roc_curve函数如何在后台计算FPR，TPR值就我而言，我从400，维输入数据中获得了53，

分类：编程问答

如何解决roc_curve函数如何在后台计算FPR，TPR值就我而言，我从400，维输入数据中获得了53，

我正在绘制大鹏曲线。我有一个带有一个基于隐藏层的分类器的神经网络。所以我的输出是我称为A2的最后一层激活功能的结果；这将是roc_curve()中输入的概率。我的A2和预测具有以下形状和数据：

print(A2.ravel().shape)
print(predictions.ravel().shape)
print(A2,predictions)

输出： 

(400,)
(400,)
[[3.22246780e-04 7.64373268e-01 7.64385217e-01 7.64372464e-01
  1.63920340e-01 7.64372463e-01 2.75254103e-04 7.65185909e-01
  2.06186064e-01 2.12094433e-01 2.75251983e-04 7.64372463e-01
  2.11985152e-01 2.10202927e-01 2.75252955e-04 9.44088883e-02
  2.02522498e-01 2.07370306e-01 2.50282683e-03 2.75260253e-04
  2.11928461e-01 2.75251291e-04 2.75251291e-04 2.75251498e-04
  2.75251306e-04 1.35809613e-01 2.75464969e-04 1.74181943e-01
  2.75435676e-04 2.75251294e-04 2.96236579e-04 2.75268578e-04
  2.76053487e-04 2.78105904e-04 2.75293008e-04 2.75251307e-04
  2.87538148e-04 2.75270689e-04 2.39320951e-06 4.45134656e-02
  2.75251367e-04 2.75251506e-04 2.75251303e-04 2.31132556e-06
  3.69449012e-04 2.75251293e-04 5.59346558e-02 2.31132310e-06
  1.82980485e-01 6.20515482e-06 2.32293394e-02 1.58108674e-03
  2.75252597e-04 1.19360888e-02 2.27051743e-01 2.31161383e-06
  2.31132421e-06 2.31132310e-06 2.31573234e-06 2.31132310e-06
  5.15530179e-01 2.31132310e-06 2.31132311e-06 2.46803695e-06
  2.31132310e-06 2.31141693e-06 2.31132314e-06 2.31181353e-06
  1.08428788e-03 3.91750347e-01 2.15413251e-01 2.31136922e-06
  2.31132310e-06 2.31135038e-06 2.31132310e-06 4.18257225e-02
  2.31132310e-06 2.31692274e-06 2.31132315e-06 2.34152146e-06
  2.31132310e-06 2.31132310e-06 2.31134156e-06 2.32276423e-06
  2.31184444e-06 2.31189807e-06 2.31132310e-06 3.03902587e-06
  2.33123340e-06 6.74029292e-03 1.37374673e-04 7.11777353e-06
  2.31332212e-06 2.31134309e-06 2.85446765e-01 8.45686446e-04
  2.95393201e-06 6.30729453e-02 2.35681287e-06 1.67406531e-05
  1.39482094e-04 1.47208937e-05 2.64716376e-05 1.48764918e-05
  2.37288319e-06 1.76484186e-05 1.47209077e-05 4.24952409e-05
  2.47222738e-04 1.53198138e-05 5.10281474e-06 1.47209298e-05
  1.47208667e-05 2.64277585e-01 1.47208667e-05 1.55307243e-01
  1.47208865e-05 2.91081049e-03 1.47208667e-05 1.47208667e-05
  1.47208667e-05 1.47903704e-05 1.47238820e-05 3.11567098e-02
  4.14289114e-01 1.50836911e-05 2.78303520e-02 1.47208667e-05
  1.47251817e-05 1.47947695e-05 1.47208667e-05 1.47208667e-05
  1.47208940e-05 1.48783712e-05 2.05607558e-04 1.47208667e-05
  4.83812804e-05 1.47208667e-05 2.09377734e-01 1.49642652e-05
  1.47221481e-05 1.47568362e-05 2.77831915e-01 4.82959556e-01
  4.50969045e-01 3.82364226e-02 4.11377002e-02 2.16308926e-01
  8.88141165e-02 2.12679453e-01 2.24050631e-02 1.47208667e-05
  2.12677744e-01 2.12677744e-01 2.12677760e-01 2.33568941e-01
  2.28926909e-01 2.13773365e-01 2.12678951e-01 1.35565877e-03
  2.47656669e-01 1.08727082e-01 2.12677744e-01 2.12678014e-01
  2.12677744e-01 2.12677744e-01 2.12677744e-01 2.12677744e-01
  2.11844159e-01 1.51525672e-03 2.12677744e-01 2.12677744e-01
  7.65697761e-01 2.12677744e-01 2.12677744e-01 2.12677744e-01
  2.12668331e-01 2.12677744e-01 2.12677744e-01 2.12677744e-01
  2.12677744e-01 2.12677744e-01 2.12677744e-01 6.01467058e-02
  2.12677744e-01 2.12677744e-01 2.12677495e-01 2.12677744e-01
  2.12677743e-01 2.12677744e-01 7.72857608e-01 2.09249431e-01
  7.86146268e-01 7.64683696e-01 8.39288704e-01 2.12677744e-01
  8.05987357e-01 7.73524718e-01 7.64722596e-01 7.64646794e-01
  2.12677744e-01 8.54868081e-01 7.66923142e-01 8.54244158e-01
