nlsq.stability.fallback.FallbackOrchestrator

class nlsq.stability.fallback.FallbackOrchestrator(strategies=None, max_attempts=10, verbose=False)

Bases: object

Orchestrates automatic fallback strategies for robust optimization.

The orchestrator tries multiple recovery strategies when optimization fails, including alternative methods, perturbed initial guesses, relaxed tolerances, and robust loss functions.

Parameters:

• strategies (list of FallbackStrategy, optional) – List of strategies to try. If None, uses default strategies.

• max_attempts (int, optional) – Maximum total attempts across all strategies. Default: 10

• verbose (bool, optional) – Print progress messages. Default: False

strategies

Active fallback strategies, sorted by priority

Type:

list of FallbackStrategy

total_attempts

Total number of fit attempts made

Type:

int

successful_strategies

Count of successes per strategy

Type:

dict

Examples

>>> from nlsq.stability.fallback import FallbackOrchestrator
>>> import numpy as np
>>>
>>> def model(x, a, b):
...     return a * np.exp(-b * x)
>>>
>>> x = np.linspace(0, 10, 100)
>>> y = 2.5 * np.exp(-0.5 * x) + 0.1 * np.random.randn(100)
>>>
>>> orchestrator = FallbackOrchestrator(verbose=True)
>>> result = orchestrator.fit_with_fallback(
...     model, x, y, p0=[1, 0.1]  # Deliberately poor p0
... )
>>>
>>> if result.fallback_strategy_used:
...     print(f"Recovered using: {result.fallback_strategy_used}")

DEFAULT_STRATEGIES: ClassVar[list[StrategyFactory]] = [<class 'nlsq.stability.fallback.AlternativeMethodStrategy'>, <class 'nlsq.stability.fallback.PerturbInitialGuessStrategy'>, <class 'nlsq.stability.fallback.AdjustTolerancesStrategy'>, <class 'nlsq.stability.fallback.AddParameterBoundsStrategy'>, <class 'nlsq.stability.fallback.UseRobustLossStrategy'>, <class 'nlsq.stability.fallback.RescaleProblemStrategy'>]

__init__(strategies=None, max_attempts=10, verbose=False)

Initialize fallback orchestrator.

fit_with_fallback(f, xdata, ydata, **kwargs)

Attempt curve fit with automatic fallback on failure.

Parameters:

• f (callable) – Model function

• xdata (array_like) – Independent variable data

• ydata (array_like) – Dependent variable data

• **kwargs – Additional arguments passed to curve_fit

Returns:

result – Optimization result with fallback metadata

Return type:

FallbackResult

Raises:

RuntimeError – If all fallback strategies fail

get_statistics()

Get statistics on fallback strategy performance.

Returns:

stats – Dictionary with success rates and attempt counts

Return type:

dict

print_statistics()

Print human-readable statistics.