Refactor API for auto_label_units
#4338
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| assert "label" in labels.columns | ||
| assert "probability" in labels.columns | ||
| assert labels.shape[0] == len(sorting_analyzer_for_curation.sorting.get_unit_ids()) |
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Can we add cases where each model is None as well. Just this test repeated but with just noise_neural_classifier=None, and just sua_mua_classifier=None.
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| # .. dropdown:: More about security | ||
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| # Sharing models, with are Python objects, is complicated. |
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Suggested change
| # Sharing models, which are Python objects, is complicated. |
cheeky docs correction
Co-authored-by: Chris Halcrow <[email protected]>
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The
auto_label_unitswas used only for the model-based labeling, but there are other ways to label units! (see #4304 )This PR renames
auto_label_unitstomodel_based_label_units. In addition, it adds aunitrefine_label_unitsthat automate the noise/neural + MUA/SUA classification. By default, lightweight models are used since they give almost the same performance as the full models, but don't depend on PCA metrics.