from sklearn.svm import SVC

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


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.classifiers.SklearnMethod.SklearnMethod`.


    **YAML specification:**

    .. indent with spaces
    .. code-block:: yaml

        definitions:
            ml_methods:
                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)

    def can_predict_proba(self) -> bool:
        return False

    def can_fit_with_example_weights(self) -> bool:
        return False

    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

    @staticmethod
    def get_documentation():
        doc = str(SVM.__doc__)

        mapping = {
            "For usage instructions, check :py:obj:`~immuneML.ml_methods.classifiers.SklearnMethod.SklearnMethod`.": SklearnMethod.get_usage_documentation("SVM"),
        }

        doc = update_docs_per_mapping(doc, mapping)
        return doc
