dpg.sklearn_normalizer
======================

.. py:module:: dpg.sklearn_normalizer

.. autoapi-nested-parse::

   Normalizer for scikit-learn ensemble models.

   Handles differences in tree storage between RandomForest, GradientBoosting,
   AdaBoost, and other ensemble methods to provide a consistent interface for DPG.



Classes
-------

.. autoapisummary::

   dpg.sklearn_normalizer.SklearnEnsembleNormalizer


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

.. py:class:: SklearnEnsembleNormalizer

   Normalizes sklearn ensemble models to have consistent tree access interface.

   Problem: Different ensemble methods store trees differently:
   - RandomForest: estimators_ is 1D list of DecisionTree objects
   - GradientBoosting: estimators_ is 2D (n_estimators, n_trees_per_iteration)
   - AdaBoost: estimators_ is 1D list of DecisionTree objects

   Solution: Normalize to 1D list for consistent access.


   .. py:attribute:: GB_MODELS


   .. py:method:: needs_normalization(model)
      :staticmethod:


      Check if a model needs tree structure normalization.

      :param model: sklearn ensemble model

      :returns: True if normalization needed
      :rtype: bool



   .. py:method:: normalize(model)
      :staticmethod:


      Normalize a sklearn ensemble model's tree structure.

      For GradientBoosting models:
      - Converts 2D estimators_ to 1D list
      - Preserves the class-slot index for multiclass classifiers

      :param model: sklearn ensemble model

      :returns: Normalized shallow copy with DPG-specific metadata
      :rtype: model



   .. py:method:: get_tree_class_index(model, tree_index)
      :staticmethod:


      Return the preserved class-slot index for a normalized GB tree.