ZeroGuess uses machine learning to improve curve fitting parameter estimation. It generates optimal starting parameters for fitting functions, reducing computation time and increasing fitting reliability.
Curve fitting in scientific applications often encounters issues with initial parameter estimation:
- Convergence to local rather than global minima
- High computational cost for complex functions
- Failed convergence with inadequate starting points
Conventional approaches rely on domain expertise, manual adjustment, or computationally intensive global optimization methods.
ZeroGuess addresses these limitations through machine learning models trained on synthetic data, providing effective parameter estimates that allow simpler fitting algorithms to achieve results comparable to advanced methods with significantly reduced computation time.
pip install zeroguessimport numpy as np
# Define a simple wavelet function directly
def wavelet(x, frequency, phase, position, width):
z = (x - position) / width
return np.exp(-z**2) * np.cos(2 * np.pi * frequency * z + phase)
# Create some synthetic experimental data with known parameters
true_params = {
"frequency": 0.5,
"phase": 1.0,
"position": 7.0,
"width": 1.5
}
# Generate x, y data points
x_data = np.linspace(0, 20, 200)
y_clean = wavelet(x_data, **true_params)
# Add noise
np.random.seed(42) # For reproducibility
noise_level = 0.05
y_data = y_clean + np.random.normal(0, noise_level * (np.max(y_clean) - np.min(y_clean)), size=y_clean.shape)from zeroguess.integration import ZeroGuessModel
# Enhanced lmfit Model with parameter estimation
model = ZeroGuessModel(
wavelet,
independent_vars_sampling={"x": x_data},
estimator_settings={
# Configure training parameters
# "n_samples": 1000,
# "n_epochs": 200,
# "validation_split": 0.2,
# "add_noise": True,
# "noise_level": 0.1,
# 'verbose': True
# Provide a function to make parameters canonical
# "make_canonical": ...,
# Save and load model automatically
"snapshot_path": "model_test.pth",
},
)
# Set parameter hints
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)
# Guess parameters with ZeroGuess estimator
params = model.guess(y_data, x=x_data)
# Run the fit
result = model.fit(y_data, x=x_data, params=params)from scipy import optimize
import zeroguess
# 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, pcov = optimize.curve_fit(wavelet, x_data, y_data, p0=list(initial_params.values()))Providing good initial parameter estimates for an arbitrary fit function is an ambitious task. ZeroGuess is motivated by the experience of a person doing many fits over an extended period of time: At some point you look at your data and can roughly predict what the parameters should be.
This intuition is implemented by ZeroGuess, where a neural network looks at many instances of synthetic data and then is trained to predict the parameters that generated this data. This approach is restricted to several limitations (see below).
Conceptual Limitations:
- Users must define appropriate parameter ranges for their specific use case. This is typically feasible as parameters in physical systems are usually bounded
- Parameter ambiguity can affect learning effectiveness (see "Canonical Representations" section)
Technical Limitations:
- Only one-dimensional independent variables are currently supported
- Requires currently a consistent sampling of the independent variable across datasets
These technical limitations can be addressed in future versions. If you need these features, please raise an issue on GitHub.
Many fitting functions produce identical outputs with different parameter combinations. For example, in a double Gaussian function, swapping the parameters of the two peaks produces the same curve:
# These two parameter sets produce identical curves
params1 = {"amplitude1": 2, "center1": -2, "width1": 1,
"amplitude2": 3, "center2": 2, "width2": 0.5}
params2 = {"amplitude1": 3, "center2": 2, "width2": 0.5,
"amplitude2": 2, "center1": -2, "width1": 1}You can provide ZeroGuess a function for make_canonical to transform parameters into a standardized form (e.g., ordering peaks from left to right), enabling the model to learn a consistent mapping and provide more reliable parameter estimates.
Accurate starting parameters are essential for local optimization methods like least_squares. Global optimization methods like dual_annealing find better solutions but require substantially more computation.
Benchmarks indicate that ZeroGuess improves the performance of least_squares fits to comparable levels with dual_annealing (~70% success rate) while reducing computation time by a factor of 100 (from 1150ms to 12ms).
This makes ZeroGuess useful for complex fitting functions with multiple parameters and for large datasets where computation efficiency is important.
- 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
- Python 3.10+
- Dependencies: numpy, scipy, torch, lmfit (optional)
MIT
ZeroGuess is benchmarked regularly to ensure optimal performance. View the latest benchmark results.