Comments (9)
@hassiahk yes, I will assign you :]
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@hassiahk sounds great :]
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Hi @SkafteNicki, can I also work on this simultaneously with KLD
?
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Hello @SkafteNicki, should we have three different metrics for this?
Tweedie deviance with power
Poisson deviance - Tweedie deviance with power=1
Gamma deviance - Tweedie deviance with power=2
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Hello @SkafteNicki, should we have three different metrics for this?
Tweedie deviance with power
Poisson deviance - Tweedie deviance with power=1
Gamma deviance - Tweedie deviance with power=2
I think in this case, it is better to just have a single metric called Deviance
(or TweedieDeviance
) and then make it clear in the docstring what power=1
and power=2
corresponds to
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I think in this case, it is better to just have a single metric called
Deviance
(orTweedieDeviance
) and then make it clear in the docstring whatpower=1
andpower=2
corresponds to
Yes, that makes sense :)
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@hassiahk @SkafteNicki how is it going here? 🐰
from torchmetrics.
@hassiahk @SkafteNicki how is it going here? 🐰
I was working on it and then got busy with something else. I will raise a draft PR by tomorrow.
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@Borda I will be drafting a PR for this by this weekend. Sorry for the delay.
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Related Issues (20)
- Documentation of ERGAS HOT 1
- Support for DLM (AIM) metric HOT 1
- Error for index tensor verification in GeneralizedDice if target is zeros HOT 1
- Incorrect caching (`m._compute`) of metrics inside a `MetricCollection` if `compute_groups` are used and `.compute` is called twice HOT 1
- Classification Report, maybe with the option to select even more metrics HOT 1
- `MeanAveragePrecision.tm_to_coco` example fails with `IndexError: list index out of range` HOT 1
- The compute_groups may lead to unexpected behavior HOT 2
- GPU ram memory increase until overflow when using PSNR and SSIM HOT 2
- Need higher level RAG metrics HOT 2
- Multi output functionality for MeanAbsoluteError HOT 1
- _cumsum moves the data to default GPU when deterministic flag is on HOT 1
- Retrieval metrics are misleading - here's how it should be done instead HOT 6
- Multiple threshold for Multilabel metrics HOT 1
- Cannot import the "clustering" module HOT 5
- Potentially Misleading Error Message for multiclass_precision HOT 2
- Accuracy incurs a GPU/CPU sync point HOT 3
- Support equivalent of torcheval's `merge_state` HOT 1
- Multilabel metrics based on threshold should accept a threshold per label HOT 1
- Recall and Accuracy produce different values when used with logits HOT 1
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