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()