tf.keras.preprocessing.sequence.TimeseriesGenerator

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Utility class for generating batches of temporal data.

Inherits From: Sequence

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tf.compat.v1.keras.preprocessing.sequence.TimeseriesGenerator

tf.keras.preprocessing.sequence.TimeseriesGenerator(
    data,
    targets,
    length,
    sampling_rate=1,
    stride=1,
    start_index=0,
    end_index=None,
    shuffle=False,
    reverse=False,
    batch_size=128
)

This class takes in a sequence of data-points gathered at equal intervals, along with time series parameters such as stride, length of history, etc., to produce batches for training/validation.

Arguments
data	Indexable generator (such as list or Numpy array) containing consecutive data points (timesteps). The data should be at 2D, and axis 0 is expected to be the time dimension.
targets	Targets corresponding to timesteps in data. It should have same length as data.
length	Length of the output sequences (in number of timesteps).
sampling_rate	Period between successive individual timesteps within sequences. For rate r, timesteps data[i], data[i-r], ... data[i - length] are used for create a sample sequence.
stride	Period between successive output sequences. For stride s, consecutive output samples would be centered around data[i], data[i+s], data[i+2*s], etc.
start_index	Data points earlier than start_index will not be used in the output sequences. This is useful to reserve part of the data for test or validation.
end_index	Data points later than end_index will not be used in the output sequences. This is useful to reserve part of the data for test or validation.
shuffle	Whether to shuffle output samples, or instead draw them in chronological order.
reverse	Boolean: if true, timesteps in each output sample will be in reverse chronological order.
batch_size	Number of timeseries samples in each batch (except maybe the last one).

Returns
A Sequence instance.

Examples:

from keras.preprocessing.sequence import TimeseriesGenerator
import numpy as np
data = np.array([[i] for i in range(50)])
targets = np.array([[i] for i in range(50)])
data_gen = TimeseriesGenerator(data, targets,
                               length=10, sampling_rate=2,
                               batch_size=2)
assert len(data_gen) == 20
batch_0 = data_gen[0]
x, y = batch_0
assert np.array_equal(x,
                      np.array([[[0], [2], [4], [6], [8]],
                                [[1], [3], [5], [7], [9]]]))
assert np.array_equal(y,
                      np.array([[10], [11]]))

Methods

get_config

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get_config()

Returns the TimeseriesGenerator configuration as Python dictionary.

Returns
A Python dictionary with the TimeseriesGenerator configuration.

on_epoch_end

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on_epoch_end()

Method called at the end of every epoch.

to_json

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to_json(
    **kwargs
)

Returns a JSON string containing the timeseries generator configuration.

Args
**kwargs	Additional keyword arguments to be passed to json.dumps().

Returns
A JSON string containing the tokenizer configuration.

__getitem__

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__getitem__(
    index
)

Gets batch at position index.

Args
index	position of the batch in the Sequence.

Returns
A batch

__iter__

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__iter__()

Create a generator that iterate over the Sequence.

__len__

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__len__()

Number of batch in the Sequence.

Returns
The number of batches in the Sequence.