def launch_mimic_training():
experiment_uids_path = Path(
__file__).parent.parent / 'data/thesis/experiment_uids.json'
dataset = 'mimic'
if experiment_uids_path.exists():
exp_uids = json2dict(experiment_uids_path)
else:
experiment_uids_path.parent.mkdir(exist_ok=True, parents=True)
exp_uids = {dataset: {}}
for params in [mopoe_mimic_args, mofop_mimic_args, mopgfm_mimic_args]:
method = params['method']
if method not in exp_uids[dataset]:
exp_uids[dataset][method] = {}
num_mods = 3
exp_uids[dataset][method][f'{num_mods}_mods'] = []
experiment_uid = get_experiment_uid('mimic', method=method)
params["experiment_uid"] = experiment_uid
exp_uids[dataset][method][f'{num_mods}_mods'].append(experiment_uid)
launch_leomed_jobs(which_dataset='mimic', params=params)
dict2json(experiment_uids_path, d=exp_uids)
def launch_polymnist_training():
experiment_uids_path = Path(
__file__).parent.parent / 'data/thesis/experiment_uids.json'
dataset = 'polymnist'
if experiment_uids_path.exists():
exp_uids = json2dict(experiment_uids_path)
else:
experiment_uids_path.parent.mkdir(exist_ok=True, parents=True)
exp_uids = {dataset: {}}
for params in [
mopoe_args, poe_args, moe_args, mopgfm_args, mofop_args,
iwmogfm2_args, mogfm_amortized_args
]:
# for params in [mofop_args]:
method = params['method']
if method not in exp_uids[dataset]:
exp_uids[dataset][method] = {}
for num_mods in range(1, 5):
params['num_mods'] = num_mods
# more evaluation steps are needed for 3 mods
if num_mods == 3:
params['eval_freq'] = 100
exp_uids[dataset][method][f'{num_mods}_mods'] = []
for _ in range(nbr_repeats):
experiment_uid = get_experiment_uid('polymnist', method=method)
params["experiment_uid"] = experiment_uid
exp_uids[dataset][method][f'{num_mods}_mods'].append(
experiment_uid)
launch_leomed_jobs(which_dataset='polymnist', params=params)
dict2json(experiment_uids_path, d=exp_uids)
# -*- coding: utf-8 -*-
# from mmvae_hub.hyperopt.search_spaces.base_search_spaces import base_search_spaces
from sklearn.model_selection import ParameterGrid
from mmvae_hub.hyperopt.search_spaces.search_spaces import *
from mmvae_hub.leomed_utils.launch_jobs import launch_leomed_jobs
for search_space in [sp_mofop_mnistsvhntext]:
# for search_space in [search_space_je, search_space_gfm, search_space_mofop]:
for params in ParameterGrid(search_space):
params["eval_freq"] = 50
launch_leomed_jobs(which_dataset='mnistsvhntext', params=params)
# -*- coding: utf-8 -*-
from sklearn.model_selection import ParameterGrid
from mmvae_hub.hyperopt.search_spaces.base_search_spaces import *
from mmvae_hub.hyperopt.search_spaces.search_spaces import *
from mmvae_hub.leomed_utils.launch_jobs import launch_leomed_jobs
# for search_space in [flow_mimic]:
# for search_space in [iwmopgfm_mimic]:
for search_space in [sp_mopoe_mimic, iwmogfm_mimic, amortized_mimic]:
# for search_space in [search_space_je, search_space_gfm, search_space_mofop]:
for params in ParameterGrid(search_space):
# params['gpu_mem'] = 15000
params["eval_freq"] = 150
launch_leomed_jobs(which_dataset='mimic', params=params)
# -*- coding: utf-8 -*-
from sklearn.model_selection import ParameterGrid
from mmvae_hub.hyperopt.search_spaces.search_spaces import *
from mmvae_hub.leomed_utils.launch_jobs import launch_leomed_jobs
# for search_space in [iwmogfm2]:
# for search_space in [sp_mofop]:
# for search_space in [mopgfm_celeba, mopoe_celeba]:
for search_space in [mopoe_celeba]:
# for search_space in [search_space_je, search_space_gfm, search_space_mofop]:
for params in ParameterGrid(search_space):
params["eval_freq"] = 50
launch_leomed_jobs(which_dataset='celeba', params=params)
# -*- coding: utf-8 -*-
# from mmvae_hub.hyperopt.search_spaces.base_search_spaces import base_search_spaces
from sklearn.model_selection import ParameterGrid
from mmvae_hub.hyperopt.search_spaces.search_spaces import *
from mmvae_hub.leomed_utils.launch_jobs import launch_leomed_jobs
# for search_space in [sp_iwmopgfm, iwmopoe]:
for search_space in [iwmopoe]:
# for search_space in [sp_mofop]:
# for search_space in [sp_joint_elbo_article]:
# for search_space in [search_space_je, search_space_gfm, search_space_mofop]:
for params in ParameterGrid(search_space):
# params["eval_freq"] = 10
launch_leomed_jobs(which_dataset='polymnist', params=params)