***********************
:py:mod:`wildboar.base`
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.. py:module:: wildboar.base

.. autoapi-nested-parse::

   Base classes for all estimators.

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Classes
-------

.. autoapisummary::

   wildboar.base.BaseEstimator
   wildboar.base.CounterfactualMixin
   wildboar.base.ExplainerMixin



Functions
---------

.. autoapisummary::

   wildboar.base.is_counterfactual
   wildboar.base.is_explainer




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   <br />

.. py:class:: BaseEstimator



   
   Base estimator for all Wildboar estimators.
















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   .. py:method:: get_metadata_routing()

      
      Get metadata routing of this object.

      Please check :ref:`User Guide <metadata_routing>` on how the routing
      mechanism works.




      :Returns:

          **routing** : MetadataRequest
              A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating
              routing information.











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   .. py:method:: get_params(deep=True)

      
      Get parameters for this estimator.


      :Parameters:

          **deep** : bool, default=True
              If True, will return the parameters for this estimator and
              contained subobjects that are estimators.



      :Returns:

          **params** : dict
              Parameter names mapped to their values.











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   .. py:method:: set_params(**params)

      
      Set the parameters of this estimator.

      The method works on simple estimators as well as on nested objects
      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have
      parameters of the form ``<component>__<parameter>`` so that it's
      possible to update each component of a nested object.

      :Parameters:

          **\*\*params** : dict
              Estimator parameters.



      :Returns:

          **self** : estimator instance
              Estimator instance.











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.. py:class:: CounterfactualMixin

   
   Mixin class for counterfactual explainer.
















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   .. py:method:: score(x, y)

      
      Score the counterfactual explainer in terms of closeness of fit.


      :Parameters:

          **x** : array-like of shape (n_samples, n_timestep)
              The samples.

          **y** : array-like of shape (n_samples, )
              The desired counterfactal label.



      :Returns:

          float
              The proximity.











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.. py:class:: ExplainerMixin

   
   Mixin class for all explainers in wildboar.
















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   .. py:method:: fit_explain(estimator, x=None, y=None, **kwargs)

      
      Fit and return the explanation.


      :Parameters:

          **estimator** : Estimator
              The estimator to explain.

          **x** : time-series, optional
              The input time series.

          **y** : array-like of shape (n_samples, ), optional
              The labels.

          **\*\*kwargs** : dict, optional
              Optional extra arguments.



      :Returns:

          ndarray
              The explanation.











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   .. py:method:: plot(x=None, y=None, ax=None)

      
      Plot the explanation.


      :Parameters:

          **x** : array-like, optional
              Optional imput samples.

          **y** : array-like, optional
              Optional target labels.

          **ax** : Axes, optional
              Optional axes to plot to.



      :Returns:

          Axes
              The axes object.











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.. py:function:: is_counterfactual(estimator)

   
   Check if estimator is a counterfactual explainer.


   :Parameters:

       **estimator** : object
           The estimator.



   :Returns:

       bool
           True if the estimator probably is a counterfactual explainer.











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.. py:function:: is_explainer(estimator)

   
   Check if estimator is an explainer.


   :Parameters:

       **estimator** : object
           The estimator.



   :Returns:

       bool
           True if the estimator probably is an explainer.











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