def __iter__(self): pid_iterators = [set_hard_coded_key_dec(ps.filtered_pid_iterator,'surgpids')(ps.all_ucla_pid_iterator(), ps.bin_f(ps.ucla_treatment_f(),ps.equals_bin([ps.ucla_treatment_f.surgery])))] filtered_data_fs = [ps.old_filtered_get_data_f(), ps.medium_filtered_get_data_f(), ps.filtered_get_data_f()] upscale_vals = [0] diffcovs_iters = [5000] diffcovs_numchains = [4] diffcovs_seeds = [1] perf_percentiles = [[0.25, 0.5, 0.75]] get_pops_fs = [ps.train_better_pops_f()] summarize_fs = [ps.get_param_mean_f()] cv_fs = [ps.cv_fold_f(4)] upscale_vals = [0.0] # perf_times = [[1,2,4,8,12,18,24,30,36,42,48]] ys_fs = [ps.modified_ys_f(ps.ys_f(ps.ys_f.sexual_function), ps.score_modifier_f(c)) for c in upscale_vals] actual_ys_f_shifts = [1] loss_fs = [ps.scaled_logistic_loss_f(10.0)] feature_sets_iterator = ps.get_feature_set_iterator([hard_coded_feature_sets.default_age_categorical_f, hard_coded_feature_sets.medium_age_categorical_f], [hard_coded_feature_sets.default_initial_categorical_f, hard_coded_feature_sets.medium_initial_categorical_f, hard_coded_feature_sets.highminus_initial_categorical_f]) hypers = [hard_coded_hypers.default_hyper, hard_coded_hypers.medium_hyper] x_abc_fs = ps.keyed_list([set_hard_coded_key_dec(ps.x_abc_fs, feature_set.get_key())(feature_set, feature_set, feature_set) for feature_set in feature_sets_iterator]) return itertools.product(pid_iterators, filtered_data_fs, diffcovs_iters, diffcovs_numchains, diffcovs_seeds, perf_percentiles, perf_times, get_pops_fs, summarize_fs, cv_fs, ys_fs, hypers, x_abc_fs, loss_fs, actual_ys_f_shifts)