Linear regression method using Numpy

Introduction

This article was written by the author at https://www.udemy.com/share/1013lqB0AedFdUR34=0 The purpose is to review what I learned in

to write

--Light derivation of least squares --Convert from pandas DataFrame to numpy array and try to calculate

Derivation of least squares method

Suppose you are given a dataset [x, y]. x is the explanatory variable and y is the objective variable. For example, if you increase your height, you will gain weight, so in this case x = height and y = weight.

And I want to predict y from the given data x. Let the predicted value at that time be $ \ hat {y} $, and assume the following relational expression.

\hat{y} = ax + b

Here, the goal is to make $ \ hat {y} $ as close as possible to the correct answer value $ y $. Therefore

Error = y - \hat{y} = y - ax + b =0

It is important to find a and b that are. Please see the following links for the following explanations. http://arduinopid.web.fc2.com/P7.html

Linear regression with numpy

First import the module

python

import pandas as pd
from pandas import Series, DataFrame
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline

Also download the dataset used this time

python

from sklearn.datasets import load_boston

boston = load_boston()

This time, we will use RM (average number of rooms per dwelling) and target (Price) in this data frame. Originally, we use sns.pairplot and sns.jointplot to search for variables that are likely to have a linear regression (proportional) relationship, but this time we will assume that these two variables have a proportional relationship in advance.

python

boston_df = DataFrame(boston.data)
#Give a column name
boston_df.columns = boston.feature_names
#Copy a new column because it is difficult to understand with target
boston_df['Price'] = boston.target
#Scatter plot and regression line display
sns.lmplot('RM', 'Price', data=boston_df)

Let's calculate this regression line. Use np.linalg.lstsq (X, Y). However, since this X requires an array with a specific shape, it is molded for that purpose.

python

X = boston_df.RM
Y = boston_df.Price
#[x,1]In the shape of
X = np.array([ [value[0], 1] for value in X])
#Convert to floating point type
X = X.astype(np.float64)
#a,Each predicted value is stored in b
a, b = np.linalg.lstsq(X, Y)[0]

This is the end of the calculation. Let's see the result

python

plt.plot(boston_df.RM, boston_df.Price, 'o')
x = boston_df.RM
plt.plot(x, a*x+b, 'r')

Supplement about np.linalg.lstsq

Click here for official documentation https://numpy.org/doc/stable/reference/generated/numpy.linalg.lstsq.html#numpy.linalg.lstsq

numpy.linalg.lstsq(a, b, rcond='warn')

--Parameters --Coefficient matrix a (M, N), independent variable b (M,) or (M, K), rcond

• Return value --Least squares solution p, total residual residues, rank rank of coefficient matrix a, singular value of a

If np.linalg.lstsq (X, Y) [1], the total residuals can be taken out.