gp-elite 0.2.2

Régression symbolique par programmation génétique — lois interprétables sur données expérimentales (pur Python/NumPy)

GP_ELITE

Genetic-programming symbolic regression — discover interpretable laws from your experimental data.

🇫🇷 Version française

GP_ELITE searches for a mathematical formula linking your variables to a target, instead of a black box. It is built for small experimental datasets (≤10 variables, 100–5000 points) where you want to understand the relationship: degradation laws, sensor calibration, engineering correlations, dose–response curves, physical laws.

Pure Python / NumPy — no Julia, no compilation, no GPU. pip install and you're ready.

Given only the 8 planets' distance and orbital period, GP_ELITE rediscovered Kepler's Third Law (T = a·√a = a^1.5) in seconds — see examples/kepler_demo.py.

from gp_elite import symbolic_regression

result = symbolic_regression(X, y, feature_names=["cycle", "temperature", "current"])
print(result.expression)        # capacity_SOH = 0.913 - 0.352·tanh(...)
print(result.r2_validation)     # 0.996  (on data never seen during training)

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What's new in 0.2.0

• Levenberg–Marquardt constant fitting (default): constants reach machine precision — Coulomb's q1·q2/(4πεr²) recovered exactly (1−R² ≈ 8e-32). 6–14× faster on constant-heavy problems.
• Multi-restart reliability (restarts=N): independent runs share one deterministic hold-out, their candidate archives are merged, and one global selection is made.
• Pareto front output (result.pareto): the full complexity ↔ accuracy staircase, each entry with its own .predict.
• Forecast / extrapolation mode (extrapolate_feature=, extrapolate_direction=): out-of-domain divergence probes, a linear safety floor, frontier-based selection. Also available as mode 7 in the interactive menu.
• Reproducibility: identical results per seed within a process; across invocations, run with PYTHONHASHSEED=0.

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Why GP_ELITE?

	GP_ELITE	Neural networks	PySR (state of the art)
Output	readable formula	black box	readable formula
Installation	pip install (pure Python)	heavy	requires Julia
Overfitting guard	built-in (hold-out)	do it yourself	do it yourself
Variable selection	importance report	no	partial

GP_ELITE's niche: zero barrier to entry. A lab engineer, a student, or a technician points at a CSV file and gets a validated law back — without becoming a developer.

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Installation

pip install gp-elite          # from PyPI
# or, from source:
git clone https://github.com/ariel95500-create/gp-elite
cd gp-elite && pip install -e .

Dependencies: numpy, pandas, scikit-learn.

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Usage

One line, on your own data (console UI)

gp-elite

Choose mode 6 (generic CSV), point to your file, and keep the defaults. GP_ELITE detects the columns, holds out a validation set, evolves, and prints the discovered law with its generalization report.

Programmatically (notebooks, pipelines)

import numpy as np
from gp_elite import symbolic_regression

X = np.random.uniform(1, 5, (200, 2))
y = 2.0 + 3.0 * np.sqrt(X[:, 0]) - 0.5 * X[:, 1]

result = symbolic_regression(
    X, y,
    feature_names=["a", "b"],
    operators="physical",   # 'physical' | 'trig' | 'full' | 'poly'
    generations=60,
    speed="fast",           # 'ultrafast' | 'fast' | 'normal'
)

print(result.expression)        # e.g. 2.0 + 3.0·sqrt(a) - 0.5·b
print(result.r2_validation)     # quality on the hold-out set
print(result.size)              # node count (readability)

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🛡️ Robust regression (outlier-resistant custom loss)

Real-world data is dirty. A handful of outliers can drag an ordinary least-squares fit far from the true relationship. GP_ELITE ships a one-switch robust mode that fits the true law even when a sizeable fraction of the data is corrupted.

from gp_elite import symbolic_regression

# X, y : your (possibly dirty) data
result = symbolic_regression(X, y, feature_names=["x"], robust=True)
print(result.expression)

Under the hood, robust=True switches the objective to a Huber loss and rescales the final coefficients with an IRLS (Iteratively Reweighted Least Squares) procedure, so the fit is governed by the bulk of the data rather than by a few extreme points. It stays a compact, readable formula.

Measured behaviour (recovering y = 2x + 1 — RMSE against the true law on clean points, lower is better):

outliers	MSE (default)	robust=True
0 %	0.063	0.063
10 %	1.398	1.374
20 %	1.925	0.543

With clean data, ordinary MSE wins by a hair — robustness isn't free. With 10–20 % outliers, robust mode recovers the true law while plain MSE derails. Use robust=True when you suspect your data contains outliers.

See examples/robust_regression.py for the full reproducible benchmark.

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Full example: battery degradation (NASA data)

python examples/battery_soh.py

From 168 real charge cycles, GP_ELITE discovers a state-of-health (SOH) law:

capacity_SOH ≈ 0.913 − 0.352 · tanh( cycle^((temperature/cycle)^0.485) )

R² validation = 0.996   (on cycles never seen)   12 nodes

A saturating degradation with cycle count, modulated by temperature — physically plausible, and certified on unseen data.

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What is GP_ELITE good (and less good) at?

Good at: physical / engineering laws with multiplicative or exponential structure, modest-size noisy experimental data, problems where interpretability matters most.

On the frozen Feynman benchmark (15 physics equations, PYTHONHASHSEED=0, restarts=4): 10/15 exact symbolic recoveries (67%) at machine precision (1−R² < 1e-9), 14/15 within 1e-3 (93%). Head-to-head against gplearn on identical data/splits (generous budget for gplearn): 67% vs 40% exact — GP_ELITE ahead on 9 equations, tied on 5, behind on 1. Real-data forecasting (NASA battery SOH, true extrapolation on unseen cycles): median R² +0.52 vs +0.34 for linear regression, with zero divergent models. Reproduce: PYTHONHASHSEED=0 python benchmarks/feynman_bench.py 0 15 and benchmarks/duel.py.

Less good at: chaotic sequences (e.g. Collatz flight time — an intrinsically random component), >15–20 variables (the search space explodes), large datasets where raw accuracy outweighs interpretability (ensemble models dominate there).

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Technical features

• Levenberg–Marquardt constant optimization (v0.2): closed-form-quality constants, deterministic, LM/Adam switchable
• Multi-restart + merged candidate archives (v0.2): seed variance turned into reliability
• Pareto front API (v0.2): non-dominated complexity/accuracy staircase
• Guarded extrapolation / forecasting mode (v0.2): beyond-domain probes, linear floor, frontier selection
• Composition motif seeding (v0.2): Pythagorean, reciprocal-sum, Gaussian templates for nested structures
• Asymmetric island model (explorer / cleaner / stigmergic) with periodic migration
• Linear scaling (Keijzer 2003): the engine searches for the shape; scale and offset coefficients are solved in closed form
• ε-lexicase selection (La Cava 2016) to preserve behavioral diversity
• Island parallelism (multi-core) — ≈ ×3 measured on 4 cores
• Hold-out validation + parsimonious champion selection (R² tolerance): built-in overfitting guard
• Shift-free normalization preserving multiplicative structure (x·y stays a clean product)
• Transferable stigmergic memory across runs (grammar export/import)

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Tests

pip install pytest
pytest -q

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License

MIT — see LICENSE. Free to use, including commercially, with retention of the copyright notice.

Citing GP_ELITE

If GP_ELITE is useful in academic work, see CITATION.cff.