def get_data(the_iterable): 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_iterable: 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)
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
def plot_figs_with_avg_error(the_iterable, scalar_fs): for pid_iterator, filter_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, actual_ys_f_shift, post_process_f in the_iterable: try: init_f = ps.set_hard_coded_key_dec(ps.s_f, 'init')(ys_f) actual_ys_f = ps.actual_ys_f(ys_f, actual_ys_f_shift) ps.abc_attributes_scatter_f(scalar_fs, init_f, actual_ys_f)(pid_iterator) ps.figs_with_avg_error_f(init_f, actual_ys_f)(pid_iterator) except Exception, e: print e ps.print_traceback() """ for frame in traceback.extract_tb(sys.exc_info()[2]): fname,lineno,fn,text = frame print "Error in %s on line %d" % (fname, lineno) """ pass
def plot_model_performances(the_iterable): for ( pid_iterator, filter_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, actual_ys_f_shift, post_process_f, ) in the_iterable: try: get_posterior_f = ps.get_pystan_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) shifted_perf_times = [t - actual_ys_f_shift for t in perf_times] logreg_trainer = ps.get_logreg_predictor_f(shifted_perf_times) trainers = ps.keyed_list([prior_trainer, logreg_trainer, diffcovs_trainer]) init_f = ps.set_hard_coded_key_dec(ps.s_f, "init")(ys_f) actual_ys_f = ps.actual_ys_f(ys_f, actual_ys_f_shift) data = ps.get_data_f(x_abc_f, init_f, actual_ys_f)(pid_iterator) filtered_data = ps.generic_filtered_get_data_f(filter_f)(data) filtered_data = post_process_f(filtered_data) ps.model_comparer_f(trainers, cv_f, loss_f, perf_percentiles, shifted_perf_times)(filtered_data) except Exception, e: for frame in traceback.extract_tb(sys.exc_info()[2]): fname, lineno, fn, text = frame print "Error in %s on line %d" % (fname, lineno) print e pdb.set_trace() pass
def plot_full_model_posterior_parameters(the_iterable): for pid_iterator, filtered_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, actual_ys_f_shift, post_process_f in the_iterable: try: get_posterior_f = ps.get_pystan_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) actual_ys_f = ps.actual_ys_f(ys_f, actual_ys_f_shift) data = ps.get_data_f(x_abc_f, init_f, actual_ys_f)(pid_iterator) filtered_data = ps.generic_filtered_get_data_f(filter_f)(data) filtered_data = post_process_f(filtered_data) ps.plot_diffcovs_posterior_f(3, 2, cv_f, get_posterior_f)(filtered_data) except Exception, e: import traceback for frame in traceback.extract_tb(sys.exc_info()[2]): fname,lineno,fn,text = frame print "Error in %s on line %d" % (fname, lineno) print e pdb.set_trace() pass
def plot_predicted_patient_curves(the_iterable): 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, actual_ys_f_shift, post_process_f, ) in the_iterable: try: get_posterior_f = ps.get_pystan_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) shifted_perf_times = [t - actual_ys_f_shift for t in perf_times] logreg_trainer = ps.get_logreg_predictor_f(shifted_perf_times) init_f = ps.set_hard_coded_key_dec(ps.s_f, "init")(ys_f) actual_ys_f = ps.actual_ys_f(ys_f, actual_ys_f_shift) data = ps.get_data_f(x_abc_f, init_f, actual_ys_f)(pid_iterator) filtered_data = ps.generic_filtered_get_data_f(filter_f)(data) filtered_data = post_process_f(filtered_data) ps.plot_all_predictions_fig_f( ps.keyed_list([prior_trainer, logreg_trainer, diffcovs_trainer]), cv_f, shifted_perf_times )(filtered_data) except: pass
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)()