Allowing a function to access a local variable within another function? /u/Formula15 Python Education

I’m teaching myself linear regression (with some help from family), and I want to expand this code to calculate the MSE between the line of regression and the actual points. I understand the written math, but writing the program to do it is proving difficult.

Here’s “my” (I edited a geeks for geeks tutorial to shorten some things and get rid of the “main()” function they provided) code:

import numpy as np

import matplotlib.pyplot as plt

from sklearn.metrics import mean_squared_error

def estimate_coef(x, y):

n = np.size(x)

m_x = np.mean(x)

m_y = np.mean(y)

SS_xy = np.sum(y*x) - n*m_y*m_x

SS_xx = np.sum(x*x) - n*m_x*m_x

b_1 = SS_xy / SS_xx

b_0 = m_y - b_1*m_x

print(f"Estimated Coefficients:nb_0 = {b_0}nb_1 = {b_1}")

return(b_0, b_1)

def plot_regression_line(x, y, b):

#plotting the actual points as a scatter plot

plt.scatter(x, y, color = "m",

marker = "o", s = 30)

y_pred = b[0] + b[1]*x

#plotting regression line

plt.plot(x, y_pred, color = "g")

plt.xlabel('x')

plt.ylabel('y')

print(f"nCalcluated y predictions...n{y_pred}")

x = np.array([-5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

y = np.array([-1, 1, 3, 2, 5, 7, 8, 8, 9, 10, 12])

b = estimate_coef(x, y)

plot_regression_line(x, y, b)

plt.show()

Ideally I would like to add a function to calculate the mean squared error without needing to manually put in the values for Y_pred. I figured I may be able to make my plot_regression_line(x, y, b) return the values for y_pred it calculates, and use that as a variable for mean_squared_error. I’m not sure if that would even work, if that’s the best way to do it, or how I could make that happen.

Thank you for your time and assistance!

I’m teaching myself linear regression (with some help from family), and I want to expand this code to calculate the MSE between the line of regression and the actual points. I understand the written math, but writing the program to do it is proving difficult.

Here’s “my” (I edited a geeks for geeks tutorial to shorten some things and get rid of the “main()” function they provided) code:

import numpy as np

import matplotlib.pyplot as plt

from sklearn.metrics import mean_squared_error

def estimate_coef(x, y):

n = np.size(x)

m_x = np.mean(x)

m_y = np.mean(y)

SS_xy = np.sum(y*x) - n*m_y*m_x

SS_xx = np.sum(x*x) - n*m_x*m_x

b_1 = SS_xy / SS_xx

b_0 = m_y - b_1*m_x

print(f"Estimated Coefficients:nb_0 = {b_0}nb_1 = {b_1}")

return(b_0, b_1)

def plot_regression_line(x, y, b):

#plotting the actual points as a scatter plot

plt.scatter(x, y, color = "m",

marker = "o", s = 30)

y_pred = b[0] + b[1]*x

#plotting regression line

plt.plot(x, y_pred, color = "g")

plt.xlabel('x')

plt.ylabel('y')

print(f"nCalcluated y predictions...n{y_pred}")

x = np.array([-5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

y = np.array([-1, 1, 3, 2, 5, 7, 8, 8, 9, 10, 12])

b = estimate_coef(x, y)

plot_regression_line(x, y, b)

plt.show()

Ideally I would like to add a function to calculate the mean squared error without needing to manually put in the values for Y_pred. I figured I may be able to make my plot_regression_line(x, y, b) return the values for y_pred it calculates, and use that as a variable for mean_squared_error. I’m not sure if that would even work, if that’s the best way to do it, or how I could make that happen.

Thank you for your time and assistance!