-
Notifications
You must be signed in to change notification settings - Fork 12
/
Copy pathoptimization_stats.py
64 lines (59 loc) · 1.27 KB
/
optimization_stats.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
from niaaml.utilities import OptimizationStats
import numpy as np
"""
This example presents how the OptimizationStats class can be used. Normally, it is used in the background when the Pipeline's optimize method is called.
"""
# dummy array with expected results of the classification process
y = np.array(
[
"Class 1",
"Class 1",
"Class 1",
"Class 2",
"Class 1",
"Class 2",
"Class 2",
"Class 2",
"Class 2",
"Class 1",
"Class 1",
"Class 2",
"Class 1",
"Class 2",
"Class 1",
"Class 1",
"Class 1",
"Class 1",
"Class 2",
"Class 1",
]
)
# dummy array with predicted classes
predicted = np.array(
[
"Class 1",
"Class 1",
"Class 1",
"Class 2",
"Class 2",
"Class 2",
"Class 1",
"Class 1",
"Class 1",
"Class 2",
"Class 1",
"Class 1",
"Class 2",
"Class 2",
"Class 1",
"Class 2",
"Class 1",
"Class 2",
"Class 2",
"Class 2",
]
)
# instantiate OptimizationStats
stats = OptimizationStats(predicted, y)
# print user-friendly text representation
print(stats.to_string())