp_folder)
run_util.create_dir(tmp_folder)
model_file = tmp_folder + '/model'
tmp_file = tmp_folder + '/tmp.txt'
train_exe_name = exe_path.liblinar_train_exe_name
test_exe_name = exe_path.liblinar_test_exe_name
#make the result dir
#cmd = 'mkdir -p %s' %dst_folder
#os.system(cmd)
run_util.create_dir(dst_folder)
result_file = './%s' % tmp_folder + '/' + result_file
#clear the file if it already exists
open(result_file, 'w').close()
#evaluate the result
train_cmd = train_exe_name + ' %s' % train_file + ' %s' % model_file
test_cmd = test_exe_name + ' %s' % test_file + ' %s' % model_file + ' %s' % tmp_file + '>> %s' % result_file
print train_cmd
start_time = time.time()
os.system(train_cmd)
end_time = time.time()
#parse learning time
train_time = (float)(end_time - start_time)
result_once[3] = train_time
#predict
print test_cmd
os.system(test_cmd)
result_once[1] = liblinear_util.parse_error_rate(result_file)
valid_dim = run_util.get_valid_dim(train_file)
model_size = liblinear_util.get_model_size(model_file)
result_once[2] = 100 - (model_size * 100.0 / valid_dim)
return [result_once]
def run_liblinear(train_file, test_file,ds, ori_result_file):
result_all = []
train_exe_name = exe_path.liblinar_train_exe_name
test_exe_name = exe_path.liblinar_test_exe_name
#make the result dir
dst_folder = './%s' %ds
run_util.create_dir(dst_folder)
c_list = l1_def.get_lambda_list(ds,'liblinear')
for c in c_list:
result_once = [0,0,0,0]
model_file = dst_folder + '/ll_model%g' %c
predict_file = dst_folder + '/ll_predict%g' %c
result_file = dst_folder + '/' + ori_result_file + '_%f' %c
#clear the file if it already exists
open(result_file,'w').close()
#evaluate the result
train_cmd = train_exe_name + ' -s 5 -c %f' %c + ' %s' %train_file + ' %s' %model_file
test_cmd = test_exe_name + ' %s' %test_file + ' %s' %model_file + ' %s' %predict_file + '>> %s' %result_file
#test_cmd = test_exe_name + ' %s' %test_file + ' %s' %model_file + ' %s' %predict_file
print train_cmd
start_time =time.time()
os.system(train_cmd)
end_time = time.time()
#parse learning time
train_time = (float)(end_time - start_time)
result_once[3] = train_time
#predict
print test_cmd
os.system(test_cmd)
result_once[1] = liblinear_util.parse_error_rate(result_file)
valid_dim = run_util.get_valid_dim(train_file)
model_size = liblinear_util.get_model_size(model_file)
result_once[2] = model_size
result_all.append(result_once)
return result_all
def run_vw(train_file, test_file, ds, result_file, is_cache=True):
tmp_folder = ds + '/vw_tmp'
#os.system('mkdir -p %s' %tmp_folder)
run_util.create_dir(tmp_folder)
model_file = tmp_folder + '/vw_model'
rd_model_file = tmp_folder + '/vw_model.txt'
tmp_file = tmp_folder + '/vw_tmp.txt'
result_file = tmp_folder + '/' + result_file
extra_cmd = ' --sgd --binary --loss_function=hinge '
model_cmd = ' --readable_model %s' % rd_model_file + ' -f %s ' % model_file
valid_dim = run_util.get_valid_dim(train_file)
#transform into vw format
if os.path.exists('%s.vw' % train_file) == False:
os.system('python ../tools/libsvm2vw.py %s' % train_file)
if os.path.exists('%s.vw' % test_file) == False:
os.system('python ../tools/libsvm2vw.py %s' % test_file)
if is_cache == True:
cache_train = train_file + "_cache.vw"
cache_test = test_file + "_cache.vw"
train_file += ".vw"
test_file += ".vw"
#evaluate the result
if is_cache == True:
train_cmd_prefix = '%s' % exe_name + ' %s' % train_file + ' --cache_file %s ' % cache_train
test_cmd_prefix = '%s' % exe_name + ' %s' % test_file + ' -t -i %s' % model_file + ' --cache_file %s ' % cache_test
else:
train_cmd_prefix = '%s' % exe_name + ' %s' % train_file
test_cmd_prefix = '%s' % exe_name + ' %s' % test_file + ' -t -i %s' % model_file
cmd_postfix = ' 2> %s' % tmp_file
result_list = []
lambda_list = l1_def.get_lambda_list(ds, 'vw')
for l1 in lambda_list:
result_item = [0, 0, 0, 0]
#train
cmd = train_cmd_prefix + ' --l1 %e' % l1 + extra_cmd + model_cmd + cmd_postfix
print cmd
start_time = time.time()
os.system(cmd)
end_time = time.time()
#parse learning time
result_item[3] = (float)(end_time - start_time)
result_item[3] = (float)('%.2f' % result_item[3])
#parse learn error rate
result_item[0] = vw_util.parse_error_rate(tmp_file)
#test
cmd = test_cmd_prefix + extra_cmd + cmd_postfix
print cmd
os.system(cmd)
