ium_440058/sacred-example-file.py
2021-05-24 15:43:42 +02:00

97 lines
3.0 KiB
Python

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
from sacred import Experiment
from sacred.observers import FileStorageObserver
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import mean_squared_error
np.set_printoptions(suppress=False)
ex = Experiment("ium_s440058", interactive=False, save_git_info=False)
ex.observers.append(FileStorageObserver('ium_s440058/my_runs_directory'))
@ex.config
def my_config():
num_epochs = 10
batch_size = 20
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)
@ex.capture
def script(num_epochs, batch_size, _run):
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)
_run.log_scalar("Batch", str(batch_size))
_run.log_scalar("epoch", str(num_epochs))
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()
_run.log_scalar("Lost", str(loss.item()))
torch.save(model.state_dict(), 'diabetes.pth')
pred = model(fTest)
accuracy = accuracy_score(tTest, np.argmax(pred.detach().numpy(), axis = 1))
f1 = f1_score(tTest, np.argmax(pred.detach().numpy(), axis = 1), average = None)
rmse = mean_squared_error(tTest, pred.detach().numpy())
_run.log_scalar("accuracy", accuracy)
_run.log_scalar("f1", f1)
_run.log_scalar("rmse", rmse)
@ex.automain
def my_main(num_epochs, batch_size, _run):
script()
ex.run()
ex.add_artifact('diabetes.pth')