python 多分类任务中按照类别分层采样

在机器学习多分类任务中有时候需要针对类别进行分层采样，比如说类别不均衡的数据，这时候随机采样会造成训练集、验证集、测试集中不同类别的数据比例不一样，这是会在一定程度上影响分类器的性能的，这时候就需要进行分层采样保证训练集、验证集、测试集中每一个类别的数据比例差不多持平。

下面python代码。

# 将数据按照类别进行分层划分
def save_file_stratified(filename, ssdfile_dir, categories):
    """
    将文件分流到3个文件中
    filename: 原数据地址，一个csv文件
    文件内容格式:  类别	内容
    """
    f_train = open('../data/usefuldata-711depart/train.txt', 'w', encoding='utf-8')
    f_val = open('../data/usefuldata-711depart/val.txt', 'w', encoding='utf-8')
    f_test = open('../data/usefuldata-711depart/test.txt', 'w', encoding='utf-8')
    # f_class = open('../data/usefuldata-37depart/class.txt', 'w', encoding='utf-8')
    dict_ssdqw = {}
    for ssdfile in os.listdir(ssdfile_dir):
        ssdfile_name = os.path.join(ssdfile_dir, ssdfile)
        f = open(ssdfile_name, 'r', encoding='utf-8')
        content_qw = ''
        content = f.readline()
        # 以下部分，因为统计整个案件基本情况他有换行，所以将多行处理在一行里面
        while content:
            content_qw += content
            content_qw = content_qw.replace('
', '')
            content = f.readline()
        ssdfile_key = str(ssdfile).replace('.txt','')
        dict_ssdqw[ssdfile_key] = content_qw
    # doc_count代表每一类数据总共有多少个
    doc_count_0 = 0
    doc_count_1 = 0
    doc_count_2 = 0
    doc_count_3 = 0
    doc_count_4 = 0
    doc_count_5 = 0
    doc_count_6 = 0
    doc_count_7 = 0
    doc_count_8 = 0
    doc_count_9 = 0
    doc_count_10 = 0
    doc_count_11 = 0
    doc_count_12 = 0
    temp_file = open(filename, 'r', encoding='utf-8')
    line = temp_file.readline()
    while line:
        line_content = line.split(',')
        name = line_content[0]
        if name in dict_ssdqw:
            label = line_content[1]
            if label == categories[0]:
                doc_count_0 += 1
            elif label == categories[1]:
                doc_count_1 += 1
            elif label == categories[2]:
                doc_count_2 += 1
            elif label == categories[3]:
                doc_count_3 += 1
            elif label == categories[4]:
                doc_count_4 += 1
            elif label == categories[5]:
                doc_count_5 += 1
            elif label == categories[6]:
                doc_count_6 += 1
            elif label == categories[7]:
                doc_count_7 += 1
            elif label == categories[8]:
                doc_count_8 += 1
            elif label == categories[9]:
                doc_count_9 += 1
            elif label == categories[10]:
                doc_count_10 += 1
            elif label == categories[11]:
                doc_count_11 += 1
            elif label == categories[12]:
                doc_count_12 += 1
        line = temp_file.readline()
    temp_file.close()
    # 总数量
    doc_count = doc_count_0 + doc_count_1 + doc_count_2 + doc_count_3 +
        doc_count_4 + doc_count_5 + doc_count_6 + doc_count_7 +
        doc_count_8 + doc_count_9 + doc_count_10 + doc_count_11 + doc_count_12
    class_set = set()
    tag_train_0 = doc_count_0 * 70 / 100
    tag_train_1 = doc_count_1 * 70 / 100
    tag_train_2 = doc_count_2 * 70 / 100
    tag_train_3 = doc_count_3 * 70 / 100
    tag_train_4 = doc_count_4 * 70 / 100
    tag_train_5 = doc_count_5 * 70 / 100
    tag_train_6 = doc_count_6 * 70 / 100
    tag_train_7 = doc_count_7 * 70 / 100
    tag_train_8 = doc_count_8 * 70 / 100
    tag_train_9 = doc_count_9 * 70 / 100
    tag_train_10 = doc_count_10 * 70 / 100
    tag_train_11= doc_count_11 * 70 / 100
    tag_train_12 = doc_count_12 * 70 / 100
    tag_val_0 = doc_count_0 * 85 / 100
    tag_val_1 = doc_count_1 * 85 / 100
    tag_val_2 = doc_count_2 * 85 / 100
    tag_val_3 = doc_count_3 * 85 / 100
    tag_val_4 = doc_count_4 * 85 / 100
    tag_val_5 = doc_count_5 * 85 / 100
    tag_val_6 = doc_count_6 * 85 / 100
    tag_val_7 = doc_count_7 * 85 / 100
    tag_val_8 = doc_count_8 * 85 / 100
    tag_val_9 = doc_count_9 * 85 / 100
    tag_val_10 = doc_count_10 * 85 / 100
    tag_val_11 = doc_count_11 * 85 / 100
    tag_val_12 = doc_count_12 * 85 / 100
    # tag_test = doc_count * 70 / 100
    tag_0 = 0
    tag_1 = 0
    tag_2 = 0
    tag_3 = 0
    tag_4 = 0
    tag_5 = 0
    tag_6 = 0
    tag_7 = 0
    tag_8 = 0
    tag_9 = 0
    tag_10 = 0
    tag_11 = 0
    tag_12 = 0
    # 有些文书行业标记是空！！我想看看有多少条？
    blank_tag = 0
    # 标记一下，每个类别有多少个训练集、验证集、测试集？
    train_class_tag = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    val_class_tag = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    test_class_tag = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    # csvfile = open(filename, 'r', encoding='utf-8')
    txtfile = open(filename, 'r', encoding='utf-8')
    process_line = txtfile.readline()
    while process_line:
        line_content = process_line.split(',')
        name = line_content[0]
        if name in dict_ssdqw:
            content = dict_ssdqw[name]
            label = line_content[1]
            # if label != '' and label != '其他行业':
            if label != '':
                class_set.add(label)
                # 对每一类进行分层采样
                if label == categories[0]:
                    tag_0 += 1
                    if tag_0 < tag_train_0:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[0] += 1
                    elif tag_0 < tag_val_0:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[0] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[0] += 1
                elif label == categories[1]:
                    tag_1 += 1
                    if tag_1 < tag_train_1:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[1] += 1
                    elif tag_1 < tag_val_1:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[1] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[1] += 1
