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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for the experimental input pipeline ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.data.python.ops import resampling
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import errors
from tensorflow.python.ops import string_ops
from tensorflow.python.platform import test
from tensorflow.python.util import compat
class ResampleTest(test.TestCase):
def testInitialKnownDistribution(self):
self._testDistribution(initial_known=True)
def testInitialNotKnownDistribution(self):
self._testDistribution(initial_known=False)
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)
if __name__ == "__main__":
test.main()
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