sklearn.rst

Sklearn Datamodule

Utilities to map sklearn or numpy datasets to PyTorch Dataloaders with automatic data splits and GPU/TPU support.

from sklearn.datasets import load_diabetes
from pl_bolts.datamodules import SklearnDataModule

X, y = load_diabetes(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_diabetes
from pl_bolts.datamodules import SklearnDataset

X, y = load_diabetes(return_X_y=True)
dataset = SklearnDataset(X, y)
loader = DataLoader(dataset)

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Sklearn Dataset Class

Transforms a sklearn or numpy dataset to a PyTorch Dataset.

pl_bolts.datamodules.sklearn_datamodule.SklearnDataset

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Sklearn DataModule Class

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.

pl_bolts.datamodules.sklearn_datamodule.SklearnDataModule