Addressing `truncate_triangles` Default For Ragged Triangles In Epidemiological Nowcasting

Hey guys! Today, we're diving into a fascinating discussion about a potential bug and a proposed solution related to the truncate_triangles function, particularly when dealing with ragged triangles. This issue was brought to light in the epinowcast and baselinenowcast categories, and it's crucial for anyone working with epidemiological nowcasting.

Understanding the Bug: truncate_triangles and Ragged Data

At the heart of the matter is the truncate_triangles function, which currently defaults to truncating nrow(reporting_triangle) - ncol(reporting_triangle) - 1 triangles. Now, this works perfectly fine in most scenarios. However, when we encounter ragged triangles – datasets where the number of columns varies across rows – this default can lead to unexpected and, frankly, problematic behavior. Specifically, the formula nrow(reporting_triangle) - ncol(reporting_triangle) - 1 can result in a negative value. Why is this an issue? Because it doesn't make sense to truncate a negative number of triangles! This unexpected behavior can throw a wrench in your analysis and potentially lead to errors in your nowcasting models. To illustrate, consider this scenario:

sim_delay_pmf <- c(0.1, 0.2, 0.3, 0.1, 0.1, 0.1)

# Generate counts for each reference date
counts <- c(150,
  160, 170, 200, 100
)

# Create a complete triangle based on the known delay PMF
complete_triangle <- lapply(counts, function(x) round(x * sim_delay_pmf))
complete_triangle <- do.call(rbind, complete_triangle)

ragged_triangle <- construct_triangle(
  complete_triangle,
 structure = 2
)

trunc_rts <- truncate_triangles(ragged_triangle, n = 2)
#> Error in truncate_triangles(ragged_triangle) :
#> Assertion on 'n' failed: Element 1 is not >= 0.

The error arises because nrow(reporting_triangle) - ncol(reporting_triangle) - 1 evaluates to a negative number in this case, causing the function to fail. This highlights the need for a more robust default behavior when dealing with ragged triangles.

Diving Deeper into the Root Cause

The core of the problem lies in the assumption that ncol(reporting_triangle) - 1 represents the number of horizons. While this holds true for complete triangles, it falls apart when dealing with jagged data structures. Think of it like trying to fit a square peg into a round hole – the underlying assumption doesn't align with the actual data structure.

Expected Behavior and a Potential Solution

So, what's the fix? Well, the suggested solution is quite elegant in its simplicity. Instead of relying on the potentially problematic nrow(reporting_triangle) - ncol(reporting_triangle) - 1, we could default to nrow(reporting_triangle) - 1. This might seem like a minor tweak, but it has significant implications for the robustness and usability of the truncate_triangles function.

Why does this work? Even though defaulting to nrow(reporting_triangle) - 1 might mean truncating triangles that aren't technically nowcastable, it avoids the error caused by negative values. In most cases, users should specify the truncation number explicitly to ensure they get the desired behavior. Defaulting to nrow(reporting_triangle) - 1 acts as a safe fallback, preventing the function from crashing when faced with ragged triangles.

The Importance of Explicit Specification

It's worth emphasizing that, in general, specifying the number of triangles to truncate is the best practice. Relying on defaults can lead to unexpected outcomes, especially when dealing with complex data structures like ragged triangles. By explicitly setting the n parameter in truncate_triangles, you gain fine-grained control over the truncation process and ensure your analysis aligns with your specific needs.

Proposed Solution: Changing the Default for truncate_triangles

To address the bug described above, a proposed solution involves modifying the default value for the number of triangles to truncate in the truncate_triangles function. Currently, the default is calculated as nrow(reporting_triangle) - ncol(reporting_triangle) - 1. However, this formula can result in negative values when dealing with ragged triangles, as demonstrated in the bug report.

The Rationale Behind the New Default

The suggestion is to change the default to nrow(reporting_triangle) - 1. This adjustment aims to provide a more robust and intuitive behavior, especially in scenarios where the reporting triangle has a jagged structure. While this new default may lead to truncating triangles that are not strictly nowcastable in certain cases, it prevents the function from crashing due to negative truncation values. In essence, it prioritizes stability and usability without sacrificing functionality.

Addressing Potential Over-Truncation

It's important to acknowledge that this new default might, in some instances, result in over-truncation – truncating more triangles than necessary. However, the consensus is that this is a reasonable trade-off. The key takeaway is that users should generally specify the desired number of truncated triangles explicitly, using the n parameter in the truncate_triangles function. This explicit specification ensures precise control over the truncation process and minimizes the risk of unexpected behavior.

Balancing Usability and Precision

Choosing a default value often involves striking a balance between usability and precision. In this case, the proposed change leans towards usability by preventing crashes and ensuring the function works out-of-the-box for a wider range of triangle structures. While precision is crucial in epidemiological modeling, providing a stable and predictable default behavior enhances the overall user experience.

Impact and Context: Weekday Filter Implementation

This bug was actually discovered while implementing a weekday filter, adding another layer of context to the issue. It's a reminder that seemingly isolated bugs can sometimes surface during the development of new features or the refinement of existing ones. The fact that this issue was caught during the implementation of a weekday filter highlights the importance of thorough testing and debugging, especially when dealing with complex epidemiological models.

The Role of Debugging in Software Development

Debugging, as many of you know, is a critical part of the software development lifecycle. It's the process of identifying and resolving defects or bugs in software code or a system that prevent correct operation. In this case, the bug in truncate_triangles was found while debugging the weekday filter feature. This is a common scenario – often, debugging one feature can uncover issues in other parts of the codebase.

Importance of Thorough Testing

Thorough testing is just as vital as debugging. It involves executing software components or a system to evaluate one or more properties of interest. Testing helps to identify errors, gaps, or missing requirements, ensuring that the software meets the specified requirements and performs as expected. Without proper testing, bugs like this one in truncate_triangles might go unnoticed and could potentially lead to incorrect results or analyses.

Contextual Awareness in Bug Fixing

Understanding the context in which a bug is discovered can provide valuable insights into the root cause and potential solutions. In this instance, knowing that the bug was found during the implementation of a weekday filter helps to focus the debugging efforts. It also underscores the need to consider how different parts of the codebase interact with each other. A bug in one function might have implications for other functions or features, so it's important to take a holistic approach to debugging and bug fixing.

Conclusion: A Step Towards More Robust Nowcasting

In conclusion, this discussion highlights the importance of careful consideration when setting default behaviors in software functions, especially when dealing with complex data structures. The proposed change to the default value in truncate_triangles represents a step forward in making the function more robust and user-friendly, particularly for those working with ragged triangles. By prioritizing stability and usability, we can empower epidemiologists and researchers to build more reliable nowcasting models and gain deeper insights into infectious disease dynamics.

So, what are your thoughts on this issue and the proposed solution? Feel free to share your insights and experiences – let's keep the conversation going!