#'unlab_loader_config':{'batch_size':2000},
'net_config':{'k':256},'trainer':Classifier,
'trainer_config':{'log_dir':os.path.expanduser('~/tb-experiments/UCI/t3layer_baseline/'),
'log_args':{'minPeriod':.01, 'timeFrac':3/10}}#[1,.1,.3,3],}#'advEps':[10,3,1,.3]}
}
if __name__=='__main__':
# thestudy = Study(PI_trial,uci_pi_spec2,study_name='uci_baseline2234_')
# thestudy.run(num_trials=3,ordered=False)
# #print(thestudy.covariates())
# covars = thestudy.covariates()
# covars['test_Acc'] = thestudy.outcomes['test_Acc'].values
# covars['dev_Acc'] = thestudy.outcomes['dev_Acc'].values
# print(covars.drop(['log_suffix','saved_at'],axis=1))
# PI model baselines for AG-NEWS w/ best hyperparameters
text_pi_cfg = {'dataset':AG_News,'num_epochs':50,'trainer':PiModel,'trainer_config':{'cons_weight':30},'opt_config':{'lr':1e-3},
'loader_config': {'amnt_labeled':200+5000,'lab_BS':200}}
text_classifier_cfg = {'dataset':AG_News,'num_epochs':500,'trainer':Classifier,'opt_config':{'lr':1e-3},
'loader_config': {'amnt_labeled':200+5000,'lab_BS':200}}
y_text_pi_cfg = {'dataset':YAHOO,'num_epochs':50,'trainer':PiModel,'trainer_config':{'cons_weight':[30]},'opt_config':{'lr':[1e-4]},
'loader_config': {'amnt_labeled':800+5000,'lab_BS':800}}
y_text_classifier_cfg = {'dataset':YAHOO,'num_epochs':500,'trainer':Classifier,'opt_config':{'lr':1e-3},
'loader_config': {'amnt_labeled':800+5000,'lab_BS':800}}
# Searched from
# text_pi_cfg = {'num_epochs':50,'trainer':PiModel,'trainer_config':{'cons_weight':[10,30]},'opt_config':{'lr':[1e-3,3e-4,3e-3]},
# 'loader_config': {'amnt_labeled':200+5000,'lab_BS':200}}
textstudy = Study(PI_trial,y_text_pi_cfg,study_name='Agnews')
textstudy.run(3)
print(textstudy.covariates())
print(textstudy.outcomes)
'opt_config': {
'lr': 3e-4
},
'dataset': MINIBOONE, #[MINIBOONE,HEPMASS,AG_News],
'trainer_config': {
'log_dir':
os.path.expanduser('~/tb-experiments/UCI/flowgmm/miniboone/'),
'unlab_weight': 1.
},
'loader_config': {
'amnt_labeled': 20 + 5000,
'amnt_dev': 5000,
'lab_BS': 20
},
'net_config': {
'k': 256,
'coupling_layers': 7,
'nperlayer': 1
}
}
#trial(one_flowgmm_cfg)
thestudy = Study(trial, text_flowgmm_cfg, study_name='text_hypers')
thestudy.run(3)
covars = thestudy.covariates()
covars['test_Acc'] = thestudy.outcomes['test_Acc'].values
covars['dev_Acc'] = thestudy.outcomes['dev_Acc'].values
print(covars.drop(['log_suffix', 'saved_at'], axis=1))
# print(thestudy.covariates())
# print(thestudy.outcomes)