mixle.data.exchangeability module

Exchangeability diagnostics for fitted and synthetic-data workflows.

Fitting one distribution to a dataset, or synthesizing “more rows like these”, assumes that row order does not carry information. When the data has a trend or a regime shift, that assumption is false and a pooled marginal model can misrepresent the process.

exchangeability_check() tests the assumption with numeric probes: a permutation test for rank correlation between value and row position, plus a first-half/second-half location-shift test. The aggregate label is one of:

  • exchangeable: no order signal found at the tested level;

  • trend: value co-moves with position;

  • shift: the halves differ in location.

mixle.inference.create() and mixle.inference.synthesize() record the verdict in provenance so downstream consumers can see when pooling deserves review.

class ExchangeabilityReport(label, fields=<factory>)[source]

Bases: object

The verdict per numeric field, plus the aggregate label the preconditions record.

Parameters:
label: str
fields: list[dict[str, Any]]
property exchangeable: bool
as_dict()[source]
Return type:

dict[str, Any]

exchangeability_check(data, *, alpha=0.01, n_perm=200, seed=0)[source]

Test whether row ORDER carries information (see module docstring). Small n -> exchangeable (no power).

alpha is deliberately strict (0.01): the check should flag clear violations, not manufacture warnings from noise. Non-numeric-only data passes vacuously (order tests need a numeric surface).

Parameters:
Return type:

ExchangeabilityReport