def test_TimeSeriesGenerator_doesnt_miss_any_sample():
x = np.array([[i] for i in range(10)])
for length in range(3, 10):
g = sequence.TimeseriesGenerator(x, x,
length=length,
batch_size=1)
expected = max(0, len(x) - length)
actual = len(g)
assert expected == actual
if len(g) > 0:
# All elements in range(length, 10) should be used as current step
expected = np.arange(length, 10).reshape(-1, 1)
y = np.concatenate([g[ix][1] for ix in range(len(g))], axis=0)
assert_allclose(y, expected)
x = np.array([[i] for i in range(23)])
strides = (1, 1, 5, 7, 3, 5, 3)
lengths = (3, 3, 4, 3, 1, 3, 7)
batch_sizes = (6, 6, 6, 5, 6, 6, 6)
shuffles = (False, True, True, False, False, False, False)
for stride, length, batch_size, shuffle in zip(strides,
lengths,
batch_sizes,
shuffles):
g = sequence.TimeseriesGenerator(x, x,
length=length,
sampling_rate=1,
stride=stride,
start_index=0,
end_index=None,
shuffle=shuffle,
reverse=False,
batch_size=batch_size)
if shuffle:
# all batches have the same size when shuffle is True.
expected_sequences = ceil(
(23 - length) / float(batch_size * stride)) * batch_size
else:
# last batch will be different if `(samples - length) / stride`
# is not a multiple of `batch_size`.
expected_sequences = ceil((23 - length) / float(stride))
expected_batches = ceil(expected_sequences / float(batch_size))
y = [g[ix][1] for ix in range(len(g))]
actual_sequences = sum(len(_y) for _y in y)
actual_batches = len(y)
assert expected_sequences == actual_sequences
assert expected_batches == actual_batches
def test_TimeseriesGenerator_serde():
data = np.array([[i] for i in range(50)])
targets = np.array([[i] for i in range(50)])
data_gen = sequence.TimeseriesGenerator(data, targets,
length=10,
sampling_rate=2,
batch_size=2)
json_gen = data_gen.to_json()
recovered_gen = sequence.timeseries_generator_from_json(json_gen)
assert data_gen.batch_size == recovered_gen.batch_size
assert data_gen.end_index == recovered_gen.end_index
assert data_gen.length == recovered_gen.length
assert data_gen.reverse == recovered_gen.reverse
assert data_gen.sampling_rate == recovered_gen.sampling_rate
assert data_gen.shuffle == recovered_gen.shuffle
assert data_gen.start_index == data_gen.start_index
assert data_gen.stride == data_gen.stride
assert (data_gen.data == recovered_gen.data).all()
assert (data_gen.targets == recovered_gen.targets).all()
def test_TimeseriesGenerator():
data = np.array([[i] for i in range(50)])
targets = np.array([[i] for i in range(50)])
data_gen = sequence.TimeseriesGenerator(data,
targets,
length=10,
sampling_rate=2,
batch_size=2)
assert len(data_gen) == 20
assert (np.allclose(
data_gen[0][0],
np.array([[[0], [2], [4], [6], [8]], [[1], [3], [5], [7], [9]]])))
assert (np.allclose(data_gen[0][1], np.array([[10], [11]])))
assert (np.allclose(
data_gen[1][0],
np.array([[[2], [4], [6], [8], [10]], [[3], [5], [7], [9], [11]]])))
assert (np.allclose(data_gen[1][1], np.array([[12], [13]])))
data_gen = sequence.TimeseriesGenerator(data,
targets,
length=10,
sampling_rate=2,
reverse=True,
batch_size=2)
assert len(data_gen) == 20
assert (np.allclose(
data_gen[0][0],
np.array([[[8], [6], [4], [2], [0]], [[9], [7], [5], [3], [1]]])))
assert (np.allclose(data_gen[0][1], np.array([[10], [11]])))
data_gen = sequence.TimeseriesGenerator(data,
targets,
length=10,
sampling_rate=2,
shuffle=True,
batch_size=1)
batch = data_gen[0]
r = batch[1][0][0]
assert (np.allclose(
batch[0], np.array([[[r - 10], [r - 8], [r - 6], [r - 4], [r - 2]]])))
assert (np.allclose(batch[1], np.array([
[r],
])))
data_gen = sequence.TimeseriesGenerator(data,
targets,
length=10,
sampling_rate=2,
stride=2,
batch_size=2)
assert len(data_gen) == 10
assert (np.allclose(
data_gen[1][0],
np.array([[[4], [6], [8], [10], [12]], [[6], [8], [10], [12], [14]]])))
assert (np.allclose(data_gen[1][1], np.array([[14], [16]])))
data_gen = sequence.TimeseriesGenerator(data,
targets,
length=10,
sampling_rate=2,
start_index=10,
end_index=30,
batch_size=2)
assert len(data_gen) == 6
assert (np.allclose(
data_gen[0][0],
np.array([[[10], [12], [14], [16], [18]], [[11], [13], [15], [17],
[19]]])))
assert (np.allclose(data_gen[0][1], np.array([[20], [21]])))
data = np.array([np.random.random_sample((1, 2, 3, 4)) for i in range(50)])
targets = np.array([np.random.random_sample((3, 2, 1)) for i in range(50)])
data_gen = sequence.TimeseriesGenerator(data,
targets,
length=10,
sampling_rate=2,
start_index=10,
end_index=30,
batch_size=2)
assert len(data_gen) == 6
assert np.allclose(
data_gen[0][0],
np.array([np.array(data[10:19:2]),
np.array(data[11:20:2])]))
assert (np.allclose(data_gen[0][1], np.array([targets[20], targets[21]])))
with assert_raises(ValueError) as context:
sequence.TimeseriesGenerator(data, targets, length=50)
error = str(context.exception)
assert '`start_index+length=50 > end_index=49` is disallowed' in error