Calculate The Confusion Matrix In Different Columns In Panda Frame?

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 Id

Solution 1:

here is one way:

  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 = FNfor 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)

output:

Accuracy:
   col1  col2  col3
0   0.6   0.4   0.4

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)