[AI Evaluation] [Regression] EvaluationResult.Get Throws When Trying To Fetch Metric With Name ViolenceEvaluator.ViolenceMetricName.

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Introduction

In recent times, a regression was introduced in the AI evaluation process, causing an exception to be thrown when trying to fetch a metric with a specific name. This issue arose during a refactoring process, highlighting the importance of thorough testing and validation in software development. In this article, we will delve into the details of this regression, its causes, and the necessary steps to resolve it.

Understanding the Issue

The Azure AI Foundry Evaluation service returns metrics with names that differ from the ones used in the Safety library. To address this discrepancy, a translation process is employed to map the service's metric names to more user-friendly names. However, this translation process only affects the returned metrics, leaving the EvaluationResult.Metrics dictionary unchanged. As a result, when the EvaluationResult.Get method attempts to fetch a metric with a name that has been translated, it throws an exception.

The Root Cause of the Regression

The root cause of this regression lies in the refactoring process that introduced the issue. During this process, the code was modified to translate the metric names returned by the Azure AI Foundry Evaluation service. While the translation process was successful, the EvaluationResult.Metrics dictionary was not updated to reflect the new metric names. This means that the dictionary still stores metrics by their original names, leading to the exception thrown by EvaluationResult.Get.

The Impact of the Regression

The impact of this regression is significant, as it affects the ability to fetch metrics with translated names. This can lead to errors and inconsistencies in the evaluation process, ultimately affecting the accuracy and reliability of the results. Furthermore, the regression highlights the importance of thorough testing and validation in software development, as it can have far-reaching consequences if left unchecked.

Resolving the Regression

To resolve this regression, it is essential to update the EvaluationResult.Metrics dictionary to reflect the new metric names. This can be achieved by patching up the keys in the dictionary to match the translated metric names. By doing so, the EvaluationResult.Get method will be able to fetch metrics with translated names without throwing an exception.

Implementation Details

To implement the fix, the following steps can be taken:

  1. Update the EvaluationResult.Metrics dictionary: Patch up the keys in the dictionary to match the translated metric names.
  2. Test the updated code: Verify that the EvaluationResult.Get method can fetch metrics with translated names without throwing an exception.
  3. Validate the results: Ensure that the updated code produces accurate and reliable results.

Best Practices for Avoiding Similar Regressions

To avoid similar regressions in the future, the following best practices can be employed:

  1. Thorough testing and validation: Ensure that code changes are thoroughly tested and validated to prevent regressions.
  2. Code reviews: Conduct regular code reviews to identify potential issues and prevent regressions.
  3. Documentation: Maintain accurate and up-to-date documentation to ensure that developers are aware of changes and potential issues.

Conclusion

In conclusion, the regression introduced in the AI evaluation process highlights the importance of thorough testing and validation in software development. By understanding the root cause of the regression and implementing the necessary fix, developers can prevent similar issues from arising in the future. By following best practices and maintaining accurate documentation, developers can ensure that their code is reliable, accurate, and free from regressions.

Introduction

In our previous article, we discussed the regression introduced in the AI evaluation process, causing an exception to be thrown when trying to fetch a metric with a specific name. In this article, we will address some of the frequently asked questions related to this regression and provide detailed answers to help developers understand the issue and its resolution.

Q1: What is the root cause of the regression?

A1: The root cause of the regression lies in the refactoring process that introduced the issue. During this process, the code was modified to translate the metric names returned by the Azure AI Foundry Evaluation service. However, the EvaluationResult.Metrics dictionary was not updated to reflect the new metric names, leading to the exception thrown by EvaluationResult.Get.

Q2: Why was the EvaluationResult.Metrics dictionary not updated?

A2: The EvaluationResult.Metrics dictionary was not updated because the translation process only affected the returned metrics, leaving the dictionary unchanged. This means that the dictionary still stores metrics by their original names, leading to the exception thrown by EvaluationResult.Get.

Q3: How can I resolve the regression?

A3: To resolve the regression, you need to update the EvaluationResult.Metrics dictionary to reflect the new metric names. This can be achieved by patching up the keys in the dictionary to match the translated metric names.

Q4: What are the steps to implement the fix?

A4: The steps to implement the fix are as follows:

  1. Update the EvaluationResult.Metrics dictionary: Patch up the keys in the dictionary to match the translated metric names.
  2. Test the updated code: Verify that the EvaluationResult.Get method can fetch metrics with translated names without throwing an exception.
  3. Validate the results: Ensure that the updated code produces accurate and reliable results.

Q5: How can I prevent similar regressions in the future?

A5: To prevent similar regressions in the future, you can follow these best practices:

  1. Thorough testing and validation: Ensure that code changes are thoroughly tested and validated to prevent regressions.
  2. Code reviews: Conduct regular code reviews to identify potential issues and prevent regressions.
  3. Documentation: Maintain accurate and up-to-date documentation to ensure that developers are aware of changes and potential issues.

Q6: What are the consequences of not resolving the regression?

A6: If the regression is not resolved, it can lead to errors and inconsistencies in the evaluation process, ultimately affecting the accuracy and reliability of the results. Furthermore, the regression highlights the importance of thorough testing and validation in software development, as it can have far-reaching consequences if left unchecked.

Q7: Can I use a different approach to resolve the regression?

A7: While there may be alternative approaches to resolve the regression, the recommended approach is to update the EvaluationResult.Metrics dictionary to reflect the new metric names. This is the most straightforward and effective way to resolve the issue.

Q8: How can I verify that the regression is resolved?

A8: To verify that the regression is resolved, you can test the updated code by fetching metrics with translated names and ensuring that the EvaluationResult.Get method does not throw an exception.

Conclusion

In conclusion, the regression introduced in the AI evaluation process highlights the importance of thorough testing and validation in software development. By understanding the root cause of the regression and implementing the necessary fix, developers can prevent similar issues from arising in the future. By following best practices and maintaining accurate documentation, developers can ensure that their code is reliable, accurate, and free from regressions.