  2.11261708e-01 7.66992993e-01 2.12677744e-01 2.12598362e-01
  7.66165847e-01 9.99643109e-01 7.65268010e-01 9.99685903e-01
  9.99685903e-01 7.65043689e-01 2.12677744e-01 2.12677744e-01
  7.64840536e-01 9.99685901e-01 9.99332786e-01 2.12677743e-01
  7.79121852e-01 9.99685785e-01 7.79074180e-01 7.65194741e-01
  8.98667738e-01 9.99684795e-01 9.58419683e-01 9.99685902e-01
  9.99685882e-01 9.99639779e-01 9.99639274e-01 9.99677983e-01
  9.99685736e-01 9.99685902e-01 9.92940564e-01 9.99685903e-01
  9.99685839e-01 8.30995491e-01 9.90611316e-01 9.99997341e-01
  9.99670704e-01 9.23825584e-01 9.99685666e-01 9.99996824e-01
  9.99685902e-01 9.40290068e-01 9.99685903e-01 9.99996965e-01
  9.99685364e-01 9.99997362e-01 9.99685801e-01 9.99997362e-01
  9.99996900e-01 9.99685513e-01 9.99997362e-01 9.99684995e-01
  9.99676405e-01 9.99997362e-01 6.89410113e-01 5.28997119e-01
  9.93019339e-01 6.62017810e-01 9.99997362e-01 9.99997362e-01
  9.99997358e-01 9.99997362e-01 9.99997362e-01 9.99997346e-01
  9.99997362e-01 9.99997352e-01 9.99997362e-01 9.99997362e-01
  9.99997362e-01 9.99071790e-01 9.99997362e-01 9.99997362e-01
  3.46195433e-01 9.99995537e-01 9.99997362e-01 9.99997362e-01
  9.99997362e-01 9.99997362e-01 9.99997362e-01 9.99996894e-01
  7.67197871e-01 9.99997179e-01 1.65047845e-01 9.99978488e-01
  2.93981729e-01 9.99997362e-01 9.99997361e-01 9.29067186e-01
  9.99997362e-01 9.48399940e-01 9.99997362e-01 9.99997362e-01
  6.78299886e-01 9.99997362e-01 9.99997362e-01 9.63677152e-01
  9.99997362e-01 3.67733752e-01 9.99997222e-01 7.74993071e-01
  6.37972260e-01 9.99943783e-01 9.77268446e-01 9.99976242e-01
  7.00255679e-01 9.99983200e-01 9.99983201e-01 9.99983138e-01
  9.99983197e-01 9.86360906e-01 9.99983201e-01 9.99389801e-01
  9.98380059e-01 9.99983201e-01 9.99983201e-01 9.99983201e-01
  9.99983199e-01 9.99983199e-01 9.99983201e-01 9.99391768e-01
  9.99983201e-01 9.99983201e-01 9.99981131e-01 9.99983201e-01
  9.99983201e-01 9.76520592e-01 8.44076103e-01 9.99983201e-01
  9.99983201e-01 9.99983201e-01 9.99983201e-01 9.99983201e-01
  9.99899640e-01 9.99983201e-01 9.99983193e-01 9.99964112e-01
  9.99983201e-01 9.99983201e-01 9.99983201e-01 7.58322592e-01
  9.99983201e-01 9.99983201e-01 9.99981971e-01 7.64372463e-01
  7.64372463e-01 9.99983201e-01 9.06823611e-01 9.99983201e-01
  7.64372463e-01 9.99983201e-01 2.01516877e-01 7.64372463e-01
  3.98768426e-01 7.64372463e-01 9.81611504e-01 7.64372463e-01
  7.64372463e-01 7.64370725e-01 7.64372463e-01 7.64372463e-01
  9.99979567e-01 1.90105310e-01 7.64372463e-01 7.64372463e-01
  4.09226724e-01 7.64372463e-01 7.64372463e-01 7.64372463e-01
  7.64372463e-01 7.64387743e-01 7.64372463e-01 7.64372463e-01
  7.64372463e-01 7.64372463e-01 7.64372463e-01 7.64372463e-01
  7.64372463e-01 7.64372463e-01 7.64372463e-01 7.64372463e-01
  7.76876797e-01 7.64372463e-01 2.07693046e-01 7.64372463e-01
  7.64372463e-01 7.59770748e-01 7.64372463e-01 7.64372463e-01
  7.66343703e-01 2.05588421e-01 7.64372828e-01 2.06636497e-01
  1.97645490e-01 2.09816835e-01 7.64372464e-01 1.77842165e-01]] [[0 1 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
  0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 0 0 1 1 1 1 1 1 0 0
  1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
  1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1
  0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
  1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1
  1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 0
  0 0 1 0]]

