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lightdatamodule

LightDataModule

Bases: LightningDataModule

A LightningDataModule for handling data.

Parameters:

Name Type Description Default
batch_size int

The batch size. Required.

 required
dataset Dataset

The dataset from the torch.utils.data Dataset class. It must implement three functions: init, len, and getitem. Required.

 required
test_size float

The test size. if test_size is float, then train_size is 1 - test_size. If test_size is int, then train_size is len(data_full) - test_size. Train size will be split into train and validation sets. So if test size is 0.7, the 0.7 train size will be split into 0.7 * 0.7 = 0.49 train set amd 0.7 * 0.3 = 0.21 validation set.

 required
test_seed int

The test seed. Defaults to 42.

 42
num_workers int

The number of workers. Defaults to 0.

 0
scaler object

The spot scaler object (e.g. TorchStandardScaler). Defaults to None.

 None

Attributes:

Name Type Description
batch_size int

The batch size.
data_full Dataset

The full dataset.
data_test Dataset

The test dataset.
data_train Dataset

The training dataset.
data_val Dataset

The validation dataset.
num_workers int

The number of workers.
test_seed int

The test seed.
test_size float

The test size.

Methods:

Name Description
prepare_data

Usually used for downloading the data. Here: Does nothing, i.e., pass.
setup

Optional[str] = None): Performs the training, validation, and test split.
train_dataloader

Returns a DataLoader instance for the training set.
val_dataloader

Returns a DataLoader instance for the validation set.
test_dataloader

Returns a DataLoader instance for the test set.

Examples:

>>> from spotpython.data.lightdatamodule import LightDataModule
    from spotpython.data.csvdataset import CSVDataset
    from spotpython.utils.scaler import TorchStandardScaler
    import torch
    # data.csv is simple csv file with 11 samples
    dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
    scaler = TorchStandardScaler()
    data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5, scaler=scaler)
    data_module.setup()
    print(f"Training set size: {len(data_module.data_train)}")
    print(f"Validation set size: {len(data_module.data_val)}")
    print(f"Test set size: {len(data_module.data_test)}")
    full_train_size: 0.5
    val_size: 0.25
    train_size: 0.25
    test_size: 0.5
    Training set size: 3
    Validation set size: 3
    Test set size: 6
References

See https://lightning.ai/docs/pytorch/stable/data/datamodule.html

Source code in spotpython/data/lightdatamodule.py 
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class LightDataModule(L.LightningDataModule):
    """
    A LightningDataModule for handling data.

    Args:
        batch_size (int):
            The batch size. Required.
        dataset (torch.utils.data.Dataset):
            The dataset from the torch.utils.data Dataset class.
            It  must implement three functions: __init__, __len__, and __getitem__.
            Required.
        test_size (float):
            The test size. if test_size is float, then train_size is 1 - test_size.
            If test_size is int, then train_size is len(data_full) - test_size.
            Train size will be split into train and validation sets.
            So if test size is 0.7, the 0.7 train size will be split into 0.7 * 0.7 = 0.49 train set
            amd 0.7 * 0.3 = 0.21 validation set.
        test_seed (int):
            The test seed. Defaults to 42.
        num_workers (int):
            The number of workers. Defaults to 0.
        scaler (object):
            The spot scaler object (e.g. TorchStandardScaler). Defaults to None.

    Attributes:
        batch_size (int): The batch size.
        data_full (Dataset): The full dataset.
        data_test (Dataset): The test dataset.
        data_train (Dataset): The training dataset.
        data_val (Dataset): The validation dataset.
        num_workers (int): The number of workers.
        test_seed (int): The test seed.
        test_size (float): The test size.

    Methods:
        prepare_data(self):
            Usually used for downloading the data. Here: Does nothing, i.e., pass.
        setup(self, stage: Optional[str] = None):
            Performs the training, validation, and test split.
        train_dataloader():
            Returns a DataLoader instance for the training set.
        val_dataloader():
            Returns a DataLoader instance for the validation set.
        test_dataloader():
            Returns a DataLoader instance for the test set.

