Fisher score error – Length of value does not match length of index?

I am working on ML problem, trying to compute the fisher score for feature selection purpose

A B Y
1 1 1
2 5 1
1 5 1
7 9 0
7 9 0
8 9 0

t = pd.read_clipboard()

I am trying to compute the fisher score for each of the feature. I am just following the tutorials as is here and here

The code is given below

!pip install skfeature-chappers
from skfeature.function.similarity_based import fisher_score
score = fisher_score.fisher_score(t[['A','B']], t['Y'])) # error here
score = fisher_score.fisher_score(t[['A','B']], t['Y']), mode='rank') # tried this but also error
score = pd.Series(fisher_score.fisher_score(t[['A','B']], t['Y']))) # error here

I get

ValueError: Length of values (1) does not match length of index (2)

If I pass only one feature as input like shown below,

score = pd.Series(fisher_score.fisher_score(t[['A']], t['Y']))

I expect my output to have a list of scores for each feature, but I get another error:

ValueError: Data must be 1-dimensional

How to fix this issue?

>Solution :

The inputs to the fisher_score method is expected a numpy array not a pandas dataframe/series.

Try this:

score = fisher_score.fisher_score(t[['A','B']].to_numpy(),
                      t['Y'].to_numpy())