zeroguess 0.8.1

Machine Learning for Curve Fitting Parameter Estimation

ZeroGuess: Machine Learning for Curve Fitting Parameter Estimation

ZeroGuess is a Python library that simplifies the estimation of starting parameters for curve fitting by leveraging machine learning. It supports SciPy and lmfit, two widely used curve fitting libraries in the scientific Python ecosystem.

Problem Statement

While curve fitting is a well-understood problem, the process of estimating starting parameters is not. It is a very tedious and error-prone process that often requires domain expertise, trial and error, or both. Poor initial parameter estimates can cause fitting algorithms to:

• Converge to suboptimal local minima
• Require more iterations to converge
• Fail to converge entirely

ZeroGuess uses machine learning to learn from the fitting function itself, providing optimal starting parameters without manual tuning.

Installation

pip install zeroguess

Quick Start

Setup experimental data

import numpy as np
from zeroguess.functions import WaveletFunction, add_gaussian_noise

# Create a double peakGaussian function
wavelet = WaveletFunction()

# Create some experimental data
true_params = wavelet.get_random_params()
x_data = np.linspace(-10, 10, 100)
y_data = add_gaussian_noise(wavelet(x_data, **true_params), sigma=0.1)

lmfit Integration

from zeroguess.integration import ZeroGuessModel

# Enhanced lmfit Model with parameter estimation
# Enhanced lmfit Model with parameter estimation
model = ZeroGuessModel(
    wavelet,
    independent_vars_sampling={"x": x_data},
    estimator_settings={
        "make_canonical": wavelet.get_canonical_params,
        # Configure training parameters
        # "n_samples": 1000,
        # "n_epochs": 200,
        # "validation_split": 0.2,
        # "add_noise": True,
        # "noise_level": 0.1,
        # 'verbose': True
        "snapshot_path": "model_dg.pth", # saves and loads model automatically
    },
)

model.set_param_hint("frequency", min=0.05, max=1.0)
model.set_param_hint("phase", min=0.0, max=2.0 * np.pi)
model.set_param_hint("position", min=5.0, max=15.0)
model.set_param_hint("width", min=0.1, max=3.0)

# Standard lmfit workflow
params = model.guess(y_data, x=x_data)

result = model.fit(y_data, x=x_data, params=params)

Scipy usage

import zeroguess
from scipy import optimize

# Create and train parameter estimator
estimator = zeroguess.create_estimator(
    function=wavelet,
    param_ranges={
        "frequency": (0.05, 1.0),
        "phase": (0.0, 2.0 * np.pi),
        "position": (5.0, 15.0),
        "width": (0.1, 3.0),
    },
    independent_vars_sampling={
        'x': x_data
    },
    snapshot_path="model_dg_plain.pth", # saves and loads model automatically
)

if not estimator.is_trained:
    estimator.train()

# Get parameter estimates for experimental data
initial_params = estimator.predict(x_data, y_data)

# Use in standard curve fitting
optimal_params, _ = optimize.curve_fit(
    wavelet, x_data, y_data,
    p0=list(initial_params.values())
)

Features

• Automatic estimation of starting parameters for curve fitting
• Support for both SciPy and lmfit curve fitting libraries
• Neural network-based parameter estimation
• Model persistence for reuse without retraining
• Detailed diagnostics and visualization tools

Requirements

• Python 3.10+
• Dependencies: numpy, scipy, torch, lmfit (optional)

License

MIT

Performance Benchmarks

ZeroGuess is benchmarked regularly to ensure optimal performance. View the latest benchmark results.