ml_grid.model_classes.TPOTClassifierWrapper
===========================================

.. py:module:: ml_grid.model_classes.TPOTClassifierWrapper

.. autoapi-nested-parse::

   TPOT Classifier Wrapper.

   This module provides a scikit-learn compatible wrapper for TPOTClassifier.



Attributes
----------

.. autoapisummary::

   ml_grid.model_classes.TPOTClassifierWrapper.TPOTClassifier
   ml_grid.model_classes.TPOTClassifierWrapper.logger


Classes
-------

.. autoapisummary::

   ml_grid.model_classes.TPOTClassifierWrapper.TPOTClassifierWrapper


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

.. py:data:: TPOTClassifier
   :value: None


.. py:data:: logger

.. py:class:: TPOTClassifierWrapper(generations: int = 5, population_size: int = 20, offspring_size: Optional[int] = None, mutation_rate: float = 0.9, crossover_rate: float = 0.1, scoring: str = 'accuracy', cv: int = 5, subsample: float = 1.0, n_jobs: int = -1, max_time_mins: Optional[int] = None, max_eval_time_mins: float = 5, random_state: int = 42, verbosity: int = 2, early_stop: Optional[int] = None)

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


   A scikit-learn compatible wrapper for TPOTClassifier.


   .. py:attribute:: generations
      :value: 5



   .. py:attribute:: population_size
      :value: 20



   .. py:attribute:: offspring_size
      :value: None



   .. py:attribute:: mutation_rate
      :value: 0.9



   .. py:attribute:: crossover_rate
      :value: 0.1



   .. py:attribute:: scoring
      :value: 'accuracy'



   .. py:attribute:: cv
      :value: 5



   .. py:attribute:: subsample
      :value: 1.0



   .. py:attribute:: n_jobs
      :value: -1



   .. py:attribute:: max_time_mins
      :value: None



   .. py:attribute:: max_eval_time_mins
      :value: 5



   .. py:attribute:: random_state
      :value: 42



   .. py:attribute:: verbosity
      :value: 2



   .. py:attribute:: early_stop
      :value: None



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



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


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


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