def _testDistribution(self, initial_known):
classes = np.random.randint(5, size=(20000,)) # Uniformly sampled
target_dist = [0.9, 0.05, 0.05, 0.0, 0.0]
initial_dist = [0.2] * 5 if initial_known else None
iterator = (dataset_ops.Dataset.from_tensor_slices(classes).shuffle(
200, seed=21).map(lambda c: (c, string_ops.as_string(c))).apply(
resampling.rejection_resample(
target_dist=target_dist,
initial_dist=initial_dist,
class_func=lambda c, _: c,
seed=27)).make_initializable_iterator())
init_op = iterator.initializer
get_next = iterator.get_next()
with self.test_session() as sess:
sess.run(init_op)
returned = []
with self.assertRaises(errors.OutOfRangeError):
while True:
returned.append(sess.run(get_next))
returned_classes, returned_classes_and_data = zip(*returned)
_, returned_data = zip(*returned_classes_and_data)
self.assertAllEqual([compat.as_bytes(str(c))
for c in returned_classes], returned_data)
total_returned = len(returned_classes)
# Subsampling rejects a large percentage of the initial data in
# this case.
self.assertGreater(total_returned, 20000 * 0.2)
class_counts = np.array([
len([True for v in returned_classes if v == c])
for c in range(5)])
returned_dist = class_counts / total_returned
self.assertAllClose(target_dist, returned_dist, atol=1e-2)
def _testDistribution(self, initial_known):
classes = np.random.randint(5, size=(20000,)) # Uniformly sampled
target_dist = [0.9, 0.05, 0.05, 0.0, 0.0]
initial_dist = [0.2] * 5 if initial_known else None
iterator = (dataset_ops.Dataset.from_tensor_slices(classes).shuffle(
200, seed=21).map(lambda c: (c, string_ops.as_string(c))).apply(
resampling.rejection_resample(
target_dist=target_dist,
initial_dist=initial_dist,
class_func=lambda c, _: c,
seed=27)).make_one_shot_iterator())
get_next = iterator.get_next()
with self.test_session() as sess:
returned = []
with self.assertRaises(errors.OutOfRangeError):
while True:
returned.append(sess.run(get_next))
returned_classes, returned_classes_and_data = zip(*returned)
_, returned_data = zip(*returned_classes_and_data)
self.assertAllEqual([compat.as_bytes(str(c))
for c in returned_classes], returned_data)
total_returned = len(returned_classes)
# Subsampling rejects a large percentage of the initial data in
# this case.
self.assertGreater(total_returned, 20000 * 0.2)
class_counts = np.array([
len([True for v in returned_classes if v == c])
for c in range(5)])
returned_dist = class_counts / total_returned
self.assertAllClose(target_dist, returned_dist, atol=1e-2)
def testDistribution(self, initial_known):
classes = np.random.randint(5, size=(20000,)) # Uniformly sampled
target_dist = [0.9, 0.05, 0.05, 0.0, 0.0]
initial_dist = [0.2] * 5 if initial_known else None
classes = math_ops.to_int64(classes) # needed for Windows build.
dataset = dataset_ops.Dataset.from_tensor_slices(classes).shuffle(
200, seed=21).map(lambda c: (c, string_ops.as_string(c))).repeat()
get_next = dataset.apply(
resampling.rejection_resample(
target_dist=target_dist,
initial_dist=initial_dist,
class_func=lambda c, _: c,
seed=27)).make_one_shot_iterator().get_next()
with self.cached_session() as sess:
returned = []
while len(returned) < 4000:
returned.append(sess.run(get_next))
returned_classes, returned_classes_and_data = zip(*returned)
_, returned_data = zip(*returned_classes_and_data)
self.assertAllEqual([compat.as_bytes(str(c))
for c in returned_classes], returned_data)
total_returned = len(returned_classes)
class_counts = np.array([
len([True for v in returned_classes if v == c])
for c in range(5)])
returned_dist = class_counts / total_returned
self.assertAllClose(target_dist, returned_dist, atol=1e-2)
def testDistribution(self, initial_known):
classes = np.random.randint(5, size=(20000, )) # Uniformly sampled
target_dist = [0.9, 0.05, 0.05, 0.0, 0.0]
initial_dist = [0.2] * 5 if initial_known else None
classes = math_ops.to_int64(classes) # needed for Windows build.
