def param_to_datasets_and_trainers(pid_iterator, filtered_data_f, diffcovs_iter, diffcovs_numchains, diffcovs_seed, perf_percentiles, perf_times, get_pops_f, summarize_f, cv_f, ys_f, hypers, x_abc_f, loss_f):
get_posterior_f = ps.get_diffcovs_posterior_f(get_pops_f, hypers, diffcovs_iter, diffcovs_numchains, diffcovs_seed)
diffcovs_trainer = ps.get_diffcovs_point_predictor_f(get_posterior_f, summarize_f)
prior_trainer = ps.get_prior_predictor_f(get_pops_f)
logreg_trainer = ps.get_logreg_predictor_f(perf_times)
init_f = ps.set_hard_coded_key_dec(ps.s_f, 'init')(ys_f)
data = ps.get_data_f(x_abc_f, init_f, ps.after_0_ys_f(ys_f))(pid_iterator)
filtered_data = filtered_data_f(data)
return ps.keyed_list([prior_trainer, logreg_trainer, diffcovs_trainer]), cv_f, loss_f, perf_percentiles, perf_times, filtered_data
ps.plot_all_predictions_fig_f(ps.keyed_list([prior_trainer, logreg_trainer]), cv_f, perf_times)(filtered_data)
"""
"""
for pid_iterator, filtered_data_f, diffcovs_iter, diffcovs_numchains, diffcovs_seed, perf_percentiles, perf_times, get_pops_f, summarize_f, cv_f, ys_f, hypers, x_abc_f, loss_f in the_iter:
init_f = ps.set_hard_coded_key_dec(ps.s_f, 'init')(ys_f)
data = ps.get_data_f(x_abc_f, init_f, ys_f)(pid_iterator)
filtered_data = filtered_data_f(data)
for training_data, testing_data in cv_f(filtered_data):
#print ps.aggregate_shape_f()(training_data)
print ps.train_shape_pops_f()(training_data)
print ps.train_better_pops_f()(training_data)
print ps.train_better_pops_f()(training_data).get_location()
pdb.set_trace()
"""
for pid_iterator, filtered_data_f, diffcovs_iter, diffcovs_numchains, diffcovs_seed, perf_percentiles, perf_times, get_pops_f, summarize_f, cv_f, ys_f, hypers, x_abc_f, loss_f in the_iter:
get_posterior_f = ps.get_diffcovs_posterior_f(get_pops_f, hypers, diffcovs_iter, diffcovs_numchains, diffcovs_seed)
init_f = ps.set_hard_coded_key_dec(ps.s_f, 'init')(ys_f)
data = ps.get_data_f(x_abc_f, init_f, ps.after_0_ys_f(ys_f))(pid_iterator)
filtered_data = filtered_data_f(data)
ps.plot_diffcovs_posterior_f(3, 2, cv_f, get_posterior_f)(filtered_data)
#save_at_specified_path_dec(ps.figure_combiner_f(ps.model_comparer_f, lambda x: (x[0:5], [x[5]])),'some_figs_old.pdf')(model_data_iter)
#for _models, _cv_f, _loss_f, _perf_percentiles, _perf_times, _filtered_data in model_data_iter:
# diffcovs_trainer = _models[2]
# ps.plot_diffcovs_posterior_f(3,2)()