Source code for immuneML.ml_methods.SVM

from sklearn.svm import SVC

from immuneML.ml_methods.SklearnMethod import SklearnMethod
from scripts.specification_util import update_docs_per_mapping

[docs]class SVM(SklearnMethod): """ This is a wrapper of scikit-learn’s SVC class. Please see the `scikit-learn documentation <>`_ of SVC for the parameters. Note: if you are interested in plotting the coefficients of the SVM model, consider running the :ref:`Coefficients` report. For usage instructions, check :py:obj:`~immuneML.ml_methods.SklearnMethod.SklearnMethod`. YAML specification: .. indent with spaces .. code-block:: yaml my_svm: # user-defined method name SVM: # 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 kernel: linear # Additional parameter that determines whether to print convergence warnings show_warnings: True # if any of the parameters under SVM 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_svm: SVM """ def __init__(self, parameter_grid: dict = None, parameters: dict = None): _parameter_grid = parameter_grid if parameter_grid is not None else {} _parameters = parameters if parameters is not None else {} super(SVM, self).__init__(parameter_grid=_parameter_grid, parameters=_parameters) def _get_ml_model(self, cores_for_training: int = 2, X=None): return SVC(**self._parameters)
[docs] def can_predict_proba(self) -> bool: return False
[docs] def get_params(self): params = self.model.get_params() params["coefficients"] = self.model.coef_[0].tolist() if self.model.kernel == 'linear' else self.model.dual_coef_ params["intercept"] = self.model.intercept_.tolist() return params
[docs] @staticmethod def get_documentation(): doc = str(SVM.__doc__) mapping = { "For usage instructions, check :py:obj:`~immuneML.ml_methods.SklearnMethod.SklearnMethod`.": SklearnMethod.get_usage_documentation("SVM"), } doc = update_docs_per_mapping(doc, mapping) return doc