62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
import torch
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import sys
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from torch import nn
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import numpy as np
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import pandas as pd
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np.set_printoptions(suppress=False)
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class LogisticRegressionModel(nn.Module):
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def __init__(self, input_dim, output_dim):
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super(LogisticRegressionModel, self).__init__()
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self.linear = nn.Linear(input_dim, output_dim)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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out = self.linear(x)
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return self.sigmoid(out)
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train = pd.read_csv("train.csv")
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test = pd.read_csv("test.csv")
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valid = pd.read_csv("valid.csv")
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xtrain = train[['age','anaemia','creatinine_phosphokinase','diabetes', 'ejection_fraction', 'high_blood_pressure', 'platelets', 'serum_creatinine', 'serum_sodium', 'sex', 'smoking']].astype(np.float32)
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ytrain = train['DEATH_EVENT'].astype(np.float32)
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xtest = test[['age','anaemia','creatinine_phosphokinase','diabetes', 'ejection_fraction', 'high_blood_pressure', 'platelets', 'serum_creatinine', 'serum_sodium', 'sex', 'smoking']].astype(np.float32)
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ytest = test['DEATH_EVENT'].astype(np.float32)
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xTrain = torch.from_numpy(xtrain.values)
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yTrain = torch.from_numpy(ytrain.values.reshape(179,1))
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xTest = torch.from_numpy(xtest.values)
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yTest = torch.from_numpy(ytest.values)
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batch_size = 10
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num_epochs = 5
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learning_rate = 0.002
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input_dim = 11
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output_dim = 1
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model = LogisticRegressionModel(input_dim, output_dim)
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criterion = torch.nn.BCELoss(reduction='mean')
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optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
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for epoch in range(num_epochs):
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# print ("Epoch #",epoch)
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model.train()
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optimizer.zero_grad()
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# Forward pass
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y_pred = model(xTrain)
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# Compute Loss
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loss = criterion(y_pred, yTrain)
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# print(loss.item())
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# Backward pass
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loss.backward()
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optimizer.step()
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y_pred = model(xTest)
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print(y_pred.data)
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torch.save(model.state_dict(), 'DEATH_EVENT.pth')
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