''' from evaluation.experiment import Experiment import data.load_data as load_data import numpy as np output_dir = '../../data/bayesian_sequence_combination/output/ner/' regen_data = False gt, annos, doc_start, text, gt_nocrowd, doc_start_nocrowd, text_nocrowd, gt_task1_val, gt_val, doc_start_val, text_val, _ = \ load_data.load_ner_data(regen_data) # ------------------------------------------------------------------------------------------------ exp = Experiment(None, 9, annos.shape[1], None, max_iter=20) exp.save_results = True exp.opt_hyper = False #True best_bac_wm = 'bac_seq' #'unknown' # choose model with best score for the different BAC worker models best_bac_wm_score = -np.inf best_nu0factor = 0.1 best_diags = 1 best_factor = 1 best_acc_bias = 0 exp.alpha0_diags = best_diags exp.alpha0_factor = best_factor exp.nu0_factor = best_nu0factor exp.alpha0_acc_bias = best_acc_bias exp.methods = [
annos, gt, doc_start, features, annos, gt_val, doc_start, features, alpha0_factor=alpha0_factor, alpha0_diags=alpha0_diags, beta0_factor=beta0_factor, max_iter=20) exp.methods = [ 'ibcc', ] exp.opt_hyper = False exp.run_methods(new_data=regen_data) # ---------------------------------------------------------------------------- beta0_factor = 0.1 alpha0_diags = 0.1 alpha0_factor = 0.1 output_dir = os.path.join( evaluation.experiment.output_root_dir, 'ner3_%f_%f_%f' % (beta0_factor, alpha0_diags, alpha0_factor)) exp = Experiment(output_dir, 9, annos, gt, doc_start,