在seaborn的两条曲线之间绘制最长的垂直线

Question

我目前有这样的情节（考虑 data 是我粘贴在最底部的数据框）：

import seaborn as sns

sns.relplot(
    data = data,
    x = "Threshold",
    y = "Value",
    kind = "line",
    hue="Metric"
).set(xlabel="Threshold")

产生：

现在，我想知道如何在此图中注释一条线，使其位于曲线之间，位于曲线之间距离最大的 x 轴值处。我还需要注释文本以显示距离值。

应该是这样的：

这是 pandas 数据框：

Threshold,Metric,Value
0.0,Recall,1.0
0.010101010101010102,Recall,0.9802536231884058
0.020202020202020204,Recall,0.9706521739130435
0.030303030303030304,Recall,0.9621376811594203
0.04040404040404041,Recall,0.9541666666666667
0.05050505050505051,Recall,0.9456521739130435
0.06060606060606061,Recall,0.9322463768115942
0.07070707070707072,Recall,0.9173913043478261
0.08080808080808081,Recall,0.908695652173913
0.09090909090909091,Recall,0.8976449275362319
0.10101010101010102,Recall,0.8813405797101449
0.11111111111111112,Recall,0.8644927536231884
0.12121212121212122,Recall,0.8498188405797101
0.13131313131313133,Recall,0.8358695652173913
0.14141414141414144,Recall,0.818659420289855
0.15151515151515152,Recall,0.7967391304347826
0.16161616161616163,Recall,0.7748188405797102
0.17171717171717174,Recall,0.7521739130434782
0.18181818181818182,Recall,0.7269927536231884
0.19191919191919193,Recall,0.6952898550724638
0.20202020202020204,Recall,0.6704710144927536
0.21212121212121213,Recall,0.648731884057971
0.22222222222222224,Recall,0.6097826086956522
0.23232323232323235,Recall,0.5847826086956521
0.24242424242424243,Recall,0.5521739130434783
0.25252525252525254,Recall,0.5023550724637681
0.26262626262626265,Recall,0.4766304347826087
0.27272727272727276,Recall,0.42047101449275365
0.2828282828282829,Recall,0.3958333333333333
0.29292929292929293,Recall,0.3539855072463768
0.30303030303030304,Recall,0.3327898550724638
0.31313131313131315,Recall,0.3036231884057971
0.32323232323232326,Recall,0.2798913043478261
0.33333333333333337,Recall,0.2371376811594203
0.3434343434343435,Recall,0.22119565217391304
0.3535353535353536,Recall,0.17300724637681159
0.36363636363636365,Recall,0.15996376811594204
0.37373737373737376,Recall,0.13568840579710145
0.38383838383838387,Recall,0.11938405797101449
0.393939393939394,Recall,0.10652173913043478
0.4040404040404041,Recall,0.09891304347826087
0.4141414141414142,Recall,0.08894927536231884
0.42424242424242425,Recall,0.07681159420289856
0.43434343434343436,Recall,0.06557971014492754
0.4444444444444445,Recall,0.05253623188405797
0.4545454545454546,Recall,0.04655797101449275
0.4646464646464647,Recall,0.024456521739130436
0.4747474747474748,Recall,0.019384057971014494
0.48484848484848486,Recall,0.009782608695652175
0.494949494949495,Recall,0.0034420289855072463
0.5050505050505051,Recall,0.002173913043478261
0.5151515151515152,Recall,0.0016304347826086956
0.5252525252525253,Recall,0.0007246376811594203
0.5353535353535354,Recall,0.00018115942028985507
0.5454545454545455,Recall,0.0
0.5555555555555556,Recall,0.0
0.5656565656565657,Recall,0.0
0.5757575757575758,Recall,0.0
0.5858585858585859,Recall,0.0
0.595959595959596,Recall,0.0
0.6060606060606061,Recall,0.0
0.6161616161616162,Recall,0.0
0.6262626262626263,Recall,0.0
0.6363636363636365,Recall,0.0
0.6464646464646465,Recall,0.0
0.6565656565656566,Recall,0.0
0.6666666666666667,Recall,0.0
0.6767676767676768,Recall,0.0
0.686868686868687,Recall,0.0
0.696969696969697,Recall,0.0
0.7070707070707072,Recall,0.0
0.7171717171717172,Recall,0.0
0.7272727272727273,Recall,0.0
0.7373737373737375,Recall,0.0
0.7474747474747475,Recall,0.0
0.7575757575757577,Recall,0.0
0.7676767676767677,Recall,0.0
0.7777777777777778,Recall,0.0
0.787878787878788,Recall,0.0
0.797979797979798,Recall,0.0
0.8080808080808082,Recall,0.0
0.8181818181818182,Recall,0.0
0.8282828282828284,Recall,0.0
0.8383838383838385,Recall,0.0
0.8484848484848485,Recall,0.0
0.8585858585858587,Recall,0.0
0.8686868686868687,Recall,0.0
0.8787878787878789,Recall,0.0
0.888888888888889,Recall,0.0
0.8989898989898991,Recall,0.0
0.9090909090909092,Recall,0.0
0.9191919191919192,Recall,0.0
0.9292929292929294,Recall,0.0
0.9393939393939394,Recall,0.0
0.9494949494949496,Recall,0.0
0.9595959595959597,Recall,0.0
0.9696969696969697,Recall,0.0
