evaluma.methods.aggregate
=========================

.. py:module:: evaluma.methods.aggregate


Attributes
----------

.. autoapisummary::

   evaluma.methods.aggregate._AGG_MODES


Functions
---------

.. autoapisummary::

   evaluma.methods.aggregate.compute_aggregate


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

.. py:data:: _AGG_MODES

.. py:function:: compute_aggregate(scores_matrix: pandas.DataFrame, agg='trimmed_mean') -> evaluma.results.AggregateResult

   Compute a point-estimate descriptive ranking from a normalized score matrix.

   .. note::
       This is a **descriptive point estimate only** (no CI). The
       trimmed-mean variant trims across datasets, not across seeds; with
       fewer than ~10 datasets the 25% trim is aggressive (e.g. 5 datasets
       → only 3 contribute). Treat results as exploratory. For a
       statistically grounded ranking with uncertainty, use
       :func:`evaluma.methods.iqm.compute_iqm` (requires multiple seeds).

   :param scores_matrix: Normalized model × dataset score matrix.
   :param agg: Aggregation mode — one of ``"trimmed_mean"``, ``"mean"``,
               ``"median"``.

   :returns: Result with ``.table`` sorted descending by ``score``.
   :rtype: AggregateResult

   :raises ValueError: If ``agg`` is not one of the supported modes.