Update evaluation.py: Added a function to evaluate the performance of EM models on validation data using a specified metric.

[abdelghanibelgaid]

Update evaluation.py: Added a function to evaluate the performance of EM models on validation data using a specified metric.

Motivation and Context

This PR was created to add a new function evaluate_em_model to the codebase. This function allows users to evaluate the performance of a trained EM model on validation data using a specified metric. It enhances the utility of the library by providing a convenient way to assess model performance.

How has this been tested?

The testing process includes the following strategies:

• Unit Testing: Unit tests have been implemented to assess the functionality of individual components of the evaluate_em_model function. These tests validate that the function behaves as expected under various conditions. Key aspects covered by unit tests include correct computation of the specified metric (e.g., accuracy, F1 score), and handling of edge cases, such as empty validation datasets.
• Integration Testing: Integration tests focus on evaluating how the evaluate_em_model function interacts with other components of the library. This involves testing the integration of the evaluate_em_model function within the existing library's ecosystem.

Checklist

• Read the contributing guidelines
• Opened this PR as a 'Draft Pull Request' if it is work-in-progress
• Updated the documentation to reflect the code changes
• Added a description of this change and added my name to the list of supporting contributions in the RELEASE.md file
• Added tests to cover my changes
• Assigned myself to the PR

Notice

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