0.9797979797979799,Recall,0.0
0.98989898989899,Recall,0.0
1.0,Recall,0.0
0.0,Fall-out,1.0
0.010101010101010102,Fall-out,0.6990465720990212
0.020202020202020204,Fall-out,0.58461408367334
0.030303030303030304,Fall-out,0.516647992727734
0.04040404040404041,Fall-out,0.4643680104855929
0.05050505050505051,Fall-out,0.4172674037587468
0.06060606060606061,Fall-out,0.3796376551170116
0.07070707070707072,Fall-out,0.3507811343889394
0.08080808080808081,Fall-out,0.33186055852694335
0.09090909090909091,Fall-out,0.3152231359533222
0.10101010101010102,Fall-out,0.29964272879098575
0.11111111111111112,Fall-out,0.2855844238208993
0.12121212121212122,Fall-out,0.27161068008371564
0.13131313131313133,Fall-out,0.25719298987379235
0.14141414141414144,Fall-out,0.24338836860241422
0.15151515151515152,Fall-out,0.2312538316808659
0.16161616161616163,Fall-out,0.22026087140350506
0.17171717171717174,Fall-out,0.2083377375642137
0.18181818181818182,Fall-out,0.19694311143056467
0.19191919191919193,Fall-out,0.18402638310466565
0.20202020202020204,Fall-out,0.17440754286197493
0.21212121212121213,Fall-out,0.16548633279073208
0.22222222222222224,Fall-out,0.15278100754709004
0.23232323232323235,Fall-out,0.14292962391391667
0.24242424242424243,Fall-out,0.1317252605542989
0.25252525252525254,Fall-out,0.11555292476164303
0.26262626262626265,Fall-out,0.10612434729298353
0.27272727272727276,Fall-out,0.08902183793839714
0.2828282828282829,Fall-out,0.08331395471745978
0.29292929292929293,Fall-out,0.07232099444009894
0.30303030303030304,Fall-out,0.06735302200706086
0.31313131313131315,Fall-out,0.061454876012092256
0.32323232323232326,Fall-out,0.05665602604485973
0.33333333333333337,Fall-out,0.048982094158932836
0.3434343434343435,Fall-out,0.045641925459273196
0.3535353535353536,Fall-out,0.03748176648415534
0.36363636363636365,Fall-out,0.0341415977844957
0.37373737373737376,Fall-out,0.029321607509037482
0.38383838383838387,Fall-out,0.026996173604211148
0.393939393939394,Fall-out,0.024353635075999407
0.4040404040404041,Fall-out,0.022514428260364035
0.4141414141414142,Fall-out,0.01940680295118703
0.42424242424242425,Fall-out,0.017165930279263473
0.43434343434343436,Fall-out,0.014459970826374648
0.4444444444444445,Fall-out,0.011035240893812233
0.4545454545454546,Fall-out,0.009386296852208105
0.4646464646464647,Fall-out,0.004756569350781135
0.4747474747474748,Fall-out,0.003868676405301989
0.48484848484848486,Fall-out,0.002135171130795087
0.494949494949495,Fall-out,0.0008033317125763693
0.5050505050505051,Fall-out,0.0004228061645138786
0.5151515151515152,Fall-out,0.00031710462338540896
0.5252525252525253,Fall-out,4.228061645138786e-05
0.5353535353535354,Fall-out,0.0
0.5454545454545455,Fall-out,0.0
0.5555555555555556,Fall-out,0.0
0.5656565656565657,Fall-out,0.0
0.5757575757575758,Fall-out,0.0
0.5858585858585859,Fall-out,0.0
0.595959595959596,Fall-out,0.0
0.6060606060606061,Fall-out,0.0
0.6161616161616162,Fall-out,0.0
0.6262626262626263,Fall-out,0.0
0.6363636363636365,Fall-out,0.0
0.6464646464646465,Fall-out,0.0
0.6565656565656566,Fall-out,0.0
0.6666666666666667,Fall-out,0.0
0.6767676767676768,Fall-out,0.0
0.686868686868687,Fall-out,0.0
0.696969696969697,Fall-out,0.0
0.7070707070707072,Fall-out,0.0
0.7171717171717172,Fall-out,0.0
0.7272727272727273,Fall-out,0.0
0.7373737373737375,Fall-out,0.0
0.7474747474747475,Fall-out,0.0
0.7575757575757577,Fall-out,0.0
0.7676767676767677,Fall-out,0.0
0.7777777777777778,Fall-out,0.0
0.787878787878788,Fall-out,0.0
0.797979797979798,Fall-out,0.0
0.8080808080808082,Fall-out,0.0
0.8181818181818182,Fall-out,0.0
0.8282828282828284,Fall-out,0.0
0.8383838383838385,Fall-out,0.0
0.8484848484848485,Fall-out,0.0
0.8585858585858587,Fall-out,0.0
0.8686868686868687,Fall-out,0.0
0.8787878787878789,Fall-out,0.0
0.888888888888889,Fall-out,0.0
0.8989898989898991,Fall-out,0.0
0.9090909090909092,Fall-out,0.0
0.9191919191919192,Fall-out,0.0
0.9292929292929294,Fall-out,0.0
0.9393939393939394,Fall-out,0.0
0.9494949494949496,Fall-out,0.0
0.9595959595959597,Fall-out,0.0
0.9696969696969697,Fall-out,0.0
0.9797979797979799,Fall-out,0.0
0.98989898989899,Fall-out,0.0
1.0,Fall-out,0.0

