s434766
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23 KiB
import torch
import torch.nn.functional as F
from torch import nn
from torch.autograd import Variable
import torchvision.transforms as transforms
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
class LogisticRegressionModel(nn.Module):
def __init__(self, input_dim, output_dim):
super(LogisticRegressionModel, self).__init__()
self.linear = nn.Linear(input_dim, output_dim)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
out = self.linear(x)
return self.sigmoid(out)
np.set_printoptions(suppress=False)
data_train = pd.read_csv("data_train.csv")
data_test = pd.read_csv("data_test.csv")
data_val = pd.read_csv("data_val.csv")
FEATURES = [ 'age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
x_train = data_train[FEATURES].astype(np.float32)
y_train = data_train['stroke'].astype(np.float32)
x_test = data_test[FEATURES].astype(np.float32)
y_test = data_test['stroke'].astype(np.float32)
fTrain = torch.from_numpy(x_train.values)
tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
fTest= torch.from_numpy(x_test.values)
tTest = torch.from_numpy(y_test.values)
batch_size = 150
n_iters = 1000
num_epochs = 10input_dim = 6
output_dim = 1
model = LogisticRegressionModel(input_dim, output_dim)
learning_rate = 0.001
criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)print(list(model.parameters())[0].size())
print(list(model.parameters())[1].size())torch.Size([1, 6]) torch.Size([1])
for epoch in range(num_epochs):
print ("Epoch #",epoch)
model.train()
optimizer.zero_grad()
# Forward pass
y_pred = model(fTrain)
# Compute Loss
loss = criterion(y_pred, tTrain)
print(loss.item())
# Backward pass
loss.backward()
optimizer.step()Epoch # 0 0.34391772747039795 Epoch # 1 0.3400452435016632 Epoch # 2 0.33628249168395996 Epoch # 3 0.3326331079006195 Epoch # 4 0.3291005790233612 Epoch # 5 0.32568827271461487 Epoch # 6 0.32239940762519836 Epoch # 7 0.3192369043827057 Epoch # 8 0.3162035048007965 Epoch # 9 0.31330153346061707
y_pred = model(fTest)
print("predicted Y value: ", y_pred.data)predicted Y value: tensor([[0.0089],
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[0.0059],
[0.0229],
[0.0692],
[0.0944],
[0.0022],
[0.0017],
[0.4130],
[0.0013],
[0.0037],
[0.0071],
[0.1049],
[0.0774],
[0.0569],
[0.2711],
[0.0290],
[0.3081],
[0.0848],
[0.0078],
[0.0015],
[0.0046],
[0.3030],
[0.0093],
[0.0481],
[0.0931],
[0.0174],
[0.0007],
[0.0695],
[0.1172],
[0.2178],
[0.1137],
[0.1141],
[0.0008],
[0.2754],
[0.0008],
[0.0167],
[0.0398],
[0.3444],
[0.0089],
[0.2858],
[0.0251],
[0.0016],
[0.0993],
[0.0009]])
print ("The accuracy is", accuracy_score(tTest, np.argmax(y_pred.detach().numpy(), axis=1)))The accuracy is 0.9480651731160896
torch.save(model, 'stroke.pkl')