python中2个列表的线性回归

Question

Hi I have 2 lists of numbers and I want to get the R^2 from a regular linear regression. I think the question has been posted a lot, but I just cannot find this somewhere.

My lists:

my_y = [2,5,6,10]
my_x = [19,23,22,30]

I have tried to change it to numpy arrays and then use sklearn to regress and get what I need, but I did not succeed. I used the following code:

from sklearn.linear_model import LinearRegression
import numpy as np

my_y = np.array([2,5,6,10]).reshape(1, -1)
my_x = np.array([19,23,22,30]).reshape(1,-1)

lm = LinearRegression()
result = lm.score(my_x, my_y)
print(result)

Does anyone have a fast way to get the R^2 from doing a linear regression between those 2 variables?

My expected output from this regression is: R^2=0.930241

Answer 1

Try:

import scipy

my_y = [2,5,6,10]
my_x = [19,23,22,30]

slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(my_x, my_y)
    
print(r_value**2)

and you get:

0.9302407516147975

---

From scipy version '1.4.1' (thanks to @FlamePrinz for having noted the issue for new versions of scipy):

from scipy import stats

my_y = [2,5,6,10]
my_x = [19,23,22,30]

slope, intercept, r_value, p_value, std_err = stats.linregress(my_x, my_y)

print(r_value**2)

Answer 2

From a quick look of the documentation, I see that linear_model needs you to provide a linear model as the name suggests. To get a simple R:

import scipy
my_y = np.array([2,5,6,10])
my_x = np.array([19,23,22,30])
R=scipy.stats.linregress(my_x, my_y)[2]
print(R)

0.9644898919194527

and R**2 yields the desired result of 0.930.