如何在测试和训练中分别使用基于时间的数据拆分 [英] How to split data using Time Based in Test and Train Respectively

问题描述

如何使用基于时间的拆分将数据拆分为训练和测试.

How to split data into Train and Test by using time-based split.

我知道train_test_split会随机拆分它，以及如何根据时间拆分它.

I know that train_test_split splits it randomly how to split it based on Time.

  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) 
  # this splits the data randomly as 67% test and 33% train

如何在67％的训练和33％的测试下基于时间拆分相同的数据集?

How to Split the same data set based on time as 67% train and 33% test?

数据集的TimeStamp列.

The dataset has a column TimeStamp.

我尝试搜索类似的问题，但不确定该方法.

I tried searching on the similar questions but was not sure about the approach.

有人可以简要解释吗

推荐答案

在时间序列数据集上，数据拆分以不同的方式进行. 请参阅此链接以获取更多信息.或者，您可以尝试从scikit-learn中 TimeSeriesSplit 包裹.所以主要思想是这样，假设您根据时间戳记有10个数据点.现在，拆分将如下所示:

On time-series datasets, data splitting takes place in a different way. See this link for more info. Alternatively, you can try TimeSeriesSplit from scikit-learn package. So the main idea is this, suppose you have 10 points of data according to timestamp. Now the splits will be like this :

Split 1 : 
Train_indices : 1 
Test_indices  : 2


Split 2 : 
Train_indices : 1, 2 
Test_indices  : 3


Split 3 : 
Train_indices : 1, 2, 3 
Test_indices  : 4

Split 4 : 
Train_indices : 1, 2, 3, 4 
Test_indices  : 5

依此类推.您可以查看上面链接中显示的示例，以更好地了解TimeSeriesSplit在sklearn中的工作方式

So on and so forth. You can check the example shown in the link above to get a better idea of how TimeSeriesSplit works in sklearn

更新 如果您有单独的时间列，则可以简单地基于该列对数据进行排序，并按照上述方法应用timeSeriesSplit来获取拆分.

Update If you have a separate time column, you can simply sort the data based on that column and apply timeSeriesSplit as mentioned above to get the splits.

为确保最终拆分中67％的训练和33％的测试数据，请指定拆分次数，如下所示:

In order to ensure 67% training and 33% testing data in final split, specify number of splits as following:

no_of_split = int((len(data)-3)/3)

示例

X = np.array([[1, 2], [3, 4], [1, 2], [3, 4],[1, 2], [3, 4],[3, 4],[1, 2],     [3, 4],[3, 4],[1, 2], [3, 4] ])
y = np.array([1, 2, 3, 4, 5, 6,7,8,9,10,11,12])
tscv = TimeSeriesSplit(n_splits=int((len(y)-3)/3))
for train_index, test_index in tscv.split(X):
     print("TRAIN:", train_index, "TEST:", test_index)

     #To get the indices 
     X_train, X_test = X[train_index], X[test_index]
     y_train, y_test = y[train_index], y[test_index]

输出:

('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]))

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