Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update evaluation.py: Added a function to evaluate the performance of EM models on validation data using a specified metric. #215

Open
wants to merge 1 commit into
base: develop
Choose a base branch
from

Conversation

abdelghanibelgaid
Copy link

@abdelghanibelgaid abdelghanibelgaid commented Sep 27, 2023

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

  • I acknowledge and agree that, by checking this box and clicking "Submit Pull Request":

  • I submit this contribution under the Apache 2.0 license and represent that I am entitled to do so on behalf of myself, my employer, or relevant third parties, as applicable.

  • I certify that (a) this contribution is my original creation and / or (b) to the extent it is not my original creation, I am authorised to submit this contribution on behalf of the original creator(s) or their licensees.

  • I certify that the use of this contribution as authorised by the Apache 2.0 license does not violate the intellectual property rights of anyone else.

… EM models on validation data using a specified metric.
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

1 participant