-
Notifications
You must be signed in to change notification settings - Fork 12
/
Copy pathutils.py
184 lines (158 loc) · 6.24 KB
/
utils.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
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
import os
import numpy as np
import random
from sklearn import metrics
from sklearn.metrics import precision_recall_curve, auc
def print_file(str_, save_file_path=None):
print(str_)
if save_file_path != None:
f = open(save_file_path, 'a')
print(str_, file=f)
class Metrictor_PPI:
def __init__(self, pre_y, truth_y, true_prob, is_binary=False):
self.TP = 0
self.FP = 0
self.TN = 0
self.FN = 0
self.pre = np.array(pre_y).squeeze()
self.tru = np.array(truth_y).squeeze()
self.true_prob = np.array(true_prob).squeeze()
if is_binary:
length = pre_y.shape[0]
for i in range(length):
if pre_y[i] == truth_y[i]:
if truth_y[i] == 1:
self.TP += 1
else:
self.TN += 1
elif truth_y[i] == 1:
self.FN += 1
elif pre_y[i] == 1:
self.FP += 1
self.num = length
else:
N, C = pre_y.shape
for i in range(N):
for j in range(C):
if pre_y[i][j] == truth_y[i][j]:
if truth_y[i][j] == 1:
self.TP += 1
else:
self.TN += 1
elif truth_y[i][j] == 1:
self.FN += 1
elif truth_y[i][j] == 0:
self.FP += 1
self.num = N * C
def show_result(self, is_print=False, file=None):
self.Accuracy = (self.TP + self.TN) / (self.num + 1e-10)
self.Precision = self.TP / (self.TP + self.FP + 1e-10)
self.Recall = self.TP / (self.TP + self.FN + 1e-10)
self.F1 = 2 * self.Precision * self.Recall / (self.Precision + self.Recall + 1e-10)
# fpr, tpr, thresholds = metrics.roc_curve(self.tru, self.pre, pos_label=1)
# self.Auc = metrics.auc(fpr, tpr)
aupr_entry_1 = self.tru
aupr_entry_2 = self.true_prob
aupr = np.zeros(7)
for i in range(7):
precision, recall, _ = precision_recall_curve(aupr_entry_1[:,i], aupr_entry_2[:,i])
aupr[i] = auc(recall,precision)
self.Aupr = aupr
if is_print:
print_file("Accuracy: {}".format(self.Accuracy), file)
print_file("Precision: {}".format(self.Precision), file)
print_file("Recall: {}".format(self.Recall), file)
print_file("F1-Score: {}".format(self.F1), file)
# print_file("AUC: {}".format(self.Auc), file)
# print_file("Aupr: {}".format(self.Aupr), file)
class UnionFindSet(object):
def __init__(self, m):
# m, n = len(grid), len(grid[0])
self.roots = [i for i in range(m)]
self.rank = [0 for i in range(m)]
self.count = m
for i in range(m):
self.roots[i] = i
def find(self, member):
tmp = []
while member != self.roots[member]:
tmp.append(member)
member = self.roots[member]
for root in tmp:
self.roots[root] = member
return member
def union(self, p, q):
parentP = self.find(p)
parentQ = self.find(q)
if parentP != parentQ:
if self.rank[parentP] > self.rank[parentQ]:
self.roots[parentQ] = parentP
elif self.rank[parentP] < self.rank[parentQ]:
self.roots[parentP] = parentQ
else:
self.roots[parentQ] = parentP
self.rank[parentP] -= 1
self.count -= 1
def get_bfs_sub_graph(ppi_list, node_num, node_to_edge_index, sub_graph_size):
candiate_node = []
selected_edge_index = []
selected_node = []
random_node = random.randint(0, node_num - 1)
while len(node_to_edge_index[random_node]) > 5:
random_node = random.randint(0, node_num - 1)
candiate_node.append(random_node)
while len(selected_edge_index) < sub_graph_size:
cur_node = candiate_node.pop(0)
selected_node.append(cur_node)
for edge_index in node_to_edge_index[cur_node]:
if edge_index not in selected_edge_index:
selected_edge_index.append(edge_index)
end_node = -1
if ppi_list[edge_index][0] == cur_node:
end_node = ppi_list[edge_index][1]
else:
end_node = ppi_list[edge_index][0]
if end_node not in selected_node and end_node not in candiate_node:
candiate_node.append(end_node)
else:
continue
# print(len(selected_edge_index), len(candiate_node))
node_list = candiate_node + selected_node
# print(len(node_list), len(selected_edge_index))
return selected_edge_index
def get_dfs_sub_graph(ppi_list, node_num, node_to_edge_index, sub_graph_size):
stack = []
selected_edge_index = []
selected_node = []
random_node = random.randint(0, node_num - 1)
while len(node_to_edge_index[random_node]) > 5:
random_node = random.randint(0, node_num - 1)
stack.append(random_node)
while len(selected_edge_index) < sub_graph_size:
# print(len(selected_edge_index), len(stack), len(selected_node))
cur_node = stack[-1]
if cur_node in selected_node:
flag = True
for edge_index in node_to_edge_index[cur_node]:
if flag:
end_node = -1
if ppi_list[edge_index][0] == cur_node:
end_node = ppi_list[edge_index][1]
else:
end_node = ppi_list[edge_index][0]
if end_node in selected_node:
continue
else:
stack.append(end_node)
flag = False
else:
break
if flag:
stack.pop()
continue
else:
selected_node.append(cur_node)
for edge_index in node_to_edge_index[cur_node]:
if edge_index not in selected_edge_index:
selected_edge_index.append(edge_index)
return selected_edge_index