def summary_result(result):
logger = get_logger(name='summary_result',
file_name=log_dir + '/summary_result',
verbose=True)
logger.info('\tTraining set\t\t\t\t\t\t\tTest set\t\t\t\t\t\t')
logger.info(
'TPR\tTNR\tAUC\tTPR\tTNR\tAUC\tR1\tR2\tR3\tR4\tR5\tP1\tP2\tP3\tP4\tP5')
for train_result, test_result, lift_result, param in result:
response_matrix = np.array(
lift_result[0])[:, 0:5] # take only first 5 decile
profit_matrix = np.array(
lift_result[1])[:, 0:5] # take only first 5 decile
# train_result = np.array()
all_metric = np.hstack(
[train_result, test_result, response_matrix, profit_matrix])
all_metric = np.vstack([
all_metric,
np.mean(all_metric, axis=0),
np.std(all_metric, axis=0)
])
# param = experiment_list[i]
logger.info(param)
for x in all_metric:
s = ''
for y in x:
s = s + str(y) + '\t'
logger.info('%s' % s)
def run_experiment_parallel():
experiment_list = []
experiment_list.append(
{'h': [60, 60], 'training_epochs': 100, 'pretraining_epochs': 20, 'finetune_lr': 0.01, 'beta': 1,
'cl': [0.1, 0.1], 'cost_vec': [1, 3], 'batch_size': 1, 'pretrain_lr': 0.001})
experiment_list.append(
{'h': [100, 100], 'training_epochs': 100, 'pretraining_epochs': 20, 'finetune_lr': 0.01, 'beta': 1,
'cl': [0.1, 0.1], 'cost_vec': [1, 3], 'batch_size': 1, 'pretrain_lr': 0.001})
experiment_list.append(
{'h': [120, 120], 'training_epochs': 100, 'pretraining_epochs': 20, 'finetune_lr': 0.01, 'beta': 1,
'cl': [0.1, 0.1], 'cost_vec': [1, 3], 'batch_size': 1, 'pretrain_lr': 0.001})
experiment_list.append(
{'h': [60], 'training_epochs': 100, 'pretraining_epochs': 20, 'finetune_lr': 0.01, 'beta': 1,
'cl': [0.1, 0.1], 'cost_vec': [1, 3], 'batch_size': 1, 'pretrain_lr': 0.001})
experiment_list.append(
{'h': [80], 'training_epochs': 100, 'pretraining_epochs': 20, 'finetune_lr': 0.01, 'beta': 1,
'cl': [0.1, 0.1], 'cost_vec': [1, 3], 'batch_size': 1, 'pretrain_lr': 0.001})
experiment_list.append(
{'h': [100], 'training_epochs': 100, 'pretraining_epochs': 20, 'finetune_lr': 0.01, 'beta': 1,
'cl': [0.1, 0.1], 'cost_vec': [1, 3], 'batch_size': 1, 'pretrain_lr': 0.001})
experiment_list.append(
{'h': [120], 'training_epochs': 100, 'pretraining_epochs': 20, 'finetune_lr': 0.01, 'beta': 1,
'cl': [0.1, 0.1], 'cost_vec': [1, 3], 'batch_size': 1, 'pretrain_lr': 0.001})
experiment_list.append(
{'h': [130], 'training_epochs': 100, 'pretraining_epochs': 20, 'finetune_lr': 0.01, 'beta': 1,
'cl': [0.1, 0.1], 'cost_vec': [1, 3], 'batch_size': 1, 'pretrain_lr': 0.001})
logger = get_logger(name='experiment_settings', file_name=log_dir + '/experiment_setting.txt', verbose=True)
for e in experiment_list:
logger.info('experiment_list.append(%s)', e)
p = multiprocessing.Pool()
func = partial(run_helper, log_dir)
result = (p.map(func, experiment_list))
summary_result(result)
def csdnn_helper(self, p_dict):
# (train_result, test_result,model_name,y_pred_score_test)
logger = get_logger(name=p_dict['nn'],
file_name=p_dict['nn'],
verbose=True)
train_result, test_result, model_path, y_pred_score_test = csdnn_classifier(
log_dir=self.log_dir, logger=logger, p_dict=p_dict)
return (train_result, test_result, p_dict), y_pred_score_test
def run_helper(log_dir, p_dict,ens_param=None): # contain dict
# If main model existed
nn = log_dir + '/' +p_dict['exp_name']+'_'+str(random.random()) + '.txt'
main_file_list.append(nn)
logger = get_logger(name=nn, file_name= nn, verbose=True)
train_result, test_result = test_SdA(p_dict=p_dict, logger=logger,ens_param=ens_param)
return train_result, test_result, p_dict
def run_helper(log_dir,p_dict): # contain dict
nn = str(random.random()) + '.txt'
logger = get_logger(name=nn, file_name=log_dir + '/' + nn, verbose=True)
train_result, test_result, test_lift = test_SdA(finetune_lr=p_dict['finetune_lr'], pretraining_epochs=p_dict['pretraining_epochs'],
pretrain_lr=p_dict['pretrain_lr'], training_epochs=p_dict['training_epochs'],
batch_size=p_dict['batch_size'], h=p_dict['h'], cl=p_dict['cl'], cost_vec=p_dict['cost_vec'],beta=p_dict['beta'],logger=logger)
return train_result, test_result, test_lift, p_dict
s = s + str(y) + '\t'
logger.info('%s' % s)
if __name__ == '__main__':
datetim = time.strftime("%d_%m_%Y_%H_%M_%S")
log_dir = 'log/' + data_dir + '/' + datetim
if not os.path.exists(log_dir):
os.makedirs(log_dir)
start_time = timeit.default_timer()
run_experiment_parallel()
#run_experiment()
end_time = (timeit.default_timer() - start_time) / 60
#logger.info('Time took:%f mn', end_time)
logger = get_logger(name='summary_result',
file_name=log_dir + '/summary_result',
verbose=True)
logger.info('Time took:%f mn', end_time)
# combine test file to summary.txt
time.sleep(5)
file_list = [f for f in os.listdir(log_dir)]
with open(log_dir + '/' + datetim + '.txt', 'w') as outfile:
for fname in file_list:
with open(log_dir + '/' + fname) as infile:
for line in infile:
outfile.write(line)
print(file_list)
if __name__ == '__main__':
main_file_list = []
datetim = time.strftime("%d_%m_%Y_%H_%M_%S")
log_dir = 'log/' + data_dir + '/' + datetim
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not os.path.exists(log_dir + '/tmp_model'):
os.makedirs(log_dir + '/tmp_model')
if not os.path.exists(log_dir + '/tmp_data'):
os.makedirs(log_dir + '/tmp_data')
settings.init(log_dir)
start_time = timeit.default_timer()
nn = log_dir + '/summary_result.txt'
main_summary_logger = get_logger(name=nn, file_name=nn, verbose=False)
# run_experiment_parallel()
# run_experiment()
run_repeat_experiment()
end_time = (timeit.default_timer() - start_time) / 60
# logger.info('Time took:%f mn', end_time)
logger = get_logger(name='summary_result',
file_name=log_dir + '/summary_result.txt',
verbose=True)
logger.info('Time took:%f mn', end_time)
time.sleep(10)
main_file_list.append(log_dir + '/summary_result.txt')
combine_log(path=log_dir + '/' + datetim + '.txt',
file_list=main_file_list)
