Version 0.1.5

New Features

Parameter Transformation Module (braintools.param)

• 7 new bijective transforms for constrained optimization and probabilistic modeling:
  • Positive: Constrains parameters to (0, +∞) using exponential transformation
  • Negative: Constrains parameters to (-∞, 0) using negative softplus
  • ScaledSigmoid: Sigmoid with adjustable sharpness/temperature parameter (beta)
  • Power: Box-Cox family power transformation for variance stabilization
  • Ordered: Ensures monotonically increasing output vectors (useful for cutpoints in ordinal regression)
  • Simplex: Stick-breaking transformation for probability vectors summing to 1
  • UnitVector: Projects vectors onto the unit sphere (L2 norm = 1)
• Jacobian computation: Added log_abs_det_jacobian() method to Transform base class and implementations for probabilistic modeling
  • Implemented for: Identity, Sigmoid, Softplus, Log, Exp, Affine, Chain, Positive

Surrogate Gradient Enhancements (braintools.surrogate)

• Gradient computation of hyperparameters of surrogate gradient functions.
• Fix batching issue in surrogate gradient functions

Improvements

API Enhancements

• __repr__ methods: Added string representations to all Transform classes and Param class for better debugging
• Enhanced documentation: Updated docs/apis/param.rst with comprehensive API reference
  • Organized sections: Base Classes, Parameter Wrapper, Bounded Transforms, Positive/Negative Transforms, Advanced Transforms, Composition Transforms
  • Descriptive explanations for each transform's use case

Code Quality

• Comprehensive test coverage: Added 28 new tests for param module (45 total tests passing)
  • Tests for all new transforms: roundtrip, constraints, repr methods
  • Tests for log_abs_det_jacobian correctness
  • Tests for edge cases and numerical stability

What's Changed

• ⬆️ Bump actions/checkout from 4 to 6 by @dependabot[bot] in #62
• Refactor surrogate gradient tests; enhance checkpointing, add ClippedTransform by @chaoming0625 in #61
• Introduce param module and refactor surrogate gradients by @chaoming0625 in #63
• Add param transforms with Jacobians; enhance surrogate gradients by @chaoming0625 in #64

Full Changelog: v0.1.4...v0.1.5