"""
Column	Meaning
crim	Per capita crime rate by town
zn	Proportion of residential land zoned for lots over 25,000 sq.ft.
indus	Proportion of non-retail business acres per town
chas	Charles River dummy variable (1 if tract bounds river, 0 otherwise)
nox	Nitric oxides concentration (parts per 10 million)
rm	Average number of rooms per dwelling
age	Proportion of owner-occupied units built prior to 1940
dis	Weighted distances to five Boston employment centers
rad	Index of accessibility to radial highways
tax	Full-value property-tax rate per $10,000
ptratio	Pupil-teacher ratio by town
black	1000(Bk - 0.63)^2, where Bk is the proportion of Black people by town
lstat	% lower status of the population
medv	Median value of owner-occupied homes in $1000’s (target variable)
"""

import pandas as pd
import torch
import torch.nn as nn

df = pd.read_csv("./RegressionModels/BostonHousing/Boston.csv")


# print(df.iloc[:,0:14])

X = torch.tensor(df.iloc[:,0:14].values, dtype=torch.float32)
Y = torch.tensor(df["medv"].values, dtype=torch.float32)

model = torch.nn.Sequential(
    torch.nn.Linear(14, 1)
)

loss_fn = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=5e-9)

for epoch in range(2000):
    predict_y = model(X)
    loss = loss_fn(predict_y, Y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    if epoch % 99 ==0:
        print(f'Epoch: {epoch}, Loss: {loss.item():.2f}')