In [1]:

    

def f1(precision,recall):
    if precision == 0.0 and recall == 0.0:
        return 0.0
    else:
        return 2 * float(precision)* float(recall) / ( precision+recall )


    

In [2]:

    

f1(0.31,0.76)


    

    
Out[2]:





0.44037383177570094

In [3]:

    

f1(0.64,0.38)


    

    
Out[3]:





0.4768627450980392

In [4]:

    

f1(0.39,0.16)


    

    
Out[4]:





0.2269090909090909

In [5]:

    

f1(0.22,0.18)


    

    
Out[5]:





0.19799999999999998

In [6]:

    

f1(0.11,0.39)


    

    
Out[6]:





0.1716

In [11]:

    

f1(0.0,0)


    

    




---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
<ipython-input-11-abd1e492a31b> in <module>()
----> 1 f1(0.0,0)

<ipython-input-1-7e34dfb05a45> in f1(precision, recall)
      1 def f1(precision,recall):
----> 2     return 2 * float(precision)* float(recall) / ( precision+recall )

ZeroDivisionError: float division by zero

In [ ]:

    

import numpy as np


    

In [ ]:

    

lst = []


    

In [ ]:

    

lst.append(np.array([1,2,3]))


    

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lst.append(np.array([4,4,4]))


    

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matrix = np.vstack(lst)


    

In [ ]:

    

matrix


    

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matrix.sum(axis=1)


    

In [ ]:

    

arr = np.array([1,2,3]).reshape(1,-1)


    

In [ ]:

    

np.sum(arr,axis=1)


    

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np.argsort(arr,axis=1)


    

In [ ]:

    

np.array([1,1]).shape


    

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original_y_true = np.array([[0.,1.],[1.,1.]]);original_y_true


    

In [ ]:

    

original_y_true.shape


    

In [ ]:

    

original_y_score = np.array([[0.,0.4],[0.9,0.1]]);original_y_score


    

In [ ]:

    

score_indices_top_k = np.array([[0,0],[0,0]]);score_indices_top_k


    

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row_indices_to_select = [i for i in range(original_y_true.shape[0])];row_indices_to_select


    

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column_indices_to_select = score_indices_top_k.T


    

In [ ]:

    

out = original_y_true[row_indices_to_select,column_indices_to_select];out


    

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out.shape


    

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arr1 = np.array([[0.5]])


    

In [ ]:

    

arr2 = np.array([[1.0]])


    

In [ ]:

    

lst = []
lst.append(arr1)
lst.append(arr2)


    

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np.vstack(lst)


    

In [ ]: