仿scikit-learn模式写的kNN算法

一、什么是kNN算法

k邻近是指每个样本都可以用它最接近的k个邻居来代表。

核心思想：如果一个样本在特征空间中的k个最相邻的样本中大多数属于一个某类别，则该样本也属于这个类别。

二、将kNN封装成kNNClassifier

1、训练样本的特征在二维空间中的表示

、

　　

2、kNN的训练过程如下图

　　

3、完整代码（kNN.py）

import numpy as np
from math import sqrt
from collections import Counter
from metrics import accuracy_score

class kNNClassifier():
    def __init__(self, k):
        """初始化kNN分类器"""
        assert k >= 1, "k must be valid"
        self.k = k
        self._x_train = None
        self._y_train = None

    def fit(self, x_train, y_train):
        """根据训练集x_train和y_train训练kNN分类器"""
        assert x_train.shape[0] == y_train.shape[0], 
            "the size of x_train must be equal to the size of y_train"
        assert x_train.shape[0] >= self.k, "the size of x_train must be at least k"
        self._x_train = x_train
        self._y_train = y_train
        return self

    def predict(self, X_predict):
        """给定待预测数据集X_train,返回表示x_train的结果向量"""
        assert self._x_train is not None and self._y_train is not None, 
            "must fit before predict"
        assert X_predict.shape[1] == self._x_train.shape[1] , 
            "the feature number of X_predict must be equal to x_train"
        y_predict = [self._predict(x) for x in X_predict]
        return np.array(y_predict)

    def _predict(self, x):
        """给定待预测数据x,返回x预测的结果值"""
        assert x.shape[0] == self._x_train.shape[1], 
            "the feature number of x must be equal tu x_train"
        distances = [sqrt(np.sum((x_train-x)**2)) for x_train in self._x_train]
        nearest = np.argsort(distances)
        topK_y = [self._y_train[i] for i in nearest[:self.k]]
        votes = Counter(topK_y)
        return votes.most_common(1)[0][0]

    def score(self, X_test, y_test):
        """根据数据集X_test 和y_test 得到当前模型的准确度"""
        y_predict = self.predict(X_test)
        return accuracy_score(y_test, y_predict)

    def __repr__(self):
        return "kNN(k=%d)" % self.k

if __name__ == "__main__":
    x_train = np.array([[0.31864691, 0.99608349],
                        [0.8609734 , 0.40706129],
                        [0.86746155, 0.20136923],
                        [0.4346735 , 0.17677379],
                        [0.42842348, 0.68055183],
                        [0.70661963, 0.76155652],
                        [0.73379517, 0.6123456 ],
                        [0.68330672, 0.52193524],
                        [0.11192091, 0.07885633],
                        [0.99273292, 0.62484263]])
    y_train = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
    k = 6
    x = np.array([0.756789,0.6123456])
    knn = kNNClassifier(k)
    knn.fit(x_train,y_train)
    x_predict = x.reshape(1,-1)
    print(knn.predict(x_predict))

三、测试结果

[1]

四、问题

1、如果直接将上面训练得到的模型直接放在真实环境中使用，但是模型没有得到验证，会造成模型很差，会有真实损失。

2、真实环境下很难拿到符合条件的数据去测试

解决办法：

1、将训练数据拿出一部分作为测试数据，通过测试数据直接判断模型好坏。

2、在模型进入真实环境前改进模型

1、train_test_split.py

import numpy as np

def train_test_split(X, Y, train_ratio=0.8, seed=None):
    """将数据X和Y按照train_ratio分割成x_train,y_train,x_test,y_test"""
    assert X.shape[0] == Y.shape[0], "the size of X must equal to the size of Y"
    assert 0.0 <= train_ratio <= 1.0, "train_ratio must be valid"

    if seed:
        np.random.seed(seed)

    shuffled_indexes = np.random.permutation(len(X))
    train_size = int(len(X) * train_ratio)
    train_indexes = shuffled_indexes[:train_size]
    test_indexes = shuffled_indexes[train_size:]

    x_train = X[train_indexes]
    y_train = Y[train_indexes]

    x_test = X[test_indexes]
    y_test = Y[test_indexes]

    return x_train,y_train,x_test,y_test

2、实际操作

2、从最终的结果来看，该模型与原始数据的标签的吻合达到100%。

五、scikit-learn中的train_test_split