Hi! Thanks for this XAI visualization package!
I am trying to use the saliency method applied to Autoencoders and reconstruction errors.
Also, I do not have images, but a feature array for each observation.
My goal is to check the "importance" of each input feature to the overall reconstruction, thus was trying to use smoothgrad method.
In this case, I do not understand what should I pass to 'score'. The output of the model is just the reconstructed input (n_samples x n_features).
I also couldn't pass a custom function to the score attribute as you suggest in the documentation.
'From the example in the repository:
Instead of using CategoricalScore object,
you can also define the function from scratch as follows:
def score_function(output):
# The output variable refers to the output of the model,
# so, in this case, output shape is (3, 1000) i.e., (samples, classes).
return (output[0][1], output[1][294], output[2][413])
,
But then, how can you pass the function to the method? It needs to be callable, so it gives "ValueError: Score object must be callable! "
Could you add an example explicitly using score_function passed to saliency() (or any other) instance, instead of the instance score from one of the defined score classes (BinaryScore, CategoricalScore) ?
Thank you in advance!
Hi! Thanks for this XAI visualization package!
I am trying to use the saliency method applied to Autoencoders and reconstruction errors.
Also, I do not have images, but a feature array for each observation.
My goal is to check the "importance" of each input feature to the overall reconstruction, thus was trying to use smoothgrad method.
In this case, I do not understand what should I pass to 'score'. The output of the model is just the reconstructed input (n_samples x n_features).
I also couldn't pass a custom function to the score attribute as you suggest in the documentation.
'From the example in the repository:
Instead of using CategoricalScore object,
you can also define the function from scratch as follows:
def score_function(output):
# The
outputvariable refers to the output of the model,# so, in this case,
outputshape is(3, 1000)i.e., (samples, classes).return (output[0][1], output[1][294], output[2][413])
,
But then, how can you pass the function to the method? It needs to be callable, so it gives "ValueError: Score object must be callable! "
Could you add an example explicitly using score_function passed to saliency() (or any other) instance, instead of the instance score from one of the defined score classes (BinaryScore, CategoricalScore) ?
Thank you in advance!