smx.predicates.generation
=========================

.. py:module:: smx.predicates.generation

.. autoapi-nested-parse::

   PredicateGenerator: generate binary predicates from quantile thresholds on
   zone aggregation scores.



Classes
-------

.. autoapisummary::

   smx.predicates.generation.PredicateGenerator


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

.. py:class:: PredicateGenerator(quantiles: List[float])

   Generate binary predicates from quantile thresholds on zone scores.

   For each spectral zone and each quantile value, two predicates are created:

   * ``zone <= q_value`` (samples below the quantile threshold)
   * ``zone > q_value``  (samples above the quantile threshold)

   Duplicate predicates (same rule) arising from identical quantile values
   are automatically removed.

   Parameters
   ----------
   quantiles : list of float
       Quantile fractions in [0, 1] to use as thresholds.
       Example: ``[0.25, 0.5, 0.75]`` creates six predicates per zone.

   Attributes (set after :meth:`fit`)
   ------------------------------------
   predicates_df\_ : pd.DataFrame
       One row per predicate. Columns: ``predicate``, ``rule``, ``zone``,
       ``thresholds``, ``operator``.
   indicator_df\_ : pd.DataFrame
       Binary indicator matrix (samples × predicates).  Columns are predicate
       rule strings; values are 1/0.
   co_occurrence_matrix\_ : pd.DataFrame
       Pairwise co-occurrence matrix of predicates.


   .. py:attribute:: quantiles


   .. py:attribute:: predicates_df_
      :type:  Optional[pandas.DataFrame]
      :value: None



   .. py:attribute:: indicator_df_
      :type:  Optional[pandas.DataFrame]
      :value: None



   .. py:attribute:: co_occurrence_matrix_
      :type:  Optional[pandas.DataFrame]
      :value: None



   .. py:method:: fit(zone_scores_df: pandas.DataFrame) -> PredicateGenerator

      Learn predicates from *zone_scores_df*.

      Parameters
      ----------
      zone_scores_df : pd.DataFrame
          Zone aggregation scores (samples × zones) as returned by
          :class:`smx.zones.aggregation.ZoneAggregator`.

      Returns
      -------
      self



   .. py:method:: transform(zone_scores_df: pandas.DataFrame) -> pandas.DataFrame

      Apply the fitted predicates to new zone scores data.

      This re-uses the thresholds learned during :meth:`fit` and is
      useful for applying predicates to a prediction (validation) set.

      Parameters
      ----------
      zone_scores_df : pd.DataFrame
          Zone scores for the samples to evaluate.

      Returns
      -------
      pd.DataFrame
          Binary indicator matrix (samples × predicates).



   .. py:method:: fit_transform(zone_scores_df: pandas.DataFrame) -> pandas.DataFrame

      Fit and return the indicator matrix in one step.