CTest
=====

.. class:: CTest(self, models: Union[Literal['all', 'big_data', 'basic', 'basic2'], list[sam_ml.models.main_classifier.Classifier]] = 'all', vectorizer: str | sam_ml.data.preprocessing.embeddings.Embeddings_builder | None | list[str | sam_ml.data.preprocessing.embeddings.Embeddings_builder | None] = None, scaler: str | sam_ml.data.preprocessing.scaler.Scaler | None | list[str | sam_ml.data.preprocessing.scaler.Scaler | None] = None, selector: str | tuple[str, int] | sam_ml.data.preprocessing.feature_selection.Selector | None | list[str | tuple[str, int] | sam_ml.data.preprocessing.feature_selection.Selector | None] = None, sampler: str | sam_ml.data.preprocessing.sampling.Sampler | sam_ml.data.preprocessing.sampling_pipeline.SamplerPipeline | None | list[str | sam_ml.data.preprocessing.sampling.Sampler | sam_ml.data.preprocessing.sampling_pipeline.SamplerPipeline | None] = None)

AutoML class for classifier  - parent class :class:`AutoML`

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

   * - Parameters
     - 
        models : {"all", "big_data", "basic", "basic2"} or list, default="all"
            - 'all':
                use all Wrapperclass models (18+ models)
            - 'big_data':
                use all Wrapperclass models except the ones that take too much space or 
                time on big data (>200.000 data points)
            - 'basic':
                use basic Wrapperclass models (8 models) which includes:
                LogisticRegression, MLP Classifier, LinearSVC, DecisionTreeClassifier,
                RandomForestClassifier, SVC, Gradientboostingmachine, KNeighborsClassifier
            - 'basic2':
                use basic (mostly tree-based) Wrapperclass models
            - list of Wrapperclass models from sam_ml library

        vectorizer : str, Embeddings_builder, or None
            object or algorithm of :class:`Embeddings_builder` class which will be used for automatic string column vectorizing (None for no vectorizing)
        scaler : str, Scaler, or None
            object or algorithm of :class:`Scaler` class for scaling the data (None for no scaling)
        selector : str, Selector, or None
            object, tuple of algorithm and feature number, or algorithm of :class:`Selector` class for feature selection (None for no selecting)
        sampler : str, Sampler, SamplerPipeline, or None
            object or algorithm of :class:`Sampler` / :class:`SamplerPipeline` class for sampling the train data (None for no sampling)



   * - Attributes
     - 
         
        models : dict
            dictionary with model names as keys and model instances as values
        
       
         
        scores : dict[str, float]
            dictionary with scores for every model as dictionary
        
       

.. note::

   If a list is provided for one or multiple of the preprocessing steps, all model with preprocessing steps combination will be added as pipelines.

.. raw:: html

   <h2>Example</h2>

>>> from sam_ml.models.automl import CTest
>>>
>>> model = CTest()
>>> print(model)
CTest(models='all', vectorizer=[None], scaler=[None], selector=[None], sampler=[None])
        

.. raw:: html

   <h2>Methods</h2>

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

   * - Method
     - Description
   
   * - :meth:`~sam_ml.models.automl.ClassifierTest.CTest.add_model`
     - Function for adding model in self.models
   
   * - :meth:`~sam_ml.models.automl.ClassifierTest.CTest.eval_models`
     - Function to train and evaluate every model
   
   * - :meth:`~sam_ml.models.automl.ClassifierTest.CTest.eval_models_cv`
     - Function to run a cross validation on every model
   
   * - :meth:`~sam_ml.models.automl.ClassifierTest.CTest.find_best_model_mass_search`
     - Function to run a successive halving hyperparameter search for every model
   
   * - :meth:`~sam_ml.models.automl.ClassifierTest.CTest.find_best_model_randomCV`
     - Function to run a random cross validation hyperparameter search for every model
   
   * - :meth:`~sam_ml.models.automl.ClassifierTest.CTest.find_best_model_smac`
     - Function to run a Hyperparametertuning with SMAC library HyperparameterOptimizationFacade for every model [can only be used in the sam_ml version with swig]
   
   * - :meth:`~sam_ml.models.automl.ClassifierTest.CTest.model_combs`
     - Function for mapping string to set of models
   
   * - :meth:`~sam_ml.models.automl.ClassifierTest.CTest.output_scores_as_pd`
     - Function to output self.scores as pd.DataFrame
   
   * - :meth:`~sam_ml.models.automl.ClassifierTest.CTest.remove_model`
     - Function for deleting model in self.models
   

.. 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.automl.ClassifierTest.CTest.add_model

.. automethod:: sam_ml.models.automl.ClassifierTest.CTest.eval_models

.. automethod:: sam_ml.models.automl.ClassifierTest.CTest.eval_models_cv

.. automethod:: sam_ml.models.automl.ClassifierTest.CTest.find_best_model_mass_search

.. automethod:: sam_ml.models.automl.ClassifierTest.CTest.find_best_model_randomCV

.. automethod:: sam_ml.models.automl.ClassifierTest.CTest.find_best_model_smac

.. automethod:: sam_ml.models.automl.ClassifierTest.CTest.model_combs

.. automethod:: sam_ml.models.automl.ClassifierTest.CTest.output_scores_as_pd

.. automethod:: sam_ml.models.automl.ClassifierTest.CTest.remove_model