SamplerPipeline
===============

.. class:: SamplerPipeline(self, algorithm: str | list[sam_ml.data.preprocessing.sampling.Sampler] = 'SMOTE_rus_20_50')

Class uses multplie up- and down-sampling algorithms instead of only one  - parent class :class:`Data`

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

   * - Parameters
     - 
        algorithm : str or list[Sampler],                 default="SMOTE_rus_20_50"
            algorithm format:
            
            - "A1\_A2\_..._An_x1_x2_..._xn" with A1, A2, ... Sampler algorithm and x1, x2, ... their sampling_strategy
            
              first, use Sampler A1 with sampling_strategy x1% on data, then Sampler A2 with sampling_strategy x2% until Sampler An with sampling_strategy xn on data (ONLY works for binary data!!!)
              
              Note:
              sampling_strategy is the percentage of class size of minority in relation to the class size of the majority

              Examples (for binary classification):

              1) ros_rus_10_50: RandomOverSampler for minority class to 10% of majority class and then RandomUnderSampler for majority class to 2*minority class

              2) SMOTE_rus_20_50: SMOTE for minority class to 20% of majority class and then RandomUnderSampler for majority class to 2*minority class

            - list[Sampler]: use each Sampler in list one after the other on data
        

   * - Attributes
     - 
         
        algorithm : str
            name of the used algorithm
        
       
         
        transformer : transformer instance
            transformer instance (e.g. StandardScaler)
        
       



.. raw:: html

   <h2>Example</h2>

>>> from sam_ml.data.preprocessing import SamplerPipeline
>>>
>>> model = SamplerPipeline()
>>> print(model)
SamplerPipeline(Sampler(algorithm='SMOTE', sampling_strategy=0.2, ), Sampler(algorithm='rus', sampling_strategy=0.5, ))
        

.. raw:: html

   <h2>Methods</h2>

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

   * - Method
     - Description
   
   * - :meth:`~sam_ml.data.preprocessing.sampling_pipeline.SamplerPipeline.get_params`
     - Function to get the parameter from the transformer instance
   
   * - :meth:`~sam_ml.data.preprocessing.sampling_pipeline.SamplerPipeline.params`
     - Function to get the recommended parameter values for the class
   
   * - :meth:`~sam_ml.data.preprocessing.sampling_pipeline.SamplerPipeline.sample`
     - Function for up- and downsampling
   
   * - :meth:`~sam_ml.data.preprocessing.sampling_pipeline.SamplerPipeline.set_params`
     - Function to set the parameter of the transformer instance
   




.. automethod:: sam_ml.data.preprocessing.sampling_pipeline.SamplerPipeline.get_params

.. automethod:: sam_ml.data.preprocessing.sampling_pipeline.SamplerPipeline.params

.. automethod:: sam_ml.data.preprocessing.sampling_pipeline.SamplerPipeline.sample

.. automethod:: sam_ml.data.preprocessing.sampling_pipeline.SamplerPipeline.set_params