在 pandas 框架的不同列中计算混淆矩阵? [英] Calculate the Confusion Matrix in different columns in panda frame?

问题描述

我有一个包含3000行和3列的数据框，如下所示:

I have a dataframe with 3000 rows and 3 columns as follows:

   0    col1  col2  col3  
  ID1     1     0     1     
  Id2     1     1     0     
  Id3     0     1     1     
  Id4     2     1     0           
  Id5     2     2     3     
   …        ..   ..    ..
  Id3000  3     1     0

在此数据帧中，每一列和每一行的值表示预测问题的结果，如下所示:每列中0表示TP，1表示FP，2表示TN，3指向FN.所以我想计算每列的准确性.像这样的东西:

In this data frame, the value of each column and row refers to a result of a prediction problem as follows: 0 means TP, 1 means FP, 2 refers to TN and 3 points to FN in each column. So I want to calculate the accuracy of each column. something like this:

 Accuracy result:
                 col1  col2  col3
                  0.67  0.68 0.79

任何想法都可以以非常有效的方式计算出重要的指标，例如准确性或f-measure.

Any idea that I can calculate the important metrics, like accuracy or f-measure in a very efficient way.

推荐答案

这是一种方法:

  data = """
 id    col1  col2  col3  
ID1     1     0     1     
Id2     1     1     0     
Id3     0     1     1     
Id4     2     1     0           
Id5     2     2     3 
  """

#coding to create a sample DataFrame for testing
df = pd.read_csv(pd.compat.StringIO(data), sep='\s+')
print(df)
#end of creation

accuracy ={}  #dict for result final
# i select all columns with name begins by 'col' and create a list
select_cols = [col for col in df.columns if col.startswith('col')]

for col in select_cols:
    df1 = df.groupby(col).size()
    t = [0,0,0,0]      #[TP, FP, TN, FN] 0 = TP, 1 = FP, 2 = TN and 3 = FN
    for v in df1.index:
        t[v] = df1[v]
    accuracy[col] = (t[0] + t[2])/(sum(t)) #Accuracy = (TP + TN)/(TP +TN + FP + FN

df_acc = pd.DataFrame.from_dict(accuracy, orient='index').T

print('Accuracy:');print(df_acc)

输出:

Accuracy:
   col1  col2  col3
0   0.6   0.4   0.4

或者另一个解决方案(我认为更好):您替换了2个循环for

Or another solution (better i think): you replace the 2 loops for

for col in select_cols:
    accuracy[col] = (df[df[col]==0].count()[0] + df[df[col]==2].count()[0]) / df[col].count()

df_acc = pd.DataFrame.from_dict(accuracy, orient='index' ).T.reset_index(drop=True)

print('Accuracy');print(df_acc)

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