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 <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_
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_.toarray().tolist() if hasattr(self.model.dual_coef_, "toarray") else self.model.dual_coef_.tolist()
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