result_item[1] = vw_util.parse_error_rate(tmp_file)
#parse sparsity
model_size = vw_util.get_model_size(rd_model_file)
result_item[2] = 100 - (model_size * 100.0 / valid_dim)
result_list.append(result_item)
vw_util.write_parse_result(result_list, result_file)
return result_list
def run_vw(train_file, test_file,ds,result_file, is_cache = True):
tmp_folder = ds + '/vw_tmp'
#os.system('mkdir -p %s' %tmp_folder)
run_util.create_dir(tmp_folder)
model_file = tmp_folder + '/vw_model'
rd_model_file = tmp_folder + '/vw_model.txt'
tmp_file = tmp_folder + '/vw_tmp.txt'
result_file = tmp_folder + '/' + result_file
extra_cmd = ' --sgd --binary --loss_function=hinge '
model_cmd = ' --readable_model %s' %rd_model_file + ' -f %s ' %model_file
valid_dim = run_util.get_valid_dim(train_file)
#transform into vw format
if os.path.exists('%s.vw' %train_file) == False:
os.system('python ../tools/libsvm2vw.py %s' %train_file)
if os.path.exists('%s.vw' %test_file) == False:
os.system('python ../tools/libsvm2vw.py %s' %test_file)
if is_cache == True:
cache_train = train_file + "_cache.vw"
cache_test = test_file + "_cache.vw"
train_file += ".vw"
test_file += ".vw"
#evaluate the result
if is_cache == True:
train_cmd_prefix = '%s' %exe_name + ' %s' %train_file +' --cache_file %s ' %cache_train
test_cmd_prefix = '%s' %exe_name + ' %s' %test_file + ' -t -i %s' %model_file + ' --cache_file %s ' %cache_test
else:
train_cmd_prefix = '%s' %exe_name + ' %s' %train_file
test_cmd_prefix = '%s' %exe_name + ' %s' %test_file + ' -t -i %s' %model_file
cmd_postfix = ' 2> %s' %tmp_file
result_list = []
lambda_list = l1_def.get_lambda_list(ds,'vw')
for l1 in lambda_list:
result_item = [0,0,0,0]
#train
cmd = train_cmd_prefix + ' --l1 %e' %l1 + extra_cmd + model_cmd + cmd_postfix
print cmd
start_time =time.time()
os.system(cmd)
end_time = time.time()
#parse learning time
result_item[3] = (float)(end_time - start_time)
result_item[3] = (float)('%.2f' %result_item[3])
#parse learn error rate
result_item[0] = vw_util.parse_error_rate(tmp_file)
#test
cmd = test_cmd_prefix + extra_cmd + cmd_postfix
print cmd
os.system(cmd)
result_item[1] = vw_util.parse_error_rate(tmp_file)
#parse sparsity
model_size = vw_util.get_model_size(rd_model_file)
result_item[2] = 100 - (model_size * 100.0 / valid_dim)
result_list.append(result_item)
vw_util.write_parse_result(result_list,result_file)
return result_list
def run_mRMR(train_file, test_file,ds, result_file):
result_all = []
train_exe_name = exe_path.mRMR
#make the result dir
dst_folder = './%s' %ds
run_util.create_dir(dst_folder)
data_valid_dim = run_util.get_valid_dim(train_file)
data_num = run_util.get_data_num(train_file)
#bs_list = l1_def.get_lambda_list(ds,'mRMR')
if 'synthetic' in ds:
bs_list = l1_def.get_lambda_list(ds,'mRMR')
else:
lambda_list = l1_def.get_lambda_list(ds,'mRMR')
bs_list = []
b_num = len(lambda_list)
for i in range(0,b_num):
dim = int(data_valid_dim * (1 - lambda_list[i]))
if dim > 0 and dim <= 500:
bs_list.append(dim)
bs_list = l1_def.get_lambda_list(ds,'mRMR')
#clear the file if it already exists
open(result_file,'w').close()
for bs in bs_list:
result_once = [0,0,0,0]
model_file = dst_folder + '/mRMR_model%d' %bs
parse_file = dst_folder + '/mRMR_model_parse%d' %bs
if os.path.exists(model_file) == False:
print model_file + ' not exist'
csv_train_file = train_file + '.csv'
if os.path.exists(csv_train_file) == False:
#convert data
print 'convert data'
cmd = exe_path.csv_converter + ' -i %s' %train_file + ' -o %s' %csv_train_file
cmd += ' -sdt libsvm -ddt csv'
print cmd
os.system(cmd)
#run mRMR
prev_cmd = train_exe_name + ' -v %d' %data_valid_dim + ' -t 0.5 -i %s' %csv_train_file
cmd = prev_cmd + ' -n %d' %bs + ' > %s' %model_file
print cmd
start_time =time.time()
os.system(cmd)
end_time = time.time()
#parse learning time
train_time = (float)(end_time - start_time)
result_once[3] = train_time
#parse result
parse_model_file(model_file,parse_file);
#run OGD
cmd_data = dataset.get_cmd_data_by_file(train_file, test_file,True)
cmd = exe_path.SOL_exe_name + cmd_data + ' -m %s' %parse_file + ' -k %d' %bs
cmd += dataset.get_model_param(ds,'SGD-FS')
cmd += ' -opt mRMR_OGD -norm -loss Hinge >> %s' %result_file
print cmd
os.system(cmd)
result_once[2] = bs
result_all.append(result_once)
return result_all