Source code for qbiocode.data_generation.make_s_curve
from sklearn.datasets import make_s_curve
import pandas as pd
import numpy as np
import itertools
import json
import os
np.random.seed(42)
# 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 my_make_s_curve(
n_samples=N_SAMPLES,
noise=NOISE,
save_path=None
):
"""
This function generates a series of 'S-curve' data sets. It uses itertools to generate a range of input arguments
to pass into the sklearn make_s_curve function. A data set is generated for each set of input arguments, which allows
the function to produce a variety of different S-curves based on varying number of samples (n_samples) and noise.
Args:
n_samples: list of integers
The number of sample points on the Swiss Roll.
noise: list of floats
The standard deviation of the gaussian noise.
Returns:
df (pandas.DataFrame): Dataset in pandas dataframe with samples in the first column, features in the middle columns, and
labels in the last column.
"""
print("Generating S Curve dataset...")
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]))
# print(configurations)
# print(len(configurations))
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_s_curve(
n_samples=n_s,
noise=n_n,
)
# 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({'s_curve_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, 's_curve_data-{}.csv'.format(count_configs)), index=False)
count_configs += 1
# print(X.shape)
# print(y.shape)
return