Added regression metrics and support for multi-modal data using dictionaries#1685
Added regression metrics and support for multi-modal data using dictionaries#1685AntonioCarta merged 6 commits intoContinualAI:masterfrom
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It seems the readthedocs failed because it is using a prior version of sklearn which uses the deprecated mean_squared_error function to calculate the RMSE and my proposed metric uses the new version which calls directly the new root_mean_squared_error. Should I modify my code to use the deprecated function? |
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Thanks, the changes look good. Can you also add the new classes to the API documentation?
is it possibile to support both? Either by checking the scikit-learn version or by try/catching the import. Otherwise we can add a version constraint, although it's better to avoid it if possible. |
…or when training with jointtraining strategy
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Hi, it seems that there is an import error: |
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I'll fix it right away |
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I have changed RMSE and R2 to be calculated directly with Torch functions. I also fixed an error in the joint training strategy, as it was not training. |
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Hi @AntonioCarta , ¿could you please help me understand what test failed? Thanks in advance. |
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Hi, it's just the style checker. I will fix that in a separate commit. Thanks for your contibution |
Hi all,
I have created a fork to contribute to Avalanche. My problem is a regression problem using multi-modal data and noticed that Avalanche did not support this natively. I have added RMSE and R2 metrics, as well as forgetting metrics for both, and modified also the library to handle batches that use dictionaries of tensors instead of just tensors, to handle multi-modal data. This second point address issue #1678. I have blackend the code as well. Please let me know if everything looks good.
Best regards,
Joaquín