Support binary case #200
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Support binary case #200
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ejm714
commented
Aug 22, 2022
| @@ -491,6 +491,12 @@ def preprocess_labels(cls, values): | |||
| .max() | |||
| ) | |||
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| species_cols = labels.filter(regex="species_").columns | |||
| # binary case | |||
| if len(species_cols) == 2: | |||
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Add check to confirm that row-wise sum is always 1 in addition to two columns
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This branch is currently failing several tests (not just code quality), even after pulling updates from master. Rather than debug them, I'm inclined to start with a new branch and create a new PR. Sound ok, @ejm714 ? |
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Closing as this is superseded by #215 |
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If a user provides only two labels, we assume these are mutually exclusive and train the binary model. We log which column we're keeping.
Outstanding:
Note: I gave this a quick try on a dataset of 100 videos balanced evenly between blank and non blank and we indeed see some learning.
Some downsides to the binary case is that these metrics can look misleading when the class are imbalanced. Not sure the best way to warn users about that. If users provide highly imbalanced data, models may learn problematically to only predict the default class, but I suppose that is the same in the multilabel case.