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.
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
length: Length of the output sequences (in number of timesteps).
sampling_rate: Period between successive individual timesteps within sequences. For rate
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+2*s], etc. start_index, end_index: Data points earlier than
start_indexor later than
end_indexwill 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).
A [Sequence](/utils/#sequence) instance.
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 x, y = batch_0 assert np.array_equal(x, np.array([[, , , , ], [, , , , ]])) assert np.array_equal(y, np.array([, ]))
__init__( data, targets, length, sampling_rate=1, stride=1, start_index=0, end_index=None, shuffle=False, reverse=False, batch_size=128 )
Creates an infinite generator that iterate over the Sequence.
Method called at the end of every epoch.