Enhancing The 'custom_experiment' Signature For Flexibility

The Challenge: Signature Mismatch in Custom Experiments

Let's dive into a common snag when working with the custom_experiment decorator, especially if you're keen on making your experiments as streamlined as possible. The core of the issue revolves around how the decorator reshapes the function's signature. This alteration can sometimes trip you up, especially if you're trying to call the experiment function directly within a script. The original, undecorated function is still accessible as an attribute of the custom_function, as documented here, but it adds an extra layer of complexity that can feel a bit cumbersome. We want to make it easier for you to use your experiments, whether they're running in a full-fledged ADO environment or a quick script.

The Core Problem and Why It Matters

The fundamental problem is that when you decorate a function with @custom_experiment, the signature of the function changes. This means the way you call the function – the arguments you pass to it – might not be what you'd initially expect. For instance, imagine you have a simple function you've written, and you want to test it or use it in a different context outside of your typical ADO (Automation Delivery Orchestrator) workflow. You might run into compatibility issues or spend extra time figuring out how to make it work. This is especially true if you are new to custom experiments.

Impact on Experiment Calls

This signature change directly impacts how you call your experiment functions. Without this change, calling the experiment directly might require extra steps, like fetching the experiment definition from the registry, creating an entity, and then calling the experiment with its specific definition. Though ADO handles this seamlessly, the process can become unwieldy if you are operating outside of the ADO framework or when you're in the rapid prototyping and testing phase of a project.

The Annoyance Factor

While the current system isn't broken, the signature mismatch can be a minor annoyance that breaks the flow of development. It forces you to remember or look up how to call the function correctly, instead of just using it as you originally intended. This creates a moment of friction, a small but unnecessary detour in the coding process that, over time, can add up to wasted time and effort. The goal is to make the entire process more intuitive.

The Proposed Solution: Streamlining the custom_experiment Process

The goal is to make using custom_experiment more straightforward. We want the decorated function to feel and behave more like its original, undecorated self, with minimal fuss. This boils down to two key changes:

Preserving the Original Function Description

First and foremost, we propose that the custom_experiment decorator should preserve the original function signature. This means that when you decorate a function, the way you call it should stay as close as possible to the way you would call the original function. You should be able to pass values to the function using a dictionary (e.g., custom_experiment(**d)) without needing to worry about the internal workings of the decorator.

Removing Unnecessary Parameter Concepts

Second, we aim to streamline the concept of parameters. The existing code uses the func tools wrapper which will be adjusted to better align with the core objective of the decorator. This helps in simplifying how parameters are managed and used within the decorated function. The current structure, while functional, can be simplified to offer a more unified and intuitive experience for users.

Code Changes

The following code adjustments are proposed to achieve these improvements:

1. Code Migration: The code currently present within the func tools wrapper needs to be moved. The goal is to relocate specific portions of code related to the function's signature management to a more appropriate function within the custom_experiments.py file.

   • Original Location: The code currently resides within lines 324-325 of the custom_experiments.py file, as shown in the original code snippet.
   • Proposed New Location: The code will be migrated to the function defined in lines 533-536 of the same file. This strategic relocation aims to enhance the clarity and maintainability of the codebase, ensuring that the critical function-signature preservation logic is clearly articulated and easily accessible.

Alternatives Considered

We looked at some alternative approaches, each with its own set of trade-offs:

Keeping the Current System

The most straightforward alternative is to do nothing and stick with the current system. The advantage of this approach is that it requires no changes and is already functioning. However, this maintains the existing issues with function signature changes, which can cause confusion for developers.

Using a Different Decorator

One could consider using a completely different decorator. This approach could offer more control over the function signature. But, the downside of this option would be significant. Changing decorators would demand that all users would have to refactor their existing code. This would significantly affect the overall impact.

More Complex Parameter Handling

Instead of simplifying parameter handling, we could introduce more complex features, such as parameter validation or more flexible ways to specify parameters. The benefit would be more powerful parameters. But, this complexity would increase the learning curve and could make the custom_experiment decorator more difficult to use.

Additional Context and Use Cases

Let's get into some specific examples to see how this enhancement affects experiment calls and parameter passing. This will give you a clearer idea of how the proposed changes improve flexibility and usability.

Calling the Function with a Dictionary

Let's say you have a dictionary d containing the values you need to pass to your custom_experiment function. With the proposed changes, you can call the function directly like this:

custom_experiment(**d)

This direct approach keeps the calling process clean and intuitive. You don't need to perform extra steps or handle internal signature changes.

Calling with an Entity

Now, imagine you have an entity, e. To use your custom_experiment function with this entity, you can use the following approach:

custom_experiment(**custom_experiment._experiment.values_from_entity(e))

This method allows you to extract the necessary values from the entity and pass them to your function. The proposed changes ensures a seamless integration, as it keeps the function calls aligned with expectations.

Using a Parameterized Instance

If you have a parameterized instance of the experiment, let's say p, you can call your function using:

custom_experiment(**p.values_from_entity(e))

This method allows you to use your function with parameterized instances, providing flexibility in different scenarios. The signature remains consistent, regardless of the parameterization used.

Benefits of the Proposed Changes

Enhanced Usability

The primary benefit is that the calling process becomes more user-friendly. Developers can call custom experiments as they naturally would, without extra steps or needing to remember how the signature has changed. This results in cleaner code and faster development cycles.

Reduced Confusion

By keeping the function signatures consistent, the changes minimize the potential for confusion. Users no longer need to investigate how the decorator transforms their original function. This leads to a smoother learning curve, especially for those new to custom experiments.

Improved Compatibility

These changes will improve how easily you can use your custom experiments in diverse contexts. Whether it's within ADO or in standalone scripts, the ability to call the experiment directly simplifies integration and reuse of experiment code across projects.

Increased Productivity

By simplifying function calls and parameter management, developers can get more done in less time. No more wrestling with signature mismatches or searching for workarounds; you can focus on the experiment logic itself, improving the efficiency of development.

Conclusion: Making Custom Experiments More Accessible

By ensuring that the custom_experiment decorator preserves the original function description and streamlining parameter handling, we're making it simpler and more intuitive to work with custom experiments. These changes will lead to increased usability, reduced confusion, and better compatibility, ultimately boosting developer productivity. This effort streamlines the experiment creation process, allowing you to focus on the core logic rather than the setup.

For more information and detailed examples, check out the official IBM ADO documentation on custom experiments.