Conditioning examples

[shackmann]

Note

Medium Risk
Introduces a new public forecast return mode and stricter response parsing on the core contract path; behavior is well covered by tests but mistakes in validation or parsing would affect all log-prob callers.

Overview
Adds first-class log_prob scoring: JointFMClient.forecast_log_prob(...) takes query_rows (one observed row per query_times) and returns LogProbResult / LogProbScores, with ForecastRequest enforcing that return_mode="log_prob" and query_rows stay paired and that requested_columns lists every readable column in schema order (pinned equality columns excepted).

Request building and adapters accept query_rows (DataFrame encoding reuses history row conversion with query_rows[...] error paths). Response parsing adds outputs.log_prob, recomputes summary fields from per-horizon values, and refuses unknown return_modes instead of mis-reading another mode’s shape. ConditionPlausibility docs clarify joint densities and how equality and region plausibility chain.

notebooks/forecast_condition.ipynb grows into a full conditioning walkthrough: unconditional vs conditional means, joint samples, quantiles inside bands, multi-interval box estimation, rally ranking via log-density differences, and conditional forecast_log_prob for comparing outcomes. README and docs/api-reference.md document the helper and wire fields; fixtures and tests/test_log_prob_mode.py cover validation and client round-trips.

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Arrays helper cannot score log_prob

Low Severity

build_forecast_payload_from_arrays still accepts return_mode="log_prob" but has no query_rows argument, so the new request check that log_prob and query_rows must appear together now rejects every arrays-built scoring payload. Callers who previously built the payload here and attached query_rows afterward can no longer get a payload back.

Additional Locations (1)

• src/jointfm_client/contract.py#L545-L550

 

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