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Currently to generate a match_weights_chart a linker (with data) is required. If I am trying to visualise a trained model which has been saved as a json, this feels unnecessary.
My current workaround is to create an empty dataframe with the correct column nomes, which isn't ideal.
Describe the solution you'd like
Alternative to linker.visualisations.match_weights_chart that is not part of the linker that allows you to pass a trained model. For example:
from splink.visualisations import match_weights_chart
chart = match_weights_chart("model.json")
Similar to the clustering methods that live outside of the linker
Describe alternatives you've considered
Additional context
The text was updated successfully, but these errors were encountered:
Is your proposal related to a problem?
Currently to generate a
match_weights_chart
a linker (with data) is required. If I am trying to visualise a trained model which has been saved as a json, this feels unnecessary.My current workaround is to create an empty dataframe with the correct column nomes, which isn't ideal.
Describe the solution you'd like
Alternative to
linker.visualisations.match_weights_chart
that is not part of the linker that allows you to pass a trained model. For example:Similar to the
clustering
methods that live outside of the linkerDescribe alternatives you've considered
Additional context
The text was updated successfully, but these errors were encountered: