凌乱的散点图回归线：Python

在python 2.7.6，matlablib，scikit learn 0.17.0中，当我在散点图上制作多项式回归线时，多项式曲线将非常混乱，如下所示：

脚本是这样的：它将读取两列浮动数据并绘制散点图和回归

import pandas as pd
import scipy.stats as stats
import pylab 
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
import pylab as pl
import sklearn
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
from sklearn import datasets, linear_model
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import Ridge

df=pd.read_csv("boston_real_estate_market_clean.csv")

LSTAT = df['LSTAT'].as_matrix()

LSTAT=LSTAT.reshape(LSTAT.shape[0], 1)

MEDV=df['MEDV'].as_matrix()

MEDV=MEDV.reshape(MEDV.shape[0], 1)

# Train test set split
X_train1, X_test1, y_train1, y_test1 =                train_test_split(LSTAT,MEDV,test_size=0.3,random_state=1)

# Ploynomial Regression-nst order

plt.scatter(X_test1, y_test1, s=10, alpha=0.3)

for degree in [1,2,3,4,5]:
    model = make_pipeline(PolynomialFeatures(degree), Ridge())
    model.fit(X_train1,y_train1)
    y_plot = model.predict(X_test1)
    plt.plot(X_test1, y_plot, label="degree %d" % degree
             +'; $q^2$: %.2f' % model.score(X_train1, y_train1)
             +'; $R^2$: %.2f' % model.score(X_test1, y_test1))


plt.legend(loc='upper right')

plt.show()

我猜原因是因为“X_test1，y_plot”没有正确排序？

X_test1 是一个像这样的 numpy 数组：

[[  5.49]
 [ 16.65]
 [ 17.09]
 ....
 [ 25.68]
 [ 24.39]]

yplot 是一个像这样的 numpy 数组：

[[ 29.78517812]
 [ 17.16759833]
 [ 16.86462359]
 [ 23.18680265]
...[ 37.7631725 ]]

我尝试用这个排序：

 [X_test1, y_plot] = zip(*sorted(zip(X_test1, y_plot), key=lambda y_plot: y_plot[0]))

     plt.plot(X_test1, y_plot, label="degree %d" % degree
              +'; $q^2$: %.2f' % model.score(X_train1, y_train1)
              +'; $R^2$: %.2f' % model.score(X_test1, y_test1))

曲线现在看起来正常，但结果很奇怪，R^2 为负。

任何大师都可以告诉我真正的问题是什么或如何正确排序吗？谢谢你！

虽然现在的图是正确的，但您在排序时弄乱了 X_test1 与 y_test1 的配对，因为您忘记了以相同的方式对 y_test1 进行排序。 最好的解决方案是在拆分后立即排序。然后稍后计算的 y_plot 将自动正确：（这里未经测试的示例使用 numpy 作为 np）

X_train1, X_test1, y_train1, y_test1 =             train_test_split(LSTAT,MEDV,test_size=0.3,random_state=1)

sorted_index = np.argsort(X_test1)
X_test1 = X_test1[sorted_index]
y_test1 = y_test1[sorted_index]