def experiment2(FLAGS,
expname='global_exp',
test_time=False,
dataname='eicu',
debug=None):
'''
Global model only experiment
'''
pct = FLAGS.train_data_subset_path.split('pct_')[1].split('_')[0]
pct_num = os.path.basename(
FLAGS.train_data_subset_path)[:-4] # remove '.pkl'
expname = "pct{}_{}_{}".format(pct, pct_num, expname)
settings = create_joint_settings(FLAGS)
if debug is not None:
if type(debug) is list:
settings = [settings[idx] for idx in debug]
else:
idx = debug
settings = settings[idx:idx + 1]
tasks = [[('--model_type', 'GLOBAL'),
('--train_data_subset_path', FLAGS.train_data_subset_path),
('--result_dir', FLAGS.eicu_cohort),
('--eicu_cohort', FLAGS.eicu_cohort), ('--dataname', dataname),
('--result_suffix', '_' + expname)] + setting
for setting in settings]
if test_time:
tasks = [['--test_time', '--bootstrap'] + setting for setting in tasks]
run('moe.py', tasks, gpus=gpus, n_concurrent_process=FLAGS.nc)
def experiment16(FLAGS,
expname='global_plus_mtl_feature_exp',
test_time=False,
debug=None):
'''
careunit separate models
'''
cluster_name = expname.split('feature')[0] + "exp"
settings = create_joint_settings(FLAGS)
if debug is not None:
if type(debug) is list:
settings = [settings[idx] for idx in debug]
else:
idx = debug
settings = settings[idx:idx + 1]
tasks = [[('--model_type', 'GLOBAL'), ('--result_dir', FLAGS.result_dir),
('--cohorts', 'custom'), '--include_cohort_as_feature',
('--cohort_filepath', str(i) + '_' + cluster_name + '.npy'),
('--result_suffix', '_' + expname)] + setting
for i, setting in enumerate(settings)]
if test_time:
tasks = [['--test_time', '--bootstrap'] + setting for setting in tasks]
run('moe.py', tasks, gpus=[5, 6], n_concurrent_process=FLAGS.nc)
def experiment4(FLAGS,
expname='mtl_val_curve',
test_time=False,
debug=None,
dataname='eicu'):
'''
mtl val curve
'''
pct = FLAGS.train_data_subset_path.split('pct_')[1].split('_')[0]
pct_num = os.path.basename(
FLAGS.train_data_subset_path)[:-4] # remove '.pkl'
expname = "pct{}_{}_{}".format(pct, pct_num, expname)
if FLAGS.pct_val < 1:
expname = "{}_val{}".format(expname, int(FLAGS.pct_val * 100))
if FLAGS.global_model_fn is None: return
cluster_settings, model_settings = create_cluster_model_settings(FLAGS)
if debug is not None:
if type(debug) is list:
cluster_settings = [cluster_settings[idx] for idx in debug]
model_settings = [model_settings[idx] for idx in debug]
else:
idx = debug
cluster_settings = cluster_settings[idx:idx + 1]
model_settings = model_settings[idx:idx + 1]
cluster_settings = [
[('--model_type', 'VAL_CURVE'),
('--train_data_subset_path', FLAGS.train_data_subset_path),
('--pct_val', FLAGS.pct_val), "--cluster_add_result_suffix",
('--result_dir', FLAGS.eicu_cohort),
('--eicu_cohort', FLAGS.eicu_cohort), ('--dataname', dataname),
('--global_model_fn', FLAGS.global_model_fn),
('--result_suffix', '_' + expname)] + setting
for setting in cluster_settings
]
model_settings = [
[('--model_type', 'MULTITASK'),
('--train_data_subset_path', FLAGS.train_data_subset_path),
('--result_dir', FLAGS.eicu_cohort),
('--eicu_cohort', FLAGS.eicu_cohort), ('--dataname', dataname),
('--result_suffix', '_' + expname),
('--global_model_fn', FLAGS.global_model_fn),
('--cohort_filepath', str(i) + '_' + expname + '.npy')] + setting
for i, setting in enumerate(model_settings)
]
# acknowledge the temporal dependence between the runs
# first run cluster_settings, followed by model_settings
# also make sure model_settings uses cluster settings' model
run('cluster_moe.py',
cluster_settings,
gpus=gpus,
n_concurrent_process=FLAGS.nc)
if test_time:
model_settings = [['--test_time', '--bootstrap'] + setting
for setting in model_settings]