    Examples:
        >>> from spotpython.data.lightdatamodule import LightDataModule
            from spotpython.data.csvdataset import CSVDataset
            from spotpython.utils.scaler import TorchStandardScaler
            import torch
            # data.csv is simple csv file with 11 samples
            dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
            scaler = TorchStandardScaler()
            data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5, scaler=scaler)
            data_module.setup()
            print(f"Training set size: {len(data_module.data_train)}")
            print(f"Validation set size: {len(data_module.data_val)}")
            print(f"Test set size: {len(data_module.data_test)}")
            full_train_size: 0.5
            val_size: 0.25
            train_size: 0.25
            test_size: 0.5
            Training set size: 3
            Validation set size: 3
            Test set size: 6

    References:
        See https://lightning.ai/docs/pytorch/stable/data/datamodule.html

    """

    def __init__(
        self,
        batch_size: int,
        dataset: object,
        test_size: float,
        test_seed: int = 42,
        num_workers: int = 0,
        scaler: Optional[object] = None,
    ):
        super().__init__()
        self.batch_size = batch_size
        self.data_full = dataset
        self.test_size = test_size
        self.test_seed = test_seed
        self.num_workers = num_workers
        self.scaler = scaler

    def prepare_data(self) -> None:
        """Prepares the data for use."""
        # download
        pass

    def setup(self, stage: Optional[str] = None) -> None:
        """
        Splits the data for use in training, validation, and testing.
        Uses torch.utils.data.random_split() to split the data.
        Splitting is based on the test_size and test_seed.
        The test_size can be a float or an int.
        If a spotpython scaler object is defined, the data will be scaled.

        Args:
            stage (Optional[str]):
                The current stage. Can be "fit" (for training and validation), "test" (testing),
                or None (for all three stages). Defaults to None.

        Examples:
            >>> from spotpython.data.lightdatamodule import LightDataModule
                from spotpython.data.csvdataset import CSVDataset
                import torch
                dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
                data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
                data_module.setup()
                print(f"Training set size: {len(data_module.data_train)}")
                Training set size: 3

        """
        # if test_size is float, then train_size is 1 - test_size
        test_size = self.test_size
        if isinstance(self.test_size, float):
            full_train_size = round(1.0 - test_size, 2)
            val_size = round(full_train_size * test_size, 2)
            train_size = round(full_train_size - val_size, 2)
        else:
            # if test_size is int, then train_size is len(data_full) - test_size
            full_train_size = len(self.data_full) - test_size
            val_size = int(full_train_size * test_size / len(self.data_full))
            train_size = full_train_size - val_size

        print(f"LightDataModule.setup(): stage: {stage}")
        # print(f"LightDataModule setup(): full_train_size: {full_train_size}")
        # print(f"LightDataModule setup(): val_size: {val_size}")
        # print(f"LightDataModule setup(): train_size: {train_size}")
        # print(f"LightDataModule setup(): test_size: {test_size}")

        # Assign train/val datasets for use in dataloaders
        if stage == "fit" or stage is None:
            print(f"train_size: {train_size}, val_size: {val_size} used for train & val data.")
            generator_fit = torch.Generator().manual_seed(self.test_seed)
            self.data_train, self.data_val, _ = random_split(
                self.data_full, [train_size, val_size, test_size], generator=generator_fit
            )
            if self.scaler is not None:
                # Fit the scaler on training data and transform both train and val data
                scaler_train_data = torch.stack([self.data_train[i][0] for i in range(len(self.data_train))]).squeeze(1)
                # train_val_data = self.data_train[:,0]
                print(scaler_train_data.shape)
                self.scaler.fit(scaler_train_data)
                self.data_train = [(self.scaler.transform(data), target) for data, target in self.data_train]
                data_tensors_train = [data.clone().detach() for data, target in self.data_train]
                target_tensors_train = [target.clone().detach() for data, target in self.data_train]
                self.data_train = TensorDataset(
                    torch.stack(data_tensors_train).squeeze(1), torch.stack(target_tensors_train)
                )
                # print(self.data_train)
                self.data_val = [(self.scaler.transform(data), target) for data, target in self.data_val]
                data_tensors_val = [data.clone().detach() for data, target in self.data_val]
                target_tensors_val = [target.clone().detach() for data, target in self.data_val]
                self.data_val = TensorDataset(torch.stack(data_tensors_val).squeeze(1), torch.stack(target_tensors_val))

