def test_simple_determinism():
"""Test to check that the extraction of the batches is deterministic."""
classes = 10
number = 100000
epochs = 100
batch_size = 10000
x = np.arange(0, number, dtype=np.int64)
y = np.random.randint(0, classes, size=number)
ms = MixedSequence(VectorSequence(x, batch_size),
VectorSequence(y, batch_size))
ms2 = MixedSequence(VectorSequence(x, batch_size),
VectorSequence(y, batch_size))
for epoch in range(epochs):
for step in range(ms.steps_per_epoch):
xi, yi = ms[step]
xj, yj = ms2[step]
if epoch == 0:
# The first epochs they must be aligned
assert (xi == xj).all()
assert (yi == yj).all()
else:
# Afterwards, since the ms2 is not shuffled, they must not be
# anymore. Or at least, is very unlikely.
assert (xi != xj).any()
assert (yi != yj).any()
assert (y[xi] == yi).all()
ms.on_epoch_end()
def test_genomic_sequence_determinism():
batch_size = 32
epochs = 5
enhancers = pd.read_csv("tests/enhancers.csv")
promoters = pd.read_csv("tests/promoters.csv")
genome = Genome("hg19", chromosomes=["chr1"])
for region in tqdm((enhancers, promoters), desc="Region types"):
y = np.arange(0, len(region), dtype=np.int64)
mixed_sequence = MixedSequence(x=BedSequence(genome, region,
batch_size),
y=VectorSequence(y, batch_size))
reference_mixed_sequence = MixedSequence(
x=BedSequence(genome,
region,
batch_size=len(region),
shuffle=False),
y=VectorSequence(y, batch_size=len(region), shuffle=False))
X, _ = reference_mixed_sequence[0]
for _ in trange(epochs, desc="Epochs", leave=False):
for step in range(mixed_sequence.steps_per_epoch):
xi, yi = mixed_sequence[step]
assert (X[yi.astype(int)] == xi).all()
mixed_sequence.on_epoch_end()