run('moe.py', model_settings, gpus=gpus, n_concurrent_process=FLAGS.nc)
def experiment8(FLAGS,
expname='snapshot_oi',
test_time=False,
debug=None,
dataname='eicu'):
'''
snapshot outcome independent (AE)
'''
pct = FLAGS.train_data_subset_path.split('pct_')[1].split('_')[0]
pct_num = os.path.basename(
FLAGS.train_data_subset_path)[:-4] # remove '.pkl'
cluster_expname = "pct{}_{}_{}".format(pct, pct_num,
"mtl_oi") # share the cluster
expname = "pct{}_{}_{}".format(pct, pct_num, expname)
if FLAGS.global_model_fn is None: return
cluster_settings, model_settings = create_cluster_model_settings(FLAGS)
if debug is not None:
if type(debug) is list:
cluster_settings = [cluster_settings[idx] for idx in debug]
model_settings = [model_settings[idx] for idx in debug]
else:
idx = debug
cluster_settings = cluster_settings[idx:idx + 1]
model_settings = model_settings[idx:idx + 1]
# cluster_settings = [[('--model_type', 'AE'),
# ('--train_data_subset_path', FLAGS.train_data_subset_path),
# "--cluster_add_result_suffix",
# ('--result_dir', FLAGS.eicu_cohort),
# ('--eicu_cohort', FLAGS.eicu_cohort),
# ('--dataname', dataname),
# ('--global_model_fn', FLAGS.global_model_fn),
# ('--result_suffix', '_' + cluster_expname)] +
# setting for setting in cluster_settings]
model_settings = [
[('--model_type', 'SNAPSHOT'),
('--train_data_subset_path', FLAGS.train_data_subset_path),
('--result_dir', FLAGS.eicu_cohort),
('--eicu_cohort', FLAGS.eicu_cohort), ('--dataname', dataname),
('--result_suffix', '_' + expname),
('--global_model_fn', FLAGS.global_model_fn),
('--cohort_filepath', str(i) + '_' + cluster_expname + '.npy')] +
setting for i, setting in enumerate(model_settings)
]
# acknowledge the temporal dependence between the runs
# first run cluster_settings, followed by model_settings
# also make sure model_settings uses cluster settings' model
# don't need to run cluster setting b/c exp5 should already ran this
# run('cluster_moe.py', cluster_settings, gpus=gpus, n_concurrent_process=FLAGS.nc)
if test_time:
model_settings = [['--test_time', '--bootstrap'] + setting
for setting in model_settings]
run('moe.py', model_settings, gpus=gpus, n_concurrent_process=FLAGS.nc)
def experiment_debug_joint(FLAGS,
expname='debug',
test_time=False,
viz_time=False,
debug=None):
'''
debug settings: see read me for manually tuned performance
'''
settings = create_joint_settings(FLAGS)
if debug is not None:
if type(debug) is list:
settings = [settings[idx] for idx in debug]
else:
idx = debug
settings = settings[idx:idx + 1]
settings = [[('--lr', 0.001), ('--wd', 1e-4)]]
tasks = [
[
('--model_type', 'MULTITASK'),
('--result_dir', 'debug'),
'--include_cohort_as_feature',
# '--test_time',
# '--bootstrap',
('--epochs', 100),
('--global_model_fn', FLAGS.global_model_fn),
('--result_suffix', '_' + expname),
('--cohorts', 'careunit')
] + setting for setting in settings
]
# tasks = [[('--model_type', 'MULTITASK'), # auroc 0.852
# ('--result_dir', FLAGS.result_dir),
# ('--epochs', 100),
# ('--global_model_fn', FLAGS.global_model_fn),
# ('--result_suffix', '_' + expname),
# ('--cohort_filepath', 'sample_y_quartile.npy'),
# ('--cohorts', 'custom')] +
# setting for setting in settings]
# tasks = [[('--model_type', 'GLOBAL'), # test auc: 0.872, val auc: 0.880
# ('--epochs', 100),
# ('--result_dir', FLAGS.result_dir),
# ('--global_model_fn', FLAGS.global_model_fn),
# ('--result_suffix', '_' + expname),
# '--include_cohort_as_feature',
# ('--cohorts', 'saps')] +
# setting for setting in settings]
if test_time:
tasks = [['--test_time', '--bootstrap'] + setting for setting in tasks]
if viz_time:
tasks = [['--viz_time'] + setting for setting in tasks]
