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


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,
    ]

    def __init__(
        self,
        n_neighbors=5,
        weights="uniform",
        algorithm="auto",
        leaf_size=30,
        p=2,
        random_seed=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_seed=random_seed
        )

    @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)