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Fix docs metrics formatting #5077

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merged 8 commits into from
Dec 12, 2020
Merged

Fix docs metrics formatting #5077

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eb72f6c
fix functional f1 fbeta formatting
s-rog Dec 11, 2020
7a267cf
Update f_beta.py
s-rog Dec 11, 2020
c7d052e
remove line breaks
s-rog Dec 11, 2020
647f48a
Update f_beta.py
s-rog Dec 11, 2020
c965710
pad linea breaks with 2 spaces instead of tab
s-rog Dec 11, 2020
d1cd509
Merge branch 'master' into s-rog-patch-2
s-rog Dec 11, 2020
2a19336
Merge branch 'master' into s-rog-patch-2
s-rog Dec 12, 2020
13aa4bb
Merge branch 'master' into s-rog-patch-2
s-rog Dec 12, 2020
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20 changes: 10 additions & 10 deletions pytorch_lightning/metrics/classification/f_beta.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,11 +52,11 @@ class FBeta(Metric):
Threshold value for binary or multi-label logits. default: 0.5

average:
* `'micro'` computes metric globally
* `'macro'` computes metric for each class and uniformly averages them
* `'weighted'` computes metric for each class and does a weighted-average,
where each class is weighted by their support (accounts for class imbalance)
* `None` computes and returns the metric per class
- ``'micro'`` computes metric globally
- ``'macro'`` computes metric for each class and uniformly averages them
- ``'weighted'`` computes metric for each class and does a weighted-average,
where each class is weighted by their support (accounts for class imbalance)
- ``'none'`` computes and returns the metric per class

multilabel: If predictions are from multilabel classification.
compute_on_step:
Expand Down Expand Up @@ -185,11 +185,11 @@ class F1(FBeta):
Threshold value for binary or multi-label logits. default: 0.5

average:
* `'micro'` computes metric globally
* `'macro'` computes metric for each class and uniformly averages them
* `'weighted'` computes metric for each class and does a weighted-average,
where each class is weighted by their support (accounts for class imbalance)
* `None` computes and returns the metric per class
- ``'micro'`` computes metric globally
- ``'macro'`` computes metric for each class and uniformly averages them
- ``'weighted'`` computes metric for each class and does a weighted-average,
where each class is weighted by their support (accounts for class imbalance)
- ``'none'`` computes and returns the metric per class

multilabel: If predictions are from multilabel classification.
compute_on_step:
Expand Down
20 changes: 10 additions & 10 deletions pytorch_lightning/metrics/functional/f_beta.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,11 +83,11 @@ def fbeta(
Threshold value for binary or multi-label logits. default: 0.5

average:
* `'micro'` computes metric globally
* `'macro'` computes metric for each class and uniformly averages them
* `'weighted'` computes metric for each class and does a weighted-average,
where each class is weighted by their support (accounts for class imbalance)
* `None` computes and returns the metric per class
- ``'micro'`` computes metric globally
- ``'macro'`` computes metric for each class and uniformly averages them
- ``'weighted'`` computes metric for each class and does a weighted-average,
where each class is weighted by their support (accounts for class imbalance)
- ``'none'`` computes and returns the metric per class

multilabel: If predictions are from multilabel classification.

Expand Down Expand Up @@ -136,11 +136,11 @@ def f1(
Threshold value for binary or multi-label logits. default: 0.5

average:
* `'micro'` computes metric globally
* `'macro'` computes metric for each class and uniformly averages them
* `'weighted'` computes metric for each class and does a weighted-average,
where each class is weighted by their support (accounts for class imbalance)
* `None` computes and returns the metric per class
- ``'micro'`` computes metric globally
- ``'macro'`` computes metric for each class and uniformly averages them
- ``'weighted'`` computes metric for each class and does a weighted-average,
where each class is weighted by their support (accounts for class imbalance)
- ``'none'`` computes and returns the metric per class

multilabel: If predictions are from multilabel classification.

Expand Down