Use _make_obs_column_categorical for CategoricalJointObsmField
#1417
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@@ Coverage Diff @@
## master #1417 +/- ##
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+ Coverage 91.04% 91.07% +0.02%
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Files 111 111
Lines 8635 8630 -5
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- Hits 7862 7860 -2
+ Misses 773 770 -3
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adamgayoso
requested changes
Mar 10, 2022
scvi/data/_utils.py
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| # make sure each category contains enough cells | ||
| unique, counts = np.unique(adata.obs[alternate_column_key], return_counts=True) | ||
| unique, counts = np.unique(getattr(df, alternate_column_key), return_counts=True) | ||
| if np.min(counts) < 3: | ||
| category = unique[np.argmin(counts)] | ||
| warnings.warn( | ||
| "Category {} in adata.obs['{}'] has fewer than 3 cells. SCVI may not train properly.".format( |
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can we change this message to say "models" instead of SCVI
scvi/data/_utils.py
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| # make sure each category contains enough cells | ||
| unique, counts = np.unique(adata.obs[alternate_column_key], return_counts=True) | ||
| unique, counts = np.unique(getattr(df, alternate_column_key), return_counts=True) |
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why getattr and not df[alternate_column_key]
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also wait isn't this call to np unique inefficient? We already called np unique on the codes earlier on 144?
adamgayoso
approved these changes
Mar 10, 2022
meeseeksmachine
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Mar 10, 2022
…egoricalJointObsmField`
adamgayoso
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Mar 10, 2022
…lJointObsmField` (#1419) Co-authored-by: Justin Hong <jjhong922@berkeley.edu>
nrclaudio
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to nrclaudio/scvi-tools-tune
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Jun 21, 2022
…cverse#1417) * refactor make obs column cat * address comments * release note
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Fixes #1410