input_dict_gnuts = {
"v_fun": v_fun_list,
"epsilon": ["dual"],
"second_order": [False],
"cov": ["adapt"],
"metric_name": ["diag_e"],
"dynamic": [True],
"windowed": [False],
"criterion": ["gnuts"],
"max_tree_depth": [8]
}
input_object_gnuts = tuneinput_class(input_dict_gnuts)
experiment_instance_gnuts = experiment(
input_object=input_object_gnuts,
experiment_setting=experiment_setting_gnuts,
fun_per_sampler=target_fun)
experiment_instance_gnuts.run()
np_store, col_names, output_names = experiment_instance_gnuts.np_output()
np_store_diagnostics, diagnostics_names = experiment_instance_gnuts.np_diagnostics(
)
np_diagnostics_gnuts[i] = np_store_diagnostics
np_store_gnuts[i] = np_store
np_store_gnuts = numpy.stack(np_store_gnuts, axis=0)
np_diagnostics_gnuts = numpy.stack(np_diagnostics_gnuts, axis=0)
gnuts_col_names = col_names
gnuts_output_names = output_names
#######################################################################################################################################
ep_list = list(numpy.linspace(ep_bounds[0],ep_bounds[1],num_grid_divides))
evolve_L_list = list(numpy.linspace(L_bounds[0],L_bounds[1],num_grid_divides))
evolve_t_list = list(numpy.linspace(converted_t_bounds[0],converted_t_bounds[1],num_grid_divides))
#print(converted_t_bounds)
#####################################################################################################################################
experiment_setting_ep_L = experiment_setting_dict(chain_length=10000,num_chains_per_sampler=4,warm_up=1000,
tune_l=0,allow_restart=True,max_num_restarts=5)
input_dict_ep_L = {"v_fun":v_fun_list,"epsilon":ep_list,"second_order":[False],
"evolve_t":evolve_t_list,"metric_name":["unit_e"],"dynamic":[False],"windowed":[False],"criterion":[None]}
input_object_ep_L = tuneinput_class(input_dict_ep_L)
experiment_instance_ep_L = experiment(input_object=input_object_ep_L,experiment_setting=experiment_setting_ep_L,fun_per_sampler=function)
experiment_instance_ep_L.run()
result_grid_ep_L= experiment_instance_ep_L.experiment_result_grid_obj
##########################################################################################################################################
experiment_setting_ep_t = experiment_setting_dict(chain_length=10000,num_chains_per_sampler=4,warm_up=1000,
tune_l=0,allow_restart=True,max_num_restarts=5)
input_dict_ep_t = {"v_fun":v_fun_list,"epsilon":ep_list,"second_order":[False],
"evolve_t":evolve_t_list,"metric_name":["unit_e"],"dynamic":[False],"windowed":[False],"criterion":[None]}
input_object_ep_t = tuneinput_class(input_dict_ep_t)
experiment_instance_ep_t = experiment(input_object=input_object_ep_t,experiment_setting=experiment_setting_ep_t,fun_per_sampler=function)
input_dict = {
"v_fun": [V_logistic_regression],
"epsilon": [0.1, 0.05],
"second_order": [False],
"evolve_L": [10],
"metric_name": ["unit_e"],
"dynamic": [False],
"windowed": [False],
"criterion": [None]
}
input_obj = tuneinput_class(input_dict)
#print(input_obj.__dict__["grid_shape"])
#exit()
exper_obj = experiment(input_object=input_obj)
exper_obj.run()
#print(len(exper_obj.id_to_multi_index))
print(exper_obj.id_to_multi_index[0])
print(exper_obj.id_to_multi_index[1])
print(exper_obj.store_grid_obj[exper_obj.id_to_multi_index[0]])
print(exper_obj.store_grid_obj[exper_obj.id_to_multi_index[1]])
#exit()
#print(exper_obj.store_grid_obj[0,0,0,0,0])
#out.input_dict["v_fun"][0]()
#print()
num_grid_divides = 2
ep_list = list(numpy.linspace(1e-2, 0.1, num_grid_divides))
evolve_t_list = list(numpy.linspace(0.15, 5.0, num_grid_divides))
v_fun_list = []
input_dict = {
"v_fun": v_fun_list,
"epsilon": ep_list,
"second_order": [False],
"evolve_t": evolve_t_list,
"metric_name": ["unit_e"],
"dynamic": [False],
"windowed": [False],
"criterion": [None]
}
experiment_setting = experiment_setting_dict(chain_length=10000,
num_repeat=20,
num_chains_per_sampler=4,
warm_up=1000,
tune_l=0,
save_name="temp_experiment.pkl")
input_object = tuneinput_class(input_dict)
experiment_instance = experiment(input_object=input_object,
experiment_setting=experiment_setting,
fun_per_sampler=function)
experiment.run()
from abstract.mcmc_sampler import mcmc_sampler, mcmc_sampler_settings_dict
from adapt_util.tune_param_classes.tune_param_setting_util import *
from experiments.experiment_obj import tuneinput_class
from experiments.experiment_obj import experiment,experiment_setting_dict
from experiments.correctdist_experiments.prototype import check_mean_var
num_per_model = 20
mcmc_meta = mcmc_sampler_settings_dict(mcmc_id=0,samples_per_chain=500,num_chains=1,num_cpu=1,thin=1,tune_l_per_chain=0,
warmup_per_chain=100,is_float=False,isstore_to_disk=False)
input_dict = {"v_fun":[V_funnel_cp],"epsilon":[0.1],"alpha":[1e6,1e2],"second_order":[True],
"evolve_L":[10],"metric_name":["softabs"],"dynamic":[False],"windowed":[False],"criterion":[None]}
input_dict2 = {"v_fun":[V_funnel_ncp],"epsilon":[0.1],"second_order":[False],
"evolve_L":[10],"metric_name":["unit_e"],"dynamic":[False],"windowed":[False],"criterion":[None]}
input_obj = tuneinput_class(input_dict)
input_obj2 = tuneinput_class(input_dict2)
experiment_setting_dict = experiment_setting_dict(chain_length=10000,num_repeat=num_per_model)
experiment_obj = experiment(input_object=input_obj,experiment_setting=experiment_setting_dict)
experiment_obj.run()
experiment_obj2 = experiment(input_object=input_obj2,experiment_setting=experiment_setting_dict)
experiment_obj2.run()