LR_GradDesc.py

#This code is submitted by Ayanabha Ghosh (ayan-cs)

import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder

df = pd.read_csv('./datasets/insurance.csv')
print(df.head(5))
le = LabelEncoder()
le.fit(df['sex'])
df['sex'] = le.transform(df['sex'])
le.fit(df['smoker'])
df['smoker'] = le.transform(df['smoker'])
le.fit(df['region'])
df['region'] = le.transform(df['region'])
print(df.head(5))

X = np.array([df.loc[i][:-1] for i in range(len(df))])
Y = np.array(df['expenses'])

size = len(df)
random_indice = np.random.permutation(size)
num_train = int(size*0.7)
num_test = int(size*0.3)

X_train = X[random_indice[:num_train]]
y_train = Y[random_indice[:num_train]]
X_test = X[random_indice[-num_test:]]
y_test = Y[random_indice[-num_test:]]

import random
w = [random.random() for _ in range(len(X_train[0]))]
print("Initial weight : ",w)

alpha = 0.0001
epoch = 100000

cost = []
for i in range(epoch):
  h = np.inner(w, X_train) # w=(1,6)  X_train=(n, 6)  h=(n,1)
  cost.append(0.5*num_train*(np.sum(np.square(h - y_train))))
  d_w = (1/num_train)*(np.inner(h - y_train, np.transpose(X_train))) #d_w=(1,6)
  w = w - alpha*np.transpose(d_w)

print("Final weight : ",w)

y_pred = np.inner(w, X_test)
u = sum((y_pred - y_test)**2)
v = sum((y_test - np.mean(y_test))**2)
r2 = 1 - (u/v)
print("Score : "+str(r2))

from sklearn.linear_model import LinearRegression
clf = LinearRegression()
clf.fit(X_train, y_train)
print("Score : "+str(clf.score(X_test, y_test)))