tf.keras.preprocessing.sequence.TimeseriesGenerator

Class TimeseriesGenerator

Inherits From: Sequence

Defined in tensorflow/python/keras/_impl/keras/preprocessing/sequence.py.

Utility class for generating batches of temporal data.

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, end_index: Data points earlier than start_index or 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](/utils/#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

__init__

__init__(
    data,
    targets,
    length,
    sampling_rate=1,
    stride=1,
    start_index=0,
    end_index=None,
    shuffle=False,
    reverse=False,
    batch_size=128
)

Initialize self. See help(type(self)) for accurate signature.

__getitem__

__getitem__(index)

Gets batch at position index.

Arguments:

• index: position of the batch in the Sequence.

Returns:

A batch

__iter__

__iter__()

Creates an infinite generator that iterate over the Sequence.

Yields:

Sequence items.

__len__

__len__()

Number of batch in the Sequence.

Returns:

The number of batches in the Sequence.

on_epoch_end

on_epoch_end()

Method called at the end of every epoch.