Regressor class
===============

.. class:: Regressor(self, model_object, model_name: str, model_type: str, grid: ConfigSpace.configuration_space.ConfigurationSpace)

Regressor parent class  - parent class :class:`Model`

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   * - Parameters
     - 
        model_object : classifier object
            model with 'fit', 'predict', 'set_params', and 'get_params' method (see sklearn API)
        model_name : str
            name of the model
        model_type : str
            kind of estimator (e.g. 'RFR' for RandomForestRegressor)
        grid : ConfigurationSpace
            hyperparameter grid for the model
        

   * - 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)
        
       





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   <h2>Methods</h2>

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   * - Method
     - Description
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor._changed_parameters`
     - Function to get parameters that differ from the default ones
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor._get_all_scores`
     - Calculate r2, rmse, d2_tweedie, and optional custom_score metrics
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor._get_score`
     - Calculate a score for given y true and y prediction values
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor._make_cv_scores`
     - Function to create from the crossvalidation results a dictionary
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor._make_scorer`
     - Function to create a dictionary with scorer for the crossvalidation
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor._print_scores`
     - Function to print out the values of a dictionary
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.cross_validation`
     - Random split crossvalidation
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.cross_validation_small_data`
     - One-vs-all cross validation for small datasets
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.evaluate`
     - Function to create multiple scores with predict function of model
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.evaluate_score`
     - Function to create a score with predict function of model
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.feature_importance`
     - Function to generate a matplotlib plot of the top45 feature importance from the model.
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.fit`
     - Function to fit the model
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.fit_warm_start`
     - Function to warm_start fit the model
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.get_deepcopy`
     - Function to create a deepcopy of object
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.get_params`
     - Function to get the parameter from the model object
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.get_random_config`
     - Function to generate one grid configuration
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.get_random_configs`
     - Function to generate grid configurations
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.load_model`
     - Function to load a pickled model class object
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.predict`
     - Function to predict with predict-method from model object
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.predict_proba`
     - Function to predict with predict_proba-method from model object
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.randomCVsearch`
     - Hyperparametertuning with randomCVsearch
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.replace_grid`
     - Function to replace self.grid
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.save_model`
     - Function to pickle and save the class object
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.set_params`
     - Function to set the parameter of the model object
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.smac_search`
     - Hyperparametertuning with SMAC library HyperparameterOptimizationFacade [can only be used in the sam_ml version with swig]
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.train`
     - Function to train the model
   
   * - :meth:`~sam_ml.models.main_regressor.Regressor.train_warm_start`
     - Function to warm_start train the model
   




.. automethod:: sam_ml.models.main_regressor.Regressor._changed_parameters

.. automethod:: sam_ml.models.main_regressor.Regressor._get_all_scores

.. automethod:: sam_ml.models.main_regressor.Regressor._get_score

.. automethod:: sam_ml.models.main_regressor.Regressor._make_cv_scores

.. automethod:: sam_ml.models.main_regressor.Regressor._make_scorer

.. automethod:: sam_ml.models.main_regressor.Regressor._print_scores

.. automethod:: sam_ml.models.main_regressor.Regressor.cross_validation

.. automethod:: sam_ml.models.main_regressor.Regressor.cross_validation_small_data

.. automethod:: sam_ml.models.main_regressor.Regressor.evaluate

.. automethod:: sam_ml.models.main_regressor.Regressor.evaluate_score

.. automethod:: sam_ml.models.main_regressor.Regressor.feature_importance

.. automethod:: sam_ml.models.main_regressor.Regressor.fit

.. automethod:: sam_ml.models.main_regressor.Regressor.fit_warm_start

.. automethod:: sam_ml.models.main_regressor.Regressor.get_deepcopy

.. automethod:: sam_ml.models.main_regressor.Regressor.get_params

.. automethod:: sam_ml.models.main_regressor.Regressor.get_random_config

.. automethod:: sam_ml.models.main_regressor.Regressor.get_random_configs

.. automethod:: sam_ml.models.main_regressor.Regressor.load_model

.. automethod:: sam_ml.models.main_regressor.Regressor.predict

.. automethod:: sam_ml.models.main_regressor.Regressor.predict_proba

.. automethod:: sam_ml.models.main_regressor.Regressor.randomCVsearch

.. automethod:: sam_ml.models.main_regressor.Regressor.replace_grid

.. automethod:: sam_ml.models.main_regressor.Regressor.save_model

.. automethod:: sam_ml.models.main_regressor.Regressor.set_params

.. automethod:: sam_ml.models.main_regressor.Regressor.smac_search

.. automethod:: sam_ml.models.main_regressor.Regressor.train

.. automethod:: sam_ml.models.main_regressor.Regressor.train_warm_start