/
budget_evaluation.py
90 lines (75 loc) · 3.02 KB
/
budget_evaluation.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
import pandas as pd
import numpy as np
import cPickle as pkl
from glob import glob
from synthetic_evaluation import group_paths
from check_k_best_trees import k_best_trees
from experiment_util import parse_result_path
def eval_setcover_obj_func(cand_trees, k):
trees = k_best_trees(cand_trees, k)
return len(set(n for t in trees for n in t.nodes_iter()))
def eval_tree_size_obj_mean(cand_trees):
sizes = [t.number_of_nodes() for t in cand_trees]
return np.mean(sizes)
def eval_tree_size_obj_median(cand_trees):
sizes = [t.number_of_nodes() for t in cand_trees]
return np.median(sizes)
def eval_calculation_time(cand_trees):
return sum(t.graph['calculation_time'] for t in cand_trees)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-e', '--experiment_paths_regexp')
parser.add_argument('-k', type=int)
parser.add_argument('-o', '--output_path', required=True)
args = parser.parse_args()
paths = glob(args.experiment_paths_regexp)
# print(args.experiment_paths_regexp)
# print(paths)
groups_by_method = group_paths(paths,
keyfunc=lambda p: p['args'][0],
sort_keyfunc=lambda k: float(k['U']))
# print(len(groups_by_method))
setcover_obj_mat = []
calculation_time_mat = []
tree_size_obj_mean_mat = []
tree_size_obj_median_mat = []
index = []
for method, paths in groups_by_method:
index.append(method)
results = [pkl.load(open(p)) for p in paths]
setcover_obj_mat.append([eval_setcover_obj_func(ctrees, args.k)
for ctrees in results])
calculation_time_mat.append([eval_calculation_time(ctrees)
for ctrees in results])
tree_size_obj_mean_mat.append([eval_tree_size_obj_mean(ctrees)
for ctrees in results])
tree_size_obj_median_mat.append([eval_tree_size_obj_median(ctrees)
for ctrees in results])
print('index:', index)
columns = [float(parse_result_path(p)['U'])
for p in paths]
print(columns)
print(np.asarray(setcover_obj_mat).shape)
print(np.asarray(tree_size_obj_mean_mat).shape)
df1 = pd.DataFrame(setcover_obj_mat,
index=index,
columns=columns)
df2 = pd.DataFrame(calculation_time_mat,
index=index,
columns=columns)
df3 = pd.DataFrame(tree_size_obj_mean_mat,
index=index,
columns=columns)
df4 = pd.DataFrame(tree_size_obj_median_mat,
index=index,
columns=columns)
ret = {
'setcover-objective': df1,
'calculation-time': df2,
'treesize-objective-mean': df3,
'treesize-objective-median': df4
}
pkl.dump(ret, open(args.output_path, 'w'))
if __name__ == "__main__":
main()