Melvin Avarez
79f51e4775
Major Features: • Comprehensive Differential Evolution with 5 strategies (rand1, best1, currenttobest1, rand2, best2) • Adaptive jDE algorithm for self-tuning F and CR parameters • Convergence tracking with history records and early stopping • Mathematical toolkit module (780 lines): gradient, hessian, jacobian, statistics, linear algebra • Optimal control framework: HJB solvers, regime switching, jump diffusion, MRSJD • Sparse optimization: Sparse PCA, Box-Tao decomposition, ADMM, Elastic Net • Rayon parallelization infrastructure (ready for pure Rust objectives) Performance: • 74-88× speedup for DE vs SciPy • 50-100× speedup overall vs pure Python Refactoring & Cleanup: • Removed 5 legacy files (de_refactored.rs, hmm_legacy.rs, hmm_refactored.rs, mcmc_legacy.rs, mcmc_refactored.rs) • Modular architecture with trait-based design • Generic implementations (no domain-specific code) • Updated Python bindings for new DE API • Fixed ALL compilation warnings (0 errors, 0 warnings) Documentation: • Updated README with v0.2.0 features and benchmarks • Created RELEASE_NOTES_v0.2.0.md (comprehensive changelog) • New optimal control tutorial notebook (03_optimal_control_tutorial.ipynb) • Updated API examples in README • Created test_release.py for release validation Version Bumps: • Cargo.toml: 0.1.0 → 0.2.0 • pyproject.toml: 0.1.0 → 0.2.0 • python/__init__.py: 0.1.0 → 0.2.0 Breaking Changes: • DE API: mutation_factor/crossover_rate → f/cr • DE API: use_adaptive_jde → adaptive • DE API: strategy names simplified (e.g., 'rand/1/bin' → 'rand1') • DE returns: (x, fun) tuple instead of dict-like object Known Items (Post-Release): • Mathematical toolkit functions available in Rust but not yet exposed to Python • MCMC Python wrapper needs API update to match new Rust implementation • Tutorial notebooks need DE API updates Tests: 34 Rust tests passing, core Python functionality validated with test_release.py
423 lines
13 KiB
Python
423 lines
13 KiB
Python
"""
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Core optimization functions with Rust acceleration
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"""
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import warnings
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from typing import Callable, List, Tuple, Optional
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import numpy as np
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# Try to import Rust backend
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try:
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from optimizr._core import (
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mcmc_sample as _rust_mcmc_sample,
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differential_evolution as _rust_differential_evolution,
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grid_search as _rust_grid_search,
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mutual_information as _rust_mutual_information,
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shannon_entropy as _rust_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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)
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RUST_AVAILABLE = True
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except ImportError:
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RUST_AVAILABLE = False
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warnings.warn(
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"Rust backend not available. Using pure Python fallbacks. "
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"Install with 'pip install optimizr' to enable Rust acceleration.",
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RuntimeWarning
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)
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def mcmc_sample(
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log_likelihood_fn: Callable[[List[float], List[float]], float],
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data: np.ndarray,
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initial_params: np.ndarray,
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param_bounds: List[Tuple[float, float]],
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n_samples: int = 10000,
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burn_in: int = 1000,
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proposal_std: float = 0.1,
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) -> np.ndarray:
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"""
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MCMC Metropolis-Hastings sampler.
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Generates samples from a target distribution using the Metropolis-Hastings
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algorithm with Gaussian random walk proposals.
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Parameters
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----------
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log_likelihood_fn : callable
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Function that computes log P(data | params). Should accept
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(params: list, data: list) and return float.
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data : np.ndarray
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Observed data (passed to log_likelihood_fn)
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initial_params : np.ndarray
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Starting parameter values
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param_bounds : list of (float, float)
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[(min, max), ...] bounds for each parameter
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n_samples : int, default=10000
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Number of samples to generate (after burn-in)
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burn_in : int, default=1000
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Number of initial samples to discard
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proposal_std : float, default=0.1
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Standard deviation of Gaussian proposals
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Returns
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-------
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samples : np.ndarray
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Array of shape (n_samples, n_params) with parameter samples
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Examples
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--------
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>>> def log_likelihood(params, data):
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... mu, sigma = params
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... residuals = (data - mu) / sigma
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... return -0.5 * np.sum(residuals**2) - len(data) * np.log(sigma)
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>>> data = np.random.randn(100) + 2.0
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>>> samples = mcmc_sample(
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... log_likelihood_fn=log_likelihood,
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... data=data,
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... initial_params=np.array([0.0, 1.0]),
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... param_bounds=[(-10, 10), (0.1, 10)],
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... n_samples=10000,
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... burn_in=1000
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... )
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>>> print(f"Posterior mean: {np.mean(samples[:, 0]):.2f}")
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"""
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if RUST_AVAILABLE:
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# Convert to lists if numpy arrays
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data_list = data.tolist() if hasattr(data, 'tolist') else list(data)
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params_list = initial_params.tolist() if hasattr(initial_params, 'tolist') else list(initial_params)
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samples = _rust_mcmc_sample(
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log_likelihood_fn=log_likelihood_fn,
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data=data_list,
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initial_params=params_list,
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param_bounds=param_bounds,
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n_samples=n_samples,
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burn_in=burn_in,
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proposal_std=proposal_std,
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)
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return np.array(samples)
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else:
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# Pure Python fallback
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return _mcmc_sample_python(
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log_likelihood_fn, data, initial_params, param_bounds,
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n_samples, burn_in, proposal_std
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)
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def differential_evolution(
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objective_fn: Callable[[np.ndarray], float],
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bounds: List[Tuple[float, float]],
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popsize: int = 15,
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maxiter: int = 1000,
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f: Optional[float] = None,
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cr: Optional[float] = None,
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strategy: str = "rand1",
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seed: Optional[int] = None,
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tol: float = 1e-6,
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atol: float = 1e-8,
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track_history: bool = False,
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parallel: bool = False,
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adaptive: bool = False,
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constraint_penalty: float = 1000.0,
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) -> Tuple[np.ndarray, float]:
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"""
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Differential Evolution global optimizer.
