def test_shuffle(self):
# Test cross-epoch random order and seed determinism
data = np.arange(10)
targets = data * 2
dataset = timeseries.timeseries_dataset_from_array(data,
targets,
sequence_length=5,
batch_size=1,
shuffle=True,
seed=123)
first_seq = None
for x, y in dataset.take(1):
self.assertNotAllClose(x, np.arange(0, 5))
self.assertAllClose(x[:, 0] * 2, y)
first_seq = x
# Check that a new iteration with the same dataset yields different results
for x, _ in dataset.take(1):
self.assertNotAllClose(x, first_seq)
# Check determism with same seed
dataset = timeseries.timeseries_dataset_from_array(data,
targets,
sequence_length=5,
batch_size=1,
shuffle=True,
seed=123)
for x, _ in dataset.take(1):
self.assertAllClose(x, first_seq)
def test_not_batched(self):
data = np.arange(100)
dataset = timeseries.timeseries_dataset_from_array(data,
None,
sequence_length=9,
batch_size=None,
shuffle=True)
sample = next(iter(dataset))
self.assertEqual(len(sample.shape), 1)
def test_errors(self):
# bad start index
with self.assertRaisesRegex(ValueError, '`start_index` must be '):
_ = timeseries.timeseries_dataset_from_array(np.arange(10),
None,
3,
start_index=-1)
with self.assertRaisesRegex(ValueError, '`start_index` must be '):
_ = timeseries.timeseries_dataset_from_array(np.arange(10),
None,
3,
start_index=11)
# bad end index
with self.assertRaisesRegex(ValueError, '`end_index` must be '):
_ = timeseries.timeseries_dataset_from_array(np.arange(10),
None,
3,
end_index=-1)
with self.assertRaisesRegex(ValueError, '`end_index` must be '):
_ = timeseries.timeseries_dataset_from_array(np.arange(10),
None,
3,
end_index=11)
# bad sampling_rate
with self.assertRaisesRegex(ValueError, '`sampling_rate` must be '):
_ = timeseries.timeseries_dataset_from_array(np.arange(10),
None,
3,
sampling_rate=0)
# bad sequence stride
with self.assertRaisesRegex(ValueError, '`sequence_stride` must be '):
_ = timeseries.timeseries_dataset_from_array(np.arange(10),
None,
3,
sequence_stride=0)
def test_start_and_end_index(self):
data = np.arange(100)
dataset = timeseries.timeseries_dataset_from_array(data,
None,
sequence_length=9,
batch_size=5,
sequence_stride=3,
sampling_rate=2,
start_index=10,
end_index=90)
for batch in dataset:
self.assertAllLess(batch[0], 90)
self.assertAllGreater(batch[0], 9)
def test_timeseries_regression(self):
# Test simple timeseries regression use case
data = np.arange(10)
offset = 3
targets = data[offset:]
dataset = timeseries.timeseries_dataset_from_array(
data, targets, sequence_length=offset, batch_size=1)
i = 0
for batch in dataset:
self.assertLen(batch, 2)
inputs, targets = batch
self.assertEqual(inputs.shape, (1, 3))
# Check values
self.assertAllClose(targets[0], data[offset + i])
self.assertAllClose(inputs[0], data[i:i + offset])
i += 1
self.assertEqual(i, 7) # Expect 7 batches
def test_no_targets(self):
data = np.arange(50)
dataset = timeseries.timeseries_dataset_from_array(data,
None,
sequence_length=10,
batch_size=5)
# Expect 9 batches
i = None
for i, batch in enumerate(dataset):
if i < 8:
self.assertEqual(batch.shape, (5, 10))
elif i == 8:
self.assertEqual(batch.shape, (1, 10))
for j in range(min(5, len(batch))):
# Check each sample in the batch
self.assertAllClose(batch[j],
np.arange(i * 5 + j, i * 5 + j + 10))
self.assertEqual(i, 8)
def test_basics(self):
# Test ordering, targets, sequence length, batch size
data = np.arange(100)
targets = data * 2
dataset = timeseries.timeseries_dataset_from_array(data,
targets,
sequence_length=9,
batch_size=5)
# Expect 19 batches
for i, batch in enumerate(dataset):
self.assertLen(batch, 2)
inputs, targets = batch
if i < 18:
self.assertEqual(inputs.shape, (5, 9))
if i == 18:
# Last batch: size 2
self.assertEqual(inputs.shape, (2, 9))
# Check target values
self.assertAllClose(targets, inputs[:, 0] * 2)
for j in range(min(5, len(inputs))):
# Check each sample in the batch
self.assertAllClose(inputs[j],
np.arange(i * 5 + j, i * 5 + j + 9))
def test_sequence_stride(self):
data = np.arange(100)
targets = data * 2
dataset = timeseries.timeseries_dataset_from_array(data,
targets,
sequence_length=9,
batch_size=5,
sequence_stride=3)
for i, batch in enumerate(dataset):
self.assertLen(batch, 2)
inputs, targets = batch
if i < 6:
self.assertEqual(inputs.shape, (5, 9))
if i == 6:
# Last batch: size 1
self.assertEqual(inputs.shape, (1, 9))
# Check target values
self.assertAllClose(inputs[:, 0] * 2, targets)
for j in range(min(5, len(inputs))):
# Check each sample in the batch
start_index = i * 5 * 3 + j * 3
end_index = start_index + 9
self.assertAllClose(inputs[j],
np.arange(start_index, end_index))
