savez_compressed

[rvandewater]

Added simple numpy compression saving. Currently testing out if this works throughout the pipeline. Reproducibility tests are appreciated!

Summary by CodeRabbit

• Performance Improvements
  • Enhanced matrix storage efficiency by using compressed file format when saving sparse matrices.

[coderabbitai]

Walkthrough

The pull request introduces a minor modification to the store_matrix function in the utils.py file. The change involves switching from np.savez to np.savez_compressed when saving a sparse matrix to disk. This update aims to improve storage efficiency by using a compressed format for saving the matrix, without altering the function's core functionality or signature.

Changes

File	Change Summary
src/MEDS_tabular_automl/utils.py	Updated store_matrix function to use np.savez_compressed instead of np.savez for more efficient sparse matrix storage

Poem

🐰 A rabbit's tale of matrix might,
Compressed and stored with storage light,
From savez to savez_compressed we leap,
Saving space with a technological peep!
Efficiency dancing, bits so neat! 📦✨

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📜 Recent review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 374e2f5 and a24a45a.

📒 Files selected for processing (1)

• src/MEDS_tabular_automl/utils.py (1 hunks)

🔇 Additional comments (1)

src/MEDS_tabular_automl/utils.py (1)

258-258: Great improvement with compressed storage!

Using np.savez_compressed can significantly reduce file size, especially for large sparse matrices. To ensure reproducibility and confirm that the compression doesn't cause any downstream issues, it might be beneficial to verify how these compressed files are handled in subsequent pipeline steps or external tools.

Below is a script to help locate usage references to store_matrix and load_matrix and check whether they are tested:

✅ Verification successful

Compressed matrix storage is well-integrated and tested

The verification shows that the compressed matrix storage implementation is properly integrated into the codebase:

• The store_matrix and load_matrix functions are used consistently throughout the codebase
• The functions are thoroughly tested in both unit tests (tests/test_tabularize.py) and integration tests (tests/test_integration.py)
• The tests verify critical aspects:
  • Matrix shape preservation
  • Non-empty data validation
  • Expected column counts
  • Proper loading of both static and time-series matrices
• The functions are also used in production code paths (cache_task.py, tabular_dataset.py)

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Search for references to store_matrix and load_matrix to confirm test coverage or usage.

echo "Searching for store_matrix usage..."
rg "store_matrix" -A 5

echo "Searching for load_matrix usage..."
rg "load_matrix" -A 5

Length of output: 6543

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