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the classification model is classification_pred = model([input_x[0], input_x[1],input_x[2], input_x[3]], os_event.size(0), {'gex': 0, 'methy': 1, 'mut':2, 'cna': 3}),the input_x of one sample is as the figure showed,how to use the IntegratedGradients
to get the four features' attribution?
The text was updated successfully, but these errors were encountered:
Hi @Sarah-air , if your model uses all 4 inputs, then normal passing is fine. but if it doesn't, you'll have to pass only the inputs that your model uses to the attribute method as input and the rest as additional forward args. The Gradient method throws an error if any input is unused. Check our example here.
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the classification model is classification_pred = model([input_x[0], input_x[1],input_x[2], input_x[3]], os_event.size(0), {'gex': 0, 'methy': 1, 'mut':2, 'cna': 3}),the input_x of one sample is as the figure showed,how to use the IntegratedGradients
to get the four features' attribution?
The text was updated successfully, but these errors were encountered: