ml_grid.model_classes.AutoKerasClassifierWrapper
================================================

.. py:module:: ml_grid.model_classes.AutoKerasClassifierWrapper

.. autoapi-nested-parse::

   AutoKeras Classifier Wrapper.

   This module provides a scikit-learn compatible wrapper for AutoKeras StructuredDataClassifier.



Attributes
----------

.. autoapisummary::

   ml_grid.model_classes.AutoKerasClassifierWrapper.ak
   ml_grid.model_classes.AutoKerasClassifierWrapper.logger


Classes
-------

.. autoapisummary::

   ml_grid.model_classes.AutoKerasClassifierWrapper.AutoKerasClassifierWrapper


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

.. py:data:: ak
   :value: None


.. py:data:: logger

.. py:class:: AutoKerasClassifierWrapper(max_trials: int = 3, epochs: int = 10, validation_split: float = 0.2, directory: Optional[str] = None, seed: int = 42, verbose: int = 1, overwrite: bool = True)

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


   A scikit-learn compatible wrapper for AutoKeras StructuredDataClassifier.


   .. py:attribute:: max_trials
      :value: 3



   .. py:attribute:: epochs
      :value: 10



   .. py:attribute:: validation_split
      :value: 0.2



   .. py:attribute:: directory
      :value: None



   .. py:attribute:: seed
      :value: 42



   .. py:attribute:: verbose
      :value: 1



   .. py:attribute:: overwrite
      :value: True



   .. py:attribute:: model_
      :value: None



   .. py:method:: fit(X: Union[numpy.ndarray, pandas.DataFrame], y: Union[numpy.ndarray, pandas.Series], **kwargs) -> AutoKerasClassifierWrapper


   .. py:method:: predict(X: Union[numpy.ndarray, pandas.DataFrame]) -> numpy.ndarray


   .. py:method:: predict_proba(X: Union[numpy.ndarray, pandas.DataFrame]) -> numpy.ndarray