                elif label == categories[2]:
                    tag_2 += 1
                    if tag_2 < tag_train_2:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[2] += 1
                    elif tag_2 < tag_val_2:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[2] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[2] += 1
                elif label == categories[3]:
                    tag_3 += 1
                    if tag_3 < tag_train_3:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[3] += 1
                    elif tag_3 < tag_val_3:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[3] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[3] += 1
                elif label == categories[4]:
                    tag_4 += 1
                    if tag_4 < tag_train_4:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[4] += 1
                    elif tag_4 < tag_val_4:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[4] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[4] += 1
                elif label == categories[5]:
                    tag_5 += 1
                    if tag_5 < tag_train_5:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[5] += 1
                    elif tag_5 < tag_val_5:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[5] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[5] += 1
                elif label == categories[6]:
                    tag_6 += 1
                    if tag_6 < tag_train_6:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[6] += 1
                    elif tag_6 < tag_val_6:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[6] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[6] += 1
                elif label == categories[7]:
                    tag_7 += 1
                    if tag_7 < tag_train_7:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[7] += 1
                    elif tag_7 < tag_val_7:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[7] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[7] += 1
                elif label == categories[8]:
                    tag_8 += 1
                    if tag_8 < tag_train_8:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[8] += 1
                    elif tag_8 < tag_val_8:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[8] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[8] += 1
                elif label == categories[9]:
                    tag_9 += 1
                    if tag_9 < tag_train_9:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[9] += 1
                    elif tag_9 < tag_val_9:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[9] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[9] += 1
                elif label == categories[10]:
                    tag_10 += 1
                    if tag_10 < tag_train_10:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[10] += 1
                    elif tag_10 < tag_val_10:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[10] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[10] += 1
                elif label == categories[11]:
                    tag_11 += 1
                    if tag_11 < tag_train_11:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[11] += 1
                    elif tag_11 < tag_val_11:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[11] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[11] += 1
                elif label == categories[12]:
                    tag_12 += 1
                    if tag_12 < tag_train_12:
                        f_train.write(label + '	' + content + '
')
                        train_class_tag[12] += 1
                    elif tag_12 < tag_val_12:
                        f_val.write(label + '	' + content + '
')
                        val_class_tag[12] += 1
                    else:
                        f_test.write(label + '	' + content + '
')
                        test_class_tag[12] += 1
            else:
                blank_tag += 1
        process_line = txtfile.readline()
    txtfile.close()
    print("有" + str(blank_tag) + "个文书的行业标记为空！")
    print("train:")
    print(train_class_tag)
    train_tag_total =0
    for i_total in train_class_tag:
        train_tag_total += i_total
    train_class_tag_distribute = []
    for i in train_class_tag:
        train_class_tag_distribute.append((i / train_tag_total) * 100)
    print("分布:")
    print(train_class_tag_distribute)
    print("val:")
    print(val_class_tag)
    val_tag_total = 0
    for i_total in val_class_tag:
        val_tag_total += i_total
    val_class_tag_distribute = []
    for i in val_class_tag:
        val_class_tag_distribute.append((i / val_tag_total) * 100)
    print("分布:")
    print(val_class_tag_distribute)
    print("test:")
    print(test_class_tag)
    test_tag_total = 0
    for i_total in test_class_tag:
        test_tag_total += i_total
    test_class_tag_distribute = []
    for i in test_class_tag:
        test_class_tag_distribute.append((i / test_tag_total) * 100)
    print("分布:")
    print(test_class_tag_distribute)
    f_train.close()
    f_test.close()
    f_val.close()
if __name__ == '__main__':
    categories = [
        "class1",
        "class2",
        "class3",
        "class4",
        "class5",
        "class6",
        "class7",
        "class8",
        "class9",
        "class10",
        "class11",
        "class12",
        "class13"
    ]
    save_file_stratified('../data/qwdata/shuffle-try3/classified_table_ms.txt', '../data/qwdata/ms-ygscplusssdqw',categories)

后面可以看到类别划分

---

这里要注意的一点是：这是我早期写的文章，需要注意的一点是，我们通常在训练集和验证集上做分层采样即可，测试集最好保持原样不要动。