In [1]:

    

from sklearn.metrics import classification_report
import pandas as pd
import pprint


    

In [2]:

    

y_true = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
y_pred = [0, 1, 1, 1, 1, 0, 0, 0, 1, 1]


    

In [3]:

    

print(classification_report(y_true, y_pred))


    

    




              precision    recall  f1-score   support

           0       0.25      0.20      0.22         5
           1       0.33      0.40      0.36         5

   micro avg       0.30      0.30      0.30        10
   macro avg       0.29      0.30      0.29        10
weighted avg       0.29      0.30      0.29        10

In [4]:

    

print(type(classification_report(y_true, y_pred)))


    

    




<class 'str'>

In [5]:

    

print(classification_report(y_true, y_pred,
                            target_names=['class_0', 'class_1']))


    

    




              precision    recall  f1-score   support

     class_0       0.25      0.20      0.22         5
     class_1       0.33      0.40      0.36         5

   micro avg       0.30      0.30      0.30        10
   macro avg       0.29      0.30      0.29        10
weighted avg       0.29      0.30      0.29        10

In [6]:

    

d = classification_report(y_true, y_pred, output_dict=True)


    

In [7]:

    

pprint.pprint(d)


    

    




{'0': {'f1-score': 0.22222222222222224,
       'precision': 0.25,
       'recall': 0.2,
       'support': 5},
 '1': {'f1-score': 0.3636363636363636,
       'precision': 0.3333333333333333,
       'recall': 0.4,
       'support': 5},
 'macro avg': {'f1-score': 0.29292929292929293,
               'precision': 0.29166666666666663,
               'recall': 0.30000000000000004,
               'support': 10},
 'micro avg': {'f1-score': 0.3, 'precision': 0.3, 'recall': 0.3, 'support': 10},
 'weighted avg': {'f1-score': 0.29292929292929293,
                  'precision': 0.29166666666666663,
                  'recall': 0.3,
                  'support': 10}}

In [8]:

    

print(d['0'])


    

    




{'precision': 0.25, 'recall': 0.2, 'f1-score': 0.22222222222222224, 'support': 5}

In [9]:

    

print(d['0']['precision'])


    

    




0.25

In [10]:

    

print(type(d['0']['precision']))


    

    




<class 'float'>

In [11]:

    

df = pd.DataFrame(d)


    

In [12]:

    

print(df)


    

    




                  0         1  micro avg  macro avg  weighted avg
f1-score   0.222222  0.363636        0.3   0.292929      0.292929
precision  0.250000  0.333333        0.3   0.291667      0.291667
recall     0.200000  0.400000        0.3   0.300000      0.300000
support    5.000000  5.000000       10.0  10.000000     10.000000

In [13]:

    

print(df.iloc[:, :-3])


    

    




                  0         1
f1-score   0.222222  0.363636
precision  0.250000  0.333333
recall     0.200000  0.400000
support    5.000000  5.000000

In [14]:

    

print(df.iloc[:, -3:])


    

    




           micro avg  macro avg  weighted avg
f1-score         0.3   0.292929      0.292929
precision        0.3   0.291667      0.291667
recall           0.3   0.300000      0.300000
support         10.0  10.000000     10.000000