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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handle_scaling_and_transform()
¶
Fits the scaler on the training data and transforms both training and validation datasets. This function is only called when self.scaler is not None.
Source code in spotpython/data/lightdatamodule.py
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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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|
prepare_data()
¶
Prepares the data for use.
Source code in spotpython/data/lightdatamodule.py
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|
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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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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|
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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|
transform_dataset(dataset)
¶
Applies the scaler transformation to the dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dataset |
List[Tuple[Tensor, Any]]
|
The dataset to transform, consisting of data and target pairs. |
required |
Returns:
Name | Type | Description |
---|---|---|
TensorDataset |
TensorDataset
|
A PyTorch TensorDataset containing the transformed and cloned data and targets. |
Raises:
Type | Description |
---|---|
ValueError
|
If the input data is not correctly formatted for transformation. |
Source code in spotpython/data/lightdatamodule.py
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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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