`set_engine()` Removes Quantile Levels: A Tidymodels Issue

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`set_engine()` Removes Quantile Levels: A tidymodels Issue

Hey guys! Today, we're diving into a quirky issue in the tidymodels and parsnip universe. It's all about how the set_engine() function interacts with quantile levels, and trust me, you'll want to know about this, especially if you're into quantile regression.

The Curious Case of Disappearing Quantile Levels

So, what's the deal? It turns out that the order in which you set your engine and mode in tidymodels can affect whether your quantile levels stick around. Let's break it down with some code examples. We're going to explore a specific behavior where setting the engine after setting the quantile levels can lead to those levels being dropped. This is definitely not the behavior we'd expect, and it's something to be mindful of when building your models.

library(tidymodels)

qnt_lvls <- (1:3) / 4

linear_reg() |>
  set_engine("quantreg") |>
  set_mode("quantile regression", quantile_levels = qnt_lvls)
#> Linear Regression Model Specification (quantile regression)
#>
#> Computational engine: quantreg
#> Quantile levels: 0.25, 0.5, and 0.75.

linear_reg() |>
  set_mode("quantile regression", quantile_levels = qnt_lvls) |>
  set_engine("quantreg")
#> Linear Regression Model Specification (quantile regression)
#>
#> Computational engine: quantreg
#> Quantile levels: .

See that? In the first chunk of code, we first set the engine to "quantreg" and then set the mode to "quantile regression", specifying our quantile levels (qnt_lvls). Everything works as expected, and the quantile levels are happily displayed: 0.25, 0.5, and 0.75.

But in the second chunk, we flip the order. We first set the mode and quantile levels, and then we set the engine. Poof! The quantile levels disappear. They're replaced by a single dot (.), which isn't quite what we wanted, right? This behavior highlights a potential pitfall in how model specifications are handled, especially when dealing with engines that have specific requirements for the model mode.

This inconsistency can be a real head-scratcher if you're not aware of it. You might spend time debugging, wondering why your quantile levels aren't being recognized. The key takeaway here is that the order of operations matters. Setting the engine last seems to wipe out the quantile levels, which is not intuitive.

Digging Deeper: Why Does This Happen?

So, why exactly does this happen? The issue likely stems from how parsnip (the package within tidymodels that handles model specifications) updates the model specification when set_engine() is called. It appears that setting the engine might reset or overwrite some of the mode-specific settings, including the quantile levels. This isn't ideal, as we'd expect the engine setting to simply configure the computational backend without affecting other aspects of the model specification.

Think of it like this: you're ordering a fancy coffee. You specify you want a latte with almond milk and a shot of vanilla. That's like setting the mode and quantile levels. Then, you tell the barista you want it iced. That's like setting the engine. You wouldn't expect the barista to suddenly forget about the almond milk and vanilla, right? But that's kind of what's happening here. The engine setting is inadvertently clearing out the quantile levels.

This behavior is a subtle bug, but it can have significant consequences. If you're not careful, you might end up fitting a quantile regression model with the wrong quantile levels, or even worse, without any specified levels at all. This could lead to incorrect results and misleading conclusions. It's crucial to be aware of this issue and to double-check your model specifications to ensure that your quantile levels are being properly set.

The Importance of Model Specification Order

The order in which you define your model components matters. When using tidymodels and parsnip, particularly with functions like set_engine() and set_mode(), you must pay attention to the sequence of operations. Setting the engine too late in the process, as we've seen, can lead to unexpected consequences, such as the loss of quantile levels.

This issue isn't just about quantile regression; it highlights a broader principle in statistical modeling: the importance of careful model specification. Every detail matters, from the choice of engine to the setting of specific parameters. A seemingly minor oversight, like the order of function calls, can have a significant impact on the final results. By understanding these nuances, you can avoid common pitfalls and ensure that your models accurately reflect your research questions.

The Proposed Solution: Constructor Functions

The good news is that the tidymodels team is aware of this issue, and they're thinking about ways to address it. One potential solution is to create constructor functions for model specifications. These functions would essentially bundle up all the necessary settings for a particular model type, ensuring that everything is set in the correct order and that no settings are inadvertently dropped. This approach would provide a more robust and user-friendly way to define models, reducing the risk of errors and inconsistencies.

What are Constructor Functions?

Constructor functions are functions that create objects. In the context of tidymodels, a constructor function for, say, quantile regression, would take all the relevant arguments (like engine, quantile levels, etc.) and return a fully specified model object. This would encapsulate the logic of setting up the model correctly, preventing users from making mistakes by setting things in the wrong order. Think of it as a recipe for creating a model – you provide the ingredients, and the constructor function ensures they're mixed in the right order.

For example, instead of having to call linear_reg(), set_engine(), and set_mode() separately, you might have a single function like quantile_reg() that takes arguments for the engine and quantile levels and returns a complete model specification. This would make the process more streamlined and less error-prone. The key advantage of constructor functions is that they enforce a specific order of operations, ensuring that all necessary settings are applied correctly. This can be particularly helpful for complex models with many options, where it's easy to make mistakes when setting things up manually.

Benefits of Constructor Functions

Constructor functions offer several benefits:

  • Improved Clarity: They make the code more readable and easier to understand. Instead of a series of function calls, you have a single, self-contained function that creates the model.
  • Reduced Errors: They prevent common errors by ensuring that settings are applied in the correct order.
  • Increased Consistency: They promote consistency across different models and analyses.
  • Simplified Syntax: They can simplify the syntax for creating models, making it easier for users to get started.

The introduction of constructor functions would be a significant step forward for tidymodels, making it even more user-friendly and robust. By encapsulating the logic of model specification, these functions would reduce the risk of errors and ensure that users can confidently build the models they need.

How to Avoid This Issue (For Now)

In the meantime, while we wait for constructor functions (or another fix) to be implemented, there are a few things you can do to avoid this issue:

  1. Set the engine first: Make sure you call set_engine() before you set the mode and any mode-specific arguments like quantile_levels. This seems to be the safest approach.
  2. Double-check your model specifications: Always print your model specification to the console and verify that all the settings are correct. This is a good practice in general, but it's especially important when dealing with this issue.
  3. Be mindful of the order: Pay close attention to the order in which you're calling functions. If you're setting an engine and then noticing that some settings are being dropped, the order is likely the culprit.

These simple steps can help you avoid the disappearing quantile levels issue and ensure that your models are correctly specified. Remember, a little bit of caution can save you a lot of headaches down the road.

Wrapping Up

So, there you have it! The mystery of the disappearing quantile levels in tidymodels and parsnip. It's a quirky issue, but one that's important to be aware of. By understanding the cause of the problem and following the steps outlined above, you can avoid this pitfall and build your quantile regression models with confidence. And with the potential introduction of constructor functions on the horizon, the future of model specification in tidymodels looks bright!

Remember, always double-check your model specifications, and be mindful of the order in which you set your engine and mode. Happy modeling, folks!