import torch import sys import torch.nn.functional as F from torch import nn from sklearn.metrics import accuracy_score, mean_squared_error import numpy as np import pandas as pd from sacred import Experiment from sacred.observers import MongoObserver np.set_printoptions(suppress=False) ex = Experiment("ium_s434766", interactive=False, save_git_info=False) ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred')) @ex.config def my_config(): num_epochs = 15 batch_size = 16 learning_rate = 0.001 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) @ex.capture def train(num_epochs, batch_size, learning_rate, _log): data_train = pd.read_csv("data_train.csv") data_test = pd.read_csv("data_test.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) input_dim = 6 output_dim = 1 info_params = "Batch size = " + str(batch_size) + " Epochs = " + str(num_epochs) _log.info(info_params) 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() info_loss = "Last loss = " + str(loss.item()) _log.info(info_loss) y_pred = model(fTest) # print("predicted Y value: ", y_pred.data) torch.save(model.state_dict(), 'stroke.pth') @ex.automain def my_main(num_epochs, batch_size, learning_rate, _run): train() r = ex.run() ex.add_artifact("stroke_model/stroke.pth")