Fix issue 376 and related bugs

[ooples]

…lculators

This commit implements comprehensive unit tests for four specialized loss function fitness calculators as requested in issue #376:

• DiceLossFitnessCalculator: Tests for medical imaging and segmentation scenarios
• JaccardLossFitnessCalculator: Tests for object detection using IoU metrics
• ContrastiveLossFitnessCalculator: Tests for similarity learning (face recognition)
• CosineSimilarityLossFitnessCalculator: Tests for document similarity and embeddings

Test Coverage Includes:

• Perfect predictions (zero loss)
• Worst case scenarios (maximum loss)
• Partial overlaps and varying degrees of similarity
• Edge cases (division by zero, empty sets, all zeros)
• Probabilistic predictions
• Different numeric types (float, double)
• Real-world scenarios (medical imaging, object detection, etc.)
• Proper null handling and exception testing

Each test file contains 20+ comprehensive test cases covering:

• Constructor behavior with different data set types
• Mathematical correctness of loss calculations
• IsBetterFitness comparison logic
• ModelEvaluationData integration
• Float and double type support
• Magnitude invariance (for cosine similarity)
• Imbalanced data handling

The tests follow the existing project patterns and conventions used in other loss function tests (e.g., MeanSquaredErrorLossTests, HuberLossTests).

Resolves #376

User Story / Context

• Reference: [US-XXX] (if applicable)
• Base branch: merge-dev2-to-master

Summary

• What changed and why (scoped strictly to the user story / PR intent)

Verification

• Builds succeed (scoped to changed projects)
• Unit tests pass locally
• Code coverage >= 90% for touched code
• Codecov upload succeeded (if token configured)
• TFM verification (net46, net6.0, net8.0) passes (if packaging)
• No unresolved Copilot comments on HEAD

Copilot Review Loop (Outcome-Based)

Record counts before/after your last push:

• Comments on HEAD BEFORE: [N]
• Comments on HEAD AFTER (60s): [M]
• Final HEAD SHA: [sha]

Files Modified

• List files changed (must align with scope)

Notes

• Any follow-ups, caveats, or migration details

[coderabbitai]

Warning

Rate limit exceeded

@ooples has exceeded the limit for the number of commits or files that can be reviewed per hour. Please wait 6 minutes and 30 seconds before requesting another review.

⌛ How to resolve this issue?

After the wait time has elapsed, a review can be triggered using the @coderabbitai review command as a PR comment. Alternatively, push new commits to this PR.

We recommend that you space out your commits to avoid hitting the rate limit.

🚦 How do rate limits work?

CodeRabbit enforces hourly rate limits for each developer per organization.

Our paid plans have higher rate limits than the trial, open-source and free plans. In all cases, we re-allow further reviews after a brief timeout.

Please see our FAQ for further information.

📥 Commits

Reviewing files that changed from the base of the PR and between 4bbb1b8 and a9dc298.

📒 Files selected for processing (4)

• tests/AiDotNet.Tests/UnitTests/FitnessCalculators/ContrastiveLossFitnessCalculatorTests.cs (1 hunks)
• tests/AiDotNet.Tests/UnitTests/FitnessCalculators/CosineSimilarityLossFitnessCalculatorTests.cs (1 hunks)
• tests/AiDotNet.Tests/UnitTests/FitnessCalculators/DiceLossFitnessCalculatorTests.cs (1 hunks)
• tests/AiDotNet.Tests/UnitTests/FitnessCalculators/JaccardLossFitnessCalculatorTests.cs (1 hunks)

✨ Finishing touches

• 📝 Generate docstrings

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch claude/fix-issue-376-011CUw5zp2S5EJB6bJL418Gy

---

Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out.

❤️ Share

• X
• Mastodon
• Reddit
• LinkedIn

_{Comment @coderabbitai help to get the list of available commands and usage tips.}

[Copilot]

Pull Request Overview

This PR adds comprehensive unit test coverage for four new fitness calculator classes used in machine learning model evaluation: JaccardLoss, DiceLoss, CosineSimilarityLoss, and ContrastiveLoss fitness calculators.

• Adds 395 lines of tests for JaccardLossFitnessCalculator covering IoU-based segmentation metrics
• Adds 337 lines of tests for DiceLossFitnessCalculator covering Dice coefficient-based segmentation metrics
• Adds 475 lines of tests for CosineSimilarityLossFitnessCalculator covering vector direction similarity
• Adds 422 lines of tests for ContrastiveLossFitnessCalculator covering similarity learning metrics

Reviewed Changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated no comments.

File	Description
tests/AiDotNet.Tests/UnitTests/FitnessCalculators/JaccardLossFitnessCalculatorTests.cs	Comprehensive tests for Jaccard Loss (IoU) fitness calculator, including edge cases, perfect/no overlap scenarios, probabilistic predictions, and comparison with Dice metric
tests/AiDotNet.Tests/UnitTests/FitnessCalculators/DiceLossFitnessCalculatorTests.cs	Complete test suite for Dice Loss fitness calculator covering segmentation scenarios, imbalanced data handling, and various overlap conditions
tests/AiDotNet.Tests/UnitTests/FitnessCalculators/CosineSimilarityLossFitnessCalculatorTests.cs	Extensive tests for Cosine Similarity Loss including vector alignment scenarios, magnitude invariance, document similarity, and text embedding use cases
tests/AiDotNet.Tests/UnitTests/FitnessCalculators/ContrastiveLossFitnessCalculatorTests.cs	Thorough tests for Contrastive Loss covering similar/dissimilar pairs, margin behavior, face recognition scenarios, and various distance calculations

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.