[docs]classSVC(SklearnMethod):""" This is a wrapper of scikit-learn’s LinearSVC class. Please see the `scikit-learn documentation <https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html>`_ of SVC for the parameters. Note: if you are interested in plotting the coefficients of the SVC model, consider running the :ref:`Coefficients` report. For usage instructions, check :py:obj:`~immuneML.ml_methods.classifiers.SklearnMethod.SklearnMethod`. **YAML specification:** .. indent with spaces .. code-block:: yaml definitions: ml_methods: my_svc: # user-defined method name SVC: # name of the ML method # sklearn parameters (same names as in original sklearn class) C: [0.01, 0.1, 1, 10, 100] # find the optimal value for C # Additional parameter that determines whether to print convergence warnings show_warnings: True # if any of the parameters under SVC is a list and model_selection_cv is True, # a grid search will be done over the given parameters, using the number of folds specified in model_selection_n_folds, # and the optimal model will be selected model_selection_cv: True model_selection_n_folds: 5 # alternative way to define ML method with default values: my_default_svc: SVC """def__init__(self,parameter_grid:dict=None,parameters:dict=None):_parameter_grid=parameter_gridifparameter_gridisnotNoneelse{}_parameters=parametersifparametersisnotNoneelse{}super(SVC,self).__init__(parameter_grid=_parameter_grid,parameters=_parameters)def_get_ml_model(self,cores_for_training:int=2,X=None):returnSklearnSVC(**self._parameters)