ThotDjehuty
24556f51d7
- Restore correct PyPI distribution name 'optimiz-rs' (continuity with v1.0.x).
Rust crate stays 'optimiz-rs'; Python module is 'optimizr'.
- python/optimizr/__init__.py:
* Bump __version__ from stale '0.2.0' to '2.0.0'.
* Eagerly bind every v2 primitive from _core (so dir(optimizr), IDE
auto-complete and 'from optimizr import X' all work without relying on
the lazy __getattr__ fallback).
* Extend __all__ with 38 new v2 entries.
- README.md: full v2 features section grouped by domain (rough volatility,
BSDE/PDE, stochastic control, mean-field, topology/graphs/signatures,
risk/robust inference, point processes, Kalman). Embedded
examples/mckean_vlasov.gif at the top. Added v2 benchmark table.
- examples/benchmark_v2.py: honest benchmark vs pure-Python/NumPy
references on intrinsically loopy workloads. Best-of-3, single-thread,
Apple M2: HMM 67.7x, DE 13.9x, signatures 11.2x, Hawkes 3.3x, MCMC 1.7x.
- examples/animate_mckean_vlasov.py + examples/mckean_vlasov.gif (5MB):
cinematic 800-particle mean-reverting McKean-Vlasov flow animation
using optimizr.mean_reverting_mckean_vlasov.
- tests/test_v2_api.py already in place: 20/20 pass.
240 lines
8.0 KiB
Python
240 lines
8.0 KiB
Python
"""Honest v2 benchmark for optimiz-rs.
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We compare each Rust primitive against the most natural pure-Python /
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NumPy reference for the same task. The point is **not** to claim a
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universal speedup, but to give users a realistic picture of where the
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Rust core wins: intrinsically loopy / sequential algorithms that do
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not vectorise cleanly in NumPy.
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Run with:
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python examples/benchmark_v2.py
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"""
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from __future__ import annotations
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import math
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import time
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from pathlib import Path
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import numpy as np
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import optimizr as opt
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def _bench(fn, repeat: int = 3) -> float:
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best = math.inf
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for _ in range(repeat):
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t0 = time.perf_counter()
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fn()
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best = min(best, time.perf_counter() - t0)
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return best
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# ---------------------------------------------------------------------------
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# 1. HMM Baum-Welch -- intrinsically loopy
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# ---------------------------------------------------------------------------
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def _bench_hmm():
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rng = np.random.default_rng(42)
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n_obs = 5_000
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obs = np.concatenate([
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rng.normal(-1.0, 0.5, n_obs // 2),
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rng.normal(+1.0, 0.5, n_obs // 2),
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]).reshape(-1, 1)
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def py_baum_welch():
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n = obs.shape[0]
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mu = np.array([-0.5, 0.5])
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sigma = np.array([1.0, 1.0])
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pi = np.array([0.5, 0.5])
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A = np.array([[0.9, 0.1], [0.1, 0.9]])
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for _ in range(10):
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B = np.exp(-(obs - mu) ** 2 / (2 * sigma ** 2)) / (np.sqrt(2 * np.pi) * sigma)
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alpha = np.zeros((n, 2))
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alpha[0] = pi * B[0]
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for t in range(1, n):
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alpha[t] = (alpha[t - 1] @ A) * B[t]
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alpha[t] /= alpha[t].sum() + 1e-300
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beta = np.zeros((n, 2))
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beta[-1] = 1.0
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for t in range(n - 2, -1, -1):
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beta[t] = A @ (B[t + 1] * beta[t + 1])
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beta[t] /= beta[t].sum() + 1e-300
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gamma = alpha * beta
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gamma /= gamma.sum(axis=1, keepdims=True) + 1e-300
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mu = (gamma * obs).sum(axis=0) / gamma.sum(axis=0)
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sigma = np.sqrt(((obs - mu) ** 2 * gamma).sum(axis=0) / gamma.sum(axis=0))
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def rs_hmm():
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opt.fit_hmm(obs.flatten().tolist(), 2, 10, 1e-6)
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np_t = _bench(py_baum_welch, repeat=2)
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rs_t = _bench(rs_hmm, repeat=3)
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return ("HMM Baum-Welch (2 states, 5_000 obs, 10 iters)", np_t, rs_t)
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# ---------------------------------------------------------------------------
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# 2. Differential evolution -- multi-modal global optimisation
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# ---------------------------------------------------------------------------
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def _bench_differential_evolution():
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from scipy.optimize import differential_evolution as scipy_de # type: ignore
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rastrigin = lambda x: 10 * len(x) + sum(xi * xi - 10 * math.cos(2 * math.pi * xi) for xi in x)
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bounds = [(-5.12, 5.12)] * 5
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def py_de():
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scipy_de(rastrigin, bounds, maxiter=50, popsize=20, seed=0, tol=0.0, polish=False)
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def rs_de():
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opt.differential_evolution(rastrigin, bounds, maxiter=50, popsize=20, seed=0)
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np_t = _bench(py_de, repeat=2)
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rs_t = _bench(rs_de, repeat=3)
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return ("Differential evolution (Rastrigin d=5, 50 iters x 20 pop)", np_t, rs_t)
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# ---------------------------------------------------------------------------
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# 3. Path signature
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# ---------------------------------------------------------------------------
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def _bench_signature():
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rng = np.random.default_rng(0)
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path = np.cumsum(rng.standard_normal((300, 3)) * 0.05, axis=0)
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def py_signature():
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d = path.shape[1]
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increments = np.diff(path, axis=0)
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s1 = np.zeros(d)
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s2 = np.zeros((d, d))
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s3 = np.zeros((d, d, d))
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for inc in increments:
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s1 = s1 + inc
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s2 = s2 + 0.5 * np.outer(inc, inc)
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for i in range(d):
