clophfit.fitting.ctr_validation
===============================

.. py:module:: clophfit.fitting.ctr_validation

.. autoapi-nested-parse::

   Control holdout / leave-one-control-out validation helpers.



Functions
---------

.. autoapisummary::

   clophfit.fitting.ctr_validation.ctr_param_name
   clophfit.fitting.ctr_validation.free_ctr_param_name
   clophfit.fitting.ctr_validation.make_ctr_holdout_scheme
   clophfit.fitting.ctr_validation.iter_ctr_holdouts
   clophfit.fitting.ctr_validation.widen_heldout_k_prior
   clophfit.fitting.ctr_validation.weighted_mean_reference
   clophfit.fitting.ctr_validation.summarize_bayesian_ctr_holdout
   clophfit.fitting.ctr_validation.summarize_ctr_loo_table
   clophfit.fitting.ctr_validation.classical_ctr_holdout_rows


Module Contents
---------------

.. py:function:: ctr_param_name(group_name)

   Return the shared-control K parameter name used by Bayesian multi-fit.


.. py:function:: free_ctr_param_name(group_name, well)

   Return the free-control K parameter name used by Bayesian multi-fit.


.. py:function:: make_ctr_holdout_scheme(scheme, *, group_name, heldout_well)

   Return a PlateScheme copy with one control well removed.

   ``PlateScheme.names`` validates strictly as ``dict[str, set[str]]`` in
   ClopHfit, so this helper preserves that type.


.. py:function:: iter_ctr_holdouts(scheme, *, min_remaining = 1)

   Yield holdout tasks from all named control groups.


.. py:function:: widen_heldout_k_prior(results, heldout_well, *, n_sd, prior_sigma = 0.6)

   Return a deepcopy with the heldout well's K prior widened.

   ``fit_binding_pymc_multi`` uses ``p.stderr * n_sd`` as the prior sigma for
   unknown wells.  Increasing ``stderr`` avoids a control-LOO posterior that is
   dominated by a tiny preliminary-fit uncertainty.


.. py:function:: weighted_mean_reference(arrays)

   Return an inverse-variance weighted posterior reference per draw.


.. py:function:: summarize_bayesian_ctr_holdout(trace, *, trace_id, ctr_group, heldout_well, remaining_ctr_wells = None, reference_mode = 'shared', rope = 0.1)

   Summarize posterior CTR holdout ``ΔK``.

   ``reference_mode="shared"`` compares heldout K to ``K_ctr_{group}``.
   ``reference_mode="weighted_mean"`` compares it to the inverse-variance
   weighted posterior mean of the remaining free-control K variables.


.. py:function:: summarize_ctr_loo_table(ctr_loo_df)

   Collapse individual holdout rows into one row per model condition.


.. py:function:: classical_ctr_holdout_rows(results, scheme, *, trace_id, rope = 0.1)

   Post-hoc CTR holdout table for classical fits.

   For each control well, compare its fitted K to the inverse-variance weighted
   mean K of the remaining control wells in the same group.