Source code for immuneML.ml_methods.PrecomputedKNN

from sklearn.neighbors import KNeighborsClassifier

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

[docs]class PrecomputedKNN(SklearnMethod): """ This is a wrapper of scikit-learn’s KNeighborsClassifier class. This ML method takes a pre-computed distance matrix, as created by the :ref:`Distance` or :ref:`CompAIRRDistance` encoders. If you would like to use a different encoding in combination with KNN, please use :ref:`KNN` instead. Please see the `scikit-learn documentation <>`_ of KNeighborsClassifier for the parameters. For usage instructions, check :py:obj:`~immuneML.ml_methods.SklearnMethod.SklearnMethod`. YAML specification: .. indent with spaces .. code-block:: yaml my_knn_method: KNN: # sklearn parameters (same names as in original sklearn class) weights: uniform # always use this setting for weights n_neighbors: [5, 10, 15] # find the optimal number of neighbors # Additional parameter that determines whether to print convergence warnings show_warnings: True # if any of the parameters under KNN 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_knn: KNN """ def __init__(self, parameter_grid: dict = None, parameters: dict = None): parameters = parameters if parameters is not None else {} parameter_grid = parameter_grid if parameter_grid is not None else {} super(PrecomputedKNN, self).__init__(parameter_grid=parameter_grid, parameters=parameters) def _get_ml_model(self, cores_for_training: int = 2, X=None): params = self._parameters params["n_jobs"] = cores_for_training params["metric"] = "precomputed" return KNeighborsClassifier(**params)
[docs] def get_params(self): return self.model.get_params(deep=True)
[docs] def can_predict_proba(self) -> bool: return True
[docs] def get_compatible_encoders(self): from immuneML.encodings.distance_encoding.CompAIRRDistanceEncoder import CompAIRRDistanceEncoder from immuneML.encodings.distance_encoding.DistanceEncoder import DistanceEncoder return [DistanceEncoder, CompAIRRDistanceEncoder]
[docs] @staticmethod def get_documentation(): doc = str(PrecomputedKNN.__doc__) mapping = { "For usage instructions, check :py:obj:`~immuneML.ml_methods.SklearnMethod.SklearnMethod`.": SklearnMethod.get_usage_documentation("KNN"), } doc = update_docs_per_mapping(doc, mapping) return doc