run('moe.py', tasks, gpus=[5, 6], n_concurrent_process=FLAGS.nc)
def experiment7(FLAGS,
expname='snapshot_val_curve',
test_time=False,
debug=None,
dataname='eicu'):
'''
snapshot val curve
'''
if FLAGS.global_model_fn is None: return
cluster_settings, model_settings = create_cluster_model_settings(FLAGS)
if debug is not None:
if type(debug) is list:
cluster_settings = [cluster_settings[idx] for idx in debug]
model_settings = [model_settings[idx] for idx in debug]
else:
idx = debug
cluster_settings = cluster_settings[idx:idx + 1]
model_settings = model_settings[idx:idx + 1]
cluster_settings = [[('--model_type', 'VAL_CURVE'),
('--result_dir', FLAGS.eicu_cohort),
('--eicu_cohort', FLAGS.eicu_cohort),
('--dataname', dataname),
('--global_model_fn', FLAGS.global_model_fn),
('--result_suffix', '_' + expname)] + setting
for setting in cluster_settings]
model_settings = [
[('--model_type', 'SNAPSHOT'), ('--result_dir', FLAGS.eicu_cohort),
('--eicu_cohort', FLAGS.eicu_cohort), ('--dataname', dataname),
('--result_suffix', '_' + expname),
('--global_model_fn', FLAGS.global_model_fn),
('--cohort_filepath', str(i) + '_' + expname + '.npy')] + setting
for i, setting in enumerate(model_settings)
]
# acknowledge the temporal dependence between the runs
# first run cluster_settings, followed by model_settings
# also make sure model_settings uses cluster settings' model
run('cluster_moe.py',
cluster_settings,
gpus=[5, 6],
n_concurrent_process=FLAGS.nc)
if test_time:
model_settings = [['--test_time', '--bootstrap'] + setting
for setting in model_settings]
run('moe.py', model_settings, gpus=[5, 6], n_concurrent_process=FLAGS.nc)
def experiment9(FLAGS, expname='MTL_saps_exp', test_time=False, debug=None):
'''
saps quartile based MTL
'''
settings = create_joint_settings(FLAGS)
if debug is not None:
if type(debug) is list:
settings = [settings[idx] for idx in debug]
else:
idx = debug
settings = settings[idx:idx + 1]
tasks = [[('--model_type', 'MULTITASK'),
('--result_dir', FLAGS.result_dir),
('--result_suffix', '_' + expname),
('--cohorts', 'saps')] + setting for setting in settings]
if test_time:
tasks = [['--test_time', '--bootstrap'] + setting for setting in tasks]
run('moe.py', tasks, gpus=[5, 6], n_concurrent_process=FLAGS.nc)
def experiment_debug_joint(FLAGS,
expname='debug',
test_time=False,
viz_time=False,
debug=None):
'''
debug settings: see read me for manually tuned performance
'''
settings = create_joint_settings(FLAGS)
if debug is not None:
if type(debug) is list:
settings = [settings[idx] for idx in debug]
else:
idx = debug
settings = settings[idx:idx + 1]
settings = [[('--lr', 0.001), ('--wd', 1e-4)]]
tasks = [
[
('--model_type', 'MULTITASK'),
('--result_dir', FLAGS.result_dir_prefix + 'debug'),
'--include_cohort_as_feature',
# '--test_time',
# '--bootstrap',
('--epochs', 100),
('--global_model_fn', FLAGS.global_model_fn),
('--result_suffix', '_' + expname),
('--cohorts', 'careunit')
] + setting for setting in settings
]
if test_time:
tasks = [['--test_time', '--bootstrap'] + setting for setting in tasks]
if viz_time:
tasks = [['--viz_time'] + setting for setting in tasks]
run('moe.py', tasks, gpus=gpus, n_concurrent_process=FLAGS.nc)
def experiment15(FLAGS,
expname='saps_feature_exp',
test_time=False,
debug=None):
'''
saps quartile separate models
'''
settings = create_joint_settings(FLAGS)
if debug is not None:
if type(debug) is list:
settings = [settings[idx] for idx in debug]
else:
idx = debug
settings = settings[idx:idx + 1]
tasks = [[('--model_type', 'GLOBAL'), ('--result_dir', FLAGS.result_dir),
('--cohorts', 'saps'), '--include_cohort_as_feature',
('--result_suffix', '_' + expname)] + setting
for setting in settings]
