uqregressors.utils.data_loader
data_loader
A collection of methods meant to help with dataset loading and cleaning.
The most useful user-facing methods are
- load_unformatted_dataset
- clean_dataset
- validate_dataset
clean_dataset(X, y)
A simple helper method to drop missing or NaN values and reshape y to the correct size
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X
|
Union[ndarray, DataFrame, Series]
|
Input features (n_samples, n_features) |
required |
y
|
Union[ndarray, DataFrame, Series]
|
Output targets (n_samples,) |
required |
Returns:
Name | Type | Description |
---|---|---|
X_clean |
ndarray
|
Input features cleaned (n_samples, n_features) |
y_clean |
ndarray
|
Output targets cleaned (n_samples, 1) |
Source code in uqregressors\utils\data_loader.py
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load_arff(path)
ARFF file loader.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
str
|
Path to the ARFF file. |
required |
Returns:
Name | Type | Description |
---|---|---|
df |
DataFrame
|
Parsed ARFF data as a DataFrame. |
Source code in uqregressors\utils\data_loader.py
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load_unformatted_dataset(path, target_column=None, drop_columns=None)
Load and standardize a dataset from a file. Note that the last column is always assumed to be the target.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path
|
str
|
Path to the dataset file (CSV, XLSX, ARFF, etc.) |
required |
target_column
|
Union[str, int, None]
|
Name or index of the target column. If not provided, it is assumed the last column |
None
|
drop_columns
|
list
|
Columns to drop (e.g., indices, column names). |
None
|
Returns:
Name | Type | Description |
---|---|---|
X |
ndarray
|
Input features (n_samples, n_features) |
y |
ndarray
|
Target values (n_samples,) |
Source code in uqregressors\utils\data_loader.py
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validate_X_input(X, input_dim=None, device='cpu', requires_grad=False)
Convert X to a torch.Tensor for inference. Called by regressors before the predict method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X
|
array - like
|
Input data to convert, should have shape (n_samples, n_features) |
required |
device
|
str
|
Target device ('cpu' or 'cuda'). |
'cpu'
|
requires_grad
|
bool
|
Whether the tensor should track gradients. |
False
|
Returns:
Type | Description |
---|---|
Tensor
|
Prediction inputs of shape (n_samples, n_features) |
Source code in uqregressors\utils\data_loader.py
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validate_and_prepare_inputs(X, y, device='cpu', requires_grad=False)
Convert X and y into compatible torch.Tensors for training. Called by regressors before the fit method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X
|
array - like
|
Feature matrix. Supports np.ndarray, pd.DataFrame, list, or torch.Tensor. |
required |
y
|
array - like
|
Target vector. Supports np.ndarray, pd.Series, list, or torch.Tensor. |
required |
device
|
str
|
Device to place tensors on (e.g., 'cpu' or 'cuda'). |
'cpu'
|
requires_grad
|
bool
|
Whether the X tensor should require gradients (for gradient-based inference). |
False
|
Returns:
Name | Type | Description |
---|---|---|
X_tensor |
Tensor
|
Input features of shape (n_samples, n_features) |
y_tensor |
Tensor
|
Output targets of shape (n_samples, 1) |
Source code in uqregressors\utils\data_loader.py
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validate_dataset(X, y, name='unnamed')
A simple helper method to validate that a dataset is ready for regression. Raises errors if X and y are not of the correct shape, or if the dataset contains NaNs or missing values. If a dataset fails this method, try to apply the clean_dataset method first, and try again.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X
|
Union[ndarray, DataFrame, Series]
|
Input features (n_samples, n_features) |
required |
y
|
Union[ndarray, DataFrame, Series]
|
Output targets (n_samples,) |
required |
Source code in uqregressors\utils\data_loader.py
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