def train_model(dataset):
model_result_dict = {}
for model in model_list:
model_result_dict[model] = util.ResultItem()
for k in range(0, rand_num):
if rand_num > 1:
print 'shuffle datset...'
dataset.shuffle_file()
for model in model_list:
print '-----------------------------------'
print ' Experiment on %s' % model + ' Random %d' % k
print '-----------------------------------'
#create destination folder
dst_folder = dataset.name + '/%s' % model
if os.path.exists(dst_folder) == False:
os.makedirs(dst_folder)
#output file
result_file = '{0}/{1}_result_{2}.txt'.format(dst_folder, model, k)
result_file = result_file.replace('/', os.sep)
#clear the file if it already exists
open(result_file, 'w').close()
if model == 'liblinear':
result_once = run_liblinear.run(dataset, model_config,
result_file)
elif model == 'FGM':
result_once = run_fgm.run(dataset, model_config, result_file)
elif model == 'mRMR':
run_mRMR.run(dataset, model_config)
continue
elif model == 'BIF':
run_bif.run(dataset, model_config)
continue
else:
param_config = ''
#get parameters
if is_default_param == False:
param_config = dataset.get_best_param(model)
result_once = run_ofs.run(dataset, model, model_config,
param_config, result_file)
model_result_dict[model].Add(result_once)
dataset.del_rand_file()
#average the result
if (rand_num > 1):
for key, val in model_result_dict.iteritems():
val.Divide(rand_num)
return model_result_dict
def train_model(dataset, model_list, model_config):
model_result_dict = {}
for model in model_list:
model_result_dict[model] = util.ResultItem()
model_type = model_config['type'].lower()
rand_num = model_config['rand_num']
for k in range(0,rand_num):
if rand_num > 1:
print 'shuffle datset...'
dataset.shuffle_file()
for model in model_list:
print '-----------------------------------'
print ' Experiment on %s' %model + ' Random %d' %k
print '-----------------------------------'
#create destination folder
dst_folder = dataset.name + '/%s' %model
if os.path.exists(dst_folder) == False:
os.makedirs(dst_folder)
if model_type == 'fs':
#output file
result_file = '{0}/{1}_rand_{2}_type_{3}.txt'.format(dst_folder,model, k, model_type)
elif model_type == 'sol' and model_config['sol_type'] == 'run_all':
#output file
result_file = '{0}/{1}_rand_{2}_type_{3}_soltype_{4}.txt'.format(dst_folder,model, k,model_type, model_config['sol_type'])
elif model_type == 'sol' and model_config['sol_type'] == 'search_l1':
result_file = '{0}/{1}_rand_{2}_type_{3}_soltype_{4}_tolerance_{5}.txt'.format(dst_folder,model, k,model_type, model_config['sol_type'], model_config['tolerance'])
else:
print 'unregnized model type: ', model['type']
sys.exit()
result_file = result_file.replace('/',os.sep)
#clear the file if it already exists
open(result_file,'w').close()
if model_type == 'fs':
result_once = train_fs(dataset, model, model_config, result_file)
elif model_type == 'sol':
result_once = train_sol(dataset, model, model_config, result_file)
model_result_dict[model].Add(result_once)
dataset.del_rand_file()
#average the result
if (rand_num > 1):
for key,val in model_result_dict.iteritems():
val.Divide(rand_num)
return model_result_dict