82 lines
2.3 KiB
Python
82 lines
2.3 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import pandas as pd
|
|
import numpy as np
|
|
import sys
|
|
import os
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn import preprocessing
|
|
from sklearn.preprocessing import StandardScaler
|
|
scaler = StandardScaler()
|
|
|
|
EPOCHS = int(sys.argv[1])
|
|
|
|
# Model
|
|
class Model(nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.linear = nn.Linear(1,1)
|
|
|
|
def forward(self, x):
|
|
y_predicted = torch.sigmoid(self.linear(x))
|
|
return y_predicted
|
|
|
|
data = pd.read_csv('data.csv')
|
|
data.dropna()
|
|
|
|
training_data = data.sample(frac=0.9, random_state=25)
|
|
testing_data = data.drop(training_data.index)
|
|
|
|
print(f"No. of training examples: {training_data.shape[0]}")
|
|
print(f"No. of testing examples: {testing_data.shape[0]}")
|
|
|
|
training_data = training_data[['sqft_living', 'price']]
|
|
|
|
testing_data = testing_data[['sqft_living', 'price']]
|
|
|
|
training_data[['price']] = training_data[['price']] / 10000000
|
|
training_data[['sqft_living']] = training_data[['sqft_living']] / 10000
|
|
|
|
testing_data[['price']] = testing_data[['price']] / 10000000
|
|
testing_data[['sqft_living']] = testing_data[['sqft_living']] / 10000
|
|
|
|
# Tensory
|
|
X_training = training_data[['sqft_living']].to_numpy()
|
|
X_testing = testing_data[['sqft_living']].to_numpy()
|
|
y_training = training_data[['price']].to_numpy()
|
|
y_testing = testing_data[['price']].to_numpy()
|
|
|
|
import torch
|
|
torch.from_file
|
|
X_training = torch.from_numpy(X_training.astype(np.float32))
|
|
X_testing = torch.from_numpy(X_testing.astype(np.float32))
|
|
y_training = torch.from_numpy(y_training.astype(np.float32))
|
|
y_testing = torch.from_numpy(y_testing.astype(np.float32))
|
|
|
|
|
|
model = Model()
|
|
criterion = nn.BCELoss()
|
|
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
|
|
|
|
# Trening
|
|
num_epochs = EPOCHS
|
|
for epoch in range(num_epochs):
|
|
y_predicted = model(X_training)
|
|
loss = criterion(y_predicted,y_training)
|
|
loss.backward()
|
|
optimizer.step()
|
|
optimizer.zero_grad()
|
|
|
|
if (epoch%100==0):
|
|
print(f'epoch:{epoch+1},loss = {loss.item():.4f}')
|
|
|
|
with torch.no_grad():
|
|
y_predicted = model(X_testing)
|
|
y_predicted_cls = y_predicted.round()
|
|
acc = y_predicted_cls.eq(y_testing).sum()/float(y_testing.shape[0])
|
|
print(f'{acc:.4f}')
|
|
result = open("output",'w+')
|
|
result.write(f'{y_predicted}')
|
|
|
|
torch.save(model, "modelP.pkl") |