ElasticNet (EN)
===============

.. class:: EN(self, model_name: str = 'ElasticNet', **kwargs)

ElasticNet Wrapper class  - parent class :class:`Regressor`

.. list-table::
   :widths: 25 75
   :header-rows: 0

   * - Parameters
     - 
        selection : str,
            if set to 'random', a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to 'random') often leads to significantly faster convergence especially when tol is higher than 1e-4
        


   * - Attributes
     - 
         
        cv_scores : dict[str, float]
            dictionary with cross validation results
        
       
         
        feature_names : list[str]
            names of all the features that the model saw during training. Is empty if model was not fitted yet.
        
       
         
        grid : ConfigurationSpace
            hyperparameter tuning grid of the model
        
       
         
        model : model object
            model with 'fit', 'predict', 'set_params', and 'get_params' method (see sklearn API)
        
       
         
        model_name : str
            name of the model. Used in loading bars and dictionaries as identifier of the model
        
       
         
        model_type : str
            kind of estimator (e.g. 'RFC' for RandomForestClassifier)
        
       
         
        rCVsearch_results : pd.DataFrame or None
            results from randomCV hyperparameter tuning. Is ``None`` if randomCVsearch was not used yet.
        
       
         
        train_score : float
            train score value
        
       
         
        train_time : str
            train time in format: "0:00:00" (hours:minutes:seconds)
        
       

.. note::

   You can use all parameters of the wrapped model when initialising the wrapper class.

.. raw:: html

   <h2>Example</h2>

>>> from sam_ml.models.regressor import EN
>>>
>>> model = EN()
>>> print(model)
EN(model_name='ElasticNet')
        

.. raw:: html

   <h2>Methods</h2>

.. list-table::
   :widths: 25 75
   :header-rows: 1

   * - Method
     - Description
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.cross_validation`
     - Random split crossvalidation
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.cross_validation_small_data`
     - One-vs-all cross validation for small datasets
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.evaluate`
     - Function to create multiple scores with predict function of model
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.evaluate_score`
     - Function to create a score with predict function of model
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.feature_importance`
     - Function to generate a matplotlib plot of the top45 feature importance from the model.
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.fit`
     - Function to fit the model
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.fit_warm_start`
     - Function to warm_start fit the model
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.get_deepcopy`
     - Function to create a deepcopy of object
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.get_params`
     - Function to get the parameter from the model object
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.get_random_config`
     - Function to generate one grid configuration
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.get_random_configs`
     - Function to generate grid configurations
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.load_model`
     - Function to load a pickled model class object
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.predict`
     - Function to predict with predict-method from model object
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.predict_proba`
     - Function to predict with predict_proba-method from model object
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.randomCVsearch`
     - Hyperparametertuning with randomCVsearch
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.replace_grid`
     - Function to replace self.grid
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.save_model`
     - Function to pickle and save the class object
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.set_params`
     - Function to set the parameter of the model object
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.smac_search`
     - Hyperparametertuning with SMAC library HyperparameterOptimizationFacade [can only be used in the sam_ml version with swig]
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.train`
     - Function to train the model
   
   * - :meth:`~sam_ml.models.regressor.ElasticNet.EN.train_warm_start`
     - Function to warm_start train the model
   




.. automethod:: sam_ml.models.regressor.ElasticNet.EN.cross_validation

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.cross_validation_small_data

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.evaluate

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.evaluate_score

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.feature_importance

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.fit

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.fit_warm_start

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.get_deepcopy

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.get_params

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.get_random_config

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.get_random_configs

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.load_model

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.predict

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.predict_proba

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.randomCVsearch

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.replace_grid

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.save_model

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.set_params

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.smac_search

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.train

.. automethod:: sam_ml.models.regressor.ElasticNet.EN.train_warm_start