nlsq.diagnostics.ParameterSensitivityReport¶

class nlsq.diagnostics.ParameterSensitivityReport(available=True, error_message=None, computation_time_ms=0.0, is_sloppy=False, eigenvalues=<factory>, eigenvectors=None, eigenvalue_range=0.0, effective_dimensionality=0.0, stiff_indices=<factory>, sloppy_indices=<factory>, issues=<factory>, health_status=HealthStatus.HEALTHY)[source]¶

Bases: AnalysisResult

Report from parameter sensitivity spectrum analysis.

Contains results from eigenvalue spectrum analysis to identify well-determined vs poorly-determined parameter directions based on the spread of eigenvalues in the Fisher Information Matrix.

is_sloppy¶

Whether the model exhibits wide eigenvalue spread (sensitivity spectrum).

Type:: bool

eigenvalues¶

Eigenvalue spectrum of the Fisher Information Matrix.

Type:: np.ndarray

eigenvectors¶

Eigenvectors of the FIM (columns are eigenvectors).

Type:: np.ndarray | None

eigenvalue_range¶

Log10 range of eigenvalues (orders of magnitude).

Type:: float

effective_dimensionality¶

Effective number of well-determined parameter combinations.

Type:: float

stiff_indices¶

Indices of stiff (well-determined) directions.

Type:: list[int]

sloppy_indices¶

Indices of poorly-determined directions.

Type:: list[int]

issues¶

List of detected sensitivity issues (SENS-001, SENS-002).

Type:: list[ModelHealthIssue]

health_status¶

Overall health status based on detected issues.

Type:: HealthStatus

is_sloppy: bool¶

eigenvalues: ndarray¶

eigenvectors: ndarray | None¶

eigenvalue_range: float¶

effective_dimensionality: float¶

stiff_indices: list[int]¶

sloppy_indices: list[int]¶

issues: list[ModelHealthIssue]¶

health_status: HealthStatus¶

get_sloppy_combinations()[source]¶

Get poorly determined parameter combinations.

Returns:: List of (eigenvector, eigenvalue) tuples for poorly-determined directions.
Return type:: list[tuple[np.ndarray, float]]

__init__(available=True, error_message=None, computation_time_ms=0.0, is_sloppy=False, eigenvalues=<factory>, eigenvectors=None, eigenvalue_range=0.0, effective_dimensionality=0.0, stiff_indices=<factory>, sloppy_indices=<factory>, issues=<factory>, health_status=HealthStatus.HEALTHY)¶