libuplift.base
==============

.. py:module:: libuplift.base

.. autoapi-nested-parse::

   Base classes for all estimators and trasformers.

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Classes
-------

.. autoapisummary::

   libuplift.base.UpliftRegressorMixin
   libuplift.base.UpliftClassifierMixin
   libuplift.base.UpliftTransformerMixin


Functions
---------

.. autoapisummary::

   libuplift.base.is_uplift


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

.. py:class:: UpliftRegressorMixin

   Bases: :py:obj:`_BaseUpliftMixin`, :py:obj:`sklearn.base.RegressorMixin`


   
   Mixin class for all uplift regression estimators in
   libuplift.
















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   .. py:method:: score(X, y, trt, n_trt=None, sample_weight=None)

      
      Score test data.

      By default difference between model predicted and sample ATE
      is returned (e_sate).















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.. py:class:: UpliftClassifierMixin

   Bases: :py:obj:`_BaseUpliftMixin`, :py:obj:`sklearn.base.ClassifierMixin`


   
   Mixin class for all uplift classification estimators in
   libuplift.
















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   .. py:method:: predict_action(X, pos_label=None)

      
      Predict most beneficial action.

      Only supported for binary classification or when pos_label is
      set.  pos_label must be an intereger between 0 and
      ``self.n_classes_-1``.















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   .. py:method:: score(X, y, trt, n_trt=None, sample_weight=None)

      
      Score test data.

      By default difference between model predicted and sample ATE
      is returned (e_sate).















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.. py:class:: UpliftTransformerMixin

   Bases: :py:obj:`object`


   .. py:method:: fit_transform(X, y=None, trt=None, n_trt=None, **fit_params)


.. py:function:: is_uplift(estimator)

   
   Returns True if the given estimator is an uplift model.


   :Parameters:

       **estimator** : object
           Estimator object to test.



   :Returns:

       **out** : bool
           True if estimator is an uplift model and False otherwise.











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