mixle.inference.refine module

Diagnosis-directed correction compared with blind structure search.

If diagnosis-directed correction does not reach the same held-out target in fewer trials than blind structure search (learn_bayesian_network run with no diagnosis, over growing prefixes of data) on the planted-fault benchmark, keep blind search as the baseline.

Only the add_edge fix is translated to a concrete structural edit here, because that is the only fix mixle.inference.explain.diagnose actually detects today (upgrade_leaf/split_region/add_factor are recognized vocabulary with no detector wired yet – see that module’s docstring). A FaultReport naming any other fix is reported as “not actionable,” never guessed at.

held_out_log_likelihood(model, data)[source]

Total log-density of a fitted network (or anything exposing dist_to_encoder/seq_log_density) over held-out data – the metric both paths below are compared on.

Parameters:
Return type:

float

apply_add_edge_fix(model, fault, data)[source]

Apply only the fix diagnose actually named: add a linear-Gaussian edge between the two fields in fault.dominant (parsed from its "field[i]|...field[j]|..." shape), refit that one factor on data, and return the corrected network – every other factor is left untouched.

Returns None (never guesses) when fault.suggested_fix is not "add_edge", or dominant does not name exactly two fields (nothing dominant, or a shape this translation doesn’t understand).

Parameters:

  • model (HeterogeneousBayesianNetwork)

  • fault (FaultReport)

  • data (Sequence[tuple])

Return type:

HeterogeneousBayesianNetwork | None

class TrialsToTarget(n_trials, final_model, history=<factory>)[source]

Bases: object

How many trials a path needed to reach its target (None = never reached), plus its score history.

Parameters:
directed_correction(model, cases, data, held_out, *, background=None)[source]

One diagnosis, one targeted edit, one verification.

Costs exactly 1 trial if diagnose names an actionable fix and it verifiably improves held-out score over the original model; costs 0 (unreached) if the fix isn’t actionable or doesn’t verify – a correction that does not improve held-out is a failed diagnosis, logged as such, never silently kept anyway.

Parameters:
Return type:

TrialsToTarget

blind_search_trials_to_target(data, held_out, target_score, *, round_size=10, max_rounds=20, max_parents=1, seed=0)[source]

The blind baseline: NO diagnosis. Re-run learn_bayesian_network from scratch on growing prefixes of data (round_size more examples each round, shuffled once up front) until the discovered structure’s held-out score reaches target_score (typically directed_correction()’s own held-out score) or max_rounds is exhausted.

Parameters:
Return type:

TrialsToTarget