CODEWITHSUNDEEP
python
pranabjebemtech
pranabjebemtech

Reputation: 25

in python TypeError: unhashable type: 'numpy.ndarray'

I tried to find recall but type error occur

import pandas as pd
y_test = {'o1':  [0,1,0,1],'o2': [1,1,0,1],'o3':[0,0,1,1]}
y_test = pd.DataFrame (y_test)
y_pred = {'o1':  [1,1,0,1],'o2': [1,0,0,1],'o3':[1,0,1,1]}
y_pred = pd.DataFrame (y_pred)
y_pred = y_pred.to_numpy()


def precision(y_test, y_pred):
    i = set(y_test).intersection(y_pred)
    len1 = len(y_pred)
    if len1 == 0:
        return 0
    else:
        return len(i) / len1

print("recall of Binary Relevance Classifier: " + str(precision(y_test, y_pred)))

This code shown an error: actually i try to find recall for multi label classification error details iven below

TypeError                                 Traceback (most recent call last)
<ipython-input-41-8f3ca706a8e6> in <module>
16         return len(i) / len1
17 
---> 18 print("recall of Binary Relevance Classifier: " + str(precision(y_test, y_pred)))
<ipython-input-41-8f3ca706a8e6> in precision(y_test, y_pred)
 9 
10 def precision(y_test, y_pred):
---> 11     i = set(y_test).intersection(y_pred)
 12     len1 = len(y_pred)
 13     if len1 == 0:

TypeError: unhashable type: 'numpy.ndarray'

Upvotes: 2

Views: 13311

Answers (1)

Carolyn Conway
Carolyn Conway

Reputation: 1506

Your numpy array y_test cannot be converted to a set (on line 11), because the array is 2 dimensional.

For an iterable to be converted to a set, the items need to all be hashable. For a 1-d numpy array that's fine, because numbers are hashable:

>>> array_1d = np.array([1, 2, 3])
>>> array_1d
array([1, 2, 3])
>>> set(array_1d)
{1, 2, 3}

But for a 2-d array, you'll get this error because the nested arrays are not themselves hashable:

>>> array_2d = np.array([[1,2,3], [1,2,3], [1,2,3]])
>>> array_2d
array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]])
>>> set(array_2d)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'numpy.ndarray'

Upvotes: 6

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