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python实现K近邻回归,采用等权重和不等权重的方法

yipeiwu_com5年前 (2020-03-06)Python基础

如下所示：

from sklearn.datasets import load_boston
 
boston = load_boston()
 
from sklearn.cross_validation import train_test_split
 
import numpy as np;
 
X = boston.data
y = boston.target
 
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 33, test_size = 0.25)
 
print 'The max target value is: ', np.max(boston.target)
print 'The min target value is: ', np.min(boston.target)
print 'The average terget value is: ', np.mean(boston.target)
 
from sklearn.preprocessing import StandardScaler
 
ss_X = StandardScaler()
ss_y = StandardScaler()
 
X_train = ss_X.fit_transform(X_train)
X_test = ss_X.transform(X_test)
y_train = ss_y.fit_transform(y_train)
y_test = ss_y.transform(y_test)
 
from sklearn.neighbors import KNeighborsRegressor
 
uni_knr = KNeighborsRegressor(weights = 'uniform')
uni_knr.fit(X_train, y_train)
uni_knr_y_predict = uni_knr.predict(X_test)
 
dis_knr = KNeighborsRegressor(weights = 'distance')
dis_knr.fit(X_train, y_train)
dis_knr_y_predict = dis_knr.predict(X_test)
 
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
 
print 'R-squared value of uniform weights KNeighorRegressor is: ', uni_knr.score(X_test, y_test)
print 'The mean squared error of uniform weights KNeighorRegressor is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(uni_knr_y_predict))
print 'The mean absolute error of uniform weights KNeighorRegressor is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(uni_knr_y_predict))
 
print 'R-squared of distance weights KNeighorRegressor is: ', dis_knr.score(X_test, y_test)
print 'the value of mean squared error of distance weights KNeighorRegressor is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(dis_knr_y_predict))
print 'the value of mean ssbsolute error of distance weights KNeighorRegressor is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(dis_knr_y_predict))

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