def main(options): now = time_utils._timestamp_pretty() meta_conf = "level%d_feature_conf_meta_linear_%s" % (options.level, now) stacking_conf = "level%d_feature_conf_%s" % (options.level, now) feat_name = "level%d_meta_linear_%s" % (options.level, now) # get meta feature conf for `level` models cmd = "python get_feature_conf_linear_stacking.py -d %d -o %s.py" % ( options.dim, meta_conf) os.system(cmd) # NOTE: using predictions from `level-1` models to generate features # for `level` models cmd = "python get_stacking_feature_conf.py -l %d -t %d -o %s.py" % ( options.level - 1, options.top, stacking_conf) os.system(cmd) # generate feature for `level` models cmd = "python feature_combiner.py -l %d -c %s -m %s -n %s -s .csv -t %f" % ( options.level, stacking_conf, meta_conf, feat_name, options.corr) os.system(cmd) # train `level` models if options.refit_once: cmd = "python task.py -m stacking -f %s -l %s -e 100 -o" % ( feat_name, options.learner) else: cmd = "python task.py -m stacking -f %s -l %s -e 100" % ( feat_name, options.learner) os.system(cmd)
def main(options): now = time_utils._timestamp_pretty() meta_conf = "level%d_feature_conf_meta_linear_%s"%(options.level, now) stacking_conf = "level%d_feature_conf_%s"%(options.level, now) feat_name = "level%d_meta_linear_%s"%(options.level, now) # get meta feature conf for `level` models cmd = "python get_feature_conf_linear_stacking.py -d %d -o %s.py"%( options.dim, meta_conf) os.system(cmd) # NOTE: using predictions from `level-1` models to generate features # for `level` models cmd = "python get_stacking_feature_conf.py -l %d -t %d -o %s.py"%( options.level-1, options.top, stacking_conf) os.system(cmd) # generate feature for `level` models cmd = "python feature_combiner.py -l %d -c %s -m %s -n %s -s .csv -t %f"%( options.level, stacking_conf, meta_conf, feat_name, options.corr) os.system(cmd) # train `level` models if options.refit_once: cmd = "python task.py -m stacking -f %s -l %s -e 100 -o"%(feat_name, options.learner) else: cmd = "python task.py -m stacking -f %s -l %s -e 100"%(feat_name, options.learner) os.system(cmd)
# -*- coding: utf-8 -*- """ @author: Chenglong Chen <*****@*****.**> @brief: script for testing 1st level model with reg_xgb_tree """ import os import sys from utils import time_utils if len(sys.argv) >= 3: suffix = sys.argv[1] threshold = float(sys.argv[2]) else: suffix = time_utils._timestamp_pretty() threshold = 0.05 cmd = "python get_feature_conf_nonlinear.py -d 10 -o feature_conf_nonlinear_%s.py"%suffix os.system(cmd) cmd = "python feature_combiner.py -l 1 -c feature_conf_nonlinear_%s -n basic_nonlinear_%s -t %.6f"%(suffix, suffix, threshold) os.system(cmd) cmd = "python task.py -m single -f basic_nonlinear_%s -l reg_xgb_tree -e 100"%suffix os.system(cmd)
# -*- coding: utf-8 -*- """ @author: Chenglong Chen <*****@*****.**> @brief: script for testing 1st level model with reg_xgb_tree """ import os import sys from utils import time_utils if len(sys.argv) >= 3: suffix = sys.argv[1] threshold = float(sys.argv[2]) else: suffix = time_utils._timestamp_pretty() threshold = 0.05 cmd = "python get_feature_conf_nonlinear.py -d 10 -o feature_conf_nonlinear_%s.py" % suffix os.system(cmd) cmd = "python feature_combiner.py -l 1 -c feature_conf_nonlinear_%s -n basic_nonlinear_%s -t %.6f" % ( suffix, suffix, threshold) os.system(cmd) cmd = "python task.py -m single -f basic_nonlinear_%s -l reg_xgb_tree -e 100" % suffix os.system(cmd)