def evaluate(data, model, scale, device):
X, y, _, _ = data.split(1, shuffle=False)
X, y = prep_data(X, y, device)
X = scale(X)
model.eval()
profiler = Profiler("INFERENCE TIME")
profiler.tick()
pred = model(X)
profiler.tock()
print(profiler, end='\n\n')
criterion = get_loss('bce')
error = criterion(pred, y)
print("loss: {:.4f}".format(error))
acc = accuracy(torch.argmin(pred, dim=1), y[:, 0])
print("accuracy: {:.4f}".format(acc))
def train(data, model, scale, config, device):
from nn.optim.sgd import SGD
from utils.train_history import TrainHistory
X_train, y_train, X_test, y_test = data.split(config.train_part,
shuffle=True)
assert len(X_train) > 0, "Wrong number of train examples"
assert len(X_test) > 0, "Wrong number of test examples"
X_train, y_train = prep_data(X_train, y_train, device)
X_test, y_test = prep_data(X_test, y_test, device)
print("first label in train part: {:.2f}%".format(
y_train[:, 0].sum().float() / y_train.shape[0] * 100))
print("first label in test part: {:.2f}%".format(
y_test[:, 0].sum().float() / y_test.shape[0] * 100))
scale.fit(torch.cat([X_train, X_test], dim=0))
X_train = scale(X_train)
X_test = scale(X_test)
criterion = get_loss(config.loss)
optimizer = SGD(model, **config.sgd_params)
profiler = Profiler("TRAIN TIME")
history = TrainHistory(config.epochs,
["loss", "val_loss", "acc", "val_acc"])
for i in range(1, config.epochs + 1):
profiler.tick()
losses = []
for batch_X, batch_y in batch_iterator(X_train,
y_train,
config.batch_size,
permute=True):
output = model(batch_X)
losses.append(criterion(output, batch_y).to("cpu"))
grad = criterion.backward(output, batch_y)
model.backward(grad)
optimizer.optimise()
test_pred = model(X_test)
test_loss = criterion(test_pred, y_test)
test_acc = accuracy(torch.argmax(test_pred, dim=1),
torch.argmax(y_test, dim=1))
pred = model(X_train)
acc = accuracy(torch.argmax(pred, dim=1), torch.argmax(y_train, dim=1))
history.update(i, np.mean(losses), test_loss, acc, test_acc)
history.print_progress()
if config.cross_validation:
idxs = np.random.permutation(
np.arange(X_train.shape[0] + X_test.shape[0]))
X = torch.cat([X_train, X_test], dim=0)
y = torch.cat([y_train, y_test], dim=0)
train_num = int(X.shape[0] * config.train_part)
X_train = X[idxs[train_num:]]
X_test = X[idxs[:train_num]]
y_train = y[idxs[train_num:]]
y_test = y[idxs[:train_num]]
profiler.tock()
history.visualize()
print('\n', profiler, sep='', end='\n\n')
