evaluma.methods.profiles
========================

.. py:module:: evaluma.methods.profiles


Functions
---------

.. autoapisummary::

   evaluma.methods.profiles.compute_profiles


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

.. py:function:: compute_profiles(scores_matrix: pandas.DataFrame, metric_direction: dict | None = None) -> evaluma.results.ProfileResult

   Compute Dolan-Moré performance profiles.

   Ratios are computed directly from raw scores without normalization:

   - **max metrics** (higher is better): r_ij = best_j / score_ij
   - **min metrics** (lower is better): r_ij = score_ij / best_j

   where best_j is the best score across all models on dataset j. Both
   definitions yield r_ij ≥ 1, with r_ij = 1 when model i is the best on
   dataset j.

   The tau grid uses the exact observed ratio values as breakpoints, giving
   the exact Dolan-Moré step function. The plot renders on a log₁₀(τ) axis,
   following ML-GYM (Batra et al., 2025) and the AutoML Decathlon (Roberts
   et al., 2022), which extended Dolan & Moré (2002). Use
   ``ProfileResult.aup`` for the scalar Area Under the Profile summary.

   :param scores_matrix: Raw model × dataset score matrix. All values must be
                         strictly positive.
   :param metric_direction: Dict mapping dataset (column) names to ``"min"`` or
                            ``"max"``. Datasets absent from the dict default to ``"max"``.

   :returns: ``tau``, ``model``,
             ``fraction_within_tau``).
   :rtype: ProfileResult with ``.table`` (long-format

   :raises ValueError: If any value in ``scores_matrix`` is zero or negative.