simple_linear_regression.py

from random import seed
from random import randrange
from csv import reader
from math import sqrt
import matplotlib.pyplot as plt
 
# Load a CSV file
def load_csv(filename):
	dataset = list()
	with open(filename, 'r') as file:
		csv_reader = reader(file)
		for row in csv_reader:
			row=row[0].split()
			if not row:
				continue
			dataset.append(row)
	return dataset
 
# Convert string column to float
def str_column_to_float(dataset, column):
	for row in dataset:
		row[column] = float(row[column].strip())
 
# Split a dataset into a train and test set
def train_test_split(dataset, split):
	train = list()
	train_size = split * len(dataset)
	dataset_copy = list(dataset)
	while len(train) < train_size:
		index = randrange(len(dataset_copy))
		train.append(dataset_copy.pop(index))
	return train, dataset_copy
 
# Calculate root mean squared error
def rmse_metric(actual, predicted):
	sum_error = 0.0
	for i in range(len(actual)):
		prediction_error = predicted[i] - actual[i]
		sum_error += (prediction_error ** 2)
	mean_error = sum_error / float(len(actual))
	return sqrt(mean_error)
 
# Evaluate an algorithm using a train/test split
def evaluate_algorithm(dataset, algorithm, split, *args):
	train, test = train_test_split(dataset, split)
	test_set = list()
	y=[]
	for row in test:
		row_copy = list(row)
		row_copy[-1] = None
		test_set.append(row_copy)
		y.append(row[-1])
	predicted = algorithm(train, test_set, y, *args)
	actual = [row[-1] for row in test]
	rmse = rmse_metric(actual, predicted)
	return rmse
 
# Calculate the mean value of a list of numbers
def mean(values):
	return sum(values) / float(len(values))
 
# Calculate covariance between x and y
def covariance(x, mean_x, y, mean_y):
	covar = 0.0
	for i in range(len(x)):
		covar += (x[i] - mean_x) * (y[i] - mean_y)
	return covar
 
# Calculate the variance of a list of numbers
def variance(values, mean):
	return sum([(x-mean)**2 for x in values])
 
# Calculate coefficients
def coefficients(dataset):
	x = [row[0] for row in dataset]
	y = [row[1] for row in dataset]
	x_mean, y_mean = mean(x), mean(y)
	b1 = covariance(x, x_mean, y, y_mean) / variance(x, x_mean)
	b0 = y_mean - b1 * x_mean
	return [b0, b1]
 
# Simple linear regression algorithm
def simple_linear_regression(train, test, y):
	predictions = list()
	b0, b1 = coefficients(train)
	x=[]
	for row in test:
		yhat = b0 + b1 * row[0]
		predictions.append(yhat)
		x.append(row[0])
	plt.figure()
	plt.plot(x,predictions,'r',x,y,'bo')
	plt.show()
	return predictions
 
# Simple linear regression on insurance dataset
seed(1)
# load and prepare data
filename = 'insurance.csv'
dataset = load_csv(filename)
for i in range(len(dataset[0])):
	str_column_to_float(dataset, i)
# evaluate algorithm
split = 0.6
rmse = evaluate_algorithm(dataset, simple_linear_regression, split)
print('RMSE: %.3f' % (rmse))