-
Notifications
You must be signed in to change notification settings - Fork 1
/
extract_dataset_info.py
41 lines (27 loc) · 1.19 KB
/
extract_dataset_info.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
import numpy as np
import pandas as pd
from collections import Counter
from datasets import load_all
if __name__ == '__main__':
rows = []
columns = ['Name', 'IR', 'Samples', 'Features']
for name, dataset in load_all().items():
(X_train, y_train), (X_test, y_test) = dataset[0][0], dataset[0][1]
X = np.concatenate([X_train, X_test])
y = np.concatenate([y_train, y_test])
n_samples = X.shape[0]
n_features = X.shape[1]
majority_class = Counter(y).most_common()[0][0]
n_majority_samples = Counter(y).most_common()[0][1]
n_minority_samples = Counter(y).most_common()[1][1]
imbalance_ratio = np.round(n_majority_samples / n_minority_samples, 2)
rows.append([name.replace('_', '').replace('-', ''), imbalance_ratio, n_samples, n_features])
df = pd.DataFrame(rows, columns=columns).sort_values('IR')
df['IR'] = df['IR'].map(lambda x: f'{x:.2f}')
for i in range(30):
row = [str(df.iloc[i][c]) for c in columns]
if i + 30 < len(df):
row += [str(df.iloc[i + 30][c]) for c in columns]
else:
row += ['' for _ in columns]
print(' & '.join(row) + ' \\\\')