python实现K近邻回归,采用等权重和不等权重的方法

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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