Source code for evalml.pipelines.components.estimators.classifiers.kneighbors_classifier

import numpy as np
from sklearn.neighbors import KNeighborsClassifier as SKKNeighborsClassifier
from skopt.space import Integer

from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes


[docs]class KNeighborsClassifier(Estimator): """ K-Nearest Neighbors Classifier. """ name = "KNN Classifier" hyperparameter_ranges = { "n_neighbors": Integer(2, 12), "weights": ["uniform", "distance"], "algorithm": ["auto", "ball_tree", "kd_tree", "brute"], "leaf_size": Integer(10, 30), "p": Integer(1, 5) } model_family = ModelFamily.K_NEIGHBORS supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS]
[docs] def __init__(self, n_neighbors=5, weights="uniform", algorithm="auto", leaf_size=30, p=2, random_state=0, **kwargs): parameters = {"n_neighbors": n_neighbors, "weights": weights, "algorithm": algorithm, "leaf_size": leaf_size, "p": p} parameters.update(kwargs) knn_classifier = SKKNeighborsClassifier(**parameters) super().__init__(parameters=parameters, component_obj=knn_classifier, random_state=random_state)
@property def feature_importance(self): """ Returns array of 0's matching the input number of features as feature_importance is not defined for KNN classifiers. """ num_features = self._component_obj.n_features_in_ return np.zeros(num_features)