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Create cat regressor #3353

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Create cat regressor #3353

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Intron7
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@Intron7 Intron7 commented Nov 11, 2024

Use numba to create the regressor for categorical regression

@Intron7 Intron7 added this to the 1.11.0 milestone Nov 11, 2024
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codecov bot commented Nov 11, 2024

Codecov Report

Attention: Patch coverage is 53.84615% with 6 lines in your changes missing coverage. Please review.

Project coverage is 76.46%. Comparing base (6dd0a7a) to head (2421bd5).
Report is 7 commits behind head on main.

Files with missing lines Patch % Lines
src/scanpy/preprocessing/_simple.py 53.84% 6 Missing ⚠️
Additional details and impacted files
@@            Coverage Diff             @@
##             main    #3353      +/-   ##
==========================================
- Coverage   76.58%   76.46%   -0.12%     
==========================================
  Files         111      111              
  Lines       12862    12874      +12     
==========================================
- Hits         9850     9844       -6     
- Misses       3012     3030      +18     
Files with missing lines Coverage Δ
src/scanpy/preprocessing/_simple.py 88.46% <53.84%> (-1.46%) ⬇️

... and 7 files with indirect coverage changes

---- 🚨 Try these New Features:

tests/test_preprocessing.py Outdated Show resolved Hide resolved
src/scanpy/preprocessing/_simple.py Outdated Show resolved Hide resolved
np.testing.assert_array_almost_equal(adata.X, tester)


def test_regressor_categorical():
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I would

  1. explain why this test exists (to test against a previous implementation? I am impartial whether it's necessary TBH since we are already testing for reproducibility, could see getting rid of this)
  2. refactor the "Create org regressors" into a helper function like create_original

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I can see your point here

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Do you have an an opinion on the first point? Is this test necessary? If so, perhaps a comment then?

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tests/test_preprocessing.py Outdated Show resolved Hide resolved
tests/test_preprocessing.py Outdated Show resolved Hide resolved
tests/test_preprocessing.py Outdated Show resolved Hide resolved
src/scanpy/preprocessing/_simple.py Outdated Show resolved Hide resolved
@@ -722,13 +737,13 @@ def regress_out(
"we regress on the mean for each category."
)
logg.debug("... regressing on per-gene means within categories")
regressors = np.zeros(X.shape, dtype="float32")
# Create numpy array's from categorical variable
cats = np.int64(len(adata.obs[keys[0]].cat.categories))
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Ditto

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Also comment why np.int64

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because it has be done because of weird typing from pandas. So this ensures that it works within the kernel

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so len doesn’t return a Python int? That’s a pandas bug.

tests/test_preprocessing.py Outdated Show resolved Hide resolved
src/scanpy/preprocessing/_simple.py Outdated Show resolved Hide resolved
np.testing.assert_array_almost_equal(adata.X, tester)


def test_regressor_categorical():
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Do you have an an opinion on the first point? Is this test necessary? If so, perhaps a comment then?

src/scanpy/preprocessing/_simple.py Outdated Show resolved Hide resolved
Comment on lines +740 to +742
number_categories = np.int64(len(adata.obs[keys[0]].cat.categories))
filters = adata.obs[keys[0]].cat.codes.to_numpy()
number_categories = number_categories.astype(filters.dtype)
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@flying-sheep flying-sheep Nov 14, 2024

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Either this or add a comment (to the code) explaining why it needs to be the other way.
Also if I do this, the test still passes, so …

Suggested change
number_categories = np.int64(len(adata.obs[keys[0]].cat.categories))
filters = adata.obs[keys[0]].cat.codes.to_numpy()
number_categories = number_categories.astype(filters.dtype)
number_categories = len(adata.obs[keys[0]].cat.categories)
filters = adata.obs[keys[0]].cat.codes.to_numpy()

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I added a comment. Other wise you have a dtype missmatch and crash of the kernel

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Other wise you have a dtype missmatch and crash of the kernel

I would say that this is the important part for the comment!

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@flying-sheep flying-sheep Nov 21, 2024

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100%!

  • refactor your code until the “what” is obvious.
  • if the “why” isn’t obvious from understanding the “what”, add the missing parts as a comment

I see that you’re

  1. convert the cat codes into a numpy array
  2. creating a numpy scalar with the same dtype as filters, holding the number of categories

So you don’t need to comment that you do any of that.

I asked because I’m confused why a Python integer is converted to a numpy scalar: Usually APIs accept either and do the converting themselves. So I’d like to see a comment removing that confusion by explaining why you convert to a numpy scalar. (a crash is a great reason)


but I also see that _create_regressor_categorical has number_categories: int and then does range(number_categories), so I’m still very confused why numba crashes unless the dtypes match.

I can’t reproduce the crash. leaving the thing as a Python int just works for me.

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Also the way to do this in one step is

Suggested change
number_categories = np.int64(len(adata.obs[keys[0]].cat.categories))
filters = adata.obs[keys[0]].cat.codes.to_numpy()
number_categories = number_categories.astype(filters.dtype)
filters = adata.obs[keys[0]].cat.codes.to_numpy()
number_categories = filters.dtype.type(len(adata.obs[keys[0]].cat.categories))

tests/test_preprocessing.py Outdated Show resolved Hide resolved
Comment on lines +740 to +742
number_categories = np.int64(len(adata.obs[keys[0]].cat.categories))
filters = adata.obs[keys[0]].cat.codes.to_numpy()
number_categories = number_categories.astype(filters.dtype)
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Other wise you have a dtype missmatch and crash of the kernel

I would say that this is the important part for the comment!

def _create_regressor_categorical(
X: np.ndarray, number_categories: int, filters: np.ndarray
) -> np.ndarray:
# create regressor matrix faster for categorical variables
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What does this comment mean?

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Benchmark changes

Change Before [6dd0a7a] After [2421bd5] Ratio Benchmark (Parameter)
+ 366M 405M 1.11 preprocessing_counts.peakmem_scrublet('pbmc68k_reduced', 'counts')
- 1.39±0.03ms 1.24±0.02ms 0.9 preprocessing_log.FastSuite.time_mean_var('pbmc3k', 'off-axis')
- 584M 480M 0.82 preprocessing_log.peakmem_pca('pbmc3k', 'off-axis')

Comparison: https://github.com/scverse/scanpy/compare/6dd0a7a72c7f8f57a082cca0f6a369dc47937b04..2421bd55496036151b73c46c5ec7ffa7e5ef71eb
Last changed: Thu, 21 Nov 2024 11:39:20 +0000

More details: https://github.com/scverse/scanpy/pull/3353/checks?check_run_id=33316268173

@flying-sheep flying-sheep removed their request for review November 21, 2024 11:39
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3 participants