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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 RandomFourierFeatureMapper."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.contrib.kernel_methods.python.mappers import dense_kernel_mapper
from tensorflow.contrib.kernel_methods.python.mappers.random_fourier_features import RandomFourierFeatureMapper
from tensorflow.python.framework import constant_op
from tensorflow.python.framework.test_util import TensorFlowTestCase
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import googletest
def _inner_product(x, y):
"""Inner product between tensors x and y.
The input tensors are assumed to be in ROW representation, that is, the method
returns x * y^T.
Args:
x: input tensor in row format
y: input tensor in row format
Returns:
the inner product of x, y
"""
return math_ops.matmul(x, y, transpose_b=True)
def _compute_exact_rbf_kernel(x, y, stddev):
"""Computes exact RBF kernel given input tensors x and y and stddev."""
diff = math_ops.subtract(x, y)
diff_squared_norm = _inner_product(diff, diff)
return math_ops.exp(-diff_squared_norm / (2 * stddev * stddev))
class RandomFourierFeatureMapperTest(TensorFlowTestCase):
def testInvalidInputShape(self):
x = constant_op.constant([[2.0, 1.0]])
with self.test_session():
rffm = RandomFourierFeatureMapper(3, 10)
with self.assertRaisesWithPredicateMatch(
dense_kernel_mapper.InvalidShapeError,
r'Invalid dimension: expected 3 input features, got 2 instead.'):
rffm.map(x)
def testMappedShape(self):
x1 = constant_op.constant([[2.0, 1.0, 0.0]])
x2 = constant_op.constant([[1.0, -1.0, 2.0], [-1.0, 10.0, 1.0],
[4.0, -2.0, -1.0]])
with self.test_session():
rffm = RandomFourierFeatureMapper(3, 10, 1.0)
mapped_x1 = rffm.map(x1)
mapped_x2 = rffm.map(x2)
self.assertEqual([1, 10], mapped_x1.get_shape())
self.assertEqual([3, 10], mapped_x2.get_shape())
def testSameOmegaReused(self):
x = constant_op.constant([[2.0, 1.0, 0.0]])
with self.test_session():
rffm = RandomFourierFeatureMapper(3, 100)
mapped_x = rffm.map(x)
mapped_x_copy = rffm.map(x)
# Two different evaluations of tensors output by map on the same input
# are identical because the same paramaters are used for the mappings.
self.assertAllClose(mapped_x.eval(), mapped_x_copy.eval(), atol=0.001)
def testTwoMapperObjects(self):
x = constant_op.constant([[2.0, 1.0, 0.0]])
y = constant_op.constant([[1.0, -1.0, 2.0]])
stddev = 3.0
with self.test_session():
# The mapped dimension is fairly small, so the kernel approximation is
# very rough.
rffm1 = RandomFourierFeatureMapper(3, 100, stddev)
rffm2 = RandomFourierFeatureMapper(3, 100, stddev)
mapped_x1 = rffm1.map(x)
mapped_y1 = rffm1.map(y)
mapped_x2 = rffm2.map(x)
mapped_y2 = rffm2.map(y)
approx_kernel_value1 = _inner_product(mapped_x1, mapped_y1)
approx_kernel_value2 = _inner_product(mapped_x2, mapped_y2)
self.assertAllClose(
approx_kernel_value1.eval(), approx_kernel_value2.eval(), atol=0.01)
def testBadKernelApproximation(self):
x = constant_op.constant([[2.0, 1.0, 0.0]])
y = constant_op.constant([[1.0, -1.0, 2.0]])
stddev = 3.0
with self.test_session():
# The mapped dimension is fairly small, so the kernel approximation is
# very rough.
rffm = RandomFourierFeatureMapper(3, 100, stddev, seed=0)
mapped_x = rffm.map(x)
mapped_y = rffm.map(y)
exact_kernel_value = _compute_exact_rbf_kernel(x, y, stddev)
approx_kernel_value = _inner_product(mapped_x, mapped_y)
self.assertAllClose(
exact_kernel_value.eval(), approx_kernel_value.eval(), atol=0.2)
def testGoodKernelApproximationAmortized(self):
# Parameters.
num_points = 20
input_dim = 5
mapped_dim = 5000
stddev = 5.0
# TODO(sibyl-vie3Poto): Reduce test's running time before moving to third_party. One
# possible way to speed the test up is to compute both the approximate and
# the exact kernel matrix directly using matrix operations instead of
# computing the values for each pair of points separately.
points_shape = [1, input_dim]
points = [
random_ops.random_uniform(shape=points_shape, maxval=1.0)
for _ in xrange(num_points)
]
normalized_points = [nn.l2_normalize(point, dim=1) for point in points]
total_absolute_error = 0.0
with self.test_session():
rffm = RandomFourierFeatureMapper(input_dim, mapped_dim, stddev, seed=0)
# Cache mappings so that they are not computed multiple times.
cached_mappings = dict((point, rffm.map(point))
for point in normalized_points)
for x in normalized_points:
mapped_x = cached_mappings[x]
for y in normalized_points:
mapped_y = cached_mappings[y]
exact_kernel_value = _compute_exact_rbf_kernel(x, y, stddev)
approx_kernel_value = _inner_product(mapped_x, mapped_y)
abs_error = math_ops.abs(exact_kernel_value - approx_kernel_value)
total_absolute_error += abs_error
self.assertAllClose(
[[0.0]],
total_absolute_error.eval() / (num_points * num_points),
atol=0.02)
if __name__ == '__main__':
googletest.main()
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