from sklearn.datasets import make_classification
import pandas as pd
import numpy as np
import json
import itertools
import os
dataset_config = {}
np.random.seed(42)
# parameters to vary across the configurations
N_SAMPLES = list(range(100, 300, 50))
N_FEATURES = list(range(10,60,10))
N_INFORMATIVE = list(range(2,8,4))
N_REDUNDANT = list(range(2,8,4))
N_CLASSES = list(range(2, 4, 6))
N_CLUSTERS_PER_CLASS = list(range(1, 2, 3))
WEIGHTS = [[0.3, 0.7], [0.4, 0.6], [0.5, 0.5]]
[docs]
def my_make_classification(
n_samples,
n_features,
n_informative,
n_redundant,
n_classes,
n_clusters_per_class,
weights,
save_path=None
):
print("Generating classes 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, n_features, n_informative, n_redundant, n_classes, n_clusters_per_class, weights]))
count_configs = 1
# populate all the configs with the corresponding argument values
for n_s, n_f, n_i, n_r, n_cla, n_clu, weights in configurations:
if (n_i + n_r) <= n_f:
config = "n_samples={}, n_features={}, n_informative={}, n_redundant={}, n_classes={}, n_clusters_per_class={}, weights={}".format(
n_s, n_f, n_i, n_r, n_cla, n_clu, weights
)
# print(count_configs)
# iteratively run the function for each combination of arguments
X, y = make_classification(
n_samples=n_s,
n_features=n_f,
n_informative=n_i,
n_redundant=n_r,
n_classes=n_cla,
n_clusters_per_class=n_clu,
weights=weights,
)
# 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({'hd_data-{}.csv'.format(count_configs):
{'n_samples': n_s,
'n_features': n_f,
'n_informative': n_i,
'n_redundant': n_r,
'n_classes': n_cla,
'n_clusters_per_class': n_clu,
'weights': weights}})
json.dump(dataset_config, outfile, indent=4)
new_dataset = dataset.to_csv( os.path.join( save_path, 'class_data-{}.csv'.format(count_configs)), index=False)
count_configs += 1
# print(X.shape)
# print(y.shape)
return
