pymixef.convergence module

Backend-neutral convergence and numerical-quality reporting.

class pymixef.convergence.HessianDiagnostics(positive_definite=None, min_eigenvalue=None, max_eigenvalue=None, condition_number=None, effective_rank=None)[source]

Bases: object

Definiteness and conditioning summary for an observed Hessian.

Parameters:

  • positive_definite (bool | None)

  • min_eigenvalue (float | None)

  • max_eigenvalue (float | None)

  • condition_number (float | None)

  • effective_rank (int | None)

positive_definite: bool | None
min_eigenvalue: float | None
max_eigenvalue: float | None
condition_number: float | None
effective_rank: int | None
classmethod from_matrix(matrix, *, relative_tolerance=1e-8)[source]
Parameters:

  • matrix (ndarray)

  • relative_tolerance (float)

Return type:

HessianDiagnostics

to_dict()[source]
Return type:

dict[str, Any]

class pymixef.convergence.BoundaryRecord(parameter, value, boundary='zero', tolerance=None)[source]

Bases: object

One natural-scale parameter on or near a numerical boundary.

Parameters:

  • parameter (str)

  • value (float)

  • boundary (str)

  • tolerance (float | None)

parameter: str
value: float
boundary: str
tolerance: float | None
to_dict()[source]
Return type:

dict[str, Any]

class pymixef.convergence.ConvergenceReport(status, optimizer_terminated, optimizer_message='', iterations=None, objective_evaluations=None, gradient_evaluations=None, scaled_gradient_inf_norm=None, parameter_step_norm=None, hessian=<factory>, boundaries=(), conditional_mode_failures=0, ode_failures=0, warnings=(), engine_metrics=<factory>)[source]

Bases: object

Structured convergence contract shared by every estimator.

Parameters:

  • status (str)

  • optimizer_terminated (bool)

  • optimizer_message (str)

  • iterations (int | None)

  • objective_evaluations (int | None)

  • gradient_evaluations (int | None)

  • scaled_gradient_inf_norm (float | None)

  • parameter_step_norm (float | None)

  • hessian (HessianDiagnostics)

  • boundaries (tuple[BoundaryRecord, ...])

  • conditional_mode_failures (int)

  • ode_failures (int)

  • warnings (tuple[WarningRecord, ...])

  • engine_metrics (Mapping[str, Any])

status: str
optimizer_terminated: bool
optimizer_message: str
iterations: int | None
objective_evaluations: int | None
gradient_evaluations: int | None
scaled_gradient_inf_norm: float | None
parameter_step_norm: float | None
hessian: HessianDiagnostics
boundaries: tuple[BoundaryRecord, ...]
conditional_mode_failures: int
ode_failures: int
warnings: tuple[WarningRecord, ...]
engine_metrics: Mapping[str, Any]
property trustworthy: bool

Whether termination and numerical checks support routine interpretation.

classmethod assess(*, optimizer_terminated, gradient=None, hessian=None, gradient_tolerance=1e-4, boundaries=(), warnings=(), **metrics)[source]

Construct a report from common deterministic optimizer diagnostics.

Parameters:

  • optimizer_terminated (bool)

  • gradient (ndarray | None)

  • hessian (ndarray | None)

  • gradient_tolerance (float)

  • boundaries (Iterable[BoundaryRecord])

  • warnings (Iterable[WarningRecord | Mapping[str, Any]])

  • metrics (Any)

Return type:

ConvergenceReport

classmethod from_dict(value)[source]
Parameters:

value (Mapping[str, Any])

Return type:

ConvergenceReport

to_dict()[source]
Return type:

dict[str, Any]