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)
