LimeTextExplainer for MultiClass classification - Facing issue for explain instance with custom classifier function

Question

LimeTextExplainer for MultiClass classification - Facing issue for explain instance with custom classifier function

Mayur

2022年5月13日 03:08

exp = explainer.explain_instance(df_val_final.Description[idx],predproba_list,num_features=5, top_labels=2)

While executing the explain instance of LimeTextExplainer, the above statement keeps on executing continuously with the below warning message. Execution stops only if I interrupt the kernel

C:\ProgramData\Anaconda3\lib\site-packages\fastai\torch_core.py:83: UserWarning: Tensor is int32: upgrading to int64; for better performance use int64 input
  warn('Tensor is int32: upgrading to int64; for better performance use int64 input')
C:\ProgramData\Anaconda3\lib\site-packages\fastai\torch_core.py:83: UserWarning: Tensor is int32: upgrading to int64; for better performance use int64 input
 warn('Tensor is int32: upgrading to int64; for better performance use int64 input')
C:\ProgramData\Anaconda3\lib\site-packages\fastai\torch_core.py:83: UserWarning: Tensor is int32: upgrading to int64; for better performance use int64 input
  warn('Tensor is int32: upgrading to int64; for better performance use int64 input')

I want to use my own custom classifier model and hence I wrote a classifier function - predproba_list, which returns a numpy array of predicted probabilities for the classes Below is the function code

def predproba_list(test1) :
    pred = learn_clf.predict(test1)
    return np.array(pred[2])

pred[2] vaue is tensor([0.1423, 0.2133, 0.6444]) which i then convert to a numpy array

Can you please advise if the return value of the function is as expected by the explain instance's classifier function, and what could be causing the code to keep on executing without any result?

Topic lime

Category Data Science

tschomacker · Accepted Answer · 2021年11月30日 15:38

I am also struggling with the LimeTextExplainer and stumbled over your question. The return value of your predproba_list should be an array of arrays of floats. e.g.:

predproba_list('a') 

return:
array([[0.07965168, 0.07578776, 0.2927914 , 0.06609259, 0.03115178]])

I hope this helps.

LimeTextExplainer for MultiClass classification - Facing issue for explain instance with custom classifier function

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