FEA Add macro-averaged mean absolute error #780
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Hello @AurelienMassiot! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:
Comment last updated at 2021-02-08 23:15:32 UTC |
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- Coverage 98.55% 98.55% -0.01%
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Files 89 89
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Branches 475 477 +2
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imblearn/metrics/_classification.py
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| all_mae = [] | ||
| y_true = np.array(y_true) | ||
| y_pred = np.array(y_pred) | ||
| for class_to_predict in np.unique(y_true): |
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I think that we should introduce a label to be able to either give class that are not present in y_true or select a subset of labels as in precision-recall: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html#sklearn.metrics.precision_recall_fscore_support
Would it make sense?
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I think it does not make sense because all we want is the MAE for each class really in y_true.
For example, if I have:
y_true = [1, 2, 2, 3]
I want the average MAE, which will be the MAE for classes 1,2,3. And if a class is missing in y_pred, it doesn't matter, for example in the following example:
y_pred= [1, 2, 2, 2]
my MAEs will be 0 for class 1, 0 for class 2, 1 for class 3, and the MA-MAE will then be 0.33.
WDYT?
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| ) | ||
| def test_macro_averaged_mean_absolute_error(y_true, y_pred, expected_ma_mae): |
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If we introduce labels, we will need another test with a bit more corner cases.
Otherwise, I think this is good.
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See comment above for labels.
imblearn/metrics/_classification.py
Outdated
| all_mae = [] | ||
| y_true = np.array(y_true) | ||
| y_pred = np.array(y_pred) | ||
| for class_to_predict in np.unique(y_true): |
There was a problem hiding this comment.
I think it does not make sense because all we want is the MAE for each class really in y_true.
For example, if I have:
y_true = [1, 2, 2, 3]
I want the average MAE, which will be the MAE for classes 1,2,3. And if a class is missing in y_pred, it doesn't matter, for example in the following example:
y_pred= [1, 2, 2, 2]
my MAEs will be 0 for class 1, 0 for class 2, 1 for class 3, and the MA-MAE will then be 0.33.
WDYT?
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||
| ], | ||
| ) | ||
| def test_macro_averaged_mean_absolute_error(y_true, y_pred, expected_ma_mae): |
There was a problem hiding this comment.
See comment above for labels.
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Could anyone make a final review ? :-) |
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@AurelienMassiot I promise you that it will be merged for the next release which will follow the release in scikit-learn 0.24 |
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Thanks @glemaitre ! This is not urgent, good luck for the release of scikit-learn ;-). |
glemaitre
changed the title
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Thanks @AurelienMassiot Good to go |
Reference Issue
As detailed in the issue #18901 I wrote in Scikit-Learn main repository, Macro-Averaged MAE should be added to imbalanced-learn repository instead.
For ordinal classification, we can use multiple metrics, for example: MAE, MSE... As we would use for regression.
But when these classes are imbalanced, one way to deal with imbalance is to use macro-averaged MAE instead, as described on StackExchange and in the original paper.
The macro-averaged MAE is like the "classic" MAE, except we compute each MAE for each class and average them, giving equal weights to MAEs. Note that macro-averaged MAE == micro-averaged (or classic) MAE when class are balanced.
To illustrate this, let's consider:
Any other comments?