Answer 1

我能想到的最简单的方法是创建两个单独的列表，其中包含指标为召回率的所有值，以及另一个包含指标为 Fall-out 的所有值的列表。这可以使用 pandas 操作轻松完成，如下所示（假设数据框的名称为 df）-

import math
import matplotlib.pyplot as plt
ls_metric = df['Metric'].to_list()
ls_value = df['Value'].to_list()
ls_threshold = df['Threshold'].to_list()
ls_value_recall = []
ls_value_fallout = []
ls_threshold_recall = []
ls_threshold_fallout = []
for i, j, k in zip(ls_metric, ls_value, ls_threshold):
    if (i == 'Recall'):
        ls_value_recall.append(j)
        ls_threshold_recall.append(k)
    elif(i == 'Fall-out'):
        ls_value_fallout.append(j)
        ls_threshold_recall.append(k)
ls_dist = []
for i, j in zip(ls_value_recall, ls_value_fallout):
    ls_dist.append(math.abs(i-j))
max_diff = max(ls_dist)
location_of_max_diff = ls_dist.index(max_diff)
value_of_threshold_at_max_diff = ls_threshold_recall[location_of_max_diff]
value_of_recall_at_max_diff = ls_value_recall[location_of_max_diff]
value_of_fallout_at_max_diff = ls_value_fallout[location_of_max_diff]
x_values = [value_of_threshold_at_max_diff, value_of_threshold_at_max_diff]
y_values = [value_of_recall_at_max_diff, value_of_fallout_at_max_diff]
plt.plot(x_values, y_values)

某些假设 - 阈值相同并且两个指标的读数数量相同，我认为这是正确的，简要浏览了数据但如果不是，我相信修改代码仍然很容易

您可以将此图添加到您自己的图形中，其语法很容易获得，现在就线条的标签而言，一种方法是使用 matplotlib.pyplot.text 添加文本框但是这样你就需要调整位置以获得所需的位置另一种方法是仅将其添加为图例

Answer 2

使用pivot将数据从长转换为宽
使用idxmax找到y1和y2（Fall-out和Recall）之间最大差异的x（阈值）
使用vlines在x处绘制从y1到y2
使用annotate在y1和y2

g = sns.relplot(data=data, x='Threshold', y='Value', hue='Metric', kind='line')

# pivot to wide form
p = data.pivot(index='Threshold', columns='Metric', values='Value')

# find x, y1, and y2 corresponding to max difference
diff = p['Fall-out'].sub(p['Recall']).abs()
x = diff.idxmax()
y1, y2 = p.loc[x]

# plot line and label
ax = g.axes.flat[0]
ax.vlines(x, y1, y2, ls='--')
ax.annotate(f'Dist = {diff.loc[x]:.2f}', ha='left', va='center',
            xy=(x, 0.5*(y1+y2)), xycoords='data',
            xytext=(5, 0), textcoords='offset pixels')

在seaborn的两条曲线之间绘制最长的垂直线

Draw longest possible vertical line between two curves in seaborn

python

matplotlib

dataframe

pandas

seaborn