我如何重新采样 "roc_curve" (fpr,tpr)？

Question

我正在寻找重新采样 "roc_curve" (sklearn) 输出。

当我在 Ipython 中绘制 fpr,tpr 时很好，但有时我想导出它（主要是为客户端），但很难理解，因为它不是线性的。

例如

fpr =[0,0.1,0.4,0.9,1]
tpr =[0,0.3,0.4,0.5,1]

我如何重新采样 fpr 以每 5% ex 线性化：

[0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.,0.85,0.9,0.95,1]

和tpr：

[0,0.15,0.3,0.3167,0.333,0.35,0.3667,0.383,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.75,1]

我该如何继续？

Answer 1

我想你要找的是分段常数插值

import numpy as np
from scipy.interpolate import spline

fpr =[0,0.1,0.4,0.9,1]
tpr =[0,0.3,0.4,0.5,1]

n = 20
x_interp = np.linspace(0,1,n+1)
y_interp = spline(fpr, tpr, x_interp, order=0)

x_interp 是 fpr 值

[ 0.    0.05  0.1   0.15  0.2   0.25  0.3   0.35  0.4   0.45  0.5   0.55
  0.6   0.65  0.7   0.75  0.8   0.85  0.9   0.95  1.  ]

y_interp是对应的tpr值

[ 0.   0.   0.3  0.3  0.3  0.3  0.3  0.3  0.4  0.4  0.4  0.4  0.4  0.4  0.4
  0.4  0.4  0.4  0.5  0.5  0.5]

我如何重新采样 "roc_curve" (fpr,tpr)？

How can i resample "roc_curve" (fpr,tpr )?

python

scikit-learn