import sys import json from FeatFair import FeatFair from train_models import train_feat_model from feat_common_args import common_args dataset = sys.argv[1] dataset_name = dataset.split('/')[-1].split('.')[0] attributes = sys.argv[2] seed = int(sys.argv[3]) rdir = sys.argv[4] common_args['obj'] = 'fitness,complexity' common_args['pop_size'] = 100 common_args['gens'] = 0 est = FeatFair( **common_args, sel='random', surv='offspring', random_state=seed, ) # set up Feat model perf, hv = train_feat_model(est, 'feat_random_p100_g0', dataset, attributes, seed, rdir)
import sys import json from FeatFair import FeatFair from train_models import train_feat_model from feat_common_args import common_args dataset = sys.argv[1] dataset_name = dataset.split('/')[-1].split('.')[0] attributes = sys.argv[2] seed = int(sys.argv[3]) rdir = sys.argv[4] common_args['obj'] = 'fitness' est = FeatFair( **common_args, sel='tournament', surv='offspring', random_state=seed, ) # set up Feat model perf, hv = train_feat_model(est, 'feat_tourn', dataset, attributes, seed, rdir)
import sys from train_models import train_feat_model from feat_common_args import common_args from FeatFair import FeatFair import json dataset = sys.argv[1] dataset_name = dataset.split('/')[-1].split('.')[0] attributes = sys.argv[2] seed = int(sys.argv[3]) rdir = sys.argv[4] est = FeatFair( **common_args, sel = 'fair_lexicase2', surv = 'nsga2', random_state=seed, ) # set up Feat NSGA2 model perf, hv = train_feat_model(est, 'feat_flex2_nsga2', dataset, attributes, seed, rdir)