このリンクは、PythonでのデータセットにTomekリンクを適用するためのコードと、プロットの詳細を提供 ここhttp://contrib.scikit-learn.org/imbalanced-learn/auto_examples/under-sampling/plot_tomek_links.html
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from imblearn.under_sampling import TomekLinks
print(__doc__)
rng = np.random.RandomState(0)
n_samples_1 = 500
n_samples_2 = 50
X_syn = np.r_[1.5 * rng.randn(n_samples_1, 2),
0.5 * rng.randn(n_samples_2, 2) + [2, 2]]
y_syn = np.array([0] * (n_samples_1) + [1] * (n_samples_2))
X_syn, y_syn = shuffle(X_syn, y_syn)
X_syn_train, X_syn_test, y_syn_train, y_syn_test = train_test_split(X_syn,
y_syn)
# remove Tomek links
tl = TomekLinks(return_indices=True)
X_resampled, y_resampled, idx_resampled = tl.fit_sample(X_syn, y_syn)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
idx_samples_removed = np.setdiff1d(np.arange(X_syn.shape[0]),
idx_resampled)
idx_class_0 = y_resampled == 0
plt.scatter(X_resampled[idx_class_0, 0], X_resampled[idx_class_0, 1],
alpha=.8, label='Class #0')
plt.scatter(X_resampled[~idx_class_0, 0], X_resampled[~idx_class_0, 1],
alpha=.8, label='Class #1')
plt.scatter(X_syn[idx_samples_removed, 0], X_syn[idx_samples_removed, 1],
alpha=.8, label='Removed samples')
はtomekリンクの使用例である:[リンク](http://stackoverflow.com/questions/12670253/fast-comput-of-tomek-link-in-r) – Amid
このリンクを使用してくださいhttp://contrib.scikit-learn.org/imbalanced-learn/auto_examples/under-sampling/plot_tomek_links.html – usct01