python机器学习基础02——sklearn之KNN

KNN分类模型

• 概念：
  • 简单地说，K-近邻算法采用测量不同特征值之间的距离方法进行分类（k-Nearest Neighbor，KNN）

这里的距离用的是欧几里得距离，也就是欧式距离

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

df = pd.read_excel('./datasets/my_films.xlsx')
#提取特征数据
feature = df[['Action Lens','Love Lens']]
#提起标签数据
target = df['target']

#数据集切分
x_train,x_test,y_train,y_test = train_test_split(feature,target,test_size=0.1,random_state=2020)

#创建算法模型对象
#n_neighbors == knn中的k
knn = KNeighborsClassifier(n_neighbors=3)
#训练模型:特征数据必须是二维的
knn.fit(x_train,y_train)

knn.predict(x_test)
print('模型分类结果：',knn.predict(x_test))
print('真实的结果：',y_test)

结果：
模型分类结果： ['Action' 'Action']
真实的结果： 2    Action
1    Action
Name: target, dtype: object

scikit-learn自带数据集合

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import sklearn.datasets as datasets
from sklearn.neighbors import KNeighborsClassifier

iris = datasets.load_iris()
feature = iris['data']
target = iris['target']
feature.shape,target.shape

x_train,x_test,y_train,y_test = train_test_split(feature,target,test_size=0.2,random_state=2020)

knn = KNeighborsClassifier(n_neighbors=8)
knn.fit(x_train,y_train)
knn.score(x_test,y_test)

print('模型分类结果：',knn.predict(x_test))
print('真实的分类结果:',y_test)

结果：
模型分类结果： [2 0 1 1 1 2 2 1 0 0 2 1 0 2 2 0 1 1 2 0 0 2 1 0 2 1 1 1 0 0]
真实的分类结果: [2 0 1 1 1 2 2 1 0 0 2 2 0 2 2 0 1 1 2 0 0 2 1 0 2 1 1 1 0 0]

• k-近邻算法之约会网站配对效果判定（datingTestSet.txt）

import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler,StandardScaler

df = pd.read_csv('./datasets/datingTestSet.txt',header=None,sep='\t')
feature = df[[0,1,2]]
target = df[3]

x_train,x_test,y_train,y_test = train_test_split(feature,target,test_size=0.2,random_state=2020)

knn = KNeighborsClassifier(n_neighbors=75)
knn.fit(x_train,y_train)
knn.score(x_test,y_test) # 0.82

#归一化
mm = MinMaxScaler()
m_feature = mm.fit_transform(feature)
x_train,x_test,y_train,y_test = train_test_split(m_feature,target,test_size=0.2,random_state=2020)
knn = KNeighborsClassifier(n_neighbors=75)
knn.fit(x_train,y_train)
knn.score(x_test,y_test) # 0.955

#标准化
s = StandardScaler()
s_feature = s.fit_transform(feature)
x_train,x_test,y_train,y_test = train_test_split(s_feature,target,test_size=0.2,random_state=2020)
knn = KNeighborsClassifier(n_neighbors=75)
knn.fit(x_train,y_train)
knn.score(x_test,y_test) # 0.96

• 学习曲线寻找最优的k值
  • 穷举不同的k值

import matplotlib.pyplot as plt
ks = np.arange(3,150,5)
scores = []
for k in ks:
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(x_train,y_train)
    score = knn.score(x_test,y_test)
    scores.append(score)
scores = np.array(scores)
plt.plot(ks,scores)
plt.xlabel('k')
plt.ylabel('score')

#如何具体找出分值最好的k值
max_value_index = scores.argmax() #argmax可以返回numpy数组中最大值的下标
best_k = ks[max_value_index] # 68

K折交叉验证

• 目的：

  • 选出最为适合的模型超参数的取值，然后将超参数的值作用到模型的创建中。

• 思想：

  • 将样本的训练数据交叉的拆分出不同的训练集和验证集，使用交叉拆分出不同的训练集和验证集测分别试模型的精准度，然就求出的精准度的均值就是此次交叉验证的结果。将交叉验证作用到不同的超参数中，选取出精准度最高的超参数作为模型创建的超参数即可！

• 实现思路：

  • 将训练数据平均分割成K个等份
  • 使用1份数据作为验证数据，其余作为训练数据
  • 计算验证准确率
  • 使用不同的测试集，重复2、3步骤
  • 对准确率做平均，作为对未知数据预测准确率的估计

• API

  • from sklearn.model_selection import cross_val_score
  • cross_val_score(estimator,X,y,cv):
    • estimator:模型对象
    • X,y:训练集数据
    • cv：折数

# k折交叉验证应用在KNN

from sklearn.model_selection import cross_val_score
feature = feature
target = target
x_train,x_test,y_train,y_test = train_test_split(feature,target,test_size=0.2,random_state=2020)
#交叉验证（作用在训练集中）
knn = KNeighborsClassifier(n_neighbors=15)
#参数1：模型对象，参数2：训练集的特征，参数3：训练集的标签，参数cv就是折数
mean_score = cross_val_score(knn,x_train,y_train,cv=10).mean() # 0.81875

ks = np.arange(3,150,5)
scores = []
for k in ks:
    knn = KNeighborsClassifier(n_neighbors=k)
    score = cross_val_score(knn,x_train,y_train,cv=5).mean()
    scores.append(score)
plt.plot(ks,scores)
plt.xlabel('k')
plt.ylabel('score')
scores = np.array(scores)

best_k = ks[scores.argmax()]
#使用最优的超参数训练模型
knn = KNeighborsClassifier(n_neighbors=best_k)
knn.fit(x_train,y_train)
knn.score(x_test,y_test) # 0.82
knn.predict(x_test) #训练好的模型进行分类操作