        # Assign test dataset for use in dataloader(s)
        if stage == "test" or stage is None:
            print(f"test_size: {test_size} used for test dataset.")
            # get test data set as test_abs percent of the full dataset
            generator_test = torch.Generator().manual_seed(self.test_seed)
            self.data_test, _ = random_split(self.data_full, [test_size, full_train_size], generator=generator_test)
            if self.scaler is not None:
                self.data_test = [(self.scaler.transform(data), target) for data, target in self.data_test]
                data_tensors_test = [data.clone().detach() for data, target in self.data_test]
                target_tensors_test = [target.clone().detach() for data, target in self.data_test]
                self.data_test = TensorDataset(
                    torch.stack(data_tensors_test).squeeze(1), torch.stack(target_tensors_test)
                )

        # if stage == "predict" or stage is None:
        #     print(f"test_size, full_train_size: {test_size}, {full_train_size}")
        #     generator_predict = torch.Generator().manual_seed(self.test_seed)
        #     full_data_predict, _ = random_split(
        #         self.data_full, [test_size, full_train_size], generator=generator_predict
        #     )
        #     # Only keep the features for prediction
        #     self.data_predict = [x for x, _ in full_data_predict]

        # Assign pred dataset for use in dataloader(s)
        if stage == "predict" or stage is None:
            print(f"test_size: {test_size} used for predict dataset.")
            # get test data set as test_abs percent of the full dataset
            generator_predict = torch.Generator().manual_seed(self.test_seed)
            self.data_predict, _ = random_split(
                self.data_full, [test_size, full_train_size], generator=generator_predict
            )
            if self.scaler is not None:
                self.data_predict = [(self.scaler.transform(data), target) for data, target in self.data_predict]
                data_tensors_predict = [data.clone().detach() for data, target in self.data_predict]
                target_tensors_predict = [target.clone().detach() for data, target in self.data_predict]
                self.data_predict = TensorDataset(
                    torch.stack(data_tensors_predict).squeeze(1), torch.stack(target_tensors_predict)
                )

    def train_dataloader(self) -> DataLoader:
        """
        Returns the training dataloader, i.e., a pytorch DataLoader instance
        using the training dataset.

        Returns:
            DataLoader: The training dataloader.

        Examples:
            >>> from spotpython.data.lightdatamodule import LightDataModule
                from spotpython.data.csvdataset import CSVDataset
                import torch
                dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
                data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
                data_module.setup()
                print(f"Training set size: {len(data_module.data_train)}")
                Training set size: 3

        """
        print(f"LightDataModule.train_dataloader(). data_train size: {len(self.data_train)}")
        # print(f"LightDataModule: train_dataloader(). batch_size: {self.batch_size}")
        # print(f"LightDataModule: train_dataloader(). num_workers: {self.num_workers}")
        # apply fit_transform to the training data
        return DataLoader(self.data_train, batch_size=self.batch_size, num_workers=self.num_workers)

    def val_dataloader(self) -> DataLoader:
        """
        Returns the validation dataloader, i.e., a pytorch DataLoader instance
        using the validation dataset.

        Returns:
            DataLoader: The validation dataloader.

        Examples:
            >>> from spotpython.data.lightdatamodule import LightDataModule
                from spotpython.data.csvdataset import CSVDataset
                import torch
                dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
                data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
                data_module.setup()
                print(f"Training set size: {len(data_module.data_val)}")
                Training set size: 3
        """
        print(f"LightDataModule.val_dataloader(). Val. set size: {len(self.data_val)}")
        # print(f"LightDataModule: val_dataloader(). batch_size: {self.batch_size}")
        # print(f"LightDataModule: val_dataloader(). num_workers: {self.num_workers}")
        # apply fit_transform to the val data
        return DataLoader(self.data_val, batch_size=self.batch_size, num_workers=self.num_workers)

    def test_dataloader(self) -> DataLoader:
        """
        Returns the test dataloader, i.e., a pytorch DataLoader instance
        using the test dataset.

        Returns:
            DataLoader: The test dataloader.

        Examples:
            >>> from spotpython.data.lightdatamodule import LightDataModule
                from spotpython.data.csvdataset import CSVDataset
                import torch
                dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
                data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
                data_module.setup()
                print(f"Test set size: {len(data_module.data_test)}")
                Test set size: 6

        """
        print(f"LightDataModule.test_dataloader(). Test set size: {len(self.data_test)}")
        # print(f"LightDataModule: test_dataloader(). batch_size: {self.batch_size}")
        # print(f"LightDataModule: test_dataloader(). num_workers: {self.num_workers}")
        # apply fit_transform to the val data
        return DataLoader(self.data_test, batch_size=self.batch_size, num_workers=self.num_workers)

    def predict_dataloader(self) -> DataLoader:
        """
        Returns the predict dataloader, i.e., a pytorch DataLoader instance
        using the predict dataset.

