'''
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score

y_t = [[1,0,0,1],[1,0,1,1]]
y_pr= [[1,0,0,1], [1,0,0,1]]


f1_scor_l = []
for i,j in zip(y_t, y_pr):
    f1_scor = f1_score(i, j, average='macro', zero_division=0)
    f1_scor_l.append(f1_scor)

print(f1_scor_l)'''


import numpy as np
a = np.array([[1,2,3,4,5], [6,7,8,9,10], [11,12,13,14,15]])
b = np.array([[1,2,3,4,5], [6,7,8,9,10], [11,12,13,14,15]])
print(a)
print('----------------------------')
a_trans = a.transpose()
print(a_trans)
print('----------------------------')
b_trans = b.transpose()
print(b_trans)
print('----------------------------')

print('----------------------------')
b_list = b_trans.tolist()
print(b_list)
print('----------------------------')
a_list = a_trans.tolist()
print(a_list)
print('----------------------------')



from sklearn.metrics import f1_score
f1_l = []
for i,j in zip(a_list,b_list):
    f1 = f1_score(i, j, average='macro', zero_division=0)
    f1_l.append(f1)

print(f1_l)
d90600a17929ddad0abca1bd99477ab841e0013a
<mask token>

<mask token>
print(a)
print('----------------------------')
<mask token>
print(a_trans)
print('----------------------------')
<mask token>
print(b_trans)
print('----------------------------')
print('----------------------------')
<mask token>
print(b_list)
print('----------------------------')
<mask token>
print(a_list)
print('----------------------------')
<mask token>
for i, j in zip(a_list, b_list):
    f1 = f1_score(i, j, average='macro', zero_division=0)
    f1_l.append(f1)
print(f1_l)

<mask token>
a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]])
b = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]])
print(a)
print('----------------------------')
a_trans = a.transpose()
print(a_trans)
print('----------------------------')
b_trans = b.transpose()
print(b_trans)
print('----------------------------')
print('----------------------------')
b_list = b_trans.tolist()
print(b_list)
print('----------------------------')
a_list = a_trans.tolist()
print(a_list)
print('----------------------------')
<mask token>
f1_l = []
for i, j in zip(a_list, b_list):
    f1 = f1_score(i, j, average='macro', zero_division=0)
    f1_l.append(f1)
print(f1_l)

<mask token>
import numpy as np
a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]])
b = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]])
print(a)
print('----------------------------')
a_trans = a.transpose()
print(a_trans)
print('----------------------------')
b_trans = b.transpose()
print(b_trans)
print('----------------------------')
print('----------------------------')
b_list = b_trans.tolist()
print(b_list)
print('----------------------------')
a_list = a_trans.tolist()
print(a_list)
print('----------------------------')
from sklearn.metrics import f1_score
f1_l = []
for i, j in zip(a_list, b_list):
    f1 = f1_score(i, j, average='macro', zero_division=0)
    f1_l.append(f1)
print(f1_l)

'''
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score

y_t = [[1,0,0,1],[1,0,1,1]]
y_pr= [[1,0,0,1], [1,0,0,1]]


f1_scor_l = []
for i,j in zip(y_t, y_pr):
    f1_scor = f1_score(i, j, average='macro', zero_division=0)
    f1_scor_l.append(f1_scor)

print(f1_scor_l)'''


import numpy as np
a = np.array([[1,2,3,4,5], [6,7,8,9,10], [11,12,13,14,15]])
b = np.array([[1,2,3,4,5], [6,7,8,9,10], [11,12,13,14,15]])
print(a)
print('----------------------------')
a_trans = a.transpose()
print(a_trans)
print('----------------------------')
b_trans = b.transpose()
print(b_trans)
print('----------------------------')

print('----------------------------')
b_list = b_trans.tolist()
print(b_list)
print('----------------------------')
a_list = a_trans.tolist()
print(a_list)
print('----------------------------')



from sklearn.metrics import f1_score
f1_l = []
for i,j in zip(a_list,b_list):
    f1 = f1_score(i, j, average='macro', zero_division=0)
    f1_l.append(f1)

print(f1_l)