"""
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.
"""
from sklearn.datasets import make_circles
import pandas as pd
import numpy as np
import itertools
import json
import os
# parameters to vary across the configurations
N_SAMPLES = list(range(100, 300, 20))
NOISE = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
[docs]
def generate_circles_datasets(
n_samples=N_SAMPLES,
noise=NOISE,
save_path=None,
random_state=42,
):
"""
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
-------
None
Saves CSV files for each dataset configuration and a JSON file with
all configuration parameters.
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...
"""
print("Generating circles dataset...")
np.random.seed(random_state)
if save_path is None:
save_path = 'circles_data'
if not os.path.exists(save_path):
os.makedirs(save_path)
# enumerate all possible combinations of parameters based on ranges above
configurations = list(itertools.product(*[n_samples, noise]))
count_configs = 1
dataset_config = {}
# populate all the configs with the corresponding argument values
for n_s, n_n in configurations:
config = "n_samples={}, noise={}".format(
n_s, n_n,
)
# print(count_configs)
# iteratively run the function for each combination of arguments
X, y = make_circles(
n_samples=n_s,
noise=n_n,
random_state=random_state,
)
# print("Configuration {}/{}: {}".format(count_configs, len(configurations), config))
dataset = pd.DataFrame(X)
dataset['class'] = y
with open( os.path.join( save_path, 'dataset_config.json' ), 'w') as outfile:
dataset_config.update({'ld_data-{}.csv'.format(count_configs):
{'n_samples': n_s,
'noise': n_n}})
json.dump(dataset_config, outfile, indent=4)
new_dataset = dataset.to_csv( os.path.join( save_path, 'circles_data-{}.csv'.format(count_configs)), index=False)
count_configs += 1
# print(X.shape)
# print(y.shape)
return
