python - 数据打印，但不写入数据框

Question

我正在尝试计算真阳性率等。的二进制混淆矩阵，并将结果输出到 csv 文件。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import csv
from sklearn.metrics import confusion_matrix



AllBinary = pd.read_csv('BinaryData.csv')


y_test = AllBinary['Binary_ac']
y_pred = AllBinary['Binary_pred']

cm = confusion_matrix(y_test, y_pred)

stats = pd.DataFrame()

TP = cm[0][0]
FP = cm[0][1]
FN = cm[1][0]
TN = cm[1][1]

stats['TruePositive'] = TP
stats['TrueNegative'] = TN
stats['FalsePositive'] = FP
stats['FalseNegative'] = FN

print(TP)
print(TN)
print(FP)
print(FN)

stats.to_csv('C:/out/' + 'BinaryStats' + '.csv', header = True)

打印结果显示，基本的混淆矩阵统计计算如下:

210483
153902
32845
10788

csv 输出创建标题，但结果为空白。我做错了什么？

更新:

print(stats)

Empty DataFrame
Columns: [TruePositive, TrueNegative, Falsepositive, FalseNegative]

Answer 1

这里的问题是您不能通过简单地将标量值分配给新列来附加到这样的 df:

In [55]:
stats = pd.DataFrame()
stats['TruePositive'] = 210483
stats

Out[55]:
Empty DataFrame
Columns: [TruePositive]
Index: []

您需要在构造函数中使用所需的值构建 df:

In [62]:
TP = 210483
FP = 153902
FN = 32845
TN = 10788
stats = pd.DataFrame({'TruePositive':[TP], 'TrueNegative':[TN], 'FalsePositive':[FP], 'FalseNegative':[FN]})
stats

Out[62]:
   FalseNegative  FalsePositive  TrueNegative  TruePositive
0          32845         153902         10788        210483

或添加一个虚拟行，然后您的代码将起作用:

In [71]:
stats = pd.DataFrame()
stats = stats.append(pd.Series('dummy'), ignore_index=True)
stats['TruePositive'] = TP
stats['TrueNegative'] = TN
stats['FalsePositive'] = FP
stats['FalseNegative'] = FN
stats

Out[71]:
       0  TruePositive  TrueNegative  FalsePositive  FalseNegative
0  dummy        210483         10788         153902          32845

然后您可以调用 drop 删除虚拟列:

In [72]:
stats.drop(0, axis=1)

Out[72]:
   TruePositive  TrueNegative  FalsePositive  FalseNegative
0        210483         10788         153902          32845

所以您尝试失败的原因是因为您的初始 df 是空的，您正在为一个新列分配一个标量值，标量值会将新列的所有行设置为此值。由于您的 df 没有行，因此失败，这就是为什么您的 df 是空的。

另一种方法是用单行创建 df(这里我放了 NaN):

In [77]:
stats = pd.DataFrame([np.NaN])
stats['TruePositive'] = TP
stats['TrueNegative'] = TN
stats['FalsePositive'] = FP
stats['FalseNegative'] = FN
stats.dropna(axis=1)

Out[77]:
   TruePositive  TrueNegative  FalsePositive  FalseNegative
0        210483         10788         153902          32845