Sweep: Update of unit-test required

[sweep-ai[bot]]

Description

This pull request introduces a comprehensive update to the unit tests for various loss functions within the fedot_ind.core.models.nn.network_modules.losses module. It adds tests for a variety of loss functions including ExpWeightedLoss, HuberLoss, LogCoshLoss, MaskedLossWrapper, CenterLoss, CenterPlusLoss, FocalLoss, TweedieLoss, SMAPELoss, and RMSELoss. Additionally, it includes tests for the lambda_prepare function with different types of inputs.

Summary

• Added unit tests for multiple loss functions to ensure correctness and stability.
• Tests cover different scenarios and input types for the lambda_prepare function.
• Introduced tests for ExpWeightedLoss focusing on its behavior over multiple time steps.
• Implemented tests for HuberLoss, LogCoshLoss, and their behavior with different reduction methods (mean, sum, none).
• Added a test for MaskedLossWrapper to verify its functionality with NaN values in the target tensor.
• Included tests for CenterLoss and CenterPlusLoss to validate loss calculation with respect to class centers.
• Provided tests for FocalLoss with different configurations of alpha, gamma, and reduction methods.
• Tested TweedieLoss with different power values to ensure correct loss computation.
• Added tests for SMAPELoss and RMSELoss to verify their accuracy in calculating respective losses.

This update ensures that the loss functions work as expected across a variety of conditions and input types, enhancing the reliability of the neural network module's loss computation capabilities.

Fixes #148.

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[pep8speaks]

Thanks for update, @sweep-ai[bot]!

• In the file tests/unit/core/models/test_losses.py:

Line 3:121: E501 line too long (207 > 120 characters)

Comment last updated at 2024-05-28 14:09:08 UTC