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Population-based stochastic optimization effective for non-convex,
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multimodal objective functions.
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Parameters
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----------
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objective_fn : callable
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Function to minimize: f(x) -> float where x is np.ndarray
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bounds : list of (float, float)
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[(min, max), ...] bounds for each parameter
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popsize : int, default=15
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Population size multiplier (total size = popsize × n_params)
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maxiter : int, default=1000
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Maximum number of generations
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f : float, optional
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Mutation factor (typically 0.5-2.0). If None, uses 0.8
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cr : float, optional
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Crossover probability (typically 0.1-0.9). If None, uses 0.7
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strategy : str, default="rand1"
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Mutation strategy: "rand1", "best1", "currenttobest1", "rand2", "best2"
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seed : int, optional
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Random seed for reproducibility
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tol : float, default=1e-6
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Convergence tolerance for function value changes
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atol : float, default=1e-8
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Absolute convergence tolerance
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track_history : bool, default=False
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Whether to track convergence history
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parallel : bool, default=False
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Whether to use parallel evaluation (not supported for Python callbacks)
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adaptive : bool, default=False
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Whether to use adaptive jDE parameter control
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constraint_penalty : float, default=1000.0
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Penalty for constraint violations
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Returns
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-------
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x : np.ndarray
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Best parameters found
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fun : float
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Best objective value (minimum)
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Examples
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--------
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>>> def rosenbrock(x):
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... return sum(100*(x[i+1] - x[i]**2)**2 + (1-x[i])**2
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... for i in range(len(x)-1))
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>>> result = differential_evolution(
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... objective_fn=rosenbrock,
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... bounds=[(-5, 5)] * 10,
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... strategy="best1",
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... adaptive=True,
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... maxiter=1000
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... )
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>>> print(f"Minimum: {result[1]:.6f} at {result[0]}")
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"""
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if RUST_AVAILABLE:
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result = _rust_differential_evolution(
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objective_fn=objective_fn,
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bounds=bounds,
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popsize=popsize,
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maxiter=maxiter,
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f=f,
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cr=cr,
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strategy=strategy,
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seed=seed,
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tol=tol,
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atol=atol,
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track_history=track_history,
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parallel=parallel,
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adaptive=adaptive,
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constraint_penalty=constraint_penalty,
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)
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return np.array(result.x), result.fun
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else:
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# Pure Python fallback (scipy)
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try:
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from scipy.optimize import differential_evolution as scipy_de
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mutation = f if f is not None else 0.8
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recombination = cr if cr is not None else 0.7
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result = scipy_de(
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objective_fn,
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bounds=bounds,
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maxiter=maxiter,
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popsize=popsize,
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mutation=mutation,
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recombination=recombination,
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seed=seed,
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tol=tol,
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atol=atol
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)
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return result.x, result.fun
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except ImportError:
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raise ImportError(
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"Rust backend not available and scipy not installed. "
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"Install scipy or build OptimizR with Rust support."
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)
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def grid_search(
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objective_fn: Callable[[np.ndarray], float],
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bounds: List[Tuple[float, float]],
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n_points: int = 10,
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) -> Tuple[np.ndarray, float]:
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"""
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Grid search optimizer.
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Exhaustively evaluates objective function at all points on a regular grid.
