机器学习-KNN分类算法（下）

机器学习-KNN分类算法（上）之前写了KNN的基本概念和python基本实现方法，并没有对模型的好坏进行判断，接下来使用训练数据集和测试数据集来判断（使用accurcay分类指标）
（一）自定义分割数据集方法 data_shuffer.py

import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt

iris = datasets.load_iris()
X = iris.data
y = iris.target
print(X.shape)
# (150, 4)
---------方法1---------
# 使用concatenate函数进行拼接，因为传入的矩阵必须具有相同的形状。
# 因此需要对label进行reshape操作，axis=1表示纵向拼接。
tempConcat = np.concatenate((X, y.reshape(-1,1)), axis=1)
#  拼接好后，直接进行乱序操作
np.random.shuffle(tempConcat)
#  再将shuffle后的数组使用split方法拆分
shuffle_X,shuffle_y = np.split(tempConcat, [4], axis=1)
#  设置划分的比例
test_ratio = 0.2
test_size = int(len(X) * test_ratio)
X_train = shuffle_X[test_size:]
y_train = shuffle_y[test_size:]
X_test = shuffle_X[:test_size]
y_test = shuffle_y[:test_size]
---------方法2---------
# 将x长度这么多的数，返回一个新的打乱顺序的数组，
注意，数组中的元素不是原来的数据，而是混乱的索引
shuffle_index = np.random.permutation(len(X))
# 指定测试数据的比例
test_ratio = 0.2
test_size = int(len(X) * test_ratio)
test_index = shuffle_index[:test_size]
train_index = shuffle_index[test_size:]
X_train = X[train_index]
X_test = X[test_index]
y_train = y[train_index]
y_test = y[test_index]
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)

（二）调用sklearn分类准确度指标

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier

digits = datasets.load_digits()
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=666)
knn_clf = KNeighborsClassifier(n_neighbors=3)
knn_clf.fit(X_train, y_train)
y_predict = knn_clf.predict(X_test)
print(accuracy_score(y_test, y_predict))

（三）寻找好的k值

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import datasets
digits = datasets.load_digits()
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=666)

# 指定最佳值的分数，初始化为0.0；设置最佳值k，初始值为-1
best_score = 0.0
best_k = -1
for k in range(1, 11):  # 暂且设定到1～11的范围内
    knn_clf = KNeighborsClassifier(n_neighbors=k)
    knn_clf.fit(X_train, y_train)
    score = knn_clf.score(X_test, y_test)
    if score > best_score:
        best_k = k
        best_score = score
print("best_k = ", best_k)
print("best_score = ", best_score)

相关代码已上传GitHub，仅供参考学习使用。