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generate_parameters_frequentist.py
385 lines (368 loc) · 13.4 KB
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generate_parameters_frequentist.py
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import os
import shutil
import pickle
def save_obj(obj, name ,folder):
with open(f'{folder}'+ name + '.pkl', 'wb') as f:
pickle.dump(obj, f, protocol=4)
def load_obj(name,folder):
with open(f'{folder}' + name, 'rb') as f:
return pickle.load(f)
def generate_job_params(
directory='job_dir/',
dataset_ind=0,
bayesian_flag=False,
mv_flag=False,
LS_flag=False,
dual_flag=True,
old_flag=False,
del_list=None,
core_flag=True,
hyperits_count=10,
epochs_count=15,
arch = 0
):
if not os.path.exists(directory):
os.makedirs(directory)
else:
shutil.rmtree(directory)
os.makedirs(directory)
lags_base = list(range(1, 25)) + list(range(7 * 24, 8 * 24))
"""
BE CAREFUL OF WHAT YOU PUT HERE
"""
#FIXED PARAMS
PATH = ['public_data_t_fixed/', 'public_movielens_data_t_fixed/', 'tensor_data_t_fixed/']
data_path = [dataset+ 'all_data.pt' for dataset in PATH]
seed = [1337]*len(PATH)
batch_size_a = [1e-3*8]*len(PATH)
batch_size_b = [1e-2*1.2]*len(PATH)
reg_para_a = [0]*len(PATH)
reg_para_b = [1]*len(PATH)
max_lr = [1e-2]*len(PATH)
max_R = [75,12,10]
architecture = [arch]*len(PATH) #[0,0,1]
temporal_tag = [2,2,2] #First find the temporal dim mark it if not None
delete_side_info = [None]*len(PATH) #Remove side info, i.e. set to no side info
delete_side_info[dataset_ind]=del_list
special_mode = [0]*len(PATH)
split_mode = [0]*len(PATH)
latent_scale = [LS_flag]*len(PATH)
old_setup = [old_flag]*len(PATH)
cuda = [True]*len(PATH)
hyperits = [hyperits_count]*len(PATH)
epochs = [epochs_count]*len(PATH)
full_grad = [False]*len(PATH)
dual = [dual_flag]*len(PATH)
task = ['regression']*len(PATH)
init_max = [1e-1]*len(PATH)
shape_permutation = [[0,1,2],[0,1,2],[0,2,1]] #Remove this swap this for dimension order
patience = [100]*len(PATH)
forecast = [False,False,False,True,False,True]
lags = [[0],[0],[0]]
base_ref_int=[0,0,0]
lambda_W_a=[0.5]*len(PATH)
lambda_W_b=[2.5]*len(PATH)
lambda_T_x_a=[50]*len(PATH)
lambda_T_x_b=[500]*len(PATH)
normalize_Y = [False,False,False,True,True,True]
periods=[7,7,7]
period_size=[24,24,24]
validation_per_epoch=[4,4,4]
validation_patience = [2,2,2]
"""
BAYESIAN PARAMS
"""
bayesian = [bayesian_flag]*len(PATH)
multivariate = [mv_flag]*len(PATH)
mu_a = [0.]*len(PATH)
mu_b =[0.]*len(PATH)
sigma_a = [-1]*len(PATH)
sigma_b = [2]*len(PATH)
base_dict={
'PATH':0,
'reg_para_a':0,
'reg_para_b':0,
'batch_size_a':0,
'batch_size_b':0,
'hyperits':0,
'architecture':0,
'task':0,
'epochs':0,
'bayesian':0,
'data_path':0,
'cuda':0,
'max_R':0,
'max_lr':0,
'old_setup':0,
'latent_scale':0,
'chunks':0,
'dual':0,
'init_max':0,
'multivariate':0,
'mu_a':0,
'sigma_a':0,
'mu_b':0,
'sigma_b':0,
'split_mode':0,
'seed':0,
'temporal_tag':0,
'delete_side_info':0,
'special_mode':0,
'shape_permutation': 0,
'full_grad':False,
'forecast':False,
'lags':0,
'base_ref_int':0,
'lambda_W_a':0.0,
'lambda_W_b':0.5,
'lambda_T_x_a': 100.,#625., for none kernel approach
'lambda_T_x_b': 100.1,#625.1,
'normalize_Y':False,
'patience': 100,
'periods':7,
'period_size':24,
'validation_per_epoch': 3,
'validation_patience': 2,
'train_core_separate': core_flag,
'temporal_folds': [0],
'log_errors':False
}
counter = 0
for i,datasets in enumerate([dataset_ind]):
for key in base_dict.keys():
if key in locals().keys():
base_dict[key]=locals()[key][datasets]
for seed in range(5):
base_dict['seed']=seed
print(base_dict)
save_obj(base_dict,f'job_{counter}',directory)
counter += 1
def generate_retail_param():
generate_job_params(directory='retail_benchmark/',
dataset_ind=2,
bayesian_flag=False,
mv_flag=False,
LS_flag=False,
dual_flag=True,
old_flag=True,
core_flag=True,
del_list=[0,1,2],
hyperits_count=10,
epochs_count=15,
arch=1
)
generate_job_params(directory='retail_20_dual_arch_1/',
dataset_ind=2,
bayesian_flag=False,