现在，当我在roc_curve()中输入这些值时，会得到fpr，tpr，以下形状和尺寸的阈值：

fpr,tpr,threshold = roc_curve(Y.ravel(),A2.ravel())
print(fpr.shape,tpr.shape,threshold.shape)
print(fpr,threshold)

输出： 

(53,) (53,)
[0.    0.    0.    0.    0.    0.    0.    0.    0.005 0.005 0.015 0.015
 0.025 0.025 0.03  0.03  0.035 0.035 0.05  0.05  0.06  0.06  0.065 0.065
 0.075 0.075 0.095 0.095 0.1   0.1   0.19  0.27  0.285 0.285 0.3   0.3
 0.32  0.32  0.325 0.325 0.335 0.335 0.34  0.34  0.345 0.345 0.35  0.35
 0.355 0.355 0.36  0.36  1.   ] [0.    0.015 0.045 0.19  0.23  0.235 0.245 0.615 0.615 0.62  0.62  0.64
 0.64  0.665 0.665 0.675 0.675 0.685 0.685 0.69  0.69  0.7   0.7   0.715
 0.825 0.895 0.895 0.905 0.905 0.92  0.92  0.935 0.935 0.945 0.945 0.95
 0.95  0.955 0.955 0.96  0.96  0.965 0.965 0.97  0.97  0.975 0.975 0.99
 0.99  0.995 0.995 1.    1.   ] [1.99999736e+00 9.99997362e-01 9.99997362e-01 9.99995537e-01
 9.99983201e-01 9.99983201e-01 9.99983201e-01 8.44076103e-01
 8.39288704e-01 8.30995491e-01 7.86146268e-01 7.74993071e-01
 7.72857608e-01 7.66165847e-01 7.65697761e-01 7.65194741e-01
 7.65185909e-01 7.64840536e-01 7.64646794e-01 7.64387743e-01
 7.64373268e-01 7.64372464e-01 7.64372464e-01 7.64372463e-01
 7.64372463e-01 5.28997119e-01 4.14289114e-01 3.98768426e-01
 3.91750347e-01 2.93981729e-01 2.12677744e-01 2.12677744e-01
 2.12677744e-01 2.12677743e-01 2.12668331e-01 2.12598362e-01
 2.11844159e-01 2.11261708e-01 2.10202927e-01 2.09816835e-01
 2.09249431e-01 2.07693046e-01 2.07370306e-01 2.06636497e-01
 2.06186064e-01 2.05588421e-01 2.02522498e-01 1.90105310e-01
 1.82980485e-01 1.77842165e-01 1.74181943e-01 1.65047845e-01
 2.31132310e-06]

因此，我的roc曲线如下所示：

plt.figure()
plt.plot(fpr,tpr)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic(ROC Curve)')

输出：

enter image description here

为什么我得到(53,)形状的FPR，TPR，阈值？我的情况只是简单的两类分类。感谢您的帮助。

解决方法

阈值的数量计算如下：

  • 第 1 步：保留唯一的分数值，加 1。

Source：

# y_score typically has many tied values. Here we extract
# the indices associated with the distinct values. We also
# concatenate a value for the end of the curve.
distinct_value_indices = np.where(np.diff(y_score))[0]
threshold_idxs = np.r_[distinct_value_indices,y_true.size - 1]
  • 第 2 步（如果 if drop_intermediate and len(fps) > 2）：丢弃对应于中间点并与其他点共线的阈值。

Source：

# Attempt to drop thresholds corresponding to points in between and
# collinear with other points. These are always suboptimal and do not
# appear on a plotted ROC curve (and thus do not affect the AUC).
# Here np.diff(_,2) is used as a "second derivative" to tell if there
# is a corner at the point. Both fps and tps must be tested to handle
# thresholds with multiple data points (which are combined in
# _binary_clf_curve). This keeps all cases where the point should be kept,# but does not drop more complicated cases like fps = [1,3,7],# tps = [1,2,4]; there is no harm in keeping too many thresholds.
if drop_intermediate and len(fps) > 2:
    optimal_idxs = np.where(np.r_[True,np.logical_or(np.diff(fps,2),np.diff(tps,2)),True])[0]
    fps = fps[optimal_idxs]
    tps = tps[optimal_idxs]
    thresholds = thresholds[optimal_idxs]

然后计算每个阈值的 FPR 和 TPR。

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