I have a dataset like such:
| index | tag | feature1 | feature2 | target |
|---|---|---|---|---|
| 1 | tag1 | 1.4342 | 88.4554 | 0.5365 |
| 2 | tag1 | 2.5656 | 54.5466 | 0.1263 |
| 3 | tag2 | 5.4561 | 845.556 | 0.8613 |
| 4 | tag3 | 6.5546 | 8.52545 | 0.7864 |
| 5 | tag3 | 8.4566 | 945.456 | 0.4646 |
The number of entries in each tag is not always the same.
And my objective is to load only the data with a specific tag or tags, so that I get only the entries in tag1 for one mini-batch and then tag2 for another mini-batch if I set batch_size=1. Or for instance tag1 and tag2 if I set batch_size=2
The code I have so far disregards completely the tag label and just chooses the batches randomly.
I built the datasets like such:
# features is a matrix with all the features columns through all rows
# target is a vector with the target column through all rows
featuresTrain, targetTrain = projutils.get_data(train=True, config=config)
train = torch.utils.data.TensorDataset(featuresTrain, targetTrain)
train_loader = make_loader(train, batch_size=config.batch_size)
And my loader (generically) looks like this:
def make_loader(dataset, batch_size):
loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True,
pin_memory=True,
num_workers=8)
return loader
Which I then train like this:
for epoch in range(config.epochs):
for _, (features, target) in enumerate(loader):
loss = train_batch(features, target, model, optimizer, criterion)
And the train_batch:
def train_batch(features, target, model, optimizer, criterion):
features, target = features.to(device), target.to(device)
# Forward pass ➡
outputs = model(features)
loss = criterion(outputs, target
return loss