Knn算法实现

Knn算法实现

 

 

 

k近邻算法¶

 

0.引入依赖¶

In [8]:

import numpy as np
import pandas as pd

#这里直接引入sklearn里面的数据集，iris 鸢尾花
from sklearn.datasets  import  load_iris
from sklearn.model_selection import train_test_split   # 切分数据集为训练集和测试集
from sklearn.metrics import accuracy_score   #计算分类预测的准确率

 

1.数据加载和预处理¶

In [23]:

iris = load_iris()
df = pd.DataFrame(data=iris.data, columns = iris.feature_names)
df['class'] = iris.target
df['class'] = df['class'].map( lambda  i:iris.target_names[i] )
df.describe()

Out[23]:

 

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)
count	150.000000	150.000000	150.000000	150.000000
mean	5.843333	3.057333	3.758000	1.199333
std	0.828066	0.435866	1.765298	0.762238
min	4.300000	2.000000	1.000000	0.100000
25%	5.100000	2.800000	1.600000	0.300000
50%	5.800000	3.000000	4.350000	1.300000
75%	6.400000	3.300000	5.100000	1.800000
max	7.900000	4.400000	6.900000	2.500000

In [24]:

x = iris.data
y = iris.target.reshape(-1,1)

In [33]:

#划分训练接和测试集
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=35,stratify = y)

Out[33]:

array([1.7, 1. , 1.3, 1.5, 3.9, 1.8, 2.1, 7. , 6.2, 0.5, 1.9, 6.2, 2.7,
       7.1, 6.9, 0. , 2. , 2.6, 1.9, 2.3, 2.6, 6.7, 3.8, 7.1, 6.7, 4.9,
       2.2, 2.1, 2.7, 1.3, 2. , 0.8, 2.7, 2.6, 1.4, 1.9, 3.7, 6.9, 2.3,
       2.2, 1.9, 1.2, 1.7, 6.6, 0.5, 6.8, 6.9, 2.5, 6.2, 6.8, 6.7, 3.6,
       7. , 1.5, 1.7, 2.1, 2.7, 3. , 2.2, 1.8, 1.8, 1.7, 2.7, 7.2, 6.9,
       2.9, 7.2, 1.4, 2.9, 2.2, 4.2, 1.5, 6.6, 6.1, 1.5, 4.6, 6.5, 1.4,
       1.3, 0.5, 3.8, 6.3, 6.8, 6.6, 1.8, 2.5, 7.4, 2.6, 6.8, 6.8, 4. ,
       1.7, 7.1, 6.5, 7.9, 1.4, 2.4, 6.6, 6.4, 7.3, 1.9, 1.8, 7.6, 0.9,
       0.8])

In [102]:

arr=np.argsort(np.array([1,5,3,4]))[:3]
test=[np.array([1,5,3,4])[a] for a in arr]
test_2=np.array([1,5,3,4])[arr]
test_2.tolist().count(1)

Out[102]:

1

In [109]:

np.argmax([1,5,3,4])
# np.bincount([1,1,2,3,'1x'])

 

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-109-feb333e2c58a> in <module>
      1 np.argmax([1,5,3,4])
----> 2np.bincount([1,1,2,3,'1x'])

ValueError: invalid literal for int() with base 10: '1x'

 

2.核心算法实现¶

In [150]:

# 距离函数定义
def l1_distance(a,b):
    return np.sum(np.abs(a-b),axis=1)
def l2_distance(a,b):
     return np.sqrt(np.sum((a-b)**2,axis=1))


# 分类器实现
class kNN(object):
    #定义一个初始化方法， __init__ 是类的构造方法
    def __init__(self,n_neighbors=1,dist_func= l1_distance):
        self.n_neighbors=n_neighbors
        self.dist_func=dist_func
    
    # 训练模型的方法
    def fit(self,x,y):
        self.x_train = x
        self.y_train = y
        
    # 模型预测
    def predict(self, x):
        # 初始化预测分类数组
        y_pred = np.zeros((x.shape[0],1),dtype=self.y_train.dtype)
        #遍历输入的x数据点
        for i,x_test  in enumerate(x):
            # x_test和全部训练数据计算距离
            distances=self.dist_func(self.x_train,x_test)
            # 对获得的距离按照由近到远排序
            nn_indexes=np.argsort(distances)[:self.n_neighbors]
            #选取其中最近的k个点，统计类别出现频率最高的那个，赋给y_predict[i]
#             y_res=[y_train[a] for a in nn_indexes]
            y_res=y_train[nn_indexes].ravel().tolist()
#             y_pred[i] = np.argmax([y_res.count(0),y_res.count(1),y_res.count(2)])
            y_pred[i] = np.argmax(np.bincount(y_res))
        return y_pred

In [160]:

kNN_model=kNN(n_neighbors=5,dist_func= l1_distance)
kNN_model.fit(x_train,y_train)
y_pred=kNN_model.predict(x_test)

In [161]:

accuracy_score(y_test,y_pred)

Out[161]:

0.9777777777777777

In [166]:

#比对各个参数的好坏
knn=kNN()
knn.fit(x_train,y_train)
result_list=[]
for p in [1,2]:
  knn.dist_func=l1_distance if p==1 else l2_distance
  #考虑不一样的k取值
  for k in range(1,10,2):
        knn.n_neighbors=k
        y_pred=knn.predict(x_test)
        accuracy= accuracy_score(y_test,y_pred)
        print(accuracy)
        result_list.append([knn.n_neighbors,knn.dist_func.__name__,accuracy])
df = pd.DataFrame(result_list,columns=['k',"距离函数","准确率"])      
df

 

0.9333333333333333
0.9333333333333333
0.9777777777777777
0.9555555555555556
0.9555555555555556
0.9333333333333333
0.9333333333333333
0.9777777777777777
0.9777777777777777
0.9777777777777777

Out[166]:

 

	k	距离函数	准确率
0	1	l1_distance	0.933333
1	3	l1_distance	0.933333
2	5	l1_distance	0.977778
3	7	l1_distance	0.955556
4	9	l1_distance	0.955556
5	1	l2_distance	0.933333
6	3	l2_distance	0.933333
7	5	l2_distance	0.977778
8	7	l2_distance	0.977778
9	9	l2_distance	0.977778

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