-
Notifications
You must be signed in to change notification settings - Fork 1
Expand file tree
/
Copy pathJDataScore.py
More file actions
57 lines (54 loc) · 1.89 KB
/
Copy pathJDataScore.py
File metadata and controls
57 lines (54 loc) · 1.89 KB
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
# 读取的csv文件都不包含表头，格式为:user_id,sku_id
import pandas as pd
# file1为预测数据文件，file2为真实数据文件,F11只关注是否下单
def f1_score(file1,file2):
data_hat = pd.read_csv(file1, header=None)
data_real = pd.read_csv(file2,header=None)
count = 0.0
preSum = float(len(data_hat.index))
realSum = float(len(data_real.index))
for i,d_hat in enumerate(data_hat.values):
user_id = d_hat[0]
sku_id = d_hat[1]
for j,d_real in enumerate(data_real.values):
user_id2 = d_real[0] # 具体索引，视情况而定
sku_id2 = d_real[1]
if(user_id==user_id2):
count = count+1
break
Precise = count/preSum
Recall = count/realSum
F11 = 6*Recall*Precise/(5*Recall+Precise)
return F11
# F2关注的是user_id和sku_id同时正确
def f2_score(file1,file2):
data_hat = pd.read_csv(file1, header=None)
data_real = pd.read_csv(file2,header=None)
count = 0.0
preSum = float(len(data_hat.index))
realSum = float(len(data_real.index))
for i,d_hat in enumerate(data_hat.values):
user_id = d_hat[0]
sku_id = d_hat[1]
for j,d_real in enumerate(data_real.values):
user_id2 = d_real[0] # 具体索引，视情况而定
sku_id2 = d_real[1]
if(user_id==user_id2 and sku_id==sku_id2):
count = count+1
break
Precise = count/preSum
Recall = count/realSum
F12 = 5*Recall*Precise/(2*Recall+3*Precise)
return F12
# 调用这个函数就好，总得分
def F(file1,file2):
F11 = f1_score(file1,file2)
F12 = f2_score(file1,file2)
Score=0.4*F11 + 0.6*F12
return Score
# 亲测可用~
if __name__ == "__main__":
file1 = "I:/JData/pre.csv"
file2 = "I:/JData/real.csv"
score = F(file1,file2)
print score