Comments (3)
bobakfb
commented on August 28, 2024
Hey @crazyn2 thanks for the post. We do have tests against Sklearn that are passing and have passed a bunch of times since auroc landed. Can you give a specific set of inputs that yields a different output?
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crazyn2
commented on August 28, 2024
Thank you for your prompt response. I've provide my code repository of github https://github.com/crazyn2/mini-ad which contained the whole procedures including dataset inputs ? If you have time, you can reproduce the result utilizing those codes.
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crazyn2
commented on August 28, 2024
I've identified the reason for the discrepancy in AUC-ROC values with sklearn. I realized that I forgot to reset the metric class in my loop. Apologies for any inconvenience.
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Related Issues (20)
- RuntimeError: "bitwise_and_cpu" not implemented for 'Float' when using binary_precison & binary_recall HOT 2
- Error in masking in the function multiclass_recall HOT 2
- FLOPs and ModuleSummary Documentation HOT 2
- More precise definition of perplexity when ignore index is not None HOT 1
- Throughput metric is not taking into account the number of processes HOT 2
- Multiple metrics sharing the same state HOT 4
- Disagreement for macro f1 with torchmetrics and sklearn HOT 5
- Docs return description of binary_confusion_matrix incorrect HOT 2
- Updating `Mean` with 0 leads to 'No calls to update() have been made...' warning HOT 1
- RetrievalRecall, RetrievalPrecision require different, 1D input than MulticlassRecall, MulticlassPrecision which accept batch input HOT 2
- Bug in MulticlassRecall example from when adding one additional class HOT 1
- The score computed by `multiclass_f1_score` for binary classification is wrong. It is not f1 score but accuracy. HOT 2
- The FID result cannot be aligned with pytorch-fid/torch-fidelity HOT 1
- Torcheval pointing to wrong directory for nvrtc-builtins64_121.dll file.
- Potentially Misleading Error Message for multiclass_precision
- metrics should have an unsafe option
- Discrepancy between code and documentation on official pytorch websit
- Stable version for Torcheval
- Mutliclass Precision Recall Curve, docs not consistent with execution HOT 1
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