XGBClassifier (XGBC)
====================

.. class:: XGBC(self, model_name: str = 'XGBClassifier', n_jobs: str = -1, random_state: int = 42, **kwargs)

XGBoostClassifier Wrapper class  - parent class :class:`Classifier`

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

   * - Parameters
     - 
        random_state : int,                 default=42
            random_state for 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)
        
       

.. note::

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

.. raw:: html

   <h2>Example</h2>

>>> from sam_ml.models.classifier import XGBC
>>>
>>> model = XGBC()
>>> print(model)
XGBC(base_score=None, booster=None, callbacks=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, device=None, early_stopping_rounds=None, enable_categorical=False, eval_metric=None, feature_types=None, gamma=None, grow_policy=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, max_delta_step=None, max_depth=None, max_leaves=None, min_child_weight=None, missing=nan, monotone_constraints=None, multi_strategy=None, n_estimators=None, num_parallel_tree=None, reg_alpha=None, reg_lambda=None, sampling_method=None, scale_pos_weight=None, subsample=None, tree_method=None, validate_parameters=None, verbosity=None, model_name='XGBClassifier')
        

.. raw:: html

   <h2>Methods</h2>

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

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

.. note::

   A lot of methods use parameters for advanced scoring. For additional information on advanced scoring, see :ref:`scoring documentation <classifier-scoring-section>`


.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.cross_validation

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.cross_validation_small_data

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.evaluate

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.evaluate_proba

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.evaluate_score

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.evaluate_score_proba

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.feature_importance

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.fit

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.fit_warm_start

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.get_deepcopy

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.get_params

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.get_random_config

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.get_random_configs

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.load_model

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.predict

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.predict_proba

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.randomCVsearch

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.replace_grid

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.save_model

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.set_params

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.smac_search

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.train

.. automethod:: sam_ml.models.classifier.XGBoostClassifier.XGBC.train_warm_start