if test_time:
tasks = [['--test_time', '--bootstrap'] + setting for setting in tasks]
run('moe.py', tasks, gpus=[5, 6], n_concurrent_process=FLAGS.nc)
def experiment12(FLAGS,
expname='separate_careunit_exp',
test_time=False,
debug=None):
'''
careunit separate models
'''
settings = create_joint_settings(FLAGS)
if debug is not None:
if type(debug) is list:
settings = [settings[idx] for idx in debug]
else:
idx = debug
settings = settings[idx:idx + 1]
tasks = [[('--model_type', 'SEPARATE'), ('--result_dir', FLAGS.result_dir),
('--cohorts', 'careunit'),
('--result_suffix', '_' + expname)] + setting
for setting in settings]
if test_time:
tasks = [['--test_time', '--bootstrap'] + setting for setting in tasks]
run('moe.py', tasks, gpus=[5, 6], n_concurrent_process=FLAGS.nc)
def experiment2(FLAGS,
expname='global_exp',
test_time=False,
dataname='mimic',
debug=None):
'''
Global model only experiment
'''
settings = create_joint_settings(FLAGS)
if debug is not None:
if type(debug) is list:
settings = [settings[idx] for idx in debug]
else:
idx = debug
settings = settings[idx:idx + 1]
tasks = [[('--model_type', 'GLOBAL'), ('--result_dir', FLAGS.result_dir),
('--dataname', dataname),
('--result_suffix', '_' + expname)] + setting
for setting in settings]
if test_time:
tasks = [['--test_time', '--bootstrap'] + setting for setting in tasks]
run('moe.py', tasks, gpus=[5, 6], n_concurrent_process=FLAGS.nc)
def run(result_dir, nmaxepochs, nthreads, cuda):
experiment = transform_experiment()
# We'll use multiple processes so disable MKL multithreading
os.environ['MKL_NUM_THREADS'] = str(nthreads)
torch.set_num_threads(nthreads)
try:
with open('../config/redis_address', 'r') as f:
address = f.read().strip()
ray.init(redis_address=address)
except:
ray.init()
ahb = AsyncHyperBandScheduler(reward_attr='negative_loss',
max_t=nmaxepochs)
trials = run(experiment,
scheduler=ahb,
raise_on_failed_trial=False,
queue_trials=True,
early_stop_all_trials=True)
trials = [trial for trial in trials if trial.last_result is not None]
losses = [
-trial.last_result.get('negative_loss', float('-inf'))
for trial in trials
]
print(np.array(losses))
# Polish solutions with L-BFGS
polish_fn = ray.remote(num_gpus=0.25 if cuda else 0)(polish)
sorted_trials = sorted(trials,
key=lambda trial: -trial.last_result.get(
'negative_loss', float('-inf')))
polished_losses = ray.get([
polish_fn.remote(trial) for trial in sorted_trials[:N_TRIALS_TO_POLISH]
])
for i in range(min(N_TRIALS_TO_POLISH, len(trials))):
sorted_trials[i].last_result[
'polished_negative_loss'] = -polished_losses[i]
print(np.sort(losses)[:N_TRIALS_TO_POLISH])
print(np.sort(polished_losses))
checkpoint_path = Path(result_dir) / experiment.name
checkpoint_path.mkdir(parents=True, exist_ok=True)
checkpoint_path /= 'trial.pkl'
with checkpoint_path.open('wb') as f:
pickle.dump(trials, f)
ex.add_artifact(str(checkpoint_path))
return min(losses + polished_losses)
# import hyperopt as hp
# from ray.tune.suggest.hyperopt import HyperOptSearch
ray.shutdown()
ray.init(local_mode=True)
config = {
# "lr": tune.grid_search([0.1, 0.5]),
"lr": tune.grid_search([0.005]),
# "arch": tune.grid_search(["resnext50_32x4d", "resnet50"]),
"arch": tune.grid_search(["resnext50_32x4d"]),
# "step": tune.grid_search([5]),
"lr_decay": tune.grid_search(["linear"]),
"loss": tune.grid_search(["focalloss"]),
# "remark": tune.grid_search(["resume_linear"])
}
# hyperopt = HyperOptSearch(metric="valid_loss", mode="min", space=config)
tune.run(
main,
config=config,
name="run_report",
local_dir="./ray_results",
# search_alg=hyperopt,
# num_samples=2,
# stop={"training_iteration": 5},
resources_per_trial={"gpu": 1})
else:
main({})
