scale function(_get_mean_var) updated for dense array, speedup upto ~4.65x

[kaushalprasadhial]

We had created this PR before #3099. This one is the same PR with editing enabled for maintainers.
Hi,
We are submitting PR for speed up of the _get_mean_var function.

	Time(sec)
Original	18.49
Updated	3.97
Speedup	4.65743073

experiment setup : AWS r7i.24xlarge

import time
import numpy as np

import pandas as pd

import scanpy as sc
from sklearn.cluster import KMeans

import os
import wget

import warnings



warnings.filterwarnings('ignore', 'Expected ')
warnings.simplefilter('ignore')
input_file = "./1M_brain_cells_10X.sparse.h5ad"

if not os.path.exists(input_file):
    print('Downloading import file...')
    wget.download('https://rapids-single-cell-examples.s3.us-east-2.amazonaws.com/1M_brain_cells_10X.sparse.h5ad',input_file)


# marker genes
MITO_GENE_PREFIX = "mt-" # Prefix for mitochondrial genes to regress out
markers = ["Stmn2", "Hes1", "Olig1"] # Marker genes for visualization

# filtering cells
min_genes_per_cell = 200 # Filter out cells with fewer genes than this expressed
max_genes_per_cell = 6000 # Filter out cells with more genes than this expressed

# filtering genes
min_cells_per_gene = 1 # Filter out genes expressed in fewer cells than this
n_top_genes = 4000 # Number of highly variable genes to retain

# PCA
n_components = 50 # Number of principal components to compute

# t-SNE
tsne_n_pcs = 20 # Number of principal components to use for t-SNE

# k-means
k = 35 # Number of clusters for k-means

# Gene ranking

ranking_n_top_genes = 50 # Number of differential genes to compute for each cluster

# Number of parallel jobs
sc._settings.ScanpyConfig.n_jobs = os.cpu_count()

start=time.time()
tr=time.time()
adata = sc.read(input_file)
adata.var_names_make_unique()
adata.shape
print("Total read time : %s" % (time.time()-tr))



tr=time.time()
# To reduce the number of cells:
USE_FIRST_N_CELLS = 1300000
adata = adata[0:USE_FIRST_N_CELLS]
adata.shape

sc.pp.filter_cells(adata, min_genes=min_genes_per_cell)
sc.pp.filter_cells(adata, max_genes=max_genes_per_cell)
sc.pp.filter_genes(adata, min_cells=min_cells_per_gene)
sc.pp.normalize_total(adata, target_sum=1e4)
print("Total filter and normalize time : %s" % (time.time()-tr))


tr=time.time()
sc.pp.log1p(adata)
print("Total log time : %s" % (time.time()-tr))


# Select highly variable genes
sc.pp.highly_variable_genes(adata, n_top_genes=n_top_genes, flavor = "cell_ranger")

# Retain marker gene expression
for marker in markers:
        adata.obs[marker + "_raw"] = adata.X[:, adata.var.index == marker].toarray().ravel()

# Filter matrix to only variable genes
adata = adata[:, adata.var.highly_variable]

ts=time.time()
#Regress out confounding factors (number of counts, mitochondrial gene expression)
mito_genes = adata.var_names.str.startswith(MITO_GENE_PREFIX)
n_counts = np.array(adata.X.sum(axis=1))
adata.obs['percent_mito'] = np.array(np.sum(adata[:, mito_genes].X, axis=1)) / n_counts
adata.obs['n_counts'] = n_counts


sc.pp.regress_out(adata, ['n_counts', 'percent_mito'])
print("Total regress out time : %s" % (time.time()-ts))

#scale

ts=time.time()
sc.pp.scale(adata)
print("Total scale time : %s" % (time.time()-ts))

add timer around _get_mean_var call

scanpy/scanpy/preprocessing/_scale.py

Line 167 in 706d4ef

mean, var = _get_mean_var(X)

we can also create _get_mean_var_std function that return std as well so we don't require to compute it in scale function(L168-L169).

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 76.43%. Comparing base (751eafa) to head (56bd438).

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #3280      +/-   ##
==========================================
- Coverage   76.49%   76.43%   -0.07%     
==========================================
  Files         111      111              
  Lines       12867    12870       +3     
==========================================
- Hits         9843     9837       -6     
- Misses       3024     3033       +9     

Files with missing lines	Coverage Δ
src/scanpy/preprocessing/_utils.py	55.88% <100.00%> (+0.32%)	⬆️

... and 2 files with indirect coverage changes

---- 🚨 Try these New Features:

• Flaky Tests Detection - Detect and resolve failed and flaky tests

[ilan-gold]

What happened to sparse?

[Intron7]

I still try to integrate all the stuff it's a mess though.

[ilan-gold]

Please remove erroneous diffs

[flying-sheep]

Hi! This seems a bit unfinished. If you need our help with a local test environment or so, we’re happy to help.

[flying-sheep]

instead of adding a second code path that handles np.ndarray, you should replace the existing one above:

@axis_mean.register(np.ndarray)
def _(X: np.ndarray, ...): ...

[flying-sheep]

pull this out of the if branch

[flying-sheep]

I mean these assignments. When you have two branches, and both start with the same 3 lines, just do those before the if statement instead.

[flying-sheep]

there is no return statement

[kaushalprasadhial]

i have updated the code please check

[flying-sheep]

Well, the tests aren’t passing, so it still doesn’t seem to be working

[Intron7]

This also doesn't work for F-Arrays