Melvin Alvarez
f5f6005f80
- Implement RustObjective trait for GIL-free parallelization
- Add 5 benchmark functions: Sphere, Rosenbrock, Rastrigin, Ackley, Griewank
* Each implements RustObjective with evaluate(), dimension(), global_optimum()
* Exposed to Python with __call__ method
- Add parallel_differential_evolution_rust() function:
* Uses Rayon for parallel population evaluation
* Works with RustObjective implementations only
* Eliminates Python GIL overhead for 10-100× speedup
* Supports all DE strategies and adaptive parameters
- Create comprehensive examples:
* parallel_de_benchmark.py: Performance benchmarks showing speedup
* polaroid_optimizr_integration.py: 4 workflows combining Polaroid + OptimizR
- Regime detection with HMM
- Strategy parameter optimization
- Portfolio risk analysis
- Pairs trading pipeline
- Module integration:
* Export benchmark functions in Python API
* Export parallel_differential_evolution_rust
* Update __init__.py and core.py with new functions
- Technical implementation:
* RustObjective trait in src/rust_objectives.rs
* Parallel evaluation uses par_iter() from Rayon
* Per-thread RNG seeding for reproducibility
* Maintains same API as standard DE for easy comparison
Part of Priority 2: Enable Rust parallelization (Enhancement Strategy)
Expected speedup: 10-100× on multi-core systems for pure Rust objectives
80 lines
1.8 KiB
Python
80 lines
1.8 KiB
Python
"""
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OptimizR - High-Performance Optimization Algorithms
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===================================================
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Fast, reliable implementations of advanced optimization and statistical
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inference algorithms with Rust acceleration and pure Python fallbacks.
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.. moduleauthor:: OptimizR Contributors
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"""
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from optimizr.hmm import HMM
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from optimizr.core import (
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mcmc_sample,
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differential_evolution,
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parallel_differential_evolution_rust,
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grid_search,
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mutual_information,
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shannon_entropy,
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sparse_pca_py,
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box_tao_decomposition_py,
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elastic_net_py,
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hurst_exponent_py,
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compute_risk_metrics_py,
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estimate_half_life_py,
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bootstrap_returns_py,
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# Time-series utilities
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prepare_for_hmm_py,
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rolling_hurst_exponent_py,
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rolling_half_life_py,
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return_statistics_py,
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create_lagged_features_py,
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rolling_correlation_py,
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# Benchmark functions
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Sphere,
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Rosenbrock,
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Rastrigin,
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Ackley,
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Griewank,
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)
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# Try to import maths_toolkit from Rust backend
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try:
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from optimizr import _core
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maths_toolkit = _core
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except (ImportError, AttributeError):
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maths_toolkit = None
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__version__ = "0.2.0"
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__all__ = [
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"HMM",
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"mcmc_sample",
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"differential_evolution",
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"parallel_differential_evolution_rust",
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"grid_search",
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"mutual_information",
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"shannon_entropy",
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"sparse_pca_py",
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"box_tao_decomposition_py",
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"elastic_net_py",
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"hurst_exponent_py",
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"compute_risk_metrics_py",
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"estimate_half_life_py",
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"bootstrap_returns_py",
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# Time-series utilities
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"prepare_for_hmm_py",
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"rolling_hurst_exponent_py",
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"rolling_half_life_py",
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"return_statistics_py",
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"create_lagged_features_py",
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"rolling_correlation_py",
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# Benchmark functions
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"Sphere",
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"Rosenbrock",
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"Rastrigin",
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"Ackley",
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"Griewank",
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"maths_toolkit",
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]
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