qbiocode.data_generation.make_class module#

Generate synthetic high-dimensional classification datasets.

This module creates multiple configurations of multi-class classification datasets with varying dimensionality, feature characteristics, and class distributions, useful for testing machine learning algorithms on high-dimensional data.

Summary#

Functions:

generate_classification_datasets

Generate multiple high-dimensional classification datasets with varying parameters.

Reference#

generate_classification_datasets(n_samples, n_features, n_informative, n_redundant, n_classes, n_clusters_per_class, weights, save_path=None, random_state=42)[source]#

Generate multiple high-dimensional classification datasets with varying parameters.

Creates a series of synthetic datasets for multi-class classification problems with configurable feature characteristics including informative features, redundant features, and class distributions.

Parameters:

  • n_samples (list of int) – List of sample sizes to generate for each configuration.

  • n_features (list of int) – List of total feature counts (must be >= n_informative + n_redundant).

  • n_informative (list of int) – List of informative feature counts that are useful for prediction.

  • n_redundant (list of int) – List of redundant feature counts (linear combinations of informative features).

  • n_classes (list of int) – List of class counts for multi-class classification.

  • n_clusters_per_class (list of int) – List of cluster counts per class.

  • weights (list of list of float) – List of class weight distributions (must sum to 1.0).

  • save_path (str, optional) – Directory path where datasets and configuration files will be saved.

  • random_state (int, default=42) – Random seed for reproducibility.

Returns:

Saves CSV files for each dataset configuration and a JSON file with all configuration parameters.

Return type:

None

Notes

  • Each dataset is saved as ‘class_data-{i}.csv’ where i is the configuration number

  • Configuration parameters are saved in ‘dataset_config.json’

  • The last column ‘class’ contains class labels

  • Only valid configurations where (n_informative + n_redundant) <= n_features are generated

Examples

>>> from qbiocode.data_generation import generate_classification_datasets
>>> generate_classification_datasets(
...     n_samples=[100], n_features=[20], n_informative=[5],
...     n_redundant=[2], n_classes=[2], n_clusters_per_class=[1],
...     weights=[[0.5, 0.5]], save_path='data'
... )
Generating classes dataset...