qxmt.datasets.generate.loader module

qxmt.datasets.generate.loader module#

class qxmt.datasets.generate.loader.GeneratedDataLoader(task_type, generate_method, random_seed, params={})#

Bases: object

This class loads the generated data and label. The loaded data and label are returned as numpy arrays (X and y). The dataset is generated by the specified task and method.

Supported task types: - classification - regression

Supported generate methods: - linear

Examples

>>> loader = GeneratedDataLoader(task_type="classification", generate_method="linear", random_seed=42)
>>> X, y = loader.load()
>>> loader = GeneratedDataLoader(task_type="regression", generate_method="linear", random_seed=42)
>>> X, y = loader.load()
Parameters:

  • task_type (str)

  • generate_method (str)

  • random_seed (int)

  • params (dict[str, Any])

__init__(task_type, generate_method, random_seed, params={})#

Initialize the GeneratedDataLoader.

Parameters:

  • task_type (str) – dataset task type

  • generate_method (str) – generate dataset method

  • random_seed (int) – random seed for reproducibility

  • params (dict[str, Any]]) – additional parameters for dataset generation

Return type:

None

load()#

Load dummy dataset. The dataset is generated by the specified task and method.

Parameters:

  • task_type (str) – dataset task type. “classification” or “regression” is supported.

  • generate_method (str) – generate dataset method. “linear” is supported.

  • random_seed (int) – random seed for reproducibility.

  • params (dict[str, Any]]) – additional parameters for dataset generation.

Raises:

ValueError – not supported task_type or generate_method

Returns:

generated data and label

Return type:

tuple[np.ndarray, np.ndarray]