KNN不调库python python knn包

以前使用knn都是调用sklearn包里面的程序，这次自己尝试编写一下程序，如果有不足之处还望大家指点~

首先knn的原理其实很简单，先给模型训练数据，接着来一条测试数据，就去与所有训练数据计算距离，选出距离最小的k条（k近邻，k最好为奇数，避免不好决策的问题），看这k条数据最多的类标，然后将测试数据的类标取为该类标。
废话不多说，直接上代码，注解都写得十分清楚了

# -*- coding: utf-8 -*-
"""
Created on Sun Apr 12 15:00:44 2020

@author: asus
"""
'''
knn实现
'''

import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier


path = r'F:\大三下\李艳老师数据挖掘实践\knn\iris.csv'
data = pd.read_csv(path)
#print(data.head())
##因为原数据按类别排序，所以打乱顺序以划分数据集
#data = data.sample(frac = 1).reset_index(drop=True)
#print(data)


def train_test_train(data,target_columns,test_size = 0.25,random_state = None):
    '''
    划分数据集，data为数据，
    target_columns为决策属性列名，如本例是'Name'
    test_size为测试集比例
    '''
    #打乱顺序以划分数据集
    shuffle_data = data.sample(frac = 1,random_state = random_state).reset_index(drop=True)
    
    split_index = int(len(shuffle_data) * (1 - test_size))
    
    #提取决策属性
    y = np.array(shuffle_data[target_columns])
    x = np.array(shuffle_data.drop(columns = target_columns))
    
    
    #划分数据
    X_train,X_test = x[:split_index],x[split_index:]
    y_train,y_test = y[:split_index],y[split_index:]
    return X_train,X_test,y_train,y_test


    
        
        
class KNNClassfier(object):
    
    def __init__(self,n_neighbors = 3):
        '''
        初始化
        默认3近邻，计算距离是用欧氏距离
        '''
        self.n_neighbors = n_neighbors
        self.X_train = None
        self.y_train = None
    
    def fit(self,X_train,y_train):
        '''
        X_train is array ,shape like [n_samples,shapes]
        X_train is array ,shape like [n_samples,1]
        '''
        self.X_train = X_train
        self.y_train = y_train
    
    def predict(self,X_test):
        '''
        X_test is array ,shape like [n_samples,shapes]
        该函数返回一个在X_test上的预测结果
        '''
        #先创建一个空的array,
        #shape:  与X_test行数相同，一列
#        y_pred = np.zeros((X_test.shape[0],1))
        y_pred = list()
        
        for i in range(X_test.shape[0]):
            #遍历X_test，对于每一条测试集的数据
            dis = []#用于存放距离
            
            for j in range(self.X_train.shape[0]):
                #计算第i条测试数据与所有训练数据的距离
                #即第i条测试数据与第j条训练数据的差的平方和开更号
                #np.linalg.norm()为矩阵整体元素平方和开根号
                #所以对于每一个i，即每一条测试数据，有几条训练数据，dis就有几个元素
                dis.append(np.linalg.norm(X_test[i] - self.X_train[j,:]))
                
            #对于第i条数据，计算完距离以后排序   
            labels = []#存放类标
            #这里的sorted(range(len(dis)),key = dis.__getitem__)意思是：
            #返回一个新的列表，里面的元素是dis从小到大排序后对应的索引，所以前面的就是距离最小的
            index = sorted(range(len(dis)),key = dis.__getitem__)

            
            for j in range(self.n_neighbors):
                #  假设n_neighbors为3，就取index前三个元素，这三个元素都是数据的索引
                #  然后去取这三个数据的类标,比方说前三个是4,78,53，那么就取4,78,53条数据的类别放入labels
                #  对于每一个i，labels都有三个类标
                labels.append(self.y_train[index[j]])
                
            counts = []#用于计数
            for label in labels:
                #统计每个类标的个数
                counts.append(labels.count(label))
#            y_pred[i] = labels[np.argmax(counts)]
            
            '''
            比方说labels = [A,B,A]
            那么count = [2,1,2]
            那么np.argmax(counts) = 0
            根据这个去取labels[0] = A 
            '''
            y_pred.append(labels[np.argmax(counts)])#返回沿最大值的索引
            
        return y_pred
    
    
    def score(self,X_test,y_test):
        '''输入测试集，返回测试集精度'''
        pred = self.predict(X_test)
        score = np.mean(y_pred == y_test)
        
        return score
        
            
if __name__ == '__main__':
    
    #划分数据集
    X_train,X_test,y_train,y_test = train_test_train(data,target_columns = 'Name',test_size = 0.3,random_state = 85)
    
    
#    knn = KNeighborsClassifier(n_neighbors = 3)
#    knn.fit(X_train,y_train)
#    y_pred = knn.predict(X_test)
#    print("Test set score: {:.2f}".format(np.mean(y_pred == y_test)))
#    print("Train set score: {:.2f}".format(knn.score(X_train, y_train)))
    
    
        
        
    knn = KNNClassfier(n_neighbors = 3)    
    knn.fit(X_train,y_train)
    y_pred = knn.predict(X_test)
    #print("Test set score: {:.2f}".format(np.mean(y_pred == y_test)))
    print("Test set score: {:.2f}".format(knn.score(X_test,y_test)))

我还自己写了划分数据的函数，也算是初步实现了。后期再改进