q8_compare_time.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from linearRegression.linearRegression import LinearRegression

import time


"""Heat map plots"""
import seaborn as sns
sns.set_theme()


grad = []
norm = []
c = []
for N in range (20,200,10):
	tempg = []
	tempn = []
	for P in range(5,20,2):
		np.random.seed(P)

		X = pd.DataFrame(np.random.randint(0,100,size=(N, P)))
		# print(X)
		y = pd.Series(np.random.randn(N))

		

		LR = LinearRegression(fit_intercept=True)
		# c.append([N,P])
		start_time = time.time()
		LR.fit_vectorised(X, y, batch_size=2) 
		end_time = time.time()

		tempg.append(end_time-start_time)

		# start_time = time.time()
		# LR.fit_normal(X, y) 
		# end_time = time.time()

		# tempn.append(end_time-start_time)

	grad.append(tempg)
	# norm.append(tempn)

# corr = pd.DataFrame(norm)
corr = pd.DataFrame(grad)

fig, ax = plt.subplots(figsize=(11, 9))

sns.heatmap(corr)

yticks = [N for N in range (20,200,10)]
xticks = [P for P in range(5,20,2)]
plt.yticks(plt.yticks()[0], labels=yticks, rotation=0)
plt.xticks(plt.xticks()[0], labels=xticks)
plt.show()

""""""


"""Normal plt plots"""
# a = []
# b = []
# c = []
# for i in range (10,200):
# 	N = 15

# 	X = pd.DataFrame(np.random.randn(N, i))
# 	y = pd.Series(np.random.randn(N))

# 	LR = LinearRegression(fit_intercept=True)
# 	c.append(i)
# 	start_time = time.time()
# 	LR.fit_vectorised(X, y,batch_size=2) 
# 	end_time = time.time()

# 	a.append(end_time-start_time)

# 	start_time = time.time()
# 	LR.fit_normal(X, y) 
# 	end_time = time.time()

# 	b.append(end_time-start_time)

# plt.plot(c,a,label = 'Gradient Descent')
# plt.plot(c,b,label = 'Normal Equation')
# plt.legend(loc = 'best')
# plt.show()


# a = []
# b = []
# c = []
# for i in range (10,10000,10):

# 	X = pd.DataFrame(np.random.randn(i, 9))
# 	y = pd.Series(np.random.randn(i))

# 	LR = LinearRegression(fit_intercept=True)
# 	c.append(i)
# 	start_time = time.time()
# 	LR.fit_vectorised(X, y,batch_size=2) 
# 	end_time = time.time()

# 	a.append(end_time-start_time)

# 	start_time = time.time()
# 	LR.fit_normal(X, y) 
# 	end_time = time.time()

# 	b.append(end_time-start_time)

# plt.plot(c,a,label = 'Gradient Descent')
# plt.plot(c,b,label = 'Normal Equation')
# plt.legend(loc = 'best')
# plt.show()
""""""