我有一个包含36个数据点的序列，我想对其进行滑动窗口的训练和测试。我查看了TimeSeriesSplit()，但它只能做类似于以下的事情：
('TRAIN:', array([0, 1, 2]), 'TEST:', array([3, 4, 5]))('TRAIN:', array([0, 1, 2, 3, 4, 5]), 'TEST:', array([6, 7, 8]))('TRAIN:', array([0, 1, 2, 3, 4, 5, 6, 7, 8]), 'TEST:', array([ 9, 10, 11]))我想实现一个固定长度为12的滑动窗口，每次移动一个点，测试集也使用一个固定长度为3的滑动窗口。例如：
('TRAIN:', array([0,1,2,3,4,5,6,7,8,9,10,11]), 'TEST:', array([12,13,14]))('TRAIN:', array([1,2,3,4,5,6,7,8,9,10,11,12]), 'TEST:', array([13,14,15]))('TRAIN:', array([2,3,4,5,6,7,8,9,10,11,12,13]), 'TEST:', array([14,15,16]))...我阅读了这篇文章（https://ntguardian.wordpress.com/2017/06/19/walk-forward-analysis-demonstration-backtrader/）并尝试了以下代码：
from sklearn.model_selection import TimeSeriesSplitfrom sklearn.utils import indexablefrom sklearn.utils.validation import _num_samplesimport numpy as npclass TimeSeriesSplitImproved(TimeSeriesSplit): def split(self, X, y=None, groups=None, fixed_length=False, train_splits=1, test_splits=1): X, y, groups = indexable(X, y, groups) n_samples = _num_samples(X) n_splits = self.n_splits n_folds = n_splits + 1 train_splits, test_splits = int(train_splits), int(test_splits) if n_folds > n_samples: raise ValueError( ("Cannot have number of folds ={0} greater" " than the number of samples: {1}.").format(n_folds, n_samples)) if (n_folds - train_splits - test_splits) <= 0 and test_splits > 0: raise ValueError( ("Both train_splits and test_splits must be positive" " integers.")) indices = np.arange(n_samples) split_size = (n_samples // n_folds) test_size = split_size * test_splits train_size = split_size * train_splits test_starts = range(train_size + n_samples % n_folds, n_samples - (test_size - split_size), split_size) if fixed_length: for i, test_start in zip(range(len(test_starts)), test_starts): rem = 0 if i == 0: rem = n_samples % n_folds yield (indices[(test_start - train_size - rem):test_start],indices[test_start:test_start + test_size]) else: for test_start in test_starts: yield (indices[:test_start],indices[test_start:test_start + test_size])model = TimeSeriesSplitImproved(n_splits=5)for train_index, test_index in model.split(X,fixed_length=True,train_splits=2, test_splits=1): print("TRAIN:", train_index, "TEST:", test_index) train, test = X[train_index], X[test_index]但只得到了以下结果：
TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11] TEST: [12 13 14 15 16 17]TRAIN: [ 6 7 8 9 10 11 12 13 14 15 16 17] TEST: [18 19 20 21 22 23]TRAIN: [12 13 14 15 16 17 18 19 20 21 22 23] TEST: [24 25 26 27 28 29]TRAIN: [18 19 20 21 22 23 24 25 26 27 28 29] TEST: [30 31 32 33 34 35]提前感谢您的帮助！
回答：
考虑到你的数据集有36个点，你可以很容易地手动完成这个操作。以下示例应该会有所帮助：
import numpy as npdata = list(range(36))window_size = 12splits = []for i in range(window_size, len(data)): train = np.array(data[i-window_size:i]) test = np.array(data[i:i+3]) splits.append(('TRAIN:', train, 'TEST:', test))# 查看结果for a_tuple in splits: print(a_tuple)# ('TRAIN:', array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]), 'TEST:', array([12, 13, 14]))# ('TRAIN:', array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), 'TEST:', array([13, 14, 15]))# ('TRAIN:', array([ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), 'TEST:', array([14, 15, 16]))