dataset = dataset_ops.Dataset.from_tensor_slices(classes).shuffle(
200, seed=21).map(lambda c: (c, string_ops.as_string(c))).repeat()
get_next = dataset.apply(
resampling.rejection_resample(
target_dist=target_dist,
initial_dist=initial_dist,
class_func=lambda c, _: c,
seed=27)).make_one_shot_iterator().get_next()
with self.cached_session() as sess:
returned = []
while len(returned) < 4000:
returned.append(sess.run(get_next))
returned_classes, returned_classes_and_data = zip(*returned)
_, returned_data = zip(*returned_classes_and_data)
self.assertAllEqual(
[compat.as_bytes(str(c)) for c in returned_classes], returned_data)
total_returned = len(returned_classes)
class_counts = np.array([
len([True for v in returned_classes if v == c]) for c in range(5)
])
returned_dist = class_counts / total_returned
self.assertAllClose(target_dist, returned_dist, atol=1e-2)
def testVariableDevicePlacement(self):
classes = np.random.randint(5, size=(20000,)) # Uniformly sampled
target_dist = [0.9, 0.05, 0.05, 0.0, 0.0]
with ops.device(
device_setter.replica_device_setter(ps_tasks=1, ps_device="/cpu:0")):
_ = (dataset_ops.Dataset.from_tensor_slices(classes).shuffle(
200, seed=21).map(lambda c: (c, string_ops.as_string(c))).apply(
resampling.rejection_resample(
target_dist=target_dist,
initial_dist=None,
class_func=lambda c, _: c,
seed=27)))
self.assertEqual(1, len(variables.local_variables()))
self.assertEqual(b"",
compat.as_bytes(variables.local_variables()[0].device))
def testVariableDevicePlacement(self):
classes = np.random.randint(5, size=(20000, )) # Uniformly sampled
target_dist = [0.9, 0.05, 0.05, 0.0, 0.0]
with ops.device(
device_setter.replica_device_setter(ps_tasks=1,
ps_device="/cpu:0")):
_ = (dataset_ops.Dataset.from_tensor_slices(classes).shuffle(
200,
seed=21).map(lambda c: (c, string_ops.as_string(c))).apply(
resampling.rejection_resample(target_dist=target_dist,
initial_dist=None,
class_func=lambda c, _: c,
seed=27)))
self.assertEqual(1, len(variables.local_variables()))
self.assertEqual(
b"", compat.as_bytes(variables.local_variables()[0].device))
def _time_resampling(test_obj, data_np, target_dist, init_dist, num_to_sample):
dataset = dataset_ops.Dataset.from_tensor_slices(data_np).repeat()
# Reshape distribution via rejection sampling.
dataset = dataset.apply(
resampling.rejection_resample(class_func=lambda x: x,
target_dist=target_dist,
initial_dist=init_dist,
seed=142))
get_next = dataset.make_one_shot_iterator().get_next()
with test_obj.test_session() as sess:
start_time = time.time()
for _ in xrange(num_to_sample):
sess.run(get_next)
end_time = time.time()
return end_time - start_time
def _time_resampling(
test_obj, data_np, target_dist, init_dist, num_to_sample):
dataset = dataset_ops.Dataset.from_tensor_slices(data_np).repeat()
# Reshape distribution via rejection sampling.
dataset = dataset.apply(
resampling.rejection_resample(
class_func=lambda x: x,
target_dist=target_dist,
initial_dist=init_dist,
seed=142))
get_next = dataset.make_one_shot_iterator().get_next()
with test_obj.test_session() as sess:
start_time = time.time()
for _ in xrange(num_to_sample):
sess.run(get_next)
end_time = time.time()
return end_time - start_time
def testEdgeCasesSampleFromInitialDataset(self, only_initial_dist):
init_dist = [0.5, 0.5]
target_dist = [0.5, 0.5] if only_initial_dist else [0.0, 1.0]
num_classes = len(init_dist)
