熊猫数据中多标签分类数据集的迭代分割

Question

我有dataset，它包含带有字符串值的文本列和值为1或0的多列(分类或否)。我想使用skmultilearn将数据分割成均匀分布，但我得到了以下错误：

KeyError: 'key of type tuple not found and not a MultiIndex'

这是我的密码：

import pandas as pd
from skmultilearn.model_selection import iterative_train_test_split


y = pd.read_csv("dataset.csv")
x = y.pop("text")

x_train, x_test, y_train, y_test = iterative_train_test_split(x, y, test_size=0.1)

Answer 1

下面是对我有用的东西(这是98/1/1分离)：

import os
import pandas as pd
from iterstrat.ml_stratifiers import MultilabelStratifiedShuffleSplit


def main():
    # load dataset
    y = pd.read_csv("dataset.csv")
    x = y.pop("text")

    # save tag names to reuse them later for creating pandas DataFrames
    tag_names = y.columns

    # Data has to be in ndarray format
    y = y.to_numpy()
    x = x.to_numpy()

    # split to train / test
    msss = MultilabelStratifiedShuffleSplit(n_splits=2, test_size=0.02, random_state=42)
    for train_index, test_index in msss.split(x, y):
        x_train, x_test_temp = x[train_index], x[test_index]
        y_train, y_test_temp = y[train_index], y[test_index]

    # make some memory space
    del x
    del y

    # split to test / validation
    msss = MultilabelStratifiedShuffleSplit(n_splits=2, test_size=0.5, random_state=42)
    for test_index, val_index in msss.split(x_test_temp, y_test_temp):
        x_test, x_val = x_test_temp[test_index], x_test_temp[val_index]
        y_test, y_val = y_test_temp[test_index], y_test_temp[val_index]

    # train dataset
    df_train = pd.DataFrame(data=y_train, columns=tag_names)
    df_train.insert(0, "snippet", x_train)

    # validation dataset
    df_val = pd.DataFrame(data=y_val, columns=tag_names)
    df_val.insert(0, "snippet", x_val)

    # test dataset
    df_test = pd.DataFrame(data=y_test, columns=tag_names)
    df_test.insert(0, "snippet", x_test)


if __name__ == "__main__":
    main()