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]]
perf_times = [[1, 2, 4, 8, 12, 18, 24, 30, 36, 42, 48]]
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, 0.2] #
ys_fs = [ps.modified_ys_f(ps.ys_f(ps.ys_f.sexual_function), ps.score_modifier_f(c)) for c in upscale_vals]
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.high_age_categorical_f,
],
[
hard_coded_feature_sets.default_initial_categorical_f,
hard_coded_feature_sets.high_initial_categorical_f,
hard_coded_feature_sets.higher_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, hard_coded_hypers.relaxed_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,
)
def __iter__(self):
pid_iterators = [ps.filtered_pid_iterator(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]))), ps.is_good_pid())]
#pid_iterators = [ps.all_ucla_pid_iterator()]
#filter_fs = [hard_coded_filter_fs.old_filter_f]
filter_fs = [ps.always_true_f()]
#filtered_data_fs = [ps.generic_filtered_get_data_f(filter_f) for filter_f in filter_fs]
upscale_vals = [0]
diffcovs_iters = [1000]
diffcovs_numchains = [1]
diffcovs_seeds = [1]
perf_percentiles = [[0.25, 0.5, 0.75]]
perf_times = [[1,2,4,8,12,18,24,30,36,42,48]]
get_pops_fs = [ps.train_better_pops_f()]
summarize_fs = [ps.get_param_mean_f()]
cv_fs = [ps.cv_fold_f(3)]
upscale_vals = [0.0] #
ys_fs = [ps.modified_ys_f(ps.ys_f(ps.ys_f.sexual_function), ps.score_modifier_f(c)) for c in upscale_vals]
post_process_fs = [ps.normalized_data_f()]
actual_ys_f_shifts = [1]
loss_fs = [ps.scaled_logistic_loss_f(10.0)]
feature_sets_iterator = [hard_coded_feature_sets.default_simple_indicators]
hypers = [hard_coded_hypers.default_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, filter_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, post_process_fs)
def __iter__(self):
pid_iterators1 = [ps.filtered_pid_iterator(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]))), ps.is_good_pid())]
pid_iterators2 = [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]))), ps.is_good_pid()]
filter_fs1 = [ps.always_true_f()]
filter_fs2 = [hard_coded_filter_fs.old_filter_f]
upscale_vals = [0]
diffcovs_iters = [500]
diffcovs_numchains = [1]
diffcovs_seeds = [1]
perf_percentiles = [[0.25, 0.5, 0.75]]
perf_times = [[1,2,4,8,12,18,24,30,36,42,48]]
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] #
ys_fs = [ps.modified_ys_f(ps.ys_f(ps.ys_f.sexual_function), ps.score_modifier_f(c)) for c in upscale_vals]
post_process_fs = [ps.normalized_data_f()]
actual_ys_f_shifts = [0,1]
loss_fs = [ps.scaled_logistic_loss_f(10.0)]
ones_f_list = set_hard_coded_key_dec(ps.keyed_list, 'ones')([ps.ones_f()])
#feature_sets_iterator = ps.get_feature_set_iterator([ones_f_list], 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])
feature_sets_iterator = ps.get_feature_set_iterator([ones_f_list], [hard_coded_feature_sets.default_age_categorical_f], [hard_coded_feature_sets.default_initial_categorical_f, hard_coded_feature_sets.highminus_initial_categorical_f])
hypers = [hard_coded_hypers.default_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.chain(\
itertools.product(pid_iterators1, filter_fs1, 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, post_process_fs), \
itertools.product(pid_iterators2, filter_fs2, 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, post_process_fs)\
)