mv_flag=True,
LS_flag=False,
dual_flag=True,
old_flag=False,
del_list=[0,1],
core_flag=True,
hyperits_count=10,
epochs_count=15,
arch=1
)
generate_job_params(directory='retail_20_bayesian_dual_LS/',
dataset_ind=2,
bayesian_flag=False,
mv_flag=True,
LS_flag=True,
dual_flag=True,
old_flag=False,
del_list=[0,1],
core_flag=True,
hyperits_count=10,
epochs_count=15,
arch=1
)
generate_job_params(directory='retail_20_bayesian_primal/',
dataset_ind=2,
bayesian_flag=False,
mv_flag=False,
LS_flag=False,
dual_flag=False,
old_flag=False,
del_list=[0,1],
core_flag=True,
hyperits_count=10,
epochs_count=15,
arch=1
)
generate_job_params(directory='retail_20_bayesian_primal_LS/',
dataset_ind=2,
bayesian_flag=False,
mv_flag=False,
LS_flag=True,
dual_flag=False,
old_flag=False,
del_list=[0,1],
core_flag=True,
hyperits_count=10,
epochs_count=15,
arch=1
)
generate_job_params(directory='retail_old_setup_primal/',
dataset_ind=2,
bayesian_flag=False,
mv_flag=False,
LS_flag=False,
dual_flag=False,
old_flag=True,
del_list=None,
core_flag=False,
hyperits_count=20,
epochs_count=10,
arch=0
)
def generate_alcohol_param():
generate_job_params(directory='alcohol_benchmark/',
dataset_ind=0,
bayesian_flag=False,
mv_flag=False,
LS_flag=False,
dual_flag=True,
old_flag=True,
core_flag=True,
del_list=[0,1,2],
hyperits_count=10,
epochs_count=20
)
generate_job_params(directory='alcohol_bayesian_dual/',
dataset_ind=0,
bayesian_flag=False,
mv_flag=True,
LS_flag=False,
dual_flag=True,
old_flag=False,
del_list=None,
core_flag=True,
hyperits_count=10,
epochs_count=20
)
generate_job_params(directory='alcohol_bayesian_dual_LS/',
dataset_ind=0,
bayesian_flag=False,
mv_flag=True,
LS_flag=True,
dual_flag=True,
old_flag=False,
del_list=None,
core_flag=True,
hyperits_count=10,
epochs_count=20
)
generate_job_params(directory='alcohol_bayesian_primal/',
dataset_ind=0,
bayesian_flag=False,
mv_flag=False,
LS_flag=False,
dual_flag=False,
old_flag=False,
del_list=None,
core_flag=True,
hyperits_count=10,
epochs_count=20
)
generate_job_params(directory='alcohol_bayesian_primal_LS/',
dataset_ind=0,
bayesian_flag=False,
mv_flag=False,
LS_flag=True,
dual_flag=False,
old_flag=False,
del_list=None,
core_flag=True,
hyperits_count=10,
epochs_count=20
)
generate_job_params(directory='alcohol_old_setup_primal/',
dataset_ind=0,
bayesian_flag=False,
mv_flag=False,
LS_flag=False,
dual_flag=False,
old_flag=True,
del_list=None,
core_flag=False,
hyperits_count=20,
epochs_count=10
)
def generate_movielens_params():
generate_job_params(directory='movielens_20_benchmark/',
dataset_ind=1,
bayesian_flag=False,
mv_flag=False,
LS_flag=False,
dual_flag=True,
old_flag=True,
core_flag=True,
del_list=[0,1,2],
hyperits_count=10,
epochs_count=20
)
generate_job_params(directory='movielens_20_bayesian_dual/',
dataset_ind=1,
bayesian_flag=False,
mv_flag=True,
LS_flag=False,
dual_flag=True,
old_flag=False,
del_list=None,
core_flag=True,
hyperits_count=10,
epochs_count=20
)
generate_job_params(directory='movielens_20_bayesian_dual_LS/',
dataset_ind=1,
bayesian_flag=False,
mv_flag=True,
LS_flag=True,
dual_flag=True,
old_flag=False,
del_list=None,
core_flag=True,
hyperits_count=10,
epochs_count=20
)
generate_job_params(directory='movielens_20_bayesian_primal/',
dataset_ind=1,
bayesian_flag=False,
mv_flag=False,
LS_flag=False,
dual_flag=False,
old_flag=False,
del_list=None,
core_flag=True,
hyperits_count=10,
epochs_count=20
)
generate_job_params(directory='movielens_20_bayesian_primal_LS/',
dataset_ind=1,
bayesian_flag=False,
mv_flag=False,
LS_flag=True,
dual_flag=False,
old_flag=False,
del_list=None,
core_flag=True,
hyperits_count=10,
epochs_count=20
)
generate_job_params(directory='movielens_primal_old_setup/',
dataset_ind=1,
bayesian_flag=False,
mv_flag=False,
LS_flag=False,
dual_flag=False,
old_flag=True,
del_list=None,
core_flag=False,
hyperits_count=20,
epochs_count=10
)
if __name__ == '__main__':
generate_retail_param()
generate_alcohol_param()
generate_movielens_params()