Utilities to map sklearn or numpy datasets to PyTorch Dataloaders with automatic data splits and GPU/TPU support.
from sklearn.datasets import load_boston
from pl_bolts.datamodules import SklearnDataModule
X, y = load_boston(return_X_y=True)
loaders = SklearnDataModule(X, y)
train_loader = loaders.train_dataloader(batch_size=32)
val_loader = loaders.val_dataloader(batch_size=32)
test_loader = loaders.test_dataloader(batch_size=32)
Or build your own torch datasets
from sklearn.datasets import load_boston
from pl_bolts.datamodules import SklearnDataset
X, y = load_boston(return_X_y=True)
dataset = SklearnDataset(X, y)
loader = DataLoader(dataset)
Transforms a sklearn or numpy dataset to a PyTorch Dataset.
.. autoclass:: pl_bolts.datamodules.sklearn_datamodule.SklearnDataset :noindex:
Automatically generates the train, validation and test splits for a Numpy dataset. They are set up as dataloaders for convenience. Optionally, you can pass in your own validation and test splits.
.. autoclass:: pl_bolts.datamodules.sklearn_datamodule.SklearnDataModule :noindex: