def estimate_weights(fold): holdout = folds == fold train_datasets = [] for dset, has_fut in zip(datasets, has_future): d = loglin.sub_dataset(dset, ~holdout & has_fut) train_datasets.append(d) weights = learn.learn_time_regularized_weights(train_datasets, censor_times, time_penalty=time_penalty, l1_penalty=l1_penalty) return weights
for dset, has_fut in zip(datasets, has_future): d = loglin.sub_dataset(dset, ~holdout & has_fut) train_datasets.append(d) weights = learn.learn_time_regularized_weights(train_datasets, censor_times, time_penalty=time_penalty, l1_penalty=l1_penalty) return weights pool = Pool(3) all_weights = pool.map(estimate_weights, [k + 1 for k in range(10)]) for i, weights in enumerate(all_weights): fold = i + 1 holdout = folds == fold for j, w in enumerate(weights): test_data = loglin.sub_dataset(datasets[j], holdout) _, marg, _ = loglin.inference(w, test_data) posteriors[j][holdout, :] = marg[0] # for fold in sorted(set(folds)): # logging.info('Staring fold {}'.format(fold)) # holdout = folds == fold # train_datasets = [] # for dset, has_fut in zip(datasets, has_future): # d = loglin.sub_dataset(dset, ~holdout & has_fut) # train_datasets.append(d) # all_weights = learn.learn_time_regularized_weights(train_datasets, censor_times, time_penalty=1e-3, l1_penalty=1e-3)
def estimate_weights(fold): holdout = folds == fold train_data = loglin.sub_dataset(dataset, ~holdout & has_future) weights = learn.learn_weights(train_data, 1e-3)