72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
import torch
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import sys
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import torch.nn.functional as F
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from torch import nn
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from torch.autograd import Variable
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import torchvision.transforms as transforms
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.metrics import accuracy_score
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import numpy as np
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import pandas as pd
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from sacred import Experiment
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from sacred.observers import FileStorageObserver
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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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data_train = pd.read_csv("data_train.csv")
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data_test = pd.read_csv("data_test.csv")
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data_val = pd.read_csv("data_val.csv")
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FEATURES = ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
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x_train = data_train[FEATURES].astype(np.float32)
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y_train = data_train['stroke'].astype(np.float32)
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x_test = data_test[FEATURES].astype(np.float32)
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y_test = data_test['stroke'].astype(np.float32)
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fTrain = torch.from_numpy(x_train.values)
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tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
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fTest= torch.from_numpy(x_test.values)
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tTest = torch.from_numpy(y_test.values)
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batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 16
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num_epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
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learning_rate = 0.001
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input_dim = 6
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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(fTrain)
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# Compute Loss
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loss = criterion(y_pred, tTrain)
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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(fTest)
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print("predicted Y value: ", y_pred.data)
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torch.save(model.state_dict(), 'stroke.pth')
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