我为 KNN 构建了自定义转换器，但我不明白为什么我的 k 数在设置时总是显示相同的精度......

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import StandardScaler
from scipy.spatial import distance 

def minkowski_dist(a, b):
    return distance.minkowski(a, b)

class MyKNN(BaseEstimator, TransformerMixin):

    def __init__(self, k, scale_data=False):
        super().__init__()
        self.k = k
        if scale_data is True:
            self.ss = StandardScaler()
        else:
            self.ss = None
        self.X_train = None

    def fit(self, X_train, Y_train):
        self.X_train = X_train
        self.Y_train = Y_train

    def predict(self, X_test):
        prediction = []
        for row in X_test:
            label =  self.closest_n(row)
            prediction.append(label)
        return prediction

    def closest_n(self, row):
        best_dist = minkowski_dist(row, self.X_train[0])
        best_index = 0
        for i in range(1, len(self.X_train)):
            dist = minkowski_dist(row, self.X_train[i])
            if dist < best_dist:
                best_dist = dist
                best_index = i
        return self.Y_train[best_index]

一直在iris 数据集上做 KNN

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

iris_dataset=load_iris()
X=iris_dataset.data
Y=iris_dataset.target

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=42, stratify=Y)

print(X_train.shape, X_test.shape, Y_train.shape, Y_test.shape)

这就是我得到的：

for k in range(1,21,2):
    knn = MyKNN(k)
    knn.fit(X_train, Y_train)
    prediction = knn.predict(X_test)
    from sklearn.metrics import accuracy_score
    print("Test accuracy for k={}:".format(k),accuracy_score(Y_test, prediction))