        Returns:
            DataLoader: The predict dataloader.

        Examples:
            >>> from spotpython.data.lightdatamodule import LightDataModule
                from spotpython.data.csvdataset import CSVDataset
                import torch
                dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
                data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
                data_module.setup()
                print(f"Predict set size: {len(data_module.data_predict)}")
                Predict set size: 6

        """
        print(f"LightDataModule.predict_dataloader(). Predict set size: {len(self.data_predict)}")
        # print(f"LightDataModule: predict_dataloader(). batch_size: {self.batch_size}")
        # print(f"LightDataModule: predict_dataloader(). num_workers: {self.num_workers}")
        # apply fit_transform to the val data

        return DataLoader(self.data_predict, batch_size=len(self.data_predict), num_workers=self.num_workers)

predict_dataloader()

Returns the predict dataloader, i.e., a pytorch DataLoader instance using the predict dataset.

Returns:

Name Type Description
DataLoader DataLoader

The predict dataloader.

Examples:

>>> from spotpython.data.lightdatamodule import LightDataModule
    from spotpython.data.csvdataset import CSVDataset
    import torch
    dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
    data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
    data_module.setup()
    print(f"Predict set size: {len(data_module.data_predict)}")
    Predict set size: 6
Source code in spotpython/data/lightdatamodule.py 
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def predict_dataloader(self) -> DataLoader:
    """
    Returns the predict dataloader, i.e., a pytorch DataLoader instance
    using the predict dataset.

    Returns:
        DataLoader: The predict dataloader.

    Examples:
        >>> from spotpython.data.lightdatamodule import LightDataModule
            from spotpython.data.csvdataset import CSVDataset
            import torch
            dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
            data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
            data_module.setup()
            print(f"Predict set size: {len(data_module.data_predict)}")
            Predict set size: 6

    """
    print(f"LightDataModule.predict_dataloader(). Predict set size: {len(self.data_predict)}")
    # print(f"LightDataModule: predict_dataloader(). batch_size: {self.batch_size}")
    # print(f"LightDataModule: predict_dataloader(). num_workers: {self.num_workers}")
    # apply fit_transform to the val data

    return DataLoader(self.data_predict, batch_size=len(self.data_predict), num_workers=self.num_workers)

prepare_data()

Prepares the data for use.

Source code in spotpython/data/lightdatamodule.py 
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def prepare_data(self) -> None:
    """Prepares the data for use."""
    # download
    pass

setup(stage=None)

Splits the data for use in training, validation, and testing. Uses torch.utils.data.random_split() to split the data. Splitting is based on the test_size and test_seed. The test_size can be a float or an int. If a spotpython scaler object is defined, the data will be scaled.

Parameters:

Name Type Description Default
stage Optional[str]

The current stage. Can be “fit” (for training and validation), “test” (testing), or None (for all three stages). Defaults to None.

 None

Examples:

>>> from spotpython.data.lightdatamodule import LightDataModule
    from spotpython.data.csvdataset import CSVDataset
    import torch
    dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
    data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
    data_module.setup()
    print(f"Training set size: {len(data_module.data_train)}")
    Training set size: 3
Source code in spotpython/data/lightdatamodule.py 
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def setup(self, stage: Optional[str] = None) -> None:
    """
    Splits the data for use in training, validation, and testing.
    Uses torch.utils.data.random_split() to split the data.
    Splitting is based on the test_size and test_seed.
    The test_size can be a float or an int.
    If a spotpython scaler object is defined, the data will be scaled.

    Args:
        stage (Optional[str]):
            The current stage. Can be "fit" (for training and validation), "test" (testing),
            or None (for all three stages). Defaults to None.

