qbiocode.data_generation.make_circles module#

Generate synthetic concentric circles datasets for binary classification tasks.

This module creates multiple configurations of 2D concentric circles datasets with varying sample sizes and noise levels, useful for testing machine learning algorithms on non-linearly separable data.

Summary#

Functions:

generate_circles_datasets

Generate multiple concentric circles datasets with varying parameters.

Reference#

generate_circles_datasets(n_samples=[100, 120, 140, 160, 180, 200, 220, 240, 260, 280], noise=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], save_path=None, random_state=42)[source]#

Generate multiple concentric circles datasets with varying parameters.

Creates a series of 2D datasets where samples form two concentric circles, providing a classic non-linearly separable binary classification problem. Each configuration varies the number of samples and noise level.

Parameters:

  • n_samples (list of int, default=range(100, 300, 20)) – List of sample sizes to generate for each configuration.

  • noise (list of float, default=[0.1, 0.2, ..., 0.9]) – List of noise standard deviations to apply to the data.

  • save_path (str, default='circles_data') – 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 ‘circles_data-{i}.csv’ where i is the configuration number

  • Configuration parameters are saved in ‘dataset_config.json’

  • The last column ‘class’ contains binary labels (0 or 1)

Examples

>>> from qbiocode.data_generation import generate_circles_datasets
>>> generate_circles_datasets(n_samples=[100, 200], noise=[0.1, 0.3])
Generating circles dataset...