import numpy as np
 pandas as pd 
from sklearn.neighbors import KNeighborsClassifier # 导入knn学习包

salary = pd.read_csv("./adults.txt")
salary.head()

#样本数据的提取
y = salary['salary']
X = salary.iloc[:,[0,1,3,5,6,8,9,-2,-3]]
X.head()

 数据去重 用于下面函数的理解
u = X[occupation].unique()
u

 np.argwhere此方法找到对应名称的索引
np.argwhere(u == Exec-managerial")[0,0]   np.argwhere

X.columns[2:-1]

 2:-1列的所有数据映射
for col in X.columns[1:-1]:   遍历所有类名
    
    u = X[col].unique()    类似上面的u = X['occupation'].unique()  得出每个分类下面的种类名称
#     print(col)

    def convert(x):   将上面得出的u 进行索引映射         print(x)
        return np.argwhere(u == x)[0,1)">  将上面得出的u 进行索引映射
    
    X[col] = X[col].map(convert)   将上面得出的u 进行索引映射
    
X.head()

 切分训练集跟测试集
from sklearn.model_selection  train_test_split

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2)   切分
print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)

 预测测试
knn = KNeighborsClassifier(n_neighbors=8)

knn.fit(X_train,y_train)   计算公式

y_ = knn.predict(X_test)  预测值

from sklearn.metrics import accuracy_score  计算分类预测的准确率

 求出预测准确率
accuracy = accuracy_score(y_test,y_)

print(预测准确率: ",accuracy)