Bayesian Principal Component Analysis 2 | Imputation

[lucas-diedrich]

Impute missing values with BPCA

$$ X_{reconstructed} = U \times L + \overline{X} $$

with BPCA factors (observations X factors) $U$ and BPCA loadings (factors x features) $L$.

Only missing values are replaced.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 88.81579% with 17 lines in your changes missing coverage. Please review.
⚠️ Please upload report for BASE (bpca-embedding@cf04e78). Learn more about missing BASE report.

Files with missing lines	Patch %	Lines
src/alphapepttools/pl/plots.py	22.22%	7 Missing ⚠️
src/alphapepttools/pp/impute.py	91.56%	7 Missing ⚠️
src/alphapepttools/tl/plot_data_handling.py	92.85%	3 Missing ⚠️

Additional details and impacted files

@@                Coverage Diff                @@
##             bpca-embedding     #142   +/-   ##
=================================================
  Coverage                  ?   77.99%           
=================================================
  Files                     ?       35           
  Lines                     ?     1809           
  Branches                  ?        0           
=================================================
  Hits                      ?     1411           
  Misses                    ?      398           
  Partials                  ?        0           

Files with missing lines	Coverage Δ
src/alphapepttools/pp/__init__.py	100.00% <100.00%> (ø)
src/alphapepttools/tl/__init__.py	100.00% <100.00%> (ø)
src/alphapepttools/tl/embeddings.py	89.61% <100.00%> (ø)
src/alphapepttools/tl/tools.py	92.30% <100.00%> (ø)
src/alphapepttools/tl/plot_data_handling.py	97.16% <92.85%> (ø)
src/alphapepttools/pl/plots.py	40.17% <22.22%> (ø)
src/alphapepttools/pp/impute.py	92.80% <91.56%> (ø)

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