# We don't need many samples to test that this works.
num_samples = 100
data_np = np.random.choice(num_classes, num_samples, p=init_dist)
dataset = dataset_ops.Dataset.from_tensor_slices(data_np)
# Reshape distribution.
dataset = dataset.apply(
resampling.rejection_resample(class_func=lambda x: x,
target_dist=target_dist,
initial_dist=init_dist))
get_next = dataset.make_one_shot_iterator().get_next()
with self.cached_session() as sess:
returned = []
with self.assertRaises(errors.OutOfRangeError):
while True:
returned.append(sess.run(get_next))
def testRandomClasses(self):
init_dist = [0.25, 0.25, 0.25, 0.25]
target_dist = [0.0, 0.0, 0.0, 1.0]
num_classes = len(init_dist)
num_samples = 100 # We don't need many samples to test a dirac-delta target distribution
data_np = np.random.choice(num_classes, num_samples, p=init_dist)
dataset = dataset_ops.Dataset.from_tensor_slices(data_np)
# Apply a random mapping that preserves the data distribution.
def _remap_fn(_):
return math_ops.cast(random_ops.random_uniform([1]) * num_classes,
dtypes.int32)[0]
dataset = dataset.map(_remap_fn)
# Reshape distribution.
dataset = dataset.apply(
resampling.rejection_resample(
class_func=lambda x: x,
target_dist=target_dist,
initial_dist=init_dist))
get_next = dataset.make_one_shot_iterator().get_next()
with self.test_session() as sess:
returned = []
with self.assertRaises(errors.OutOfRangeError):
while True:
returned.append(sess.run(get_next))
classes, _ = zip(*returned)
bincount = np.bincount(
np.array(classes),
minlength=num_classes).astype(np.float32) / len(classes)
self.assertAllClose(target_dist, bincount, atol=1e-2)
def testRandomClasses(self):
init_dist = [0.25, 0.25, 0.25, 0.25]
target_dist = [0.0, 0.0, 0.0, 1.0]
num_classes = len(init_dist)
# We don't need many samples to test a dirac-delta target distribution.
num_samples = 100
data_np = np.random.choice(num_classes, num_samples, p=init_dist)
dataset = dataset_ops.Dataset.from_tensor_slices(data_np)
# Apply a random mapping that preserves the data distribution.
def _remap_fn(_):
return math_ops.cast(
random_ops.random_uniform([1]) * num_classes, dtypes.int32)[0]
dataset = dataset.map(_remap_fn)
# Reshape distribution.
dataset = dataset.apply(
resampling.rejection_resample(class_func=lambda x: x,
target_dist=target_dist,
initial_dist=init_dist))
get_next = dataset.make_one_shot_iterator().get_next()
with self.cached_session() as sess:
returned = []
with self.assertRaises(errors.OutOfRangeError):
while True:
returned.append(sess.run(get_next))
classes, _ = zip(*returned)
bincount = np.bincount(np.array(classes),
minlength=num_classes).astype(
np.float32) / len(classes)
self.assertAllClose(target_dist, bincount, atol=1e-2)
def testEdgeCasesSampleFromInitialDataset(self, only_initial_dist):
init_dist = [0.5, 0.5]
target_dist = [0.5, 0.5] if only_initial_dist else [0.0, 1.0]
num_classes = len(init_dist)
# We don't need many samples to test that this works.
num_samples = 100
data_np = np.random.choice(num_classes, num_samples, p=init_dist)
dataset = dataset_ops.Dataset.from_tensor_slices(data_np)
# Reshape distribution.
dataset = dataset.apply(
resampling.rejection_resample(
class_func=lambda x: x,
target_dist=target_dist,
initial_dist=init_dist))
get_next = dataset.make_one_shot_iterator().get_next()
with self.cached_session() as sess:
returned = []
with self.assertRaises(errors.OutOfRangeError):
while True:
returned.append(sess.run(get_next))
