从 pandas 日期时间索引创建时间序列上的滑动窗口数据集

Question

考虑以下代码：

import pandas as pd
import numpy as np
import tensorflow as tf


def random_dates(start, end, n=10):

    start_u = start.value//10**9
    end_u = end.value//10**9

    return pd.to_datetime(np.random.randint(start_u, end_u, n), unit='s')


start = pd.to_datetime('2015-01-01')
end = pd.to_datetime('2018-01-01')
dates=random_dates(start, end)

此代码使用以下输出创建随机日期：

print(dates)
DatetimeIndex(['2015-06-25 22:00:34', '2015-05-05 19:20:11',
               '2016-04-11 21:52:28', '2015-10-23 21:07:46',
               '2017-04-06 04:01:23', '2015-07-17 06:13:32',
               '2017-06-18 12:33:27', '2015-11-04 06:48:28',
               '2017-08-20 17:10:17', '2016-04-14 07:46:59'],
              dtype='datetime64[ns]', freq=None)

我想通过以下命令使用日期时间索引作为输入来创建滑动窗口数据集：

tensorflow_dataset=tf.keras.preprocessing.timeseries_dataset_from_array(dates.values, None, sequence_length=1,sequence_stride=2, batch_size=1)

当我这样做时，我收到以下错误：

ValueError: Failed to convert a NumPy array to a Tensor (Unsupported numpy type: NPY_DATETIME).

任何想法如何解决这个问题？

Answer 1

您可以尝试将每个 numpy 数据时间对象转换为字符串：

import pandas as pd
import numpy as np
import tensorflow as tf

def random_dates(start, end, n=10):
    start_u = start.value//10**9
    end_u = end.value//10**9

    return pd.to_datetime(np.random.randint(start_u, end_u, n), unit='s')

start = pd.to_datetime('2015-01-01')
end = pd.to_datetime('2018-01-01')
dates=random_dates(start, end)
tensorflow_dataset=tf.keras.preprocessing.timeseries_dataset_from_array(np.datetime_as_string(dates.values), None, sequence_length=1,sequence_stride=2, batch_size=1)

for d in tensorflow_dataset:
  print(d)

tf.Tensor([[b'2016-11-16T02:46:49.000000000']], shape=(1, 1), dtype=string)
tf.Tensor([[b'2015-07-27T04:07:14.000000000']], shape=(1, 1), dtype=string)
tf.Tensor([[b'2015-09-10T14:57:51.000000000']], shape=(1, 1), dtype=string)
tf.Tensor([[b'2017-11-01T20:48:49.000000000']], shape=(1, 1), dtype=string)
tf.Tensor([[b'2017-08-25T11:34:42.000000000']], shape=(1, 1), dtype=string)

之后，您可以将字符串转换为您想要的任何内容。您还可以使用np.datetime_as_string 的unit 参数来获得不同的输出。

np.datetime_as_string(dates.values, unit='D'):

tf.Tensor([[b'2016-04-22']], shape=(1, 1), dtype=string)
tf.Tensor([[b'2015-04-03']], shape=(1, 1), dtype=string)
tf.Tensor([[b'2015-02-14']], shape=(1, 1), dtype=string)
tf.Tensor([[b'2017-02-09']], shape=(1, 1), dtype=string)
tf.Tensor([[b'2015-02-19']], shape=(1, 1), dtype=string)

np.datetime_as_string(dates.values, unit='h'):

tf.Tensor([[b'2017-01-19T15']], shape=(1, 1), dtype=string)
tf.Tensor([[b'2015-11-02T15']], shape=(1, 1), dtype=string)
tf.Tensor([[b'2016-12-11T06']], shape=(1, 1), dtype=string)
tf.Tensor([[b'2017-07-24T04']], shape=(1, 1), dtype=string)
tf.Tensor([[b'2016-06-22T04']], shape=(1, 1), dtype=string)