    Examples:
        >>> from spotpython.data.lightdatamodule import LightDataModule
            from spotpython.data.csvdataset import CSVDataset
            import torch
            dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
            data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
            data_module.setup()
            print(f"Training set size: {len(data_module.data_train)}")
            Training set size: 3

    """
    # if test_size is float, then train_size is 1 - test_size
    test_size = self.test_size
    if isinstance(self.test_size, float):
        full_train_size = round(1.0 - test_size, 2)
        val_size = round(full_train_size * test_size, 2)
        train_size = round(full_train_size - val_size, 2)
    else:
        # if test_size is int, then train_size is len(data_full) - test_size
        full_train_size = len(self.data_full) - test_size
        val_size = int(full_train_size * test_size / len(self.data_full))
        train_size = full_train_size - val_size

    print(f"LightDataModule.setup(): stage: {stage}")
    # print(f"LightDataModule setup(): full_train_size: {full_train_size}")
    # print(f"LightDataModule setup(): val_size: {val_size}")
    # print(f"LightDataModule setup(): train_size: {train_size}")
    # print(f"LightDataModule setup(): test_size: {test_size}")

    # Assign train/val datasets for use in dataloaders
    if stage == "fit" or stage is None:
        print(f"train_size: {train_size}, val_size: {val_size} used for train & val data.")
        generator_fit = torch.Generator().manual_seed(self.test_seed)
        self.data_train, self.data_val, _ = random_split(
            self.data_full, [train_size, val_size, test_size], generator=generator_fit
        )
        if self.scaler is not None:
            # Fit the scaler on training data and transform both train and val data
            scaler_train_data = torch.stack([self.data_train[i][0] for i in range(len(self.data_train))]).squeeze(1)
            # train_val_data = self.data_train[:,0]
            print(scaler_train_data.shape)
            self.scaler.fit(scaler_train_data)
            self.data_train = [(self.scaler.transform(data), target) for data, target in self.data_train]
            data_tensors_train = [data.clone().detach() for data, target in self.data_train]
            target_tensors_train = [target.clone().detach() for data, target in self.data_train]
            self.data_train = TensorDataset(
                torch.stack(data_tensors_train).squeeze(1), torch.stack(target_tensors_train)
            )
            # print(self.data_train)
            self.data_val = [(self.scaler.transform(data), target) for data, target in self.data_val]
            data_tensors_val = [data.clone().detach() for data, target in self.data_val]
            target_tensors_val = [target.clone().detach() for data, target in self.data_val]
            self.data_val = TensorDataset(torch.stack(data_tensors_val).squeeze(1), torch.stack(target_tensors_val))

    # Assign test dataset for use in dataloader(s)
    if stage == "test" or stage is None:
        print(f"test_size: {test_size} used for test dataset.")
        # get test data set as test_abs percent of the full dataset
        generator_test = torch.Generator().manual_seed(self.test_seed)
        self.data_test, _ = random_split(self.data_full, [test_size, full_train_size], generator=generator_test)
        if self.scaler is not None:
            self.data_test = [(self.scaler.transform(data), target) for data, target in self.data_test]
            data_tensors_test = [data.clone().detach() for data, target in self.data_test]
            target_tensors_test = [target.clone().detach() for data, target in self.data_test]
            self.data_test = TensorDataset(
                torch.stack(data_tensors_test).squeeze(1), torch.stack(target_tensors_test)
            )

    # if stage == "predict" or stage is None:
    #     print(f"test_size, full_train_size: {test_size}, {full_train_size}")
    #     generator_predict = torch.Generator().manual_seed(self.test_seed)
    #     full_data_predict, _ = random_split(
    #         self.data_full, [test_size, full_train_size], generator=generator_predict
    #     )
    #     # Only keep the features for prediction
    #     self.data_predict = [x for x, _ in full_data_predict]

    # Assign pred dataset for use in dataloader(s)
    if stage == "predict" or stage is None:
        print(f"test_size: {test_size} used for predict dataset.")
        # get test data set as test_abs percent of the full dataset
        generator_predict = torch.Generator().manual_seed(self.test_seed)
        self.data_predict, _ = random_split(
            self.data_full, [test_size, full_train_size], generator=generator_predict
        )
        if self.scaler is not None:
            self.data_predict = [(self.scaler.transform(data), target) for data, target in self.data_predict]
            data_tensors_predict = [data.clone().detach() for data, target in self.data_predict]
            target_tensors_predict = [target.clone().detach() for data, target in self.data_predict]
            self.data_predict = TensorDataset(
                torch.stack(data_tensors_predict).squeeze(1), torch.stack(target_tensors_predict)
            )

test_dataloader()

Returns the test dataloader, i.e., a pytorch DataLoader instance using the test dataset.

