l_tau = ['tau_dr', 'tau_ols', 'tau_ols_ps', 'tau_resid']
output = 'results/2019-10-30_' + exp_name + '.csv'
l_scores = []
for args['citcio'] in [False, True]:
for args['model'] in ["dlvm", "lrmf"]:
for args['seed'] in range_seed:
for args['prop_miss'] in range_prop_miss:
for args['n'] in range_n:
for args['p'] in range_p:
t0 = time.time()
score = exp_mi(**args)
args['time'] = int(time.time() - t0)
l_scores.append(
np.concatenate((list(args.values()), score)))
print('exp with ', args)
print('........... DONE')
print('in ', int(args["time"]), ' s \n\n')
score_data = pd.DataFrame(l_scores,
columns=list(args.keys()) +
l_tau)
score_data.to_csv(output + '_temp')
print('saving ' + exp_name + 'at: ' + output)
score_data.to_csv(output)
print('*' * 20)
print('Exp: ' + exp_name + ' succesfully ended.')
print('*' * 20)
l_scores = []
for args['seed'] in range_seed:
for args['citcio'] in [False, True]:
for args['model'] in ["dlvm", "lrmf"]:
for args['n'] in range_n:
for args['sig_prior'] in range_sig_prior:
for args['n_epochs'] in range_n_epochs:
for args['prop_miss'] in range_prop_miss:
for args['p'] in range_p:
t0 = time.time()
score = exp_miwae(**args)
args['time'] = int(time.time() - t0)
l_scores.append(
np.concatenate(
(list(args.values()), score)))
print('exp with ', args)
print('........... DONE')
print('in ', int(args["time"]), ' s \n\n')
score_data = pd.DataFrame(
l_scores, columns=list(args.keys()) + l_tau)
score_data.to_csv(output + '_temp')
print('saving ' + exp_name + 'at: ' + output)
score_data.to_csv(output)
print('*' * 20)
print('Exp: ' + exp_name + ' succesfully ended.')
print('*' * 20)
args['m'] = 10
print('starting exp: ' + exp_name)
l_tau = ['tau_dr', 'tau_ols', 'tau_ols_ps']
output = 'results/2019-10-24_'+exp_name+'.csv'
l_scores = []
for args['citcio'] in [False, True]:
for args['model'] in ["dlvm","lrmf"]:
for args['seed'] in range_seed:
for args['prop_miss'] in range_prop_miss:
for args['n'] in range_n:
for args['p'] in range_p:
t0 = time.time()
score = exp_mi(**args)
args['time'] = int(time.time() - t0)
l_scores.append(np.concatenate((list(args.values()),score)))
print('exp with ', args)
print('........... DONE')
print('in ', int(args["time"]) , ' s \n\n')
score_data = pd.DataFrame(l_scores, columns=list(args.keys()) + l_tau)
score_data.to_csv(output + '_temp')
print('saving ' +exp_name + 'at: ' + output)
score_data.to_csv(output)
print('*'*20)
print('Exp: '+ exp_name+' succesfully ended.')
print('*'*20)
if args['model'] == "lrmf":
Z, X, w, y, ps = gen_lrmf(n=args['n'], d=args['d'], p=args['p'],
citcio = args['citcio'], prop_miss = args['prop_miss'],
seed = args['seed'])
elif args['model'] == "dlvm":
Z, X, w, y, ps = gen_dlvm(n=args['n'], d=args['d'], p=args['p'],
citcio = args['citcio'], prop_miss = args['prop_miss'],
seed = args['seed'])
X_miss = ampute(X, prop_miss = args['prop_miss'], seed = args['seed'])
# Complete
t0 = time.time()
tau = exp_complete(Z, X, w, y)
args['time'] = int(time.time() - t0)
l_scores.append(np.concatenate((['Z'], list(args.values()), [None]*7, tau['Z'])))
l_scores.append(np.concatenate((['X'], list(args.values()), [None]*7, tau['X'])))
# Mean-imputation
t0 = time.time()
tau = exp_mean(X_miss, w, y)
args['time'] = int(time.time() - t0)
l_scores.append(np.concatenate((['Mean_imp'], list(args.values()), [None]*7, tau)))
# MI
tau = []
t0 = time.time()
for m in range_m:
tau.append(exp_mi(X_miss, w, y, m=m))
