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v1.2.3 - tam - Initial Public Release

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@yallioux yallioux released this 08 Jun 13:38
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v1.2.3
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TAM Framework v1.2.3 - Initial Public Release

This release marks the first official open-source release of the unified TAM framework. It aggregates all internal development milestones (v1.1.1 through v1.2.2), representing a complete architectural overhaul from the legacy weakl package (v0.0.6).

⚠️ Note to JOSS Reviewers: Features tagged with (BETA) (active research) and (EXP) (experimental) are strictly excluded from the stable JOSS evaluation scope.

🏗️ Major Architectural Overhaul

  • Formula API: Transitioned from a legacy dictionary-based configuration to an intuitive, R-like Formula API (e.g., y ~ s(temp) + l(day)).
  • Meta-Learner Ecosystem: Introduced a complete suite of object-oriented meta-learners, including StaticTAM (core fitting), AdaptiveTAM (online learning), OperaTAM (GPU-accelerated expert aggregation), KalmanTAM (BETA), AutoTAM (EXP) (evolutionary AutoML), SafetyTAM (EXP) (Conformal Prediction), and HierarchicalTAM (BETA).
  • Math & Hardware Dispatchers: Implemented an intelligent routing layer that dynamically switches between chunked direct solvers and Matrix-Free Sparse Conjugate Gradient solvers based on topological complexity and available VRAM.

🌌 The "Spectrum" Core Effects Library

This release completes the "Spectrum" library, flattening diverse physical and statistical topologies into a single unified API:

  • Standard & Continuous Bases: Added standard Linear terms (l), Real-valued Fourier harmonics (f) (transitioned from complex exponentials to real sine/cosine bases), Splines (s), and Chebyshev polynomials (p).
  • Categorical & Signal Processing: Introduced Categorical effects (c) supporting nominal, ordinal, and fourier topological mappings, alongside Ricker Wavelets (w), and PID controller effects (pid) for dynamic derivative penalization.
  • Advanced Functional Bases: Added RBF with both Gaussian and Matérn kernels (rbf), and Neural projections (n) (EXP) for embedding deep layers.
  • Tree & Forest Integration: Integrated native GPU-based Random Forests (t), Flat N-ary Histograms to prevent matrix singularities, and varying-coefficient Linear Trees (lt).
  • Interactions & Physics: Added Multivariate Tensor Products (te) for surface modeling and Universal Physics effects (phys) (BETA) for Physics-Informed Kernel Learning (PIKL) using ODEs/PDEs.

🚀 Performance & Mathematical Stability

  • Native GPU Acceleration: Fully migrated intensive design matrix constructions (Splines, Wavelets, RBF) from CPU to GPU.
  • Hardware Safeguards: Built a robust memory management module with smart chunking and dummy-pass VRAM footprint estimation to prevent CUDA Out-of-Memory crashes.
  • OOD Extrapolation Wrapper: Introduced native Out-Of-Distribution extrapolation utilizing multidimensional directional derivatives, strictly bounding feature maps to the $[-1, 1]^F$ hypercube.
  • GCV Auto-ML: Added Generalized Cross Validation (GCV) for automatic global regularization parameter selection, eliminating the need for validation sets.

🛠️ Ecosystem, MLOps, and Tooling

  • Academic Benchmarks: Included official reproduction scripts for foundational load forecasting architectures (Pierrot-Goude 2011, Doumèche et al. 2025).
  • MLOps Dashboard: Enhanced the plotting suite with chronological forecast smoothing, Test Set Vulnerability heatmaps, and unified model color mapping.
  • Comprehensive Documentation: Published extensive markdown documentation bridging mathematical theory and framework architecture, alongside a full cheatsheet.py.
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