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Parameters
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----------
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objective_fn : callable
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Function to maximize: f(x) -> float where x is np.ndarray
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bounds : list of (float, float)
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[(min, max), ...] bounds for each parameter
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n_points : int, default=10
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Number of grid points per dimension
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Returns
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-------
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x : np.ndarray
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Best parameters found
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fun : float
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Best objective value (maximum)
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Examples
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--------
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>>> def objective(x):
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... return -(x[0]**2 + x[1]**2) # Peak at (0, 0)
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>>> result = grid_search(
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... objective_fn=objective,
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... bounds=[(-5, 5), (-5, 5)],
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... n_points=50
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... )
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>>> print(f"Maximum: {result[1]:.6f} at {result[0]}")
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"""
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if RUST_AVAILABLE:
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result = _rust_grid_search(
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objective_fn=objective_fn,
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bounds=bounds,
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n_points=n_points,
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)
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return np.array(result.x), result.fun
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else:
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# Pure Python fallback
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return _grid_search_python(objective_fn, bounds, n_points)
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def mutual_information(
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x: np.ndarray,
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y: np.ndarray,
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n_bins: int = 10,
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) -> float:
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"""
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Compute mutual information between two variables.
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I(X;Y) = H(X) + H(Y) - H(X,Y)
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Measures how much knowing one variable reduces uncertainty about the other.
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Parameters
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----------
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x : np.ndarray
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Sample values from first variable
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y : np.ndarray
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Sample values from second variable (must be same length as x)
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n_bins : int, default=10
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Number of bins for histogram estimation
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Returns
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-------
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mi : float
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Mutual information in nats (multiply by 1/ln(2) for bits)
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Examples
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--------
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>>> x = np.random.randn(10000)
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>>> y = 2 * x + np.random.randn(10000) * 0.5
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>>> mi = mutual_information(x, y, n_bins=20)
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>>> print(f"MI: {mi:.4f} nats")
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"""
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if RUST_AVAILABLE:
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return _rust_mutual_information(x.tolist(), y.tolist(), n_bins=n_bins)
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else:
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# Pure Python fallback
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return _mutual_information_python(x, y, n_bins)
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def shannon_entropy(
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x: np.ndarray,
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n_bins: int = 10,
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) -> float:
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"""
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Compute Shannon entropy of a variable.
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H(X) = -Σ p(x) log(p(x))
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Quantifies the uncertainty/information content of a random variable.
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Parameters
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----------
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x : np.ndarray
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Sample values from the variable
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n_bins : int, default=10
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Number of bins for histogram estimation
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Returns
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-------
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entropy : float
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Shannon entropy in nats (multiply by 1/ln(2) for bits)
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Examples
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--------
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>>> x_uniform = np.random.uniform(0, 1, 10000)
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>>> h_uniform = shannon_entropy(x_uniform, n_bins=20)
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>>> x_peaked = np.random.normal(0, 0.1, 10000)
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>>> h_peaked = shannon_entropy(x_peaked, n_bins=20)
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>>> print(f"Uniform: {h_uniform:.4f}, Peaked: {h_peaked:.4f}")
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"""
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if RUST_AVAILABLE:
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return _rust_shannon_entropy(x.tolist(), n_bins=n_bins)
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else:
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# Pure Python fallback
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return _shannon_entropy_python(x, n_bins)
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# Pure Python fallback implementations
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def _mcmc_sample_python(log_likelihood_fn, data, initial_params, param_bounds,
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n_samples, burn_in, proposal_std):
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"""Pure Python MCMC implementation"""
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current_params = initial_params.copy()
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samples = []
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current_ll = log_likelihood_fn(current_params.tolist(), data.tolist())
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for _ in range(n_samples + burn_in):
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# Propose
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proposed = current_params + np.random.randn(len(current_params)) * proposal_std
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for i, (low, high) in enumerate(param_bounds):
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proposed[i] = np.clip(proposed[i], low, high)
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# Accept/reject
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proposed_ll = log_likelihood_fn(proposed.tolist(), data.tolist())
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if np.log(np.random.rand()) < proposed_ll - current_ll:
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current_params = proposed
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current_ll = proposed_ll
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if len(samples) >= burn_in:
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samples.append(current_params.copy())
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return np.array(samples)
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def _grid_search_python(objective_fn, bounds, n_points):
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"""Pure Python grid search implementation"""
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n_params = len(bounds)
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grids = [np.linspace(low, high, n_points) for low, high in bounds]
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best_params = None
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best_score = float('-inf')
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import itertools
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for point in itertools.product(*grids):
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score = objective_fn(np.array(point))
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if score > best_score:
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best_score = score
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best_params = np.array(point)
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return best_params, best_score
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def _mutual_information_python(x, y, n_bins):
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"""Pure Python MI implementation"""
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hist_2d, x_edges, y_edges = np.histogram2d(x, y, bins=n_bins)
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pxy = hist_2d / np.sum(hist_2d)
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px = np.sum(pxy, axis=1)
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py = np.sum(pxy, axis=0)
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px_py = px[:, None] * py[None, :]
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# Only compute where both are nonzero
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nonzero = (pxy > 0) & (px_py > 0)
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mi = np.sum(pxy[nonzero] * np.log(pxy[nonzero] / px_py[nonzero]))
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return max(0.0, mi)
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def _shannon_entropy_python(x, n_bins):
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"""Pure Python entropy implementation"""
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hist, _ = np.histogram(x, bins=n_bins)
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probs = hist[hist > 0] / np.sum(hist)
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return -np.sum(probs * np.log(probs))
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