Returns:

Name Type Description
DataLoader DataLoader

The test dataloader.

Examples:

>>> from spotpython.data.lightdatamodule import LightDataModule
    from spotpython.data.csvdataset import CSVDataset
    import torch
    dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
    data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
    data_module.setup()
    print(f"Test set size: {len(data_module.data_test)}")
    Test set size: 6
Source code in spotpython/data/lightdatamodule.py 
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def test_dataloader(self) -> DataLoader:
    """
    Returns the test dataloader, i.e., a pytorch DataLoader instance
    using the test dataset.

    Returns:
        DataLoader: The test dataloader.

    Examples:
        >>> from spotpython.data.lightdatamodule import LightDataModule
            from spotpython.data.csvdataset import CSVDataset
            import torch
            dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
            data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
            data_module.setup()
            print(f"Test set size: {len(data_module.data_test)}")
            Test set size: 6

    """
    print(f"LightDataModule.test_dataloader(). Test set size: {len(self.data_test)}")
    # print(f"LightDataModule: test_dataloader(). batch_size: {self.batch_size}")
    # print(f"LightDataModule: test_dataloader(). num_workers: {self.num_workers}")
    # apply fit_transform to the val data
    return DataLoader(self.data_test, batch_size=self.batch_size, num_workers=self.num_workers)

train_dataloader()

Returns the training dataloader, i.e., a pytorch DataLoader instance using the training dataset.

Returns:

Name Type Description
DataLoader DataLoader

The training dataloader.

Examples:

>>> from spotpython.data.lightdatamodule import LightDataModule
    from spotpython.data.csvdataset import CSVDataset
    import torch
    dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
    data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
    data_module.setup()
    print(f"Training set size: {len(data_module.data_train)}")
    Training set size: 3
Source code in spotpython/data/lightdatamodule.py 
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def train_dataloader(self) -> DataLoader:
    """
    Returns the training dataloader, i.e., a pytorch DataLoader instance
    using the training dataset.

    Returns:
        DataLoader: The training dataloader.

    Examples:
        >>> from spotpython.data.lightdatamodule import LightDataModule
            from spotpython.data.csvdataset import CSVDataset
            import torch
            dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
            data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
            data_module.setup()
            print(f"Training set size: {len(data_module.data_train)}")
            Training set size: 3

    """
    print(f"LightDataModule.train_dataloader(). data_train size: {len(self.data_train)}")
    # print(f"LightDataModule: train_dataloader(). batch_size: {self.batch_size}")
    # print(f"LightDataModule: train_dataloader(). num_workers: {self.num_workers}")
    # apply fit_transform to the training data
    return DataLoader(self.data_train, batch_size=self.batch_size, num_workers=self.num_workers)

val_dataloader()

Returns the validation dataloader, i.e., a pytorch DataLoader instance using the validation dataset.

Returns:

Name Type Description
DataLoader DataLoader

The validation dataloader.

Examples:

>>> from spotpython.data.lightdatamodule import LightDataModule
    from spotpython.data.csvdataset import CSVDataset
    import torch
    dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
    data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
    data_module.setup()
    print(f"Training set size: {len(data_module.data_val)}")
    Training set size: 3
Source code in spotpython/data/lightdatamodule.py 
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def val_dataloader(self) -> DataLoader:
    """
    Returns the validation dataloader, i.e., a pytorch DataLoader instance
    using the validation dataset.

    Returns:
        DataLoader: The validation dataloader.

    Examples:
        >>> from spotpython.data.lightdatamodule import LightDataModule
            from spotpython.data.csvdataset import CSVDataset
            import torch
            dataset = CSVDataset(csv_file='data.csv', target_column='prognosis', feature_type=torch.long)
            data_module = LightDataModule(dataset=dataset, batch_size=5, test_size=0.5)
            data_module.setup()
            print(f"Training set size: {len(data_module.data_val)}")
            Training set size: 3
    """
    print(f"LightDataModule.val_dataloader(). Val. set size: {len(self.data_val)}")
    # print(f"LightDataModule: val_dataloader(). batch_size: {self.batch_size}")
    # print(f"LightDataModule: val_dataloader(). num_workers: {self.num_workers}")
    # apply fit_transform to the val data
    return DataLoader(self.data_val, batch_size=self.batch_size, num_workers=self.num_workers)