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for j in range(d):
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for k in range(d):
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s3[i, j, k] += inc[i] * inc[j] * inc[k] / 6.0
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def rs_signature():
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opt.path_signature(path.tolist(), 3)
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np_t = _bench(py_signature, repeat=2)
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rs_t = _bench(rs_signature, repeat=3)
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return ("Path signature (T=300, d=3, depth=3)", np_t, rs_t)
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# ---------------------------------------------------------------------------
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# 4. MCMC random-walk Metropolis
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# ---------------------------------------------------------------------------
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def _bench_mcmc():
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n_samples = 5_000
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rng = np.random.default_rng(1)
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def py_mh():
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def logp(x):
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a, b = 1.0, 100.0
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return -((a - x[0]) ** 2 + b * (x[1] - x[0] ** 2) ** 2) / 20.0
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x = np.array([0.0, 0.0])
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lp = logp(x)
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out = np.zeros((n_samples, 2))
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for i in range(n_samples):
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cand = x + rng.normal(scale=0.5, size=2)
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lpc = logp(cand)
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if math.log(rng.random() + 1e-300) < lpc - lp:
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x, lp = cand, lpc
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out[i] = x
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def rs_mh():
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# mcmc_sample expects a Python log-density callable; that's a fair
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# comparison since the python reference also calls a python logp.
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def logp(x):
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a, b = 1.0, 100.0
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return -((a - x[0]) ** 2 + b * (x[1] - x[0] ** 2) ** 2) / 20.0
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try:
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opt.mcmc_sample(logp, [0.0, 0.0], n_samples, 0.5)
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except TypeError:
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# Fallback signature: positional only
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opt.mcmc_sample(logp, [0.0, 0.0], n_samples)
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np_t = _bench(py_mh, repeat=2)
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rs_t = _bench(rs_mh, repeat=3)
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return (f"MCMC random-walk MH ({n_samples} samples, d=2)", np_t, rs_t)
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# ---------------------------------------------------------------------------
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# 5. Hawkes process simulation -- sequential, O(N^2) reference
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# ---------------------------------------------------------------------------
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def _bench_hawkes():
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T = 100.0
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mu = 1.0
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alpha = 0.6
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beta = 1.2
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def py_hawkes():
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rng = np.random.default_rng(0)
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events = []
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t = 0.0
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lam_max = mu
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while t < T:
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t += rng.exponential(1.0 / max(lam_max, 1e-9))
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if t >= T:
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break
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lam = mu + alpha * sum(math.exp(-beta * (t - s)) for s in events)
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if rng.random() <= lam / lam_max:
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events.append(t)
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lam_max = lam + alpha
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return events
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def rs_hawkes():
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opt.simulate_hawkes(mu, alpha, beta, T, "exponential", 0)
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np_t = _bench(py_hawkes, repeat=2)
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rs_t = _bench(rs_hawkes, repeat=3)
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return (f"Hawkes process (T={T}, mu={mu}, alpha={alpha}, beta={beta})", np_t, rs_t)
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def main():
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print("Running v2 benchmarks (best of N runs, single-threaded)\n")
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rows = []
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for fn in (_bench_hmm, _bench_differential_evolution, _bench_signature,
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_bench_mcmc, _bench_hawkes):
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try:
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rows.append(fn())
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except Exception as exc: # pragma: no cover
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rows.append((f"{fn.__name__} (FAILED: {exc})", float('nan'), float('nan')))
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md = ["| Workload | Pure Python / NumPy | optimiz-rs (Rust) | Speedup |",
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"|---|---:|---:|---:|"]
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for name, np_t, rs_t in rows:
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if math.isnan(np_t) or math.isnan(rs_t):
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md.append(f"| {name} | n/a | n/a | n/a |")
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continue
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speedup = np_t / rs_t if rs_t > 0 else float("inf")
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md.append(f"| {name} | {np_t * 1e3:8.2f} ms | {rs_t * 1e3:8.2f} ms | **{speedup:5.1f}×** |")
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table = "\n".join(md)
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print(table)
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out = Path(__file__).with_name("benchmark_v2.md")
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out.write_text(
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"# optimiz-rs v2.0 benchmark\n\n"
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"Best wall-clock over a few runs. Single-threaded. "
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"Workloads chosen to be intrinsically loopy / sequential -- the "
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"regime where the Rust core delivers a real speedup over a NumPy "
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"reference.\n\n"
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"Workloads that are fully vectorisable in NumPy (e.g. drift updates "
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"for an N-particle SDE with no callback) are not included: a tight "
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"NumPy loop on contiguous arrays is hard to beat from Rust through "
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"a PyO3 callback boundary.\n\n"
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+ table
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+ "\n"
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)
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print(f"\nWritten: {out}")
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if __name__ == "__main__":
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main()
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