79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
|
import torch
|
||
|
|
import sys
|
||
|
|
from torch import nn
|
||
|
|
import numpy as np
|
||
|
|
import pandas as pd
|
||
|
|
import mlflow
|
||
|
|
from sklearn.metrics import accuracy_score
|
||
|
|
|
||
|
|
np.set_printoptions(suppress=False)
|
||
|
|
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
def readAndtrain(epochs, batch_size):
|
||
|
|
train = pd.read_csv("train.csv")
|
||
|
|
test = pd.read_csv("test.csv")
|
||
|
|
|
||
|
|
xtrain = train[['age','anaemia','creatinine_phosphokinase','diabetes', 'ejection_fraction', 'high_blood_pressure', 'platelets', 'serum_creatinine', 'serum_sodium', 'sex', 'smoking']].astype(np.float32)
|
||
|
|
ytrain = train['DEATH_EVENT'].astype(np.float32)
|
||
|
|
xtest = test[['age','anaemia','creatinine_phosphokinase','diabetes', 'ejection_fraction', 'high_blood_pressure', 'platelets', 'serum_creatinine', 'serum_sodium', 'sex', 'smoking']].astype(np.float32)
|
||
|
|
ytest = test['DEATH_EVENT'].astype(np.float32)
|
||
|
|
xTrain = torch.from_numpy(xtrain.values)
|
||
|
|
yTrain = torch.from_numpy(ytrain.values.reshape(179,1))
|
||
|
|
xTest = torch.from_numpy(xtest.values)
|
||
|
|
yTest = torch.from_numpy(ytest.values)
|
||
|
|
|
||
|
|
learning_rate = 0.002
|
||
|
|
input_dim = 11
|
||
|
|
output_dim = 1
|
||
|
|
|
||
|
|
model = LogisticRegressionModel(input_dim, output_dim)
|
||
|
|
model.load_state_dict(torch.load('DEATH_EVENT.pth'))
|
||
|
|
|
||
|
|
criterion = torch.nn.BCELoss(reduction='mean')
|
||
|
|
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
|
||
|
|
|
||
|
|
for epoch in range(epochs):
|
||
|
|
# print ("Epoch #",epoch)
|
||
|
|
model.train()
|
||
|
|
optimizer.zero_grad()
|
||
|
|
# Forward pass
|
||
|
|
y_pred = model(xTrain)
|
||
|
|
# Compute Loss
|
||
|
|
loss = criterion(y_pred, yTrain)
|
||
|
|
# print(loss.item())
|
||
|
|
# Backward pass
|
||
|
|
loss.backward()
|
||
|
|
optimizer.step()
|
||
|
|
|
||
|
|
|
||
|
|
torch.save(model.state_dict(), 'DEATH_EVENT.pth')
|
||
|
|
prediction= model(xTest)
|
||
|
|
|
||
|
|
return prediction, loss.item(), yTest
|
||
|
|
|
||
|
|
print("accuracy_score", accuracy_score(yTest, np.argmax(prediction.detach().numpy(), axis=1)))
|
||
|
|
# print("F1", f1_score(yTest, np.argmax(prediction.detach().numpy(), axis=1), average=None))
|
||
|
|
|
||
|
|
|
||
|
|
batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 10
|
||
|
|
epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
|
||
|
|
with mlflow.start_run():
|
||
|
|
|
||
|
|
prediction, loss, yTest = readAndtrain(epochs, batch_size)
|
||
|
|
|
||
|
|
mlflow.log_metric("accuracy_score", accuracy_score(yTest, np.argmax(prediction.detach().numpy(), axis=1)))
|
||
|
|
mlflow.log_param("Last loss", loss)
|
||
|
|
mlflow.log_param("epochs", epochs)
|
||
|
|
mlflow.log_param("batch size", batch_size)
|