pymixef.results module

Stable backend-neutral fit result and archival format.

class pymixef.results.FitResult(model_ir, parameters, unconstrained_parameters, parameter_covariance, fitted_values, residuals, random_effects, objective, log_likelihood, method, engine, convergence, manifest, warnings=(), diagnostic_data=<factory>, extra=<factory>, result_schema_version='1.0.0')

Bases: object

Result contract shared by frequentist, stochastic, and Bayesian engines.

Parameters:

• model_ir (Any)

• parameters (Mapping[str, float])

• unconstrained_parameters (Mapping[str, float])

• parameter_covariance (ndarray | None)

• fitted_values (ndarray)

• residuals (ndarray)

• random_effects (Mapping[str, Any])

• objective (float)

• log_likelihood (float | None)

• method (str)

• engine (str)

• convergence (ConvergenceReport)

• manifest (RunManifest)

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

• diagnostic_data (Mapping[str, DiagnosticTable])

• extra (Mapping[str, Any])

• result_schema_version (str)

model_ir: Any

parameters: Mapping[str, float]

unconstrained_parameters: Mapping[str, float]

parameter_covariance: ndarray | None

fitted_values: ndarray

residuals: ndarray

random_effects: Mapping[str, Any]

objective: float

log_likelihood: float | None

method: str

engine: str

convergence: ConvergenceReport

manifest: RunManifest

warnings: tuple[WarningRecord, ...]

diagnostic_data: Mapping[str, DiagnosticTable]

extra: Mapping[str, Any]

result_schema_version: str

property success: bool

A compatibility convenience; inspect convergence for real detail.

property n_observations: int

prediction(*, mode='conditional')

Return an explicitly named prediction mode for the analysis rows.

Parameters:

mode (str)

Return type:

ndarray

diagnostic(name)

Parameters:

name (str)

Return type:

DiagnosticTable

residual_diagnostics(*, observed=None, variance=None)

Parameters:

• observed (Sequence[float] | None)

• variance (Sequence[float] | float | None)

Return type:

DiagnosticTable

simulate(*, n_replicates=1, seed=None, parameter_uncertainty='none', random_effects=True, residual_error=True, output='numpy', design=None)

Simulate from archived Gaussian calculations or a backend simulator.

The arguments follow PyMixEF’s public simulation contract. A backend may archive a callable simulator for an in-memory result, but archival reloads use the standardized Gaussian fallback only when its assumptions are explicit.

Parameters:

• n_replicates (int)

• seed (int | None)

• parameter_uncertainty (str)

• random_effects (bool)

• residual_error (bool)

• output (str)

• design (Any)

Return type:

ndarray | DiagnosticTable

vpc(*, data=None, independent=None, bins='adaptive', prediction_corrected=False, simulations=1000, seed=None)

Parameters:

• data (Sequence[float] | None)

• independent (Sequence[float] | None)

• bins (str | int | Sequence[float])

• prediction_corrected (bool)

• simulations (int)

• seed (int | None)

Return type:

DiagnosticTable

to_dict()

Return type:

dict[str, Any]

save(path)

Save the full result as versioned JSON; never pickle.

Parameters:

path (str | Path)

Return type:

Path

classmethod from_dict(value)

Parameters:

value (Mapping[str, Any])

Return type:

FitResult

classmethod load(path, *, verify_integrity=True, require_sidecar=False)

Load an archived result and verify its hash sidecar when available.

Legacy or externally produced JSON may omit the sidecar. Set require_sidecar=True when the calling workflow requires an integrity record. Integrity verification can be disabled only explicitly.

Parameters:

• path (str | Path)

• verify_integrity (bool)

• require_sidecar (bool)

Return type:

FitResult

summary()

Return a concise text summary separating estimates and convergence.

Return type:

str