def run_learning_and_mapping(args,
fixed_params,
task_param,
seeds,
slurm_args=None):
logging.info('Running final tests with...')
import copy
task_params = [copy.copy(task_param) for s in seeds]
param_ids = range(len(task_params))
for param, seed, param_id in zip(task_params, seeds, param_ids):
param.param_id = param_id
param.seed = seed
ensure_dir_exists("run_parameters")
common_params = SimpleNamespace(
**fixed_params.__dict__,
priv_cancertype_pairs=[],
pub_cancertypes=sum(cancer_type_pairs, []),
task_type='learn_and_map',
)
args.wait = True
batch2.run_tasks(args,
common_params,
task_params,
slurm_args=slurm_args,
params_file=("run_parameters/batch-%s.pkl" %
(fixed_params.test_name)))
def run_optimization_batch(args,
fixed_params,
task_params,
seeds,
slurm_args=None):
ensure_dir_exists("run_parameters")
param_ids = range(len(task_params))
for param, seed, param_id in zip(task_params, seeds, param_ids):
param.param_id = param_id
param.seed = seed
assert len(task_params) == gpyopt_batch_size
nonpriv_cancertype_pairs = [
ctp for ctp in cancer_type_pairs
if ctp != fixed_params.priv_cancertype_pair
]
assert len(nonpriv_cancertype_pairs) == len(cancer_type_pairs) - 1
res = np.zeros((len(task_params), fixed_params.param_opt_folds))
for fold in range(fixed_params.param_opt_folds):
val_cancertype_pairs = [
ctp for (i, ctp) in enumerate(nonpriv_cancertype_pairs)
if i % fixed_params.param_opt_folds == fold
]
learn_cancertype_pairs = [
ctp for (i, ctp) in enumerate(nonpriv_cancertype_pairs)
if i % fixed_params.param_opt_folds != fold
]
assert (len(val_cancertype_pairs) +
len(learn_cancertype_pairs) == len(nonpriv_cancertype_pairs))
common_params = SimpleNamespace(
**fixed_params.__dict__,
priv_cancertype_pairs=val_cancertype_pairs,
pub_cancertypes=sum(learn_cancertype_pairs, []),
task_type='paramopt',
)
args.wait = True
batch2.run_tasks(args,
common_params,
task_params,
slurm_args=slurm_args,
params_file=("run_parameters/batch-%s.pkl" %
(fixed_params.test_name)))
# get results
for param_id in param_ids:
full_model_id = "%s-%s" % (fixed_params.test_name, param_id)
filename = "param_opt/opt_result-%s.txt" % (full_model_id)
try:
res[param_id, fold] = np.loadtxt(filename)
import os
os.remove(filename)
except:
res[param_id, fold] = gpyopt_fail_res + np.random.randn(
) * gpyopt_fail_res_std
logging.info('Warning, could not load "%s"' % filename)
return np.mean(res, axis=1, keepdims=True)
def run_test(args, fixed_params, task_param, seeds, slurm_args=None):
logging.info('Running final tests with...')
import copy
task_params = [copy.copy(task_param) for s in seeds]
param_ids = range(len(task_params))
for param, seed, param_id in zip(task_params, seeds, param_ids):
param.param_id = param_id
param.seed = seed
ensure_dir_exists("run_parameters")
nonpriv_cancertype_pairs = [
ctp for ctp in cancer_type_pairs
if ctp != fixed_params.priv_cancertype_pair
]
assert len(nonpriv_cancertype_pairs) == len(cancer_type_pairs) - 1
common_params = SimpleNamespace(
**fixed_params.__dict__,
priv_cancertype_pairs=[fixed_params.priv_cancertype_pair],
pub_cancertypes=sum(nonpriv_cancertype_pairs, []),
task_type='test',
)
args.wait = True
batch2.run_tasks(args,
common_params,
task_params,
slurm_args=slurm_args,
params_file=("run_parameters/batch-%s.pkl" %
(fixed_params.test_name)))
#
res = np.zeros((len(task_params), 1))
for param_id in param_ids:
full_model_id = "%s-%s" % (fixed_params.test_name, param_id)
filename = "param_opt/opt_result-%s.txt" % (full_model_id)
try:
res[param_id] = np.loadtxt(filename)
import os
os.remove(filename)
except:
res[param_id] = np.nan #gpyopt_fail_res
logging.info('Warning, could not load "%s"' % filename)
filename = "res/test_results-%s.txt" % (fixed_params.test_name)
logging.info("Writing final results to '%s'" % filename)
np.savetxt(filename, res)
def run_optimization_batch(args,
fixed_params,
task_params,
seeds,
slurm_args=None):
ensure_dir_exists("run_parameters")
param_ids = range(len(task_params))
for param, seed, param_id in zip(task_params, seeds, param_ids):
param.param_id = param_id
#param.priv_cancertypes = val_cancertypes
#param.skip_cancertypes = priv_cancertypes
param.seed = seed
#np.save("run_parameters/params-%s.npy" % (test_name), params)
assert len(task_params) == gpyopt_batch_size
common_params = SimpleNamespace(
**fixed_params.__dict__,
pred_cancertypes=fixed_params.val_cancertypes,
skip_cancertypes=fixed_params.priv_cancertypes,
task_type='paramopt',
)
args.wait = True
batch2.run_tasks(args,
common_params,
task_params,
slurm_args=slurm_args,
params_file=("run_parameters/batch-%s.pkl" %
(fixed_params.test_name)))
# get results
#return np.random.randn(gpyopt_batch_size, 1)
res = np.zeros((len(task_params), 1))
for param_id in param_ids:
full_model_id = "%s-%s" % (fixed_params.test_name, param_id)
filename = "param_opt/opt_result-%s.txt" % (full_model_id)
try:
res[param_id] = np.loadtxt(filename)
import os
os.remove(filename)
except:
res[param_id] = gpyopt_fail_res + np.random.randn(
) * gpyopt_fail_res_std
logging.info('Warning, could not load "%s"' % filename)
return res
def run_test(args, fixed_params, task_param, seeds, slurm_args=None):
logging.info('Running tests...')
task_params = [[
SimpleNamespace(
pred_cancertypes=priv_cancertypes,
seed=seed,
) for seed in seeds
] for priv_cancertypes in cancer_type_pairs]
task_params = sum(task_params, [])
param_ids = range(len(task_params))
for param, param_id in zip(task_params, param_ids):
param.param_id = param_id
ensure_dir_exists("run_parameters")
#np.save("run_parameters/params-%s.npy" % (test_name), params)
common_params = SimpleNamespace(**fixed_params.__dict__, )
args.wait = True
batch2.run_tasks(args,
common_params,
task_params,
slurm_args=slurm_args,
params_file=("run_parameters/batch-%s.pkl" %
(fixed_params.test_name)))
#
res = np.zeros((len(task_params), 1))
for param_id in param_ids:
full_model_id = "%s-%s" % (fixed_params.test_name, param_id)
filename = "param_opt/opt_result-%s.txt" % (full_model_id)
try:
res[param_id] = np.loadtxt(filename)
import os
os.remove(filename)
except:
res[param_id] = np.nan
logging.info('Warning, could not load "%s"' % filename)
res = np.reshape(res, (len(cancer_type_pairs), len(seeds)))
filename = "res/test_results-%s.txt" % (fixed_params.test_name)
logging.info("Writing final results to '%s'" % filename)
np.savetxt(filename, res)
