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, 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
