python - 从每个类别标签中抽取 X 个样本

Question

我有一个数据集(numpy 向量)，其中包含 50 个类和 9000 个训练示例。

x_train=(9000,2048)
y_train=(9000,)  # Classes are strings 
classes=list(set(y_train))

我想构建一个子数据集，每个类都有 5 个示例

这意味着我得到了5*50=250个训练样本。因此我的子数据集将采用这种形式:

sub_train_data=(250,2048)
sub_train_labels=(250,)

备注:我们从每个类别中随机抽取 5 个示例(类别总数 = 50)

谢谢

Answer 1

这是该问题的解决方案:

from collections import Counter
import numpy as np
import matplotlib.pyplot as plt

def balanced_sample_maker(X, y, sample_size, random_seed=42):
    uniq_levels = np.unique(y)
    uniq_counts = {level: sum(y == level) for level in uniq_levels}

    if not random_seed is None:
        np.random.seed(random_seed)

    # find observation index of each class levels
    groupby_levels = {}
    for ii, level in enumerate(uniq_levels):
        obs_idx = [idx for idx, val in enumerate(y) if val == level]
        groupby_levels[level] = obs_idx
    # oversampling on observations of each label
    balanced_copy_idx = []
    for gb_level, gb_idx in groupby_levels.items():
        over_sample_idx = np.random.choice(gb_idx, size=sample_size, replace=True).tolist()
        balanced_copy_idx+=over_sample_idx
    np.random.shuffle(balanced_copy_idx)

    data_train=X[balanced_copy_idx]
    labels_train=y[balanced_copy_idx]
    if  ((len(data_train)) == (sample_size*len(uniq_levels))):
        print('number of sampled example ', sample_size*len(uniq_levels), 'number of sample per class ', sample_size, ' #classes: ', len(list(set(uniq_levels))))
    else:
        print('number of samples is wrong ')

    labels, values = zip(*Counter(labels_train).items())
    print('number of classes ', len(list(set(labels_train))))
    check = all(x == values[0] for x in values)
    print(check)
    if check == True:
        print('Good all classes have the same number of examples')
    else:
        print('Repeat again your sampling your classes are not balanced')
    indexes = np.arange(len(labels))
    width = 0.5
    plt.bar(indexes, values, width)
    plt.xticks(indexes + width * 0.5, labels)
    plt.show()
    return data_train,labels_train

X_train,y_train=balanced_sample_maker(X,y,10)

灵感来自Scikit-learn balanced subsampling