nnpiv.diagnostics.relative_wellposedness_from_data

nnpiv.diagnostics.relative_wellposedness_from_data(data, *, A, B=None, C, C_prime, mask_s=None, mask_t=None, **kwargs)[source]

Run the pre-estimation diagnostic from dataset-level selectors.

Parameters

  • data (DataFrame, mapping, or 2D array-like) – Source container holding all blocks.

  • A – Block selectors for each argument required by relative_wellposedness_diagnostic().

  • C – Block selectors for each argument required by relative_wellposedness_diagnostic().

  • C_prime – Block selectors for each argument required by relative_wellposedness_diagnostic().

  • B –

    Optional selector accepted only for interface consistency with (A, B, C, C') notation. It is intentionally ignored by this diagnostic.

    Supported selector forms: - callable selector(data) -> array-like - direct array-like matrix/vector - DataFrame/mapping columns (string or list of strings) - DataFrame/array-style integer selectors (int, list[int], slice)

  • mask_s (array-like, str, or callable, optional) – Optional subset selectors for stage-specific sample restrictions.

  • mask_t (array-like, str, or callable, optional) – Optional subset selectors for stage-specific sample restrictions.

  • **kwargs – Forwarded to relative_wellposedness_diagnostic().

Returns

Output dictionary returned by relative_wellposedness_diagnostic().

Return type

dict

Notes

A, C, and C_prime must resolve to arrays with the same row count.