fix: fix filtering of NaNs in Prophet preprocessing

[sd2k]

Fixes #209.

Summary by CodeRabbit

• New Features

  • Introduced a method to filter out NaN values from training data, ensuring data integrity.
  • Added regression tests to verify handling of NaN values in model fitting.

• Bug Fixes

  • Improved error handling in data preprocessing to maintain robustness against invalid entries.

• Documentation

  • Updated test conditions to reflect new features and ensure clarity on functionality.

[coderabbitai]

Walkthrough

The pull request addresses the handling of NaN values in the Prophet forecasting library by introducing a new filter_nans method in the TrainingData struct. This method removes NaN entries from the y vector while simultaneously updating corresponding entries in other vectors like ds, cap, floor, x, and seasonality_conditions. The changes modify the preprocessing workflow to handle NaN values more robustly, ensuring that the data preparation process can handle incomplete or invalid data without causing panics.

Changes

File	Change Summary
crates/augurs-prophet/src/data.rs	Added filter_nans() method to TrainingData struct to remove NaN values and update associated vectors
crates/augurs-prophet/src/forecaster.rs	Updated test module compilation attribute to #[cfg(all(test, feature = "wasmstan"))]
crates/augurs-prophet/src/prophet.rs	Added fit_with_nans() regression test to verify NaN handling
crates/augurs-prophet/src/prophet/prep.rs	Simplified preprocess method signature and integrated filter_nans() method

Assessment against linked issues

Objective	Addressed	Explanation
Handle NaNs without panicking [#209]	✅
Create TrainingData::filter_nans() method [#209]	✅
Consume self and return new TrainingData [#209]	✅

Possibly related PRs

• feat: add Forecaster wrapper for Prophet #191: Introduces ProphetForecaster utilizing TrainingData struct, which is conceptually related to NaN handling improvements

Poem

🐰 A Rabbit's Ode to NaN Filtering 🧹

In data's wild and winding maze,
Where numbers dance in misty haze,
Our Prophet learns to gently sift,
No more shall NaNs cause a rift!
Filtering magic, clean and bright! 🌟

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[coderabbitai]

Actionable comments posted: 0

🧹 Nitpick comments (3)

crates/augurs-prophet/src/data.rs (2)

141-179: Efficient and clean NaN filtering logic.

This implementation properly filters out NaN entries in the y vector, updates corresponding fields, and keeps the data structure coherent. The use of a helper retain function is concise.
• Consider if the caller needs feedback on the number of removed rows or warnings (e.g. for large fraction of NaNs).
• Adding extra tests for edge cases, such as all NaNs or multiple consecutive NaNs, could further improve reliability.

---

347-362: Test covers a basic NaN scenario.

The test checks that a single NaN is filtered from both the y vector and ds. Consider adding broader test coverage (e.g., multiple NaNs or no NaNs).

crates/augurs-prophet/src/prophet.rs (1)

866-877: Test ensures no panic with NaN entries.

“fit_with_nans” successfully validates that including a NaN in the y column won’t crash the process. It matches the PR objective of robustly handling NaNs.
• You could extend coverage to verify the final fitted model state or that the resulting set of data is correct in length.

📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL
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📥 Commits

Reviewing files that changed from the base of the PR and between 8ed69b7 and 23fa3b3.

📒 Files selected for processing (4)

• crates/augurs-prophet/src/data.rs (2 hunks)
• crates/augurs-prophet/src/forecaster.rs (1 hunks)
• crates/augurs-prophet/src/prophet.rs (1 hunks)
• crates/augurs-prophet/src/prophet/prep.rs (2 hunks)

✅ Files skipped from review due to trivial changes (1)

• crates/augurs-prophet/src/forecaster.rs

🔇 Additional comments (2)

crates/augurs-prophet/src/data.rs (1)

135-140: Documentation is nice and clear.

These doc comments thoroughly describe the intent and outcome of removing NaN values from the y column and the corresponding entries in others. The mention that “NaN values in other columns are retained” ensures clarity in known behavior.

crates/augurs-prophet/src/prophet/prep.rs (1)

Line range hint 197-210: Great integration of “filter_nans” into preprocess.

Calling “data.filter_nans()” ensures the dataset is cleansed before proceeding. This matches Issue #209 and PR 219 objectives for robust Prophet preprocessing.
• The direct call is straightforward; be sure any upstream code that depends on “n” is now consistent with the filtered size.