我有一个非常基础的脚本来演示这个问题：
from imblearn.over_sampling import ADASYNimport pandas as pd, numpy as npfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.model_selection import train_test_splitdata = pd.read_csv('glass.csv')classes = data.values[:, -1]data = data.iloc[:, :-1]adasyn = ADASYN(sampling_strategy='not majority', random_state=8, n_neighbors=3)new_data, new_classes = adasyn.fit_resample(data, classes)X_train, X_test, y_train, y_test = train_test_split(new_data, new_classes, test_size = 0.20)rfc = RandomForestClassifier()rfc.fit(X_train, y_train)print("Score: {}".format(rfc.score(X_test, y_test)))意图是平衡下面的类别不平衡情况：
(214, 10)Class=1, Count=70, Percentage=32.710%Class=2, Count=76, Percentage=35.514%Class=3, Count=17, Percentage=7.944%Class=5, Count=13, Percentage=6.075%Class=6, Count=9, Percentage=4.206%Class=7, Count=29, Percentage=13.551%以达到相等（或接近相等）的样本数。然而，运行上述代码会产生：
ValueError: No samples will be generated with the provided ratio settings.
将ADASYN的sampling_strategy更改为minority可以成功地对少数类别6进行过采样，并将其增加到74个样本，但其他类别仍然不平衡。因此，我正在寻找一种方法来使用ADASYN完全对所有少数类别进行过采样。
ADASYN文档说明：'not majority': resample all classes but the majority class;
但显然这并未发生。
回答：
为了解决这个问题，我所做的是对除两个主要多数类别之外的所有类别进行重新采样，并继续通过以下方式进行：
adasyn = ADASYN(sampling_strategy='minority', random_state=8, n_neighbors=3)new_data = datanew_classes = classesfor i in range(len(classes)-2): new_data, new_classes = adasyn.fit_resample(data, classes)