Added test case for #73 and a fix

mmcdermott commented 3 months ago

Closes #73 as not reproducible.

Summary by CodeRabbit

Documentation
- Improved clarity of the query function documentation.
- Added new examples demonstrating valid usage of the query function, including configuration setups and sample outputs.
- Enhanced documentation for the check_constraints function with a sample DataFrame and usage example.
- Improved formatting and readability of the extract_subtree function documentation with updated examples.

coderabbitai[bot] commented 3 months ago

Walkthrough

The changes involve enhancements to the docstring of the query function in src/aces/query.py, improving clarity and adding new examples to demonstrate valid usage. A new code block showcasing the check_constraints function with a sample DataFrame has been introduced in src/aces/constraints.py. Minor formatting adjustments were made in the extract_subtree function to improve code readability and ensure output uniqueness.

Changes

Files	Change Summary
src/aces/query.py	Updated docstring for the `query` function for clarity and added new usage examples.
src/aces/constraints.py	Added usage example for the `check_constraints` function with a sample DataFrame.
src/aces/extract_subtree.py	Reformatted method calls in the `extract_subtree` function for improved readability and ensured unique output.

Assessment against linked issues

Objective	Addressed	Explanation
Duplication of rows when using _ANY_EVENT (73)	❌	The changes do not address the duplication issue.

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With clearer docs, the path's now bright,
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codecov[bot] commented 3 months ago

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Files with missing lines	Coverage Δ
src/aces/constraints.py	`100.00% <ø> (ø)`
src/aces/extract_subtree.py	`94.59% <ø> (ø)`
src/aces/query.py	`93.61% <ø> (+4.25%)`	:arrow_up:

... and 2 files with indirect coverage changes

🚨 Try these New Features:

Flaky Tests Detection - Detect and resolve failed and flaky tests

mmcdermott commented 3 months ago

Ahh, I see, makes sense. I'll update the code.

mmcdermott commented 3 months ago

So I just pushed what I think is a fix in 46690b41251fe8455d18616e3657d4b8ccc2e86b

Basically, the fix takes the line:

        child_anchor_realizations = window_summary_df.select(
            "subject_id",
            pl.col("child_anchor_timestamp").alias("subtree_anchor_timestamp"),
        )

and adds

        child_anchor_realizations = window_summary_df.select(
            "subject_id",
            pl.col("child_anchor_timestamp").alias("subtree_anchor_timestamp"),
        ).unique()

Why is this appropriate? Because, there is literally no point to recursing on a dataframe where multiple rows are identical. If everything else is working appropriately, such an action should just likewise return a set of duplicate outputs. If two cases of possible child anchors are the same event in a patient's record, then we can stremaline subsequent window criteria checks by just analyzing those windows only once, instead of multiple times.

In any event bound window case, it is totally possible that multiple input windows with different subtree anchors will end at the same event in a patient's record. This means they'd have the same child_anchor_timestamp for subsequent iterations, even though they have different subtree_anchor_timestamps. In that case, it is appropriate to determine if the subtrees from that shared child anchor are valid only once, then join it back to all possible subtree anchors in one one-to-many join, rather than doing a bunch of duplicate calculations and using a many-to-many join in the output, which is what we're doing now and what results in duplication.

justin13601 / ACES