def run():
problems = [
'llvm_input', 'noc_CM_log', 'sort_256', 'wc+rs-3d-c4', 'wc+sol-3d-c4',
'wc+wc-3d-c4', 'wc-3d-c4', 'wc-5d-c5', 'wc-6d-c1', 'wc-c1-3d-c1',
'wc-c3-3d-c1', 'rs-6d-c3'
]
for problem in problems:
print ">>> ", problem
if problem == 'wc-3d-c4':
files = [
"./Data/" + f for f in os.listdir("./Data") if problem in f
]
files = [f for f in files if '+' not in f]
else:
files = [
"./Data/" + f for f in os.listdir("./Data") if problem in f
]
assert (len(files) == 20), "Somethign is wrong"
for file in files:
# if "POM" in file:
# name = file.split('/')[-1].split('_')[0]
# indeps, deps = collect_all_points(file, 9, 3)
# elif "xomo" in file:
# if "xomoo2" in file:
# name = "xomoo2"
# else:
# name = "_".join(file.split('/')[-1].split('_')[:2])
# indeps, deps = collect_all_points(file, 27, 4)
# elif "MONRP" in file:
# name = "_".join(file.split('/')[-1].split('_')[:6])
# indeps, deps = collect_all_points(file, 50, 3)
name = file.split('/')[-1].split('_')[0]
indeps, deps = collect_all_points(file, None, 2)
keys = lessismore.keys()
if name == 'wc-3d-c4':
key = [k for k in keys if name in k]
key = [k for k in key if '+' not in k]
else:
key = [k for k in keys if name in k]
try:
assert (len(key) == 1), "Something is wrong"
except:
import pdb
pdb.set_trace()
key = key[0]
transformed_training_dep = get_cdom_values(deps, lessismore[key])
assert (len(deps) == len(transformed_training_dep)
), "Something is wrong"
from sklearn.tree import DecisionTreeRegressor, export_graphviz
model = DecisionTreeRegressor(min_samples_leaf=2)
model.fit(indeps, transformed_training_dep)
tree_name = "./tree/" + file.split('/')[-1][:-4] + '.dot'
export_graphviz(model, out_file=tree_name)
print tree_name
def parallelize(subdir, name, rep_no, decisions, objectives):
print subdir, name, rep_no
indeps, deps = collect_all_points(subdir, decisions, objectives)
keys = lessismore.keys()
key = [k for k in keys if name in k]
try:
assert (len(key) == 1), "Something is wrong"
except:
import pdb
pdb.set_trace()
key = key[0]
transformed_training_dep = get_cdom_values(deps, lessismore[key])
assert (len(deps) == len(transformed_training_dep)), "Something is wrong"
from sklearn.tree import DecisionTreeRegressor, export_graphviz
model = DecisionTreeRegressor(min_samples_leaf=2)
model.fit(indeps, transformed_training_dep)
tree_name = "./tree/" + name + "_" + str(rep_no) + '.dot'
export_graphviz(model, out_file=tree_name)
print tree_name
dep = []
content = open(file).readlines()
for c in content:
indep.append(map(int, map(float, c.strip().split(';'))))
temp = ",".join(
map(str, map(int, map(float,
c.strip().split(';')))))
try:
dep.append(objectives_dict[temp])
except:
import pdb
pdb.set_trace()
assert (len(indep) == len(dep)), "Something is wrong"
# appropriate key for lessismore
keys = lessismore.keys()
if name == 'wc-3d-c4': key = "./Data/wc-3d-c4.csv"
else:
key = [k for k in keys if name in k.lower()]
try:
assert (len(key) == 1), "Something is wrong"
except:
import pdb
pdb.set_trace()
key = key[0]
transformed_training_dep = get_cdom_values(dep, lessismore[key])
assert (
len(dep) == len(transformed_training_dep)), "Something is wrong"
from sklearn.tree import DecisionTreeRegressor, export_graphviz
model = DecisionTreeRegressor(min_samples_leaf=2)