from sklearn.model_selection import train_test_split import torch import torch.nn as nn import pandas as pd import numpy as np import torch.nn.functional as F from torch.utils.data import DataLoader, TensorDataset, random_split from sklearn import preprocessing import sys class LogisticRegressionModel(torch.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) results = pd.read_csv('diabetes2.csv') results.dropna() data_train, data_valid, data_test = np.split(results.sample(frac=1), [int(.6*len(results)), int(.8*len(results))]) columns_to_train = ['Glucose', 'BloodPressure', 'Insulin', 'Age'] x_train = data_train[columns_to_train].astype(np.float32) y_train = data_train['Outcome'].astype(np.float32) x_test = data_test[columns_to_train].astype(np.float32) y_test = data_test['Outcome'].astype(np.float32) fTrain = torch.from_numpy(x_train.values) tTrain = torch.from_numpy(y_train.values.reshape(460,1)) fTest= torch.from_numpy(x_test.values) tTest = torch.from_numpy(y_test.values) batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 20 n_iters = 900 num_epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 10 learning_rate = 0.005 input_dim = 4 output_dim = 1 model = LogisticRegressionModel(input_dim, output_dim) criterion = torch.nn.BCELoss(reduction='mean') optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate) 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() y_pred = model(fTest) torch.save(model, 'diabetes.pkl')